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 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


import warnings
warnings.filterwarnings('ignore')



In [2]: 

import os

,In [3]: 

os.getcwd()


Out[3]:

'C:\\Users\\Hakers Zone\\Desktop\\Data Science\\Great Lakes\\Core Course fil
es\\11. Finance and Risk Analytics\\FRA Milestone -1\\Working files'


Importing the dataset

In [4]: 

df = pd.read_excel('Company_Data2015-1.xlsx')


In [5]: 

#Glimpse of Data
df.head()


Out[5]:

Networth Equity Net
Capital Total Gross C
Co_Code Co_Name Next Paid Networth Working
Employed Debt Block
Year Up Capital

0 16974 Hind.Cables -8021.60 419.36 -7027.48 -1007.24 5936.03 474.30 -1076.34

Tata Tele.
1 21214 -3986.19 1954.93 -2968.08 4458.20 7410.18 9070.86 -1098.88
Mah.

ABG
2 14852 -3192.58 53.84 506.86 7714.68 6944.54 1281.54 4496.25 9
Shipyard

3 2439 GTL -3054.51 157.30 -623.49 2353.88 2326.05 1033.69 -2612.42 1

Bharati
4 23505 -2967.36 50.30 -1070.83 4675.33 5740.90 1084.20 1836.23 4
Defence


5 rows × 67 columns




Fixing messy column names (containing spaces) for ease of use

In [6]: 

df.columns = df.columns.str.replace(' ', '_').str.replace('(', '').str.replace(')', '').str

,In [7]: 

#### Checking top 5 rows again
df.head()


Out[7]:

Co_Code Co_Name Networth_Next_Year Equity_Paid_Up Networth Capital_Employed Tot

0 16974 Hind.Cables -8021.60 419.36 -7027.48 -1007.24

Tata Tele.
1 21214 -3986.19 1954.93 -2968.08 4458.20
Mah.

ABG
2 14852 -3192.58 53.84 506.86 7714.68
Shipyard

3 2439 GTL -3054.51 157.30 -623.49 2353.88

Bharati
4 23505 -2967.36 50.30 -1070.83 4675.33
Defence


5 rows × 67 columns




In [69]: 

df.dtypes.value_counts()


Out[69]:

float64 63
int64 3
object 1
dtype: int64



Now, let us check the number of rows (observations) and the number of columns (variables)

In [8]: 

print('The number of rows (observations) is =',df.shape[0],'\n''The number of columns (vari


The number of rows (observations) is = 3586
The number of columns (variables) is = 67



Checking datatype of all columns

, In [9]: 

df.info()


<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3586 entries, 0 to 3585
Data columns (total 67 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Co_Code 3586 non-null int64
1 Co_Name 3586 non-null object
2 Networth_Next_Year 3586 non-null float64
3 Equity_Paid_Up 3586 non-null float64
4 Networth 3586 non-null float64
5 Capital_Employed 3586 non-null float64
6 Total_Debt 3586 non-null float64
7 Gross_Block_ 3586 non-null float64
8 Net_Working_Capital_ 3586 non-null float64
9 Current_Assets_ 3586 non-null float64
10 Current_Liabilities_and_Provisions_ 3586 non-null float64
11 Total_Assets_to_Liabilities_ 3586 non-null float64
12 Gross_Sales 3586 non-null float64
13 Net_Sales 3586 non-null float64
14 Other_Income 3586 non-null float64
15 Value_Of_Output 3586 non-null float64
16 Cost_of_Production 3586 non-null float64
17 Selling_Cost 3586 non-null float64
18 PBIDT 3586 non-null float64
19 PBDT 3586 non-null float64
20 PBIT 3586 non-null float64
21 PBT 3586 non-null float64
22 PAT 3586 non-null float64
23 Adjusted_PAT 3586 non-null float64
24 CP 3586 non-null float64
25 Revenue_earnings_in_forex 3586 non-null float64
26 Revenue_expenses_in_forex 3586 non-null float64
27 Capital_expenses_in_forex 3586 non-null float64
28 Book_Value_Unit_Curr 3586 non-null float64
29 Book_Value_Adj._Unit_Curr 3582 non-null float64
30 Market_Capitalisation 3586 non-null float64
31 CEPS_annualised_Unit_Curr 3586 non-null float64
32 Cash_Flow_From_Operating_Activities 3586 non-null float64
33 Cash_Flow_From_Investing_Activities 3586 non-null float64
34 Cash_Flow_From_Financing_Activities 3586 non-null float64
35 ROG-Net_Worth_perc 3586 non-null float64
36 ROG-Capital_Employed_perc 3586 non-null float64
37 ROG-Gross_Block_perc 3586 non-null float64
38 ROG-Gross_Sales_perc 3586 non-null float64
39 ROG-Net_Sales_perc 3586 non-null float64
40 ROG-Cost_of_Production_perc 3586 non-null float64
41 ROG-Total_Assets_perc 3586 non-null float64
42 ROG-PBIDT_perc 3586 non-null float64
43 ROG-PBDT_perc 3586 non-null float64
44 ROG-PBIT_perc 3586 non-null float64
45 ROG-PBT_perc 3586 non-null float64
46 ROG-PAT_perc 3586 non-null float64
47 ROG-CP_perc 3586 non-null float64
48 ROG-Revenue_earnings_in_forex_perc 3586 non-null float64
49 ROG-Revenue_expenses_in_forex_perc 3586 non-null float64
50 ROG-Market_Capitalisation_perc 3586 non-null float64