修复预测问题（Fixing Prediction Problem）

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2023-12-01

在本章中，我们将着重于在特定场景的帮助下修复预测问题。

考虑到公司希望根据在线申请表提供的客户详细信息自动化贷款资格详情。详细信息包括客户姓名，性别，婚姻状况，贷款金额和其他强制性细节。

详细信息记录在CSV文件中，如下所示 -

执行以下代码来评估预测问题 -

import pandas as pd
from sklearn import ensemble
import numpy as np
from scipy.stats import mode
from sklearn import preprocessing,model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
#loading the dataset
data=pd.read_csv('train.csv',index_col='Loan_ID')
def num_missing(x):
   return sum(x.isnull())
#imputing the the missing values from the data
data['Gender'].fillna(mode(list(data['Gender'])).mode[0], inplace=True)
data['Married'].fillna(mode(list(data['Married'])).mode[0], inplace=True)
data['Self_Employed'].fillna(mode(list(data['Self_Employed'])).mode[0], inplace=True)
# print (data.apply(num_missing, axis=0))
# #imputing mean for the missing value
data['LoanAmount'].fillna(data['LoanAmount'].mean(), inplace=True)
mapping={'0':0,'1':1,'2':2,'3+':3}
data = data.replace({'Dependents':mapping})
data['Dependents'].fillna(data['Dependents'].mean(), inplace=True)
data['Loan_Amount_Term'].fillna(method='ffill',inplace=True)
data['Credit_History'].fillna(method='ffill',inplace=True)
print (data.apply(num_missing,axis=0))
#converting the cateogorical data to numbers using the label encoder
var_mod = ['Gender','Married','Education','Self_Employed','Property_Area','Loan_Status']
le = LabelEncoder()
for i in var_mod:
   le.fit(list(data[i].values))
   data[i] = le.transform(list(data[i]))
#Train test split
x=['Gender','Married','Education','Self_Employed','Property_Area','LoanAmount', 'Loan_Amount_Term','Credit_History','Dependents']
y=['Loan_Status']
print(data[x])
X_train,X_test,y_train,y_test=model_selection.train_test_split(data[x],data[y], test_size=0.2)
#
# #Random forest classifier
# clf=ensemble.RandomForestClassifier(n_estimators=100,
criterion='gini',max_depth=3,max_features='auto',n_jobs=-1)
clf=ensemble.RandomForestClassifier(n_estimators=200,max_features=3,min_samples
   _split=5,oob_score=True,n_jobs=-1,criterion='entropy')
clf.fit(X_train,y_train)
accuracy=clf.score(X_test,y_test)
print(accuracy)

输出 (Output)

上面的代码生成以下输出。