From f1c846e17e52b10ba2f9dc54ce7ed7e23cc22604 Mon Sep 17 00:00:00 2001
From: ZestyZeeshan <141507225+ZestyZeeshan@users.noreply.github.com>
Date: Tue, 3 Oct 2023 17:26:08 +0530
Subject: [PATCH 1/2] Created using Colaboratory

---
 notebook_1.ipynb | 2577 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 2577 insertions(+)
 create mode 100644 notebook_1.ipynb

diff --git a/notebook_1.ipynb b/notebook_1.ipynb
new file mode 100644
index 0000000..79fa458
--- /dev/null
+++ b/notebook_1.ipynb
@@ -0,0 +1,2577 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/ZestyZeeshan/ML-X/blob/main/notebook_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a3d8b64f",
+      "metadata": {
+        "id": "a3d8b64f"
+      },
+      "source": [
+        "# Video Game Sales"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d3e899bf",
+      "metadata": {
+        "id": "d3e899bf"
+      },
+      "source": [
+        "This dataset contains a list of video games with sales greater than 100,000 copies."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e22b5b22",
+      "metadata": {
+        "id": "e22b5b22"
+      },
+      "source": [
+        "# Import necessary libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "5eca13ae",
+      "metadata": {
+        "id": "5eca13ae"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np # for numerical data and arrays\n",
+        "import matplotlib.pyplot as plt # for visualizing data\n",
+        "import pandas as pd # for loading dataset and creating dataframe"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Read Data from local drive for further analysis.\n",
+        "To upload the file from the local drive write the following code in the cell and run it."
+      ],
+      "metadata": {
+        "id": "t8bU3ZI7Tcm0"
+      },
+      "id": "t8bU3ZI7Tcm0"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "\n",
+        "\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "uPWqYxfsSFWi",
+        "outputId": "f3bd99af-45b9-4ea5-bc56-d507555e5087"
+      },
+      "id": "uPWqYxfsSFWi",
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-b957813e-1270-4eae-b05a-81bb6b5c01cc\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-b957813e-1270-4eae-b05a-81bb6b5c01cc\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving video games.csv to video games (1).csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Click on “choose files”, then select and download the CSV file from your local drive.  Later write the following code snippet to import it into a pandas dataframe.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "P0EDFZ31Ttid"
+      },
+      "id": "P0EDFZ31Ttid"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import io\n",
+        "\n",
+        "df = pd.read_csv(io.BytesIO(uploaded['video games (1).csv']))\n",
+        "print(df)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IzGg3liRSjma",
+        "outputId": "f83b3ab7-5167-46e9-8724-0da09020b440"
+      },
+      "id": "IzGg3liRSjma",
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "        Rank                                              Name Platform  \\\n",
+            "0          1                                        Wii Sports      Wii   \n",
+            "1          2                                 Super Mario Bros.      NES   \n",
+            "2          3                                    Mario Kart Wii      Wii   \n",
+            "3          4                                 Wii Sports Resort      Wii   \n",
+            "4          5                          Pokemon Red/Pokemon Blue       GB   \n",
+            "...      ...                                               ...      ...   \n",
+            "16593  16596                Woody Woodpecker in Crazy Castle 5      GBA   \n",
+            "16594  16597                     Men in Black II: Alien Escape       GC   \n",
+            "16595  16598  SCORE International Baja 1000: The Official Game      PS2   \n",
+            "16596  16599                                        Know How 2       DS   \n",
+            "16597  16600                                  Spirits & Spells      GBA   \n",
+            "\n",
+            "         Year         Genre   Publisher  NA_Sales  EU_Sales  JP_Sales  \\\n",
+            "0      2006.0        Sports    Nintendo     41.49     29.02      3.77   \n",
+            "1      1985.0      Platform    Nintendo     29.08      3.58      6.81   \n",
+            "2      2008.0        Racing    Nintendo     15.85     12.88      3.79   \n",
+            "3      2009.0        Sports    Nintendo     15.75     11.01      3.28   \n",
+            "4      1996.0  Role-Playing    Nintendo     11.27      8.89     10.22   \n",
+            "...       ...           ...         ...       ...       ...       ...   \n",
+            "16593  2002.0      Platform       Kemco      0.01      0.00      0.00   \n",
+            "16594  2003.0       Shooter  Infogrames      0.01      0.00      0.00   \n",
+            "16595  2008.0        Racing  Activision      0.00      0.00      0.00   \n",
+            "16596  2010.0        Puzzle    7G//AMES      0.00      0.01      0.00   \n",
+            "16597  2003.0      Platform     Wanadoo      0.01      0.00      0.00   \n",
+            "\n",
+            "       Other_Sales  Global_Sales  \n",
+            "0             8.46         82.74  \n",
+            "1             0.77         40.24  \n",
+            "2             3.31         35.82  \n",
+            "3             2.96         33.00  \n",
+            "4             1.00         31.37  \n",
+            "...            ...           ...  \n",
+            "16593         0.00          0.01  \n",
+            "16594         0.00          0.01  \n",
+            "16595         0.00          0.01  \n",
+            "16596         0.00          0.01  \n",
+            "16597         0.00          0.01  \n",
+            "\n",
+            "[16598 rows x 11 columns]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "b74iao69Trac"
+      },
+      "id": "b74iao69Trac"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f106904c",
+      "metadata": {
+        "id": "f106904c"
+      },
+      "source": [
+        "# Load the dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "c4d4ec20",
+      "metadata": {
+        "id": "c4d4ec20",
+        "outputId": "bce883be-677b-415d-f9a7-66f3e6787bf4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Rank                      Name Platform    Year         Genre Publisher  \\\n",
+              "0     1                Wii Sports      Wii  2006.0        Sports  Nintendo   \n",
+              "1     2         Super Mario Bros.      NES  1985.0      Platform  Nintendo   \n",
+              "2     3            Mario Kart Wii      Wii  2008.0        Racing  Nintendo   \n",
+              "3     4         Wii Sports Resort      Wii  2009.0        Sports  Nintendo   \n",
+              "4     5  Pokemon Red/Pokemon Blue       GB  1996.0  Role-Playing  Nintendo   \n",
+              "\n",
+              "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  \n",
+              "0     41.49     29.02      3.77         8.46         82.74  \n",
+              "1     29.08      3.58      6.81         0.77         40.24  \n",
+              "2     15.85     12.88      3.79         3.31         35.82  \n",
+              "3     15.75     11.01      3.28         2.96         33.00  \n",
+              "4     11.27      8.89     10.22         1.00         31.37  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-96ae44ff-0a37-43cb-972f-2fbdf3d4b389\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Rank</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Platform</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Genre</th>\n",
+              "      <th>Publisher</th>\n",
+              "      <th>NA_Sales</th>\n",
+              "      <th>EU_Sales</th>\n",
+              "      <th>JP_Sales</th>\n",
+              "      <th>Other_Sales</th>\n",
+              "      <th>Global_Sales</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Wii Sports</td>\n",
+              "      <td>Wii</td>\n",
+              "      <td>2006.0</td>\n",
+              "      <td>Sports</td>\n",
+              "      <td>Nintendo</td>\n",
+              "      <td>41.49</td>\n",
+              "      <td>29.02</td>\n",
+              "      <td>3.77</td>\n",
+              "      <td>8.46</td>\n",
+              "      <td>82.74</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Super Mario Bros.</td>\n",
+              "      <td>NES</td>\n",
+              "      <td>1985.0</td>\n",
+              "      <td>Platform</td>\n",
+              "      <td>Nintendo</td>\n",
+              "      <td>29.08</td>\n",
+              "      <td>3.58</td>\n",
+              "      <td>6.81</td>\n",
+              "      <td>0.77</td>\n",
+              "      <td>40.24</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Mario Kart Wii</td>\n",
+              "      <td>Wii</td>\n",
+              "      <td>2008.0</td>\n",
+              "      <td>Racing</td>\n",
+              "      <td>Nintendo</td>\n",
+              "      <td>15.85</td>\n",
+              "      <td>12.88</td>\n",
+              "      <td>3.79</td>\n",
+              "      <td>3.31</td>\n",
+              "      <td>35.82</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Wii Sports Resort</td>\n",
+              "      <td>Wii</td>\n",
+              "      <td>2009.0</td>\n",
+              "      <td>Sports</td>\n",
+              "      <td>Nintendo</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>11.01</td>\n",
+              "      <td>3.28</td>\n",
+              "      <td>2.96</td>\n",
+              "      <td>33.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>Pokemon Red/Pokemon Blue</td>\n",
+              "      <td>GB</td>\n",
+              "      <td>1996.0</td>\n",
+              "      <td>Role-Playing</td>\n",
+              "      <td>Nintendo</td>\n",
+              "      <td>11.27</td>\n",
+              "      <td>8.89</td>\n",
+              "      <td>10.22</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>31.37</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96ae44ff-0a37-43cb-972f-2fbdf3d4b389')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-96ae44ff-0a37-43cb-972f-2fbdf3d4b389 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-96ae44ff-0a37-43cb-972f-2fbdf3d4b389');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-ff62efe5-7db1-44d0-8bda-c7bedbb50dc7\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ff62efe5-7db1-44d0-8bda-c7bedbb50dc7')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-ff62efe5-7db1-44d0-8bda-c7bedbb50dc7 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "#df = pd.read_csv('vgsales.csv')\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "D-0j4FxfTEDf",
+        "outputId": "a462beaa-189d-4c30-ea96-911611e866c8"
+      },
+      "id": "D-0j4FxfTEDf",
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        Rank                                              Name Platform  \\\n",
+              "16593  16596                Woody Woodpecker in Crazy Castle 5      GBA   \n",
+              "16594  16597                     Men in Black II: Alien Escape       GC   \n",
+              "16595  16598  SCORE International Baja 1000: The Official Game      PS2   \n",
+              "16596  16599                                        Know How 2       DS   \n",
+              "16597  16600                                  Spirits & Spells      GBA   \n",
+              "\n",
+              "         Year     Genre   Publisher  NA_Sales  EU_Sales  JP_Sales  \\\n",
+              "16593  2002.0  Platform       Kemco      0.01      0.00       0.0   \n",
+              "16594  2003.0   Shooter  Infogrames      0.01      0.00       0.0   \n",
+              "16595  2008.0    Racing  Activision      0.00      0.00       0.0   \n",
+              "16596  2010.0    Puzzle    7G//AMES      0.00      0.01       0.0   \n",
+              "16597  2003.0  Platform     Wanadoo      0.01      0.00       0.0   \n",
+              "\n",
+              "       Other_Sales  Global_Sales  \n",
+              "16593          0.0          0.01  \n",
+              "16594          0.0          0.01  \n",
+              "16595          0.0          0.01  \n",
+              "16596          0.0          0.01  \n",
+              "16597          0.0          0.01  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1f801e9d-b15c-44f5-93a9-94bb5e783649\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Rank</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Platform</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Genre</th>\n",
+              "      <th>Publisher</th>\n",
+              "      <th>NA_Sales</th>\n",
+              "      <th>EU_Sales</th>\n",
+              "      <th>JP_Sales</th>\n",
+              "      <th>Other_Sales</th>\n",
+              "      <th>Global_Sales</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>16593</th>\n",
+              "      <td>16596</td>\n",
+              "      <td>Woody Woodpecker in Crazy Castle 5</td>\n",
+              "      <td>GBA</td>\n",
+              "      <td>2002.0</td>\n",
+              "      <td>Platform</td>\n",
+              "      <td>Kemco</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16594</th>\n",
+              "      <td>16597</td>\n",
+              "      <td>Men in Black II: Alien Escape</td>\n",
+              "      <td>GC</td>\n",
+              "      <td>2003.0</td>\n",
+              "      <td>Shooter</td>\n",
+              "      <td>Infogrames</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16595</th>\n",
+              "      <td>16598</td>\n",
+              "      <td>SCORE International Baja 1000: The Official Game</td>\n",
+              "      <td>PS2</td>\n",
+              "      <td>2008.0</td>\n",
+              "      <td>Racing</td>\n",
+              "      <td>Activision</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16596</th>\n",
+              "      <td>16599</td>\n",
+              "      <td>Know How 2</td>\n",
+              "      <td>DS</td>\n",
+              "      <td>2010.0</td>\n",
+              "      <td>Puzzle</td>\n",
+              "      <td>7G//AMES</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16597</th>\n",
+              "      <td>16600</td>\n",
+              "      <td>Spirits &amp; Spells</td>\n",
+              "      <td>GBA</td>\n",
+              "      <td>2003.0</td>\n",
+              "      <td>Platform</td>\n",
+              "      <td>Wanadoo</td>\n",
+              "      <td>0.01</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.01</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f801e9d-b15c-44f5-93a9-94bb5e783649')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1f801e9d-b15c-44f5-93a9-94bb5e783649 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-1f801e9d-b15c-44f5-93a9-94bb5e783649');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-04878133-3a10-402c-b719-4e1b2347666f\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-04878133-3a10-402c-b719-4e1b2347666f')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-04878133-3a10-402c-b719-4e1b2347666f button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "977564ce",
+      "metadata": {
+        "id": "977564ce",
+        "outputId": "4df450b2-7304-410c-a4fb-a050108b12ed",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(16598, 11)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ],
+      "source": [
+        "df.shape # Shape of dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "742f8791",
+      "metadata": {
+        "id": "742f8791"
+      },
+      "source": [
+        "# Begin Analysis"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "81c0e3bd",
+      "metadata": {
+        "id": "81c0e3bd"
+      },
+      "source": [
+        "Let us check for null values first."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "75960eda",
+      "metadata": {
+        "id": "75960eda",
+        "outputId": "ff7feec7-eff1-43b0-a781-0a7e55876005",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Rank              0\n",
+              "Name              0\n",
+              "Platform          0\n",
+              "Year            271\n",
+              "Genre             0\n",
+              "Publisher        58\n",
+              "NA_Sales          0\n",
+              "EU_Sales          0\n",
+              "JP_Sales          0\n",
