Data Pipeline Engineering

4/8

Pipeline Architecture

Data Ingestion

Load CSV → Train/Test Split → Save Artifacts

Data Transformation

Imputation → Encoding → Scaling → Pipeline

Model Training

Multiple Models → Hyperparameter Tuning → Best Model Selection

Prediction Pipeline

Load Model & Preprocessor → Transform Inputs → Predict

Implementation Highlights

Data Transformation Pipeline

# Feature engineering with scikit-learn pipeline
num_pipeline = Pipeline(
    steps=[
        ("imputer", SimpleImputer(strategy="median")),
        ("scaler", StandardScaler())
    ]
)

cat_pipeline = Pipeline(
    steps=[
        ("imputer", SimpleImputer(strategy="most_frequent")),
        ("encoder", OneHotEncoder())
    ]
)

preprocessor = ColumnTransformer([
    ("num", num_pipeline, numerical_cols),
    ("cat", cat_pipeline, categorical_cols)
])
                        

Features Processing

Numerical Features 3 features

Categorical Features 5 features

One-Hot Encoded 15 dimensions

Final Feature Space 18 dimensions

Artifacts Management

train.csv & test.csv

preprocessor.pkl

model.pkl

Modular architecture

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