Untitled

# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score


# Define a function to calculate credit score
def calculate_credit_score(data):
    # Implement credit score calculation logic here
    # For demonstration purposes, let's assume a simple calculation
    credit_score = (data['payment_history'] + data['credit_utilization'] + data['credit_age']) / 3
    return credit_score


# Define a function to provide credit score improvement recommendations
def provide_recommendations(credit_score):
    # Implement recommendation logic here
    # For demonstration purposes, let's assume simple recommendations
    if credit_score < 600:
        return "Improve payment history and reduce credit utilization."
    elif credit_score < 700:
        return "Continue making timely payments and monitor credit utilization."
    else:
        return "Maintain good credit habits and consider exploring new credit options."


# Define main function
def main():
    # Load sample credit data (replace with actual data)
    data = pd.DataFrame({
        'payment_history': [0.8, 0.7, 0.9, 0.6, 0.8],
        'credit_utilization': [0.3, 0.4, 0.2, 0.5, 0.3],
        'credit_age': [5, 3, 7, 2, 5]
    })

    # Calculate credit score
    credit_score = calculate_credit_score(data)

    # Provide recommendations
    recommendations = provide_recommendations(credit_score)

    # Print results
    print("Credit Score:", credit_score)
    print("Recommendations:", recommendations)


# Run main function
if __name__ == "__main__":
    main()