This project, conducted by Group 5 for Drexel University's INFO442 course, focuses on analyzing the Rossmann Store Sales dataset. The primary objective is to derive insights and build predictive models to forecast sales for the Rossmann drug store chain.
Dataset The dataset used for this project is sourced from Kaggle: Rossmann Store Sales Dataset
Dataset Description
The dataset contains historical sales data for Rossmann stores. Each row represents a specific store's sales data on a particular date. Dataset
The project is organized as follows:
- code/: Jupyter notebooks with exploratory data analysis (EDA), data cleaning, and model development.
- data/: Contains the raw and processed data files.
- reports/: Generated reports and visualizations.
- README.md: Project overview and instructions.
Prerequisites Python 3.x Jupyter Notebook Required Python libraries: pandas numpy scikit-learn matplotlib seaborn Installation Clone the repository:
git clone https://github.com/Emilye42/INFO442
Environment setup:
pip install -r requirements.txt
Contributors
Group 5 Members(alphabetical):
- Emily Ye
- Jerry Li
- Junkai Ge
- Shenyang Dong
License
This project is licensed under the MIT License.
Acknowledgements
We would like to thank Kaggle for providing the dataset and the Drexel University INFO442 instructors for their guidance and support.