Heart Disease Dataset Analysis & Classification using Machine Learning models such as linear regression, support vector machine, k-means, k-nearest neighbors and logistic regression to predict cardiac diseases.

• This branch contains all files for both branches heartD-1 & heartD-2

• Machine Learning with different Regressions on different Dataset

• Branch heartD-1 -

  HD_Linear-Reg.ipynb: Jupyter Notebook file with python code.
  Heart Disease.csv: Dataset of Heart Disease in which Machine Learning was performed.
  heartD-1_singlecodefile.py: Single .py file which contains all python codes from notebook file (for easy copying of codes).
  

• Branch heartD-2 -

  HD2_Logistic,KNN,SVM-Reg.ipynb: Jupyter Notebook file with python code.
  heart disease classification dataset.csv: Dataset of Heart Disease in which Machine Learning was performed.
  heartD-2_singlecodefile.py: Single .py file which contains all python codes from notebook file (for easy copying of codes).
  

• Install Any IDE which supports .ipynb and .py format.
• Import the .iypnb file along with dataset for respective branch (Check branch details to understand file structure for each branch)
• Recommended IDE is Jupyter Notebook, you can also use Visual Studio Code.
• If you are unable to import .iypnb file or the file is not supported. Then create a new .iypnb file.
• Copy the codes line by line from the singlecodefile.py for the respective Dataset and Notebook file.

• Handling .ipynb files.
• About Machine Learning.
• Info about Numpy, Pandas, matplotlib, SciKit-Learn.