An R package for Private Evaporative Cooling feature selection and classification with Relief-F and Random Forests
-
Updated
May 20, 2020 - R
An R package for Private Evaporative Cooling feature selection and classification with Relief-F and Random Forests
The project concerns an international e-commerce company* based in the USA who want to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers.
This repository contains the necessary scripts to derive off-target models through (1) A neural network framework based on Keras and Tensorflow (2)An autmomated machine learning framework based on AutoGluon
Applying predictive models on "Framingham Heart Study" dataset.
comparing many classification algorithms (Naive Bayes, Logistic Regression, Support Vector Machine) on Spiral Data with tuning SVM's parameters with mentioning Decision Trees and K-Nearest Neighbors implementation.
Exploratoy Data Analysis,Logistic Regression,Penalized Logistic Regression (LASSO), LDA, Decision Trees, Bagging, Random Forest
A multi-label classification model for classifying comments from Wikipedia talk page edits into different types of toxicity(insult, threat, identity hate, etc).
The project focused on building a joint computational toolbox for credit card risk analysis. It consists of removing irrelevant attributes from dataset to get meaningful model while building decision trees from the possible subset or combination of attributes and further applying these pools of trees as an initial seed to Multi - Objective Evolu…
Implementation of data exploration using ggplot, data visualization and machine learning algorithms for predictive modelling.
Add a description, image, and links to the classification-algorithims topic page so that developers can more easily learn about it.
To associate your repository with the classification-algorithims topic, visit your repo's landing page and select "manage topics."