diff --git a/ann.py b/ann.py
index 1ac39d6..a9589ee 100644
--- a/ann.py
+++ b/ann.py
@@ -44,24 +44,27 @@
 # Part 2 - Now let's make the ANN!
 
 # Importing the Keras libraries and packages
-import keras
 from keras.models import Sequential
-from keras.layers import Dense
+from keras.layers import Dense, Dropout, Conv1D,  Masking, Embedding, LSTM
+from keras.initializers import RandomNormal
 
 # Initialising the ANN
 classifier = Sequential()
 
-# Adding the input layer and the first hidden layer
-classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
+classifier.add(Dense(256, input_dim=11, activation='relu'))
 
-# Adding the second hidden layer
-classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
+classifier.add(Dropout(0.5))
 
-# Adding the output layer
-classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
+classifier.add(Dense(256, activation='relu'))
+
+classifier.add(Dropout(0.5))
+
+classifier.add(Dense(1, activation='sigmoid'))
 
 # Compiling the ANN
-classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
+classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])
+
+print(classifier.summary())
 
 # Fitting the ANN to the Training set
 classifier.fit(X_train, y_train, batch_size = 10, epochs = 30)
diff --git a/eitlstm.py b/eitlstm.py
new file mode 100644
index 0000000..1e47357
--- /dev/null
+++ b/eitlstm.py
@@ -0,0 +1,155 @@
+
+# Artificial Neural Network
+
+# Installing Theano
+# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
+
+# Installing Tensorflow
+# pip install tensorflow
+
+# Installing Keras
+# pip install --upgrade keras
+
+# Part 1 - Data Preprocessing
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Churn_Modelling.csv')
+X = dataset.iloc[:, 3:13].values
+y = dataset.iloc[:, 13].values
+
+# Encoding categorical data
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder_X_1 = LabelEncoder()
+X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
+labelencoder_X_2 = LabelEncoder()
+X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
+onehotencoder = OneHotEncoder(categorical_features = [1])
+X = onehotencoder.fit_transform(X).toarray()
+X = X[:, 1:]
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
+
+# Feature Scaling
+from sklearn.preprocessing import StandardScaler
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+
+# Part 2 - Now let's make the ANN!
+
+# Importing the Keras libraries and packages
+import keras
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import Dropout
+
+# Initialising the ANN
+classifier = Sequential()
+
+# Adding the input layer and the first hidden layer
+classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
+classifier.add(Dropout(0.5))
+classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
+classifier.add(Dropout(0.5))
+classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
+
+# Compiling the ANN
+classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])
+
+# Fitting the ANN to the Training set
+classifier.fit(X_train, y_train, batch_size = 10, epochs = 10)
+
+
+# Before optimization loss: 0.3864 - acc: 0.8512
+
+# Part 3 - Making predictions and evaluating the model
+
+# Predicting the Test set results
+y_pred = classifier.predict(X_test)
+y_pred = (y_pred > 0.5)
+
+# Predicting a single new observation
+"""Predict if the customer with the following informations will leave the bank:
+Geography: France
+Credit Score: 600
+Gender: Male
+Age: 40
+Tenure: 3
+Balance: 60000
+Number of Products: 2
+Has Credit Card: Yes
+Is Active Member: Yes
+Estimated Salary: 50000"""
+new_prediction = classifier.predict(sc.transform(np.array([[0.0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])))
+new_prediction = (new_prediction > 0.5)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
+
+# Part 4 - Evaluating, Improving and Tuning the ANN
+
+# Evaluating the ANN
+from keras.wrappers.scikit_learn import KerasClassifier
+from sklearn.model_selection import cross_val_score
+from keras.models import Sequential
+from keras.layers import Dense
+#from keras.layers import Dropout
+def build_classifier():
+    classifier = Sequential()
+    classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
+    classifier.add(Dropout(rate = 0.5))
+    classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
+    classifier.add(Dropout(rate = 0.5))
+    classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
+    classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])
+    return classifier
+classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 10)
+accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
+mean = accuracies.mean()
+variance = accuracies.std()
+
+# Improving the ANN
+# Dropout Regularization to reduce overfitting if needed
+
+# Tuning the ANN
+from keras.wrappers.scikit_learn import KerasClassifier
+from sklearn.model_selection import GridSearchCV
+from keras.models import Sequential
+from keras.layers import Dense
+
+def build_classifier(optimizer):
+    classifier = Sequential()
+    classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
+    classifier.add(Dropout(rate = 0.5))
+    classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
+    classifier.add(Dropout(rate = 0.5))
+    classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
+    classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy'])
+    return classifier
+
+classifier = KerasClassifier(build_fn = build_classifier)
+
+parameters = {'batch_size': [20,32],
+              'epochs': [20, 32],
+              'optimizer': ['adam', 'rmsprop']}
+
+grid_search = GridSearchCV(estimator = classifier,
+                           scoring = 'accuracy',
+                           param_grid = parameters,
+                           cv = 10)
+
+grid_search = grid_search.fit(X_train, y_train)
+
+best_parameters = grid_search.best_params_ 
+best_accuracy = grid_search.best_score_
+
+# Best Accuracy: 0.861375
+# Best parameter: {'batch_size': 30, 'epochs': 32, 'optimizer': 'adam'}
diff --git a/evaluating_improving_tuning.py b/eitmlp.py
similarity index 74%
rename from evaluating_improving_tuning.py
rename to eitmlp.py
index 42a9c67..0260e7f 100644
--- a/evaluating_improving_tuning.py
+++ b/eitmlp.py
@@ -1,3 +1,4 @@
+
 # Artificial Neural Network
 
