🧪 A/B Testing: Conversion Rate Analysis

This is a basic A/B testing project written in Python. It compares the conversion rates between two groups — control and treatment — and checks whether the difference is statistically significant using a t-test.

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📁 Project Structure

ab_testing_project/ ├── analyze.py ├── data/ │ └── ab_data.csv ├── output/ │ ├── conversion_chart.png # Bar chart of conversion rates │ └── report.md # Markdown report of the results ├── .gitignore └── requirements.txt

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📊 What This Project Does

• Loads a dataset with user conversion data
• Calculates conversion rates for control and treatment groups
• Performs a t-test to check if the difference is statistically significant
• Saves:
  • A bar chart comparing the two groups
  • A Markdown summary report in the output/ folder

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🧪 Sample Output

Conversion Rates:

Control: 12.04%

Treatment: 11.89%

T-statistic: 1.2369 P-value: 0.2161

Conclusion: ❌ Not statistically significant — the difference may be due to chance.

Since the p-value is above 0.05, we conclude there is no strong evidence that one version outperforms the other.

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🛠️ Technologies Used

• Python 3
• pandas
• seaborn
• matplotlib
• scipy

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📎 Dataset

The dataset used is from Kaggle, and contains simulated data for an A/B test.

Columns include:

user_id

group (control or treatment)

converted (0 or 1)

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📬 How to Run This Project

# (Optional) Set up virtual environment
python -m venv venv
venv\Scripts\activate      # On Windows
# or
source venv/bin/activate   # On macOS/Linux

# Install dependencies
pip install -r requirements.txt

# Run analysis
python analyze.py

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📈 Output Files

After running analyze.py, you’ll find:

output/conversion_chart.png: A bar chart showing conversion rates

output/report.md: A Markdown report summarizing the results

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📌 Notes

This is a small and simple project, perfect for learning A/B testing fundamentals. It doesn’t use Flask or machine learning — just clean, understandable Python code and basic statistics.

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