
# Importing essential libraries
import numpy as np
import pandas as pd

from matplotlib import pyplot as plt
import seaborn as sns

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA, IncrementalPCA
from sklearn.feature_selection import RFE
from sklearn.model_selection import KFold, GridSearchCV, RandomizedSearchCV, cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, roc_curve, roc_auc_score
import statsmodels.api as sm
from statsmodels.stats.outliers_influence import variance_inflation_factor
from imblearn.over_sampling import SMOTENC

import warnings
warnings.filterwarnings('ignore')


pd.set_option('display.max_columns', 500)


# Loading data
df = pd.read_csv('telecom_churn_data.csv')
df.head()


df.shape

(99999, 226)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 99999 entries, 0 to 99998
Columns: 226 entries, mobile_number to sep_vbc_3g
dtypes: float64(179), int64(35), object(12)
memory usage: 172.4+ MB


df.describe()


# Average recharge amount in Good phase (months 6 & 7)
df['avg_rech_amt_good_phase'] = df.apply(lambda x: (x['total_rech_amt_6'] + x['total_rech_amt_7'])/2, axis=1)


# Calculating threshold for High Value Customers
hvc_threshold = np.percentile(df['avg_rech_amt_good_phase'], 70)
hvc_threshold

368.5


# Filtering High Value Customers
df = df[df['avg_rech_amt_good_phase'] >= hvc_threshold]


df.shape

(30011, 227)


# Dropping temporary variables
df.drop(['avg_rech_amt_good_phase'], axis=1, inplace=True)


df_missing = df.isnull().mean() * 100
df_missing[df_missing > 40]

date_of_last_rech_data_6    62.023925
date_of_last_rech_data_7    61.140915
date_of_last_rech_data_8    60.834361
date_of_last_rech_data_9    61.810669
total_rech_data_6           62.023925
total_rech_data_7           61.140915
total_rech_data_8           60.834361
total_rech_data_9           61.810669
max_rech_data_6             62.023925
max_rech_data_7             61.140915
max_rech_data_8             60.834361
max_rech_data_9             61.810669
count_rech_2g_6             62.023925
count_rech_2g_7             61.140915
count_rech_2g_8             60.834361
count_rech_2g_9             61.810669
count_rech_3g_6             62.023925
count_rech_3g_7             61.140915
count_rech_3g_8             60.834361
count_rech_3g_9             61.810669
av_rech_amt_data_6          62.023925
av_rech_amt_data_7          61.140915
av_rech_amt_data_8          60.834361
av_rech_amt_data_9          61.810669
arpu_3g_6                   62.023925
arpu_3g_7                   61.140915
arpu_3g_8                   60.834361
arpu_3g_9                   61.810669
arpu_2g_6                   62.023925
arpu_2g_7                   61.140915
arpu_2g_8                   60.834361
arpu_2g_9                   61.810669
night_pck_user_6            62.023925
night_pck_user_7            61.140915
night_pck_user_8            60.834361
night_pck_user_9            61.810669
fb_user_6                   62.023925
fb_user_7                   61.140915
fb_user_8                   60.834361
fb_user_9                   61.810669
dtype: float64


# Removing features with more than 40% missing values
df.drop(df_missing[df_missing > 40].index, axis=1, inplace=True)


df.shape

(30011, 186)


# Deleting the rows having more than 50% missing values
df.drop(df[(df.isnull().sum(axis=1)) > (len(df.columns)//2)].index, inplace=True)
df.shape

(29897, 186)


df_missing = df.isnull().mean() * 100
df_missing[df_missing > 0].sort_values(ascending=False)

loc_ic_mou_9            5.318259
roam_og_mou_9           5.318259
loc_og_t2t_mou_9        5.318259
loc_ic_t2f_mou_9        5.318259
loc_og_t2m_mou_9        5.318259
                          ...   
std_ic_t2m_mou_7        0.632170
last_date_of_month_8    0.474964
date_of_last_rech_7     0.327792
date_of_last_rech_6     0.207379
last_date_of_month_7    0.053517
Length: 123, dtype: float64


# Deleting those rows with missing values in MOU columns 
df.dropna(subset=filter(lambda x: 'mou' in x, df_missing[df_missing > 0].index), inplace=True)


df.shape

(27991, 186)


df_missing = df.isnull().mean() * 100
df_missing[df_missing > 0].sort_values(ascending=False)

date_of_last_rech_9    1.021757
date_of_last_rech_8    1.011039
date_of_last_rech_7    0.203637
date_of_last_rech_6    0.096460
dtype: float64


# Dropping date columns
df.drop([c for c in df.columns if 'date' in c], axis=1, inplace=True)


# Dropping mobile_number column as it can't be used in analysis
df.drop('mobile_number', axis=1, inplace=True)


df.shape

(27991, 177)


# Dropping features with only one category as they add no value to the model building
df_nunique = df.nunique()
df.drop(df_nunique[df_nunique < 2].index, axis=1, inplace=True)
df.shape

(27991, 165)


df.duplicated().sum()

0


# Check for possible wrong data type
df.select_dtypes(include=['object']).columns

Index([], dtype='object')


# Calculating target variable based on usage in the last month
def calculate_target(x):
  if ((x['total_ic_mou_9'] == 0) & (x['total_og_mou_9'] == 0) & (x['vol_2g_mb_9'] == 0) & (x['vol_3g_mb_9'] == 0)):
    return 1
  else:
    return 0

df['churn'] = df.apply(calculate_target, axis=1)


# Making sure that `churn` variable isn't treated as numerical variable
df['churn'] = df['churn'].astype('category')


df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 27991 entries, 8 to 99997
Columns: 166 entries, arpu_6 to churn
dtypes: category(1), float64(132), int64(33)
memory usage: 35.5 MB


plt.figure(figsize=(8,8))
plt.pie(df['churn'].value_counts(), labels=['No Churn', 'Churn'], autopct='%.2f%%', colors=sns.color_palette('bright'))
plt.title('Target Variable Data Imbalance', fontsize=20)
plt.show()


[i for i in df.columns if i[-1:] not in ['6', '7', '8', '9']]

['aon', 'aug_vbc_3g', 'jul_vbc_3g', 'jun_vbc_3g', 'sep_vbc_3g', 'churn']


# Making `vbc_3g` feature names consistent
df.rename(columns={'jun_vbc_3g': 'vbc_3g_6','jul_vbc_3g': 'vbc_3g_7','aug_vbc_3g': 'vbc_3g_8', 'sep_vbc_3g': 'vbc_3g_9'}, inplace=True)
[i for i in df.columns if i[-1:] not in ['6', '7', '8', '9']]

['aon', 'churn']


# Listing primary available predictors that have been repeated for multiple months
unique_columns = map(lambda x: x[0:-2], [i for i in df.columns if i[-1:] in ['6', '7', '8', '9']])
unique_columns = list(set(unique_columns))
unique_columns

['og_others',
 'isd_ic_mou',
 'isd_og_mou',
 'loc_og_mou',
 'monthly_3g',
 'roam_og_mou',
 'std_ic_t2f_mou',
 'std_og_t2m_mou',
 'roam_ic_mou',
 'last_day_rch_amt',
 'std_og_t2f_mou',
 'total_og_mou',
 'loc_og_t2c_mou',
 'loc_og_t2f_mou',
 'offnet_mou',
 'std_ic_t2t_mou',
 'total_rech_num',
 'std_ic_mou',
 'sachet_3g',
 'loc_ic_t2t_mou',
 'ic_others',
 'loc_ic_t2f_mou',
 'sachet_2g',
 'spl_ic_mou',
 'spl_og_mou',
 'total_rech_amt',
 'loc_og_t2t_mou',
 'std_og_t2t_mou',
 'max_rech_amt',
 'vbc_3g',
 'vol_3g_mb',
 'total_ic_mou',
 'loc_ic_t2m_mou',
 'arpu',
 'std_ic_t2m_mou',
 'vol_2g_mb',
 'monthly_2g',
 'onnet_mou',
 'loc_og_t2m_mou',
 'loc_ic_mou',
 'std_og_mou']


len(unique_columns)

41


unrepeated_columns = ['aon']


def treat_outliers(col):
    q1 = np.percentile(df[col], 25)
    q3 = np.percentile(df[col], 75)
    IQR = q3-q1
    lwr_bound = q1-(1.5*IQR)
    upr_bound = q3+(1.5*IQR)
    df[col] = df[col].apply(lambda x: lwr_bound if x < lwr_bound else upr_bound if x > upr_bound else x)


for col_set in unique_columns:
  for col in [col_set+'_6', col_set+'_7', col_set+'_8', col_set+'_9']:
    treat_outliers(col)


for col in unique_columns:
  df[col+'_good'] = (df[col+'_6'] + df[col+'_7'])/2
  df[col+'_action'] = df[col+'_8']
  df[col+'_churn'] = df[col+'_9']
  df.drop([col+'_'+str(i) for i in [6, 7, 8, 9]], axis=1, inplace=True)
df.head()


df['total_mou_good'] = df['total_og_mou_good'] + df['total_ic_mou_good']
df['total_mou_action'] = df['total_og_mou_action'] + df['total_ic_mou_action']
df['total_mou_churn'] = df['total_og_mou_churn'] + df['total_ic_mou_churn']
unique_columns.append('total_mou')
df.head()


df['mou_decrease'] = df.apply(lambda x: 1 if (x['total_mou_action'] - x['total_mou_good']) < 0 else 0, axis=1)
df['mou_decrease'] = df['mou_decrease'].astype('category')
df.head()


df['arpu_decrease'] = df.apply(lambda x: 1 if (x['arpu_action'] - x['arpu_good']) < 0 else 0, axis=1)
df['arpu_decrease'] = df['arpu_decrease'].astype('category')
df.head()


df['rech_amt_decrease'] = df.apply(lambda x: 1 if (x['total_rech_amt_action'] - x['total_rech_amt_good']) < 0 else 0, axis=1)
df['rech_amt_decrease'] = df['rech_amt_decrease'].astype('category')
df.head()


df['rech_num_decrease'] = df.apply(lambda x: 1 if (x['total_rech_num_action'] - x['total_rech_num_good']) < 0 else 0, axis=1)
df['rech_num_decrease'] = df['rech_num_decrease'].astype('category')
df.head()


# Helper functions for Numerical Univariate Analysis
def num_uni_box_analysis(var, friendly_name=None):
  if friendly_name is None:
    friendly_name = var
  ax = sns.boxplot(data=df, x=var)
  ax.set_xticks(list(df[var].quantile([0,0.25,0.5,0.75,0.95,1])))
  ax.set_xlabel(friendly_name)
  ax.set_title(f'{friendly_name} Distribution Univariate Analysis', fontsize=20)
  ax.figure.set_size_inches(16,8)
  return ax

def num_uni_hist_analysis(var, friendly_name=None, bins=25):
  if friendly_name is None:
    friendly_name = var
  ax = sns.histplot(data=df ,x=var, stat='percent', bins=bins)
  ax.set_xlabel(friendly_name)
  ax.set_title(f'{friendly_name} Distribution Histogram', fontsize=20)
  ax.figure.set_size_inches(16,8)
  return ax


# Helper functions for Numerical Segmented Univariate Analysis
def num_uni_box_seg_analysis(var, friendly_name=None):
  if friendly_name is None:
    friendly_name = var
  ax = sns.boxplot(data=df, x='churn', y=var)
  ax.legend(['No', 'Yes'])
  ax.set_ylabel(friendly_name)
  ax.set_title(f'{friendly_name} Distribution Univariate Analysis', fontsize=20)
  ax.figure.set_size_inches(16,8)
  return ax

def num_uni_hist_seg_analysis(var, friendly_name=None, bins=25):
  if friendly_name is None:
    friendly_name = var
  ax = sns.histplot(data=df ,x=var, hue='churn', stat='percent', multiple='fill', bins=bins)
  ax.legend(['No', 'Yes'])
  ax.set_xlabel(friendly_name)
  ax.set_title(f'{friendly_name} Distribution Histogram (Normalised)', fontsize=20)
  ax.figure.set_size_inches(16,8)
  return ax


# Helper function for Categorical Univariate Analysis
def cat_uni_analysis(col, friendly_name=None):
  if friendly_name is None:
    friendly_name = col
  tmp_df = df[col].value_counts(normalize=True).mul(100).rename('Percent').reset_index().rename(columns={'index': col})
  ax = sns.barplot(data=tmp_df, x=col, y='Percent')
  for p in ax.patches:
    txt = str(p.get_height().round(2)) + '%'
    txt_x = p.get_x() + (p.get_width()/2)
    txt_y = p.get_height()
    ax.annotate(txt, (txt_x, txt_y), size=11, ha='center', va='bottom')
  ax.set_title(f'{friendly_name} Univariate Analysis (Normalised)', fontsize=20)
  ax.set_xlabel(friendly_name)
  ax.figure.set_size_inches(16,8)
  return ax


# Helper function for Categorical Segmented Univariate Analysis
def cat_seg_analysis(col, friendly_name=None):
  if friendly_name is None:
    friendly_name = col
  tmp_df = df.groupby(col)['churn'].value_counts(normalize=True).mul(100).rename('Percent').reset_index().rename({'level_1': 'churn'}, axis=1).replace(0,'No').replace(1,'Yes')
  ax = sns.barplot(data=tmp_df, x=col, y='Percent', hue='churn')
  for p in ax.patches:
    txt = str(p.get_height().round(2)) + '%'
    txt_x = p.get_x() + (p.get_width()/2)
    txt_y = p.get_height()
    ax.annotate(txt, (txt_x, txt_y), size=11, ha='center', va='bottom')
  ax.set_title(f'{friendly_name} Segmented Univariate Analysis (Normalised)', fontsize=20)
  ax.set_xlabel(friendly_name)
  ax.figure.set_size_inches(16,8)
  return ax


plt.figure(figsize=(24, 12*len(unique_columns)))

for i in range(len(unique_columns)*2):
  col=unique_columns[i//2]
  plt.subplot(len(unique_columns), 2, i+1)
  if i%2 == 0:
    ax = sns.boxplot(data=df[[col+'_good', col+'_action', col+'_churn']])
    ax.set_title(col + ' Unsegmented Analysis')
  else:
    ax = sns.boxplot(data=pd.melt(df[[col+'_good', col+'_action', col+'_churn', 'churn']], 'churn'), x='variable', y='value', hue='churn')
    ax.set_title(col + ' Segmented Analysis')
  ax.set_xticklabels(['Good Phase', 'Action Phase', 'Churn Phase'])
  plt.tight_layout()


# Useless numerical variables with negligible variance or huge data imbalance
useless_columns = ['roam_og_mou', 'roam_ic_mou', 'std_og_t2f_mou', 'spl_ic_mou', 'og_others', 'isd_ic_mou', 'isd_og_mou', 'sachet_3g', 'sachet_2g', 'monthly_3g', 'monthly_2g']


# Removing useless features
for col in useless_columns:
  df.drop([col+'_good', col+'_action', col+'_churn'], axis=1, inplace=True)
unique_columns = [i for i in unique_columns if i not in useless_columns]
df.shape

