minimum result: create a model that performs better than just predict all that worse 3.5 (or 4) is bad, all is more than 3.5 (4) is good.
For training models, I used Yelp Academic Dataset available here: https://www.yelp.com/dataset
For validation, I used data scraped from yelp. The folder
Scraping contains code for web scraping and working with YELP API
First download data through yelp api for (king county zip codes) files.
Then with BeautifulSoup, I scraped user reviews for about 100 user files. …
When news came out last week that Paypal was holding a panel debate on gender equality that only included male speakers, public response was less than helpful. “A female organizer’s statement explaining that the panel was specifically intended to feature “male allies” satisfied no one, drawing answers such as, “Wow. “I see that if you needed someone to take the blame, you could find a woman.” And that comment came from a man.
You’d better think about putting women on the agenda if you’re organizing a tech event today.
You should do so because numerous groups of individuals bring different points of view to the discussion, bringing depth and scope that allows us to better understand and solve the challenges we face. If you can’t grasp that then just do it to stop being a laughing stock all over social media. …
In a 2013 New York Times post, 2012 is the Big Data breakout year. The big data promise was appealing, digital web data too large for conventional data processing was collected, new software innovations were applied for mining, and infinite problems could be solved. Big data has a record of progress since 2012. Since then. By 2017 Gartner Research reported a failure rate of nearly 85% in large data projects. Another 2018 Gartner study found that 91 percent were not transformational market intelligence levels from the 196 firms interviewed for big data and analysis.
The most relevant and compelling question for all companies as interesting as they understand what AI is and how it functions is whether and why it should be used. Although the specifics vary by industry, vision, and objective for each enterprise, there are certain key factors that are the basis for each answer.
The basis of business cases for AI will be the three factors in any organization: the business goals, the decision-making predictions, and the value of acting on those decisions more rapidly, accurately, and consistently over time than existing methods or by unlocking a certain amount of new capabilities. …
According to the fraud & AML practice of Aite Group, the financial institutions of Covid-19 were granted a 10:1 ratio of bot-based malice to legitimate login. Every month, malicious login efforts set new records.
In 2018–2019, the number of infringed data records rose by 84% to 15.1B of the previous year’s accounts.
Organized crime-supported fraud operations are close to legitimate firms, complete with ongoing AI recruitment programs, experience in bot and learning machinery, and the creation of infringement techniques at the offices.
By June 2020 on average online banking registration credentials for the dark web were about $35 while payment card information was between $12 and $20 each, as analyzed by Aid Net Security again. …
It is no longer a secret that big data is the reason behind the success of many large technology companies. However, as more and more businesses adopt it in order to store, process, and derive value from their large amount of data, it is becoming a challenge for them to make the most effective use of the data collected.
This is where machine learning will support them. Data is a bonus for machine learning systems. The more data the system collects, the more it learns to work for companies. …
If you were asked a year ago how quickly it would be to shift into a situation where almost everyone in your company worked from home away every day — what would you have said?
One year? A year? 2 years? 2 years? In a matter of days, it is impossible that anyone will envision it. But this is exactly what happened to all of us at the beginning of this year because of elements outside our control.
This unbelievable drive to transform, which resulted from necessity, encouraged companies to do what they previously thought impossible. It was also important to balance the company’s needs — to function in a secure and comfortable atmosphere — with the needs of employees .. …
Freight trucks contributed 23 percent of greenhouse gas emissions in 2018, and transport contributes a total of 28 percent of those emissions by industry.
According to Lu Saenz, VP Engineering and Product Development at Flock Freight shared truckloads on less than load (LTL) freight, which allow many shippers to share space in one complete semi-trailer, could reduce carbon emissions.
Saenz says there is a significant advantage to the introduction of a new shared truckload model in the logistics industry.
“With the conventional LTL model, freight zigzags through the obsolete hub-and-spoke system and is inefficient in its approach, both in terms of the time it takes shipments to arrive on the shelves and in terms of the climate, but also because goods are constantly damaged by being taken on and off the trucks along the road,” Saenz said. …