Product Sales Forecasting: EDA, Hypothesis Testing & Ensemble Modeling
📰 Medium · Machine Learning
Learn to forecast product sales using EDA, hypothesis testing, and ensemble modeling with XGBoost, Random Forest, and Facebook Prophet
Action Steps
- Explore the data using EDA to identify trends and patterns
- Apply hypothesis testing to validate assumptions about sales data
- Build an ensemble model using XGBoost, Random Forest, and Facebook Prophet
- Configure hyperparameters for each model to optimize performance
- Test and compare the performance of each model using metrics such as MAE and RMSE
Who Needs to Know This
Data scientists and analysts on a retail team can benefit from this project to improve sales forecasting and inform business decisions
Key Insight
💡 Ensemble modeling with XGBoost, Random Forest, and Facebook Prophet can improve the accuracy of sales forecasts
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Boost your sales forecasting game with EDA, hypothesis testing, and ensemble modeling!
Key Takeaways
Learn to forecast product sales using EDA, hypothesis testing, and ensemble modeling with XGBoost, Random Forest, and Facebook Prophet
Full Article
A complete end-to-end data science project on retail sales prediction using XGBoost, Random Forest, and Facebook Prophet Continue reading on Medium »
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