Election Data Analysis: Insights from 2014 and 2019

This repository contains the code and analysis for a project presented to AtliQ Media. The project aims to provide unbiased insights from the 2014 and 2019 Lok Sabha elections in India, focusing on less explored themes like voter turnout percentage. This analysis was conducted as part of the Resume Project challenge 11 of Code Basics.

Project Overview

AtliQ Media, a private media company, plans to telecast a show on the Lok Sabha elections 2024 in India. Unlike other channels, they aim to present data-driven insights from past elections without any bias, rather than debating the potential winners of the upcoming election.

Objectives

The main objectives of this project are:

1. To analyze voter turnout percentages across various constituencies.
2. To compare the performance of major political parties (INC and BJP) between 2014 and 2019.
3. To identify constituencies with significant changes in party preferences and voter turnout.
4. To provide meaningful visualizations of the findings.

Analysis and Insights

The analysis is divided into several key sections:

1. Voter Turnout Analysis: Examining the voter turnout percentage in different constituencies.
2. Party Performance Comparison: Comparing the vote shares of INC and BJP in 2014 and 2019.
3. Change in Party Preferences: Identifying constituencies where the winning party changed between 2014 and 2019.
4. Top Constituencies by Vote Share Gain: Highlighting constituencies where INC and BJP gained significant vote shares.
5. NOTA Analysis: Determining which constituency voted the most for NOTA.

File Structure

• codebasics1.ipynb: The Jupyter Notebook containing all the code and visualizations for the analysis.

Getting Started

Prerequisites

To run the code in this repository, you need the following:

• Python 3.x
• Jupyter Notebook
• Required Python libraries: pandas, matplotlib

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/election-data-analysis.git

2. Navigate to the project directory:

   cd election-data-analysis

3. Install the required libraries:

   pip install pandas matplotlib

Usage

1. Open the Jupyter Notebook:

   jupyter notebook codebasics1.ipynb

2. Run the cells sequentially to perform the analysis and generate visualizations.

Video Presentation

A detailed video presentation explaining the analysis is available on YouTube. Watch the video to get a comprehensive understanding of the insights and findings.

🔗 YouTube Video: Link to the video

Contributing

Contributions are welcome! If you have any suggestions or improvements, feel free to create an issue or submit a pull request.

Acknowledgements

Thanks codebasics, Dhaval Patel, and Hemanand Vadivel for this challenge

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Author: Prateek Sharma
Contact: ps2798@rit.edu/prateeksharma1809@gmail.com