Housing Price Prediction (Regression)

Goal

Given housing data, you will:

• Explore price drivers
• Prepare features (missing values, encoding)
• Build a baseline regression pipeline

Analysis pipeline

diagram Housing regression pipeline mermaid

  flowchart LR
  A["Raw housing data"] --> B["Explore
(price distribution)"]
  B --> C["Preprocess
(impute, encode, scale)"]
  C --> D["Train
(baseline regressor)"]
  D --> E["Evaluate
(MAE / RMSE)"]

From raw listings to a baseline price-prediction model.

From the book

McKinney always checks a column’s shape with frame.info() before doing anything else, because dtypes and missing values quietly break later steps. Housing prices are a classic case where the raw distribution is also worth a look: prices are usually right-skewed (a few very expensive homes stretch the tail), which is why many regression pipelines model log(price) instead of raw price.

Step 1: Load

Load housing

import pandas as pd
 
df = pd.read_csv("data/housing.csv")
print(df.shape)
print(df.head())

Step 2: EDA: price distribution

Price distribution

import seaborn as sns
import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
sns.histplot(df["price"], bins=30, kde=True)
plt.title("House price distribution")
plt.tight_layout()
plt.show()

Visualize it

sketch House price distribution (right-skewed) p5.js Pause Restart

Most homes cluster on the left; a long tail of expensive homes stretches right.

Step 3: Baseline preprocessing

• Identify numeric/categorical
• Impute missing values
• One-hot encode
• Scale numeric (optional)

Use the Phase 4 pipeline approach.

Step 4: Train baseline model

Baseline regression (concept)

# Use scikit-learn Pipeline + ColumnTransformer
# Choose a baseline model like LinearRegression or RandomForestRegressor
# Evaluate using MAE/RMSE on a held-out test set

Deliverable

• Key drivers (most correlated features)
• Data quality issues
• Baseline model performance

🧪 Try It Yourself

Exercise 1 – Check missing values

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Exercise 2 – Correlation with price

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Exercise 3 – One-hot encode a categorical column

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