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IPO project - EDA

The dataset consists of 20,452 rows, each representing an IPO. \ Each row is comprised of 22 columns providing various information about the company, including country, deal value of the IPO, financial and economic details before the IPO, sector of the company, stock price after IPO, stock price after one week, and stock price after one month.

(For more info on dataset, check https://github.com/EdoPedrocchi/RicercaMente/blob/main/Projects/IPO/indexIPO.md)

import pandas as pd
import os
import matplotlib.pyplot as plt
import category_encoders as ce
import numpy as np

df = pd.read_excel("../IPOdataset.xlsx")

df.head(10)

	Target country code	Deal value\nth USD	Target business description(s)	Completed date	Deal equity value\nth USD	Pre-deal target operating revenue/turnover\nth USD Last avail. yr	Pre-deal target EBITDA\nth USD Last avail. yr	Pre-deal target EBIT\nth USD Last avail. yr	Pre-deal target profit before tax\nth USD Last avail. yr	Pre-deal target profit after tax\nth USD Last avail. yr	...	Pre-deal target net assets\nth USD Last avail. yr	Pre-deal target current liabilities\nth USD Last avail. yr	Pre-deal target shareholders funds\nth USD Last avail. yr	Pre-deal target market capitalisation (Last available year)\nth USD	Pre-deal target number of months\nLast avail. yr	Target stock price 1 month after completion\nUSD	Target major sector	Target stock price after completion\nUSD	Target stock price after completion\nUSD.1	Target stock price 1 week after completion\nUSD
0	US	113000000	Cigarette manufacturer	2008-03-28	n.a.	22810000	9850000	8894000	8884000	6314000	...	15595000	8091000	15595000	n.a.	12	52.02	Food, beverages, tobacco	52.02	52.02	52.02
1	US	47000000	Online payment processing services holding com...	2015-07-17	47000000	8025000	1784000	1268000	1261000	419000	...	8248000	13283000	8248000	n.a.	12	38.73	Other services	40.470001	40.470001	37
2	IE	22000000	Automotive dashboard displays manufacturer, Au...	2016-10-31	22000000	n.a.	n.a.	n.a.	n.a.	n.a.	...	n.a.	n.a.	n.a.	n.a.	n.a.	53.560001	Machinery, equipment, furniture, recycling	45.299999	45.299999	44.599998
3	US	17640000	Crude oil pipeline operator, Natural gas distr...	2006-12-14	n.a.	9454000	2589000	1853000	2335000	1409000	...	12253000	7314000	11504000	n.a.	12	25.85	Gas, Water, Electricity	28.620001	28.620001	28.620001
4	IT	16446774.375436	Electricity distribution services, Electricity...	1999-11-02	16446774.375436	n.a.	n.a.	n.a.	n.a.	n.a.	...	n.a.	n.a.	n.a.	n.a.	n.a.	5.233764	Gas, Water, Electricity	5.233764	5.233764	5.233764
5	US	13000000	Print and Internet yellow pages directories pu...	2006-11-20	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	...	n.a.	n.a.	n.a.	n.a.	n.a.	100	NaN	100	100	100
6	FR	12471846.893149	Investment services, Telecommunications services	1997-10-21	12471846.893149	n.a.	n.a.	n.a.	n.a.	n.a.	...	n.a.	n.a.	n.a.	n.a.	n.a.	34.754611	Post and telecommunications	34.754611	34.754611	34.754611
7	BE	11089670.828604	Residential property management services, Resi...	2006-10-23	11089670.828604	4202.295371	1509.648127	1334.342288	-204.31328	-279.984866	...	71882.961281	4372.556438	71880.438895	n.a.	7	57.050868	Other services	57.631681	57.631681	57.631681
8	GB	10499455.139847	Media services, Motion picture production serv...	2004-02-02	10499455.139847	3183799.491464	477660.733745	319651.289502	54486.015256	-12713.40356	...	3007628.042136	937159.462405	3005811.841627	6133727.172356	n.a.	2.543876	Other services	2.678734	2.678734	2.562684
9	BM	9290000	Broadband internet services provider, Telecomm...	2017-12-29	9290000	n.a.	n.a.	n.a.	n.a.	n.a.	...	n.a.	n.a.	n.a.	n.a.	n.a.	23.48	Post and telecommunications	21.58	21.58	21.09

