albertoviciana/fifaproject

EXPLANATORY ANALYSIS

For the FIFA21 project, we were presented with a table of data regarding professional football player’s personal, professional and skills information. The goal was to build a model so in the future, we could predict the overall player’s rating score. Our group first imported the different packages to be able to: get, read and work with the data. After reading the data, we started cleaning the data by dividing the multiple columns into 2 groups of 51 (out of the 102), so it would be easier to check its values and work with a smaller list. When cleaning the data, we performed the following actions:

• Standardized the column names by lowering its cases and replacing the spaces with underscores

• Noticed the relationships with the different variables: dropped the ones that we tought were going to be redundant for our model, kept the variables we thought would be relevant.

  Dropped variables:

• Unnamed: most likely a copy of the ID variable. Noticed it with “Group by” function

• Club related variables: we got rid of all club related variables like Team & Contract, Joined, Loan Date End, etc.

• Position: kept the BP value as it added more value to our model

• Dropped all football skills variables: all mostly included in Base Stats and Total Stats

  Kept values:

• Height, Weight, Foot: tought physical attributed could be a factor which impacts the overall rating of a player (ova)

• Base Stats & Total Stats: highly correlated with ova.

• Variables that describre player money valuation: could be related that the most a player is paid is because it is correlated with its ova.

• BP & player best position score: the players best position needs to be in the model because the ova is most likely coming from the player best position rating score.

  Divided the numerical and categorical variables and started the following EDA steps:

• Transforming numerical types stored as categorical into numerical variables by defining functions and pandas commands. Example: removing euro symbols

• Visualization analysis: pairplot, displots, boxplots and heatmap.

• Creation of new data frames with cleaned values Concatenated each cleaned variable into a final data frame.

• Checked correlations between final variables using heatmap to decided whether to drop any other before creating the prediction model or not.

  Creation of the prediction model.

• X-y split by defining “ova” as target variable

• Build the model applying the linear regression method

• Train-test split to fit the model with our final data Model validation.

• Calculated R2, MSE, RMSE and MAE Noticed our model was better than expected with a model score of 0.79 and mean absolute error of 2.4.

  #### However, we tried to improve the model prediction by:

• Normalizing the numerical variables with MinMaxScale

• Introduced relevant categorical variables like international rating(?) and BP

• Introduced a key numerical variable by using panda’s melt function: players best position score

• After repeating the prediction model steps, we reached a model score of 0.98 with a mean absolute error of 0.8(?). In conclusion, it is clear that the overall rating of a player depends mostly on the players best position score. If we just used the total and base stats we would have been able to predict a players overall rating but not super accuratelly. On the other hand, physical and personal information values were not useful when trying to predict a player’s ova. In the future, if we would like to build a new model, we should focus on the players best position score which would lead to lesser use of redundant variables, speeding up the computer prediction model results.