🎯 Project: Housing Price Prediction
This project will help you apply everything you've learned about regression models. You'll build a complete housing price prediction system using linear regression, evaluate it with regression metrics, and use regularization techniques.
This is a real-world regression problem that combines data preprocessing, feature engineering, model training, and evaluation into one comprehensive project.
Project Workflow
A complete regression project follows these steps:
print("Complete ML Project Workflow:")
print("=" * 50")
print("\n1. Data Collection & Exploration:")
print(" - Load housing data (size, bedrooms, location, etc.)")
print(" - Explore data distributions")
print(" - Identify missing values and outliers")
print("\n2. Data Preprocessing:")
print(" - Handle missing values")
print(" - Encode categorical features")
print(" - Normalize/standardize features")
print("\n3. Feature Engineering:")
print(" - Create new features (e.g., price per sqft)")
print(" - Select relevant features")
print("\n4. Model Training:")
print(" - Split data (train/test)")
print(" - Train Linear Regression")
print(" - Try Ridge and Lasso regularization")
print("\n5. Model Evaluation:")
print(" - Calculate MAE, RMSE, R²")
print(" - Compare models")
print(" - Check for overfitting")
print("\n6. Make Predictions:")
print(" - Predict prices for new houses")
print(" - Interpret results")
Sample Housing Data
Here's what housing data typically looks like:
import pandas as pd
data = {
'size_sqft': [1200, 1500, 1800, 2000, 2500],
'bedrooms': [2, 3, 3, 4, 4],
'bathrooms': [1, 2, 2, 2.5, 3],
'age_years': [10, 5, 15, 2, 8],
'price': [200000, 300000, 350000, 450000, 500000]
}
df = pd.DataFrame(data)
print("Housing Dataset:")
print(df)
print("\nFeatures (X):")
print(" - size_sqft: House size in square feet")
print(" - bedrooms: Number of bedrooms")
print(" - bathrooms: Number of bathrooms")
print(" - age_years: House age")
print("\nTarget (y):")
print(" - price: House price (what we want to predict)")
print("\nFeature Engineering Ideas:")
print(" - price_per_sqft = price / size_sqft")
print(" - total_rooms = bedrooms + bathrooms")
print(" - sqft_per_bedroom = size_sqft / bedrooms")
Training and Evaluating Models
Compare different regression models:
print("Model Comparison for Housing Price Prediction:")
print("=" * 50")
print("\n1. Linear Regression:")
print(" from sklearn.linear_model import LinearRegression")
print(" model = LinearRegression()")
print(" - Simple baseline")
print(" - May overfit with many features")
print("\n2. Ridge Regression:")
print(" from sklearn.linear_model import Ridge")
print(" model = Ridge(alpha=1.0)")
print(" - Prevents overfitting")
print(" - Good for many correlated features")
print("\n3. Lasso Regression:")
print(" from sklearn.linear_model import Lasso")
print(" model = Lasso(alpha=0.1)")
print(" - Feature selection")
print(" - Removes irrelevant features")
print("\nEvaluation Metrics:")
print(" - MAE: Average prediction error in dollars")
print(" - RMSE: Penalizes large errors")
print(" - R²: How much variance is explained")
Exercise: Complete Housing Price Prediction Project
Complete the exercise on the right side:
- Task 1: Create a DataFrame with housing features and prices
- Task 2: Create a new feature (e.g., price_per_sqft or total_rooms)
- Task 3: Calculate basic statistics (mean, max, min price)
- Task 4: Simulate training a model and making predictions
- Task 5: Calculate MAE and RMSE for the predictions
Write your code to complete this housing price prediction project!