tf-idf-vectorization

✨ This is my university unit assignment on AI Algorithms. A movies recommendation website using TF-IDF and Cosine Simillarity.

Retrieve Information from Text Documents with TF-IDF model and dimention reduction with (Latent Semantic Indexing)LSI.

Full-featured information retrieval system that indexes and enables searching through the CACM (Communications of the ACM) corpus.

Understanding human emotions in text is crucial for many applications such as customer feedback analysis

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.

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

Recommendation Engine for SHL assessment product catalog based on user's job role, skills, etc.

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.

Plagiarism detector made by using NLP and TF-IDF which is able to differentiate between original and coied/plagiarised.

AniVerse: A comprehensive anime recommendation platform with a React/Next.js frontend, Node.js backend for user management, and a Python/Flask API for personalized recommendations.

A hybrid Steam game recommendation engine combining Content-Based Filtering, Collaborative Filtering, and MMR re-ranking for diverse, personalized suggestions.

Email Spam Detector - Machine Learning Model (Dockerized) that classifies messages as spam or not spam using a trained Naive Bayes model. The model is built using scikit-learn and is packaged inside a Docker container for easy deployment and usage.

Build a Web App called AI-Powered Recipe Recommender App

Tool for processing, categorizing, and searching through PDF documents and images using machine learning and OCR.

An end-to-end review analysis pipeline that processes 21,000+ real Amazon reviews using both traditional machine learning (TF-IDF + Logistic Regression) and modern AI (Google Gemini zero-shot classification, aspect extraction, and topic modeling) -wrapped in an interactive Streamlit dashboard. Built with Python, scikit-learn, Streamlit, and Gemini

System to recommend movies based on user-inputted movie

CADL Activites of NLP (PMC2421A).

AI-powered chatbot using NLP & Machine Learning (Logistic Regression) with TF-IDF vectorization and a Streamlit interface. Trained on predefined intents and logs conversations.

AI-powered Resume Screener Internship Project built with Python and Flask. The system uploads resumes, extracts skills using NLP, matches them with job descriptions and generates match percentage analysis. Technologies: Python, Flask, NLP, PyPDF2, pdfplumber, HTML, CSS, JavaScript.