250x250
Notice
Recent Posts
Recent Comments
Link
| 일 | 월 | 화 | 수 | 목 | 금 | 토 |
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| 13 | 14 | 15 | 16 | 17 | 18 | 19 |
| 20 | 21 | 22 | 23 | 24 | 25 | 26 |
| 27 | 28 | 29 | 30 |
Tags
- __sub__
- choice()
- View
- glob
- __getitem__
- items()
- shutil
- CSS
- JS
- locals()
- remove()
- node.js
- discard()
- MySqlDB
- Database
- zipfile
- __annotations__
- mro()
- shuffle()
- count()
- HTML
- fnmatch
- inplace()
- __len__
- fileinput
- randrange()
- decode()
- MySQL
- 파이썬
- 오버라이딩
Archives
- Today
- Total
흰둥이는 코드를 짤 때 짖어 (왈!왈!왈!왈!왈!왈!왈!왈!왈!왈!왈!)
(Python) lightGBM 본문
728x90
반응형
1. credit 데이터셋 살펴보기
In [ ]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
In [ ]:
credit_df = pd.read_csv('/content/drive/MyDrive/KDT/4. 머신러닝과 딥러닝/credit.csv')
In [ ]:
credit_df
Out[ ]:
| ID | Customer_ID | Name | Age | SSN | Occupation | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | ... | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Payment_of_Min_Amount | Total_EMI_per_month | Amount_invested_monthly | Payment_Behaviour | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0x1602 | CUS_0xd40 | Aaron Maashoh | 23 | 821-00-0265 | Scientist | 19114.12 | 3 | 4 | 3 | ... | 4.0 | 809.98 | 26.822620 | 22 Years and 1 Months | No | 49.574949 | 80.41529544 | High_spent_Small_value_payments | 312.494089 | Good |
| 1 | 0x160e | CUS_0x21b1 | Rick Rothackerj | 28_ | 004-07-5839 | _______ | 34847.84 | 2 | 4 | 6 | ... | 2.0 | 605.03 | 24.464031 | 26 Years and 7 Months | No | 18.816215 | 104.2918252 | Low_spent_Small_value_payments | 470.690627 | Standard |
| 2 | 0x161a | CUS_0x2dbc | Langep | 34 | 486-85-3974 | _______ | 143162.64 | 1 | 5 | 8 | ... | 3.0 | 1303.01 | 28.616735 | 17 Years and 9 Months | No | 246.992320 | 168.4137027 | !@9#%8 | 1043.315978 | Good |
| 3 | 0x1626 | CUS_0xb891 | Jasond | 54 | 072-31-6145 | Entrepreneur | 30689.89 | 2 | 5 | 4 | ... | 4.0 | 632.46 | 26.544229 | 17 Years and 3 Months | No | 16.415452 | 81.22885871 | Low_spent_Large_value_payments | 433.604773 | Standard |
| 4 | 0x1632 | CUS_0x1cdb | Deepaa | 21 | 615-06-7821 | Developer | 35547.71_ | 7 | 5 | 5 | ... | 4.0 | 943.86 | 39.797764 | 30 Years and 8 Months | Yes | 0.000000 | 276.7253943 | !@9#%8 | 288.605522 | Standard |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 12495 | 0x25fb6 | CUS_0x372c | Lucia Mutikanik | 18 | 340-85-7301 | Lawyer | 42903.79 | 0 | 4 | 6 | ... | 1.0 | 1079.48 | 27.289440 | 28 Years and 1 Months | No | 50894.000000 | 78.51494451 | High_spent_Small_value_payments | 493.341182 | Good |
| 12496 | 0x25fc2 | CUS_0xf16 | Maria Sheahanb | 44 | #F%$D@*&8 | Media_Manager | 16680.35 | 1 | 1 | 5 | ... | 4.0 | 897.16 | 39.868572 | NaN | NM | 41.113561 | 52.95197782 | High_spent_Small_value_payments | 318.737378 | Good |
| 12497 | 0x25fce | CUS_0xaf61 | Chris Wickhamm | 49 | 133-16-7738 | Writer | 37188.1 | 1 | 4 | 5 | ... | 3.0 | 620.64 | 39.080823 | 29 Years and 9 Months | No | 84.205949 | 223.8750182 | Low_spent_Small_value_payments | 291.619866 | Good |
| 12498 | 0x25fda | CUS_0x8600 | Sarah McBridec | 28 | 031-35-0942 | Architect | 20002.88 | 10 | 8 | 29 | ... | 9.0 | 3571.7_ | 22.895966 | 5 Years and 8 Months | Yes | 60.964772 | 43.37067007 | High_spent_Large_value_payments | 328.655224 | Poor |
| 12499 | 0x25fe6 | CUS_0x942c | Nicks | 24 | 078-73-5990 | Mechanic | 39628.99 | 4 | 6 | 7 | ... | 3.0 | 502.38 | 32.991333 | 31 Years and 3 Months | No | 35.104023 | 401.1964806 | Low_spent_Small_value_payments | 189.641080 | Poor |
12500 rows × 24 columns
In [ ]:
pd.set_option('display.max_columns', 50)
In [ ]:
credit_df.head()
Out[ ]:
| ID | Customer_ID | Name | Age | SSN | Occupation | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Type_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Payment_of_Min_Amount | Total_EMI_per_month | Amount_invested_monthly | Payment_Behaviour | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0x1602 | CUS_0xd40 | Aaron Maashoh | 23 | 821-00-0265 | Scientist | 19114.12 | 3 | 4 | 3 | 4 | Auto Loan, Credit-Builder Loan, Personal Loan,... | 3 | 7 | 4.0 | 809.98 | 26.822620 | 22 Years and 1 Months | No | 49.574949 | 80.41529544 | High_spent_Small_value_payments | 312.494089 | Good |
| 1 | 0x160e | CUS_0x21b1 | Rick Rothackerj | 28_ | 004-07-5839 | _______ | 34847.84 | 2 | 4 | 6 | 1 | Credit-Builder Loan | 3 | 4 | 2.0 | 605.03 | 24.464031 | 26 Years and 7 Months | No | 18.816215 | 104.2918252 | Low_spent_Small_value_payments | 470.690627 | Standard |
| 2 | 0x161a | CUS_0x2dbc | Langep | 34 | 486-85-3974 | _______ | 143162.64 | 1 | 5 | 8 | 3 | Auto Loan, Auto Loan, and Not Specified | 5 | 8 | 3.0 | 1303.01 | 28.616735 | 17 Years and 9 Months | No | 246.992320 | 168.4137027 | !@9#%8 | 1043.315978 | Good |
| 3 | 0x1626 | CUS_0xb891 | Jasond | 54 | 072-31-6145 | Entrepreneur | 30689.89 | 2 | 5 | 4 | 1 | Not Specified | 0 | 6 | 4.0 | 632.46 | 26.544229 | 17 Years and 3 Months | No | 16.415452 | 81.22885871 | Low_spent_Large_value_payments | 433.604773 | Standard |
| 4 | 0x1632 | CUS_0x1cdb | Deepaa | 21 | 615-06-7821 | Developer | 35547.71_ | 7 | 5 | 5 | 0 | NaN | 5 | NaN | 4.0 | 943.86 | 39.797764 | 30 Years and 8 Months | Yes | 0.000000 | 276.7253943 | !@9#%8 | 288.605522 | Standard |
In [ ]:
credit_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12500 entries, 0 to 12499
Data columns (total 24 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ID 12500 non-null object
1 Customer_ID 12500 non-null object
2 Name 11273 non-null object
3 Age 12500 non-null object
4 SSN 12500 non-null object
5 Occupation 12500 non-null object
6 Annual_Income 12500 non-null object
7 Num_Bank_Accounts 12500 non-null int64
8 Num_Credit_Card 12500 non-null int64
9 Interest_Rate 12500 non-null int64
10 Num_of_Loan 12500 non-null object
11 Type_of_Loan 11074 non-null object
12 Delay_from_due_date 12500 non-null int64
13 Num_of_Delayed_Payment 11657 non-null object
14 Num_Credit_Inquiries 12264 non-null float64
15 Outstanding_Debt 12500 non-null object
16 Credit_Utilization_Ratio 12500 non-null float64
17 Credit_History_Age 11387 non-null object
18 Payment_of_Min_Amount 12500 non-null object
19 Total_EMI_per_month 12500 non-null float64
20 Amount_invested_monthly 11935 non-null object
21 Payment_Behaviour 12500 non-null object
22 Monthly_Balance 12366 non-null float64
23 Credit_Score 12500 non-null object
dtypes: float64(4), int64(4), object(16)
memory usage: 2.3+ MB
| Left column (영어) | Right column (한글) |
|---|---|
| ID | 고유 식별자 |
| Customer_ID | 고객 ID |
| Name | 이름 |
| Age | 나이 |
| SSN | 주민등록번호 |
| Occupation | 직업 |
| Annual_Income | 연간 소득 |
| Num_Bank_Accounts | 은행 계좌 수 |
| Num_Credit_Card | 신용 카드 수 |
| Interest_Rate | 이자율 |
| Num_of_Loan | 대출 수 |
| Type_of_Loan | 대출 유형 |
| Delay_from_due_date | 마감일로부터 연체 기간 |
| Num_of_Delayed_Payment | 연체된 결제 수 |
| Num_Credit_Inquiries | 신용조회 수 |
| Outstanding_Debt | 미상환 잔금 |
| Credit_Utilization_Ratio | 신용카드 사용률 |
| Credit_History_Age | 카드 사용 기간 |
| Payment_of_Min_Amount | 리볼빙 여부 |
| Total_EMI_per_month | 월별 총 지출 금액 |
| Amount_invested_monthly | 매월 투자 금액 |
| Payment_Behaviour | 지불 행동 |
| Monthly_Balance | 월별 잔고 |
| Credit_Score | 신용 점수 |
In [ ]:
credit_df.drop(['ID', 'Customer_ID', 'Name', 'SSN'], axis=1, inplace=True)
In [ ]:
credit_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12500 entries, 0 to 12499
Data columns (total 20 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Age 12500 non-null object
1 Occupation 12500 non-null object
2 Annual_Income 12500 non-null object
3 Num_Bank_Accounts 12500 non-null int64
4 Num_Credit_Card 12500 non-null int64
5 Interest_Rate 12500 non-null int64
6 Num_of_Loan 12500 non-null object
7 Type_of_Loan 11074 non-null object
8 Delay_from_due_date 12500 non-null int64
9 Num_of_Delayed_Payment 11657 non-null object
10 Num_Credit_Inquiries 12264 non-null float64
11 Outstanding_Debt 12500 non-null object
12 Credit_Utilization_Ratio 12500 non-null float64
13 Credit_History_Age 11387 non-null object
14 Payment_of_Min_Amount 12500 non-null object
15 Total_EMI_per_month 12500 non-null float64
16 Amount_invested_monthly 11935 non-null object
17 Payment_Behaviour 12500 non-null object
18 Monthly_Balance 12366 non-null float64
19 Credit_Score 12500 non-null object
dtypes: float64(4), int64(4), object(12)
memory usage: 1.9+ MB
In [ ]:
credit_df['Credit_Score'].value_counts()
Out[ ]:
Standard 6943
Poor 3582
Good 1975
Name: Credit_Score, dtype: int64In [ ]:
credit_df['Credit_Score'] = credit_df['Credit_Score'].replace({'Poor':0, 'Standard':1, 'Good':2})
In [ ]:
credit_df.head()
Out[ ]:
