Decision-Tree-Model

In this Decision tree model, we predicted heart disease from medical records with the help of a classification model.

Problem Statement:

Analyze the heart disease dataset to explore the machine learning algorithms and build a decision tree model to predict the disease.

About the dataset:

This dataset contains 303 rows and 14 columns, the following are different factors from which our conclusions were phrased:

1. age: Age of the patient
2. Gender: {0, 1} - {Male, Female}
3. chest_pain: Chest pain experienced by the patient {0, 1, 2, 3}
4. rest_bps: Blood pressure of the patient while resting(in mm/Hg)
5. cholesterol: Patient's cholesterol level (in mg/dl)
6. fasting_blood_sugar: The blood sugar of the patient while fasting
7. rest_ecg: Potassium level {0, 1, 2}
8. thalach: The patient’s maximum heart rate
9. exer_angina: It refers to exercise-induced angina - (1=Yes, 0=No)
10. slope: It refers to the slope of the peak of the exercise ST-Segment- (0,1,2)
11. ca: Number of major vessels - (0,1,2,3,4)
12. thalassemia: It refers to thalassemia which is a blood disorder - (0,1,2,3)
13. old_peak: It is the ST depression induced by exercise relative to rest(ST relates to the position on ECG plots)
14. Target: The patient has heart disease or not - (1=Yes, 0=No)