Project goal
Predict house prices using property features.
Business value
• Real estate valuation
• Investment decisions
• Pricing strategy
• Classic regression interview problem
Dataset
Housing data. Typical columns
• area
• bedrooms
• bathrooms
• location
• parking
• price
Target price.
Tech stack
• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
Step 1. Import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
Step 2. Load data
df = pd.read_csv("house_prices.csv")
df.head()
Step 3. Basic checks
df.shape
df.info()
df.isnull().sum()
Step 4. Data cleaning
Fill missing values.
df.fillna(df.median(numeric_only=True), inplace=True)
Step 5. Encode categorical variables
le = LabelEncoder()
for col in df.select_dtypes(include='object').columns:
df[col] = le.fit_transform(df[col])
Step 6. Feature scaling
scaler = StandardScaler()
X = df.drop('price', axis=1)
y = df['price']
X_scaled = scaler.fit_transform(X)
Step 7. Train test split
X_train, X_test, y_train, y_test = train_test_split(
X_scaled, y, test_size=0.3, random_state=42
)
Step 8. Build model
Linear Regression.
model = LinearRegression()
model.fit(X_train, y_train)
Step 9. Predictions
y_pred = model.predict(X_test)
Step 10. Evaluation
mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
r2 = r2_score(y_test, y_pred)
print("MAE:", mae)
print("RMSE:", rmse)
print("R2:", r2)
Typical results
• R2 between 0.70 to 0.85
• Location and area dominate price
Step 11. Feature importance
importance = pd.DataFrame({
'Feature': X.columns,
'Coefficient': model.coef_
}).sort_values(by='Coefficient', ascending=False)
importance
Interpretation: Positive coefficient increases price. Negative reduces price.
Step 12. Model improvements
• Ridge regression for multicollinearity
• Lasso for feature selection
• Random Forest for non-linear patterns
Resume bullet example
• Built house price prediction model using regression
• Achieved R2 score above 0.8
• Identified key price drivers
Interview explanation flow
• Why RMSE matters
• How multicollinearity affects coefficients
• Why tree models outperform linear sometimes
Mini task for you
• Try Ridge and Lasso
• Compare RMSE
• Plot actual vs predicted
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