
#!pip install jupyterthemes


#Importing libararies

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import Accuracy
from jupyterthemes import jtplot

#jtplot.style(theme = 'monokai', context = 'notebook', ticks = True, grid = False)


bank_df = pd.read_csv('UniversalBank.csv')


bank_df.head()


#Obtain dataframe info

bank_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 14 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   ID                  5000 non-null   int64  
 1   Age                 5000 non-null   int64  
 2   Experience          5000 non-null   int64  
 3   Income              5000 non-null   int64  
 4   ZIP Code            5000 non-null   int64  
 5   Family              5000 non-null   int64  
 6   CCAvg               5000 non-null   float64
 7   Education           5000 non-null   int64  
 8   Mortgage            5000 non-null   int64  
 9   Personal Loan       5000 non-null   int64  
 10  Securities Account  5000 non-null   int64  
 11  CD Account          5000 non-null   int64  
 12  Online              5000 non-null   int64  
 13  CreditCard          5000 non-null   int64  
dtypes: float64(1), int64(13)
memory usage: 547.0 KB


# Obtain the statistical summary of the dataframe
bank_df.describe()


# For better visualization
bank_df.describe().T


# See how many null values exist in the dataframe
bank_df.isnull().sum()

ID                    0
Age                   0
Experience            0
Income                0
ZIP Code              0
Family                0
CCAvg                 0
Education             0
Mortgage              0
Personal Loan         0
Securities Account    0
CD Account            0
Online                0
CreditCard            0
dtype: int64


# Visualize personal Loan column 
# Percentage of customers who accepted personal loan ~ 9%
plt.figure(figsize = (10, 7))
sns.countplot(bank_df['Personal Loan']);


# Visualize Education feature
plt.figure(figsize = (10, 7))
sns.countplot(bank_df['Education'])

<matplotlib.axes._subplots.AxesSubplot at 0x203d6af3320>


# Visualize Age
# Uniform distribution between 30-60 years
plt.figure(figsize = (20, 10))
sns.countplot(bank_df['Age'])

<matplotlib.axes._subplots.AxesSubplot at 0x203d6af3fd0>


# Visualize credit card availability feature
# Recall that ~29% of customers have credit cards
plt.figure(figsize = (10, 7))
sns.countplot(bank_df['CreditCard']);


# Visualize income data
# Most customers have incomes that range between 45K and 60K per year
# Data is skewed with less customers earning above 100K
plt.figure(figsize = (20, 10))
sns.distplot(bank_df['Income'])

<matplotlib.axes._subplots.AxesSubplot at 0x203d6f54e48>


# Create two dataframes for the two classes
personalloans = bank_df[bank_df['Personal Loan'] == 1]
no_personalloans = bank_df[bank_df['Personal Loan'] == 0]


personalloans


personalloans.describe()
# Mean income of customers who have personal loans is generally high ~ 144K and average CC of 3.9K


no_personalloans


no_personalloans.describe()
# Mean income of customers who have do not have personal loans is generally low ~ 66K and average CC of 1.7K


# Plot the distribution plot for both classes separately 
# Customers who took personal loans tend to have higher income
plt.figure(figsize = (20, 10))
sns.distplot(personalloans['Income'], color = 'g')
sns.distplot(no_personalloans['Income'], color = 'r')

<matplotlib.axes._subplots.AxesSubplot at 0x203d7030550>


# Plot pairplot
plt.figure(figsize = (30, 30))
sns.pairplot(bank_df)

<seaborn.axisgrid.PairGrid at 0x203d7071ba8>

<Figure size 2160x2160 with 0 Axes>


# Correlation plot
# Stong Positive correlation between experience and age
# Strong positive correlation between CC average and income
plt.figure(figsize = (20, 20))
cm = bank_df.corr()
ax = plt.subplot()
sns.heatmap(cm, annot = True, ax = ax)

<matplotlib.axes._subplots.AxesSubplot at 0x203dd2c7c18>


# List all column names
bank_df.columns

Index(['ID', 'Age', 'Experience', 'Income', 'ZIP Code', 'Family', 'CCAvg',
       'Education', 'Mortgage', 'Personal Loan', 'Securities Account',
       'CD Account', 'Online', 'CreditCard'],
      dtype='object')


