Kaggle Machine Learning Competition Project : To classify activities into one of the six activities performed by individuals by reading the inertial sensors data collected using Smartphone.

Using the ES algorithm to train RBF-network and implement regression and classification algorithms on the dataset

evolutionary-based approach in RBF neural network training

SPPU - BE ENTC (2015 Pattern) - Elective III

Using data mining techniques to predict if the organization is prone to bankruptcy using the data with 250 records and 6 nominal attributes per record. Machine learning techniques used: Linear and non-linear SVM, Decision Tree Classifier, Gaussian Naive Bayes.

Two pattern classification problem using Radial Basis Functions (RBF) Neural Networks, with center vectors selected via self-organizing map (SOM) neural networks.

Persian text simplification using lexical simplification

This repository describes the application of numerical Collocation method in Machine Learning as a Supervised learning algorithm