FRA Milestone-1 - Predicting Credit Risk University of New South Wales FINANCE 123
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Course
FINANCE 123
Institution
FINANCE 123
FRA PROJECT (MILESTONE - 1)
Predicting Credit Risk ¶
Problem Statement
Businesses or companies can fall prey to default if they are not able to keep up their debt obligations. Defaults
will lead to a lower credit rating for the company which in turn reduces its chances of getting credit in the...
fra milestone 1 predicting credit risk university of new south wales finance 123
fra project milestone 1 predicting credit risk ¶ problem statement businesses or companies can fa
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FRA PROJECT (MILESTONE - 1)
Predicting Credit Risk ¶
Problem Statement
Businesses or companies can fall prey to default if they are not able to keep up their debt obligations. Defaults
will lead to a lower credit rating for the company which in turn reduces its chances of getting credit in the future
and may have to pay higher interests on existing debts as well as any new obligations. From an investor's point
of view, he would want to invest in a company if it is capable of handling its financial obligations, can grow
quickly, and is able to manage the growth scale.
A balance sheet is a financial statement of a company that provides a snapshot of what a company owns,
owes, and the amount invested by the shareholders. Thus, it is an important tool that helps evaluate the
performance of a business.
Data that is available includes information from the financial statement of the companies for the previous year
(2015). Also, information about the Networth of the company in the following year (2016) is provided which can
be used to drive the labeled field.
Explanation of data fields available in Data Dictionary, 'Credit Default Data Dictionary.xlsx'
In [175]:
# Importing the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns # for making plots with seaborn
import sklearn
color = sns.color_palette()
import sklearn.metrics as mertics
import scipy.stats as stats
import sklearn.metrics as metrics
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