This project aims to predict the sales of Big Mart outlets using machine learning techniques. Specifically, we utilize the XGBoost Regressor algorithm to forecast sales based on various features such as item weight, outlet size, outlet location, and other relevant factors.

• data: Contains the datasets used in the project.
• notebook: Jupyter notebook for data exploration, preprocessing, model development, and evaluation.
• results: Visualizations generated during the analysis.

The dataset used in this project is sourced from Big Mart, containing historical sales data for various products across different outlets. It includes features such as item weight, item visibility, outlet size, outlet location type, etc.

We employ the XGBoost Regressor model to predict sales based on the provided features. XGBoost is a powerful gradient boosting algorithm known for its efficiency and performance in regression tasks.

1. Setup Environment: Ensure you have Python installed along with necessary libraries (numpy, pandas, scikit-learn, xgboost).
2. Data Preparation: Preprocess the data to handle missing values, encode categorical variables, and perform feature scaling.
3. Model Training: Train the XGBoost Regressor model using the provided training dataset.
4. Model Evaluation: Evaluate the model's performance using appropriate metrics such as R-squared.
5. Prediction: Use the trained model to make predictions on new or unseen data.

• Model Performance: The XGBoost Regressor achieved a R-squared of 0.5017253991620692 on the test data, indicating its effectiveness in predicting sales.
• Feature Importance: Visualizations depicting the importance of different features in influencing sales predictions.

This project demonstrates the application of machine learning techniques, specifically XGBoost Regressor, in predicting sales for Big Mart outlets. By leveraging historical sales data and relevant features, we can make informed decisions to optimize inventory management and maximize sales revenue.