Heart Disease Detection Model

[MJakash]

Learning Goals:

• Understand the concept and use of the Random Forest Classifier and K-Nearest Neighbors (KNN) algorithm.
• Learn how to preprocess medical data for machine learning, particularly in the context of heart disease detection.
• Explore feature selection and model optimization techniques.
• Evaluate model performance using metrics such as accuracy,cross_val_score.
• Gain proficiency in using scikit-learn for implementing and comparing machine learning models.

Exercise Statement:

• Build a machine learning model to detect whether a person is suffering from heart disease or not.
• Implement the model using two different algorithms: Random Forest Classifier and K-Nearest Neighbors (KNN).
• Compare the performance of these models and choose the one that gives the best results based on evaluation metrics.

Prerequisites:

• Familiarity with Random Forest Classifier and KNN algorithms.
• Knowledge of basic machine learning concepts, such as feature scaling, cross-validation, and hyperparameter tuning.
• Familiarity with scikit-learn for model building and evaluation.
• Prior experience with medical datasets will be helpful.

Data Source/Summary:

The dataset used in this exercise typically involves patient data containing attributes such as age, gender, cholesterol levels, blood pressure, etc., to predict whether a person has heart disease.