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The Success Story of Microsoft’s Senior Data Scientist

Analytics Vidhya

Among these trailblazers stands an exceptional individual, Mr. Nirmal, a visionary in the realm of data science, who has risen to become a driving […] The post The Success Story of Microsoft’s Senior Data Scientist appeared first on Analytics Vidhya.

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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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Feature Engineering in Machine Learning

Pickl AI

Key takeaways Feature engineering transforms raw data for ML, enhancing model performance and significance. EDA, imputation, encoding, scaling, extraction, outlier handling, and cross-validation ensure robust models. Steps of Feature Engineering 1.

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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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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset. Techniques such as statistical summaries, data visualisation, and correlation analysis help uncover patterns, anomalies, and relationships within the data.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Data Science is the art and science of extracting valuable information from data. It encompasses data collection, cleaning, analysis, and interpretation to uncover patterns, trends, and insights that can drive decision-making and innovation.

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Meet the winners of the Kelp Wanted challenge

DrivenData Labs

Summary of approach: In the end I managed to create two submissions, both employing an ensemble of models trained across all 10-fold cross-validation (CV) splits, achieving a private leaderboard (LB) score of 0.7318.