Election Prediction Project

Overview

This project is focused on predicting election outcomes for California's 33rd Congressional District (CA-33). It utilizes historical data and advanced machine learning techniques to analyze trends and forecast results. Done By: Yaswanth Mopada (A20585424) Vishwas Reddy Dodle (A20562449)

Features

• Data Preparation: Handles historical datasets for election analysis.
• Model Development: Includes preprocessing, model training, and prediction using Python-based tools.
• Insights: Provides detailed analytics on voter turnout and candidate performance.

Requirements

• Python 3.x
• Jupyter Notebook
• Libraries:
  • pandas
  • numpy
  • scikit-learn
  • matplotlib
  • seaborn

Structure

• Code Cells: Implements the logic for data preprocessing, modeling, and predictions.
• Markdown Cells: Contains documentation and context for the steps in the notebook.

How to Run

1. Ensure you have all the required libraries installed.
2. Open the notebook (Election_prediction.ipynb) in Jupyter Notebook or Google Colab.
3. Execute the cells sequentially to process data and generate predictions.

Notes

• The project uses a reproducible workflow. All steps should be executed in sequence for consistent results.
• Future updates will include enhanced UI/UX integration and visualization dashboards.

Author

Vishwas Reddy Dodle
Contact: [Your Email or GitHub Link]

License

This project is open-source and available under the MIT License.