kaggle-titanic and How-to-score-0.8134-in-Titanic-Kaggle-Challenge
These are complements: the tutorial provides foundational techniques for data munging and supervised learning that form the basis for implementing the advanced feature engineering and model optimization strategies demonstrated in the competition solution.
About kaggle-titanic
agconti/kaggle-titanic
A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.
This project provides a detailed example of how to analyze historical data to predict outcomes using machine learning. It takes raw passenger data from the Titanic disaster and guides you through cleaning, visualizing, and applying predictive models. Aspiring data analysts or data scientists new to Python will find this useful for understanding competitive data analysis workflows.
About How-to-score-0.8134-in-Titanic-Kaggle-Challenge
ahmedbesbes/How-to-score-0.8134-in-Titanic-Kaggle-Challenge
Solution of the Titanic Kaggle competition
This project helps data science practitioners understand how to approach a classification problem like predicting survival. It takes raw passenger data, performs analysis and feature engineering, and outputs a predictive model for survival. This is ideal for aspiring data scientists or those new to Kaggle competitions seeking a structured approach to common machine learning tasks.
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