Module 19: Create and Understand Classification Models with Python

In this exercise, you’ll delve into the world of classification models in machine learning using Python. Through hands-on exercises, you'll gain insights into various classification techniques and their applications in predictive modeling.

Exercise Overview

In this module, you will learn:

• Discover how classification differs from classical regression.
• Build models that can perform classification tasks.
• Explore how to assess and improve classification models.

This module provides a comprehensive introduction to classification modeling, enabling you to understand the principles, build models, and improve their performance.

Jupyter Notebooks

To get started, access the following Jupyter notebooks:

1. Train and Build a Classification Model - Learn how to train and build a classification model using Python, exploring techniques for preprocessing data, selecting features, and training the model.
2. Assessing a Logistic Regression Model - Dive deeper into logistic regression, a common classification algorithm, and learn how to assess its performance using evaluation metrics such as accuracy, precision, and recall.
3. Improving Classification Models - Discover strategies for improving classification models.

These notebooks will guide you through practical exercises where you'll apply classification techniques, evaluate model performance, and enhance model accuracy and robustness.

Happy learning!