Tutorial 07: Missing Value Imputation

Level: Beginner · Part of: ML Learning Roadmap

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Case Study: Health Survey Data Cleaning

Scenario

Patient survey responses have missing BMI and blood pressure readings. You must impute sensibly without leaking test-set statistics.

Learning Objectives

• Detect missing values with pandas
• Use SimpleImputer for mean/median/mode strategies
• Use KNNImputer for multivariate imputation
• Impute inside a Pipeline

Prerequisites

• Tutorial 06
• Python 3.9+, scikit-learn, NumPy, pandas, matplotlib

Dataset

Modified diabetes dataset with injected missing values

Hands-On Solution

Copy and run the complete script below:

import numpy as np
from sklearn.datasets import load_diabetes
from sklearn.impute import SimpleImputer, KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import mean_absolute_error

X, y = load_diabetes(return_X_y=True)
rng = np.random.RandomState(42)
mask = rng.random(X.shape) < 0.1
X_missing = X.copy()
X_missing[mask] = np.nan

X_train, X_test, y_train, y_test = train_test_split(
    X_missing, y, test_size=0.2, random_state=42
)

for strategy in ["mean", "median"]:
    pipe = Pipeline([
        ("imputer", SimpleImputer(strategy=strategy)),
        ("model", LinearRegression()),
    ])
    pipe.fit(X_train, y_train)
    mae = mean_absolute_error(y_test, pipe.predict(X_test))
    print(f"SimpleImputer({strategy:6s}) MAE: {mae:.2f}")

pipe_knn = Pipeline([
    ("imputer", KNNImputer(n_neighbors=5)),
    ("model", LinearRegression()),
])
pipe_knn.fit(X_train, y_train)
mae_knn = mean_absolute_error(y_test, pipe_knn.predict(X_test))
print(f"KNNImputer         MAE: {mae_knn:.2f}")

Expected Output

When you run the script, you should see evaluation metrics printed to the console. Some tutorials also save .png plot files in the current directory.

Exercises

1. Count missing values per column before imputation.
2. Try SimpleImputer(strategy='constant', fill_value=0).
3. Why must imputation happen inside CV folds, not before splitting?

Key Takeaways

• Never impute using statistics from the full dataset before splitting
• Mean imputation is simple; KNN imputation uses feature relationships
• Imputers are transformers — they belong in Pipelines

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