👋 Welcome

# Start writing code here...# Import all the libraries import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import matplotlib.pyplot as plt#visualization from PIL import Image %matplotlib inline import seaborn as sns#visualization import itertools import warnings warnings.filterwarnings("ignore") import plotly.graph_objs as go#visualization import plotly.offline as py#visualization

# Load the cleaned file telcom = pd.read_csv("clean_df.csv")

telcom.head()

Unnamed: 0int64

customerIDobject

7590-VHVEG

5575-GNVDE

3668-QPYBK

7795-CFOCW

9237-HQITU

telcom = telcom.drop('Unnamed: 0', axis = 1)

telcom.head()

customerIDobject

genderobject

7590-VHVEG

Female

5575-GNVDE

Male

3668-QPYBK

Male

7795-CFOCW

Male

9237-HQITU

Female

from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import StandardScaler #customer id col Id_col = ['customerID'] #Target columns target_col = ["Churn"] #categorical columns cat_cols = telcom.nunique()[telcom.nunique() < 6].keys().tolist() cat_cols = [x for x in cat_cols if x not in target_col] #numerical columns num_cols = [x for x in telcom.columns if x not in cat_cols + target_col + Id_col] #Binary columns with 2 values bin_cols = telcom.nunique()[telcom.nunique() == 2].keys().tolist() #Columns more than 2 values multi_cols = [i for i in cat_cols if i not in bin_cols]

multi_cols

bin_cols

#Label encoding Binary columns le = LabelEncoder() for i in bin_cols : telcom[i] = le.fit_transform(telcom[i]) #Duplicating columns for multi value columns telcom = pd.get_dummies(data = telcom,columns = multi_cols)

telcom

customerIDobject

7590-VHVEG0%

5575-GNVDE0%

7030 others100%

genderint64

0 - 1

7590-VHVEG

5575-GNVDE

3668-QPYBK

7795-CFOCW

9237-HQITU

9305-CDSKC

1452-KIOVK

6713-OKOMC

7892-POOKP

6388-TABGU

#Scaling Numerical columns std = StandardScaler() scaled = std.fit_transform(telcom[num_cols]) scaled = pd.DataFrame(scaled,columns=num_cols)

scaled

tenurefloat64

-1.2802480369298874 - 1.6125732136881425

MonthlyChargesfloat64

-1.5472830783322116 - 1.7933814803098413

-1.2802480369298874

-1.1616939352451685

0.06430268518835193

-0.2608779199297493

-1.2395040756535771

-0.3639232943754246

0.512486259227765

-0.747850415293989

-1.2395040756535771

0.19617817640187488

-0.9950403079957154

1.1584890119510036

-0.42462485012737144

0.8078023344020117

-0.9135523854430948

-1.1650179795821258

-0.18016108246950976

1.3296772953043026

1.2051336009250397

-0.28747027462540753

telcom = telcom.drop(columns = num_cols,axis = 1) telcom = telcom.merge(scaled,left_index=True,right_index=True,how = "left")

telcom

customerIDobject

7590-VHVEG0%

5575-GNVDE0%

7030 others100%

genderint64

0 - 1

7590-VHVEG

5575-GNVDE

3668-QPYBK

7795-CFOCW

9237-HQITU

9305-CDSKC

1452-KIOVK

6713-OKOMC

7892-POOKP

6388-TABGU

What is the purpose of the above code?

