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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Customer Churn Analysis
Churn rate, when applied to a customer base, refers to the proportion of contractual customers or subscribers who leave a supplier during a given time period. It is a possible indicator of customer dissatisfaction, cheaper and/or better offers from the competition, more successful sales and/or marketing by the competition, or reasons having to do with the customer life cycle.
Churn is closely related to the concept of average customer life time. For example, an annual churn rate of 25 percent implies an average customer life of four years. An annual churn rate of 33 percent implies an average customer life of three years. The churn rate can be minimized by creating barriers which discourage customers to change suppliers (contractual binding periods, use of proprietary technology, value-added services, unique business models, etc.), or through retention activities such as loyalty programs. It is possible to overstate the churn rate, as when a consumer drops the service but then restarts it within the same year. Thus, a clear distinction needs to be made between "gross churn", the total number of absolute disconnections, and "net churn", the overall loss of subscribers or members. The difference between the two measures is the number of new subscribers or members that have joined during the same period. Suppliers may find that if they offer a loss-leader "introductory special", it can lead to a higher churn rate and subscriber abuse, as some subscribers will sign on, let the service lapse, then sign on again to take continuous advantage of current specials. https
Step1:  Load The Trifacta Prepared Dataset
Step2:  Scatterplot Matrix
Step3:  Automatic Machine Learning
The Automatic Machine Learning (AutoML) function automates the supervised machine learning model training process. The current version of AutoML trains and cross-validates a Random Forest, an Extremely-Randomized Forest, a random grid of Gradient Boosting Machines (GBMs), a random grid of Deep Neural Nets, and a Stacked Ensemble of all the models.
Step4:  Leaderboard
Step5:  Variable Importances
Below we plot variable importances as reported by the best performing algo in the ensemble.
Step6:  Best Model vs the Base Learners
This plot shows the ROC curves for the Super Model, the Best Base Model, and 9 next best models in the ensemble.
Step7:  Confusion Matrix
Step8:  Business Impact Matrix
Weighting Predictions With a Dollar Value
-   Correctly predicting retain
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Python Code:
%%capture
import numpy as np
import pandas as pd
import h2o
from h2o.automl import H2OAutoML
from __future__ import print_function
import pandas_profiling
# Suppress unwatned warnings
import warnings
warnings.filterwarnings('ignore')
import logging
logging.getLogger("requests").setLevel(logging.WARNING)
# Load our favorite visualization library
import os
import plotly
import plotly.plotly as py
import plotly.figure_factory as ff
import plotly.graph_objs as go
import cufflinks as cf
plotly.offline.init_notebook_mode(connected=True)
# Sign into Plotly with masked, encrypted API key
myPlotlyKey = os.environ['SECRET_ENV_BRETTS_PLOTLY_KEY']
py.sign_in(username='bretto777',api_key=myPlotlyKey)
Explanation: Customer Churn Analysis
Churn rate, when applied to a customer base, refers to the proportion of contractual customers or subscribers who leave a supplier during a given time period. It is a possible indicator of customer dissatisfaction, cheaper and/or better offers from the competition, more successful sales and/or marketing by the competition, or reasons having to do with the customer life cycle.
