• Mrs. Jyoti Sharma
• Ms. Neelam Sharma
• Mr. L. P. Bhaiya

Data Analytics has emerged as an important domain in the digital space due to the explosion of tremendous volume of data by various sources such as social media, sensors and business organizations. Social media contribute in generation of huge varied data formats in various representations. WhatsApp has attracted large volume of users because of the easy chat conversations. In current scenario WhatsApp is used for small scale business, understanding the context in this chat text is important to identify the insights. WhatsApp data is left behind unnoticed as there exist no standard to represent in conventional machine understandable text. In this paper we are studying the various classification algorithms. Classification is the process of dividing the data to some groups that can act either dependently or independently. Our main aim is to show the comparison of the various classification algorithms like KNN, Naïve Bayes, Decision Tree, Random Forest and Support Vector Machine (SVM) with rapid miner and find out which algorithm will be most suitable for the users. The overall results for the all the algorithms are shown in Table1. From the results it is clear that, the K-NN algorithm is better than other algorithms on both the datasets.

"Comparative Analysis of Various Text Classification Algorithms on WhatsApp Chat Log", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.65-72, June-2019, Available :http://www.jetir.org/papers/JETIR1906792.pdf

"Comparative Analysis of Various Text Classification Algorithms on WhatsApp Chat Log", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp65-72, June-2019, Available at : http://www.jetir.org/papers/JETIR1906792.pdf