Code
Building a Customer Segmentation Tool for a SaaS Scale-up
Overview
What this challenge is about.
You are provided with a JSON file containing user data: user_id, total_logins, days_since_last_login, features_used (count), subscription_tier (free/basic/premium). Your task is to write a Python script that: (1) loads and preprocesses the data (normalize numeric features, encode subscription tier), (2) applies k-means clustering with k=4, (3) assigns each user to a segment, (4) outputs a CSV with user_id and segment label, and (5) generates a scatter plot (PCA-reduced) colored by segment. Success means the script runs end-to-end and the segments are interpretable. Constraints: use only scikit-learn, pandas, matplotlib; no external APIs.
The Brief
What you'll do, and what you'll demonstrate.
How can TaskFlow automatically segment its user base to enable targeted marketing and reduce churn?
Earning criteria — what you'll demonstrate
- Use pandas to load and manipulate JSON data into a DataFrame
- Apply data preprocessing techniques (normalization, encoding) for machine learning
- Implement k-means clustering using scikit-learn and determine optimal k via elbow method
- Visualize clustering results using PCA and matplotlib
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Skills you'll demonstrate.
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