🗳️ Election Prediction App

Welcome to the Election Prediction API, a Flask-powered service that predicts the election outcome for a given state and year. The API uses pre-trained machine learning models to deliver accurate predictions, enabling insightful analyses.

🚀 Features

Predict Election Outcomes: Predict whether a state leans Republican or Democratic for a specified year.
Dynamic Model Integration: Plug in various ML models and preprocessors seamlessly.
State and Year Filters: Use state and year data to refine predictions.
Error Handling: Built-in robust validation and user-friendly error messages.

📋 Prerequisites

Python 3.9 recommended or above
Deps in Requirments.txt

🛠️ Installation

Clone the repository:

https://github.com/sjhaleprogrammer/CSCI6020-final-project.git
cd CSCI6020-final-project

Create a virtual environment:

py -m venv venv # For Windows
python3 -m venv venv # For Linux/Mac

source venv/bin/activate  # For Linux/Mac
venv\Scripts\activate     # For Windows

Install dependencies:
```
pip install -r requirements.txt
```
Prepare your models and data:
- Place your pre-trained models and preprocessors in the models/ directory.
- Add your processed dataset (e.g., all_states_prediction.csv) to the data_processed/ directory.

🏃‍♂️ Run the API

Start the Flask server:
```
flask --app frontend/app.py run
```
Access the API at:
```
http://127.0.0.1:5000
```

📡 Endpoints

`POST /predict`

Predicts the election outcome for a given state and year.

Request

Headers: Content-Type: application/json

Body:

{
    "model_name": "example_model",
    "state": "North Carolina",
    "year": 2024
}

Response

Success (200):

{
    "state": "North Carolina",
    "year": 2024,
    "predicted_result": "Democratic"
}

Error:
- 400: Missing or invalid input fields.
- 404: Dataset or model files not found.
- 500: Internal server error.

🧠 Behind the Scenes

Models: Built using robust machine learning algorithms, each tailored for election prediction tasks.
Data Preprocessing: Features standardized and normalized using a custom preprocessor.
Prediction Logic: Combines state-year data with pre-trained models for high-accuracy results.

📖 Example Use Case

Imagine you're a political analyst preparing for an election year. Using this API, you can:

Quickly assess the likelihood of a state leaning Democratic or Republican.
Integrate predictions into dashboards or visualizations.

🌟 Future Enhancements

Add support for more advanced ML models (e.g., neural networks).
Introduce visual dashboards for predictions.
Expand dataset support for county-level predictions.

🤝 Contributions

Feel free to contribute! Fork the repository, make your changes, and submit a pull request.

📜 License

This project is licensed under the MIT License.

SamuelH98/CSCI6020-final-project