Retail Sales Data Analysis

Overview

This project performs an in-depth analysis of a retail sales dataset. It includes data preprocessing, exploratory data analysis (EDA), customer segmentation, and visualization of key business insights using Python and Plotly.

Features

• Data loading and preprocessing
• Handling missing values and understanding data types
• Customer segmentation based on age groups
• Analyzing purchase patterns by different demographic segments
• Identifying the most popular product categories
• Determining revenue generation trends over time
• Interactive visualizations using Plotly

Dataset

The dataset used in this project contains the following columns:

• Transaction ID: Unique identifier for each transaction
• Date: Date of the transaction
• Customer ID: Unique identifier for each customer
• Gender: Gender of the customer (Male/Female)
• Age: Age of the customer
• Product Category: Category of the purchased product
• Quantity: Number of units purchased
• Price per Unit: Price of a single unit
• Total Amount: Total cost of the transaction

Installation & Requirements

Ensure you have Python installed along with the required libraries.

Install dependencies using:

pip install pandas numpy plotly

Usage

1. Clone the repository:

   git clone https://github.com/mehtadigisha/Descriptive-and-Predictive-Analysis-with-Interactive-Dashboard.git

2. Navigate to the project folder:

   cd Descriptive-and-Predictive-Analysis-with-Interactive-Dashboard

3. Run the script:

   python Descriptive and Predictive Analysis with Interactive Dashboard.ipynb

Key Analysis & Insights

• Customer Segmentation: Classifies customers into four age groups - Child, Teenager, Adult, and Senior Citizen.
• Popular Product Categories: Identifies the most purchased product categories by each age group.
• Revenue Analysis: Determines which product category generates the highest revenue.
• Gender-based Spending: Analyzes whether males or females contribute more to total sales.
• Time-based Trends: Tracks total sales trends over time.

Visualizations

• Number of Customers per Product Category (Bar Chart)
• Total Sales by Product Category (Bar Chart)
• Sales Trend Over Time (Line Chart)
• Revenue Distribution by Gender (Pie Chart)

Example Output