📊 Amazon Sales Analysis

This repository contains a data analysis project performed using a Jupyter Notebook to extract meaningful insights from Amazon sales data.

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📌 Overview

The project focuses on analyzing Amazon product data, including pricing, discounts, ratings, and categories. It involves data cleaning, exploratory data analysis (EDA), and visualization to understand trends and patterns.

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• WordCloud

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📂 Dataset

• Contains 1465 records with 16 features

• Includes:

  • Product Name
  • Category
  • Price (Original & Discounted)
  • Rating & Rating Count
  • Discount Percentage

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⚙️ Steps Performed

1. Data Cleaning (handling missing values, duplicates)
2. Data Type Conversion (price & percentage columns)
3. Exploratory Data Analysis (EDA)
4. Data Visualization (graphs & word clouds)

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📊 Key Insights

• Most products fall in 41–60% discount range (~35%)
• Average discounted price: ₹3129.98
• Average discount: 47.67%
• Weak correlation (0.12) between price and rating
• Top categories: Computers & Accessories, Home & Kitchen

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📈 Visualizations

• Category-wise rating analysis
• Discount distribution plots
• Word clouds for product keywords

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🚀 How to Run

1. Clone the repository
2. Place amazon.csv in the root directory
3. Open and run Amazon-Sales-Analysis.ipynb in Jupyter Notebook

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📌 Future Improvements

• Build a recommendation system
• Apply machine learning for price prediction
• Deploy as a dashboard (Streamlit/Power BI)

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👨‍💻 Author

Kunal BCA Final Year Student | Aspiring Data Scientist