Website-AB-Testing-Python
🌗 Light vs Dark Theme Website Performance
Analyzing User Behavior Insights & A/B Testing Website Metrics with Python
This project analyzes website performance metrics to compare “Light Theme” and “Dark Theme” using a dataset (data/abt.csv) with 1000 user records. Built in Python within a Jupyter Notebook (abt.ipynb), it leverages pandas, numpy, scipy, seaborn, matplotlib, and scikit-learn for data processing, statistical testing, and visualizations.
Dataset Overview
The dataset (data/abt.csv) contains website user interaction data with 1000 rows (486 Light Theme, 514 Dark Theme). A preview of key columns:
| Theme | Click_Through_Rate | Conversion_Rate | Bounce_Rate | Scroll_Depth | Age | Location | Session_Duration | Purchases | Added_to_Cart |
|---|---|---|---|---|---|---|---|---|---|
| Light Theme | 0.032 | 0.208 | 0.754 | 73.02 | 52 | Chennai | 1176 | No | Yes |
| Light Theme | 0.143 | 0.028 | 0.306 | 35.02 | 33 | Pune | 780 | Yes | Yes |
| Dark Theme | 0.323 | 0.179 | 0.297 | 45.74 | 27 | New Delhi | 912 | No | No |
| Light Theme | 0.489 | 0.326 | 0.649 | 79.37 | 41 | Chennai | 1345 | No | Yes |
| Light Theme | 0.098 | 0.273 | 0.438 | 72.18 | 36 | Pune | 645 | Yes | Yes |
- Size: 1000 rows (sample: 5 shown).
- Key Metrics: CTR, Conversion, Bounce, Scroll, Purchases, Demographics.
Analysis Sections and Visualizations
1. Data Summary
Provides descriptive statistics for numerical and categorical variables.
- Metrics: Means, std, ranges for
Click_Through_Rate,Conversion_Rate, etc. - Visualizations: None (tabular output).
- Insights:
- Mean CTR: 0.256, Conversion: 0.253, Bounce: 0.506, Scroll: 50.32.
- Age range: 18-65, Session: ~15.5min avg.
2. Theme Performance Comparison
Compares mean metrics between themes.
- Metrics:
Click_Through_Rate,Conversion_Rate,Bounce_Rate,Scroll_Depth. - Visualizations: Styled table.
- Insights:
- Dark Theme CTR: 0.265 vs. Light 0.247 (higher).
- Light Theme Conversion: 0.255 vs. Dark 0.251 (slight edge).
3. Hypothesis Testing
Tests for significant differences using Welch’s t-tests (alpha = 0.05).
- Metrics:
Click_Through_Rate,Conversion_Rate,Bounce_Rate,Scroll_Depth. - Hypotheses:
- H₀: No difference in means.
- H₁: Difference exists.
- Visualizations: Styled table with p-values.
- Insights:
- CTR: p = 0.048 (significant, Dark > Light).
- Conversion: p = 0.635 (not significant).
4. Correlation Analysis
Examines linear relationships between metrics.
- Metrics:
Click_Through_Rate,Conversion_Rate,Bounce_Rate,Scroll_Depth. - Visualizations: Heatmap (
seaborn). - Insights:
- Weak correlations (< 0.3) → metrics are independent.
5. Distribution Analysis
Explores metric distributions by theme.
- Metrics:
Conversion_Rate. - Visualizations: Histogram with KDE (
seaborn). - Insights:
- Light Theme: Bimodal (~0.15, ~0.40) → two user groups.
- Dark Theme: Unimodal (~0.25) → consistent, lower conversions.
6. Location-Based Analysis
Assesses bounce rates by location and theme.
- Metrics:
Bounce_Rate. - Visualizations: Box plot (
seaborn). - Insights:
- New Delhi: Dark Theme higher median (~0.60) vs. Light (~0.50).
- Location > Theme influence.
7. Predictive Modeling
Predicts Purchases using logistic regression.
- Features:
Click_Through_Rate,Conversion_Rate,Bounce_Rate,Scroll_Depth,Age. - Visualizations: None (tabular coefficients).
- Insights:
- Accuracy: ~60% (weak).
- Positive:
Conversion_Rate(0.02),Scroll_Depth(0.01). - Negative:
Bounce_Rate(-0.03),Click_Through_Rate(-0.01).
Key Findings
- Theme Performance: Dark Theme boosts CTR significantly (p = 0.048); Light Theme slightly better for conversions (not significant).
- User Behavior: Weak metric correlations; bimodal Conversion in Light Theme suggests segmentation.
- Location Impact: New Delhi favors Light Theme (lower bounce).
- Prediction: Current features poorly predict purchases—needs non-linear models.
Methodology
- Tools: Python with
pandas,numpy,scipy.stats,seaborn,matplotlib,scikit-learn. - Visualizations: Static plots (heatmap, histogram, box plot) with
seaborn. - Processing: T-tests, correlation, regression, data cleaning (
pd.to_numeric).
Usage
- Setup: Install dependencies: ```bash pip install pandas numpy scipy seaborn matplotlib scikit-learn IPython
Author
Author: [Pavan Yellathakota]
Date: MAR 2025
Contact Information
You can reach out to me through the following channels:
- Email: pavanyellathakota@gmail.com
- LinkedIn: Pavan Yellathakota
For more projects and resources, check out:
- GitHub: Pavan Yellathakota
- Portfolio: pye.pages.dev
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