Consumer Propensity Forecasting is a machine learning project designed to predict the likelihood of a customer making a purchase in an e-commerce environment. By analyzing customer browsing behavior and session characteristics, the system helps businesses identify potential buyers and optimize marketing strategies.
The project provides valuable insights into customer behavior, enabling data-driven decision-making and improved conversion rates.
E-commerce platforms attract thousands of visitors daily, but only a small percentage complete a purchase. Identifying high-intent customers can help businesses target promotions, improve user experiences, and increase revenue.
This project aims to predict whether a customer session will result in a purchase based on behavioral and session-level data.
- Purchase likelihood prediction
- Customer behavior analysis
- Data preprocessing and feature engineering
- Machine learning classification models
- Performance evaluation using classification metrics
- Interactive visualization dashboard
- Python
- Pandas
- NumPy
- Scikit-Learn
- Matplotlib
- Streamlit
- Machine Learning
- Data Collection
- Data Cleaning and Preprocessing
- Feature Engineering
- Model Training
- Model Evaluation
- Prediction Generation
- Dashboard Visualization
The model analyzes customer behavior patterns such as:
- Page visits
- Time spent on website
- Bounce rates
- Exit rates
- Visitor type
- Traffic source
- Session characteristics
Using these features, the system predicts the probability of a purchase.
- Improve conversion rates
- Identify high-value customers
- Optimize marketing campaigns
- Enhance customer targeting
- Support data-driven decision making
The trained machine learning model successfully identifies customers with a higher likelihood of making a purchase, enabling proactive business actions and improved sales performance.
- Real-time prediction system
- Recommendation engine integration
- Customer segmentation
- Revenue forecasting
- Explainable AI (SHAP/LIME)
- Personalized marketing suggestions
Harshita Pawar
This project is intended for educational and research purposes.