| Age | Occupation | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Type_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Payment_of_Min_Amount | Total_EMI_per_month | Amount_invested_monthly | Payment_Behaviour | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 23 | Scientist | 19114.12 | 3 | 4 | 3 | 4 | Auto Loan, Credit-Builder Loan, Personal Loan,... | 3 | 7 | 4.0 | 809.98 | 26.822620 | 22 Years and 1 Months | No | 49.574949 | 80.41529544 | High_spent_Small_value_payments | 312.494089 | 2 |
| 1 | 28_ | _______ | 34847.84 | 2 | 4 | 6 | 1 | Credit-Builder Loan | 3 | 4 | 2.0 | 605.03 | 24.464031 | 26 Years and 7 Months | No | 18.816215 | 104.2918252 | Low_spent_Small_value_payments | 470.690627 | 1 |
| 2 | 34 | _______ | 143162.64 | 1 | 5 | 8 | 3 | Auto Loan, Auto Loan, and Not Specified | 5 | 8 | 3.0 | 1303.01 | 28.616735 | 17 Years and 9 Months | No | 246.992320 | 168.4137027 | !@9#%8 | 1043.315978 | 2 |
| 3 | 54 | Entrepreneur | 30689.89 | 2 | 5 | 4 | 1 | Not Specified | 0 | 6 | 4.0 | 632.46 | 26.544229 | 17 Years and 3 Months | No | 16.415452 | 81.22885871 | Low_spent_Large_value_payments | 433.604773 | 1 |
| 4 | 21 | Developer | 35547.71_ | 7 | 5 | 5 | 0 | NaN | 5 | NaN | 4.0 | 943.86 | 39.797764 | 30 Years and 8 Months | Yes | 0.000000 | 276.7253943 | !@9#%8 | 288.605522 | 1 |
In [ ]:
credit_df.describe()
Out[ ]:
| Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Delay_from_due_date | Num_Credit_Inquiries | Credit_Utilization_Ratio | Total_EMI_per_month | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|
| count | 12500.000000 | 12500.000000 | 12500.00000 | 12500.000000 | 12264.000000 | 12500.000000 | 12500.000000 | 12366.000000 | 12500.000000 |
| mean | 17.275120 | 21.647680 | 69.46520 | 21.051440 | 24.591650 | 32.291949 | 1303.781040 | 405.815391 | 0.871440 |
| std | 118.518214 | 123.789969 | 455.95698 | 14.859994 | 183.422458 | 5.084327 | 8118.261086 | 218.136964 | 0.654268 |
| min | 0.000000 | 1.000000 | 1.00000 | -5.000000 | 0.000000 | 20.992914 | 0.000000 | 0.088628 | 0.000000 |
| 25% | 3.000000 | 4.000000 | 8.00000 | 10.000000 | 2.000000 | 28.110034 | 29.128806 | 271.785749 | 0.000000 |
| 50% | 6.000000 | 5.000000 | 13.00000 | 18.000000 | 4.000000 | 32.297912 | 66.372879 | 337.169588 | 1.000000 |
| 75% | 7.000000 | 7.000000 | 20.00000 | 28.000000 | 8.000000 | 36.458660 | 149.904496 | 475.222487 | 1.000000 |
| max | 1779.000000 | 1479.000000 | 5788.00000 | 67.000000 | 2592.000000 | 49.564519 | 82122.000000 | 1602.040519 | 2.000000 |
In [ ]:
sns.barplot(x='Payment_of_Min_Amount', y='Credit_Score', data=credit_df)
Out[ ]:
<Axes: xlabel='Payment_of_Min_Amount', ylabel='Credit_Score'>
In [ ]:
plt.figure(figsize=(20, 5))
sns.barplot(x='Occupation', y='Credit_Score', data=credit_df)
Out[ ]:
<Axes: xlabel='Occupation', ylabel='Credit_Score'>
In [ ]:
# corr(): 각 열 간의 상관 계수를 반환
# 피어슨, 켄달-타우, 스피어맨
plt.figure(figsize=(12, 12))
sns.heatmap(credit_df.corr(), cmap='coolwarm', vmin=-1, vmax=1, annot=True)
<ipython-input-15-755eee53a13a>:4: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
sns.heatmap(credit_df.corr(), cmap='coolwarm', vmin=-1, vmax=1, annot=True)
Out[ ]:
<Axes: >
In [ ]:
credit_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12500 entries, 0 to 12499
Data columns (total 20 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Age 12500 non-null object
1 Occupation 12500 non-null object
2 Annual_Income 12500 non-null object
3 Num_Bank_Accounts 12500 non-null int64
4 Num_Credit_Card 12500 non-null int64
5 Interest_Rate 12500 non-null int64
6 Num_of_Loan 12500 non-null object
7 Type_of_Loan 11074 non-null object
8 Delay_from_due_date 12500 non-null int64
9 Num_of_Delayed_Payment 11657 non-null object
10 Num_Credit_Inquiries 12264 non-null float64
11 Outstanding_Debt 12500 non-null object
12 Credit_Utilization_Ratio 12500 non-null float64
13 Credit_History_Age 11387 non-null object
14 Payment_of_Min_Amount 12500 non-null object
15 Total_EMI_per_month 12500 non-null float64
16 Amount_invested_monthly 11935 non-null object
17 Payment_Behaviour 12500 non-null object
18 Monthly_Balance 12366 non-null float64
19 Credit_Score 12500 non-null int64
dtypes: float64(4), int64(5), object(11)
memory usage: 1.9+ MB
In [ ]:
for i in credit_df.columns:
if credit_df[i].dtype == 'O':
print(i)
Age
Occupation
Annual_Income
Num_of_Loan
Type_of_Loan
Num_of_Delayed_Payment
Outstanding_Debt
Credit_History_Age
Payment_of_Min_Amount
Amount_invested_monthly
Payment_Behaviour
In [ ]:
credit_df.head()
Out[ ]:
| Age | Occupation | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Type_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Payment_of_Min_Amount | Total_EMI_per_month | Amount_invested_monthly | Payment_Behaviour | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 23 | Scientist | 19114.12 | 3 | 4 | 3 | 4 | Auto Loan, Credit-Builder Loan, Personal Loan,... | 3 | 7 | 4.0 | 809.98 | 26.822620 | 22 Years and 1 Months | No | 49.574949 | 80.41529544 | High_spent_Small_value_payments | 312.494089 | 2 |
| 1 | 28_ | _______ | 34847.84 | 2 | 4 | 6 | 1 | Credit-Builder Loan | 3 | 4 | 2.0 | 605.03 | 24.464031 | 26 Years and 7 Months | No | 18.816215 | 104.2918252 | Low_spent_Small_value_payments | 470.690627 | 1 |
| 2 | 34 | _______ | 143162.64 | 1 | 5 | 8 | 3 | Auto Loan, Auto Loan, and Not Specified | 5 | 8 | 3.0 | 1303.01 | 28.616735 | 17 Years and 9 Months | No | 246.992320 | 168.4137027 | !@9#%8 | 1043.315978 | 2 |
| 3 | 54 | Entrepreneur | 30689.89 | 2 | 5 | 4 | 1 | Not Specified | 0 | 6 | 4.0 | 632.46 | 26.544229 | 17 Years and 3 Months | No | 16.415452 | 81.22885871 | Low_spent_Large_value_payments | 433.604773 | 1 |
| 4 | 21 | Developer | 35547.71_ | 7 | 5 | 5 | 0 | NaN | 5 | NaN | 4.0 | 943.86 | 39.797764 | 30 Years and 8 Months | Yes | 0.000000 | 276.7253943 | !@9#%8 | 288.605522 | 1 |
In [ ]:
for i in ['Age', 'Annual_Income', 'Num_of_Loan', 'Num_of_Delayed_Payment', 'Outstanding_Debt', 'Amount_invested_monthly']:
credit_df[i] = pd.to_numeric(credit_df[i].str.replace('_', ''))
In [ ]:
# Credit_History_Age의 데이터를 개월로 변경
# 22 Years and 1 Months -> 22 * 12 + 1
credit_df['Credit_History_Age'] = credit_df['Credit_History_Age'].str.replace(' Months', '')
credit_df['Credit_History_Age'] = pd.to_numeric(credit_df['Credit_History_Age'].str.split(' Years and ', expand=True)[0])*12 + pd.to_numeric(credit_df['Credit_History_Age'].str.split(' Years and ', expand=True)[1])
In [ ]:
credit_df.head()
Out[ ]:
| Age | Occupation | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Type_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Payment_of_Min_Amount | Total_EMI_per_month | Amount_invested_monthly | Payment_Behaviour | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 23 | Scientist | 19114.12 | 3 | 4 | 3 | 4 | Auto Loan, Credit-Builder Loan, Personal Loan,... | 3 | 7.0 | 4.0 | 809.98 | 26.822620 | 265.0 | No | 49.574949 | 80.415295 | High_spent_Small_value_payments | 312.494089 | 2 |
| 1 | 28 | _______ | 34847.84 | 2 | 4 | 6 | 1 | Credit-Builder Loan | 3 | 4.0 | 2.0 | 605.03 | 24.464031 | 319.0 | No | 18.816215 | 104.291825 | Low_spent_Small_value_payments | 470.690627 | 1 |
| 2 | 34 | _______ | 143162.64 | 1 | 5 | 8 | 3 | Auto Loan, Auto Loan, and Not Specified | 5 | 8.0 | 3.0 | 1303.01 | 28.616735 | 213.0 | No | 246.992320 | 168.413703 | !@9#%8 | 1043.315978 | 2 |
| 3 | 54 | Entrepreneur | 30689.89 | 2 | 5 | 4 | 1 | Not Specified | 0 | 6.0 | 4.0 | 632.46 | 26.544229 | 207.0 | No | 16.415452 | 81.228859 | Low_spent_Large_value_payments | 433.604773 | 1 |
| 4 | 21 | Developer | 35547.71 | 7 | 5 | 5 | 0 | NaN | 5 | NaN | 4.0 | 943.86 | 39.797764 | 368.0 | Yes | 0.000000 | 276.725394 | !@9#%8 | 288.605522 | 1 |
In [ ]:
credit_df.describe()
Out[ ]:
| Age | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Total_EMI_per_month | Amount_invested_monthly | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 12500.000000 | 1.250000e+04 | 12500.000000 | 12500.000000 | 12500.00000 | 12500.00000 | 12500.000000 | 11657.000000 | 12264.000000 | 12500.000000 | 12500.000000 | 11387.000000 | 12500.000000 | 11935.000000 | 12366.000000 | 12500.000000 |
| mean | 113.371280 | 1.888617e+05 | 17.275120 | 21.647680 | 69.46520 | 3.43656 | 21.051440 | 31.280089 | 24.591650 | 1426.220376 | 32.291949 | 217.588127 | 1303.781040 | 643.291976 | 405.815391 | 0.871440 |
| std | 691.223297 | 1.482707e+06 | 118.518214 | 123.789969 | 455.95698 | 65.35565 | 14.859994 | 229.911798 | 183.422458 | 1155.169458 | 5.084327 | 99.638681 | 8118.261086 | 2063.324328 | 218.136964 | 0.654268 |
| min | -500.000000 | 7.005930e+03 | 0.000000 | 1.000000 | 1.00000 | -100.00000 | -5.000000 | -3.000000 | 0.000000 | 0.230000 | 20.992914 | 1.000000 | 0.000000 | 0.000000 | 0.088628 | 0.000000 |
