https://github.com/istinnew/eniac_ab_insight
Dive into a comprehensive analysis aimed at boosting iPhone 13 sales by optimizing the Click-Through Rate (CTR) of the “SHOP NOW” button, compare different button designs and determine the most effective strategy for increasing engagement.
https://github.com/istinnew/eniac_ab_insight
ab-testing data data-analysis data-engineering data-science data-visualization google googlecolab libraries python testing testing-tools visual-studio-code
Last synced: about 1 month ago
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Dive into a comprehensive analysis aimed at boosting iPhone 13 sales by optimizing the Click-Through Rate (CTR) of the “SHOP NOW” button, compare different button designs and determine the most effective strategy for increasing engagement.
- Host: GitHub
- URL: https://github.com/istinnew/eniac_ab_insight
- Owner: IstinNew
- License: mit
- Created: 2024-11-24T19:48:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-25T18:35:14.000Z (over 1 year ago)
- Last Synced: 2025-02-09T03:27:10.562Z (over 1 year ago)
- Topics: ab-testing, data, data-analysis, data-engineering, data-science, data-visualization, google, googlecolab, libraries, python, testing, testing-tools, visual-studio-code
- Language: Jupyter Notebook
- Homepage:
- Size: 347 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Eniac A/B Testing Project
## Overview
This repository contains the final project for Eniac's A/B testing. The project focuses on improving iPhone 13 sales by increasing the Click-Through Rate (CTR) of the “SHOP NOW” button through A/B testing. This repository includes a detailed analysis, visual interpretation, and relevant scripts.
## Project Structure
- `Eniac AB Test.ipynb`: Jupyter notebook containing the full analysis and code for the A/B test.
- `chart.png`: Image file with the A/B test results.
- `data/`: Folder containing sample datasets used for the analysis.
- `notebook/`: Folder containing notebook used for the analysis.
- `results/`: Folder containing the results and plots generated from the analysis.
## Highlights
- **Data Analysis**: Comprehensive analysis using Jupyter Notebook, providing insights into user behavior and engagement.
- **Visualization**: Clear and informative visual representations of A/B testing results.
- **Statistical Methods**: Application of statistical techniques, including chi-square tests, to validate the results.
- **Reproducibility**: Ensuring reproducibility of results with clearly defined steps and code.
## Challenges
- **Data Collection**: Ensuring accurate and unbiased data collection for the A/B test.
- **Analysis Accuracy**: Applying appropriate statistical methods to derive meaningful conclusions.
- **User Engagement**: Designing effective experiments to measure user engagement accurately.
-
## Links to Previous Projects
1. **First Project**: [Magist Eniac Case Study](https://github.com/IstinNew/magist-eniac-case-study)
2. **Second Project**: [Eniac's Discount Strategy Analysis](https://github.com/IstinNew/Enaic-s-Discount-Strategy-Analysis)
## Getting Started
1. **Clone the Repository**:
```sh
git clone https://github.com/YOUR_GITHUB_USERNAME/Eniac-A-B-testing.git
cd Eniac-A-B-testing
```
2. **Install Dependencies**:
```sh
pip install -r requirements.txt
```
3. **Run the Jupyter Notebook**:
```sh
jupyter notebook "Eniac AB Test.ipynb"
```
## Results (incluing chart)
[Chart](https://github.com/IstinNew/Eniac_AB_Insight/blob/main/results/chart.png)
**Personal Opinion**:
As per performed Chi-square test to check for statistical significance, version C comes as the winner.
Howvever, between Versions A & C there is a marginal difference as compared to when comparing with Versions B & D. Hence would suggest to re-assess the metrics.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
Happy coding! 😊📊✨