+              "Other_Sales       0\n",
+              "Global_Sales      0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ],
+      "source": [
+        "df.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "140dbd77",
+      "metadata": {
+        "id": "140dbd77"
+      },
+      "source": [
+        "Year and publisher seem to have null values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = df.dropna(how='any',axis=0)"
+      ],
+      "metadata": {
+        "id": "5nVjsU-dUr14"
+      },
+      "id": "5nVjsU-dUr14",
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We removed null values how we can see that our data is more clean."
+      ],
+      "metadata": {
+        "id": "6Y_ZIXWiVGDC"
+      },
+      "id": "6Y_ZIXWiVGDC"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ekq64C7SU_S8",
+        "outputId": "7786fba8-3258-4b84-85d3-cf345eb69772"
+      },
+      "id": "ekq64C7SU_S8",
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Rank            0\n",
+              "Name            0\n",
+              "Platform        0\n",
+              "Year            0\n",
+              "Genre           0\n",
+              "Publisher       0\n",
+              "NA_Sales        0\n",
+              "EU_Sales        0\n",
+              "JP_Sales        0\n",
+              "Other_Sales     0\n",
+              "Global_Sales    0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bcacc961",
+      "metadata": {
+        "id": "bcacc961"
+      },
+      "source": [
+        "Let us also get more information about the columns like datatypes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "SkZT-UzBU0Dv"
+      },
+      "id": "SkZT-UzBU0Dv"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "05b79ffe",
+      "metadata": {
+        "id": "05b79ffe",
+        "outputId": "f228d353-c058-464d-a879-8e1fe8821a09",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 16291 entries, 0 to 16597\n",
+            "Data columns (total 11 columns):\n",
+            " #   Column        Non-Null Count  Dtype  \n",
+            "---  ------        --------------  -----  \n",
+            " 0   Rank          16291 non-null  int64  \n",
+            " 1   Name          16291 non-null  object \n",
+            " 2   Platform      16291 non-null  object \n",
+            " 3   Year          16291 non-null  float64\n",
+            " 4   Genre         16291 non-null  object \n",
+            " 5   Publisher     16291 non-null  object \n",
+            " 6   NA_Sales      16291 non-null  float64\n",
+            " 7   EU_Sales      16291 non-null  float64\n",
+            " 8   JP_Sales      16291 non-null  float64\n",
+            " 9   Other_Sales   16291 non-null  float64\n",
+            " 10  Global_Sales  16291 non-null  float64\n",
+            "dtypes: float64(6), int64(1), object(4)\n",
+            "memory usage: 1.5+ MB\n"
+          ]
+        }
+      ],
+      "source": [
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c7a5ec53",
+      "metadata": {
+        "id": "c7a5ec53"
+      },
+      "source": [
+        "For the numeric datatypes, we can get some useful stats which will help us analyse the data better."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "id": "ae484739",
+      "metadata": {
+        "id": "ae484739",
+        "outputId": "97de6e24-3abd-47c5-cb04-2b203e363730",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               Rank          Year      NA_Sales      EU_Sales      JP_Sales  \\\n",
+              "count  16291.000000  16291.000000  16291.000000  16291.000000  16291.000000   \n",
+              "mean    8290.190228   2006.405561      0.265647      0.147731      0.078833   \n",
+              "std     4792.654450      5.832412      0.822432      0.509303      0.311879   \n",
+              "min        1.000000   1980.000000      0.000000      0.000000      0.000000   \n",
+              "25%     4132.500000   2003.000000      0.000000      0.000000      0.000000   \n",
+              "50%     8292.000000   2007.000000      0.080000      0.020000      0.000000   \n",
+              "75%    12439.500000   2010.000000      0.240000      0.110000      0.040000   \n",
+              "max    16600.000000   2020.000000     41.490000     29.020000     10.220000   \n",
+              "\n",
+              "        Other_Sales  Global_Sales  \n",
+              "count  16291.000000  16291.000000  \n",
+              "mean       0.048426      0.540910  \n",
+              "std        0.190083      1.567345  \n",
+              "min        0.000000      0.010000  \n",
+              "25%        0.000000      0.060000  \n",
+              "50%        0.010000      0.170000  \n",
+              "75%        0.040000      0.480000  \n",
+              "max       10.570000     82.740000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-62f5634a-bab5-40a2-8d9d-672515d40df9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Rank</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>NA_Sales</th>\n",
+              "      <th>EU_Sales</th>\n",
+              "      <th>JP_Sales</th>\n",
+              "      <th>Other_Sales</th>\n",
+              "      <th>Global_Sales</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>16291.000000</td>\n",
+              "      <td>16291.000000</td>\n",
+              "      <td>16291.000000</td>\n",
+              "      <td>16291.000000</td>\n",
+              "      <td>16291.000000</td>\n",
+              "      <td>16291.000000</td>\n",
+              "      <td>16291.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>8290.190228</td>\n",
+              "      <td>2006.405561</td>\n",
+              "      <td>0.265647</td>\n",
+              "      <td>0.147731</td>\n",
+              "      <td>0.078833</td>\n",
+              "      <td>0.048426</td>\n",
+              "      <td>0.540910</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>4792.654450</td>\n",
+              "      <td>5.832412</td>\n",
+              "      <td>0.822432</td>\n",
+              "      <td>0.509303</td>\n",
+              "      <td>0.311879</td>\n",
+              "      <td>0.190083</td>\n",
+              "      <td>1.567345</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1980.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.010000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>4132.500000</td>\n",
+              "      <td>2003.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.060000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>8292.000000</td>\n",
+              "      <td>2007.000000</td>\n",
+              "      <td>0.080000</td>\n",
+              "      <td>0.020000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.010000</td>\n",
+              "      <td>0.170000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>12439.500000</td>\n",
+              "      <td>2010.000000</td>\n",
+              "      <td>0.240000</td>\n",
+              "      <td>0.110000</td>\n",
+              "      <td>0.040000</td>\n",
+              "      <td>0.040000</td>\n",
+              "      <td>0.480000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>16600.000000</td>\n",
+              "      <td>2020.000000</td>\n",
+              "      <td>41.490000</td>\n",
+              "      <td>29.020000</td>\n",
+              "      <td>10.220000</td>\n",
+              "      <td>10.570000</td>\n",
+              "      <td>82.740000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62f5634a-bab5-40a2-8d9d-672515d40df9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-62f5634a-bab5-40a2-8d9d-672515d40df9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-62f5634a-bab5-40a2-8d9d-672515d40df9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-26292d02-a85e-4b3e-b65f-6fbca5c0af36\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26292d02-a85e-4b3e-b65f-6fbca5c0af36')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-26292d02-a85e-4b3e-b65f-6fbca5c0af36 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
+      "source": [
+        "df.describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6bf0dd4b",
+      "metadata": {
+        "id": "6bf0dd4b"
+      },
+      "source": [
+        "So, from the above table we see that the data is from year 1980 to 2020.\n",
+        "Also, we see the stats region-wise (NA, EU, JP, Other) as well as the global sales. NA sales seem to be the highest and JP is the lowest."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b5d1ea7",
+      "metadata": {
+        "id": "2b5d1ea7"
+      },
+      "source": [
+        "We saw some null values before. We can impute the null values and fill them. It won't be 100% accurate but it will be a good approximation for us to work with. Since the null values were in the year and Publisher, we can use mode to impute the null values.\n",
+        "<br>\n",
+        "Let us save it to a new dataframe."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "id": "ea6ce41a",
+      "metadata": {
+        "id": "ea6ce41a"
+      },
+      "outputs": [],
+      "source": [
+        "df1 = df.copy()\n",
+        "df1.Year = df1.Year.fillna(df1.Year.mode()[0])\n",
+        "df1.Publisher =df1.Publisher.fillna(df1.Publisher.mode()[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "e9720d32",
+      "metadata": {
+        "id": "e9720d32",
+        "outputId": "2d4d3ec0-097d-4aef-9f37-9c11202ee83a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Rank            0\n",
+              "Name            0\n",
+              "Platform        0\n",
+              "Year            0\n",
+              "Genre           0\n",
+              "Publisher       0\n",
+              "NA_Sales        0\n",
+              "EU_Sales        0\n",
+              "JP_Sales        0\n",
+              "Other_Sales     0\n",
+              "Global_Sales    0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ],
+      "source": [
+        "df1.isnull().sum() # Checking for null values again"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5e57d7e5",
+      "metadata": {
+        "id": "5e57d7e5"
+      },
+      "source": [
+        "# Visualizations"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3ea2d5a7",
+      "metadata": {
+        "id": "3ea2d5a7"
+      },
+      "source": [
+        "Let us see which genre had the most sales."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "id": "cee5f07d",
+      "metadata": {
+        "id": "cee5f07d",
+        "outputId": "4633f267-cdcd-4920-ae0d-b571eacd1639",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 521
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "genre = df1.Genre.value_counts().values\n",
+        "labels =df1.Genre.value_counts().index\n",
+        "plt.figure(figsize=(6, 6))\n",
+        "plt.title('Genre by Sales',fontdict={'fontweight':'bold'})\n",
+        "plt.pie(genre,labels=labels,autopct='%.2f %%',pctdistance=0.9)\n",
+        "plt.legend(bbox_to_anchor=(1.5,1.0),loc='upper left')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bd1eadbc",
+      "metadata": {
+        "id": "bd1eadbc"
+      },
+      "source": [
+        "<b> Action </b> seems to have highest sales followed by <b> Sports </b>. <b>Puzzle</b> is the least sold genre."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "234557d0",
+      "metadata": {
+        "id": "234557d0"
+      },
+      "source": [
+        "Let us now visualize the top 10 years of sales."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "id": "461a0b9a",
+      "metadata": {
+        "id": "461a0b9a",
+        "outputId": "6afdf3a0-0a74-4dd1-fc2c-c90a728aa9d1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 701
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "year= df1.Year.value_counts().head(10)\n",
+        "plt.figure(figsize=(12,8))\n",
+        "\n",
+        "plt.xticks(year.index)\n",
+        "plt.title('Top 10 sales by Years',fontdict={'fontweight':'bold','fontsize':16})\n",
+        "\n",
+        "plt.bar(year.index,year.values)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "af00b75b",
+      "metadata": {
+        "id": "af00b75b"
+      },
+      "source": [
+        "Year <b> 2009 </b> seems to have the highest sales."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "219f5bcb",
+      "metadata": {
+        "id": "219f5bcb"
+      },
+      "source": [
+        "Now let us see which platform / console made the highest sales."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "id": "4d2dc044",
+      "metadata": {
+        "id": "4d2dc044",
+        "outputId": "eb034923-75eb-4689-c423-7f7e9f6c3894",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 719
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "platform = df1.Platform.value_counts()\n",
+        "plt.figure(figsize=(12,8))\n",
+        "\n",
+        "plt.barh(platform.index,platform.values)\n",
+        "\n",
+        "for i,v in enumerate(platform.values):\n",
+        "    plt.text(v,i,str(v))\n",
+        "\n",
+        "plt.title('Sales by Platform',fontdict={'fontweight':'bold','fontsize':'14'})\n",
+        "plt.xlabel('Counts')\n",
+        "plt.ylabel(\"Platform\")\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8f951e6f",
+      "metadata": {
+        "id": "8f951e6f"
+      },
+      "source": [
+        "Nintendo <b> DS </b> is in the lead with Sony's <b> PS2 </b> right behind it."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8ef07027",
+      "metadata": {
+        "id": "8ef07027"
+      },
+      "source": [
+        "Let us now visualize the sales by countries."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "91877501",
+      "metadata": {
+        "id": "91877501",
+        "outputId": "5f28218f-2751-4feb-c980-cea13a6e9ed1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 521
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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FJpNJdAxyAGq1GkqlskjrspgREREREQGQZRmxsbFISUkRHYUciLe3NwICAiBJ0j3XYzEjIiIiIgJspczf3x+urq73/SBNdC+yLCMrKwtxcXEAgMDAwHuuz2JGRERERE7PYrHYSplerxcdhxyETqcDAMTFxcHf3/+ehzVy8g8iIiIicnq3zylzdXUVnIQcze2/U/c7b5HFjIiIiIjoHzx8kUpbUf9OsZgREREREREJxmJGRERERETFMnz4cPTt21d0jAe2Z88eSJJUIWbi5OQfRERERET3EPTG1nLbV9S8x4u1/vDhw7Fy5UrMnTsXb7zxhm35pk2b0K9fP8iy/GB5oqIQHByMEydOoEmTJg+0rduys7NRpUoVKBQK3LhxA1qttlS2WxLt2rVDTEwMvLy8hGW4jSNmRERERER2zMXFBe+//z6Sk5NLdbtGo7FUt3fbDz/8gAYNGqBu3brYtGlTmeyjKEwmEzQaTZGuMVYeWMyIiIiIiOzYI488goCAAMydO/ee690uRFqtFkFBQfjoo4/yPR4UFIR33nkHzz33HDw9PfHCCy8gODgYANC0aVNIkoTOnTvne878+fMRGBgIvV6PcePG3XfmQQBYtmwZnn32WTz77LNYtmzZXY9LkoQvv/wSvXr1gqurK+rVq4dDhw7h0qVL6Ny5M9zc3NCuXTtERkbme95PP/2EZs2awcXFBTVr1sSsWbNgNpvzbXfx4sV44okn4Obmhjlz5hR4KOOBAwfQuXNnuLq6wsfHB927d7eV3u3bt6NDhw7w9vaGXq9Hr1697spRUixmRERERER2TKlU4r333sPnn3+O69evF7jOsWPHMHDgQDz99NM4c+YMZs6ciWnTpmHFihX51ps/fz4aN26MEydOYNq0afjrr78AAL/99htiYmLw448/2tbdvXs3IiMjsXv3bqxcuRIrVqy4a3v/FRkZiUOHDmHgwIEYOHAg9u3bh6tXr9613u2CePLkSdStWxfPPPMMxowZgzfffBNHjx6FLMsYP368bf19+/bhueeew//+9z+Eh4fjyy+/xIoVKzBnzpx82505cyb69euHM2fOYMSIEXft9+TJk+jatSvq16+PQ4cOYf/+/ejduzcsFgsAIDMzE6+++iqOHj2KXbt2QaFQoF+/frBarfd83UXBc8yIiIiIiOxcv3790KRJE8yYMaPAUaiPP/4YXbt2xbRp0wAAtWvXRnh4OD788EMMHz7ctt7DDz+M1157zfbz7Qsi6/V6BAQE5Numj48PFi5cCKVSibp16+Lxxx/Hrl27MHr06EJzfvPNN+jZsyd8fHwAAN27d8fy5csxc+bMfOs9//zzGDhwIADg9ddfR9u2bTFt2jR0794dAPC///0Pzz//vG39WbNm4Y033sCwYcMAADVr1sQ777yDKVOmYMaMGbb1nnnmmXzPu3z5cr79fvDBB2jRogUWLVpkW9agQQPb/f79+9/1evz8/BAeHo6wsLBCX3dRcMSMiIiIiMgBvP/++1i5ciUiIiLueiwiIgLt27fPt6x9+/a4ePGibTQIAFq0aFHk/TVo0MBW3AAgMDAQcXFxha5vsViwcuVKPPvss7Zlzz77LFasWHHXiFOjRo1s9ytVqgQAaNiwYb5lOTk5SEtLAwCcOnUKs2fPhru7u+02evRoxMTEICsrq8iv7/aIWWEuXryIwYMHo2bNmvD09ERQUBAAIDo6+p7bLQqOmBEREREROYCHHnoI3bt3x5tvvplvFKw43NzciryuWq3O97MkSfc8pG/Hjh24ceMGBg0alG+5xWLBrl270K1btwK3fXtijoKW3d5fRkYGZs2ahSeffPKu/bq4uNju3+/16XS6ez7eu3dv1KhRA0uXLkXlypVhtVoRFhZWKhOlcMSMiEodr21CREQkxrx58/Dzzz/j0KFD+ZbXq1cPBw4cyLfswIEDqF27dr5Rr//SaDQAkG9UraSWLVuGp59+GidPnsx3e/rppws8/LI4mjVrhgsXLiAkJOSum0JR9MrTqFEj7Nq1q8DHEhMTceHCBbz99tvo2rUr6tWrV6ozYbKYETmw4cOHQ5IkzJs3L9/yTZs2lcq0sFFRUZAkCSdPnnzgbd2WnZ0NX19fGAwG5Obmltp2S6IiXduEiIioKBo2bIghQ4bgs88+y7f8tddew65du/DOO+/g77//xsqVK7Fw4UJMmjTpntvz9/eHTqfD9u3bcevWLaSmppYoV3x8PH7++WcMGzYMYWFh+W7PPfccNm3ahKSkpBJtGwCmT5+OVatWYdasWTh37hwiIiKwdu1avP3228XazptvvokjR45g7NixOH36NM6fP4/FixcjISEBPj4+0Ov1+Oqrr3Dp0iX8/vvvePXVV0uc+b9YzIgcHK9tUjIV7domRERERTV79uy7Dils1qwZ1q9fj7Vr1yIsLAzTp0/H7Nmz73vIo0qlwmeffYYvv/wSlStXRp8+fUqUadWqVXBzcyvw/K2uXbtCp9Nh9erVJdo2kDeJyJYtW7Bz5060bNkSbdq0wYIFC1CjRo1ibad27drYuXMnTp06hVatWqFt27b46aefoFKpoFAosHbtWhw7dgxhYWF45ZVX8OGHH5Y4811kInJYw4YNk3v16iXXrVtXnjx5sm35xo0b5f/+779hwwa5fv36skajkWvUqCHPnz8/3+M1atSQZ8+eLQ8dOlT28PCQhw0bJgPId+vUqZNtv3369JE//PBDOSAgQPb19