10 rows × 22 columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20452 entries, 0 to 20451
Data columns (total 22 columns):
 #   Column                                                              Non-Null Count  Dtype         
---  ------                                                              --------------  -----         
 0   Target country code                                                 20452 non-null  object        
 1   Deal value
th USD                                                   20448 non-null  object        
 2   Target business description(s)                                      20439 non-null  object        
 3   Completed date                                                      20448 non-null  datetime64[ns]
 4   Deal equity value
th USD                                            20448 non-null  object        
 5   Pre-deal target operating revenue/turnover
th USD Last avail. yr    20452 non-null  object        
 6   Pre-deal target EBITDA
th USD Last avail. yr                        20452 non-null  object        
 7   Pre-deal target EBIT
th USD Last avail. yr                          20452 non-null  object        
 8   Pre-deal target profit before tax
th USD Last avail. yr             20452 non-null  object        
 9   Pre-deal target profit after tax
th USD Last avail. yr              20452 non-null  object        
 10  Pre-deal target net profit
th USD Last avail. yr                    20452 non-null  object        
 11  Pre-deal target total assets
th USD Last avail. yr                  20452 non-null  object        
 12  Pre-deal target net assets
th USD Last avail. yr                    20452 non-null  object        
 13  Pre-deal target current liabilities
th USD Last avail. yr           20452 non-null  object        
 14  Pre-deal target shareholders funds
th USD Last avail. yr            20452 non-null  object        
 15  Pre-deal target market capitalisation (Last available year)
th USD  20452 non-null  object        
 16  Pre-deal target number of months
Last avail. yr                     20452 non-null  object        
 17  Target stock price 1 month after completion
USD                     20452 non-null  object        
 18  Target major sector                                                 19435 non-null  object        
 19  Target stock price after completion
USD                             20452 non-null  object        
 20  Target stock price after completion
USD.1                           20452 non-null  object        
 21  Target stock price 1 week after completion
USD                      20452 non-null  object        
dtypes: datetime64[ns](1), object(21)
memory usage: 3.4+ MB

We've observed both null values and "n.a." values in the dataset, which are inappropriate. Let's start by counting the total number of non-valid values in the dataset.

print(df.isnull().sum())

Target country code                                                       0
Deal value\nth USD                                                        4
Target business description(s)                                           13
Completed date                                                            4
Deal equity value\nth USD                                                 4
Pre-deal target operating revenue/turnover\nth USD Last avail. yr         0
Pre-deal target EBITDA\nth USD Last avail. yr                             0
Pre-deal target EBIT\nth USD Last avail. yr                               0
Pre-deal target profit before tax\nth USD Last avail. yr                  0
Pre-deal target profit after tax\nth USD Last avail. yr                   0
Pre-deal target net profit\nth USD Last avail. yr                         0
Pre-deal target total assets\nth USD Last avail. yr                       0
Pre-deal target net assets\nth USD Last avail. yr                         0
Pre-deal target current liabilities\nth USD Last avail. yr                0
Pre-deal target shareholders funds\nth USD Last avail. yr                 0
Pre-deal target market capitalisation (Last available year)\nth USD       0
Pre-deal target number of months\nLast avail. yr                          0
Target stock price 1 month after completion\nUSD                          0
Target major sector                                                    1017
Target stock price after completion\nUSD                                  0
Target stock price after completion\nUSD.1                                0
Target stock price 1 week after completion\nUSD                           0
dtype: int64

(df == 'n.a.').sum()

