Intro to Data Mining: Scikit-Learn Exercises

Scikit-Learn exercises: fitting classifiers and regressors, then scoring them honestly.

coursework Winter 2025 Python, Machine Learning, Scikit-Learn, Data Mining, Model Training

Scikit_Learn_Exercise.ipynb ↓Scikit_Learn_Exercise.html ↓

Overview

This notebook introduces Scikit-Learn, Python’s premier machine learning library. Through hands-on exercises, you’ll learn to build, train, and evaluate machine learning models.

Topics Covered

Model Building

Supervised Learning: Classification and regression algorithms
Model Selection: Choosing the right algorithm for the task
Training Process: Fitting models to data
Hyperparameter Tuning: Optimizing model performance

Evaluation Techniques

Metrics: Accuracy, precision, recall, F1-score, RMSE
Cross-Validation: Robust model evaluation
Confusion Matrix: Understanding classification results
Learning Curves: Diagnosing model performance

Data Preprocessing

Feature Scaling: Normalization and standardization
Encoding: Handling categorical variables
Train-Test Split: Proper data partitioning

Learning Objectives

Implement common machine learning algorithms
Evaluate model performance using appropriate metrics
Preprocess data for optimal model training
Diagnose and address overfitting/underfitting

Skills Developed

By completing these exercises, you’ll be able to:

Build end-to-end machine learning pipelines
Compare different algorithms for the same task
Interpret model results and make data-driven decisions
Apply best practices in model development