{"id":24300566,"url":"https://github.com/ituvtu/Data-Science-AB-Testing","last_synced_at":"2025-09-26T02:31:44.775Z","repository":{"id":272622568,"uuid":"917215367","full_name":"ituvtu/DataMining-AB-Testing","owner":"ituvtu","description":"This project focuses on conducting A/B testing to evaluate the effectiveness of two marketing campaigns. 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Using statistical analysis and hypothesis testing, we determine which campaign is more effective in improving conversion rates.\n\n## Project Structure\nThe project consists of three main parts, each represented in a corresponding Jupyter Notebook:\n\n1. **01_data_preprocessing.ipynb**: Data loading and preprocessing.\n2. **02_stat_analysis.ipynb**: Statistical analysis and visualization of results.\n3. **03_conclusion.ipynb**: Conclusions and recommendations based on the analysis.\n\n## Files\n- **datasets/control_group.csv**: Data for the control group.\n- **datasets/test_group.csv**: Data for the test group.\n- **datasets/control_i.csv**: Processed data for the control group.\n- **datasets/test_i.csv**: Processed data for the test group.\n- **functions.py**: File containing functions for data processing and analysis.\n\n## Key Skills\n- **Data Processing**: Using Pandas and NumPy libraries for data loading, cleaning, and preprocessing.\n- **Statistical Analysis**: Performing hypothesis testing (t-test) to assess statistical significance between groups.\n- **Data Visualization**: Utilizing Matplotlib and Seaborn libraries to create graphs and visualize results.\n- **Data Consistency Checks**: Verifying data for logical consistency and correcting anomalies.\n\n## Findings\nBased on the analysis, we concluded that the new (test) marketing campaign is more effective compared to the control campaign. Key insights include:\n\n- The conversion rate for the control group was **1.23%**, while for the test group, it reached **2.54%**.\n- The test group was **2.07 times more effective** than the control group.\n- 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.\n![Histogram of Conversion Comparison](https://github.com/user-attachments/assets/889afde6-8350-481b-ab52-9a1669002553).\n\n## Recommendations\nBased on the results, it is recommended to adopt the test campaign as the primary marketing strategy to optimize user engagement and improve conversions.\n\n## How to Run the Project\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/ituvtu/DataMining-AB-Testing\n2. Install the required libraries:\n   ```bash\n   pip install -r requirements.txt\n3. Launch Jupyter Notebook and open the following files for execution and analysis:\n- 01_data_preprocessing.ipynb\n- 02_stat_analysis.ipynb\n- 03_conclusion.ipynb\n\n## Author\nituvtu ([LinkedIn](https://www.linkedin.com/in/ivanturenko/)).\n\n\n## This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fituvtu%2FData-Science-AB-Testing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fituvtu%2FData-Science-AB-Testing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fituvtu%2FData-Science-AB-Testing/lists"}