heshamhashem/combrise-between-Logistic-regression-SVM-KNN-Nieve-Bayes-Decision-Tree-and-Random-Forest-for

In this project we will combrise between Logistic regression & SVM & KNN & Nieve Bayes & Decision Tree and Random Forest for models 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.¶ We will go through 5 tasks to implement our project: Task 1: Import the important library and exploring the dataset. Task 2: Identifying Missing Data and dealing with them. Task 3: Split the Data into Dependent and Independent Variables Task 4: One-Hot Encoding Task 5: Centering and Scaling Task 6: Logistic regression model Tssk 7: Support vector machine classifier model. Task 8: K nearest neighbore classifier model. Task 9: Nieve Bayes model. Task 10: Decision Tree model. Task 11: Random Forest model.

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