(27991, 99)


num_uni_box_analysis('aon', 'Age on Network')
plt.show()


num_uni_box_seg_analysis('aon', 'Age on Network')
plt.show()


num_uni_hist_analysis('aon', 'Age on Network')
plt.show()


num_uni_hist_seg_analysis('aon', 'Age on Network')
plt.show()


cat_uni_analysis('mou_decrease', 'Decrease in Minutes of Usage')
plt.show()


cat_seg_analysis('mou_decrease', 'Decrease in Minutes of Usage')
plt.show()


cat_uni_analysis('arpu_decrease', 'Decrease in Average Revenue per Customer')
plt.show()


cat_seg_analysis('arpu_decrease', 'Decrease in Average Revenue per Customer')
plt.show()


cat_uni_analysis('rech_amt_decrease', 'Decrease in Recharge Amount')
plt.show()


cat_seg_analysis('rech_amt_decrease', 'Decrease in Recharge Amount')
plt.show()


cat_uni_analysis('rech_num_decrease', 'Decrease in No. of Recharges')
plt.show()


cat_seg_analysis('rech_num_decrease', 'Decrease in No. of Recharges')
plt.show()


# Removing churn phase data
df.drop([i+'_churn' for i in unique_columns], axis=1, inplace=True)


# Dropping few derived columns that are not required in further analysis
df.drop(['total_mou'+'_'+p for p in ['good', 'action']], axis=1, inplace=True)
unique_columns = [i for i in unique_columns if i != 'total_mou']


df.shape

(27991, 66)


df.head()


df_train, df_test = train_test_split(df, train_size=0.7, random_state=100)


df_train.shape

(19593, 66)


df_test.shape

(8398, 66)


# Extract Target Variable
y_train = df_train.pop('churn')
X_train = df_train


# Fitting SMOTE-NC to the train set
smote_nc = SMOTENC(categorical_features=list(map(lambda x: 'decrease' in x, X_train.columns)), random_state=42)
X_train, y_train = smote_nc.fit_resample(X_train, y_train)


X_train.shape

(37862, 65)


y_train.shape

(37862,)


num_vars = ['aon']
for col in unique_columns:
  num_vars = num_vars + [col+'_good', col+'_action']


# Learn Min & Max values
scaler = MinMaxScaler()
scaler.fit(X_train[num_vars])

MinMaxScaler()

MinMaxScaler()


# Scale
X_train[num_vars] = scaler.transform(X_train[num_vars])
X_train.head()


X_train_pca = X_train[num_vars].copy()


pca = PCA(random_state=42)


pca.fit(X_train_pca)

PCA(random_state=42)

PCA(random_state=42)


pca.components_

array([[ 0.07336113,  0.15964022,  0.19426862, ...,  0.20760663,
        -0.12861914,  0.01011346],
       [-0.03377833,  0.0097072 ,  0.03149767, ...,  0.01475655,
         0.25204477,  0.30253814],
       [-0.05105141, -0.1562654 , -0.13065015, ..., -0.11793345,
         0.03544626,  0.11383457],
       ...,
       [-0.00116137, -0.23425505,  0.32667731, ...,  0.08765565,
         0.38786571, -0.23735821],
       [-0.00169729, -0.14672795,  0.09875055, ...,  0.520221  ,
        -0.18195387,  0.12803139],
       [-0.00067344,  0.51554079, -0.38051691, ...,  0.18301135,
         0.3148663 , -0.21369201]])


pca.explained_variance_ratio_

array([2.11122456e-01, 1.45584838e-01, 7.94797021e-02, 6.16009695e-02,
       5.39088167e-02, 4.62920669e-02, 4.39221279e-02, 3.06820837e-02,
       2.81091998e-02, 2.29584650e-02, 2.16429002e-02, 1.95213907e-02,
       1.90167818e-02, 1.69256524e-02, 1.50889467e-02, 1.30643786e-02,
       1.23308193e-02, 1.19547998e-02, 1.14146754e-02, 1.00386598e-02,
       9.81173001e-03, 9.50723891e-03, 8.82044018e-03, 8.11904421e-03,
       7.49962780e-03, 6.90580286e-03, 6.74601241e-03, 6.29822005e-03,
       5.83295291e-03, 4.52271230e-03, 4.40614721e-03, 4.33308617e-03,
       4.14525124e-03, 3.79351625e-03, 3.64783933e-03, 3.18520592e-03,
       2.89762609e-03, 2.69207318e-03, 2.60851376e-03, 2.37011180e-03,
       2.08448829e-03, 1.97898619e-03, 1.91867584e-03, 1.61837923e-03,
       1.43560381e-03, 1.35566382e-03, 9.98824756e-04, 9.70046922e-04,
       8.77691698e-04, 6.81438273e-04, 5.48528065e-04, 4.93316389e-04,
       4.62360328e-04, 3.63470751e-04, 3.57522334e-04, 3.01620543e-04,
       2.42118232e-04, 1.87151018e-04, 1.59123721e-04, 8.93422371e-05,
       7.27652434e-05])


# Scree Plot
pca_variance_cumulative = np.cumsum(pca.explained_variance_ratio_)
plt.figure(figsize=(16,10))
plt.plot(range(1,len(pca_variance_cumulative)+1), pca_variance_cumulative)
plt.show()


# Picking 25 PCA components
pca = IncrementalPCA(n_components=25)


X_train_pca = pca.fit_transform(X_train_pca)
X_train_pca = pd.DataFrame(X_train_pca, columns=['PC'+str(i) for i in range(1,26)])
X_train_pca.head()


plt.figure(figsize=[24,24])
sns.heatmap(np.corrcoef(X_train_pca.transpose()), annot=True, cmap='Blues')
plt.show()


# Creating function to easily calculate VIF
def calculate_vif(x_df):
  vif_df = pd.DataFrame({'Feature': x_df.columns, 'VIF': [ variance_inflation_factor(x_df.values, i) for i in range(x_df.shape[1])]})
  vif_df['VIF'] = vif_df['VIF'].round(2)
  vif_df = vif_df.sort_values(by='VIF', ascending=False)
  return vif_df


# Creating function to easily train Logistics Regression models
def train_model(y_df, x_df):
  # Preparing for intercept
  x_df_sm = sm.add_constant(x_df)
  # Training model
  lr = sm.GLM(y_df, x_df_sm, sm.families.Binomial())
  lr_model = lr.fit()
  return lr_model


# Creating function to easily select features
def auto_select_features(n, x_df=X_train):
  logreg = LogisticRegression(max_iter=1000)
  selector = RFE(logreg, n_features_to_select=n)
  selector = selector.fit(x_df, y_train)
  top_features_df = pd.DataFrame({'Feature': x_df.columns, 'Selected': selector.support_, 'Rank': selector.ranking_})
  selected_vars = top_features_df[top_features_df['Selected'] == True]['Feature'].to_list()
  return selected_vars


# Creating function to easily evaluate performance of model over multiple probability cutoffs
def model_performance(model, x_df, y_df=y_train, lib='sm'):
  prob = [float(x)/10 for x in range(10)]

  y_final = y_df.to_frame()
  if lib == 'sm':
    x_df_sm = sm.add_constant(x_df)
    y_final['churn_prob'] = model.predict(x_df_sm)
  else:
    y_final['churn_prob'] = model.predict_proba(x_df)[:,1]

  for i in prob:
    y_final[i] = y_final['churn_prob'].map(lambda x: 1 if x > i else 0)

  performance_df = pd.DataFrame( columns = ['probability_cutoff','accuracy','sensitivity','specificity'])
  for i in prob:
    cm = confusion_matrix(y_final['churn'], y_final[i])
    total=sum(sum(cm))
    accuracy = (cm[0,0]+cm[1,1])/total
    sensitivity = cm[1,1]/(cm[1,0]+cm[1,1])
    specificity = cm[0,0]/(cm[0,0]+cm[0,1])
    performance_df.loc[i] =[ i ,accuracy,sensitivity,specificity]
  return performance_df


# Creating function to draw ROC curve
def roc(model, x_df, y_df=y_train, lib='sm'):
    y_final = y_df.to_frame()
    if lib == 'sm':
        x_df_sm = sm.add_constant(x_df)
        y_final['churn_prob'] = model.predict(x_df_sm)
    else:
        y_final['churn_prob'] = model.predict_proba(x_df)[:,1]
    fpr, tpr, threshold = roc_curve( y_final['churn'], y_final['churn_prob'], drop_intermediate = False )
    auc_score = roc_auc_score( y_final['churn'], y_final['churn_prob'])
    plt.figure(figsize=(8, 8))
    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )
    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver Operating Characteristic (ROC) Curve')
    plt.legend(loc="lower right")
    plt.show()
    return None


# Auto selecting 15 features
X_train_1 = X_train[auto_select_features(15)]


# Training Logistic Regression Model
train_model(y_train, X_train_1).summary()


calculate_vif(X_train_1)


# Dropping 'loc_ic_mou_good' because it has high VIF
X_train_1 = X_train_1.drop('loc_ic_mou_good', axis=1)


# Re-train model
train_model(y_train, X_train_1).summary()


calculate_vif(X_train_1)


# Dropping 'total_og_mou_action' because it has high VIF
X_train_1 = X_train_1.drop('total_og_mou_action', axis=1)


# Re-train model
train_model(y_train, X_train_1).summary()


calculate_vif(X_train_1)


# Dropping 'std_og_mou_action' because it has high VIF
X_train_1 = X_train_1.drop('std_og_mou_action', axis=1)


# Re-train model
train_model(y_train, X_train_1).summary()


calculate_vif(X_train_1)


# Dropping 'loc_ic_t2m_mou_good' because it has high VIF
X_train_1 = X_train_1.drop('loc_ic_t2m_mou_good', axis=1)


# Re-train model
train_model(y_train, X_train_1).summary()


calculate_vif(X_train_1)


# Dropping 'offnet_mou_action' because it has high VIF
X_train_1 = X_train_1.drop('offnet_mou_action', axis=1)


# Re-train model
train_model(y_train, X_train_1).summary()


calculate_vif(X_train_1)


# Dropping 'std_og_t2t_mou_action' because it has high VIF
X_train_1 = X_train_1.drop('std_og_t2t_mou_action', axis=1)


# Re-train model
train_model(y_train, X_train_1).summary()


calculate_vif(X_train_1)


# Dropping 'loc_ic_t2t_mou_action' because it has high VIF
X_train_1 = X_train_1.drop('loc_ic_t2t_mou_action', axis=1)


# Re-train model
train_model(y_train, X_train_1).summary()


calculate_vif(X_train_1)


model_1 = train_model(y_train, X_train_1)


roc(model_1, X_train_1)


train_perf_1 = model_performance(model_1, X_train_1)
train_perf_1


train_perf_1.plot(x='probability_cutoff', y=['accuracy','sensitivity','specificity'], figsize=(10,8))
plt.show()


train_cutoff_1 = 0.4
train_perf_1.loc[[train_cutoff_1]]


# Auto selecting top 10 features
X_train_2 = X_train[auto_select_features(10)]


# Training Logistic Regression Model
train_model(y_train, X_train_2).summary()


calculate_vif(X_train_2)


# Dropping 'total_og_mou_action' because it has high VIF
X_train_2 = X_train_2.drop('total_og_mou_action', axis=1)


# Re-train model
train_model(y_train, X_train_2).summary()


calculate_vif(X_train_2)


# Dropping 'std_og_mou_action' because it has high VIF
X_train_2 = X_train_2.drop('std_og_mou_action', axis=1)


# Re-train model
train_model(y_train, X_train_2).summary()


calculate_vif(X_train_2)


# Dropping 'std_og_t2t_mou_action' because it has high VIF
X_train_2 = X_train_2.drop('std_og_t2t_mou_action', axis=1)


# Re-train model
train_model(y_train, X_train_2).summary()


calculate_vif(X_train_2)


# Dropping 'offnet_mou_action' because it has high VIF
X_train_2 = X_train_2.drop('offnet_mou_action', axis=1)


# Re-train model
train_model(y_train, X_train_2).summary()


calculate_vif(X_train_2)


model_2 = train_model(y_train, X_train_2)


roc(model_2, X_train_2)


train_perf_2 = model_performance(model_2, X_train_2)
train_perf_2


train_perf_2.plot(x='probability_cutoff', y=['accuracy','sensitivity','specificity'], figsize=(10,8))
plt.show()


train_cutoff_2 = 0.4
train_perf_2.loc[[train_cutoff_2]]


X_train_3 = X_train_pca


# Creating KFold object with 5 splits
folds = KFold(n_splits=5, shuffle=True, random_state=4)


# Specifying hyperparameters to control regularisation
params = {"C": [0.01, 0.1, 1, 10, 100, 1000]}


lg = LogisticRegression()


# Specifing score as recall as we are more focused on acheiving the higher sensitivity than the accuracy
grid_search_lg = GridSearchCV(estimator = lg, param_grid = params, scoring= 'recall', cv = folds, verbose = 1, return_train_score=True)


# Fitting the model
grid_search_lg.fit(X_train_pca, y_train)

Fitting 5 folds for each of 6 candidates, totalling 30 fits

GridSearchCV(cv=KFold(n_splits=5, random_state=4, shuffle=True),
             estimator=LogisticRegression(),
             param_grid={'C': [0.01, 0.1, 1, 10, 100, 1000]},
             return_train_score=True, scoring='recall', verbose=1)

GridSearchCV(cv=KFold(n_splits=5, random_state=4, shuffle=True),
             estimator=LogisticRegression(),
             param_grid={'C': [0.01, 0.1, 1, 10, 100, 1000]},
             return_train_score=True, scoring='recall', verbose=1)

LogisticRegression()

LogisticRegression()


# results of grid search CV
cv_results = pd.DataFrame(grid_search_lg.cv_results_)
cv_results


# plot of C versus train and validation scores

plt.figure(figsize=(12, 10))
plt.plot(cv_results['param_C'], cv_results['mean_test_score'])
plt.plot(cv_results['param_C'], cv_results['mean_train_score'])
plt.xlabel('C')
plt.ylabel('sensitivity')
plt.legend(['test result', 'train result'], loc='upper left')
plt.xscale('log')


# Best score with best C
best_score = grid_search_lg.best_score_
best_C = grid_search_lg.best_params_['C']

print(" The highest test sensitivity is {0} at C: {1}".format(best_score, best_C))

 The highest test sensitivity is 0.8627828946525055 at C: 0.01


model_3 = grid_search_lg.best_estimator_


roc(model_3, X_train_3, lib='sklearn')


train_perf_3 = model_performance(model_3, X_train_3, lib='sklearn')
train_perf_3


train_perf_3.plot(x='probability_cutoff', y=['accuracy','sensitivity','specificity'], figsize=(10,8))
plt.show()


train_cutoff_3 = 0.4
train_perf_3.loc[[train_cutoff_3]]


X_train_4 = X_train_pca


dtree = DecisionTreeClassifier()


# Creating hyperparameter grid
params = {
    'max_depth': range(5, 15, 5),
    'min_samples_leaf': range(50, 150, 50),
    'min_samples_split': range(50, 150, 50),
}


grid_search_dt = GridSearchCV(estimator = dtree, param_grid = params, scoring= 'recall', cv = 5, verbose = 1)


grid_search_dt.fit(X_train_4, y_train)

Fitting 5 folds for each of 8 candidates, totalling 40 fits

GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),
             param_grid={'max_depth': range(5, 15, 5),
                         'min_samples_leaf': range(50, 150, 50),
                         'min_samples_split': range(50, 150, 50)},
             scoring='recall', verbose=1)

GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),
             param_grid={'max_depth': range(5, 15, 5),
                         'min_samples_leaf': range(50, 150, 50),
                         'min_samples_split': range(50, 150, 50)},
             scoring='recall', verbose=1)

DecisionTreeClassifier()

DecisionTreeClassifier()


cv_results = pd.DataFrame(grid_search_dt.cv_results_)
cv_results


# Best sensitivity score on 0.5 cutoff
grid_search_dt.best_score_

0.8861658681359241


model_4 = grid_search_dt.best_estimator_
model_4

DecisionTreeClassifier(max_depth=10, min_samples_leaf=50, min_samples_split=50)

DecisionTreeClassifier(max_depth=10, min_samples_leaf=50, min_samples_split=50)


roc(model_4, X_train_4, lib='sklearn')


train_perf_4 = model_performance(model_4, X_train_4, lib='sklearn')
train_perf_4


train_perf_4.plot(x='probability_cutoff', y=['accuracy','sensitivity','specificity'], figsize=(10,8))
plt.show()


train_cutoff_4 = 0.1
train_perf_4.loc[[train_cutoff_4]]


X_train_5 = X_train_pca


rf = RandomForestClassifier()


params = {
    'max_depth': range(5,10,5),
    'min_samples_leaf': range(50, 150, 50),
    'min_samples_split': range(50, 150, 50),
    'n_estimators': [100,200,300], 
    'max_features': [10, 20]
}


grid_search_rf = GridSearchCV(estimator = rf, param_grid = params, cv = 3, n_jobs = -1, verbose = 1, return_train_score=True)


# Fitting the model
grid_search_rf.fit(X_train_5, y_train)

Fitting 3 folds for each of 24 candidates, totalling 72 fits

GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,
             param_grid={'max_depth': range(5, 10, 5), 'max_features': [10, 20],
                         'min_samples_leaf': range(50, 150, 50),
                         'min_samples_split': range(50, 150, 50),
                         'n_estimators': [100, 200, 300]},
             return_train_score=True, verbose=1)

GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,
             param_grid={'max_depth': range(5, 10, 5), 'max_features': [10, 20],
                         'min_samples_leaf': range(50, 150, 50),
                         'min_samples_split': range(50, 150, 50),
                         'n_estimators': [100, 200, 300]},
             return_train_score=True, verbose=1)

RandomForestClassifier()

RandomForestClassifier()


grid_search_rf.best_score_

0.8474461538770628


model_5 = grid_search_rf.best_estimator_
model_5

RandomForestClassifier(max_depth=5, max_features=20, min_samples_leaf=50,
                       min_samples_split=50, n_estimators=200)

RandomForestClassifier(max_depth=5, max_features=20, min_samples_leaf=50,
                       min_samples_split=50, n_estimators=200)


roc(model_5, X_train_5, lib='sklearn')


train_perf_5 = model_performance(model_5, X_train_5, lib='sklearn')
train_perf_5


train_perf_5.plot(x='probability_cutoff', y=['accuracy','sensitivity','specificity'], figsize=(10,8))
plt.show()


train_cutoff_5 = 0.3
train_perf_5.loc[[train_cutoff_5]]


# Feature Scaling on Test Set
df_test[num_vars] = scaler.transform(df_test[num_vars])


# Extract target variable
y_test = df_test.pop('churn')
X_test = df_test


# PCA transform
X_test_pca = X_test[num_vars].copy()
X_test_pca = pca.transform(X_test_pca)
X_test_pca = pd.DataFrame(X_test_pca, columns=['PC'+str(i) for i in range(1,26)])
X_test_pca.head()


test_perf_1 = model_performance(model_1, X_test[X_train_1.columns], y_test)
test_perf_1


test_perf_1.plot(x='probability_cutoff', y=['accuracy','sensitivity','specificity'], figsize=(10,8))
plt.show()


test_cutoff_1 = 0.5
test_perf_1.loc[[test_cutoff_1]]


test_perf_2 = model_performance(model_2, X_test[X_train_2.columns], y_test)
test_perf_2


test_perf_2.plot(x='probability_cutoff', y=['accuracy','sensitivity','specificity'], figsize=(10,8))
plt.show()


# Choosing cutoff to maximise Sensitivity
test_cutoff_2 = 0.5
test_perf_2.loc[[test_cutoff_2]]


test_perf_3 = model_performance(model_3, X_test_pca[X_train_3.columns], y_test, lib='sklearn')
test_perf_3


test_perf_3.plot(x='probability_cutoff', y=['accuracy','sensitivity','specificity'], figsize=(10,8))
plt.show()


test_cutoff_3 = 0.5
test_perf_3.loc[[test_cutoff_3]]


test_perf_4 = model_performance(model_4, X_test_pca[X_train_4.columns], y_test, lib='sklearn')
test_perf_4


test_perf_4.plot(x='probability_cutoff', y=['accuracy','sensitivity','specificity'], figsize=(10,8))
plt.show()


test_cutoff_4 = 0.3
test_perf_4.loc[[test_cutoff_4]]


test_perf_5 = model_performance(model_5, X_test_pca[X_train_5.columns], y_test, lib='sklearn')
test_perf_5


test_perf_5.plot(x='probability_cutoff', y=['accuracy','sensitivity','specificity'], figsize=(10,8))
plt.show()


test_cutoff_5 = 0.4
test_perf_5.loc[[test_cutoff_5]]


model_names = ['Model 1', 'Model 2', 'Model 3', 'Model 4', 'Model 5']
models = [model_1, model_2, model_3, model_4, model_5]
libs = ['sm', 'sm', 'sklearn', 'sklearn', 'sklearn']
used_pca = ['No', 'No', 'Yes', 'Yes', 'Yes']
train_perfs = [train_perf_1, train_perf_2, train_perf_3, train_perf_4, train_perf_5]
test_perfs = [test_perf_1, test_perf_2, test_perf_3, test_perf_4, test_perf_5]
train_cutoffs = [train_cutoff_1, train_cutoff_2, train_cutoff_3, train_cutoff_4, train_cutoff_5]
test_cutoffs = [test_cutoff_1, test_cutoff_2, test_cutoff_3, test_cutoff_4, test_cutoff_5]


num_predictors = []
train_accuracies = []
test_accuracies = []
train_sensitivities = []
test_sensitivities = []

for model, lib, train_perf, test_perf, train_cutoff, test_cutoff in zip(models, libs, train_perfs, test_perfs, train_cutoffs, test_cutoffs):
  p = 0
  if lib == 'sm':
    p = len(model.params) - 1 # Subtracted 1 to not count intercept
  else:
    p = 25
  train_accuracy = train_perf.loc[train_cutoff, 'accuracy']
  test_accuracy = test_perf.loc[test_cutoff, 'accuracy']
  train_sensitivity = train_perf.loc[train_cutoff, 'sensitivity']
  test_sensitivity = test_perf.loc[test_cutoff, 'sensitivity']
  num_predictors.append(p)
  train_accuracies.append(train_accuracy)
  test_accuracies.append(test_accuracy)
  train_sensitivities.append(train_sensitivity)
  test_sensitivities.append(test_sensitivity)

result_df = pd.DataFrame({'Model': model_names, 'Used PCA': used_pca, 'No. of Predictors': num_predictors, 'Accuracy (Train)': train_accuracies, 'Accuracy (Test)': test_accuracies, 'Sensitivity (Train)': train_sensitivities, 'Sesitivity (Test)': test_sensitivities})
result_df


# Final Models
final_model_1 = model_2
final_model_2 = model_3


final_model_1.summary()


num_uni_box_seg_analysis('onnet_mou_action', 'Same network minutes of usage in action phase')
plt.show()


num_uni_box_seg_analysis('loc_ic_t2t_mou_action', 'Local incoming from same network minutes of usage in action phase')
plt.show()


num_uni_box_seg_analysis('std_og_t2t_mou_good', 'STD outgoing to same network minutes of usage in good phase')
plt.show()


num_uni_box_seg_analysis('loc_ic_t2m_mou_action', 'Local incoming from other network minutes of usage in action phase')
plt.show()


num_uni_box_seg_analysis('std_og_t2m_mou_good', 'STD outgoing to other network minutes of usage in good phase')
plt.show()


num_uni_box_seg_analysis('std_og_t2m_mou_action', 'STD outgoing to other network minutes of usage in action phase')
plt.show()

	mobile_number	circle_id	last_date_of_month_6	last_date_of_month_7	last_date_of_month_8	last_date_of_month_9	arpu_6	arpu_7	arpu_8	arpu_9	onnet_mou_6	onnet_mou_7	onnet_mou_8	onnet_mou_9	offnet_mou_6	offnet_mou_7	offnet_mou_8	offnet_mou_9	roam_ic_mou_6	roam_ic_mou_7	roam_ic_mou_8	roam_ic_mou_9	roam_og_mou_6	roam_og_mou_7	roam_og_mou_8	roam_og_mou_9	loc_og_t2t_mou_6	loc_og_t2t_mou_7	loc_og_t2t_mou_8	loc_og_t2t_mou_9	loc_og_t2m_mou_6	loc_og_t2m_mou_7	loc_og_t2m_mou_8	loc_og_t2m_mou_9	loc_og_t2f_mou_6	loc_og_t2f_mou_7	loc_og_t2f_mou_8	loc_og_t2f_mou_9	loc_og_t2c_mou_6	loc_og_t2c_mou_7	loc_og_t2c_mou_8	loc_og_t2c_mou_9	loc_og_mou_6	loc_og_mou_7	loc_og_mou_8	loc_og_mou_9	std_og_t2t_mou_6	std_og_t2t_mou_7	std_og_t2t_mou_8	std_og_t2t_mou_9	std_og_t2m_mou_6	std_og_t2m_mou_7	std_og_t2m_mou_8	std_og_t2m_mou_9	std_og_t2f_mou_6	std_og_t2f_mou_7	std_og_t2f_mou_8	std_og_t2f_mou_9	std_og_t2c_mou_6	std_og_t2c_mou_7	std_og_t2c_mou_9	std_og_mou_6	std_og_mou_7	std_og_mou_8	std_og_mou_9	isd_og_mou_6	isd_og_mou_7	isd_og_mou_9	spl_og_mou_6	spl_og_mou_7	spl_og_mou_8	spl_og_mou_9	og_others_6	og_others_7	og_others_9	total_og_mou_6	total_og_mou_7	total_og_mou_8	total_og_mou_9	loc_ic_t2t_mou_6	loc_ic_t2t_mou_7	loc_ic_t2t_mou_8	loc_ic_t2t_mou_9	loc_ic_t2m_mou_6	loc_ic_t2m_mou_7	loc_ic_t2m_mou_8	loc_ic_t2m_mou_9	loc_ic_t2f_mou_6	loc_ic_t2f_mou_7	loc_ic_t2f_mou_8	loc_ic_t2f_mou_9	loc_ic_mou_6	loc_ic_mou_7	loc_ic_mou_8	loc_ic_mou_9	std_ic_t2t_mou_6	std_ic_t2t_mou_7	std_ic_t2t_mou_8	std_ic_t2t_mou_9	std_ic_t2m_mou_6	std_ic_t2m_mou_7	std_ic_t2m_mou_8	std_ic_t2m_mou_9	std_ic_t2f_mou_6	std_ic_t2f_mou_7	std_ic_t2f_mou_8	std_ic_t2f_mou_9	std_ic_t2o_mou_6	std_ic_t2o_mou_7	std_ic_t2o_mou_9	std_ic_mou_6	std_ic_mou_7	std_ic_mou_8	std_ic_mou_9	total_ic_mou_6	total_ic_mou_7	total_ic_mou_8	total_ic_mou_9	spl_ic_mou_6	spl_ic_mou_7	spl_ic_mou_9	isd_ic_mou_6	isd_ic_mou_7	isd_ic_mou_9	ic_others_6	ic_others_7	ic_others_9	total_rech_num_6	total_rech_num_7	total_rech_num_8	total_rech_num_9	total_rech_amt_6	total_rech_amt_7	total_rech_amt_8	total_rech_amt_9	max_rech_amt_6	max_rech_amt_7	max_rech_amt_8	max_rech_amt_9	date_of_last_rech_6	date_of_last_rech_7	date_of_last_rech_8	date_of_last_rech_9	last_day_rch_amt_6	last_day_rch_amt_7	last_day_rch_amt_8	last_day_rch_amt_9	date_of_last_rech_data_6	date_of_last_rech_data_7	date_of_last_rech_data_8	date_of_last_rech_data_9	total_rech_data_6	total_rech_data_7	total_rech_data_8	total_rech_data_9	max_rech_data_6	max_rech_data_7	max_rech_data_8	max_rech_data_9	count_rech_2g_6	count_rech_2g_7	count_rech_2g_8	count_rech_2g_9	count_rech_3g_6	count_rech_3g_7	count_rech_3g_8	count_rech_3g_9	av_rech_amt_data_6	av_rech_amt_data_7	av_rech_amt_data_8	av_rech_amt_data_9	vol_2g_mb_6	vol_2g_mb_7	vol_2g_mb_8	vol_3g_mb_6	vol_3g_mb_7	vol_3g_mb_8	vol_3g_mb_9	arpu_3g_6	arpu_3g_7	arpu_3g_8	arpu_3g_9	arpu_2g_6	arpu_2g_7	arpu_2g_8	arpu_2g_9	night_pck_user_6	night_pck_user_7	night_pck_user_8	night_pck_user_9	monthly_2g_7	sachet_2g_6	sachet_2g_8	sachet_2g_9	monthly_3g_6	monthly_3g_7	monthly_3g_8	fb_user_6	fb_user_7	fb_user_8	fb_user_9	aon	aug_vbc_3g	jun_vbc_3g	sep_vbc_3g
0	7000842753	109	6/30/2014	7/31/2014	8/31/2014	9/30/2014	197.385	214.816	213.803	21.100	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	NaN	NaN	NaN	0.00	NaN	NaN	NaN	NaN	NaN	NaN	0.00	NaN	NaN	NaN	NaN	0.00	0.00	0.00	0.00	NaN	NaN	0.16	NaN	NaN	NaN	4.13	NaN	NaN	NaN	1.15	NaN	NaN	NaN	5.44	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	NaN	NaN	NaN	0.00	NaN	0.00	0.00	5.44	0.00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4	3	2	6	362	252	252	0	252	252	252	0	6/21/2014	7/16/2014	8/8/2014	9/28/2014	252	252	252	0	6/21/2014	7/16/2014	8/8/2014	NaN	1.0	1.0	1.0	NaN	252.0	252.0	252.0	NaN	0.0	0.0	0.0	NaN	1.0	1.0	1.0	NaN	252.0	252.0	252.0	NaN	30.13	1.32	5.75	83.57	150.76	109.61	0.00	212.17	212.17	212.17	NaN	212.17	212.17	212.17	NaN	0.0	0.0	0.0	NaN	0	0	0	0	1	1	1	1.0	1.0	1.0	NaN	968	30.4	101.20	3.58
1	7001865778	109	6/30/2014	7/31/2014	8/31/2014	9/30/2014	34.047	355.074	268.321	86.285	24.11	78.68	7.68	18.34	15.74	99.84	304.76	53.76	0.0	0.00	0.00	0.00	0.0	0.00	0.00	0.00	23.88	74.56	7.68	18.34	11.51	75.94	291.86	53.76	0.00	0.00	0.00	0.00	0.0	2.91	0.00	0.00	35.39	150.51	299.54	72.11	0.23	4.11	0.00	0.00	0.00	0.46	0.13	0.00	0.00	0.00	0.00	0.0	0.0	0.0	0.0	0.23	4.58	0.13	0.00	0.0	0.0	0.0	4.68	23.43	12.76	0.00	0.00	0.0	0.0	40.31	178.53	312.44	72.11	1.61	29.91	29.23	116.09	17.48	65.38	375.58	56.93	0.00	8.93	3.61	0.00	19.09	104.23	408.43	173.03	0.00	0.00	2.35	0.00	5.90	0.00	12.49	15.01	0.00	0.00	0.00	0.00	0.0	0.0	0.0	5.90	0.00	14.84	15.01	26.83	104.23	423.28	188.04	0.00	0.0	0.00	1.83	0.00	0.00	0.00	0.00	0.00	4	9	11	5	74	384	283	121	44	154	65	50	6/29/2014	7/31/2014	8/28/2014	9/30/2014	44	23	30	0	NaN	7/25/2014	8/10/2014	NaN	NaN	1.0	2.0	NaN	NaN	154.0	25.0	NaN	NaN	1.0	2.0	NaN	NaN	0.0	0.0	NaN	NaN	154.0	50.0	NaN	0.00	108.07	365.47	0.00	0.00	0.00	0.00	NaN	0.00	0.00	NaN	NaN	28.61	7.60	NaN	NaN	0.0	0.0	NaN	1	0	2	0	0	0	0	NaN	1.0	1.0	NaN	1006	0.0	0.00	0.00
2	7001625959	109	6/30/2014	7/31/2014	8/31/2014	9/30/2014	167.690	189.058	210.226	290.714	11.54	55.24	37.26	74.81	143.33	220.59	208.36	118.91	0.0	0.00	0.00	38.49	0.0	0.00	0.00	70.94	7.19	28.74	13.58	14.39	29.34	16.86	38.46	28.16	24.11	21.79	15.61	22.24	0.0	135.54	45.76	0.48	60.66	67.41	67.66	64.81	4.34	26.49	22.58	8.76	41.81	67.41	75.53	9.28	1.48	14.76	22.83	0.0	0.0	0.0	0.0	47.64	108.68	120.94	18.04	0.0	0.0	0.0	46.56	236.84	96.84	42.08	0.45	0.0	0.0	155.33	412.94	285.46	124.94	115.69	71.11	67.46	148.23	14.38	15.44	38.89	38.98	99.48	122.29	49.63	158.19	229.56	208.86	155.99	345.41	72.41	71.29	28.69	49.44	45.18	177.01	167.09	118.18	21.73	58.34	43.23	3.86	0.0	0.0	0.0	139.33	306.66	239.03	171.49	370.04	519.53	395.03	517.74	0.21	0.0	0.45	0.00	0.85	0.01	0.93	3.14	0.36	5	4	2	7	168	315	116	358	86	200	86	100	6/17/2014	7/24/2014	8/14/2014	9/29/2014	0	200	86	0	NaN	NaN	NaN	9/17/2014	NaN	NaN	NaN	1.0	NaN	NaN	NaN	46.0	NaN	NaN	NaN	1.0	NaN	NaN	NaN	0.0	NaN	NaN	NaN	46.0	0.00	0.00	0.00	0.00	0.00	0.00	8.42	NaN	NaN	NaN	2.84	NaN	NaN	NaN	0.0	NaN	NaN	NaN	0.0	0	0	0	1	0	0	0	NaN	NaN	NaN	1.0	1103	0.0	4.17	0.00
3	7001204172	109	6/30/2014	7/31/2014	8/31/2014	9/30/2014	221.338	251.102	508.054	389.500	99.91	54.39	310.98	241.71	123.31	109.01	71.68	113.54	0.0	54.86	44.38	0.00	0.0	28.09	39.04	0.00	73.68	34.81	10.61	15.49	107.43	83.21	22.46	65.46	1.91	0.65	4.91	2.06	0.0	0.00	0.00	0.00	183.03	118.68	37.99	83.03	26.23	14.89	289.58	226.21	2.99	1.73	6.53	9.99	0.00	0.00	0.00	0.0	0.0	0.0	0.0	29.23	16.63	296.11	236.21	0.0	0.0	0.0	10.96	0.00	18.09	43.29	0.00	0.0	0.0	223.23	135.31	352.21	362.54	62.08	19.98	8.04	41.73	113.96	64.51	20.28	52.86	57.43	27.09	19.84	65.59	233.48	111.59	48.18	160.19	43.48	66.44	0.00	129.84	1.33	38.56	4.94	13.98	1.18	0.00	0.00	0.00	0.0	0.0	0.0	45.99	105.01	4.94	143.83	280.08	216.61	53.13	305.38	0.59	0.0	0.55	0.00	0.00	0.00	0.00	0.00	0.80	10	11	18	14	230	310	601	410	60	50	50	50	6/28/2014	7/31/2014	8/31/2014	9/30/2014	30	50	50	30	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00	0.00	0.00	0.00	0.00	0.00	0.00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	0	0	0	0	0	0	NaN	NaN	NaN	NaN	2491	0.0	0.00	0.00
4	7000142493	109	6/30/2014	7/31/2014	8/31/2014	9/30/2014	261.636	309.876	238.174	163.426	50.31	149.44	83.89	58.78	76.96	91.88	124.26	45.81	0.0	0.00	0.00	0.00	0.0	0.00	0.00	0.00	50.31	149.44	83.89	58.78	67.64	91.88	124.26	37.89	0.00	0.00	0.00	1.93	0.0	0.00	0.00	0.00	117.96	241.33	208.16	98.61	0.00	0.00	0.00	0.00	9.31	0.00	0.00	0.00	0.00	0.00	0.00	0.0	0.0	0.0	0.0	9.31	0.00	0.00	0.00	0.0	0.0	0.0	0.00	0.00	0.00	5.98	0.00	0.0	0.0	127.28	241.33	208.16	104.59	105.68	88.49	233.81	154.56	106.84	109.54	104.13	48.24	1.50	0.00	0.00	0.00	214.03	198.04	337.94	202.81	0.00	0.00	0.86	2.31	1.93	0.25	0.00	0.00	0.00	0.00	0.00	0.00	0.0	0.0	0.0	1.93	0.25	0.86	2.31	216.44	198.29	338.81	205.31	0.00	0.0	0.18	0.00	0.00	0.00	0.48	0.00	0.00	5	6	3	4	196	350	287	200	56	110	110	50	6/26/2014	7/28/2014	8/9/2014	9/28/2014	50	110	110	50	6/4/2014	NaN	NaN	NaN	1.0	NaN	NaN	NaN	56.0	NaN	NaN	NaN	1.0	NaN	NaN	NaN	0.0	NaN	NaN	NaN	56.0	NaN	NaN	NaN	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	NaN	NaN	NaN	0.00	NaN	NaN	NaN	0.0	NaN	NaN	NaN	0	1	0	0	0	0	0	0.0	NaN	NaN	NaN	1526	0.0	0.00	0.00

