Scikit-learn Projects
Customer Analytics & Data Mining for E-Commerce (Data Mining & Machine Learning):
🛒 Online Retail Data-Mining & Analytics Platform
Role: Data Scientist / ML Engineer • Tech: Python ( Pandas · NumPy · scikit-learn · Prophet · LightGBM · Matplotlib / Seaborn ), SQL, Jupyter, Git
🚀 1-Sentence Pitch
I designed an end-to-end analytics pipeline that converts 500 K+ e-commerce transactions into segment-driven marketing actions, personalised product recommendations, and six-month sales forecasts—all from a single notebook.
🎯 Problem & Goal
The retailer held raw invoice data but lacked answers to three core questions:
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Who are our most valuable / risky customers?
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What should we recommend to each shopper right now?
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How much will we sell in the next season, and when?
My goal was to extract these answers with minimum engineering overhead and maximum business clarity.
🛠️ Solution Architecture
| Stage | Key Tasks | Algorithms & Tools |
|---|---|---|
| Data Prep | Excel → DataFrame, null & duplicate pruning, outlier detection | Tukey fences · Z-score · pd.to_datetime() |
| Exploratory EDA | KPI dashboards by Country, Invoice, Customer | Seaborn heatmaps, log-scaled histograms |
| RFM + Feature Eng. | Recency, Frequency, Monetary, AvgOrderValue, CLV, LoyaltyScore | Vectorised Pandas · discount factor |
| Customer Clustering | PCA → K-means (k = 5) + VIP outlier bucket | sklearn.decomposition.PCA · KMeans |
| Recommendation Engine | - User-based CF (cosine) - Content-based (Euclidean on price, popularity, brand) |
Top-N recommender API |
| Predictive Modeling | GradientBoostingRegressor with GridSearchCV → RMSE ↓ 12 % vs. baseline | LightGBM · XGBoost comparison |
| Time-Series Forecast | Daily revenue aggregation → Prophet: trend + weekly/annual seasonality | RMSE validation · holiday regressor |
| Visual Reporting | Cluster scatterplots, SHAP feature importances, forecast bands | Matplotlib · SHAP |
📈 Impact & Insights
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VIP cohort (2 % of users) drives 31 % of revenue → triggered targeted loyalty e-mails.
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Return-prone cluster identified (avg. 18 % return rate) → flagged for UX & description fixes.
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Seasonality: December peak + April dip predicted; procurement aligned 6 weeks earlier.
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Recommendation lift (offline test): +9 % expected AOV for top-decile customers.
🌟 Why It Matters
The project shows how small data teams can bootstrap a full analytics stack—from cleaning to forecasting—without heavyweight infra. Every notebook cell is reproducible; business users get clear graphs and CSV-ready outputs for CRM uploads.