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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

Flipboard

Models were trained and cross-validated on the 2018, 2019, and 2020 seasons and tested on the 2021 season. To avoid leakage during cross-validation, we grouped all plays from the same game into the same fold. For more information on how to use GluonTS SBP, see the following demo notebook.

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[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. What is Data Science? Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. It further performs badly on the test data set.

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AI in Time Series Forecasting

Pickl AI

Summary: AI in Time Series Forecasting revolutionizes predictive analytics by leveraging advanced algorithms to identify patterns and trends in temporal data. Advanced algorithms recognize patterns in temporal data effectively. Cleaning Data: Address any missing values or outliers that could skew results.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Analysis: This step involves applying statistical and Machine Learning techniques to analyse the cleaned data and uncover patterns, trends, and relationships.

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Cheat Sheets for Data Scientists – A Comprehensive Guide

Pickl AI

Cheat sheets for Data Scientists Cheat sheets are like treasure maps for Data Scientists, helping them navigate the vast sea of information and tools available to them. These reference guides condense complex concepts, algorithms, and commands into easy-to-understand formats.

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Types of Feature Extraction in Machine Learning

Pickl AI

Raw data, such as images or text, often contain irrelevant or redundant information that hinders the model’s performance. By extracting key features, you allow the Machine Learning algorithm to focus on the most critical aspects of the data, leading to better generalisation. What is Feature Extraction?

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. We design an algorithm that automatically identifies the ambiguity between these two classes as the overlapping region of the clusters. Each season consists of around 17,000 plays.

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