agconti/kaggle-titanic

Implements a complete competitive analysis workflow in a single IPython Notebook, covering feature engineering, exploratory data analysis with Matplotlib visualizations, and model comparison across multiple algorithms (logistic regression, SVM with multiple kernels, random forests). Leverages the PyData stack—NumPy, Pandas, scikit-learn, and StatsModels—to demonstrate k-fold cross-validation for local evaluation and direct submission to Kaggle's competition API. Includes benchmark reference scripts to help newcomers understand foundational approaches to the prediction task.

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