Feature Engineering and Prediction of Survivors on the Titanic Dataset
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Updated
Sep 6, 2017 - HTML
Feature Engineering and Prediction of Survivors on the Titanic Dataset
This project predicts credit scores ('Good', 'Standard', 'Poor') using a streamlined ML pipeline. It includes data extraction, cleaning, and preprocessing. Key techniques are Mutual Information for feature selection, PCA for dimensionality reduction, and XGBoost for accurate and efficient model training, ensuring reliable and robust predictions.
"TensorFlow Image Classification Project" This project demonstrates image classification using TensorFlow. The CIFAR-10 dataset, consisting of 60,000 32x32 color images across 10 classes, is explored and analyzed. Key components include data loading, dataset characteristics, and a machine learning model built using the functional API.
predict where the patient will be discharged to before surgery
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