| 25% | 24.000000 | 1.948777e+04 | 3.000000 | 4.000000 | 8.00000 | 1.00000 | 10.000000 | 9.000000 | 2.000000 | 566.072500 | 28.110034 | 141.000000 | 29.128806 | 73.810753 | 271.785749 | 0.000000 |
| 50% | 33.000000 | 3.765508e+04 | 6.000000 | 5.000000 | 13.00000 | 3.00000 | 18.000000 | 14.000000 | 4.000000 | 1166.155000 | 32.297912 | 215.000000 | 66.372879 | 134.201478 | 337.169588 | 1.000000 |
| 75% | 42.000000 | 7.289813e+04 | 7.000000 | 7.000000 | 20.00000 | 5.00000 | 28.000000 | 18.000000 | 8.000000 | 1945.962500 | 36.458660 | 298.000000 | 149.904496 | 264.555831 | 475.222487 | 1.000000 |
| max | 8592.000000 | 2.365819e+07 | 1779.000000 | 1479.000000 | 5788.00000 | 1496.00000 | 67.000000 | 4388.000000 | 2592.000000 | 4998.070000 | 49.564519 | 397.000000 | 82122.000000 | 10000.000000 | 1602.040519 | 2.000000 |
In [ ]:
credit_df = credit_df[credit_df['Age'] >= 0]
In [ ]:
credit_df.sort_values('Age').tail(30)
Out[ ]:
| Age | Occupation | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Type_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Payment_of_Min_Amount | Total_EMI_per_month | Amount_invested_monthly | Payment_Behaviour | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9449 | 7615 | Architect | 34589.720 | 6 | 9 | 25 | 5 | Personal Loan, Student Loan, Home Equity Loan,... | 23 | 18.0 | 8.0 | 3783.35 | 32.711927 | 65.0 | Yes | 111.689044 | 128.119632 | High_spent_Small_value_payments | 318.438990 | 0 |
| 7151 | 7711 | Manager | 15814.770 | 7 | 4 | 13 | 7 | Debt Consolidation Loan, Not Specified, Home E... | 22 | 7.0 | 5.0 | 902.47 | 39.316759 | 233.0 | NM | 58.119995 | 10000.000000 | Low_spent_Small_value_payments | 190.621479 | 1 |
| 8974 | 7733 | Doctor | 21505.640 | 2 | 4 | 3 | 1 | Not Specified | 4 | 2.0 | 3.0 | 204.35 | 35.150663 | 184.0 | No | 11.620501 | 55.276752 | Low_spent_Medium_value_payments | 367.916413 | 2 |
| 11565 | 7753 | _______ | 19826.670 | 6 | 3 | 33 | 5 | Student Loan, Home Equity Loan, Personal Loan,... | 6 | 15.0 | 8.0 | 2052.89 | 35.653249 | 64.0 | Yes | 45.868928 | 121.807133 | Low_spent_Small_value_payments | 302.846189 | 0 |
| 6851 | 7762 | Journalist | 43549.860 | 7 | 5 | 24 | 9 | Student Loan, Debt Consolidation Loan, Mortgag... | 27 | 21.0 | 6.0 | 4639.59 | 28.221137 | 145.0 | Yes | 193.114379 | 96.851718 | High_spent_Small_value_payments | 337.249403 | 1 |
| 9388 | 7849 | Doctor | 66554.310 | 4 | 6 | 15 | 3 | Payday Loan, Credit-Builder Loan, and Auto Loan | 14 | 19.0 | 3.0 | 485.70 | 33.200830 | 223.0 | No | 101.923115 | 349.739377 | Low_spent_Medium_value_payments | 402.756758 | 1 |
| 565 | 7851 | Engineer | 20083.060 | 8 | 5 | 15 | 3 | Credit-Builder Loan, Mortgage Loan, and Credit... | 20 | 23.0 | 7.0 | 2381.47 | 37.416601 | 107.0 | Yes | 33.819519 | 38.558298 | High_spent_Medium_value_payments | 363.281016 | 0 |
| 5718 | 7894 | Teacher | 99868.830 | 2 | 3 | 2 | 3 | Auto Loan, Credit-Builder Loan, and Auto Loan | 5 | 9.0 | 1.0 | 693.76 | 30.253597 | 263.0 | No | 146.701253 | 838.623220 | Low_spent_Small_value_payments | 137.915778 | 2 |
| 12415 | 7901 | Entrepreneur | 64363.920 | 4 | 6 | 6 | 5 | Personal Loan, Credit-Builder Loan, Auto Loan,... | 25 | NaN | 5.0 | 978.79 | 30.205522 | 110.0 | Yes | 176.843978 | 211.321489 | High_spent_Medium_value_payments | 423.000533 | 1 |
| 1414 | 7935 | Developer | 116809.000 | 3 | 1 | 3 | 3 | Debt Consolidation Loan, Credit-Builder Loan, ... | 2 | NaN | 3.0 | 1312.44 | 34.458738 | 219.0 | No | 191.675705 | 190.976717 | High_spent_Large_value_payments | 838.555911 | 2 |
| 1258 | 7980 | Scientist | 44941.950 | 4 | 6 | 3 | 1 | Payday Loan | 0 | 9.0 | 2.0 | 1150.28 | 27.894834 | 367.0 | No | 22.434725 | 133.727501 | !@9#%8 | 508.454024 | 2 |
| 7445 | 8035 | Doctor | 32753.050 | 2 | 7 | 6 | 4 | Credit-Builder Loan, Debt Consolidation Loan, ... | 2 | 5.0 | 3.0 | 1300.30 | 36.662384 | 359.0 | No | 79.182771 | 63.646267 | !@9#%8 | 400.513046 | 2 |
| 10824 | 8052 | Manager | 35820.620 | 0 | 6 | 8 | 4 | Student Loan, Not Specified, Student Loan, and... | 10 | 10.0 | 3.0 | 731.26 | 26.763490 | 237.0 | No | 87.981091 | 169.322300 | Low_spent_Medium_value_payments | 298.501775 | 2 |
| 857 | 8080 | Entrepreneur | 62835.080 | 0 | 4 | 1 | 2 | Home Equity Loan, and Debt Consolidation Loan | 10 | 14.0 | 0.0 | 306.85 | 40.859818 | 392.0 | No | 67.384074 | 181.104372 | Low_spent_Medium_value_payments | 577.937220 | 2 |
| 4233 | 8081 | Accountant | 19609.380 | 9 | 7 | 23 | 8 | Payday Loan, Mortgage Loan, Home Equity Loan, ... | 16 | 23.0 | 12.0 | 4186.04 | 25.656993 | 62.0 | Yes | 87.281543 | 93.293367 | Low_spent_Small_value_payments | 276.936589 | 1 |
| 7108 | 8172 | Doctor | 23838.090 | 5 | 6 | 13 | 773 | Debt Consolidation Loan, Payday Loan, Home Equ... | 24 | 10.0 | 7.0 | 802.30 | 31.348708 | 358.0 | Yes | 53.308099 | 10000.000000 | Low_spent_Small_value_payments | 232.429877 | 1 |
| 4046 | 8179 | Scientist | 49847.550 | 9 | 1384 | 18 | 3 | Home Equity Loan, Personal Loan, and Student Loan | 21 | 18.0 | 8.0 | 2173.47 | 39.903631 | 233.0 | Yes | 71.675700 | 318.284663 | Low_spent_Medium_value_payments | 278.535887 | 0 |
| 10197 | 8198 | Journalist | 20488.290 | 8 | 5 | 33 | 9 | Auto Loan, Home Equity Loan, Home Equity Loan,... | 56 | 18.0 | 7.0 | 3218.07 | 35.075428 | 92.0 | Yes | 131.465294 | 122.316046 | Low_spent_Medium_value_payments | 207.354411 | 0 |
| 5788 | 8246 | Accountant | 42900.210 | 7 | 4 | 17 | 4 | Personal Loan, Payday Loan, Mortgage Loan, and... | 18 | 15.0 | 4.0 | 2696.09 | 31.455247 | 117.0 | Yes | 111.209311 | 150.713217 | Low_spent_Small_value_payments | 376.079222 | 0 |
| 2725 | 8249 | Developer | 35372.360 | 8 | 5 | 25 | 9 | Home Equity Loan, Auto Loan, Mortgage Loan, Au... | 15 | 24.0 | 6.0 | 3850.97 | 31.673174 | 85.0 | Yes | 173.241966 | 80.806393 | High_spent_Small_value_payments | 292.521308 | 1 |
| 11176 | 8279 | _______ | 37443.130 | 2 | 4 | 7 | 0 | NaN | 13 | 0.0 | 2.0 | 843.53 | 34.062221 | 302.0 | No | 0.000000 | NaN | High_spent_Small_value_payments | 490.572275 | 0 |
| 4738 | 8306 | Developer | 27647.080 | 7 | 6 | 13 | 5 | Not Specified, Credit-Builder Loan, Mortgage L... | 17 | 8.0 | 7.0 | 159.63 | 26.757773 | 62.0 | Yes | 100.740203 | 10000.000000 | High_spent_Small_value_payments | 294.462237 | 1 |
| 8284 | 8348 | Accountant | 19323.400 | 10 | 5 | 18 | 8 | Debt Consolidation Loan, Debt Consolidation Lo... | 48 | 15.0 | 7.0 | 3033.25 | 30.197723 | 100.0 | Yes | 99.101347 | 110.085452 | Low_spent_Medium_value_payments | 245.741535 | 0 |
| 9384 | 8403 | Developer | 7756.465 | 10 | 7 | 19 | 2 | Payday Loan, and Student Loan | 45 | 21.0 | 11.0 | 2338.09 | 25.301853 | 67.0 | Yes | 9.682065 | NaN | High_spent_Medium_value_payments | 270.333736 | 0 |
| 7630 | 8409 | Lawyer | 34413.760 | 6 | 5 | 8 | 5 | Home Equity Loan, Home Equity Loan, Home Equit... | 23 | 20.0 | 6.0 | 113.06 | 39.139526 | 69.0 | Yes | 74.328709 | 37.339760 | High_spent_Large_value_payments | 420.012864 | 1 |
| 12472 | 8425 | Writer | 18512.970 | 7 | 5 | 18 | 3 | Student Loan, Student Loan, and Mortgage Loan | 15 | 11.0 | 6.0 | 1366.56 | 29.253092 | 131.0 | Yes | 24.621173 | 63.282651 | High_spent_Medium_value_payments | 311.570927 | 1 |
| 11218 | 8481 | Journalist | 15874.010 | 1 | 3 | 5 | 0 | NaN | 12 | 6.0 | 1.0 | 1173.38 | 24.443887 | 380.0 | No | 0.000000 | 53.610174 | High_spent_Small_value_payments | 311.373243 | 0 |
| 12439 | 8490 | Lawyer | 150131.680 | 5 | 1 | 4 | 0 | NaN | 8 | -2.0 | 0.0 | 1138.36 | 30.013470 | 376.0 | No | 0.000000 | 949.847265 | Low_spent_Small_value_payments | 599.850069 | 1 |
| 9255 | 8587 | Journalist | 28286.240 | 4 | 7 | 7 | 1 | Mortgage Loan | 18 | 16.0 | 4.0 | 1406.94 | 33.599043 | 350.0 | No | 17.431672 | 41.114532 | High_spent_Medium_value_payments | 418.772462 | 1 |
| 2963 | 8592 | _______ | 81815.020 | 0 | 5 | 4 | 1 | Mortgage Loan | 4 | NaN | 3.0 | 1434.75 | 28.645438 | 394.0 | No | 53.927785 | 277.263979 | Low_spent_Medium_value_payments | 637.300069 | 2 |
In [ ]:
sns.boxplot(y=credit_df['Age'])
Out[ ]:
<Axes: ylabel='Age'>
In [ ]:
credit_df[credit_df['Age'] > 100].sort_values('Age')
Out[ ]:
| Age | Occupation | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Type_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Payment_of_Min_Amount | Total_EMI_per_month | Amount_invested_monthly | Payment_Behaviour | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3911 | 102 | Musician | 38460.280 | 6 | 9 | 16 | 7 | Personal Loan, Personal Loan, Home Equity Loan... | 56 | 16.0 | 11.0 | 4106.50 | 26.537577 | 73.0 | Yes | 179.135821 | 267.336505 | Low_spent_Medium_value_payments | 157.630007 | 1 |
| 2416 | 126 | Teacher | 22050.560 | 5 | 4 | 12 | 1 | Home Equity Loan | 5 | 13.0 | 4.0 | 37.42 | 28.956967 | 387.0 | NM | 13.485884 | NaN | High_spent_Medium_value_payments | 359.927004 | 1 |
