Project title: Bank Customer Churn Classification

Problem Statement: Predicting if a Customer will exit bank or not from the given Bank Customer data.

1.0 Business Understanding

The Banking Inducstry has experienced massive growth over the years and with many new high tech entrants, competition has also increased tremendously.

There are limitless number of options where customers can put their money, from traditional Banks to online solid startups. To understand how your customers are likely to behave will equip the bank in addressing issues that in the first place cause them to leave.

Customer experience or customer service has been cited as the number one reason why clients move banks followed by unfavourable fees among other reasons such as lack of new digital products.

Once customers leave, it is hard to get them back. For this reason post event analysis will only give reasons why they left and cannot prevent the action from happening. However, with the help of AI, we can use the same data to predict who might leave and therefore have an opportunity to seek to understand why.

For most companies, the customer acquisition cost (cost of acquiring a new customer) is higher than the cost of retaining an existing customer. Therefore, the challenge of implementing a successful churn project is to increase customer loyalty and, consequently, increase company revenue.

It is therefore neccessary to analyze data of customers who left to find insights which might help us predict the ones who are likely to leave and develop plans to reduce these number.

2.0 Data Understanding

Creating a Churner Profile and Identifying Churn Behavior

In trying to gain insights into the data, I create visual representations that aggregate and summarize the following:

1. Creating Customer Segmentation based on behavior, characteristics, patterns and address the question, "Which Customer do we care about? The best vs the most valuable.
2. Compare to Control population: Understanding one time customers vs regular engaged ones
3. Identifying what makes your Churner different

Implementing the Churn Scoring Mechanism

In looking for a solution, I determine the best approach and algorithms by first:

1. Finding relevant features, then
2. Computing a Churn Score

Feature description¶

Bank client data:

1 - ID (numeric)
2 - Surname (String)
3 - age (numeric)
4 - Credit Score (Numeric)
5 - Geographical Area : (categorical: 'France', 'German', 'Spain')
6 - Gender : (categorical: 'Male' or 'Female')
7 - Tenure : Number of years member has been with Bank (Numeric)
8 - Balance: Amount in Account (Numeric)
9 - NumofProducts: Number of Products member has with Bank (Numeric)

Other Attributes:

10 - HasCrCard: Has Credit Card (binary: '1 = yes',0 = 'no')
11 - IsActiveMember: Is Member Still current Account Holder (binary: '1 = yes',0 = 'no')
12 - EstimatedSalary: Estimated Salary (Numeric)

Output variable (desired target):

13 - y - has the client Exited? (binary: '1 = yes',0 = 'no')

!python -m pip install scikit-learn==0.22.0 --user

#!python -m pip install cufflinks

from toggle_cell import toggle_code as hide_solution

# Importing Libraries
from __future__ import print_function
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns #visualization
sns.set()

import matplotlib.pyplot as plt #visualization
%matplotlib inline

import itertools
import warnings
warnings.filterwarnings("ignore")
import os
import io
import plotly.offline as py #visualization
py.init_notebook_mode(connected=True) #visualization
import plotly.graph_objs as go #visualization
import plotly.tools as tls #visualization
import plotly.figure_factory as ff #visualization

# Required to make it work in Google Colab
from plotly import __version__

import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot 

cf.go_offline()

hide_solution()

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Setting Notebook Mode to False

# training dataset import
#cust_data = pd.read_csv("C:\\Users\\Welcome\\PROF790\\Bank_Churn\\Churn_Modelling.csv")
cust_data = pd.read_csv("Churn_Modelling.csv")
cust_data.head(10)

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	RowNumber	CustomerId	Surname	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	1	15634602	Hargrave	619	France	Female	42	2	0.00	1	1	1	101348.88	1
1	2	15647311	Hill	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0
2	3	15619304	Onio	502	France	Female	42	8	159660.80	3	1	0	113931.57	1
3	4	15701354	Boni	699	France	Female	39	1	0.00	2	0	0	93826.63	0
4	5	15737888	Mitchell	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0
5	6	15574012	Chu	645	Spain	Male	44	8	113755.78	2	1	0	149756.71	1
6	7	15592531	Bartlett	822	France	Male	50	7	0.00	2	1	1	10062.80	0
7	8	15656148	Obinna	376	Germany	Female	29	4	115046.74	4	1	0	119346.88	1
8	9	15792365	He	501	France	Male	44	4	142051.07	2	0	1	74940.50	0
9	10	15592389	H?	684	France	Male	27	2	134603.88	1	1	1	71725.73	0

Percentage of Exited Customers Vs Remaining

# Percentage per category for the target column.
percentage_labels = cust_data['Exited'].value_counts(normalize = True) * 100
percentage_labels

0    79.63
1    20.37
Name: Exited, dtype: float64

Note: Of the 10,000 Bank Customer data provided, 20.37% of them Left the Bank

Exploratory Data Analysis

How does the provided data Features Correlate?

# Drop the irrelevant columns  as shown above
cust_data = cust_data.drop(["RowNumber", "CustomerId", "Surname"], axis = 1)

# Build correlation matrix
corr = cust_data.corr()
corr.style.background_gradient(cmap='PuBu')

Note

The graphical analysis shows all values are very small and less than +0.5 or -0.5. We can therefore say features are not correlated.

#price range correlation
corr.sort_values(by=["CreditScore"], ascending=False).iloc[0].sort_values(ascending=False)

CreditScore        1.000000
IsActiveMember     0.025651
NumOfProducts      0.012238
Balance            0.006268
Tenure             0.000842
EstimatedSalary   -0.001384
Age               -0.003965
HasCrCard         -0.005458
Exited            -0.027094
Name: CreditScore, dtype: float64

Taking a look at how Data is Distributed using Scatter Plots

#scatterplot
sns.set()
cols_pr = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts','HasCrCard', 'EstimatedSalary']
sns.pairplot(cust_data[cols_pr], height = 2.0)
plt.show()

Deductions:

Credit Score seems to be normally distributed while
There are high number of customers with low balances and
Most Customers either have one or two bank products.

Checking for Missing Data

There some algorithms that can't handle missing data or may perform poorly when present with such data. We check and appropriate handle any missing values for that matter.

#missing data
total_null = cust_data.isnull().sum().sort_values(ascending=False)
percent_null = (cust_data.isnull().sum()/cust_data.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total_null, percent_null], axis=1, keys=['Total', 'Percent'])

missing_data

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	Total	Percent
Exited	0	0.0
EstimatedSalary	0	0.0
IsActiveMember	0	0.0
HasCrCard	0	0.0
NumOfProducts	0	0.0
Balance	0	0.0
Tenure	0	0.0
Age	0	0.0
Gender	0	0.0
Geography	0	0.0
CreditScore	0	0.0

Inspecting for Ouliers

cust_data.describe()

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	CreditScore	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
count	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.00000	10000.000000	10000.000000	10000.000000
mean	650.528800	38.921800	5.012800	76485.889288	1.530200	0.70550	0.515100	100090.239881	0.203700
std	96.653299	10.487806	2.892174	62397.405202	0.581654	0.45584	0.499797	57510.492818	0.402769
min	350.000000	18.000000	0.000000	0.000000	1.000000	0.00000	0.000000	11.580000	0.000000
25%	584.000000	32.000000	3.000000	0.000000	1.000000	0.00000	0.000000	51002.110000	0.000000
50%	652.000000	37.000000	5.000000	97198.540000	1.000000	1.00000	1.000000	100193.915000	0.000000
75%	718.000000	44.000000	7.000000	127644.240000	2.000000	1.00000	1.000000	149388.247500	0.000000
max	850.000000	92.000000	10.000000	250898.090000	4.000000	1.00000	1.000000	199992.480000	1.000000

Data Visualization

Distribution by Geographical Area

from itertools import chain
countmale = cust_data[cust_data['Gender']=='Male']['Gender'].count()
countfemale = cust_data[cust_data['Gender']=='Female']['Gender'].count()    
fig,aix = plt.subplots(figsize=(8,6))
#print(countmale)
#print(countfemale)
aix = sns.countplot(hue='Exited',y='Geography',data=cust_data)

Note: 0 = 'Male' 1 = 'Female'

from matplotlib import rcParams

# figure size in inches
#rcParams['figure.figsize'] = 11.7,8.27

g = sns.FacetGrid(cust_data,hue = 'Exited', height = 6.27, aspect=9.7/6.27)
(g.map(plt.hist,'Age',edgecolor="w").add_legend())

<seaborn.axisgrid.FacetGrid at 0x28d18d37ba8>

Note:

Legend - 1 = 'Yes', 0 = 'No'

Observation:

From above graph, Current Customer Age data distribution is skewed to the left meaning more younger generation tend to stick with the bank.

sns.kdeplot(cust_data.CreditScore[cust_data.Gender=='Male'], label='Men', shade=True)
sns.kdeplot(cust_data.CreditScore[cust_data.Gender=='Female'], label= 'Women', shade=True)
plt.xlabel('Credit Score')

Text(0.5, 0, 'Credit Score')

Note:

Credit Score data tends to be normally distributed both in men and women

# Drop the irrelevant columns  as shown above
training_data = cust_data

training_data.head()

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	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	619	France	Female	42	2	0.00	1	1	1	101348.88	1
1	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0
2	502	France	Female	42	8	159660.80	3	1	0	113931.57	1
3	699	France	Female	39	1	0.00	2	0	0	93826.63	0
4	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0

