Survey on machine leaning based game predictions

Abstract

Abstract In the world wide millions of people interested on games and competitive matches. The stakeholders stand for one team and produce the sponsorship to the players. Huge amount of money transferred from one hand to other hand. So that stakeholder wants to select a good players into his teams. Here Machine Learning based multi variant regression algorithms used to calculate the progress of each player based on previous datasets to predict the performance at on-going match. To extract the features from on-going match characterized with learned datasets by implementing the Support Vector Machine (SVM), Gaussian Fit-chime (GAU) and KNN algorithms which perform the optimal classification on trained datasets. Feature selection and game predictions are become critical analytical process. The performance of the model effected and produces the outcome based on the feature selection. In this process some irrelevant variables removed to reduce the burden of algorithms and input datasets dimensions. This process speed up the dataset learning using various algorithms to produce the game predictions. The machine learning models mostly preferred algorithms to implement in feature selection are Linear Regression, Decision Tree Regression, Random Forest Regression and Boosting Algorithm like Adaptive Boosting (AdaBoost) Algorithm. In this paper we discussed about how to predict the game score based on trained datasets using various algorithms on Machine Learning platform.