Heart-Disease-Prediction using Logistic Regression

Steps:

1.Loading the required libraries and dataset

2.Data Cleaning: This step involves identifying and handling missing values, handling outliers, and dealing with duplicated data.

3.Data Transformation: This step involves transforming the data in various ways such as scaling, normalization, or encoding categorical variables.

4.Splitting the Data: Split the data into training and testing sets to evaluate the performance of the model on unseen data.

5.Training the Model: Train the classification model on the training data using Logistic Regression.

6.Evaluating the Model: Evaluate the performance of the classification model on the testing data using techniques like classification report,confusion matrix and accuracy.