​ In this project we will built all machine learning models for classification (shown below) using scikit-learn. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease. ​ In this lesson you will learn about... ​

• Task 1: Import the important library and exploring the dataset
• Task 2: Missing Data
  • Identifying Missing Data
  • Dealing with Missing Data
• Task 3: Split the Data into Dependent and Independent Variables
• Task 4: One-Hot Encoding
• Task 5: Logistic regression model
• Task 6: Support vector machine classifier model
• Task 7: K nearest neighbore classifier model
• Task 8: Naieve Bayes model
• Task 9: Decision Tree model
• Task 10: Random Forest model

This tutorial assumes that you are already know the basics of coding in Python and are familiar with the theory behind Logistic Reression, Support Vector Machines,KNN,Naieve Bayes, Decision Tree and Random Forest models. Also you should be aware about The GridSearchCV method, Radial Basis Function (RBF), Regularization, Cross Validation and Confusion Matrices.