{"id":25702,"date":"2019-11-29T09:00:59","date_gmt":"2019-11-29T14:00:59","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=25702"},"modified":"2022-11-21T09:44:40","modified_gmt":"2022-11-21T14:44:40","slug":"k-means-clustering-algo-python-part-viii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-viii\/","title":{"rendered":"K-Means Clustering Algorithm For Pair Selection In Python \u2013 Part VIII"},"content":{"rendered":"\n<p><em>See the prior installments in this series: <\/em><a href=\"\/campus\/ibkr-quant-news\/k-means-clustering-algo-part-i\/\"><em>Part I<\/em><\/a><em>, <\/em><a href=\"\/campus\/ibkr-quant-news\/k-means-clustering-algo-part-ii\/\"><em>Part II<\/em><\/a><em>, <\/em><a href=\"\/campus\/ibkr-quant-news\/k-means-clustering-algo-part-iii\/\"><em>Part III<\/em><\/a><em>, <\/em><a href=\"\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-iv\/\"><em>Part IV<\/em><\/a><em>,&nbsp;<\/em><a href=\"\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-v\/\"><em>Part V<\/em><\/a><em>&nbsp;,&nbsp;<a href=\"\/campus\/contributors\/k-means-clustering-algo-python-part-vi\/\">Part VI<\/a><\/em> <em>and <a href=\"\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-vii\/\">Part VII<\/a><\/em>. <\/p>\n\n\n\n<p>Now that we have imported our data analysis, visualization and the <code>make_blobs<\/code> method from <code>sklearn<\/code>, we&#8217;re ready to create our toy data to begin our analysis.<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">#creating fake data<br>\ndata=make_blobs(n_samples=500, n_features=8,centers=5, cluster_std=1.5, random_state=201)<\/p>\n\n\n\n<p>In the above line of code, we have created a variable named data and have initialized it using our make_blobs object imported from sklearn. The make blobs object allows us to create and specify the parameters associated with the data we&#8217;re going to create. We&#8217;re able to assign the number of samples, or the number of observations equally divided between clusters, the number of features, clusters, cluster standard deviation, and a random state. Using the centres variable, we can determine the number of clusters that we want to create from our toy data.<\/p>\n\n\n\n<p>Now that we have initialized our method, let&#8217;s take a look at our data.<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">#Let&#8217;s take a look at our fake data<br>\ndata[0] #produces an array of our samples<\/pre>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" data-src=\"https:\/\/d1rwhvwstyk9gu.cloudfront.net\/2019\/09\/code-1.jpg\" alt=\"K-Means Clustering Algorithm For Pair Selection In Python\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" \/><\/figure>\n\n\n\n<p><em>Stay tuned -for the next installment in this series. Lamarcus will <\/em> <em>view the cluster assignments we created.<\/em><\/p>\n\n\n\n<hr class=\"wp-block-separator\"\/>\n\n\n\n<p><em>Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.<\/em><\/p>\n\n\n\n<p><em>Disclaimer: All investments and trading in the stock market involve risk. Any decisions to place trades in the financial markets, including trading in stock or options or other financial instruments is a personal decision that should only be made after thorough research, including a personal risk and financial assessment and the engagement of professional assistance to the extent you believe necessary. The trading strategies or related information mentioned in this article is for informational purposes only.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Now that we have imported our data analysis, visualization and the make_blobs method from sklearn, we&#8217;re ready to create our toy data to begin our analysis.<\/p>\n","protected":false},"author":261,"featured_media":20116,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,350,341,344],"tags":[851,4658,4656,806,4582,4124,852,4659,1225,4657,1224,595,4580],"contributors-categories":[13654],"class_list":{"0":"post-25702","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-python-development","10":"category-ibkr-quant-news","11":"category-quant-asia-pacific","12":"category-quant-development","13":"category-quant-regions","14":"tag-algo-trading","15":"tag-augmented-dickey-fuller-test","16":"tag-correlation-coefficient","17":"tag-data-science","18":"tag-dataframe","19":"tag-k-means-clustering","20":"tag-machine-learning","21":"tag-matplotlib","22":"tag-numpy","23":"tag-p-value","24":"tag-pandas","25":"tag-python","26":"tag-seaborn","27":"contributors-categories-quantinsti"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- 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