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Project Overview

Steps
  1. 01Project Overview
  2. 02Dataset & Downloads
  3. 03Project Roadmap
  4. 04Step-by-Step Implementation
  5. 05Data Cleaning Phase — Summary
  6. 06Exploratory Data Analysis
  7. 07Business Questions (as Formal Hypotheses)
  8. 08Tool-Specific Implementation (Python / SciPy Stats)
  9. 09Expected Output After Every Major Step — Recap
  10. 10Visualization & Dashboard
  11. 11Insights & Recommendations
  12. 12Final Project Deliverables
  13. 13Final Project Checklist

Project Overview

Project Title: Cascade Retail Group — Customer Behaviour & Hypothesis Testing

Business Scenario: Cascade Retail Group sells Furniture, Office Supplies, and Technology to Consumer, Corporate, and Home Office customers across the US. The analytics team keeps noticing patterns in dashboards — "Corporate customers seem to spend more," "the West region looks stronger than Central" — and leadership keeps asking whether these patterns are real or just noise in the data.

Business Problem: Decisions are being made on eyeballed differences in bar charts. A difference that looks big on a chart might not be statistically real, and a difference that looks small might still be highly significant with enough data. Cascade needs a rigorous way to tell the two apart before acting.

Project Objective: Use Python's statistical testing tools to formally test specific business claims about customer segments, regions, categories, and discounting — each as a proper null/alternative hypothesis pair, with a real p-value and effect size, not a guess from a chart.

Key Stakeholders: Head of Analytics (wants rigor before rollout decisions), Marketing (segment-targeted campaigns), Pricing team (discount strategy), Regional Sales Managers (want fair comparisons between regions).

Final Business Goal: A short set of statistically defensible answers to "is this difference real?" — with the honesty to say "no, that one's not significant" exactly as often as the data actually says so.

What You Will Finally Build

  • A cleaned dataset ready for statistical testing

  • A Python (Jupyter Notebook) pipeline using pandas, SciPy's stats module, and Matplotlib/Seaborn

  • 6 formally conducted hypothesis tests (2 t-tests, 1 ANOVA, 2 chi-square tests, 1 correlation test), each with assumption checks, a real p-value, and an effect size

  • 7 supporting charts, including one combined "hypothesis test summary" chart

  • A written insights & recommendations report that clearly separates statistical significance from practical significance

  • A short final presentation summary

Why this matters more than it sounds: Of the 6 tests in this project, half turn out not to be statistically significant — including one Cascade's own team assumed was obviously true. That's not a failure of the analysis; catching a false pattern before it becomes a business decision is exactly what hypothesis testing is for.

Tools: Python, pandas, scipy.stats (the statistical engine of this project), matplotlib and seaborn for visualization. No SQL, Excel, or BI tool is required.