Target country code                                                        0
Deal value\nth USD                                                      5057
Target business description(s)                                             0
Completed date                                                             0
Deal equity value\nth USD                                               5706
Pre-deal target operating revenue/turnover\nth USD Last avail. yr      12326
Pre-deal target EBITDA\nth USD Last avail. yr                          13538
Pre-deal target EBIT\nth USD Last avail. yr                            11866
Pre-deal target profit before tax\nth USD Last avail. yr               11694
Pre-deal target profit after tax\nth USD Last avail. yr                11963
Pre-deal target net profit\nth USD Last avail. yr                      14974
Pre-deal target total assets\nth USD Last avail. yr                    11451
Pre-deal target net assets\nth USD Last avail. yr                      15005
Pre-deal target current liabilities\nth USD Last avail. yr             11590
Pre-deal target shareholders funds\nth USD Last avail. yr              11443
Pre-deal target market capitalisation (Last available year)\nth USD    19423
Pre-deal target number of months\nLast avail. yr                       11226
Target stock price 1 month after completion\nUSD                        3528
Target major sector                                                        0
Target stock price after completion\nUSD                                3507
Target stock price after completion\nUSD.1                              3507
Target stock price 1 week after completion\nUSD                         3509
dtype: int64

(df["Deal value\nth USD"] == df["Deal equity value\nth USD"]).sum()

19742

(df["Target stock price after completion\nUSD"] == df["Target stock price after completion\nUSD.1"]).sum()

20452

Some more information that we find:

• Columns containing "Pre-deal" information are populated with "n.a." values instead of integer type values, making them unsuitable for analysis. They will be excluded from our analysis.
• The column "Pre-deal target market capitalization" contains approximately 19,500 null values. We can remove this column.
• Information regarding target stock prices (after completion, 1 week, and 1 month) are essential for our analysis, so rows with null values in these columns will be removed.
• The column "Deal equity value" duplicates the information in the "Deal value" column and can be dropped.
• The column "Target stock price after completion\nUSD.1" is a duplicate and will be dropped as well.

To handle invalid values, one approach is to remove all rows with these types of values (NaN or "n.a."). \ Following this approach, the final dataset will contain approximately 4,000 rows.

Another approach could involve replacing null values with the mean value for each column. However, for now, we will investigate the first approach.

## Remove columns -> "Pre-deal market capitalisation",
##                   "Deal equity value","Target businees description","Target stock price after completion\nUSD.1"

df = df.drop(columns=["Pre-deal target market capitalisation (Last available year)\nth USD", \
                      "Deal equity value\nth USD", "Target business description(s)", \
                      "Target stock price after completion\nUSD.1"])
df.head()

	Target country code	Deal value\nth USD	Completed date	Pre-deal target operating revenue/turnover\nth USD Last avail. yr	Pre-deal target EBITDA\nth USD Last avail. yr	Pre-deal target EBIT\nth USD Last avail. yr	Pre-deal target profit before tax\nth USD Last avail. yr	Pre-deal target profit after tax\nth USD Last avail. yr	Pre-deal target net profit\nth USD Last avail. yr	Pre-deal target total assets\nth USD Last avail. yr	Pre-deal target net assets\nth USD Last avail. yr	Pre-deal target current liabilities\nth USD Last avail. yr	Pre-deal target shareholders funds\nth USD Last avail. yr	Pre-deal target number of months\nLast avail. yr	Target stock price 1 month after completion\nUSD	Target major sector	Target stock price after completion\nUSD	Target stock price 1 week after completion\nUSD
0	US	113000000	2008-03-28	22810000	9850000	8894000	8884000	6314000	6038000	31777000	15595000	8091000	15595000	12	52.02	Food, beverages, tobacco	52.02	52.02
1	US	47000000	2015-07-17	8025000	1784000	1268000	1261000	419000	419000	21917000	8248000	13283000	8248000	12	38.73	Other services	40.470001	37
2	IE	22000000	2016-10-31	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	53.560001	Machinery, equipment, furniture, recycling	45.299999	44.599998
3	US	17640000	2006-12-14	9454000	2589000	1853000	2335000	1409000	674000	35056000	12253000	7314000	11504000	12	25.85	Gas, Water, Electricity	28.620001	28.620001
4	IT	16446774.375436	1999-11-02	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	n.a.	5.233764	Gas, Water, Electricity	5.233764	5.233764

We replace the "n.a." values with null values and then drop all the rows containing these null values.

