iamjr15,Jigyansu Rout,iamjr015,github

Hello 01110111 01101111 01110010 01101100 01100100 👋 !

Hi, I'm Jigyansu Rout, a web and app developer, with a love for greatly designed products. I take great care in the experience, architecture, and code quality of the things I build, along with a deep obsession with elegant interfaces.

🔭 I’m currently working on a ton of side projects and a startup maybe :P
🌱 I’m currently learning Machine Learning, Data Science and Blockchain stuff!
💬 Talk to me about football, crypto/stocks, great design, new products and moonshot projects
📫 How to reach me: if you have an idea or think we can collaborate on something, drop me a text through any of the mediums below!

🤝Let's connect !

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Jigyansu Rout's Projects

21-days-of-code

21 Days of Code Challenge from 18 December 2021 to 8 January 2022

bank-loan-approval-prediction

Models bank loan applications to classify and predict approval decisions using customer demographic, financial, and loan data. Applies machine learning algorithms like logistic regression and random forest for enhanced automation.

chatbot_tflearn

This is a chat bot, and it has a intents.json, which consists of the training data.

ensemble-ai-text-detection

This project detects AI-generated text using an ensemble of classifiers: Multinomial Naive Bayes, Logistic Regression, LightGBM, and CatBoost. It includes robust data preprocessing, model development, and evaluation, ensuring accurate identification of AI-generated content from a diverse text dataset.

facenet-recognition-pytorch

Face recognition using Facenet, SVM, MTCNN and PyTorch. Face detection, feature extraction and training for custom datasets.

hackathon_5.0

UPES CSI Hackathon 5.0

jos

PKU OS Practice/mit 6.828 JOS labs

lokdhaba_js

Development repository for lokdhaba in javascript

makemyday

MakeMyDay is a general utility multipurpose application for Android. It was built using App Inventor for my entry for Google Code to Learn 2017.

ml-heart-and-kidney-disease-prediction

This machine learning project focused on predicting chronic kidney disease and heart issues by training and evaluating classification models including SVM, Logistic Regression on medical datasets. The models were assessed using accuracy, precision/recall metrics to determine the best approach, along with analysis of performance factors.

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