 # Installing Theano
@@ -53,21 +54,20 @@
 classifier = Sequential()
 
 # Adding the input layer and the first hidden layer
-classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
-# classifier.add(Dropout(rate = 0.1))
-
-# Adding the second hidden layer
-classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
-# classifier.add(Dropout(rate = 0.1))
-
-# Adding the output layer
+classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
+classifier.add(Dropout(0.5))
+classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
+classifier.add(Dropout(0.5))
 classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
 
 # Compiling the ANN
-classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
+classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])
 
 # Fitting the ANN to the Training set
-classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
+classifier.fit(X_train, y_train, batch_size = 10, epochs = 10)
+
+
+# Before optimization loss: 0.3864 - acc: 0.8512
 
 # Part 3 - Making predictions and evaluating the model
 
@@ -104,12 +104,12 @@
 #from keras.layers import Dropout
 def build_classifier():
     classifier = Sequential()
-    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
-    classifier.add(Dropout(rate = 0.1))
-    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
-    classifier.add(Dropout(rate = 0.1))
+    classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
+    classifier.add(Dropout(rate = 0.5))
+    classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
+    classifier.add(Dropout(rate = 0.5))
     classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
-    classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
+    classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])
     return classifier
 classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 10)
 accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
@@ -127,18 +127,18 @@ def build_classifier():
 
 def build_classifier(optimizer):
     classifier = Sequential()
-    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
-    classifier.add(Dropout(rate = 0.1))
-    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
-    classifier.add(Dropout(rate = 0.1))
+    classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
+    classifier.add(Dropout(rate = 0.5))
+    classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
+    classifier.add(Dropout(rate = 0.5))
     classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
     classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy'])
     return classifier
 
 classifier = KerasClassifier(build_fn = build_classifier)
 
-parameters = {'batch_size': [25, 32],
-              'epochs': [10, 50],
+parameters = {'batch_size': [10, 12],
+              'epochs': [5, 10],
               'optimizer': ['adam', 'rmsprop']}
 
 grid_search = GridSearchCV(estimator = classifier,
@@ -148,5 +148,8 @@ def build_classifier(optimizer):
 
 grid_search = grid_search.fit(X_train, y_train)
 
-best_parameters = grid_search.best_params_
-best_accuracy = grid_search.best_score_
\ No newline at end of file
+best_parameters = grid_search.best_params_ 
+best_accuracy = grid_search.best_score_
+
+# Best Accuracy: 0.857875
+# Best parameter: {'batch_size': 12, 'epochs': 10, 'optimizer': 'adam'}
\ No newline at end of file
diff --git a/lstm.py b/lstm.py
new file mode 100644
index 0000000..220fd47
--- /dev/null
+++ b/lstm.py
@@ -0,0 +1,83 @@
+# Artificial Neural Network
+
+# Installing Theano
+# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
+
+# Installing Tensorflow
+# pip install tensorflow
+
+# Installing Keras
+# pip install --upgrade keras
+
+# Part 1 - Data Preprocessing
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Churn_Modelling.csv')
+X = dataset.iloc[:, 3:13].values
+y = dataset.iloc[:, 13].values
+
+# Encoding categorical data
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder_X_1 = LabelEncoder()
+X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
+labelencoder_X_2 = LabelEncoder()
+X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
+onehotencoder = OneHotEncoder(categorical_features = [1])
+X = onehotencoder.fit_transform(X).toarray()
+X = X[:, 1:]
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
+
+# Feature Scaling
+from sklearn.preprocessing import StandardScaler
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+
+# Convert to 3d (not recommended)
+X_test = np.reshape(X_test, X_test.shape + (1,))
+X_train = np.reshape(X_train, X_train.shape + (1,))
+
+# Part 2 - Now let's make the ANN!
+
+# Importing the Keras libraries and packages
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Conv1D,  Masking, Embedding, LSTM
+from keras.initializers import RandomNormal
+
+# Initialising the ANN
+
+model = Sequential()
+
+model.add(LSTM(128))
+
+model.add(Dropout(0.5))
+
+model.add(Dense(1, activation='sigmoid'))
+
+# Compiling the ANN
+model.compile(loss='binary_crossentropy',
+              optimizer='rmsprop',
+              metrics=['accuracy'])
+
+# Fitting the ANN to the Training set
+model.fit(X_train, y_train, batch_size=16, epochs=10)
+print(model.summary())
+
+
+# Part 3 - Making predictions and evaluating the model
+
+# Predicting the Test set results
+y_pred = model.predict(X_test)
+y_pred = (y_pred > 0.5)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
\ No newline at end of file