Big Mart Sales Prediction System using Python and Machine Learning

Big Mart Sales Prediction System

Amazon Big Mart Sales Prediction is a machine learning project that helps predict sales for different Big Mart stores using real retail data. The system uses a regression model trained on past sales data and is built using Python and Flask.Users can enter store and product details, and the system will forecast the expected sales. It’s a great project for students and beginners who want to learn how machine learning can be used in retail and business analytics.

Project Overview

This is a ready-to-integrate machine learning project that trains and deploys a regression model for retail sales forecasting. It includes structured code for data preprocessing, model training, and a web interface for predictions.

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Project Details

• Language Used: Python
• Python Version (Recommended): 3.6+
• Type: Web Application

Available Features

• Machine learning model for predicting Big Mart sales
• Data preprocessing and feature engineering scripts
• Jupyter Notebook for model development (model.ipynb)
• Flask-based web application (app.py) for user interaction
• Pretrained model files (.pkl) stored under /models/
• Input form interface built with HTML templates
• Prediction based on user-provided data
• Requirement file (requirements.txt) for environment setup

🔍 Topics Covered

• Machine Learning Models: Sales forecasting using regression models
• Data Preprocessing: Handling missing values, encoding features
• Feature Engineering: Creating input variables for better accuracy
• Model Evaluation: Assessing prediction performance
• Deployment: Web interface using Flask for user interaction

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