Ans: Here we have dropped the original numerical values so that we can put our scaled values in the data frame. So in the next line, we are merging our scaled values (which are monthly charges, total charges and tenure)so that we can get a standard format of all our numerical column and within a range of 0 to 1.

summary = telcom.describe().transpose() summary

countfloat64

7032.0 - 7032.0

meanfloat64

-1.1266427053078948e-16 - 0.9032992036405005

gender

7032.0

0.5046928327645052

SeniorCitizen

7032.0

0.16240045506257111

Partner

7032.0

0.4825085324232082

Dependents

7032.0

0.2984926052332196

PhoneService

7032.0

0.9032992036405005

OnlineSecurity

7032.0

0.286547212741752

OnlineBackup

7032.0

0.34485210466439137

DeviceProtection

7032.0

0.3438566552901024

TechSupport

7032.0

0.2901023890784983

StreamingTV

7032.0

0.3843856655290102

# To make it look nice we can do some additional stuff if needed summary = (telcom[[i for i in telcom.columns if i not in Id_col]]. describe().transpose().reset_index()) summary = summary.rename(columns = {"index" : "feature"}) summary = np.around(summary,3) val_lst = [summary['feature'], summary['count'], summary['mean'],summary['std'], summary['min'], summary['25%'], summary['50%'], summary['75%'], summary['max']] trace = go.Table(header = dict(values = summary.columns.tolist(), line = dict(color = ['#506784']), fill = dict(color = ['#119DFF']), ), cells = dict(values = val_lst, line = dict(color = ['#506784']), fill = dict(color = ["lightgrey",'#F5F8FF']) ), columnwidth = [200,60,100,100,60,60,80,80,80]) layout = go.Layout(dict(title = "Variable Summary")) figure = go.Figure(data=[trace],layout=layout) py.iplot(figure)

#correlation correlation = telcom.corr() #tick labels matrix_cols = correlation.columns.tolist()

#convert to array corr_array = np.array(correlation)

correlation

genderfloat64

-0.016327823070701356 - 1.0

SeniorCitizenfloat64

-0.21055006112685165 - 1.0

gender

1.0

-0.00181939061341898

SeniorCitizen

-0.00181939061341898

1.0

Partner

-0.0013790513218356554

0.016956614532022372

Dependents

0.010348917127614234

-0.21055006112685165

PhoneService

-0.007514979909200452

0.00839161191121723

OnlineSecurity

-0.016327823070701356

-0.03857639016860375

OnlineBackup

-0.013092839264554514

0.06666279065142326

DeviceProtection

-0.0008067457759127595

0.05951387148203744

TechSupport

-0.008507162405233496

-0.06057683940618956

StreamingTV

-0.007124396986724548

0.10544501753679582

#Plotting trace = go.Heatmap(z = corr_array, x = matrix_cols, y = matrix_cols, colorscale = "Viridis", colorbar = dict(title = "Pearson Correlation coefficient", titleside = "right" ) , ) layout = go.Layout(dict(title = "Correlation Matrix for variables", autosize = False, height = 720, width = 800, margin = dict(r = 0 ,l = 210, t = 25,b = 210, ), yaxis = dict(tickfont = dict(size = 9)), xaxis = dict(tickfont = dict(size = 9)) ) ) data = [trace] fig = go.Figure(data=data,layout=layout) py.iplot(fig)

Q. What do you observe?

Ans: The correlation matrix above shows the correlation coefficients between several variables related to churn:

Below are few insights needs to be considered:

Tenure correlated with Total charges and Contract_Two Years

Monthly charges correlated with Totalcharges, MultipleLines, InternetService,OnlineSecurity

Total charges corelated with Tenure and Monthly charges.

Customers with multiple lines are correlated with tenure, monthly and total charges

No correlation has been observed for customer with OnlineSecurity, OnlineBackUp, DeviceProtection, TechSupport, Streamingmovies.

Strangely, 2-years contracts are corelated with tenure, but not 1-year contract

Phone service and multiple lines_no phone service are negatively correlated.

Model Building (We will build Decision Tree and Logistics Regression models)

!pip install statsmodels==0.13.2

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WARNING: You are using pip version 22.0.4; however, version 22.2.2 is available.
You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.

# Import all the modules from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import confusion_matrix,accuracy_score,classification_report from sklearn.metrics import f1_score import statsmodels.api as sm from sklearn.metrics import precision_score,recall_score

#splitting train and test data train,test = train_test_split(telcom,test_size = .25 ,random_state = 111)

Q. What is the purpose of random_state parameter?

Ans: We use random_state parameter so that we could reuse the train_test_spilt data so that we can reproduce our results to get best accuracy.

