submission: Raushan

.devcontainer/devcontainer.json

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+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "submissions/Raushan/main.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run submissions/Raushan/main.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

submissions/Raushan/.python-version

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+3.13

submissions/Raushan/README.md

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+# India Growth Metrics Dashboard
+
+_Submitted by: Raushan Bhanu_
+
+## 🌟 Live Demo
+
+🔗 **[View Live Demo](https://india-growth-metrics-dashboard.streamlit.app)**
+
+## 📸 Screenshots
+
+- **Dashboard Overview**
+
+![Dashboard Overview](./demo-images/dashboard-overview.png)
+
+- **Cities Comparison**
+
+![Cities Comparison](./demo-images/cities-comparison.png)
+
+- **Mobile View**
+
+![Mobile View](./demo-images/mobile-view.png)
+
+## 🛠️ Tech Stack
+
+- **Frontend**: Streamlit (Python)
+- **Database**: CSV (Pandas DataFrames)
+- **Visualization**: Plotly, Seaborn, Matplotlib, Pydeck
+- **Deployment**: Streamlit Cloud
+
+## ✨ Unique Features
+
+- **AI-Powered Analytics**: Trend prediction, anomaly detection, and correlation analysis across urban metrics.
+- **Interactive City Mapping**: Visualizes metrics geospatially using scalable Pydeck markers.
+- **Dynamic Data Exploration**: Customizable visualizations, comparisons, and filtering tools for city-wise insights.
+
+---
+
+## 🚀 Getting Started
+
+### Prerequisites
+
+- Python 3.8+
+- Option 1: [`uv` package manager](https://github.com/astral-sh/uv)
+- Option 2: Traditional `pip` and `requirements.txt`
+
+---
+
+### 🧩 Installation & Running the App
+
+#### ✅ Option 1: Using `uv` (recommended)
+
+```bash
+# Step 1: Clone the repository
+git clone https://github.com/RaushanBhanu/Letscode-Community-Challenge-1.git
+cd Letscode-Community-Challenge-1/submissions/Raushan
+
+# Step 2: Create and activate a virtual environment
+uv venv
+source .venv/bin/activate       # Linux/macOS
+.venv\Scripts\activate          # Windows
+
+# Step 3: Install dependencies using uv
+uv sync
+
+# Step 4: Generate mock data (if needed)
+uv run generate_mock_data.py
+
+# Step 5: Run the Streamlit app
+streamlit run main.py
+```
+
+#### ✅ Option 2: Using `pip`
+
+```bash
+# Step 1: Clone the repository
+git clone https://github.com/RaushanBhanu/Letscode-Community-Challenge-1.git
+cd Letscode-Community-Challenge-1/submissions/Raushan
+
+# Step 2: Install dependencies
+pip install -r requirements.txt
+
+# Step 3: Generate mock data (if needed)
+python generate_mock_data.py
+
+# Step 4: Run the app
+streamlit run main.py
+```

submissions/Raushan/generate_mock_data.py

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+import pandas as pd
+import random
+
+# Define cities and years
+cities = [
+    "Mumbai",
+    "Delhi",
+    "Bangalore",
+    "Hyderabad",
+    "Chennai",
+    "Kolkata",
+    "Pune",
+    "Ahmedabad",
+    "Jaipur",
+    "Lucknow",
+]
+years = list(range(2019, 2029))  # 5 years
+
+# Define metrics and value ranges
+metrics = {
+    "GDP": (50, 300),
+    "GNI": (45, 290),
+    "GDP per Capita": (5000, 25000),
+    "Unemployment Rate": (3, 15),
+    "Inflation Rate": (2, 10),
+    "FDI": (0.5, 15),
+    "Export/Import Ratio": (0.5, 2.0),
+    "Public Debt % of GDP": (30, 90),
+    "HDI": (0.5, 0.9),
+    "Life Expectancy": (65, 82),
+    "Infant Mortality Rate": (2, 30),
+    "Literacy Rate": (70, 99),
+    "Education Index": (0.4, 0.95),
+    "Gender Inequality Index": (0.2, 0.7),
+    "Population Growth Rate": (0.5, 3.0),
+    "Urban Population %": (50, 100),
+    "Healthcare Expenditure per Capita": (200, 2000),
+    "Physicians per 1000": (0.5, 4),
+    "Hospital Beds per 1000": (1, 6),
+    "Access to Clean Water %": (60, 100),
+    "Vaccination Coverage %": (60, 100),
+    "CO2 Emissions per Capita": (1, 8),
+    "Renewable Energy %": (10, 60),
+    "Forest Area %": (5, 40),
+    "Air Quality Index": (50, 300),
+    "Environmental Performance Index": (20, 80),
+    "Corruption Perceptions Index": (20, 80),
+    "Internet Penetration %": (40, 95),
+    "Mobile Subscriptions per 100": (60, 130),
+    "Infrastructure Quality Index": (2, 8),
+    "Political Stability Index": (-2.5, 2.5),
+    "Gini Coefficient": (25, 50),
+    "Poverty Rate": (5, 40),
+    "Social Protection Coverage %": (30, 100),
+}
+
+# Generate mock data
+data = []
+for city in cities:
+    for year in years:
+        entry = {"City": city, "Year": year}
+        for metric, (low, high) in metrics.items():
+            entry[metric] = round(random.uniform(low, high), 2)
+        data.append(entry)
+
+# Create DataFrame
+df_mock_data = pd.DataFrame(data)
+
+# Save to CSV
+csv_path = "india_city_growth_metrics_mock_data.csv"
+df_mock_data.to_csv(csv_path, index=False)