This project aims to provide a comparative analysis of heart disease prediction using various machine learning models. The focus is on cleaning and preprocessing data, model training, and evaluation.

Heart Failure Prediction Dataset.

In this section, we load the dataset and perform necessary preprocessing steps such as handling missing values and encoding categorical variables:

import pandas as pd import numpy as np df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv') df.head()

We use several models including K-Nearest Neighbors, Decision Trees, and Random Forests for training:

from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) knn = KNeighborsClassifier(n_neighbors=5) knn.fit(X_train, y_train)

from sklearn.tree import DecisionTreeClassifier dt = DecisionTreeClassifier(random_state=42) dt.fit(X_train, y_train)

from sklearn.ensemble import RandomForestClassifier rf = RandomForestClassifier(random_state=42) rf.fit(X_train, y_train)

After training the models, we evaluate their performance using metrics such as accuracy and confusion matrix:

from sklearn.metrics import accuracy_score, confusion_matrix knn_pred = knn.predict(X_test) print(f"Accuracy of KNN: {accuracy_score(y_test, knn_pred)}")

This approach allows for a better understanding of which models perform optimally for heart disease prediction, enabling practitioners to utilize the most effective strategies in real-world applications.