This project explores customer segmentation and prediction of purchase behavior using machine learning techniques on e-commerce customer data. It was developed by Pratik Bokade as part of a data analytics learning journey.
To analyze e-commerce customer behavior, segment users based on purchasing patterns, and predict the likelihood of future purchases using clustering and classification models.
- 🧹 Data Cleaning and Preprocessing
- 📊 Exploratory Data Analysis (EDA)
- 🔍 Customer Segmentation using KMeans Clustering
- 📈 Purchase Prediction using Logistic Regression, Random Forest, and XGBoost
- 📉 Model Evaluation using Accuracy, Precision, Recall, and Confusion Matrix
- 📌 Insights and Actionable Recommendations for marketers
- Python 3.9+
- Pandas – Data manipulation
- NumPy – Numerical operations
- Matplotlib & Seaborn – Data visualization
- Scikit-learn – ML models and evaluation
- XGBoost – Gradient boosting classification
- Jupyter Notebook – Development environment
- RFM (Recency, Frequency, Monetary) Analysis
- Clustered customer segments
- Correlation heatmap
- Feature importance for prediction model
- Identified 3–5 customer segments using KMeans clustering.
- Best performing model: Random Forest with over 85% accuracy in predicting purchases.
- Derived actionable insights for targeted marketing and personalization.
- Integrate with real-time customer databases or dashboards (Power BI/Tableau).
- Implement deep learning models for advanced predictions.
- Create a recommendation engine for product suggestions.
This project helped in strengthening my knowledge of:
- Customer behavior modeling
- Supervised vs. unsupervised learning
- Real-world implementation of ML models in business contexts
Pratik Bokade
📧 Email Me
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