ModelAnalysisStroke.ipynb 408 KB
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{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ModelAnalysisStroke.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "XUQ1vTYKsqAH"
      },
      "outputs": [],
      "source": [
        "# Imports\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline \n",
        "from sklearn.impute import SimpleImputer, KNNImputer\n",
        "from sklearn.metrics import roc_curve, roc_auc_score, average_precision_score, f1_score \n",
        "from sklearn.model_selection import StratifiedKFold, cross_val_score, train_test_split\n",
        "from sklearn.neural_network import MLPClassifier \n",
        "from sklearn.neighbors import KNeighborsClassifier \n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler, LabelEncoder \n",
        "from sklearn.svm import SVC \n",
        "from sklearn.tree import DecisionTreeClassifier \n",
        "from imblearn.under_sampling import RandomUnderSampler"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading of data"
      ],
      "metadata": {
        "id": "jeOeEPUwxwbd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stroke = pd.read_csv('./stroke.csv')\n",
        "\n",
        "stroke = stroke.drop(columns = ['id'])\n",
        "\n",
        "\n",
        "\n",
        "categorical = ['hypertension', \n",
        "               'heart_disease', \n",
        "               'ever_married', \n",
        "               'work_type', \n",
        "               'Residence_type', \n",
        "               'smoking_status']\n",
        "\n",
        "numerical = ['age', \n",
        "             'avg_glucose_level']\n",
        "\n",
        "X = stroke.drop(columns = 'stroke')\n",
        "y = stroke['stroke']\n",
        "\n",
        "us = RandomUnderSampler()\n",
        "\n",
        "rus_X, rus_y = us.fit_resample(X, y)\n",
        "display()"
      ],
      "metadata": {
        "id": "erZfIBRsxvzg"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "ky2xUEduOUzt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Distribution of Target Values"
      ],
      "metadata": {
        "id": "uYJy00zqIBRN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "is_true = y == 1\n",
        "is_false = y == 0\n",
        "\n",
