Natural Language Processing Projects
Explore the Future with Our AI Projects
Welcome to our AI Projects section, where innovation meets intelligence!
Optimizing Chunk Sizes for Efficient and Accurate Document Retrieval Using HyDE Evaluation
Optimize document retrieval with GPT-4, using vector indexing and chunk size tuning for fast, accurate real-time and real-world AI search insights.
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Corrective Retrieval-Augmented Generation (RAG) with Dynamic Adjustments
Corrective Retrieval-Augmented Generation (RAG) enhances response accuracy by dynamically adjusting the retrieval process, ensuring relevant, up-to-date information.
Enhancing Document Retrieval with Contextual Overlapping Windows
Improve document retrieval with contextual overlapping windows, PDF processing, text chunking, FAISS, and OpenAI embeddings for more coherent search results.
Document Augmentation through Question Generation for Enhanced Retrieval
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.
Time Series Analysis with Facebook Prophet Python and Cesium
Forecast healthcare call volumes using Prophet with enhanced features from Cesium. Improve accuracy with statistical features and seasonal patterns.
Build ARCH and GARCH Models in Time Series using Python
This project forecasts stock market volatility using ARCH and GARCH models, helping traders and investors predict market changes and manage financial risks effectively.
Build A Book Recommender System With TF-IDF And Clustering(Python)
Create a book recommendation system with machine learning using TF-IDF, KMeans clustering, and cosine similarity for accurate, data-driven suggestions
Build Multi-Class Text Classification Models with RNN and LSTM
Multi-class text classification using RNN and LSTM for analyzing customer complaints, and providing real-world business insights and solutions.
Build a Hybrid Recommender System in Python using LightFM
Develop a hybrid recommendation system using collaborative and content-based filtering with LightFM for personalized product recommendations based on customer behavior.
Sentiment Analysis for Mental Health Using NLP & ML
Classify mental health statements using NLP and ML techniques. Includes preprocessing, TF-IDF, XGBoost, Random Over-Sampling, and real-world prediction applications.
Skip Gram Model Python Implementation for Word Embeddings
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.
NLP Project for Beginners on Text Processing and Classification
Get hands-on experience in NLP with a project focused on text processing and supervised classification.
Topic modeling using K-means clustering to group customer reviews
Analyze customer reviews with NLP, sentiment analysis, topic modeling, and K-Means clustering to uncover trends, and insights and improve business strategies.
Word2Vec and FastText Word Embedding with Gensim in Python
Understand how CBOW, Skip-Gram, and FastText models capture word meanings, visualize embeddings, and evaluate model performance for various NLP tasks.
Question Answer System Training With Distilbert Base Uncased
Question Answering system built on Pegasus+SQuAD for accurate responses. Optimized for high accuracy and user experience across applications
Semantic Search Using Msmarco Distilbert Base & Faiss Vector Database
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.
Document Summarization Using Sentencepiece Transformers
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.
Customer Service Chatbot Using LLMs
The goal of this project is to create a customer support chatbot by using advanced methods for natural language processing.