ZXHjh0rG43G+2bu3LmzvGTJEnnx4sVyt27d7nocgLxkyRL58ccfl3U6nVy3bl354MGD8sWLF+VOnTrJrq6uctu2beVLly7le96mTZvkpk2bylqtVg4ODpZnzpwpm0ymfNtdtGiR3Lt3b9nV1VWeMWOGvHv3bhmAnJycbFtv//79cqdOnWSdTid7e3vLjz76qJyUlCTLsixv27ZNbt++vezl5SX7+vrKjz/++F05iIioYsrOzpbDw8Pl7Oxs0VHIwRT17xZHzIgcHK9t4hjXNiEiIiIHVz49kYhEuD1yJcuy3KZNG3nEiBGyLN89YvbMM8/cNTo1efJkuX79+rafa9SoIfft2zffOleuXJEByCdOnLhrvzVq1JDNZrNt2YABA+RBgwbdM+9bb72Vbx99+vSRZ8yYkW8dAPLbb79t+/nQoUMyAHnZsmW2Zd99953s4uJi+7lr167ye++9l2873377rRwYGJhvuxMnTsy3zn9HzAYPHiy3b9/+nq/hTvHx8TIA+cyZM0V+DhERicERMyorHDEjonx4bRP7vrYJEREROTZex4zISfDaJvZ9bRMiIiJybCxmRE5k3rx5aNKkCerUqZNveUW6tsnUqVPzLZ8zZw6WLVuWr5gV153XNnkQt69tMmvWrLseu31tk6VLl6Jjx44AgP379z/Q/oiIiMh5sJgROZF7XdukZcuWeOeddzBo0CAcOnQICxcuxKJFi+65vTuvbVK1alW4uLiU6Jpft69tsnnzZoSFheV77LnnnkO/fv2QlJQEX1/fYm8byLu2Sa9evVC9enU89dRTUCgUOHXqFM6ePYt33323yNt588030bBhQ4wdOxYvvvgiNBoNdu/ejQEDBsDX19d2bZPAwEBER0fjjTfeKFFeIiIicj48x4zIyfDaJnZ6bRMiIiJyaJIsy7LoEEREREREIuXk5ODKlSsIDg7Od/4x0YMq6t8tjpgREREREREJxmJGREREREQkGCf/ICIiIiK6l5nFn9iq5PtKLfZThg8fjpUrV961vHv37ti+fXtppKJywGJGRERERGTnevTogeXLl+dbptVqS7QtWZZhsVigUrEqlCceykhEREREZOe0Wi0CAgLy3Xx8fBAVFQVJknDy5EnbuikpKZAkCXv27AEA7NmzB5IkYdu2bWjevDm0Wi3279+P3NxcTJgwAf7+/nBxcUGHDh1w5MgR23ZuP2/r1q1o1KgRXFxc0KZNG5w9ezZftv3796Njx47Q6XSoVq0aJkyYgMzMzPJ4W+wKixkREREREeGNN97AvHnzEBERgUaNGmHKlCn44YcfsHLlShw/fhwhISHo3r07kpKS8j1v8uTJ+Oijj3DkyBH4+fmhd+/eMJlMAIDIyEj06NED/fv3x+nTp7Fu3Trs378f48ePF/ESKzQWMyIiIiIiO7dlyxa4u7vnu7333nvF2sbs2bPRrVs31KpVC1qtFosXL8aHH36Inj17on79+li6dCl0Oh2WLVuW73kzZsxAt27d0LBhQ6xcuRK3bt3Cxo0bAQBz587FkCFDMHHiRISGhqJdu3b47LPPsGrVKuTk5JTa63cEPHCUiIiIiMjOdenSBYsXL863zNfXF2lpaUXeRosWLWz3IyMjYTKZ0L59e9sytVqNVq1aISIiIt/z2rZtm2+fderUsa1z6tQpnD59GmvWrLGtI8syrFYrrly5gnr16hU5n6NjMSMiIiIisnNubm4ICQm5a3lGRgaAvDJ02+3DDAvaRmnLyMjAmDFjMGHChLseq169eqnvz57xUEYiIiIiIgfl5+cHAIiJibEtu3MikMLUqlULGo0GBw4csC0zmUw4cuQI6tevn2/dw4cP2+4nJyfj77//to2ENWvWDOHh4QgJCbnrptFoHuSlORyOmBERERER2bnc3FzExsbmW6ZSqWAwGNCmTRvMmzcPwcHBiIuLw9tvv33f7bm5ueGll17C5MmT4evri+rVq+ODDz5AVlYWRo4cmW/d2bNnQ6/Xo1KlSpg6dSoMBgP69u0LAHj99dfRpk0bjB8/HqNGjYKbmxvCw8Px66+/YuHChaX2+h0BixkRERER0b2U4KLP5W379u0IDAzMt6xOnTo4f/48vvnmG4wcORLNmzdHnTp18MEHH+DRRx+97zbnzZsHq9WKoUOHIj09HS1atMCOHTvg4+Nz13r/+9//cPHiRTRp0gQ///yzbTSsUaNG2Lt3L6ZOnYqOHTtClmXUqlULgwYNKr0X7yAk+c4DTomIiIiInFBOTg6uXLmC4OBguLi4iI5jF/bs2YMuXbogOTkZ3t7eouNUWEX9u8URMyJBZFlGWo4ZqVkmpGQbkZJlQkq2CalZ/95PyzYh12yF2WqFySLDZLHCbJFhtFhhtuQtM1tlqBQSVEoJaoUCSoUEjUoBF7UCLmolXFRK6DRKeOnU8HXTwNs1778+rhr4uGng46qGq4a/CoiIiIhE4qcxojKSY7LgRko2biRn42ZKdt792z+nZiM2NQcmS8UYsNaqFNC7aVDZW4eqPjpU9XHN99/K3jpoVJwriIiIiKissJgRPaC0HBMu3krHhdgM/H0rHRfj0nHxVgbi0nNFRyuyXLMVN1NzcDM1B0evJt/1uEIC/D1cUF3vijqVPFAn4N+bp4taQGIiIiISrXPnzuBZUaWHxYyoGK4kZOLktWScvZGWV8JuZSA2zfGvWm+Vgdi0HMSm5eCvK0n5Hqvs5YI6AR6oHeCBugEeCKvshRB/d0iSJCgtERERkf1hMSMqRFqOCSejU3AiOgUnriXj1LUUJGcVfEFGZ3Z7pG33hXjbMk8XFZpU90Hz6j5oVsMbTav7wF3LXzdEREREheEnJaJ/xKXn4MClBByKTMTx6BRExmeAo/Mlk5Zjxh9/x+OPv/PKmkICalfyQNPqPmhRwwftQvQI9NIJTklERERUcbCYkdPKNlrw55VE7L+YgP2XEnA+Nl10JIdllYHzsek4H5uO7/6KBgCE+LvjoVA/dKxtQNuaeriolYJTEhEREYnDYkZO5eyNVOz9Ox77Lsbj+NUUGC1W0ZGc1qW4DFyKy8A3B65Ao1KgVZAvOoYa8FBtP9QL9BQdj4iIiKhcsZiRQ7NaZRyLTsb2s7HYcS4W15OzRUeiAhjNVuy/lDdyOXfbeQR4uqBHWAB6hgWgZZAvFApOJEJERESOjcWMHI7ZYsWhy4nYfjYWO8NvId6Opq2nPLFpOVhxMAorDkbBz0OL7g0q4bGwQLSuqYeSJY2IiIgcEIsZOQRZlnHgUiI2nbyBX8NvITWbsyc6ivj0XKw+HI3Vh6Ohd9Pg0QaV8FjDQLSrZWBJIyKictFwZcNy29eZYWeK/Zzhw4cjJSUFmzZtKv1AVG5YzMiuRSVkYsOx69h44gZupPAwRUeXmGnEd39dw3d/XUMlTy2ebFYVA1tUQ7DBTXQ0IiIiogeiEB2AqLgycs1Y+1c0nlp8EJ3n78HC3ZdYypzQrbRcLN4TiS7z92DAkoNYf/Qasoxm0bGIiIiE2r59Ozp06ABvb2/o9Xr06tULkZGRtsejoqIgSRLWrl2Ldu3awcXFBWFhYdi7d69tHYvFgpEjRyI4OBg6nQ516tTBp59+mm8/w4cPR9++fTF//nwEBgZCr9dj3LhxMJl41FJJccSM7MaRqCSsOXwVO87dQrbJIjpOiaUe/h4pe1fCo/kT8H3kBdtyU3IMkncvQ+71cMgWE3TBzeHbbQyUbj6Fbivn2lmk/fkDjLciYclIgl+/qXCt3bbQ9RN3LETGye3weXg0PFv2KXS9jHO7kbJ3JWRjNtwaPgLfrqNtj5lTb+HWumkIHPYJFFrXYr76snEkKhlHopIxa/M5PN4oEANbVEOLIF/RsYiIiMpdZmYmXn31VTRq1AgZGRmYPn06+vXrh5MnT0Kh+HdMZvLkyfjkk09Qv359fPzxx+jduzeuXLkCvV4Pq9WKqlWr4vvvv4der8fBgwfxwgsvIDAwEAMHDrRtY/fu3QgMDMTu3btx6dIlDBo0CE2aNMHo0aMLikb3wWJGFVqu2YLNJ29ixcEonLuZJjrOA8uN+RvpJ7dD7ReUb7nVmIO49dOg9g9GpcHvAQBS9q1G3A+zETD0I0hSwYPbsjEHav+acG/UDfEb37vnvrP+PojcmxegdL93YbFkpSJp++fQPzYRKu8AxG2YBZcajeEa0goAkLhzEXw6Da8wpexOmUYL1h+9jvVHr6NOJQ+M6BCEPk2q8BppRETkNPr375/v52+++QZ+fn4IDw9HWFiYbfn48eNt6y5evBjbt2/HsmXLMGXKFKjVasyaNcu2bnBwMA4dOoT169fnK2Y+Pj5YuHAhlEol6tati8cffxy7du1iMSshHspIFdKttBzM33EB7eb+jskbTjtEKbMas5Hw83zoe7wMhYt7vsdyb4TDnBoHw2OvQOMXBI1fEAyPvwJjzCXkXD1d6DZ1tVrA56GhcK3d7p77NqcnIOnXL2HoNQlQ3Pv7GHNKLCStK9zqPQRtYG24VG8EU+I1AEBm+F5IChVc69x7fxXBhVvpeP2HM2g/73d8tPMC4tJyREciIiIqcxcvXsTgwYNRs2ZNeHp6IigoCAAQHR2db722bf89wkalUqFFixaIiIiwLfviiy/QvHlz+Pn5wd3dHV999dVd22jQoAGUyn+//AwMDERcXFwZvCrnwGJGFcqxq8l4+bsT6PD+71i4+xISM42iI5WapF8XQ1erJXRBTe56TLbkHY8tKdW2ZZJSA0gScq+fe6D9yrIVCVs+hmfrJ6Hxq3Hf9VW+VSCbcvMOj8xOhzHmb2j8gmDJyUDKvtXw7fbiA+Upb4mZRnz++yV0eH83Xll3EmdvpIqOREREVGZ69+6NpKQkLF26FH/++Sf+/PNPAIDRWPTPVGvXrsWkSZMwcuRI7Ny5EydPnsTzzz9/1zbUanW+nyVJgtVqffAX4aR4KCNVCHsuxOHz3y/h2NVk0VHKRGb4XhhjIxE4bEGBj2sr14WkdkHynuXw7vQcIAMpe1cAshWWjAd7T9IOb4CkUMKj+RNFWl/p4g7D468gYcvHkM1GuIU9DF3N5kj45VN4NOsFc+otxP3wDmA1w6v9M3Cr2+GB8pUXo8WKjSduYOOJG2gZ5IMXHqqFbvUriY5FRERUahITE3HhwgUsXboUHTt2BADs37+/wHUPHz6Mhx56CABgNptx7NgxjB8/HgBw4MABtGvXDmPHjrWtf+cEIlQ2WMxIGFmW8VtEHBb+fhGnrjvuKIY5LR5Ju5ai0qB3IKk0Ba6jdPWCX983kLRzEdKP/QxIEtzqd4KmUi1AKvm1unJjLyHt2GYEDvsUUjG241q7Xb7DI3Oiz8AUHwXfbmNw86sXYOg9GUo3H8SsehUu1cKgdPMucUYR8iYLOYoGlT0xoWsoHq1fqVjvDxERUUXk4+MDvV6Pr776CoGBgYiOjsYbb7xR4LpffPEFQkNDUa9ePSxYsADJyckYMWIEACA0NBSrVq3Cjh07EBwcjG+//RZHjhxBcHBweb4cp8NiRuVOlmVsOxuLz3+/hIgY+z937H6MsZdgzUpBzIr//btQtiL32jmkH9+C6pM2QlIooQtuhipjvoYlKxWSQgmFizuuLXwWrt4BJd537rVzsGam4sbi5/PtO3n3MqQd/QlVX/rmvtuQzSYk7VwMfa9XYU6OgWy1wKV63oU21b5VkBtzAa4hrUucUaRzN9Mw5ttjqB+YV9C6N2BBIyKiu5Xkos/lyWq1QqVSQaFQYO3atZgwYQLCwsJQp04dfPbZZ+jcufNdz5k3bx7mzZuHkydPIiQkBJs3b4bBYAAAjBkzBidOnMCgQYMgSRIGDx6MsWPHYtu2beX8ypwLixmVG6tVxs+nb2Lh75dwMS5DdJxy41KjMQJHLMy3LPGXT6HWV4Vn6/6QFPlnDFS6egEAsq+egjUz9YFKj1tYF7gENc63LG79dLg1eBjuDR8p0jZSD66FS81m0AaEwHgrErD+e6kC2WoGHOBY8vCYNLy4+hjqBnjgf11D0SMsgAWNiIjsRlxcHEJCQgAAjzzyCMLDw/M9LsvyXc+pV6+e7fyz/9JqtVi+fDmWL1+eb/ncuXNt91esWHHX8z755JNiJqc7sZhRudh9Pg5zt0Xg71vOU8huU2hdofnP9PiSWguFi0e+5Rmnf4VaXw0KVy/k3jyP5N++gkfLPlDrq9rWubX2LehC28KzeW8AeTM9mpNjbI+bU2/BeOsyFDp3qDz9odR5Qqnz/E8gFZRuPvm2WxhjQjQyz+9D4PDPAAAq36qApED6qZ1QuvvAlHgdmsDQYr4jFdf52HS8tOY46gZ44NVutfFog5KPVhIREZW15ORkHDhwAHv27MGLL9rX5Fx0NxYzKlNnb6TivV8icDAyUXSUCs+UdAPJf6yENTsDKi9/eLUdCI+WffOvkxwLbfa/h38aYy/i1ndv2X5O/v1rAIBbWFcYHn/lgfLIsoyk7Qvh8/AoKDQuAACFWgv9YxOR9OtiyBYTfLu9CJWH4YH2UxGdj03HC98eQ+tgX7z9eH00rOolOhIREdFdRowYgSNHjuC1115Dnz59RMehByTJBY1tEj2g68lZmL/jAn46dRP8G0b2TJKAvk2qYHL3OqjsrRMdh4iIykhOTg6uXLmC4OBguLi4iI5DDqSof7c4YkalKjXbhEW7L2H5wSgYzfZ/7hGRLAMbT9zAtrMxGNkhGC91DoG7lr86iYiIqHTx0wWVClmW8X9/RWP+jgtIzjKJjkNU6nJMVnyxOxLrjlzDxEdq45lW1aFQcIIQIiIiKh0sZvTAzt1MxdSNZ3HyWoroKERlLiHDiLc3ncW6I9fwXr+GPP+MiIiISgWLGZVYRq4ZH+/8GysPRcFi5Ylk5FzO3EhF30UHMLRNDUzqXoeHNxIREdED4ScJKpGtp2PwzpZwxKbliI5CJIzFKmPFwShsPxuLmU/UR4+wQNGRiIiIyE6xmFGxRCdmYdpPZ7H373jRUYgqjNi0HLy4+jgeqeePWX3CUIWzNxIREVExKUQHIPsgyzJWHoxC90/+YCkjKsRvEXHo9vFefL3vMqw8vJeIiOzAihUr4O3tLTpGsQwfPhx9+/YVHaPUccSM7utmSjambDiN/ZcSREchqvCyjBa8uzUCv4bfwkcDG6Oqj6voSERE9IAi6tYrt33VOx9R7Odcu3YNM2bMwPbt25GQkIDAwED07dsX06dPh16vt60XFBSEiRMnYuLEiaWYuHhOnTqFadOm4fDhw0hLS0NAQABat26Nzz//HP7+/sJyVQQcMaN72nDsOrp/8gdLGVEx/XklCT0/2Yfvj14THYWIiBzY5cuX0aJFC1y8eBHfffcdLl26hCVLlmDXrl1o27YtkpKShOQyme6+fFJ8fDy6du0KX19f7NixAxEREVi+fDkqV66MzMxMASkrFhYzKlBCRi5GrzqKSd+fQnqOWXQcIruUnmvG5A2nMebbo0jKNIqOQ0REDmjcuHHQaDTYuXMnOnXqhOrVq6Nnz5747bffcOPGDUydOhUA0LlzZ1y9ehWvvPIKJEmCJOW/FueOHTtQr149uLu7o0ePHoiJicn3+Ndff4169erBxcUFdevWxaJFi2yPRUVFQZIkrFu3Dp06dYKLiwvWrFlzV9YDBw4gNTUVX3/9NZo2bYrg4GB06dIFCxYsQHBwMADAYrFg5MiRCA4Ohk6nQ506dfDpp5/e8z2wWq2YO3eu7TmNGzfGhg0bbI8nJydjyJAh8PPzg06nQ2hoKJYvX168N7oc8FBGusv2szGYuvEsEvlBkqhU7Dh3C8eu/oH3+zdE13qVRMchIiIHkZSUhB07dmDOnDnQ6fJPPBUQEIAhQ4Zg3bp1WLRoEX788Uc0btwYL7zwAkaPHp1v3aysLMyfPx/ffvstFAoFnn32WUyaNMlWrtasWYPp06dj4cKFaNq0KU6cOIHRo0fDzc0Nw4YNs23njTfewEcffYSmTZvCxcXlrrwBAQEwm83YuHEjnnrqqbvKIZBXsqpWrYrvv/8eer0eBw8exAsvvIDAwEAMHDiwwPdh7ty5WL16NZYsWYLQ0FD88ccfePbZZ+Hn54dOnTph2rRpCA8Px7Zt22AwGHDp0iVkZ2cX+/0uayxmZJNrtmDm5nB891e06ChEDichIxcjVx7F0y2rYUbvBtBplKIjERGRnbt48SJkWUa9egWfA1evXj0kJycjPj4e/v7+UCqV8PDwQEBAQL71TCYTlixZglq1agEAxo8fj9mzZ9senzFjBj766CM8+eSTAIDg4GCEh4fjyy+/zFfMJk6caFunIG3atMFbb72FZ555Bi+++CJatWqFhx9+GM899xwqVcr74lKtVmPWrFm25wQHB+PQoUNYv359gcUsNzcX7733Hn777Te0bdsWAFCzZk3s378fX375JTp16oTo6Gg0bdoULVq0AJB3rl1FxEMZCQBwNTETTy46yFJGVMbWHrmGfosO4HJ8hugoRETkIGT5wWYCdnV1tZUyAAgMDERcXBwAIDMzE5GRkRg5ciTc3d1tt3fffReRkZH5tnO7+NzLnDlzEBsbiyVLlqBBgwZYsmQJ6tatizNnztjW+eKLL9C8eXP4+fnB3d0dX331FaKjC/6MeunSJWRlZaFbt2758q1atcqW76WXXsLatWvRpEkTTJkyBQcPHiz2e1QeWMwIv5yJQa/P9uPczTTRUYicwvnYdDyx8AC2no65/8pERESFCAkJgSRJiIgoeCbHiIgI+Pj4wM/P757bUavV+X6WJMlW9jIy8r5IXLp0KU6ePGm7nT17FocPH873PDc3tyLl1uv1GDBgAObPn4+IiAhUrlwZ8+fPBwCsXbsWkyZNwsiRI7Fz506cPHkSzz//PIzGgk+xuZ1v69at+fKFh4fbzjPr2bOn7fy6mzdvomvXrpg0aVKRspYnHsroxIxmK+ZsDcfKQ1dFRyFyOhm5Zoz7v+M4EhWEqY/Xg1rJ78mIiKh49Ho9unXrhkWLFuGVV17Jd55ZbGws1qxZg+eee852LpdGo4HFYinWPipVqoTKlSvj8uXLGDJkSKnmv52pVq1atlkZDxw4gHbt2mHs2LG2df47Mnen+vXrQ6vVIjo6Gp06dSp0PT8/PwwbNgzDhg1Dx44dMXnyZFsZrChYzJzUtaQsjP+/4zh1PVV0FCKntuJgFE5dT8EXzzRDZW/d/Z9ARER0h4ULF6Jdu3bo3r073n33XQQHB+PcuXOYPHkyqlSpgjlz5tjWDQoKwh9//IGnn34aWq0WBoOhSPuYNWsWJkyYAC8vL/To0QO5ubk4evQokpOT8eqrrxY565YtW7B27Vo8/fTTqF27NmRZxs8//4xffvnFNktiaGgoVq1ahR07diA4OBjffvstjhw5Ypu18b88PDwwadIkvPLKK7BarejQoQNSU1Nx4MABeHp6YtiwYZg+fTqaN2+OBg0aIDc3F1u2bCn0vDyRWMyc0N6/4zHhuxNIzb77+hJEVP5ORKeg1+f78cmgJnio9r0PNyEiovJXkos+l5fQ0FAcPXoUM2bMwMCBA5GUlISAgAD07dsXM2bMgK+vr23d2bNnY8yYMahVqxZyc3OLfG7aqFGj4Orqig8//BCTJ0+Gm5sbGjZsWOwLVdevXx+urq547bXXcO3aNWi1WoSGhuLrr7/G0KFDAQBjxozBiRMnMGjQIEiShMGDB2Ps2LHYtm1bodt955134Ofnh7lz5+Ly5cvw9vZGs2bN8NZbbwHIG5V78803ERUVBZ1Oh44dO2Lt2rXFyl4eJPlBzxYku7L8wBW8uzUCFiv/2IkqGoUEvNmzHkY/VFN0FCIip5OTk4MrV64gODi4wKneiUqqqH+3OGLmJMwWK6ZvPof/+5OzLhJVVFYZmPNLBC4nZGB2nzCed0ZEROREWMycQEqWEWPXHMfByETRUYioCL776xquJmZh8ZDm8HJV3/8JREREZPf4dayDi4zPQL9FB1nKiOzMwchE9Ft8AFEJmaKjEBERUTlgMXNg+y7Go98XB3CFH+yI7NLl+Ez0XXQAhy/zixUiIiJHx2LmoNYfvYbnlx9BWo5ZdBQiegApWSYMXfYn1h+9JjoKEZFT4Lx4VNqK+neKxcwBfbH7EqZsOA0zZ14kcggmi4wpG05j8Z7CL7BJREQPRq3OO6c3KytLcBJyNLf/Tt3+O1YYTv7hQGRZxuwt4Vh+IEp0FCIqA+9vP4/UbBPe6FlXdBQiIoejVCrh7e2NuLg4AICrqyskSRKciuyZLMvIyspCXFwcvL29oVQq77k+r2PmIMwWKyZ9fwqbTt4UHYWIytgzravj3T5hUCj4gYGIqDTJsozY2FikpKSIjkIOxNvbGwEBAfct+ixmDiDXbMG4NSfwW8Qt0VGIqJz0ahSIBYOa8FpnRERlwGKxwGQyiY5BDkCtVt93pOw2FjM7l5lrxuhVRzkdPpET6lzHD0uebQ4XddF+4RMREVHFxWJmxzJyzRi67E+ciE4RHYWIBGkZ5INlw1vC04UXoiYiIrJnLGZ2KjPXjGHf/IWjV5NFRyEiwRpX88a3I1uxnBEREdkxnpxgh7KMZjy//AhLGREBAE5dS8Fzy/5Ceg7PhyAiIrJXLGZ2JttowYgVR/BXVJLoKERUgZy8loJh3/yFjFxeVJ6IiMgesZjZkRyTBaNWHcHhyyxlRHS349EpeH75X8g2WkRHISIiomJiMbMTuWYLRq86igOXOPsiERXuSFQyRq86ilwzyxkREZE9YTGzAyaLFS9+ewz7LiaIjkJEdmD/pQSMXX0cJotVdBQiIiIqIhazCk6WZUzZcBq7L8SLjkJEdmTX+Ti8su4kOPEuERGRfWAxq+De334BG0/cEB2DiOzQltMxmLM1QnQMIiIiKgIWswpsxYErWLI3UnQMIrJjX++/gm/2XxEdg4iIiO6DxayC+uVMDGZvCRcdg4gcwLtbw7HtTIzoGERERHQPLGYV0J+XEzFx3UlYeWoIEZUCqwxMXHcSR3n9QyIiogqLxayC+ftWOkavOgqjmbOpEVHpyTVbMWrVUUTGZ4iOQkRERAVgMatA4tNzMfybv5CWYxYdhYgcUEqWCcO++Qvx6bmioxAREdF/sJhVEEazFS+tPoabqTmioxCRA7uenM0LUBMREVVALGYVxIzN53D0arLoGETkBE5eS8H0TedExyAiIqI7sJhVAKsPX8V3f0WLjkFETmTd0Wv49vBV0TGIiIjoHyxmgv11JQmzfuY310RU/mb/fI4zNRIREVUQLGYC3UzJxtg1x2CycF58Iip/JouMl9YcRyzPbSUiIhKOxUyQHJMFY749hoQMo+goROTE4tNz8eLqY5wMhIiISDAWM0GmbTqLMzdSRccgIuJkIERERBUAi5kAm07cwPfHrouOQURks+7oNXx/9JroGERERE6LxaycRSVk4u1NZ0XHICK6y8zN53AlIVN0DCIiIqfEYlaOjGYrXv7uBDJyzaKjEBHdJdNowcvfHYfRbBUdhYiIyOmwmJWjD7af53llRFShnb2Rhg93nBcdg4iIyOmwmJWT3efjsOzAFdExiIju6+v9V7D373jRMYiIiJwKi1k5iEvLwaTvT0Hm5cqIyA7IMvDa+lNIyMgVHYWIiMhpsJiVMVmW8er6U0jM5PXKiMh+JGTk/vOFEr9RIiIiKg8sZmXs//6Kxv5LCaJjEBEV254L8Vh16KroGERERE6BxawM3UjJxtxfeBI9Edmv97efx7WkLNExiIiIHB6LWRl644fTnBqfiOxaltGCtzaeER2DiIjI4bGYlZF1R6Kx7yIPYSQi+7fvYgLWH7kmOgYREZFDYzErA7GpOXh3a4ToGEREpebdreGIS8sRHYOIiMhhsZiVgTd/PI30HB7CSESOIy3HjKmbzoqOQURE5LBYzErZD8euY/cFXpiViBzPr+G38POpm6JjEBEROSQWs1KUmmXCnF94CCMROa6Zm88hmddlJCIiKnUsZqXoo18vIIkfWIjIgSVmGvHBjguiYxARETkcFrNSEn4zDWv+jBYdg4iozK07Eo2zN1JFxyAiInIoLGalZMbms7BYZdExiIjKnFXOO6SRiIiISg+LWSnYeOI6jkQli45BRFRujl5NxqYTN0THICIichgsZg8oI9eMub+cFx2DiKjczdt2HllGXhqEiIioNLCYPaBPf/sbcem5omMQEZW72LQcLPz9kugYREREDoHF7AFcjs/AioNRomMQEQnz9f4ruJqYKToGERGR3WMxewDzd16AycIJP4jIeRnNVszZyus3EhERPSgWsxI6fT0F287Gio5BRCTczvBbOBHNCZCIiIgeBItZCX2w/QJkDpYREQEAPuRFp4mIiB4Ii1kJHLiUgP2XEkTHICKqMA5GJuIAfy8SERGVGItZCXywndPjExH9F0fNiIiISo7FrJi2nYnBqeupomMQEVU4J6+lYOc5nntLRERUEixmxWCxypi/k98IExEV5uNf/4bVyhNwiYiIiovFrBh+OnkDkfG8Xg8RUWHOx6Zj86mbomMQERHZHRazIpJlGYv3RIqOQURU4X266yJHzYiIiIqJxayIdobfwsW4DNExiIgqvCsJmdjOc82IiIiKhcWsiDhaRkRUdEv28ncmERFRcbCYFcHByAScvJYiOgYRkd04fT0VByN5XTMiIqKiYjErAo6WEREV35K9l0VHICIishssZvdx9kYq9l3kt75ERMX1x9/xCL+ZJjoGERGRXWAxu49Fey6JjkBEZLe+/INHHBARERUFi9k9RCdmYftZzixGRFRSW0/H4FpSlugYREREFR6L2T2s/vMqeCkeIqKSM1tlLD8QJToGERFRhcdiVogckwXfH70mOgYRkd374fh15JgsomMQERFVaCxmhdh6OgbJWSbRMYiI7F5qtgk/n7opOgYREVGFxmJWiG8PXxUdgYjIYaz5M1p0BCIiogqNxawAZ2+k8oLSRESl6OS1FJy7mSo6BhERUYXFYlaAbw9xtIyIqLRx1IyIiKhwLGb/kZptwmaeC0FEVOp+OnEDGblm0TGIiIgqJBaz/9h4/DqyOXsYEVGpyzRasOnEDdExiIiIKiQWs//4kR8aiIjKzP/xcEYiIqICsZjdITI+A6ev8+R0IqKyEh6Thr9vpYuOQUREVOGwmN2Bh9gQEZW9n07ydy0REdF/sZjdYRM/LBARlTlOsERERHQ3FrN/HLuahGtJ2aJjEBE5vGtJ2Th2NVl0DCIiogqFxewfG3kYIxFRudnMIxSIiIjyYTEDYLJYsfV0jOgYREROY+uZGFissugYREREFQaLGYC9F+KRnGUSHYOIyGkkZBix/1KC6BhEREQVBosZgF/OcrSMiKi8bT7JSUCIiIhuc/piZrHK2HMhXnQMIiKn81vELZgtVtExiIiIKgSnL2bHriYjKdMoOgYRkdNJzTbhKGdnJCIiAsBiht8ibomOQETktH4/Hyc6AhERUYXAYhbOYkZEJAq/HCMiIsrj1MUsMj4DlxMyRccgInJal+MzEcXfw0RERM5dzH7laBkRkXAcNSMiInLyYsbDGImIxON5ZkRERE5czFKzTDgezdnAiIhEOxKVhLQck+gYREREQjltMTt8JRFWWXQKIiIyWWTsv5ggOgYREZFQTlvMDkUmio5ARET/OHyZv5OJiMi5OW0x44cAIqKK48/LSaIjEBERCeWUxSwp04gLt9JFxyAion/8HZeO5Eyj6BhERETCOGUxO3w5ETLPLyMiqjBkGfjzCo9kICIi5+WUxYznlxERVTyHeTgjERE5MacsZjy/jIio4vnzCosZERE5L6crZvHpubgYlyE6BhER/ceF2DSkZvF6ZkRE5Jycrpgdu8qLShMRVURWnmdGREROzOmK2dkbqaIjEBFRIY5Hp4iOQEREJITTFbPTLGZERBXWuZv8HU1ERM7J6YoZR8yIiCou/o4mIiJn5VTF7FpSFpJ4AVMiogorOcuE68lZomMQERGVO6cqZvwmloio4jt7I010BCIionLnVMWM55cREVV8PM+MiIickVMVszPX+Y89EVFFd4ZfohERkRNyqmJ2lt/CEhFVeDyUkYiInJHTFLO49BykZJlExyAiovtIyMjFrbQc0TGIiIjKldMUs8vxmaIjEBFREV2KyxAdgYiIqFyxmBERUYVzOYG/s4mIyLk4TTGLjOe3r0RE9uIKv0wjIiIn4zTF7DKLGRGR3YhKZDEjIiLn4jzFjIfFEBHZjSv8nU1ERE7GKYqZ0WzF9eRs0TGIiKiIriVlwWyxio5BRERUbpyimF1NzITFKouOQURERWS2yrjGL9SIiMiJOEUx4yExRET250oCzw0mIiLn4RTFLCaVFyolIrI3UQlZoiMQERGVG6coZrFpLGZERPbmFn93ExGRE3GKYsZ/3ImI7E9ceq7oCEREROWGxYyIiCqkuHT+7iYiIufhJMWM37oSEdmbOP7uJiIiJ+IkxYzfuhIR2RseykhERM7E4YtZltGM9Byz6BhERFRMqdkm5JgsomMQERGVC4cvZjyMkYjIfsVz1IyIiJyEwxezOB7GSERkt3g4IxEROQuHL2Yp2SbREYiIqIQSMljMiIjIOTh8MUtjMSMislsZPEeYiIichOMXM/6jTkRktzKN/B1ORETOweGLWXoOR8yIiOxVZi5nZSQiIuegEh2grE20rMT/3JfBqnGHRe0Os9odJqUrcpVuyFG4IUtyRSZ0yIAOaVYd0mQXJFtckGLWItGsRYJJg7hcDW4Z1UgxqUW/HCIip5KZyxEzIiJyDg5fzGDMgGTOhtKcDSXioSnpdpSArFZB1rjDervgqdxhVLoiR+GKbIUbsqBDBlyRLrsgzeqCFKsLks0uSLJokWTSIN6owS2jBrdy1bDIDj9YSUT0wHgoIxEROQsnKGaZpbYpyWqGlJMCRU4KVABcSrohLSCr3WyjeCaVO0wqN+QqXJGtcEX2P6N46f+M4qVaXZBicUGSWYskswYJJi3icjWINaqRYXb8P0Iicl4cMSMiImfh+J/qjVmiExRIMmVCacqEErdKPoqnAmStxjaKZ1K7w6R0g1Hlhhwpr+TdHsVLk12QZtUhxeKCZEveYZqJJi3ijWrEGTWIM6ohy1JpvkQiogeWaeQ5ZkRE5Bwcv5iZSm/ErCKSLEZI2UlQZCdBBUBXoo0AslYCNG6wajxgVrvDrHKDUemGXKUbsiVXZEk6ZML1n1E8F6RadUg2a5Fk0SLBpEWiSYP4XDVijRpkW5Sl/CqJyFlxxIyIiJyF4xczs1F0ArsgQQaMGVAaM6AEoAXgVpINqQHZRQtZ4wGLxh1mlTuM/5S8HEVewcuCKzKgQ7rsgpTbo3jm26N4GsQZ80byEoycbIXI2WVxxIyIiJyE4xczKneSJRdSdi4U2QlQo4SjeApA1ikBjRssGg9Y/jkPz6h0Q84/JS8Turxz8eS82TRTrC5IMbv8e5imbUZNDUxWHqZJZI8sVll0BCIionLBYkYVliRbgNw0qHLToELeKF6JaABZpfvPJRNuFzw3ZEu6uy+Z8M9smolmLRKM/86omWri/zJE5ckqs5gREZFz4KdMcgoFXTLBq7gbKfSSCW7/XDLBNd9hmv+9ZEKC8d9RvDijipdMICoCDpgREZGzYDEjKoZSuWTC7clW1K53XTIhR3H7XLzbFz53yXfJhNuHaSaYNIgzanArV4NMTrZCDkzmiBkRETkJFjMiASTIQGlcMkENfOHXG11/vAo5LqE0IxJVCC5hYcD4DqJjEBERlTkeS0Vk57ZUuYWXnjch/rGWgIL/S5NjkZQcESYiIufAT3FEds6gckOCIhPjGp/AN+NDgVo1REciKj0sZkRE5CRYzIjsnF7x73yV290i8eyAWFwY1AqSS4nOgCOqUCSOAhMRkZNw/H/xJF6/ihybAflHFIySBdNqHsc74/QwNa8vKBVRKeGIGREROQnHL2bKEk+rQGQX9IVMWndacwtDHv0b+0c0g+TjXa6ZiEqLpHT8f6aIiIgAZyhmWg/RCYjKlN5kvOfjn1U6jYmjFUh9pHk5JSIqRUpOHkxERM6BxYzIzhmMOfdd54YyDaNbnsL6cQ0gVatSDqmISofSg7/DiYjIObCYEdk5fXZakdfd4HkBzw9JRnS/VoCKIxFU8Sm9vURHICIiKhcsZkR2zpCZXKz1MyQjJtU9jk9ergZrWO0ySkVUOpTe3qIjEBERlQvHL2Yad9EJiMqUIT2uRM876HINg3tdxvGhLSF58P8TqpiUXhwxIyIi5+D4xYwjZuTgtOYcuKvdSvRcWQLmVT2BN150RVaHJqUbjKgUKFjMiIjISThBMfMUnYCozBk0D/bhNVKVhOEdz2Lri40hVfIvpVRED44jZkRE5CycoJjxEC1yfHpVyUbM/mulzzm8MDwbtx5vCSgc/9cDVXxKL2/REYiIiMqF43/y4ogZOQG9ovQupJ6syMbLjU7g6/EhQGhwqW2XqCQ4KyMRETkLxy9m7jwsixyfAcpS3+ZOt8t4tv9NRAxuBUnnUurbJyoKHspIRETOwvGLmUeg6AREZU5vsZbJdo2SBTOCjmPWOD2MLcPKZB9E98JiRkREzsLxi5nWnYczksMzmHLLdPtn1bfw7CPnsWdkM0i+PmW6L6LbFG5ukHghdCIichKOX8wAwLOy6AREZcqQm10u+1nkfxovj5KR0q05IEnlsk9yXhwtIyIiZ+IcxYyHM5KD02enldu+YpUZeKHFKXw3rh6kGlXLbb