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Hi, I'm Anurag šŸ‘‹āœØ

šŸ’« About Me:

I'm currently a junior at Guru Gobind Singh Indraprastha University in New Delhi, working towards a Bachelor of Technology in Artificial Intelligence & Data Science. I have a strong passion for data science, Machine Learing, Generative AI LLMs, Python programming, and contributing to open source projects. I am always eager to learn and help in any way I can.

šŸ”­ Iā€™m working on some personal projects right now.
šŸ‘Æ Iā€™d like to work with others on cool machine learning projects.
šŸ¤ Iā€™m looking for help to find an internship.
šŸŒ± Iā€™m currently learning more about integrating deep learning with LLMs.
šŸ’¬ Ask me about: Artificial Intelligence, Data Science & Machine Learning.
āš” Fun fact: AI revolution has started and we are not even prepared for it fully!

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šŸ’» Technical SKills:

  • Programming Language: Python
  • Machine Learning Skills: Neural Networks, APIs, Deep Learning, Computer Vision, CNN, LLM, Langchain
  • Libraries: Numpy, Pandas, Seaborn, Scikit-Learn, TensorFlow, Keras, Pytorch, LLM, OPENAI, Gradio, Streamlit
  • Web Devlopment: HTML, CSS
  • Software & Utilities: VS Code, Anaconda, Jupyter, Basics of MS Office

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itsANURAG's Projects

custom_yolov9_room_cleanliness_detector icon custom_yolov9_room_cleanliness_detector

This project leverages a custom-trained YOLOv9 model to detect objects related to room cleanliness. Built with Gradio, it provides an interactive web interface where users can upload images and adjust detection parameters. The app returns images with annotated bounding boxes around detected objects, aiding in room organization tasks.

driver-drowsiness-detection-system icon driver-drowsiness-detection-system

"Driver Drowsiness Detection System" is a comprehensive repository aimed at enhancing road safety by leveraging cutting-edge technology. Our system employs state-of-the-art computer vision and machine learning techniques to detect signs of driver drowsiness in real-time, thus preventing potential accidents caused by driver fatigue.

exploratory_data_analysis_on_house_price_prediction_dataset icon exploratory_data_analysis_on_house_price_prediction_dataset

Comprehensive EDA on House Price Dataset. This project includes feature engineering, feature selection, and visualizations to uncover insights and prepare data for advanced regression techniques. Data sourced from Kaggle's House Prices competition.

friday3_a_chatbot_powered_by_openai icon friday3_a_chatbot_powered_by_openai

friday2 is a user friendly Streamlit app which acts as a interactive chatbot powered by OPENAI and used gpt-model. With availability of chatgpt already in market, friday provides additional features like "option to use mic rather than write","get the response translated in diff languages" etc

friday_a_chatbot_powered_by_openai icon friday_a_chatbot_powered_by_openai

TrienGPT-Streamlit-App is a user-friendly web application powered by OpenAI's GPT-3.5 model, TrienGPT. It simplifies language tasks with two key features: text translation and text-to-speech conversion. Translate text between multiple languages and generate lifelike audio output with customizable accents.

handwritten_digit_recognition_using_cnn icon handwritten_digit_recognition_using_cnn

"Welcome to the 'Handwritten Digit Recognition using CNN' project. My goal is to harness the power of Convolutional Neural Networks (CNNs) to accurately identify handwritten digits. With a pre-trained model on the MNIST dataset, you can jump right into digit recognition or fine-tune the model to suit your specific needs."

invoice-data-extraction-using-machine-learning icon invoice-data-extraction-using-machine-learning

Develop a Python application to extract key information from invoices using machine learning. The project involves training a model, optimizing it for deployment, and running it on a client desktop. The solution should handle various invoice formats in English, Dutch, and French without hardcoded labels.

lane_detection_for_autonomous_vehicles icon lane_detection_for_autonomous_vehicles

This repository contains a lane detection pipeline using OpenCV and a Tkinter-based GUI for video comparison. The tool processes input videos to detect and highlight lane lines, and displays both the original and processed videos side by side. Features include curvature calculation, speed control, and dark mode.A project leveraging futuristic ideas

optical_text_extractor_from_image icon optical_text_extractor_from_image

šŸŽ‰ EasyOCR Streamlit App: Extract text from images effortlessly using EasyOCR and Streamlit. Upload images in PNG, JPG, or JPEG formats, and let the AI do the rest! Built with Python, PIL, and NumPy, hosted on Hugging Face Spaces. Perfect for quick and accurate OCR needs. Made with ā¤ļø by @itsANURAG. šŸš€

spotify_playlist_recommender icon spotify_playlist_recommender

This code is a Python script that uses the Streamlit library to create a simple web application for generating Spotify playlists based on the top songs in the US for specific years or a range of years.

voice-chat-enabled-rag-chatbot-with-real-time-internet-access icon voice-chat-enabled-rag-chatbot-with-real-time-internet-access

An open-source project that uses cutting-edge NLP models and real-time web search to provide dynamic voice query responses. Features include speech-to-text with Nemo, text generation with Mistral-7B, DuckDuckGo search integration, and text-to-speech with edge-tts, all in a user-friendly Gradio interface.

youtube_video_ai_assistant- icon youtube_video_ai_assistant-

A Gradio-powered web app that answers questions about YouTube videos using their transcripts. It extracts the video transcript, splits it into chunks, creates embeddings with Google Generative AI, and performs similarity searches. Responses are generated using the Gemini Pro language model.

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