{"id":29819323,"url":"https://github.com/kanishk1420/e-commerce-dynamic-pricing","last_synced_at":"2026-05-16T11:02:38.112Z","repository":{"id":305827432,"uuid":"1024031381","full_name":"Kanishk1420/E-Commerce-Dynamic-Pricing","owner":"Kanishk1420","description":"A comprehensive data science solution for dynamic pricing strategy optimization in e-commerce, leveraging price elasticity analysis, demand forecasting, and profit optimization techniques","archived":false,"fork":false,"pushed_at":"2025-07-22T06:01:15.000Z","size":8285,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-22T08:35:49.502Z","etag":null,"topics":["data-science","e-commerce","matplotlib","python","random-forest","regression","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Kanishk1420.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-07-22T04:59:05.000Z","updated_at":"2025-07-22T06:04:00.000Z","dependencies_parsed_at":"2025-07-22T08:46:33.682Z","dependency_job_id":null,"html_url":"https://github.com/Kanishk1420/E-Commerce-Dynamic-Pricing","commit_stats":null,"previous_names":["kanishk1420/e-commerce-dynamic-pricing"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/Kanishk1420/E-Commerce-Dynamic-Pricing","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kanishk1420%2FE-Commerce-Dynamic-Pricing","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kanishk1420%2FE-Commerce-Dynamic-Pricing/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kanishk1420%2FE-Commerce-Dynamic-Pricing/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kanishk1420%2FE-Commerce-Dynamic-Pricing/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Kanishk1420","download_url":"https://codeload.github.com/Kanishk1420/E-Commerce-Dynamic-Pricing/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kanishk1420%2FE-Commerce-Dynamic-Pricing/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":280583894,"owners_count":26355258,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-23T02:00:06.710Z","response_time":142,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","e-commerce","matplotlib","python","random-forest","regression","seaborn"],"created_at":"2025-07-28T21:10:20.350Z","updated_at":"2025-10-23T07:59:40.520Z","avatar_url":"https://github.com/Kanishk1420.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# E-Commerce Pricing Elasticity Analysis\r\n\r\nA comprehensive data science solution for dynamic pricing strategy optimization in e-commerce, leveraging price elasticity analysis, demand forecasting, and profit optimization techniques.\r\n\r\n## 🎯 Project Overview\r\n\r\nThis project analyzes e-commerce sales data to develop intelligent pricing strategies that maximize profitability while maintaining competitive positioning. The solution combines statistical analysis, machine learning, and business intelligence to provide actionable pricing recommendations.\r\n\r\n### Key Features\r\n\r\n- **Price Elasticity Analysis**: Understand how price changes affect customer demand across different product categories\r\n- **Demand Forecasting**: Predict sales volumes using Random Forest regression models\r\n- **Profit Optimization**: Calculate optimal price points to maximize revenue and profit margins\r\n- **Interactive Visualizations**: Comprehensive dashboards with both static and interactive charts\r\n- **Business Intelligence**: Actionable recommendations for different pricing scenarios\r\n\r\n### Business Impact\r\n\r\nThe analysis demonstrates potential for **10.80% overall profit improvement** ($245,067 additional profit) through strategic pricing optimization, with category-specific recommendations ranging from conservative price adjustments to aggressive margin expansion.\r\n\r\n## 📊 Solution Architecture\r\n\r\n### 1. Data Integration \u0026 Cleaning\r\n- Merged multiple CSV datasets (Amazon sales, international reports, expense data)\r\n- Implemented robust data validation and quality checks\r\n- Created unified analysis dataset with 9,172+ records\r\n\r\n### 2. Feature Engineering\r\n- **Price Positioning**: Quantile-based price positioning within categories\r\n- **Temporal Features**: Season, quarter, day-of-week analysis\r\n- **Discount Analysis**: Realistic discount distribution modeling\r\n- **Profitability Metrics**: Margin calculations and cost ratios\r\n\r\n### 3. Price Elasticity Modeling\r\n- **Cross-Elasticity Analysis**: How price changes affect demand across categories\r\n- **Segmentation**: Elastic vs. inelastic product categorization\r\n- **Seasonal Patterns**: Time-based elasticity