Name: Debadri Sengupta
Type: User
Company: Jadavpur University
Bio: C++, Python coder. Does cool stuff with data. JU student.
Location: Kolkata, West Bengal, India
Blog: https://www.linkedin.com/in/debadri-sengupta-61665a14b/
Debadri Sengupta's Projects
"20 newsgroups" dataset - Text Classification using Multinomial Naive Bayes in Python.
An attempt to learn general cs skills, and refine my web dev and ml knowledge.
500 AI Machine learning Deep learning Computer vision NLP Projects with code
A personal project for product similarity and image similarity based on text features and image features. Libraries involved: Scikit Learn, Numpy, Pandas, Scikit-learn, Matplotlib, NLTK, Beautiful Soup.
A curated list of awesome Python frameworks, libraries, software and resources
Public dataset benchmarks used for measuring the performance of MindsDB.
Community-made poetry about infrastructure
Classifying models of electric guitar using MobileNet V2 architecture
Descriptive project segmenting customers of a wholesale retailer using GMM etc.
NLP datasets
Config files for my GitHub profile.
Example Repo for the Udemy Course "Deployment of Machine Learning Models"
Used PCA to classify face data obtained from LFW
Data Engineering with Google Cloud Platform, published by Packt
Fork and Create a Pull Request
Hexagon is a microservices toolkit written in Kotlin. Its purpose is to ease the building of services (Web applications, APIs or queue consumers) that run inside a cloud platform.
Clustering movie raters on the basis of their tastes alongwith identifying most rated movies and users.
Personal project for prediction of a software as a malware based on ASM and bytes file features. Libraries used: Scikit-learn, Matplotlib, Seaborn, Numpy, Pandas, etc
Predictive AI layer for existing databases.
Official documentation website of MindsDB
Predicting possible donors in a charity project
My take on Kaggle Competition Titanic Survival prediction using classification model.
Personal project for prediction of movies based on user-user similarity and movie-movie similarity matrix. Libraries used: Surprise, Scikit-learn, Matplotlib, Seaborn, Numpy, Pandas, XGBoostetc
Your regression training app, without code