#Separating churn and non churn customers
churn     = training_data[training_data["Exited"] == 1]
not_churn = training_data[training_data["Exited"] == 0]

target_col = ["Exited"]
cat_cols   = training_data.nunique()[training_data.nunique() < 6].keys().tolist()
cat_cols   = [x for x in cat_cols if x not in target_col]
num_cols   = [x for x in training_data.columns if x not in cat_cols + target_col]

Setting Display Function for Colab

# Setting to Allow for Colab Display of interactive Plots
#configure_plotly_browser_state()

# Function which plot box plot for detecting outliers
trace = []
def gen_boxplot(df):
    for feature in df:
        trace.append(
            go.Box(
                name = feature,
                y = df[feature]
            )
        )

new_df = training_data[num_cols[:1]]
gen_boxplot(new_df)
data = trace
py.iplot(data)

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Note:

Credit Score data is normally didtributed but with a concentration of low Credit Score for substantial number 
of customers

configure_plotly_browser_state()

# Function which plot box plot for detecting outliers
trace = []
def gen_boxplot(df):
    for feature in df:
        trace.append(
            go.Box(
                name = feature,
                y = df[feature]
            )
        )
new_df = training_data[num_cols[1:3]]
gen_boxplot(new_df)
data = trace
py.iplot(data)

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9, 3, 3, 7, 7, 3, 6, 6, 9, 5, 3, 1, 9, 2, 10, 8, 9, 10, 5, 3, 4, 6, 6, 8, 1, 5, 8, 8, 5, 9, 8, 5, 9, 5, 10, 4, 0, 4, 4, 5, 6, 1, 5, 8, 9, 1, 6, 4, 1, 9, 2, 4, 8, 0, 1, 2, 6, 8, 7, 5, 2, 1, 5, 3, 1, 3, 2, 0, 8, 3, 7, 10, 6, 5, 8, 9, 9, 4, 3, 2, 4, 4, 3, 3, 6, 3, 4, 1, 6, 9, 9, 7, 9, 4, 2, 3, 5, 8, 4, 8, 6, 7, 0, 6, 5, 1, 3, 5, 2, 9, 8, 2, 5, 1, 8, 1, 4, 8, 5, 9, 6, 9, 3, 4, 6, 6, 3, 1, 6, 8, 9, 9, 2, 8, 4, 5, 4, 2, 1, 9, 6, 6, 2, 6, 3, 9, 7, 1, 5, 5, 0, 7, 9, 1, 9, 5, 3, 4, 1, 7, 9, 1, 2, 6, 4, 8, 6, 3, 5, 7, 1, 3, 5, 5, 10, 7, 4, 6, 8, 4, 7, 2, 7, 5, 4, 2, 9, 2, 7, 6, 9, 9, 8, 4, 6, 2, 6, 6, 1, 9, 4, 1, 3, 2, 8, 7, 6, 9, 1, 4, 5, 2, 7, 4, 10, 7, 9, 8, 10, 6, 5, 3, 7, 3, 5, 6, 2, 6, 8, 7, 9, 1, 2, 8, 4, 9, 2, 0, 4, 9, 2, 4, 1, 3, 7, 5, 9, 9, 1, 1, 9, 8, 8, 2, 5, 6, 8, 9, 2, 9, 2, 2, 8, 6, 8, 4, 9, 9, 3, 2, 1, 1, 2, 9, 1, 0, 8, 4, 4, 9, 5, 2, 9, 6, 5, 6, 2, 8, 9, 3, 2, 6, 8, 5, 8, 2, 8, 0, 10, 6, 3, 2, 2, 3, 6, 3, 2, 6, 6, 10, 6, 2, 1, 9, 8, 8, 7, 7, 6, 1, 2, 4, 5, 4, 9, 1, 2, 2, 0, 5, 4, 4, 9, 4, 8, 5, 4, 7, 9, 6, 2, 6, 5, 9, 7, 5, 1, 9, 7, 3, 5, 4, 7, 8, 2, 8, 2, 0, 9, 7, 9, 9, 2, 2, 10, 8, 4, 0, 7, 0, 2, 6, 4, 9, 6, 9, 5, 9, 3, 6, 5, 2, 2, 9, 5, 6, 1, 6, 2, 10, 7, 5, 2, 3, 5, 0, 6, 8, 1, 6, 7, 9, 3, 5, 0, 7, 5, 5, 4, 9, 4, 7, 3, 5, 3, 7, 2, 7, 2, 1, 4, 4, 1, 3, 5, 5, 4, 9, 2, 5, 9, 9, 10, 7, 2, 9, 1, 3, 4, 6, 9, 9, 4, 9, 4, 7, 1, 9, 10, 4, 4, 4, 4, 1, 7, 3, 4, 4, 9, 8, 3, 10, 8, 7, 4, 3, 3, 4, 8, 9, 8, 0, 7, 2, 1, 9, 2, 8, 9, 6, 9, 8, 6, 0, 7, 2, 4, 10, 2, 4, 3, 5, 2, 8, 4, 4, 3, 6, 2, 8, 4, 8, 1, 6, 7, 10, 1, 0, 4, 5, 8, 2, 10, 5, 10, 5, 0, 9, 1, 5, 4, 1, 10, 9, 1, 8, 9, 2, 4, 10, 3, 4, 9, 6, 3, 9, 6, 4, 8, 10, 9, 8, 5, 8, 2, 1, 4, 3, 6, 6, 7, 3, 1, 6, 4, 4, 3, 10, 5, 2, 4, 3, 8, 8, 10, 5, 0, 6, 0, 4, 8, 1, 2, 10, 2, 7, 1, 10, 4, 4, 6, 2, 2, 0, 7, 4, 1, 1, 4, 10, 6, 6, 8, 10, 3, 4, 2, 5, 5, 1, 3, 0, 6, 6, 8, 3, 9, 1, 5, 0, 1, 0, 4, 8, 0, 3, 7, 8, 2, 9, 4, 4, 8, 2, 6, 1, 9, 5, 6, 4, 6, 1, 6, 2, 5, 7, 9, 2, 9, 5, 4, 8, 1, 0, 6, 0, 5, 7, 0, 9, 6, 8, 5, 8, 7, 2, 6, 7, 6, 10, 4, 2, 1, 7, 10, 9, 0, 7, 5, 5, 3, 1, 1, 2, 4, 3, 3, 7, 3, 9, 9, 6, 1, 8, 6, 5, 0, 9, 3, 5, 2, 6, 2, 3, 3, 1, 2, 1, 9, 4, 2, 0, 9, 3, 8, 6, 5, 4, 5, 5, 5, 9, 4, 6, 5, 4, 0, 3, 3, 2, 5, 9, 2, 4, 2, 2, 8, 2, 3, 9, 7, 3, 2, 5, 9, 7, 2, 2, 7, 5, 1, 10, 10, 4, 5, 5, 3, 9, 7, 6, 0, 3, 4, 8, 2, 1, 8, 2, 3, 8, 0, 7, 5, 8, 4, 2, 4, 7, 2, 4, 9, 3, 1, 4, 1, 9, 9, 9, 8, 1, 3, 6, 3, 9, 8, 3, 9, 8, 8, 4, 3, 2, 10, 1, 3, 7, 1, 5, 7, 2, 8, 4, 3, 7, 4, 8, 2, 4, 7, 9, 8, 10, 8, 6, 9, 8, 8, 6, 0, 7, 1, 4, 6, 7, 2, 6, 3, 6, 7, 9, 1, 10, 4, 1, 5, 7, 6, 1, 1, 3, 7, 8, 10, 9, 8, 9, 8, 6, 5, 7, 9, 6, 1, 0, 4, 3, 2, 5, 8, 7, 6, 2, 6, 8, 9, 8, 4, 7, 2, 5, 0, 3, 7, 2, 10, 9, 2, 7, 2, 1, 4, 8, 4, 5, 1, 10, 3, 5, 9, 1, 4, 7, 9, 6, 1, 0, 4, 8, 9, 6, 8, 6, 2, 1, 4, 5, 2, 6, 2, 10, 4, 7, 5, 9, 5, 5, 6, 4, 1, 5, 3, 3, 3, 4, 7, 6, 8, 9, 8, 6, 9, 1, 8, 9, 1, 0, 3, 1, 9, 7, 7, 5, 1, 6, 8, 2, 3, 8, 3, 5, 6, 1, 2, 9, 5, 5, 3, 9, 3, 7, 2, 9, 6, 7, 8, 2, 1, 7, 3, 7, 6, 0, 9, 6, 2, 8, 1, 6, 7, 1, 2, 6, 4, 9, 4, 8, 6, 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                    {"responsive": true}
                ).then(function(){

// Listen for the removal of the full notebook cells var notebookContainer = gd.closest('#notebook-container'); if (notebookContainer) {{ x.observe(notebookContainer, {childList: true}); }}

// Listen for the clearing of the current output cell var outputEl = gd.closest('.output'); if (outputEl) {{ x.observe(outputEl, {childList: true}); }}

                    })
            };
            });
        </script>
    </div>

Note:

While the lower age limit for banking is determined by law and pegged at minimum age of 18, there is no upper age limit and Banks have a good number of Customer well past the upper quartle of Age 63 years.

