abhiwalia15

AI-for-Finance-Stocks-real-time-analysis-

1. First we fetch data of stocks in realtime from nse India website, perform basis data visualizations using python to analyze the stock. 2. Then we use machine learning LSTM technique to predict the future stock price and at last create an interactive web-app using Streamlit in python.

COVID-19

Performing Exploratory Data Analysis on COVID-19 Dataset and display the results by plotting of various types such as the plot of Worldwide Cases- confirmed, deaths, recovered and analysis by country. • After performing EDA, the cleaning and preprocessing of the dataset to make predictions using the ML technique(LightGBM, which is a gradient boosting framework that uses tree-based learning algorithms).

Deep-Learning-with-OpenCV

Deep Learning is a fast growing domain of Machine Learning and if you’re working in the field of computer vision/image processing already (or getting up to speed), it’s a crucial area to explore. With OpenCV 3.3, we can utilize pre-trained networks with popular deep learning frameworks. The fact that they are pre-trained implies that we don’t need to spend many hours training the network — rather we can complete a forward pass and utilize the output to make a decision within our application. OpenCV does not (and does not intend to be) to be a tool for training networks — there are already great frameworks available for that purpose. Since a network (such as a CNN) can be used as a classifier, it makes logical sense that OpenCV has a Deep Learning module that we can leverage easily within the OpenCV ecosystem. Popular network architectures compatible with OpenCV 3.3 include: GoogleLeNet (used in this blog post) AlexNet SqueezeNet VGGNet (and associated flavors) ResNet

Document-Scanner-Using-OpenCV-Python

• In this project, you will learn how to extract email and phone number from a business card or any document and save the output in a JSON file. • Initially we need to resize the images so OpenCV can handle it and then the following steps are applied-detecting the edges, finding contours, applying perspective transform to get top-down view, using pytesseract to extract text and then finally using regex expressions to identify only email and phone number.

Face-Classification-into-Blood-No-Blood

Competition

Facial-Expression-Recognition

• First we recognize the emotion of the person using Opencv and Keras by training our model on Data provided from Kaggle, program is trained for 30 epochs and we have got an accuracy of 71%. • After detecting the emotion out of 7 labels, we as user for his favourite artist and then recommend songs from Spotify using its API and movies from IMDB depending upon its mood.

fuel-consumption-and-Carbon-dioxide-emission-of-cars

In this notebook, we learn how to use scikit-learn to implement simple linear regression. We download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. Then, we split our data into training and test sets, create a model using training set, evaluate your model using test set, and finally use model to predict unknown value.

Python-for-Data-Science-and-Machine-Learning-Bootcamp

program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning: Programming with Python NumPy with Python Using pandas Data Frames to solve complex tasks Use pandas to handle Excel Files Web scraping with python Connect Python to SQL Use matplotlib and seaborn for data visualizations Use plotly for interactive visualizations Machine Learning with SciKit Learn, including: Linear Regression K Nearest Neighbors K Means Clustering Decision Trees Random Forests Natural Language Processing Neural Nets and Deep Learning Support Vector Machines and much, much more!

Web-Scraping-in-Python

Implementing Web Scraping in Python with BeautifulSoup There are mainly two ways to extract data from a website: Use the API of the website (if it exists). For example, Facebook has the Facebook Graph API which allows retrieval of data posted on Facebook. Access the HTML of the webpage and extract useful information/data from it. This technique is called web scraping or web harvesting or web data extraction. This article discusses the steps involved in web scraping using implementation of Web Scraping in Python with Beautiful Soup Steps involved in web scraping: Send a HTTP request to the URL of the webpage you want to access. The server responds to the request by returning the HTML content of the webpage. For this task, we will use a third-party HTTP library for python requests. Once we have accessed the HTML content, we are left with the task of parsing the data. Since most of the HTML data is nested, we cannot extract data simply through string processing. One needs a parser which can create a nested/tree structure of the HTML data. There are many HTML parser libraries available but the most advanced one is html5lib. Now, all we need to do is navigating and searching the parse tree that we created, i.e. tree traversal. For this task, we will be using another third-party python library, Beautiful Soup. It is a Python library for pulling data out of HTML and XML files.

