AIAerobics-OpenPoseNet-CodeExamples

This is a collection of some of the important machine learning algorithms which are implemented with out using any libraries. Libraries such as numpy and pandas are used to improve computational complexity of algorithms

Final project of IBM's course https://www.coursera.org/learn/machine-learning-with-python on coursera

Diabetes Prediction is my weekend practice project. In this I used KNN Neighbors Classifier to trained model that is used to predict the positive or negative result. Given set of inputs are BMI(Body Mass Index),BP(Blood Pressure),Glucose Level,Insulin Level based on this features it predict whether you have diabetes or not.

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This MATLAB code implements the binary Grass hopper optimization algorithm to select the features and train with KNN

Insurance claim fraud detection using machine learning algorithms.

simple implementation of machine learning algorithm in pyhton

KNN using brute force and ball trees implemented in Python/Cython

sentiment-analysis,document-classification,svm,logistic-regression,rcnn-text-classification, cnn-text-classification, lstm-text-classification, naive-bayes-classifier, sklearn-classify, pytorch

This project involves training of Machine Learning models to predict the Heart Failure for Heart Disease event. In this KNN gives a high Accuracy of 89%.