Shambhavi Aggarwal's repositories
MoneyGame-AndroidApp
Learn investment in the stock market without risks. 🎉 (Mobile App) Demo 🔽
Paper-Implementations
Pytorch implementation of some of the popular papers
Glasses-Removal-Network
Pytorch implementation of glasses removal network using CycleGan
Intelligent-Vision
A simple tool meant for Person Re-identification ✨ Demo 🔽
MLH-Fellow-Map
https://fellowmap.space/
MLH-Projects
Portfolio page consisting of the work I did as a part of the MLH Fellowship(Fall 2020): Explorer Program
MoneyGame-Backend
Learn investment in the stock market without risks. 🎉Demo 🔽
MoneyGame-Frontend
Learn investment in the stock market without risks. 🎉 (WebApp) Demo 🔽
Real-time-edge-detector
using OpenCV and canny edge detector
Stanford-Dogs-
Compared various state of the art deep learning models on Stanford Dogs dataset
volunteery
Help the helpers. 🤝
HelpingHand
Leveraging Intelligent Processing Tools and Algorithms to help the Visually Impaired see and navigate 💥✨
acadivity
A web app to help students to optimize their everyday life, and keep their productivity high!
audio-processing
Getting started with Librosa Library
batch-1-photos
Batch 1 Graduation Photos
Boston-Housing
I have used scikit-learn's Boston dataset that has a given set of features that describe a house in Boston and our machine learning model must predict the house price. Performed Exploratory Data Analysis on the data and used Linear Regression to predict the target variable.
Deep-Learning
Learning basic deep learning pipeline
gitg0
A magnificent tool to auto-suggest everything you need before pushing a git commit.
KnightInTheNight
This is a Halloween based Godot game 🎃. You need to help Rohan the Knight fight his way through the haunted mansion and get candy ⚔️
Machine-Learning
This repository contains all the notebooks I made while learning Machine Learning.
PARKS-Gaze
Github page for the PARKS-gaze dataset to describe and provide links for the dataset
SneakySketchers
A python desktop application that allows you to do image inpainting by directly drawing on it.
Spam-or-Ham
I have used the SMS Spam Collection Dataset. It contains the text of 5572 SMS messages and a label, classifying the message as "spam" or "ham". I have implemented Bag of Words from scratch and then I have used scikit learn's implementation of Bag of Words to transform the entire dataset. Further, I have used the Naive Bayes algorithm to classify the messages into "spam" or "ham".