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Exploratory Data Analysis on Supermarket Sales Dataset

🛒 Project Overview

This project focuses on analyzing a Supermarket Sales Dataset to uncover actionable insights that can help supermarket management make data-driven decisions. The dataset includes records of sales transactions, customer demographics, and other key features.

The analysis provides insights into branch performance, customer preferences, payment methods, and factors influencing sales and customer ratings.

📝 Problem Statement

Supermarkets play a vital role in retail, and leveraging data effectively can improve profitability and operational efficiency. The objective of this project is to:

  • Analyze the dataset to identify high-performing branches.
  • Understand customer behavior and preferences.
  • Examine payment methods and their impact on sales.
  • Explore factors influencing customer ratings and sales trends.

📂 Dataset

  • Source: Kaggle - Supermarket Sales Dataset
  • The dataset contains detailed information on supermarket sales, including customer demographics, branches, payment methods, and ratings.

⚙️ Tools and Technologies

  • Programming Language: Python
  • Libraries Used:
    • pandas
    • NumPy
    • Matplotlib
    • Seaborn

🔍 Key Steps

  1. Data Loading and Understanding:

    • Imported the dataset using pandas and explored it using .head(), .info(), .describe(), and .shape().
    • Checked and handled missing values, duplicates, and inconsistent data formats.

  2. Data Cleaning:

    • Processed data to ensure consistency and accuracy (e.g., formatted date columns and categorical data).

  3. Exploratory Data Analysis (EDA):

    • Analyzed sales trends based on customer demographics, branches, and payment methods.
    • Explored relationships between features like sales, ratings, and branches.

  4. Data Visualization:

    • Pie Charts: Distribution of sales by branch.
    • Bar Charts: Payment methods vs. total sales.
    • Heatmaps: Correlation among numerical features.
    • Boxplots: Gender-wise sales distribution.

  5. Insights and Findings:

    • Highlighted high-performing branches.
    • Identified the most preferred payment methods.
    • Analyzed factors influencing customer ratings and sales.

📊 Key Insights

  • The most revenue-generating branch was identified.
  • The most popular payment method was analyzed.
  • Sales trends across genders and customer demographics were explored.

📈 Results

This analysis provides actionable insights for supermarket management to make informed, data-driven decisions and improve profitability.