Common Workflows

Two worked end-to-end workflows: building a classification model and performing a clustering analysis.

Common Workflows

Building a Classification Model

Load and explore data

python

import pandas as pd
df = pd.read_csv('data.csv')
X = df.drop('target', axis=1)
y = df['target']

Split data with stratification

python

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

Create preprocessing pipeline

python

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer

# Handle numeric and categorical features separately
preprocessor = ColumnTransformer([
    ('num', StandardScaler(), numeric_features),
    ('cat', OneHotEncoder(), categorical_features)
])

Build complete pipeline

python

model = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', RandomForestClassifier(random_state=42))
])

Tune hyperparameters

python

from sklearn.model_selection import GridSearchCV

param_grid = {
    'classifier__n_estimators': [100, 200],
    'classifier__max_depth': [10, 20, None]
}

grid_search = GridSearchCV(model, param_grid, cv=5)
grid_search.fit(X_train, y_train)

Evaluate on test set

python

from sklearn.metrics import classification_report

best_model = grid_search.best_estimator_
y_pred = best_model.predict(X_test)
print(classification_report(y_test, y_pred))

Performing Clustering Analysis

Preprocess data

python

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

Find optimal number of clusters

python

from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

scores = []
for k in range(2, 11):
    kmeans = KMeans(n_clusters=k, random_state=42)
    labels = kmeans.fit_predict(X_scaled)
    scores.append(silhouette_score(X_scaled, labels))

optimal_k = range(2, 11)[np.argmax(scores)]

Apply clustering

python

model = KMeans(n_clusters=optimal_k, random_state=42)
labels = model.fit_predict(X_scaled)

Visualize with dimensionality reduction

python

from sklearn.decomposition import PCA

pca = PCA(n_components=2)
X_2d = pca.fit_transform(X_scaled)

plt.scatter(X_2d[:, 0], X_2d[:, 1], c=labels, cmap='viridis')