{"id":143648,"date":"2024-12-03T19:05:53","date_gmt":"2024-12-03T13:35:53","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=143648"},"modified":"2026-06-01T14:40:25","modified_gmt":"2026-06-01T09:10:25","slug":"car-price-prediction-in-machine-learning","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/car-price-prediction-in-machine-learning\/","title":{"rendered":"Machine Learning Project &#8211; Car Price Prediction"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1xT8R1yYryCeF5DG5q2WIknUbtLthiez4\/view?usp=drive_link\" target=\"_blank\" rel=\"noopener\"><strong>Machine Learning Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn import metrics \r\n#pandas                    2.2.2\r\n#numpy                     1.24.3\r\n#matplotlib                3.7.1\r\n#scikit-learn              1.5.1\r\n\r\n\r\n\r\n# Read data set \r\ndf=pd.read_csv(\"D:\/\/scikit_data\/car\/car_data.csv\")\r\ndf.head()\r\n\r\n\r\ntype(df)\r\n\r\n\r\n# No of Rows &amp; Columns \r\ndf.shape\r\n\r\n\r\n# Dispaly information about dataset\r\ndf.info()\r\n\r\n\r\n# Infomrmation about missing values\r\ndf.isnull().sum()\r\n\r\n\r\n# Check categorical data\r\nprint(df.Fuel_Type.value_counts())\r\nprint(df.Seller_Type.value_counts())\r\nprint(df.Transmission.value_counts())\r\n\r\n\r\n# Replace Character values\r\n\r\ndf.replace({'Fuel_Type':{'Petrol':0,'Diesel':1,'CNG':2}},inplace=True)\r\ndf.replace({'Seller_Type':{'Dealer':0,'Individual':1}},inplace=True)\r\ndf.replace({'Transmission':{'Manual':0,'Automatic':1}},inplace=True)\r\n\r\n\r\n\r\n\r\ndf.head()\r\n\r\n\r\n# Find Depended and Independed variables\r\nx=df.drop(['Car_Name','Selling_Price'],axis='columns')  # Independed variables \r\n\r\n\r\ny=df['Selling_Price']  # Depended variables\r\n\r\n\r\nx\r\n\r\n\r\ny\r\n\r\n\r\n# Split data into training and testing data\r\nx_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.1,random_state=2)\r\n\r\n\r\n# x_train  ---&gt; training independed variable\r\n# y_train ---&gt;  training depended variable\r\n# x_test  ---&gt; test independed variable\r\n# y_test ---&gt;  test depended variable\r\n\r\n\r\n\r\nlen(x_train)\r\n\r\n\r\nlen(x_test)\r\n\r\n\r\n# Model Prepration\r\nmodel=LinearRegression()\r\nmodel.fit(x_train,y_train)\r\n\r\n\r\ny_pred_train=model.predict(x_train)\r\n\r\n\r\ny_train\r\n\r\n\r\nplt.scatter(y_train,y_pred_train,marker='.',color='blue')\r\nplt.xlabel(\"Actual Price\")\r\nplt.ylabel(\"Predicated Price\")\r\nplt.title(\"Actual Price vs Predicated Price\")\r\nplt.show()\r\n\r\n\r\n\r\nerror_score=metrics.r2_score(y_train,y_pred_train)\r\nprint(\"R squared Error for training data set \", error_score)\r\n\r\n\r\n# Predication an testing data\r\n\r\ny_pred_test=model.predict(x_test)\r\n\r\n\r\nplt.scatter(y_test,y_pred_test,marker='.',color='red')\r\nplt.xlabel(\"Actual Price\")\r\nplt.ylabel(\"Predicated Price\")\r\nplt.title(\"Actual Price vs Predicated Price\")\r\nplt.show()\r\n\r\n\r\n\r\nerror_score=metrics.r2_score(y_test,y_pred_test)\r\nprint(\"R squared Error for training data set \", error_score)\r\n\r\n\r\nmodel.score(x_test,y_test)\r\n<\/pre>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:2646,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1xT8R1yYryCeF5DG5q2WIknUbtLthiez4\\\/view?usp=drive_link&quot;,&quot;archived_href&quot;:&quot;&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[],&quot;broken&quot;:false,&quot;last_checked&quot;:null,&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Machine Learning Dataset import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics #pandas 2.2.2 #numpy 1.24.3 #matplotlib&#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":[33214,33212,33213,8431,33127,33128,20697],"class_list":["post-143648","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-car-price-prediction","tag-car-price-prediction-in-machine-learning","tag-car-price-prediction-project-in-machine-learning","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - 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