NLP based approach to automatically categorize your bookmarks!

Forecasting weather Using Multinomial Logistic Regression, Decision Tree, Naïve Bayes Multinomial, and Support Vector Machine

This is the project that I created while working at TCS iON. The model is deployed on Heroku using Flask.

I have used Multinomial Naive Bayes, Random Trees Embedding, Random Forest Regressor, Random Forest Classifier, Multinomial Logistic Regression, Linear Support Vector Classifier, Linear Regression, Extra Tree Regressor, Extra Tree Classifier, Decision Tree Classifier, Binary Logistic Regression and calculated accuracy score, confusion matrix and…

Documentation of multinomial naivebayes from scratch.

AI chatbot designed for the coaching institute to respond to the students regarding the course details and deployed in the Flask web framework. Apart from that it can respond to the uses anything they ask.

Webapp para classificar comentários (positivos, negativos e neutros) advindos do Facebook usando Natural Language Toolkit (NLTK) + Django e Bootstrap na interface Web.

Text Classification using scikit-learn. Classify BBC articles.

Predict location of twitter users based on text contents (TF-IDF, chi-square)

The aim of this project is to help people to figure the disease they might have based on the symptoms in their bodies currently

Graduation Project - Sentiment Mining

Youtube Spam Comment Detector

AI chatbot designed for the coaching institute to respond to the students regarding the course details and deployed in the Flask web framework. Apart from that it can respond to the uses anything they ask.

UIUC MCS-DS Fall 2018 CS410 Project

Analyse of election machine texts from the Finnish parliament elections 2015

Big Data Project - SSML - Spark Streaming for Machine Learning

Email Spam detection using python and three different algorithms.

A document classifier built using Multinomial Naive Bayes model available in scikit-learn. Also contains scikit-learn packages built to be used in AWS lambda environment(2018.03)