import seaborn as sns

from sklearn.preprocessing import StandardScaler

from sklearn.decomposition import FactorAnalysis

from sklearn.impute import SimpleImputer

from factor$\_$analyzer import Rotator

# Load the data

df $=$ pd$.$read$\_$csv$(\text{'}$C:$\backslash \backslash$Users$\backslash \backslash$tingc$\backslash \backslash$Desktop$\backslash \backslash$Final report$\backslash \backslash$FinalData$\_$Final.csv$\text{'},$ encoding$=$'latin$1\text{'})$

# Selecting only numerical columns for factor analysis

numerical$\_$columns $=$ df$.$select$\_$dtypes$($include$=[$np$.$number$]).$columns.tolist$()$

# Dropping the $\text{'}$mrt$\_$flow' column if it exists in the numerical columns

if $\text{'}$mrt$\_$flow' in numerical$\_$columns:

numerical$\_$columns.remove$(\text{'}$mrt$\_$flow'$)$

# Update DataFrame to use only the selected numerical columns

df$\_$numerical $=$ df$[$numerical$\_$columns$]$

# Imputing missing values

imputer $=$ SimpleImputer$($strategy$=$'median'$)$

df$\_$numerical$\_$imputed $=$ imputer.fit$\_$transform$($df$\_$numerical$)$

# Standardizing the data

scaler $=$ StandardScaler$()$

df$\_$numerical$\_$scaled $=$ scaler.fit$\_$transform$($df$\_$numerical$\_$imputed$)$

# Performing factor analysis

fa $=$ FactorAnalysis$($n$\_$components$=5,$ random$\_$state$=0)$

fa$\_$components $=$ fa$.$fit$\_$transform$($df$\_$numerical$\_$scaled$)$

# Creating DataFrame for factor analysis results

fa$\_$df $=$ pd$.$DataFrame$($fa$\_$components, columns$=[$f$\text{'}$Factor$\{$i$+1\}\text{'}$ for i in range$($fa$.$n$\_$components$)])$

# Factor loadings before rotation

loadings$\_$before$\_$rotation $=$ fa$.$components$\_$

# Applying Varimax rotation

rotator $=$ Rotator$($method$=$'varimax'$)$

loadings$\_$rotated $=$ rotator.fit$\_$transform$($loadings$\_$before$\_$rotation$)$

Above is the code for factor analysis, now I need to use the results$($first $5$ factors before rotated & first $5$ factors after rotated$)$ to predict y$(\text{'}$mrt$\_$flow'$),$ using the SVM method$($training:$0\mathrm{.}9,$testing:$0\mathrm{.}1),$ and use MAE to evaluate the performance. please give me the complete code$($not the steps but the code that can run it directly$).$ Thank you very much!:$)$