其中有三個地方需要注意一下

首先是要對資料進行預處理

# -*- coding: utf-8 -*- """ Created on Wed Sep 19 14:16:42 2018

@author: Administrator """

#--*- coding:utf-8 -*-- import pandas as pd import numpy as np

#Load data: train = pd.read_csv('Train_nyOWmfK.csv') test = pd.read_csv('Test_bCtAN1w.csv') print train.shape, test.shape print train.dtypes

""" City variable dropped because of too many categories DOB converted to Age | DOB dropped EMI_Loan_Submitted_Missing created which is 1 if EMI_Loan_Submitted was missing else 0 | Original variable EMI_Loan_Submitted dropped EmployerName dropped because of too many categories Existing_EMI imputed with 0 (median) since only 111 values were missing Interest_Rate_Missing created which is 1 if Interest_Rate was missing else 0 | Original variable Interest_Rate dropped Lead_Creation_Date dropped because made little intuitive impact on outcome Loan_Amount_Applied, Loan_Tenure_Applied imputed with median values Loan_Amount_Submitted_Missing created which is 1 if Loan_Amount_Submitted was missing else 0 | Original variable Loan_Amount_Submitted dropped Loan_Tenure_Submitted_Missing created which is 1 if Loan_Tenure_Submitted was missing else 0 | Original variable Loan_Tenure_Submitted dropped LoggedIn, Salary_Account dropped Processing_Fee_Missing created which is 1 if Processing_Fee was missing else 0 | Original variable Processing_Fee dropped Source – top 2 kept as is and all others combined into different category Numerical and One-Hot-Coding performed """

#Combine into data: train['source']= 'train' test['source'] = 'test' data=pd.concat([train, test],ignore_index=True) print data.shape

# checking missing print ("-------------check missing--------------") print (data.apply(lambda x: sum(x.isnull())))

## look at categories of all object variables var = ['Gender','Salary_Account','Mobile_Verified','Var1','Filled_Form','Device_Type','Var2','Source'] for v in var:     print '\nFrequency count for variable %s'%v     print data[v].value_counts()

## handle individual variables print(len(data['City'].unique())) #drop city because too many unique data.drop('City', axis=1, inplace=True)

data['DOB'].head()

#Create age variable: data['Age'] = data['DOB'].apply(lambda x: 115 - int(x[-2:])) data['Age'].head()

#drop DOB: data.drop('DOB',axis=1,inplace=True) data.boxplot(column=['EMI_Loan_Submitted'],return_type='axes')

data['EMI_Loan_Submitted_Missing'] = data['EMI_Loan_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0) print data[['EMI_Loan_Submitted','EMI_Loan_Submitted_Missing']].head(10)

#drop original vaiables: data.drop('EMI_Loan_Submitted',axis=1,inplace=True)

data.drop('Employer_Name',axis=1,inplace=True)

print data['Existing_EMI'].describe() #Impute by median (0) because just 111 missing: data['Existing_EMI'].fillna(0, inplace=True)

#Majority values missing so I'll create a new variable stating whether this is missing or note: data['Interest_Rate_Missing'] = data['Interest_Rate'].apply(lambda x: 1 if pd.isnull(x) else 0) print data[['Interest_Rate','Interest_Rate_Missing']].head(10) data.drop('Interest_Rate',axis=1,inplace=True)

#Drop this variable because doesn't appear to affect much intuitively data.drop('Lead_Creation_Date',axis=1,inplace=True)

#Impute with median because only 111 missing: data['Loan_Amount_Applied'].fillna(data['Loan_Amount_Applied'].median(),inplace=True) data['Loan_Tenure_Applied'].fillna(data['Loan_Tenure_Applied'].median(),inplace=True)

#High proportion missing so create a new var whether present or not data['Loan_Amount_Submitted_Missing'] = data['Loan_Amount_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0) data['Loan_Tenure_Submitted_Missing'] = data['Loan_Tenure_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)

#Remove old vars data.drop(['Loan_Amount_Submitted','Loan_Tenure_Submitted'],axis=1,inplace=True)

data.drop('LoggedIn',axis=1,inplace=True)

#Salary account has mnay banks which have to be manually grouped data.drop('Salary_Account',axis=1,inplace=True)

#High proportion missing so create a new var whether present or not data['Processing_Fee_Missing'] = data['Processing_Fee'].apply(lambda x: 1 if pd.isnull(x) else 0) #drop old data.drop('Processing_Fee',axis=1,inplace=True)

data['Source'] = data['Source'].apply(lambda x: 'others' if x not in ['S122','S133'] else x) data['Source'].value_counts()

data.apply(lambda x: sum(x.isnull()))

## Numberical Coding from sklearn.preprocessing import LabelEncoder le = LabelEncoder() var_to_encode = ['Device_Type','Filled_Form','Gender','Var1','Var2','Mobile_Verified','Source'] for col in var_to_encode:     data[col] = le.fit_transform(data[col])

# One-Hot Coding data = pd.get_dummies(data, columns=var_to_encode) train = data.loc[data['source']=='train'] test = data.loc[data['source']=='test']

train.drop('source',axis=1,inplace=True) test.drop(['source','Disbursed'],axis=1,inplace=True)

train.to_csv('train_modified.csv',index=False) test.to_csv('test_modified.csv',index=False) 2、解決錯誤：TypeError: cv() got an unexpected keyword argument 'show_progress'

需要把modelfit函式中的show_progress修改為verbose_eval

3、出現錯誤：'str' object is not callable

需要把booster()修改為get_booster()。