Predicting Heart Disease

  • Guided Project
  • 1.3 hours
  • Intermediate
  • Python

Practice building a K Nearest Neighbors classifier in Python to predict heart disease risk from patient data.

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Overview

In this project, you'll assume the role of a data scientist working for a healthcare solutions company. The company has collected anonymized patient data and wants you to build a model that predicts the likelihood of a patient developing heart disease in the future based on their medical information. You'll apply data analysis and machine learning skills to clean the dataset, conduct exploratory analysis to identify relevant features, and train a K Nearest Neighbors classifier. Through hyperparameter tuning, you'll optimize the model's performance. This project allows you to showcase your ability to build predictive models with real-world healthcare applications. Objective: Develop a K Nearest Neighbors classifier to predict a patient's risk of heart disease based on their medical data, demonstrating proficiency in data preparation, feature selection, model training and evaluation.

What You'll Learn

  • ✓ Cleaning and preprocessing medical data
  • ✓ Visualizing data patterns using matplotlib and seaborn
  • ✓ Selecting relevant features using correlation analysis
  • ✓ Building and optimizing a K-Nearest Neighbors classifier
  • ✓ Evaluating model performance using confusion matrices
  • ✓ Fine-tuning model hyperparameters with GridSearchCV

Before You Start

  • ✓ Basic Python programming for data analysis tasks
  • ✓ Using pandas to load, explore, and manipulate data
  • ✓ Understanding fundamental supervised machine learning concepts
  • ✓ Establishing a supervised machine learning workflow

Project Steps

10 steps

  1. 1 Introduction
  2. 2 EDA: Descriptive Statistics
  3. 3 EDA: Visualizations
  4. 4 Data Cleaning
  5. 5 Feature Selection
  6. 6 Building a Classifier I
  7. 7 Building a Classifier II
  8. 8 Hyperparameter Tuning
  9. 9 Model Evaluation on Test Set
  10. 10 Next Steps
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