🛒 Big Mart Sales Prediction Project

This project focuses on building a Machine Learning model to predict sales for retail products across various Big Mart stores. The goal is to identify the key factors affecting product sales and build a regression model to forecast sales more accurately.

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📁 Project Structure

├── Big Mart Sales Prediction.ipynb     # Jupyter Notebook with end-to-end analysis & model
├── Train/Test CSV Files (optional)     # Training and testing data (not included here)
└── README.md                           # Project documentation (you are here)

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📊 Problem Statement

Retail companies like Big Mart want to understand which features influence the sales of products and how to forecast them. This project analyzes sales data of various products across multiple outlets of Big Mart.

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🔍 Workflow Overview

1. Data Loading & Exploration
2. Handling Missing Values
3. Feature Engineering
4. Data Visualization
5. Model Building
6. Performance Evaluation
7. Final Predictions

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🚀 Technologies Used

• Python 🐍
• Pandas & NumPy
• Matplotlib & Seaborn (EDA)
• Scikit-learn (ML models: Linear Regression, Decision Tree, Random Forest)
• Jupyter Notebook

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📈 Model Performance

• Evaluated using Root Mean Squared Error (RMSE) and R² Score
• Cross-validation used to avoid overfitting
• Best performing model saved (e.g., Random Forest Regressor)

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📂 Data Summary

Dataset includes fields like:

• Item_Identifier, Item_Weight, Item_Fat_Content, Item_Visibility
• Outlet_Identifier, Outlet_Establishment_Year, Outlet_Size, Outlet_Location_Type
• Target Variable: Item_Outlet_Sales

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🔮 Goal

Predict Item_Outlet_Sales using given features and generate submission for competition or business insight.

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🛠️ To Run Locally

1. Clone the repo or open the notebook
2. Install required libraries:

pip install pandas numpy matplotlib seaborn scikit-learn

3. Open the notebook using Jupyter or VSCode and run the cells

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📈 Future Enhancements

• Hyperparameter tuning using GridSearchCV
• Deploy model with Streamlit/Flask UI
• Try advanced models (XGBoost, LightGBM)
• Feature selection via Recursive Feature Elimination (RFE)

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🤝 Acknowledgments

• Big Mart Dataset on Analytics Vidhya / Kaggle
• Scikit-learn

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📜 License

This project is licensed under the MIT License.