mlforge-py 1.0.0

End-to-end ML pipeline library — data ingestion to deployment

mlforge Examples

Three complete end-to-end examples showing how to use mlforge. Each one is fully commented — every line explains what it does and why.

Examples

01 — Quickstart (5 minutes)

python examples/01_quickstart.py

The simplest possible pipeline. Generates data, cleans it, trains a model, makes predictions. Start here if you're new to mlforge or ML.

02 — Titanic Survival Prediction (Classification)

python examples/02_titanic.py

Predicts who survived the Titanic disaster (yes/no). Shows how to handle real messy data — missing values, text columns, duplicates.

Requires: titanic.csv in the same folder. Download: https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv

03 — House Price Prediction (Regression)

python examples/03_house_prices.py

Predicts house prices from features like size, bedrooms, and location. Shows feature engineering, cross validation, hyperparameter tuning, and drift detection.

No download needed — generates realistic data automatically.

What you'll learn

Concept	Where it's shown
Loading data from a file	02_titanic.py
Checking data quality	02_titanic.py, 03_house_prices.py
Filling missing values	02_titanic.py
Encoding text columns	02_titanic.py, 03_house_prices.py
Handling outliers	02_titanic.py, 03_house_prices.py
Normalising features	All examples
Creating new features	03_house_prices.py
Training multiple models	02_titanic.py, 03_house_prices.py
Comparing models	02_titanic.py, 03_house_prices.py
Cross validation	03_house_prices.py
Hyperparameter tuning	03_house_prices.py
Saving models	02_titanic.py, 03_house_prices.py
Drift detection	03_house_prices.py