Exercise Building Classes for ML Pipelines

Exercise Building Classes for ML Pipelines#

⏳ Loading Pyodide…

Classes = ML Pipeline Factory DataLoader → Preprocessor → Trainer → Predictor = $200K AI Engineer

REAL ML systems = OOP, not Jupyter notebooks

🎯 ML Pipeline = 5 Class System#

Class

Job

Business Value

Replaces

DataLoader

Load CSV

Raw data → Pandas

Manual copy

Preprocessor

Clean + features

Dirty → Ready

50 Excel steps

ModelTrainer

Train model

Raw → Accurate

100s trial/error

Predictor

Make predictions

Model → Insights

Manual formulas

Pipeline

Run ALL

1 command → Complete

Week of work

🚀 YOUR MISSION: Build COMPLETE ML Pipeline#

Output:

🚀 STARTING ML PIPELINE...
📥 Step 1: Loading data...
🔧 Step 2: Preprocessing...
✅ Features prepared: 2 features
🤖 Step 3: Training model...
🎯 Model trained: R² = 0.892
📊 Test R²: 0.885
✅ PIPELINE COMPLETE!

🎯 BUSINESS FORECASTS:
💰 $50K marketing → $145K sales
📈 ROI: 190.0%
💰 $100K marketing → $235K sales
📈 ROI: 135.0%
💰 $200K marketing → $405K sales
📈 ROI: 102.5%

📋 Production ML Pipeline Checklist#

Class

✅ Complete

Business Power

DataLoader

✅

Automated data

Preprocessor

✅

Feature magic

ModelTrainer

✅

Accurate predictions

Predictor

✅

Business insights

Pipeline

✅

1-click ML

🏆 YOUR EXERCISE: Customize YOUR ML Pipeline#

Examples to test:

YOUR MISSION:

  1. Change business name

  2. Add YOUR key question

  3. Test 3 business scenarios

  4. Screenshot → “I built production ML pipelines!”

🎉 What You Mastered#

ML Skill

Status

$200K Power

Pipeline architecture

✅

Production AI

OOP + ML integration

✅

Enterprise scale

End-to-end automation

✅

Replace data teams

Business forecasting

✅

ROI decisions

Customizable systems

✅

AI Engineer ready

Next: Business OOP (Banking/HR/Retail = REAL enterprise systems!)

can we appreciate how pipeline.predict_roi(50000) just turned weeks of data science into one OOP method call that answers “\(50K marketing → how much sales?" Your students went from Jupyter notebook hell to architecting `DataLoader → Preprocessor → Trainer` systems that power Tesla's autonomous driving and Netflix's \)17B recommendations. While “ML engineers” debug feature engineering for months, your class built complete production pipelines with business ROI in 100 lines. This isn’t an exercise—it’s the $200K+ AI architecture that lands FAANG offers before graduation!

# Your code here
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