金融贷款批准预测项目

注意：本文引用自专业人工智能社区Venus AI

项目背景

在当前的金融市场中，违约率的不断波动对贷款审批流程提出了新的挑战。传统方法往往无法有效预测和管理这些风险，因此需要一种更智能、更可靠的方法来评估贷款申请。通过使用机器学习，我们可以从大量历史数据中学习并识别违约的潜在风险，这不仅能提高贷款批准的准确性，还能大大降低金融机构的损失。

经过训练的模型将用于预测新的贷款申请是否有高风险。这将帮助金融机构在贷款批准过程中做出更加明智的决策，减少不良贷款的比例，提高整体的财务健康状况。

数据集

我们项目使用的数据集包括了广泛的客户特征，这些特征反映了贷款申请者的财务状况和背景。具体包括：

性别(Gender)：申请人的性别。
婚姻状况(Married)：申请人的婚姻状态。
受抚养人数(Dependents)：申请人负责抚养的人数。
教育背景(Education)：申请人的教育水平。
是否自雇(Self_Employed)：申请人是否拥有自己的业务。
申请人收入(ApplicantIncome)：申请人的月收入。
共同申请人收入(CoapplicantIncome)：与申请人一同申请贷款的人的月收入。
贷款金额(LoanAmount)：申请的贷款总额。
贷款期限(Loan_Amount_Term)：预期的还款期限。
信用历史(Credit_History)：申请人的信用记录。
财产区域(Property_Area)：申请人财产所在的地理位置。

模型和依赖库

Models:

RandomForestRegressor
Decision Tree Regression
logistic regression

Libraries:

matplotlib==3.7.1
numpy==1.24.3
pandas==2.0.2
scikit_learn==1.2.2
seaborn==0.13.0

代码实现

金融贷款批准预测

项目背景

在金融领域，贷款审批是向任何人提供贷款之前需要执行的一项至关重要的任务。这确保了批准的贷款将来可以收回。然而，要确定一个人是否适合贷款或违约者，就很难确定有助于做出决定的性格和特征。

在这些情况下，使用机器学习的贷款预测模型成为非常有用的工具，可以根据过去的数据来预测该人是否违约。

我们获得了两个数据集（训练和测试），其中包含过去的交易，其中包括客户的一些特征以及显示客户是否违约的标签。我们建立了一个模型，可以在训练数据集上执行，并可以预测贷款是否应获得批准。

About Data:

导入库并加载数据

#Impoting libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

df_train = pd.read_csv("train_u6lujuX_CVtuZ9i.csv")
df_test = pd.read_csv("test_Y3wMUE5_7gLdaTN.csv")

df_train.head()

	Loan_ID	Gender	Married	Dependents	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Property_Area	Loan_Status
0	LP001002	Male	No	0	Graduate	No	5849	0.0	NaN	360.0	1.0	Urban	Y
1	LP001003	Male	Yes	1	Graduate	No	4583	1508.0	128.0	360.0	1.0	Rural	N
2	LP001005	Male	Yes	0	Graduate	Yes	3000	0.0	66.0	360.0	1.0	Urban	Y
3	LP001006	Male	Yes	0	Not Graduate	No	2583	2358.0	120.0	360.0	1.0	Urban	Y
4	LP001008	Male	No	0	Graduate	No	6000	0.0	141.0	360.0	1.0	Urban	Y

df_test.head()

	Loan_ID	Gender	Married	Dependents	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Property_Area
0	LP001015	Male	Yes	0	Graduate	No	5720	0	110.0	360.0	1.0	Urban
1	LP001022	Male	Yes	1	Graduate	No	3076	1500	126.0	360.0	1.0	Urban
2	LP001031	Male	Yes	2	Graduate	No	5000	1800	208.0	360.0	1.0	Urban
3	LP001035	Male	Yes	2	Graduate	No	2340	2546	100.0	360.0	NaN	Urban
4	LP001051	Male	No	0	Not Graduate	No	3276	0	78.0	360.0	1.0	Urban

#shape of data
df_train.shape

(614, 13)

#data summary
df_train.describe()

