International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 02 | Feb 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 827 Analysis of Multiple Classification algorithms using Real Time Twitter Data Sharmila Bhargava 1 , Dr. Rachna Dubey 2 , Priyanka Gupta 3 1 M.Tech Scholar, Department of Computer Science & Engineering, Lakshmi Narain College of Technology& Excellence Bhopal (M.P), India. 2 Professor & Head, Department of CSE, Lakshmi Narain College of Technology& Excellence, Bhopal (M.P), India. 3 Assistant Professor, Department of CSE, Lakshmi Narain College of Technology& Excellence, Bhopal (M.P), India. ---------------------------------------------------------------------***---------------------------------------------------------------------- ABSTRACT:- The spreading amount of data usually generates interesting demand for the data analysis tools that spot regularities in these data. Data mining has turned up as great domain that contributes mechanism for data analysis, to find out the hidden knowledge, and self-ruling decision making in many operation domains. Supervised machine learning is using to find out the search for algorithms that reason from clearly supplied instances to produce general interpretation, which then makes predictions about future scenario or events. In other words, the goal of supervised learning is to make a small model of the distribution of class labels (distribution or classification) in terms of finding (predictor) features. The resulting classifier is then used to assign class labels (attributes) to the testing instances where the values of the predictor (attributes or properties) features are known, but the value of the class label is unknown. This paper explains various supervised machine learning classification techniques. In this paper, we have discussed the about the classification algorithm which are available today, how they works, and what are their advantages and disadvantages. The algorithms which we will discuss are Naïve Bayes, SVM, random forest, decision tree and logistic regression. Keywords: Classification, Naïve Bayes, Random forest, Multiple regression dependent variable, independent variables, predictor variable, response variable I. INTRODUCTION Social Predictive modelling can be explained in terms of mathematics to find out the goal there is relationship between a target, response, or “dependent” variable and various predictor (attributes) or “independent” variables with the goal in mind of measuring future (attributes) values of those predictors and inserting them into the mathematical relationship to predict future values of the target variable, it is mandatory to give some measure of mistrust for the predictions, typically a prediction interval that gives some assigned level of confidence like some percentage value i.e (95%).Regression analysis establishes a relationship between a dependent o r outcome variable and a set of predictors. Regression, as a data mining technique, is supervised learning. Supervised learning partitions the database into training and validation data. The techniques used in this research were simple linear regression and multiple linear regressions. Some divergence between the uses of regression in statistics verses data mining are: in statistics the data is a sample from a (Data Storage) population, but in Data Mining the data is taken from a large database (e.g. 1 million records). Also in stats the regression model is created from a given sample, but in Data Mining the regression model is created from a part of the data (training data). Predictive (To guess) analytics enclose a number of mechanism from stats, data mining and game theory that find out current and historical facts to make guesses about future events. The variety of techniques is sometimes divided in three ways: predictive models, descriptive models and decision models. Predictive models explain for sure relationships and some patterns that usually edge to a certain behaviour, point to fraud, predict system failures, and so many. By explaining the explanatory variables, we can find out or predict results in the dependent variables. Descriptive models explain for creating partition or segment; generally it is used to classify (find out) customers based on for instance (behaviour of customers in different locations) socio-demographic characteristics, life cycle, profits, required product and many more. Where predictive models focus on a specific (individual) event or behaviour, descriptive models identify as many different (general) relationships as possible. Decision models explain to find out optimization ways to predict (find out) results of decisions. This branch of predictive analytics apply in operations research, including areas such as resource optimization in networking & many places, route planning in many industries . 1.1 Data Mining Techniques It is used for decision making in a business is very poor even though data storage grows exponentially. Data mining also known as Knowledge discovery or finding some important information in some scenario. The Knowledge extracted allows predicting the behaviour and future behaviour. This allows the business owners to take positive, knowledge driven decisions. Data mining is