        "bar1 = plt.bar(0.5, y[is_false].size, width = 0.4, label=\"Cereberovascular accident not present\")\n",
        "bar2 = plt.bar(1.3, y[is_true].size, width = 0.4, label=\"Cereberovascular accident present\")\n",
        "\n",
        "for rect in bar1 + bar2:\n",
        "    height = rect.get_height()\n",
        "    plt.text(rect.get_x() + rect.get_width() / 2.0, height, f'{height:.0f}', ha='center', va='bottom')\n",
        "\n",
        "plt.xticks([0.5,1.3], labels = [f'0', f'1'])\n",
        "plt.title(\"Distribution of Target Value\")\n",
        "plt.xlabel(\"Target Value\")\n",
        "plt.ylabel(\"Number of Observations\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "j9-w_tdnIKiM",
        "outputId": "e45a8fe4-68d6-43ee-91fa-1ca85110b162"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation matrix between numerical data"
      ],
      "metadata": {
        "id": "ScrJK_GLOYg8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(stroke[list(set(numerical).union({'bmi'}))].corr(), annot=True)\n",
        "plt.title(\"Correlation Matrix Between Numerical Attributes\")\n",
        "plt.savefig('corr.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "0KGHay0xObb7",
        "outputId": "4f85493c-72d1-42c5-95d8-0916dbf1549f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JvMVHJ3sJH53MOZeJInQ1SBogaVbMNCCmpobA8pj5FeGyWHOA08LHpwI7Stq9rPCinNVwDdCfrUcneyLC9s45lxoRzmows1HAqAo821XAw5IuAKYDK4Ey+zDKNTqZpN2ARuH95J1zLrMk72yFlUDjmPlG4bIiZpZL2OKVVAc43cx+KKvSKGc1TJW0U5h0PyJIwPcnur1zzqVM8m79MxNoLqmZpJpAH2BsbAFJdSUV5tJrgSfjVRqlj3dnM/uJILM/a2aHAcdG2N4551IjSaeTmVkeMBiYCHwGvGhm8yUNl3RKWKwLsFDSF0A94LZ44UVJvNUl1Qd6A+MibFel3HD7SI46uQ+9+g5MdyhV2okndGH+p9P5fME7/PXqy7ZZf+QRh/HhjDfYtHEpp5128lbr7rzjeubMfot5c6dy/8jhqQq5yul87OFMmTGW6bNeZ9DQ/tusv2hQP958/xUmvv0yz//3HzRsVH+r9XV2rM2MT//H8LuuS1XIqZO8sxows/Fmtr+Z7Wtmt4XLhpnZ2PDxGDNrHpa5yMx+jVdnlMQ7nCDrLzazmZL2Ab6MsH2V0Ouk43l85Ih0h1Gl5eTk8OADt9G9R19atT6as87qxYEHNt+qzLLlK+l/0Z95fvQrWy3v1LE9f+zUgUPaHkfrNsfQoX0bOh/lo40Wl5OTw4i7r+f83oM4tlNPTjm9G81b7LNVmflzP+PkY/pw4pGn8/rYyVx3y5Vbrb/qusHMeO+jVIadOtly5ZqZvWRmB5vZpeH8EjM7vfJCS4/2bVqx8047xi/oSnVoh0NYvPhrvvpqGZs3b+bFF1/llB4nblVm6dIVzJv3GQXFdnwzY7ta21GzZk22264m1WtUZ80336Yy/CqhTbtWfP3VMpYtXcHmzXm89n8TOKHb0VuVef+dmWz6ZRMAn8yaS/0G9YrWtWrdkrp77M70Ke+lNO6UyfDbu0c5uNZI0n8lfRNOLydyaZz7/WnQcC+Wr8gtml