mohiteakshay Goto Github PK

autoscraper

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

bear_voila

Demo bear classifier with fastai and Voila

counterfeit-medicine-sales-predict

Counterfeit medicines are fake medicines which are either contaminated or contain wrong or no active ingredient. They could have the right active ingredient but at the wrong dose. Counterfeit drugs are illegal and are harmful to health. 10% of the world's medicine is counterfeit and the problem is even worse in developing countries. Up to 30% of medicines in developing countries are counterfeit. Millions of pills, bottles and sachets of counterfeit and illegal medicines are being traded across the world. The World Health Organization (WHO) is working with International Criminal Police Organization (Interpol) to dislodge the criminal networks raking in billions of dollars from this cynical trade. Despite all these efforts, counterfeit medicine selling rackets don’t seem to stop popping here and there. It has become a challenge to deploy resources to counter these; without spreading them too thin and eventually rendering them ineffective. Government has decided that they should focus on illegal operations of high net worth first instead of trying to control all of them. In order to do that they have collected data which will help them to predict sales figures given an illegal operation's characteristics.

demo

fastai

The fastai deep learning library, plus lessons and tutorials

fastbook

Draft of the fastai book

gdriveplotter

greendeck

lion

mahindra-first-choice

music-genre-identification

odi-match-winner-weekend-hackathon-9

Hackathon On MachineHack

practical-deep-learning-for-coders-2.0

Notebooks for the "A walk with fastai2" Study Group and Lecture Series

project-4-distracted-driver-multiaction-classification

rest-api-using-flask

restapidocs

Templates for documenting REST APIs

scylla

Intelligent proxy pool for Humans™ (Maintainer needed)

solar-power-predict

We have to predict the power generated by a solar plant given the date time and weather conditions.

stock-market-predicition

ASKNBID assignment

traffic-signal-image-classification-using-cnn

twitter-sentiment-analysis

Problem Statement Twitter has now become a useful way to build one's business as it helps in giving the brand a voice and a personality. The platform is also a quick, easy and inexpensive way to gain valuable insight from the desired audience. Identifying the sentiments about the product/brand can help the business take better actions. You have with you evaluated tweets about multiple brands. The evaluators(random audience) were asked if the tweet expressed positive, negative, or no emotion towards a product/brand and labelled accordingly. Dataset Description This dataset contains around 7k tweet text with the sentiment label. The file train.csv has 3 columns tweet_id - Unique id for tweets. tweet - Tweet about the brand/product sentiment - 0: Negative, 1: Neutral, 2: Positive, 3: Can't Tell Evaluation Metric We will be using ‘weighted’ F1-measure as the evaluation metric for this competition. For more information on the F1-metric refer to https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html Submission format Submission file should have two columns, one for tweet_id and sencond for sentiment [0: Negative, 1: Neutral, 2: Positive, 3: Can't Tell]. A sample submission file has also been attached for reference.