Dataset Information:

The dataset consists of 303 rows and 14 columns with label Target. Data contains categorical as well as continuous data.

Data Cleansing:

There is only one column that contains null value in which only two rows have null value that can easily be dropped by removing NA function.

Data Visualization:

Data Visualization is done by step by step process with critical analysis I use correlation matrix to find most dependent variable to the label which is Age. I plot graph of label (Target) to show the ratio of heart Disease.

Methodology:

To explain and identify the problem and resolve medical objectives, different data Science technique, which interpret the medical goals, have been implemented to diagnose the heart disease and to improve the success standards of the algorithms for prediction. Suitable machine learning algorithms, like: Random Forest, SVM (Support Vector Machine), Decision Tree and Logistics Regression were preferred for the training and implementation in python for developing and evolving the predictive model. These algorithms executed on the model will help medical experts to predict and diagnose heart attacks in the patient dataset. The main goal is to identify which machine-learning algorithm has the best accuracy for the prediction of heart disease from the patient dataset.

Result:

Cross Validation is also done for all the models. The results are same but have some variance in accuracy. After Cross Validation the result become clear that Logistic regression is good for this problem

Installation Guide:

->Install Anaconda Destribution
->Install Jupyter Notebook
->Copy Heart Health.ipynb to path C:\Users\xyz along with heart.csv
->Run Jupyter Notebook and open file from its home page
->Change the path of read_csv() as your file location