Project for classes "Machine Learning 2: predictive models, deep learning, neural network" called "Heart failure classification".

The main goal of this project was to show usage of different machine learning techniques in classification where the dependent variable was death event. Dataset contained the medical records of 299 patients who had heart failure, collected during their follow-up period, where each patient profile had 13 clinical features. It was downloaded from https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records. Project was divided into two parts. Firstly, the data was described, visualized and verified for occurence of incorrect values. After it, the feature selection methods were applied. Properly prepared dataset was used in the second part of this project, where selected variables were used in modelling. Before implementing various types of methods, the dataset was divided into train and test sample. The following classification techniques were selected:
-Logistic Regression
-SVM
-Decision Tree
-Random Forest
-XGBoost

Creation date: 02.2022