support_Vector_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "support_Vector_Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNztmFsB8WgmPmqaB54lz0d",
      "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/antonio2505/SuperDataScience_Projects_ML/blob/main/support_Vector_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eS94u74B5I7m"
      },
      "source": [
        "## **Support Vector Regression (SVM)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wnTXnPRR5JBZ"
      },
      "source": [
        "importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5DHImnZ7gNe"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MwYxD8l5JGs"
      },
      "source": [
        "Import the dataset "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fOSqmWfh7v4V"
      },
      "source": [
        "dataset = pd.read_csv('Position_Salaries.csv')\n",
        "\n",
        "x = dataset.iloc[ : , 1:-1].values\n",
        "y = dataset.iloc[ : , -1].values"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3JCvQ-F-5Nz"
      },
      "source": [
        "#to apply the feature scaling, reshape the y before to display like the x (2D array)\n",
        "y = y.reshape(len(y), 1)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqcL_tgD5JOh"
      },
      "source": [
        "Feature scaling "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raQk0P3U_0Fu"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc_x = StandardScaler()\n",
        "sc_y = StandardScaler()\n",
        "\n",
        "x = sc_x.fit_transform(x)\n",
        "y = sc_y.fit_transform(y)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WIzaKIj1CDT4",
        "outputId": "088da260-323e-4f63-a9e0-30c32e7b5e72"
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-1.5666989 ]\n",
            " [-1.21854359]\n",
            " [-0.87038828]\n",
            " [-0.52223297]\n",
            " [-0.17407766]\n",
            " [ 0.17407766]\n",
            " [ 0.52223297]\n",
            " [ 0.87038828]\n",
            " [ 1.21854359]\n",
            " [ 1.5666989 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2P0qGkqCDXq",
        "outputId": "bb50efc3-42c4-42b0-87dc-b4470e7aec2f"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-0.72004253]\n",
            " [-0.70243757]\n",
