Remove Cross Validation Remove Data Analysis Remove EDA
article thumbnail

Announcing the Winners of ‘The NFL Fantasy Football’ Data Challenge

Ocean Protocol

Fantasy Football is a popular pastime for a large amount of the world, we gathered data around the past 6 seasons of player performance data to see what our community of data scientists could create. By leveraging cross-validation, we ensured the model’s assessment wasn’t reliant on a singular data split.

article thumbnail

Feature Engineering in Machine Learning

Pickl AI

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. Through Exploratory Data Analysis , imputation, and outlier handling, robust models are crafted. Steps of Feature Engineering 1.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

What is cross-validation, and why is it used in Machine Learning? Cross-validation is a technique used to assess the performance and generalization ability of Machine Learning models. The process is repeated multiple times, with each subset serving as both training and testing data.

article thumbnail

Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models. Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset.

article thumbnail

New Data Challenge: Aviation Weather Forecasting Using METAR Data

Ocean Protocol

This is a unique opportunity for data people to dive into real-world data and uncover insights that could shape the future of aviation safety, understanding, airline efficiency, and pilots driving planes. When implementing these models, you’ll typically start by preprocessing your time series data (e.g.,

article thumbnail

The AI Process

Towards AI

Data description: This step includes the following tasks: describe the dataset, including the input features and target feature(s); include summary statistics of the data and counts of any discrete or categorical features, including the target feature. Training: This step includes building the model, which may include cross-validation.

AI 74
article thumbnail

What is Regression Analysis?

Pickl AI

The process of conducting Regression Analysis typically involves several steps: Step 1: Data Collection: Gather relevant data for both dependent and independent variables. This data can come from various sources such as surveys, experiments, or historical records.

EDA 52