Question

Can someone please just run this on their system and post the screenshot so I can know it works. The .csv file and program are given below, please just run it and provide the screenshot. Also, I am using 3.6 Python in Pycharm. Thanks!

Bank_Predictions.csv

use a portion of the dataset Bank_Predictions which I have provided below (it is only 10 lines because the actual file has over 1000 lines so here is a small snippet);

Number	Customer_ID	Last_Name	Cr_Score	Location	Gender	Age	History	Current_Balance	Num_Of_Products	Has_CrCard	IsActiveMember	Customer_Salary	Acc_Closed
1	15634602	Hargrave	619	France	Female	42	2	0	1	1	1	101348.9	1
2	15647311	Hill	608	Spain	Female	41	1	83807.86	1	0	1	112542.6	0
3	15619304	Onio	502	France	Female	42	8	159660.8	3	1	0	113931.6	1
4	15701354	Boni	699	France	Female	39	1	0	2	0	0	93826.63	0
5	15737888	Mitchell	850	Spain	Female	43	2	125510.8	1	1	1	79084.1	0
6	15574012	Chu	645	Spain	Male	44	8	113755.8	2	1	0	149756.7	1
7	15592531	Bartlett	822	France	Male	50	7	0	2	1	1	10062.8	0
8	15656148	Obinna	376	Germany	Female	29	4	115046.7	4	1	0	119346.9	1
9	15792365	He	501	France	Male	44	4	142051.1	2	0	1	74940.5	0
10	15592389	Hanah	684	France	Male	27	2	134603.9	1	1	1	71725.73	0
11	15767821	Bearce	528	France	Male	31	6	102016.7	2

PROGRAM:

import numpy as np
import matplotlib as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Bank_Predictions.csv')

# Splitting the attributes into independent and dependent attributes
X = dataset.iloc[:, :-1].values # attributes to determine dependent variable / Class
Y = dataset.iloc[:, -1].values # dependent variable / Class

# ------ Part-1: Data preprocessing ----------

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
onehotencoder = OneHotEncoder(categorical_features=[0])
X = onehotencoder.fit_transform(X).toarray()

labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)


# Splitting the dataset into the Training and Test sets
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: Build the ANN --------

# import keras library and packages
import keras
from keras.models import Sequential
from keras.layers import Dense

# Initializing the ANN
classifier = Sequential()

# Adding the input layer and the first hidden layer
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu', input_dim = 11))

# Adding second hidden layer
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu'))

# Adding output layer
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))

# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Fitting the ANN to the training set
classifier.fit(X_train, Y_train, batch_size = 10, nb_epoch = 100)

# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
print(y_pred)

# Making the confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(Y_test, y_pred)
print(cm)

accuracy = classifier.evaluate(X, Y)
print(accuracy)

Answer 1

Answer) I have executed the code using python 3.6. The screen shot is below:

At the end of the last photo, you can see the confusion matrix and accuracy 0.875 that is 87%.

[2] import import numpy as np import matplotlib as plt import pandas as pd [5] # Importing the dataset dataset = pd. read_csv('Bank_Predictions.csv') # Splitting the attributes into independent and dependent attributes X = dataset.iloc[:, :-1].values # attributes to determine dependent variable / class Y = dataset.iloc[:, -1].values # dependent variable / class [7] # ------ Part-1: Data preprocessing ---------- # Encoding categorical data from sklearn.preprocessing import LabelEncoder, OneHotEncoder labelencoder_X = LabelEncoder) X[:, ®] = labelencoder_x.fit_transform(X[:, 0]) onehotencoder = OneHotEncoder) X = onehotencoder.fit_transform(x).toarray() labelencoder_Y = LabelEncoder) Y = labelencoder_Y.fit_transform(Y) # Splitting the dataset into the Training and Test sets 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) [9] + ------- Part-2: Build the ANN ------- # import keras library and packages import keras from keras.models import Sequential from keras.layers import Dense # Initializing the ANN classifier = Sequential() # Adding the input layer and the first hidden layer classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu', input_dim = 11)) # Adding second hidden layer classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu')) # Adding output layer classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid)) # Compiling the ANN classifier.compile( optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