df_withoutNull = df.replace("n.a.",np.nan,inplace=False)

print(df_withoutNull.isnull().sum())

Target country code                                                      0
Deal value\nth USD                                                    5061
Completed date                                                           4
Pre-deal target operating revenue/turnover\nth USD Last avail. yr    12326
Pre-deal target EBITDA\nth USD Last avail. yr                        13538
Pre-deal target EBIT\nth USD Last avail. yr                          11866
Pre-deal target profit before tax\nth USD Last avail. yr             11694
Pre-deal target profit after tax\nth USD Last avail. yr              11963
Pre-deal target net profit\nth USD Last avail. yr                    14974
Pre-deal target total assets\nth USD Last avail. yr                  11451
Pre-deal target net assets\nth USD Last avail. yr                    15005
Pre-deal target current liabilities\nth USD Last avail. yr           11590
Pre-deal target shareholders funds\nth USD Last avail. yr            11443
Pre-deal target number of months\nLast avail. yr                     11226
Target stock price 1 month after completion\nUSD                      3528
Target major sector                                                   1017
Target stock price after completion\nUSD                              3507
Target stock price 1 week after completion\nUSD                       3509
dtype: int64

df_withoutNull.dropna(inplace=True)
df_withoutNull.isnull().sum()

Target country code                                                  0
Deal value\nth USD                                                   0
Completed date                                                       0
Pre-deal target operating revenue/turnover\nth USD Last avail. yr    0
Pre-deal target EBITDA\nth USD Last avail. yr                        0
Pre-deal target EBIT\nth USD Last avail. yr                          0
Pre-deal target profit before tax\nth USD Last avail. yr             0
Pre-deal target profit after tax\nth USD Last avail. yr              0
Pre-deal target net profit\nth USD Last avail. yr                    0
Pre-deal target total assets\nth USD Last avail. yr                  0
Pre-deal target net assets\nth USD Last avail. yr                    0
Pre-deal target current liabilities\nth USD Last avail. yr           0
Pre-deal target shareholders funds\nth USD Last avail. yr            0
Pre-deal target number of months\nLast avail. yr                     0
Target stock price 1 month after completion\nUSD                     0
Target major sector                                                  0
Target stock price after completion\nUSD                             0
Target stock price 1 week after completion\nUSD                      0
dtype: int64

df_withoutNull.info()

<class 'pandas.core.frame.DataFrame'>
Index: 3565 entries, 0 to 15385
Data columns (total 18 columns):
 #   Column                                                            Non-Null Count  Dtype         
---  ------                                                            --------------  -----         
 0   Target country code                                               3565 non-null   object        
 1   Deal value
th USD                                                 3565 non-null   float64       
 2   Completed date                                                    3565 non-null   datetime64[ns]
 3   Pre-deal target operating revenue/turnover
th USD Last avail. yr  3565 non-null   float64       
 4   Pre-deal target EBITDA
th USD Last avail. yr                      3565 non-null   float64       
 5   Pre-deal target EBIT
th USD Last avail. yr                        3565 non-null   float64       
 6   Pre-deal target profit before tax
th USD Last avail. yr           3565 non-null   float64       
 7   Pre-deal target profit after tax
th USD Last avail. yr            3565 non-null   float64       
 8   Pre-deal target net profit
th USD Last avail. yr                  3565 non-null   float64       
 9   Pre-deal target total assets
th USD Last avail. yr                3565 non-null   float64       
 10  Pre-deal target net assets
th USD Last avail. yr                  3565 non-null   float64       
 11  Pre-deal target current liabilities
th USD Last avail. yr         3565 non-null   float64       
 12  Pre-deal target shareholders funds
th USD Last avail. yr          3565 non-null   float64       
 13  Pre-deal target number of months
Last avail. yr                   3565 non-null   float64       
 14  Target stock price 1 month after completion
USD                   3565 non-null   float64       
 15  Target major sector                                               3565 non-null   object        
 16  Target stock price after completion
USD                           3565 non-null   float64       
 17  Target stock price 1 week after completion
USD                    3565 non-null   float64       
dtypes: datetime64[ns](1), float64(15), object(2)
memory usage: 529.2+ KB

After removing the rows with null values, the dimensions of our dataset have decreased, and now we have around 3.5k rows.

