This is an end-to-end ML project, which aims at developing a regression model for the problem of predicting the sales of a given product, based on its properties like item category, weight, visibility, MRP, type of outlet the roduct is sold, size of the outlet etc.
Multiple regressor like Linear Regression, RandomForestRegressor, Lasso, XGBoost Regressor were tested and the best one was selected. The performance of the selected model was further improved using hyperparameter tuning.
Deployed in Railway.app.
Link to the application : https://store-sales-prediction.up.railway.app/
Nowadays, shopping malls and Big Marts keep track of individual item sales data in order to forecast future client demand and adjust inventory management. In a data warehouse, these data stores hold a significant amount of consumer information and particular item details. By mining the data store from the data warehouse, more anomalies and common patterns can be discovered.
The goal of this project is to build a solution that should able to predict the sales of the items in different stores of the supermarket chain, according to the provided dataset.
This dataset is taken from the kaggle. Link: https://www.kaggle.com/brijbhushannanda1979/bigmart-sales-data
The Code is written in Python 3.7. If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip.
