Project: Predicting Heart Disease with Classification Machine Learning Algorithms
Author: Jarar Zaidi
Date: 6/11/2020
Medium Link to project: https://medium.com/@jararzaidi/project-predicting-heart-disease-with-classification-machine-learning-algorithms-fd69e6fdc9d6
This project is organized as follows:
Table of Contents
- Introduction: Scenario & Goals, Features & Predictor
- Data Wrangling
- Exploratory Data Analysis: Correlations, Violin & Box Plots, Filtering data by positive & negative Heart Disease patient
- Machine Learning + Predictive Analytics: Prepare Data for Modeling, Modeling/Training, Confusion Matrix, Feature Importance, Predictions
- Conclusions
Scenario: You have just been hired as a Data Scientist at a Hospital with an alarming number of patients coming in reporting various cardiac symptoms. A cardiologist measures vitals & hands you this data to perform Data Analysis and predict whether certain patients have Heart Disease. We would like to make a Machine Learning algorithm where we can train our AI to learn & improve from experience. Thus, we would want to classify patients as either positive or negative for Heart Disease.
Goal: Predict whether a patient should be diagnosed with Heart Disease. This is a binary outcome. Positive (+) = 1, patient diagnosed with Heart Disease Negative (-) = 0, patient not diagnosed with Heart Disease Experiment with various Classification Models & see which yields greatest accuracy. Examine trends & correlations within our data Determine which features are most important to Positive/Negative Heart Disease diagnosis
Files:
Predicting Heart Disease with Classification Machine Learning Algorithms.ipynb - Jupyter Notebook (.ipynb) version
Predicting Heart Disease with Classification Machine Learning Algorithms.py - the Python (.py) version project
heartDisease.csv - Original dataset used from Kaggle.com in CSV (.csv) format