Distribution of Continous Variables

#function  for histogram for customer churn types
def histogram(column) :
    trace1 = go.Histogram(x  = churn[column],
                          histnorm= "percent",
                          name = "Churn Customers",
                          marker = dict(line = dict(width = .5,
                                                    color = "black"
                                                    )
                                        ),
                         opacity = .9 
                         ) 
    
    trace2 = go.Histogram(x  = not_churn[column],
                          histnorm = "percent",
                          name = "Non Churn Customers",
                          marker = dict(line = dict(width = .5,
                                              color = "black"
                                             )
                                 ),
                          opacity = .9
                         )
    
    data = [trace1,trace2]
    layout = go.Layout(dict(title =column + " Distribution in Customer Attrition ",
                            plot_bgcolor  = "rgb(243,243,243)",
                            paper_bgcolor = "rgb(243,243,243)",
                            xaxis = dict(gridcolor = 'rgb(255, 255, 255)',
                                             title = column,
                                             zerolinewidth=1,
                                             ticklen=5,
                                             gridwidth=2
                                            ),
                            yaxis = dict(gridcolor = 'rgb(255, 255, 255)',
                                             title = "percent",
                                             zerolinewidth=1,
                                             ticklen=5,
                                             gridwidth=2
                                            ),
                           )
                      )
    fig  = go.Figure(data = data,layout=layout)
    
    py.iplot(fig)

#configure_plotly_browser_state()

# Calling the function for plotting the histogram for creditscore column 
histogram(num_cols[0])

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Note:

It is good to note that a section of Customers with very good Credit Card Score also churn.

We can hypothesize that, Banks compete for these High value Customers and therefore are likely to be given great offers by competing banks to join them.

#configure_plotly_browser_state()

# Calling the function for plotting the histogram for creditscore column 
# Pass the mouse hover the graph for more information. 
histogram(num_cols[1])

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Note:

From the above graph, Bank Customers who churn tend to be older compared to those who do note.

Hypothesis:

1. Older Customers tend to look for value adding products from their Banks and if not available they can easily switch Banks.
2. Older Customers are a very busy lot and value their time and therefore poor customer service can easily drive them away.

#configure_plotly_browser_state()

# Calling the function for plotting the histogram for balance column 
histogram(num_cols[3])

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"white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Balance Distribution in Customer Attrition "}, "xaxis": {"gridcolor": "rgb(255, 255, 255)", "gridwidth": 2, "ticklen": 5, "title": {"text": "Balance"}, "zerolinewidth": 1}, "yaxis": {"gridcolor": "rgb(255, 255, 255)", "gridwidth": 2, "ticklen": 5, "title": {"text": "percent"}, "zerolinewidth": 1}},
                    {"responsive": true}
                ).then(function(){

// Listen for the removal of the full notebook cells var notebookContainer = gd.closest('#notebook-container'); if (notebookContainer) {{ x.observe(notebookContainer, {childList: true}); }}

// Listen for the clearing of the current output cell var outputEl = gd.closest('.output'); if (outputEl) {{ x.observe(outputEl, {childList: true}); }}

                    })
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    </div>

Note:

There is an abnormaly high number of customers with very low Bank Balance who exited the Bank

Hypothesis:

We can hypothesize that customers with very low balances have less attachment and trust in the bank and therefore much easier to detach.

#function  for pie plot for customer attrition types
def plot_pie(column) :
    
    trace1 = go.Pie(values  = churn[column].value_counts().values.tolist(),
                    labels  = churn[column].value_counts().keys().tolist(),
                    hoverinfo = "label+percent+name",
                    domain  = dict(x = [0,.48]),
                    name    = "Churn Customers",
                    marker  = dict(line = dict(width = 2,
                                               color = "rgb(243,243,243)")
                                  ),
                    hole    = .6
                   )
    trace2 = go.Pie(values  = not_churn[column].value_counts().values.tolist(),
                    labels  = not_churn[column].value_counts().keys().tolist(),
                    hoverinfo = "label+percent+name",
                    marker  = dict(line = dict(width = 2,
                                               color = "rgb(243,243,243)")
                                  ),
                    domain  = dict(x = [.52,1]),
                    hole    = .6,
                    name    = "Non churn customers" 
                   )


    layout = go.Layout(dict(title = column + " Distribution in Customer Attrition ",
                            plot_bgcolor  = "rgb(243,243,243)",
                            paper_bgcolor = "rgb(243,243,243)",
                            annotations = [dict(text = "churn customers",
                                                font = dict(size = 13),
                                                showarrow = False,
                                                x = .15, y = .5),
                                           dict(text = "Non churn customers",
                                                font = dict(size = 13),
                                                showarrow = False,
                                                x = .88,y = .5
                                               )
                                          ]
                           )
                      )
    data = [trace1,trace2]
    fig  = go.Figure(data = data,layout = layout)
    py.iplot(fig)

#configure_plotly_browser_state()

# Calling the function for plotting the pie plot for geography column
plot_pie(cat_cols[0])

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                    {"annotations": [{"font": {"size": 13}, "showarrow": false, "text": "churn customers", "x": 0.15, "y": 0.5}, {"font": {"size": 13}, "showarrow": false, "text": "Non churn customers", "x": 0.88, "y": 0.5}], "paper_bgcolor": "rgb(243,243,243)", "plot_bgcolor": "rgb(243,243,243)", "template": {"data": {"bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "bar"}], "barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "barpolar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contour": [{"colorbar": {"outlinewidth": 0, 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Note:

Percentage wise, Spain had 20.3% at half the %age of those who exited than that of Germany and France

#configure_plotly_browser_state()

# Calling the function for plotting the pie plot for gender column
plot_pie(cat_cols[1])

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Note:

Female Customers are the ones who are most likely to leave the Banks.

3.0 Data Preparation & Preprocessing

cust_data.columns

Index(['CreditScore', 'Geography', 'Gender', 'Age', 'Tenure', 'Balance',
       'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary',
       'Exited'],
      dtype='object')

cust_data.head()

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	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	619	France	Female	42	2	0.00	1	1	1	101348.88	1
1	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0
2	502	France	Female	42	8	159660.80	3	1	0	113931.57	1
3	699	France	Female	39	1	0.00	2	0	0	93826.63	0
4	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0

Splitting the Independent and Dependent (Target) variables

X_columns = cust_data.columns.tolist()[0:10]
y_columns = cust_data.columns.tolist()[-1:]
print(f'All columns: {cust_data.columns.tolist()}')
print()
print(f'X values: {X_columns}')
print()
print(f'y values: {y_columns}')

All columns: ['CreditScore', 'Geography', 'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Exited']

X values: ['CreditScore', 'Geography', 'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary']

y values: ['Exited']

X = cust_data[X_columns].values # Credit Score through Estimated Salary
y = cust_data[y_columns].values # Exited

print(X[:5])

[[619 'France' 'Female' 42 2 0.0 1 1 1 101348.88]
 [608 'Spain' 'Female' 41 1 83807.86 1 0 1 112542.58]
 [502 'France' 'Female' 42 8 159660.8 3 1 0 113931.57]
 [699 'France' 'Female' 39 1 0.0 2 0 0 93826.63]
 [850 'Spain' 'Female' 43 2 125510.82 1 1 1 79084.1]]

Encoding categorical (string based) data.

# Encoding categorical (string based) data. Country: there are 3 options: France, Spain and Germany
# This will convert those strings into scalar values for analysis
print(X[:8,1], '... will now become: ')
from sklearn.preprocessing import LabelEncoder
label_X_country_encoder = LabelEncoder()
X[:,1] = label_X_country_encoder.fit_transform(X[:,1])
print(X[:8,1])

['France' 'Spain' 'France' 'France' 'Spain' 'Spain' 'France' 'Germany'] ... will now become: 
[0 2 0 0 2 2 0 1]

# We will do the same thing for gender. this will be binary in this dataset
print(X[:6,2], '... will now become: ')
from sklearn.preprocessing import LabelEncoder
label_X_gender_encoder = LabelEncoder()
X[:,2] = label_X_gender_encoder.fit_transform(X[:,2])
print(X[:6,2])

['Female' 'Female' 'Female' 'Female' 'Female' 'Male'] ... will now become: 
[0 0 0 0 0 1]

Creating Dummy Variables with OneHotEncoder

from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline


pipeline = Pipeline(
    [('Categorizer', ColumnTransformer(
         [ # Gender
          ("Gender Label encoder", OneHotEncoder(categories='auto', drop='first'), [2]),
           # Geography
          ("Geography One Hot", OneHotEncoder(categories='auto', drop='first'), [1])
         ], remainder='passthrough', n_jobs=1)),
     # Standard Scaler for the classifier
    ('Normalizer', StandardScaler())
    ])

X_data = pipeline.fit_transform(X)

# Splitting the dataset into the Training and Testing set.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_data,
                                                    y, 
                                                    test_size = 0.2, 
                                                    random_state = 0)

print(f'training shapes: {X_train.shape}, {y_train.shape}')
print(f'testing shapes: {X_test.shape}, {y_test.shape}')

training shapes: (8000, 11), (8000, 1)
testing shapes: (2000, 11), (2000, 1)

4.0 Modelling

With the advancement of data-driven Machine Learning, It is now possible to identify the potential inactive customers that are likely to churn and take measurable steps to retain them quickly.

