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This project utilizes historical customer data to predict future purchasing behavior and monetary value.\n\u003cbr/\u003e\n\n**Key Features**\n\u003cbr/\u003e\n- Data preprocessing and feature engineering\n- Implementation of Gamma-Gamma and Beta-Geometric/NBD models\n- Random Forest Regressor for CLV prediction\n- Feature importance analysis\n- Data visualization for customer segmentation and CLV distribution\n\u003cbr/\u003e\n\n**Technologies Used**\n\u003cbr/\u003e\n- Python 3.x\n- pandas\n- numpy\n- scikit-learn\n- matplotlib\n- seaborn\n\u003cbr/\u003e\n\n**Model Components**\n\u003cbr/\u003e\n1. Data Preprocessing: Handling missing values and feature engineering\n2. Gamma-Gamma Model: Estimating customer monetary value\n3. Beta-Geometric/NBD Model: Predicting customer purchase behavior\n4. Random Forest Regressor: Predicting overall Customer Lifetime Value\n5. Feature Importance Analysis: Identifying key factors influencing CLV\n\u003cbr/\u003e\n\n**Results**\n\u003cbr/\u003e\nThe model successfully predicts Customer Lifetime Value, enabling:\n\u003cbr/\u003e\n- Targeted marketing strategies\n- Improved customer retention efforts\n- Efficient allocation of marketing resources\n- Personalized customer engagement\n\u003cbr/\u003e\n\n**Future Improvements**\n\u003cbr/\u003e\n- Incorporate additional data sources for more accurate predictions\n- Experiment with other machine learning algorithms for comparison\n- Develop a web application for easy CLV prediction by non-technical users\n  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F1401dev%2Fcustomer-lifetime-value-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F1401dev%2Fcustomer-lifetime-value-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F1401dev%2Fcustomer-lifetime-value-prediction/lists"}