set_title("Features Correlating with Attrited Customers",fontdict={"fontsize":18}, pad=16); fig.tight_layout(pad=5) plt.show() 小结:从上面右侧的热力图中能看到下面的字段和流失类型客户是无相关的。相关系数的值在正负0.1之间(右图) Credit Limit Ave
接下来,最重要的流失用户分布: ex.pie(c_data,names='Attrition_Flag',title='Proportion of churn vs not churn customers') 我们可以看到,只有16%的数据样本代表流失客户,在接下来的步骤中,我将使用SMOTE对流失样本进行采样,使其与常规客户的样本大小匹配,以便给后面选择的模型一个更好的机会来捕捉小细节。 3....
count(), labels=['high-value customers', 'low-value customers'], autopct='%.0f%%', shadow=True) plt.show() ActiveUser[ActiveUser.customerValue=='high-value'].tail(1) 从以上的分析可以看到,TotalCharges在2395.7以上的客户就属于是高净值的客户,这四成用户贡献了Telco公司的80%的利润。 Active...
接下来,最重要的流失用户分布: ex.pie(c_data,names='Attrition_Flag',title='Proportion of churn vs not churn customers') 1. 我们可以看到,只有16%的数据样本代表流失客户,在接下来的步骤中,我将使用SMOTE对流失样本进行采样,使其与常规客户的样本大小匹配,以便给后面选择的模型一个更好的机会来捕捉小...
important role in solving problems related to credit risk. Hence role of predictive modelers and data... Scorecards 1. Application Scorecard : It applies to new (first time) customers applying for loan or credit Risk Management and Financial Institutions Chapter 6 —— The Credit Crisis of 2007...
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使用来自 Kaggle 的 BankChurners 数据集来分析 10127 名客户的数据。 数据可在https://www.kaggle.com/sakshigoyal7/credit-card-customers找到 代码使用的是R 语言 不是python 上传者:weixin_54707168时间:2022-01-29 kaggle房价预测数据集.rar 比赛概述 影响房价的因素有很多,在本题的数据集中有79个变量几乎描...
'Grade' metric is added by another single digit number to further distingusih between different customers When we model loan status, it is considered common to include 'Grade' and 'Subgrade'. But when we model 'Grade' and 'Subgrade', it is debetable to include Loan Status or not However,...
whatif_data=train_data[lambdadf:df['Card_Category']=='Blue']out_orig=whatif_data[outcome]value_sliver=whatif_data['Card_Category'].map(lambda_:'Silver')out_silver=why.whatif(whatif_data,value_sliver,treatment='Card_Category')print('Selected customers:',len(whatif_data))print(f'Mean{...
This seems to be a dataset about a telecom company's customers, and whether they churned (i.e., left the company) or not. It could be used for a variety of purposes, such as understanding the characteristics of customers who churn, or building a predictive model to predict churn. ...