/kfJS+vqIjEBERlRvnKGaeVUQnICpT+sykct/nRo+/MezpRET1bwWo1eW+f3J86mos/kRE5DycpJhxxIwcmz49HhLK/9DCLIUJU2ofx0cvV4alUZ1y3z85Nk31GqIjEBERlRsnKWY8x4wcm9pqgqfGQ9j+/9TewDOPReLIsBaQPMTlIMeiqV5ddAQiIqJy4xzFzIPFjByfQSN29lFZAj6sfBKvv+iCzIeaCs1CjkFTg8WMiIich3MUMy+eY0aOT690FR0BAHBZlYzn25/B5pcaQQqsJDoO2TFNDR7KSEREzsM5iplvLUDA+TdE5Umv0IiOkM9q73CMei4TMb1bAkql6DhkZxSurlD5+YmOQUREVG6co5hpXAHvaqJTEJUpg1zx/ndOVeTgf2EnsGR8MOQ6NUXHITui5vllRETkZCreJ7myYuCMceTY9BaL6AiF+t01CkP6Xce5Z1pB0ulExyE7wIk/iIjI2ThPMfNjMSPHZjAZRUe4J7NkxawaxzFzrA9yWzcUHYcqOJ5fRkREzobFjMhBGHIzRUcoknOaOAx9OAK7RjWFwuArOg5VUJyRkYiInI3zFDMeykgOTp+VJjpCsXzpdwZjR1qQ2L05IHFyHsqP55gREZGzcZ5ixhEzcnCGzETREYotTpGJl5qdwurxdSEF84M4/YuHMhIRkbNxnmKm8wbceU0lclw+GQlQSPb5v/Rm94sYOigOkQNaQdJUrGn/qfwp3N2h8vcXHYOIiKhc2eenuJLiqBk5MKVsgbfGU3SMEsuRzHgz5DjeHx8Ac5N6ouOQQC4NwyDx8FYiInIyzlXM/OuLTkBUpgxq+y1mtx3V3sQzPS/i8PDmkLzs//VQ8ekaNRYdgYiIqNw5VzGr0lx0AqIypVc5zjXCPg48hUkvaJDeuanoKFTOdI14OQUiInI+zlXMqrYQnYCoTBkktegIpeqqKgUj257BxrENIVUOEB2HyomuUSPREYiIiMqdcxUz35qAq150CqIyY5Ad87yc77wiMOq5DNzo0wpQqUTHoTKkqhwIlZ+f6BhERETlzrmKGQBU4agZOS69xSI6QplJlXLwSv3j+GJ8Dcj1aomOQ2WE55cREZGzcr5iVrWl6AREZUZvNIqOUOb26q7imb7ROPNsK0iurqLjUCnjYYxEROSsnLCYcQIQclz63AzREcqFBTLeqXYc08Z6IqctP8g7El1j/nkSEZFzkmRZlkWHKFc5qcC8GgCc62WTc7hYqQ6edM0WHaPcjUoMQ/eN1yHHJ4iOUiFZZBlfJCbg57Q0JJjN8Fep0NfTCy/q9fe8Xtj/JSfj/1KSccNkQqBKjTF6Pfp4eeVbJ81iwacJ8fg1PR2pVisqq1R4w78SOrm7F7jNGyYj3oiJQXhODuq7uGBeYCCqqP+5qLhKhUnVqmLEqFHo379/qb1+IiIie+B8I2YuXoChtugURGXCkOGcxeRr/Vm8NMKEhJ4tAIXz/Vq7n6+TErE2JQVv+1fCluBgvOrnh2VJSVidklzoc9YmJ2NBQjzG6Q3YHBSM8QYD3rl1C7sz0m3rGGUZo65fww2TCZ9UroJfgoMxOyAQle4xQcsHcXGopFLhh6Bg+KlU+DAu3vbYrzodlGo1SxkRETkl55zerGpLIOGC6BREpc47MwkqyQtm2Sw6SrlLUGRibJOTeCw0FMO35ACXr4qOVGGczM7Gw+7utlGsKmoNfklLw5nsHMCn4OdsTkvDQC9v9PTMu8h3NY0GZ3KysSwpCV3cPQAAP6amINViwZrqNaD+Z+TNNvpViEijEa/7+yNIo0FfTy98GB8HIG/k7ZPLkdj74w+l8ZKJiIjsjnN+tRzUXnQCojIhQYav1uv+KzqwX9wiMXRgLC4ObAVJqxUdp0JootPhcGYmov6ZHOZ8Tg6OZ2ejo7tboc8xylZoFfkPc3SRFDidnQ3TP0fA787IQGMXHd69FYuOly7iiSuX8WViAiz3OEK+rlaLQ5lZsMoyDmZlos4/f0bz4+Mw+ok+qFat2oO+XCIiIrvknMWsZhfRCYjKjF7tITqCcLmSBVNrHcd74/1hblpPdBzhRvvq8ZinJx6/chmNLpxH/6tRGOrji96ehZf49m7u2JCSgnM5OZBlGWdzsrEhNQVmACn/XJbhusmEnRnpsABYUrUaXtIbsCIpCUsSEwvd7mQ/f1wx5uKRy5G4ajRisp8/jmZl4XxuLp5/ZSIGDhyImjVr4sUXX4TRCWYZJSIius05D2X0DAT86gHxEaKTEJU6vdJFdIQK44QmBs/0AP7XtDk6bIyEnJwiOpIQ29PTsSUtDR8GVkaIVoPzObmYG3crbxIQr4LL2Ut6PRLMZgy+GgUZgP6fdZclJeH2OJpVluGrVGJWpQAoJQkNXFxwy2zGN0mJGGcwFLjdSmo1Flf9d1TMaLVi9PVr+LBpM3ywYgU8PDxw4cIF9OjRA19++SVefvnlUn43iIiIKibnHDEDgFoPi05AVCYMklp0hArn00qn8MooJdIebiY6ihDz4+Mw6p9Rs9paFzzh5YVhvr5YmlT4yJaLQoE5gYE4VrsOfq1ZC7tq1kIVlRpuCgV8lUoAgJ9KhSCNBso7ZnasqdEgwWKBsYgT/n6ZlIj2bm5o81hP7NmzB/3794darcaTTz6JPXv2PNDrJiIisifOW8xCWMzIMel5JYgCXVelYlTr09gwLgxS1cqi45SrbKv1rl/2CkiwFqE8qSUJAWo1lJKEX9LT0NnNHYp/ilhTnSuijcZ827lqMsJPqYLmHtPw3xaZm4utaWl42eAHt/btYbFYYDKZAAAmkwmWfw6ZJCIicgbOW8xqtAdUPOSLHI/B7HwzMhbHes/zGDEkFdf6tQLuMa27I+ni7o4vkxKxNyMDN0xG/JaejpXJSXjE49/zET+Oj8MbMTdtP0cZjdicmooooxGns7Px2s0buJibi4l+/x6i+LS3N1KtVrwXdwtRRiP2ZmTgq8REDPbxvm8mWZYx41Ys3vCvBFetFq6t26B9+/ZYunQpIiIisGrVKrRvz4maiIjIeThvMVPrgOptRKcgKnUGY67oCBVeuiIXr9U9js9ergZrg1DRccrc1EqV8KiHB2bfikWvK1fwYXwcBnp542WDn22dBLMZMf+MVgF5F6VekZyEJ6OuYNT1a8iVZfxfjRr5psMPVKuxtGo1nM3JQd+oK3gv7hae9fHBaF/9fTOtT02BQalCZ3d3uDZuDKW7G2bOnImcnBy0bt0aISEhGDduXOm+EUWwYsUKeHt7l/t+H8Tw4cPRt29f0TGIiOgBOcfXxYWp9TBweY/oFESlSp+T4cxfuRTLfpdrOPSEhClNW6DZpgjIGZmiI5UJN4USb/pXwpv+lQpd573A/Id31tJq8WNQ8H233USnw9oaQcXONMjbB4O88y6i5taxIwDA398fv/32W7G39V/Xrl3DjBkzsH37diQkJCAwMBB9+/bF9OnTodf/WxqDgoIwceJETJw48YH3WVKnTp3CtGnTcPjwYaSlpSEgIACtW7fG559/Dn9/f2G5iIio/Dn3xzdOAEIOSJ+dIjqCXbFAxtxqJ/HWS+7Ibt9IdByn5N6lc6lt6/Lly2jRogUuXryI7777DpcuXcKSJUuwa9cutG3bFklJSaW2r+Iw3TEaeVt8fDy6du0KX19f7NixAxEREVi+fDkqV66MzEzH/JKAiIgK59zFLKAh4B4gOgVRqdKnx4uOYJcuqhIx7KFwbBvTGFIlv/s/gUqFumpVuNSuXWrbGzduHDQaDXbu3IlOnTqhevXq6NmzJ3777TfcuHEDU6dOBQB07twZV69exSuvvAJJkiD9Z7KSHTt2oF69enB3d0ePHj0QExOT7/Gvv/4a9erVg4uLC+rWrYtFixbZHouKioIkSVi3bh06deoEFxcXrFmz5q6sBw4cQGpqKr7++ms0bdoUwcHB6NKlCxYsWIDg4LzRSovFgpEjRyI4OBg6nQ516tTBp59+es/3wGq1Yu7cubbnNG7cGBs2bLA9npycjCFDhsDPzw86nQ6hoaFYvnx58d5oIiIqdc5dzACgXi/RCYhKlVd2KjQKzf1XpAIt9z2HF4fnIu7xloCCvyLLmnuXLqW2raSkJOzYsQNjx46FTqfL91hAQACGDBmCdevWQZZl/Pjjj6hatSpmz56NmJiYfMUrKysL8+fPx7fffos//vgD0dHRmDRpku3xNWvWYPr06ZgzZw4iIiLw3nvvYdq0aVi5cmW+fb7xxhv43//+h4iICHTv3v2uvAEBATCbzdi4cSPkQmbItFqtqFq1Kr7//nuEh4dj+vTpeOutt7B+/fpC34e5c+di1apVWLJkCc6dO4dXXnkFzz77LPbu3QsAmDZtGsLDw7Ft2zZERERg8eLFMBRy3TkiIio/zn2OGQDU7wsc+Vp0CqJSpdd6ISabI2cllajIwvhGJ9A9JAQjtxqBS1GiIzksjy6dS21bFy9ehCzLqFevXoGP16tXD8nJyYiPj4e/vz+USiU8PDwQEJD/yAmTyYQlS5agVq1aAIDx48dj9uzZtsdnzJiBjz76CE8++SQAIDg4GOHh4fjyyy8xbNgw23oTJ060rVOQNm3a4K233sIzzzyDF198Ea1atcLDDz+M5557DpUq5Z0PqFarMWvWLNtzgoODcejQIaxfvx4DBw68a5u5ubl477338Ntvv6Ft27YAgJo1a2L//v348ssv0alTJ0RHR6Np06Zo0aIFgLxz7YiISDx+HVyjPeBe+AnxRPZIr3YXHcEh7HC9jGefisGFp1tCcuHlNUqbwt0dri1blvp2Cxt9KipXV1dbKQOAwMBAxMXFAQAyMzMRGRmJkSNHwt3d3XZ79913ERkZmW87t4vPvcyZMwexsbFYsmQJGjRogCVLlqBu3bo4c+aMbZ0vvvgCzZs3h5+fH9zd3fHVV18hOjq6wO1dunQJWVlZ6NatW758q1atsuV76aWXsHbtWjRp0gRTpkzBwYMHi/0eERFR6WMxUyiAer1FpyAqVQYFS0RpMUoWTAs+gXfG6WFq0UB0HIfi/lBHSGp1qW0vJCQEkiQhIiKiwMcjIiLg4+MDP797n0Oo/k8mSZJsZS8jIwMAsHTpUpw8edJ2O3v2LA4fPpzveW5ubkXKrdfrMWDAAMyfPx8RERGoXLky5s+fDwBYu3YtJk2ahJEjR2Lnzp04efIknn/+eRiNxgK3dTvf1q1b8+ULDw+3nWfWs2dP2/l1N2/eRNeuXfMdqklERGKwmAF5hzMSORC9xKOUS9tpzS0M6XYBf4xsBqkIF1Cm+/PsVbpfiun1enTr1g2LFi1CdnZ2vsdiY2OxZs0aDBo0yDbRh0ajgcViKdY+KlWqhMqVK+Py5csICQnJd7s9YceD0Gg0qFWrlm1WxgMHDqBdu3YYO3YsmjZtipCQkLtG5u5Uv359aLVaREdH35WvWrVqtvX8/PwwbNgwrF69Gp988gm++uqrB85OREQPhp/egLzDGd38gcw40UmISoXeKjqB41rofxo/jPbArOPN4f3bMdFx7JbS1xfuD3Us9e0uXLgQ7dq1Q/fu3fHuu+8iODgY586dw+TJk1GlShXMmTPHtm5QUBD++OMPPP3009BqtUWeAGPWrFmYMGECvLy80KNHD+Tm5uLo0aNITk7Gq6++WuSsW7Zswdq1a/H000+jdu3akGUZP//8M3755RfbLImhoaFYtWoVduzYgeDgYHz77bc4cuRIoSXQw8MDkyZNwiuvvAKr1YoOHTogNTUVBw4cgKenJ4YNG4bp06ejefPmaNCgAXJzc7Fly5ZCz8sjIqLywxEzgIczksMxmM2iIzi0GGU6Xmh5CuvGN4BUrYroOHbJ8/HHIalK/7vB0NBQHD16FDVr1sTAgQNRq1YtvPDCC+jSpQsOHToEX19f27qzZ89GVFQUatWqdd/DG+80atQofP3111i+fDkaNmyITp06YcWKFcUeMatfvz5cXV3x2muvoUmTJmjTpg3Wr1+Pr7/+GkOHDgUAjBkzBk8++SQGDRqE1q1bIzExEWPHjr3ndt955x1MmzYNc+fORb169dCjRw9s3brVlk+j0eDNN99Eo0aN8NBDD0GpVGLt2rXFyk5ERKVPkh/0LGlHcWUfsJJT55Nj2Fn7IbxmihIdwym4yxrM+DsMNTYfB1iIiyxowwbownjOHhER0W0cMbvt9uGMRA7AkJ0uOoLTyJCMmFznOBa8XBXWhqV3oWRHpg0NYSkjIiL6Dxaz2xQKoOFTolMQlQpDdoroCE7nkMt1DH78Mo491xKSBy9XcC+eTzwhOgIREVGFw2J2pyZDRCcgKhX6dF5cWgRZAt6vcgJvvOiKrA6NRcepmBQKeLGYERER3YXF7E4BYUBgE9EpiB6YW24GdCqd6BhOK1KVhOEdz2HLi40hBfAQ6Tu5tWkNdaVKomMQERFVOCxm/9X0WdEJiEqFXuMlOoLTW+VzDi8My8atXi0BpVJ0nArBq08f0RGIiIgqJBaz/2o4AOBIAzkAvcpNdAQCkKzIxssNT+DLl4OB2g9+AWJ7Jrm6wqNbN9ExiIiIKiQWs//SeQMN+opOQfTADEqt6Ah0h126KDzz5A2ED24FSeecX/54dnsECldX0TGIiIgqJBazgjR/XnQCogemBw+dq2jMkhUzg45j1jhfGFuFiY5T7rz69hUdgYiIqMJiMStI9daAP6+xQ/bNYOW14yuqs+pbeLbreewZ1QyS3ld0nHKhCakF1zZtRMcgIiKqsFjMCtOCo2Zk3/Qmk+gIdB+L/E7j5ZFWJD/aHJAk0XHKlH74cEgO/hqJiIgeBItZYRoNArSeolMQlZjemC06AhVBrDIDY5qfwupxdSEFVRUdp0woDQZeVJqIiOg+WMwK4+IJNHtOdAqiEjNkp4uOQMWw2eMihj6dgCv9WwFqteg4pcrnmcFQaDSiYxAREVVokizLPBGlMKk3gE8bA1YeEkb254ZvdfTgpczsUqvcKnhllwuUpy6IjvLAJJ0OIb/vgsrHR3QUIiKiCo0jZvfiVQVo+JToFEQlok+PFx2BSugv7Q080zMSR4a3gORp34dUe/Xtw1JGRERUBCxm99PuZdEJiErExZQNdzUvMm2vZAn4MPAkJo/RIKNTU9FxSkahgH7YMNEpiIiI7AKL2f1UagCEPCI6BVGJ6DX2PdpCQJQqBSPancFPLzWEVDlAdJxice/SBZqgINExiIiI7AKLWVG0myA6AVGJ6FUcMXMUa7wjMOq5DMT0bgko7ePi4frnh4uOQEREZDdYzIqiZicgsLHoFETFZlBoRUegUpQq5eB/YSew+OVgyHVriY5zTy4NG8K1RQvRMYiIiOwGi1lRcdSM7JBe5v/ijmi3LgpD+l7D2WdaQnJ1FR2nQBwtIyIiKh5+aiuqBv0A35qiUxAVi8HKq2E4KrNkxewaJzBjrBdy2zQUHScfdZUq8OjeXXQMIiIiu8JiVlQKJdD5LdEpiIrFYDKKjkBlLFwdj6FdIvDb6CZQGHxFxwEA+E14GZKdnAdHRERUUbCYFUfDp4BKYaJTEBWZPjdLdAQqJ18ZzmLsCCsSu7cAJElYDm3t2vDs3VvY/omIiOwVi1lxSBLQZaroFERFZshJFx2BylGcMgMvNTuJVePrQAquLiSD36uvQFLwnxYiIqLi4r+exVX3MaBqS9EpiIpEn5kkOgIJsMX9EoYOikPkgFaQNJpy269rixbw6Ny53PZHRETkSFjMSqLrdNEJiIpEnx4PCeIOayNxciQz3gw5jvfHB8DcpF657NN/0mvlsh8iIiJHxGJWEsEPAcGdRKcgui+1xQhPjYfoGCTQUe1NPNPzIg4/3wKSl2eZ7cf9ka7QNWlSZtsnIiJydCxmJdV1hugEREWiV7OYEfBxwEm8NkaD9M5NS3/jSiX8X3ml9LdLRETkRFjMSqpqc6DO46JTEN2XQeUmOgJVENHKFIxsewY/jg2DVCWw1Lbr1a8vtLVqldr2iIiInBGL2YN4ZCagUItOQXRPekX5Tf5A9mGt13mMeDYNN/q0BFSqB9qWpNXCb/z4UkpGRETkvFjMHoRfbaDNi6JTEN2TXubkH3S3dEUuXql/Al+MrwG5XkiJt+Pz7BCoAwJKMRkREZFzYjF7UJ1eBzxK75AgotJmsFhFR6AKbK/uKp7pexWnn20Jya14h70qPD1heOGFMkpGRETkXFjMHpTWA+g2W3QKokLpTUbREaiCs0DGu9VO4O2XPJDTtlGRn+c3fjyUXl5lmIyIiMh5sJiVhkYDgertRKcgKpAhN1N0BLITF9QJeK5zOHaMaQLJ33DPdV3q14fPkGfKKRkREZHjYzErLY99CEhK0SmI7mLIThMdgezMMt+zeOl5E+J7tgAUBfwzoVAgYNZMSEr+ziMiIiotLGalJSAMaDlKdAqiu+gzE0VHIDuUoMjEuCYnsXx8KFCrRr7HvAcNhK5hQ0HJiIiIHJMky7IsOoTDyEkFPm8OZMaLTkJkY5GUaBZcDVaZk4BQyWhlJaZdbow6P52CwsMDtbb9AqUHL1xORERUmjhiVppcvIBHZolOQZSPUrbAW8MJGqjkciUL3q51HHPG+8H93aksZURERGWAxay0NR0C1OoqOgVRPnq1u+gI5ADcg0NQuXMP0TGIiIgcEotZWXjic0DLEQqqOAwqV9ERyM65qlwxvc100TGIiIgcFotZWfCqAnSfIzoFkY1eUouOQHZuQrMJCHQPFB2DiIjIYbGYlZVmQ4GQbqJTEAEADLIkOgLZscZ+jTG47mDRMYiIiBwai1lZeuIzHtJIFYLBYhEdgeyUTqXDO+3fgULiPxdERERlif/SliXPykCP90SnIIKvMVd0BLJTU1pOQbBXsOgYREREDo/FrKw1fRYIfVR0CnJyhtxM0RHIDj1S/RE8Vfsp0TGIiIicAotZeej9Wd41zogE0Welio5Adsbf1R8z280UHYOIiMhpsJiVB89A4ImFolOQEzNkJIiOQHZEISnwXof34MVzZImIiMoNi1l5qf8E0GqM6BTkpHwyk6CSVKJjkJ0YVn8YWge2Fh2DiIjIqbCYladH3wUqNxWdgpyQBBk+Wk/RMcgO1POth5ebvSw6BhERkdNhMStPKg0wYAWn0CchDGoP0RGogtOpdHj/ofehVvCC5EREROWNxay8+QQBfXi+GZU/vVInOgJVcK+3fJ1T4xMREQnCYiYCzzcjAfQ8x4zuoX9of/Sv3V90DCIiIqfFYibKo+8CgU1EpyAnYpAl0RGogmrs1xhTW08VHYOIiMip8St0UW6fb/ZVJyAnFXP35eLH8yacT7BCp5LQrpoS7z+iRR2DEgAQlWJF8KcZBW5q/VM6DGhQ8DkhM/fkYO1ZM66lWaFRAs0DlZjzsBatq/77R//Ed1k4GWtBXKYMH52ER2qq8P4jWlT2KLy3v7ojBytOGuGmkTCvqwuGNPp3/9+fM2HVaRN+HuxagjeGyorebBEdgSogP50fFnReALWS55URERGJJMmyLIsO4dQu7QLWDECPb9PwdJgaLSsrYbYCb/2ei7NxFoSPdYebRoLFKiM+K/8f1VfHTPjwYC5iXvOAu6bg0ZD/O2OCv5uEmj4KZJtkLDhsxPfhJlx62R1+bnnFa8GhXLStpkSguwI30q2YtDMXAHBwpFuB2/z5ggmjf87BlmdccTHRihGbs3HtFXcYXBVIzZHRcmkmfnvOFdW9OCBbkWyr0xlTjJdFx6AKRK1QY3mP5Wjs11h0FCIiIqfHETPRQroCPeZhuzw53+IVfVzgPz8Dx2IseKiGCkqFhAD3/OVr43kTBtZXF1rKAOCZhvm/Bf+4uwuWnTDh9C0rutbMK06vtNXaHq/hrcAbHWT0XZsNk0WGWnn3tiMSrOgcpESLynm3iTtycCVZhsEVmPJrDl5qoWYpq4AMuZkAj2akO7zd5m2WMiIiogqCn54rgtYvAC1H5VuUmjdoBV9dwZ+kj9204GSsFSObFf3wI6NFxlfHjPDSAo0DCv6jT8qWseaMCe2qKQssZQDQuJISR29akJwt49hNC7JNMkJ8FdgfbcbxWAsmtNYUOROVH31WsugIVIEMqjMIT4Y+KToGERER/YMjZhVFj/eBxEvA5T2wyjImbs9B+2pKhPkrC1x92Qkj6hkUaFft/n+EW/424ekN2cgyAYEeEn4d6gaDa/5i9vqvOVh4xIgsE9CmqhJbBhc+tXr3EBWebaRGy6UZ0KklrOyrg5sGeGlrDlb00WHxURM+/8sIg6uEr3q5oEEhr4HKlz49HnDjNfQIaObfDK+3el10DCIiIroDzzGrSLJTgK8fwUsrT2PbJTP2j3BDVc+7R7ayTTICP0rHtIe0eK2d9u7t/EemUUZMhoyELCuWHjPh9ygz/hzlBn+3f7edkGVFUraMqykyZu3NhZeLhC2DdZCkoh37NmtPLlJyZDzfVI1Hv83CmZfcsOVvMxYeMeLYC+5FfguobDWvFQKj1Sg6BglUybUS1vVaB71OLzoKERER3YGHMlYkOm+MP1UPWy5ZsXtYwaUMADaEm5BlAp5rXLTDGN00EkJ8FWhTVYVlfXRQKSQsO27Kt47BVYHaeiW61VJh7VM6/HLRjMPXizaL3/kEC1afMeGdh7XYE2XGQzWU8HNTYGADNY7HWJGey+5fUfhqOWLmzDw0Hlj0yCKWMiIiogqIxayCkGUZ48ePx8Ztv+P3H1chWF/4SNiyEyY8UUdlm1WxuKyyjFxL4WXJ+s9DuUXoZbIsY8yWHHz8qBbuGgkWK2Cy5j12+7/32BWVM4Oao5fOSqvU4rMun6G2T23RUYiIiKgALGYVxLhx47B69Wr83//9HzzqP4zYNjMQm2FFtil/q7mUZMUfVy0Y1azgCTbqLszAxoi80bBMo4y3duXg8HUzrqZYceymBSN+ysaNNBkD6ueNtv153YyFfxlxMtaCqylW/H7FjME/ZKOWj4S2Ve9/btjXx03wc5XQu07e9tpXV+H3K2Ycvm7GgkO5qO+ngLcLpwKsKPQKF9ERSAClpMT7Hd9Hi4AWoqMQERFRITj5RwWxePFiAEDnzp3zLV/exwXDm/xbwr45YURVTwmP1iq4NF1ItCL1n0MHlQrgfIIVK09lIyFLhl4noWUVJfY972abkMNVLeHHCBNm7MlFplFGoIeEHrVUePspHbSqexeqWxlWzNmXm+96Z62qKPFaWy0e/79s+LvlTQxCFYdB4v/yzuit1m+ha42uomMQERHRPXDyj4rut1nA/o9FpyAH8XmTx/FV6hnRMagcvdT4JYxtMlZ0DCIiIroPHspY0T0yA2gxQnQKchB6s+n+K5HDGFB7AEsZERGRnWAxswePfQSEPSU6BTkAgzFHdAQqJ12rd8Xbbd4WHYOIiIiKiMXMHigUQL8vgdDuopOQnTPkZIiOQOWgeaXmeP+h96GQ+CueiIjIXvBfbXuhVAEDVwI12otOQnZMn5UsOgKVsWb+zbCo6yJolfe/+DwRERFVHCxm9kStAwavBSo3FZ2E7JQhPUF0BCpDLQNaYvEji+GqdhUdhYiIiIqJxczeuHgCQzcBVZqLTkJ2yC03HTolr2XmiFoHtMYXXb9gKSMiIrJTLGb2SOedV86qthKdhOyQr9ZLdAQqZW0D22Jh14XQqXjdQCIiInvFYmavXDyBoT8C1duKTkJ2xqByFx2BSlH7yu3xedfP4aLiSCgREZE9YzGzZ1oP4NkfgOBOopOQHdFzUgiH0aFKB3z28Gec6IOIiMgBsJjZO40bMOR7oM5jopOQnTBAKToClYJOVTvhsy6fQaPUiI5CREREpYDFzBGotMDAb4GGA0QnITtgsIpOQA+qZ3BPLOi8AGqlWnQUIiIiKiUq0QGolChVQL+vAK0ncHSZ6DRUgelNRtERSizzQiYSfklA9tVsmFPMqP5ydXg29yx0/RsrbiB5TzICBgfA0N1Q6Hq3Nt5C/E/x+ZZpAjSoPa+27WdTigmx62KReS4TlhwLtIFa+PXyg1fLwidTSTmYgtgNsbDmWOHT0QeBgwNtjxnjjYiaH4VaM2tBqSv6KOaIsBGY2GwiJEkq8nOIiIio4mMxcyQKBdDrY8C7OvDbTACy6ERUAemNOaIjlJg11wqX6i7wecgH0Z9H33PdtGNpyI7Mhsq7aL/mtFW0CJocZPtZUuYvPteXXoc1y4rqE6tD5a5CyuEUXFt0DZqZGuhq3D0bojndjBvLb6DqqKpQ+6lxdcFVuNVzg2eTvCJ589ubqDSgUpFLmVJS4o1Wb+Dpuk8XaX0iIiKyLzyU0RF1mAgMWA5wljYqgD4nTXSEEvNo5IFK/Svdc5QMAEzJJtxcfRNVX6x6V8EqjKSQoPZW224qj/yFLvtSNnwf8YVrTVdo/DXwf8IfSlclsqOyC9yeMd4IpU4Jr9ZecK3pCrd6bsi9mQsASDmcAkkpwatF0S5doFPpsKDzApYyIiIiB8Zi5qga9AOG/Qy4Fn74FjknQ2ay6AhlSrbKuP7VdRh6GuBSpehfTuTeysX5iedxYfIFXFtyDcbE/Id86kJ0SPsrDeYMM2SrjJTDKbCarHCr61bg9rSVtLAarXmHXWaYkX0lGy7VXGDJtCDuxzgEPhtY4PP+y9fFF18/+jW6VO9S5NdCRERE9oeHMjqyaq2AUb8BawYAiRdFp6EKwpAeD3j5iY5RZhJ+SQAUgL6bvsjPca3liqqjqkIbqIUpxYS4n+Jw5b0rCHk3xHaoYfWx1XFt8TWcH38eUAIKjQLVJ1SHtlLBU9Ur3ZSoOroqri+9Dtkow7udNzwaeuD6suvw7eoLU4IJ0Z9GQ7bI8O/rX+C5atU9qmPJI0tQzbNayd4MIiIishsOOWI2fPhwSJJ0161Hjx6io5U/32Bg5E6gRnvRSaiCcDFlw03lKjpGmciOykbizkRUHVW1WJNjeDTygFcrL7hUc4FHQw8EvRIES5YFqX+l2ta59eMtWLIsCJoShFozasHQ3YBrX1xDzrXCz9nzbO6J0HdDUfuD2qjUrxIyz2ci93oufDv74triawh8JhDVx1fHjW9uwJxmzvfcRn6NsPqx1SxlRERETsJhR8x69OiB5cuX51um1ZbsIqyyLMNisUClstO3y9UXGLoJ+GkccGa96DRUARi0Xsg0Z4mOUeoyL2TCnG7Ghdcu/LvQCsSujUXizkTU+ahOkbajdFNCG6CFMS7vcMbcuFwk7UpCyJwQ2+GRuuo6ZP6dicRdiagyvMp9t2k1WXFz1U1UfaEqjHFGyBbZdhikNkCLrMgseDbNO3euV81emNF2Blx4nigREZHTcMgRMyCvhAUEBOS7+fj4ICoqCpIk4eTJk7Z1U1JSIEkS9uzZAwDYs2cPJEnCtm3b0Lx5c2i1Wuzfvx+5ubmYMGEC/P394eLigg4dOuDIkSO27dx+3tatW9GoUSO4uLigTZs2OHv2bL5s+/fvR8eOHaHT6VCtWjVMmDABmZmZZfuGqDRA/6VAt9mAxAsMOzu9quDzouydd3tvhLwTgpDZ/95U3ioYehoQNCmoyNux5FhgjDNC7Z13nTA5958ZTv8zCCcppCJPfhq/OR7uDd2hC9JBtsrAHdeTk815P6sUKrzV+i3M7TiXpYyIiMjJOGwxKw1vvPEG5s2bh4iICDRq1AhTpkzBDz/8gJUrV+L48eMICQlB9+7dkZSUlO95kydPxkcffYQjR47Az88PvXv3hslkAgBERkaiR48e6N+/P06fPo1169Zh//79GD9+fPm8qPb/A4ZtBtz8y2d/VCHpFSUbPRbNkmNB9tVsZF/NmwnRmGBE9tVs20QdKncVXKq65LtJSgkqLxW0gf++5ivvX0Hib4m2n2PWxiDzfCaM8UZkXczKm4pfAXi1zjvvSxuohaaSBjdX3ETW5SzkxuUiYVsCMs5lwLPZvWeIBICcGzlI/SsVlZ6sZNseJCBpbxLST6YjNyYX1epXw/LuyzG47uBSe7+IiIjIftjpsXn3t2XLFri7u+db9tZbb+GZZ54p8jZmz56Nbt26AQAyMzOxePFirFixAj179gQALF26FL/++iuWLVuGyZMn2543Y8YM2/NWrlyJqlWrYuPGjRg4cCDmzp2LIUOGYOLEiQCA0NBQfPbZZ+jUqRMWL14MF5dy+JY8qAPw4j7g++FA9KGy3x9VOAbZPkdNs69kI+r9KNvPsd/FAsgbKas6umqRt2OMM8Kc/u85XeYkM64tuQZLhgVKDyXcQt1Qc1pNqDzzfkVKKgk1XqmBW9/fwtVPrsKaY4W2khZVRlWBR2OPe+5LlmXcXHETAYMDoNDmfRem0ChQZVQVxHwbA9kko824Ntg0bBMMOs6iSkRE5Kwctph16dIFixcvzrfM19cXaWlFv4ZTixYtbPcjIyNhMpnQvv2/k2io1Wq0atUKERER+Z7Xtm3bfPusU6eObZ1Tp07h9OnTWLNmjW0dWZZhtVpx5coV1KtXr8j5HohHADBsC/DrNODwovLZJ1UYeqv1/itVQO713BG2IqxYzynovLL/Lqs29v4TbGgDtKj+cvVi7RsAJElCzak171ru2cQTnk088Wy9Z/Fai9egUjjsr2MiIiIqAof9JODm5oaQkJC7lmdkZADIK0O33T7MsKBtlLaMjAyMGTMGEyZMuOux6tWL/6HvgShVQI+5edPq//QyYEwv3/2TMAaT8f4rUZnSqXSY1W4Wegb3FB2FiIiIKgCnO8fMzy/v+k0xMTG2ZXdOBFKYWrVqQaPR4MCBA7ZlJpMJR44cQf369fOte/jwYdv95ORk/P3337aRsGbNmiE8PBwhISF33TQazYO8tJJr0A8Y/TvgV1fM/qncGYzZoiM4tSDPIKx5bA1LGREREdk47IhZbm4uYmNj8y1TqVQwGAxo06YN5s2bh+DgYMTFxeHtt9++7/bc3Nzw0ksvYfLkyfD19UX16tXxwQcfICsrCyNHjsy37uzZs6HX61GpUiVMnToVBoMBffv2BQC8/vrraNOmDcaPH49Ro0bBzc0N4eHh+PXXX7Fw4cJSe/3F5lc7r5ztmAocW37/9cmu6bPTAEHfAzgzCRIG1RmE11q8xlkXiYiIKB+HLWbbt29HYGBgvmV16tTB+fPn8c0332DkyJFo3rw56tSpgw8++ACPPvrofbc5b948WK1WDB06FOnp6WjRogV27NgBHx+fu9b73//+h4sXL6JJkyb4+eefbaNhjRo1wt69ezF16lR07NgRsiyjVq1aGDRoUOm9+JLSuAG9PwHqPAZsHg9k3BKdiMqIITMZ0DjdgLlQ/jp/zG4/G+2r8GLvREREdDdJvvNkK3oge/bsQZcuXZCcnAxvb2/RcR5MVhKwZSIQ/pPoJFQGTEoNmlUPEB3DaTxa41FMbzsdXlov0VGIiIioguJX5lQwV19g4Cqg35cAP0w6HLXFCE/1vad5pwfnofbAex3ew0edP2IpIyIiontiMaN7a/w08NIBIKij6CRUygwsCmWqdUBr/NjnR/Su1Vt0FCIiIrIDPJSRikaWgcOLgV2zATNn9HMEI5p0xZHUi6JjOBwXpQtebvoyhtYfCkmSRMchIiIiO8ERMyoaSQLajgXGHQZCu4tOQ6XAoNCKjuBw2ldujx/7/IjnGjzHUkZERETF4rCzMlIZ8QkChqwHwjcD298A0m6ITkQlpJf5vUxp0bvoMaXlFDxW8zHRUYiIiMhO8ZMZlUz9J4BxfwFtxwMK9nt7pLdYREewewpJgadqP4Wf+v7EUkZEREQPhJ+oqeS07kD3OUDjwcCWV4Drf4lORMVgMJtER7BrYfowTG0zFWGGMNFRiIiIyAGwmNGDCwgDRu4Ejq8EfpsJZCeLTkRFoM/NFB3BLnlrvTGh2QT0D+0PhcSDDoiIiKh0cFZGKl3ZycAf84G/lgKWXNFp6B4iAutjoEuG6Bh2Q61QY1CdQRjTaAy8XbxFxyEiIiIHw2JGZSMlGvh9DnBmPSBbRaehAsR7BuBhvUZ0jApPISnwWPBjGN90PKq4VxEdh4iIiBwUixmVrZjTwG8zgMjfRSeh/zArVGgeVBVWFudCdajSARObTUQd3zqioxAREZGDYzGj8hG5G/h1OhB7WnQSukOnuo2QlJsiOkaF08jQCBObT0TLgJaioxAREZGTYDGj8iPLwJnvgd3vAclXRKchAE827ICLGdGiY1QYQZ5B+F+z/+GRGo+IjkJEREROhrMyUvmRJKDRQCCsP3BuI7DvYyDunOhUTk2vcsFF0SEqgGCvYIwIG4HeNXtDqVCKjkNEREROiMWMyp9CCTR8Kq+g/b0d2PcRcP2I6FROySA59+Qfjf0aY0TYCHSp1gWSJImOQ0RERE6MxYzEkSSgTs+825U/8kbQLu8Wncqp6GXnKyMSJHSs2hEjwkageaXmouMQERERAWAxo4oi+KG8243jeSNoF37hNPvlwGCxiI5QblSSCj2De+L5sOcR6hMqLMfw4cORkpKCTZs2CctAREREFQ+LGVUsVZoBT68Bkq4Ax5YDJ1YDWYmiUzksvckoOkKZc1e7o29IXzxX/zkEugeKjkNERERUIIXoAEQF8g0Gus0GXo0A+n0FVG0lOpFD0udkiI5QZhoaGmJWu1nYNWAXXm/1eoUsZdu3b0eHDh3g7e0NvV6PXr16ITIy0vZ4VFQUJEnC2rVr0a5dO7i4uCAsLAx79+61rWOxWDBy5EgEBwdDp9OhTp06+PTTT/PtZ/jw4ejbty/mz5+PwMBA6PV6jBs3DiaTqdxeKxEREd0bR8yoYlNpgcaD