Project — House Price Prediction

Let's stop and think about this for a second

A real-world regression project means chaining together, in sequence, every step you learned in the Basic/Intermediate chapters — Data Loading → Preprocessing (missing values, scaling) → Feature Engineering → Train/Test Split → Model Training (comparing Linear Regression and Random Forest) → Evaluation (R², cross-validation) → selecting the best model. Running this full workflow yourself is what really cements how each individual concept connects and gets used together.

Let's connect it to a real scenario

Take a house dataset (size, bedrooms, age, location) and run the full pipeline yourself: (1) handle missing values, (2) One-Hot Encode the categorical features, (3) scale the numeric features, (4) do an 80/20 train/test split, (5) train and compare Linear Regression and Random Forest, (6) get a more stable estimate with cross-validation, and (7) pick the best model.

Ran through the full regression pipeline (Preprocessing → Feature Engineering → Train/Test → Model → Evaluation) yourself
Trained and compared more than one model (Linear Regression, Random Forest)
Confidently picked a final model using cross-validation

Let's look at it together

python

from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler

# 1-3: preprocess, encode, scale (see Basic/Intermediate chapters)
# 4: split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 5: train and compare models
lr = LinearRegression().fit(X_train, y_train)
rf = RandomForestRegressor(n_estimators=100, random_state=42).fit(X_train, y_train)

print(f"Linear Regression R^2: {lr.score(X_test, y_test):.2f}")
print(f"Random Forest R^2: {rf.score(X_test, y_test):.2f}")

# 6: cross-validate the better model
cv_scores = cross_val_score(rf, X, y, cv=5)
print(f"Random Forest CV mean: {cv_scores.mean():.2f}")

You should see

Linear Regression R^2: 0.82
Random Forest R^2: 0.89
Random Forest CV mean: 0.87

Try it in 5 minutes

Run a house price dataset (create your own sample data, or use a public dataset) through the full pipeline, compare the two models, and pick a final model.

One thing to watch out for

Before deploying the final model to production, you need to retrain it on all of the data (train + test combined) — you only split off train/test for evaluation purposes; the deployment model should use as much of the available data as possible.