澳洲统计学作业ETB1100代写 Business Statistics课程assignment代做

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来自澳洲代写的顾客授权发布的Business Statistics,ETB1100作业要求片段,我们不会发布ETB1100的answer在网站,我们曾经写过ETB1100及相关的Business Statistics写过很多作业,考试,如果你也需要代写这个课程的作业请联系客服WX:QQ 5757940 ,代写人的代写服务覆盖全球华人留学生,可以为AU的学生提供非常准时精湛的服务,小作业assignment代写、essay代写享适时优惠,project、paper代写、论文代写支持分期付款,网课、exam代考预约时刻爆单中赶紧来撩。
您的任务是复制并修改两个ML示例。1.信用/风险评分2.使用k-means聚类的客户细分首先,您需要从Knime下载一本电子书,名为:Practicing Data Science。Knime的Stefan Helfrich非常乐意提供一个代码,这样您就可以免费下载任何电子书:在课堂上,我们将查看模型,因此您还需要下载工作流或在Knime Explorer中的示例中找到它们...

 

Questions:

Your assignment will be to copy and then modify two ML examples.
1. Credit/Risk Scoring
2. Customer Segmentation using k-means clustering
To start, you will need to download an e-book from Knime called: Practicing Data Science.  Knime's Stefan Helfrich was so kind to provide a code so you can download any e-book for free:
In class, we are going to go over the models so you will also need to download the workflows or find them in the Examples in our Knime Explorer.  You will find the locations to download the workflows in the e-book.

We might go through most of this in class first (that is the copy part of the assignment).  For homework, you are going to modify each one. 1.  For the Credit/Risk model, I want you to swap ML technique from Random Forest to decision tree.  You will submit a simple table that shows your AUC and accuracy percent for the two ways that you did the modeling.  For example, it should look like this:model type    AUC (area under curve in ROC graph)    Accuracy  (from Scorer Node)
Decision Tree    .801    .795
Random Forest    .822    .792

You should also write a short statement of which model you would recommend and why you would recommend it.  For example, you might say that "Bank should use the Random Forest model to predict risk/score because the area under the curve was higher."  Or  you could say, "Telco should use the Decision Tree model because the accuracy was higher."  Your next sentence should say why you chose either AUC or accuracy to make your decision.

You should also submit your Knime export as a knwf file also with your one-page write-up.
2.  For the customer segmentation example, you will use two different values for k (10, 5) which represents the number of different customer segments.  After you run both, you will submit a simple table that shows the denormalized means for your cluster_0 values for day_mins and eve_mins.  For example, it might look like this:
cluster 0    day mins    eve mins
k=5    128    105
k=10    89    101
You should also submit your Knime export as a knwf file K Means Clustering
Here is a video explaining kMeans Clustering:

If you are a student from an English-speaking country, please feel free to contact us at [email protected] and we will provide you with an excellent writing service.

 

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