🗳️ Bihar Assembly Election ML Model

This project uses machine learning to analyze historical Bihar Vidhan Sabha election data (2000–2020) and predict whether a legislative race will be tight or safe.

📌 Project Objective

The aim is to assist political analysts, journalists, and data scientists by providing a predictive model that classifies election races based on:

• Voter turnout
• Party affiliation
• Constituency type (General/SC/ST)
• Margin and percentage of victory
• Total electors and votes polled

📂 Project Structure

├── app.py # Flask web app ├── election_race_model.pkl # Trained machine learning model ├── IndiaVotes_Bihar.csv # Historical election data (2000–2020) ├── templates/ # HTML templates for the web app ├── .gitignore └── README.md # Project documentation (this file)

🚀 How to Run Locally

1. Clone the repository

git clone https://github.com/Vivek-ML001/BiharElection.git
cd BiharElection

2. Install dependencies

Make sure Python is installed. Then run:

pip install -r requirements.txt

> If requirements.txt is not available, use:



pip install pandas scikit-learn flask

3. Run the app

python app.py

Then open your browser at http://127.0.0.1:5000

 📊 Model Details

Algorithm: (e.g., Random Forest, Logistic Regression)

Target Label: Tight Race vs Safe Seat

Input Features: Voter Turnout, Party, Constituency Type, Margin %, etc.


✨ Future Improvements

Add visual analytics dashboard

Integrate real-time data scraping

Improve accuracy using ensemble models

Deploy on cloud (Render/Heroku/AWS)


📮 Contact

Made by Vivek Kumar | GitHub Profile


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