๐ฆ Overview This project analyzes an Amazon sales dataset to identify the most selling and in-demand products in the electronics category. By treating rating count as a stand-in for sales, the goal is to extract insights into:
What consumers actually want
Which brands are winning
What pricing strategies might be killing it
Ideal for: ๐ E-commerce businesses ๐ง Data analysts ๐ Product launch wizards
๐ Dataset Details Source: Amazon sales dataset (source Kaggle)
Filtered Scope: Only electronics category products are included โ surgically extracted from a larger multi-category dataset.
๐งพ Columns Used: ->Product ->Product Name ->Category ->Rating Count (proxy for sales) ->Actual Price ->Discounted Price ->Ratings
๐งน Data Preprocessing ๐ Extracted brand names from Product Name using Excel magic
๐ฏ Filtered down to only electronics products
๐ง Parsed specific product names for cleaner analysis
๐ฏ Project Goals ๐ Identify top-selling, most demanded electronics products
๐ Analyze trends in rating counts, actual vs. discounted prices
๐ Provide actionable insights for product strategy & marketing
๐ Tools Used ๐งฎ Excel Brand extraction
Category filtering
๐ Python pandas: data cleaning & manipulation
matplotlib / seaborn: plotting those pretty graphs
๐ Jupyter Notebook Used for the full-blown, annotated analysis
๐ How to Use Clone this repo
๐ Key Findings ๐ง Rating count used as a smart substitute for sales data
๐ฅ Top Product: Wired Earphones โ especially models like boAt Bassheads 100 โ Discounted Price ~ โน379
๐ก Insight: Cheaper products with high ratings dominate โ value for money is king ๐
Some subcategory data may be missing
๐ฎ Future Improvements Add review sentiment or return rate metrics Expand to other product categories Create an interactive dashboard using Tableau or Streamlit