	mobile_number	circle_id	loc_og_t2o_mou	std_og_t2o_mou	loc_ic_t2o_mou	arpu_6	arpu_7	arpu_8	arpu_9	onnet_mou_6	onnet_mou_7	onnet_mou_8	onnet_mou_9	offnet_mou_6	offnet_mou_7	offnet_mou_8	offnet_mou_9	roam_ic_mou_6	roam_ic_mou_7	roam_ic_mou_8	roam_ic_mou_9	roam_og_mou_6	roam_og_mou_7	roam_og_mou_8	roam_og_mou_9	loc_og_t2t_mou_6	loc_og_t2t_mou_7	loc_og_t2t_mou_8	loc_og_t2t_mou_9	loc_og_t2m_mou_6	loc_og_t2m_mou_7	loc_og_t2m_mou_8	loc_og_t2m_mou_9	loc_og_t2f_mou_6	loc_og_t2f_mou_7	loc_og_t2f_mou_8	loc_og_t2f_mou_9	loc_og_t2c_mou_6	loc_og_t2c_mou_7	loc_og_t2c_mou_8	loc_og_t2c_mou_9	loc_og_mou_6	loc_og_mou_7	loc_og_mou_8	loc_og_mou_9	std_og_t2t_mou_6	std_og_t2t_mou_7	std_og_t2t_mou_8	std_og_t2t_mou_9	std_og_t2m_mou_6	std_og_t2m_mou_7	std_og_t2m_mou_8	std_og_t2m_mou_9	std_og_t2f_mou_6	std_og_t2f_mou_7	std_og_t2f_mou_8	std_og_t2f_mou_9	std_og_t2c_mou_6	std_og_t2c_mou_7	std_og_t2c_mou_8	std_og_t2c_mou_9	std_og_mou_6	std_og_mou_7	std_og_mou_8	std_og_mou_9	isd_og_mou_6	isd_og_mou_7	isd_og_mou_8	isd_og_mou_9	spl_og_mou_6	spl_og_mou_7	spl_og_mou_8	spl_og_mou_9	og_others_6	og_others_7	og_others_8	og_others_9	total_og_mou_6	total_og_mou_7	total_og_mou_8	total_og_mou_9	loc_ic_t2t_mou_6	loc_ic_t2t_mou_7	loc_ic_t2t_mou_8	loc_ic_t2t_mou_9	loc_ic_t2m_mou_6	loc_ic_t2m_mou_7	loc_ic_t2m_mou_8	loc_ic_t2m_mou_9	loc_ic_t2f_mou_6	loc_ic_t2f_mou_7	loc_ic_t2f_mou_8	loc_ic_t2f_mou_9	loc_ic_mou_6	loc_ic_mou_7	loc_ic_mou_8	loc_ic_mou_9	std_ic_t2t_mou_6	std_ic_t2t_mou_7	std_ic_t2t_mou_8	std_ic_t2t_mou_9	std_ic_t2m_mou_6	std_ic_t2m_mou_7	std_ic_t2m_mou_8	std_ic_t2m_mou_9	std_ic_t2f_mou_6	std_ic_t2f_mou_7	std_ic_t2f_mou_8	std_ic_t2f_mou_9	std_ic_t2o_mou_6	std_ic_t2o_mou_7	std_ic_t2o_mou_8	std_ic_t2o_mou_9	std_ic_mou_6	std_ic_mou_7	std_ic_mou_8	std_ic_mou_9	total_ic_mou_6	total_ic_mou_7	total_ic_mou_8	total_ic_mou_9	spl_ic_mou_6	spl_ic_mou_7	spl_ic_mou_8	spl_ic_mou_9	isd_ic_mou_6	isd_ic_mou_7	isd_ic_mou_8	isd_ic_mou_9	ic_others_6	ic_others_7	ic_others_8	ic_others_9	total_rech_num_6	total_rech_num_7	total_rech_num_8	total_rech_num_9	total_rech_amt_6	total_rech_amt_7	total_rech_amt_8	total_rech_amt_9	max_rech_amt_6	max_rech_amt_7	max_rech_amt_8	max_rech_amt_9	last_day_rch_amt_6	last_day_rch_amt_7	last_day_rch_amt_8	last_day_rch_amt_9	total_rech_data_6	total_rech_data_7	total_rech_data_8	total_rech_data_9	max_rech_data_6	max_rech_data_7	max_rech_data_8	max_rech_data_9	count_rech_2g_6	count_rech_2g_7	count_rech_2g_8	count_rech_2g_9	count_rech_3g_6	count_rech_3g_7	count_rech_3g_8	count_rech_3g_9	av_rech_amt_data_6	av_rech_amt_data_7	av_rech_amt_data_8	av_rech_amt_data_9	vol_2g_mb_6	vol_2g_mb_7	vol_2g_mb_8	vol_2g_mb_9	vol_3g_mb_6	vol_3g_mb_7	vol_3g_mb_8	vol_3g_mb_9	arpu_3g_6	arpu_3g_7	arpu_3g_8	arpu_3g_9	arpu_2g_6	arpu_2g_7	arpu_2g_8	arpu_2g_9	night_pck_user_6	night_pck_user_7	night_pck_user_8	night_pck_user_9	monthly_2g_6	monthly_2g_7	monthly_2g_8	monthly_2g_9	sachet_2g_6	sachet_2g_7	sachet_2g_8	sachet_2g_9	monthly_3g_6	monthly_3g_7	monthly_3g_8	monthly_3g_9	sachet_3g_6	sachet_3g_7	sachet_3g_8	sachet_3g_9	fb_user_6	fb_user_7	fb_user_8	fb_user_9	aon	aug_vbc_3g	jul_vbc_3g	jun_vbc_3g	sep_vbc_3g
count	9.999900e+04	99999.0	98981.0	98981.0	98981.0	99999.000000	99999.000000	99999.000000	99999.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.0	96140.0	94621.0	92254.0	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	99999.000000	99999.000000	99999.000000	99999.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.0	96140.0	94621.0	92254.0	96062.000000	96140.000000	94621.000000	92254.000000	99999.000000	99999.000000	99999.000000	99999.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	96062.000000	96140.000000	94621.000000	92254.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	25153.000000	25571.000000	26339.000000	25922.000000	25153.000000	25571.000000	26339.000000	25922.00000	25153.000000	25571.000000	26339.000000	25922.000000	25153.000000	25571.000000	26339.000000	25922.000000	25153.000000	25571.000000	26339.000000	25922.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	25153.000000	25571.000000	26339.000000	25922.000000	25153.000000	25571.000000	26339.000000	25922.000000	25153.000000	25571.000000	26339.000000	25922.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000	25153.000000	25571.000000	26339.000000	25922.000000	99999.000000	99999.000000	99999.000000	99999.000000	99999.000000
mean	7.001207e+09	109.0	0.0	0.0	0.0	282.987358	278.536648	279.154731	261.645069	132.395875	133.670805	133.018098	130.302327	197.935577	197.045133	196.574803	190.337222	9.950013	7.149898	7.292981	6.343841	13.911337	9.818732	9.971890	8.555519	47.100763	46.473010	45.887806	44.584446	93.342088	91.397131	91.755128	90.463192	3.751013	3.792985	3.677991	3.655123	1.123056	1.368500	1.433821	1.232726	144.201175	141.670476	141.328209	138.709970	79.829870	83.299598	83.282673	82.342919	87.299624	90.804137	89.838390	86.276622	1.129011	1.115010	1.067792	1.042362	0.0	0.0	0.0	0.0	168.261218	175.221436	174.191498	169.664466	0.798277	0.776572	0.791247	0.723892	3.916811	4.978279	5.053769	4.412767	0.454157	0.030235	0.033372	0.047456	305.133424	310.231175	304.119513	289.279198	47.922365	47.990520	47.211362	46.281794	107.475650	107.120493	108.460515	106.155471	12.084305	12.599697	11.751834	12.173105	167.491059	167.719540	167.432575	164.619293	9.575993	10.011904	9.883921	9.432479	20.722240	21.656415	21.183211	19.620913	2.156397	2.216923	2.085004	2.173419	0.0	0.0	0.0	0.0	32.457179	33.887833	33.154735	31.229344	200.130037	202.853055	198.750783	189.214260	0.061557	0.033585	0.040361	0.163137	7.460608	8.334936	8.442001	8.063003	0.854656	1.012960	0.970800	1.017162	7.558806	7.700367	7.212912	6.893019	327.514615	322.962970	324.157122	303.345673	104.637486	104.752398	107.728207	101.943889	63.156252	59.385804	62.641716	43.901249	2.463802	2.666419	2.651999	2.441170	126.393392	126.729459	125.717301	124.94144	1.864668	2.044699	2.016288	1.781807	0.599133	0.621720	0.635711	0.659363	192.600982	200.981292	197.526489	192.734315	51.904956	51.229937	50.170154	44.719701	121.396219	128.995847	135.410689	136.056613	89.555057	89.384120	91.173849	100.264116	86.398003	85.914450	86.599478	93.712026	0.025086	0.023034	0.020844	0.015971	0.079641	0.083221	0.081001	0.068781	0.389384	0.439634	0.450075	0.393104	0.075921	0.078581	0.082941	0.086341	0.074781	0.080401	0.084501	0.084581	0.914404	0.908764	0.890808	0.860968	1219.854749	68.170248	66.839062	60.021204	3.299373
std	6.956694e+05	0.0	0.0	0.0	0.0	328.439770	338.156291	344.474791	341.998630	297.207406	308.794148	308.951589	308.477668	316.851613	325.862803	327.170662	319.396092	72.825411	73.447948	68.402466	57.137537	71.443196	58.455762	64.713221	58.438186	150.856393	155.318705	151.184830	147.995390	162.780544	157.492308	156.537048	158.681454	14.230438	14.264986	13.270996	13.457549	5.448946	7.533445	6.783335	5.619021	251.751489	248.731086	245.914311	245.934517	252.476533	263.631042	265.486090	267.184991	255.617850	269.347911	271.757783	261.407396	7.984970	8.599406	7.905971	8.261770	0.0	0.0	0.0	0.0	389.948499	408.922934	411.633049	405.138658	25.765248	25.603052	25.544471	21.310751	14.936449	20.661570	17.855111	16.328227	4.125911	2.161717	2.323464	3.635466	463.419481	480.031178	478.150031	468.980002	140.258485	145.795055	137.239552	140.130610	171.713903	169.423620	169.723759	165.492803	40.140895	42.977442	39.125379	43.840776	254.124029	256.242707	250.025523	249.845070	54.330607	57.411971	55.073186	53.376273	80.793414	86.521393	83.683565	74.913050	16.495594	16.454061	15.812580	15.978601	0.0	0.0	0.0	0.0	106.283386	113.720168	110.127008	101.982303	291.651671	298.124954	289.321094	284.823024	0.160920	0.155725	0.146147	0.527860	59.722948	65.219829	63.813098	63.505379	11.955164	12.673099	13.284348	12.381172	7.078405	7.070422	7.203753	7.096261	398.019701	408.114237	416.540455	404.588583	120.614894	124.523970	126.902505	125.375109	97.356649	95.915385	104.431816	90.809712	2.789128	3.031593	3.074987	2.516339	108.477235	109.765267	109.437851	111.36376	2.570254	2.768332	2.720132	2.214701	1.274428	1.394524	1.422827	1.411513	192.646318	196.791224	191.301305	188.400286	213.356445	212.302217	212.347892	198.653570	544.247227	541.494013	558.775335	577.394194	193.124653	195.893924	188.180936	216.291992	172.767523	176.379871	168.247852	171.384224	0.156391	0.150014	0.142863	0.125366	0.295058	0.304395	0.299568	0.278120	1.497320	1.636230	1.630263	1.347140	0.363371	0.387231	0.384947	0.384978	0.568344	0.628334	0.660234	0.650457	0.279772	0.287950	0.311885	0.345987	954.733842	267.580450	271.201856	253.938223	32.408353
min	7.000000e+09	109.0	0.0	0.0	0.0	-2258.709000	-2014.045000	-945.808000	-1899.505000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.500000	0.500000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-30.820000	-26.040000	-24.490000	-71.090000	-35.830000	-15.480000	-55.830000	-45.740000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	180.000000	0.000000	0.000000	0.000000	0.000000
25%	7.000606e+09	109.0	0.0	0.0	0.0	93.411500	86.980500	84.126000	62.685000	7.380000	6.660000	6.460000	5.330000	34.730000	32.190000	31.630000	27.130000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.660000	1.630000	1.600000	1.360000	9.880000	10.025000	9.810000	8.810000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	17.110000	17.480000	17.110000	15.560000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	44.740000	43.010000	38.580000	25.510000	2.990000	3.230000	3.280000	3.290000	17.290000	18.590000	18.930000	18.560000	0.000000	0.000000	0.000000	0.000000	30.390000	32.460000	32.740000	32.290000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.000000	0.000000	0.010000	0.000000	38.530000	41.190000	38.290000	32.370000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000	3.000000	3.000000	3.000000	109.000000	100.000000	90.000000	52.000000	30.000000	30.000000	30.000000	28.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	1.000000	25.000000	25.000000	25.000000	25.00000	1.000000	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	82.000000	92.000000	87.000000	69.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	1.000000	467.000000	0.000000	0.000000	0.000000	0.000000
50%	7.001205e+09	109.0	0.0	0.0	0.0	197.704000	191.640000	192.080000	176.849000	34.310000	32.330000	32.360000	29.840000	96.310000	91.735000	92.140000	87.290000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	11.910000	11.610000	11.730000	11.260000	41.030000	40.430000	40.360000	39.120000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	65.110000	63.685000	63.730000	61.840000	0.000000	0.000000	0.000000	0.000000	3.950000	3.635000	3.310000	2.500000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	11.640000	11.090000	10.410000	8.410000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	145.140000	141.530000	138.610000	125.460000	15.690000	15.740000	16.030000	15.660000	56.490000	57.080000	58.240000	56.610000	0.880000	0.930000	0.930000	0.960000	92.160000	92.550000	93.830000	91.640000	0.000000	0.000000	0.000000	0.000000	2.030000	2.040000	2.030000	1.740000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	5.890000	5.960000	5.880000	5.380000	114.740000	116.340000	114.660000	105.890000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.000000	6.000000	5.000000	5.000000	230.000000	220.000000	225.000000	200.000000	110.000000	110.000000	98.000000	61.000000	30.000000	30.000000	30.000000	0.000000	1.000000	1.000000	1.000000	2.000000	145.000000	145.000000	145.000000	145.00000	1.000000	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	154.000000	154.000000	154.000000	164.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.480000	0.420000	0.880000	2.605000	10.830000	8.810000	9.270000	14.800000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	1.000000	863.000000	0.000000	0.000000	0.000000	0.000000
75%	7.001812e+09	109.0	0.0	0.0	0.0	371.060000	365.344500	369.370500	353.466500	118.740000	115.595000	115.860000	112.130000	231.860000	226.815000	228.260000	220.505000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	40.960000	39.910000	40.110000	39.280000	110.390000	107.560000	109.090000	106.810000	2.080000	2.090000	2.040000	1.940000	0.000000	0.000000	0.000000	0.000000	168.270000	164.382500	166.110000	162.225000	30.807500	31.132500	30.580000	28.230000	53.290000	54.040000	52.490000	48.560000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	144.837500	150.615000	147.940000	142.105000	0.000000	0.000000	0.000000	0.000000	2.430000	3.710000	3.990000	3.230000	0.000000	0.000000	0.000000	0.000000	372.860000	378.570000	369.900000	353.480000	46.840000	45.810000	46.290000	45.180000	132.387500	130.960000	133.930000	130.490000	8.140000	8.282500	8.110000	8.140000	208.075000	205.837500	207.280000	202.737500	4.060000	4.230000	4.080000	3.510000	15.030000	15.740000	15.360000	14.260000	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	26.930000	28.310000	27.710000	25.690000	251.670000	250.660000	248.990000	236.320000	0.000000	0.000000	0.000000	0.060000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	9.000000	10.000000	9.000000	9.000000	437.500000	428.000000	434.500000	415.000000	120.000000	128.000000	144.000000	144.000000	110.000000	110.000000	130.000000	50.000000	3.000000	3.000000	3.000000	3.000000	177.000000	177.000000	179.000000	179.00000	2.000000	2.000000	2.000000	2.000000	1.000000	1.000000	1.000000	1.000000	252.000000	252.000000	252.000000	252.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	122.070000	119.560000	122.070000	140.010000	122.070000	122.070000	122.070000	140.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1807.500000	0.000000	0.000000	0.000000	0.000000
max	7.002411e+09	109.0	0.0	0.0	0.0	27731.088000	35145.834000	33543.624000	38805.617000	7376.710000	8157.780000	10752.560000	10427.460000	8362.360000	9667.130000	14007.340000	10310.760000	13724.380000	15371.040000	13095.360000	8464.030000	3775.110000	2812.040000	5337.040000	4428.460000	6431.330000	7400.660000	10752.560000	10389.240000	4729.740000	4557.140000	4961.330000	4429.880000	1466.030000	1196.430000	928.490000	927.410000	342.860000	916.240000	502.090000	339.840000	10643.380000	7674.780000	11039.910000	11099.260000	7366.580000	8133.660000	8014.430000	9382.580000	8314.760000	9284.740000	13950.040000	10223.430000	628.560000	544.630000	516.910000	808.490000	0.0	0.0	0.0	0.0	8432.990000	10936.730000	13980.060000	11495.310000	5900.660000	5490.280000	5681.540000	4244.530000	1023.210000	2372.510000	1390.880000	1635.710000	800.890000	370.130000	394.930000	787.790000	10674.030000	11365.310000	14043.060000	11517.730000	6626.930000	9324.660000	10696.230000	10598.830000	4693.860000	4455.830000	6274.190000	5463.780000	1872.340000	1983.010000	2433.060000	4318.280000	7454.630000	9669.910000	10830.160000	10796.290000	5459.560000	5800.930000	4309.290000	3819.830000	5647.160000	6141.880000	5645.860000	5689.760000	1351.110000	1136.080000	1394.890000	1431.960000	0.0	0.0	0.0	0.0	5712.110000	6745.760000	5957.140000	5956.660000	7716.140000	9699.010000	10830.380000	10796.590000	19.760000	21.330000	16.860000	62.380000	6789.410000	5289.540000	4127.010000	5057.740000	1362.940000	1495.940000	2327.510000	1005.230000	307.000000	138.000000	196.000000	131.000000	35190.000000	40335.000000	45320.000000	37235.000000	4010.000000	4010.000000	4449.000000	3399.000000	4010.000000	4010.000000	4449.000000	3399.000000	61.000000	54.000000	60.000000	84.000000	1555.000000	1555.000000	1555.000000	1555.00000	42.000000	48.000000	44.000000	40.000000	29.000000	35.000000	45.000000	49.000000	7546.000000	4365.000000	4076.000000	4061.000000	10285.900000	7873.550000	11117.610000	8993.950000	45735.400000	28144.120000	30036.060000	39221.270000	6362.280000	4980.900000	3716.900000	13884.310000	6433.760000	4809.360000	3483.170000	3467.170000	1.000000	1.000000	1.000000	1.000000	4.000000	5.000000	5.000000	4.000000	42.000000	48.000000	44.000000	40.000000	14.000000	16.000000	16.000000	11.000000	29.000000	35.000000	41.000000	49.000000	1.000000	1.000000	1.000000	1.000000	4337.000000	12916.220000	9165.600000	11166.210000	2618.570000

