svr.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNNtBA73eYgklPq5O6CERDC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Saani765/machine_learning-basics/blob/main/svr.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wCbf1SVFWSQX"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv(\"Position_Salaries.csv\")\n",
        "X=dataset.iloc[:,1:-1].values\n",
        "y=dataset.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "Y4H66G4KZEHk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "FEATURE SCALING"
      ],
      "metadata": {
        "id": "gM9Dabf9ZjQy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y=y.reshape(len(y),1)\n",
        "print(y)"
      ],
      "metadata": {
        "id": "d5cl3kyPZD74"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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)\n"
      ],
      "metadata": {
        "id": "-fHUOEVoZD3m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)\n",
        "print(y)"
      ],
      "metadata": {
        "id": "2LSaorg_ZD0C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": 129
        },
        "id": "KKbl7IEzZDtj",
        "outputId": "78d29c2b-7d24-4163-dad2-acfff99bc86a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: 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-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "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": "9ZcDbLIFZDkZ",
        "outputId": "510d8777-f1d5-4d5f-cc4c-cc119c1ece07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[170370.0204065]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "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')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "ktZa3u5tej6z",
        "outputId": "7b2b15f0-67d5-4bc2-b501-b5f2d5b344f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4a18c789a0>]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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nfUZsYJ8RAAD8T43sMwIAAFDdCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAQczjkTZssFsDYQQAgCD1/ffSZZdJvXpJP/9srw7CCAAAQebAAem++6Tu3aVVq0zvyNq19uohjAAAECQ8HmnhQql9e2nGDMnlkq6/3gzTXHutvbp8PpsGAAD4n40bpTFjpMxM8/ycc6Tnn5euuspuXRI9IwAABLTCQmnSJKlLFxNEIiKkP//ZzBepC0FEomcEAICA5PFI77wjjR8v5eSYtmuukZ59VmrTxmZl5RFGAAAIMFu2SPfeK330kXneqpX03HN254WcDMM0AAAEiF9/laZNkzp1MkEkLEyaPFlav77uBhGJnhEAAALChx+a3pCtW83zK6+UZs6U2rWzW5c36BkBAMCPbd8uDRpk5oNs3SrFxUlvvCF9/LF/BBGJMAIAgF8qKpIef1zq2FF6912pXj3pgQfMEt4hQySHw3aF3mOYBgAAP7N8udkzZPNm8/yyy6RZs6Tzz7dbV1XRMwIAgJ/4+Wfphhukfv1MEImOll57TVqxwn+DiEQYAQCgzjtyRHrmGbON+5tvSiEhZrLqpk3SsGH+NSRTEYZpAACowz77TBo9Wvrvf83zpCTphRekbt2sllWt6BkBAKAOysuThg+XLr/cBJFmzaT586UvvgisICLRMwIAgO9cLmnVKmnXLikmRkpOlkJDq+Wljx41PR9TpkhOpxmCuesus3KmadNqeYs6hzACAIAvliyRxo0zs0lLxMebQ19SU0/rpbOypD/+UcrONs979jTBpFev03rZOo9hGgAAvLVkiTR4cNkgIkk7dpj2JUuq9LJ79ki33y5dfLEJIk2aSC++KH39deAHEYkwAgCAd1wu0yPi8ZT/WUnb+PHmOh9e8qWXpPPOM/NBJOm228wqmbvvrraRnzqPMAIAgDdWrSrfI3I8j0fKzTXXeeHbb83KmLvvln75ReraVfryS2nePOnss6upZj9BGAEAwBu7dlXLdfv2SffcI114ofTNN1JkpJlu8u23ZpgmGDGBFQAAb8TEnNZ1brf0yivSn/4k7d1r2oYNk/7yF+9fOlDRMwIAgDeSk82qmcq2O3U4pIQEc90J/v1v03zbbSaIdOxotnB/7TWCiEQYAQDAO6GhZjxFKh9ISp5nZJSZdVpQYOa0XnCB9NVXUsOG0tNPmxUzl19eCzX7CcIIAADeSk2VFi+W4uLKtsfHm/bf9hnxeKS//92cJfPss2aIZvBgaeNG6YEHpPr1LdRehzFnBAAAX6SmStddV+kOrOvXm7NkVq40l597rjRzpjlpFxUjjAAA4KvQ0HLjLAcPSn/+s/TXv5ot3Rs0kCZPlu6/XwoPt1OmvyCMAABwGjwe6a23pAkTjm1Dcu21ZngmMdFqaX6DMAIAQBVt3iyNHSt98ol53rq19Nxz0jXX2K3L3zCBFQAAH3k8Unq61LmzCSJhYdLUqdJ//0sQqQp6RgAA8IHHY1bETJ9unl91lfT889I559ity58RRgAA8JLHI913n5mkKpkhmTFjKt8HDd4hjAAA4AWPx0xSLdn3bPZs6a677NYUKAgjAACcgscj3Xuv2S9EkubMke68025NgYQwAgDASbjdZsXMCy+Y4Zi5c6Xbb7ddVWAhjAAAUAm32+ymOnu2CSLz5kmjRtmuKvAQRgAAqIDbLd19t+kJcTikl1+WRo60XVVgIowAAHACt1v6wx9MT0hIiPTKK9Itt9iuKnARRgAAOI7bLd1xh+kJCQmR/vY36eabbVcV2AgjAAD8xuUyk1NfecUEkb//XbrxRttVBT7CCAAAMkFk1CjTExIaaoLI0KG2qwoOhBEAQNA7elS69VYTQEJDpX/8QxoyxHZVwYMwAgAIakePSiNGmABSr560cKF0/fW2qwouhBEAQNA6etSsklm0yASRN96Q/t//s11V8CGMAACC0pEj0rBh0ptvSvXrmz+vu852VcGJMAIACDpHjkg33SS99ZYUFiYtXiwNHGi7quBFGAEABJXiYrNc9+23TRBZskQaMMB2VcGNMAIACBrFxdINN0jvviuFh5tAcvXVtqsCYQQAEBSKisxy3fffN0Hk3Xel/v1tVwWJMAIACAJFRWa57ocfShERJoj062e7KpQgjAAAAtrhw1JqqvTRR