text_plain_output_1.png
import pandas as pd

import seaborn as sns

import six

import warnings

import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
import pandas as pd
import re
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('talk', font_scale=0.65, rc={'grid.linewidth': 5})
pd.set_option('display.max_columns', 300)
pd.set_option('display.max_rows', 400)
from sklearn.linear_model import LogisticRegression, LinearRegression, LassoCV, Lasso, Ridge, LogisticRegressionCV
from sklearn.feature_selection import RFE
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA, IncrementalPCA
from sklearn.model_selection import GridSearchCV, cross_val_score, KFold, StratifiedKFold, RandomizedSearchCV
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, precision_score, recall_score
from sklearn.metrics import precision_recall_curve, roc_auc_score, roc_curve
from imblearn.over_sampling import SMOTE, RandomOverSampler, ADASYN
from sklearn.preprocessing import StandardScaler, MinMaxScaler, QuantileTransformer
from scipy.stats import skew
from fancyimpute import IterativeImputer, KNN
from sklearn.impute import IterativeImputer
from sklearn.impute import KNNImputer
from sklearn.naive_bayes import MultinomialNB, BernoulliNB, GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier
from sklearn.svm import SVC
from xgboost import XGBClassifier
from statsmodels.stats.outliers_influence import variance_inflation_factor
from sklearn.feature_selection import RFE
import statsmodels.api as sm

telecom = pd.read_csv('../input/telecom-churn-dataset/telecom_churn_data.csv')

telecom.describe(percentiles=[0.25, 0.5, 0.75, 0.99])