# Specify model input features (all data except for the target variable) 
X = bank_df.drop(columns = ['Personal Loan'])
X


# Model output (target variable)
y = bank_df['Personal Loan']
y

0       0
1       0
2       0
3       0
4       0
       ..
4995    0
4996    0
4997    0
4998    0
4999    0
Name: Personal Loan, Length: 5000, dtype: int64


from tensorflow.keras.utils import to_categorical
y = to_categorical(y)
y

array([[1., 0.],
       [1., 0.],
       [1., 0.],
       ...,
       [1., 0.],
       [1., 0.],
       [1., 0.]], dtype=float32)


# scale the data before training the model
from sklearn import metrics
from sklearn.preprocessing import StandardScaler, MinMaxScaler

scaler_x = StandardScaler()
X = scaler_x.fit_transform(X)


# spliting the data in to test and train sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1)


# print the shapes
X_train.shape, X_test.shape, y_train.shape, y_test.shape

((4500, 13), (500, 13), (4500, 2), (500, 2))


# Create keras sequential model
ANN_model = keras.Sequential()

# Add dense layer
ANN_model.add(Dense(250, input_dim = 13, kernel_initializer = 'normal',activation = 'relu'))

# Add dropout layer to make sure ann isn't overfitting the training data
ANN_model.add(Dropout(0.3))

# Add dense layer
ANN_model.add(Dense(500, activation = 'relu'))

# Add dropout layer
ANN_model.add(Dropout(0.3))

# Add dense layer
ANN_model.add(Dense(500, activation = 'relu'))

# Add dropout layer
ANN_model.add(Dropout(0.4))

# Add dense layer
ANN_model.add(Dense(250, activation = 'linear'))

# Add dropout layer
ANN_model.add(Dropout(0.5))

# Add dense layer with softmax activation
ANN_model.add(Dense(2, activation = 'softmax'))
ANN_model.summary()

Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_20 (Dense)             (None, 250)               3500      
_________________________________________________________________
dropout_16 (Dropout)         (None, 250)               0         
_________________________________________________________________
dense_21 (Dense)             (None, 500)               125500    
_________________________________________________________________
dropout_17 (Dropout)         (None, 500)               0         
_________________________________________________________________
dense_22 (Dense)             (None, 500)               250500    
_________________________________________________________________
dropout_18 (Dropout)         (None, 500)               0         
_________________________________________________________________
dense_23 (Dense)             (None, 250)               125250    
_________________________________________________________________
dropout_19 (Dropout)         (None, 250)               0         
_________________________________________________________________
dense_24 (Dense)             (None, 2)                 502       
=================================================================
Total params: 505,252
Trainable params: 505,252
Non-trainable params: 0
_________________________________________________________________


# Compiling the model
ANN_model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])


#Splitting into validation and training set to avoid overfitting
history = ANN_model.fit(X_train, y_train, epochs = 20, validation_split = 0.2, verbose = 1)