1KDBtJ770kpKbarwvEIIwCAgHX4sDRokPTxxyaIfPCB9Lvf2a4KJwqpyk2zZs1SYmKiIiIi1Lt3b61evdqr+xYuXCiHw6FBgwZV5W0BAPDar79K115rgsgZZ0hLlxJE6iqfw8iiRYuUlpamadOmae3ateratav69++v3bt3n/S+7du36/7771dycnKViwUAwBuHDpkgsny51LChGaK5/HLbVaEyPoeRGTNm6M4779SoUaPUsWNHzZ49W2eccYbmz59f6T0ul0vDhg3TI488ojZt2pxWwQAAnMyhQ2YDs08/lRo1MkHk0kttV4WT8SmMFBcXa82aNUo5buZPSEiIUlJSlJWVVel9f/7zn9W8eXPdfvvtXr1PUVGRnE5nmQcAAKdSWGg2MPvnP00QWbZMokO+7vMpjOzdu1cul0vR0dFl2qOjo5WXl1fhPV988YXmzZunuXPnev0+6enpioqKKn0kJCT4UiYAIAgdPCj9/vfSypVS48ZmrkifPrargjeqNIHVWwcOHNDw4cM1d+5cNWvWzOv7Jk2apIKCgtJHbm5uDVYJAPB3Bw6YIPL551JkpPTJJ9LFF9uuCt7yaWlvs2bNFBoaqvz8/DLt+fn5atGiRbnrf/zxR23fvl0Djzt9yO12mzeuV0+bNm1S27Zty90XHh6u8PBwX0oDAASpAwfMlu5ffilFRZkgcuGFtquCL3zqGQkLC1OPHj2UmZlZ2uZ2u5WZmamkpKRy17dv317ff/+9srOzSx/XXnut+vbtq+zsbIZfAACnxek0W7p/+aV05plm9QxBxP/4vOlZWlqaRo4cqZ49e+rCCy9URkaGCgsLNWrUKEnSiBEjFBcXp/T0dEVERKhTp05l7j/zzDMlqVw7AAC+KCiQrrpK+vprqUkTE0R69LBdFarC5zAydOhQ7dmzR1OnTlVeXp66deumZcuWlU5qzcnJUUhIjU5FAQAEuf37TY/I6tUmiHz6qXTBBbarQlU5PB6Px3YRp+J0OhUVFaWCggJFRkbaLgcAYNH+/eaQu2++kZo2lTIzpW7dbFeFinj7/c3ZNAAAv/HLL9KVV0pr1khnnWWCSNeutqvC6SKMAAD8wr59JoisXSs1a2Y2Nuvc2XZVqA6EEQBAnfe//0kpKVJ2tnT22SaIsA4icDDTFABQp+3dK11xhQkizZtLK1YQRAINPSMAgDprzx4TRL7/XoqONkGkQwfbVaG60TMCAKiTdu+Wfvc7E0RiYsyZMwSRwETPCACgzsnPN0Fk/XopNtb0iLRrZ7sq1BR6RgAAdUpentS3rwkicXGmR4QgEtgIIwCAOmPXLhNENmyQ4uNNEDn3XNtVoaYxTAMAqBN27jRBZPNmKSHBDM1UcLA7AhA9IwAA63bskC6/3ASRVq2kzz4jiAQTekYAAFbl5poekR9/NEFk5UopMdF2VahN9IwAAKzJyTE9Ij/+KLVubXpECCLBhzACALDip59MENm6VWrTxvSItGpluyrYQBgBANS67dtNENm2zcwNWblSatnSclGwhjkjAIBatW2bCSI5OWbZ7ooVZj8RBC96RgAAteY//5EuvdQEkXbtTI8IQQSEEQBArVi6VOrTR/r5Z6l9exNEYmNtV4W6gDACAKhxzz8vDRwoHTxolvF+9ZU5/A6QCCMAgBp09Kg0dqx0772S2y3dfru0bJnUpIntylCXMIEVAFAjnE7pxhuljz4yz596SnrgAcnhsFsX6h7CCACg2uXkSNdcI33/vdSggfTaa1Jqqu2qUFcRRgAA1eqbb8z8kPx8qUUL6b33pF69bFeFuowwAgCoNm+9JQ0fLv36q9S5s/TBBydsZuZySatWSbt2mRmsyclSaKi1elE3MIEVAHDaPB7pySelwYNNEPn976UvvzwhiCxZYg6e6dtXuvlm82diomlHUCOMAABOS3GxdMcd0qRJ5vnYsdK770qNGx930ZIlJqn8/HPZm3fsMO0EkqBGGAEAVNkvv0hXXSXNny+FhJj9RJ57Tqp3/CQAl0saN850n5yopG38eHMdghJhBABQJVu2SBddZM6WadRIev99acyYCi5ctap8j8jxPB4pN9dch6DEBFYAgM9WrZIGDZL27ZMSEsxE1S5dKrl41y7vXtTb6xBw6BkBAPjkb3+TrrjCBJFevaTVq08SRCTv931nf/igRRgBAHjF45GmTpVGjJCOHJGuv94cdteixSluTE6W4uMr33rV4TDdK8nJ1V0y/ARhBABwSocPm9W4jz5qnk+cKL3xhnTGGV7cHBoqPfus+fuJgaTkeUYG+40EMcIIAOCkdu+Wfvc7aeFCs0pm/nwpPd2snvFaaqq0eLEUF1e2PT7etLNXfFBjAisAoFL//a85Y2b7dnPS7ltvmb3KqiQ1VbruOnZgRTmEEQBAhZYvN/uROZ1S27bShx9K5513mi8aGipdfnl1lIcAwjANAKCcl16Srr7aBJHkZOnrr6shiACVIIwAAEq5XNJ990l3323+Pny46SFp1sx2ZQhkDNMAACRJBw9Kw4ZJ771nnj/6qDR5cuUrcoHqQhgBAGjHDmngQOm776TwcGnBAunGG21XhWBBGAGAIPfdd2bFzM6d0tlnmxN3k5JsV4VgwpwRAAhi770nXXKJCSIdO0r/+hdBBLWPMAIAQcjjkWbMMIfdHTokXXml9NVXUuvWtitDMCKMAECQOXJEuuces2rG45HuusvsIRIVZbsyBCvmjABAECkokIYMMct1HQ7pmWekCRNYMQO7CCMAECS2bTMTVdevNwfc/eMf0rXX2q4KIIwAQFDIyjLHwuzZI8XGSu+/L11wge2qAIM5IwAQ4BYuNIfb7dkjde8urV5NEEHdQhgBgADl8UiPPSbddJNUVGSGZD7/XIqLs10ZUBZhBAACUFGRNHKkNGWKeZ6WJi1ZIjVqZLcuoCLMGQGAALN3r5SaKq1aJYWGSrNmmeW7QF1FGAGAALJpkzRggPTjj1JkpLR4sdnQDKjLCCMAECBWrDA9Ivv3S4mJZiOzjh1tVwWcGnNGACAAvPyy1K+fCSIXXWTOmCGIwF8QRgDAj7nd0qRJ0m23SUePSkOHSv/8p9S8ue3KAO8RRgDATx06JN1wg/Tkk+b5lCnS669LDRrYrQvwFXNGAMAP7dpldlT95hupfn1p3jxp+HDbVQFVQxgBAD/z/fdmxUxurtS0qfT229Kll9quCqg6hmkAwI989JHUp48JIu3amYmqBBH4O8IIAPiBI4dd+uvoLbpmgFsHDkh9L/coK0s65xzblQGnr0phZNasWUpMTFRERIR69+6t1atXV3rt3LlzlZycrCZNmqhJkyZKSUk56fUAgGMKCqTpt/5HbRvlKe2Fc+T2hGiU5mvZD23VdOUS2+UB1cLnMLJo0SKlpaVp2rRpWrt2rbp27ar+/ftr9+7dFV6/cuVK3XTTTVqxYoWysrKUkJCgfv36aceOHaddPAAEqpwc6b77pISYI7r/lS7KdcWpufL1rO7VPN2usJ3bpcGDzYEzgJ9zeDwejy839O7dW7169dLMmTMlSW63WwkJCRo7dqwmTpx4yvtdLpeaNGmimTNnasSIEV69p9PpVFRUlAoKChQZGelLuQDgV779Vpo+XXrzTcnlMm0dtF5pmqFb9JoiVHTsYodDio+Xtm0zh9AAdYy3398+9YwUFxdrzZo1SklJOfYCISFKSUlRVlaWV69x6NAhHTlyRE2bNq30mqKiIjmdzjIPAAhUbrf07rtmImqvXtLChSaIXHHBL1qqq7VOnXSH5pUNIpLk8ZiZrKtW2SkcqCY+hZG9e/fK5XIpOjq6THt0dLTy8vK8eo0HH3xQsbGxZQLNidLT0xUVFVX6SEhI8KVMAPALhw5JL74otW8vDRpkMkW9ema/kO++kz69f5mu1jKF6BQd2Lt21Uq9QE2p1X1GnnzySS1cuFArV65UREREpddNmjRJaWlppc+dTieBBEDAyMuTZs0yQeR//zNtZ54p3XWXNHasFBf324X7Y7x7wRgvrwPqKJ/CSLNmzRQaGqr8/Pwy7fn5+WrRosVJ733mmWf05JNP6tNPP1WXLl1Oem14eLjCw8N9KQ0A6rx166S//lV67TWpuNi0tW4tjR9vzpZp1OiEG5KTzZyQHTvMkMyJSuaMJCfXdOlAjfJpmCYsLEw9evRQZmZmaZvb7VZmZqaSkpIqve/pp5/Wo48+qmXLlqlnz55VrxYA/IzHIy1fLl11ldS5szR/vgkiSUnS4sXSDz9I995bQRCRzKTUZ581f3c4yv6s5HlGBpNX4fd8XtqblpamuXPn6pVXXtGGDRt0zz33qLCwUKNGjZIkjRgxQpMmTSq9/qmnntKUKVM0f/58JSYmKi8vT3l5eTp48GD1fQoAqGOKi6VXXpG6dZP69ZM+/lgKCZGuv1766ivzuP56L3JEaqpJLaVjN7+Jjzftqak19RGAWuPznJGhQ4dqz549mjp1qvLy8tStWzctW7asdFJrTk6OQkKOZZwXX3xRxcXFGjx4cJnXmTZtmv7v//7v9KoHgDpm3z7ppZek558/Nq+0YUMzDDN+vNSmTRVeNDXVnIq3apV50ZgYMzRDjwgChM/7jNjAPiMA6roffzQjJvPnm1UykhQbayak3nWX1KSJ1fIAK7z9/ubUXgCoIo/HDLfMmGFOzi35X7uuXc3uqUOHSmFhdmsE/AFhBAB8dPSoCR/Tp5tTc0tcfbUJIb/7Xfn5pgAqRxgBAC8dOGCGYTIypO3bTVtYmNmkLC1N6tjRZnWA/yKMAMAp/Pyz9Nxz0pw55hRdSWrWTPrjH83jhE2pAfiIMAIAlfjuOzMUs2iRGZqRpHbtTC/IiBFSgwZ26wMCBWEEAI7jdksffWRCyIoVx9ovu8zMBxkwwOwXAqD6EEYABDaXy6v9OQ4flv72N7MyZuNG0xYaKt1wgwkhPXrUct1AECGMAAhcS5ZI48aZSR8l4uPNFuu/7Vy6e7c5sG7WLGnPHnNJZKR0551mm/aWLS3UDQQZwgiAwLRkiTR4cPkD5nbskAYP1saMZZqxrp9efVUqKjI/atnS7JJ6++0mkACoHYQRAIHH5TI9IicEEY+klZ7LNF336cNx/Urbe/UyQzHXXy/V419FoNbxnx2AwLNqVZmhmSOqpzd0g6brPn2nCyRJDrl1bZ99ui+9mS65hE3KAJsIIwAChscj5edLGz51aYPu0Xp11AZ10H/URXt1tiSpgQ7pVi3QBP1V547+s5R8k+WqARBGAPgdt1v66Sdpw4ayj/Xrpf37JemK3x7HRCtPY/W87tZsnaV9pjEmppYrB1ARwgiAOuvIEWnLlmNBoyR0bNwo/fprxfeEhEitW3vUYcen6nj4O3XQenXQBnVTtsJVbC5yOMyqmuTk2vswACpFGAFg3aFDJmCc2MuxZcuxnU9PFBZmdkPt0ME8OnY0f7ZrJ0VEOKQlB6TBE83Fx09kLZkckpFR4X4jAGofYQRArfnll/K9HBs2HDt0riKNGknt2x8LGyWPNm1OsfIlNVVavLjifUYyMkr3GQFgH2EEQLXyeMxmpyf2cmzYYCaXVqZZs7JhoyR8xMefxkqX1FTpuuu82oEVgD2EEQBV4nKZSaQn9nJs2HDsZNuKxMeX7+Xo0EE6++waKjQ0VLr88hp6cQDVgTAC4KSKi6Uffijfy7FpkznPpSIhIVLbtuV7Odq3lxo3rt36AdR9hBEgiBUWSjt3mhGMnTuPPUqe//yztG2b6QWpSHi4dN555Xs52rUzPwMAbxBGgAB06FDlAeP4vzud3r1e4zOOqsP5oerQ0VFmiKV1a6ZfADh9hBHAj/z667EgcbKwcbI5Gydq2FCKjTWPmJjf/v6//yj2/TmK2bdO5+oHxR7aKceueGnis6xCAVDtCCNAHXD4sAkTJwsYO3eW7C7qnQYNpLi44wLGiYHjt783bnzCapVTnHarxYsJJACqlcPjOfFfnLrH6XQqKipKBQUFiuRcb/iRoiIpL6/ycFHyfN8+718zIqLycHH888jIKiyJdbmkxMSy+3Icr2Tn0m3bGJ8BcErefn/TMwKcBrfbfG9v2VL28eOPpiPhf//z/rXCw08eLkoeUVE1eMLsCafdluPxSLm55jqWywKoJoQR4BSOHpVycsoHji1bpK1bTe/HyYSFnTpgxMRITZrUgWPsd+2q3usAwAuEEUBmL43t2ysOHNu2VX4+iiTVr29WlZxzzrFH27ZSQoIJGk2b1oGQ4