This project is a machine learning model that predicts the sales of Big Mart items based on various attributes. The model is built using XGBoost Regressor and the evaluation metrics used to evaluate the model's performance is R Squared. The model is built on the Big Mart Sales data that contains the sales of various products in different outlets of Big Mart.
Getting Started These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Prerequisites What things you need to install the software and how to install them
streamlit numpy pandas scikit-learn xgboost matplotlib seaborn
Deployment This model can be deployed as a web application using streamlit. The show_predict_page function can be run as a streamlit app to start the web application. The app takes in various attributes as inputs and gives the prediction of sales as the output.
Built With streamlit - The web framework used numpy - Numerical computing library pandas - Data analysis library scikit-learn - Machine learning library xgboost - Gradient Boosting library matplotlib - Plotting library seaborn - Plotting library based on matplotlib
Authors Your Name - Nyanda Freddy
License This project is licensed under the MIT License - see the LICENSE.md file for details