Practical ML using Scikit-learn · Lesson 1 of 1
Practical ML using Scikit-learn
Build an end-to-end model workflow with splits, baselines, preprocessing, pipelines, metrics, and persistence.
Lesson
1. Introduction
A useful machine-learning project is a sequence of validated decisions, not a single call to fit(). Scikit-learn provides consistent estimator interfaces and pipeline tools that help keep preprocessing aligned between training and inference.
2. Core concepts
Split data before fitting imputers, encoders, or scalers. Pipelines fit these transformations on training folds and apply them consistently. Stratified splits preserve class proportions when appropriate; cross-validation estimates variability across splits, while a final held-out test should remain independent of tuning.
3. Topics covered in this module
- Real-world datasets, target definition, and train/test creation
- Iris classification and house-price regression examples
- MAE, RMSE, accuracy, confusion matrix, and stratified splitting
- Missing data, categorical encoding, feature scaling, pipelines, ColumnTransformer, Joblib, and inference
4. Syntax and how to read it
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
model = make_pipeline(SimpleImputer(), StandardScaler(), LogisticRegression())The pipeline ensures each transformation is fitted as part of model training and reused in the same order for prediction.
5. Practical workflow
Create a baseline and a leakage-safe split, build preprocessing and model steps into a pipeline, tune only on training folds, and report relevant metrics. Save the entire fitted pipeline with version and feature-schema information.
6. Common mistakes
- Scaling or imputing the full dataset before splitting.
- Reporting a metric without a baseline, units, split method, or target definition.
7. Best practices
- Use a pipeline and preserve the feature schema with the artifact.
- Track the random seed, data version, package versions, and final test evaluation.
8. Practice task
Where should a scaler be fitted if you want a fair test-set evaluation?
9. One possible solution
Fit it on the training data only, preferably inside a scikit-learn pipeline, and apply the learned transformation to validation and test data.10. Summary
Build an end-to-end model workflow with splits, baselines, preprocessing, pipelines, metrics, and persistence. Use a pipeline and preserve the feature schema with the artifact. Track the random seed, data version, package versions, and final test evaluation.
11. What comes next
The next module, Deep Learning and Neural Networks, builds on this foundation and applies it to a new part of the analytics workflow.
Visual guide
A reliable Practical ML using Scikit-learn workflow
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Step 01
Frame
Define the question, input scope, and intended output.
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Step 02
Work
Apply the method and validate assumptions as you go.
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Step 03
Check
Compare results with known cases and communicate limitations.
Code examples
from sklearn.dummy import DummyRegressor
from sklearn.metrics import mean_absolute_error
X_train, X_test = [[1], [2], [3]], [[4]]
y_train, y_test = [10, 12, 14], [16]
model = DummyRegressor(strategy='mean').fit(X_train, y_train)
predictions = model.predict(X_test)
print(mean_absolute_error(y_test, predictions))2.0 days (the target units depend on the dataset).
import joblib
joblib.dump(model, 'delivery_model.joblib')
loaded = joblib.load('delivery_model.joblib')Saves and reloads the complete fitted preprocessing/model pipeline.
Key points
- Build an end-to-end model workflow with splits, baselines, preprocessing, pipelines, metrics, and persistence.
- Use a pipeline and preserve the feature schema with the artifact.
- Track the random seed, data version, package versions, and final test evaluation.
Check your understanding
Practice
Your learning progress