tldr

KggleのTelco Customer ChurnをPredicting Customer Churn - Data Every Day #040に沿ってやっていきます。

実行環境はGoogle Colaboratorです。

インポート

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import sklearn.preprocessing as sp
from sklearn.model_selection import train_test_split
import sklearn.linear_model as slm

import tensorflow as tf

データのダウンロード

Google Driveをマウントします。

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

KaggleのAPIクライアントを初期化し、認証します。
認証情報はGoogle Drive内（/content/drive/My Drive/Colab Notebooks/Kaggle）にkaggle.jsonとして置いてあります。

import os
kaggle_path = "/content/drive/My Drive/Colab Notebooks/Kaggle"
os.environ['KAGGLE_CONFIG_DIR'] = kaggle_path

from kaggle.api.kaggle_api_extended import KaggleApi
api = KaggleApi()
api.authenticate()

Kaggle APIを使ってデータをダウンロードします。

dataset_id = 'blastchar/telco-customer-churn'
dataset = api.dataset_list_files(dataset_id)
file_name = dataset.files[0].name
file_path = os.path.join(api.get_default_download_dir(), file_name)
file_path

'/content/WA_Fn-UseC_-Telco-Customer-Churn.csv'

api.dataset_download_file(dataset_id, file_name, force=True, quiet=False)

100%|██████████| 955k/955k [00:00<00:00, 95.4MB/s]

Downloading WA_Fn-UseC_-Telco-Customer-Churn.csv to /content









True

データの読み込み

Pedumagalhaes/quality-prediction-in-a-mining-processadasを使ってダウンロードしてきたCSVファイルを読み込みます。

data = pd.read_csv(file_path)

data

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	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	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	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	No	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	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	6840-RESVB	Male	0	Yes	Yes	24	Yes	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.5	No
7039	2234-XADUH	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.9	No
7040	4801-JZAZL	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No
7041	8361-LTMKD	Male	1	Yes	No	4	Yes	Yes	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.6	Yes
7042	3186-AJIEK	Male	0	No	No	66	Yes	No	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.5	No

7043 rows × 21 columns

下準備

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

data = data.drop(['customerID'], axis=1)

エンコード

def get_uniques(df, columns):
    return {column: list(df[column].unique()) for column in columns}

def get_categorical_columns(df):
    return [column for column in df.columns if df.dtypes[column] == 'object']

get_uniques(data, get_categorical_columns(data))

{'Churn': ['No', 'Yes'],
 'Contract': ['Month-to-month', 'One year', 'Two year'],
 'Dependents': ['No', 'Yes'],
 'DeviceProtection': ['No', 'Yes', 'No internet service'],
 'InternetService': ['DSL', 'Fiber optic', 'No'],
 'MultipleLines': ['No phone service', 'No', 'Yes'],
 'OnlineBackup': ['Yes', 'No', 'No internet service'],
 'OnlineSecurity': ['No', 'Yes', 'No internet service'],
 'PaperlessBilling': ['Yes', 'No'],
 'Partner': ['Yes', 'No'],
 'PaymentMethod': ['Electronic check',
  'Mailed check',
  'Bank transfer (automatic)',
  'Credit card (automatic)'],
 'PhoneService': ['No', 'Yes'],
 'StreamingMovies': ['No', 'Yes', 'No internet service'],
 'StreamingTV': ['No', 'Yes', 'No internet service'],
 'TechSupport': ['No', 'Yes', 'No internet service'],
 'TotalCharges': ['29.85',
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  ...],
 'gender': ['Female', 'Male']}

sorted(data['TotalCharges'].unique())

