I am new to data science and am currently working on a data science project and have to answer a few questions about the following data set with 18k data points: https://www.kaggle.com/karangadiya/fifa19
The question I have to answer is as follows: To which extent can you determine the value of a player using the players most important features?
Features in this case being the columns from 'Crossing' to 'GKReflexes'. I grouped up all the players as follows: Att = ['RF', 'LF', 'RS', 'LS', 'RS', 'RW', 'LW', 'CF', 'ST'] # attack positions Mid = ['LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LDM', 'CDM', 'RDM'] # midfield positions Def = ['LCB', 'CB', 'LB', 'RCB', 'RB', 'LWB', 'RWB'] # defense positions GK
I already looked at the data and was able to determine that certain player features are much higher for certain positions.
I plan on using multiple linear regression to answer the research question, but I'm not sure on how to split the data into a train and test set.
I'm thinking of one of the following approaches:
- First split the large data set into smaller data sets for each position (GK, DEF, ATT and MID) and then make a train and test set for each position.
- Leave the large dataset as it is with all of the positions combined and split that into a training and test set.
I am currently leaning more towards the first approach because I think I'll be able to better determine the price of the player using the features that are important to the players position, but I am not sure if that's the correct way to go at it.