| 7418 | 169 | Doctor | 50109.760 | 6 | 3 | 4 | 1 | Personal Loan | 16 | 17.0 | 0.0 | 893.62 | 27.560776 | 191.0 | NM | 22.847438 | 351.547678 | Low_spent_Large_value_payments | 331.086217 | 2 |
| 952 | 181 | _______ | 87957.020 | 2 | 5 | 9 | 4 | Home Equity Loan, Auto Loan, Credit-Builder Lo... | 9 | 1.0 | 2.0 | 811.01 | 41.470014 | 231.0 | No | 195.913703 | 265.660815 | High_spent_Medium_value_payments | 533.800649 | 0 |
| 3197 | 216 | Media_Manager | 15829.875 | 4 | 2 | 4 | 1 | Student Loan | 10 | NaN | 3.0 | 968.61 | 34.361779 | 236.0 | No | 6.719535 | NaN | Low_spent_Small_value_payments | 352.096096 | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 12472 | 8425 | Writer | 18512.970 | 7 | 5 | 18 | 3 | Student Loan, Student Loan, and Mortgage Loan | 15 | 11.0 | 6.0 | 1366.56 | 29.253092 | 131.0 | Yes | 24.621173 | 63.282651 | High_spent_Medium_value_payments | 311.570927 | 1 |
| 11218 | 8481 | Journalist | 15874.010 | 1 | 3 | 5 | 0 | NaN | 12 | 6.0 | 1.0 | 1173.38 | 24.443887 | 380.0 | No | 0.000000 | 53.610174 | High_spent_Small_value_payments | 311.373243 | 0 |
| 12439 | 8490 | Lawyer | 150131.680 | 5 | 1 | 4 | 0 | NaN | 8 | -2.0 | 0.0 | 1138.36 | 30.013470 | 376.0 | No | 0.000000 | 949.847265 | Low_spent_Small_value_payments | 599.850069 | 1 |
| 9255 | 8587 | Journalist | 28286.240 | 4 | 7 | 7 | 1 | Mortgage Loan | 18 | 16.0 | 4.0 | 1406.94 | 33.599043 | 350.0 | No | 17.431672 | 41.114532 | High_spent_Medium_value_payments | 418.772462 | 1 |
| 2963 | 8592 | _______ | 81815.020 | 0 | 5 | 4 | 1 | Mortgage Loan | 4 | NaN | 3.0 | 1434.75 | 28.645438 | 394.0 | No | 53.927785 | 277.263979 | Low_spent_Medium_value_payments | 637.300069 | 2 |
260 rows × 20 columns
In [ ]:
credit_df = credit_df[credit_df['Age'] < 120]
In [ ]:
credit_df.describe()
Out[ ]:
| Age | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Total_EMI_per_month | Amount_invested_monthly | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 12126.000000 | 1.212600e+04 | 12126.000000 | 12126.000000 | 12126.000000 | 12126.000000 | 12126.000000 | 11304.000000 | 11892.000000 | 12126.000000 | 12126.000000 | 11045.000000 | 12126.000000 | 11584.000000 | 11995.000000 | 12126.000000 |
| mean | 33.049398 | 1.897604e+05 | 17.275606 | 21.458601 | 68.507917 | 3.179861 | 21.053604 | 31.464084 | 24.994871 | 1426.321231 | 32.293466 | 217.614396 | 1308.972585 | 641.812701 | 406.077932 | 0.870856 |
| std | 10.810043 | 1.486221e+06 | 118.546216 | 122.910157 | 453.592030 | 64.030581 | 14.857112 | 230.020040 | 185.478101 | 1155.255348 | 5.084805 | 99.405014 | 8131.974016 | 2059.907639 | 218.484165 | 0.653729 |
| min | 14.000000 | 7.005930e+03 | 0.000000 | 1.000000 | 1.000000 | -100.000000 | -5.000000 | -3.000000 | 0.000000 | 0.230000 | 20.992914 | 1.000000 | 0.000000 | 0.000000 | 0.088628 | 0.000000 |
| 25% | 24.000000 | 1.945750e+04 | 4.000000 | 4.000000 | 8.000000 | 1.000000 | 10.000000 | 9.000000 | 2.000000 | 565.967500 | 28.118929 | 141.000000 | 29.275040 | 73.698246 | 271.752715 | 0.000000 |
| 50% | 33.000000 | 3.765508e+04 | 6.000000 | 5.000000 | 13.000000 | 3.000000 | 18.000000 | 14.000000 | 4.000000 | 1166.555000 | 32.285050 | 215.000000 | 66.196875 | 134.363758 | 337.123394 | 1.000000 |
| 75% | 41.000000 | 7.305204e+04 | 7.000000 | 7.000000 | 20.000000 | 5.000000 | 28.000000 | 18.000000 | 8.000000 | 1945.677500 | 36.452073 | 297.000000 | 149.873130 | 265.384383 | 474.903731 | 1.000000 |
| max | 102.000000 | 2.365819e+07 | 1779.000000 | 1479.000000 | 5788.000000 | 1496.000000 | 67.000000 | 4388.000000 | 2592.000000 | 4998.070000 | 49.564519 | 397.000000 | 82122.000000 | 10000.000000 | 1602.040519 | 2.000000 |
In [ ]:
# 연봉
2.365819e+07
Out[ ]:
23658190.0In [ ]:
len(credit_df[credit_df['Num_Bank_Accounts'] > 30]) / len(credit_df)
Out[ ]:
0.013029853207982847In [ ]:
credit_df = credit_df[credit_df['Num_Bank_Accounts'] <= 10]
In [ ]:
credit_df.describe()
Out[ ]:
| Age | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Total_EMI_per_month | Amount_invested_monthly | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 11968.000000 | 1.196800e+04 | 11968.000000 | 11968.000000 | 11968.000000 | 11968.000000 | 11968.000000 | 11155.000000 | 11739.000000 | 11968.000000 | 11968.000000 | 10902.000000 | 11968.000000 | 11435.000000 | 11840.000000 | 11968.000000 |
| mean | 33.059241 | 1.906275e+05 | 5.374749 | 21.289606 | 68.378509 | 3.202958 | 21.070438 | 31.480592 | 25.259392 | 1426.112040 | 32.290274 | 217.519171 | 1316.881794 | 637.877772 | 406.065193 | 0.870572 |
| std | 10.805276 | 1.493985e+06 | 2.589361 | 121.976512 | 454.393273 | 64.430573 | 14.868583 | 231.016220 | 186.667973 | 1155.106304 | 5.084843 | 99.394703 | 8162.702164 | 2052.261335 | 218.516737 | 0.653619 |
| min | 14.000000 | 7.005930e+03 | 0.000000 | 1.000000 | 1.000000 | -100.000000 | -5.000000 | -3.000000 | 0.000000 | 0.230000 | 20.992914 | 1.000000 | 0.000000 | 0.000000 | 0.088628 | 0.000000 |
| 25% | 24.000000 | 1.943611e+04 | 3.000000 | 4.000000 | 8.000000 | 1.000000 | 10.000000 | 9.000000 | 2.000000 | 565.175000 | 28.110034 | 141.000000 | 29.292854 | 73.446749 | 271.845069 | 0.000000 |
| 50% | 33.000000 | 3.757821e+04 | 6.000000 | 6.000000 | 13.000000 | 3.000000 | 18.000000 | 14.000000 | 4.000000 | 1167.045000 | 32.286924 | 215.000000 | 66.182932 | 133.519013 | 337.124205 | 1.000000 |
| 75% | 41.000000 | 7.305026e+04 | 7.000000 | 7.000000 | 20.000000 | 5.000000 | 28.000000 | 18.000000 | 8.000000 | 1947.157500 | 36.441411 | 297.000000 | 149.824990 | 263.272841 | 475.278504 | 1.000000 |
| max | 102.000000 | 2.365819e+07 | 10.000000 | 1479.000000 | 5788.000000 | 1496.000000 | 67.000000 | 4388.000000 | 2592.000000 | 4998.070000 | 49.564519 | 397.000000 | 82122.000000 | 10000.000000 | 1602.040519 | 2.000000 |
In [ ]:
len(credit_df[credit_df['Num_Credit_Card'] > 10]) / len(credit_df)
Out[ ]:
0.022142379679144383In [ ]:
credit_df = credit_df[credit_df['Num_Credit_Card'] <= 10]
In [ ]:
credit_df.describe()
Out[ ]:
| Age | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Total_EMI_per_month | Amount_invested_monthly | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 11703.000000 | 1.170300e+04 | 11703.000000 | 11703.000000 | 11703.000000 | 11703.000000 | 11703.000000 | 10903.000000 | 11480.000000 | 11703.00000 | 11703.000000 | 10652.000000 | 11703.000000 | 11180.000000 | 11578.000000 | 11703.000000 |
| mean | 33.049987 | 1.884282e+05 | 5.376399 | 5.537127 | 68.856362 | 3.186448 | 21.088268 | 31.785013 | 25.401742 | 1428.30559 | 32.285310 | 217.163068 | 1309.521881 | 641.250210 | 405.784155 | 0.869948 |
| std | 10.808971 | 1.484002e+06 | 2.593310 | 2.068797 | 457.627124 | 64.380015 | 14.899153 | 233.335849 | 187.642780 | 1156.44015 | 5.079688 | 99.525006 | 8141.405469 | 2059.087456 | 218.301576 | 0.653669 |
| min | 14.000000 | 7.005930e+03 | 0.000000 | 1.000000 | 1.000000 | -100.000000 | -5.000000 | -3.000000 | 0.000000 | 0.23000 | 20.992914 | 1.000000 | 0.000000 | 0.000000 | 0.088628 | 0.000000 |
| 25% | 24.000000 | 1.942758e+04 | 3.000000 | 4.000000 | 8.000000 | 1.000000 | 10.000000 | 9.000000 | 2.000000 | 565.31000 | 28.112036 | 140.000000 | 29.562116 | 73.229301 | 271.681508 | 0.000000 |
| 50% | 33.000000 | 3.764810e+04 | 6.000000 | 5.000000 | 13.000000 | 3.000000 | 18.000000 | 14.000000 | 4.000000 | 1167.04000 | 32.282544 | 215.000000 | 66.400432 | 133.865834 | 336.838358 | 1.000000 |
| 75% | 41.000000 | 7.304866e+04 | 7.000000 | 7.000000 | 20.000000 | 5.000000 | 28.000000 | 18.000000 | 8.000000 | 1953.03500 | 36.424405 | 297.000000 | 150.046222 | 263.681454 | 475.301587 | 1.000000 |
| max | 102.000000 | 2.365819e+07 | 10.000000 | 10.000000 | 5788.000000 | 1496.000000 | 67.000000 | 4388.000000 | 2592.000000 | 4998.07000 | 49.564519 | 397.000000 | 82122.000000 | 10000.000000 | 1602.040519 | 2.000000 |
In [ ]:
credit_df = credit_df[credit_df['Interest_Rate'] <= 40]
In [ ]:
credit_df.describe()
Out[ ]:
| Age | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Total_EMI_per_month | Amount_invested_monthly | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 11487.000000 | 1.148700e+04 | 11487.000000 | 11487.000000 | 11487.000000 | 11487.000000 | 11487.000000 | 10695.000000 | 11270.000000 | 11487.000000 | 11487.000000 | 10454.000000 | 11487.000000 | 10974.000000 | 11363.000000 | 11487.000000 |
| mean | 33.049534 | 1.866684e+05 | 5.378950 | 5.537564 | 14.581788 | 3.085836 | 21.072691 | 32.148481 | 25.614907 | 1428.211763 | 32.274850 | 217.182227 | 1309.892277 | 643.294691 | 405.462098 | 0.868982 |
| std | 10.807228 | 1.473331e+06 | 2.592557 | 2.069015 | 8.770185 | 63.612514 | 14.868582 | 235.577742 | 188.958957 | 1156.852697 | 5.081655 | 99.618121 | 8148.091946 | 2063.815555 | 217.887346 | 0.653006 |
