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在金融服务行业#xff0c;贷款审批是一项关键任务#xff0c;它不仅关系到资金的安全#xff0c;还直接影响到金融机构的运营效率和风险管理…注意本文引用自专业人工智能社区Venus AI
更多AI知识请参考原站 [www.aideeplearning.cn]
在金融服务行业贷款审批是一项关键任务它不仅关系到资金的安全还直接影响到金融机构的运营效率和风险管理。传统的审批流程往往依赖于人工审核这不仅效率低下而且容易受到主观判断的影响。为了解决这些问题我们引入了一种基于机器学习的贷款预测模型旨在提高贷款审批的准确性和效率。
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:
RandomForestRegressorDecision Tree Regressionlogistic regression
Libraries:
matplotlib3.7.1numpy1.24.3pandas2.0.2scikit_learn1.2.2seaborn0.13.0
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_IDGenderMarriedDependentsEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryProperty_AreaLoan_Status0LP001002MaleNo0GraduateNo58490.0NaN360.01.0UrbanY1LP001003MaleYes1GraduateNo45831508.0128.0360.01.0RuralN2LP001005MaleYes0GraduateYes30000.066.0360.01.0UrbanY3LP001006MaleYes0Not GraduateNo25832358.0120.0360.01.0UrbanY4LP001008MaleNo0GraduateNo60000.0141.0360.01.0UrbanY
df_test.head()
Loan_IDGenderMarriedDependentsEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryProperty_Area0LP001015MaleYes0GraduateNo57200110.0360.01.0Urban1LP001022MaleYes1GraduateNo30761500126.0360.01.0Urban2LP001031MaleYes2GraduateNo50001800208.0360.01.0Urban3LP001035MaleYes2GraduateNo23402546100.0360.0NaNUrban4LP001051MaleNo0Not GraduateNo3276078.0360.01.0Urban
#shape of data
df_train.shape
(614, 13)#data summary
df_train.describe()
ApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_Historycount614.000000614.000000592.000000600.00000564.000000mean5403.4592831621.245798146.412162342.000000.842199std6109.0416732926.24836985.58732565.120410.364878min150.0000000.0000009.00000012.000000.00000025%2877.5000000.000000100.000000360.000001.00000050%3812.5000001188.500000128.000000360.000001.00000075%5795.0000002297.250000168.000000360.000001.000000max81000.00000041667.000000700.000000480.000001.000000
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 float648 LoanAmount 592 non-null float649 Loan_Amount_Term 600 non-null float6410 Credit_History 564 non-null float6411 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(columnsLoan_ID,axis1, inplaceTrue)
df_train.shape
(480, 12)#data summary
df_train.describe()
ApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_Historycount480.000000480.000000480.000000480.000000480.000000mean5364.2312501581.093583144.735417342.0500000.854167std5668.2512512617.69226780.50816465.2124010.353307min150.0000000.0000009.00000036.0000000.00000025%2898.7500000.000000100.000000360.0000001.00000050%3859.0000001084.500000128.000000360.0000001.00000075%5852.5000002253.250000170.000000360.0000001.000000max81000.00000033837.000000600.000000480.0000001.000000
数据分析(EDA)
df_train.head()
GenderMarriedDependentsEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryProperty_AreaLoan_Status1MaleYes1GraduateNo45831508.0128.0360.01.0RuralN2MaleYes0GraduateYes30000.066.0360.01.0UrbanY3MaleYes0Not GraduateNo25832358.0120.0360.01.0UrbanY4MaleNo0GraduateNo60000.0141.0360.01.0UrbanY5MaleYes2GraduateYes54174196.0267.0360.01.0UrbanY
#distribution of Churn data
sns.displot(datadf_train,xLoan_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_featureslist(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(nrowsn_rows, ncolsn_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(kindbar, stackedTrue, axaxes[i])axes[i].set_title(fTotal 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(nrowsn_rows, ncolsn_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(xLoan_Status, ycol, datadf_train, axaxes[i])axes[i].set_title(fDistribution 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。
相关性分析 ## Correlation between variables
plt.figure(figsize(15,8))
correlation df_train.corr()
sns.heatmap((correlation), annotTrue, cmapcoolwarm)Axes: 没有观察到任何显着的相关性。
数据预处理
df_train.head()
GenderMarriedDependentsEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryProperty_AreaLoan_Status1MaleYes1GraduateNo45831508.0128.0360.01.0RuralN2MaleYes0GraduateYes30000.066.0360.01.0UrbanY3MaleYes0Not GraduateNo25832358.0120.0360.01.0UrbanY4MaleNo0GraduateNo60000.0141.0360.01.0UrbanY5MaleYes2GraduateYes54174196.0267.0360.01.0UrbanY
df_train[Property_Area].value_counts()
Semiurban 191
Urban 150
Rural 139
Name: Property_Area, dtype: int64df_train[Credit_History].value_counts()
1.0 410
0.0 70
Name: Credit_History, dtype: int64df_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_newdf_train_new.replace({col:{Yes:1,No:0}})
