📌 Project Overview This project focuses on Exploratory Data Analysis (EDA) of sales data to uncover insights about revenue, product categories, customer spending, and rating patterns. The analysis aims to help businesses make data-driven decisions by identifying top-performing products, high-value customers, and seasonal trends.
🎯 Objectives The analysis addresses the following key questions:
Total Revenue: What is the overall revenue generated?
Top Revenue-Generating Category: Which category contributed the most revenue?
Average Ratings by Category: How do ratings vary across product categories?
Monthly Revenue Trends: How does revenue fluctuate over time?
Top 5 Customers: Which customers have the highest total spend?
📂 Dataset Source: (Add source or dataset link if available)
Structure: Includes product details, categories, revenue, customer IDs, ratings, and transaction dates.
🛠️ Tools & Technologies Used Programming Language: Python
Libraries:
pandas – Data manipulation and analysis
numpy – Numerical computations
matplotlib & seaborn – Data visualization
Environment: Jupyter Notebook
📊 Key Insights Identified the category generating the highest revenue.
Analyzed monthly sales patterns to detect peak and low seasons.
Determined customer segments with the highest contribution to sales.
Compared product ratings to identify quality trends.