Titanic ML Foundations
Completed a first machine learning submission in the Kaggle Titanic competition to familiarize myself with ML workflows; plan to revisit later using advanced techniques to improve my score.
Context
My very first ML project: a beginner-friendly Kaggle competition (Titanic — Machine Learning from Disaster), where the goal is to predict survival of Titanic passengers using features like age, sex, passenger class, and others :contentReference[oaicite:0]{index=0}. It’s a classic introductory task widely used to learn data preprocessing, model training and evaluation. I chose it to build hands-on experience in ML and plan to return later with more advanced techniques to improve my performance.
Approach
- Data: Loaded and explored Kaggle’s Titanic dataset—including train/test splits with passenger attributes such as age, sex, fare, class, etc. :contentReference[oaicite:1]{index=1}
- Methods: Preprocessed data (e.g., filled missing ages, encoded categorical features), trained a simple classification model (e.g., logistic regression), evaluated on validation set and submitted predictions to Kaggle leaderboard.
- Learning Outcome: Went through the full ML pipeline—from loading CSVs, preprocessing, modeling to submission—gaining practical familiarity with each step.
Results
The project served as a comprehensive hands-on introduction to ML application. My initial submission yielded a baseline score, serving as a reference point. I aim to revisit this challenge in the future using feature engineering and advanced models to enhance predictive performance.