4.1 Using Keras Neural Network without Dropout

import keras
from keras.models import Sequential
from keras.layers import Dense #to add layers

classifier = Sequential()

#init --> initialize weights according to uniform distribution
#input_dim is required for the first hidden layer, as it is the first starting point. --> number of nodes.
#output_dim --> number of nodes of the hidden layer
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu', input_dim = 11))

#input_dim --> remove it as it already knows what to expect.
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu'))


#the output layer: output_dim should be 1, as output is binary outcome, and activation should be 'sigmoid'
#If dependent variables have more than two categories, use activation = 'softmax'
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))

#compile the model --> backpropagation -> gradient descent
#optimizer = algorithm to find the optimal set of weights in ANN
#loss = functions that should be optimized. if more than two categories, use "categorical_crossentropy"
#metrics = criterion used to calculate the performance of the model.
classifier.compile(optimizer = 'adam', loss = "binary_crossentropy", metrics = ['accuracy'])

Using TensorFlow backend.

Fitting The Model

#batch_size = the number of observations after which you want to update the weights
#           batch size and epochs should be tuned through experiments.
#epoch = going through the whole dataset
classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 40)

hide_solution()

Epoch 1/40
8000/8000 [==============================] - 1s 108us/step - loss: 0.4764 - accuracy: 0.7968
Epoch 2/40
8000/8000 [==============================] - 1s 99us/step - loss: 0.4075 - accuracy: 0.8246
Epoch 3/40
8000/8000 [==============================] - 1s 91us/step - loss: 0.3926 - accuracy: 0.8304
Epoch 4/40
8000/8000 [==============================] - 1s 90us/step - loss: 0.3832 - accuracy: 0.8310
Epoch 5/40
8000/8000 [==============================] - 1s 90us/step - loss: 0.3760 - accuracy: 0.8393
Epoch 6/40
8000/8000 [==============================] - 1s 92us/step - loss: 0.3715 - accuracy: 0.8455
Epoch 7/40
8000/8000 [==============================] - 1s 88us/step - loss: 0.3668 - accuracy: 0.8475
Epoch 8/40
8000/8000 [==============================] - 1s 91us/step - loss: 0.3641 - accuracy: 0.8493
Epoch 9/40
8000/8000 [==============================] - 1s 89us/step - loss: 0.3611 - accuracy: 0.8515
Epoch 10/40
8000/8000 [==============================] - 1s 90us/step - loss: 0.3584 - accuracy: 0.8511
Epoch 11/40
8000/8000 [==============================] - 1s 90us/step - loss: 0.3583 - accuracy: 0.8546
Epoch 12/40
8000/8000 [==============================] - 1s 90us/step - loss: 0.3565 - accuracy: 0.8536
Epoch 13/40
8000/8000 [==============================] - 1s 93us/step - loss: 0.3554 - accuracy: 0.8549
Epoch 14/40
8000/8000 [==============================] - 1s 93us/step - loss: 0.3534 - accuracy: 0.8590
Epoch 15/40
8000/8000 [==============================] - 1s 89us/step - loss: 0.3538 - accuracy: 0.8575
Epoch 16/40
8000/8000 [==============================] - 1s 93us/step - loss: 0.3537 - accuracy: 0.8565
Epoch 17/40
8000/8000 [==============================] - 1s 97us/step - loss: 0.3524 - accuracy: 0.8577
Epoch 18/40
8000/8000 [==============================] - 1s 99us/step - loss: 0.3517 - accuracy: 0.8569
Epoch 19/40
8000/8000 [==============================] - 1s 89us/step - loss: 0.3522 - accuracy: 0.8568
Epoch 20/40
8000/8000 [==============================] - 1s 90us/step - loss: 0.3516 - accuracy: 0.8580
Epoch 21/40
8000/8000 [==============================] - 1s 91us/step - loss: 0.3509 - accuracy: 0.8577
Epoch 22/40
8000/8000 [==============================] - 1s 93us/step - loss: 0.3510 - accuracy: 0.8577
Epoch 23/40
8000/8000 [==============================] - 1s 95us/step - loss: 0.3511 - accuracy: 0.8554
Epoch 24/40
8000/8000 [==============================] - 1s 95us/step - loss: 0.3502 - accuracy: 0.8558
Epoch 25/40
8000/8000 [==============================] - 1s 94us/step - loss: 0.3509 - accuracy: 0.8587
Epoch 26/40
8000/8000 [==============================] - 1s 90us/step - loss: 0.3495 - accuracy: 0.8568
Epoch 27/40
8000/8000 [==============================] - 1s 93us/step - loss: 0.3490 - accuracy: 0.8595
Epoch 28/40
8000/8000 [==============================] - 1s 101us/step - loss: 0.3486 - accuracy: 0.8584
Epoch 29/40
8000/8000 [==============================] - 1s 91us/step - loss: 0.3483 - accuracy: 0.8575
Epoch 30/40
8000/8000 [==============================] - 1s 93us/step - loss: 0.3480 - accuracy: 0.8584
Epoch 31/40
8000/8000 [==============================] - 1s 90us/step - loss: 0.3472 - accuracy: 0.8587
Epoch 32/40
8000/8000 [==============================] - 1s 94us/step - loss: 0.3474 - accuracy: 0.8596
Epoch 33/40
8000/8000 [==============================] - 1s 94us/step - loss: 0.3468 - accuracy: 0.8587
Epoch 34/40
8000/8000 [==============================] - 1s 94us/step - loss: 0.3460 - accuracy: 0.8610
Epoch 35/40
8000/8000 [==============================] - 1s 92us/step - loss: 0.3462 - accuracy: 0.8596
Epoch 36/40
8000/8000 [==============================] - 1s 91us/step - loss: 0.3453 - accuracy: 0.8602
Epoch 37/40
8000/8000 [==============================] - 1s 103us/step - loss: 0.3442 - accuracy: 0.8564
Epoch 38/40
8000/8000 [==============================] - 1s 91us/step - loss: 0.3446 - accuracy: 0.8585
Epoch 39/40
8000/8000 [==============================] - 1s 89us/step - loss: 0.3440 - accuracy: 0.8597
Epoch 40/40
8000/8000 [==============================] - 1s 89us/step - loss: 0.3441 - accuracy: 0.8595

Predict The Results

#predicting the results

y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5) #to classify each probability into True or False

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print (cm, '\n\n', y_pred[:10, :])

[[1546   49]
 [ 218  187]] 

 [[False]
 [False]
 [False]
 [False]
 [False]
 [ True]
 [False]
 [False]
 [False]
 [ True]]

Calculating Accuracy From Confusion Matrix

# accuracy
print('The Test Accuracy is: ',((1547 + 138)/2000)*100,'%')

The Test Accuracy is:  84.25 %

4.2 Using Neural Network with Dropout

from keras.models import Sequential
from keras.layers import Dense, Dropout

# Initializing the ANN
classifier = Sequential()

# This adds the input layer (by specifying input dimension) AND the first hidden layer (units)
classifier.add(Dense(6, 
                     activation = 'relu', 
                     input_shape = (X_train.shape[1], )))
classifier.add(Dropout(rate=0.1))

Implementing hidden layer (Deep Neural Network)

# Adding the second hidden layer
# Notice that we do not need to specify input dim. 
classifier.add(Dense(6, activation = 'relu')) 
classifier.add(Dropout(rate=0.1))

Adding Output Layer

# Adding the output layer
# Notice that we do not need to specify input dim. 
# we have an output of 1 node, which is the the desired dimensions of our output (stay with the bank or not)
# We use the sigmoid because we want probability outcomes
classifier.add(Dense(1, activation = 'sigmoid'))

Compiling Neural Network

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

Fitting The Neural Network

history = classifier.fit(X_train, y_train, batch_size=32, epochs= 200, validation_split=0.1, verbose=2)

