Sharda Links
DAA Exp 5-8
DAA Exp 5-8 Code
Experiment No-8
Problem Statement: Write a program to Normalize the data used in linear regression problem above before predicting prices, and then predict the housing prices.
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
X = np.array([[2100, 3], [2400, 4], [2000, 4], [1200, 2], [2500, 4], [1800, 3],
[3000, 5], [2200, 3], [1600, 3], [2800, 4]
])
y = np.array([300, 380, 250, 450, 340, 270, 520, 410, 320, 490])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
model = LinearRegression()
model.fit(X_train_scaled, y_train)
y_pred = model.predict(X_test_scaled)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("--- Model Evaluation ---")
print(f"Mean Squared Error: {mse:.2f}")
print(f"R-squared Score: {r2:.2f}\n")
new_house = np.array([[2100, 3]])
new_house_scaled = scaler.transform(new_house)
predicted_price = model.predict(new_house_scaled)
print("--- New Prediction ---")
print(f"Features: {new_house[0][0]} sq ft, {new_house[0][1]} bedrooms")
print(f"Predicted Price: ${predicted_price[0] * 1000:,.2f}")
0 Comments