Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ituvtu/datamining-ab-testing
This project focuses on conducting A/B testing to evaluate the effectiveness of two marketing campaigns. Using statistical analysis and hypothesis testing, we determine which campaign is more effective in improving conversion rates.
https://github.com/ituvtu/datamining-ab-testing
a-b-testing data-analysis data-analysis-python data-mining ipynb jupyter jupyter-notebook python
Last synced: about 21 hours ago
JSON representation
This project focuses on conducting A/B testing to evaluate the effectiveness of two marketing campaigns. Using statistical analysis and hypothesis testing, we determine which campaign is more effective in improving conversion rates.
- Host: GitHub
- URL: https://github.com/ituvtu/datamining-ab-testing
- Owner: ituvtu
- Created: 2025-01-15T15:18:24.000Z (2 days ago)
- Default Branch: main
- Last Pushed: 2025-01-15T15:50:54.000Z (2 days ago)
- Last Synced: 2025-01-15T17:38:45.965Z (2 days ago)
- Topics: a-b-testing, data-analysis, data-analysis-python, data-mining, ipynb, jupyter, jupyter-notebook, python
- Language: Jupyter Notebook
- Homepage:
- Size: 141 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# A/B Testing of Marketing Campaigns
## Project Description
This project focuses on conducting A/B testing to evaluate the effectiveness of two marketing campaigns. Using statistical analysis and hypothesis testing, we determine which campaign is more effective in improving conversion rates.
## Project Structure
The project consists of three main parts, each represented in a corresponding Jupyter Notebook:
1. **01_data_preprocessing.ipynb**: Data loading and preprocessing.
2. **02_stat_analysis.ipynb**: Statistical analysis and visualization of results.
3. **03_conclusion.ipynb**: Conclusions and recommendations based on the analysis.
## Files
- **datasets/control_group.csv**: Data for the control group.
- **datasets/test_group.csv**: Data for the test group.
- **datasets/control_i.csv**: Processed data for the control group.
- **datasets/test_i.csv**: Processed data for the test group.
- **functions.py**: File containing functions for data processing and analysis.
## Key Skills
- **Data Processing**: Using Pandas and NumPy libraries for data loading, cleaning, and preprocessing.
- **Statistical Analysis**: Performing hypothesis testing (t-test) to assess statistical significance between groups.
- **Data Visualization**: Utilizing Matplotlib and Seaborn libraries to create graphs and visualize results.
- **Data Consistency Checks**: Verifying data for logical consistency and correcting anomalies.
## Findings
Based on the analysis, we concluded that the new (test) marketing campaign is more effective compared to the control campaign. Key insights include:
- The conversion rate for the control group was **1.23%**, while for the test group, it reached **2.54%**.
- The test group was **2.07 times more effective** than the control group.
- Statistical significance was confirmed via a t-test, showing a p-value of **0.001**, indicating that the improvement in conversion rate is not random.
.
## Recommendations
Based on the results, it is recommended to adopt the test campaign as the primary marketing strategy to optimize user engagement and improve conversions.
## How to Run the Project
1. Clone the repository:
```bash
git clone https://github.com/ituvtu/DataMining-AB-Testing
2. Install the required libraries:
```bash
pip install -r requirements.txt
3. Launch Jupyter Notebook and open the following files for execution and analysis:
- 01_data_preprocessing.ipynb
- 02_stat_analysis.ipynb
- 03_conclusion.ipynb
## Author
ituvtu ([LinkedIn](https://www.linkedin.com/in/ivanturenko/)).
## This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).