Churn is closely related to the concept of average customer life time. For example, an annual churn rate of 25 percent implies an average customer life of four years. An annual churn rate of 33 percent implies an average customer life of three years. The churn rate can be minimized by creating barriers which discourage customers to change suppliers (contractual binding periods, use of proprietary technology, value-added services, unique business models, etc.), or through retention activities such as loyalty programs. It is possible to overstate the churn rate, as when a consumer drops the service but then restarts it within the same year. Thus, a clear distinction needs to be made between "gross churn", the total number of absolute disconnections, and "net churn", the overall loss of subscribers or members. The difference between the two measures is the number of new subscribers or members that have joined during the same period. Suppliers may find that if they offer a loss-leader "introductory special", it can lead to a higher churn rate and subscriber abuse, as some subscribers will sign on, let the service lapse, then sign on again to take continuous advantage of current specials. https://en.wikipedia.org/wiki/Churn_rate
End of explanation
accessKey = os.environ['BRETT_AWS_ACCESS_KEY']
s3file = 'https://trifactapro.s3.amazonaws.com/churn.csv?AWSAccessKeyId=' + accessKey + '&Expires=1522155539&Signature=Imj9nbjqsdarjbAGKkjeHB9PwWE%3D'
# Load some data
churnDF = pd.read_csv(s3file, delimiter=',')
churnDF["Churn"] = churnDF["Churn"].replace(to_replace=False, value='Retain')
churnDF["Churn"] = churnDF["Churn"].replace(to_replace=True, value='Churn')
churnDFs = churnDF.sample(frac=0.07) # Sample for speedy viz
churnDF.head(5)
%%capture
pandas_profiling.ProfileReport(churnDF)
Explanation: Load The Trifacta Prepared Dataset
End of explanation
# separate the calls data for plotting
churnDFs = churnDFs[['Account Length','Day Calls','Eve Calls','CustServ Calls','Churn']]
# Create scatter plot matrix of call data
splom = ff.create_scatterplotmatrix(churnDFs, diag='histogram', index='Churn',  
                                  colormap= dict(
                                      Churn = '#9CBEF1',
                                      Retain = '#04367F'
                                      ),
                                  colormap_type='cat',
                                  height=560, width=650,
                                  size=4, marker=dict(symbol='circle'))
py.iplot(splom)
%%capture
#h2o.connect(ip="35.225.239.147")
h2o.init(nthreads=1, max_mem_size="768m")
%%capture
# Split data into training and testing frames
from sklearn import cross_validation
from sklearn.model_selection import train_test_split
training, testing = train_test_split(churnDF, train_size=0.8, stratify=churnDF["Churn"], random_state=9)
train = h2o.H2OFrame(python_obj=training).drop("State")
test = h2o.H2OFrame(python_obj=testing).drop("State")
# Set predictor and response variables
y = "Churn"
x = train.columns
x.remove(y)
Explanation: Scatterplot Matrix
End of explanation
%%capture
# Run AutoML until 11 models are built
autoModel = H2OAutoML(max_models = 20)
autoModel.train(x = x, y = y,
          training_frame = train,
          validation_frame = test, 
          leaderboard_frame = test)
Explanation: Automatic Machine Learning
The Automatic Machine Learning (AutoML) function automates the supervised machine learning model training process. The current version of AutoML trains and cross-validates a Random Forest, an Extremely-Randomized Forest, a random grid of Gradient Boosting Machines (GBMs), a random grid of Deep Neural Nets, and a Stacked Ensemble of all the models.
End of explanation
leaders = autoModel.leaderboard
leaders
Explanation: Leaderboard
End of explanation
importances = h2o.get_model(leaders[2, 0]).varimp(use_pandas=True)
importances = importances.loc[:,['variable','relative_importance']].groupby('variable').mean()
importances.sort_values(by="relative_importance", ascending=False).iplot(kind='bar', colors='#5AC4F2', theme='white')
import matplotlib.pyplot as plt
plt.figure()
bestModel = h2o.get_model(leaders[2, 0])
plt = bestModel.partial_plot(data=test, cols=["Day Mins","CustServ Calls","Day Charge"])
Explanation: Variable Importances
Below we plot variable importances as reported by the best performing algo in the ensemble.
End of explanation
Model0 = np.array(h2o.get_model(leaders[0,0]).roc(xval=True))
Model1 = np.array(h2o.get_model(leaders[1,0]).roc(xval=True))
Model2 = np.array(h2o.get_model(leaders[2,0]).roc(xval=True))
Model3 = np.array(h2o.get_model(leaders[3,0]).roc(xval=True))
Model4 = np.array(h2o.get_model(leaders[4,0]).roc(xval=True))
