main.py

# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load the dataset
data = pd.read_csv('heart-disease.csv')

# Define features (X) and target (y)
x = data.drop("target", axis=1)  # assuming the target column is named 'target'
y = data["target"]

# 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)

# Initialize the Logistic Regression model
model = LogisticRegression(max_iter=1000)  # Increase max_iter


# Train the model
model.fit(X_train, y_train)

# Evaluate the model
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy * 100:.2f}%")
import joblib

# # Save the model
# joblib.dump(model, 'logistic_regression_model.pkl')