Regression Repository

This repository contains two projects related to regression and time series analysis: bike sharing demand prediction and Rossmann sales prediction.

Bike Sharing Demand Prediction

The goal of this project was to predict the number of bikes rented per hour in a bike sharing program based on weather and time-related variables. The dataset used in this project was obtained from the UCI Machine Learning Repository.

We performed regression analysis using Linear Regression, Ridge Regression, Lasso Regression,ElasticNet Regression,Random forest,XGboost,LGBM etc. We also used time-series analysis with FBProphet.

Rossmann Sales Prediction

The goal of this project was to forecast the weekly sales volume of 10k+ Rossmann drug stores for efficient inventory management. We used the dataset obtained from the Kaggle Rossmann Store Sales dataset.

We performed regression analysis using Linear Regression, Ridge Regression, Lasso Regression, ElasticNet Regression,Random forest,XGboost,LGBM etx. We also used time-series analysis with FBProphet.

Files and Folders

• bike_sharing_demand_prediction: contains the code and data related to the bike sharing demand prediction project.
• rossmann_sales_prediction: contains the code and data related to the Rossmann sales prediction project.
• README.md: provides an overview of the repository.

Getting Started

To run the code in this repository, you need to have Python installed along with the following libraries: numpy, pandas, matplotlib, seaborn, scikit-learn, fbprophet etc which you can find in the first line of the notebook.

Conclusion

Overall, the regression models were able to predict the bike sharing demand and Rossmann sales with reasonable accuracy. Time-series analysis with FBProphet also proved to be a useful tool for forecasting future sales.

Thank you for visiting our regression repository!