🛒 E-Commerce Data Analysis using Python

📌 Project Overview:

• This project focuses on analyzing E-Commerce sales data using Python to identify business insights related to customer behavior, product performance, and revenue trends.
• In this project, Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn are used to clean, analyze, and visualize the dataset.

🎯 Project Objectives

• The main objectives of this project include:
• Analyze overall sales performance
• Identify top-selling products
• nderstand customer purchasing behavior
• Analyze monthly and yearly sales trends
• dentify high revenue categories
• valuate profitability across products and regions

📂 Dataset Description

• The dataset represents transactional data from an e-commerce platform.
• Each row in the dataset represents a product purchase within an order.

🛠 Tools & Technologies Used

• Pandas: Data manipulation
• NumPy: Numerical operations
• Matplotlib: Data visualization
• Seaborn: Statistical visualization
• Google colab: Development environment

🔄 Data Analysis Workflow (Data Pipeline)

• The project follows a structured data analysis pipeline.
• Step 1: Data Collection
• Step 2: Data Cleaning
• Step 3: Data Transformation
• step 4: Exploratory Data Analysis (EDA)

📊 Key Performance Indicators (KPIs)

• KPIs are important business metrics used to measure performance.
• Total Sales
• Total Orders
• Total Profit
• Average Order Value
• Top Selling Products

📉 Data Visualization

• Visualization helps communicate insights clearly.
• Sales Trend Over Time
• Sales by Category
• Top 10 Products
• Correlation Heatmap

💡 Key Insights

• Some insights discovered from the analysis include:
• A few product categories generate the majority of revenue
• High discounts can reduce profit margins
• Certain regions contribute significantly more sales
• Sales show seasonal patterns across months
• A small group of products drives most of the revenue

👨💻 Author

• Mekala Anusha
• Location:Ballari,Karnataka, India
• Languages: English