Heart_Attack-EDA-Prediction

Welcome to this project on Heart Attack Analysis & Prediction.

Objective:

1. Introduction to the problem
2. Exploratory Data Analysis (EDA) and PreProcessing
3. Model building

Description

To perform EDA and predict if a person is prone to a heart attack or not.

Heart disease is the leading cause of death for people of most racial and ethnic groups.
One person dies every 37 seconds just in the United States alone from cardiovascular disease.
Complete analysis of Heart Disease UCI dataset both visually and statistically to obtain critical observations which can be used for inference.
To predict whether a person has a heart disease or not based on the various biological and physical parameters of the body
To make a model having high accuracy and precision and can predict the results with greater confidence.
Make these predictions accessible to users and patients anywhere, anytime so that they can get complete picture of their Health \

Dependencies

Python kaggle - Heart Attack Analysis & Prediction Dataset pandas sklearn

Datasets

The data used for training and testing is the Heart Disease UCI downloaded from Kaggle. This database contains 14 attributes.

Exploratory Data Analysis \

It's a clean, easy to understand set of data. However, the meaning of some of the column headers are not obvious. Here's what they mean,

• age: The person's age in years
• sex: The person's sex (1 = male, 0 = female)
• cp: The chest pain experienced (Value 1: typical angina, Value 2: atypical angina, Value 3: non-anginal pain, Value 4: asymptomatic)
• trestbps: The person's resting blood pressure (mm Hg on admission to the hospital)
• chol: The person's cholesterol measurement in mg/dl
• fbs: The person's fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)
• restecg: Resting electrocardiographic measurement (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy by Estes' criteria)
• thalach: The person's maximum heart rate achieved
• exang: Exercise induced angina (1 = yes; 0 = no)
• ldpeak: ST depression induced by exercise relative to rest ('ST' relates to positions on the ECG plot. See more here)
• slope: the slope of the peak exercise ST segment (Value 1: upsloping, Value 2: flat, Value 3: downsloping)
• ca: The number of major vessels (0-3)
• thal: A blood disorder called thalassemia (3 = normal; 6 = fixed defect; 7 = reversable defect)
• target: Heart disease (0 = no, 1 = yes)

Inputs

Here our Model is trained to predict whether a person has a heart disease or not based on the following common features as input:

• age
• gender
• chest pain
• blood pressure
• cholesterol level
• max heart rate
• exercise induced angina