30Jun - by aiuniverse - 0 - In Data Science

Source – https://www.analyticsinsight.net/

Winning these Kaggle competitions will be a Great Addition to Data Science Resumes.

If you are a data science professional or a machine learning engineer, you may have heard of Kaggle. Kaggle is the world’s largest data science community with numerous courses, books, and tutorials that address various concepts of this field. Apart from all the matter, another aspect that makes Kaggle exciting for tech enthusiasts is their competition.

Kaggle competitions are machine learning tasks made by Kaggle or other companies. These competitions vary on the basis of problems and complexity. For beginners or experienced professionals, there are competitions for everyone to participate with cash prizes.

Out of all the active ones, there are the most exciting competitions for ML engineers and data scientists, and analysts.


Objective: To Identify and localize COVID-19 abnormalities on chest radiographs

Entry Deadline: August 2

Cash Prize: US$100,000

COVID-19 is wreaking havoc in the world and healthcare professionals are having a tough time controlling the epidemic. COVID-19 looks very similar to other viral and bacterial pneumonia on chest radiographs, which makes it difficult to diagnose. You have to build a computer vision model to detect and localize COVID-19. It would help doctors provide a quick and confident diagnosis. As a result, patients could get the right treatment before the most severe effects of the virus takes hold.

2. Optiver Realized Volatility Prediction

Objective: Apply your data science skills to make financial markets better

Entry Deadline: September 20

Cash Prize: US$100,000

Volatility is one of the most prominent terms you’ll hear on any trading floor – and for good reason. In financial markets, volatility captures the amount of fluctuation in prices. High volatility is associated with periods of market turbulence and large price swings, while low volatility describes more calm and quiet markets. For trading firms like Optiver, accurately predicting volatility is essential for the trading of options, whose price is directly related to the volatility of the underlying product.

In the first three months of this competition, you’ll build models that predict short-term volatility for hundreds of stocks across different sectors. You will have hundreds of millions of rows of highly granular financial data at your fingertips, with which you’ll design your model forecasting volatility over 10-minute periods. Your models will be evaluated against real market data collected in the three-month evaluation period after training.

3. CommonLit Readability Prize

Objective: Rate the complexity of literary passages for grades 3-12 classroom use

Entry Deadline: July 26

Cash Prize: US$60,000

Reading is an important fundamental skill, and currently, most educational texts are matched to readers using traditional readability methods or commercially available formulas. However, each has its issues. Tools like Flesch-Kincaid Grade Level are based on weak proxies of text decoding (i.e., characters or syllables per word) and syntactic complexity (i.e., number or words per sentence). As a result, they lack construct and theoretical validity. At the same time, commercially available formulas, such as Lexile, can be cost-prohibitive, lack suitable validation studies, and suffer from transparency issues when the formula’s features aren’t publicly available.

In this competition, you’ll build algorithms to rate the complexity of reading passages for grade 3-12 classroom use. To accomplish this, you’ll pair your machine learning skills with a dataset that includes readers from a wide variety of age groups and a large collection of texts taken from various domains. Winning models will be sure to incorporate text cohesion and semantics.

4.  MLB Player Digital Engagement Forecasting

Objective: Predict fan engagement with baseball player digital content

Entry Deadline: July 20

Cash Prize: US$50,000

You need to tend to the interests of baseball fans. Major League Baseball (MLB) and Google Cloud want the Kaggle community’s help to identify the many other factors which pique supporter engagement and create deeper relationships between players and fans. In this competition, you’ll predict how fans engage with MLB players’ digital content on a daily basis for a future date range. You’ll have access to player performance data, social media data, and team factors like market size. Successful models will provide new insights into what signals most strongly correlate with and influence engagement.

5. SETI Breakthrough Listen – E.T Signal Search

Objective: Find extraterrestrial signals in data from deep space

Entry Deadline: July 21

Cash Prize: US$15,000

“Are we alone in this universe” is a question that is a mystery in itself. To find the answers, the University of California, Berkeley, employs the world’s most powerful telescopes to scan millions of stars for signs of technology. Now it wants the Kaggle community to help interpret the signals they pick up.

In this competition, use your data science skills to help identify anomalous signals in scans of Breakthrough Listen to targets. Because there are no confirmed examples of alien signals to use to train machine learning algorithms, the team included some simulated signals (that they call “needles”) in the haystack of data from the telescope. They have identified some of the hidden needles so that you can train your model to find more. The data consist of two-dimensional arrays, so there may be approaches from computer vision that are promising, as well as digital signal processing, anomaly detection, and more. The algorithm that’s successful at identifying the most needles will win a cash prize but also has the potential to help answer one of the biggest questions in science.

6. Google Smartphone Decimeter Challenge

Objective: Improve high precision GNSS positioning and navigation accuracy on smartphones

Entry Deadline: July 28

Cash Prize: US$10,000

The world needs a navigation app that is more accurate with regards to bumps and potholes. Machine learning and precision GNSS algorithms are expected to improve this accuracy and provide billions of Android phone users with a more fine-tuned positioning experience. This competition, hosted by the Android GPS team, is being presented at the ION GNSS+ 2021 Conference. They seek to advance research in smartphone GNSS positioning accuracy and help people better navigate the world around them.

In this competition, you’ll use data collected from the host team’s own Android phones to compute location down to decimeter or even centimeter resolution, if possible. You’ll have access to precise ground truth, raw GPS measurements, and assistance data from nearby GPS stations, in order to train and test your submissions.

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