This project presents an Exploratory Data Analysis (EDA) of an Amazon sales dataset to identify: -Key revenue drivers -Category performance -Impact of discount strategies -Influence of ratings on revenue -Structural business patterns

Category Revenue Distribution All six product categories contributed almost equally (~16–17% each) to total revenue. Insight: -No category dominates revenue. -Revenue distribution is highly balanced.

Key Findings: -Revenue is Price-Driven -Discounts Do Not Increase Sales -Ratings Have No Revenue Impact -Balanced Category Performance

Top revenue transactions were generated by: -High-priced products -Maximum quantity sold -Zero discount

Based on the analysis: -The business model appears value-driven rather than volume-driven. -Premium pricing strategy contributes more to revenue than discount campaigns. -Aggressive discounting may not be an effective growth strategy. -Marketing efforts may not need heavy category prioritization due to balanced distribution.

The dataset shows highly uniform patterns across: -Category revenue -Discount distribution -Ratings -Revenue spread This suggests the dataset may be simulated or structurally balanced rather than reflecting real-world e-commerce variability.