shubhamdarak37 Goto Github PK

chatbot-develoment-using-nlp-and-deep-learning

covid19-confirmed-cases-prediction

crowd-counting-for-optimal-resource-management

BEProject

data-structures-and-algorithms

The Repository is comprised of competitive coding questions solved using C++ language

fake-news-classifier

Implemented the fake news classifier using Django as a server-side framework and Html, CSS, Bootstrap as a frontend technology. The Deep Learning model included an Embedding layer and Long Short-Term Memory layers which provide complete sentiment of the sentence. The system accepts text news as an input and predicts the probability of news being True or fake.

lane-detection-and-departure-warning-system

System will detect Lane and will warn if car is moving out of the lane.

lane_departure_warning_system-using-cnn

lane is detected and warning is generated when user departs from Lane

malaria-detection-system

System is composed of Deep Residual network used to classify the cells as Infected or Uninfected.

moneymize

Money management application

optical-character-recognition-using-googles-tesseract

shubham.github.io

shubhamdarak37.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

statistical-crowd-counting

Implemented the project using PostgreSQL as a database, Django as a server-side framework, and Html, CSS, bootstrap as a frontend technology. The Deep Learning model was used to predict crowd count from video frames. The project provides area wise statistical analysis of crowd flow throughout the shops, malls, etc.

thinkstats2

Text and supporting code for Think Stats, 2nd Edition

traffic-sign-recognition-system

system comprises of three sets of CONV -> RELU -> BN -> POOL layers and 2 fully connected layers, system gained validation accuracy of 95%. System recognizes 43 different classes of traffic signs.