Sales Analytics Project

Project Overview

This project analyzes retail sales data to identify key business insights such as revenue trends, regional performance, top-performing products, and category profitability.

The objective of this project is to demonstrate an end-to-end data analytics workflow, starting from raw data processing to business insights using Python, SQL, and Tableau.

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Technologies Used

Python (Jupyter Notebook)

Python was used for data cleaning, preprocessing, and exploratory data analysis.

Libraries used:

• Pandas – Data cleaning and transformation
• Matplotlib – Data visualization

Tasks performed in Python:

• Loaded the raw dataset

• Checked for missing values

• Removed duplicates

• Standardized column names

• Created new features such as:

  • Year
  • Month
  • Profit Margin

• Performed exploratory data analysis

• Generated visualizations

The analysis was conducted inside a Jupyter Notebook.

Notebook file:

Notebook/sales_analysis.ipynb

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SQL (MySQL)

SQL was used to perform business-level queries on the cleaned dataset.

Tasks performed using SQL:

• Created a database

• Created the sales table

• Loaded the cleaned dataset into MySQL

• Performed analytical queries including:

  • Total Sales
  • Total Profit
  • Sales by Region
  • Sales by Category
  • Top 10 Products
  • Monthly Sales Trends
  • Profit analysis

SQL script location:

sql/sales_analysis.sql

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Tableau Visualization

Tableau was used to create visualizations that help analyze sales patterns and business performance.

The Tableau workbook contains visualizations such as:

• Sales by Category
• Sales by Region
• Top 10 Products by Sales
• Monthly Sales Trend

Tableau File:

dashboard/sales_dashboard.twb

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Dataset

The dataset used in this project is the Superstore Sales dataset, which contains retail transaction records including:

• Order details
• Customer information
• Product categories
• Sales and profit values
• Shipping details
• Geographic regions

The raw dataset was first cleaned and prepared using Python before performing further analysis.

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Project Workflow

Raw Dataset ↓ Data Cleaning (Python – Pandas) ↓ Exploratory Data Analysis (Python – Jupyter Notebook) ↓ Business Queries (SQL – MySQL) ↓ Data Visualization (Tableau / Charts)

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Business Questions

The analysis focuses on answering the following key questions:

• What is the total sales revenue and total profit?
• Which regions generate the highest revenue?
• Which product categories perform best?
• What are the top selling products?
• How do sales change over time?

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Visualizations

Sales by Category

Sales by Region

Top 10 Products by Sales

Monthly Sales Trend

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Project Structure

Sales-Analytics
│
├── Data
│   ├── raw
│   │   sample_superstore.xls
│   │
│   └── cleaned
│       sales_cleaned.csv
│
├── Notebook
│   └── sales_analysis.ipynb
│
├── sql
│   └── sales_analysis.sql
│
├── Visuals
│   ├── monthly_sales.png
│   ├── sales_by_category.png
│   ├── sales_region_pie.png
│   └── top_products.png
│
├── dashboard
│
├── index.html
├── style.css
└── README.md

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Key Insights

Some important insights discovered from the analysis:

• The West region generates the highest sales revenue
• Technology products contribute the most to overall sales
• A small number of products account for a large portion of revenue
• Sales trends show consistent activity across months

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Author

Bindhu Saahithi

Master’s Student – Data Science Aspiring Data Analyst