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, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

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

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.

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

Sentiment Analysis & Topic Modeling with Amazon Reviews

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 using LSTM and Word Embeddings to gain accuracy of 97.84%.

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

Undergraduate Final Year Project

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 ROC(Receiver Operating Characteristic) and AUC(Area Under Curve) and finally shown how they are classifying the tweet in positive and negative.

The template project for three way and five way sentiment classification

A python based machine learning model,which uses algorithms like the Naive Bayes algorithm and Decision tree classifier algorithm,to predict whether a posted job is fake or real.

Understand and Run Naive Bayes Algorithm on Dry Beans dataset

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

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

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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.

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

This is a program I created in Jupyter Notebook to classify tweet data on social media Twitter using the Multinomial Naive Bayes algorithm.

Legal Up recommends suitable lawyers⚖️ to clients based on concise case descriptions🔍 using advanced algorithms, ensuring clients find the right legal expertise. 💼

An efficient text classification pipeline for email subjects, leveraging NLP techniques and Multinomial Naive Bayes. Easily preprocess data, train the model, and categorize new email subjects. Ideal for NLP enthusiasts and those building practical email categorization systems using Python.

THIS PROJECT IS ABOUT TURKISH DICTIONARY(RULES) BASED SENTIMENT ANALYSIS

In this repository I have utilised 6 different NLP Models to predict the sentiments of the user as per the twitter reviews on airline. The dataset is Twitter US Airline Sentiment. The best models each from ML and DL have been deployed. It employs text preprocessing,

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.

Text Classification using scikit-learn. Classify BBC articles.

Implementation of Gaussian and Multinomial Naive Bayes Classifier using Python, Pandas, and NumPy without using any off the shelf library usi