Pulsar Star Classification using SVM

This project focuses on classifying pulsar stars using the Support Vector Machine (SVM) algorithm, a powerful method in the realm of supervised learning. The goal is to automate the identification process of pulsar stars from candidates collected during surveys, based on predictive modeling.

Project Structure

• Datasets: Holds the processed and raw datasets.
  • Processed_data: Contains processed data ready for analysis.
  • Raw_data: Contains raw data files.
• v_pred_test: Stores predicted outcomes on test data.
• notebooks: Jupyter notebooks for Exploratory Data Analysis (EDA) and model training.
• venv: A virtual environment directory for project dependencies.
• .gitignore: Specifies untracked files to ignore.
• README.md: Provides an overview of the project.
• requirements.txt: Lists all the necessary Python packages.

Setup

To run this project, follow these steps:

1. Make sure Python 3.8 or later is installed on your machine.

2. Clone the repository to your local environment.

3. Navigate to the project's root directory and set up a Python virtual environment:

   python -m venv venv

4. Activate the virtual environment:

   On Windows:

   .\venv\Scripts\activate

   On macOS and Linux:

   source venv/bin/activate

5. Install the required dependencies:

   pip install -r requirements.txt

Usage

To perform EDA or train the SVM model, open the Jupyter notebooks located in the notebooks directory:

• EDA_Test_Data.ipynb: For exploratory data analysis on test data.
• EDA_Train_Data.ipynb: For exploratory data analysis on training data.
• MODEL_TRAINING.ipynb: For training the SVM model.

Run the notebooks sequentially to explore the data and train the model.

Data

The Datasets directory is organized as follows:

• Processed_data: Processed files like pulsar_data_test_processed.csv for use in modeling.
• Raw_data: The original, unprocessed data files.

Predictions from the test data are saved in v_pred_test with filenames indicating they are predictions, such as Pulsar_data_test_Predicted.csv.

Contributing

If you'd like to contribute, please fork the repository and create a pull request with your features or changes.

License

Open-sourced software licensed under the MIT license.