This project is based on machine learning using Python language.We apply several machine learning classifiers to predict the patients survival.
This project is based on machine learning using Python language. We apply several machine learning classifiers to predict the patient's survival.
The heart Failure Clinical dataset contains the medical records of 299 patients who had heart failure. The dataset contains 11 clinical features, the follow-up period, and the label DEATH_EVENT that indicates whether or not the patient has died.
In this project we take a dataset then we do EDA and understand the data set then applying some preprocessing and apply a classification algorithm.
We can find some features strictly related to medical aspects like levels of enzymes, sodium, creatinine, and platelets in the blood and others that are more common like age, sex, or smoking. In this project, we take the first dataset and then we do EDA to understand the dataset and applying to preprocess and apply some classification algorithms like Decision Tree Classifier, KNeighborsClassifier, Random Forest classifier, SVM, Logistic Regression.
Main libraries are
Numpy: standard library for math operations.
Pandas: Used to manipulate data inside data frames and for basic computations.
Sklearn: used to apply different ML models.
Pyplot: To plot Visualizations
Seaborn: built on top of Pyplot
import all libraries
Import CSV file
ii)Decision Tree Classifier
iv)Random Forest classifier
Submitted by Debolina Poddar (debolinapoddar55)
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