from DataCleaner import *
from classifcation import*
from HandelMissingValues import*
credits_file="tmdb_5000_credits_test.csv"
movies_reg="tmdb_5000_movies_testing_regression.xlsx"
movies_class="tmdb_5000_movies_testing_classification.xlsx"
#for classification

train, test=runDataCleaner(movies_class,credits_file , 'c')
X_train,X_test,y_train,y_test,features=handle_test_and_train_missings(train, test,'c')
#X_train,X_test,y_train,y_test= train_test_split(X_train, y_train, test_size=0.2 ,random_state=42 )

print("classification Without PCA")
classification_withoutPCA(X_train,X_test,y_train,y_test,0)

print("classificstion With PCA")
classification_with_PCA(X_train,X_test,y_train,y_test,1)


#for regression:

train, test=runDataCleaner(movies_reg,credits_file , 'r')

X_train,X_test,y_train,y_test,features=handle_test_and_train_missings(train, test,'r')
regression(X_train,X_test,y_train,y_test,features)





ebcf7d78292ff5fa70f5e3cdbf6b3b80cc9f71b8
<mask token>

<mask token>
print('classification Without PCA')
classification_withoutPCA(X_train, X_test, y_train, y_test, 0)
print('classificstion With PCA')
classification_with_PCA(X_train, X_test, y_train, y_test, 1)
<mask token>
regression(X_train, X_test, y_train, y_test, features)

<mask token>
credits_file = 'tmdb_5000_credits_test.csv'
movies_reg = 'tmdb_5000_movies_testing_regression.xlsx'
movies_class = 'tmdb_5000_movies_testing_classification.xlsx'
train, test = runDataCleaner(movies_class, credits_file, 'c')
X_train, X_test, y_train, y_test, features = handle_test_and_train_missings(
    train, test, 'c')
print('classification Without PCA')
classification_withoutPCA(X_train, X_test, y_train, y_test, 0)
print('classificstion With PCA')
classification_with_PCA(X_train, X_test, y_train, y_test, 1)
train, test = runDataCleaner(movies_reg, credits_file, 'r')
X_train, X_test, y_train, y_test, features = handle_test_and_train_missings(
    train, test, 'r')
regression(X_train, X_test, y_train, y_test, features)

from DataCleaner import *
from classifcation import *
from HandelMissingValues import *
credits_file = 'tmdb_5000_credits_test.csv'
movies_reg = 'tmdb_5000_movies_testing_regression.xlsx'
movies_class = 'tmdb_5000_movies_testing_classification.xlsx'
train, test = runDataCleaner(movies_class, credits_file, 'c')
X_train, X_test, y_train, y_test, features = handle_test_and_train_missings(
    train, test, 'c')
print('classification Without PCA')
classification_withoutPCA(X_train, X_test, y_train, y_test, 0)
print('classificstion With PCA')
classification_with_PCA(X_train, X_test, y_train, y_test, 1)
train, test = runDataCleaner(movies_reg, credits_file, 'r')
X_train, X_test, y_train, y_test, features = handle_test_and_train_missings(
    train, test, 'r')
regression(X_train, X_test, y_train, y_test, features)

from DataCleaner import *
from classifcation import*
from HandelMissingValues import*
credits_file="tmdb_5000_credits_test.csv"
movies_reg="tmdb_5000_movies_testing_regression.xlsx"
movies_class="tmdb_5000_movies_testing_classification.xlsx"
#for classification

train, test=runDataCleaner(movies_class,credits_file , 'c')
X_train,X_test,y_train,y_test,features=handle_test_and_train_missings(train, test,'c')
#X_train,X_test,y_train,y_test= train_test_split(X_train, y_train, test_size=0.2 ,random_state=42 )

print("classification Without PCA")
classification_withoutPCA(X_train,X_test,y_train,y_test,0)

print("classificstion With PCA")
classification_with_PCA(X_train,X_test,y_train,y_test,1)


#for regression:

train, test=runDataCleaner(movies_reg,credits_file , 'r')

X_train,X_test,y_train,y_test,features=handle_test_and_train_missings(train, test,'r')
regression(X_train,X_test,y_train,y_test,features)




