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Copyright by Pierian Data Inc. For more information, visit us at www.pieriandata.com

Supervised Learning Capstone Project - Tree Methods Focus - SOLUTIONS

Make sure to review the introduction video to understand the 3 ways of approaching this project exercise!

Ways to approach the project:

  1. Open a new notebook, read in the data, and then analyze and visualize whatever you want, then create a predictive model.
  2. Use this notebook as a general guide, completing the tasks in bold shown below.
  3. Skip to the solutions notebook and video, and treat project at a more relaxing code along walkthrough lecture series.

GOAL: Create a model to predict whether or not a customer will Churn .

Complete the Tasks in Bold Below!

Part 0: Imports and Read in the Data

TASK: Run the filled out cells below to import libraries and read in your data. The data file is "Telco-Customer-Churn.csv"

In [132]:
# RUN THESE CELLS TO START THE PROJECT!
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
In [133]:
df = pd.read_csv('../DATA/Telco-Customer-Churn.csv')
In [134]:
df.head()
Out[134]:
customerID gender SeniorCitizen Partner Dependents tenure PhoneService MultipleLines InternetService OnlineSecurity ... DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod MonthlyCharges TotalCharges Churn
0 7590-VHVEG Female 0 Yes No 1 No No phone service DSL No ... No No No No Month-to-month Yes Electronic check 29.85 29.85 No
1 5575-GNVDE Male 0 No No 34 Yes No DSL Yes ... Yes No No No One year No Mailed check 56.95 1889.50 No
2 3668-QPYBK Male 0 No No 2 Yes No DSL Yes ... No No No No Month-to-month Yes Mailed check 53.85 108.15 Yes
3 7795-CFOCW Male 0 No No 45 No No phone service DSL Yes ... Yes Yes No No One year No Bank transfer (automatic) 42.30 1840.75 No
4 9237-HQITU Female 0 No No 2 Yes No Fiber optic No ... No No No No Month-to-month Yes Electronic check 70.70 151.65 Yes

5 rows × 21 columns

Part 1: Quick Data Check

TASK: Confirm quickly with .info() methods the datatypes and non-null values in your dataframe.

In [135]:
# CODE HERE
In [136]:


<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7032 entries, 0 to 7031
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7032 non-null   object 
 1   gender            7032 non-null   object 
 2   SeniorCitizen     7032 non-null   int64  
 3   Partner           7032 non-null   object 
 4   Dependents        7032 non-null   object 
 5   tenure            7032 non-null   int64  
 6   PhoneService      7032 non-null   object 
 7   MultipleLines     7032 non-null   object 
 8   InternetService   7032 non-null   object 
 9   OnlineSecurity    7032 non-null   object 
 10  OnlineBackup      7032 non-null   object 
 11  DeviceProtection  7032 non-null   object 
 12  TechSupport       7032 non-null   object 
 13  StreamingTV       7032 non-null   object 
 14  StreamingMovies   7032 non-null   object 
 15  Contract          7032 non-null   object 
 16  PaperlessBilling  7032 non-null   object 
 17  PaymentMethod     7032 non-null   object 
 18  MonthlyCharges    7032 non-null   float64
 19  TotalCharges      7032 non-null   float64
 20  Churn             7032 non-null   object 
dtypes: float64(2), int64(2), object(17)
memory usage: 1.1+ MB

TASK: Get a quick statistical summary of the numeric columns with .describe() , you should notice that many columns are categorical, meaning you will eventually need to convert them to dummy variables.

In [137]:
# CODE HERE
In [138]:
Out[138]:
SeniorCitizen tenure MonthlyCharges TotalCharges
count 7032.000000 7032.000000 7032.000000 7032.000000
mean 0.162400 32.421786 64.798208 2283.300441
std 0.368844 24.545260 30.085974 2266.771362
min 0.000000 1.000000 18.250000 18.800000
25% 0.000000 9.000000 35.587500 401.450000
50% 0.000000 29.000000 70.350000 1397.475000
75% 0.000000 55.000000 89.862500 3794.737500
max 1.000000 72.000000 118.750000 8684.800000

Part 2: Exploratory Data Analysis

General Feature Exploration

TASK: Confirm that there are no NaN cells by displaying NaN values per feature column.

