TeachingDataScience/Code/interview/sample_ml.py at master · yogeshhk/TeachingDataScience · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
# Building Classification Models with Sklearn
# Ref: https://towardsdatascience.com/building-classification-models-with-sklearn-6a8fd107f0c1

import pandas as pd

df = pd.read_csv("data/Customer_Churn.csv")
print(df.head())

df.gender = pd.Categorical(df.gender)
df['gender_code'] = df.gender.cat.codes

df.SeniorCitizen = pd.Categorical(df.SeniorCitizen)
df['SeniorCitizen_code'] = df.SeniorCitizen.cat.codes

df.PhoneService = pd.Categorical(df.PhoneService)
df['PhoneService_code'] = df.PhoneService.cat.codes

df.MultipleLines = pd.Categorical(df.MultipleLines)
df['MultipleLines_code'] = df.MultipleLines.cat.codes

df.InternetService = pd.Categorical(df.InternetService)
df['InternetService_code'] = df.InternetService.cat.codes

df.Partner = pd.Categorical(df.Partner)
df['Partner_code'] = df.Partner.cat.codes

df.Dependents = pd.Categorical(df.Dependents)
df['Dependents_code'] = df.Dependents.cat.codes

df.PaymentMethod = pd.Categorical(df.PaymentMethod)
df['PaymentMethod_code'] = df.PaymentMethod.cat.codes

df.PaperlessBilling = pd.Categorical(df.PaperlessBilling)
df['PaperlessBilling_code'] = df.PaperlessBilling.cat.codes

df.Contract = pd.Categorical(df.Contract)
df['Contract_code'] = df.Contract.cat.codes

df.StreamingMovies = pd.Categorical(df.StreamingMovies)
df['StreamingMovies_code'] = df.StreamingMovies.cat.codes

df.TechSupport = pd.Categorical(df.TechSupport)
df['TechSupport_code'] = df.TechSupport.cat.codes

df.DeviceProtection = pd.Categorical(df.DeviceProtection)
df['DeviceProtection_code'] = df.DeviceProtection.cat.codes

df.OnlineBackup = pd.Categorical(df.OnlineBackup)
df['OnlineBackup_code'] = df.OnlineBackup.cat.codes

df.OnlineSecurity = pd.Categorical(df.OnlineSecurity)
df['OnlineSecurity_code'] = df.OnlineSecurity.cat.codes

df.Dependents = pd.Categorical(df.Dependents)
df['Dependents_code'] = df.Dependents.cat.codes

df.Dependents = pd.Categorical(df.Dependents)
df['Dependents_code'] = df.Dependents.cat.codes

df.StreamingTV = pd.Categorical(df.StreamingTV)
df['StreamingTV_code'] = df.StreamingTV.cat.codes

df.Churn = pd.Categorical(df.Churn)
df['Churn_code'] = df.Churn.cat.codes

import numpy as np
features = ['gender_code', 'SeniorCitizen_code', 'PhoneService_code', 'MultipleLines_code',
                 'InternetService_code', 'Partner_code', 'Dependents_code', 'PaymentMethod_code',
                 'PaperlessBilling_code','Contract_code', 'StreamingMovies_code',
                 'StreamingTV_code', 'TechSupport_code', 'DeviceProtection_code', 'OnlineBackup_code',
                 'OnlineSecurity_code', 'Dependents_code', 'tenure', 'MonthlyCharges']

X = np.array(df[features])
y = np.array(df['Churn_code'])

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)

from sklearn.linear_model import LogisticRegression

reg_log = LogisticRegression()
reg_log.fit(X_train, y_train)
y_pred = reg_log.predict(X_test)

from sklearn import metrics
print(metrics.classification_report(y_test, y_pred))

print("roc_auc_score: ", metrics.roc_auc_score(y_test, y_pred))
print("f1 score: ", metrics.f1_score(y_test, y_pred))

# -----------------------------------------------------------------

from sklearn.ensemble import RandomForestClassifier
reg_rf = RandomForestClassifier()
reg_rf.fit(X_train, y_train)
y_pred = reg_rf.predict(X_test)
print(metrics.classification_report(y_test, y_pred))
print("roc_auc_score: ", metrics.roc_auc_score(y_test, y_pred))
print("f1 score: ", metrics.f1_score(y_test, y_pred))
feature_df = pd.DataFrame({'Importance':reg_rf.feature_importances_, 'Features': features })
print(feature_df)