50 results for “topic:tf-idf-vectorization”
Full-featured information retrieval system that indexes and enables searching through the CACM (Communications of the ACM) corpus.
Retrieve Information from Text Documents with TF-IDF model and dimention reduction with (Latent Semantic Indexing)LSI.
Understanding human emotions in text is crucial for many applications such as customer feedback analysis
Interactive NLP-based AI system designed to manage cinema bookings and provide a seamless user experience.
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.
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.
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.
Recommendation Engine for SHL assessment product catalog based on user's job role, skills, etc.
No description provided.
Build a Web App called AI-Powered Recipe Recommender App
AI-powered chatbot using NLP & Machine Learning (Logistic Regression) with TF-IDF vectorization and a Streamlit interface. Trained on predefined intents and logs conversations.
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.
System to recommend movies based on user-inputted movie
Tool for processing, categorizing, and searching through PDF documents and images using machine learning and OCR.
Netflix Data Analysis and Recommendation Algorithm
Built an end-to-end text classification model using TF-IDF vectorization and models like Logistic Regression and SVM. Includes exploratory data analysis, model evaluation
Repository for the course Essentials in Text and Speech Processing Fall 2024
This Spam Detection model classifies emails as spam or not using TF-IDF and Logistic Regression. It includes evaluation metrics and sample tests. The repository provides the complete code and dataset for easy use and modification.
Market trends and investment insights
Spam Email Detector
AutoJudge is a machine learning system that predicts the difficulty class and score of programming problems using only textual descriptions, with a Flask-based web interface.
Predicting the topic of news articles
CADL Activites of NLP (PMC2421A).
One million StockTwits messages collected via API and stored in MongoDB, preprocessed with emoji/punctuation preservation, and classified using TF-IDF with Logistic Regression, SVM, Naïve Bayes, Random Forest, and MLP; extended with n-gram analysis, forecasting regressions, and event-study tests linking sentiment to short-term stock returns
The Fake News Detection system features a user-friendly Tkinter-based GUI that allows users to input a news article, title, and author. Users can select from six machine learning models to instantly classify the news as Real or Fake. The interface provides quick and interactive predictions, making it ideal for real-time demonstrations.
Production-ready sentiment analysis system using classical NLP (TF-IDF + Logistic Regression) with FastAPI and Streamlit. Part of my pre-transformer NLP project series.
A Machine Learning project to detect spam messages using Natural Language Processing (NLP), TF-IDF vectorization, SMOTE for imbalance handling, and a Logistic Regression classifier — all wrapped up in Streamlit web app.
Flask API + web UI to classify text as AI‑generated or human‑written using TF‑IDF and Logistic Regression. Includes CSV upload, training, prediction, and a model status panel with charts.
Sentiment analysis done over Customer Reviews using Various ML Models. It includes preprocessing, TF-IDF vectorization, model evaluation, and a lightweight script to load trained models and make predictions on custom inputs.
A professional Django-based influencer marketing platform with GraphQL API, AI-powered recommendations, and comprehensive brand-influencer collaboration features.