https://github.com/msaakaash/data-driven-ab-testing
A data-driven analysis of student mobile operator preferences using A/B Testing and Chi-Square hypothesis testing.
https://github.com/msaakaash/data-driven-ab-testing
a-b-testing chisquare-test python scipy
Last synced: 2 months ago
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A data-driven analysis of student mobile operator preferences using A/B Testing and Chi-Square hypothesis testing.
- Host: GitHub
- URL: https://github.com/msaakaash/data-driven-ab-testing
- Owner: msaakaash
- License: mit
- Created: 2025-05-01T07:00:11.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-15T04:25:33.000Z (about 1 year ago)
- Last Synced: 2025-05-15T05:20:01.292Z (about 1 year ago)
- Topics: a-b-testing, chisquare-test, python, scipy
- Language: Jupyter Notebook
- Homepage:
- Size: 42 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Data Driven A/B Testing Analysis of Student Behavior
This project presents the analysis of student behavior using A/B Testing and hypothesis testing with the Chi-Square test to evaluate differences in mobile operator preferences between users from Tamil Nadu and outside Tamil Nadu. The analysis includes data cleaning, preprocessing, hypothesis testing, and visualization of the test results.
## Project Overview
The goal of this project is to analyze user preferences regarding mobile operators based on their geographical region (Tamil Nadu vs. Outside Tamil Nadu). This analysis provides actionable insights to help businesses and educational institutions enhance student engagement and improve learning outcomes.
## Features
- **A/B Testing**: Perform hypothesis testing to compare user behavior across different groups (Control vs Test).
- **Chi-Square Test**: Statistical analysis to evaluate differences in mobile operator preferences between users from Tamil Nadu and users outside Tamil Nadu.
- **Data Cleaning**: Cleaning and preprocessing raw user data for analysis.
- **Segmentation**: Grouping users into control and test groups for comparative analysis.
- **Visualization**: Visualizing key metrics using Matplotlib and Seaborn to provide a clear view of the test results.
- **Data-Backed Recommendations**: Actionable recommendations derived from statistical results with a 95% confidence level.
## Tech Stack
- **Python**: Programming language used for analysis.
- **Pandas**: Data manipulation and cleaning.
- **Matplotlib**: Data visualization.
- **Seaborn**: Statistical data visualization.
- **SciPy**: Statistical functions for hypothesis testing, specifically the Chi-Square test.
## Installation
Follow these steps to get the project up and running locally:
### 1. Clone the repository
```bash
git clone https://github.com/msaakaash/data-driven-ab-testing.git
cd data-driven-ab-testing
```
## License
This project is licensed under the [MIT License](LICENSE).
## Author
[**Aakaash M S**](https://github.com/msaakaash)