In this guided project, we will develop a predictive model capable of accurately predicting the presence or absence of heart disease from clinical and laboratory data using a K-Nearest-Neighbors Classifier. This project, which we'll run on Google Colab, was designed for those who are taking their first steps in Machine Learning algorithms, but the student should be already familiar with Python and basic ML concepts.

In this Guided Project, you will:

- Proceed EDA and data pre processing;

- Train a KNearestNeighbors binary classifier;

- Evaluate your model using the best metrics for it.

Learn step-by-step

- Exploratory Data Analysis

- Training the model using Grid Search

- Evaluating the model

- Importing needed modules + uploading dataset

- Splitting and pre-processing

Predictive Modeling

Heart Disease

Machine Learning

Python Programming

EDA

Exploratory Data Analysis

Data Analysis

Binary Classification

Grid Search

K-Nearest Neighbors

KNN