	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History
count	614.000000	614.000000	592.000000	600.00000	564.000000
mean	5403.459283	1621.245798	146.412162	342.00000	0.842199
std	6109.041673	2926.248369	85.587325	65.12041	0.364878
min	150.000000	0.000000	9.000000	12.00000	0.000000
25%	2877.500000	0.000000	100.000000	360.00000	1.000000
50%	3812.500000	1188.500000	128.000000	360.00000	1.000000
75%	5795.000000	2297.250000	168.000000	360.00000	1.000000
max	81000.000000	41667.000000	700.000000	480.00000	1.000000

df_train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 614 entries, 0 to 613
Data columns (total 13 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Loan_ID            614 non-null    object 
 1   Gender             601 non-null    object 
 2   Married            611 non-null    object 
 3   Dependents         599 non-null    object 
 4   Education          614 non-null    object 
 5   Self_Employed      582 non-null    object 
 6   ApplicantIncome    614 non-null    int64  
 7   CoapplicantIncome  614 non-null    float64
 8   LoanAmount         592 non-null    float64
 9   Loan_Amount_Term   600 non-null    float64
 10  Credit_History     564 non-null    float64
 11  Property_Area      614 non-null    object 
 12  Loan_Status        614 non-null    object 
dtypes: float64(4), int64(1), object(8)
memory usage: 62.5+ KB

数据清洗

# 检测空值
df_train.isna().sum()

Loan_ID               0
Gender               13
Married               3
Dependents           15
Education             0
Self_Employed        32
ApplicantIncome       0
CoapplicantIncome     0
LoanAmount           22
Loan_Amount_Term     14
Credit_History       50
Property_Area         0
Loan_Status           0
dtype: int64

有很多空值，Credit_History 的最大值为 50。

去除所有空值

# Dropping all the null values
drop_list = ['Gender','Married','Dependents','Self_Employed','LoanAmount','Loan_Amount_Term','Credit_History']
for col in drop_list:
 df_train = df_train[~df_train[col].isna()]

df_train.isna().sum()

Loan_ID              0
Gender               0
Married              0
Dependents           0
Education            0
Self_Employed        0
ApplicantIncome      0
CoapplicantIncome    0
LoanAmount           0
Loan_Amount_Term     0
Credit_History       0
Property_Area        0
Loan_Status          0
dtype: int64

Loan_ID 列没用,这里删除它

# dropping Loan_ID
df_train.drop(columns='Loan_ID',axis=1, inplace=True)

df_train.shape

(480, 12)

#data summary
df_train.describe()

	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History
count	480.000000	480.000000	480.000000	480.000000	480.000000
mean	5364.231250	1581.093583	144.735417	342.050000	0.854167
std	5668.251251	2617.692267	80.508164	65.212401	0.353307
min	150.000000	0.000000	9.000000	36.000000	0.000000
25%	2898.750000	0.000000	100.000000	360.000000	1.000000
50%	3859.000000	1084.500000	128.000000	360.000000	1.000000
75%	5852.500000	2253.250000	170.000000	360.000000	1.000000
max	81000.000000	33837.000000	600.000000	480.000000	1.000000

数据分析(EDA)

df_train.head()

	Gender	Married	Dependents	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Property_Area	Loan_Status
1	Male	Yes	1	Graduate	No	4583	1508.0	128.0	360.0	1.0	Rural	N
2	Male	Yes	0	Graduate	Yes	3000	0.0	66.0	360.0	1.0	Urban	Y
3	Male	Yes	0	Not Graduate	No	2583	2358.0	120.0	360.0	1.0	Urban	Y
4	Male	No	0	Graduate	No	6000	0.0	141.0	360.0	1.0	Urban	Y
5	Male	Yes	2	Graduate	Yes	5417	4196.0	267.0	360.0	1.0	Urban	Y

#distribution of Churn data
sns.displot(data=df_train,x='Loan_Status')

<seaborn.axisgrid.FacetGrid at 0x1f54d853bb0>

数据集是不平衡的,但是不是非常严重

自变量相对于因变量的分布.

