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8. Machine Learning with Scikit-learn

Scikit-learn (sklearn) is the most popular open-source machine learning library in Python. It provides simple, consistent, and efficient tools for data mining and analysis — from preprocessing to model evaluation and deployment.

Install Scikit-learn (if not using Anaconda)

Bash

pip install scikit-learn

Standard import

Python

import sklearn from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import accuracy_score, mean_squared_error

8.1 Supervised Learning – Regression & Classification

Supervised Learning = Learning from labeled data (input + correct output).

Regression → Predict continuous values (e.g., house price, temperature, salary)

Classification → Predict discrete classes (e.g., spam/not spam, disease/no disease)

Common algorithms in Scikit-learn

Regression:

• Linear Regression

• Ridge / Lasso (regularized)

• Decision Tree / Random Forest Regressor

• Gradient Boosting (XGBoost, LightGBM often used via sklearn interface)

Classification:

• Logistic Regression

• Decision Tree / Random Forest Classifier

• Support Vector Machine (SVM)

• k-Nearest Neighbors (KNN)

• Gradient Boosting Classifier

Basic Regression Example (House Price Prediction)

Python

from sklearn.linear_model import LinearRegression from sklearn.datasets import fetch_california_housing # Load data housing = fetch_california_housing(as_frame=True) X = housing.data y = housing.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = LinearRegression() model.fit(X_train, y_train) predictions = model.predict(X_test) rmse = mean_squared_error(y_test, predictions, squared=False) print(f"RMSE: {rmse:.3f}")

Basic Classification Example (Iris Dataset)

Python

from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier iris = load_iris(as_frame=True) X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) clf = RandomForestClassifier(n_estimators=100, random_state=42) clf.fit(X_train, y_train) accuracy = accuracy_score(y_test, clf.predict(X_test)) print(f"Accuracy: {accuracy:.3f}")

8.2 Model Training, Evaluation & Hyperparameter Tuning

Training = model.fit(X_train, y_train)

Prediction = model.predict(X_test)

Evaluation Metrics

Regression:

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

• Mean Absolute Error (MAE)

• R² Score

Classification:

• Accuracy

• Precision / Recall / F1-Score

• Confusion Matrix

• ROC-AUC (especially for imbalanced data)

Hyperparameter Tuning (Grid Search / Random Search)

Python

from sklearn.model_selection import GridSearchCV param_grid = { 'n_estimators': [50, 100, 200], 'max_depth': [None, 10, 20], 'min_samples_split': [2, 5, 10] } grid_search = GridSearchCV( RandomForestClassifier(random_state=42), param_grid, cv=5, scoring='f1_macro', n_jobs=-1 ) grid_search.fit(X_train, y_train) print("Best parameters:", grid_search.best_params_) print("Best score:", grid_search.best_score_)

Best practice (2026):

• Use RandomizedSearchCV for large search spaces (faster)

• Use cross-validation for reliable evaluation

• Never tune on test set — use validation set or cross-validation

8.3 Cross-Validation & Model Selection

Cross-Validation = Splitting data multiple times to get reliable performance estimate.

Most common: K-Fold CV

Python

from sklearn.model_selection import cross_val_score scores = cross_val_score( RandomForestClassifier(random_state=42), X, y, cv=5, # 5-fold scoring='accuracy' ) print("Cross-validation scores:", scores) print("Mean accuracy:", scores.mean()) print("Std deviation:", scores.std())

Stratified K-Fold (for imbalanced classification)

Python

from sklearn.model_selection import StratifiedKFold cv = StratifiedKFold(n_splits=5) scores = cross_val_score(model, X, y, cv=cv, scoring='f1_macro')

Model Selection Flow (recommended)

1. Split data → train / validation / test (80/10/10 or 70/15/15)

2. Preprocess → fit on train only, transform validation/test

3. Try multiple models with cross-validation on train+validation

4. Select best model → tune hyperparameters

5. Final evaluation on hold-out test set

8.4 Unsupervised Learning – Clustering & Dimensionality Reduction

Unsupervised Learning → No labels. Discover hidden structure in data.

Clustering – Group similar data points

Most popular: K-Means

Python

from sklearn.cluster import KMeans from sklearn.datasets import make_blobs X, = makeblobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0) kmeans = KMeans(n_clusters=4, random_state=42) kmeans.fit(X) labels = kmeans.labels_ centers = kmeans.cluster_centers_ plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis') plt.scatter(centers[:, 0], centers[:, 1], c='red', marker='x', s=200) plt.title("K-Means Clustering") plt.show()

Dimensionality Reduction – Reduce number of features while preserving information

PCA (Principal Component Analysis)

Python

from sklearn.decomposition import PCA pca = PCA(n_components=2) X_pca = pca.fit_transform(X) plt.scatter(X_pca[:, 0], X_pca[:, 1], c=labels, cmap='viridis') plt.title("PCA – 2D Visualization") plt.xlabel("PC1") plt.ylabel("PC2") plt.show() print("Explained variance ratio:", pca.explained_variance_ratio_)

t-SNE (non-linear, great for visualization)

Python

from sklearn.manifold import TSNE tsne = TSNE(n_components=2, random_state=42) X_tsne = tsne.fit_transform(X) plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=labels, cmap='viridis') plt.title("t-SNE Visualization") plt.show()

Mini Summary Project – End-to-End ML Pipeline

Python

from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split, cross_val_score X = df.drop('target', axis=1) # your features y = df['target'] pipeline = Pipeline([ ('scaler', StandardScaler()), ('classifier', RandomForestClassifier(random_state=42)) ]) scores = cross_val_score(pipeline, X, y, cv=5, scoring='f1') print("F1 scores:", scores) print("Mean F1:", scores.mean())

This completes the full Machine Learning with Scikit-learn section — now you can build, train, evaluate, and tune real ML models!es the full Classes and Objects – Basic Building Blocks section — the heart of OOP in Python!

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