基于机器学习的心脏病检测的分类算法内含数据集以及运行环境说明.zip

# Project-Predicting-Heart-Disease-with-Classification-Machine-Learning-Algorithms 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 1. Introduction: Scenario & Goals, Features & Predictor 2. Data Wrangling 3. Exploratory Data Analysis: Correlations, Violin & Box Plots, Filtering data by positive & negative Heart Disease patient 4. Machine Learning + Predictive Analytics: Prepare Data for Modeling, Modeling/Training, Confusion Matrix, Feature Importance, Predictions 5. 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 ---------------