claude-scaffold: Skill for Claude Code

Predictive Analytics

When to Load This Skill

Load when working with: scikit-learn pipelines, feature engineering, tabular ML, time series, model training/evaluation, MLflow experiment tracking, model registry, cross-validation.

Project Structure for ML Projects

src/{project_name}/
├── core/
│   └── domain.py
├── ml/
│   ├── __init__.py
│   ├── features/
│   │   ├── __init__.py
│   │   ├── builder.py       # FeatureBuilder — assembles feature matrix
│   │   └── transformers.py  # custom sklearn transformers
│   ├── models/
│   │   ├── __init__.py
│   │   ├── trainer.py       # training pipeline
│   │   └── evaluator.py     # metrics computation
│   └── registry/
│       └── mlflow_adapter.py

Sklearn Pipeline Standard

Always use Pipeline — never apply transformations outside of it. This ensures train/test consistency and prevents data leakage.

import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler


def build_pipeline(
    numeric_features: list[str],
    categorical_features: list[str],
) -> Pipeline:
    numeric_transformer = Pipeline([
        ("imputer", SimpleImputer(strategy="median")),
        ("scaler", StandardScaler()),
    ])

    categorical_transformer = Pipeline([
        ("imputer", SimpleImputer(strategy="most_frequent")),
        ("encoder", OneHotEncoder(handle_unknown="ignore", sparse_output=False)),
    ])

    preprocessor = ColumnTransformer([
        ("num", numeric_transformer, numeric_features),
        ("cat", categorical_transformer, categorical_features),
    ])

    return Pipeline([
        ("preprocessor", preprocessor),
        ("classifier", GradientBoostingClassifier(n_estimators=200, random_state=42)),
    ])

Custom Transformer Pattern

import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin


class DateFeatureExtractor(BaseEstimator, TransformerMixin):
    def __init__(self, date_column: str) -> None:
        self.date_column = date_column

    def fit(self, X: pd.DataFrame, y=None) -> "DateFeatureExtractor":
        return self

    def transform(self, X: pd.DataFrame) -> pd.DataFrame:
        X = X.copy()
        dt = pd.to_datetime(X[self.date_column])
        X[f"{self.date_column}_year"] = dt.dt.year
        X[f"{self.date_column}_month"] = dt.dt.month
        X[f"{self.date_column}_dayofweek"] = dt.dt.dayofweek
        X[f"{self.date_column}_quarter"] = dt.dt.quarter
        return X.drop(columns=[self.date_column])