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Résumé : one of the central motivations behind NLP (Natural Language Processing) is distinguishing designs. Agreed a content report, errand of recognizing the setting is referred to be as theme order. Text classification is an important advance in the characteristic non-standard speech process. It is also performed utilizing totally different classification algorithms. Its cut back is wide utilized in text classification to perform classification on large live of text information. However, Map Reduce needed plenty of your time to perform the tasks thereby increasing latency and since the information is distributed over the cluster it builds time and so reducing process speed. Also, It utilizes a long queue of code. driven by this, we propose a basic nonetheless compelling machine learning methodology that uses a Naïve mathematician classifier for text information. In the Machine Learning approach, the classifier is made mechanically by learning the properties of classes from a collection of predefined coaching information. Hence, it will method complicated moreover, multi assortment information in dynamic things. Here we tend to propose a naïve Bayes classifier that scales directly with a range of indicators and information points that may be used for each binary and multiclass classification issue. We tend to enforce the conferred schemes victimization Machine Learning tool. The experiment results demonstrate the performance improvement within the classification technique. .This manuscript investigates the performance of three various classifiers to be specific Decision Tree, and Naive Bayes and KNN on a theme characterization commission (within six subject divisions). An Assessment is finished based on meticulousness, truthfulness, recall, and f1-attain based outcomes. Along with those three previously mentione