This repository contains the data analysis and visualization project for the Lok Sabha 2024 elections in India. The project leverages various data analytics and visualization tools to analyze and present insights from the election data.
The project includes the following key aspects:
- Power BI: Utilized for interactive data visualization and business intelligence, creating charts, graphs, and interactive dashboards to present insights.
- Seaborn and Pandas: Used for data preprocessing and analysis, uncovering trends and correlations in the election data.
- NLTK (Natural Language Toolkit): Employed for sentiment analysis on political speeches and manifestos, gauging the public sentiment towards different parties.
- Logistic Regression: Developed a predictive model to forecast the winning party in each region based on historical data, demographic factors, and sentiment analysis.
- Selenium: Integrated for web scraping to gather comprehensive data on candidate profiles, campaign events, and voter turnout, further enriching the analysis.
The insights derived from the analysis highlighted the dominant parties in various regions, their past performance, and how the current data correlated with historical trends, providing valuable information for election strategizing.
The repository includes the following files and directories:
powerBi.pbix: The Power BI report file containing the interactive visualizations and dashboards.data/: Directory containing the relevant data files used in the analysis.scripts/: Directory with the Python scripts for data preprocessing, analysis, and model development.README.md: This README file providing an overview of the project.
- Clone or download the repository to your local machine.
- Open the Power BI report file (
powerBi.pbix) using Power BI Desktop to interact with the visualizations and insights. - Install the required Python libraries (
seaborn,pandas,nltk,selenium) using pip if you intend to use or modify the Python scripts provided.
Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please feel free to create a new issue or submit a pull request.
This project is licensed under the MIT License.