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#Elemental Library
import pandas as pd 
import numpy as np 
import math 

import re 
from scipy import stats
from scipy.stats import norm, skew
import string

#Visualization
import matplotlib.pyplot as plt 
import seaborn as sns 

import matplotlib.gridspec as gridspec
%matplotlib inline
sns.set_style('whitegrid')
import cufflinks as cf
cf.go_offline()
from IPython.display import display
from PIL import Image

import warnings
warnings.filterwarnings("ignore")

Greeting

As Always I'm following the OSEM Methodology..!!! Hopefully you can have some new knowledge from Here..!!

path="../input/loanimages/os.png"
display(Image.open(path))
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1705x1315 at 0x7FA22468EB50>

1. Obtain Data

train = pd.read_csv('../input/my-dataset/credit_train.csv')
print("----------Technical Information-------------")
print('Data Set Shape = {}'.format(train.shape))
print('Data Set Memory Usage = {:.2f} MB'.format(train.memory_usage().sum()/1024**2))
print("Data columns type""\n""{}".format(train.dtypes.value_counts()))
train.describe()
----------Technical Information------------- Data Set Shape = (100514, 19) Data Set Memory Usage = 14.57 MB Data columns type float64 12 object 7 dtype: int64