8m6xZ4AjXwOnvwdMmaKTOQRDVirgKjpF6fFQe+Dxmo/jqdpP2cVFoTMzM/Hqq6+iUaNGyMjIwPTp09GvXz+cPHkSCsW/35tNnjwZn3zyCerXr4+PP/4YvXv3xpUrV6DX62G1WlG1alV8//330Ov1OHjwIF544QUEBgZi4MCBtm3s3r0bgYGB2L17Ny5duoRBgwahSZMmGD16tIiXTkRERP/B65iR/clJA06tBY6vBG6dFZ3GriW5GdDJ3/6bWRO/Juhfuz+6B3WHTqUTHeee7nWOWUJCAvz8/HDmzBmEhYUhKioKwcHBmDdvHl5//XUAgNlsRnBwMF5++WVMmTKlwH2MHz8esbGx2LBhg22fe/bsQWRkJJTKvMsBDBw4EAqFAmvXri2bF0pERETFwhEzsj8unkDrF/JusWeB0+uAMxuA9Juik9kd76wkqCRPmGWz6CjFZtAZ0COoB/qH9keIT4joOCVy8eJFTJ8+HX/++ScSEhJgteZNeBMdHY2wsDDbem3btrXdV6lUaNGiBSIiImzLvvjiC3zzzTeIjo5GdnY2jEYjmjRpkm9fDRo0sJUyAAgMDMSZM2fK6JURERFRcbGYkX0LCMu7PTILiPojr6BF/AzkpIhOZhcUshU+Wk/E5ySJjlIkfjo/PFLjETxa41E0q9QMCsm+T5Pt3bs3atSogaVLl6Jy5cqwWq0ICwuD0Vj0SVnWrl2LSZMm4aOPPkLbtm3h4eGBDz/8EH/++We+9dRqdb6fJUmyFUEiIiISj8WMHINCAdTsnHfrtQCI3A2c+xE4vxXITROdrkLTqz0qdDHz1/nnlbGgR9HUv6ndl7HbEhMTceHCBSxduhQdO3YEAOzfv7/AdQ8fPoyHHnoIQN6hjMeOHcP48eMBAAcOHEC7du0wduxY2/p3TiBCRERE9oHFjByPUg3UfjTvZjYCVw8AF3fm3RIviU5X4eiVFe+cLEctY3fy8fGBXq/HV199hcDAQERHR+ONN94ocN0vvvgCoaGhqFevHhYsWIDk5GSMGDECABAaGopVq1Zhx44dCA4OxrfffosjR44gODi4PF8OERERPSAWM3JsKg1Qq0vercdcIDESuPgrcHEHEHUAsOSKTiicQVLff6UyplVq0cy/GdpVboe2lduitk9tSJIkOlaZsFqtUKlUtok3JkyYgLCwMNSpUwefffYZOnfufNdz5s2bh3nz5uHkyZMICQnB5s2bYTAYAABjxozBiRMnMGjQIEiShMGDB2Ps2LHYtm1bOb8yIiIiehCclZGclzETuLw3byTt8m4gOUp0IiEWNHkc36SW7yQQEiTU9a2LNpXboG1gWzSr1AxapbZcM4jSo0cPhISEYOHChfdd9/asjCdOnLhrMg8iIiJyLBwxI+elcQPqPpZ3A4D0WCD6EBB9OO8WewaQLWIzlgODpexfowQJ1T2ro6l/U7QNbIs2ldvA18W3zPdbkSQnJ+PAgQPYs2cPXnzxRdFxiIiIqIJhMSO6zSMAaNAv7wYAuRnA9SP/FLVDwPWjDnlha4Mpp/S3qTMgzBCGhoaGCDOEoYG+Aby0XqW+H3syYsQIHDlyBK+99hr69OkjOg4RERFVMDyUkaiorJa8yUNuncu7xYXnXeA65RoA+/3f6K+glhgp3Srx8z00HqjnWy9fEQtwCyjFhERERESOj8WM6EHlpgO3/ilpceFAXASQfDXvgtdyxb9O1GX/UPRxu/ckKGqFGlU9qiLIMyjv5hWEGp41EOQZBL1OX05JiYiIiBwXixlRWbGYgLQbQEp03qhaSjSQ+s9/U64CaTGA1SQ6JVJ13nikij8MOgP8Xf3h5+oHf1d/BLgGIMgrr4hVca8CpUIpOioRERGRw2IxIxIpNx3ITr7jlvLv/Zx/7udm5BU4q+Wfm/mf2533zYCkANS6O26ugMol77+3f1a7AC5egKsBcPvn5moAXDxFvxNERERETo3FjIiIiIiISDCF6ABERERERETOjsWMiIiIiIhIMBYzIiIiIiIiwVjMiIiIiIiIBGMxIyIiIiIiEozFjIiIiIiISDAWMyIiIiIiIsFYzIiIiIiIiARjMSMiIiIiIhKMxYyIiIiIiEgwFjMiIiIiIiLBWMyIiIiIiIgEYzEjIiIiIiISjMWMiIiIiIhIMBYzIiIiIiIiwVjMiIiIiIiIBGMxIyIiIiIiEozFjIiIiIiISDAWMyIiIiIiIsFYzIiIiIiIiARjMSMiIiIiIhKMxYyIiIiIiEgwFjMiIiIiIiLBWMyIiIiIiIgEYzEjIiIiIiISjMWMiIiIiIhIMBYzIiIiIiIiwVjMiIiIiIiIBGMxIyIiIiIiEozFjIiIiIiISDAWMyIiIiIiIsFYzIiIiIiIiARjMSMiIiIiIhKMxYyIiIiIiEgwFjMiIiIiIiLBWMyIiIiIiIgEYzEjIiIiIiISjMWMiIiIiIhIMBYzIiIiIiIiwVjMiIiIiIiIBGMxIyIiIiIiEozFjIiIiIiISDAWMyIiIiIiIsFYzIiIiIiIiARjMSMiIiIiIhKMxYyIiIiIiEgwFjMiIiIiIiLBWMyIiIiIiIgEYzEjIiIiIiISjMWMiIiIiIhIMBYzIiIiIiIiwVjMiIiIiIiIBGMxIyIiIiIiEozFjIiIiIiISDAWMyIiIiIiIsH+H1Chz5GtsrQrAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "a = df1.NA_Sales.sum().round()\n",
+        "b = df1.EU_Sales.sum().round()\n",
+        "c = df1.JP_Sales.sum().round()\n",
+        "d = df1.Other_Sales.sum().round()\n",
+        "e = df1.Global_Sales.sum().round()\n",
+        "values = [a,b,c,d]\n",
+        "labels=['North America','Europe','Japan','Other Sales']\n",
+        "\n",
+        "plt.figure(figsize=(6, 6))\n",
+        "plt.title('Sales by Countries',fontdict={'fontweight':'bold'})\n",
+        "plt.pie(values,labels=labels,autopct='%.2f %%',pctdistance=0.9)\n",
+        "plt.legend(bbox_to_anchor=(1.5,1.0),loc='upper left')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e1f81b0e",
+      "metadata": {
+        "id": "e1f81b0e"
+      },
+      "source": [
+        "<b> NA </b> is clearly in the lead with almost 50% sales recorded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from mpl_toolkits.mplot3d import Axes3D\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "import matplotlib.pyplot as plt # plotting"
+      ],
+      "metadata": {
+        "id": "zjCPN3ZzWv4a"
+      },
+      "id": "zjCPN3ZzWv4a",
+      "execution_count": 37,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "1UnaaZJwYXfE"
+      },
+      "id": "1UnaaZJwYXfE",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Most genre at all"
+      ],
+      "metadata": {
+        "id": "RIkxBSIVYhon"
+      },
+      "id": "RIkxBSIVYhon"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "!pip install seaborn"
+      ],
+      "metadata": {
+        "id": "GKoGOk3UWP5y",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "21e0c9fb-bf0d-4f15-94bc-d2a4ca587cca"
+      },
+      "id": "GKoGOk3UWP5y",
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.12.2)\n",
+            "Requirement already satisfied: numpy!=1.24.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.23.5)\n",
+            "Requirement already satisfied: pandas>=0.25 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.5.3)\n",
+            "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /usr/local/lib/python3.10/dist-packages (from seaborn) (3.7.1)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.1.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.42.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.1)\n",
+            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.1.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25->seaborn) (2023.3.post1)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import plotly.graph_objects as go\n",
+        "\n",
+        "x = [\"NA_Sales\",\"EU_Sales\",\"JP_Sales\",'Other_Sales','Global_Sales']\n",
+        "y = [df1['NA_Sales'].max(),df1['EU_Sales'].max(),df1['JP_Sales'].max(),df1['Other_Sales'].max() ,df1['Global_Sales'].max()]\n",
+        "labels = ['Wii Sports\t','Wii Sports\t', 'Pokemon Red/Pokemon Blue', 'Grand Theft Auto: San Andreas\t', 'Wii Sports\t']\n",
+        "colors = [\"palette\"[i] for i in range(len(x))]\n",
+        "\n",
+        "fig = go.Figure()\n",
+        "fig.add_trace(go.Histogram(histfunc='max', y=y, x=x, text=labels, marker_color=c ))\n",
+        "\n",
+        "\n",
+        "\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "jC5a-4bZXdTq",
+        "outputId": "c40cabce-6167-4254-9f05-ebf5df200278"
+      },
+      "id": "jC5a-4bZXdTq",
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
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+              "<head><meta charset=\"utf-8\" /></head>\n",
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+              "                            \n",
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+              "            Plotly.purge(gd);\n",
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+              "}});\n",
+              "\n",
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+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "r=df1.groupby('Genre')['Global_Sales'].sum().sort_values(ascending = False)\n",
+        "r=r.reset_index()\n",
+        "r"
+      ],
+      "metadata": {
+        "id": "6oDdlockYc82",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 425
+        },
+        "outputId": "bcc0c8a2-eef9-40ad-ffa7-15807d71dce3"
+      },
+      "id": "6oDdlockYc82",
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           Genre  Global_Sales\n",
+              "0         Action       1722.84\n",
+              "1         Sports       1309.24\n",
+              "2        Shooter       1026.20\n",
+              "3   Role-Playing        923.83\n",
+              "4       Platform        829.13\n",
+              "5           Misc        789.87\n",
+              "6         Racing        726.76\n",
+              "7       Fighting        444.05\n",
+              "8     Simulation        389.98\n",
+              "9         Puzzle        242.21\n",
+              "10     Adventure        234.59\n",
+              "11      Strategy        173.27"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5b12899d-105b-4812-9ae7-3b6f84cb6800\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
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+              "      <th>1</th>\n",
+              "      <td>Sports</td>\n",
+              "      <td>1309.24</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Shooter</td>\n",
+              "      <td>1026.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Role-Playing</td>\n",
+              "      <td>923.83</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Platform</td>\n",
+              "      <td>829.13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Misc</td>\n",
+              "      <td>789.87</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Racing</td>\n",
+              "      <td>726.76</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Fighting</td>\n",
+              "      <td>444.05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Simulation</td>\n",
+              "      <td>389.98</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Puzzle</td>\n",
+              "      <td>242.21</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Adventure</td>\n",
+              "      <td>234.59</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Strategy</td>\n",
+              "      <td>173.27</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "        const element = document.querySelector('#df-5b12899d-105b-4812-9ae7-3b6f84cb6800');\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-ee1b600b-b889-43f4-8a7d-228a00d2a30e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee1b600b-b889-43f4-8a7d-228a00d2a30e')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-ee1b600b-b889-43f4-8a7d-228a00d2a30e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n"
+      ],
+      "metadata": {
+        "id": "8ikZ-xE2Xfrj"
+      },
+      "id": "8ikZ-xE2Xfrj",
+      "execution_count": 50,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.figure(figsize=(13, 7))\n",
+        "sns.barplot(data=r, x='Global_Sales', y='Genre', palette='husl')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "id": "69CIhXInRBx2",
+        "outputId": "bc5ab499-ff6c-4601-e6c8-32ef838bf117"
+      },
+      "id": "69CIhXInRBx2",
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1300x700 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "LPNN6pfXRHFh"
+      },
+      "id": "LPNN6pfXRHFh",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.3"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file

From fd5935fe89082df1a84c17f1cb32159f52d86bd7 Mon Sep 17 00:00:00 2001
From: ZestyZeeshan <141507225+ZestyZeeshan@users.noreply.github.com>
Date: Sun, 15 Oct 2023 12:15:40 +0530
Subject: [PATCH 2/2] Created using Colaboratory

---
 Copy_of_notebook.ipynb | 8792 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 8792 insertions(+)
 create mode 100644 Copy_of_notebook.ipynb

diff --git a/Copy_of_notebook.ipynb b/Copy_of_notebook.ipynb
new file mode 100644
index 0000000..b2e3485
--- /dev/null
+++ b/Copy_of_notebook.ipynb
@@ -0,0 +1,8792 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/ZestyZeeshan/ML-X/blob/main/Copy_of_notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a3d8b64f",
+      "metadata": {
+        "id": "a3d8b64f"
+      },
+      "source": [
+        "# Video Game Sales"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d3e899bf",
+      "metadata": {
+        "id": "d3e899bf"
+      },
+      "source": [
+        "This dataset contains a list of video games with sales greater than 100,000 copies."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e22b5b22",
+      "metadata": {
+        "id": "e22b5b22"
+      },
+      "source": [
+        "# Import necessary libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5eca13ae",
+      "metadata": {
+        "id": "5eca13ae"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np # for numerical data and arrays\n",
+        "import matplotlib.pyplot as plt # for visualizing data\n",
+        "import pandas as pd # for loading dataset and creating dataframe"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f106904c",
+      "metadata": {
+        "id": "f106904c"
+      },
+      "source": [
+        "# Load the dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c4d4ec20",
+      "metadata": {
+        "id": "c4d4ec20",
+        "outputId": "bf1a2e9a-1361-483b-92ea-41aceae73ce9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-3d6b55be-acd6-4314-a030-4eb864e395bc\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-3d6b55be-acd6-4314-a030-4eb864e395bc\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving video games.csv to video games.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import files\n",
+        "upload=files.upload()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df=pd.read_csv('video games.csv')\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "X6TrTaXmeOZ8",
+        "outputId": "32ae2bfa-86de-480a-f944-7ed416ecc444"
+      },
+      "id": "X6TrTaXmeOZ8",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Rank                      Name Platform    Year         Genre Publisher  \\\n",
+              "0     1                Wii Sports      Wii  2006.0        Sports  Nintendo   \n",
+              "1     2         Super Mario Bros.      NES  1985.0      Platform  Nintendo   \n",
+              "2     3            Mario Kart Wii      Wii  2008.0        Racing  Nintendo   \n",
+              "3     4         Wii Sports Resort      Wii  2009.0        Sports  Nintendo   \n",
+              "4     5  Pokemon Red/Pokemon Blue       GB  1996.0  Role-Playing  Nintendo   \n",
+              "\n",
+              "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  \n",
+              "0     41.49     29.02      3.77         8.46         82.74  \n",
+              "1     29.08      3.58      6.81         0.77         40.24  \n",
+              "2     15.85     12.88      3.79         3.31         35.82  \n",
+              "3     15.75     11.01      3.28         2.96         33.00  \n",
+              "4     11.27      8.89     10.22         1.00         31.37  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-381bece8-8f08-4a41-a473-016580e87563\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Rank</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Platform</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Genre</th>\n",
+              "      <th>Publisher</th>\n",
+              "      <th>NA_Sales</th>\n",
+              "      <th>EU_Sales</th>\n",
+              "      <th>JP_Sales</th>\n",
+              "      <th>Other_Sales</th>\n",
+              "      <th>Global_Sales</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Wii Sports</td>\n",
+              "      <td>Wii</td>\n",
+              "      <td>2006.0</td>\n",
+              "      <td>Sports</td>\n",
+              "      <td>Nintendo</td>\n",
+              "      <td>41.49</td>\n",
+              "      <td>29.02</td>\n",
+              "      <td>3.77</td>\n",
+              "      <td>8.46</td>\n",
+              "      <td>82.74</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Super Mario Bros.</td>\n",
+              "      <td>NES</td>\n",
+              "      <td>1985.0</td>\n",
+              "      <td>Platform</td>\n",
+              "      <td>Nintendo</td>\n",
+              "      <td>29.08</td>\n",
+              "      <td>3.58</td>\n",
+              "      <td>6.81</td>\n",
+              "      <td>0.77</td>\n",
+              "      <td>40.24</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Mario Kart Wii</td>\n",
+              "      <td>Wii</td>\n",
+              "      <td>2008.0</td>\n",
+              "      <td>Racing</td>\n",
+              "      <td>Nintendo</td>\n",
+              "      <td>15.85</td>\n",
+              "      <td>12.88</td>\n",
+              "      <td>3.79</td>\n",
+              "      <td>3.31</td>\n",
+              "      <td>35.82</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Wii Sports Resort</td>\n",
+              "      <td>Wii</td>\n",
+              "      <td>2009.0</td>\n",
+              "      <td>Sports</td>\n",
+              "      <td>Nintendo</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>11.01</td>\n",
+              "      <td>3.28</td>\n",
+              "      <td>2.96</td>\n",
+              "      <td>33.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>Pokemon Red/Pokemon Blue</td>\n",
+              "      <td>GB</td>\n",
+              "      <td>1996.0</td>\n",
+              "      <td>Role-Playing</td>\n",
+              "      <td>Nintendo</td>\n",
+              "      <td>11.27</td>\n",
+              "      <td>8.89</td>\n",
