Tshe Speculative Study On Machine Learning Algorithm For An Efficient Classification Of Text Document.

Abstract

Document Classification Is A Problem In Information Science. The Text Documents Are Assigned To
One Or More Classes Or Categories For Classification. Classifying Text Documents Using Various
Classifier Algorithms And Are Compared With Different Metrics. Based On The Experiment It Is
Demonstrated That Svm Classifier Model Proves To Be Better Than Other Models, Whereas While
Comparing The Accuracy Random Forests Model Proves To Have Good Accuracy Score But The
Time Taken By The Training Phase Is Not Considerable. For This Experiment Three Classes Have
Been Used Among That The "Administrative" Category Is The Most Frequent Category As Its Count
Value Is Greater And Also It Confirms That There Are No Missing Values. The Accuracy Score Of
Random Forest And Non Linear Svm Classifiers' Is Quite Appealing The Magnitude Is High When
Considering The Training Time. The Chi-Square Test Is Applied To Select Less Number Of Features
And The Algorithms Are Applied, And It Demonstrated That The Accuracy Rate Remains The Same.