Project 02 — Market Basket & Customer Segmentation

Overview

A small grocery store wants to understand purchase patterns and segment its customers. This project mines association rules from shopping baskets and clusters customers by demographics and spending behaviour.

Chapters Combined

Part	Chapter	Topic
A	Ch2/Ch3	Descriptive statistics + correlation matrix
B	Ch4	Min-max normalization
C	Ch6	Apriori frequent itemset + association rules
D	Ch5	K-means customer segmentation (K=3)

Dataset

20 synthetic grocery customers. Each customer has: - Age, Visits (per month), Spend (monthly spend in $) - Items: list of purchased product categories

How to Run

Python

python market_analysis.py

No external libraries required. Pure Python stdlib only.

R

Rscript market_analysis.R

Requires: arules (install.packages("arules"))

Expected Output (Python)

PART A -- DESCRIPTIVE STATISTICS
Column     Mean    Std     Min     Max
Age        37.90   11.77   19      61
Visits     9.75    5.64    1       22
Spend      283.25  130.97  30      480

PART A -- CORRELATION MATRIX
           Age       Visits    Spend
Age        1.000     0.057     0.135
Visits     0.057     1.000     0.951
Spend      0.135     0.951     1.000

PART A -- ASCII SCATTER (Age vs Spend)
...

PART B -- NORMALIZED (first 3 rows shown):
C01: Age=0.357, Visits=0.524, Spend=0.556

PART C -- FREQUENT ITEMSETS (min_support=5/20=0.25)
{bread}: support=12 (0.60)
{milk}: support=12 (0.60)
...
ASSOCIATION RULES (sorted by lift):
milk -> yogurt: support=7, confidence=0.583, lift=1.750
...

PART D -- CUSTOMER SEGMENTS (K=3)
Cluster 0 (Young Low-Spend): C02, C05, C11, C15, C19 ...
Cluster 1 (Regular Shoppers): C01, C07, C09, C13, C17 ...
Cluster 2 (Loyal High-Spend): C03, C06, C08, C12, C14, C18 ...

File Structure

08-03-project-02/
    project_README.md      -- this file
    market_analysis.py     -- Python implementation (pure stdlib)
    market_analysis.R      -- R implementation