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The objective is to identify patterns influencing purchases and build a predictive model for the `Made_Purchase` column.  \n\n---\n\n## Dataset Overview  \n\nThe dataset consists of the following files:  \n\n- **`train.csv`**: Training data with user attributes and purchase indicators.  \n- **`test.csv`**: Test data for predictions.  \n- **`sample_submission.csv`**: Example submission format.  \n\n### Key Features  \n\n1. **Page Metrics**  \n   - Visit counts and durations for `HomePage`, `LandingPage`, and `ProductDescriptionPage`.  \n\n2. **Google Metrics**  \n   - `Bounce Rate`, `Exit Rate`, and `Page Value` provide insights into user engagement and exit behavior.  \n\n3. **Seasonal Indicators**  \n   - `SeasonalPurchase` and `Month_SeasonalPurchase` capture purchase trends during seasonal events.  \n\n4. **Demographics and Contextual Attributes**  \n   - Includes `OS`, `Search Engine`, `Zone`, `Type of Traffic`, `Customer Type`, `Gender`, and more.  \n\n---\n\n## Approach  \n\n1. **Preprocessing**: Cleaning and handling inaccuracies in user data.  \n2. **EDA**: Identifying patterns and relationships between features.  \n3. **Feature Engineering**: Enhancing predictive capabilities through derived metrics.  \n4. **Modeling**: Training machine learning models to predict purchase behavior.  \n5. **Evaluation**: Optimizing results based on F1-Score, focusing on precision and recall balance.  \n\n---\n\n## Tools and Dependencies  \n\n- **Scikit-learn**: Model development and evaluation.  \n- **Pandas, NumPy**: Data handling and preprocessing.  \n- **XGBoost**: Advanced gradient boosting for predictions.  \n- **Matplotlib, Seaborn**: Data visualization.  \n- **Imbalanced-learn**: Addressing class imbalances.  \n\n---\n\n## Business Impact  \n\n- **Improved Engagement**: Insights into bounce and exit rates for better user retention.  \n- **Targeted Marketing**: Seasonal purchase patterns for campaign optimization.  \n- **Enhanced Conversions**: Data-driven strategies to boost sales performance.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpushpakrai%2Funderstanding-e-commerce-consumer-behavior","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpushpakrai%2Funderstanding-e-commerce-consumer-behavior","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpushpakrai%2Funderstanding-e-commerce-consumer-behavior/lists"}