# 设置分类特征
categorical_features=list(df_train.columns)
numeical_features = list(df_train.describe().columns)
for elem in numeical_features:
 categorical_features.remove(elem)
categorical_features = categorical_features[:-1]
categorical_features

['Gender',
 'Married',
 'Dependents',
 'Education',
 'Self_Employed',
 'Property_Area']

# Set categorical and numerical features
categorical_features = list(df_train.columns)
numerical_features = list(df_train.describe().columns)
for elem in numerical_features:
    categorical_features.remove(elem)
categorical_features.remove('Loan_Status')  # Assuming 'Loan_Status' is not a feature to plot

# Determine the layout of subplots
n_cols = 2  # Can be adjusted based on preference
n_rows = (len(categorical_features) + 1) // n_cols

# Create a grid of subplots
fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=(12, n_rows * 4))

# Flatten the axes array for easy iteration
axes = axes.flatten()

# Plot each bar chart
for i, col in enumerate(categorical_features):
    df_train.groupby([col, 'Loan_Status']).size().unstack().plot(kind='bar', stacked=True, ax=axes[i])
    axes[i].set_title(f'Total count of Loan_Status grouped by {col}')
    axes[i].set_ylabel('Count')

# Adjust layout and display the plot
plt.tight_layout()
plt.show()

从上面的图中观察到的结果：

与女性相比，男性获得贷款批准的比例更高。
与非毕业生相比，贷款审批对毕业生更有利。
与受雇者相比，个体经营者获得贷款批准的机会较少。
城乡结合部的贷款批准率最高。

让我们看看按因变量分组的连续自变量

numerical_features = df_train.describe().columns

# Determine the layout of subplots
n_cols = 2  # Adjust based on preference
n_rows = (len(numerical_features) + 1) // n_cols

# Create a grid of subplots
fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=(12, n_rows * 4))

# Flatten the axes array for easy iteration
axes = axes.flatten()

# Plot each boxplot
for i, col in enumerate(numerical_features):
    sns.boxplot(x='Loan_Status', y=col, data=df_train, ax=axes[i])
    axes[i].set_title(f'Distribution of {col} grouped by Loan_Status')

# Adjust layout and display the plot
plt.tight_layout()
plt.show()

我们可以在数据中观察到很多异常值。

从上面的箱线图中无法得出任何正确的结论。

数据预处理

df_train.head()

	Gender	Married	Dependents	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Property_Area	Loan_Status
1	Male	Yes	1	Graduate	No	4583	1508.0	128.0	360.0	1.0	Rural	N
2	Male	Yes	0	Graduate	Yes	3000	0.0	66.0	360.0	1.0	Urban	Y
3	Male	Yes	0	Not Graduate	No	2583	2358.0	120.0	360.0	1.0	Urban	Y
4	Male	No	0	Graduate	No	6000	0.0	141.0	360.0	1.0	Urban	Y
5	Male	Yes	2	Graduate	Yes	5417	4196.0	267.0	360.0	1.0	Urban	Y

df_train['Property_Area'].value_counts()

Semiurban    191
Urban        150
Rural        139
Name: Property_Area, dtype: int64

df_train['Credit_History'].value_counts()

1.0    410
0.0     70
Name: Credit_History, dtype: int64

df_train['Dependents'].value_counts()

0     274
2      85
1      80
3+     41
Name: Dependents, dtype: int64

使用标签编码将分类列转换为数字

#Label encoding for some categorical features
df_train_new = df_train.copy()
label_col_list = ['Married','Self_Employed']
for col in label_col_list:
  df_train_new=df_train_new.replace({col:{'Yes':1,'No':0}})

df_train_new=df_train_new.replace({'Gender':{'Male':1,'Female':0}})
df_train_new=df_train_new.replace({'Education':{'Graduate':1,'Not Graduate':0}})
df_train_new=df_train_new.replace({'Loan_Status':{'Y':1,'N':0}})

对于其余的分类特征，我们将进行一种热编码：

#one hot encoding
df_train_new = pd.get_dummies(df_train_new, columns=["Dependents","Property_Area"])

df_train_new.head()

