Lesson 50: Final Capstone Project

🎯 Final Capstone Project

Congratulations on reaching the final project! This capstone integrates everything you've learned throughout the Machine Learning course. You'll build a complete end-to-end ML pipeline from data preparation to model evaluation.

This project challenges you to apply: data preprocessing, feature scaling, model training, evaluation metrics, and best practices. It's your opportunity to demonstrate mastery of the complete ML workflow.

Project Objectives

By completing this capstone, you will:

Implement the complete ML pipeline from start to finish
Apply best practices: proper data splitting, scaling, and evaluation
Train and evaluate a machine learning model
Interpret model performance using multiple metrics
Reflect on the end-to-end ML workflow

Complete ML Pipeline
# Capstone Project Workflow:
# 1. Load and explore data
# 2. Split into train/test sets (with stratification)
# 3. Preprocess features (scaling, normalization)
# 4. Train a machine learning model
# 5. Evaluate on test set
# 6. Interpret results and reflect

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

print("Complete ML Pipeline Implementation")

Project Requirements

Your capstone project should include:

Data Splitting: Proper train/test split with stratification
Preprocessing: Feature scaling without data leakage
Model Training: Train a classification model on training data
Evaluation: Calculate accuracy and generate classification report
Reflection: Summarize the complete workflow and key learnings

💡 Capstone Success Tips

This project synthesizes all your learning. Take your time, follow best practices, and think critically about each step. The goal is to demonstrate your understanding of the complete ML process!

Exercise: Complete ML Pipeline

In the exercise on the right, you'll implement a complete machine learning pipeline from data splitting through model evaluation. This comprehensive exercise brings together all the concepts you've learned.

This is your final challenge - build it step by step, applying all the best practices you've learned throughout the course!

Final Capstone Project

🎯 Final Capstone Project

Project Objectives

Project Requirements

💡 Capstone Success Tips

Exercise: Complete ML Pipeline

Lesson Complete!