📊 E-commerce Sales Analysis with Python

Company : CodeAlpha Pvt Ltd
Name : Sudeb Paul
Student ID : CA/AU1/7749
Domain : Data Analytics
Duration : August to September 2025

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📌 Project Title

Exploratory Data Analysis and Visualization on E-commerce Sales

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✅ Internship Progress

Successfully completed Task 2 and Task 3 of the Data Analyst Internship at CodeAlpha

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📂 Table of Contents

• 📌 Project Title
• 📖 Project Introduction
• 🎯 Objective
• 🗂 Dataset
• 🛠 Tools & Technologies
• 🔍 Methodology & Key Features
• 📊 EDA Summary
• 📈 Key Findings
• ✅ Recommendations
• 🏁 Conclusion

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📖 Project Introduction

This project performs a detailed analysis of e-commerce sales data to extract actionable insights into business performance. The goal is to understand sales and profit patterns across time, products, and customer segments, helping businesses improve marketing, inventory, and profitability strategies.

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🎯 Objective

• Analyze monthly sales and profit trends
• Identify top and bottom-performing categories and sub-categories
• Understand customer segment profitability
• Provide actionable, data-driven business recommendations

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🗂 Dataset

• Dataset: Superstore.csv
• Records: 9,994
• Features: 21 columns
• Key columns:
  • Order Date, Sales, Profit, Category, Sub-Category, Segment, Region, City, etc.

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🛠 Tools & Technologies

• Python (Google Colab)
• pandas – Data manipulation
• plotly.express & plotly.graph_objects – Interactive visualizations
• plotly.colors, plotly.io – Styling and layout
• Google Drive – Data storage

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🔍 Methodology & Key Features

• Data Cleaning: Converted date columns to datetime format
• Feature Engineering: Extracted year, month, day from Order Date
• EDA (Exploratory Data Analysis):
  • Monthly trends (sales & profit)
  • Sales/profit by category & sub-category
  • Segment-wise performance
  • Sales-to-profit ratio

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📊 EDA Summary

Metric	Insight
Monthly Sales	🔻 Lowest: Feb, 🔺 Highest: Nov
Category Sales	🥇 Technology > 🥈 Furniture > 🥉 Office Supplies
Sub-Category Sales	📱 Phones, 🪑 Chairs are top; 📎 Fasteners lowest
Monthly Profit	🔻 Jan-Feb low, 🔺 December peak
Category Profit	🥇 Technology, 🥉 Furniture (lowest)
Sub-Category Profit	💰 Copiers, Phones lead; ❌ Tables in loss
Segment Performance	👑 Consumer: Highest sales/profit
Efficiency	🏡 Home Office: Best sales-to-profit ratio

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📈 Key Findings

• Seasonality: Peak in Nov-Dec, low in Jan-Feb
• Category:
  • Technology = top profit + sales
  • Furniture = high sales but poor profit
• Sub-Categories:
  • Phones & Copiers = best performers
  • Tables & Bookcases = unprofitable
• Segments:
  • Consumer = highest revenue
  • Home Office = most efficient (profit/sale)

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✅ Recommendations

• Reassess Tables and Bookcases for profitability
• Boost campaigns in low-sales months (Feb)
• Prioritize Tech and Office Supplies to Home Office segment
• Focus on seasonal inventory planning for peak months

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🏁 Conclusion

This EDA project provides clear, actionable insights into e-commerce performance by analyzing patterns across products, time, and customer segments. The findings empower smarter decision-making in marketing, product management, and inventory optimization.

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🔗 Connect with Me

• LinkedIn
• GitHub

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