R - Churn of Customers: is a model that uses a logistic regression associated with a threshold to predict which customers present the greater risk to be lost.
R- Data Cleaning + Multinomial Regression: is a model that presents data cleaning and a multinomial regression using package nnet to classify customers according to their level of loyalty.
R - Face Recognition: is a code to detect faces and objects in R.
R - Geolocation Brazil: is a file for geo-spatial localization, brazilian map.
R - Geolocation USA: is also a file for geo-spatial localization, USA map.
R - Geolocation World: is a file for geo-spatial localization, world map, zoom available, customizable icons.
R - Gradient Descent Logistic: is a model that performs a gradient descent to define a threshold for the sigmoid function in a Logistic Regression. Boosting was implemented and ROC curves compared.
R - H2O Deep Learning: is a Neural Network model developed to predict recommendations and word-of-mouth advertising.
R - Imbalanced classes is a model for employee churn, where features have no correlation with target variable and also there are imbalanced classes in the proportion 1/20. A logistic regression from scratch is applied, a hill climbing gradient is used to define the best threshold for the logistic function and after that, boosting was compared regarding AUC in a ROC plot.
Logistic Regression + Gradient Descent + Boosting is a model where features have no correlation with target variable. Logistic Regression with Gradient Descent was applied, and then Boosting.
R - MNIST: is a solution for the MNIST dataset, developed from scratch.
R - Markov Chains: is a simple visualization of Markov Chains and probabilities associated.
R - NeuralNet: is a Neural Network model developed to predict and classify word-of-mouth advertising.
R - Ridge Regression: is a model with Ridge Regularization made from scratch to prevent overfitting.
R - Deep Learning: is a Neural Network model with 2 hidden layers for prediction of a continuous variable.