Train on 3600 samples, validate on 900 samples
Epoch 1/20
3600/3600 [==============================] - 4s 1ms/sample - loss: 0.1787 - acc: 0.9311 - val_loss: 0.0711 - val_acc: 0.9744
Epoch 2/20
3600/3600 [==============================] - 2s 469us/sample - loss: 0.0975 - acc: 0.9642 - val_loss: 0.0623 - val_acc: 0.9789
Epoch 3/20
3600/3600 [==============================] - 2s 449us/sample - loss: 0.0786 - acc: 0.9722 - val_loss: 0.0554 - val_acc: 0.9778
Epoch 4/20
3600/3600 [==============================] - 1s 416us/sample - loss: 0.0743 - acc: 0.9753 - val_loss: 0.0441 - val_acc: 0.9856
Epoch 5/20
3600/3600 [==============================] - 2s 431us/sample - loss: 0.0739 - acc: 0.9739 - val_loss: 0.0484 - val_acc: 0.9844
Epoch 6/20
3600/3600 [==============================] - 2s 439us/sample - loss: 0.0643 - acc: 0.9778 - val_loss: 0.0467 - val_acc: 0.9822
Epoch 7/20
3600/3600 [==============================] - 2s 425us/sample - loss: 0.0564 - acc: 0.9814 - val_loss: 0.0499 - val_acc: 0.9822
Epoch 8/20
3600/3600 [==============================] - 2s 570us/sample - loss: 0.0610 - acc: 0.9808 - val_loss: 0.0516 - val_acc: 0.9844
Epoch 9/20
3600/3600 [==============================] - 4s 1ms/sample - loss: 0.0548 - acc: 0.9794 - val_loss: 0.0481 - val_acc: 0.9844
Epoch 10/20
3600/3600 [==============================] - 4s 1ms/sample - loss: 0.0497 - acc: 0.9836 - val_loss: 0.0405 - val_acc: 0.9856
Epoch 11/20
3600/3600 [==============================] - 4s 1ms/sample - loss: 0.0457 - acc: 0.9831 - val_loss: 0.0381 - val_acc: 0.9867
Epoch 12/20
3600/3600 [==============================] - 4s 1ms/sample - loss: 0.0474 - acc: 0.9831 - val_loss: 0.0422 - val_acc: 0.9811
Epoch 13/20
3600/3600 [==============================] - 3s 781us/sample - loss: 0.0471 - acc: 0.9844 - val_loss: 0.0390 - val_acc: 0.9878
Epoch 14/20
3600/3600 [==============================] - 3s 938us/sample - loss: 0.0414 - acc: 0.9839 - val_loss: 0.0465 - val_acc: 0.9833
Epoch 15/20
3600/3600 [==============================] - 2s 644us/sample - loss: 0.0482 - acc: 0.9836 - val_loss: 0.0363 - val_acc: 0.9856
Epoch 16/20
3600/3600 [==============================] - 2s 431us/sample - loss: 0.0371 - acc: 0.9878 - val_loss: 0.0466 - val_acc: 0.9844
Epoch 17/20
3600/3600 [==============================] - 2s 419us/sample - loss: 0.0403 - acc: 0.9861 - val_loss: 0.0303 - val_acc: 0.9867
Epoch 18/20
3600/3600 [==============================] - 2s 436us/sample - loss: 0.0347 - acc: 0.9875 - val_loss: 0.0456 - val_acc: 0.9867
Epoch 19/20
3600/3600 [==============================] - 2s 495us/sample - loss: 0.0331 - acc: 0.9881 - val_loss: 0.0368 - val_acc: 0.9878
Epoch 20/20
3600/3600 [==============================] - 3s 796us/sample - loss: 0.0303 - acc: 0.9886 - val_loss: 0.0492 - val_acc: 0.9844


# Plot the model performance across epochs
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train_loss','val_loss'], loc = 'upper right')
plt.show()


# Make predictions
predictions = ANN_model.predict(X_test)

# Append the index of max value using argmax function
predict = []
for i in predictions:
    predict.append(np.argmax(i))


# Get the acccuracy of the model
result = ANN_model.evaluate(X_test, y_test)

print("Accuracy : {}".format(result[1]))

500/500 [==============================] - 0s 373us/sample - loss: 0.1368 - acc: 0.9700
Accuracy : 0.9700000286102295


# Get the original values
y_original = []

for i in y_test:
    y_original.append(np.argmax(i))


# Plot Confusion Matrix to plot original values against the model
confusion_matrix = metrics.confusion_matrix(y_original, predict)
sns.heatmap(confusion_matrix, annot = True)

<matplotlib.axes._subplots.AxesSubplot at 0x203dffd7cc0>


# Print out the classification report
from sklearn.metrics import classification_report
print(classification_report(y_original, predict))

              precision    recall  f1-score   support

           0       0.98      0.99      0.98       459
           1       0.84      0.78      0.81        41

    accuracy                           0.97       500
   macro avg       0.91      0.88      0.90       500
weighted avg       0.97      0.97      0.97       500