Team-Predictions

Predicting an IPL ALL Stars team based on past performance of Players.

abhiwalia15/AI-for-Finance-Stocks-real-time-analysis-

1. First we fetch data of stocks in realtime from nse India website, perform basis data visualizations using python to analyze the stock. 2. Then we use machine learning LSTM technique to predict the future stock price and at last create an interactive web-app using Streamlit in python.

abhiwalia15/Facial-Expression-Recognition

• First we recognize the emotion of the person using Opencv and Keras by training our model on Data provided from Kaggle, program is trained for 30 epochs and we have got an accuracy of 71%. • After detecting the emotion out of 7 labels, we as user for his favourite artist and then recommend songs from Spotify using its API and movies from IMDB depending upon its mood.

abhiwalia15/Face-Classification-into-Blood-No-Blood

Competition

abhiwalia15/abhiwalia15

abhiwalia15/Network-Project-Attacks

Various network attacks

abhiwalia15/season-of-docs

Supporting materials for Google's Season of Docs 2021

abhiwalia15/Sitting-Posture-Recognition

Final Year Project

abhiwalia15/Building-A-Baseline-Convolutional-AutoEncoder-Network-For-Image-Denoising-On-Fashion-MNIST-Dataset

We will build a simple baseline autoencoder model using TensorFlow and the CNN network. Then we will use this network on the FASHION MNIST dataset to show our results and accuracy. We will evaluate our model using a simple CNN network to show how our autoencoder model performs better than a stand-alone CNN model.

abhiwalia15/community

NimbleEdge community content and more.

abhiwalia15/COMP8610_Assignment_1

Assignment_1

abhiwalia15/COMP8610_Assignment_2

Aayushi Navinchandra Patel (Student ID:110087817) Aaditya Pradipbhai Parekh (Student ID:- 110084734) Dhruvkumar Arvind Patel (Student ID:- 110055817) Mrinal Walia (Student ID:- 110066886)

abhiwalia15/design

This is the main hub for those interested in design in the OpenMined community

abhiwalia15/EnvisEdge

Deploy recommendation engines with Edge Computing

abhiwalia15/FederatedScope

An easy-to-use federated learning platform

abhiwalia15/Generating-Anime-Character-Faces-Using-ACGANs

Abstract-This paper will implement a baseline Auxiliary Classifier Generative Adversarial Network capable of generating new anime character faces. We aim to design a style-guided discriminator and a generator network and train it on 63,000 synthesized anime faces. ACGANs can be notoriously hard to train as the generator, and the discriminator architectures are susceptible to parameters, hyperparameters, regularization, learning rate, and activation functions. However, unlike a conventional GANs network, our proposed framework uses a GPU for faster training and can improve its performance given more c- omputational power and datasets. This problem has previously been solved but lacks a generic architecture. Hence, our proposed framework can be used as a baseline network across different applications of GANs. The generated anime faces from our final model are visually pleasing and resemble designer-generated characters. As generative modelling is an unsupervised task and does not expect images to have labels, the dataset used in this project has 63,000 unlabeled cropped anime faces available at this URL: https://github.com/bchao1/Anime-Face-Dataset. Abbreviations: Auxiliary Classifier Generative Adversarial Networks (ACGANs), Generative Adversarial Networks (GANs), Artificial Intelligence (A.I.), Machine Learning (ML), Convolutional Neural Network (CNN)

abhiwalia15/geomstats

Computations and statistics on manifolds with geometric structures.

abhiwalia15/google-images-download

Google/Bing Images Web Downloader

abhiwalia15/ip-1_assignment-1_backend

Internship/Project-1 Assignment 1 - Master of Applied Computing @ University of Windsor

abhiwalia15/Kubernetes-Docs-Enhancement

This repository aims to improve the clarity and quality of Kubernetes documentation to make it more accessible and user-friendly for individuals with varying levels of Kubernetes expertise.

abhiwalia15/LogoDet

Neural Nets for logo detection

abhiwalia15/Mrinal-Portfolio-Hugo

Hugo: Portfolio

abhiwalia15/mrinalwalia

-Portfolio Website: Hugo

abhiwalia15/Open-Source-Requests

A curation of paid and unpaid requests for the community to work on.

abhiwalia15/PySyft

A library for answering questions using data you cannot see

abhiwalia15/RealTime

abhiwalia15/reverse-interview

Questions to ask the company during your interview

abhiwalia15/Root-Cause-Analysis-Deep-Learning-Analytis-

IT Operation: Root Cause Analysis

abhiwalia15/Sentinels

Code related to any applications / bots serving our discord community.

abhiwalia15/ShootAR

Comp ASE Game.

abhiwalia15/The-Grand-Complete-Data-Science-Materials