In [139]:
# CODE HERE
In [140]:
Out[140]:
customerID          0
gender              0
SeniorCitizen       0
Partner             0
Dependents          0
tenure              0
PhoneService        0
MultipleLines       0
InternetService     0
OnlineSecurity      0
OnlineBackup        0
DeviceProtection    0
TechSupport         0
StreamingTV         0
StreamingMovies     0
Contract            0
PaperlessBilling    0
PaymentMethod       0
MonthlyCharges      0
TotalCharges        0
Churn               0
dtype: int64

TASK:Display the balance of the class labels (Churn) with a Count Plot.

In [141]:
# CODE HERE
In [ ]:

TASK: Explore the distrbution of TotalCharges between Churn categories with a Box Plot or Violin Plot.

In [143]:
# CODE HERE
In [ ]:

TASK: Create a boxplot showing the distribution of TotalCharges per Contract type, also add in a hue coloring based on the Churn class.

In [145]:
#CODE HERE
In [ ]:

TASK: Create a bar plot showing the correlation of the following features to the class label. Keep in mind, for the categorical features, you will need to convert them into dummy variables first, as you can only calculate correlation for numeric features.

['gender', 'SeniorCitizen', 'Partner', 'Dependents','PhoneService', 'MultipleLines', 
 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'InternetService',
   'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']

*Note, we specifically listed only the features above, you should not check the correlation for every feature, as some features have too many unique instances for such an analysis, such as customerID*

In [147]:
#CODE HERE
In [148]:
Out[148]:
Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],
      dtype='object')
In [149]:
In [150]:
Out[150]:
Contract_Two year                         -0.301552
StreamingMovies_No internet service       -0.227578
StreamingTV_No internet service           -0.227578
TechSupport_No internet service           -0.227578
DeviceProtection_No internet service      -0.227578
OnlineBackup_No internet service          -0.227578
OnlineSecurity_No internet service        -0.227578
InternetService_No                        -0.227578
PaperlessBilling_No                       -0.191454
Contract_One year                         -0.178225
OnlineSecurity_Yes                        -0.171270
TechSupport_Yes                           -0.164716
Dependents_Yes                            -0.163128
Partner_Yes                               -0.149982
PaymentMethod_Credit card (automatic)     -0.134687
InternetService_DSL                       -0.124141
PaymentMethod_Bank transfer (automatic)   -0.118136
PaymentMethod_Mailed check                -0.090773
OnlineBackup_Yes                          -0.082307
DeviceProtection_Yes                      -0.066193
MultipleLines_No                          -0.032654
MultipleLines_No phone service            -0.011691
PhoneService_No                           -0.011691
gender_Male                               -0.008545
gender_Female                              0.008545
PhoneService_Yes                           0.011691
MultipleLines_Yes                          0.040033
StreamingMovies_Yes                        0.060860
StreamingTV_Yes                            0.063254
StreamingTV_No                             0.128435
StreamingMovies_No                         0.130920
Partner_No                                 0.149982
SeniorCitizen                              0.150541
Dependents_No                              0.163128
PaperlessBilling_Yes                       0.191454
DeviceProtection_No                        0.252056
OnlineBackup_No                            0.267595
PaymentMethod_Electronic check             0.301455
InternetService_Fiber optic                0.307463
TechSupport_No                             0.336877
OnlineSecurity_No                          0.342235
Contract_Month-to-month                    0.404565
Name: Churn_Yes, dtype: float64
In [ ]:

Part 3: Churn Analysis

This section focuses on segementing customers based on their tenure, creating "cohorts", allowing us to examine differences between customer cohort segments.

TASK: What are the 3 contract types available?

In [152]:
# CODE HERE
In [153]:
Out[153]:
array(['Month-to-month', 'One year', 'Two year'], dtype=object)

TASK: Create a histogram displaying the distribution of 'tenure' column, which is the amount of months a customer was or has been on a customer.

In [154]:
#CODE HERE
In [ ]:

TASK: Now use the seaborn documentation as a guide to create histograms separated by two additional features, Churn and Contract.

In [156]:
#CODE HERE
In [ ]:

TASK: Display a scatter plot of Total Charges versus Monthly Charges, and color hue by Churn.

In [158]:
#CODE HERE
In [159]:
Out[159]:
Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],
      dtype='object')
In [ ]:

Creating Cohorts based on Tenure

Let's begin by treating each unique tenure length, 1 month, 2 month, 3 month...N months as its own cohort.

TASK: Treating each unique tenure group as a cohort, calculate the Churn rate (percentage that had Yes Churn) per cohort. For example, the cohort that has had a tenure of 1 month should have a Churn rate of 61.99%. You should have cohorts 1-72 months with a general trend of the longer the tenure of the cohort, the less of a churn rate. This makes sense as you are less likely to stop service the longer you've had it.