	ID	Age	Experience	Income	ZIP Code	Family	CCAvg	Education	Mortgage	Personal Loan	Securities Account	CD Account	Online	CreditCard
count	5000.000000	5000.000000	5000.000000	5000.000000	5000.000000	5000.000000	5000.000000	5000.000000	5000.000000	5000.000000	5000.000000	5000.00000	5000.000000	5000.000000
mean	2500.500000	45.338400	20.104600	73.774200	93152.503000	2.396400	1.937938	1.881000	56.498800	0.096000	0.104400	0.06040	0.596800	0.294000
std	1443.520003	11.463166	11.467954	46.033729	2121.852197	1.147663	1.747659	0.839869	101.713802	0.294621	0.305809	0.23825	0.490589	0.455637
min	1.000000	23.000000	-3.000000	8.000000	9307.000000	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000
25%	1250.750000	35.000000	10.000000	39.000000	91911.000000	1.000000	0.700000	1.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000
50%	2500.500000	45.000000	20.000000	64.000000	93437.000000	2.000000	1.500000	2.000000	0.000000	0.000000	0.000000	0.00000	1.000000	0.000000
75%	3750.250000	55.000000	30.000000	98.000000	94608.000000	3.000000	2.500000	3.000000	101.000000	0.000000	0.000000	0.00000	1.000000	1.000000
max	5000.000000	67.000000	43.000000	224.000000	96651.000000	4.000000	10.000000	3.000000	635.000000	1.000000	1.000000	1.00000	1.000000	1.000000

	count	mean	std	min	25%	50%	75%	max
ID	5000.0	2500.500000	1443.520003	1.0	1250.75	2500.5	3750.25	5000.0
Age	5000.0	45.338400	11.463166	23.0	35.00	45.0	55.00	67.0
Experience	5000.0	20.104600	11.467954	-3.0	10.00	20.0	30.00	43.0
Income	5000.0	73.774200	46.033729	8.0	39.00	64.0	98.00	224.0
ZIP Code	5000.0	93152.503000	2121.852197	9307.0	91911.00	93437.0	94608.00	96651.0
Family	5000.0	2.396400	1.147663	1.0	1.00	2.0	3.00	4.0
CCAvg	5000.0	1.937938	1.747659	0.0	0.70	1.5	2.50	10.0
Education	5000.0	1.881000	0.839869	1.0	1.00	2.0	3.00	3.0
Mortgage	5000.0	56.498800	101.713802	0.0	0.00	0.0	101.00	635.0
Personal Loan	5000.0	0.096000	0.294621	0.0	0.00	0.0	0.00	1.0
Securities Account	5000.0	0.104400	0.305809	0.0	0.00	0.0	0.00	1.0
CD Account	5000.0	0.060400	0.238250	0.0	0.00	0.0	0.00	1.0
Online	5000.0	0.596800	0.490589	0.0	0.00	1.0	1.00	1.0
CreditCard	5000.0	0.294000	0.455637	0.0	0.00	0.0	1.00	1.0

	ID	Age	Experience	Income	ZIP Code	Family	CCAvg	Education	Mortgage	Personal Loan	Securities Account	CD Account	Online	CreditCard
count	480.000000	480.000000	480.000000	480.000000	480.000000	480.000000	480.000000	480.000000	480.000000	480.0	480.000000	480.000000	480.00000	480.000000
mean	2390.650000	45.066667	19.843750	144.745833	93153.202083	2.612500	3.905354	2.233333	100.845833	1.0	0.125000	0.291667	0.60625	0.297917
std	1394.393674	11.590964	11.582443	31.584429	1759.223753	1.115393	2.097681	0.753373	160.847862	0.0	0.331064	0.455004	0.48909	0.457820
min	10.000000	26.000000	0.000000	60.000000	90016.000000	1.000000	0.000000	1.000000	0.000000	1.0	0.000000	0.000000	0.00000	0.000000
25%	1166.500000	35.000000	9.000000	122.000000	91908.750000	2.000000	2.600000	2.000000	0.000000	1.0	0.000000	0.000000	0.00000	0.000000
50%	2342.000000	45.000000	20.000000	142.500000	93407.000000	3.000000	3.800000	2.000000	0.000000	1.0	0.000000	0.000000	1.00000	0.000000
75%	3566.000000	55.000000	30.000000	172.000000	94705.500000	4.000000	5.347500	3.000000	192.500000	1.0	0.000000	1.000000	1.00000	1.000000
max	4981.000000	65.000000	41.000000	203.000000	96008.000000	4.000000	10.000000	3.000000	617.000000	1.0	1.000000	1.000000	1.00000	1.000000

