📊 E-Commerce Exploratory Data Analysis (EDA)

📌 Project Overview

This project focuses on performing Exploratory Data Analysis (EDA) on a cleaned E-commerce dataset using Python. The objective is to understand the dataset by analyzing distributions, identifying trends, detecting outliers, and discovering meaningful business insights through statistical analysis and visualizations.

🎯 Objectives

• Analyze the cleaned dataset.
• Calculate descriptive statistics.
• Identify trends and patterns.
• Detect outliers using Boxplots and the IQR method.
• Visualize data using charts and graphs.
• Summarize key business insights.

🛠 Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• VS Code

📂 Dataset

• Cleaned E-commerce Dataset
• File: "Cleaned_Dataset.xlsx"

📈 Analysis Performed

• Dataset Overview
• Data Quality Checks
• Missing Value Analysis
• Duplicate Record Check
• Descriptive Statistics
• Histogram
• Boxplot
• Correlation Heatmap
• Pairplot
• Count Plots
• Average Total Price by Product
• Order Status by Payment Method
• Monthly Order Volume & Revenue Trend

📊 Key Findings

• No missing values or duplicate records were found.
• TotalPrice shows a right-skewed distribution.
• Strong positive correlation exists between Quantity, UnitPrice, and TotalPrice.
• Only a few outliers were identified.
• Product categories are well distributed.
• Customer order behavior and payment trends were analyzed successfully.

📁 Project Files

• EDA_Project.py
• Cleaned_Dataset.xlsx
• EDA_Report.pdf
• PNG Visualizations

✅ Outcome

This project demonstrates the application of Exploratory Data Analysis techniques to transform raw business data into meaningful insights using Python.