Titanic ML Pipeline

Exploring exploratory data analysis (EDA), feature engineering, and model evaluation on the classic Titanic dataset.
This project is designed as a hands‑on learning resource for understanding how preprocessing choices and algorithm selection impact predictive performance.

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Project Overview

The goal is to predict passenger survival on the Titanic using machine learning.
This matters because the dataset is a benchmark problem for classification, widely used to practice end‑to‑end ML workflows: data cleaning, feature engineering, model training, evaluation, and interpretability.

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Dataset Description

• Source: Kaggle Titanic Dataset
• Key Features:
  • Passenger demographics (Age, Sex, SibSp, Parch)
  • Ticket and cabin information
  • Socio‑economic indicators (Fare, Class)
  • Survival outcome (target variable)

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Pipeline Summary

1. EDA → Inspect missing values, distributions, correlations
2. Feature Engineering → Encode categorical variables, impute missing data, create derived features
3. Modeling → Train multiple ML algorithms (Logistic Regression, Decision Trees, Random Forests, Gradient Boosting)
4. Evaluation → Compare models using accuracy, precision, recall, F1 score, and ROC‑AUC

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⚙️ How to Run

Setup

pip install -r requirements.txt