Train on 7200 samples, validate on 800 samples
Epoch 1/200
 - 1s - loss: 0.6659 - accuracy: 0.6162 - val_loss: 0.5078 - val_accuracy: 0.7950
Epoch 2/200
 - 0s - loss: 0.4935 - accuracy: 0.7939 - val_loss: 0.4560 - val_accuracy: 0.7950
Epoch 3/200
 - 0s - loss: 0.4647 - accuracy: 0.7960 - val_loss: 0.4371 - val_accuracy: 0.7975
Epoch 4/200
 - 0s - loss: 0.4543 - accuracy: 0.7971 - val_loss: 0.4273 - val_accuracy: 0.8012
Epoch 5/200
 - 0s - loss: 0.4457 - accuracy: 0.8018 - val_loss: 0.4195 - val_accuracy: 0.8087
Epoch 6/200
 - 0s - loss: 0.4438 - accuracy: 0.7983 - val_loss: 0.4142 - val_accuracy: 0.8100
Epoch 7/200
 - 0s - loss: 0.4355 - accuracy: 0.8062 - val_loss: 0.4071 - val_accuracy: 0.8138
Epoch 8/200
 - 0s - loss: 0.4328 - accuracy: 0.8099 - val_loss: 0.3999 - val_accuracy: 0.8275
Epoch 9/200
 - 0s - loss: 0.4246 - accuracy: 0.8179 - val_loss: 0.3918 - val_accuracy: 0.8288
Epoch 10/200
 - 0s - loss: 0.4180 - accuracy: 0.8225 - val_loss: 0.3831 - val_accuracy: 0.8413
Epoch 11/200
 - 0s - loss: 0.4172 - accuracy: 0.8275 - val_loss: 0.3772 - val_accuracy: 0.8438
Epoch 12/200
 - 0s - loss: 0.4068 - accuracy: 0.8301 - val_loss: 0.3694 - val_accuracy: 0.8500
Epoch 13/200
 - 0s - loss: 0.4004 - accuracy: 0.8347 - val_loss: 0.3624 - val_accuracy: 0.8575
Epoch 14/200
 - 0s - loss: 0.3949 - accuracy: 0.8351 - val_loss: 0.3578 - val_accuracy: 0.8575
Epoch 15/200
 - 0s - loss: 0.3921 - accuracy: 0.8338 - val_loss: 0.3535 - val_accuracy: 0.8625
Epoch 16/200
 - 0s - loss: 0.3938 - accuracy: 0.8317 - val_loss: 0.3519 - val_accuracy: 0.8625
Epoch 17/200
 - 0s - loss: 0.3874 - accuracy: 0.8372 - val_loss: 0.3497 - val_accuracy: 0.8600
Epoch 18/200
 - 0s - loss: 0.3865 - accuracy: 0.8381 - val_loss: 0.3486 - val_accuracy: 0.8612
Epoch 19/200
 - 0s - loss: 0.3819 - accuracy: 0.8396 - val_loss: 0.3485 - val_accuracy: 0.8600
Epoch 20/200
 - 0s - loss: 0.3816 - accuracy: 0.8392 - val_loss: 0.3467 - val_accuracy: 0.8600
Epoch 21/200
 - 0s - loss: 0.3818 - accuracy: 0.8361 - val_loss: 0.3477 - val_accuracy: 0.8562
Epoch 22/200
 - 0s - loss: 0.3789 - accuracy: 0.8414 - val_loss: 0.3441 - val_accuracy: 0.8637
Epoch 23/200
 - 0s - loss: 0.3789 - accuracy: 0.8392 - val_loss: 0.3434 - val_accuracy: 0.8650
Epoch 24/200
 - 0s - loss: 0.3775 - accuracy: 0.8419 - val_loss: 0.3419 - val_accuracy: 0.8650
Epoch 25/200
 - 0s - loss: 0.3805 - accuracy: 0.8394 - val_loss: 0.3427 - val_accuracy: 0.8637
Epoch 26/200
 - 0s - loss: 0.3793 - accuracy: 0.8399 - val_loss: 0.3426 - val_accuracy: 0.8625
Epoch 27/200
 - 0s - loss: 0.3749 - accuracy: 0.8408 - val_loss: 0.3403 - val_accuracy: 0.8650
Epoch 28/200
 - 0s - loss: 0.3796 - accuracy: 0.8390 - val_loss: 0.3411 - val_accuracy: 0.8625
Epoch 29/200
 - 0s - loss: 0.3785 - accuracy: 0.8408 - val_loss: 0.3412 - val_accuracy: 0.8637
Epoch 30/200
 - 0s - loss: 0.3815 - accuracy: 0.8376 - val_loss: 0.3413 - val_accuracy: 0.8625
Epoch 31/200
 - 0s - loss: 0.3765 - accuracy: 0.8414 - val_loss: 0.3400 - val_accuracy: 0.8675
Epoch 32/200
 - 0s - loss: 0.3745 - accuracy: 0.8425 - val_loss: 0.3392 - val_accuracy: 0.8687
Epoch 33/200
 - 0s - loss: 0.3731 - accuracy: 0.8461 - val_loss: 0.3398 - val_accuracy: 0.8675
Epoch 34/200
 - 0s - loss: 0.3735 - accuracy: 0.8417 - val_loss: 0.3386 - val_accuracy: 0.8675
Epoch 35/200
 - 0s - loss: 0.3707 - accuracy: 0.8460 - val_loss: 0.3373 - val_accuracy: 0.8687
Epoch 36/200
 - 0s - loss: 0.3717 - accuracy: 0.8468 - val_loss: 0.3375 - val_accuracy: 0.8625
Epoch 37/200
 - 0s - loss: 0.3739 - accuracy: 0.8415 - val_loss: 0.3376 - val_accuracy: 0.8675
Epoch 38/200
 - 0s - loss: 0.3640 - accuracy: 0.8479 - val_loss: 0.3353 - val_accuracy: 0.8700
Epoch 39/200
 - 0s - loss: 0.3741 - accuracy: 0.8431 - val_loss: 0.3360 - val_accuracy: 0.8687
Epoch 40/200
 - 0s - loss: 0.3722 - accuracy: 0.8444 - val_loss: 0.3370 - val_accuracy: 0.8662
Epoch 41/200
 - 0s - loss: 0.3701 - accuracy: 0.8422 - val_loss: 0.3348 - val_accuracy: 0.8675
Epoch 42/200
 - 0s - loss: 0.3729 - accuracy: 0.8454 - val_loss: 0.3338 - val_accuracy: 0.8687
Epoch 43/200
 - 0s - loss: 0.3687 - accuracy: 0.8453 - val_loss: 0.3333 - val_accuracy: 0.8712
Epoch 44/200
 - 0s - loss: 0.3788 - accuracy: 0.8397 - val_loss: 0.3357 - val_accuracy: 0.8662
Epoch 45/200
 - 0s - loss: 0.3710 - accuracy: 0.8447 - val_loss: 0.3340 - val_accuracy: 0.8687
Epoch 46/200
 - 0s - loss: 0.3718 - accuracy: 0.8458 - val_loss: 0.3336 - val_accuracy: 0.8687
Epoch 47/200
 - 0s - loss: 0.3756 - accuracy: 0.8432 - val_loss: 0.3335 - val_accuracy: 0.8687
Epoch 48/200
 - 0s - loss: 0.3683 - accuracy: 0.8453 - val_loss: 0.3323 - val_accuracy: 0.8712
Epoch 49/200
 - 0s - loss: 0.3622 - accuracy: 0.8494 - val_loss: 0.3305 - val_accuracy: 0.8687
Epoch 50/200
 - 0s - loss: 0.3649 - accuracy: 0.8504 - val_loss: 0.3297 - val_accuracy: 0.8725
Epoch 51/200
 - 0s - loss: 0.3726 - accuracy: 0.8457 - val_loss: 0.3310 - val_accuracy: 0.8637
Epoch 52/200
 - 0s - loss: 0.3651 - accuracy: 0.8483 - val_loss: 0.3306 - val_accuracy: 0.8687
Epoch 53/200
 - 0s - loss: 0.3656 - accuracy: 0.8493 - val_loss: 0.3295 - val_accuracy: 0.8662
Epoch 54/200
 - 0s - loss: 0.3637 - accuracy: 0.8507 - val_loss: 0.3273 - val_accuracy: 0.8687
Epoch 55/200
 - 0s - loss: 0.3692 - accuracy: 0.8469 - val_loss: 0.3289 - val_accuracy: 0.8675
Epoch 56/200
 - 0s - loss: 0.3698 - accuracy: 0.8494 - val_loss: 0.3291 - val_accuracy: 0.8725
Epoch 57/200
 - 0s - loss: 0.3609 - accuracy: 0.8536 - val_loss: 0.3276 - val_accuracy: 0.8675
Epoch 58/200
 - 0s - loss: 0.3709 - accuracy: 0.8474 - val_loss: 0.3271 - val_accuracy: 0.8662
Epoch 59/200
 - 0s - loss: 0.3651 - accuracy: 0.8500 - val_loss: 0.3265 - val_accuracy: 0.8687
Epoch 60/200
 - 0s - loss: 0.3701 - accuracy: 0.8511 - val_loss: 0.3271 - val_accuracy: 0.8700
Epoch 61/200
 - 0s - loss: 0.3657 - accuracy: 0.8507 - val_loss: 0.3261 - val_accuracy: 0.8687
Epoch 62/200
 - 0s - loss: 0.3661 - accuracy: 0.8503 - val_loss: 0.3257 - val_accuracy: 0.8662
Epoch 63/200
 - 0s - loss: 0.3667 - accuracy: 0.8489 - val_loss: 0.3266 - val_accuracy: 0.8650
Epoch 64/200
 - 0s - loss: 0.3643 - accuracy: 0.8483 - val_loss: 0.3262 - val_accuracy: 0.8675
Epoch 65/200
 - 0s - loss: 0.3645 - accuracy: 0.8504 - val_loss: 0.3252 - val_accuracy: 0.8712
Epoch 66/200
 - 0s - loss: 0.3598 - accuracy: 0.8535 - val_loss: 0.3256 - val_accuracy: 0.8687
Epoch 67/200
 - 0s - loss: 0.3614 - accuracy: 0.8503 - val_loss: 0.3253 - val_accuracy: 0.8662