+              "      <td>10.22</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>31.37</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-381bece8-8f08-4a41-a473-016580e87563')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-381bece8-8f08-4a41-a473-016580e87563 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-381bece8-8f08-4a41-a473-016580e87563');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-8ac70372-89d6-4a49-9c1d-8c2f9b957751\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8ac70372-89d6-4a49-9c1d-8c2f9b957751')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8ac70372-89d6-4a49-9c1d-8c2f9b957751 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "977564ce",
+      "metadata": {
+        "id": "977564ce",
+        "outputId": "3ce5927b-b5de-41cd-bb5a-9a7106d236ab",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(16598, 11)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "df.shape # Shape of dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "742f8791",
+      "metadata": {
+        "id": "742f8791"
+      },
+      "source": [
+        "# Begin Analysis"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "81c0e3bd",
+      "metadata": {
+        "id": "81c0e3bd"
+      },
+      "source": [
+        "Let us check for null values first."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "75960eda",
+      "metadata": {
+        "id": "75960eda",
+        "outputId": "04810512-4f95-4e6a-92d3-2027cb338749",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Rank              0\n",
+              "Name              0\n",
+              "Platform          0\n",
+              "Year            271\n",
+              "Genre             0\n",
+              "Publisher        58\n",
+              "NA_Sales          0\n",
+              "EU_Sales          0\n",
+              "JP_Sales          0\n",
+              "Other_Sales       0\n",
+              "Global_Sales      0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "df.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "140dbd77",
+      "metadata": {
+        "id": "140dbd77"
+      },
+      "source": [
+        "Year and publisher seem to have null values."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bcacc961",
+      "metadata": {
+        "id": "bcacc961"
+      },
+      "source": [
+        "Let us also get more information about the columns like datatypes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "05b79ffe",
+      "metadata": {
+        "id": "05b79ffe",
+        "outputId": "836a4a91-c072-461e-901d-7738bcafd8a3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 16598 entries, 0 to 16597\n",
+            "Data columns (total 11 columns):\n",
+            " #   Column        Non-Null Count  Dtype  \n",
+            "---  ------        --------------  -----  \n",
+            " 0   Rank          16598 non-null  int64  \n",
+            " 1   Name          16598 non-null  object \n",
+            " 2   Platform      16598 non-null  object \n",
+            " 3   Year          16327 non-null  float64\n",
+            " 4   Genre         16598 non-null  object \n",
+            " 5   Publisher     16540 non-null  object \n",
+            " 6   NA_Sales      16598 non-null  float64\n",
+            " 7   EU_Sales      16598 non-null  float64\n",
+            " 8   JP_Sales      16598 non-null  float64\n",
+            " 9   Other_Sales   16598 non-null  float64\n",
+            " 10  Global_Sales  16598 non-null  float64\n",
+            "dtypes: float64(6), int64(1), object(4)\n",
+            "memory usage: 1.4+ MB\n"
+          ]
+        }
+      ],
+      "source": [
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c7a5ec53",
+      "metadata": {
+        "id": "c7a5ec53"
+      },
+      "source": [
+        "For the numeric datatypes, we can get some useful stats which will help us analyse the data better."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ae484739",
+      "metadata": {
+        "id": "ae484739",
+        "outputId": "5c740938-663e-4e7c-8a1a-8c4df342eafa",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               Rank          Year      NA_Sales      EU_Sales      JP_Sales  \\\n",
+              "count  16598.000000  16327.000000  16598.000000  16598.000000  16598.000000   \n",
+              "mean    8300.605254   2006.406443      0.264667      0.146652      0.077782   \n",
+              "std     4791.853933      5.828981      0.816683      0.505351      0.309291   \n",
+              "min        1.000000   1980.000000      0.000000      0.000000      0.000000   \n",
+              "25%     4151.250000   2003.000000      0.000000      0.000000      0.000000   \n",
+              "50%     8300.500000   2007.000000      0.080000      0.020000      0.000000   \n",
+              "75%    12449.750000   2010.000000      0.240000      0.110000      0.040000   \n",
+              "max    16600.000000   2020.000000     41.490000     29.020000     10.220000   \n",
+              "\n",
+              "        Other_Sales  Global_Sales  \n",
+              "count  16598.000000  16598.000000  \n",
+              "mean       0.048063      0.537441  \n",
+              "std        0.188588      1.555028  \n",
+              "min        0.000000      0.010000  \n",
+              "25%        0.000000      0.060000  \n",
+              "50%        0.010000      0.170000  \n",
+              "75%        0.040000      0.470000  \n",
+              "max       10.570000     82.740000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-edbe06ef-6330-4bd0-9e74-35f1d0f4b89a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Rank</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>NA_Sales</th>\n",
+              "      <th>EU_Sales</th>\n",
+              "      <th>JP_Sales</th>\n",
+              "      <th>Other_Sales</th>\n",
+              "      <th>Global_Sales</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>16598.000000</td>\n",
+              "      <td>16327.000000</td>\n",
+              "      <td>16598.000000</td>\n",
+              "      <td>16598.000000</td>\n",
+              "      <td>16598.000000</td>\n",
+              "      <td>16598.000000</td>\n",
+              "      <td>16598.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>8300.605254</td>\n",
+              "      <td>2006.406443</td>\n",
+              "      <td>0.264667</td>\n",
+              "      <td>0.146652</td>\n",
+              "      <td>0.077782</td>\n",
+              "      <td>0.048063</td>\n",
+              "      <td>0.537441</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>4791.853933</td>\n",
+              "      <td>5.828981</td>\n",
+              "      <td>0.816683</td>\n",
+              "      <td>0.505351</td>\n",
+              "      <td>0.309291</td>\n",
+              "      <td>0.188588</td>\n",
+              "      <td>1.555028</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1980.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.010000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>4151.250000</td>\n",
+              "      <td>2003.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.060000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>8300.500000</td>\n",
+              "      <td>2007.000000</td>\n",
+              "      <td>0.080000</td>\n",
+              "      <td>0.020000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.010000</td>\n",
+              "      <td>0.170000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>12449.750000</td>\n",
+              "      <td>2010.000000</td>\n",
+              "      <td>0.240000</td>\n",
+              "      <td>0.110000</td>\n",
+              "      <td>0.040000</td>\n",
+              "      <td>0.040000</td>\n",
+              "      <td>0.470000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>16600.000000</td>\n",
+              "      <td>2020.000000</td>\n",
+              "      <td>41.490000</td>\n",
+              "      <td>29.020000</td>\n",
+              "      <td>10.220000</td>\n",
+              "      <td>10.570000</td>\n",
+              "      <td>82.740000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edbe06ef-6330-4bd0-9e74-35f1d0f4b89a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-edbe06ef-6330-4bd0-9e74-35f1d0f4b89a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-edbe06ef-6330-4bd0-9e74-35f1d0f4b89a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-a95c673e-3f17-48f4-8150-eebc58c3a7f1\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a95c673e-3f17-48f4-8150-eebc58c3a7f1')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-a95c673e-3f17-48f4-8150-eebc58c3a7f1 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "df.describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6bf0dd4b",
+      "metadata": {
+        "id": "6bf0dd4b"
+      },
+      "source": [
+        "So, from the above table we see that the data is from year 1980 to 2020.\n",
+        "Also, we see the stats region-wise (NA, EU, JP, Other) as well as the global sales. NA sales seem to be the highest and JP is the lowest."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b5d1ea7",
+      "metadata": {
+        "id": "2b5d1ea7"
+      },
+      "source": [
+        "We saw some null values before. We can impute the null values and fill them. It won't be 100% accurate but it will be a good approximation for us to work with. Since the null values were in the year and Publisher, we can use mode to impute the null values.\n",
+        "<br>\n",
+        "Let us save it to a new dataframe."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ea6ce41a",
+      "metadata": {
+        "id": "ea6ce41a"
+      },
+      "outputs": [],
+      "source": [
+        "df1 = df.copy()\n",
+        "df1.Year = df1.Year.fillna(df1.Year.mode()[0])\n",
+        "df1.Publisher =df1.Publisher.fillna(df1.Publisher.mode()[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e9720d32",
+      "metadata": {
+        "id": "e9720d32",
+        "outputId": "910bec35-1caf-4fd4-bef8-233942c81556",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Rank            0\n",
+              "Name            0\n",
+              "Platform        0\n",
+              "Year            0\n",
+              "Genre           0\n",
+              "Publisher       0\n",
+              "NA_Sales        0\n",
+              "EU_Sales        0\n",
+              "JP_Sales        0\n",
+              "Other_Sales     0\n",
+              "Global_Sales    0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "df1.isnull().sum() # Checking for null values again"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5e57d7e5",
+      "metadata": {
+        "id": "5e57d7e5"
+      },
+      "source": [
+        "# Visualizations"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3ea2d5a7",
+      "metadata": {
+        "id": "3ea2d5a7"
+      },
+      "source": [
+        "Let us see which genre had the most sales."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cee5f07d",
+      "metadata": {
+        "id": "cee5f07d",
+        "outputId": "aeb34212-bb4e-4ff1-d193-05bacaee0ec5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 521
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "genre = df1.Genre.value_counts().values\n",
+        "labels =df1.Genre.value_counts().index\n",
+        "plt.figure(figsize=(6, 6))\n",
+        "plt.title('Genre by Sales',fontdict={'fontweight':'bold'})\n",
+        "plt.pie(genre,labels=labels,autopct='%.2f %%',pctdistance=0.9)\n",
+        "plt.legend(bbox_to_anchor=(1.5,1.0),loc='upper left')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bd1eadbc",
+      "metadata": {
+        "id": "bd1eadbc"
+      },
+      "source": [
+        "<b> Action </b> seems to have highest sales followed by <b> Sports </b>. <b>Puzzle</b> is the least sold genre."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "234557d0",
+      "metadata": {
+        "id": "234557d0"
+      },
+      "source": [
+        "Let us now visualize the top 10 years of sales."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "461a0b9a",
+      "metadata": {
+        "id": "461a0b9a",
+        "outputId": "c6e952b9-c290-4d6e-b0be-73a86c4bd64b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 701
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "year= df1.Year.value_counts().head(10)\n",
+        "plt.figure(figsize=(12,8))\n",
+        "\n",
+        "plt.xticks(year.index)\n",
+        "plt.title('Top 10 sales by Years',fontdict={'fontweight':'bold','fontsize':16})\n",
+        "\n",
+        "plt.bar(year.index,year.values)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "af00b75b",
+      "metadata": {
+        "id": "af00b75b"
+      },
+      "source": [
+        "Year <b> 2009 </b> seems to have the highest sales."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "219f5bcb",
+      "metadata": {
+        "id": "219f5bcb"
+      },
+      "source": [
+        "Now let us see which platform / console made the highest sales."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "4d2dc044",
+      "metadata": {
+        "id": "4d2dc044",
+        "outputId": "15448f49-a3b8-4ca9-9e4d-4a3c79915e01",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 719
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "platform = df1.Platform.value_counts()\n",
+        "plt.figure(figsize=(12,8))\n",
+        "\n",
+        "plt.barh(platform.index,platform.values)\n",
+        "\n",
+        "for i,v in enumerate(platform.values):\n",
+        "    plt.text(v,i,str(v))\n",
+        "\n",
+        "plt.title('Sales by Platform',fontdict={'fontweight':'bold','fontsize':'14'})\n",
+        "plt.xlabel('Counts')\n",
+        "plt.ylabel(\"Platform\")\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8f951e6f",
+      "metadata": {
+        "id": "8f951e6f"
+      },
+      "source": [
+        "Nintendo <b> DS </b> is in the lead with Sony's <b> PS2 </b> right behind it."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8ef07027",
+      "metadata": {
+        "id": "8ef07027"
+      },
+      "source": [
+        "Let us now visualize the sales by countries."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "91877501",
+      "metadata": {
+        "id": "91877501",
+        "outputId": "fdcb7275-10be-43a9-b242-b76b9a54429d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 521
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "a = df1.NA_Sales.sum().round()\n",
+        "b = df1.EU_Sales.sum().round()\n",
+        "c = df1.JP_Sales.sum().round()\n",
+        "d = df1.Other_Sales.sum().round()\n",
+        "e = df1.Global_Sales.sum().round()\n",
+        "values = [a,b,c,d]\n",
+        "labels=['North America','Europe','Japan','Other Sales']\n",
+        "\n",
+        "plt.figure(figsize=(6, 6))\n",
+        "plt.title('Sales by Countries',fontdict={'fontweight':'bold'})\n",
+        "plt.pie(values,labels=labels,autopct='%.2f %%',pctdistance=0.9)\n",
+        "plt.legend(bbox_to_anchor=(1.5,1.0),loc='upper left')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e1f81b0e",
+      "metadata": {
+        "id": "e1f81b0e"
+      },
+      "source": [
+        "<b> NA </b> is clearly in the lead with almost 50% sales recorded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "42fbcefc",
+      "metadata": {
+        "id": "42fbcefc"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "A new dataset for video games sales analysis"
+      ],
+      "metadata": {
+        "id": "8QeDAhscYHA8"
+      },
+      "id": "8QeDAhscYHA8"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import plotly.express as px\n",
+        "from plotly.offline import init_notebook_mode\n",
+        "init_notebook_mode(connected=True)\n",
+        "import plotly.graph_objects as go\n",
+        "from plotly.subplots import make_subplots"
+      ],
+      "metadata": {
+        "id": "6h5WkE7ZYMyE",
+        "outputId": "fa69581c-8dcf-4ddc-b493-95f34a71eabb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        }
+      },
+      "id": "6h5WkE7ZYMyE",
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "        <script type=\"text/javascript\">\n",
+              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+              "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+              "        if (typeof require !== 'undefined') {\n",
+              "        require.undef(\"plotly\");\n",
+              "        requirejs.config({\n",
+              "            paths: {\n",
+              "                'plotly': ['https://cdn.plot.ly/plotly-2.24.1.min']\n",
+              "            }\n",
+              "        });\n",
+              "        require(['plotly'], function(Plotly) {\n",
+              "            window._Plotly = Plotly;\n",