	aon	loc_og_mou_good	loc_og_mou_action	loc_og_mou_churn	std_ic_t2f_mou_good	std_ic_t2f_mou_action	std_og_t2m_mou_good	std_og_t2m_mou_action	std_og_t2m_mou_churn	last_day_rch_amt_good	last_day_rch_amt_action	last_day_rch_amt_churn	total_og_mou_good	total_og_mou_action	total_og_mou_churn	loc_og_t2c_mou_action	loc_og_t2f_mou_good	loc_og_t2f_mou_action	loc_og_t2f_mou_churn	offnet_mou_good	offnet_mou_action	offnet_mou_churn	std_ic_t2t_mou_good	std_ic_t2t_mou_action	std_ic_t2t_mou_churn	total_rech_num_good	total_rech_num_action	total_rech_num_churn	std_ic_mou_good	std_ic_mou_action	std_ic_mou_churn	loc_ic_t2t_mou_good	loc_ic_t2t_mou_action	loc_ic_t2t_mou_churn	ic_others_good	ic_others_action	ic_others_churn	loc_ic_t2f_mou_good	loc_ic_t2f_mou_action	loc_ic_t2f_mou_churn	spl_og_mou_good	spl_og_mou_action	spl_og_mou_churn	total_rech_amt_good	total_rech_amt_action	total_rech_amt_churn	loc_og_t2t_mou_good	loc_og_t2t_mou_action	loc_og_t2t_mou_churn	std_og_t2t_mou_good	std_og_t2t_mou_action	std_og_t2t_mou_churn	max_rech_amt_good	max_rech_amt_action	max_rech_amt_churn	vbc_3g_good	vbc_3g_action	vol_3g_mb_good	vol_3g_mb_action	total_ic_mou_good	total_ic_mou_action	total_ic_mou_churn	loc_ic_t2m_mou_good	loc_ic_t2m_mou_action	loc_ic_t2m_mou_churn	arpu_good	arpu_action	arpu_churn	std_ic_t2m_mou_good	std_ic_t2m_mou_action	std_ic_t2m_mou_churn	vol_2g_mb_good	vol_2g_mb_action	onnet_mou_good	onnet_mou_action	onnet_mou_churn	loc_og_t2m_mou_good	loc_og_t2m_mou_action	loc_og_t2m_mou_churn	loc_ic_mou_good	loc_ic_mou_action	loc_ic_mou_churn	std_og_mou_good	std_og_mou_action	std_og_mou_churn
8	315	333.135	63.04	60.71	0.000	0.0	11.8650	75.69	74.13	25.0	10.0	0.0	470.010	171.56	142.18	0.325	0.000	0.00	0.00	87.645	136.48	108.71	0.2900	0.1000	0.00	20.0	14.0	15.0	13.5450	0.750	13.53	16.840	0.31	4.03	0.00	0.0	0.000	0.000	0.0000	0.000	0.00	10.23	0.00	519.0	120.0	186.0	219.75375	12.49	26.13	124.9950	22.58	7.33	122.0	30.0	36.0	4.03125	21.03	6.76875	1.425	66.175	16.5400	34.91	35.780	15.48	17.34	435.4720	137.362	166.787	13.255	0.6500	13.5300	20.64375	0.03	382.3600	35.08	33.46	75.770	50.54	34.58	52.630	15.79	21.38	136.870	98.28	81.46
13	2607	129.395	353.99	119.69	0.000	0.7	154.1500	156.94	96.01	80.0	0.0	0.0	549.750	1015.26	446.09	0.325	2.270	12.90	5.29	266.295	482.46	214.06	27.0125	24.0125	21.20	5.0	11.0	7.0	91.4850	111.305	103.81	37.730	52.58	24.98	0.15	0.2	0.125	2.405	7.4900	8.510	0.00	4.78	0.00	380.0	717.0	353.0	27.91000	36.01	6.14	263.8775	370.75	230.38	110.0	130.0	130.0	0.00000	0.00	0.00000	0.000	514.170	849.6075	631.86	80.705	195.18	104.79	349.2585	593.260	322.732	49.050	67.2475	61.2425	0.00000	0.02	305.0750	534.24	244.81	99.205	294.46	108.24	120.845	255.26	138.29	420.355	655.18	326.39
16	511	265.775	205.93	233.04	0.255	0.7	43.7500	18.29	13.79	100.0	130.0	220.0	316.410	233.38	255.74	0.000	2.240	10.26	4.66	263.015	162.76	224.39	27.0125	6.3300	16.66	8.0	2.0	1.0	105.8075	82.440	46.24	56.385	28.89	50.23	0.00	0.0	0.000	8.695	31.6375	31.275	1.63	5.91	0.00	459.0	160.0	220.0	53.88500	67.38	26.88	5.2350	3.23	4.46	110.0	130.0	220.0	1.22500	0.00	0.00000	0.000	373.210	294.4600	334.56	188.250	150.16	172.86	365.4220	187.894	206.490	45.880	67.2475	29.5800	0.00000	0.00	62.2600	70.61	31.34	209.640	128.28	201.49	253.345	211.78	288.31	48.990	21.53	22.69
17	667	48.770	0.00	45.79	0.000	0.0	0.7750	0.00	78.51	15.0	0.0	0.0	625.170	0.00	631.08	0.000	0.000	0.00	0.00	30.820	5.54	87.89	4.3150	0.0000	0.00	10.5	4.0	10.0	4.9550	0.000	1.63	17.270	0.00	40.91	0.00	0.0	0.000	0.000	0.0000	0.710	1.29	0.00	2.64	408.0	30.0	335.0	19.49500	0.00	36.41	221.2925	0.00	309.45	55.0	30.0	130.0	0.00000	0.00	0.00000	0.000	47.095	0.0000	87.13	24.860	0.00	43.86	354.4940	25.499	257.583	0.640	0.0000	1.6300	0.00000	0.00	413.7025	7.79	558.51	29.270	0.00	9.38	42.135	0.00	85.49	574.635	0.00	582.63
21	720	95.565	129.74	137.53	0.000	0.0	539.6925	444.08	381.95	25.0	50.0	30.0	944.565	1068.54	1031.53	0.000	0.375	1.58	0.00	827.305	983.39	869.89	10.0200	0.1800	16.74	24.0	25.0	17.0	62.9850	101.930	103.81	6.335	19.54	17.99	0.00	0.0	0.000	3.725	10.3900	8.410	0.00	0.00	0.00	640.0	718.0	680.0	5.32000	23.34	29.98	111.9350	61.79	131.64	50.0	50.0	50.0	0.00000	0.00	0.00000	0.000	169.490	261.0400	252.61	96.430	129.16	113.46	556.1030	637.760	578.596	52.960	67.2475	61.2425	0.00000	0.00	117.2600	85.14	161.63	89.870	104.81	107.54	106.505	159.11	139.88	848.990	938.79	893.99

