Data-Driven-Sales-Forecasting-for-Big-Mart-Retail-Outlets

This repository contains the implementation of a predictive model to estimate the sales of products at different BigMart outlets using the 2013 sales dataset. The project leverages various machine learning techniques to build a robust model for sales forecasting.

Table of Contents

• Project Overview
• Dataset
• Project Structure
• Modeling Approach
• Technologies Used
• How to Use
• Results
• Conclusion
• Future Improvements
• Contact

Project Overview

The goal of this project is to predict the sales of each product in different outlets based on various product attributes and store-related information. The data was collected from 10 stores for 1559 products across different cities in 2013. This project can help BigMart optimize inventory management, improve pricing strategies, and ultimately boost revenue by accurately predicting future sales.

Dataset

The dataset used for this project contains the following features:

• Item_Identifier: Unique product ID
• Item_Weight: Weight of the product
• Item_Fat_Content: Whether the product is low-fat or regular
• Item_Visibility: The percentage of total display area allocated to the product
• Item_Type: The category to which the product belongs
• Item_MRP: Maximum retail price of the product
• Outlet_Identifier: Unique store ID
• Outlet_Establishment_Year: The year the outlet was established
• Outlet_Size: The size of the outlet (Small, Medium, High)
• Outlet_Location_Type: The type of area where the outlet is located
• Outlet_Type: Whether the outlet is a grocery store or a supermarket
• Item_Outlet_Sales: The target variable (sales)

You can find the dataset here.

Project Structure

Big-Mart-Sales-Prediction/
│
├── data/                  # Dataset and data preparation scripts
├── notebooks/             # Jupyter notebooks for data exploration and EDA
├── models/                # Trained models and model training scripts
├── src/                   # Core scripts for data processing and modeling
├── results/               # Output results and visualizations
├── README.md              # Project description and instructions
└── requirements.txt       # Dependencies for the project

Modeling Approach

1. Exploratory Data Analysis (EDA):

   • Analyze the distribution of product attributes and sales.
   • Handle missing values, outliers, and feature transformations.

2. Feature Engineering:

   • Create new features from existing attributes, such as age of the outlet and item categories.
   • Encode categorical features using techniques like one-hot encoding and label encoding.

3. Model Selection:

   • Tried multiple machine learning algorithms such as:
     • Linear Regression
     • Decision Tree
     • Random Forest
     • XGBoost

4. Model Evaluation:

   • Models were evaluated using metrics like RMSE (Root Mean Squared Error) and R-squared.

Technologies Used

• Python: Programming language
• Pandas & NumPy: For data manipulation and preprocessing
• Matplotlib & Seaborn: For visualizations
• Scikit-learn: Machine learning algorithms
• XGBoost: Advanced gradient boosting algorithm
• Jupyter Notebooks: For interactive data exploration
• Streamlit: (optional) To create a web app for visualization (if used)

How to Use

1. Clone the repository:

   git clone https://github.com/Gourav052003/Data-Driven-Sales-Forecasting-for-Big-Mart-Retail-Outlets.git
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the notebooks: Open the Jupyter notebooks for data exploration, feature engineering, and model training.

4. Predict sales: Run the model training script in the src/ directory and use the trained model to predict sales for new data.

Results

The best model achieved a RMSE of 0.087 on the test set. The model was able to capture the general trends in sales, but there is room for improvement in handling the variability for certain products.

Conclusion

This project successfully demonstrates the process of building a predictive model for retail sales forecasting. By applying data preprocessing, feature engineering, and experimenting with different machine learning algorithms, the project provides insights into how product attributes and store information can be used to predict sales.

Future Improvements

Incorporate more external factors such as seasonality, holiday effects, and promotions. Experiment with advanced time series techniques or deep learning models. Optimize hyperparameters using more advanced search techniques like Bayesian Optimization.

Contact

For any questions or collaboration opportunities, feel free to reach out:

• Email: [gourav052003@gmail.com]
• LinkedIn: linkedin.com/in/gourav-kashyap-data-scientist-analytics
• GitHub: github.com/Gourav052003