Test_the_model

import pickle
import pandas as pd

# Path to the saved model
model_path = r"C:\Users\user\Downloads\heart_disease_model.p"

# Load the trained model
try:
    with open(model_path, 'rb') as file:
        model = pickle.load(file)
    print(" Model loaded successfully.")
except Exception as e:
    print(f" Error loading model: {e}")
    exit()

# Define the feature names (replace these with the actual feature names used during training)
feature_names = [
    "age", "sex", "cp", "trestbps", "chol", "fbs", "restecg",
    "thalach", "exang", "oldpeak", "slope", "ca", "thal"
]

# Example custom input data (replace these values with your own data)
custom_data = {
    "age": [50],      
    "sex": [1],        
    "cp": [2],         
    "trestbps": [120], 
    "chol": [200],    
    "fbs": [0],        
    "restecg": [1],   
    "thalach": [150],  
    "exang": [0],      
    "oldpeak": [1.5],  
    "slope": [1],      
    "ca": [0],         
    "thal": [2]        
}

# Convert custom data to a DataFrame
custom_df = pd.DataFrame(custom_data)

# Ensure the column order matches the training data
custom_df = custom_df[feature_names]  # Reorder columns to match the training data

# Predict probabilities for the custom data
try:
    probabilities = model.predict_proba(custom_df)[0]  # Get probabilities for the first sample
    print("\nClass Probabilities:")
    for i, prob in enumerate(probabilities):
        print(f"Probability of Class {i}: {prob:.4f}")
    
    # Interpret the result
    if len(probabilities) == 2:  # Binary classification case
        print(f"\nProbability of No Heart Disease: {probabilities[0]:.4f}")
        print(f"Probability of Heart Disease: {probabilities[1]:.4f}")
        predicted_class = 1 if probabilities[1] > 0.5 else 0
        print(f"Predicted Class: {predicted_class} ({'Heart Disease' if predicted_class == 1 else 'No Heart Disease'})")
except Exception as e:
    print(f" Error during prediction: {e}")