titanic-pipeline 0.1.13

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

titanic-pipeline

A classical machine learning pipeline built on the Titanic survival dataset. Covers the full ML workflow — EDA, preprocessing, feature engineering, model selection, evaluation, and hyperparameter tuning. Supports binary classification, multiclass classification, and regression, for both single-file and separate train/test datasets.

Install

pip install titanic-pipeline

Usage

Single dataset (binary classification)

from titanic_pipeline import print_pipeline

print_pipeline()                    # print full pipeline
print_pipeline('preprocessing')     # print a single section

Separate train / test datasets (binary classification)

from titanic_pipeline import print_pipeline_separate_dataset

print_pipeline_separate_dataset()
print_pipeline_separate_dataset('tuning')

Separate train / test datasets (multiclass classification)

from titanic_pipeline import print_pipeline_multi_class

print_pipeline_multi_class()
print_pipeline_multi_class('tuning')

Separate train / test datasets (regression)

from titanic_pipeline import print_pipeline_regression

print_pipeline_regression()
print_pipeline_regression('tuning')

Available Sections

Pass any of these as the section argument to any function:

Key	Stage
'imports'	All library imports
'eda'	Data loading and exploration
'preprocessing'	Imputation, feature engineering, encoding, scaling
'model_selection'	Train and compare 5 models
'evaluation'	Metrics suited to the task type
'tuning'	GridSearchCV on all 5 models
'final'	Select best model, retrain on full data, save predictions to CSV

Function Comparison

Function	Task	Models	Key Metrics
print_pipeline	Binary classification	LR, DT, k-NN, RF, SVM	Accuracy, F1, ROC-AUC
print_pipeline_separate_dataset	Binary classification	LR, DT, k-NN, RF, SVM	Accuracy, F1, ROC-AUC
print_pipeline_multi_class	Multiclass classification	LR, DT, k-NN, RF, SVM	Weighted F1, ROC-AUC (OvR)
print_pipeline_regression	Regression	Ridge, DT, k-NN, RF, SVR	MAE, RMSE, R²

Pipeline Stages

#	Stage	What it covers
1	EDA	Shape, dtypes, missing values, target distribution
2	Preprocessing & Feature Engineering	Imputation, family size, age bins, fare-per-person, encoding, scaling
3	Model Selection & Training	Train and compare 5 models side-by-side
4	Evaluation	Metrics suited to task type
5	Hyperparameter Tuning	GridSearchCV on all 5 models
Final	Best Model & Predictions	Retrain on full data, predict on test set, save predictions.csv

Requirements

• Python >= 3.8
• numpy, pandas, matplotlib, seaborn, scikit-learn

License

MIT