Super-Market Analysis and Prediction using Python and MySQL Database

The Supermarket Sales Analysis and Prediction project is a comprehensive data science workflow that combines data analysis and machine learning to derive actionable insights and predictions from supermarket sales data. The project is divided into two main components:

1. Supermarket Sales Analysis:

Objective: Analyze sales data to uncover trends and patterns. Technologies Used: Python, MySQL, Pandas, Matplotlib. Key Features:

• Run the MySQL database application as docker container and connect with the container using python connect and mysql client libraries.
• Connects to a MySQL database to fetch sales data.
• Performs exploratory data analysis (EDA) to identify trends, such as top-selling products, sales by branch, and customer demographics.
• Visualizes data using plots to provide insights into sales performance and customer preferences.

2. Sales Prediction:

Objective: Predict customer types (e.g., “Member” or “Normal”) based on sales data. Technologies Used: Python, MySQL, Scikit-learn. Key Features:

• Preprocesses data by encoding categorical variables and splitting it into training and testing sets.
• Trains a logistic regression model to predict customer types.
• Evaluates the model’s performance using accuracy and classification reports.
• Simulates predictions on new data and stores the results in a MySQL table (sales_predictions).
• Exports predictions to a CSV file for further analysis.