Rossman Store Sales Prediction Project Rossman Store
Overview This repository contains my implementation of the Rossman Store Sales Prediction project. The goal of this project was to predict the sales of various Rossmann stores across different parameters, enabling effective sales forecasting.
Key Features Data Exploration and Analysis: In-depth exploration of the provided dataset, uncovering trends and insights that influenced sales. Preprocessing: Cleaning and preprocessing of the data, handling missing values, and transforming features for machine learning models. Feature Engineering: Creation of relevant features to enhance model performance and capture additional patterns in the data. Modeling: Implementation of machine learning models, including but not limited to XGBoost and Random Forest, for accurate sales predictions. Evaluation: Rigorous evaluation of models using appropriate metrics to ensure robust performance. Project Structure data/: Contains the raw and processed datasets used in the project. notebooks/: Jupyter notebooks detailing the step-by-step process of data exploration, preprocessing, and modeling. scripts/: Any custom scripts or utility functions used in the project. models/: Saved models or model artifacts. results/: Results and output files. How to Use Clone the repository to your local machine.
bash Copy code git clone https://github.com/MuhammadTalha121/ML-Rossman-Sales-Prediction-.git Install the required dependencies.
bash Copy code pip install -r requirements.txt Navigate to the notebooks/ directory and run the Jupyter notebooks in sequential order.
Results Include any notable results, insights gained, or challenges faced during the project.
Future Improvements Share ideas or plans for future improvements to the project.
Acknowledgments Give credit to any external libraries, resources, or tutorials that you found particularly helpful in completing the project.
Feel free to explore the notebooks and provide feedback. I welcome collaboration and contributions to enhance the project further.