I developed a sophisticated ML model using LLMs to predict user preferences in chatbot interactions.implemented a comprehensive data preprocessing pipeline,including feature extraction and encoding,to optimize performance. conducted extensive hyperparameter tuning and evaluation, enhancing accuracy and in AI-driven conversational systems.

Learn Hub is a web application that helps to learn any technology in a structured & organized way. The idea is to enhance Internet learning by producing search query results based on higher accuracy and relevance of the content, instead of traditional ranking methods.

Build a Web App called AI-Powered Recipe Recommender App

In this notebook we analyze and classify news articles using machine learning techniques, including Logistic Regression, Naive Bayes, Support Vector Machines, and Random Forests. Explore text vectorization and NLP for accurate news categorization.

Interactive NLP-based AI system designed to manage cinema bookings and provide a seamless user experience.

Market trends and investment insights

The purpose of this project is to build a machine learning model to classify SMS messages as either "spam" or "ham" (not spam). Using TF-IDF vectorization and LinearSVC, it reads an SMS dataset, transforms text data into numerical features, and trains a model to distinguish between spam and ham. The "SMSSpamCollection" dataset has labeled messages.

AniVerse is an anime recommender system which recommends user based on explicit feedback from user.

Amazon product review sentiment analysis using Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes (NB) multiclass as classifier models, Synthetic Minority Oversampling Technique (SMOTE) as feature oversampler, and TF-IDF vectorization as feature, Synthetic Minority Oversampling Technique (SMOTE) as oversampler, and k-fold CV.

Codebase ideation (for better understanding in Django way) for LLM without using pre-trained models, with custom embeddings (TF-IDF or Word2Vec), FAISS for vector storage.