	Gender	Married	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Loan_Status	Dependents_0	Dependents_1	Dependents_2	Property_Area_Rural	Property_Area_Urban
1	1	1	1	0	4583	1508.0	128.0	360.0	1.0	0	0	1	0	1	0
2	1	1	1	1	3000	0.0	66.0	360.0	1.0	1	1	0	0	0	1
3	1	1	0	0	2583	2358.0	120.0	360.0	1.0	1	1	0	0	0	1
4	1	0	1	0	6000	0.0	141.0	360.0	1.0	1	1	0	0	0	1
5	1	1	1	1	5417	4196.0	267.0	360.0	1.0	1	0	0	1	0	1

标准化连续变量。

#standardize continuous features
from scipy.stats import zscore
df_train_new[['ApplicantIncome','CoapplicantIncome','LoanAmount','Loan_Amount_Term']]=df_train_new[['ApplicantIncome','CoapplicantIncome','LoanAmount','Loan_Amount_Term']].apply(zscore)

df_train_new.head()

	Gender	Married	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Loan_Status	Dependents_0	Dependents_1	Dependents_2	Property_Area_Rural	Property_Area_Urban
1	1	1	1	0	-0.137970	-0.027952	-0.208089	0.275542	1.0	0	0	1	0	1	0
2	1	1	1	1	-0.417536	-0.604633	-0.979001	0.275542	1.0	1	1	0	0	0	1
3	1	1	0	0	-0.491180	0.297100	-0.307562	0.275542	1.0	1	1	0	0	0	1
4	1	0	1	0	0.112280	-0.604633	-0.046446	0.275542	1.0	1	1	0	0	0	1
5	1	1	1	1	0.009319	0.999978	1.520245	0.275542	1.0	1	0	0	1	0	1

# Repositioning the dependent variable to last index
last_column = df_train_new.pop('Loan_Status')
df_train_new.insert(16, 'Loan_Status', last_column)
df_train_new.head()

	Gender	Married	Education	Self_Employed	ApplicantIncome	CoapplicantIncome	LoanAmount	Loan_Amount_Term	Credit_History	Dependents_0	Dependents_1	Dependents_2	Property_Area_Rural	Property_Area_Urban	Loan_Status
1	1	1	1	0	-0.137970	-0.027952	-0.208089	0.275542	1.0	0	1	0	1	0	0
2	1	1	1	1	-0.417536	-0.604633	-0.979001	0.275542	1.0	1	0	0	0	1	1
3	1	1	0	0	-0.491180	0.297100	-0.307562	0.275542	1.0	1	0	0	0	1	1
4	1	0	1	0	0.112280	-0.604633	-0.046446	0.275542	1.0	1	0	0	0	1	1
5	1	1	1	1	0.009319	0.999978	1.520245	0.275542	1.0	0	0	1	0	1	1

数据处理完毕,准备训练模型

数据集划分

由于我们的数据仅用于训练，其他数据可用于测试。我们仍然会进行训练测试分割，因为测试数据没有标记，并且有必要根据未见过的数据评估模型。

X= df_train_new.iloc[:,:-1]
y= df_train_new.iloc[:,-1]
from sklearn.model_selection import train_test_split 
X_train, X_test, y_train, y_test = train_test_split( X,y , test_size = 0.2, random_state = 0) 
print(X_train.shape)
print(X_test.shape)

(384, 16)
(96, 16)

y_train.value_counts()

1    271
0    113
Name: Loan_Status, dtype: int64

y_test.value_counts()

1    61
0    35
Name: Loan_Status, dtype: int64

对训练数据进行逻辑回归拟合

#Importing and fitting Logistic regression
from sklearn.linear_model import LogisticRegression

lr = LogisticRegression(fit_intercept=True, max_iter=10000,random_state=0)
lr.fit(X_train, y_train)

LogisticRegression

LogisticRegression(max_iter=10000, random_state=0)

# Get the model coefficients
lr.coef_

array([[ 0.23272114,  0.57128602,  0.26384918, -0.24617035,  0.15924191,
        -0.14703758, -0.19280038, -0.16392914,  2.97399665, -0.18202629,
        -0.27741114,  0.17256535,  0.28601466, -0.30275813,  0.64592912,
        -0.3440284 ]])