+              "        });\n",
+              "        }\n",
+              "        </script>\n",
+              "        "
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Importing our dataset in our google colab\n",
+        "dataset link https://www.kaggle.com/datasets/muhammadadiltalay/imdb-video-games/code .\n",
+        "following steps how we import"
+      ],
+      "metadata": {
+        "id": "4S9vBOLkdGJ_"
+      },
+      "id": "4S9vBOLkdGJ_"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "upload=files.upload()"
+      ],
+      "metadata": {
+        "id": "rpcPWwYZY7Aq",
+        "outputId": "e7977efa-fb4b-4d43-8463-579e6e5290b1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        }
+      },
+      "id": "rpcPWwYZY7Aq",
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-80fdfdd2-c3e1-4b84-acbb-b71e5d57a8be\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-80fdfdd2-c3e1-4b84-acbb-b71e5d57a8be\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving video games new.csv to video games new.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df=pd.read_csv('video games new.csv')\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "id": "L1HE3Ax8ZgPg",
+        "outputId": "fe5b2102-5fa4-4cd1-bb84-a69038cb04d9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 625
+        }
+      },
+      "id": "L1HE3Ax8ZgPg",
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Unnamed: 0                        name  \\\n",
+              "0           0                  Spider-Man   \n",
+              "1           1      Red Dead Redemption II   \n",
+              "2           2          Grand Theft Auto V   \n",
+              "3           3                  God of War   \n",
+              "4           4  Uncharted 4: A Thief's End   \n",
+              "\n",
+              "                                                 url    year certificate  \\\n",
+              "0  https://www.imdb.com/title/tt5807780/?ref_=adv...  2018.0           T   \n",
+              "1  https://www.imdb.com/title/tt6161168/?ref_=adv...  2018.0           M   \n",
+              "2  https://www.imdb.com/title/tt2103188/?ref_=adv...  2013.0           M   \n",
+              "3  https://www.imdb.com/title/tt5838588/?ref_=adv...  2018.0           M   \n",
+              "4  https://www.imdb.com/title/tt3334704/?ref_=adv...  2016.0           T   \n",
+              "\n",
+              "   rating   votes                                               plot  Action  \\\n",
+              "0     9.2  20,759  When a new villain threatens New York City, Pe...    True   \n",
+              "1     9.7  35,703  Amidst the decline of the Wild West at the tur...    True   \n",
+              "2     9.5  59,986  Three very different criminals team up for a s...    True   \n",
+              "3     9.6  26,118  After wiping out the gods of Mount Olympus, Kr...    True   \n",
+              "4     9.5  28,722  Thrown back into the dangerous underworld he'd...    True   \n",
+              "\n",
+              "   Adventure  Comedy  Crime  Family  Fantasy  Mystery  Sci-Fi  Thriller  \n",
+              "0       True   False  False   False     True    False   False     False  \n",
+              "1       True   False   True   False    False    False   False     False  \n",
+              "2      False   False   True   False    False    False   False     False  \n",
+              "3       True   False  False   False    False    False   False     False  \n",
+              "4       True   False  False   False    False    False   False     False  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-08093042-3a92-40ae-97b8-75a417bc3c3a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>name</th>\n",
+              "      <th>url</th>\n",
+              "      <th>year</th>\n",
+              "      <th>certificate</th>\n",
+              "      <th>rating</th>\n",
+              "      <th>votes</th>\n",
+              "      <th>plot</th>\n",
+              "      <th>Action</th>\n",
+              "      <th>Adventure</th>\n",
+              "      <th>Comedy</th>\n",
+              "      <th>Crime</th>\n",
+              "      <th>Family</th>\n",
+              "      <th>Fantasy</th>\n",
+              "      <th>Mystery</th>\n",
+              "      <th>Sci-Fi</th>\n",
+              "      <th>Thriller</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Spider-Man</td>\n",
+              "      <td>https://www.imdb.com/title/tt5807780/?ref_=adv...</td>\n",
+              "      <td>2018.0</td>\n",
+              "      <td>T</td>\n",
+              "      <td>9.2</td>\n",
+              "      <td>20,759</td>\n",
+              "      <td>When a new villain threatens New York City, Pe...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Red Dead Redemption II</td>\n",
+              "      <td>https://www.imdb.com/title/tt6161168/?ref_=adv...</td>\n",
+              "      <td>2018.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>9.7</td>\n",
+              "      <td>35,703</td>\n",
+              "      <td>Amidst the decline of the Wild West at the tur...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>https://www.imdb.com/title/tt2103188/?ref_=adv...</td>\n",
+              "      <td>2013.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>59,986</td>\n",
+              "      <td>Three very different criminals team up for a s...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>God of War</td>\n",
+              "      <td>https://www.imdb.com/title/tt5838588/?ref_=adv...</td>\n",
+              "      <td>2018.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>9.6</td>\n",
+              "      <td>26,118</td>\n",
+              "      <td>After wiping out the gods of Mount Olympus, Kr...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Uncharted 4: A Thief's End</td>\n",
+              "      <td>https://www.imdb.com/title/tt3334704/?ref_=adv...</td>\n",
+              "      <td>2016.0</td>\n",
+              "      <td>T</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>28,722</td>\n",
+              "      <td>Thrown back into the dangerous underworld he'd...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08093042-3a92-40ae-97b8-75a417bc3c3a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-08093042-3a92-40ae-97b8-75a417bc3c3a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-08093042-3a92-40ae-97b8-75a417bc3c3a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-3aeb9cd8-8dc2-4897-ac57-fb72dcfe15e8\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3aeb9cd8-8dc2-4897-ac57-fb72dcfe15e8')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-3aeb9cd8-8dc2-4897-ac57-fb72dcfe15e8 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "rpMtmQhmbq88"
+      },
+      "id": "rpMtmQhmbq88",
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**DATA CLEANING**"
+      ],
+      "metadata": {
+        "id": "YkRQSZeZbzIy"
+      },
+      "id": "YkRQSZeZbzIy"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.info()"
+      ],
+      "metadata": {
+        "id": "v75GsVVob_yh",
+        "outputId": "da50e977-00ad-4ea3-ca99-4bd64388d68e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "v75GsVVob_yh",
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 20803 entries, 0 to 20802\n",
+            "Data columns (total 17 columns):\n",
+            " #   Column       Non-Null Count  Dtype  \n",
+            "---  ------       --------------  -----  \n",
+            " 0   Unnamed: 0   20803 non-null  int64  \n",
+            " 1   name         20803 non-null  object \n",
+            " 2   url          20803 non-null  object \n",
+            " 3   year         20536 non-null  float64\n",
+            " 4   certificate  7903 non-null   object \n",
+            " 5   rating       11600 non-null  float64\n",
+            " 6   votes        11600 non-null  object \n",
+            " 7   plot         20803 non-null  object \n",
+            " 8   Action       20803 non-null  bool   \n",
+            " 9   Adventure    20803 non-null  bool   \n",
+            " 10  Comedy       20803 non-null  bool   \n",
+            " 11  Crime        20803 non-null  bool   \n",
+            " 12  Family       20803 non-null  bool   \n",
+            " 13  Fantasy      20803 non-null  bool   \n",
+            " 14  Mystery      20803 non-null  bool   \n",
+            " 15  Sci-Fi       20803 non-null  bool   \n",
+            " 16  Thriller     20803 non-null  bool   \n",
+            "dtypes: bool(9), float64(2), int64(1), object(5)\n",
+            "memory usage: 1.4+ MB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['votes'].head(2)"
+      ],
+      "metadata": {
+        "id": "ZjjR9zxpcCX_",
+        "outputId": "4086f8d6-744e-4d25-d241-146185fe6a77",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "ZjjR9zxpcCX_",
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    20,759\n",
+              "1    35,703\n",
+              "Name: votes, dtype: object"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "As we can see, we have commas in votes means its not in proper format(int or float), so we need to transform it in proper data type.\n"
+      ],
+      "metadata": {
+        "id": "ZmWldfHBcPeP"
+      },
+      "id": "ZmWldfHBcPeP"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# first, replace comma (,) with '' then convert it into int\n",
+        "df['votes'] = df['votes'].str.replace(',', '').astype(np.float)\n",
+        "df['votes'] = df['votes'].round(2)\n",
+        "\n",
+        "# drop the unused column\n",
+        "df.drop(columns=['Unnamed: 0'], inplace=True)"
+      ],
+      "metadata": {
+        "id": "BOmPCns3cWj9",
+        "outputId": "be739180-db8e-4d50-a6f2-847a7c42c875",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "BOmPCns3cWj9",
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-7-ab9e666dcb2b>:2: DeprecationWarning:\n",
+            "\n",
+            "`np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "Jyw1RB7jcWAq"
+      },
+      "id": "Jyw1RB7jcWAq",
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "there are multiple duplicates in our dataset"
+      ],
+      "metadata": {
+        "id": "LEll6bNWcmAd"
+      },
+      "id": "LEll6bNWcmAd"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[(df['name'] == 'Grand Theft Auto V') & (df['year'] == 2013)]"
+      ],
+      "metadata": {
+        "id": "Vwcox9KFcVtq",
+        "outputId": "755a2955-0683-4d2c-9a47-b1344539f3e6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 233
+        }
+      },
+      "id": "Vwcox9KFcVtq",
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     name                                                url  \\\n",
+              "2      Grand Theft Auto V  https://www.imdb.com/title/tt2103188/?ref_=adv...   \n",
+              "15914  Grand Theft Auto V  https://www.imdb.com/title/tt2103188/?ref_=adv...   \n",
+              "\n",
+              "         year certificate  rating    votes  \\\n",
+              "2      2013.0           M     9.5  59986.0   \n",
+              "15914  2013.0           M     9.5  59996.0   \n",
+              "\n",
+              "                                                    plot  Action  Adventure  \\\n",
+              "2      Three very different criminals team up for a s...    True      False   \n",
+              "15914  Three very different criminals team up for a s...    True      False   \n",
+              "\n",
+              "       Comedy  Crime  Family  Fantasy  Mystery  Sci-Fi  Thriller  \n",
+              "2       False   True   False    False    False   False     False  \n",
+              "15914   False   True   False    False    False   False     False  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e5293bf1-630c-4477-a9f6-cb9471ebcc73\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>url</th>\n",
+              "      <th>year</th>\n",
+              "      <th>certificate</th>\n",
+              "      <th>rating</th>\n",
+              "      <th>votes</th>\n",
+              "      <th>plot</th>\n",
+              "      <th>Action</th>\n",
+              "      <th>Adventure</th>\n",
+              "      <th>Comedy</th>\n",
+              "      <th>Crime</th>\n",
+              "      <th>Family</th>\n",
+              "      <th>Fantasy</th>\n",
+              "      <th>Mystery</th>\n",
+              "      <th>Sci-Fi</th>\n",
+              "      <th>Thriller</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>https://www.imdb.com/title/tt2103188/?ref_=adv...</td>\n",
+              "      <td>2013.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>59986.0</td>\n",
+              "      <td>Three very different criminals team up for a s...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15914</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>https://www.imdb.com/title/tt2103188/?ref_=adv...</td>\n",
+              "      <td>2013.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>59996.0</td>\n",
+              "      <td>Three very different criminals team up for a s...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5293bf1-630c-4477-a9f6-cb9471ebcc73')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e5293bf1-630c-4477-a9f6-cb9471ebcc73 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e5293bf1-630c-4477-a9f6-cb9471ebcc73');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-0fd9d1f4-cf83-40cb-a804-2e6b8e8cc699\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0fd9d1f4-cf83-40cb-a804-2e6b8e8cc699')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0fd9d1f4-cf83-40cb-a804-2e6b8e8cc699 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.drop_duplicates(['year', 'name'], keep='last', inplace = True)"
+      ],
+      "metadata": {
+        "id": "0v2g3iihcrSQ"
+      },
+      "id": "0v2g3iihcrSQ",
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[(df['name'] == 'Grand Theft Auto V') & (df['year'] == 2013)]"
+      ],
+      "metadata": {
+        "id": "l5K1KWagc98t",
+        "outputId": "9b9c7bf7-e39a-4748-a4eb-921834fe877b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 150
+        }
+      },
+      "id": "l5K1KWagc98t",
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     name                                                url  \\\n",
+              "15914  Grand Theft Auto V  https://www.imdb.com/title/tt2103188/?ref_=adv...   \n",
+              "\n",
+              "         year certificate  rating    votes  \\\n",
+              "15914  2013.0           M     9.5  59996.0   \n",
+              "\n",
+              "                                                    plot  Action  Adventure  \\\n",
+              "15914  Three very different criminals team up for a s...    True      False   \n",
+              "\n",
+              "       Comedy  Crime  Family  Fantasy  Mystery  Sci-Fi  Thriller  \n",
+              "15914   False   True   False    False    False   False     False  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d7991c2d-0bc3-4a56-9998-09d0f096aeb1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>url</th>\n",
+              "      <th>year</th>\n",
+              "      <th>certificate</th>\n",
+              "      <th>rating</th>\n",
+              "      <th>votes</th>\n",
+              "      <th>plot</th>\n",
+              "      <th>Action</th>\n",
+              "      <th>Adventure</th>\n",
+              "      <th>Comedy</th>\n",
+              "      <th>Crime</th>\n",
+              "      <th>Family</th>\n",
+              "      <th>Fantasy</th>\n",
+              "      <th>Mystery</th>\n",
+              "      <th>Sci-Fi</th>\n",
+              "      <th>Thriller</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>15914</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>https://www.imdb.com/title/tt2103188/?ref_=adv...</td>\n",
+              "      <td>2013.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>59996.0</td>\n",
+              "      <td>Three very different criminals team up for a s...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7991c2d-0bc3-4a56-9998-09d0f096aeb1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d7991c2d-0bc3-4a56-9998-09d0f096aeb1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d7991c2d-0bc3-4a56-9998-09d0f096aeb1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "mjLuKIwTdAvh"
+      },
+      "id": "mjLuKIwTdAvh",
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> transforming genres We need to shape df from wide to long, we can do this in multiple steps showed below."