+xchUNGuyV0LYfzPiIaVPfY8Wyj1mx7BMmT57G558vqqxQq6y96u9J7srVRfOrctdQr369Usuf1fc0pvzvHQAkccOtVzFi2H2VHmfaZEuLF3iK4Gheg3B6LVzmXNLsu29TDjigOU2atWfvpu04usvhHHH4oekOq0o79czuHHxIS/7+UPBx7de/D1Mmv83q3DVpjqwSZXiLN8oFFHuYWWyifVrSFaUVDq/+GADw6H0juKjf2eUM0VU1uStX07hRg6L5Rg3rk5u7uowttujVsyszPvyYDRs2AvDGxLfo2LEd77z7YaXEWlWtXvUNDRpu+RVRv0E91qzaNpEe0bkjg/9yMb27X8hvvwWDg7ft0JpDO7XlvP5nUbv2DtSoWYONGzZy5/C/pSz+SpdXxe8yHGOdpL7A8+H82cC60grHXg2yee0Sv9Did2TmrNnst18zmjZtzMqVq+nduyfn9dv2zIaSLFuey0V/Ooc7q1VDEkcd2YkHH/ILJIub8/GnNNunCY33bsjqVWvocVo3Lh/w/7Yqc1CrA7hj5DDOO3Mg69Z+V7R86CVbBtg64+yeHNzmoOxKugAZfm1XlK6GPxGcSrYaWAWcAVxYGUGl09U33cm5l/yZr5et4NhefXn5tYnpDqnKyc/PZ+gVNzD+9f/w6dypjBnzGgsWfMHNN11F9+7HA9C+XWu+XjKLM07vzmOP3MWc2cEYIy+/PI7FS5Yy+5M3+fijycydu4Bxr09O58vJSPn5+dz419t5bszjvPXBWMa9MpEvPl/MlddexvFduwBw/S1/YYfaO/DYU/cxYdpL/PPfD6Y36FTK8D5epeKqX2/xVr7tGxyZ7hCyXoM6u6U7hN+FZd/Nq/DdbH75940J55ztz7015XfPiXJWwzOSdomZ31VS3EvjnHMu5bLo4NrBsQM/mNn3kg6phJicc65isuhmlzmSdjWz7wHCwXL8LsXOucyTX/adJdItSuK8D3hf0ksEdxQ+gwQGg3DOuZTLlhavmT0raRZwTLjoNDNbUDlhOedcBWT47d0TTryS9gbWEzMWpaS9zWxZZQTmnHPlZQWZfSJVlK6G19lyc8vtgWbAQuCgZAflnHMVkkVdDa1i5yW1BQYlPSLnnKuobOlqKM7MPpZ0WDKDcc65pMjLkrMaJMWOopwDtAVySynunHPpk8SuBkldgQeAasATZnZnsfV7A88Au4RlrjGz8WXVGaXFGzs6eB5Bn+/LEbZ3zrnUSNJQCJKqAY8AxwMrgJmSxhY7o+sGgnuxPSapJTAeaFpWvVH6eG+JHLVzzqVD8lq8hwKLzGwJgKTRQE8gNvEasFP4eGcS6AmIm3glvcaWsxm2YWanlLbOOefSIsLpZLFjh4dGhcPaAjQElsesWwEUP7Z1MzBJ0hCgNnBcvOdMpMV7bwJlnHMuc0S4ZDh27PByOht42szuk9QJeE7SH8xKP7UibuI1s2kVCMg551LOktfVsBJoHDPfKFwWqz/QFcDM3pdUC6gLfFNapVHOapjHtl0OPwKzgBFmVurdKJxzLqWSd+XaTKC5pGYECbcPcE6xMsuAYwluh3YgUAso89bYUc5qmADkA/8J5/sAOxDckeJpoEeEupxzrvIk6QIKM8uTNBiYSHCq2JNmNl/ScGCWmY0F/gL8Q9KfCRqnF1icO0xESbzHmVnbmPl5kj42s7bhvdiccy4zJHGshvCc3PHFlg2LebwAODxKnVHuuVZNUtF9tiV1IPgGgOC8XuecywwZfs+1KC3ei4AnJdUJ538G+kuqDdyR9Micc668smUgdDObCbSStHM4/2PM6hclnW9mzyQ7QOeciyzDh4WM0tUABAm3WNItNDQJ8TjnXIVZQUHCUzok855pKb9FsnPOlSjDW7zJTLyZ/Uqdc78fv6PE6y1e51xmyNaB0EvwbhLrcs65crO8LEm8xQZCL/Qj8JGZzTazwckLyznnKiCLuhrah9Nr4Xx3YC4wUNJLZnZ3soNzzrlyyZabXRKMytPWzNYDSLqJ4C4URwEfAZ54nXOZIYtavHsCv8bMbwbqmdkvkn4tZRvnnEu9LEq8/wZmSHo1nO8B/Ce8ZHhB6Zs551xqWX6WdDWY2a2SJrBlFJ6BZjYrfHxuWdtu3+DIcobnEvVL7tvpDiHr9Wzrx4+rjGxp8Up6EBhtZg9UYjzOOVdhluGJN8pYDR8BN0haLOleSe0rKyjnnKuQAkt8SoOEE6+ZPWNmJwEdgIXAXZK+rLTInHOuvAoiTGkQeXQyYD/gAKAJ8Hlyw3HOuYqzAkt4ikdSV0kLJS2SdE0J6++XNDucvpD0Q7w6