🤖 04 — Modeling Pipeline

Baseline

from sklearn.dummy import DummyClassifier

baseline = DummyClassifier(strategy="most_frequent")
baseline.fit(X_train, y_train)

Real Models

Try:

• Logistic Regression
• Random Forest
• Gradient Boosting

Pipeline Template

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

preprocess = ColumnTransformer([
    ("num", StandardScaler(), numeric_features),
    ("cat", OneHotEncoder(handle_unknown="ignore"), categorical_features)
])

model = Pipeline([
    ("preprocess", preprocess),
    ("model", LogisticRegression(max_iter=1000))
])

model.fit(X_train, y_train)

---

Next

➡️ 05-evaluation-and-report