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2025

E-commerce Sales Analysis

  • Python & Jupyter Notebook
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Overview

Analyzes e-commerce sales data in Jupyter notebooks: funnel analysis to find where customers drop off, customer segmentation, and trend analysis over time, with Pandas, NumPy, Plotly and scikit-learn.

01 · Problem

Raw e-commerce data does not show at which step visitors abandon a purchase, which customers are the most valuable, or how sales change over time.

02 · Solution

I processed a generated dataset of more than 365,000 records with Pandas and NumPy and analyzed it in Jupyter notebooks: a view → cart → checkout → purchase funnel analysis, RFM customer segmentation, and daily/weekly/monthly trend analysis. I turned the results into interactive charts with Plotly and reports with Matplotlib and Seaborn.

03 · Outcome

The funnel step with the biggest drop-off was identified, more than 500 customers were grouped into RFM segments (the Champions segment accounts for about 25% of revenue), and an executive summary and KPI dashboards were prepared.