Project 1

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LogisticRegression

from sklearn.neighbors import KNeighborsClassifier

from sklearn.svm import SVC

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score, classification_report

# Assuming your dataset is in a CSV file named 'heart_data.csv'

data = pd.read_csv(r"C:\Users\hp\Downloads\heart.csv")

# Display the first few rows of the dataset

print(data.head())

# Extract features (X) and target variable (y)

X = data.drop('output', axis=1)

y = data['output']

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize the features

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.transform(X_test)

# Function to evaluate a model

def evaluate_model(model, X_train, X_test, y_train, y_test):

    model.fit(X_train, y_train)

    y_pred = model.predict(X_test)

    accuracy = accuracy_score(y_test, y_pred)

    print(f"Accuracy: {accuracy}")

    print(f"Classification Report:\n{classification_report(y_test, y_pred)}")

# Logistic Regression

lr_model = LogisticRegression()

print("\nLogistic Regression:")

evaluate_model(lr_model, X_train, X_test, y_train, y_test)

# K-Nearest Neighbours

knn_model = KNeighborsClassifier()

print("\nK-Nearest Neighbours:")

evaluate_model(knn_model, X_train, X_test, y_train, y_test)

# Support Vector Machine

svm_model = SVC()

print("\nSupport Vector Machine:")

evaluate_model(svm_model, X_train, X_test, y_train, y_test)

# Decision Tree Classifier

dt_model = DecisionTreeClassifier()

print("\nDecision Tree Classifier:")

evaluate_model(dt_model, X_train, X_test, y_train, y_test)

# Random Forest Classifier

rf_model = RandomForestClassifier()

print("\nRandom Forest Classifier:")

evaluate_model(rf_model, X_train, X_test, y_train, y_test)