Flipboard

FEBRUARY 2, 2023

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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Pickl AI

DECEMBER 16, 2024

Step 3: Data Preprocessing and Exploration Before modeling, it’s essential to preprocess and explore the data thoroughly.This step ensures that you have a clean and well-understood dataset before moving on to modeling. Cleaning Data: Address any missing values or outliers that could skew results.

AI 52

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Mlearning.ai

MAY 23, 2023

The following figure represents the life cycle of data science. It starts with gathering the business requirements and relevant data. Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. What is Cross-Validation?

Data Science 52

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Pickl AI

SEPTEMBER 12, 2024

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.

Data Analyst 52

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Pickl AI

NOVEMBER 2, 2023

Here, we’ll explore why Data Science is indispensable in today’s world. Understanding Data Science At its core, Data Science is all about transforming raw data into actionable information. It includes data collection, data cleaning, data analysis, and interpretation.

Data Scientist 52

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. He has collaborated with the Amazon Machine Learning Solutions Lab in providing clean data for them to work with as well as providing domain knowledge about the data itself.

ML 77

ML ML Machine Learning Machine Learning 77

Pickl AI

DECEMBER 10, 2024

This process often involves cleaning data, handling missing values, and scaling features. Feature extraction automatically derives meaningful features from raw data using algorithms and mathematical techniques. Cross-validation ensures these evaluations generalise across different subsets of the data.

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