E-commerce Sales Analysis
Analyzed a dataset of 5,000+ e-commerce orders generating approximately ₹20.67M in revenue. Identified top-performing regions, product categories, and high-value customer segments through detailed exploratory data analysis and visualization.
1. Overview
An e-commerce data analysis Python project dissecting a 5,000-order dataset to extract actionable business insights from raw retail transactional noise.
2. Problem Statement
Retail businesses generate massive amounts of transactional data. The challenge is identifying top-performing regions, product categories, and high-value customer patterns to optimize sales strategies.
3. Dataset
A retail dataset of over 5,000 e-commerce orders, representing approximately ₹20.67M in total revenue.
4. Methodology & Data Preprocessing
Utilized Pandas data analysis for comprehensive data cleaning, aggregation, and customer segment profiling.
5. Analysis & Model
Conducted in-depth customer sales analysis and retail analytics to map out purchasing behaviors across different demographics and regions.
6. Results & Key Findings
Identified key high-value customer segments and top-performing regional markets, creating a robust data visualization project presentation.