https://github.com/tanmayee2010/heart-attack-prediction

Heart Attack Prediction Using Machine Learning Algorithm
https://github.com/tanmayee2010/heart-attack-prediction

datetime datetime-library jupyter-notebook logistic-regression-algorithm machine-learning machine-learning-algorithms matplotlib-python mysql numpy pandas-python pycharm-ide tkinter

Last synced: about 1 month ago
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Heart Attack Prediction Using Machine Learning Algorithm

• Host: GitHub
• URL: https://github.com/tanmayee2010/heart-attack-prediction
• Owner: Tanmayee2010
• Created: 2024-06-05T06:41:03.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-05T09:36:38.000Z (5 months ago)
• Last Synced: 2024-10-09T19:01:37.835Z (about 1 month ago)
• Topics: datetime, datetime-library, jupyter-notebook, logistic-regression-algorithm, machine-learning, machine-learning-algorithms, matplotlib-python, mysql, numpy, pandas-python, pycharm-ide, tkinter
• Language: Python
• Homepage:
• Size: 861 KB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Heart-Attack-Prediction

It is a Graphical User Interface System which is based on tkinter library

**What is Logistic Regression ?**

Logistic Regression is a statistical and machine-learning techniques classifying records of a dataset based on the values of the input fields . It predicts a dependent variable based on one or more set of independent variables to predict outcomes . It can be used both for binary classification and multi-class classification.

**##Objective**

The primary objective of this study was to classify heart disease using logistic regression model and a real-world dataset. 

The logistic regression algorithm was applied to a dataset of patients with heart disease to predict the presence of the disease.

## Dataset 

[Dataset: (https://github.com/Tanmayee2010/Heart-Attack-Prediction/blob/main/heart.csv)]

    

#### Columns Information

 - age

 - sex

 - Chest pain type (4 values)

 - Resting blood pressure

 - Serum cholestoral in mg/dl

 - Fasting blood sugar > 120 mg/dl

 - Resting electrocardiographic results (values 0,1,2)

 - Maximum heart rate achieved

 - Exercise induced angina

 - Oldpeak = ST depression induced by exercise relative to rest

 - The slope of the peak exercise ST segment

 - Number of major vessels (0-3) colored by flourosopy

 - Thal: 0 = normal; 1 = fixed defect; 2 = reversable defect

## Libraries Used - 

  1. Pandas *(for data manipulation)*

  2. Matplotlib *(for data visualization)*

  3. Numpy *(for numerical calculation)*

  4. Scikit-Learn *(for data modeling)*

  5. tkinter *(for GUI)*