{"id":783,"date":"2018-07-07T09:08:27","date_gmt":"2018-07-07T09:08:27","guid":{"rendered":"http:\/\/muthu.co\/?p=783"},"modified":"2021-05-24T03:35:19","modified_gmt":"2021-05-24T03:35:19","slug":"k-means-on-iris-dataset","status":"publish","type":"post","link":"http:\/\/write.muthu.co\/k-means-on-iris-dataset\/","title":{"rendered":"K-Means on Iris Dataset"},"content":{"rendered":"\n<p>Read my <a href=\"http:\/\/muthu.co\/mathematics-behind-k-mean-clustering-algorithm\/\">previous post<\/a> to understand how K-Means algorithm works. In this post I will try to run the K-Means on Iris dataset to classify our 3 classes of flowers,&nbsp;Iris setosa, Iris versicolor, Iris virginica (our classess) using the flowers sepal-length, sepal-width, petal-length and petal-width (our features)<\/p>\n\n\n\n<p>Getting data:<\/p>\n\n\n\n<pre class=\"wp-block-code EnlighterJSRAW\"><code>import numpy as np  \nimport matplotlib.pyplot as plt  \nimport pandas as pd\n\nurl = \"https:\/\/archive.ics.uci.edu\/ml\/machine-learning-databases\/iris\/iris.data\"\n#the imported dataset does not have the required column names so lets add it\ncolnames = &#91;'sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']\nirisdata = pd.read_csv(url, names=colnames)<\/code><\/pre>\n\n\n\n<p>describe the data:<\/p>\n\n\n\n<pre class=\"wp-block-code EnlighterJSRAW\"><code>irisdata.head()<\/code><\/pre>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><a href=\"https:\/\/muthu.co\/wp-content\/uploads\/2018\/07\/Snip20180707_107.png\"><img loading=\"lazy\" decoding=\"async\" width=\"601\" height=\"115\" src=\"https:\/\/muthu.co\/wp-content\/uploads\/2018\/07\/Snip20180707_107.png\" alt=\"\" class=\"wp-image-784\" srcset=\"http:\/\/write.muthu.co\/wp-content\/uploads\/2018\/07\/Snip20180707_107.png 601w, http:\/\/write.muthu.co\/wp-content\/uploads\/2018\/07\/Snip20180707_107-300x57.png 300w\" sizes=\"(max-width: 601px) 100vw, 601px\" \/><\/a><\/figure><\/div>\n\n\n\n<p>Converting the class names into numerical categories for analysis.<\/p>\n\n\n\n<pre class=\"wp-block-code EnlighterJSRAW\"><code>irisdata&#91;'Class'] = pd.Categorical(irisdata&#91;\"Class\"])\nirisdata&#91;\"Class\"] = irisdata&#91;\"Class\"].cat.codes<\/code><\/pre>\n\n\n\n<p>Preparing our dataset:<\/p>\n\n\n\n<pre class=\"wp-block-code EnlighterJSRAW\"><code>X = irisdata.values&#91;:, 0:4]\ny = irisdata.values&#91;:, 4]<\/code><\/pre>\n\n\n\n<p>Running K-Means on it:<\/p>\n\n\n\n<pre class=\"wp-block-code EnlighterJSRAW\"><code>from sklearn.cluster import KMeans\n\n# Number of clusters\nkmeans = KMeans(n_clusters=3)\n# Fitting the input data\nkmeans = kmeans.fit(X)\n# Getting the cluster labels\nlabels = kmeans.predict(X)\n# Centroid values\ncentroids = kmeans.cluster_centers_<\/code><\/pre>\n\n\n\n<p>Checking the clustering accuracy of our program:<\/p>\n\n\n\n<pre class=\"wp-block-code EnlighterJSRAW\"><code>from sklearn.metrics import classification_report\n\ntarget_names = &#91;'Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n\nprint(classification_report(irisdata&#91;'Class'],kmeans.labels_,target_names=target_names))<\/code><\/pre>\n\n\n\n<p>The output of classification looks like this:<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><a href=\"https:\/\/muthu.co\/wp-content\/uploads\/2018\/07\/Snip20180707_108.png\"><img loading=\"lazy\" decoding=\"async\" width=\"495\" height=\"137\" src=\"https:\/\/muthu.co\/wp-content\/uploads\/2018\/07\/Snip20180707_108.png\" alt=\"\" class=\"wp-image-785\" srcset=\"http:\/\/write.muthu.co\/wp-content\/uploads\/2018\/07\/Snip20180707_108.png 495w, http:\/\/write.muthu.co\/wp-content\/uploads\/2018\/07\/Snip20180707_108-300x83.png 300w\" sizes=\"(max-width: 495px) 100vw, 495px\" \/><\/a><\/figure><\/div>\n\n\n\n<p>You can see in the classification report that, 91% of our data was predicted accurately. That&#8217;s pretty good for an unsupervised algorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Read my previous post to understand how K-Means algorithm works. In this post I will try to run the K-Means on Iris dataset to classify our 3 classes of flowers,&nbsp;Iris setosa, Iris versicolor, Iris virginica (our classess) using the flowers sepal-length, sepal-width, petal-length and petal-width (our features) Getting data: describe the data: Converting the class [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[24,37],"tags":[46,49],"_links":{"self":[{"href":"http:\/\/write.muthu.co\/wp-json\/wp\/v2\/posts\/783"}],"collection":[{"href":"http:\/\/write.muthu.co\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/write.muthu.co\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/write.muthu.co\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/write.muthu.co\/wp-json\/wp\/v2\/comments?post=783"}],"version-history":[{"count":2,"href":"http:\/\/write.muthu.co\/wp-json\/wp\/v2\/posts\/783\/revisions"}],"predecessor-version":[{"id":1890,"href":"http:\/\/write.muthu.co\/wp-json\/wp\/v2\/posts\/783\/revisions\/1890"}],"wp:attachment":[{"href":"http:\/\/write.muthu.co\/wp-json\/wp\/v2\/media?parent=783"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/write.muthu.co\/wp-json\/wp\/v2\/categories?post=783"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/write.muthu.co\/wp-json\/wp\/v2\/tags?post=783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}