{"id":143478,"date":"2024-09-25T12:04:59","date_gmt":"2024-09-25T06:34:59","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=143478"},"modified":"2025-06-24T15:09:09","modified_gmt":"2025-06-24T09:39:09","slug":"insurance-price-prediction-using-machine-learning","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/insurance-price-prediction-using-machine-learning\/","title":{"rendered":"Insurance Price Prediction using Machine Learning"},"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\/multiple-variable-linear-regression-model-for-insurance-price-predication.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\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn import linear_model\r\nfrom sklearn.model_selection import  train_test_split\r\nfrom sklearn.metrics import r2_score\r\ndf=pd.read_csv(\"D:\/\/scikit_data\/insurancedata\/insurance.csv\")\r\nmap_dict={'female':0,'male':1}\r\ndf['sex']=df['sex'].map(map_dict)\r\nmap_dict={'southwest':0,'southeast':1,'northwest':2,'northeast':3}\r\ndf['region']=df['region'].map(map_dict)\r\nmap_dict={'yes':1,'no':0}\r\ndf['smoker']=df['smoker'].map(map_dict)\r\n# print(df)\r\n# print(df.isnull().sum())\r\nx=df.drop('charges',axis='columns')  # Independed Variables\r\ny=df.charges  # Depended Variables\r\nx_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)\r\n\r\n# x_train---&gt; Training data set for Independed variable\r\n# y_train---&gt; Training data set for depended variable\r\n# x_test---&gt; Testing data set for Independed variable\r\n# y_test---&gt; Testing data set for depended variable\r\n\r\nmodel=linear_model.LinearRegression()\r\nmodel.fit(x_train,y_train)\r\n# Predication by training dataset\r\n\r\ny_pred=model.predict(x_train)\r\n#print(y_pred)\r\nplt.scatter(y_train,y_pred,color='red')\r\nplt.xlabel(\"Actual Price\")\r\nplt.ylabel(\"Predicated Price\")\r\nplt.show()\r\nprint(\"R2 Score for Training data:\",r2_score(y_train,y_pred))\r\n\r\n# Predication by test dataset\r\ny_pred=model.predict(x_test)\r\n#print(y_pred)\r\nplt.scatter(y_test,y_pred,color='blue',marker='+')\r\nplt.xlabel(\"Actual Price\")\r\nplt.ylabel(\"Predicated Price\")\r\nplt.show()\r\nprint(\"R2 Score for Testing data:\",r2_score(y_test,y_pred))\r\n\r\n\r\n\r\n\r\n\r\n\r\n# print(model)\r\n# print(\"Coeficent values\")\r\n# print(model.coef_)\r\n# print(\"Intercept values\")\r\n# print(model.intercept_)\r\n# print(x)\r\n# print(y)\r\n<\/pre>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:113,&quot;href&quot;:&quot;https:\\\/\\\/data-flair.s3.ap-south-1.amazonaws.com\\\/ai-ml-course-dataset-vivek-uprit\\\/multiple-variable-linear-regression-model-for-insurance-price-predication.zip&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251205214920\\\/https:\\\/\\\/data-flair.s3.ap-south-1.amazonaws.com\\\/ai-ml-course-dataset-vivek-uprit\\\/multiple-variable-linear-regression-model-for-insurance-price-predication.zip&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-29 06:35:54&quot;,&quot;http_code&quot;:503},{&quot;date&quot;:&quot;2026-01-06 11:19:10&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-01 04:03:06&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-04 17:27:05&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-11 23:07:42&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-17 22:02:12&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-08 07:15:27&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-04-15 02:16:14&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-23 12:57:04&quot;,&quot;http_code&quot;:403}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-23 12:57:04&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 import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model from sklearn.model_selection import train_test_split from sklearn.metrics import r2_score df=pd.read_csv(&#8220;D:\/\/scikit_data\/insurancedata\/insurance.csv&#8221;) map_dict={&#8216;female&#8217;:0,&#8217;male&#8217;:1} df[&#8216;sex&#8217;]=df[&#8216;sex&#8217;].map(map_dict) map_dict={&#8216;southwest&#8217;:0,&#8217;southeast&#8217;:1,&#8217;northwest&#8217;:2,&#8217;northeast&#8217;:3} df[&#8216;region&#8217;]=df[&#8216;region&#8217;].map(map_dict)&#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":[33146,33145,8431,33127,33128,33147],"class_list":["post-143478","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-insurance-price-prediction","tag-insurance-price-prediction-using-machine-learning","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-program-on-insurance-price-prediction"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Insurance Price Prediction using Machine Learning - 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