{"id":22787,"date":"2023-07-02T23:55:24","date_gmt":"2023-07-02T23:55:24","guid":{"rendered":"https:\/\/www.goodacademic.com\/blog\/questions\/data-mining-this-assignment-is-about-two-things-1-doing-a-simple-knn-analysis-using-a-built-in-package-from-scikit-learn-sklearn-2-making-an-elbow-plot\/"},"modified":"2023-07-02T23:55:24","modified_gmt":"2023-07-02T23:55:24","slug":"data-mining-this-assignment-is-about-two-things-1-doing-a-simple-knn-analysis-using-a-built-in-package-from-scikit-learn-sklearn-2-making-an-elbow-plot","status":"publish","type":"questions","link":"https:\/\/www.goodacademic.com\/blog\/questions\/data-mining-this-assignment-is-about-two-things-1-doing-a-simple-knn-analysis-using-a-built-in-package-from-scikit-learn-sklearn-2-making-an-elbow-plot\/","title":{"rendered":"Data Mining This assignment is about two things: (1) doing a simple KNN analysis using a built-in package from Scikit-learn\/sklearn (2) making an \u2018elbow\u2019 plot."},"content":{"rendered":"

.Data Mining<\/span><\/p>\n


<\/span>This assignment is about two things:<\/span>
<\/span>(1) doing a simple KNN analysis using a built-in package from Scikit-learn\/sklearn<\/span>
<\/span>(2) making an \u2018elbow\u2019 plot.<\/span><\/p>\n


<\/span>(1)<\/span>
<\/span>(a)<\/span>
<\/span>Now before we get into the data mining, let\u2019s first \u2018pre-process\u2019 the dataset. So, we<\/span> <\/span><\/span>pretend we only have access to 70 percent of this iris data set.<\/span> <\/span><\/span>If you think of the entire data set as a dataframe, then you are going to only use <\/span><\/span>the top<\/span> <\/span>70% of the<\/span><\/span>rows for your \u2018data mining\u2019 purpose. You will reserve the bottom 30%of the rows as the data<\/span> <\/span><\/span>set to see how well your model performs. To make it more relatable, imagine the data vendor<\/span> <\/span><\/span>tells you that the bottom 30% is premium data which you would have to pay a lot of money for<\/span> <\/span><\/span>the access. For the sake of clarity, the top 70% data is called \u2018seen_data_set\u2019 and the bottom<\/span><\/span>30% is called \u2018unseen_data_set\u2019.<\/span>
<\/span>Please write a few lines of code to do this. It should be easy.<\/span><\/p>\n


<\/span>(b)<\/span>
<\/span>Now we are going to further split this seen_data_set into 80\/20. Make sure <\/span><\/span>to use<\/span> <\/span>random state =<\/span> <\/span><\/span>1234 in the split for the result to be reproducible. Set the K value to be 5. Find the<\/span><\/span>corresponding test_score which measures the precision of your prediction on the test data set.<\/span>
<\/span><\/p>\n

(2)<\/span><\/p>\n

Please write a short report to answer this question.<\/span>
<\/span>The test_score you obtain in the first part is probably very good.<\/span><\/span>But how does K value affect the precision of your prediction? Suppose K value varies from 3 to<\/span> <\/span><\/span>15.<\/span> <\/span><\/span>Also, how good is it when we apply our mining model to the \u2018unseen_data_set\u2019? Imagine the<\/span> <\/span><\/span>bottom 30% of the original data is so prohibitively expensive that you are going to have to<\/span> <\/span><\/span>predict it by yourself.<\/span> <\/span><\/span>Make a plot where the X measures how K value <\/span><\/span>varies,<\/span> <\/span>and Y measures the precision score of<\/span> <\/span><\/span>the test_data_set of \u2018seen_data_set\u2019.<\/span>
<\/span>Make a plot where the X measures how K value varies and Y<\/span><\/span>measures the precision score when<\/span> <\/span><\/span>you apply your model to the unseen data set.<\/span> <\/span><\/span>Write the code required to make these two plots include the two plots in the report<\/span> <\/span><\/span>make a few comments on both of the plots<\/span>.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

.Data Mining This assignment is about two things:(1) doing a simple KNN analysis using a built-in package from Scikit-learn\/sklearn(2) making an \u2018elbow\u2019 plot. (1)(a)Now before we get into the data mining, let\u2019s first \u2018pre-process\u2019 the dataset. So, we pretend we only have access to 70 percent of this iris data set. If you think of the entire […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":[],"disciplines":[734],"paper_types":[],"tagged":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions\/22787"}],"collection":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions"}],"about":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/types\/questions"}],"author":[{"embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/comments?post=22787"}],"version-history":[{"count":0,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions\/22787\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/media?parent=22787"}],"wp:term":[{"taxonomy":"disciplines","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/disciplines?post=22787"},{"taxonomy":"paper_types","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/paper_types?post=22787"},{"taxonomy":"tagged","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/tagged?post=22787"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}