S1vT7HltFsA1YgwgqBx+LDpyagocPz0kxlyqUx4uAkYxweOkkdCwinOSPEnyclmTsiOHeUnsEqcdgugRgTKP6GAJLOJV0WB44cfzFSIk03XPuOMisPGOeeYVSkhIbX3OawJDZWefdasmnE4OO0WQK0gjMDvOJ1mgmhFPRw7d5783saNpXPPrThwtGjhR8MpNYnTbgHUMsII6qRffqk4bGzZIu3effJ7zzyzfOAoed6sWS0EDpfL/0+J5bRbALWIMAIrDhww8zSOf2zffmwS6an23Tj77MqHVJo2rY1PUIklSyruUXjWD3cu5bRbALWEMIJq5/GYnUK3bz8WMo4PHD/95N0mXzExFYeNtm3NXht1DjuXAkCVsAMrfObxmKGSE4PG8c8PHDj165x5ptnss1Ur8yj5+znnSG3aSI0a1ejHqF7sXAoA5bADK6rM5TLTBCrq0Sh5HD586tdp3rxsyDgxdARUrmTnUgCoMsJIEDpyxHwvVhQ0tm83PzvZJl+S+R/92NiKezZatZJatjRLZYMGO5cCQJUFdRh55hnTa16//rFHvXpln3vzs6q2h4bWzMqOX38125dXNl9j586Tb/AlmRoTEirv2YiPN9uc4zfsXAoAVRbUYeStt6Svv7ZbQ3WFHZfrWADJzz/1+0ZEmN6Lyno2YmNreWqDvy+HZedSAKiyoA4jd9wh9etnhi1KHkePln1ene0VOdnPTkejRsfCRUU9G82b16ENvgJhOSw7lwJAlbGappZ4POZ//msq7Dgcx4ZVWrWqIyfAeqOy5bAlxfvbctiKglVCAjuXAghK3n5/E0ZgT6Auh/X3IScAqCYs7Q0W/vzFF6jLYdm5FAB8ErxhxJ+/xEv4+1wLlsMCACQFw6Ho5S1ZYoYH+vaVbr7Z/JmYaNr9RclcixN7Fkq2HveHz8JyWACAgnHOSCBMmAyUuRYln+NUy2Hr+ucAAFTI2+/v4OoZcbnMsEZFX3wlbePHm+vqMl/mWtRlJcthpfJLf1gOCwBBI7jCSKB8iQfSXIvUVNMbFRdXtj0+3j96qQAApy24JrAGypd4oM21SE2VrrvO/ycUAwCqJLjCSKB8iQfi1uMshwWAoBVcwzQlX+KVbU1aso1pXf8SZ64FACCABFcYCaQvceZaAAACRPAt7ZUC6/yQQNi8DQAQkDib5lT4EgcAoEZxNs2pMGESAIA6IbjmjAAAgDqHMAIAAKwijAAAAKsIIwAAwCrCCAAAsKpKYWTWrFlKTExURESEevfurdWrV5/0+jfffFPt27dXRESEOnfurKVLl1apWAAAEHh8DiOLFi1SWlqapk2bprVr16pr167q37+/du/eXeH1X331lW666Sbdfvvt+u677zRo0CANGjRI69atO+3iAQCA//N507PevXurV69emjlzpiTJ7XYrISFBY8eO1cSJE8tdP3ToUBUWFuqDDz4obbvooovUrVs3zZ4926v3rJFNzwAAQI3y9vvbp56R4uJirVmzRikpKcdeICREKSkpysrKqvCerKysMtdLUv/+/Su9XpKKiorkdDrLPAAAQGDyaQfWvXv3yuVyKTo6ukx7dHS0Nm7cWOE9eXl5FV6fl5dX6fukp6frkUceKddOKAEAwH+UfG+fahCmTm4HP2nSJKWlpZU+37Fjhzp27KiEhASLVQEAgKo4cOCAoqKiKv25T2GkWbNmCg0NVX5+fpn2/Px8tWjRosJ7WrRo4dP1khQeHq7w8PDS540aNVJubq4aN24sh8PhS8lBwel0KiEhQbm5ucypqSP4ndQt/D7qFn4fdUtN/j48Ho8OHDig2NjYk17nUxgJCwtTjx49lJmZqUGDBkkyE1gzMzM1ZsyYCu9JSkpSZmamxo8fX9q2fPlyJSUlef2+ISEhio+P96XUoBQZGcl/2HUMv5O6hd9H3cLvo26pqd/HyXpESvg8TJOWlqaRI0eqZ8+euvDCC5WRkaHCwkKNGjVKkjRixAjFxcUpPT1dkjRu3Dhddtllmj59ugYMGKCFCxfq22+/1Zw5c3x9awAAEIB8DiNDhw7Vnj17NHXqVOXl5albt25atmxZ6STVnJwchYQcW6Rz8cUX6/XXX9fDDz+shx56SOeee67eeecdderUqfo+BQAA8FtVmsA6ZsyYSodlVq5cWa5tyJAhGjJkSFXeCl4IDw/XtGnTysyzgV38TuoWfh91C7+PuqUu/D583vQMAACgOnFQHgAAsIowAgAArCKMAAAAqwgjAADAKsKIH0tPT1evXr3UuHFjNW/eXIMGDdKmTZtsl4XfPPnkk3I4HGU2/EPt2rFjh2655RadddZZatCggTp37qxvv/3WdllBy+VyacqUKWrdurUaNGigtm3b6tFHHz3luSWoHp9//rkGDhyo2NhYORwOvfPOO2V+7vF4NHXqVMXExKhBgwZKSUnRDz/8UCu1EUb82GeffabRo0fr66+/1vLly3XkyBH169dPhYWFtksLet98841eeukldenSxXYpQeuXX35Rnz59VL9+fX300Udav369pk+friZNmtguLWg99dRTevHFFzVz5kxt2LBBTz31lJ5++mk9//zztksLCoWFheratatmzZpV4c+ffvppPffcc5o9e7b+9a9/qWHDhurfv78OHz5c47WxtDeA7NmzR82bN9dnn32mSy+91HY5QevgwYO64IIL9MILL+ixxx5Tt27dlJGRYbusoDNx4kR9+eWXWrVqle1S8JtrrrlG0dHRmjdvXmnb9ddfrwYNGui1116zWFnwcTgcevvtt0uPdvF4PIqNjdV9992n+++/X5JUUFCg6OhoLViwQDfeeGON1kPPSAApKCiQJDVt2tRyJcFt9OjRGjBggFJSUmyXEtTee+899ezZU0OGDFHz5s3VvXt3zZ0713ZZQe3iiy9WZmamNm/eLEn697//rS+++EJXX3215cqwbds25eXllfl3KyoqSr1791ZWVlaNv3+VdmBF3eN2uzV+/Hj16dOHrfYtWrhwodauXatvvvnGdilBb+vWrXrxxReVlpamhx56SN98843uvfdehYWFaeTIkbbLC0oTJ06U0+lU+/btFRoaKpfLpccff1zDhg2zXVrQy8vLk6TSo11KREdHl/6sJhFGAsTo0aO1bt06ffHFF7ZLCVq5ubkaN26cli9froiICNvlBD23262ePXvqiSeekCR1795d69at0+zZswkjlrzxxhv6+9//rtdff13nn3++srOzNX78eMXGxvI7CXIM0wSAMWPG6IMPPtCKFSsUHx9vu5ygtWbNGu3evVsXXHCB6tWrp3r16umzzz7Tc889p3r16snlctkuMajExMSoY8eOZdo6dOignJwcSxXhgQce0MSJE3XjjTeqc+fOGj58uCZMmFB6yjvsadGihSQpPz+/THt+fn7pz2oSYcSPeTwejRkzRm+//bb++c9/qnXr1rZLCmpXXHGFvv/+e2VnZ5c+evbsqWHDhik7O1uhoaG2Swwqffr0KbfUffPmzWrVqpWlinDo0KEyp7pLUmhoqNxut6WKUKJ169Zq0aKFMjMzS9ucTqf+9a9/KSkpqcbfn2EaPzZ69Gi9/vrrevfdd9W4cePScb2oqCg1aNDAcnXBp3HjxuXm6zRs2FBnnXUW83gsmDBhgi6++GI98cQTuuGGG7R69WrNmTNHc+bMsV1a0Bo4cKAef/xxtWzZUueff76+++47zZgxQ7fddpvt0oLCwYMHtWXLltLn27ZtU3Z2tpo2baqWLVtq/Pjxeuyxx3TuueeqdevWmjJlimJjY0tX3NQoD/yWpAofL7/8su3S8JvLLrvMM27cONtlBK3333/f06lTJ094eLinffv2njlz5tguKag5nU7PuHHjPC1btvRERER42rRp45k8ebKnqKjIdmlBYcWKFRV+Z4wcOdLj8Xg8brfbM2XKFE90dLQnPDzcc8UVV3g2bdpUK7WxzwgAALCKOSMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACr/j8d6M7cdV6TkAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nYm14QR-ekH7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}