Model5 = np.array(h2o.get_model(leaders[5,0]).roc(xval=True))
Model6 = np.array(h2o.get_model(leaders[6,0]).roc(xval=True))
Model7 = np.array(h2o.get_model(leaders[7,0]).roc(xval=True))
Model8 = np.array(h2o.get_model(leaders[8,0]).roc(xval=True))
Model9 = np.array(h2o.get_model(leaders[9,0]).roc(xval=True))
layout = go.Layout(autosize=False, width=725, height=575,  xaxis=dict(title='False Positive Rate', titlefont=dict(family='Arial, sans-serif', size=15, color='grey')), 
                                                           yaxis=dict(title='True Positive Rate', titlefont=dict(family='Arial, sans-serif', size=15, color='grey')))
traceChanceLine = go.Scatter(x = [0,1], y = [0,1], mode = 'lines+markers', name = 'chance', line = dict(color = ('rgb(136, 140, 150)'), width = 4, dash = 'dash'))
Model0Trace = go.Scatter(x = Model0[0], y = Model0[1], mode = 'lines', name = 'Model 0', line = dict(color = ('rgb(26, 58, 126)'), width = 3))
Model1Trace = go.Scatter(x = Model1[0], y = Model1[1], mode = 'lines', name = 'Model 1', line = dict(color = ('rgb(156, 190, 241))'), width = 1))
Model2Trace = go.Scatter(x = Model2[0], y = Model2[1], mode = 'lines', name = 'Model 2', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model3Trace = go.Scatter(x = Model3[0], y = Model3[1], mode = 'lines', name = 'Model 3', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model4Trace = go.Scatter(x = Model4[0], y = Model4[1], mode = 'lines', name = 'Model 4', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model5Trace = go.Scatter(x = Model5[0], y = Model5[1], mode = 'lines', name = 'Model 5', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model6Trace = go.Scatter(x = Model6[0], y = Model6[1], mode = 'lines', name = 'Model 6', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model7Trace = go.Scatter(x = Model7[0], y = Model7[1], mode = 'lines', name = 'Model 7', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model8Trace = go.Scatter(x = Model8[0], y = Model8[1], mode = 'lines', name = 'Model 8', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
Model9Trace = go.Scatter(x = Model9[0], y = Model9[1], mode = 'lines', name = 'Model 9', line = dict(color = ('rgb(156, 190, 241)'), width = 1))
fig = go.Figure(data=[Model0Trace,Model1Trace,Model2Trace,Model3Trace,Model4Trace,Model5Trace,Model6Trace,Model8Trace,Model9Trace,traceChanceLine], layout=layout)
py.iplot(fig)
Explanation: Best Model vs the Base Learners
This plot shows the ROC curves for the Super Model, the Best Base Model, and 9 next best models in the ensemble.
End of explanation
cm = h2o.get_model(leaders[1, 0]).confusion_matrix(xval=True)
cm = cm.table.as_data_frame()
cm
confusionMatrix = ff.create_table(cm)
confusionMatrix.layout.height=300
confusionMatrix.layout.width=800
confusionMatrix.layout.font.size=17
py.iplot(confusionMatrix)
Explanation: Confusion Matrix
End of explanation
CorrectPredictChurn = cm.loc[0,'Churn']
CorrectPredictChurnImpact = 75
cm1 = CorrectPredictChurn*CorrectPredictChurnImpact
IncorrectPredictChurn = cm.loc[1,'Churn']
IncorrectPredictChurnImpact = -5
cm2 = IncorrectPredictChurn*IncorrectPredictChurnImpact
IncorrectPredictRetain = cm.loc[0,'Retain']
IncorrectPredictRetainImpact = -150
cm3 = IncorrectPredictRetain*IncorrectPredictRetainImpact
CorrectPredictRetain = cm.loc[0,'Retain']
CorrectPredictRetainImpact = 5
cm4 = IncorrectPredictRetain*CorrectPredictRetainImpact
data_matrix = [['Business Impact', '($) Predicted Churn', '($) Predicted Retain', '($) Total'],
               ['($) Actual Churn', cm1, cm3, '' ],
               ['($) Actual Retain', cm2, cm4, ''],
               ['($) Total', cm1+cm2, cm3+cm4, cm1+cm2+cm3+cm4]]
impactMatrix = ff.create_table(data_matrix, height_constant=20, hoverinfo='weight')
impactMatrix.layout.height=300
impactMatrix.layout.width=800
impactMatrix.layout.font.size=17
py.iplot(impactMatrix)
print("Total customers evaluated: 2132")
print("Total value created by the model: $" + str(cm1+cm2+cm3+cm4))
print("Total value per customer: $" +str(round(((cm1+cm2+cm3+cm4)/2132),3)))
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# Save the best model
path = h2o.save_model(model=h2o.get_model(leaders[0, 0]), force=True)
os.rename(h2o.get_model(leaders[0, 0]).model_id, "AutoML-leader")    
%%capture
LoadedEnsemble = h2o.load_model(path="AutoML-leader")
print(LoadedEnsemble)
Explanation: Business Impact Matrix
Weighting Predictions With a Dollar Value
-   Correctly predicting retain: +$5
-   Correctly predicting churn: +$75
-   Incorrectly predicting retain: -$150
-   Incorrectly predicting churn: -$1.5
End of explanation
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