BingBingBoong

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

def scatter_plot():
    data = datasets.load_diabetes()
    X, y = data.data[:, np.newaxis, 2], data.target  # Using one feature for visualization
    plt.scatter(X, y, color='blue')
    plt.title("Scatter Plot of Diabetes Dataset")
    plt.xlabel("Feature")
    plt.ylabel("Target")
    plt.show()

def simple_linear_regression():
    data = datasets.load_diabetes()
    X, y = data.data[:, np.newaxis, 2], data.target
    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)

    plt.scatter(X_test, y_test, color='blue', label='Actual')
    plt.plot(X_test, y_pred, color='red', linewidth=2, label='Predicted')
    plt.title("Simple Linear Regression")
    plt.legend()
    plt.show()

def polynomial_regression(degree=2):
    data = datasets.load_diabetes()
    X, y = data.data[:, np.newaxis, 2], data.target
    poly = PolynomialFeatures(degree=degree)
    X_poly = poly.fit_transform(X)
    X_train, X_test, y_train, y_test = train_test_split(X_poly, y, test_size=0.2, random_state=42)
    model = LinearRegression()
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)

    plt.scatter(X, y, color='blue', label='Actual')
    plt.scatter(X_test[:, 1], y_pred, color='red', label='Predicted')
    plt.title(f"Polynomial Regression (Degree {degree})")
    plt.legend()
    plt.show()

def multiple_linear_regression():
    data = datasets.load_diabetes()
    X = data.data[:, :3]  # Selecting first 3 features for simplicity
    y = data.target
    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)

    print("Coefficients:", model.coef_)
    print("Intercept:", model.intercept_)
    print("Mean Squared Error:", np.mean((y_pred - y_test) ** 2))
    print("R^2 Score:", model.score(X_test, y_test))

    plt.scatter(y_test, y_pred, color='blue')
    plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color='red', linewidth=2)
    plt.xlabel("Actual Values")
    plt.ylabel("Predicted Values")
    plt.title("Multiple Linear Regression: Actual vs Predicted")
    plt.show()

def line_chart():
    data = datasets.load_diabetes()
    X, y = data.data[:, np.newaxis, 2], data.target
    sorted_indices = np.argsort(X[:, 0])
    X_sorted, y_sorted = X[sorted_indices], y[sorted_indices]

    plt.plot(X_sorted, y_sorted, color='green', marker='o', linestyle='dashed')
    plt.title("Line Chart of Diabetes Dataset")
    plt.xlabel("Feature")
    plt.ylabel("Target")
    plt.show()

# Uncomment the functions you want to run:
# scatter_plot()
# simple_linear_regression()
# polynomial_regression(degree=3)
# multiple_linear_regression()
# line_chart()