Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

Basic Machine Learning implementation with python

NLP based approach to automatically categorize your bookmarks!

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

Sentiment Analysis & Topic Modeling with Amazon Reviews

A web app that classifies text as a spam or ham. I am using my own ML algorithm in the backend, Code to that can be found under machine_learning_section. For Live Demo: Checkout this link

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

A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomial-naive-bayes,logistic regression,svm,decision trees to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer,TFIDF Vetorizer,WordnetLemmatizer. It is implemented usi…

THIS PROJECT IS ABOUT TURKISH SENTIMENT ANALYSIS

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…

The template project for three way and five way sentiment classification

Phony News Classifier is a repository which contains analysis of a natural language processing application i.e fake news classifier with the help of various text preprocessing strategies like bag of words,tfidf vectorizer,lemmatization,Stemming with Naive bayes and other deep learning RNN (LSTM) and maintaining the detailed accuracy below

A data science project aimed at creating a machine learning-based email spam detection system. It effectively identifies and classifies emails into spam and non-spam categories, enhancing email security and user experience.

Undergraduate Final Year Project

The project has text vectorization, handling big data with merging and cleaning the text and getting the required columns while boosting the performance by feature extraction and parameter tuning for NN, compares the Performances through applied different models treating the problem as classification and regression both.

Identifying and distinguishing spam SMS and Email using the multinomial Naïve Bayes model.

Understand and Run Naive Bayes Algorithm on Dry Beans dataset

Spam SMS Detection Project implemented using NLP & Transformers. DistilBERT - a hugging face Transformer model for text classification is used to fine-tune to best suit data to achieve the best results. Multinomial Naive Bayes achieved an F1 score of 0.94, the model was deployed on the Flask server. Application deployed in Google Cloud Platform

A model that could accurately predict the Industry Domain for different start-ups and companies based on descriptions, titles and categories.