            " [-0.66722767]\n",
            " [-0.59680786]\n",
            " [-0.49117815]\n",
            " [-0.35033854]\n",
            " [-0.17428902]\n",
            " [ 0.17781001]\n",
            " [ 0.88200808]\n",
            " [ 2.64250325]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3pFPTnTw5JXS"
      },
      "source": [
        "Training the SVM on the whole Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLp8gcCJGmGJ",
        "outputId": "34452638-24b6-48aa-ee66-5e1b286b4315"
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "\n",
        "regressor = SVR(kernel = 'rbf')\n",
        "regressor.fit(x,y)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6o1_oKz5Jd2"
      },
      "source": [
        "Predicting a New Result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y6flyCGdHEoA",
        "outputId": "ee4b6af8-5074-4901-b8a9-cd4e70baf2b8"
      },
      "source": [
        "sc_y.inverse_transform(regressor.predict(sc_x.transform([[6.5]])))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([170370.0204065])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3I-rHuD55Jlo"
      },
      "source": [
        "Visualising the SVR Result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "LDmHf-KcIJcd",
        "outputId": "0edee936-faee-4335-f918-f1289613646f"
      },
      "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)), color = 'blue')\n",
        "plt.title('Truth or Bluff(Support Vector Regression)')\n",
        "plt.xlabel('Position Level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cJ6x6FT5Jph"
      },
      "source": [
        "Visualising the SVR Result (for Hight Resolution and smoother curve)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "GuWEgNDHKWC1",
        "outputId": "a2d162aa-1c7b-4f1e-fa23-1b4103a86c38"
      },
      "source": [
        "\n",
        "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",
        "\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))), color = 'blue')\n",
        "plt.title('Truth or Bluff(Support Vector Regression)')\n",