	aon	loc_og_mou_good	loc_og_mou_action	loc_og_mou_churn	std_ic_t2f_mou_good	std_ic_t2f_mou_action	std_og_t2m_mou_good	std_og_t2m_mou_action	std_og_t2m_mou_churn	last_day_rch_amt_good	last_day_rch_amt_action	last_day_rch_amt_churn	total_og_mou_good	total_og_mou_action	total_og_mou_churn	loc_og_t2c_mou_action	loc_og_t2f_mou_good	loc_og_t2f_mou_action	loc_og_t2f_mou_churn	offnet_mou_good	offnet_mou_action	offnet_mou_churn	std_ic_t2t_mou_good	std_ic_t2t_mou_action	std_ic_t2t_mou_churn	total_rech_num_good	total_rech_num_action	total_rech_num_churn	std_ic_mou_good	std_ic_mou_action	std_ic_mou_churn	loc_ic_t2t_mou_good	loc_ic_t2t_mou_action	loc_ic_t2t_mou_churn	ic_others_good	ic_others_action	ic_others_churn	loc_ic_t2f_mou_good	loc_ic_t2f_mou_action	loc_ic_t2f_mou_churn	spl_og_mou_good	spl_og_mou_action	spl_og_mou_churn	total_rech_amt_good	total_rech_amt_action	total_rech_amt_churn	loc_og_t2t_mou_good	loc_og_t2t_mou_action	loc_og_t2t_mou_churn	std_og_t2t_mou_good	std_og_t2t_mou_action	std_og_t2t_mou_churn	max_rech_amt_good	max_rech_amt_action	max_rech_amt_churn	vbc_3g_good	vbc_3g_action	vol_3g_mb_good	vol_3g_mb_action	total_ic_mou_good	total_ic_mou_action	total_ic_mou_churn	loc_ic_t2m_mou_good	loc_ic_t2m_mou_action	loc_ic_t2m_mou_churn	arpu_good	arpu_action	arpu_churn	std_ic_t2m_mou_good	std_ic_t2m_mou_action	std_ic_t2m_mou_churn	vol_2g_mb_good	vol_2g_mb_action	onnet_mou_good	onnet_mou_action	onnet_mou_churn	loc_og_t2m_mou_good	loc_og_t2m_mou_action	loc_og_t2m_mou_churn	loc_ic_mou_good	loc_ic_mou_action	loc_ic_mou_churn	std_og_mou_good	std_og_mou_action	std_og_mou_churn	total_mou_good	total_mou_action	total_mou_churn
8	315	333.135	63.04	60.71	0.000	0.0	11.8650	75.69	74.13	25.0	10.0	0.0	470.010	171.56	142.18	0.325	0.000	0.00	0.00	87.645	136.48	108.71	0.2900	0.1000	0.00	20.0	14.0	15.0	13.5450	0.750	13.53	16.840	0.31	4.03	0.00	0.0	0.000	0.000	0.0000	0.000	0.00	10.23	0.00	519.0	120.0	186.0	219.75375	12.49	26.13	124.9950	22.58	7.33	122.0	30.0	36.0	4.03125	21.03	6.76875	1.425	66.175	16.5400	34.91	35.780	15.48	17.34	435.4720	137.362	166.787	13.255	0.6500	13.5300	20.64375	0.03	382.3600	35.08	33.46	75.770	50.54	34.58	52.630	15.79	21.38	136.870	98.28	81.46	536.185	188.1000	177.09
13	2607	129.395	353.99	119.69	0.000	0.7	154.1500	156.94	96.01	80.0	0.0	0.0	549.750	1015.26	446.09	0.325	2.270	12.90	5.29	266.295	482.46	214.06	27.0125	24.0125	21.20	5.0	11.0	7.0	91.4850	111.305	103.81	37.730	52.58	24.98	0.15	0.2	0.125	2.405	7.4900	8.510	0.00	4.78	0.00	380.0	717.0	353.0	27.91000	36.01	6.14	263.8775	370.75	230.38	110.0	130.0	130.0	0.00000	0.00	0.00000	0.000	514.170	849.6075	631.86	80.705	195.18	104.79	349.2585	593.260	322.732	49.050	67.2475	61.2425	0.00000	0.02	305.0750	534.24	244.81	99.205	294.46	108.24	120.845	255.26	138.29	420.355	655.18	326.39	1063.920	1864.8675	1077.95
16	511	265.775	205.93	233.04	0.255	0.7	43.7500	18.29	13.79	100.0	130.0	220.0	316.410	233.38	255.74	0.000	2.240	10.26	4.66	263.015	162.76	224.39	27.0125	6.3300	16.66	8.0	2.0	1.0	105.8075	82.440	46.24	56.385	28.89	50.23	0.00	0.0	0.000	8.695	31.6375	31.275	1.63	5.91	0.00	459.0	160.0	220.0	53.88500	67.38	26.88	5.2350	3.23	4.46	110.0	130.0	220.0	1.22500	0.00	0.00000	0.000	373.210	294.4600	334.56	188.250	150.16	172.86	365.4220	187.894	206.490	45.880	67.2475	29.5800	0.00000	0.00	62.2600	70.61	31.34	209.640	128.28	201.49	253.345	211.78	288.31	48.990	21.53	22.69	689.620	527.8400	590.30
17	667	48.770	0.00	45.79	0.000	0.0	0.7750	0.00	78.51	15.0	0.0	0.0	625.170	0.00	631.08	0.000	0.000	0.00	0.00	30.820	5.54	87.89	4.3150	0.0000	0.00	10.5	4.0	10.0	4.9550	0.000	1.63	17.270	0.00	40.91	0.00	0.0	0.000	0.000	0.0000	0.710	1.29	0.00	2.64	408.0	30.0	335.0	19.49500	0.00	36.41	221.2925	0.00	309.45	55.0	30.0	130.0	0.00000	0.00	0.00000	0.000	47.095	0.0000	87.13	24.860	0.00	43.86	354.4940	25.499	257.583	0.640	0.0000	1.6300	0.00000	0.00	413.7025	7.79	558.51	29.270	0.00	9.38	42.135	0.00	85.49	574.635	0.00	582.63	672.265	0.0000	718.21
21	720	95.565	129.74	137.53	0.000	0.0	539.6925	444.08	381.95	25.0	50.0	30.0	944.565	1068.54	1031.53	0.000	0.375	1.58	0.00	827.305	983.39	869.89	10.0200	0.1800	16.74	24.0	25.0	17.0	62.9850	101.930	103.81	6.335	19.54	17.99	0.00	0.0	0.000	3.725	10.3900	8.410	0.00	0.00	0.00	640.0	718.0	680.0	5.32000	23.34	29.98	111.9350	61.79	131.64	50.0	50.0	50.0	0.00000	0.00	0.00000	0.000	169.490	261.0400	252.61	96.430	129.16	113.46	556.1030	637.760	578.596	52.960	67.2475	61.2425	0.00000	0.00	117.2600	85.14	161.63	89.870	104.81	107.54	106.505	159.11	139.88	848.990	938.79	893.99	1114.055	1329.5800	1284.14

	aon	loc_og_mou_good	loc_og_mou_action	loc_og_mou_churn	std_ic_t2f_mou_good	std_ic_t2f_mou_action	std_og_t2m_mou_good	std_og_t2m_mou_action	std_og_t2m_mou_churn	last_day_rch_amt_good	last_day_rch_amt_action	last_day_rch_amt_churn	total_og_mou_good	total_og_mou_action	total_og_mou_churn	loc_og_t2c_mou_action	loc_og_t2f_mou_good	loc_og_t2f_mou_action	loc_og_t2f_mou_churn	offnet_mou_good	offnet_mou_action	offnet_mou_churn	std_ic_t2t_mou_good	std_ic_t2t_mou_action	std_ic_t2t_mou_churn	total_rech_num_good	total_rech_num_action	total_rech_num_churn	std_ic_mou_good	std_ic_mou_action	std_ic_mou_churn	loc_ic_t2t_mou_good	loc_ic_t2t_mou_action	loc_ic_t2t_mou_churn	ic_others_good	ic_others_action	ic_others_churn	loc_ic_t2f_mou_good	loc_ic_t2f_mou_action	loc_ic_t2f_mou_churn	spl_og_mou_good	spl_og_mou_action	spl_og_mou_churn	total_rech_amt_good	total_rech_amt_action	total_rech_amt_churn	loc_og_t2t_mou_good	loc_og_t2t_mou_action	loc_og_t2t_mou_churn	std_og_t2t_mou_good	std_og_t2t_mou_action	std_og_t2t_mou_churn	max_rech_amt_good	max_rech_amt_action	max_rech_amt_churn	vbc_3g_good	vbc_3g_action	vol_3g_mb_good	vol_3g_mb_action	total_ic_mou_good	total_ic_mou_action	total_ic_mou_churn	loc_ic_t2m_mou_good	loc_ic_t2m_mou_action	loc_ic_t2m_mou_churn	arpu_good	arpu_action	arpu_churn	std_ic_t2m_mou_good	std_ic_t2m_mou_action	std_ic_t2m_mou_churn	vol_2g_mb_good	vol_2g_mb_action	onnet_mou_good	onnet_mou_action	onnet_mou_churn	loc_og_t2m_mou_good	loc_og_t2m_mou_action	loc_og_t2m_mou_churn	loc_ic_mou_good	loc_ic_mou_action	loc_ic_mou_churn	std_og_mou_good	std_og_mou_action	std_og_mou_churn	total_mou_good	total_mou_action	total_mou_churn	mou_decrease
8	315	333.135	63.04	60.71	0.000	0.0	11.8650	75.69	74.13	25.0	10.0	0.0	470.010	171.56	142.18	0.325	0.000	0.00	0.00	87.645	136.48	108.71	0.2900	0.1000	0.00	20.0	14.0	15.0	13.5450	0.750	13.53	16.840	0.31	4.03	0.00	0.0	0.000	0.000	0.0000	0.000	0.00	10.23	0.00	519.0	120.0	186.0	219.75375	12.49	26.13	124.9950	22.58	7.33	122.0	30.0	36.0	4.03125	21.03	6.76875	1.425	66.175	16.5400	34.91	35.780	15.48	17.34	435.4720	137.362	166.787	13.255	0.6500	13.5300	20.64375	0.03	382.3600	35.08	33.46	75.770	50.54	34.58	52.630	15.79	21.38	136.870	98.28	81.46	536.185	188.1000	177.09	1
13	2607	129.395	353.99	119.69	0.000	0.7	154.1500	156.94	96.01	80.0	0.0	0.0	549.750	1015.26	446.09	0.325	2.270	12.90	5.29	266.295	482.46	214.06	27.0125	24.0125	21.20	5.0	11.0	7.0	91.4850	111.305	103.81	37.730	52.58	24.98	0.15	0.2	0.125	2.405	7.4900	8.510	0.00	4.78	0.00	380.0	717.0	353.0	27.91000	36.01	6.14	263.8775	370.75	230.38	110.0	130.0	130.0	0.00000	0.00	0.00000	0.000	514.170	849.6075	631.86	80.705	195.18	104.79	349.2585	593.260	322.732	49.050	67.2475	61.2425	0.00000	0.02	305.0750	534.24	244.81	99.205	294.46	108.24	120.845	255.26	138.29	420.355	655.18	326.39	1063.920	1864.8675	1077.95	0
16	511	265.775	205.93	233.04	0.255	0.7	43.7500	18.29	13.79	100.0	130.0	220.0	316.410	233.38	255.74	0.000	2.240	10.26	4.66	263.015	162.76	224.39	27.0125	6.3300	16.66	8.0	2.0	1.0	105.8075	82.440	46.24	56.385	28.89	50.23	0.00	0.0	0.000	8.695	31.6375	31.275	1.63	5.91	0.00	459.0	160.0	220.0	53.88500	67.38	26.88	5.2350	3.23	4.46	110.0	130.0	220.0	1.22500	0.00	0.00000	0.000	373.210	294.4600	334.56	188.250	150.16	172.86	365.4220	187.894	206.490	45.880	67.2475	29.5800	0.00000	0.00	62.2600	70.61	31.34	209.640	128.28	201.49	253.345	211.78	288.31	48.990	21.53	22.69	689.620	527.8400	590.30	1
17	667	48.770	0.00	45.79	0.000	0.0	0.7750	0.00	78.51	15.0	0.0	0.0	625.170	0.00	631.08	0.000	0.000	0.00	0.00	30.820	5.54	87.89	4.3150	0.0000	0.00	10.5	4.0	10.0	4.9550	0.000	1.63	17.270	0.00	40.91	0.00	0.0	0.000	0.000	0.0000	0.710	1.29	0.00	2.64	408.0	30.0	335.0	19.49500	0.00	36.41	221.2925	0.00	309.45	55.0	30.0	130.0	0.00000	0.00	0.00000	0.000	47.095	0.0000	87.13	24.860	0.00	43.86	354.4940	25.499	257.583	0.640	0.0000	1.6300	0.00000	0.00	413.7025	7.79	558.51	29.270	0.00	9.38	42.135	0.00	85.49	574.635	0.00	582.63	672.265	0.0000	718.21	1
21	720	95.565	129.74	137.53	0.000	0.0	539.6925	444.08	381.95	25.0	50.0	30.0	944.565	1068.54	1031.53	0.000	0.375	1.58	0.00	827.305	983.39	869.89	10.0200	0.1800	16.74	24.0	25.0	17.0	62.9850	101.930	103.81	6.335	19.54	17.99	0.00	0.0	0.000	3.725	10.3900	8.410	0.00	0.00	0.00	640.0	718.0	680.0	5.32000	23.34	29.98	111.9350	61.79	131.64	50.0	50.0	50.0	0.00000	0.00	0.00000	0.000	169.490	261.0400	252.61	96.430	129.16	113.46	556.1030	637.760	578.596	52.960	67.2475	61.2425	0.00000	0.00	117.2600	85.14	161.63	89.870	104.81	107.54	106.505	159.11	139.88	848.990	938.79	893.99	1114.055	1329.5800	1284.14	0