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 '1400.85',
 '1401.15',
 '1401.4',
 '1402.25',
 '1403.1',
 '1404.65',
 '1405.3',
 '1406',
 '1406.65',
 '1406.9',
 '141.1',
 '141.45',
 '141.5',
 '141.6',
 '141.65',
 '141.7',
 '1410.25',
 '1411.2',
 '1411.35',
 '1411.65',
 '1411.9',
 '1412.4',
 '1412.65',
 '1413',
 '1414.2',
 '1414.45',
 '1414.65',
 '1414.8',
 '1415',
 '1415.55',
 '1415.85',
 '1416.2',
 '1416.5',
 '1416.75',
 '1417.9',
 '1419.4',
 '142.35',
 '1421.75',
 '1421.9',
 '1422.05',
 '1422.1',
 '1422.65',
 '1423.05',
 '1423.15',
 '1423.35',
 '1423.65',
 '1423.85',
 '1424.2',
 '1424.4',
 '1424.5',
 '1424.6',
 '1424.9',
 '1424.95',
 '1425.45',
 '1426.4',
 '1426.45',
 '1426.75',
 '1427.55',
 '1429.65',
 '143.35',
 '143.65',
 '143.9',
 '1430.05',
 '1430.25',
 '1430.95',
 '1431.65',
 '1432.55',
 '1433.8',
 '1434.1',
 '1434.6',
 '1436.95',
 '1438.05',
 '1439.35',
 '144',
 '144.15',
 '144.35',
 '144.55',
 '144.8',
 '144.95',
 '1440.75',
 '1441.1',
 '1441.65',
 '1441.8',
 '1441.95',
 '1442',
 '1442.2',
 '1442.6',
 '1442.65',
 '1443.65',
 '1444.05',
 '1444.65',
 '1445.2',
 '1445.3',
 '1445.95',
 '1446.8',
 '1447.9',
 '1448.6',
 '1448.8',
 '145',
 '145.15',
 '145.4',
 '1451.1',
 '1451.6',
 '1451.9',
 '1453.1',
 '1454.15',
 '1454.25',
 '1457.25',
 '1458.1',
 '1459.35',
 '146.05',
 '146.3',
 '146.4',
 '146.6',
 '146.65',
 '146.9',
 '1460.65',
 '1460.85',
 '1461.15',
 '1461.45',
 '1462.05',
 '1462.6',
 '1463.45',
 '1463.5',
 '1463.7',
 '1465.75',
 '1466.1',
 '1468.75',
 '1468.9',
 '147.15',
 '147.5',
 '147.55',
 '147.75',
 '147.8',
 '1470.05',
 '1470.95',
 '1471.75',
 '1474.35',
 '1474.75',
 '1474.9',
 '1476.25',
 '1477.65',
 '1478.85',
 '148.05',
 '1482.3',
 '1483.25',
 '1489.3',
 '149.05',
 '149.55',
 '1490.4',
 '1490.95',
 '1492.1',
 '1493.1',
 '1493.2',
 '1493.55',
 '1493.75',
 '1494.5',
 '1495.1',
 '1496.45',
 '1496.9',
 '1497.05',
 '1497.9',
 '1498.2',
 '1498.35',
 '1498.55',
 '1498.65',
 '1498.85',
 '150',
 '150.35',
 '150.6',
 '150.75',
 '150.85',
 '1500.25',
 '1500.5',
 '1500.95',
 '1501.75',
 '1502.25',
 '1502.65',
 '1504.05',
 '1505.05',
 '1505.15',
 '1505.35',
 '1505.45',
 '1505.85',
 '1505.9',
 '1506.4',
 '1507',
 '1509.8',
 '1509.9',
 '151.3',
 '151.65',
 '151.75',
 '151.8',
 '1510.3',
 '1510.5',
 '1511.2',
 '1513.6',
 '1514.85',
 '1515.1',
 '1516.6',
 '1517.5',
 '1519',
 '152.3',
 '152.45',
 '152.6',
 '152.7',
 '152.95',
 '1520.1',
 '1520.9',
 '1521.2',
 '1522.65',
 '1522.7',
 '1523.4',
 '1524.85',
 '1525.35',
 '1527.35',
 '1527.5',
 '1529.2',
 '1529.45',
 '1529.65',
 '153.05',
 '153.3',
 '153.8',
 '153.95',
 '1530.6',
 '1531.4',
 '1532.45',
 '1533.8',
 '1534.05',
 '1534.75',
 '1536.75',
 '1537.85',
 '1537.9',
 '1538.6',
 '1539.45',
 '1539.75',
 '1539.8',
 '154.3',
 '154.55',
 '154.65',
 '154.8',
 '154.85',
 '1540.05',
 '1540.2',
 '1540.35',
 '1544.05',
 '1545.4',
 '1546.3',
 '1547.35',
 '1548.65',
 '1549.75',
 '155.35',
 '155.65',
 '155.8',
 '155.9',
 '1551.6',
 '1553.2',
 '1553.9',
 '1553.95',
 '1554',
 '1554.9',
 '1555.65',
 '1556.85',
 '1558.65',
 '1558.7',
 ...]

data['TotalCharges'] = data['TotalCharges'].replace(' ', np.NaN)

data['TotalCharges'] = data['TotalCharges'].astype(np.float)

data['TotalCharges'] = data['TotalCharges'].fillna(data['TotalCharges'].mean())

get_uniques(data, get_categorical_columns(data))

{'Churn': ['No', 'Yes'],
 'Contract': ['Month-to-month', 'One year', 'Two year'],
 'Dependents': ['No', 'Yes'],
 'DeviceProtection': ['No', 'Yes', 'No internet service'],
 'InternetService': ['DSL', 'Fiber optic', 'No'],
 'MultipleLines': ['No phone service', 'No', 'Yes'],
 'OnlineBackup': ['Yes', 'No', 'No internet service'],
 'OnlineSecurity': ['No', 'Yes', 'No internet service'],
 'PaperlessBilling': ['Yes', 'No'],
 'Partner': ['Yes', 'No'],
 'PaymentMethod': ['Electronic check',
  'Mailed check',
  'Bank transfer (automatic)',
  'Credit card (automatic)'],
 'PhoneService': ['No', 'Yes'],
 'StreamingMovies': ['No', 'Yes', 'No internet service'],
 'StreamingTV': ['No', 'Yes', 'No internet service'],
 'TechSupport': ['No', 'Yes', 'No internet service'],
 'gender': ['Female', 'Male']}