Epoch 68/200
 - 0s - loss: 0.3635 - accuracy: 0.8531 - val_loss: 0.3243 - val_accuracy: 0.8700
Epoch 69/200
 - 0s - loss: 0.3666 - accuracy: 0.8501 - val_loss: 0.3241 - val_accuracy: 0.8675
Epoch 70/200
 - 0s - loss: 0.3664 - accuracy: 0.8492 - val_loss: 0.3247 - val_accuracy: 0.8650
Epoch 71/200
 - 0s - loss: 0.3647 - accuracy: 0.8518 - val_loss: 0.3247 - val_accuracy: 0.8650
Epoch 72/200
 - 0s - loss: 0.3607 - accuracy: 0.8510 - val_loss: 0.3238 - val_accuracy: 0.8687
Epoch 73/200
 - 0s - loss: 0.3639 - accuracy: 0.8506 - val_loss: 0.3231 - val_accuracy: 0.8687
Epoch 74/200
 - 0s - loss: 0.3601 - accuracy: 0.8540 - val_loss: 0.3239 - val_accuracy: 0.8700
Epoch 75/200
 - 0s - loss: 0.3606 - accuracy: 0.8540 - val_loss: 0.3231 - val_accuracy: 0.8712
Epoch 76/200
 - 0s - loss: 0.3605 - accuracy: 0.8540 - val_loss: 0.3217 - val_accuracy: 0.8725
Epoch 77/200
 - 0s - loss: 0.3610 - accuracy: 0.8522 - val_loss: 0.3225 - val_accuracy: 0.8700
Epoch 78/200
 - 0s - loss: 0.3609 - accuracy: 0.8515 - val_loss: 0.3220 - val_accuracy: 0.8725
Epoch 79/200
 - 0s - loss: 0.3636 - accuracy: 0.8511 - val_loss: 0.3236 - val_accuracy: 0.8712
Epoch 80/200
 - 0s - loss: 0.3598 - accuracy: 0.8519 - val_loss: 0.3232 - val_accuracy: 0.8700
Epoch 81/200
 - 0s - loss: 0.3648 - accuracy: 0.8507 - val_loss: 0.3235 - val_accuracy: 0.8737
Epoch 82/200
 - 0s - loss: 0.3575 - accuracy: 0.8533 - val_loss: 0.3212 - val_accuracy: 0.8712
Epoch 83/200
 - 0s - loss: 0.3605 - accuracy: 0.8515 - val_loss: 0.3221 - val_accuracy: 0.8712
Epoch 84/200
 - 0s - loss: 0.3663 - accuracy: 0.8512 - val_loss: 0.3228 - val_accuracy: 0.8712
Epoch 85/200
 - 0s - loss: 0.3602 - accuracy: 0.8521 - val_loss: 0.3222 - val_accuracy: 0.8712
Epoch 86/200
 - 0s - loss: 0.3645 - accuracy: 0.8486 - val_loss: 0.3224 - val_accuracy: 0.8712
Epoch 87/200
 - 0s - loss: 0.3638 - accuracy: 0.8500 - val_loss: 0.3232 - val_accuracy: 0.8712
Epoch 88/200
 - 0s - loss: 0.3587 - accuracy: 0.8525 - val_loss: 0.3237 - val_accuracy: 0.8700
Epoch 89/200
 - 0s - loss: 0.3630 - accuracy: 0.8526 - val_loss: 0.3231 - val_accuracy: 0.8725
Epoch 90/200
 - 0s - loss: 0.3627 - accuracy: 0.8499 - val_loss: 0.3246 - val_accuracy: 0.8700
Epoch 91/200
 - 0s - loss: 0.3588 - accuracy: 0.8550 - val_loss: 0.3232 - val_accuracy: 0.8700
Epoch 92/200
 - 0s - loss: 0.3594 - accuracy: 0.8531 - val_loss: 0.3234 - val_accuracy: 0.8700
Epoch 93/200
 - 0s - loss: 0.3611 - accuracy: 0.8542 - val_loss: 0.3244 - val_accuracy: 0.8712
Epoch 94/200
 - 0s - loss: 0.3577 - accuracy: 0.8517 - val_loss: 0.3237 - val_accuracy: 0.8700
Epoch 95/200
 - 0s - loss: 0.3585 - accuracy: 0.8536 - val_loss: 0.3231 - val_accuracy: 0.8712
Epoch 96/200
 - 0s - loss: 0.3616 - accuracy: 0.8515 - val_loss: 0.3240 - val_accuracy: 0.8712
Epoch 97/200
 - 0s - loss: 0.3585 - accuracy: 0.8531 - val_loss: 0.3231 - val_accuracy: 0.8675
Epoch 98/200
 - 0s - loss: 0.3589 - accuracy: 0.8544 - val_loss: 0.3239 - val_accuracy: 0.8687
Epoch 99/200
 - 0s - loss: 0.3593 - accuracy: 0.8556 - val_loss: 0.3246 - val_accuracy: 0.8712
Epoch 100/200
 - 0s - loss: 0.3655 - accuracy: 0.8487 - val_loss: 0.3250 - val_accuracy: 0.8662
Epoch 101/200
 - 0s - loss: 0.3589 - accuracy: 0.8536 - val_loss: 0.3235 - val_accuracy: 0.8712
Epoch 102/200
 - 0s - loss: 0.3604 - accuracy: 0.8533 - val_loss: 0.3244 - val_accuracy: 0.8687
Epoch 103/200
 - 0s - loss: 0.3584 - accuracy: 0.8526 - val_loss: 0.3259 - val_accuracy: 0.8650
Epoch 104/200
 - 0s - loss: 0.3584 - accuracy: 0.8544 - val_loss: 0.3236 - val_accuracy: 0.8700
Epoch 105/200
 - 0s - loss: 0.3594 - accuracy: 0.8539 - val_loss: 0.3248 - val_accuracy: 0.8650
Epoch 106/200
 - 0s - loss: 0.3579 - accuracy: 0.8539 - val_loss: 0.3236 - val_accuracy: 0.8700
Epoch 107/200
 - 0s - loss: 0.3597 - accuracy: 0.8547 - val_loss: 0.3245 - val_accuracy: 0.8687
Epoch 108/200
 - 0s - loss: 0.3615 - accuracy: 0.8512 - val_loss: 0.3235 - val_accuracy: 0.8675
Epoch 109/200
 - 0s - loss: 0.3621 - accuracy: 0.8532 - val_loss: 0.3239 - val_accuracy: 0.8662
Epoch 110/200
 - 0s - loss: 0.3599 - accuracy: 0.8532 - val_loss: 0.3232 - val_accuracy: 0.8675
Epoch 111/200
 - 0s - loss: 0.3645 - accuracy: 0.8537 - val_loss: 0.3247 - val_accuracy: 0.8687
Epoch 112/200
 - 0s - loss: 0.3589 - accuracy: 0.8553 - val_loss: 0.3225 - val_accuracy: 0.8662
Epoch 113/200
 - 0s - loss: 0.3608 - accuracy: 0.8537 - val_loss: 0.3225 - val_accuracy: 0.8687
Epoch 114/200
 - 0s - loss: 0.3610 - accuracy: 0.8557 - val_loss: 0.3230 - val_accuracy: 0.8712
Epoch 115/200
 - 0s - loss: 0.3552 - accuracy: 0.8589 - val_loss: 0.3233 - val_accuracy: 0.8687
Epoch 116/200
 - 0s - loss: 0.3577 - accuracy: 0.8544 - val_loss: 0.3241 - val_accuracy: 0.8675
Epoch 117/200
 - 0s - loss: 0.3613 - accuracy: 0.8564 - val_loss: 0.3234 - val_accuracy: 0.8650
Epoch 118/200
 - 0s - loss: 0.3601 - accuracy: 0.8562 - val_loss: 0.3236 - val_accuracy: 0.8725
Epoch 119/200
 - 0s - loss: 0.3613 - accuracy: 0.8542 - val_loss: 0.3233 - val_accuracy: 0.8712
Epoch 120/200
 - 0s - loss: 0.3591 - accuracy: 0.8529 - val_loss: 0.3241 - val_accuracy: 0.8700
Epoch 121/200
 - 0s - loss: 0.3622 - accuracy: 0.8569 - val_loss: 0.3242 - val_accuracy: 0.8662
Epoch 122/200
 - 0s - loss: 0.3580 - accuracy: 0.8537 - val_loss: 0.3240 - val_accuracy: 0.8687
Epoch 123/200
 - 0s - loss: 0.3591 - accuracy: 0.8551 - val_loss: 0.3236 - val_accuracy: 0.8687
Epoch 124/200
 - 0s - loss: 0.3622 - accuracy: 0.8565 - val_loss: 0.3229 - val_accuracy: 0.8662
Epoch 125/200
 - 0s - loss: 0.3596 - accuracy: 0.8547 - val_loss: 0.3241 - val_accuracy: 0.8687
Epoch 126/200
 - 0s - loss: 0.3614 - accuracy: 0.8531 - val_loss: 0.3253 - val_accuracy: 0.8662
Epoch 127/200
 - 0s - loss: 0.3595 - accuracy: 0.8554 - val_loss: 0.3263 - val_accuracy: 0.8662
Epoch 128/200
 - 0s - loss: 0.3605 - accuracy: 0.8543 - val_loss: 0.3253 - val_accuracy: 0.8675
Epoch 129/200
 - 0s - loss: 0.3548 - accuracy: 0.8549 - val_loss: 0.3230 - val_accuracy: 0.8687
Epoch 130/200
 - 0s - loss: 0.3569 - accuracy: 0.8561 - val_loss: 0.3228 - val_accuracy: 0.8712
Epoch 131/200
 - 0s - loss: 0.3561 - accuracy: 0.8551 - val_loss: 0.3233 - val_accuracy: 0.8637
Epoch 132/200
 - 0s - loss: 0.3570 - accuracy: 0.8556 - val_loss: 0.3238 - val_accuracy: 0.8687
Epoch 133/200
 - 0s - loss: 0.3596 - accuracy: 0.8549 - val_loss: 0.3239 - val_accuracy: 0.8675
Epoch 134/200
