Random Forest Regression

Predicting Sales

Project Details

Summary

In my project, I leveraged random forest regression to predict sales based on factors like ship mode, quantity, and region. Starting with an exploratory data analysis and addressing outliers, I transformed the data (log normalization, categorical encoding) and built a robust model. Feature importance assessment and multicollinearity testing ensured its reliability. The final model explained 83.11% of sales variance (R-squared 0.8311) and provided statistically significant insights to stakeholders. Understanding this influence allows for optimizing sales strategies and maximizing revenue, while robust evaluation metrics empower data-driven decision-making for business success.

Tools

I used Python and its powerful data science libraries to predict sales. I specifically leveraged Matplotlib for visualizations, seaborn for exploratory analysis, and scikit-learn for building and testing the model. This comprehensive analysis, powered by Python's vast data science ecosystem, empowered stakeholders with valuable insights for data-driven decision-making.