	ID	Age	Experience	Income	ZIP Code	Family	CCAvg	Education	Mortgage	Personal Loan	Securities Account	CD Account	Online	CreditCard
count	4520.000000	4520.000000	4520.000000	4520.000000	4520.000000	4520.000000	4520.000000	4520.000000	4520.000000	4520.0	4520.000000	4520.000000	4520.000000	4520.000000
mean	2512.165487	45.367257	20.132301	66.237389	93152.428761	2.373451	1.729009	1.843584	51.789381	0.0	0.102212	0.035841	0.595796	0.293584
std	1448.299331	11.450427	11.456672	40.578534	2156.949654	1.148771	1.567647	0.839975	92.038931	0.0	0.302961	0.185913	0.490792	0.455454
min	1.000000	23.000000	-3.000000	8.000000	9307.000000	1.000000	0.000000	1.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
25%	1259.750000	35.000000	10.000000	35.000000	91911.000000	1.000000	0.600000	1.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
50%	2518.500000	45.000000	20.000000	59.000000	93437.000000	2.000000	1.400000	2.000000	0.000000	0.0	0.000000	0.000000	1.000000	0.000000
75%	3768.250000	55.000000	30.000000	84.000000	94608.000000	3.000000	2.300000	3.000000	98.000000	0.0	0.000000	0.000000	1.000000	1.000000
max	5000.000000	67.000000	43.000000	224.000000	96651.000000	4.000000	8.800000	3.000000	635.000000	0.0	1.000000	1.000000	1.000000	1.000000

Problem Statement/ background¶

A simple deep neural network model to predict the likekleyhood of a liability customer's (depositor) request of a personal loan getting approved based on features such as age, experience, income, locations, family, education, existing mortgage etc.¶

Features:¶

ID: Customer ID¶

Age: Customer's age in years¶

Experience: Professional experience in years¶

Income: Annual income of customer¶

Zipcode: home address zip code¶

Family: Customer's family size¶

CCAvg: Average spending on credit cards per month¶

Education: Education level (1: Undergraduate, 2: Graduate, 3: Advanced/Proffessional)¶

Mortgage: Value of house mortgage¶

Personal Loan: did the customer accept personal loan in the last campaign?¶

Securities Account: does the customer have a securities account with the bank?¶

CD Account: does the customer have a CD (certificate of deposit) account with the bank?¶

Online: does the customer use internet banking facilities?¶

CreditCard: does the customer use a credit card issued by this bank?¶

Data visualization¶

Preparing data before training the model¶

Building a simple multi layer neural network model¶

Training the deep learning model¶

Assessing performance of the trained model¶

	ID	Age	Experience	Income	ZIP Code	Family	CCAvg	Education	Securities Account	CreditCard
0	1	25	1	49	91107	4	1.6	1	1	0
1	2	45	19	34	90089	3	1.5	1	1	0
2	3	39	15	11	94720	1	1.0	1	0	0
3	4	35	9	100	94112	1	2.7	2	0	0
4	5	35	8	45	91330	4	1.0	2	0	1

	ID	Age	Experience	Income	ZIP Code	Family	CCAvg	Education	Mortgage	Personal Loan	Securities Account	CD Account	Online	CreditCard
9	10	34	9	180	93023	1	8.9	3	0	1	0	0	0	0
16	17	38	14	130	95010	4	4.7	3	134	1	0	0	0	0
18	19	46	21	193	91604	2	8.1	3	0	1	0	0	0	0
29	30	38	13	119	94104	1	3.3	2	0	1	0	1	1	1
38	39	42	18	141	94114	3	5.0	3	0	1	1	1	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4883	4884	38	13	129	92646	3	4.1	3	0	1	0	1	1	1
4927	4928	43	19	121	94720	1	0.7	2	0	1	0	1	1	1
4941	4942	28	4	112	90049	2	1.6	2	0	1	0	0	1	0
4962	4963	46	20	122	90065	3	3.0	3	0	1	0	1	1	1
4980	4981	29	5	135	95762	3	5.3	1	0	1	0	1	1	1