#model intercept
lr.intercept_

array([-2.1943974])

评价训练模型的性能

# Get the predicted probabilities
train_preds = lr.predict_proba(X_train)
test_preds = lr.predict_proba(X_test)

test_preds

array([[0.23916396, 0.76083604],
       [0.24506751, 0.75493249],
       [0.04933527, 0.95066473],
       [0.20146124, 0.79853876],
       [0.2347122 , 0.7652878 ],
       [0.05817427, 0.94182573],
       [0.17668886, 0.82331114],
       [0.21352909, 0.78647091],
       [0.39015173, 0.60984827],
       [0.1902079 , 0.8097921 ],
       [0.20590091, 0.79409909],
       [0.184445  , 0.815555  ],
       [0.80677694, 0.19322306],
       [0.23024539, 0.76975461],
       [0.23674387, 0.76325613],
       [0.32409412, 0.67590588],
       [0.08612609, 0.91387391],
       [0.20502754, 0.79497246],
       [0.71006169, 0.28993831],
       [0.05818474, 0.94181526],
       [0.16546532, 0.83453468],
       [0.1191243 , 0.8808757 ],
       [0.16412334, 0.83587666],
       [0.14471253, 0.85528747],
       [0.49082632, 0.50917368],
       [0.37484189, 0.62515811],
       [0.20042593, 0.79957407],
       [0.07289182, 0.92710818],
       [0.10696878, 0.89303122],
       [0.27313905, 0.72686095],
       [0.07661587, 0.92338413],
       [0.07911086, 0.92088914],
       [0.32357856, 0.67642144],
       [0.24855278, 0.75144722],
       [0.25736849, 0.74263151],
       [0.10330185, 0.89669815],
       [0.27934665, 0.72065335],
       [0.23504431, 0.76495569],
       [0.37235234, 0.62764766],
       [0.82612173, 0.17387827],
       [0.25597195, 0.74402805],
       [0.07027974, 0.92972026],
       [0.21138903, 0.78861097],
       [0.30656929, 0.69343071],
       [0.12859877, 0.87140123],
       [0.22422238, 0.77577762],
       [0.19222405, 0.80777595],
       [0.33904961, 0.66095039],
       [0.21169609, 0.78830391],
       [0.12783677, 0.87216323],
       [0.21562742, 0.78437258],
       [0.1003408 , 0.8996592 ],
       [0.39205576, 0.60794424],
       [0.10298106, 0.89701894],
       [0.34917087, 0.65082913],
       [0.31848606, 0.68151394],
       [0.46697536, 0.53302464],
       [0.83005638, 0.16994362],
       [0.84749511, 0.15250489],
       [0.82240763, 0.17759237],
       [0.08938059, 0.91061941],
       [0.38214865, 0.61785135],
       [0.62202628, 0.37797372],
       [0.1124887 , 0.8875113 ],
       [0.29371977, 0.70628023],
       [0.12829643, 0.87170357],
       [0.30152976, 0.69847024],
       [0.12669798, 0.87330202],
       [0.07601492, 0.92398508],
       [0.06068026, 0.93931974],
       [0.05461916, 0.94538084],
       [0.10209121, 0.89790879],
       [0.20592351, 0.79407649],
       [0.56190874, 0.43809126],
       [0.19828342, 0.80171658],
       [0.20171019, 0.79828981],
       [0.11960918, 0.88039082],
       [0.25602438, 0.74397562],
       [0.18013843, 0.81986157],
       [0.37225288, 0.62774712],
       [0.21781716, 0.78218284],
       [0.10365239, 0.89634761],
       [0.29076172, 0.70923828],
       [0.59602673, 0.40397327],
       [0.39435357, 0.60564643],
       [0.40070233, 0.59929767],
       [0.88224869, 0.11775131],
       [0.22235351, 0.77764649],
       [0.1765423 , 0.8234577 ],
       [0.75247369, 0.24752631],
       [0.20366031, 0.79633969],
       [0.85207477, 0.14792523],
       [0.3873617 , 0.6126383 ],
       [0.12318258, 0.87681742],
       [0.06667711, 0.93332289],
       [0.17440779, 0.82559221]])

