An end-to-end Data Analytics project built using Python, Pandas, and Power BI to analyze Amazon product data and generate actionable business insights.
This project focuses on transforming raw Amazon product data into meaningful business insights through:
- Data Cleaning & Transformation
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Business Insight Generation
- Interactive Power BI Dashboard Development
The objective was to understand customer engagement, product ratings, discount strategies, and category performance across Amazon products.
Source: Kaggle Amazon Product Dataset
| Metric | Value |
|---|---|
| Total Products | 1,465 |
| Categories | Multiple |
| Reviews | 26.7 Million+ |
| Average Rating | 4.10 |
| Average Discount | 47.69% |
- Python
- Pandas
- NumPy
- Power BI
- Git & GitHub
- Dataset Inspection
- Data Auditing
- Missing Value Analysis
- Data Type Validation
Performed:
- Removed ₹ currency symbols
- Removed commas from numeric values
- Converted prices to numerical format
- Converted discount percentages to numeric values
- Cleaned review counts
- Handled missing values
- Created category hierarchy
Created:
- Main Category Extraction
- KPI Metrics
- Category-Based Aggregations
- Dashboard Summary Tables
Analyzed:
- Product Distribution
- Category Performance
- Customer Ratings
- Review Volume
- Discount Trends
- Product Popularity
Built an interactive Power BI dashboard with:
- KPI Cards
- Category Analysis
- Product Analysis
- Customer Engagement Metrics
- Discount Analysis
- Correlation Analysis
Electronics, Computers & Accessories, and Home & Kitchen account for approximately 97% of all products in the dataset.
Computers & Accessories maintains one of the highest average ratings among major categories.
Products generating the highest review volumes include:
- Amazon Basics HDMI Cables
- boAt Earphones
- Redmi Smartphones
These categories drive the majority of customer engagement.
Several products offer discounts exceeding 90%, particularly in accessories and smart device categories.
No strong relationship was observed between discount percentage and customer ratings.
This suggests that higher discounts do not necessarily lead to better customer satisfaction.
Features:
- Total Products KPI
- Average Rating KPI
- Average Discount KPI
- Total Reviews KPI
- Products by Category
- Average Rating by Category
- Category Filter
Features:
- Top 10 Most Reviewed Products
- Top 10 Highest Discount Products
- Discount vs Rating Scatter Analysis
- Category Rating Comparison
amazon-product-analysis/
│
├── data/
│ ├── amazon.csv
│ ├── amazon_clean.csv
│ └── amazon_final.csv
│
├── scripts/
│ ├── data_overview.py
│ ├── data_audit.py
│ ├── inspect_values.py
│ ├── clean_data.py
│ ├── feature_engineering.py
│ ├── product_analysis.py
│ ├── category_analysis.py
│ └── dashboard_kpis.py
│
├── dashboard/
│ └── Amazon_Product_Analytics.pbix
│
├── screenshots/
│ ├── executive_overview.png
│ └── product_analysis.png
│
├── README.md
├── requirements.txt
└── .gitignore
- Data Cleaning
- Data Transformation
- Exploratory Data Analysis
- Feature Engineering
- Data Visualization
- Business Intelligence
- Dashboard Design
- Data Storytelling
- Power BI
- Python Programming
- Sentiment Analysis on Customer Reviews
- Product Recommendation Insights
- Sales Forecasting
- Advanced DAX Measures
- Interactive Drill-Through Reports
Shivi Tiwari
- Integrated B.Tech + M.Tech (Information Technology)
- International Institute of Professional Studies (IIPS), DAVV
- Aspiring Data Analyst & Full Stack Developer