About

This project builds an end-to-end machine learning system to predict passenger survival on the Titanic dataset.

The workflow includes:
- Data cleaning and preprocessing
- Encoding categorical features
- Train-test split
- Logistic Regression model training
- Model evaluation using accuracy, classification report, and confusion matrix

The trained model is used to generate survival predictions and probabilities for new passenger inputs, simulating a production-ready prediction API.
This project demonstrates a fully measurable and deployable analytical system built and validated in Zerve AI