Add Column "Success"

Now we add the column "Success" (used as target label) based on the prices of the stocks on the IPO completion, after 1 week and after 1 month.

• y = 1 if "Target stock price after completion USD" < "Target stock price 1 week after completion USD" < "Target stock price 1 month after completion USD" , The IPO is a "succes"
• y = 0 if "Target stock price after completion USD" = "Target stock price 1 week after completion USD" = "Target stock price 1 month after completion USD" , The IPO is "constant"
• y = -1 if "Target stock price after completion USD" > "Target stock price 1 week after completion USD" > "Target stock price 1 month after completion USD" , The IPO is a "failure"

def IPOsuccess(row):
    
    if int(row["Target stock price after completion\nUSD"]) < int(row["Target stock price 1 week after completion\nUSD"]) and \
       int(row["Target stock price 1 week after completion\nUSD"]) < int(row["Target stock price 1 month after completion\nUSD"]):
        
        return 1
    
    elif int(row["Target stock price after completion\nUSD"]) > int(row["Target stock price 1 week after completion\nUSD"]) and \
       int(row["Target stock price 1 week after completion\nUSD"]) > int(row["Target stock price 1 month after completion\nUSD"]):
        return -1
    
    return 0

df_withoutNull["Y"] = df_withoutNull.apply(IPOsuccess,axis=1)

df_withoutNull.head(10)

	Target country code	Deal value\nth USD	Completed date	Pre-deal target operating revenue/turnover\nth USD Last avail. yr	Pre-deal target EBITDA\nth USD Last avail. yr	Pre-deal target EBIT\nth USD Last avail. yr	Pre-deal target profit before tax\nth USD Last avail. yr	Pre-deal target profit after tax\nth USD Last avail. yr	Pre-deal target net profit\nth USD Last avail. yr	Pre-deal target total assets\nth USD Last avail. yr	Pre-deal target net assets\nth USD Last avail. yr	Pre-deal target current liabilities\nth USD Last avail. yr	Pre-deal target shareholders funds\nth USD Last avail. yr	Pre-deal target number of months\nLast avail. yr	Target stock price 1 month after completion\nUSD	Target major sector	Target stock price after completion\nUSD	Target stock price 1 week after completion\nUSD	Y
0	US	1.130000e+08	2008-03-28	2.281000e+07	9.850000e+06	8.894000e+06	8.884000e+06	6.314000e+06	6.038000e+06	3.177700e+07	1.559500e+07	8.091000e+06	1.559500e+07	12.0	52.020000	Food, beverages, tobacco	52.020000	52.020000	0
1	US	4.700000e+07	2015-07-17	8.025000e+06	1.784000e+06	1.268000e+06	1.261000e+06	4.190000e+05	4.190000e+05	2.191700e+07	8.248000e+06	1.328300e+07	8.248000e+06	12.0	38.730000	Other services	40.470001	37.000000	0
3	US	1.764000e+07	2006-12-14	9.454000e+06	2.589000e+06	1.853000e+06	2.335000e+06	1.409000e+06	6.740000e+05	3.505600e+07	1.225300e+07	7.314000e+06	1.150400e+07	12.0	25.850000	Gas, Water, Electricity	28.620001	28.620001	0
7	BE	1.108967e+07	2006-10-23	4.202295e+03	1.509648e+03	1.334342e+03	-2.043133e+02	-2.799849e+02	-2.799849e+02	1.557145e+05	7.188296e+04	4.372556e+03	7.188044e+04	7.0	57.050868	Other services	57.631681	57.631681	0
11	US	8.680000e+06	2001-06-13	2.292200e+07	4.874000e+06	3.840000e+06	3.415000e+06	2.001000e+06	2.001000e+06	5.207100e+07	1.404800e+07	7.590000e+06	1.404800e+07	12.0	35.610001	Food, beverages, tobacco	35.610001	35.610001	0
15	US	6.354828e+06	2008-05-07	5.695000e+06	1.236000e+06	1.080000e+06	8.170000e+05	4.950000e+05	4.970000e+05	1.052800e+07	5.021000e+06	1.135000e+06	5.021000e+06	12.0	22.540001	Food, beverages, tobacco	24.950001	24.950001	0
22	NL	4.600000e+06	2012-06-12	3.767048e+06	5.978655e+05	4.724712e+05	5.431359e+05	3.823868e+05	3.823868e+05	6.664664e+06	4.751552e+06	1.663429e+06	4.751552e+06	12.0	11.110000	Food, beverages, tobacco	10.674000	10.674000	0
23	US	4.500000e+06	2006-09-05	4.472832e+06	5.478040e+05	4.336000e+05	4.163200e+05	3.224930e+05	3.224930e+05	4.903886e+06	3.229134e+06	1.624765e+06	3.229134e+06	12.0	5.787500	Textiles, wearing apparel, leather	5.277500	5.482500	0
44	US	3.034222e+06	2016-10-03	9.701000e+05	3.267000e+05	2.798000e+05	2.787000e+05	2.199000e+05	2.120000e+05	1.043800e+06	-1.034000e+05	1.043000e+05	-1.373000e+05	12.0	22.620001	Chemicals, rubber, plastics, non-metallic prod...	24.490000	23.490000	-1
45	US	3.000000e+06	2006-11-16	2.373100e+06	2.182010e+05	1.802010e+05	1.801090e+05	1.101930e+05	1.101930e+05	1.338841e+06	1.005967e+06	2.911820e+05	1.005967e+06	12.0	7.760000	Wholesale & retail trade	7.760000	7.760000	0

