Modeling Pipeline Guidebook (Regression & Classification)
🎯 Purpose¶
This guidebook outlines a modular, notebook-driven pipeline for regression and classification modeling in Python. It supports scikit-learn and statsmodels workflows, emphasizes clarity, testability, and includes checkpoint logic.
📊 1. Pipeline Overview¶
[ Phase 1: Import + Config ]
[ Phase 2: Load + Explore ]
[ Phase 3: Clean + Transform ]
[ Phase 4: Split + Model ]
[ Phase 5: Evaluate + Explain ]
[ Phase 6: Export + Save ]
⚙️ 2. Modeling Skeleton (Core Workflow)¶
# Phase 1: Imports
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
# Phase 2: Load Data
df = pd.read_csv("cleaned_dataset.csv")
# Phase 3: Preprocessing
X = df.drop(columns=['target'])
y = df['target']
# Phase 4: Split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)
# Phase 5: Model + Evaluate
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
# Phase 6: Export
pd.DataFrame({"y_true": y_test, "y_pred": y_pred}).to_csv("predictions.csv", index=False)
🔧 3. Optional Layers¶
| Add-on | Module |
|---|---|
| Cross-validation | cross_val_score, GridSearchCV |
| Pipelines | Pipeline() + ColumnTransformer() for scale/encode |
| Statsmodels | Use ols or glm for parametric model summaries |
| Feature Importance | .coef_, .feature_importances_, SHAP |
| Export formats | joblib, pickle, .csv, .json |
📁 4. Directory Expectations¶
| Folder | Contents |
|---|---|
/data/ |
Cleaned inputs or feature-engineered sets |
/outputs/ |
Model predictions or coefficients |
/models/ |
Pickled/scored models if applicable |
/notebooks/ |
Final delivery notebook |
✅ Modeling Checklist¶
- [ ] Clear definition of
Xandy - [ ] Feature transformation applied after split (to avoid leakage)
- [ ] Evaluation printed and plotted
- [ ] Model exported or saved to
/outputs/ - [ ] Modeling assumptions or limitations noted
💡 Tip¶
“A great model isn't just accurate — it’s repeatable, modular, and easy to explain.”