	aon	loc_og_mou_good	loc_og_mou_action	std_ic_t2f_mou_good	std_og_t2m_mou_good	std_og_t2m_mou_action	last_day_rch_amt_good	last_day_rch_amt_action	total_og_mou_good	total_og_mou_action	loc_og_t2c_mou_good	loc_og_t2c_mou_action	loc_og_t2f_mou_good	loc_og_t2f_mou_action	offnet_mou_good	offnet_mou_action	std_ic_t2t_mou_good	std_ic_t2t_mou_action	total_rech_num_good	total_rech_num_action	std_ic_mou_good	std_ic_mou_action	loc_ic_t2t_mou_good	loc_ic_t2t_mou_action	ic_others_good	ic_others_action	loc_ic_t2f_mou_good	loc_ic_t2f_mou_action	spl_og_mou_good	spl_og_mou_action	total_rech_amt_good	total_rech_amt_action	loc_og_t2t_mou_good	loc_og_t2t_mou_action	std_og_t2t_mou_good	std_og_t2t_mou_action	max_rech_amt_good	max_rech_amt_action	vbc_3g_good	vbc_3g_action	vol_3g_mb_good	vol_3g_mb_action	total_ic_mou_good	total_ic_mou_action	loc_ic_t2m_mou_good	loc_ic_t2m_mou_action	arpu_good	arpu_action	std_ic_t2m_mou_good	std_ic_t2m_mou_action	vol_2g_mb_good	vol_2g_mb_action	onnet_mou_good	onnet_mou_action	loc_og_t2m_mou_good	loc_og_t2m_mou_action	loc_ic_mou_good	loc_ic_mou_action	std_og_mou_good	std_og_mou_action	mou_decrease	arpu_decrease	rech_amt_decrease	rech_num_decrease
0	0.439266	0.000753	0.000000	0.568966	0.000000	0.000000	0.942056	0.841402	0.000352	0.000000	0.0	0.0	0.000000	0.000000	0.000045	0.000000	0.030171	0.000000	0.125000	0.08	0.018155	0.000000	0.030531	0.090721	1.000000	0.0	0.005611	0.019281	0.000000	0.000000	0.060720	0.168168	0.002726	0.000000	0.000000	0.000000	0.740625	0.724138	1.0	1.0	1.0	1.0	0.033216	0.043420	0.043528	0.044735	0.367054	0.308416	0.002318	0.000000	0.523953	1.0	0.000725	0.000000	0.000089	0.000000	0.036193	0.052577	0.000000	0.000000	0	1	1	1
1	0.223376	0.484550	0.520507	0.000000	0.000000	0.005922	0.280374	0.434057	0.227779	0.249945	0.0	1.0	0.608680	1.000000	0.270714	0.346164	0.000000	0.000000	0.267857	0.24	0.005157	0.005391	0.312619	0.516148	0.000000	0.0	0.294869	0.255709	0.139497	0.886982	0.214168	0.408408	0.539007	0.440392	0.000410	0.000000	0.581250	0.373563	1.0	0.0	1.0	0.0	0.382417	0.448019	0.564860	0.614799	0.516485	0.450727	0.008641	0.008922	0.523953	0.0	0.145381	0.131155	0.514664	0.579382	0.459395	0.541649	0.000130	0.002075	0	1	0	1
2	0.074137	0.036821	0.005858	0.000000	0.049880	1.000000	0.000000	0.166945	0.451439	0.551327	1.0	0.0	0.000000	0.000000	0.061752	0.574698	0.079778	0.256533	0.669643	0.64	0.033626	0.299717	0.032237	0.000173	0.000000	0.0	0.038391	0.000000	0.582320	1.000000	0.186397	0.457791	0.058213	0.000000	0.631673	1.000000	0.109375	0.143678	0.0	0.0	0.0	0.0	0.049520	0.049882	0.067733	0.019539	0.461139	0.551748	0.025994	0.404327	0.000000	0.0	0.585271	0.508687	0.033893	0.009046	0.053944	0.012841	0.532326	0.744378	0	0	0	1
3	0.579812	1.000000	1.000000	0.568966	0.002677	0.000000	0.676636	0.434057	0.687304	1.000000	1.0	1.0	0.036166	0.103101	0.884923	1.000000	0.006108	0.199063	0.553571	0.52	0.035513	0.042945	0.585951	0.503443	0.000000	0.0	0.035585	0.000000	0.606650	1.000000	0.931278	1.000000	0.521616	0.405325	0.000000	0.004073	0.740625	0.724138	0.0	0.0	1.0	1.0	0.666923	0.734916	0.807062	1.000000	0.901167	1.000000	0.047843	0.000000	0.641117	1.0	0.138713	0.118805	1.000000	1.000000	0.753886	0.883094	0.000988	0.001192	0	0	0	1
4	0.121227	0.328143	0.300437	1.000000	0.101234	0.197667	0.330841	0.661102	0.251824	0.222433	0.0	0.0	0.074503	0.000000	0.238420	0.263962	0.624896	1.000000	0.428571	0.32	0.559695	0.380576	0.243881	0.163888	0.583333	1.0	0.022148	0.027815	0.374047	0.000000	0.268298	0.407741	0.359301	0.256736	0.265714	0.172650	0.656250	0.568966	1.0	1.0	1.0	1.0	0.588922	0.582398	0.370852	0.404463	0.492980	0.509685	0.648869	0.195992	0.560406	1.0	0.243332	0.160179	0.363204	0.362740	0.303008	0.306655	0.121722	0.119777	1	1	1	1

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	PC10	PC11	PC12	PC13	PC14	PC15	PC16	PC17	PC18	PC19	PC20	PC21	PC22	PC23	PC24	PC25
0	0.392464	-1.742748	1.586044	-0.042025	-0.121037	0.157551	-0.516437	-0.080412	0.491018	0.423980	0.004110	0.370351	0.035072	0.333412	-0.318460	0.199650	-0.245777	-0.173004	0.666346	0.194335	-0.002042	0.049495	0.360444	0.044814	0.035534
1	1.384197	-0.762394	-0.122334	-0.251210	-0.760379	-0.217034	0.218068	0.255680	-0.286098	-0.146520	0.637099	-0.238962	-0.760972	0.042142	0.376890	0.280950	0.180151	0.041385	0.009923	-0.583209	0.422560	-0.694506	-0.201579	0.222967	0.154847
2	-0.673997	1.229669	0.525259	-0.168532	-0.506183	-0.898676	0.264304	0.048469	-0.144008	-0.346142	-0.583583	-0.046268	0.031594	-0.241607	0.093328	-0.052417	0.465217	-0.164916	0.270171	0.006156	0.018946	0.001294	0.119512	0.013196	-0.060196
3	2.520927	0.219104	0.031615	0.150144	-1.617824	0.200662	0.868365	0.219290	-0.905711	0.570897	0.456307	-0.453094	0.617976	-0.276454	-0.674340	0.604019	0.022137	0.065132	0.030868	0.303848	0.041241	-0.105478	0.256652	0.883187	-0.396427
4	1.540153	-0.478254	1.290122	0.079554	0.672998	0.401171	0.031713	-0.806560	0.377509	0.561806	-0.036438	0.134148	0.151481	-0.187351	-0.215492	0.296445	0.053143	0.335259	-0.064859	0.529772	0.405330	0.008008	0.487486	0.047578	0.379151

Dep. Variable:	churn	No. Observations:	37862
Model:	GLM	Df Residuals:	37846
Model Family:	Binomial	Df Model:	15
Link Function:	Logit	Scale:	1.0000
Method:	IRLS	Log-Likelihood:	-14693.
Date:	Thu, 08 Dec 2022	Deviance:	29387.
Time:	22:20:48	Pearson chi2:	6.22e+04
No. Iterations:	7	Pseudo R-squ. (CS):	0.4567
Covariance Type:	nonrobust

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	0.4491	0.034	13.053	0.000	0.382	0.517
std_og_t2m_mou_good	2.2325	0.068	32.695	0.000	2.099	2.366
std_og_t2m_mou_action	-3.0590	0.115	-26.553	0.000	-3.285	-2.833
last_day_rch_amt_action	-1.6380	0.070	-23.562	0.000	-1.774	-1.502
total_og_mou_action	-7.7027	0.283	-27.240	0.000	-8.257	-7.149
offnet_mou_action	4.0173	0.151	26.571	0.000	3.721	4.314
loc_ic_t2t_mou_good	3.2288	0.210	15.358	0.000	2.817	3.641
loc_ic_t2t_mou_action	-2.6098	0.139	-18.750	0.000	-2.883	-2.337
std_og_t2t_mou_good	1.9969	0.068	29.554	0.000	1.864	2.129
std_og_t2t_mou_action	-3.0147	0.120	-25.219	0.000	-3.249	-2.780
loc_ic_t2m_mou_good	4.7021	0.454	10.356	0.000	3.812	5.592
loc_ic_t2m_mou_action	-2.8968	0.180	-16.093	0.000	-3.250	-2.544
onnet_mou_action	3.7515	0.147	25.549	0.000	3.464	4.039
loc_og_t2m_mou_good	-2.2143	0.147	-15.029	0.000	-2.503	-1.925
loc_ic_mou_good	-7.1385	0.594	-12.020	0.000	-8.303	-5.975
std_og_mou_action	3.4397	0.229	15.040	0.000	2.991	3.888

	Feature	VIF
13	loc_ic_mou_good	36.84
9	loc_ic_t2m_mou_good	28.85
3	total_og_mou_action	26.99
14	std_og_mou_action	23.03
11	onnet_mou_action	11.55
4	offnet_mou_action	11.01
8	std_og_t2t_mou_action	10.67
5	loc_ic_t2t_mou_good	9.26
1	std_og_t2m_mou_action	8.92
10	loc_ic_t2m_mou_action	8.06
12	loc_og_t2m_mou_good	6.24
6	loc_ic_t2t_mou_action	5.38
7	std_og_t2t_mou_good	3.39
0	std_og_t2m_mou_good	3.13
2	last_day_rch_amt_action	1.91

	Feature	VIF
3	total_og_mou_action	26.93
13	std_og_mou_action	22.99
11	onnet_mou_action	11.54
4	offnet_mou_action	11.01
8	std_og_t2t_mou_action	10.66
9	loc_ic_t2m_mou_good	9.75
1	std_og_t2m_mou_action	8.92
10	loc_ic_t2m_mou_action	8.05
12	loc_og_t2m_mou_good	6.19
6	loc_ic_t2t_mou_action	5.35
5	loc_ic_t2t_mou_good	5.32
7	std_og_t2t_mou_good	3.39
0	std_og_t2m_mou_good	3.13
2	last_day_rch_amt_action	1.91

	Feature	VIF
12	std_og_mou_action	11.38
7	std_og_t2t_mou_action	9.70
8	loc_ic_t2m_mou_good	9.49
3	offnet_mou_action	9.26
1	std_og_t2m_mou_action	8.66
10	onnet_mou_action	8.47
9	loc_ic_t2m_mou_action	7.66
11	loc_og_t2m_mou_good	5.46
4	loc_ic_t2t_mou_good	5.31
5	loc_ic_t2t_mou_action	5.25
6	std_og_t2t_mou_good	3.37
0	std_og_t2m_mou_good	3.11
2	last_day_rch_amt_action	1.88

	Feature	VIF
8	loc_ic_t2m_mou_good	9.47
3	offnet_mou_action	8.76
7	std_og_t2t_mou_action	8.04
10	onnet_mou_action	7.75
9	loc_ic_t2m_mou_action	7.64
1	std_og_t2m_mou_action	6.98
4	loc_ic_t2t_mou_good	5.30
11	loc_og_t2m_mou_good	5.27
5	loc_ic_t2t_mou_action	5.24
6	std_og_t2t_mou_good	3.37
0	std_og_t2m_mou_good	3.11
2	last_day_rch_amt_action	1.88

	Feature	VIF
3	offnet_mou_action	8.55
7	std_og_t2t_mou_action	8.04
9	onnet_mou_action	7.74
1	std_og_t2m_mou_action	6.98
5	loc_ic_t2t_mou_action	5.09
4	loc_ic_t2t_mou_good	4.82
10	loc_og_t2m_mou_good	4.30
8	loc_ic_t2m_mou_action	3.88
6	std_og_t2t_mou_good	3.33
0	std_og_t2m_mou_good	2.99
2	last_day_rch_amt_action	1.87

	Feature	VIF
6	std_og_t2t_mou_action	7.83
8	onnet_mou_action	7.52
4	loc_ic_t2t_mou_action	5.09
3	loc_ic_t2t_mou_good	4.77
7	loc_ic_t2m_mou_action	3.79
9	loc_og_t2m_mou_good	3.39
5	std_og_t2t_mou_good	3.32
0	std_og_t2m_mou_good	2.99
1	std_og_t2m_mou_action	2.97
2	last_day_rch_amt_action	1.78

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	0.4469	0.034	13.013	0.000	0.380	0.514
std_og_t2m_mou_good	2.2718	0.068	33.371	0.000	2.138	2.405
std_og_t2m_mou_action	-3.0465	0.115	-26.493	0.000	-3.272	-2.821
last_day_rch_amt_action	-1.6410	0.069	-23.710	0.000	-1.777	-1.505
total_og_mou_action	-7.8446	0.283	-27.762	0.000	-8.398	-7.291
offnet_mou_action	3.9904	0.151	26.445	0.000	3.695	4.286
loc_ic_t2t_mou_good	1.1043	0.116	9.503	0.000	0.877	1.332
loc_ic_t2t_mou_action	-2.6940	0.137	-19.631	0.000	-2.963	-2.425
std_og_t2t_mou_good	2.0182	0.067	30.100	0.000	1.887	2.150
std_og_t2t_mou_action	-2.9703	0.119	-24.945	0.000	-3.204	-2.737
loc_ic_t2m_mou_good	-0.3792	0.169	-2.240	0.025	-0.711	-0.047
loc_ic_t2m_mou_action	-2.8245	0.178	-15.896	0.000	-3.173	-2.476
onnet_mou_action	3.7157	0.147	25.356	0.000	3.428	4.003
loc_og_t2m_mou_good	-2.0341	0.145	-14.062	0.000	-2.318	-1.751
std_og_mou_action	3.5698	0.228	15.685	0.000	3.124	4.016