df_withoutNull["Y"].value_counts()

Y
 0    3193
-1     259
 1     113
Name: count, dtype: int64

We know that only 113 company out of 3500 have had a success IPO

Let's find some simple statistics about the distribution of the data about the IPO

1. Number of IPO per states

# number of IPO per states (without considering NaN values) -> first 15 countries
IPOperStates = df_withoutNull["Target country code"].value_counts()
plt.bar(IPOperStates.keys()[:15],IPOperStates.values[:15])

<BarContainer object of 15 artists>

2. Number of IPOs per year (from 1997 to 2023)

df_year = df_withoutNull["Completed date"].map(lambda x: str(x).split("-")[0])

df_year = df_year[df_year != "NaT"]
#sorted(dict(df_year.value_counts()).items())
## 1995, 1996 missing, we remove year 1994 (just one row)

df_year = df_year[df_year != "1994"]
IPOcountPerYear = dict(sorted(dict(df_year.value_counts()).items()))
for i in IPOcountPerYear.items():
    print(i)

plt.figure(figsize=(15,9))
plt.bar(IPOcountPerYear.keys(),IPOcountPerYear.values())

('1997', 6)
('1998', 6)
('1999', 13)
('2000', 135)
('2001', 41)
('2002', 53)
('2003', 73)
('2004', 177)
('2005', 187)
('2006', 263)
('2007', 326)
('2008', 159)
('2009', 104)
('2010', 179)
('2011', 230)
('2012', 226)
('2013', 191)
('2014', 183)
('2015', 123)
('2016', 73)
('2017', 107)
('2018', 109)
('2019', 105)
('2020', 144)
('2021', 232)
('2022', 95)
('2023', 25)

<BarContainer object of 27 artists>

3. Number of IPO for Target sector

IPOcountPerSector = df_withoutNull["Target major sector"].value_counts()

print(IPOcountPerSector)

plt.figure(figsize=(18,13))
plt.bar(IPOcountPerSector.keys(),IPOcountPerSector.values)

plt.xticks(rotation=45,ha='right')

plt.show()

Target major sector
Other services                                        1626
Machinery, equipment, furniture, recycling             519
Metals & metal products                                334
Chemicals, rubber, plastics, non-metallic products     217
Wholesale & retail trade                               181
Primary Sector (agriculture, mining, etc.)             172
Publishing, printing                                   139
Food, beverages, tobacco                                59
Hotels & restaurants                                    57
Construction                                            53
Transport                                               52
Education, Health                                       40
Post and telecommunications                             38
Textiles, wearing apparel, leather                      32
Gas, Water, Electricity                                 26
Insurance companies                                     10
Wood, cork, paper                                        6
Public administration and defence                        3
Banks                                                    1
Name: count, dtype: int64

