model_class.py

# model_class.py
import numpy as np

class LinearRegressionNumpy:
    """Simple Linear Regression using only NumPy"""
    def __init__(self):
        self.weights = None
        self.bias = None

    def fit(self, X, y, learning_rate=0.01, epochs=1000):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0
        for _ in range(epochs):
            y_pred = np.dot(X, self.weights) + self.bias
            dw = (1/n_samples) * np.dot(X.T, (y_pred - y))
            db = (1/n_samples) * np.sum(y_pred - y)
            self.weights -= learning_rate * dw
            self.bias -= learning_rate * db

    def predict(self, X):
        return np.dot(X, self.weights) + self.bias

    def score(self, X, y):
        y_pred = self.predict(X)
        ss_tot = np.sum((y - np.mean(y)) ** 2)
        ss_res = np.sum((y - y_pred) ** 2)
        return 1 - (ss_res / ss_tot)