The goal of this report is: * predicting a transformation of the player Position
variable, * predicting player's market Value
using the fifa_exam.RDS
dataset by using predictive machine learning modeling in R with "tidymodels" package.
It can be seen that in the raw data set we don't have the Overall
variable, so it makes it more complicated to only implement random models. As always, we prepare the data set as much as possible in order to get the best models based on our goals.
Applied Classification models, KNN, Decision Tree, Random Forest and Linear Modelling
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The goal of this report is: -predicting a transformation of the player `Position` variable, -predicting player's market `Value` by predictive machine learning modeling in R with "tidymodels" package.
BabakBar/FIFA-Predictive-Machine-Learning
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The goal of this report is: -predicting a transformation of the player `Position` variable, -predicting player's market `Value` by predictive machine learning modeling in R with "tidymodels" package.
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