data['MultipleLines'] = data['MultipleLines'].replace('No phone service', 'No')

data[['DeviceProtection', 'OnlineBackup', 'OnlineSecurity', 'StreamingMovies', 'StreamingTV', 'TechSupport']] = data[['DeviceProtection', 'OnlineBackup', 'OnlineSecurity', 'StreamingMovies', 'StreamingTV', 'TechSupport']].replace('No internet service', 'No')

binary_features = ['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'DeviceProtection', 'OnlineBackup', 'OnlineSecurity', 'StreamingMovies', 'StreamingTV', 'TechSupport', 'PaperlessBilling']
ordinal_features = ['InternetService', 'Contract']
nominal_features = ['PaymentMethod']
target_column = ['Churn']

internet_ordering = ['No', 'DSL', 'Fiber optic']
contract_ordering = ['Month-to-month', 'One year', 'Two year']

def binary_encode(df, column, positive_value):
    df = df.copy()
    df[column] = df[column].apply(lambda x: 1 if x == positive_value else 0)
    return df

def ordinal_encode(df, column, ordering):
    df = df.copy()
    df[column] = df[column].apply(lambda x: ordering.index(x))
    return df

def onehot_encode(df, column):
    df = df.copy()
    dummies = pd.get_dummies(df[column])
    df = pd.concat([df, dummies], axis=1)
    df = df.drop(column, axis=1)
    return df

data = binary_encode(data, 'gender', 'Male')

yes_features = ['Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'DeviceProtection', 'OnlineBackup', 'OnlineSecurity', 'StreamingMovies', 'StreamingTV', 'TechSupport', 'PaperlessBilling']
for column in yes_features:
    data = binary_encode(data, column, 'Yes')

data = ordinal_encode(data, 'InternetService', internet_ordering)
data = ordinal_encode(data, 'Contract', contract_ordering)

data = onehot_encode(data, 'PaymentMethod')

data = binary_encode(data, 'Churn', 'Yes')

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 23 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   gender                     7043 non-null   int64  
 1   SeniorCitizen              7043 non-null   int64  
 2   Partner                    7043 non-null   int64  
 3   Dependents                 7043 non-null   int64  
 4   tenure                     7043 non-null   int64  
 5   PhoneService               7043 non-null   int64  
 6   MultipleLines              7043 non-null   int64  
 7   InternetService            7043 non-null   int64  
 8   OnlineSecurity             7043 non-null   int64  
 9   OnlineBackup               7043 non-null   int64  
 10  DeviceProtection           7043 non-null   int64  
 11  TechSupport                7043 non-null   int64  
 12  StreamingTV                7043 non-null   int64  
 13  StreamingMovies            7043 non-null   int64  
 14  Contract                   7043 non-null   int64  
 15  PaperlessBilling           7043 non-null   int64  
 16  MonthlyCharges             7043 non-null   float64
 17  TotalCharges               7043 non-null   float64
 18  Churn                      7043 non-null   int64  
 19  Bank transfer (automatic)  7043 non-null   uint8  
 20  Credit card (automatic)    7043 non-null   uint8  
 21  Electronic check           7043 non-null   uint8  
 22  Mailed check               7043 non-null   uint8  
dtypes: float64(2), int64(17), uint8(4)
memory usage: 1.0 MB

分割とスケーリング

y = data['Churn']
X = data.drop('Churn', axis=1)

scaelr = sp.StandardScaler()
X = scaelr.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)

トレーニング

X.shape

(7043, 22)

model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(22,)),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid'),
])

model.summary()

model.compile(
    optimizer='adam',
    loss='binary_crossentropy',
    metrics=[tf.keras.metrics.AUC(name='auc')],
)

batch_size=64
epochs=100

history = model.fit(
    X_train,
    y_train,
    validation_split=0.2,
    batch_size=batch_size,
    epochs=epochs,
    callbacks=[tf.keras.callbacks.ReduceLROnPlateau],
    verbose=0,
)

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_6 (Dense)              (None, 64)                1472      
_________________________________________________________________
dense_7 (Dense)              (None, 64)                4160      
_________________________________________________________________
dense_8 (Dense)              (None, 1)                 65        
=================================================================
Total params: 5,697
Trainable params: 5,697
Non-trainable params: 0
_________________________________________________________________

結果

plt.figure(figsize=(14, 10))

epochs_range = range(1, epochs+1)
train_loss = history.history['loss']
val_loss = history.history['val_loss']

plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')

plt.title('Training and Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()

plt.show()

np.argmin(val_loss)

model.evaluate(X_test, y_test)

67/67 [==============================] - 0s 1ms/step - loss: 0.4744 - auc: 0.8117





[0.4744356870651245, 0.8116970062255859]