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	0.5403	0.034	16.071	0.000	0.474	0.606
std_og_t2m_mou_good	2.3211	0.068	34.189	0.000	2.188	2.454
std_og_t2m_mou_action	-2.5191	0.107	-23.570	0.000	-2.729	-2.310
last_day_rch_amt_action	-1.4903	0.066	-22.502	0.000	-1.620	-1.360
offnet_mou_action	2.4788	0.123	20.230	0.000	2.239	2.719
loc_ic_t2t_mou_good	1.1698	0.115	10.191	0.000	0.945	1.395
loc_ic_t2t_mou_action	-3.2579	0.136	-24.033	0.000	-3.524	-2.992
std_og_t2t_mou_good	2.0582	0.067	30.899	0.000	1.928	2.189
std_og_t2t_mou_action	-1.7543	0.105	-16.681	0.000	-1.960	-1.548
loc_ic_t2m_mou_good	0.4874	0.165	2.954	0.003	0.164	0.811
loc_ic_t2m_mou_action	-4.2297	0.175	-24.204	0.000	-4.572	-3.887
onnet_mou_action	1.5633	0.108	14.530	0.000	1.352	1.774
loc_og_t2m_mou_good	-3.4691	0.133	-26.157	0.000	-3.729	-3.209
std_og_mou_action	-1.6838	0.118	-14.237	0.000	-1.916	-1.452

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	0.6332	0.033	19.177	0.000	0.568	0.698
std_og_t2m_mou_good	2.2238	0.067	33.074	0.000	2.092	2.356
std_og_t2m_mou_action	-3.0080	0.101	-29.793	0.000	-3.206	-2.810
last_day_rch_amt_action	-1.4945	0.066	-22.695	0.000	-1.624	-1.365
offnet_mou_action	1.9554	0.115	17.075	0.000	1.731	2.180
loc_ic_t2t_mou_good	1.1709	0.115	10.208	0.000	0.946	1.396
loc_ic_t2t_mou_action	-3.1436	0.135	-23.281	0.000	-3.408	-2.879
std_og_t2t_mou_good	2.0012	0.066	30.175	0.000	1.871	2.131
std_og_t2t_mou_action	-2.1915	0.101	-21.783	0.000	-2.389	-1.994
loc_ic_t2m_mou_good	0.3606	0.164	2.194	0.028	0.038	0.683
loc_ic_t2m_mou_action	-4.1269	0.175	-23.623	0.000	-4.469	-3.784
onnet_mou_action	0.9868	0.099	9.995	0.000	0.793	1.180
loc_og_t2m_mou_good	-3.1443	0.129	-24.397	0.000	-3.397	-2.892

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	0.6401	0.033	19.478	0.000	0.576	0.705
std_og_t2m_mou_good	2.2429	0.067	33.635	0.000	2.112	2.374
std_og_t2m_mou_action	-3.0093	0.101	-29.848	0.000	-3.207	-2.812
last_day_rch_amt_action	-1.4913	0.066	-22.665	0.000	-1.620	-1.362
offnet_mou_action	1.9253	0.113	16.973	0.000	1.703	2.148
loc_ic_t2t_mou_good	1.2518	0.109	11.513	0.000	1.039	1.465
loc_ic_t2t_mou_action	-3.2001	0.133	-24.106	0.000	-3.460	-2.940
std_og_t2t_mou_good	2.0120	0.066	30.411	0.000	1.882	2.142
std_og_t2t_mou_action	-2.1972	0.101	-21.854	0.000	-2.394	-2.000
loc_ic_t2m_mou_action	-3.9030	0.141	-27.741	0.000	-4.179	-3.627
onnet_mou_action	0.9795	0.099	9.929	0.000	0.786	1.173
loc_og_t2m_mou_good	-3.0284	0.117	-25.908	0.000	-3.258	-2.799

	Feature	VIF
4	loc_ic_t2t_mou_action	5.07
3	loc_ic_t2t_mou_good	4.64
6	loc_ic_t2m_mou_action	3.74
8	loc_og_t2m_mou_good	3.34
7	onnet_mou_action	3.07
0	std_og_t2m_mou_good	2.95
1	std_og_t2m_mou_action	2.77
5	std_og_t2t_mou_good	2.72
2	last_day_rch_amt_action	1.76

	Feature	VIF
2	total_og_mou_action	23.47
9	std_og_mou_action	19.84
8	onnet_mou_action	10.86
6	std_og_t2t_mou_action	10.10
3	offnet_mou_action	9.16
1	std_og_t2m_mou_action	8.39
7	loc_ic_t2m_mou_action	3.39
5	std_og_t2t_mou_good	3.05
4	loc_ic_t2t_mou_action	2.82
0	std_og_t2m_mou_good	2.78

	Feature	VIF
8	std_og_mou_action	10.90
5	std_og_t2t_mou_action	9.43
7	onnet_mou_action	8.14
1	std_og_t2m_mou_action	7.94
2	offnet_mou_action	6.39
6	loc_ic_t2m_mou_action	3.13
4	std_og_t2t_mou_good	3.03
0	std_og_t2m_mou_good	2.77
3	loc_ic_t2t_mou_action	2.76

	Feature	VIF
5	std_og_t2t_mou_action	7.70
7	onnet_mou_action	7.52
2	offnet_mou_action	6.23
1	std_og_t2m_mou_action	5.70
6	loc_ic_t2m_mou_action	3.07
4	std_og_t2t_mou_good	3.03
0	std_og_t2m_mou_good	2.77
3	loc_ic_t2t_mou_action	2.71

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_C	params	split0_test_score	split1_test_score	split2_test_score	split3_test_score	split4_test_score	mean_test_score	std_test_score	rank_test_score	split0_train_score	split1_train_score	split2_train_score	split3_train_score	split4_train_score	mean_train_score	std_train_score
0	0.038329	0.006521	0.004342	0.000482	0.01	{'C': 0.01}	0.863904	0.870831	0.858095	0.85447	0.866614	0.862783	0.005862	1	0.866302	0.863063	0.863391	0.864019	0.860933	0.863542	0.001727
1	0.035730	0.000773	0.004010	0.000534	0.1	{'C': 0.1}	0.862567	0.867920	0.855428	0.85317	0.865566	0.860930	0.005719	2	0.862814	0.859896	0.861547	0.863223	0.859543	0.861405	0.001487
2	0.037409	0.001791	0.003714	0.000164	1	{'C': 1}	0.861765	0.866596	0.856228	0.85343	0.864780	0.860560	0.005006	3	0.862155	0.859566	0.861217	0.862494	0.859742	0.861035	0.001204
3	0.038531	0.001883	0.003837	0.000360	10	{'C': 10}	0.861497	0.866331	0.855428	0.85343	0.864780	0.860293	0.005076	4	0.862089	0.859368	0.861151	0.862428	0.859676	0.860942	0.001237
4	0.038309	0.003175	0.003868	0.000541	100	{'C': 100}	0.861230	0.866331	0.855428	0.85343	0.864780	0.860240	0.005064	5	0.862089	0.859368	0.861151	0.862428	0.859808	0.860969	0.001211
5	0.045172	0.007460	0.004252	0.000610	1000	{'C': 1000}	0.861230	0.866331	0.855428	0.85343	0.864780	0.860240	0.005064	5	0.862089	0.859368	0.861151	0.862428	0.859808	0.860969	0.001211

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_max_depth	param_min_samples_leaf	param_min_samples_split	params	split0_test_score	split1_test_score	split2_test_score	split3_test_score	split4_test_score	mean_test_score	std_test_score	rank_test_score
0	0.366884	0.024186	0.004141	0.000300	5	50	50	{'max_depth': 5, 'min_samples_leaf': 50, 'min_...	0.847332	0.851862	0.902536	0.828051	0.852879	0.856532	0.024687	5
1	0.343261	0.005531	0.004130	0.000283	5	50	100	{'max_depth': 5, 'min_samples_leaf': 50, 'min_...	0.847332	0.851862	0.902536	0.828051	0.852879	0.856532	0.024687	5
2	0.339829	0.001362	0.003939	0.000045	5	100	50	{'max_depth': 5, 'min_samples_leaf': 100, 'min...	0.841521	0.846844	0.902800	0.828843	0.853936	0.854789	0.025370	7
3	0.343904	0.006597	0.004173	0.000214	5	100	100	{'max_depth': 5, 'min_samples_leaf': 100, 'min...	0.841521	0.846844	0.902800	0.828843	0.853936	0.854789	0.025370	7
4	0.558816	0.009316	0.004671	0.000379	10	50	50	{'max_depth': 10, 'min_samples_leaf': 50, 'min...	0.889065	0.880116	0.893819	0.875594	0.892235	0.886166	0.007102	1
5	0.570881	0.018938	0.004177	0.000186	10	50	100	{'max_depth': 10, 'min_samples_leaf': 50, 'min...	0.889065	0.880116	0.893819	0.875594	0.892235	0.886166	0.007102	1
6	0.536892	0.013234	0.004166	0.000256	10	100	50	{'max_depth': 10, 'min_samples_leaf': 100, 'mi...	0.879820	0.856615	0.893291	0.879028	0.870312	0.875813	0.012089	3
7	0.525380	0.010356	0.003997	0.000064	10	100	100	{'max_depth': 10, 'min_samples_leaf': 100, 'mi...	0.879820	0.856615	0.893291	0.879028	0.870312	0.875813	0.012089	3

	probability_cutoff	accuracy	sensitivity	specificity
0.0	0.0	0.764909	1.000000	0.529819
0.1	0.1	0.829248	0.994242	0.664254
0.2	0.2	0.862210	0.980561	0.743859
0.3	0.3	0.884000	0.956738	0.811262
0.4	0.4	0.892953	0.933548	0.852359
0.5	0.5	0.894591	0.911785	0.877397
0.6	0.6	0.892425	0.888648	0.896202
0.7	0.7	0.881279	0.843220	0.919339
0.8	0.8	0.842586	0.729227	0.955945
0.9	0.9	0.786012	0.598278	0.973747

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	PC10	PC11	PC12	PC13	PC14	PC15	PC16	PC17	PC18	PC19	PC20	PC21	PC22	PC23	PC24	PC25
0	-0.440440	-0.697849	-0.423436	0.050620	-0.139523	-0.366688	0.434308	0.390053	-0.156415	-0.048298	-0.627741	0.107684	0.065399	-0.099949	-0.148811	0.040262	-0.114201	0.024325	-0.030826	0.245024	0.253547	-0.002278	0.320436	0.072466	-0.083523
1	-0.263751	-0.202711	0.556703	-0.287392	0.072033	-0.940119	-0.644798	-0.528677	0.341877	0.288094	-0.260406	0.110319	-0.287287	-0.322323	0.119659	-0.078429	0.264867	0.491032	-0.406629	-0.342707	0.202471	0.101099	0.309833	-0.380015	0.139167
2	1.791507	-0.277309	-1.355858	-0.882396	-0.199006	0.045232	-0.469308	0.100522	-0.440546	-0.055695	-0.118625	0.820371	0.187019	0.151478	-0.655997	-0.384681	0.101573	0.154003	0.148422	-0.232870	-0.183229	0.176190	-0.129028	0.080129	0.260594
3	0.211306	-0.298223	1.121458	-0.252266	-0.194304	-0.072581	-0.453454	-0.450398	0.294236	-0.424393	-0.103451	-0.340706	0.431403	0.627435	-0.051703	0.363340	-0.260935	-0.359697	0.214642	0.358296	-0.256062	0.206435	0.117578	-0.269853	-0.039681
4	-0.827084	1.260245	0.324048	-0.827283	-0.600716	0.079294	-0.943727	0.005158	0.147385	-0.300392	-0.171619	0.105876	-0.016424	0.114586	-0.166453	-0.072910	0.064564	-0.266438	-0.105746	0.001399	0.008426	-0.087106	0.007990	-0.022491	0.077490

	probability_cutoff	accuracy	sensitivity	specificity
0.0	0.0	0.034175	1.000000	0.000000
0.1	0.1	0.394975	0.975610	0.374430
0.2	0.2	0.524172	0.930314	0.509802
0.3	0.3	0.620862	0.891986	0.611269
0.4	0.4	0.704573	0.839721	0.699790
0.5	0.5	0.775542	0.787456	0.775120
0.6	0.6	0.840438	0.710801	0.845025
0.7	0.7	0.905096	0.592334	0.916163
0.8	0.8	0.940462	0.452962	0.957712
0.9	0.9	0.956775	0.198606	0.983603

	probability_cutoff	accuracy	sensitivity	specificity
0.0	0.0	0.532627	0.926829	0.518678
0.1	0.1	0.652774	0.867596	0.645173
0.2	0.2	0.725292	0.832753	0.721489
0.3	0.3	0.790307	0.763066	0.791271
0.4	0.4	0.828650	0.714286	0.832696
0.5	0.5	0.851869	0.675958	0.858094
0.6	0.6	0.870326	0.665505	0.877574
0.7	0.7	0.887950	0.609756	0.897793
0.8	0.8	0.924506	0.505226	0.939342
0.9	0.9	0.944034	0.435540	0.962027

Telecom Churn Prediction¶

Exploratory Data Analysis¶

Loading & Exploring Data¶

High Value Customers¶

Cleaning Data¶

Missing Values¶

Unnecessary Features¶

Duplicate Values¶

Data Types¶

Target Variable¶

Predictor Variables¶

Outlier Treatment¶

Derive New Features¶

Combine data of June & July to create good phase features¶

New Feature: 'total_mou'¶

New feature: mou_decrease¶

New feature: arpu_decrease¶

New feature: rech_amt_decrease¶

New feature: rech_num_decrease¶

Univariate/ Bivariate Analysis¶

Preprocessing¶

Cleaning Data¶

Train-Test Split¶

Data Imbalance¶

Feature Scaling on Training Data¶

PCA Dimensionality Reduction¶

Model Training¶

Interpretable Models¶

Model 1¶

Model 2¶

Black Box Models¶

Model 3¶

Model 4¶

Model 5¶

Model Evaluation¶

Model 1¶

Model 2¶

Model 3¶

Model 4¶

Model 5¶

Final Models¶

Recommendations¶

Visual analysis of model's predictor variables¶

New feature: `mou_decrease`¶

New feature: `arpu_decrease`¶

New feature: `rech_amt_decrease`¶

New feature: `rech_num_decrease`¶

	Model	Used PCA	No. of Predictors	Accuracy (Train)	Accuracy (Test)	Sensitivity (Train)	Sesitivity (Test)
0	Model 1	No	8	0.794702	0.775542	0.894195	0.787456
1	Model 2	No	6	0.798030	0.766254	0.907718	0.808362
2	Model 3	Yes	25	0.824864	0.809121	0.906344	0.801394
3	Model 4	Yes	25	0.829248	0.790307	0.994242	0.763066
4	Model 5	Yes	25	0.819608	0.772089	0.948444	0.832753

Variables	Coefficients
onnet_mou_action	-0.8666
loc_ic_t2t_mou_action	-2.1579
std_og_t2t_mou_good	1.4393
loc_ic_t2m_mou_action	-5.7799
std_og_t2m_mou_good	2.4009
std_og_t2m_mou_action	-2.1431