C /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: UserWarning: Update your 'Dense' call to the Keras 2 API: ‘Dens if _name__ == '__main__': /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: UserWarning: Update your 'Dense' call to the Keras 2 API: 'Den if sys.path[0] == "': /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: Userwarning: Update your 'Dense' call to the Keras 2 API: 'Den from ipykernel import kernelapp as app [45] # Fitting the ANN to the training set classifier.fit(x_train[:8,:11], Y_train, batch_size = 10, nb_epoch = 100) # Predicting the Test set results y_pred = classifier.predict(x_test[:8,:11]) y_pred = (y_pred > 0.5) print(y_pred) # Making the confusion Matrix from sklearn.metrics import confusion_matrix cm = confusion_matrix(Y_test, y_pred) print(cm) accuracy = classifier.evaluate(X[:8,:11], Y[:8]) print(accuracy)

Epoch 1/100 8/8 [==============================] - Os 412us/step - loss: 0.1326 - acc: 1.0000 Epoch 2/100 8/8 [==============================] - Os 512us/step - loss: 0.1321 - acc: 1.0000 Epoch 3/100 8/8 [==============================] - Os 489us/ step - loss: 0. 1315 - acc: 1.0000 Epoch 4/100 8/8 [ ============= ================== ] - Os 238us/step - loss: 0.1309 - acc: 1.0000 Epoch 5/100 8/8 [==============================] - Os 237us/step - loss: 0.1303 - acc: 1.0000 Epoch 6/100 8/8 [==============================] - Os 234us/step - loss: 0.1297 - acc: 1.0000 Epoch 7/100 8/8 [==============================] - Os 175us/step - loss: 0.1291 - acc: 1.0000 Epoch 8/100 8/8 [==============================] - Os 189us/ step - loss: 0.1286 - acc: 1.0000 Epoch 9/100 8/8 [==============================] - Os 193us/step - loss: 0.1280 - acc: 1.0000 Epoch 10/100 8/8 [============================== ] - Os 228us/step - loss: 0.1274 - acc: 1.0000 Epoch 11/100 8/8 [==============================] - Os 238us/ step - loss: 0.1269 - acc: 1.0000 Epoch 12/100 8/8 [==============================] - Os 224us/ step - loss: 0.1263 - acc: 1.0000 Epoch 13/100 8/8 [==============================] - Os 284us/ step - loss: 0.1257 - acc: 1.0000 Epoch 14/100 8/8 [==============================] - Os 261us/step - loss: 0.1252 - acc: 1.0000 Epoch 15/100 8/8 [================= =====] - Os 341us/step - loss: 0.1246 - acc: 1.0000 = = = = = =

Epoch 89/100 8/8 [==============================] - Os 276us/ step - loss: 0.0883 - acc: 1.0000 Epoch 90/100 8/8 [==============================] - Os 300us/step - loss: 0.0879 - acc: 1.0000 Epoch 91/100 8/8 [==============================] - Os 246us/ step - loss: 0.0875 - acc: 1.0000 Epoch 92/100 8/8 [==============================] - Os 238us/step - loss: 0.0870 - acc: 1.0000 Epoch 93/100 8/8 [==============================] - Os 209us/ step - loss: 0.0866 - acc: 1.0000 Epoch 94/100 8/8 [============================== ] - Os 273us/step - loss: 0.0862 - acc: 1.0000 Epoch 95/100 8/8 [==============================] - Os 249us/step - loss: 0.0858 - acc: 1.0000 Epoch 96/100 8/8 [==============================] - Os 332us/step - loss: 0.0854 - acc: 1.0000 Epoch 97/100 8/8 [==============================] - Os 529us/ step - loss: 0.0850 - acc: 1.0000 Epoch 98/100 8/8 ======= ===============] - Os 357us/step - loss: 0.0846 - acc: 1.0000 Epoch 99/100 8/8 ======== ==============] - Os 375us/ step - loss: 0.0843 - acc: 1.0000 Epoch 100/100 8/8 =================== =========== ] - Os 316us/step - loss: 0.0839 - acc: 1.0000 [[False] [False]] [[10] [10]] 8/8 [==============================] - Os 227us/step [0.35099542140960693, 0.875]