anirudh217sharma Goto Github PK

2048

Minimax algorithm for 2048 game

anirudh217sharma.github.io

apparel_dataset_using_cnn

application-of-data-analysis-factor-graphs-in-hpc-security

autogluon

AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data

autonomous-vehicle-safety-analysis

autoscraper

A Smart, Automatic, Fast and Lightweight Web Scraper for Python

baseball_hit_prediction

Using Logistic regression to predict whether a pitch will be a hit or not based on a big data with more than 70 dimensions Using the Trackman pitch-level data building a model that predicts the probability of a batted ball being a hit based on batted ball characteristics and relevant contextual data. Please regard fielding errors as non-out hits for the purposes of this analysis.

canadianbillexplainer

Explains Canadian Bills

code_snippets

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

covid19_tweets_sentiment_analysis

The dataset has been pulled from Kaggle with an objective to perform text classification using Deep Learning

covid19india-cluster

:microscope: COVID19 India Cluster Network

deployment-flask

fall2019

final_project_2020sp

Each team creates a fork from this for their course project

finals_spr2021

fuzzywuzzy

Fuzzy String Matching in Python

glacier-tax-1099-b-stock-transactions-helper

Chrome extension for helping nonresident alien fill in the 1099-B stock transaction

glmnet_python

happy_whale_and_dolphins

We use fingerprints and facial recognition to identify people, but can we use similar approaches with animals? In fact, researchers manually track marine life by the shape and markings on their tails, dorsal fins, heads and other body parts. Identification by natural markings via photographs—known as photo-ID—is a powerful tool for marine mammal science. It allows individual animals to be tracked over time and enables assessments of population status and trends. With your help to automate whale and dolphin photo-ID, researchers can reduce image identification times by over 99%. More efficient identification could enable a scale of study previously unaffordable or impossible. Currently, most research institutions rely on time-intensive—and sometimes inaccurate—manual matching by the human eye. Thousands of hours go into manual matching, which involves staring at photos to compare one individual to another, finding matches, and identifying new individuals. While researchers enjoy looking at a whale photo or two, manual matching limits the scope and reach. Algorithms developed in this competition will be implemented in Happywhale, a research collaboration and citizen science web platform. Its mission is to increase global understanding and caring for marine environments through high quality conservation science and education. Happywhale aims to make it easy and rewarding for the public to participate in science by building innovative tools to engage anyone interested in marine mammals. The platform also serves the research community with powerful collaborative tools. In this competition, you’ll develop a model to match individual whales and dolphins by unique—but often subtle—characteristics of their natural markings. You'll pay particular attention to dorsal fins and lateral body views in image sets from a multi-species dataset built by 28 research institutions. The best submissions will suggest photo-ID solutions that are fast and accurate. If successful, you'll have a hand in building advanced technology to better understand and manage the impact on the world’s changing oceans. Previous automation attempts resulted in a global database of over 50,000 whales and an agreement with cruise ships to operate at a maximum speed of 11 mph in the most whale-rich region. Your ideas to automate the identification of marine life will help overcome increasing human impacts on oceans, providing a critical tool for conservation science. If there's a whale, there's a way!

house_prices_advanced_regression_techniques

https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/overview

is-590-dv--final-project

Human movement analysis based on accelerometer and gyroscope movements in their watch and phone for different activities such as walking, running etc

leetcode-notes

microblog

A microblogging web application written in Python and Flask that I developed as part of my Flask Mega-Tutorial series.

noveltmmeta

Code and data supporting "NovelTM Data Sets for English-Language Fiction."

polars

Fast multi-threaded, hybrid-out-of-core DataFrame library in Rust | Python | Node.js

polars-tutorial

Polars Tutorial

pygame-sudoku

Generates a sudoku board on python which can be solved by user input

pytorch-sentiment-analysis

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.