LinearRegressionScript.py

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
import matplotlib.pyplot as plt

# Synthetic dataset
data = {
    'Size': [1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400],
    'Price': [300000, 320000, 340000, 360000, 380000, 400000, 420000, 440000, 460000, 480000]
}

df = pd.DataFrame(data)

# Features and target variable
X = df[['size']]
y = df['price']

# Split the data into training and testing sets
X_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create the model
model = LinearRegression()

# Train the model
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f'Mean Squared Error: {mse}')
print(f'R² Score: {r2}')

# Plot the results
plt.scatter(X_train, y_train, color='blue', label='Training data')
plt.scatter(X_test, y_test, color='green', label='Testing data')
plt.plot(X_test, y_pred, color='red', linewidth=2, label='Regression line')
plt.xlabel('size')
plt.ylabel('price')
plt.legend()
plt.show