2003HARSH
Aspiring Data Scientist | Artificial Intelligence | Machine Learning | Deep Learning | Natural Language Processing | Generative AI | RAG | Statistics
Pinned Repositories
2003HARSH
Hi , This is my profile
2003HARSH.github.io
Welcome to my portfolio! Here you'll find a collection of my projects and work experience in the field of data science and artificial intelligence. Feel free to explore the projects and reach out if you have any questions or collaboration opportunities.
AI-Guesses-the-movie
An AI-powered application that can guess movie titles based on plot summaries. Built using LangChain, Google Palm LLM, CSVLoader, RetrievalQA, Google Palm Embeddings, and FAISS. Deployed on Streamlit for an interactive user experience, allowing you to enter a plot summary and receive a predicted movie title.
AutoML
"🚀 Build ML models effortlessly! Our user-friendly platform empowers beginners with no ML background. Features include drag-and-drop functionality, pre-built templates, AutoML, and visual model representation. Learn, create, and deploy with real-time feedback. Join our supportive community! 🌐 #MachineLearning #NoCodeML"
BCA-Result-Analysis
BCA Result Analysis: 📊 Dive into student performance trends with data-driven insights. Clean, visualize, and analyze data for informed decisions in education. 🎓💡
Blog-Generation-using-Llama2
The Blog Generation project uses advanced AI technologies like Llama 2, LangChain, and Hugging Face to create custom blog content. With Streamlit, users can input a topic, word count, and audience type to generate blogs quickly and efficiently. The project combines the power of LLms with a simple, interactive interface for easy content creation.
Book_Recommender_System
This repository contains a collaborative filtering-based book recommender system built with Python Flask. 📚✨ The system utilizes cosine similarity to suggest books based on user preferences and historical data. 📊🔍
Chat-with-databases-using-RAG
"Chat with Databases using RAG" is a cutting-edge project that seamlessly integrates natural language inputs with database interactions. By leveraging advanced techniques like RAG and few-shot learning, it generates SQL queries from plain text and retrieves human-like responses from the database, revolutionizing the way we interact with data.
Document-QnA-using-Llama3-and-Groq
Document QnA is a webapp that lets users upload multiple documents and ask questions about their content. It uses Llama3, Groq API, LangChain, FAISS, and Google Palm Embeddings to identify relevant documents and provide answers with page numbers. The Streamlit interface ensures easy and efficient use.
Sentiment-Analysis-on-movie-reviews
🎬 Analyze movie reviews sentiment in real-time with "Sentiment Analysis on Movie Reviews using Word2Vec"! Powered by advanced NLP and deployed using Streamlit, this app categorizes reviews as positive or negative. Perfect for film enthusiasts and industry professionals! 🍿📊
2003HARSH's Repositories
2003HARSH/Document-QnA-using-Llama3-and-Groq
Document QnA is a webapp that lets users upload multiple documents and ask questions about their content. It uses Llama3, Groq API, LangChain, FAISS, and Google Palm Embeddings to identify relevant documents and provide answers with page numbers. The Streamlit interface ensures easy and efficient use.
2003HARSH/2003HARSH
Hi , This is my profile
2003HARSH/2003HARSH.github.io
Welcome to my portfolio! Here you'll find a collection of my projects and work experience in the field of data science and artificial intelligence. Feel free to explore the projects and reach out if you have any questions or collaboration opportunities.
2003HARSH/AI-Guesses-the-movie
An AI-powered application that can guess movie titles based on plot summaries. Built using LangChain, Google Palm LLM, CSVLoader, RetrievalQA, Google Palm Embeddings, and FAISS. Deployed on Streamlit for an interactive user experience, allowing you to enter a plot summary and receive a predicted movie title.
2003HARSH/AutoML
"🚀 Build ML models effortlessly! Our user-friendly platform empowers beginners with no ML background. Features include drag-and-drop functionality, pre-built templates, AutoML, and visual model representation. Learn, create, and deploy with real-time feedback. Join our supportive community! 🌐 #MachineLearning #NoCodeML"
2003HARSH/BCA-Result-Analysis
BCA Result Analysis: 📊 Dive into student performance trends with data-driven insights. Clean, visualize, and analyze data for informed decisions in education. 🎓💡
2003HARSH/Blog-Generation-using-Llama2
The Blog Generation project uses advanced AI technologies like Llama 2, LangChain, and Hugging Face to create custom blog content. With Streamlit, users can input a topic, word count, and audience type to generate blogs quickly and efficiently. The project combines the power of LLms with a simple, interactive interface for easy content creation.
2003HARSH/Book_Recommender_System
This repository contains a collaborative filtering-based book recommender system built with Python Flask. 📚✨ The system utilizes cosine similarity to suggest books based on user preferences and historical data. 📊🔍
2003HARSH/Chat-with-databases-using-RAG
"Chat with Databases using RAG" is a cutting-edge project that seamlessly integrates natural language inputs with database interactions. By leveraging advanced techniques like RAG and few-shot learning, it generates SQL queries from plain text and retrieves human-like responses from the database, revolutionizing the way we interact with data.
2003HARSH/Sentiment-Analysis-on-movie-reviews
🎬 Analyze movie reviews sentiment in real-time with "Sentiment Analysis on Movie Reviews using Word2Vec"! Powered by advanced NLP and deployed using Streamlit, this app categorizes reviews as positive or negative. Perfect for film enthusiasts and industry professionals! 🍿📊
2003HARSH/CUET_MCA_scores
The Automated CUET MCA Score Checker simplifies score checking for CUET aspirants. This online tool matches your answers with the original sheet, providing quick and accurate scores. To use it, save your CUET answersheet as an HTML file and upload it. No installation is needed, making it a convenient solution for students. Contributions are welcome
2003HARSH/Encryptix
During my Data Science internship at Encryptix, I tackled three distinct tasks: predicting Titanic survival rates, classifying Iris flowers, and detecting credit card fraud. Each project provided valuable hands-on experience in diverse aspects of data analysis and machine learning.
2003HARSH/Laptop-price-predictor
"Laptop Price Predictor 🎮🔍 - ML-driven app using Random Forest Regressor. Predict prices based on features like RAM, memory, and processor. Achieved R2 score of 90%. Built with Scikit-learn, Pandas, and Numpy. #MachineLearning #DataScience #Streamlit 🚀"
2003HARSH/Next-Word-Prediction-using-RNN-LSTM-GRU
The Next Word Predictor using LSTM is a project that builds a text prediction model using Long Short-Term Memory (LSTM) neural networks. It predicts the most likely next word in a given sequence, useful for text composition and natural language processing tasks. The project allows customizable training and includes an interactive script for testing
2003HARSH/OpenAI-Whisper-Automated-Hindi-Speech-Recognition
This project adapts OpenAI's Whisper model to create an automated speech recognition system for Hindi. The goal is to accurately transcribe Hindi audio into text for applications like transcription, voice commands, and accessibility. By fine-tuning the model, the project aims to improve recognition accuracy and performance in Hindi-language context
2003HARSH/Sentiment-Analysis-using-BERT
"Sentiment Analysis using BERT" utilizes advanced NLP with BERT for precise sentiment analysis, surpassing traditional methods like Word2Vec. Gain valuable insights into customer feedback and social sentiment effortlessly. 🚀💬 #NLP #SentimentAnalysis #BERT
2003HARSH/Sms-Spam-Classifier
ML model for spam detection using Naive Bayes & TF-IDF. Achieved 0.98 accuracy. Utilized Scikit-learn, Numpy, nltk. Implements NLP concepts. Explore precise spam classification effortlessly. #MachineLearning #SpamDetection 🚀✉️📱