Pranjal Bisht's repositories
NLP-Based-Price-Prediction
Use Machine Learning / Natural Language Processing to Predict Price of Product listed and get rmsle < 0.5 and Create a web app to take inputs and display the predicted price.
Geotagged-Web-Tweets-
GIS based Events detection using geo-tagged tweets web scrapping and clustering using machine learning techniques
Image_Processing
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
machine-learning-interview
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
ML-Notebooks
:fire: A series of code examples for all sorts of machine learning tasks and applications.
Movies-Recommender
Movies Recommender website using Content based filtering
applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
CovidTracker-Web-Application
This website contains all the information related to Covid-19. You can also donate to the PM care fund ( Test Mode) using the integrated Razorpay payment gateway.
NewsMonkey-React-App
A react App to view the latest news in different categories.
DsaForPlacements
Collection of 280 questions asked by Product based companies
inotebook-app
The Web Application is a clone of Google Keep and is built on MERN stack. The users can create,read, update and delete their notes using iNotebook.
ivy
The Unified Machine Learning Framework
ml-system-design-pattern
System design patterns for machine learning
Pranjal-bisht
Config files for my GitHub profile.
SparksBank-php-Web-App
Basic Banking Website to transfer money between multiple users.
Text-Analyzer
Basic React App to manipulate your text. It offers different functions to manipulate your text like lower to upper case or vice versa, removing extra spaces. It also has a light/dark mode facility which is built using the concept of useState hooks in ReactJS.
Vector-Map-Generation-from-Aerial-Imagery-using-Deep-Learning-GeoSpatial-UNET
We propose a simple yet efficient technique to leverage semantic segmentation model to extract and separate individual buildings in densely compacted areas using medium resolution satellite/UAV orthoimages. We adopted standard UNET architecture, additionally added batch normalization layer after every convolution, to label every pixel in the image.