Encode categorical variable with binary encoding

We first remove the column "Completed date".

df_withoutNull.drop(columns=["Completed date"],inplace=True)

Let's encode the categorical variable (Target country code, Target major sector) by using the binary encoding

encoder = ce.BinaryEncoder(cols=['Target country code',"Target major sector"])
df_encoded = encoder.fit_transform(df_withoutNull)

df_encoded.head()

	Target country code_0	Target country code_1	Target country code_2	Target country code_3	Target country code_4	Deal value\nth USD	Pre-deal target operating revenue/turnover\nth USD Last avail. yr	Pre-deal target EBITDA\nth USD Last avail. yr	Pre-deal target EBIT\nth USD Last avail. yr	Pre-deal target profit before tax\nth USD Last avail. yr	...	Pre-deal target number of months\nLast avail. yr	Target stock price 1 month after completion\nUSD	Target major sector_0	Target major sector_1	Target major sector_2	Target major sector_3	Target major sector_4	Target stock price after completion\nUSD	Target stock price 1 week after completion\nUSD	Y
0	0	0	0	0	1	1.130000e+08	2.281000e+07	9.850000e+06	8.894000e+06	8.884000e+06	...	12.0	52.020000	0	0	0	0	1	52.020000	52.020000	0
1	0	0	0	0	1	4.700000e+07	8.025000e+06	1.784000e+06	1.268000e+06	1.261000e+06	...	12.0	38.730000	0	0	0	1	0	40.470001	37.000000	0
3	0	0	0	0	1	1.764000e+07	9.454000e+06	2.589000e+06	1.853000e+06	2.335000e+06	...	12.0	25.850000	0	0	0	1	1	28.620001	28.620001	0
7	0	0	0	1	0	1.108967e+07	4.202295e+03	1.509648e+03	1.334342e+03	-2.043133e+02	...	7.0	57.050868	0	0	0	1	0	57.631681	57.631681	0
11	0	0	0	0	1	8.680000e+06	2.292200e+07	4.874000e+06	3.840000e+06	3.415000e+06	...	12.0	35.610001	0	0	0	0	1	35.610001	35.610001	0

5 rows × 26 columns

df_encoded.info()

<class 'pandas.core.frame.DataFrame'>
Index: 3565 entries, 0 to 15385
Data columns (total 26 columns):
 #   Column                                                            Non-Null Count  Dtype  
---  ------                                                            --------------  -----  
 0   Target country code_0                                             3565 non-null   int64  
 1   Target country code_1                                             3565 non-null   int64  
 2   Target country code_2                                             3565 non-null   int64  
 3   Target country code_3                                             3565 non-null   int64  
 4   Target country code_4                                             3565 non-null   int64  
 5   Deal value
th USD                                                 3565 non-null   float64
 6   Pre-deal target operating revenue/turnover
th USD Last avail. yr  3565 non-null   float64
 7   Pre-deal target EBITDA
th USD Last avail. yr                      3565 non-null   float64
 8   Pre-deal target EBIT
th USD Last avail. yr                        3565 non-null   float64
 9   Pre-deal target profit before tax
th USD Last avail. yr           3565 non-null   float64
 10  Pre-deal target profit after tax
th USD Last avail. yr            3565 non-null   float64
 11  Pre-deal target net profit
th USD Last avail. yr                  3565 non-null   float64
 12  Pre-deal target total assets
th USD Last avail. yr                3565 non-null   float64
 13  Pre-deal target net assets
th USD Last avail. yr                  3565 non-null   float64
 14  Pre-deal target current liabilities
th USD Last avail. yr         3565 non-null   float64
 15  Pre-deal target shareholders funds
th USD Last avail. yr          3565 non-null   float64
 16  Pre-deal target number of months
Last avail. yr                   3565 non-null   float64
 17  Target stock price 1 month after completion
USD                   3565 non-null   float64
 18  Target major sector_0                                             3565 non-null   int64  
 19  Target major sector_1                                             3565 non-null   int64  
 20  Target major sector_2                                             3565 non-null   int64  
 21  Target major sector_3                                             3565 non-null   int64  
 22  Target major sector_4                                             3565 non-null   int64  
 23  Target stock price after completion
USD                           3565 non-null   float64
 24  Target stock price 1 week after completion
USD                    3565 non-null   float64
 25  Y                                                                 3565 non-null   int64  
dtypes: float64(15), int64(11)
memory usage: 752.0 KB

