This SQL-based project focuses on the comprehensive analysis of Amazon product listings using a dataset that includes product metadata, pricing details, customer reviews, and ratings. The project is implemented in pgAdmin (PostgreSQL) and leverages a wide range of SQL techniques such as filtering, aggregation, data cleaning, typecasting, pattern matching, and text processing. By executing over 30 analytical queries, the project extracts valuable insights into customer behavior, product performance, pricing strategies, and sentiment trends within the e-commerce marketplace.
The primary objective of this project is to explore, analyze, and derive actionable insights from an Amazon product dataset using structured query language (SQL). Specific goals include:
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Identifying best-rated and most-reviewed products.
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Detecting pricing patterns and discount strategies.
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Classifying user sentiments through review content analysis.
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Analyzing product descriptions and keyword frequencies to determine marketing effectiveness.
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Recognizing top-performing categories and active reviewers.
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Cleaning and normalizing data for accurate numerical analysis (e.g., converting price and rating formats).
This objective helps in simulating real-world business intelligence practices for an e-commerce environment.
Here are several practical use cases for your SQL project on Amazon product analysis using pgAdmin:
- Product Performance Evaluation
- Dynamic Priceing & Discount Optimization
- Customer Sentiment Monitoring
- Category-Based Market Research
- Reviewer Behaviour Tracking
- Marketing & Content Strategy
- Quality Control & Product Improvement
- Multimedia & Lising Integrity Audit
- Data Cleaning Normalization
- Inventory & Vendor Decision Support
Through this SQL project, we successfully demonstrated how raw Amazon product data can be transformed into meaningful insights using powerful SQL queries. The analysis provided a deeper understanding of product performance, customer preferences, review quality, and market trends. The queries also highlight potential issues such as products with missing ratings, frequent complaints, or misleading discounts. This analytical approach not only supports data-driven decision-making but also showcases the practical value of SQL in handling and interpreting large-scale retail data.