titanic-pipeline 0.1.6

A classical ML pipeline for the Titanic survival dataset — EDA, preprocessing, feature engineering, model selection, evaluation, and hyperparameter tuning.

titanic-pipeline

A classical ML pipeline for the Titanic survival dataset. Covers the full workflow: EDA, preprocessing, feature engineering, model selection, evaluation, and hyperparameter tuning — all accessible via a single print_pipeline() call.

Install

pip install titanic-pipeline

Usage

from titanic_pipeline import print_pipeline

# Print the entire pipeline
print_pipeline()

# Print a specific section
print_pipeline('eda')
print_pipeline('preprocessing')
print_pipeline('feature_engineering')
print_pipeline('model_selection')
print_pipeline('evaluation')
print_pipeline('tuning')

Sections

Key	Contents
imports	All library imports
eda	Data loading, missing value audit, target distribution, visualizations
preprocessing	Drop redundant columns, impute missing values, encode categoricals, scale, train/test split
feature_engineering	family_size, is_alone, age_bin, fare_per_person with survival correlation plots
model_selection	Logistic Regression, Decision Tree, k-NN, Random Forest, SVM — side-by-side comparison
evaluation	Confusion matrix, classification report, ROC curves, feature importance
tuning	GridSearchCV for Decision Tree and k-NN