To conclude , we remove the columns: "Target stock price 1 month after completion" and "Target stock price 1 week after completion". This because these two columns have a strong correlation with the target label "Success" and so they can influence the algorithms to estimate the Success or not of the IPO.

df_encoded.drop(columns=["Target stock price 1 month after completion\nUSD","Target stock price 1 week after completion\nUSD"],inplace=True)

df_encoded.head(10)

	Target country code_0	Target country code_1	Target country code_2	Target country code_3	Target country code_4	Deal value\nth USD	Pre-deal target operating revenue/turnover\nth USD Last avail. yr	Pre-deal target EBITDA\nth USD Last avail. yr	Pre-deal target EBIT\nth USD Last avail. yr	Pre-deal target profit before tax\nth USD Last avail. yr	...	Pre-deal target current liabilities\nth USD Last avail. yr	Pre-deal target shareholders funds\nth USD Last avail. yr	Pre-deal target number of months\nLast avail. yr	Target major sector_0	Target major sector_1	Target major sector_2	Target major sector_3	Target major sector_4	Target stock price after completion\nUSD	Y
0	0	0	0	0	1	1.130000e+08	2.281000e+07	9.850000e+06	8.894000e+06	8.884000e+06	...	8.091000e+06	1.559500e+07	12.0	0	0	0	0	1	52.020000	0
1	0	0	0	0	1	4.700000e+07	8.025000e+06	1.784000e+06	1.268000e+06	1.261000e+06	...	1.328300e+07	8.248000e+06	12.0	0	0	0	1	0	40.470001	0
3	0	0	0	0	1	1.764000e+07	9.454000e+06	2.589000e+06	1.853000e+06	2.335000e+06	...	7.314000e+06	1.150400e+07	12.0	0	0	0	1	1	28.620001	0
7	0	0	0	1	0	1.108967e+07	4.202295e+03	1.509648e+03	1.334342e+03	-2.043133e+02	...	4.372556e+03	7.188044e+04	7.0	0	0	0	1	0	57.631681	0
11	0	0	0	0	1	8.680000e+06	2.292200e+07	4.874000e+06	3.840000e+06	3.415000e+06	...	7.590000e+06	1.404800e+07	12.0	0	0	0	0	1	35.610001	0
15	0	0	0	0	1	6.354828e+06	5.695000e+06	1.236000e+06	1.080000e+06	8.170000e+05	...	1.135000e+06	5.021000e+06	12.0	0	0	0	0	1	24.950001	0
22	0	0	0	1	1	4.600000e+06	3.767048e+06	5.978655e+05	4.724712e+05	5.431359e+05	...	1.663429e+06	4.751552e+06	12.0	0	0	0	0	1	10.674000	0
23	0	0	0	0	1	4.500000e+06	4.472832e+06	5.478040e+05	4.336000e+05	4.163200e+05	...	1.624765e+06	3.229134e+06	12.0	0	0	1	0	0	5.277500	0
44	0	0	0	0	1	3.034222e+06	9.701000e+05	3.267000e+05	2.798000e+05	2.787000e+05	...	1.043000e+05	-1.373000e+05	12.0	0	0	1	0	1	24.490000	-1
45	0	0	0	0	1	3.000000e+06	2.373100e+06	2.182010e+05	1.802010e+05	1.801090e+05	...	2.911820e+05	1.005967e+06	12.0	0	0	1	1	0	7.760000	0

10 rows × 24 columns