About repo-

This is a heart disease prediction system implemented using Logistic Regression Algorithm. Logistic regression is used to predict a binary outcome based on a set of independent variables.

Data dictionary

age - age in years
sex - (1 = male; 0 = female)
cp - chest pain type
0: Typical Angina: Chest pain related decrease blood supply to the heart
1: Atypical Angeina: Chest pain not related to heart
2: Non-anginal pain: typically esophagal spasms(non heart related)
3: Asymptomatic: chest pain not showing signs of disease

trestbps - resting blood pressure (in mm Hg on admission to the hospital)
chol - serum cholestoral in mg/dl s erum = LDL + HDL +.2triglvcerides above 200 is cause for concern

fbs - (fasting blood sugar > 120 mg/dl) - (1 = true; 0 = false) '>126' mg/dL signals diabetes

restecg - resting electrocardiographic results
0: Nothing to note
1: ST-T wave abnormality can range from mild symptoms to severe problems signals non-normal heart beat
2: Possible or definite left ventricular hypertrophy. Enlarged heart's main pumping chamber

thalach - maximum heart rate achieved
exang exercise induced angina - (1 = yes; 0 = no)
oldpeakST - depression induced by exercise relative to rest
slope the - slope of the peak exercise ST segment
ca number of major vessels - (0-3) colored by flourosopy
thal 3 = normal; 6 = fixed defect; 7 = reversable defect
target - 1 or 0

Plots-

• Relation between age and max heart rate achieved among all those tested positive or negative-
  

• Heart disease found in patients of different sex-
  

• Distribution of cholestrol among heart patients-
  

• Relation between chest pain type and heart disease-
  

• Who have higher cholestrol at particular ages, males or females-
  

• Checking if max heart was achieved by people having very high fasting blood sugar-
  

• Correlation matrix-
  

Technology used:

Python

Packages used:

sklearn
matplotlib
seaborn
pandas
numpy

Environment Setup and Local Installation:

1. Drop a ⭐ on the Github Repository.

2. Make sure to install python on your computer- https://www.python.org/downloads/
   

3. Download Python IDE or text editor for python code
   

• Install Anaconda for Windows
  
• Install Anaconda for MacOS
  
• Install Anaconda for Linux
  
• Install VS code for Windows/Mac/Linux
  

1. Clone the Repo by going to your local Git Client and pushing this command:
   git clone https://github.com/Pranav016/heart-disease-prediction-system.git

2. Go to the AnacondaPrompt/CMD and use this command to install the packages:
   pip install -r requirements.txt

3. Open the project in the Jupyter Notebook/VS code to use it.