Predict future weekly sales, enhance supply chain management, and optimize decision-making for Walmart stores.
In the dynamic retail landscape, accurate sales prediction is pivotal. This project delves deep into how both internal and external factors impact the future weekly sales of one of the largest US companies - Walmart.
Retail chains grapple with complex supply chain management. This project aims to forecast demand, enabling efficient supply planning, reducing overstocking, and streamlining logistics.
The project leverages a comprehensive dataset containing three key components:
- Stores: Details about store types, sizes, and numbers.
- Sales: Records of weekly sales, departments, and holidays.
- Features: Information on temperature, fuel prices, markdowns, CPI, and unemployment.
- Exploratory Data Analysis (EDA) and Time Series Analysis to gain insights.
- Utilize various Regression Models for accurate sales predictions.
- Lasso Regressor
- Random Forest Regressor
- Gradient Boosting Regressor
- Support Vector Regressor
- Time Series Analysis
- Clone this repository.
- Install required packages using
requirements.txt. - Explore the Jupyter notebooks for EDA, modeling, and predictions.
- Run the code to gain insights and make predictions.
By employing advanced analytics and predictive modeling, we empower Walmart to make informed decisions and optimize their supply chain management, thus enhancing their competitiveness in the retail industry.
Feel free to contribute, ask questions, and collaborate to drive this project forward!
This project is licensed under the MIT License.