Online-Retail-Analysis

A data visualization & analysis project using Python Libraries

🛒 Online Retail Data Analysis & Visualization

This project explores and analyzes the UCI Online Retail Dataset using Python.
The goal is to extract business insights using data cleaning, transformation, and visualization techniques.

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📊 Dataset Overview

• Contains UK-based online retail transactions from 2010–2011
• Over 500,000 rows and 8 columns
• Fields include:
  • InvoiceNo, StockCode, Description
  • Quantity, UnitPrice, CustomerID, Country, InvoiceDate

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🧰 Tools & Libraries Used

• Python (Jupyter Notebook)
• Pandas – data cleaning & grouping
• NumPy – numerical ops
• Matplotlib & Seaborn – charts & visualizations
• Datetime – date features for trend analysis

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🔍 Key Analyses Performed

• Data cleaning & preprocessing
• Revenue trends (daily, hourly, weekly)
• Top-selling and most profitable products
• Country-wise sales breakdown
• RFM segmentation (Recency, Frequency, Monetary)
• Basket size and average spend per invoice
• Correlation heatmaps and customer behavior insights

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📌 Project Goals

• Understand sales patterns
• Identify high-value customers
• Recommend actions to improve business strategy

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📎 How to Use

Clone the repo or download the .ipynb notebook and run it in Jupyter Notebook or Google Colab

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🙌 Author

Made by [Ashmeet Singh Sandhu]
📧 sandhuashmeet40@gmail.com
🔗 LinkedIn