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In [1]:



    


import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
%matplotlib inline



    











In [2]:



    


df = pd.read_csv('../data/telecom_churn.csv')



    











In [3]:



    


df.head()



    


















    
Out[3]:











  
    

      
      State
      Account length
      Area code
      International plan
      Voice mail plan
      Number vmail messages
      Total day minutes
      Total day calls
      Total day charge
      Total eve minutes
      Total eve calls
      Total eve charge
      Total night minutes
      Total night calls
      Total night charge
      Total intl minutes
      Total intl calls
      Total intl charge
      Customer service calls
      Churn
    

  
  
    

      0
      KS
      128
      415
      No
      Yes
      25
      265.1
      110
      45.07
      197.4
      99
      16.78
      244.7
      91
      11.01
      10.0
      3
      2.70
      1
      False
    

    

      1
      OH
      107
      415
      No
      Yes
      26
      161.6
      123
      27.47
      195.5
      103
      16.62
      254.4
      103
      11.45
      13.7
      3
      3.70
      1
      False
    

    

      2
      NJ
      137
      415
      No
      No
      0
      243.4
      114
      41.38
      121.2
      110
      10.30
      162.6
      104
      7.32
      12.2
      5
      3.29
      0
      False
    

    

      3
      OH
      84
      408
      Yes
      No
      0
      299.4
      71
      50.90
      61.9
      88
      5.26
      196.9
      89
      8.86
      6.6
      7
      1.78
      2
      False
    

    

      4
      OK
      75
      415
      Yes
      No
      0
      166.7
      113
      28.34
      148.3
      122
      12.61
      186.9
      121
      8.41
      10.1
      3
      2.73
      3
      False

1. Признаки по одному

1.1. Количественные

Гистограмма и боксплот





In [4]:



    


df['Total day minutes'].hist();



    


















    
























In [5]:



    


sns.boxplot(df['Total day minutes']);



    


















    
























In [6]:



    


df.hist();

1.2. Категориальные

countplot





In [7]:



    


df['State'].value_counts().head()



    


















    
Out[7]:








WV    106
MN     84
NY     83
AL     80
OH     78
Name: State, dtype: int64

















In [8]:



    


df['Churn'].value_counts()



    


















    
Out[8]:








False    2850
True      483
Name: Churn, dtype: int64

















In [9]:



    


sns.countplot(df['Churn']);



    


















    
























In [10]:



    


sns.countplot(df['State']);



    


















    
























In [11]:



    


sns.countplot(df[df['State'].\
                 isin(df['State'].value_counts().head().index)]['State']);

2. Взаимодействия признаков

2.1. Количественный с количественным

pairplot, scatterplot, корреляции, heatmap





In [12]:



    


feat = [f for f in df.columns if 'charge' in f]

df[feat].hist();



    


















    
























In [13]:



    


sns.pairplot(df[feat]);



    


















    
























In [14]:



    


df['Churn'].map({False: 'blue', True: 'orange'}).head()



    


















    
Out[14]:








0    blue
1    blue
2    blue
3    blue
4    blue
Name: Churn, dtype: object

















In [15]:



    


df[~df['Churn']].head()



    


















    
Out[15]:











  
    

      
      State
      Account length
      Area code
      International plan
      Voice mail plan
      Number vmail messages
      Total day minutes
      Total day calls
      Total day charge
      Total eve minutes
      Total eve calls
      Total eve charge
      Total night minutes
      Total night calls
      Total night charge
      Total intl minutes
      Total intl calls
      Total intl charge
      Customer service calls
      Churn
    

  
  
    

      0
      KS
      128
      415
      No
      Yes
      25
      265.1
      110
      45.07
      197.4
      99
      16.78
      244.7
      91
      11.01
      10.0
      3
      2.70
      1
      False
    

    

      1
      OH
      107
      415
      No
      Yes
      26
      161.6
      123
      27.47
      195.5
      103
      16.62
      254.4
      103
      11.45
      13.7
      3
      3.70
      1
      False
    

    

      2
      NJ
      137
      415
      No
      No
      0
      243.4
      114
      41.38
      121.2
      110
      10.30
      162.6
      104
      7.32
      12.2
      5
      3.29
      0
      False
    

    

      3
      OH
      84
      408
      Yes
      No
      0
      299.4
      71
      50.90
      61.9
      88
      5.26
      196.9
      89
      8.86
      6.6
      7
      1.78
      2
      False
    