| min | 14.000000 | 7.005930e+03 | 0.000000 | 1.000000 | 1.000000 | -100.000000 | -5.000000 | -3.000000 | 0.000000 | 0.230000 | 20.992914 | 1.000000 | 0.000000 | 0.000000 | 0.088628 | 0.000000 |
| 25% | 24.000000 | 1.939846e+04 | 3.000000 | 4.000000 | 7.000000 | 1.000000 | 10.000000 | 9.000000 | 2.000000 | 565.410000 | 28.109912 | 140.000000 | 29.601005 | 73.162675 | 271.686486 | 0.000000 |
| 50% | 33.000000 | 3.747152e+04 | 6.000000 | 5.000000 | 13.000000 | 3.000000 | 18.000000 | 14.000000 | 4.000000 | 1166.640000 | 32.257865 | 215.000000 | 66.158038 | 133.820509 | 336.822178 | 1.000000 |
| 75% | 41.000000 | 7.289110e+04 | 7.000000 | 7.000000 | 20.000000 | 5.000000 | 28.000000 | 18.000000 | 8.000000 | 1951.100000 | 36.402664 | 297.000000 | 149.533496 | 263.669812 | 474.566180 | 1.000000 |
| max | 102.000000 | 2.365819e+07 | 10.000000 | 10.000000 | 34.000000 | 1496.000000 | 67.000000 | 4388.000000 | 2592.000000 | 4998.070000 | 49.564519 | 397.000000 | 82122.000000 | 10000.000000 | 1602.040519 | 2.000000 |
In [ ]:
len(credit_df[credit_df['Num_of_Loan'] > 10]) / len(credit_df)
Out[ ]:
0.005310350831374598In [ ]:
credit_df = credit_df[(credit_df['Num_of_Loan'] <= 10) & (credit_df['Num_of_Loan'] >= 0)]
In [ ]:
credit_df.describe()
Out[ ]:
| Age | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Total_EMI_per_month | Amount_invested_monthly | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 10959.000000 | 1.095900e+04 | 10959.000000 | 10959.000000 | 10959.000000 | 10959.000000 | 10959.000000 | 10208.000000 | 10753.000000 | 10959.000000 | 10959.000000 | 9964.000000 | 10959.000000 | 10465.000000 | 10837.000000 | 10959.000000 |
| mean | 33.041427 | 1.884072e+05 | 5.386805 | 5.536363 | 14.593211 | 3.551966 | 21.081121 | 32.159483 | 25.398400 | 1431.045234 | 32.280924 | 216.876355 | 1323.307649 | 646.361810 | 405.453936 | 0.867871 |
| std | 10.814220 | 1.479380e+06 | 2.592922 | 2.068353 | 8.772065 | 2.445093 | 14.866980 | 235.548964 | 187.855849 | 1157.934074 | 5.083605 | 99.449671 | 8211.325128 | 2068.674211 | 218.063190 | 0.651887 |
| min | 14.000000 | 7.005930e+03 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | -5.000000 | -3.000000 | 0.000000 | 0.230000 | 20.992914 | 1.000000 | 0.000000 | 0.000000 | 0.088628 | 0.000000 |
| 25% | 24.000000 | 1.939791e+04 | 3.000000 | 4.000000 | 7.000000 | 2.000000 | 10.000000 | 9.000000 | 2.000000 | 566.540000 | 28.134686 | 140.000000 | 29.634492 | 73.207141 | 271.789642 | 0.000000 |
| 50% | 33.000000 | 3.767919e+04 | 6.000000 | 5.000000 | 13.000000 | 3.000000 | 18.000000 | 14.000000 | 4.000000 | 1166.470000 | 32.242619 | 214.000000 | 66.334750 | 133.568502 | 336.822178 | 1.000000 |
| 75% | 41.000000 | 7.305115e+04 | 7.000000 | 7.000000 | 20.000000 | 5.000000 | 28.000000 | 18.000000 | 8.000000 | 1961.510000 | 36.409723 | 296.000000 | 150.135520 | 265.706212 | 474.061722 | 1.000000 |
| max | 102.000000 | 2.365819e+07 | 10.000000 | 10.000000 | 34.000000 | 9.000000 | 67.000000 | 4388.000000 | 2592.000000 | 4998.070000 | 49.564519 | 397.000000 | 82122.000000 | 10000.000000 | 1602.040519 | 2.000000 |
In [ ]:
credit_df = credit_df[credit_df['Delay_from_due_date'] >= 0]
In [ ]:
len(credit_df[credit_df['Num_of_Delayed_Payment'] > 40]) / len(credit_df)
Out[ ]:
0.007340122947059363In [ ]:
credit_df = credit_df[(credit_df['Num_of_Delayed_Payment'] >= 0) & (credit_df['Num_of_Delayed_Payment'] <= 30)]
In [ ]:
credit_df.describe()
Out[ ]:
| Age | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Total_EMI_per_month | Amount_invested_monthly | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 10002.000000 | 1.000200e+04 | 10002.000000 | 10002.000000 | 10002.000000 | 10002.000000 | 10002.00000 | 10002.000000 | 9814.000000 | 10002.000000 | 10002.000000 | 9104.000000 | 10002.000000 | 9547.000000 | 9893.000000 | 10002.000000 |
| mean | 32.968406 | 1.935583e+05 | 5.417716 | 5.554789 | 14.709758 | 3.570886 | 21.29764 | 13.465007 | 25.625943 | 1439.794782 | 32.292003 | 215.808216 | 1339.797436 | 641.939750 | 403.918970 | 0.858528 |
| std | 10.831651 | 1.511306e+06 | 2.582985 | 2.065096 | 8.784414 | 2.445123 | 14.83699 | 6.201547 | 188.187465 | 1161.828644 | 5.093325 | 99.435323 | 8284.933619 | 2060.942992 | 216.227940 | 0.649646 |
| min | 14.000000 | 7.005930e+03 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.340000 | 20.992914 | 1.000000 | 0.000000 | 0.000000 | 0.088628 | 0.000000 |
| 25% | 24.000000 | 1.938372e+04 | 4.000000 | 4.000000 | 8.000000 | 2.000000 | 10.00000 | 9.000000 | 2.000000 | 572.750000 | 28.139668 | 139.750000 | 29.867829 | 72.716328 | 271.565853 | 0.000000 |
| 50% | 33.000000 | 3.779779e+04 | 6.000000 | 5.000000 | 13.000000 | 3.000000 | 18.00000 | 14.000000 | 4.000000 | 1173.755000 | 32.255683 | 213.000000 | 66.419970 | 132.962276 | 336.452745 | 1.000000 |
| 75% | 41.000000 | 7.258242e+04 | 7.000000 | 7.000000 | 20.000000 | 5.000000 | 28.00000 | 18.000000 | 8.000000 | 1981.990000 | 36.433628 | 296.000000 | 149.683104 | 265.027815 | 471.852376 | 1.000000 |
| max | 102.000000 | 2.365819e+07 | 10.000000 | 10.000000 | 34.000000 | 9.000000 | 67.00000 | 28.000000 | 2592.000000 | 4998.070000 | 49.564519 | 397.000000 | 82122.000000 | 10000.000000 | 1602.040519 | 2.000000 |
In [ ]:
credit_df['Num_Credit_Inquiries'] = credit_df['Num_Credit_Inquiries'].fillna(0)
In [ ]:
credit_df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10002 entries, 0 to 12498
Data columns (total 20 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Age 10002 non-null int64
1 Occupation 10002 non-null object
2 Annual_Income 10002 non-null float64
3 Num_Bank_Accounts 10002 non-null int64
4 Num_Credit_Card 10002 non-null int64
5 Interest_Rate 10002 non-null int64
6 Num_of_Loan 10002 non-null int64
7 Type_of_Loan 8893 non-null object
8 Delay_from_due_date 10002 non-null int64
9 Num_of_Delayed_Payment 10002 non-null float64
10 Num_Credit_Inquiries 10002 non-null float64
11 Outstanding_Debt 10002 non-null float64
12 Credit_Utilization_Ratio 10002 non-null float64
13 Credit_History_Age 9104 non-null float64
14 Payment_of_Min_Amount 10002 non-null object
15 Total_EMI_per_month 10002 non-null float64
16 Amount_invested_monthly 9547 non-null float64
17 Payment_Behaviour 10002 non-null object
18 Monthly_Balance 9893 non-null float64
19 Credit_Score 10002 non-null int64
dtypes: float64(9), int64(7), object(4)
memory usage: 1.6+ MB
In [ ]:
credit_df.isna().sum()
Out[ ]:
Age 0
Occupation 0
Annual_Income 0
Num_Bank_Accounts 0
Num_Credit_Card 0
Interest_Rate 0
Num_of_Loan 0
Type_of_Loan 1109
Delay_from_due_date 0
Num_of_Delayed_Payment 0
Num_Credit_Inquiries 0
Outstanding_Debt 0
Credit_Utilization_Ratio 0
Credit_History_Age 898
Payment_of_Min_Amount 0
Total_EMI_per_month 0
Amount_invested_monthly 455
Payment_Behaviour 0
Monthly_Balance 109
Credit_Score 0
dtype: int64In [ ]:
credit_df.head()
Out[ ]:
| Age | Occupation | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Type_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Payment_of_Min_Amount | Total_EMI_per_month | Amount_invested_monthly | Payment_Behaviour | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 23 | Scientist | 19114.12 | 3 | 4 | 3 | 4 | Auto Loan, Credit-Builder Loan, Personal Loan,... | 3 | 7.0 | 4.0 | 809.98 | 26.822620 | 265.0 | No | 49.574949 | 80.415295 | High_spent_Small_value_payments | 312.494089 | 2 |
| 1 | 28 | _______ | 34847.84 | 2 | 4 | 6 | 1 | Credit-Builder Loan | 3 | 4.0 | 2.0 | 605.03 | 24.464031 | 319.0 | No | 18.816215 | 104.291825 | Low_spent_Small_value_payments | 470.690627 | 1 |
| 2 | 34 | _______ | 143162.64 | 1 | 5 | 8 | 3 | Auto Loan, Auto Loan, and Not Specified | 5 | 8.0 | 3.0 | 1303.01 | 28.616735 | 213.0 | No | 246.992320 | 168.413703 | !@9#%8 | 1043.315978 | 2 |
| 3 | 54 | Entrepreneur | 30689.89 | 2 | 5 | 4 | 1 | Not Specified | 0 | 6.0 | 4.0 | 632.46 | 26.544229 | 207.0 | No | 16.415452 | 81.228859 | Low_spent_Large_value_payments | 433.604773 | 1 |
| 6 | 33 | Lawyer | 131313.40 | 0 | 1 | 8 | 2 | Credit-Builder Loan, and Mortgage Loan | 0 | 3.0 | 2.0 | 352.16 | 32.200509 | 367.0 | NM | 137.644605 | 378.171253 | High_spent_Medium_value_payments | 858.462474 | 2 |
In [ ]:
sns.displot(credit_df['Credit_History_Age'])
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x7fbfb6a859c0>
In [ ]:
sns.displot(credit_df['Amount_invested_monthly'])
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x7fbfb799fac0>
In [ ]:
sns.displot(credit_df['Monthly_Balance'])
Out[ ]:
<seaborn.axisgrid.FacetGrid at 0x7fbfb7a196f0>
In [ ]:
credit_df = credit_df.fillna(credit_df.median())
<ipython-input-52-98962993b203>:1: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.