variations\r\n\r\n### 4. Demand Forecasting\r\n- **Random Forest Models**: Trained separate models for each product category\r\n- **Feature Importance**: Identified key drivers of demand\r\n- **Model Performance**: R² scores ranging from 0.14-0.42 across categories\r\n\r\n### 5. Profit Optimization Engine\r\n- **Price-Volume Trade-offs**: Optimal pricing considering elasticity constraints\r\n- **Scenario Analysis**: Multiple pricing strategies with impact projections\r\n- **Risk Assessment**: Identification of high-risk pricing changes\r\n\r\n### 6. Advanced Segmentation Analysis\r\n- **Seasonal Elasticity**: How price sensitivity varies across seasons\r\n- **Geographic Segmentation**: Location-based pricing optimization\r\n- **Price Tier Analysis**: Elasticity differences across budget/premium segments\r\n- **Temporal Patterns**: Day-of-week and time-based pricing insights\r\n\r\n### 7. Comprehensive Evaluation Framework  \r\n- **Baseline vs. Optimized Comparison**: Current performance vs. predicted outcomes\r\n- **Scenario Testing**: Multiple pricing strategies (conservative, aggressive, promotional)\r\n- **Risk Assessment**: Impact analysis under different market conditions\r\n- **ROI Projections**: Financial impact quantification\r\n\r\n## 🛠️ Technical Stack\r\n\r\n**Core Libraries:**\r\n- **Data Processing**: pandas, numpy\r\n- **Machine Learning**: scikit-learn (Random Forest)\r\n- **Visualization**: matplotlib, seaborn, plotly\r\n- **Statistical Analysis**: scipy, statsmodels\r\n\r\n**Development Environment:**\r\n- **Platform**: Jupyter Notebook\r\n- **Language**: Python 3.x\r\n- **Data Format**: CSV files with multi-source integration\r\n\r\n## 📁 Project Structure\r\n\r\n```\r\nDelveAI Assignment/\r\n├── pricing_elasticity_analysis.ipynb    # Main analysis notebook\r\n├── README.md                            # Project documentation\r\n├── Datasets/                            # Raw data files\r\n│   ├── Amazon Sale Report.csv\r\n│   ├── International sale Report.csv\r\n│   ├── May-2022.csv\r\n│   ├── P L March 2021.csv\r\n│   ├── Sale Report.csv\r\n│   ├── Cloud Warehouse Comparison Chart.csv\r\n│   └── Expense IIGF.csv\r\n└── Data Scientist Interview Challenge.docx.pdf  # Project requirements\r\n```\r\n\r\n## 🚀 Setup Instructions\r\n\r\n### Prerequisites\r\n- Python 3.7 or higher\r\n- Jupyter Notebook or JupyterLab\r\n- Minimum 4GB RAM recommended\r\n\r\n### Installation\r\n\r\n1. **Clone or download the project files**\r\n   ```bash\r\n   # Navigate to your desired directory\r\n   cd \"your-project-directory\"\r\n   ```\r\n\r\n2. **Install required Python packages**\r\n   ```bash\r\n   pip install pandas numpy matplotlib seaborn plotly scikit-learn scipy statsmodels\r\n   ```\r\n   \r\n   Or using conda:\r\n   ```bash\r\n   conda install pandas numpy matplotlib seaborn plotly scikit-learn scipy statsmodels\r\n   ```\r\n\r\n3. **Verify data files are in place**\r\n   Ensure all CSV files are in the `Datasets/` folder as shown in the project structure above.\r\n\r\n## 🎮 How to Run\r\n\r\n### Method 1: Complete Analysis (Recommended)\r\n1. **Launch Jupyter Notebook**\r\n   ```bash\r\n   jupyter notebook\r\n   ```\r\n\r\n2. **Open the main analysis file**\r\n   - Navigate to `pricing_elasticity_analysis.ipynb`\r\n   - Click to open the notebook\r\n\r\n3. **Run all cells sequentially**\r\n   - Click \"Kernel\" → \"Restart \u0026 Run All\"\r\n   - Or press `Shift + Enter` to run cells one by one\r\n\r\n### Method 2: Section-by-Section Execution\r\nYou can run specific sections based on your needs:\r\n\r\n- **Section 1-2**: Data loading and cleaning\r\n- **Section 3**: Exploratory data analysis and visualizations\r\n- **Section 4-5**: Feature engineering and correlation analysis\r\n- **Section 6**: Demand forecasting models\r\n- **Section 7**: Profit optimization and recommendations\r\n- **Section 8**: Strategic pricing framework\r\n- **Section 9**: Segmentation analysis (seasonal, geographic, price tier analysis)\r\n- **Section 10**: Evaluation \u0026 scenario testing (baseline vs optimized pricing)\r\n\r\n### Expected Runtime\r\n- **Complete analysis**: 5-10 minutes\r\n- **Individual sections**: 30 seconds - 2 minutes each\r\n\r\n## 📈 Key Results \u0026 Insights\r\n\r\n### Price Elasticity Findings\r\n- **Most Price-Sensitive**: Western Dress, Ethnic Dress categories\r\n- **Least Price-Sensitive**: Set, Top categories\r\n- **Optimal Price Increases**: 10% for inelastic categories\r\n\r\n### Profit Optimization Results\r\n- **Set Category**: +21.7% profit improvement