    

      4
      OK
      75
      415
      Yes
      No
      0
      166.7
      113
      28.34
      148.3
      122
      12.61
      186.9
      121
      8.41
      10.1
      3
      2.73
      3
      False
    

  




















In [16]:



    


plt.scatter(df[df['Churn']]['Total eve charge'], 
            df[df['Churn']]['Total intl charge'],
           color='orange', label='churn');
plt.scatter(df[~df['Churn']]['Total eve charge'], 
            df[~df['Churn']]['Total intl charge'],
           color='blue', label='loyal');
plt.xlabel('Вечерние начисления');
plt.ylabel('Межнар. начисления');
plt.title('Распределение начислений для лояльных/ушедших');
plt.legend();



    


















    
























In [17]:



    


sns.heatmap(df.corr());



    


















    
























In [18]:



    


df.drop(feat, axis=1, inplace=True)



    











In [19]:



    


sns.heatmap(df.corr());

2.2. Количественный с категориальным

boxplot, violinplot





In [20]:



    


sns.boxplot(x='Churn', y='Total day minutes', data=df);



    


















    
























In [21]:



    


sns.boxplot(x='State', y='Total day minutes', data=df);



    


















    
























In [22]:



    


sns.violinplot(x='Churn', y='Total day minutes', data=df);



    


















    
























In [23]:



    


df.groupby('International plan')['Total day minutes'].mean()



    


















    
Out[23]:








International plan
No     178.893887
Yes    187.986997
Name: Total day minutes, dtype: float64

















In [24]:



    


sns.boxplot(x='International plan', y='Total day minutes', data=df);

2.3. Категориальный с категориальным

countplot





In [25]:



    


pd.crosstab(df['Churn'], df['International plan'])



    


















    
Out[25]:











  
    

      International plan
      No
      Yes
    

    

      Churn
      
      
    

  
  
    

      False
      2664
      186
    

    

      True
      346
      137
    

  




















In [26]:



    


sns.countplot(x='International plan', hue='Churn', data=df);



    


















    
























In [27]:



    


sns.countplot(x='Customer service calls', hue='Churn', data=df);

3. Прочее

Manifold learning, один из представителей – t-SNE





In [28]:



    


from sklearn.manifold import TSNE



    











In [29]:



    


tsne = TSNE(random_state=0)



    











In [30]:



    


df2 = df.drop(['State', 'Churn'], axis=1)



    











In [31]:



    


df2['International plan'] = df2['International plan'].map({'Yes': 1, 
                                                             'No': 0})
df2['Voice mail plan'] = df2['Voice mail plan'].map({'Yes': 1, 
                                                             'No': 0})



    











In [32]:



    


df2.info()



    


















    






<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3333 entries, 0 to 3332
Data columns (total 14 columns):
Account length            3333 non-null int64
Area code                 3333 non-null int64
International plan        3333 non-null int64
Voice mail plan           3333 non-null int64
Number vmail messages     3333 non-null int64
Total day minutes         3333 non-null float64
Total day calls           3333 non-null int64
Total eve minutes         3333 non-null float64
Total eve calls           3333 non-null int64
Total night minutes       3333 non-null float64
Total night calls         3333 non-null int64
Total intl minutes        3333 non-null float64
Total intl calls          3333 non-null int64
Customer service calls    3333 non-null int64
dtypes: float64(4), int64(10)
memory usage: 364.6 KB

















In [33]:



    


%%time
tsne.fit(df2)



    


















    






CPU times: user 3min 40s, sys: 5.54 s, total: 3min 46s
Wall time: 3min 53s










    
Out[33]:








TSNE(angle=0.5, early_exaggeration=12.0, init='random', learning_rate=200.0,
   method='barnes_hut', metric='euclidean', min_grad_norm=1e-07,
   n_components=2, n_iter=1000, n_iter_without_progress=300,
   perplexity=30.0, random_state=0, verbose=0)

















In [34]:



    


plt.scatter(tsne.embedding_[df['Churn'].values, 0], 
            tsne.embedding_[df['Churn'].values, 1], 
            color='orange', alpha=.7);
plt.scatter(tsne.embedding_[~df['Churn'].values, 0], 
            tsne.embedding_[~df['Churn'].values, 1], 
            color='blue', alpha=.7);

Content source: andreyf/machine-learning-examples

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