GitHub / virchan / predictive_modeling_workflow

This project explores the predictive modeling workflow using the Kaggle competition "Titanic - Machine Learning from Disaster." It emphasizes key stages like data analysis and model evaluation, aiming to identify the optimal model. Through a real-world approach, we enhance our understanding of the workflow and emphasize rigorous model evaluation.

JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/virchan%2Fpredictive_modeling_workflow

Topics: adaboost-classifier, bernoulli-naive-bayes-classifier, confusion-matrix, data-preparation, data-preprocessing, decision-tree-classifier, dummy-classifier, feedforward-neural-network, gaussian-naive-bayes-classifier, hypothesis-testing, kaggle-competition, logistic-regression, model-evaluation-and-selection, model-training, predictive-modeling, random-forest-classifier, support-vector-classifier, titanic-survival-prediction

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