This repository contains the code and dataset for forecasting the order quantity of a bike store chain's products based on historical sales data from 2011 to 2015. Developed a Gradient Boosting Regression model and a Random Forest Regressor model to predict future order quantities.
The dataset used for this project is available in Sales.csv. It includes historical sales data from 2011 to 2015, which serves as the foundation for training and testing the predictive model.
bike_sales_forecasting.ipynb contains is the Jupyter Notebook for this project. You can explore the entire data preprocessing, feature engineering, model development, and evaluation process in this notebook.
For RandomForestRegressor :
| Data | RMSE | r2_score |
|---|---|---|
| Train | 4.04 | 82.34% |
| Val | 4.72 | 74.50% |
| Test | 4.77 | 73.98% |
For GradientBoostingRegressor:
| Data | RMSE | r2_score |
|---|---|---|
| Train | 3.76 | 84.70% |
| Val | 4.16 | 80.18% |
| Test | 4.24 | 79.50% |