Predictive Analytics Engine

Build a machine learning system that predicts customer behavior and lifetime value

ProgressSection 1 of 3

Section 1: Introduction to Predictive Analytics

📖

EXPLAIN: Understanding the Concept

🔮 What is Predictive Analytics?

Predictive Analytics: Using historical data to predict future outcomes.

Common applications:

Customer Churn: Will a customer leave?
Lifetime Value (LTV): How much will a customer spend?
Sales Forecasting: What will next quarter's revenue be?

What you'll build: A system that analyzes customer data to predict churn risk and calculate lifetime value.

👀

SHOW: Here's How It Works

👀 Predictive Model Example

Customer Analysis:
  ID: C001
  Recency: 30 days (LOW RISK)
  Frequency: 12 purchases (LOYAL)
  Monetary: $2,400 total (HIGH VALUE)

Predictions:
  Churn Risk: 15% (LOW)
  Predicted LTV: $3,600
  Segment: VIP CUSTOMER
  Recommendation: Maintain engagement

✍️

PRACTICE: Now You Build It!

✍️ Your Turn: Calculate Churn Risk

Run the basic churn prediction model to see how customer metrics translate to risk scores.

Code Editor

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💻 Output:

Click 'Run Code' to see output here...