model.py

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor


def linear_regression(X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = LinearRegression()
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)
    coefficients = model.coef_
    intercept = model.intercept_
    return mse, r2, coefficients, intercept

def knn_regression(X, y, k=5):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = KNeighborsRegressor(n_neighbors=k)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)
    return mse, r2

def random_forest_regression(X, y, n_estimators, max_depth=None, random_state=42):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state)
    model = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, random_state=random_state)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    return mse, r2