Supermarket Sales EDA and Visualization

This repository contains exploratory data analysis (EDA) and visualization code for analyzing supermarket sales data. The dataset used is "supermarket_sales - Sheet1.csv". The analysis covers various aspects of the data, including descriptive statistics, data cleaning, feature engineering, outlier detection, and visualization.

Getting Started

To get started, clone this repository to your local machine:

git clone https://github.com/X31N0M/EDA-and-Visualization-of-Sales-Data.git

Ensure you have Python and necessary libraries installed to run the code.

Dataset

The dataset contains information about supermarket sales, including attributes such as:

• Branch: The supermarket branch (A, B, or C).
• Date: Date of the transaction.
• Customer type: Type of customer (Member or Normal).
• Product line: Category of the product.
• Unit price: Price of a single unit of the product.
• Quantity: Number of units purchased.
• Gross income: Total gross income from the transaction.
• Tax 5%: Tax amount (5% of total).
• Total: Total transaction amount.
• Payment: Payment method.
• COGS: Cost of goods sold.

Analysis Overview

1. Data Loading and Initial Exploration:

   • Loading the dataset using Pandas.
   • Displaying the first and last few rows of the dataset.
   • Checking the shape, info, and descriptive statistics of the dataset.

2. Data Cleaning and Preprocessing:

   • Handling missing values.
   • Converting data types (e.g., date column to datetime).
   • Creating new columns for month and year.
   • Encoding categorical variables.

3. Feature Engineering:

   • Calculating percentage change in gross income.
   • Adding new features like month and year from the date column.
   • Scaling numerical features using MinMaxScaler.

4. Exploratory Data Analysis (EDA):

   • Visualizing data distributions using histograms and density plots.
   • Exploring the relationship between variables using scatter plots, pair plots, and correlation analysis.
   • Creating frequency tables and cross-tabulations.
   • Analyzing sales trends over time.

5. Outlier Detection:

   • Detecting outliers using statistical methods (Z-score, IQR).
   • Using Isolation Forest and Local Outlier Factor algorithms for outlier detection.
   • Handling outliers through techniques like log transformation, clipping, and mapping.

6. Visualization:

   • Visualizing sales trends, distributions, and relationships using line plots, bar charts, pie charts, histograms, box plots, and scatter plots.
   • Utilizing libraries like Matplotlib and Seaborn for visualization.