Retail Sales Exploratory Data Analysis Project Using Python | Pandas, Matplotlib, Seaborn
In this video, I present my Retail Sales Exploratory Data Analysis (EDA) project using Python.
This project focuses on analyzing retail sales data to uncover trends, customer behavior, and actionable business insights.
🔗 GitHub Repository: Check Pin Comment
📌 Project Highlights:
• Data cleaning and preprocessing using Pandas
• Feature engineering (Date, Month, Year, Age Groups)
• Descriptive statistics and distribution analysis
• Time series analysis of sales trends
• Customer analysis (Age, Gender, Repeat Customers)
• Product category sales analysis
• Data visualization using Matplotlib and Seaborn
• Business insights and recommendations
📂 Tools & Technologies Used:
• Python
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib
• Seaborn
📁 Dataset:
Retail sales dataset containing transaction-level information including date, customer details, product category, quantity, price, and total amount.
📓 Notebook:
All analysis is performed in a Jupyter Notebook with well-structured code and explanations.
🎯 Who is this video for?
• Data Analyst aspirants
• Students learning EDA
• Python beginners
• Anyone building a data analytics portfolio
If you find this project helpful, don’t forget to ⭐ star the GitHub repo and 👍 like the video.
Feel free to share feedback or questions in the comments!
#Python #DataAnalysis #EDA #RetailSales #Pandas #Matplotlib #Seaborn #DataAnalytics #PortfolioProject
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