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Generative AI
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Generative AI
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chandadiya2004/README.md

Hi, I'm Diya Chanda 👋

Welcome to my GitHub profile!

👋 Hello World!

I'm Diya Chanda, an aspiring AI/ML Engineer, passionate about crafting intelligent solutions and building impactful projects. Currently pursuing B.Tech in CSE (AIML) at The Neotia University, I enjoy working on Machine Learning and Deep Learning, and I'm always eager to learn new technologies.

🔥 My Skills

🖥️ Programming Languages

📊 Machine Learning & Deep Learning

☁️ Databases & Cloud

🏆 Featured Projects

🔍 Project 🚀 Tech Stack 🔗 Repository
Car Price Prediction Streamlit, Python, Scikit-learn GitHub Repo
Diabetes Prediction Streamlit, Python, Scikit-learn GitHub Repo
Movie Recommendation System Streamlit, Python, Scikit-learn, Cosine-similarity GitHub Repo
Attendance Tracker Python, Tkinter, Pandas GitHub Repo
MNIST Digit Classification Python, Matplotlib, Seaborn, OpenCV, TensorFlow GitHub Repo
Gold Price Prediction Python, Sklearn, Streamlit GitHub Repo
caesar-cipher-app Python, pip, Streamlit GitHub Repo

🌐 Live Demos

Here are some live demos of my projects that you can explore:

🌱 What I'm Currently Learning

  • 🧠 Advanced Machine Learning Algorithms
  • 🌐 Deep Learning
  • 🚀 Cloud Deployment strategies (AWS, Render)

🎓 Research & Publications

📄 Paper 1: FruitQ-GradeX: Determining Fruit Quality and Grading with Explainable Deep Learning

Authors: Shibdas Dutta, Subhrendu Guha Neogi, Diya Chanda, Arpan Pramanik, Özgün Girgin, Enes Ladin Öncül
DOI: 10.1109/ICRITO66076.2025.11241706

A multi-task deep learning framework for simultaneous fruit classification and quality assessment using multi-headed CNN. Achieves 98% fruit classification and 99% quality detection accuracy with Grad-CAM interpretability, deployed via Streamlit.

📄 Paper 2: CropSense: Explainable Deep Learning Framework for Accurate Quality Detection in Solanaceous Crops

Authors: Shibdas Dutta, Subhrendu Guha Neogi, Shiladitya Chowdhury, Vikrant Chole, Arpan Pramanik, Diya Chanda
DOI: 10.1109/ICRITO66076.2025.11241535

Lightweight multi-headed CNN for potato and tomato quality classification. Achieves 99.9% crop classification and 98.5% quality detection accuracy with Grad-CAM visualizations, deployed via Streamlit for real-time precision agriculture.

🌍 Connect With Me

🎯 Fun Fact

"Machine learning is not magic; it's just math." 😎

Thanks for visiting my profile! Feel free to explore my repositories and connect with me. 🚀

Popular repositories Loading

  1. mnist-digit-classification mnist-digit-classification Public

    This repository demonstrates MNIST digit classification using deep learning. It includes a neural network model built with frameworks like TensorFlow to classify handwritten digits (0-9) from the M…

    Jupyter Notebook 1 1

  2. diabetes-prediction diabetes-prediction Public

    The Diabetes Prediction project utilizes machine learning techniques to determine the probability of an individual having diabetes based on various health metrics like age, BMI, and blood pressure.…

    Jupyter Notebook

  3. car-price-prediction car-price-prediction Public

    This project combines a Random Forest model with a Streamlit web app to predict car prices based on various factors such as make, model, year, mileage, and more. The overall goal is to create an in…

    Jupyter Notebook

  4. attendance-tracker attendance-tracker Public

    A Python GUI app using Tkinter for managing student attendance. It allows student registration, daily attendance tracking, and stores data in a CSV file. The app adds a new column for each date and…

    Python

  5. chandadiya2004 chandadiya2004 Public

    Config files for my GitHub profile.

  6. movie-recommendation-system movie-recommendation-system Public

    A Movie Recommendation System built using TfidfVectorizer and cosine similarity. The model processes a large dataset of movies and recommends similar movies based on a given input movie by analyzin…

    Python