df_train_newdf_train_new.replace({Gender:{Male:1,Female:0}})
df_train_newdf_train_new.replace({Education:{Graduate:1,Not Graduate:0}})
df_train_newdf_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()
GenderMarriedEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryLoan_StatusDependents_0Dependents_1Dependents_2Dependents_3Property_Area_RuralProperty_Area_SemiurbanProperty_Area_Urban1111045831508.0128.0360.01.0001001002111130000.066.0360.01.0110000013110025832358.0120.0360.01.0110000014101060000.0141.0360.01.0110000015111154174196.0267.0360.01.010010001
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()
GenderMarriedEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryLoan_StatusDependents_0Dependents_1Dependents_2Dependents_3Property_Area_RuralProperty_Area_SemiurbanProperty_Area_Urban11110-0.137970-0.027952-0.2080890.2755421.00010010021111-0.417536-0.604633-0.9790010.2755421.01100000131100-0.4911800.297100-0.3075620.2755421.011000001410100.112280-0.604633-0.0464460.2755421.011000001511110.0093190.9999781.5202450.2755421.010010001
# 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()
GenderMarriedEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryDependents_0Dependents_1Dependents_2Dependents_3Property_Area_RuralProperty_Area_SemiurbanProperty_Area_UrbanLoan_Status11110-0.137970-0.027952-0.2080890.2755421.00100100021111-0.417536-0.604633-0.9790010.2755421.01000001131100-0.4911800.297100-0.3075620.2755421.010000011410100.112280-0.604633-0.0464460.2755421.010000011511110.0093190.9999781.5202450.2755421.000100011
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: int64y_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 LogisticRegressionlr LogisticRegression(fit_interceptTrue, max_iter10000,random_state0)
lr.fit(X_train, y_train)
LogisticRegression
LogisticRegression(max_iter10000, random_state0)# 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], dtypeint64)
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 support0 0.45 0.89 0.60 571 0.98 0.81 0.89 327accuracy 0.82 384macro 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 support0 1.00 0.34 0.51 351 0.73 1.00 0.84 61accuracy 0.76 96macro 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 datalabels [Notapproved, approved]
cm confusion_matrix(y_train, train_class_preds)
print(cm)ax plt.subplot()
sns.heatmap(cm, annotTrue, ax ax) #annotTrue 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 datalabels [Notapproved, approved]
cm confusion_matrix(y_test, test_class_preds)
print(cm)ax plt.subplot()
sns.heatmap(cm, annotTrue, ax ax); #annotTrue 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, cv5)
clf.fit(X_train, y_train) clf.best_params_
{criterion: gini, max_depth: 4}#applying decision tree classifier
dt DecisionTreeClassifier(criteriongini,max_depth4,random_state0)
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 0.71875roc_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 support0 0.54 0.90 0.67 681 0.97 0.84 0.90 316accuracy 0.85 384macro 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 support0 0.70 0.40 0.51 351 0.72 0.90 0.80 61accuracy 0.72 96macro 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 datalabels [Notapproved, approved]
cm confusion_matrix(y_train, train_class_preds)
print(cm)ax plt.subplot()
sns.heatmap(cm, annotTrue, ax ax) #annotTrue 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 datalabels [Notapproved, approved]
cm confusion_matrix(y_test, test_class_preds)
print(cm)ax plt.subplot()
sns.heatmap(cm, annotTrue, ax ax); #annotTrue 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, cv5)# 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 0.75roc_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 0.75Confusion Matrix
# Get the confusion matrix for trained datalabels [Notapproved, approved]
cm confusion_matrix(y_train, train_class_preds)
print(cm)ax plt.subplot()
sns.heatmap(cm, annotTrue, ax ax) #annotTrue 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 datalabels [Notapproved, approved]
cm confusion_matrix(y_test, test_class_preds)
print(cm)ax plt.subplot()
sns.heatmap(cm, annotTrue, ax ax); #annotTrue 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()
[[ 72 41][ 1 270]][[13 22][ 2 59]]最佳 roc_auc 分数源于随机森林分类器因此随机森林是该模型的最佳预测模型。
代码与数据集下载
详情请见金融贷款批准预测项目-VenusAI (aideeplearning.cn)
