Background on KNN

What You’ll Learn

• Understand the principles of the KNN algorithm and learn why it’s a preferred choice for classification problems in various sectors, especially healthcare.
• Perform data preprocessing techniques such as scaling and normalization to prepare healthcare data for effective KNN modeling.
• Select the optimal number of neighbors for the KNN algorithm by using methods like hyperparameter tuning and cross-validation to enhance the model’s prediction accuracy.
• Evaluate the performance of your KNN model by using metrics such as accuracy and confusion matrices, enabling you to fine-tune your approaches based on comprehensive feedback.

Table of Contents

1. Background
2. What is KNN?
3. Objectives
4. Setup
   1. Installing required libraries
   2. Importing required libraries
5. Load the data
6. Split the data set
7. Fit the KNN model
8. Hyperparameter tuning
9. ANOVA for feature selection
10. Downsampling
11. Fitting on simpler model
12. Evaluating KNN
13. Exercises

What You’ll Need

• Basic to intermediate knowledge of Python: Familiarity with Python’s core programming concepts and ability to write and understand Python code.
• Understanding of basic machine learning concepts: Although detailed explanations will be provided, some prior knowledge of machine learning principles will be beneficial.
• An environment that supports Python and scikit-learn: The IBM Skills Network Labs environment is equipped with all necessary tools pre-installed, but you can also set up your local environment with Python, scikit-learn, NumPy, and pandas.