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Types of Statistical Models in R for Data Scientists

Pickl AI

Data Scientists are highly in demand across different industries for making use of the large volumes of data for analysisng and interpretation and enabling effective decision making. One of the most effective programming languages used by Data Scientists is R, that helps them to conduct data analysis and make future predictions.

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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.

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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.

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Popular Statistician certifications that will ensure professional success

Pickl AI

programs offer comprehensive Data Analysis and Statistical methods training, providing a solid foundation for Statisticians and Data Scientists. It emphasises probabilistic modeling and Statistical inference for analysing big data and extracting information. You will learn by practising Data Scientists.

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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Data Science interviews are pivotal moments in the career trajectory of any aspiring data scientist. Having the knowledge about the data science interview questions will help you crack the interview. What is cross-validation, and why is it used in Machine Learning? Here is a brief description of the same.

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Unlocking the Power of KNN Algorithm in Machine Learning

Pickl AI

Experimentation and cross-validation help determine the dataset’s optimal ‘K’ value. Distance Metrics Distance metrics measure the similarity between data points in a dataset. Cross-Validation: Employ techniques like k-fold cross-validation to evaluate model performance and prevent overfitting.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. Data Analytics Certification Course by Pickl.AI