 - 0s - loss: 0.3554 - accuracy: 0.8571 - val_loss: 0.3248 - val_accuracy: 0.8650
Epoch 135/200
 - 0s - loss: 0.3604 - accuracy: 0.8532 - val_loss: 0.3237 - val_accuracy: 0.8637
Epoch 136/200
 - 0s - loss: 0.3601 - accuracy: 0.8561 - val_loss: 0.3251 - val_accuracy: 0.8662
Epoch 137/200
 - 0s - loss: 0.3587 - accuracy: 0.8542 - val_loss: 0.3234 - val_accuracy: 0.8662
Epoch 138/200
 - 0s - loss: 0.3597 - accuracy: 0.8554 - val_loss: 0.3241 - val_accuracy: 0.8675
Epoch 139/200
 - 0s - loss: 0.3571 - accuracy: 0.8550 - val_loss: 0.3238 - val_accuracy: 0.8675
Epoch 140/200
 - 0s - loss: 0.3580 - accuracy: 0.8551 - val_loss: 0.3245 - val_accuracy: 0.8687
Epoch 141/200
 - 0s - loss: 0.3585 - accuracy: 0.8557 - val_loss: 0.3237 - val_accuracy: 0.8687
Epoch 142/200
 - 0s - loss: 0.3595 - accuracy: 0.8567 - val_loss: 0.3241 - val_accuracy: 0.8675
Epoch 143/200
 - 0s - loss: 0.3559 - accuracy: 0.8560 - val_loss: 0.3245 - val_accuracy: 0.8675
Epoch 144/200
 - 0s - loss: 0.3612 - accuracy: 0.8536 - val_loss: 0.3257 - val_accuracy: 0.8700
Epoch 145/200
 - 0s - loss: 0.3553 - accuracy: 0.8603 - val_loss: 0.3239 - val_accuracy: 0.8687
Epoch 146/200
 - 0s - loss: 0.3610 - accuracy: 0.8546 - val_loss: 0.3243 - val_accuracy: 0.8687
Epoch 147/200
 - 0s - loss: 0.3634 - accuracy: 0.8512 - val_loss: 0.3245 - val_accuracy: 0.8700
Epoch 148/200
 - 0s - loss: 0.3543 - accuracy: 0.8564 - val_loss: 0.3242 - val_accuracy: 0.8687
Epoch 149/200
 - 0s - loss: 0.3543 - accuracy: 0.8574 - val_loss: 0.3230 - val_accuracy: 0.8687
Epoch 150/200
 - 0s - loss: 0.3553 - accuracy: 0.8556 - val_loss: 0.3229 - val_accuracy: 0.8662
Epoch 151/200
 - 0s - loss: 0.3596 - accuracy: 0.8524 - val_loss: 0.3231 - val_accuracy: 0.8687
Epoch 152/200
 - 0s - loss: 0.3577 - accuracy: 0.8560 - val_loss: 0.3240 - val_accuracy: 0.8712
Epoch 153/200
 - 0s - loss: 0.3603 - accuracy: 0.8568 - val_loss: 0.3256 - val_accuracy: 0.8700
Epoch 154/200
 - 0s - loss: 0.3533 - accuracy: 0.8589 - val_loss: 0.3240 - val_accuracy: 0.8687
Epoch 155/200
 - 0s - loss: 0.3586 - accuracy: 0.8567 - val_loss: 0.3246 - val_accuracy: 0.8662
Epoch 156/200
 - 0s - loss: 0.3573 - accuracy: 0.8544 - val_loss: 0.3252 - val_accuracy: 0.8675
Epoch 157/200
 - 0s - loss: 0.3585 - accuracy: 0.8549 - val_loss: 0.3248 - val_accuracy: 0.8650
Epoch 158/200
 - 0s - loss: 0.3605 - accuracy: 0.8562 - val_loss: 0.3241 - val_accuracy: 0.8700
Epoch 159/200
 - 0s - loss: 0.3572 - accuracy: 0.8561 - val_loss: 0.3244 - val_accuracy: 0.8687
Epoch 160/200
 - 0s - loss: 0.3575 - accuracy: 0.8542 - val_loss: 0.3238 - val_accuracy: 0.8700
Epoch 161/200
 - 0s - loss: 0.3578 - accuracy: 0.8556 - val_loss: 0.3241 - val_accuracy: 0.8700
Epoch 162/200
 - 0s - loss: 0.3564 - accuracy: 0.8544 - val_loss: 0.3238 - val_accuracy: 0.8725
Epoch 163/200
 - 0s - loss: 0.3600 - accuracy: 0.8574 - val_loss: 0.3244 - val_accuracy: 0.8712
Epoch 164/200
 - 0s - loss: 0.3551 - accuracy: 0.8551 - val_loss: 0.3239 - val_accuracy: 0.8700
Epoch 165/200
 - 0s - loss: 0.3582 - accuracy: 0.8571 - val_loss: 0.3236 - val_accuracy: 0.8700
Epoch 166/200
 - 0s - loss: 0.3560 - accuracy: 0.8556 - val_loss: 0.3245 - val_accuracy: 0.8750
Epoch 167/200
 - 0s - loss: 0.3581 - accuracy: 0.8578 - val_loss: 0.3246 - val_accuracy: 0.8687
Epoch 168/200
 - 0s - loss: 0.3579 - accuracy: 0.8553 - val_loss: 0.3258 - val_accuracy: 0.8687
Epoch 169/200
 - 0s - loss: 0.3610 - accuracy: 0.8526 - val_loss: 0.3240 - val_accuracy: 0.8712
Epoch 170/200
 - 0s - loss: 0.3592 - accuracy: 0.8579 - val_loss: 0.3245 - val_accuracy: 0.8712
Epoch 171/200
 - 0s - loss: 0.3572 - accuracy: 0.8587 - val_loss: 0.3241 - val_accuracy: 0.8687
Epoch 172/200
 - 0s - loss: 0.3626 - accuracy: 0.8571 - val_loss: 0.3248 - val_accuracy: 0.8700
Epoch 173/200
 - 0s - loss: 0.3543 - accuracy: 0.8589 - val_loss: 0.3251 - val_accuracy: 0.8725
Epoch 174/200
 - 0s - loss: 0.3574 - accuracy: 0.8542 - val_loss: 0.3245 - val_accuracy: 0.8700
Epoch 175/200
 - 0s - loss: 0.3552 - accuracy: 0.8582 - val_loss: 0.3235 - val_accuracy: 0.8687
Epoch 176/200
 - 0s - loss: 0.3567 - accuracy: 0.8532 - val_loss: 0.3243 - val_accuracy: 0.8725
Epoch 177/200
 - 0s - loss: 0.3547 - accuracy: 0.8594 - val_loss: 0.3242 - val_accuracy: 0.8650
Epoch 178/200
 - 0s - loss: 0.3571 - accuracy: 0.8569 - val_loss: 0.3249 - val_accuracy: 0.8700
Epoch 179/200
 - 0s - loss: 0.3589 - accuracy: 0.8551 - val_loss: 0.3263 - val_accuracy: 0.8675
Epoch 180/200
 - 0s - loss: 0.3573 - accuracy: 0.8553 - val_loss: 0.3253 - val_accuracy: 0.8700
Epoch 181/200
 - 0s - loss: 0.3566 - accuracy: 0.8549 - val_loss: 0.3257 - val_accuracy: 0.8687
Epoch 182/200
 - 0s - loss: 0.3557 - accuracy: 0.8579 - val_loss: 0.3254 - val_accuracy: 0.8700
Epoch 183/200
 - 0s - loss: 0.3524 - accuracy: 0.8594 - val_loss: 0.3246 - val_accuracy: 0.8637
Epoch 184/200
 - 0s - loss: 0.3609 - accuracy: 0.8575 - val_loss: 0.3263 - val_accuracy: 0.8662
Epoch 185/200
 - 0s - loss: 0.3585 - accuracy: 0.8544 - val_loss: 0.3266 - val_accuracy: 0.8675
Epoch 186/200
 - 0s - loss: 0.3589 - accuracy: 0.8594 - val_loss: 0.3253 - val_accuracy: 0.8675
Epoch 187/200
 - 0s - loss: 0.3591 - accuracy: 0.8543 - val_loss: 0.3251 - val_accuracy: 0.8725
Epoch 188/200
 - 0s - loss: 0.3559 - accuracy: 0.8550 - val_loss: 0.3251 - val_accuracy: 0.8687
Epoch 189/200
 - 0s - loss: 0.3574 - accuracy: 0.8524 - val_loss: 0.3259 - val_accuracy: 0.8687
Epoch 190/200
 - 0s - loss: 0.3595 - accuracy: 0.8550 - val_loss: 0.3258 - val_accuracy: 0.8687
Epoch 191/200
 - 0s - loss: 0.3568 - accuracy: 0.8593 - val_loss: 0.3243 - val_accuracy: 0.8725
Epoch 192/200
 - 0s - loss: 0.3578 - accuracy: 0.8567 - val_loss: 0.3241 - val_accuracy: 0.8687
Epoch 193/200
 - 0s - loss: 0.3536 - accuracy: 0.8592 - val_loss: 0.3263 - val_accuracy: 0.8687
Epoch 194/200
 - 0s - loss: 0.3579 - accuracy: 0.8564 - val_loss: 0.3252 - val_accuracy: 0.8662
Epoch 195/200
 - 0s - loss: 0.3590 - accuracy: 0.8550 - val_loss: 0.3261 - val_accuracy: 0.8687
Epoch 196/200
 - 0s - loss: 0.3528 - accuracy: 0.8578 - val_loss: 0.3255 - val_accuracy: 0.8700
Epoch 197/200
 - 0s - loss: 0.3598 - accuracy: 0.8558 - val_loss: 0.3262 - val_accuracy: 0.8700
Epoch 198/200
 - 0s - loss: 0.3579 - accuracy: 0.8569 - val_loss: 0.3258 - val_accuracy: 0.8662
Epoch 199/200
 - 0s - loss: 0.3585 - accuracy: 0.8556 - val_loss: 0.3260 - val_accuracy: 0.8687
Epoch 200/200
 - 0s - loss: 0.3595 - accuracy: 0.8551 - val_loss: 0.3242 - val_accuracy: 0.8712