credit_df = credit_df.fillna(credit_df.median())
In [ ]:
credit_df.isna().sum()
Out[ ]:
Age 0
Occupation 0
Annual_Income 0
Num_Bank_Accounts 0
Num_Credit_Card 0
Interest_Rate 0
Num_of_Loan 0
Type_of_Loan 1109
Delay_from_due_date 0
Num_of_Delayed_Payment 0
Num_Credit_Inquiries 0
Outstanding_Debt 0
Credit_Utilization_Ratio 0
Credit_History_Age 0
Payment_of_Min_Amount 0
Total_EMI_per_month 0
Amount_invested_monthly 0
Payment_Behaviour 0
Monthly_Balance 0
Credit_Score 0
dtype: int64In [ ]:
credit_df.head()
Out[ ]:
| Age | Occupation | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Type_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Payment_of_Min_Amount | Total_EMI_per_month | Amount_invested_monthly | Payment_Behaviour | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 23 | Scientist | 19114.12 | 3 | 4 | 3 | 4 | Auto Loan, Credit-Builder Loan, Personal Loan,... | 3 | 7.0 | 4.0 | 809.98 | 26.822620 | 265.0 | No | 49.574949 | 80.415295 | High_spent_Small_value_payments | 312.494089 | 2 |
| 1 | 28 | _______ | 34847.84 | 2 | 4 | 6 | 1 | Credit-Builder Loan | 3 | 4.0 | 2.0 | 605.03 | 24.464031 | 319.0 | No | 18.816215 | 104.291825 | Low_spent_Small_value_payments | 470.690627 | 1 |
| 2 | 34 | _______ | 143162.64 | 1 | 5 | 8 | 3 | Auto Loan, Auto Loan, and Not Specified | 5 | 8.0 | 3.0 | 1303.01 | 28.616735 | 213.0 | No | 246.992320 | 168.413703 | !@9#%8 | 1043.315978 | 2 |
| 3 | 54 | Entrepreneur | 30689.89 | 2 | 5 | 4 | 1 | Not Specified | 0 | 6.0 | 4.0 | 632.46 | 26.544229 | 207.0 | No | 16.415452 | 81.228859 | Low_spent_Large_value_payments | 433.604773 | 1 |
| 6 | 33 | Lawyer | 131313.40 | 0 | 1 | 8 | 2 | Credit-Builder Loan, and Mortgage Loan | 0 | 3.0 | 2.0 | 352.16 | 32.200509 | 367.0 | NM | 137.644605 | 378.171253 | High_spent_Medium_value_payments | 858.462474 | 2 |
- Type_of_Loan에 존재하는 모든 대출 이름을 확인하기
- type_list에 저장
In [ ]:
credit_df['Type_of_Loan'] = credit_df['Type_of_Loan'].str.replace('and ', '')
In [ ]:
credit_df.head()
Out[ ]:
| Age | Occupation | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Type_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Payment_of_Min_Amount | Total_EMI_per_month | Amount_invested_monthly | Payment_Behaviour | Monthly_Balance | Credit_Score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 23 | Scientist | 19114.12 | 3 | 4 | 3 | 4 | Auto Loan, Credit-Builder Loan, Personal Loan,... | 3 | 7.0 | 4.0 | 809.98 | 26.822620 | 265.0 | No | 49.574949 | 80.415295 | High_spent_Small_value_payments | 312.494089 | 2 |
| 1 | 28 | _______ | 34847.84 | 2 | 4 | 6 | 1 | Credit-Builder Loan | 3 | 4.0 | 2.0 | 605.03 | 24.464031 | 319.0 | No | 18.816215 | 104.291825 | Low_spent_Small_value_payments | 470.690627 | 1 |
| 2 | 34 | _______ | 143162.64 | 1 | 5 | 8 | 3 | Auto Loan, Auto Loan, Not Specified | 5 | 8.0 | 3.0 | 1303.01 | 28.616735 | 213.0 | No | 246.992320 | 168.413703 | !@9#%8 | 1043.315978 | 2 |
| 3 | 54 | Entrepreneur | 30689.89 | 2 | 5 | 4 | 1 | Not Specified | 0 | 6.0 | 4.0 | 632.46 | 26.544229 | 207.0 | No | 16.415452 | 81.228859 | Low_spent_Large_value_payments | 433.604773 | 1 |
| 6 | 33 | Lawyer | 131313.40 | 0 | 1 | 8 | 2 | Credit-Builder Loan, Mortgage Loan | 0 | 3.0 | 2.0 | 352.16 | 32.200509 | 367.0 | NM | 137.644605 | 378.171253 | High_spent_Medium_value_payments | 858.462474 | 2 |
In [ ]:
credit_df['Type_of_Loan'] = credit_df['Type_of_Loan'].fillna('No Loan')
In [ ]:
credit_df.isna().sum()
Out[ ]:
Age 0
Occupation 0
Annual_Income 0
Num_Bank_Accounts 0
Num_Credit_Card 0
Interest_Rate 0
Num_of_Loan 0
Type_of_Loan 0
Delay_from_due_date 0
Num_of_Delayed_Payment 0
Num_Credit_Inquiries 0
Outstanding_Debt 0
Credit_Utilization_Ratio 0
Credit_History_Age 0
Payment_of_Min_Amount 0
Total_EMI_per_month 0
Amount_invested_monthly 0
Payment_Behaviour 0
Monthly_Balance 0
Credit_Score 0
dtype: int64In [ ]:
type_list = set(credit_df['Type_of_Loan'].str.split(', ').sum())
type_list
Out[ ]:
{'Auto Loan',
'Credit-Builder Loan',
'Debt Consolidation Loan',
'Home Equity Loan',
'Mortgage Loan',
'No Loan',
'Not Specified',
'Payday Loan',
'Personal Loan',
'Student Loan'}In [ ]:
# credit_df['Type_of_Loan'].unique()
In [ ]:
# type_set = set()
# for i in credit_df['Type_of_Loan'].unique():
# if type(i) == str:
# i = i.replace('and ', '')
# for j in i.split(', '):
# type_set.add(j)
In [ ]:
# type_list = list(type_set)
In [ ]:
# type_list
- 모든 대출이름을 파생변수로 생성
- 해당 대출을 발생한 경우 1 아니면 0으로 채움
In [ ]:
for i in type_list:
credit_df[i] = credit_df['Type_of_Loan'].apply(lambda x: 1 if i in x else 0)
In [ ]:
credit_df.head()
Out[ ]:
| Age | Occupation | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Type_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Payment_of_Min_Amount | Total_EMI_per_month | Amount_invested_monthly | Payment_Behaviour | Monthly_Balance | Credit_Score | Home Equity Loan | Personal Loan | Credit-Builder Loan | Mortgage Loan | No Loan | Debt Consolidation Loan | Auto Loan | Payday Loan | Student Loan | Not Specified | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 23 | Scientist | 19114.12 | 3 | 4 | 3 | 4 | Auto Loan, Credit-Builder Loan, Personal Loan,... | 3 | 7.0 | 4.0 | 809.98 | 26.822620 | 265.0 | No | 49.574949 | 80.415295 | High_spent_Small_value_payments | 312.494089 | 2 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1 | 28 | _______ | 34847.84 | 2 | 4 | 6 | 1 | Credit-Builder Loan | 3 | 4.0 | 2.0 | 605.03 | 24.464031 | 319.0 | No | 18.816215 | 104.291825 | Low_spent_Small_value_payments | 470.690627 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 34 | _______ | 143162.64 | 1 | 5 | 8 | 3 | Auto Loan, Auto Loan, Not Specified | 5 | 8.0 | 3.0 | 1303.01 | 28.616735 | 213.0 | No | 246.992320 | 168.413703 | !@9#%8 | 1043.315978 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 3 | 54 | Entrepreneur | 30689.89 | 2 | 5 | 4 | 1 | Not Specified | 0 | 6.0 | 4.0 | 632.46 | 26.544229 | 207.0 | No | 16.415452 | 81.228859 | Low_spent_Large_value_payments | 433.604773 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6 | 33 | Lawyer | 131313.40 | 0 | 1 | 8 | 2 | Credit-Builder Loan, Mortgage Loan | 0 | 3.0 | 2.0 | 352.16 | 32.200509 | 367.0 | NM | 137.644605 | 378.171253 | High_spent_Medium_value_payments | 858.462474 | 2 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
In [ ]:
# credit_df['Type_of_Loan'].isna().sum()
In [ ]:
# credit_df['Type_of_Loan'] = credit_df['Type_of_Loan'].fillna('')
In [ ]:
# for i in type_list:
# string = 'Type_of_Loan_' + i.replace(' ', '_')
# credit_df[string] = credit_df['Type_of_Loan'].apply(lambda x: 0 if str(x).find(i) else 1)
In [ ]:
# credit_df
In [ ]:
credit_df.drop('Type_of_Loan', axis=1, inplace=True)
In [ ]:
credit_df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10002 entries, 0 to 12498
Data columns (total 29 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Age 10002 non-null int64
1 Occupation 10002 non-null object
2 Annual_Income 10002 non-null float64
3 Num_Bank_Accounts 10002 non-null int64
4 Num_Credit_Card 10002 non-null int64
5 Interest_Rate 10002 non-null int64
6 Num_of_Loan 10002 non-null int64
7 Delay_from_due_date 10002 non-null int64
8 Num_of_Delayed_Payment 10002 non-null float64
9 Num_Credit_Inquiries 10002 non-null float64
10 Outstanding_Debt 10002 non-null float64
11 Credit_Utilization_Ratio 10002 non-null float64
12 Credit_History_Age 10002 non-null float64
13 Payment_of_Min_Amount 10002 non-null object
14 Total_EMI_per_month 10002 non-null float64
15 Amount_invested_monthly 10002 non-null float64
16 Payment_Behaviour 10002 non-null object
17 Monthly_Balance 10002 non-null float64
18 Credit_Score 10002 non-null int64
19 Home Equity Loan 10002 non-null int64
20 Personal Loan 10002 non-null int64
21 Credit-Builder Loan 10002 non-null int64
22 Mortgage Loan 10002 non-null int64
23 No Loan 10002 non-null int64