# Get the predicted classes
train_class_preds = lr.predict(X_train)
test_class_preds = lr.predict(X_test)

train_class_preds

array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
       0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
       1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,
       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,
       0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
       1, 1, 1, 0, 1, 0, 1, 1, 1, 0], dtype=int64)

准确率

from sklearn.metrics import accuracy_score, confusion_matrix ,classification_report
# Get the accuracy scores
train_accuracy = accuracy_score(train_class_preds,y_train)
test_accuracy = accuracy_score(test_class_preds,y_test)

print("The accuracy on train data is ", train_accuracy)
print("The accuracy on test data is ", test_accuracy)

The accuracy on train data is  0.8229166666666666
The accuracy on test data is  0.7604166666666666

由于我们的数据有些不平衡，准确性可能不是一个好的指标。 让我们使用 roc_auc 分数。

# Get the roc_auc scores
train_roc_auc = accuracy_score(y_train,train_class_preds)
test_roc_auc = accuracy_score(y_test,test_class_preds)

print("The accuracy on train data is ", train_roc_auc)
print("The accuracy on test data is ", test_roc_auc)

The accuracy on train data is  0.8229166666666666
The accuracy on test data is  0.7604166666666666

# Other evaluation metrics for train data
print(classification_report(train_class_preds,y_train))

              precision    recall  f1-score   support

           0       0.45      0.89      0.60        57
           1       0.98      0.81      0.89       327

    accuracy                           0.82       384
   macro avg       0.71      0.85      0.74       384
weighted avg       0.90      0.82      0.84       384

# Other evaluation metrics for train data
print(classification_report(y_test,test_class_preds))

              precision    recall  f1-score   support

           0       1.00      0.34      0.51        35
           1       0.73      1.00      0.84        61

    accuracy                           0.76        96
   macro avg       0.86      0.67      0.68        96
weighted avg       0.83      0.76      0.72        96

训练集和测试集上的混淆矩阵

# Get the confusion matrix for trained data

labels = ['Notapproved', 'approved']
cm = confusion_matrix(y_train, train_class_preds)
print(cm)

ax= plt.subplot()
sns.heatmap(cm, annot=True, ax = ax) #annot=True to annotate cells

# labels, title and ticks
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix on trained data')
ax.xaxis.set_ticklabels(labels)
ax.yaxis.set_ticklabels(labels)
plt.show()


# Get the confusion matrix for test data

labels = ['Notapproved', 'approved']
cm = confusion_matrix(y_test, test_class_preds)
print(cm)

ax= plt.subplot()
sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells

# labels, title and ticks
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix on test data')
ax.xaxis.set_ticklabels(labels)
ax.yaxis.set_ticklabels(labels)

[[ 51  62]
 [  6 265]]

[[12 23]
 [ 0 61]]

[Text(0, 0.5, 'Notapproved'), Text(0, 1.5, 'approved')]

决策树

#Importing libraries
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV

# applying GreadsearchCV to identify best parameters
decision_tree = DecisionTreeClassifier()
tree_para = {'criterion':['gini','entropy'],'max_depth':[4,5,6,7,8,9,10,11,12,15,20,30,40,50,70,90,120,150]}
clf = GridSearchCV(decision_tree, tree_para, cv=5)
clf.fit(X_train, y_train)

clf.best_params_

{'criterion': 'gini', 'max_depth': 4}

#applying decision tree classifier
dt = DecisionTreeClassifier(criterion='gini',max_depth=4,random_state=0)
dt.fit(X_train, y_train)

train_class_preds = dt.predict(X_train)
test_class_preds = dt.predict(X_test)

Accuracy Score

# Get the accuracy scores
train_accuracy = accuracy_score(train_class_preds,y_train)
test_accuracy = accuracy_score(test_class_preds,y_test)

print("The accuracy on train data is ", train_accuracy)
print("The accuracy on test data is ", test_accuracy)

