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from sklearn.cross_validation import train_test_split # Whole dataset X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 0) print("Number transactions train dataset: ", len(X_train)) print("Number transactions test dataset: ", len(X_test)) ...
Lending Club(LC)的数据见:https://www.kaggle.com/wendykan/lending-club-loan-data kaggle提供的数据包含从2007年到2015年的发放的887k贷款记录,每个贷款记录有75个维度的特征。 主要的数据特征及语义: 特征语义备注 issue_d贷款发放时间年-月 gradeLC的信用评级主要根据FICO分和贷款数额、期限确定 ...
@misc{lendingclub, title = { LendingClub贷款数据 } author = { 小鲸 }, howpublished = { \url{https://www.heywhale.com/mw/dataset/58a7fab4fbe7a30f28357645} } year = { 2017 } } 信息 挂载目录 /home/mw/input/lendingclub 数据格式 .csv 文件大小 421.3 MB 主题 商业 挂载目录 /home/mw...
Implemented a Artificial Neural Network on the Lending Club dataset . pythonartificial-neural-networkslendingclubtensorflow2 UpdatedJun 18, 2020 Jupyter Notebook In this project, based on the historical data of customers, who has applied for loans, we will identify whether loan should be approved or...
offlinenoteslendingclub UpdatedApr 15, 2019 C++ Implemented a Artificial Neural Network on the Lending Club dataset . pythonartificial-neural-networkslendingclubtensorflow2 UpdatedJun 18, 2020 Jupyter Notebook In this project, based on the historical data of customers, who has applied for loans, we...
lendingclub的借款人数据集 文件列表 lendingclub.zip lendingclub.zip (378.77M) 下载 File Name Size Update Time LoanStats_securev1_2016Q1.csv.zip 26541778 2020-06-25 16:49:46 LoanStats_securev1_2016Q2.csv.zip 19332618 2020-06-25 16:49:32 LoanStats_securev1_2016Q3.csv.zip 19606066 2020...
An investigation of different credit risk models and methods based on the Lending Club dataset with over 1.3 millions loans. Inspiration taken from the course:https://udemy.com/course/credit-risk-modeling-in-python Setup Download the dataset from:https://www.kaggle.com/wordsforthewise/lending-cl...
本文对Lending Club贷款数据进行了初步的解读,数据来源于 Kaggle, 并由科斯网(https://www.kesci.com/home/dataset/58a7fab4fbe7a30f28357645/document)资料对数据进行翻译,整个数据集大小是421MB,对空白字段进行删减后剩余48个字段,如下所示: Id 个人ID号 ...
1. 面对大量陌生字段的初步分析 2. 挑选有用的字段 3. 初步建模 数据集在 : https://www.kaggle.com/wendykan/lending-club-loan-data 数据处理 首先载入数据,并导入必要的函数 importnumpyasnpimportpandasaspdimportnumpyasnpimportjsonimportseabornassnsimportmatplotlib.pyplotaspltfromscipy.statsimportentropysns....