Machine Learning Techniques to Reduce Error in the Internet of Things

Over the years, Machine Learning (ML) techniques have been implemented to improve data processing speed and result in Internet of Things (IoT) devices. Some of the most common ML algorithms include Bayesian Statistics, Neural Networks, K Nearest Neighbors (KNN), Support Vector Machines (SVM), K means Clustering, Genetic Algorithms, decision trees, Principal Component Analysis (PCA), Random Forest, Regression Analysis. The aforementioned algorithms have been used for classification for various uses such as finding faults in the data, improve speeds by forming clusters of similar data points. ML can be used in varied fields such as to predict heart diseases, energy consumption, the state and location of IoT devices etc. This paper details the techniques and their usage in IoT.