[SIGE-MII-UGR-2016-17] Competición en Kaggle: Titanic
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Updated
Apr 28, 2017 - R
[SIGE-MII-UGR-2016-17] Competición en Kaggle: Titanic
Experiments on Porto Seguro's Safe Driver Prediction Challenge
Bank Precision Marketing Solutions-- using Logistic Regression and Tree Algorithms
A binary classification task: predicting the direction of increase and decrease in US crude oil inventories with SVM
Creating simple ANN with the help of Keras library for binary-classification
Convolutional neural network classifying dogs and cats
Several binary classifiers based on data preprocessed with K-mers
Maximizing Bank's Profitability
Using machine learning methods to predict demand for bike sharing.
Credit Risk Modelling For Dummies: But With Fewer Dummies
Predicts which customers are at high risk of cancelling the subscription to a service, based on their behaviour.
Creating a model to predict employee retention, aiding HR in risk identification and retention strategies. Emphasizing accuracy, precision, recall, and F1 score. Encompasses data collection, preprocessing, feature engineering, model training, and evaluation stages.
Binary classification for tabular data.
Binary Classification in R and application to classify patients with diabetes
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