In [161]:
#CODE HERE
In [162]:
In [163]:
In [164]:
Out[164]:
tenure
1     61.990212
2     51.680672
3     47.000000
4     47.159091
5     48.120301
        ...    
68     9.000000
69     8.421053
70     9.243697
71     3.529412
72     1.657459
Name: customerID, Length: 72, dtype: float64

TASK: Now that you have Churn Rate per tenure group 1-72 months, create a plot showing churn rate per months of tenure.

In [165]:
#CODE HERE
In [ ]:

Broader Cohort Groups

TASK: Based on the tenure column values, create a new column called Tenure Cohort that creates 4 separate categories:

  • '0-12 Months'
  • '24-48 Months'
  • '12-24 Months'
  • 'Over 48 Months'
In [167]:
# CODE HERE
In [168]:
In [169]:
In [170]:
Out[170]:
tenure Tenure Cohort
0 1 0-12 Months
1 34 24-48 Months
2 2 0-12 Months
3 45 24-48 Months
4 2 0-12 Months
5 8 0-12 Months
6 22 12-24 Months
7 10 0-12 Months
8 28 24-48 Months
9 62 Over 48 Months

TASK: Create a scatterplot of Total Charges versus Monthly Charts,colored by Tenure Cohort defined in the previous task.

In [171]:
#CODE HERE
In [ ]:

TASK: Create a count plot showing the churn count per cohort.

In [1]:
# CODE HERE
In [ ]:

TASK: Create a grid of Count Plots showing counts per Tenure Cohort, separated out by contract type and colored by the Churn hue.

In [174]:
#CODE HERE
In [ ]:

Part 4: Predictive Modeling

Let's explore 4 different tree based methods: A Single Decision Tree, Random Forest, AdaBoost, Gradient Boosting. Feel free to add any other supervised learning models to your comparisons!

Single Decision Tree

TASK : Separate out the data into X features and Y label. Create dummy variables where necessary and note which features are not useful and should be dropped.

In [178]:
#CODE HERE
In [181]:
In [182]:

TASK: Perform a train test split, holding out 10% of the data for testing. We'll use a random_state of 101 in the solutions notebook/video.

In [183]:
#CODE HERE
In [184]:
In [185]:

TASK: Decision Tree Perfomance. Complete the following tasks:

  1. Train a single decision tree model (feel free to grid search for optimal hyperparameters).
  2. Evaluate performance metrics from decision tree, including classification report and plotting a confusion matrix.
  3. Calculate feature importances from the decision tree.
  4. OPTIONAL: Plot your tree, note, the tree could be huge depending on your pruning, so it may crash your notebook if you display it with plot_tree.
In [222]:
In [227]:


              precision    recall  f1-score   support

          No       0.87      0.89      0.88       557
         Yes       0.55      0.49      0.52       147

    accuracy                           0.81       704
   macro avg       0.71      0.69      0.70       704
weighted avg       0.80      0.81      0.81       704
In [228]:
Out[228]:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2d1e9601d90>
In [229]:
In [230]:
In [231]:
In [ ]:

Random Forest

TASK: Create a Random Forest model and create a classification report and confusion matrix from its predicted results on the test set.

In [259]:
#CODE HERE
In [269]:


              precision    recall  f1-score   support

          No       0.86      0.89      0.87       557
         Yes       0.52      0.44      0.48       147

    accuracy                           0.80       704
   macro avg       0.69      0.67      0.68       704
weighted avg       0.79      0.80      0.79       704
In [270]:
Out[270]:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2d1e6a54040>

Boosted Trees

TASK: Use AdaBoost or Gradient Boosting to create a model and report back the classification report and plot a confusion matrix for its predicted results

In [ ]:
#CODE HERE
In [292]:


              precision    recall  f1-score   support

          No       0.88      0.90      0.89       557
         Yes       0.60      0.54      0.57       147

    accuracy                           0.83       704
   macro avg       0.74      0.72      0.73       704
weighted avg       0.82      0.83      0.83       704
In [293]:
Out[293]:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2d1e9373a30>

TASK: Analyze your results, which model performed best for you?

In [294]:
# With base models, we got best performance from an AdaBoostClassifier, but note, we didn't do any gridsearching AND most models performed about the same on the data set.

Great job!

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