potential\r\n- **Top Category**: +16.4% profit improvement potential\r\n- **Overall Business**: +10.80% total profit enhancement\r\n\r\n### Strategic Recommendations\r\n1. **Immediate Action**: Implement 10% price increases for inelastic categories\r\n2. **Gradual Rollout**: Test price changes with A/B testing methodology\r\n3. **Monitoring Strategy**: Track competitor responses and market reactions\r\n4. **Risk Mitigation**: Focus on categories with positive elasticity patterns\r\n\r\n## 📋 Features \u0026 Capabilities\r\n\r\n### Visualization Suite\r\n- **Static Charts**: Distribution plots, correlation heatmaps, time series\r\n- **Interactive Dashboards**: Plotly-powered dynamic charts with hover details\r\n- **Business Dashboards**: Executive-ready profit optimization visualizations\r\n\r\n### Analysis Modules\r\n- **Data Quality Assessment**: Automated validation and cleaning processes\r\n- **Statistical Analysis**: Correlation analysis, distribution fitting\r\n- **Predictive Modeling**: Machine learning-based demand forecasting\r\n- **Optimization Engine**: Profit maximization with constraint handling\r\n- **Segmentation Engine**: Multi-dimensional elasticity analysis\r\n- **Scenario Testing**: Risk assessment and strategy validation\r\n\r\n### Export Capabilities\r\n- **Results Tables**: Formatted DataFrames for further analysis\r\n- **Visualization Export**: High-quality PNG/HTML chart exports\r\n- **Recommendation Reports**: Structured business intelligence outputs\r\n\r\n## 🤖 AI-Assisted Development\r\n\r\nThis project was developed with the assistance of advanced AI coding tools to ensure high-quality, efficient, and well-documented code:\r\n\r\n- **GitHub Copilot**: Used for intelligent code completion, function suggestions, and boilerplate generation\r\n- **Claude Sonnet 4**: Utilized for complex problem-solving, algorithm optimization, and comprehensive code review\r\n\r\nThe AI assistance helped accelerate development while maintaining best practices in data science methodology, code structure, and documentation standards.\r\n\r\n## 🔧 Troubleshooting\r\n\r\n### Common Issues\r\n\r\n**Data Loading Errors:**\r\n- Verify CSV files are in the correct `Datasets/` folder\r\n- Check file names match exactly as specified in the code\r\n- Ensure files are not corrupted or partially downloaded\r\n\r\n**Memory Issues:**\r\n- Close other applications to free up RAM\r\n- Consider reducing the data sample size if memory is limited\r\n- Run sections individually rather than all at once\r\n\r\n**Visualization Problems:**\r\n- Update matplotlib and plotly to latest versions\r\n- Clear notebook output and restart kernel if plots don't display\r\n- Check browser JavaScript is enabled for interactive charts\r\n\r\n**Missing Dependencies:**\r\n```bash\r\n# Install missing packages\r\npip install package_name\r\n\r\n# Update existing packages\r\npip install --upgrade package_name\r\n```\r\n\r\n## 📚 Documentation\r\n\r\n- **Code Comments**: Extensive inline documentation throughout the notebook\r\n- **Markdown Cells**: Detailed explanations of methodology and interpretation\r\n- **Function Docstrings**: Clear descriptions of custom functions and parameters\r\n- **Results Interpretation**: Business-focused explanations of technical findings\r\n\r\n## 🔄 Future Enhancements\r\n\r\n- **Real-time Integration**: Connect to live e-commerce APIs for dynamic pricing\r\n- **Advanced Models**: Implement deep learning for complex elasticity patterns\r\n- **Multi-variate Testing**: Expand to A/B testing framework integration\r\n- **Competitive Intelligence**: Incorporate competitor pricing data\r\n- **Customer Segmentation**: Personalized pricing based on customer behavior\r\n\r\n## 📞 Support\r\n\r\nFor questions, issues, or suggestions regarding this pricing elasticity analysis:\r\n\r\n- Review the troubleshooting section above\r\n- Check code comments and markdown explanations in the notebook\r\n- Ensure all prerequisites and setup steps are completed correctly\r\n\r\n---\r\n\r\n**Note**: This analysis is designed for educational and business intelligence purposes. Always validate recommendations with domain experts and conduct appropriate testing before implementing pricing changes in production environments.\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkanishk1420%2Fe-commerce-dynamic-pricing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkanishk1420%2Fe-commerce-dynamic-pricing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkanishk1420%2Fe-commerce-dynamic-pricing/lists"}