Sales Data Analysis & EDA | Python, Pandas, Seaborn

Superstore Sales Data Analysis Using Python

Project Overview

This project focuses on analyzing retail sales data to uncover meaningful business insights, understand sales and profitability patterns, and support data-driven decision-making.

Tools & Technologies

* Python

* Pandas & NumPy

* Matplotlib & Seaborn

* Jupyter Notebook

Project Workflow

1. Data Exploration & Inspection

Explored the dataset structure, columns, data types, and descriptive statistics.

Examined missing values and duplicate records.

2. Data Cleaning & Preparation

* Investigated missing values, duplicate records, and outliers.

* Prepared the dataset for further analysis.

* Exported cleaned and processed datasets.

3. Feature Engineering

* Created additional analytical features, including profit margin, shipping duration, and sales performance categories.

4. Exploratory Data Analysis (EDA)

* Analyzed sales and profit distributions.

* Compared sales and profitability across product categories, regions, and customer segments.

* Identified top-performing products by sales and profit.

* Analyzed monthly sales trends.

* Examined average shipping duration across shipping methods.

5. KPI Analysis & Business Insights

* Developed a reusable Python class to calculate key performance indicators.

* Analyzed total sales, total profit, profit margin, order and customer counts, average order value, and shipping duration.

* Generated an analytical summary and business insights.

* Examined correlations between numerical variables.

Deliverables

* Jupyter Notebook containing the analysis workflow.

* Cleaned and feature-engineered datasets.

* Data visualizations and KPI summaries.

* Business insights derived from the analysis.

Project Objective

To transform raw retail data into structured, meaningful insights that help businesses understand their performance, identify sales opportunities, and make better-informed decisions.