🛒 Supermarket Data Analysis Using Python

📄 Overview

Analyzed supermarket data to reveal top products, city sales trends, payment preferences, and customer ratings. Explored revenue by category and customer type, and assessed time-based sales patterns.

🔄 Workflow

1. Importing the required libraries
2. Loading the Dataset
3. Basic Understanding of Data
4. Data Preprocessing
5. Exploratory Data Analysis (EDA) along with Insights
6. Summary of Insights

🔑 Key Insights

• Top Performing Product Line
• Total Sales and Revenue Trend
• City-wise Sales Distribution
• Top Performing Cities
• Preferred Payment Methods
• Customer Rating Analysis
• Category-wise Revenue
• Customer Type Analysis
• Time-based Sales Trend

🛠️ Tools, Concepts & Technologies

Technologies Used
Developed using Python with:

• Pandas: Data manipulation and analysis
• NumPy: Numerical calculations
• Matplotlib & Seaborn: Data visualization
• Jupyter Notebook: Interactive analysis

CSV Dataset: Analyzed data from a CSV file using Pandas

📊 Datasets Used

• supermarket_sales

📊 Visualizations

• Bar Charts
• Line Graphs
• Heatmaps
• Boxplots
• Pie Charts

🔍 Analysis Techniques

• Pandas Functions: groupby, merge, describe, time_series_analysis
• Feature Engineering: Creating new features and transforming existing ones for improved analysis
• EDA: Univariate and Bivariate Analysis and more.