+      ],
+      "metadata": {
+        "id": "g4us9ZGeddID"
+      },
+      "id": "g4us9ZGeddID"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# first replace true false values for genres with 0 and 1\n",
+        "df.replace({False : 0, True : 1}, inplace = True)\n",
+        "# then create another dataframe by melt function as showed below.\n",
+        "df_genre = df.melt(id_vars=['name'], value_vars=['Action', 'Adventure', 'Comedy', 'Crime', 'Family', 'Fantasy', 'Mystery', 'Sci-Fi', 'Thriller'])\n",
+        "df_genre.head()"
+      ],
+      "metadata": {
+        "id": "dOsbT7e4deH6",
+        "outputId": "69a64bf7-2da2-408d-cdba-096884fd6723",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        }
+      },
+      "id": "dOsbT7e4deH6",
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                         name variable  value\n",
+              "0           Ghost of Tsushima   Action      1\n",
+              "1  Uncharted: Drake's Fortune   Action      1\n",
+              "2        God of War: Ragnarök   Action      1\n",
+              "3                   Far Cry 3   Action      1\n",
+              "4          Fallout: New Vegas   Action      1"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5d198009-8e97-4c69-ac39-903edbd7a69e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>variable</th>\n",
+              "      <th>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Ghost of Tsushima</td>\n",
+              "      <td>Action</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Uncharted: Drake's Fortune</td>\n",
+              "      <td>Action</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>God of War: Ragnarök</td>\n",
+              "      <td>Action</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Far Cry 3</td>\n",
+              "      <td>Action</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Fallout: New Vegas</td>\n",
+              "      <td>Action</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d198009-8e97-4c69-ac39-903edbd7a69e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-5d198009-8e97-4c69-ac39-903edbd7a69e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-5d198009-8e97-4c69-ac39-903edbd7a69e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-333daded-bbe8-48a9-93d8-272ae057be1f\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-333daded-bbe8-48a9-93d8-272ae057be1f')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-333daded-bbe8-48a9-93d8-272ae057be1f button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_genre[df_genre['name'] == 'Grand Theft Auto V']"
+      ],
+      "metadata": {
+        "id": "6r969EwEdhE9",
+        "outputId": "52126f6a-5611-42df-8c4d-b70f1168c326",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 331
+        }
+      },
+      "id": "6r969EwEdhE9",
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                      name   variable  value\n",
+              "15239   Grand Theft Auto V     Action      1\n",
+              "35334   Grand Theft Auto V  Adventure      0\n",
+              "55429   Grand Theft Auto V     Comedy      0\n",
+              "75524   Grand Theft Auto V      Crime      1\n",
+              "95619   Grand Theft Auto V     Family      0\n",
+              "115714  Grand Theft Auto V    Fantasy      0\n",
+              "135809  Grand Theft Auto V    Mystery      0\n",
+              "155904  Grand Theft Auto V     Sci-Fi      0\n",
+              "175999  Grand Theft Auto V   Thriller      0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-df01170e-b068-4a49-a788-4dc7bea93488\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
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+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
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+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>variable</th>\n",
+              "      <th>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>15239</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>Action</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35334</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>Adventure</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>55429</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>Comedy</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75524</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>Crime</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>95619</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>Family</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>115714</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>Fantasy</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>135809</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>Mystery</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>155904</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>Sci-Fi</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>175999</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>Thriller</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df01170e-b068-4a49-a788-4dc7bea93488')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-df01170e-b068-4a49-a788-4dc7bea93488 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-df01170e-b068-4a49-a788-4dc7bea93488');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-b794da63-93ac-48b1-add4-67f564fdb9ff\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b794da63-93ac-48b1-add4-67f564fdb9ff')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-b794da63-93ac-48b1-add4-67f564fdb9ff button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> we have seen above that gta 5 have only 2 genres, so we need to remove unnecessary rows (having 'value' =0)"
+      ],
+      "metadata": {
+        "id": "LofIluKAdxJO"
+      },
+      "id": "LofIluKAdxJO"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_genre.drop(df_genre.index[df_genre['value'] == 0], inplace = True)"
+      ],
+      "metadata": {
+        "id": "EO6ndw5GdvC2"
+      },
+      "id": "EO6ndw5GdvC2",
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_genre[df_genre['name'] == 'Grand Theft Auto V']"
+      ],
+      "metadata": {
+        "id": "cxI04DU0d1CY",
+        "outputId": "ed6ab0d0-e935-4b15-eab5-5706872b2189",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 112
+        }
+      },
+      "id": "cxI04DU0d1CY",
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     name variable  value\n",
+              "15239  Grand Theft Auto V   Action      1\n",
+              "75524  Grand Theft Auto V    Crime      1"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-66600228-1991-4388-a279-ebc9d584ad5c\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>variable</th>\n",
+              "      <th>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>15239</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>Action</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75524</th>\n",
+              "      <td>Grand Theft Auto V</td>\n",
+              "      <td>Crime</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-66600228-1991-4388-a279-ebc9d584ad5c')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-66600228-1991-4388-a279-ebc9d584ad5c button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-66600228-1991-4388-a279-ebc9d584ad5c');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-36057866-2a32-4344-828e-c22b185610f7\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-36057866-2a32-4344-828e-c22b185610f7')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-36057866-2a32-4344-828e-c22b185610f7 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "I2QxHoIjd3Tb"
+      },
+      "id": "I2QxHoIjd3Tb",
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> now lets merge it with original df"
+      ],
+      "metadata": {
+        "id": "qbCItPUqd6lU"
+      },
+      "id": "qbCItPUqd6lU"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.merge(df, df_genre, on = 'name')"
+      ],
+      "metadata": {
+        "id": "NVxWvZCid7RH"
+      },
+      "id": "NVxWvZCid7RH",
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "id": "D96LXS7zd9ZN",
+        "outputId": "bc0e40ec-ad75-41ed-cee5-620dd220fd79",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 712
+        }
+      },
+      "id": "D96LXS7zd9ZN",
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                         name  \\\n",
+              "0           Ghost of Tsushima   \n",
+              "1           Ghost of Tsushima   \n",
+              "2  Uncharted: Drake's Fortune   \n",
+              "3  Uncharted: Drake's Fortune   \n",
+              "4  Uncharted: Drake's Fortune   \n",
+              "\n",
+              "                                                 url    year certificate  \\\n",
+              "0  https://www.imdb.com/title/tt7651352/?ref_=adv...  2020.0           M   \n",
+              "1  https://www.imdb.com/title/tt7651352/?ref_=adv...  2020.0           M   \n",
+              "2  https://www.imdb.com/title/tt1000777/?ref_=adv...  2007.0           T   \n",
+              "3  https://www.imdb.com/title/tt1000777/?ref_=adv...  2007.0           T   \n",
+              "4  https://www.imdb.com/title/tt1000777/?ref_=adv...  2007.0           T   \n",
+              "\n",
+              "   rating    votes                                               plot  Action  \\\n",
+              "0     9.3   8452.0  Set in 1274 on the Tsushima Island, the last s...       1   \n",
+              "1     9.3   8452.0  Set in 1274 on the Tsushima Island, the last s...       1   \n",
+              "2     8.5  20343.0  Treasure hunter Nathan Drake, embarks in the a...       1   \n",
+              "3     8.5  20343.0  Treasure hunter Nathan Drake, embarks in the a...       1   \n",
+              "4     8.5  20343.0  Treasure hunter Nathan Drake, embarks in the a...       1   \n",
+              "\n",
+              "   Adventure  Comedy  Crime  Family  Fantasy  Mystery  Sci-Fi  Thriller  \\\n",
+              "0          1       0      0       0        0        0       0         0   \n",
+              "1          1       0      0       0        0        0       0         0   \n",
+              "2          1       0      0       0        1        0       0         0   \n",
+              "3          1       0      0       0        1        0       0         0   \n",
+              "4          1       0      0       0        1        0       0         0   \n",
+              "\n",
+              "    variable  value  \n",
+              "0     Action      1  \n",
+              "1  Adventure      1  \n",
+              "2     Action      1  \n",
+              "3  Adventure      1  \n",
+              "4    Fantasy      1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c283e8a6-bf73-4bc8-9697-d28945ecd49c\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>url</th>\n",
+              "      <th>year</th>\n",
+              "      <th>certificate</th>\n",
+              "      <th>rating</th>\n",
+              "      <th>votes</th>\n",
+              "      <th>plot</th>\n",
+              "      <th>Action</th>\n",
+              "      <th>Adventure</th>\n",
+              "      <th>Comedy</th>\n",
+              "      <th>Crime</th>\n",
+              "      <th>Family</th>\n",
+              "      <th>Fantasy</th>\n",
+              "      <th>Mystery</th>\n",
+              "      <th>Sci-Fi</th>\n",
+              "      <th>Thriller</th>\n",
+              "      <th>variable</th>\n",
+              "      <th>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Ghost of Tsushima</td>\n",
+              "      <td>https://www.imdb.com/title/tt7651352/?ref_=adv...</td>\n",
+              "      <td>2020.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>9.3</td>\n",
+              "      <td>8452.0</td>\n",
+              "      <td>Set in 1274 on the Tsushima Island, the last s...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Action</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Ghost of Tsushima</td>\n",
+              "      <td>https://www.imdb.com/title/tt7651352/?ref_=adv...</td>\n",
+              "      <td>2020.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>9.3</td>\n",
+              "      <td>8452.0</td>\n",
+              "      <td>Set in 1274 on the Tsushima Island, the last s...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Adventure</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Uncharted: Drake's Fortune</td>\n",
+              "      <td>https://www.imdb.com/title/tt1000777/?ref_=adv...</td>\n",
+              "      <td>2007.0</td>\n",
+              "      <td>T</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>20343.0</td>\n",
+              "      <td>Treasure hunter Nathan Drake, embarks in the a...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Action</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Uncharted: Drake's Fortune</td>\n",
+              "      <td>https://www.imdb.com/title/tt1000777/?ref_=adv...</td>\n",
+              "      <td>2007.0</td>\n",
+              "      <td>T</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>20343.0</td>\n",
+              "      <td>Treasure hunter Nathan Drake, embarks in the a...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Adventure</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Uncharted: Drake's Fortune</td>\n",
+              "      <td>https://www.imdb.com/title/tt1000777/?ref_=adv...</td>\n",
+              "      <td>2007.0</td>\n",
+              "      <td>T</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>20343.0</td>\n",
+              "      <td>Treasure hunter Nathan Drake, embarks in the a...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Fantasy</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c283e8a6-bf73-4bc8-9697-d28945ecd49c')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c283e8a6-bf73-4bc8-9697-d28945ecd49c button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c283e8a6-bf73-4bc8-9697-d28945ecd49c');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-8bee0cab-5b79-4382-a5ae-1545dac67632\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8bee0cab-5b79-4382-a5ae-1545dac67632')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8bee0cab-5b79-4382-a5ae-1545dac67632 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "jeo_pEZNeAfD"
+      },
+      "id": "jeo_pEZNeAfD",
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        ">Some of games have no rating and votes (which is must for future visualizations), as of now am dropping them"
+      ],
+      "metadata": {
+        "id": "Nw4kU0oa0DaL"
+      },
+      "id": "Nw4kU0oa0DaL"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.dropna(inplace=True)"
+      ],
+      "metadata": {
+        "id": "OJF4tjHu0IRu"
+      },
+      "id": "OJF4tjHu0IRu",
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "2) DataVisualization"
+      ],
+      "metadata": {
+        "id": "2OveDV8l0PwE"
+      },
+      "id": "2OveDV8l0PwE"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Top 3 selling games per year\n",
+        "\n",
+        "Lets have a look at top 3 games released per year"
+      ],
+      "metadata": {
+        "id": "V-kvK2--0dDz"
+      },
+      "id": "V-kvK2--0dDz"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "top_selling_games = df.groupby(['year', 'name']).votes.mean().sort_values(ascending=False).reset_index()"
+      ],
+      "metadata": {
+        "id": "oXpcFgnM0d_I"
+      },
+      "id": "oXpcFgnM0d_I",
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "top_selling_games = top_selling_games.groupby('year').head(3)"
+      ],
+      "metadata": {
+        "id": "kajaVBYl0i_0"
+      },
+      "id": "kajaVBYl0i_0",
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Top 3 selling games per year\n"
+      ],
+      "metadata": {
+        "id": "E6orKLwi0rge"
+      },
+      "id": "E6orKLwi0rge"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install plotly --upgrade\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 321
+        },
+        "id": "QzEOWYKyf_Vz",
+        "outputId": "577afaba-a34b-4cad-abf8-224752633ab7"
+      },
+      "id": "QzEOWYKyf_Vz",
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.15.0)\n",
+            "Collecting plotly\n",
+            "  Downloading plotly-5.17.0-py2.py3-none-any.whl (15.6 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m31.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (8.2.3)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (23.2)\n",
+            "Installing collected packages: plotly\n",
+            "  Attempting uninstall: plotly\n",
+            "    Found existing installation: plotly 5.15.0\n",
+            "    Uninstalling plotly-5.15.0:\n",
+            "      Successfully uninstalled plotly-5.15.0\n",
+            "Successfully installed plotly-5.17.0\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.colab-display-data+json": {
+              "pip_warning": {
+                "packages": [
+                  "_plotly_utils",
+                  "plotly"
+                ]
+              }
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig = px.sunburst(top_selling_games, path= ['year', 'name'], values= 'votes', title='Top 3 selling games per year')\n",
+        "fig.update_layout(autosize = False, width = 1000, height = 800)\n",
+        "fig.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 817
+        },
+        "id": "afpYUO_W0sWo",
+        "outputId": "0ad1d597-0c31-42dc-a1ed-d9b2f4fa2a40"
+      },
+      "id": "afpYUO_W0sWo",
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.plotly.v1+json": {
+              "data": [
+                {
+                  "branchvalues": "total",
+                  "domain": {
+                    "x": [
+                      0,
+                      1
+                    ],
+                    "y": [
+                      0,
+                      1
+                    ]
+                  },
+                  "hovertemplate": "labels=%{label}<br>votes=%{value}<br>parent=%{parent}<br>id=%{id}<extra></extra>",
+                  "ids": [
+                    "1984.0/1942",
+                    "2012.0/Assassin's Creed III",
+                    "2017.0/Assassin's Creed: Origins",
+                    "1979.0/Asteroids",
+                    "1984.0/Balloon Fight",
+                    "1978.0/Basketball",
+                    "2005.0/Batman Begins",
+                    "2009.0/Batman: Arkham Asylum",
+                    "2011.0/Batman: Arkham City",
+                    "2015.0/Batman: Arkham Knight",
+                    "2016.0/Battlefield 1",
+                    "1980.0/Berzerk",
+                    "2007.0/BioShock",
+                    "2013.0/BioShock Infinite",
+                    "1978.0/Bowling",
+                    "2003.0/Call of Duty",
+                    "2007.0/Call of Duty 4: Modern Warfare",
+                    "2010.0/Call of Duty: Black Ops",
+                    "2009.0/Call of Duty: Modern Warfare 2",
+                    "2008.0/Call of Duty: World at War",
+                    "1995.0/Chrono Trigger",
+                    "1977.0/Combat",
+                    "1987.0/Contra",
+                    "2020.0/Cyberpunk 2077",
+                    "2019.0/Death Stranding",
+                    "2000.0/Diablo II",
+                    "1982.0/Dig Dug",
+                    "1981.0/Donkey Kong",
+                    "1994.0/Donkey Kong Country",
+                    "1982.0/Donkey Kong Junior",
+                    "1993.0/Doom",
+                    "1994.0/Doom II: Hell on Earth",
+                    "1988.0/Double Dragon II: The Revenge",
+                    "1990.0/Dr. Mario",
+                    "1983.0/Dragon's Lair",
+                    "1984.0/Duck Hunt",
+                    "1996.0/Duke Nukem 3D",
+                    "1982.0/E.T.: The Extra-Terrestrial",
+                    "2022.0/Elden Ring",
+                    "2008.0/Fallout 3",
+                    "2012.0/Far Cry 3",
+                    "2014.0/Far Cry 4",
+                    "2000.0/Final Fantasy IX",
+                    "1994.0/Final Fantasy VI",
+                    "1997.0/Final Fantasy VII",
+                    "1999.0/Final Fantasy VIII",
+                    "1981.0/Frogger",
+                    "1981.0/Galaga",
+                    "1979.0/Galaxian",
+                    "2006.0/Gears of War",
+                    "2020.0/Ghost of Tsushima",
+                    "2005.0/God of War",
+                    "2018.0/God of War",
+                    "1989.0/Golden Axe",
+                    "1997.0/GoldenEye 007",
+                    "1997.0/Grand Theft Auto",
+                    "2001.0/Grand Theft Auto III",
+                    "2008.0/Grand Theft Auto IV",
+                    "2013.0/Grand Theft Auto V",
+                    "2004.0/Grand Theft Auto: San Andreas",
+                    "2002.0/Grand Theft Auto: Vice City",
+                    "1998.0/Half-Life",
+                    "2004.0/Half-Life 2",
+                    "2001.0/Halo: Combat Evolved",
+                    "2022.0/Horizon Forbidden West",
+                    "2017.0/Horizon Zero Dawn",
+                    "1985.0/Ice Climber",
+                    "1985.0/Indiana Jones and the Temple of Doom",
+                    "2021.0/It Takes Two",
+                    "2002.0/Kingdom Hearts",
+                    "2022.0/Lego Star Wars: The Skywalker Saga",
+                    "2002.0/Mafia: The City of Lost Heaven",
+                    "2021.0/Marvel's Guardians of the Galaxy",
+                    "2010.0/Mass Effect 2",
+                    "2012.0/Mass Effect 3",
+                    "2001.0/Max Payne",
+                    "2003.0/Max Payne 2: The Fall of Max Payne",
+                    "1988.0/Mega Man 2",
+                    "1987.0/Metal Gear",
+                    "1998.0/Metal Gear Solid",
+                    "2004.0/Metal Gear Solid 3: Snake Eater",
+                    "1986.0/Metroid",
+                    "1980.0/Missile Command",
+                    "1992.0/Mortal Kombat",
+                    "1995.0/Mortal Kombat 3",
+                    "1993.0/Mortal Kombat II",
+                    "1976.0/Night Driver",
+                    "1980.0/Pac-Man",
+                    "2006.0/Pirates of the Caribbean: The Legend of Jack Sparrow",
+                    "1972.0/Pong",
+                    "1989.0/Prince of Persia",
+                    "2010.0/Red Dead Redemption",
+                    "2018.0/Red Dead Redemption II",
+                    "1996.0/Resident Evil",
+                    "2019.0/Resident Evil 2",
+                    "1999.0/Resident Evil 3: Nemesis",
+                    "2005.0/Resident Evil 4",
+                    "2017.0/Resident Evil VII: Biohazard",
+                    "2021.0/Resident Evil Village",
+                    "1985.0/Road Runner",
+                    "1999.0/Silent Hill",
+                    "1991.0/Sonic the Hedgehog",
+                    "1992.0/Sonic the Hedgehog 2",
+                    "1983.0/Space Ace",
+                    "1978.0/Space Invaders",
+                    "2018.0/Spider-Man",
+                    "1983.0/Star Wars",
+                    "2019.0/Star Wars Jedi: Fallen Order",
+                    "2003.0/Star Wars: Knights of the Old Republic",
+                    "1991.0/Street Fighter II: The World Warrior",
+                    "1996.0/Super Mario 64",
+                    "1988.0/Super Mario Bros. 2",
+                    "1986.0/Super Mario Bros.: The Lost Levels",
+                    "1989.0/Super Mario Land",
+                    "1990.0/Super Mario World",
+                    "1995.0/Super Mario World 2: Yoshi's Island",
+                    "1979.0/Superman",
+                    "2006.0/The Elder Scrolls IV: Oblivion",
+                    "2011.0/The Elder Scrolls V: Skyrim",
+                    "1998.0/The Fifth Element",
+                    "2013.0/The Last of Us",
+                    "2014.0/The Last of Us: Left Behind",
+                    "2020.0/The Last of Us: Part II",
+                    "1986.0/The Legend of Zelda",
+                    "1991.0/The Legend of Zelda: A Link to the Past",
+                    "1993.0/The Legend of Zelda: Link's Awakening",
+                    "2000.0/The Legend of Zelda: Majora's Mask",
+                    "1990.0/The Secret of Monkey Island",
+                    "2015.0/The Witcher 3: Wild Hunt",
+                    "2016.0/The Witcher 3: Wild Hunt - Blood and Wine",
+                    "2009.0/Uncharted 2: Among Thieves",
+                    "2011.0/Uncharted 3: Drake's Deception",
+                    "2016.0/Uncharted 4: A Thief's End",
+                    "2007.0/Uncharted: Drake's Fortune",
+                    "2015.0/Until Dawn",
+                    "2014.0/Watch Dogs",
+                    "1992.0/Wolfenstein 3D",
+                    "1987.0/Zelda II: The Adventure of Link",
+                    "1972.0",
+                    "1976.0",
+                    "1977.0",
+                    "1978.0",
+                    "1979.0",
+                    "1980.0",
+                    "1981.0",
+                    "1982.0",
+                    "1983.0",
+                    "1984.0",
+                    "1985.0",
+                    "1986.0",
+                    "1987.0",
+                    "1988.0",
+                    "1989.0",
+                    "1990.0",
+                    "1991.0",
+                    "1992.0",
+                    "1993.0",
+                    "1994.0",
+                    "1995.0",
+                    "1996.0",
+                    "1997.0",
+                    "1998.0",
+                    "1999.0",
+                    "2000.0",
+                    "2001.0",
+                    "2002.0",
+                    "2003.0",
+                    "2004.0",
+                    "2005.0",
+                    "2006.0",
+                    "2007.0",
+                    "2008.0",
+                    "2009.0",
+                    "2010.0",
+                    "2011.0",
+                    "2012.0",
+                    "2013.0",
+                    "2014.0",
+                    "2015.0",
+                    "2016.0",
+                    "2017.0",
+                    "2018.0",
+                    "2019.0",
+                    "2020.0",
+                    "2021.0",
+                    "2022.0"
+                  ],
+                  "labels": [
+                    "1942",
+                    "Assassin's Creed III",
+                    "Assassin's Creed: Origins",
+                    "Asteroids",
+                    "Balloon Fight",
+                    "Basketball",
+                    "Batman Begins",
+                    "Batman: Arkham Asylum",
+                    "Batman: Arkham City",
+                    "Batman: Arkham Knight",
+                    "Battlefield 1",
+                    "Berzerk",
+                    "BioShock",
+                    "BioShock Infinite",
+                    "Bowling",
+                    "Call of Duty",
+                    "Call of Duty 4: Modern Warfare",
+                    "Call of Duty: Black Ops",
+                    "Call of Duty: Modern Warfare 2",
+                    "Call of Duty: World at War",
+                    "Chrono Trigger",
+                    "Combat",
+                    "Contra",
+                    "Cyberpunk 2077",
+                    "Death Stranding",
+                    "Diablo II",
+                    "Dig Dug",
+                    "Donkey Kong",
+                    "Donkey Kong Country",
+                    "Donkey Kong Junior",
+                    "Doom",
+                    "Doom II: Hell on Earth",
+                    "Double Dragon II: The Revenge",
+                    "Dr. Mario",
+                    "Dragon's Lair",
+                    "Duck Hunt",
+                    "Duke Nukem 3D",
+                    "E.T.: The Extra-Terrestrial",
+                    "Elden Ring",
+                    "Fallout 3",
+                    "Far Cry 3",
+                    "Far Cry 4",
+                    "Final Fantasy IX",
+                    "Final Fantasy VI",
+                    "Final Fantasy VII",
+                    "Final Fantasy VIII",
+                    "Frogger",
+                    "Galaga",
+                    "Galaxian",
+                    "Gears of War",
+                    "Ghost of Tsushima",
+                    "God of War",
+                    "God of War",
+                    "Golden Axe",
+                    "GoldenEye 007",
+                    "Grand Theft Auto",
+                    "Grand Theft Auto III",
+                    "Grand Theft Auto IV",
+                    "Grand Theft Auto V",
+                    "Grand Theft Auto: San Andreas",
+                    "Grand Theft Auto: Vice City",
+                    "Half-Life",
+                    "Half-Life 2",
+                    "Halo: Combat Evolved",
+                    "Horizon Forbidden West",
+                    "Horizon Zero Dawn",
+                    "Ice Climber",
+                    "Indiana Jones and the Temple of Doom",
+                    "It Takes Two",
+                    "Kingdom Hearts",
+                    "Lego Star Wars: The Skywalker Saga",
+                    "Mafia: The City of Lost Heaven",
+                    "Marvel's Guardians of the Galaxy",
+                    "Mass Effect 2",
+                    "Mass Effect 3",
+                    "Max Payne",
+                    "Max Payne 2: The Fall of Max Payne",
+                    "Mega Man 2",
+                    "Metal Gear",
+                    "Metal Gear Solid",
+                    "Metal Gear Solid 3: Snake Eater",
+                    "Metroid",
+                    "Missile Command",
+                    "Mortal Kombat",
+                    "Mortal Kombat 3",
+                    "Mortal Kombat II",
+                    "Night Driver",
+                    "Pac-Man",
+                    "Pirates of the Caribbean: The Legend of Jack Sparrow",
+                    "Pong",
+                    "Prince of Persia",
+                    "Red Dead Redemption",
+                    "Red Dead Redemption II",
+                    "Resident Evil",
+                    "Resident Evil 2",
+                    "Resident Evil 3: Nemesis",
+                    "Resident Evil 4",
+                    "Resident Evil VII: Biohazard",
+                    "Resident Evil Village",
+                    "Road Runner",
+                    "Silent Hill",
+                    "Sonic the Hedgehog",
+                    "Sonic the Hedgehog 2",
+                    "Space Ace",
+                    "Space Invaders",
+                    "Spider-Man",
+                    "Star Wars",
+                    "Star Wars Jedi: Fallen Order",
+                    "Star Wars: Knights of the Old Republic",
+                    "Street Fighter II: The World Warrior",
+                    "Super Mario 64",
+                    "Super Mario Bros. 2",
+                    "Super Mario Bros.: The Lost Levels",
+                    "Super Mario Land",
+                    "Super Mario World",
+                    "Super Mario World 2: Yoshi's Island",
+                    "Superman",
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+      "source": [
+        "import plotly.express as px\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Assuming you have a DataFrame 'top_selling_games'\n",
+        "# Group the data by 'year' and calculate the sum of 'votes' for each year\n",
+        "yearly_votes = top_selling_games.groupby('year')['votes'].sum().reset_index()\n",
+        "\n",
+        "# Create a pie chart from the grouped data\n",
+        "fig = px.pie(yearly_votes, names='year', values='votes', title='Total Votes Per Year')\n",
+        "\n",
+        "# Show the pie chart\n",
+        "fig.show()\n"
+      ],
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+          "metadata": {}
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+    {
+      "cell_type": "code",
+      "source": [
+        "fig = px.scatter(data_frame=top_selling_games, x = 'year', y = 'votes', color = 'votes', size = 'votes',color_continuous_scale=px.colors.sequential.Sunset, hover_name='name', title = 'Votes over Year')\n",
+        "fig.update_layout(autosize = False, width = 1000, height = 800)\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
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+          "height": 817
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+        "id": "_Y1qQC3g0wUU",
+        "outputId": "ac75ecdb-283e-4cfa-97f4-e401c3f381d8"
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+      "id": "_Y1qQC3g0wUU",
+      "execution_count": 38,
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