classifier.summary()

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_4 (Dense)              (None, 6)                 72        
_________________________________________________________________
dropout_1 (Dropout)          (None, 6)                 0         
_________________________________________________________________
dense_5 (Dense)              (None, 6)                 42        
_________________________________________________________________
dropout_2 (Dropout)          (None, 6)                 0         
_________________________________________________________________
dense_6 (Dense)              (None, 1)                 7         
=================================================================
Total params: 121
Trainable params: 121
Non-trainable params: 0
_________________________________________________________________

Evaluation The Neural Network

Predicting The Test Set Results

plt.plot(np.array(history.history['accuracy']) * 100)
plt.plot(np.array(history.history['val_accuracy']) * 100)
plt.ylabel('accuracy')
plt.xlabel('epochs')
plt.legend(['train', 'validation'])
plt.title('Accuracy over epochs')
plt.show()

y_pred = classifier.predict(X_test)
print(y_pred[:5])

[[0.29572767]
 [0.22071445]
 [0.19559735]
 [0.05812448]
 [0.11317214]]

y_pred = (y_pred > 0.5).astype(int)
print(y_pred[:8])

[[0]
 [0]
 [0]
 [0]
 [0]
 [1]
 [0]
 [0]]

Confusion Matrix

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print(cm)

[[1532   63]
 [ 208  197]]

print (((cm[0][0]+cm[1][1])*100)/(len(y_test)), '% of testing data was classified correctly')

86.45 % of testing data was classified correctly

Note:

At 86.5% Test Accuracy, the Neural Network performance is satisfactory.

It is good to note that, Neural Net with Dropout improves model accuracy significantly.

Ploting The Receiver Operator Characteristic (ROC) Curve

# Plot the roc curve
plt.figure(figsize = (12,6), linewidth= 1)
plt.plot(fpr_knn, tpr_knn, label = 'KNN Score: ' + str(round(knn_roc_auc, 5)))
plt.plot(fpr_lr, tpr_lr, label = 'LR score: ' + str(round(lr_roc_auc, 5)))
plt.plot(fpr_ada, tpr_ada, label = 'AdaBoost Score: ' + str(round(ada_roc_auc, 5)))
plt.plot(fpr_gb, tpr_gb, label = 'GB Score: ' + str(round(gb_roc_auc, 5)))
plt.plot(fpr_rf, tpr_rf, label = 'RF score: ' + str(round(rf_roc_auc, 5)))
plt.plot([0,1], [0,1], 'k--', label = 'Random guessing: 0.5')
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC Curve ')
plt.legend(loc='best')
plt.show()

The ROC-AUC score, is more significant for us because the mean accuracy score considers only one threshold value, whereas the ROC-AUC score takes into consideration all possible threshold values and gives us the score. score() function of scikit-learn, which give us the mean accuracy score.

Note:

GradientBoosting score of 0.86 and AdaBoost 0.83 classifiers show high ROC-AUC score on the validation dataset. 
Other classifiers, such as logistic regression, KNN, and RandomForest do not perform well on the validation set.

Therefore we shall fine tune GradientBoosting and AdaBoost classifiers in order to improve their accuracy score.

We perform some parameter optimization in the following steps.

5.3 Hyperparameter Optimization

In this section, I will use the following techniques in order to improve the accuracy of the classifiers :

Cross-validation
Hyperparameter tuning

With Cross Validation, instead of splitting the valuable training data into a separate training and validation set, I use KFold cross validation.

The models we shall tune are:

- AdaBoost and
- Gradient Boosting Machine

AdaBoost is adaptive in the sense that subsequent weak learners are tweaked in favor of those instances misclassified by previous classifiers. It is also senstive to noisy data and outliers.

GBM is an ensemble method that works by training many individual learners, almost always decision trees where the trees are trained sequentially with each tree learning from the mistakes of the previous ones unlike in a random forest where the trees are trained in parallel.

Since a GBM model has many hyperparameters with complex interactions between them, the only way to find the optimal hyperparameter values is to try many different combinations on a dataset. A range of hyperparameters that control both the overall ensemble (such as the learning rate) and the individual decision trees (such as the number of leaves in the tree or the maximum depth of the tree) is determined through a random search.

The performance of each set of hyperparameters is determined by Receiver Operating Characteristic Area Under the Curve (ROC AUC) from the cross-validation

Here, we can use the Scikit-Learn functions RandomizedSearchCV or GridSearchCV.

7.0 Conclusion

Observations:

1. Female customers are the most likely to churn, 
2. Customers that are located in Germany are the most churned, and also 
3. Customers using only one product are the most churned.

Results:

After building several models, I ended up with three very promising ones namely:

Keras Neural Network with Dropout,
GradientBoosting and 
AdaBoost

which performed better than KNN and Random Forest.

This will allow to choose the best model.

I compared several algorithms which included random forest, KNN, AdaBoost, GradientBoosting and neural networks for the same. The accuracy of both the AdaBoost and GradientBoosting algorithms are comparable to the Neural Networks , hence it is hard to tell which is better. 

I dicided to tune the hyper parameter for AdaBoost and GradientBoosting by implemented a voting-based approach as their initial performance was low but better than KNN and RF.

Neural Network with dropout has proven to be a great algorithm if the dataset is well prepared and clean. AdaBoost and GradientBoosting required hyper parameter tuning but less preprocessing and the training process is also much simpler. 

This shows with more hyper-parameter tuning neural networks will yield higher performance than both.

Since the problem is about binary classification with a imbalance dataset, we have used the most efficient metric for model performance which is the ROC-AUC score and the model achieved about  87%  accuracy.   
  
This score compares very well as per industry standards on Banking Churn prediction rates. The model can achieve better performance providing a lot of historical data for the training phase.

Recommendations:

In this project, I demonstrated how a business can predict customers likely to exit. With this information, a customer retension plan can be more effective and less costly as its targeted to a specific group.

Although Churn Prediction models do a great job at predicting which custoers may churn, Single models have challenges in solving the problem at hand. The fact that:

1. No clarity about customer value: A churn prediction model doesn't tell you which of the identified at-risk customers is more valuable. In this scenario, your retention agents end up giving costly offers to low-value customers

2. No insight into the context of risk: Not knowing why a particular customer would want to cancel in the first place makes it harder to know what to do to retain them.
      
3. It doesn't allow timely and proactive engagement: Models don't provide information on time aspect to know when a predicted high-risk customer will cancel and therefore Marketers can't prioritize their planning on who to target first.

4. The lost opportunity of customer winback: Winning back lost customers is more profitable than new customer acquisition. The single model approach will only predict the risk status of active customers and won't even consider winback chances of recently canceled customers.

Since multiple factors drive a customer's desire to cancel, the objective of predicting churn analytics should go from identifying who is most likely to cancel to understanding finer and subtler details like why customers will cancel, when will they cancel, how valuable these customers are, what can be done to save them, what offers might work for them and which of the canceled customers can be won back.

I therefore recommend a Multimodel approach each predicting different dimensions of customer behavior to target various aspects of the problem.

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DavidMwasikira/Predicting-Customers-Churn

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Project title: Bank Customer Churn Classification

Problem Statement: Predicting if a Customer will exit bank or not from the given Bank Customer data.

1.0 Business Understanding

2.0 Data Understanding

Creating a Churner Profile and Identifying Churn Behavior

Implementing the Churn Scoring Mechanism

Feature description¶

Bank client data:

Other Attributes:

Output variable (desired target):

Percentage of Exited Customers Vs Remaining

Exploratory Data Analysis

How does the provided data Features Correlate?

Note

Taking a look at how Data is Distributed using Scatter Plots

Deductions:

Checking for Missing Data

Inspecting for Ouliers

Data Visualization

Distribution by Geographical Area

Note:

Observation:

Note:

Note:

Note:

Distribution of Continous Variables

Note:

Note:

Hypothesis:

Note:

Hypothesis:

Note:

Note:

3.0 Data Preparation & Preprocessing

Splitting the Independent and Dependent (Target) variables

Encoding categorical (string based) data.

Creating Dummy Variables with OneHotEncoder

4.0 Modelling

4.1 Using Keras Neural Network without Dropout

Fitting The Model

Predict The Results

Calculating Accuracy From Confusion Matrix

4.2 Using Neural Network with Dropout

Implementing hidden layer (Deep Neural Network)

Adding Output Layer

Compiling Neural Network

Fitting The Neural Network

Evaluation The Neural Network

Confusion Matrix

Note:

Ploting The Receiver Operator Characteristic (ROC) Curve

5.3 Hyperparameter Optimization

7.0 Conclusion

Observations:

Results:

Recommendations:

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