24 Debt Consolidation Loan 10002 non-null int64
25 Auto Loan 10002 non-null int64
26 Payday Loan 10002 non-null int64
27 Student Loan 10002 non-null int64
28 Not Specified 10002 non-null int64
dtypes: float64(9), int64(17), object(3)
memory usage: 2.3+ MB
In [ ]:
credit_df['Occupation'].value_counts()
Out[ ]:
_______ 673
Lawyer 664
Mechanic 646
Scientist 640
Engineer 640
Architect 632
Teacher 624
Developer 621
Entrepreneur 620
Media_Manager 616
Accountant 611
Doctor 608
Musician 607
Journalist 606
Manager 602
Writer 592
Name: Occupation, dtype: int64In [ ]:
credit_df['Occupation'] = credit_df['Occupation'].replace('_______', 'Unknown')
In [ ]:
credit_df['Occupation'].value_counts()
Out[ ]:
Unknown 673
Lawyer 664
Mechanic 646
Scientist 640
Engineer 640
Architect 632
Teacher 624
Developer 621
Entrepreneur 620
Media_Manager 616
Accountant 611
Doctor 608
Musician 607
Journalist 606
Manager 602
Writer 592
Name: Occupation, dtype: int64In [ ]:
credit_df['Payment_of_Min_Amount'].value_counts()
Out[ ]:
Yes 5315
No 3489
NM 1198
Name: Payment_of_Min_Amount, dtype: int64In [ ]:
credit_df['Payment_Behaviour'].value_counts()
Out[ ]:
Low_spent_Small_value_payments 2505
High_spent_Medium_value_payments 1794
High_spent_Large_value_payments 1453
Low_spent_Medium_value_payments 1376
High_spent_Small_value_payments 1136
Low_spent_Large_value_payments 994
!@9#%8 744
Name: Payment_Behaviour, dtype: int64In [ ]:
credit_df['Payment_Behaviour'] = credit_df['Payment_Behaviour'].replace('!@9#%8', 'Unknown')
In [ ]:
credit_df['Payment_Behaviour'].value_counts()
Out[ ]:
Low_spent_Small_value_payments 2505
High_spent_Medium_value_payments 1794
High_spent_Large_value_payments 1453
Low_spent_Medium_value_payments 1376
High_spent_Small_value_payments 1136
Low_spent_Large_value_payments 994
Unknown 744
Name: Payment_Behaviour, dtype: int64In [ ]:
credit_df = pd.get_dummies(credit_df, columns=['Occupation', 'Payment_of_Min_Amount', 'Payment_Behaviour'])
In [ ]:
credit_df.head()
Out[ ]:
| Age | Annual_Income | Num_Bank_Accounts | Num_Credit_Card | Interest_Rate | Num_of_Loan | Delay_from_due_date | Num_of_Delayed_Payment | Num_Credit_Inquiries | Outstanding_Debt | Credit_Utilization_Ratio | Credit_History_Age | Total_EMI_per_month | Amount_invested_monthly | Monthly_Balance | Credit_Score | Home Equity Loan | Personal Loan | Credit-Builder Loan | Mortgage Loan | No Loan | Debt Consolidation Loan | Auto Loan | Payday Loan | Student Loan | ... | Occupation_Architect | Occupation_Developer | Occupation_Doctor | Occupation_Engineer | Occupation_Entrepreneur | Occupation_Journalist | Occupation_Lawyer | Occupation_Manager | Occupation_Mechanic | Occupation_Media_Manager | Occupation_Musician | Occupation_Scientist | Occupation_Teacher | Occupation_Unknown | Occupation_Writer | Payment_of_Min_Amount_NM | Payment_of_Min_Amount_No | Payment_of_Min_Amount_Yes | Payment_Behaviour_High_spent_Large_value_payments | Payment_Behaviour_High_spent_Medium_value_payments | Payment_Behaviour_High_spent_Small_value_payments | Payment_Behaviour_Low_spent_Large_value_payments | Payment_Behaviour_Low_spent_Medium_value_payments | Payment_Behaviour_Low_spent_Small_value_payments | Payment_Behaviour_Unknown | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 23 | 19114.12 | 3 | 4 | 3 | 4 | 3 | 7.0 | 4.0 | 809.98 | 26.822620 | 265.0 | 49.574949 | 80.415295 | 312.494089 | 2 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 1 | 28 | 34847.84 | 2 | 4 | 6 | 1 | 3 | 4.0 | 2.0 | 605.03 | 24.464031 | 319.0 | 18.816215 | 104.291825 | 470.690627 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2 | 34 | 143162.64 | 1 | 5 | 8 | 3 | 5 | 8.0 | 3.0 | 1303.01 | 28.616735 | 213.0 | 246.992320 | 168.413703 | 1043.315978 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 3 | 54 | 30689.89 | 2 | 5 | 4 | 1 | 0 | 6.0 | 4.0 | 632.46 | 26.544229 | 207.0 | 16.415452 | 81.228859 | 433.604773 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 6 | 33 | 131313.40 | 0 | 1 | 8 | 2 | 0 | 3.0 | 2.0 | 352.16 | 32.200509 | 367.0 | 137.644605 | 378.171253 | 858.462474 | 2 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
5 rows × 52 columns
In [ ]:
from sklearn.model_selection import train_test_split
In [ ]:
len(credit_df)
Out[ ]:
10002In [ ]:
X_train, X_test, y_train, y_test = train_test_split(credit_df.drop('Credit_Score', axis=1), credit_df['Credit_Score'], test_size=0.2, random_state=10)
2. lightGBM(LGBM)
- 트리기반 학습 알고리즘인 gradient boosting 방식의 프레임 워크
- 의사결정나무, 랜덤포레스트는 균형 트리 분할(level wise) 방식이라면, LGBM은 리프 중심 트리 분할(leaf wise)
- GBM(Gradient Boosting): 모델1을 통해 y를 예측하고, 모델2에 데이터를 넣어 y를 예측, 모델3에 넣어 y를 예측하는 방식
- 학습하는데 걸리는 시간이 적음(빠른 속도)
- 메모리 사용량이 상대적으로 적은편
- 적은 데이터셋을 사용할 경우 과적합 가능성이 매우 큼(일반적으로 데이터가 10000개 이상은 사용해야 함)
In [ ]:
from lightgbm import LGBMClassifier
In [ ]:
base_model = LGBMClassifier(random_state=10)
In [ ]:
base_model.fit(X_train, y_train)
LGBMClassifier(random_state=10)In [ ]:
pred1 = base_model.predict(X_test)
In [ ]:
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_auc_score
In [ ]:
accuracy_score(y_test, pred1)
Out[ ]:
0.7351324337831084In [ ]:
confusion_matrix(y_test, pred1)
Out[ ]:
array([[407, 148, 27],
[145, 896, 91],
[ 3, 116, 168]])In [ ]:
print(classification_report(y_test, pred1))
precision recall f1-score support
0 0.73 0.70 0.72 582
1 0.77 0.79 0.78 1132
2 0.59 0.59 0.59 287
accuracy 0.74 2001
macro avg 0.70 0.69 0.69 2001
weighted avg 0.73 0.74 0.73 2001
In [ ]:
proba1 = base_model.predict_proba(X_test)
proba1
Out[ ]:
array([[6.98737764e-02, 8.38143193e-01, 9.19830307e-02],
[8.50755391e-01, 1.49084385e-01, 1.60223719e-04],
[2.74240729e-03, 9.96809944e-01, 4.47648956e-04],
...,
[8.60948233e-01, 1.38960947e-01, 9.08196360e-05],
[8.96972732e-01, 1.02822925e-01, 2.04342759e-04],
[6.65766332e-02, 2.29493501e-01, 7.03929866e-01]])In [ ]:
6.98737764e-02
Out[ ]:
0.0698737764In [ ]:
8.38143193e-01
Out[ ]:
0.838143193In [ ]:
9.19830307e-02
Out[ ]:
0.0919830307In [ ]:
roc_auc_score(y_test, proba1, multi_class='ovr')
Out[ ]:
0.89725664252795173. RandomizedSearchCV
- 분류기를 결정하고 해당 분류기의 최적의 하이퍼 파라미터를 찾기 위한 방법
- 튜닐하고싶은 파라미터를 지정하여 파라미터 값의 범위를 정하고, n_iter값을 설정하여 해당 수 만큼 Random하게 조합하여 반복 적용
In [ ]:
# n_estimators: 반복 수행하는 트리의 갯수(기본값: 100), 값을 크게 지정하면 학습시간도 오래걸리며, 과적합이 발생할 수 있음
# max_depth: 트리의 최대 깊이(기본값: -1) 최대 깊이 지정 x = -1
# learning_rate: 학습률(기본값:0.1)
params = {
'n_estimators':[100, 300, 500, 1000],
'max_depth':[-1, 30, 50, 100],
'num_leaves':[5, 10, 20, 50],
'learning_rate':[0.01, 0.05, 0.1, 0.5]
}
In [ ]:
lgbm = LGBMClassifier(random_state=10)
In [ ]:
from sklearn.model_selection import RandomizedSearchCV
In [ ]:
rand_lgbm = RandomizedSearchCV(lgbm, params, n_iter=30, random_state=10)
In [ ]:
rand_lgbm.fit(X_train, y_train)
RandomizedSearchCV(estimator=LGBMClassifier(random_state=10), n_iter=30,
param_distributions={'learning_rate': [0.01, 0.05, 0.1, 0.5],
'max_depth': [-1, 30, 50, 100],
'n_estimators': [100, 300, 500, 1000],
'num_leaves': [5, 10, 20, 50]},
random_state=10)LGBMClassifier(random_state=10)LGBMClassifier(random_state=10)In [ ]:
rand_lgbm.cv_results_
Out[ ]:
{'mean_fit_time': array([5.6484364 , 1.73388333, 0.36017942, 0.96539888, 1.69560804,
2.40308676, 2.60327592, 0.34279518, 2.41570749, 2.37531099,
0.83692245, 1.27582097, 8.6895473 , 1.64173074, 0.59101348,
1.63705478, 2.68141861, 4.75189834, 2.8852468 , 1.26868601,
0.27608867, 1.08244863, 1.06560321, 8.9055347 , 3.77189927,
4.54627204, 2.85441103, 0.80371432, 1.79698 , 3.0112771 ]),
'std_fit_time': array([1.57765455, 0.72479626, 0.22021282, 0.01859736, 0.98745104,
0.96295703, 0.9920637 , 0.00792347, 0.96262164, 1.04525756,
0.01926879, 0.97446837, 1.38614156, 0.91807949, 0.02578438,
0.88184976, 0.98658718, 1.27220549, 1.17661432, 0.9898592 ,
0.01082565, 0.01307149, 0.8267257 , 1.36993971, 1.16350347,
1.21595639, 1.03128059, 0.02613693, 0.97689834, 0.82171394]),