        "plt.xlabel('Position Level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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79+5dpnDNzPKpnIngCWCYpCGSupMuBk8sWmc2sD+ApJ1IicCn/GZm7ahsiSAiVgMnAncBL5BaBz0v6RxJh2arnQKcIOkZ4Hrg+IiIcsVkZmbrKufFYiLiDuCOonlnFDyfCuxdzhjMzKx5lb5YbGZmFeZEYGaWc04EZmY550RgZpZzTgRmZjnnRGBmlnNOBGZmOedEYGaWc04EZmY550RgZpZzTgRmZjnnRGBmlnNOBGZmOedEYGaWc04EZmY550RgZpZzTgRmZjnnRGBmlnNOBGZmOedEYGaWc04EZmY550RgZpZzTgRmZjnnRGBmlnNOBGZmOedEYGaWc04EZmY550RgZpZzTgRmZjnnRGBmlnNOBGZmOedEYGaWc04EZmY5V9ZEIOkgSdMkTZd0ahPrfEXSVEnPS7qunPGYmdm6upVrw5K6ApcAnwHmAk9ImhgRUwvWGQb8CNg7IpZK6lOueMzMrLRylgh2B6ZHxIyIWAncABxWtM4JwCURsRQgIhaWMR4zMyuhnImgPzCn4PXcbF6hHYAdJD0iaZKkg0ptSNJYSXWS6hYtWlSmcM3M8qnSF4u7AcOA/YCjgcslbVW8UkSMj4iRETGyd+/e7RyimVnnVs5EMA8YWPB6QDav0FxgYkSsioiXgZdIicHMzNpJqxJBduF3fT0BDJM0RFJ34ChgYtE6t5FKA0jqRaoqmrEB+zIzsw3U2hLBvyT9TNLw1m44IlYDJwJ3AS8AN0XE85LOkXRottpdwGuSpgL3A/8dEa+tR/xmZvY+KSJaXknagnRG/1VS8rgSuCEi3ihveOsaOXJk1NXVtfduzcw6NEmTI2JkqWWtKhFExPKIuDwiRgH/A5wJzJd0taShbRirmZm1s1ZfI5B0qKQ/Ab8CfgFsB/wFuKOM8ZmZWZm19s7if5Hq8H8WEY8WzL9F0j5tH5aZWb5FwMyZMGRI+ffVYokgazE0ISLGFCUBACLiu2WJzMwsZyLgiSfghz+E7beH4cPhzTfLv98WE0FErAE+X/5QzMzyaf58+OlPYdgw2H13uOgi2HFH+O1voUs73Pbb2qqhRyT9FrgReKt+ZkQ8WZaozMw6uQh48EH41a/g9tthzRrYbz8YNw4OOwy23rr9YmltIhiRPZ5TMC+AT7dtOGZmndvq1XDLLfDzn8PkydCrF5xyCnz966lEUAmtSgQR8alyB2Jm1pmtWQPXXQdnnw3//neq+hk/Ho45BjbdtLKxtXo8AkmfA3YGNqmfFxHnNP0OMzOLgJtvhjPPhBdfhBEj4E9/gkMPbZ/6/9Zo7X0EvweOBE4CBHwZGFzGuMzMOrxJk2DUKDjySOjaNVUJTZ4MX/xi9SQBaH1fQ6Mi4jhgaUScDexF6iDOzMyKzJsHo0fDXnulewGuuAKeeQaOOKK6EkC91ob0dva4QtK2wCqgX3lCMjPrmFavhl//Gj70Ibj11tQC6KWX4GtfSyWCatXaRHB7NmDMz4AngZnA9eUKysysKtTWQk1NOo2vqUmvm1BXl+4BOPlk+MQn4Pnn4dxzYYst2i3aDdbaVkP/mz39o6TbgU0iYln5wjIzq7DaWhg7FlasSK9nzUqvIdX7ZN59F846Cy68EPr2hZtugi99CaT2D3lDNdsNtaTDm3tzRNza5hG1wN1Qm1m7qKlJB/9igwenin9SdxDHHw9Tp6bqn1/+Enr2bM8gW6+5bqhbKhF8oZllAbR7IjAzaxezZzc5f/Xq1CXE2WfDBz8Id9wBBx/cvuG1pWYTQUR8tb0CMTOrKoMGlSwRzNx2FMfsB488km4Gu/hi2Gqr9g+vLfmGMjOzUs47r/E1AuDG7scydskfYHm6hPCf/1nB+NqQbygzMytl9OjUB8TgwbzDJnxr82s5auU17LJrd555pvMkAfANZWZmTRs9mun3zGSvEW/z+zeP4Yc/hAceSNeRO5PWVg0V31C2BN9QZmad3MSJcOyx0K0b/OUv8PlOOjLL+t5QdiEwGXgZ31BmZp3UmjXw4x+ncQF22AGefLLzJgFooUQgaTdgTv0NZZI2B54FXgQuKn94Zmbta8mSVP9/110wZkwaJWyTTVp+X0fWUongMmAlQDZI/fnZvGXA+PKGZmbWvqZOTd1E3Hdfuk78hz90/iQALV8j6BoRS7LnRwLjI+KPpK4mni5vaGZm7ef221NJoEePdEF41KhKR9R+WioRdJVUnyz2B+4rWNbqexDMzKpVBFxwQRooZocdUrcReUoC0PLB/HrgQUmLSS2HHgaQNJRUPWRm1mGtXAnf+AZMmJAGj7nyylQiyJuWupg4T9K9pKaid0dDD3VdSDeXmZl1SIsXp4FiHnoo9R56xhkdq8fQttRi9U5ETCox76XyhGNmVn7TpsHnPgdz56YB5Y8+utIRVZbr+c0sVx56KI0Z3K0b3H9/Gk4y76pw9Ewzs/KorYXPfAb69EkDyzsJJE4EZtbpRaTORI85Jh38H30Uttuu0lFVj7ImAkkHSZomabqkU5tZ7whJIank6DlmZhtq9erUMuj001OHonfdBVtvXemoqkvZEoGkrsAlwMHAcOBoScNLrLcF8D3g8XLFYmb59Oab6f6Ayy+H006Da6+FjTeudFTVp5wlgt2B6RExIyJWAjcAh5VY73+BC4B3yhiLmeXMggWw336pBHDZZalqKK/NQ1tSzkTQH5hT8HpuNu89kj4GDIyIvza3IUljJdVJqlu0aFHbR2pmncpLL6VrAS+8AH/+cxpozJpWsYvFkroAvwROaWndiBgfESMjYmTv3r3LH5yZdViTJqUuIpYvT81DO3P30W2lnIlgHjCw4PWAbF69LYBdgAckzQT2BCb6grGZbaiJE+HTn06DyT/2WOpJ1FpWzkTwBDBM0hBJ3YGjgIn1CyNiWUT0ioiaiKgBJgGHRkRdGWMys07q8svhP/4Ddt45NQ8dOrTSEXUcZUsEEbEaOBG4C3gBuCkinpd0jqRDy7VfM8uXiNRX0NixcOCBqTqoT59KR9WxlLWLiYi4A7ijaN4ZTay7XzljMbPOZ/Vq+Pa3U2ngq19NrYM22qjSUXU8vrPYzDqkFStSVdDll8O4cXDFFU4CG8qdzplZh7N4cWoN9MQT8LvfwTe/WemIOjYnAjPrUGbMgIMPhtmz4Y9/TD2J2vvjRGBmHcbkyXDIIbBqFdxzD+y9d6Uj6hx8jcDMOoQ774R994VNN4VHHnESaEtOBGZW9a66Kl0TGDYs3Si2006VjqhzcSIws6pVf4/A176W7hh+8EHo16/SUXU+vkZgZlVp1ap0k9iECb5HoNxcIjCzqrNsWRpcfsKEVCLwPQLl5RKBmVWVWbNSEpg2LV0bOP74SkfU+TkRmFnVmDw5XRR+++00oMynP13piPLBVUNmVhVuuw322ScNJfnoo04C7cmJwMwqKgIuvBAOPxw+/OE0sMzwp2qhpga6dEmPtbWVDrNTc9WQmVXMypWpn6CrroIjj0yPm95am5oLrViRVpo1q2GsydGjKxdsJ+YSgZlVxKJFcMAB6eB/xhlw3XXprmHGjWtIAvVWrEjzrSxcIjCzdvfMM3DYYbBgQUoARx9dsHD27NJvamq+vW8uEZhZu7r11jS4/OrV8PDDRUkAYNCg0m9sar69b04EZtYu1qyB00+HI45IF4WfeAJGjiyx4nnnQY8ejef16JHmW1k4EZhZ2S1dCl/4QjqWjxkDDzzQTJ9Bo0fD+PEweDBI6XH8eF8oLiNfIzCzspoyJTUNnT0bfv/71ABIauFNo0f7wN+OXCIws7KZMAH22CM1+nngAfjGN1qRBKzdORGYWZt7+234+tdTr6GjRsFTT6VHq05OBGbWpl58EfbcM/UYOm4c3H039O1b6aisOb5GYGZtIgKuvhq+853UyOevf03jC1v1c4nAzN63ZcvguONSVdDuu8PTTzsJdCROBGb2vjz8MHz0o+kO4bPPhnvugf79Kx2VrQ8nAjPbICtXwmmnwb77Qrdu8I9/pD6DunatdGS2vnyNwMzW21NPpZHDpkxJrYMuugg237zSUdmGconAzFpt5Uo488x0HWDhQpg4ES6/3Emgo3OJwMxa5bHH0l3Bzz0Hxx4Lv/oVbL11paOytuASgZk1a9ky+Pa3Ye+94fXXUyngmmucBDoTJwIzKykitQTaaSe47DL47ndh6tTUeZx1Lq4aMrN1TJkCJ56YmoZ+/OPw5z/DbrtVOiorl7KWCCQdJGmapOmSTi2x/PuSpkqaIuleSYPLGY+ZNW/hpbfw7S2uZdePrmHqI0sYP2YSjz/uJNDZlS0RSOoKXAIcDAwHjpY0vGi1p4CREfER4BbgwnLFY2ZNW7ECfvKVpxn6nc9y+ZtH8W0u5aW1Qznh+v3pekNtpcOzMitniWB3YHpEzIiIlcANwGGFK0TE/RFRP0r1JGBAGeMxsyIrV6YxAnbYAcbdPIL9uZfn2IWL+S5bs9SDxudEORNBf2BOweu52bymjAH+VmqBpLGS6iTVLVq0qA1DNMunVavgqqtgxx3hW9+Cmhp4kH35E4ezIy81XtmDxnd6VdFqSNIxwEjgZ6WWR8T4iBgZESN79+7dvsGZdSLvvAO/+10qAXzta9CrF9x5Z7oovM/gWaXf5EHjO71yJoJ5wMCC1wOyeY1IOgAYBxwaEe+WMR6z3Fq6FC64AIYMSfcE9O2b7gf45z/hwAOzUcM8aHxulbP56BPAMElDSAngKOA/C1eQtCtwGXBQRCwsYyxmuTRtGvz612mcgBUrYP/9obYWPvWpEkNG1o8RPG5cqg4aNCglAY8d3OmVLRFExGpJJwJ3AV2BKyPieUnnAHURMZFUFbQ5cLPSX+XsiDi0XDGZ5cHKlXDbbekmsPvug+7d07H85JPhIx9p4c0eND6XynpDWUTcAdxRNO+MgucHlHP/Znny7LOp64drrkkdwg0eDOeem3oH9VCR1hzfWWzWgc2eDbfcAtdem0YF69YNPve51DncgQd6bABrnapoNWSWe7W1qQ1nly7psbbpm7hmzEj9/48alc76TzklJYCLL4ZXXknVQocc4iRgrecSgVml1damU/gV2b2Vs2al1wCjR7NqFUyaBHfckVr6TJ2aFo0YAT/5CXz5yzB0aGVCt87BicCs0saNa0gCwFrE8yu248GTpvH3m+D++2H58nTWv88+cMIJqQfQ7bevYMzWqTgRmFXYW7MWU8c+TGJPHmUUD/NJlrI1LIXtnkuNeD772dTkc6utKh2tdUZOBGbt6PXX0whfTz0FTz6Zpud5nTXZv+JQ/sV/8Cf25UH26T+Dmn//o8IRWx44EZi1sQhYtCjdzDVtGrz4YqrXf+45mFPQ+1afPqmv/0OHvMBed57J7u8+RC9eSwt79IALxlfmA1juOBGYraeIdGY/d25qvjlzZsM0fTr8+9+pTr/eJpukvn322Qd22QU+/GHYdVfo16/+7t4PQ+0RMO5JmL3Ed/Rau3MiMMvUH+AXLIBXX22Y5s9PzTLrpzlz4K23Gr93441Tq8/tt4dPfjI97rhjmgYNakVTTt/RaxXkRGCdVv2BffHiVFXT1LTwxSUsnPsuC9dswyq6r7Od7t1h223TtPPO6UatgQPTNGhQSgB9+qRbAMw6IicC6zAi4I030hn7woWNp1IH+MWLYfXq0tvq0QN694a+3RbTf84/GbH2VfqygL4soE/3ZfT9/mj6HXsA/fqlljrrdNBm1ok4EVjFrVqVDu71VS/z56epvmqmvqpmwQJ4t4mOyrfaKh3Ye/eG7baDPfZIz3v1aphf+Py93pZrRsLaon74VwLX3w8/nVm+D21WRZwIrKzeeSfVqc+Zky6uzp2bns+b1zAtXJjO9gtJqbrlgx9MHabtuGN6rJ/69Gl47NUrVd9skKZG3/KoXJYjTgS2wSLSgCezZqUWM7NmNUyzZ6ep1MiiW28NAwZA//7wsY+lx/o6+H790tSnT7qTtuwGDUoBl5pvlhNOBNas5cvh5ZcbT/VNJV9+uXEzSYDNNksdoQ0alNrIDxrUcGF14MB00N9000p8kiacd17jfn7Ao3JZ7jgR5Nzq1amq5uWXU6+WM2Y0fr54ceP1N988DXdYUwP77pse66fBg9PZfoe6sOpRucxQFFfOVrmRI0dGXeNGj0YAAAjVSURBVF1dpcPoMCLSwbzwjL7wYD9rFqxZ07B+t27pWLjddg3TkCEN0zbbtOGBvrbWB2CzdiJpckSMLLXMJYIOLiJdbC2sn6+vs6+f3nyz8XvqW9bsvjscdVTDwX677VLdfbvUzbfQ9bKZtR+XCKrY6tUNzSrnz29odTNvXqrOmT07vS5uUtmzZ+Mqm8Iz+iFDUvVOxdXUlL5IO3hwyl5m1qZcIgBuvhmuvDI1R+zXLz3Wty3fZptUt92zJ2y5ZduP7BSRDtZvvJGmZctgyZLU4mbJkoY7XxcvTgf++mnx4nWbVXbrllrX9O8Pu+0Ghx+eLsIOHtwwdYiuit1s06xq5CYRvPtuOrA++2w6yDZ1xymkli+bbZYaj2y2WepHpnt32GijNEmpOwEJ1q5Ndexr1qQbo1auTPt6551U6/HWW2lqbn+QElCvXqnZ5NChsPfeqZ18YZPKAQPS8vedqKqhbt7NNs2qRm4SwTHHpAnSwbv+TLx+Wro0nanXTytWNBzI33238UE+Ik1r16aDcpcu6XGzzeADH0iJY+ONGyeULbdsmHr2TCWQD3wgTe/rhqj1VS118262aVY1fI2gPVXDmXg11c1Xw/dhlhPNXSPIR3+JtbXpANilS3qsra1MDGPHpoNwRMOZeHvHUk1186NHp+Szdm16dBIwq4jOnwiq5QBcNEA5kF6PG9e+cTRVB++6ebPc6vyJoFoOwNVyJn7eeQVdb2ZcN2+Wa50/EVTLAbhazsRHj4bx49M1ASk9jh/vahmzHOv8iaBaDsDVdCbuunkzK9D5E0G1HIB9Jm5mVarz30dQTb1LeoByM6tCnT8RgA/AZmbN6PxVQ2Zm1qyyJgJJB0maJmm6pFNLLN9Y0o3Z8scl1ZQzHjMzW1fZEoGkrsAlwMHAcOBoScOLVhsDLI2IocBFwAXlisfMzEorZ4lgd2B6RMyIiJXADcBhRescBlydPb8F2F/qUAMdmpl1eOVMBP2BOQWv52bzSq4TEauBZcA2xRuSNFZSnaS6RYsWlSlcM7N86hCthiJiPDAeQNIiSSW6z+xQegGLW1wrP/x9NPB30Zi/j8bez/cxuKkF5UwE84CBBa8HZPNKrTNXUjegJ/BacxuNiN5tGWQlSKprqjvYPPL30cDfRWP+Phor1/dRzqqhJ4BhkoZI6g4cBUwsWmci8F/Z8y8B90VHGyDBzKyDK1uJICJWSzoRuAvoClwZEc9LOgeoi4iJwBXAtZKmA0tIycLMzNpRWa8RRMQdwB1F884oeP4O8OVyxlClxlc6gCrj76OBv4vG/H00Vpbvo8MNVWlmZm3LXUyYmeWcE4GZWc45EbQjSQMl3S9pqqTnJX2v0jFVmqSukp6SdHulY6k0SVtJukXSi5JekLRXpWOqJEn/L/s/eU7S9ZI2qXRM7UXSlZIWSnquYN7Wkv4u6V/Z4wfaan9OBO1rNXBKRAwH9gS+U6L/pbz5HvBCpYOoEr8G7oyIDwEfJcffi6T+wHeBkRGxC6nlYZ5aFU4ADiqadypwb0QMA+7NXrcJJ4J2FBHzI+LJ7Ply0j96cbcbuSFpAPA54A+VjqXSJPUE9iE1qSYiVkbE65WNquK6AZtmN5v2AF6pcDztJiIeIjWpL1TYN9vVwBfban9OBBWSdbm9K/B4ZSOpqF8BPwTWVjqQKjAEWARclVWV/UHSZpUOqlIiYh7wc2A2MB9YFhF3VzaqiusbEfOz568Cfdtqw04EFSBpc+CPwMkR8Ual46kESZ8HFkbE5ErHUiW6AR8DfhcRuwJv0YZF/44mq/8+jJQgtwU2k3RMZaOqHlkPDG3W9t+JoJ1J2oiUBGoj4tZKx1NBewOHSppJ6qL805L+r7IhVdRcYG5E1JcQbyElhrw6AHg5IhZFxCrgVmBUhWOqtAWS+gFkjwvbasNOBO0oG2vhCuCFiPhlpeOppIj4UUQMiIga0kXA+yIit2d8EfEqMEfSjtms/YGpFQyp0mYDe0rqkf3f7E+OL55nCvtm+y/gz221YSeC9rU3cCzp7PfpbDqk0kFZ1TgJqJU0BRgB/KTC8VRMVjK6BXgSeJZ0rMpNdxOSrgceA3aUNFfSGOB84DOS/kUqMZ3fZvtzFxNmZvnmEoGZWc45EZiZ5ZwTgZlZzjkRmJnlnBOBmVnOORFYpyBpTdYc9zlJN0vqsZ7v31bSLdnzEYXNeiUdKqlN7vKV9GZbbKeJbZ8l6Qfl2r51Xk4E1lm8HREjsp4qVwLfXJ83R8QrEfGl7OUI4JCCZRMjos3abJtVGycC64weBoZm/bffJmmKpEmSPgIgad+CG/qekrSFpJqsNNEdOAc4Mlt+pKTjJf02e2+NpPuybd4raVA2f4Kk30h6VNIMSV9qMroikraXdKekyZIelvQhST0lzZLUJVtnM0lzJG1Uav02/wYtV5wIrFPJuiw+mHQ36tnAUxHxEeA04JpstR8A34mIEcAngbfr3x8RK4EzgBuzEsaNRbu4GLg622Yt8JuCZf2ATwCfZ/3u+hwPnBQRH89iuzQilgFPA/tm63weuCvrd2ed9ddjX2br6FbpAMzayKaSns6eP0zq0+lx4AiAiLhP0jaStgQeAX4pqRa4NSLmpu5sWmUv4PDs+bXAhQXLbouItcBUSa3qIjjriXYUcHNBDBtnjzcCRwL3k/pjurSF9c02iBOBdRZvZ2f472nq4B4R50v6K+k6wCOSDgTeaYMY3i3cfSvf0wV4vTj2zETgJ5K2Bj4O3Ads1sz6ZhvEVUPWmT0MjAaQtB+wOCLekLR9RDwbERcATwDFdezLgS2a2OajNAyZODrbxwbLxqN4WdKXszgl6aPZsjez+H4N3B4Ra5pb32xDORFYZ3YW8PGsN8/zaejC9+TswvAUYBXwt6L33Q8Mr79YXLTsJOCr2XuPJY25vD56ZL1J1k/fJyWUMZKeAZ4nDchS70bgmOyxXnPrm6039z5qZpZzLhGYmeWcE4GZWc45EZiZ5ZwTgZlZzjkRmJnlnBOBmVnOORGYmeXc/wcGh07IpJzo2AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}