support_vector_regression(done).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1G9JzdFlssZajJfqBjSuq0OtgIGpWCMQE","timestamp":1730601886213}]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"m3PAEPRDRLA3"},"source":["# Support Vector Regression (SVR)"]},{"cell_type":"markdown","metadata":{"id":"0VCUAVIjRdzZ"},"source":["## Importing the libraries"]},{"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd"],"metadata":{"id":"eCJoeO3X6ccM","executionInfo":{"status":"ok","timestamp":1730602125409,"user_tz":-420,"elapsed":1786,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}}},"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fXVXoFWtSF4_"},"source":["## Importing the dataset"]},{"cell_type":"code","source":["dataset = pd.read_csv('Position_Salaries.csv')\n","x = dataset.iloc[:,1:2].values\n","y = dataset.iloc[:,2].values"],"metadata":{"id":"lXdbynYJ6jj5","executionInfo":{"status":"ok","timestamp":1730602350087,"user_tz":-420,"elapsed":803,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["print(x)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GW-9dAT57VNK","executionInfo":{"status":"ok","timestamp":1730602350578,"user_tz":-420,"elapsed":2,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"b0fa3099-3edd-41b0-9f34-957620db9987"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 1]\n"," [ 2]\n"," [ 3]\n"," [ 4]\n"," [ 5]\n"," [ 6]\n"," [ 7]\n"," [ 8]\n"," [ 9]\n"," [10]]\n"]}]},{"cell_type":"code","source":["print(y)\n","print(len(y))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gFweYidA7j_s","executionInfo":{"status":"ok","timestamp":1730602351111,"user_tz":-420,"elapsed":2,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"3ee721fe-23b2-4a78-ea16-49e171d3d4e0"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["[  45000   50000   60000   80000  110000  150000  200000  300000  500000\n"," 1000000]\n","10\n"]}]},{"cell_type":"code","source":["y = y.reshape(len(y),1)\n","print(y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y8m3HQaq7ue_","executionInfo":{"status":"ok","timestamp":1730602382863,"user_tz":-420,"elapsed":835,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"c2046260-c649-4952-d07d-670cc13f3409"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["[[  45000]\n"," [  50000]\n"," [  60000]\n"," [  80000]\n"," [ 110000]\n"," [ 150000]\n"," [ 200000]\n"," [ 300000]\n"," [ 500000]\n"," [1000000]]\n"]}]},{"cell_type":"markdown","metadata":{"id":"YS8FeLHYS-nI"},"source":["## Feature Scaling"]},{"cell_type":"code","source":["from sklearn.preprocessing import StandardScaler\n","sc_x = StandardScaler()\n","sc_y = StandardScaler()\n","x = sc_x.fit_transform(x)\n","y = sc_y.fit_transform(y)"],"metadata":{"id":"Klm9-svg8s_A","executionInfo":{"status":"ok","timestamp":1730602826061,"user_tz":-420,"elapsed":450,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}}},"execution_count":27,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"eiU6D2QFRjxY"},"source":["## Training the SVR model on the whole dataset"]},{"cell_type":"code","source":["from sklearn.svm import SVR\n","regressor = SVR(kernel = 'rbf')\n","regressor.fit(x,y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":135},"id":"YC7-Wflr91yz","executionInfo":{"status":"ok","timestamp":1730602962636,"user_tz":-420,"elapsed":848,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"548fdaea-ba5a-47e0-887f-e174fda7261e"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n","  y = column_or_1d(y, warn=True)\n"]},{"output_type":"execute_result","data":{"text/plain":["SVR()"],"text/html":["<style>#sk-container-id-1 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-1 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-1 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-1 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-1 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-1 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-1 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-1 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-1 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-1 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-1 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-1 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-1 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-1 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-1 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-1 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-1 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"deDnDr8UR5vq"},"source":["## Predicting a new result"]},{"cell_type":"code","source":["sc_y.inverse_transform(regressor.predict(sc_x.transform([[6.5]])).reshape(-1,1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-T_5pLce-LRH","executionInfo":{"status":"ok","timestamp":1730603101066,"user_tz":-420,"elapsed":3,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"f1cd8086-f1d1-41a4-a187-3119be6880f3"},"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[252789.13921624]])"]},"metadata":{},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"zzedFlUISSu_"},"source":["## Visualising the SVR results"]},{"cell_type":"code","source":["plt.scatter(sc_x.inverse_transform(x), sc_y.inverse_transform(y), color = 'red')\n","plt.plot(sc_x.inverse_transform(x), sc_y.inverse_transform(regressor.predict(x).reshape(-1,1)), color = 'blue')\n","plt.title('Truth or Bluff (SVR)')\n","plt.xlabel('Position level')\n","plt.ylabel('Salary')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"cxZDd6FE-Mze","executionInfo":{"status":"ok","timestamp":1730602968845,"user_tz":-420,"elapsed":959,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"5d26f913-4811-47df-f393-bbf8720ab7dc"},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"UahPVNlJSZ-K"},"source":["## Visualising the SVR results (for higher resolution and smoother curve)"]},{"cell_type":"code","source":["x_grid = np.arange(min(sc_x.inverse_transform(x)), max(sc_x.inverse_transform(x)), 0.1)\n","x_grid = x_grid.reshape((len(x_grid), 1))\n","plt.scatter(sc_x.inverse_transform(x), sc_y.inverse_transform(y), color = 'red')\n","plt.plot(x_grid, sc_y.inverse_transform(regressor.predict(sc_x.transform(x_grid)).reshape(-1,1)), color = 'blue')\n","plt.title('Truth or Bluff (SVR)')\n","plt.xlabel('Position level')\n","plt.ylabel('Salary')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":527},"id":"1o37VUXE-Ol2","executionInfo":{"status":"ok","timestamp":1730603149435,"user_tz":-420,"elapsed":501,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"684ecc04-33a5-46cd-eda0-c51d272445ea"},"execution_count":35,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-35-d59cbf43e0c3>:1: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n","  x_grid = np.arange(min(sc_x.inverse_transform(x)), max(sc_x.inverse_transform(x)), 0.1)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]}]}