'mean_score_time': array([0.1926908 , 0.06419392, 0.01278734, 0.06288419, 0.06781793,
0.13456798, 0.10979614, 0.01137953, 0.13978257, 0.10621195,
0.01889424, 0.04018569, 0.37844009, 0.060566 , 0.03051939,
0.07253356, 0.10876837, 0.17245636, 0.14127903, 0.02844281,
0.01511965, 0.06118155, 0.0365643 , 0.36838446, 0.17110085,
0.22610383, 0.09424386, 0.04367356, 0.08677335, 0.22115645]),
'std_score_time': array([0.00677176, 0.01582909, 0.00082717, 0.004041 , 0.00899382,
0.00247296, 0.00562526, 0.00058882, 0.01294144, 0.01212454,
0.00042924, 0.00172177, 0.00892722, 0.00132718, 0.00057893,
0.01510708, 0.00513922, 0.00994854, 0.00465873, 0.0024984 ,
0.00176426, 0.0085586 , 0.0134188 , 0.01687875, 0.00380738,
0.00953068, 0.00611555, 0.00236528, 0.00348549, 0.06316808]),
'param_num_leaves': masked_array(data=[50, 20, 5, 5, 20, 5, 20, 10, 5, 50, 50, 10, 50, 20, 5,
20, 50, 50, 5, 50, 10, 5, 5, 50, 10, 50, 50, 10, 10,
10],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False],
fill_value='?',
dtype=object),
'param_n_estimators': masked_array(data=[500, 300, 100, 500, 300, 1000, 500, 100, 1000, 300,
100, 300, 1000, 300, 300, 300, 300, 500, 1000, 100,
100, 500, 300, 1000, 1000, 1000, 300, 300, 500, 1000],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False],
fill_value='?',
dtype=object),
'param_max_depth': masked_array(data=[30, 50, 30, 30, 50, 50, 30, 100, -1, 30, 50, 50, 100,
100, 30, 30, 30, 100, 100, 30, 30, 100, 100, 100, 100,
-1, 100, 30, 100, -1],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False],
fill_value='?',
dtype=object),
'param_learning_rate': masked_array(data=[0.05, 0.01, 0.1, 0.1, 0.05, 0.5, 0.01, 0.01, 0.05, 0.5,
0.01, 0.5, 0.05, 0.5, 0.1, 0.5, 0.05, 0.01, 0.1, 0.5,
0.5, 0.01, 0.5, 0.01, 0.01, 0.5, 0.01, 0.1, 0.1, 0.5],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False],
fill_value='?',
dtype=object),
'params': [{'num_leaves': 50,
'n_estimators': 500,
'max_depth': 30,
'learning_rate': 0.05},
{'num_leaves': 20,
'n_estimators': 300,
'max_depth': 50,
'learning_rate': 0.01},
{'num_leaves': 5,
'n_estimators': 100,
'max_depth': 30,
'learning_rate': 0.1},
{'num_leaves': 5,
'n_estimators': 500,
'max_depth': 30,
'learning_rate': 0.1},
{'num_leaves': 20,
'n_estimators': 300,
'max_depth': 50,
'learning_rate': 0.05},
{'num_leaves': 5,
'n_estimators': 1000,
'max_depth': 50,
'learning_rate': 0.5},
{'num_leaves': 20,
'n_estimators': 500,
'max_depth': 30,
'learning_rate': 0.01},
{'num_leaves': 10,
'n_estimators': 100,
'max_depth': 100,
'learning_rate': 0.01},
{'num_leaves': 5,
'n_estimators': 1000,
'max_depth': -1,
'learning_rate': 0.05},
{'num_leaves': 50,
'n_estimators': 300,
'max_depth': 30,
'learning_rate': 0.5},
{'num_leaves': 50,
'n_estimators': 100,
'max_depth': 50,
'learning_rate': 0.01},
{'num_leaves': 10,
'n_estimators': 300,
'max_depth': 50,
'learning_rate': 0.5},
{'num_leaves': 50,
'n_estimators': 1000,
'max_depth': 100,
'learning_rate': 0.05},
{'num_leaves': 20,
'n_estimators': 300,
'max_depth': 100,
'learning_rate': 0.5},
{'num_leaves': 5,
'n_estimators': 300,
'max_depth': 30,
'learning_rate': 0.1},
{'num_leaves': 20,
'n_estimators': 300,
'max_depth': 30,
'learning_rate': 0.5},
{'num_leaves': 50,
'n_estimators': 300,
'max_depth': 30,
'learning_rate': 0.05},
{'num_leaves': 50,
'n_estimators': 500,
'max_depth': 100,
'learning_rate': 0.01},
{'num_leaves': 5,
'n_estimators': 1000,
'max_depth': 100,
'learning_rate': 0.1},
{'num_leaves': 50,
'n_estimators': 100,
'max_depth': 30,
'learning_rate': 0.5},
{'num_leaves': 10,
'n_estimators': 100,
'max_depth': 30,
'learning_rate': 0.5},
{'num_leaves': 5,
'n_estimators': 500,
'max_depth': 100,
'learning_rate': 0.01},
{'num_leaves': 5,
'n_estimators': 300,
'max_depth': 100,
'learning_rate': 0.5},
{'num_leaves': 50,
'n_estimators': 1000,
'max_depth': 100,
'learning_rate': 0.01},
{'num_leaves': 10,
'n_estimators': 1000,
'max_depth': 100,
'learning_rate': 0.01},
{'num_leaves': 50,
'n_estimators': 1000,
'max_depth': -1,
'learning_rate': 0.5},
{'num_leaves': 50,
'n_estimators': 300,
'max_depth': 100,
'learning_rate': 0.01},
{'num_leaves': 10,
'n_estimators': 300,
'max_depth': 30,
'learning_rate': 0.1},
{'num_leaves': 10,
'n_estimators': 500,
'max_depth': 100,
'learning_rate': 0.1},
{'num_leaves': 10,
'n_estimators': 1000,
'max_depth': -1,
'learning_rate': 0.5}],
'split0_test_score': array([0.73329169, 0.73016864, 0.7339163 , 0.73891318, 0.73454091,
0.72517177, 0.72767021, 0.71205497, 0.73766396, 0.73266708,
0.71767645, 0.71642723, 0.7339163 , 0.72267333, 0.74203623,
0.72267333, 0.7339163 , 0.72891943, 0.7339163 , 0.72329794,
0.72829482, 0.7339163 , 0.72392255, 0.73516552, 0.73079325,
0.72829482, 0.72517177, 0.7270456 , 0.72579638, 0.72517177]),
'split1_test_score': array([0.725 , 0.741875, 0.754375, 0.740625, 0.7325 , 0.718125,
0.745625, 0.736875, 0.741875, 0.72125 , 0.735 , 0.728125,
0.72875 , 0.723125, 0.741875, 0.723125, 0.7275 , 0.738125,
0.733125, 0.725 , 0.725625, 0.76 , 0.7275 , 0.7325 ,
0.744375, 0.720625, 0.74 , 0.73625 , 0.72125 , 0.71375 ]),
'split2_test_score': array([0.738125, 0.7525 , 0.7425 , 0.725 , 0.7275 , 0.726875,
0.743125, 0.719375, 0.725 , 0.735 , 0.7325 , 0.731875,
0.736875, 0.73625 , 0.7325 , 0.73625 , 0.734375, 0.74625 ,
0.730625, 0.73875 , 0.7325 , 0.741875, 0.735 , 0.7375 ,
0.741875, 0.738125, 0.738125, 0.734375, 0.73875 , 0.72875 ]),
'split3_test_score': array([0.73125 , 0.7475 , 0.735625, 0.72875 , 0.73 , 0.715625,
0.745625, 0.726875, 0.73 , 0.7275 , 0.733125, 0.716875,
0.734375, 0.71125 , 0.73125 , 0.71125 , 0.731875, 0.73625 ,
0.723125, 0.731875, 0.71875 , 0.73375 , 0.721875, 0.729375,
0.738125, 0.725 , 0.7475 , 0.72625 , 0.72125 , 0.71375 ]),
'split4_test_score': array([0.725625, 0.731875, 0.73 , 0.72625 , 0.72625 , 0.715625,
0.73125 , 0.728125, 0.725 , 0.713125, 0.7325 , 0.72625 ,
0.72625 , 0.730625, 0.72875 , 0.730625, 0.7225 , 0.730625,
0.719375, 0.72125 , 0.721875, 0.728125, 0.725625, 0.729375,
0.740625, 0.718125, 0.735625, 0.726875, 0.720625, 0.71125 ]),
'mean_test_score': array([0.73065834, 0.74078373, 0.73928326, 0.73190764, 0.73015818,
0.72028435, 0.73865904, 0.72466099, 0.73190779, 0.72590842,
0.73016029, 0.72391045, 0.73203326, 0.72478467, 0.73528225,
0.72478467, 0.73003326, 0.73603389, 0.72803326, 0.72803459,
0.72540896, 0.73953326, 0.72678451, 0.7327831 , 0.73915865,
0.72603396, 0.73728435, 0.73015912, 0.72553428, 0.71853435]),
'std_test_score': array([0.00490687, 0.00866736, 0.00856124, 0.00655385, 0.00306781,
0.00480428, 0.00765039, 0.00840163, 0.0068052 , 0.00794967,
0.00630883, 0.00619945, 0.00391658, 0.00843757, 0.00558123,
0.00843757, 0.00448304, 0.00614264, 0.00576731, 0.00643807,
0.004806 , 0.01113118, 0.00450876, 0.00320111, 0.004643 ,
0.00698831, 0.00723753, 0.00425754, 0.00686346, 0.00703216]),
'rank_test_score': array([13, 1, 3, 12, 16, 29, 5, 27, 11, 22, 14, 28, 10, 25, 8, 25, 17,
7, 19, 18, 24, 2, 20, 9, 4, 21, 6, 15, 23, 30], dtype=int32)}In [ ]:
rand_lgbm.best_params_
Out[ ]:
{'num_leaves': 20, 'n_estimators': 300, 'max_depth': 50, 'learning_rate': 0.01}In [ ]:
lgbm = LGBMClassifier(random_state=10, num_leaves= 20, n_estimators= 300, max_depth= 50, learning_rate= 0.01)
In [ ]:
lgbm.fit(X_train, y_train)
LGBMClassifier(learning_rate=0.01, max_depth=50, n_estimators=300,
num_leaves=20, random_state=10)In [ ]:
proba = lgbm.predict_proba(X_test)
In [ ]:
roc_auc_score(y_test, proba, multi_class='ovr')
Out[ ]:
0.9024448184054178In [ ]:
# 하이퍼 파라미터 튜닝전: 0.8972566425279517
# 하이퍼 파라미터 튜닝후: 0.9024448184054178
0.9024448184054178 - 0.8972566425279517
Out[ ]:
0.005188175877466117728x90
반응형
'파이썬 머신러닝, 딥러닝' 카테고리의 다른 글
| (Python) 파이토치 (0) | 2023.06.15 |
|---|---|
| (Python) KMeans (1) | 2023.06.15 |
| (Python) 랜덤 포레스트 (0) | 2023.06.15 |
| (Python) 서포트 벡터 머신 (0) | 2023.06.14 |
| (Python) 로지스틱 회귀 (0) | 2023.06.14 |
'파이썬 머신러닝, 딥러닝' Related Articles
more