##seperating dependent and independent variables cols = [i for i in telcom.columns if i not in Id_col + target_col] X_train = train[cols] Y_train = train[target_col] X_test = test[cols] Y_test = test[target_col]

Logistics Regression

classifier = LogisticRegression(random_state = 0,penalty='l2') classifier.fit(X_train, Y_train)

# Predicting test set y_pred = classifier.predict(X_test)

classifier.coef_

#Evaluating the Results cm = confusion_matrix(Y_test, y_pred) print(accuracy_score(Y_test, y_pred)) print(precision_score(Y_test, y_pred)) print(recall_score(Y_test, y_pred)) print(f1_score(Y_test, y_pred))

0.8020477815699659
0.6888297872340425
0.5285714285714286
0.5981524249422633

Q. What do the scores mean? Is this a good model fit based on the scores. Make sure you print all the scores.

Scores are between 0 and 1, with a larger score indicating a better fit.

We can calculate scores in 4 different ways:

Accuracy is the most logical performance metric, and it is just the proportion of properly predicted observations to all observations.

Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

Recall is the ratio of correctly predicted positive observations to the all observations in actual class

F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account

# Build the confusion matrix df_cm = pd.DataFrame(cm, index = (0, 1), columns = (0, 1)) plt.figure(figsize = (10, 7)) sns.heatmap(df_cm, annot = True, fmt ='g') print("Test Data Accuracy: %.4f" %accuracy_score(Y_test, y_pred))

Test Data Accuracy: 0.8020

Decision Tree

model_tree = DecisionTreeClassifier(random_state = 2) model_tree.fit(X_train, Y_train)

# Predicting test set y_pred = model_tree.predict(X_test)

0.732650739476678
0.521551724137931
0.49387755102040815
0.5073375262054507

Q. What do the scores mean? Is this a good model fit based on the scores. Make sure you print all the scores.

Scores are between 0 and 1, with a larger score indicating a better fit.

We can calculate scores in 4 different ways:

Accuracy is the most logical performance metric, and it is just the proportion of properly predicted observations to all observations.

Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

Recall is the ratio of correctly predicted positive observations to the all observations in actual class

F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account

# Build Confusion Matrix df_cm = pd.DataFrame(cm, index = (0, 1), columns = (0, 1)) plt.figure(figsize = (10, 7)) sns.heatmap(df_cm, annot = True, fmt ='g') print("Test Data Accuracy: %.4f" %accuracy_score(Y_test, y_pred))

Test Data Accuracy: 0.7327

Q Which model performs better? (Hint: compare the metrics)

Ans: After comparing different models, we have observed that Logistic regression has better accuracy which is (0.80) as compared to decission tree model, this defines that the Logistic regression performs better.

K- fold Cross Validation

Q. What is K-fold cross validation?

Ans: The process contains a single parameter, k, that designates how many groups should be created from a given data sample. As a result, the process is frequently referred to as k-fold cross-validation. When a particular number for k is selected, it may be substituted for k in the model's reference, such as when k=10 is used to refer to cross-validation by a 10-fold factor.

from sklearn.model_selection import cross_val_score accuracies = cross_val_score(estimator = classifier, X= X_train, y =Y_train, cv =10)

# Check for accuracies accuracies

Q. What do accuracies tell?

Ans: Accuracy is the measure of how closely a measurement resembles the actual value.

