<jupyter_start><jupyter_text>deep learning 4 intermediate hiddenfrom sklearn.metrics import accuracy_score from keras.models import Sequentialfrom keras.layers import Activationfrom keras.layers.core import Densefrom keras.optimizers import Adamfrom keras.metrics import categorical_crossentropy,binary_crossentropyfrom keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropoutn_inp=np.array(X_train)model=Sequential([Dense(units=16,input_dim=30,activation="relu"),                  Dense(units=32,activation="relu"),                  Dropout(0.5),                  Dense(units=20,activation="relu"),                  Dense(units=16,activation="relu"),                  Dense(units=1,activation="sigmoid")                                  ])model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])model.fit(x_residual_train_adasyn,y_residual_train_adasyn,batch_size=25,epochs=5,shuffle=False,verbose=2)score=model.evaluate(X_test,Y_test)print("Score")print(score)y_deep_predict=model.predict(X_test)print("Confusion matrix")print(confusion_matrix(Y_test,y_deep_predict.round()))print(accuracy_score(Y_test,y_deep_predict.round()))<jupyter_code>#xbgboost classifier
#boosting trees

from sklearn.metrics import accuracy_score 
from xgboost import XGBClassifier as xg
model_xgb= xg(n_estimators=100,random_state=42)
model_xgb.fit(x_residual_train_adasyn,y_residual_train_adasyn)
y_pred_lgbm=model_xgb.predict(X_test)
print("Confusion matrix")
print(confusion_matrix(Y_test,y_pred_xgb))
print(accuracy_score(Y_test,y_pred_xgb.round()))
                                                                        


#LGBMClassifier
from sklearn.metrics import accuracy_score 
from lightgbm import LGBMClassifier as lg
model_lgbm= lg(n_estimators=100,random_state=42)
model_lgbm.fit(x_residual_train_adasyn,y_residual_train_adasyn)
y_pred_lgbm=model_lgbm.predict(X_test)
print("Confusion matrix")
print(confusion_matrix(Y_test,y_pred_lgbm))
print(accuracy_score(Y_test,y_pred_lgbm.round()))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10137120470127327, 'fraction_numerical': 0.014691478942213516, 'mean_word_length': 5.855704697986577, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 4, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '408244', 'license': 'MIT', 'n_tokens_mistral': 712, 'n_tokens_neox': 674, 'n_words': 78, 'path': 'Credit_Card_Fraud_Kaggle.ipynb', 'repo_name': 'abhilash1910/Credit_Card_Fraud_Kaggle', 'size': '2042'}