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Objective:

To perform Exploratory Data Aanalysis on the data collected by a bank of its customers to identify whether a customer associated with the bank who had taken a loan has fallen into the list of defaulter or not.

Bank has collected information based on various parameters that would help in analysing that if a customer will tend to fall under the defaulter. Further to this, the modelling based on this analysis will help the bank company to predict the likelihood that customer will be a defaulter. Depending on the risk score bank can decide on their decion to approve or reject any future loan applications.

The EDA is carried out by following the classic procedural steps as below:

  • Data understanding
  • Data cleaning
  • Data manipulation
  • Analysis of Data (univariate/bivariate/multivariate)
    -Governing factors

Libraries required for analysis

# Importing the required libraries & Filtering out the warnings

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import os
import warnings
warnings.filterwarnings('ignore')
#changing the display options for rows and columns
pd.set_option('display.max_rows',200)
pd.set_option('display.max_columns',200)
pd.set_option('display.width',200)
pd.set_option('float_format','{:2f}'.format)

1. Reading and understanding the Data sets