The Unofficial Google Data Science Blog
APRIL 17, 2017
by ERIC TASSONE, FARZAN ROHANI We were part of a team of data scientists in Search Infrastructure at Google that took on the task of developing robust and automatic large-scale time series forecasting for our organization. So it should come as no surprise that Google has compiled and forecast time series for a long time.
Smart Data Collective
OCTOBER 13, 2020
They found that predictive analytics algorithms were using social media data to forecast asset prices. But it’s clear they need help in choosing the best altcoins to buy from the more than 2000 high-risk cryptocurrencies available. High Profit Potential Matched by High Volatility and High Risk. Very carefully.
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Smarten
NOVEMBER 28, 2023
McKinsey recently surveyed 2000 businesses and found that 83% of high-tech/media/telecom, 76% of banking, and more than 50% of consumer companies identified as continuous improvement organizations. Find out how Augmented Analytics products can help your business plan and forecast for success. There is good reason for these results.
Rita Sallam
APRIL 2, 2023
Qlik Key Findings: In the US alone, there’s $367 billion in agricultural commodities at risk to flooding in the US alone. A large part of under-developed Asian countries ranging from Bangladesh to Vietnam are at high risk of flooding events. In 2000, the Netherlands had 8.5 million people at risk of catastrophic, flooding.
FineReport
APRIL 26, 2023
Predictive Analytics assesses the probability of a specific occurrence in the future, such as early warning systems, fraud detection, preventative maintenance applications, and forecasting. With Big Data Analytics, businesses can make better and quicker decisions, model and forecast future events, and enhance their Business Intelligence.
Domino Data Lab
MAY 20, 2021
Other techniques include simple re-sampling, where the minority class is continuously re-sampled until the number of obtained observations matches the size of the majority class, and focused under-sampling, where the discarded observations from the majority class are carefully selected to be away from the decision boundary (Japkowicz, 2000).
Sisense
FEBRUARY 24, 2021
Whether that data is generated internally or gathered from an external application used by customers, organizations now use on-demand cloud computing resources to make sense of the data, discover trends, and make intelligent forecasts. Or do they encourage novel ideas at the risk of having unconnected data?
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