{"id":143645,"date":"2024-12-03T18:59:47","date_gmt":"2024-12-03T13:29:47","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=143645"},"modified":"2025-06-24T15:30:39","modified_gmt":"2025-06-24T10:00:39","slug":"employee-retention-prediction-in-logistic-regression-in-ml","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/employee-retention-prediction-in-logistic-regression-in-ml\/","title":{"rendered":"Employee Retention Prediction in Logistic Regression in ML"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/data-flair.s3.ap-south-1.amazonaws.com\/ai-ml-course-dataset-vivek-uprit\/practical-logistic-regression-example-2.zip\" target=\"_blank\" rel=\"noopener\"><strong>Machine Learning Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import pandas as pd\r\nfrom matplotlib import pyplot as plt\r\n\r\n\r\ndf = pd.read_csv(\"D:\/\/scikit_data\/Employee\/HR_comma_sep.csv\")\r\n\r\n\r\ndf.head(10)\r\n\r\n\r\nleft = df[df.left==1]\r\nleft.shape\r\n\r\n\r\nleft.shape\r\n\r\n\r\nleft\r\n\r\n\r\nretained = df[df.left==0]\r\nretained.shape\r\n\r\n\r\nretained\r\n\r\n\r\npd.crosstab(df.salary,df.left).plot(kind='bar')\r\n\r\n\r\npd.crosstab(df.Department,df.left).plot(kind='bar')\r\n\r\n\r\nsubdf = df[['satisfaction_level','average_montly_hours','promotion_last_5years','salary']]\r\nsubdf.head()\r\n\r\n\r\nsalary_dummies = pd.get_dummies(subdf.salary, prefix=\"salary\")\r\n\r\n\r\nsalary_dummies\r\n\r\n\r\ndf_with_dummies = pd.concat([subdf,salary_dummies],axis='columns')\r\n\r\n\r\ndf_with_dummies.head()\r\n\r\n\r\ndf_with_dummies.drop('salary',axis='columns',inplace=True)\r\ndf_with_dummies.head()\r\n\r\n\r\nx = df_with_dummies  # Independed variables\r\nx.head()\r\n\r\n\r\ny = df.left  # Depended variable\r\n\r\n\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n\r\nx_train,x_test, y_train, y_test = train_test_split(x,y,train_size=0.3)\r\n\r\n\r\nlen(x_train)\r\n\r\n\r\nlen(x_test)\r\n\r\nfrom sklearn.linear_model import LogisticRegression\r\n\r\n\r\nmodel=LogisticRegression()\r\n\r\n\r\nmodel.fit(x_train,y_train)\r\n\r\n\r\nmodel.predict(x_test)\r\n\r\n\r\nmodel\r\n\r\n\r\nx=model.predict(x_test)\r\n\r\n\r\nprint(x)\r\n\r\n\r\nmodel.score(x_test,y_test)\r\n\r\n\r\nmodel.score(x_train,y_train)\r\n\r\n\r\nx_test\r\n\r\n\r\nmodel.predict(x_test)\r\n\r\n\r\n\r\nmodel.predict([[0,0,0,0,0,1]])\r\n\r\n\r\nmodel.predict([[0,0,0,1,0,0]])\r\n\r\n\r\nmodel.predict([[1,0,1,0,0,1]])\r\n\r\n\r\nmodel.predict([[0,0,1,1,0,0]])\r\n\r\n\r\nmodel.predict([[1,0,0,0,1,0]])\r\n<\/pre>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:100,&quot;href&quot;:&quot;https:\\\/\\\/data-flair.s3.ap-south-1.amazonaws.com\\\/ai-ml-course-dataset-vivek-uprit\\\/practical-logistic-regression-example-2.zip&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251205203726\\\/https:\\\/\\\/data-flair.s3.ap-south-1.amazonaws.com\\\/ai-ml-course-dataset-vivek-uprit\\\/practical-logistic-regression-example-2.zip&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-22 10:43:45&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-01-03 07:17:52&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-01-16 22:13:12&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-02-20 05:18:00&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-26 10:37:54&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-18 08:32:56&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-21 20:28:57&quot;,&quot;http_code&quot;:403}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-21 20:28:57&quot;,&quot;http_code&quot;:403},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Machine Learning Dataset import pandas as pd from matplotlib import pyplot as plt df = pd.read_csv(&#8220;D:\/\/scikit_data\/Employee\/HR_comma_sep.csv&#8221;) df.head(10) left = df[df.left==1] left.shape left.shape left retained = df[df.left==0] retained.shape retained pd.crosstab(df.salary,df.left).plot(kind=&#8217;bar&#8217;) pd.crosstab(df.Department,df.left).plot(kind=&#8217;bar&#8217;) subdf =&#46;&#46;&#46;<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[33210,33209,33211,33208,8431,33127,33128],"class_list":["post-143645","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-employee-retention-prediction","tag-employee-retention-prediction-in-logistic-regression-in-machine-learning","tag-employee-retention-prediction-in-machine-learning","tag-logistic-regression-in-machine-learning","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Employee Retention Prediction in Logistic Regression in ML - DataFlair<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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