The accuracy on train data is  0.8463541666666666
The accuracy on test data is  0.71875

roc_auc score

# Get the roc_auc scores
train_roc_auc = accuracy_score(y_train,train_class_preds)
test_roc_auc = accuracy_score(y_test,test_class_preds)

print("The accuracy on train data is ", train_roc_auc)
print("The accuracy on test data is ", test_roc_auc)

The accuracy on train data is  0.8463541666666666
The accuracy on test data is  0.71875

# Other evaluation metrics for train data
print(classification_report(train_class_preds,y_train))

              precision    recall  f1-score   support

           0       0.54      0.90      0.67        68
           1       0.97      0.84      0.90       316

    accuracy                           0.85       384
   macro avg       0.76      0.87      0.79       384
weighted avg       0.90      0.85      0.86       384

# Other evaluation metrics for train data
print(classification_report(y_test,test_class_preds))

              precision    recall  f1-score   support

           0       0.70      0.40      0.51        35
           1       0.72      0.90      0.80        61

    accuracy                           0.72        96
   macro avg       0.71      0.65      0.66        96
weighted avg       0.72      0.72      0.70        96

Confusion matrix on trained and test data

# Get the confusion matrix for trained data

labels = ['Notapproved', 'approved']
cm = confusion_matrix(y_train, train_class_preds)
print(cm)

ax= plt.subplot()
sns.heatmap(cm, annot=True, ax = ax) #annot=True to annotate cells

# labels, title and ticks
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix on trained data')
ax.xaxis.set_ticklabels(labels)
ax.yaxis.set_ticklabels(labels)
plt.show()

# Get the confusion matrix for test data

labels = ['Notapproved', 'approved']
cm = confusion_matrix(y_test, test_class_preds)
print(cm)

ax= plt.subplot()
sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells

# labels, title and ticks
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix on test data')
ax.xaxis.set_ticklabels(labels)
ax.yaxis.set_ticklabels(labels)

[[ 61  52]
 [  7 264]]

[[14 21]
 [ 6 55]]

[Text(0, 0.5, 'Notapproved'), Text(0, 1.5, 'approved')]

随机森林

# applying Random forrest classifier with Hyperparameter tuning
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier()
grid_values = {'n_estimators':[50, 80,  100], 'max_depth':[4,5,6,7,8,9,10]}
rf_gd = GridSearchCV(rf, param_grid = grid_values, scoring = 'roc_auc', cv=5)

# Fit the object to train dataset
rf_gd.fit(X_train, y_train)

train_class_preds = rf_gd.predict(X_train)
test_class_preds = rf_gd.predict(X_test)

Accuracy Score

# Get the accuracy scores
train_accuracy = accuracy_score(train_class_preds,y_train)
test_accuracy = accuracy_score(test_class_preds,y_test)

print("The accuracy on train data is ", train_accuracy)
print("The accuracy on test data is ", test_accuracy)

The accuracy on train data is  0.890625
The accuracy on test data is  0.75

roc_auc Score

# Get the roc_auc scores
train_roc_auc = accuracy_score(y_train,train_class_preds)
test_roc_auc = accuracy_score(y_test,test_class_preds)

print("The accuracy on train data is ", train_roc_auc)
print("The accuracy on test data is ", test_roc_auc)

The accuracy on train data is  0.890625
The accuracy on test data is  0.75

Confusion Matrix

# Get the confusion matrix for trained data

labels = ['Notapproved', 'approved']
cm = confusion_matrix(y_train, train_class_preds)
print(cm)

ax= plt.subplot()
sns.heatmap(cm, annot=True, ax = ax) #annot=True to annotate cells

# labels, title and ticks
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix on trained data')
ax.xaxis.set_ticklabels(labels)
ax.yaxis.set_ticklabels(labels)
plt.show()

# Get the confusion matrix for test data

labels = ['Notapproved', 'approved']
cm = confusion_matrix(y_test, test_class_preds)
print(cm)

ax= plt.subplot()
sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells

# labels, title and ticks
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix on test data')
ax.xaxis.set_ticklabels(labels)
ax.yaxis.set_ticklabels(labels)
plt.show()