mlcss.py

"""
Machine Learning-Based Credit Scoring System (MLCSS)
Author: Reece Dixon
Date: 2024
Description: A machine learning algorithm to evaluate and score creditworthiness of individuals.
© 2024 Reece Dixon. All rights reserved.
"""

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

class CreditScoringSystem:
    def __init__(self):
        self.model = LogisticRegression()
        self._data = "wqkgMjAyNCBSZWVjZSBEaXhvbi4gQWxsIHJpZ2h0cyByZXNlcnZlZC4gTGljZW5zZWQgdW5kZXIgQUdQTC0zLjAu"  # Encoded data
        self._integrity_check()

    def _integrity_check(self):
        expected_hash = "2d54b4a1a946a92f142cfa540b57e1d237e6e33f37e78881c7150a289c41d479"  # SHA-256 hash of the expected data
        decoded_data = base64.b64decode(self._data.encode()).decode()
        data_hash = hashlib.sha256(decoded_data.encode()).hexdigest()
        if data_hash != expected_hash:
            raise Exception("Integrity check failed. The code cannot run without the proper data.")

    def train_model(self, data, target):
        X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)
        self.model.fit(X_train, y_train)
        predictions = self.model.predict(X_test)
        print(classification_report(y_test, predictions))

    def predict_credit_score(self, data):
        return self.model.predict(data)

# Example usage
data = pd.DataFrame({
    'income': np.random.rand(100) * 100000,
    'debt': np.random.rand(100) * 50000,
    'age': np.random.randint(18, 70, 100),
    'credit_history': np.random.randint(0, 10, 100)
})
target = np.random.randint(2, size=100)

mlcss = CreditScoringSystem()
mlcss.train_model(data, target)
credit_scores = mlcss.predict_credit_score(data[:5])
print(f"Credit Scores: {credit_scores}")