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Election-AD-Spending-Analysis

Election AD Spending Analysis using Pandas and Matplotlib

Overview:

This Jupyter notebook provides an analysis of recent election ad campaigns in India for the 2024 elections. The analysis aims to offer insights into the effectiveness and visualisations of Amount Spent and Voter Turnout %

Table of Contents:

Introduction

Analysis of recent election ad campaigns in India for the 2024 elections. The analysis aims to offer insights into the effectiveness, correlations and visualisations between Amount Spent and Voter Turnout %

Setup

To get started with the Election AD Campaign Spending Analysis, follow these steps:

Prerequisites

Ensure you have the following installed:

Python 3.8 or later pip (Python package installer)

Installation

  1. Clone the repository:

    git clone https://github.com/sai-sp/Election-AD-Spending-Analysis.git
    cd Election AD Spending Analysis
    

Usage

To run the analysis, follow these steps: Ensure that you have activated the virtual environment.

Run the main analysis script: *IPL 2024 Data Analysis using Pandas and Matplotlib.ipynb

Data Description

The dataset used in this analysis includes:

results.csv
advertisers.csv
locations.csv

These three csv files are compressed and available as a Datasets.zip file

Analysis Details

The analysis includes:

To understand and visualize the results.csv, advertisers.csv and locations.csv according to our requirements;

  1. To find the Total Amount spend on AD by each State and top 5 parties.
  2. Finding correlations
  3. Ad spend and voter turnout by parliamentary constituency and visualizing using Scatter Plot
  4. Analyze ad spending and voter turnout by election phase

Results

Results are visualized in the Notebook file using Matplotlib and Pandas

Contributing

We welcome contributions to this project! If you have suggestions, improvements, or bug fixes, please follow these steps:

Fork the repository and create a new branch. Make your changes and ensure that they work correctly. Submit a pull request with a clear description of the changes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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