pd.concat([pd.DataFrame(X_train.columns, columns =["features"]), pd.DataFrame(np.transpose(classifier.coef_), columns =["Coef"])], axis = 1)

featuresobject

gender3%

SeniorCitizen3%

31 others93.9%

Coeffloat64

-1.3738370163432916 - 0.7215887493187053

gender

-0.08956551403380318

SeniorCitizen

0.21558038363067025

Partner

0.002618106783772618

Dependents

-0.13203196164194522

PhoneService

-0.1786241552364546

OnlineSecurity

-0.427249585064374

OnlineBackup

-0.1889261422307553

DeviceProtection

-0.008706823725299377

TechSupport

-0.3112658540769839

StreamingTV

0.17963667373711875

Feature Selection/Feature Engineering

from sklearn.feature_selection import RFE classifier = LogisticRegression() rfe = RFE(classifier,step=10) rfe = rfe.fit(X_train,Y_train)

print(rfe.support_)

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

X_train.columns[rfe.support_]

rfe.ranking_

# Build the model again after Feature Selection classifier = LogisticRegression(random_state = 2) classifier.fit(X_train[X_train.columns[rfe.support_]], Y_train)

# Predicting test set y_pred = classifier.predict(X_test[X_test.columns[rfe.support_]])

0.7986348122866894
0.6857923497267759
0.5122448979591837
0.5864485981308412

df_cm = pd.DataFrame(cm, index = (0, 1), columns = (0, 1)) plt.figure(figsize = (10, 7)) sns.heatmap(df_cm, annot = True, fmt ='g') print("Test Data Accuracy: %.4f" %accuracy_score(Y_test, y_pred))

Test Data Accuracy: 0.7986

Q. Has the model improved after feature selection?

Ans: As per the above confusion matrix ,we can see there is not much difference between before and after selection of features .Before feature selection the accuracy was 0.73 and now it is 0.79, However, we are getting accuracy by using limited features.

Meaning, we are getting better accuracy with less features so that we can ignore other features.

# Subset the coefficents for RFE pd.concat([pd.DataFrame(X_train.columns[rfe.support_], columns =["features"]), pd.DataFrame(np.transpose(classifier.coef_), columns =["Coef"])], axis = 1)

featuresobject

OnlineSecurity6.3%

TechSupport6.3%

14 others87.5%

Coeffloat64

-1.4004279611146393 - 0.8628106207698113

OnlineSecurity

-0.42003843548127345

TechSupport

-0.2900058760206293

StreamingMovies

0.40358589090520386

PaperlessBilling

0.3830685811263155

MultipleLines_No

-0.44480978090478274

InternetService_Fiber optic

0.8628106207698113

InternetService_No

-0.7547700746952272

Contract_Month-to-month

0.7080785636158646

Contract_Two year

-0.7771475158855855

PaymentMethod_Electronic check

0.3288030210806862

This chart is empty

final_results = pd.concat([Y_test, telcom.customerID], axis =1).dropna() final_results['predicted_churn'] = y_pred final_results = final_results[['customerID', 'Churn', 'predicted_churn']].reset_index(drop = True)

Q. Print the final Results

print(final_results)

      customerID  Churn  predicted_churn
0     3668-QPYBK    1.0                0
1     7795-CFOCW    0.0                0
2     9237-HQITU    1.0                0
3     8091-TTVAX    0.0                0
4     6865-JZNKO    0.0                0
...          ...    ...              ...
1753  2823-LKABH    0.0                0
1754  6894-LFHLY    1.0                0
1755  0639-TSIQW    1.0                0
1756  4801-JZAZL    0.0                0
1757  3186-AJIEK    0.0                0

[1758 rows x 3 columns]

Q. Provide recommendations based on the feature selection. What should company target for to reduce churn?

Customer churn has a negative impact on a company's profitability. There are numerous tactics that can be used to reduce client churn. Knowing a company's customers well is the best strategy to prevent customer churn. This entails identifying clients who run the danger of leaving and making an effort to increase their contentment. Naturally, the primary priority for solving this problem is to improve customer service.

In order to do that, for example, organization could start loyalty program for senior citizens with some benefits so that they sould not leave the company. Another tactic would be when customer joins the organization for getting the service, company should give some additional beneficial services at the beginning itself to prevent early churning.