Natural Language Processing Projects

Optimize document retrieval with GPT-4, using vector indexing and chunk size tuning for fast, accurate real-time and real-world AI search insights.

Corrective Retrieval-Augmented Generation (RAG) enhances response accuracy by dynamically adjusting the retrieval process, ensuring relevant, up-to-date information.

Improve document retrieval with contextual overlapping windows, PDF processing, text chunking, FAISS, and OpenAI embeddings for more coherent search results.

Improve document retrieval with OpenAI's GPT-4 and FAISS, generating context-based questions and accurate answers for efficient processing and information extraction from PDFs.

Forecast healthcare call volumes using Prophet with enhanced features from Cesium. Improve accuracy with statistical features and seasonal patterns.

This project forecasts stock market volatility using ARCH and GARCH models, helping traders and investors predict market changes and manage financial risks effectively.

Create a book recommendation system with machine learning using TF-IDF, KMeans clustering, and cosine similarity for accurate, data-driven suggestions

Multi-class text classification using RNN and LSTM for analyzing customer complaints, and providing real-world business insights and solutions.

Develop a hybrid recommendation system using collaborative and content-based filtering with LightFM for personalized product recommendations based on customer behavior.

Classify mental health statements using NLP and ML techniques. Includes preprocessing, TF-IDF, XGBoost, Random Over-Sampling, and real-world prediction applications.

Everyone understands the fact that a language is made up of words. Moreover, combining them appropriately is essential in many intricate activities such as natural language processing (NLP) and machine learning.

Get hands-on experience in NLP with a project focused on text processing and supervised classification.

Analyze customer reviews with NLP, sentiment analysis, topic modeling, and K-Means clustering to uncover trends, and insights and improve business strategies.

Understand how CBOW, Skip-Gram, and FastText models capture word meanings, visualize embeddings, and evaluate model performance for various NLP tasks.

Question Answering system built on Pegasus+SQuAD for accurate responses. Optimized for high accuracy and user experience across applications

The Semantic Search System with Transformers and Faiss vectors can speed up and improve the accuracy of your searches. Find out about advanced information retrieval and personalized suggestions for a wide range of businesses.

Advanced transformer models and tokenization methods can be used to automate the summarization of documents. Quickly make high-quality abstracts to help people find knowledge and make decisions.

The goal of this project is to create a customer support chatbot by using advanced methods for natural language processing.