#Project Title: Supermarket Performance Analytics Dashboard (Python, Pandas, Matplotlib

#Project Description: This project analyzes a supermarket’s store data to discover sales trends, top-performing stores, and customer efficiency. It uses Python libraries such as Pandas for data cleaning and analysis, NumPy for numerical calculations, and Matplotlib for visualizations.

Dataset

The dataset contains sales information for multiple stores, including:

• Store ID
• Store Area (sq. ft.)
• Items Available
• Daily Customer Count
• Store Sales

Features / Steps Performed

1. Loaded and cleaned the dataset (handled missing values and duplicates)
2. Calculated key metrics:
   • Sales per Customer
   • Sales per Area
3. Identified top 5 and bottom 5 performing stores
4. Analyzed correlations between:
   • Store area & sales
   • Customer count & sales
5. Visualized insights using:
   • Bar charts for sales per store
   • Scatter plots for customer vs sales
   • Histograms for sales efficiency
   • Subplots dashboard combining all charts
6. Exported a cleaned summary CSV with all metrics

Key Insights

• Larger stores and stores with more customers generally have higher sales
• Some smaller stores achieve high efficiency per area or per customer
• The dashboard visualizations clearly show top-performing and underperforming stores

Visualizations

• Bar chart: Total sales per store
• Scatter plot: Customers vs Sales
• Histogram: Sales per Customer
• Subplots dashboard showing all insights together

Technologies

• Python 3.x
• Pandas
• NumPy
• Matplotlib
• Pycharm