o/Tx3g2cCiwGRgO3mlncJ3DOuZTLS04XgqRqwCPA8cAKYKakseHtfgAwsz/HlB8CHBKv3iinky0G/gjsA2wHHCwJM5seoQ7nnKt0STy4diiwyMyWAEgaDfSk9FNozwZuildplMRbALxFcAXbbKAj8D5wTIQ6nHOu8iWv77YhsDxmfgVwWEkFJTUBmhHkyTJF6eO9nODA2lIzO5qgOe1dDc65jBOlj1fSAEmzYqYB5XzaPsAYM4t7w7coLd5NZrZJEpK2M7PPJbUoZ4DOOVd5IrR4zWwUMKqU1SuBxjHzjcJlJekDXJbIc0ZJvCsk7QK8AkyW9D2wNML2zjmXEpaXtKpmAs0lNSNIuH2Ac4oXknQAsCtB92tcUS4ZPjV8eLOkKcDOwBuJbu+cc6mSrBtQmFmepMHARKAa8KSZzZc0HJhlZmPDon0IruxN6Kheue5AYWbTyrOdc86lRBIvjDCz8cD4YsuGFZu/OUqdybz1j3POZYQMv+WaJ17nXPbxxOuccynmidc551LM8pXuEMrkidc5l3WswBOvc86llHc1OOdcipl5i9c551LKW7zOOZdi3sfrnHMpVuBnNTjnXGp5i9c551IssaFq0scTr3Mu63iL1znnUsxPJ3POuRTL94NrzjmXWt7idc65FPM+XuecS7FMP6sh7u3dJb0Y/j9P0tyYaZ6kuZUfonPORWMFSniKR1JXSQslLZJ0TSllektaIGm+pP/EqzORFu/Q8P/uCZR1zrm0K0hSH6+kasAjwPHACmCmpLFmtiCmTHPgWuBwM/te0p7x6o3b4jWzVeH/S81sKfA98HPMVKWceEIX5n86nc8XvMNfr75sm/VHHnEYH854g00bl3LaaSdvte7OO65nzuy3mDd3KvePHJ6qkLPODbeP5KiT+9Cr78B0h1KltevcjlFTRvHE9Cc4c9CZ26w/qe9JPDrpUR6a8BD3vHwPjZs3BmD/1vvz0ISHeGjCQzz8xsN0OrFTqkOvdAUFSniK41BgkZktMbPfgNFAz2JlLgYeMbPvAczsm3iVxk28hSRdImk1MBf4KJxmJbp9JsjJyeHBB26je4++tGp9NGed1YsDD2y+VZlly1fS/6I/8/zoV7Za3qlje/7YqQOHtD2O1m2OoUP7NnQ+Kvt22FToddLxPD5yRLrDqNJycnIYNGIQw84fxsBjB9L5lM5FibXQlFemMOiEQQzpNoQxj4/h4hsvBmDpwqUM7T6UId2GcGO/GxlyxxByqiWcCqqEAlPCk6QBkmbFTANiqmoILI+ZXxEui7U/sL+kdyV9IKlrvPiiHFy7CviDma2NsE1GObTDISxe/DVffbUMgBdffJVTepzIZ599WVRm6dIVABQUbD2unJmxXa3tqFmzJhJUr1GdNd98m7rgs0j7Nq1YuWpNusOo0vZvsz+5X+eyetlqAKa/Np1OJ3Ri+ZdbcsQv638pelxr+1oQHnD6ddOvRctrblcTy/QjUeUQ5XQyMxsFjKrA01UHmgNdgEbAdEmtzOyHsjZI1GJgYwWCS7sGDfdi+YrcovkVK1dxaIdDEtr2gxkfMW3qe6xY9jGSePSxp/n880WVFapzZdp9r91Zm7ulDbR21VpatGmxTbnu/bpz6sWnUr1Gda7tc23R8hZtWnDFvVewZ8M9ufeKeynIz/ABbCNK4nfJSiD2p0SjcFmsFcAMM9sMfCXpC4JEPLO0SqP8vrgWeE/S3yU9WDiVVji2+V5QsCHC02SmffdtygEHNKdJs/bs3bQdR3c5nCMOPzTdYTlXpnHPjqP/kf156o6n6HN5n6LlC2cv5NLjLuWKHlfQ+7Le1NiuRhqjTL4oXQ1xzASaS2omqSbQBxhbrMwrBK1dJNUl6HpYUlalURLv34G3gA/Y0sf7UWmFzWyUmbU3s/Y5ObUjPE3lyV25msaNGhTNN2pYn9zc1Qlt26tnV2Z8+DEbNmxkw4aNvDHxLTp2bFdZoTpXpnWr11G3Qd2i+br167JuzbpSy08bO41OJ2x7TGL5ouVs2rCJpi2aVkaYaWOmhKey67E8YDAwEfgMeNHM5ksaLumUsNhEYJ2kBcAU4GozK/2PQbTEW8PMrjSzp8zsmcIpwvZpN3PWbPbbrxlNmzamRo0a9O7dk9fGTUpo22XLcznqyI5Uq1aN6tWrc9SRnbyrwaXNF3O+oEGzBtRrXI/qNapzVI+j+GDyB1uVadB0SyOjw7EdyP066Gar17he0cG0PRvuSaP9GrFmeXb1ueebEp7iMbPxZra/me1rZreFy4aZ2djwsYW5saWZtTKz0fHqjNLHOyE82vcaUNQ7b2bfRagjrfLz8xl6xQ2Mf/0/VMvJ4elnXmDBgi+4+aarmPXRHMaNm0z7dq0Z89I/2XXXnel+8vHcNOwvtG5zDC+/PI6juxzO7E/exMyYNHEq416fnO6XVCVdfdOdzPxkLj/88BPH9urLoP7ncXqPE9MdVpVSkF/AYzc+xojnRpBTLYdJL0xi2RfL6HtlX76c9yUzJs+gxwU9aHNEG/I257H+x/Xcd+V9ABzU4SDOHHQmeZvzsALj0esf5afvf0rzK0quZJ3HW1mU6BFNSV9RdFx0CzPbJ9621Ws2zL7Dphnml9y30x1C1uvZdnC6Q/hdGL9sfIWz5rt7nZFwzjl89ZiUZ+koLd6WwCDgCIIE/DbweGUE5ZxzFZHp52hESbzPAD8BhWcynBMu653soJxzriKMzO5qiJJ4/2BmLWPmp4RH8ZxzLqPkZXgfb5SzGj6W1LFwRtJhVLFLhp1zvw+GEp7SIW6LV9I8gj7dGgQXUCwL55sAn1dueM45FyU3HzEAABENSURBVF029PH6cJDOuSqlyvfxhkNBOudclZENLV7nnKtSPPE651yK5auKdzU451xVU1DV+3idc66qyfQxCjzxOueyjvfxOudcihV4H69zzqWWdzU451yK5WV2gzfSWA3OOVclFKCEp3gkdZW0UNIiSdeUsP4CSd9Kmh1OF8Wr01u8zrmsk6yuBknVgEeA4wnuJjxT0lgzKz4y4wtmlvBI+d7idc5lnQIlPsVxKLDIzJaY2W/AaKBnRePzxOucyzoFESZJAyTNipkGxFTVEFgeM78iXFbc6ZLmShojqXG8+LyrwTmXdfIjHFwzs1HAqAo83WvA82b2q6RLCO7Mc0xZG3iL1zmXdaK0eONYCcS2YBuFy4qY2TozK7zz+hNAu3iVeuJ1zmWdJCbemUBzSc0k1QT6AGNjC0iqHzN7CvBZvEq9q8E5l3WSdcs1M8uTNBiYCFQDnjSz+ZKGA7PMbCxwuaRTgDzgO+CCePV64nXOZZ1kjtVgZuOB8cWWDYt5fC1wbZQ6PfE657KOD5LjnHMpFuWshnTwxOucyzre4nXOuRTzxOuccynmw0I651yKJTAGQ1p54nXOZZ38dAcQR0oSb4M6u6XiaX7XerZNeEQ6V06vfvxwukNwCSrI8M4Gb/E657KOH1xzzrkUy+z2ride51wW8havc86lmJ/V4JxzKZaf4Z0Nnnidc1nHuxqccy7F/HQy55xLscxOu554nXNZKNO7Gvyea865rFOAJTzFI6mrpIWSFkm6poxyp0sySe3j1ektXudc1knWWA2SqgGPAMcDK4CZksaa2YJi5XYEhgIzEqnXW7zOuaxjEf7FcSiwyMyWmNlvwGigZwnlbgXuAjYlEp8nXudc1kni7d0bAstj5leEy4pIags0NrPXE43Puxqcc1knyulkkgYAA2IWjTKzUQlumwOMJIFbusfyxOucyzpRTicLk2xpiXYl0DhmvlG4rNCOwB+AqZIA9gLGSjrFzGaV9pyeeJ1zWScveWfyzgSaS2pGkHD7AOcUrjSzH4G6hfOSpgJXlZV0wft4nXNZKFkH18wsDxgMTAQ+A140s/mShks6pbzxeYvXOZd1knkBhZmNB8YXWzaslLJdEqnTE69zLuskcJpYWnnidc5lnUy/ZNgTr3Mu6xSYt3idcy6lfCB055xLMe/jdc65FPM+XuecSzG/A4VzzqWYdzU451yKeVeDc86lWL5ldur1xOucyzqZnXbLkXgl7WBmGysjGOecS4ZM7+NNeHQySX+UtAD4PJxvLenRSovMOefKKZk3u6wMUYaFvB84EVgHYGZzgKMqI6jK1PnYw5kyYyzTZ73OoKH9t1l/0aB+vPn+K0x8+2We/+8/aNio/lbr6+xYmxmf/o/hd12XqpCrpHad2zFqyiiemP4EZw46c5v1J/U9iUcnPcpDEx7inpfvoXHzYKzp/Vvvz0MTHuKhCQ/x8BsP0+nETqkOPSvccPtIjjq5D736Dkx3KGlhZglP6RBpPF4zW15sUbJu5pkSOTk5jLj7es7vPYhjO/XklNO70bzFPluVmT/3M04+pg8nHnk6r4+dzHW3XLnV+quuG8yM9z5KZdhVTk5ODoNGDGLY+cMYeOxAOp/SuSixFpryyhQGnTCIId2GMObxMVx848UALF24lKHdhzKk2xBu7HcjQ+4YQk41HzY6ql4nHc/jI0ekO4y0yccSntIhyh69XNIfAZNUQ9JVBAMDVxlt2rXi66+WsWzpCjZvzuO1/5vACd2O3qrM++/MZNMvwY1CP5k1l/oN6hWta9W6JXX32J3pU95LadxVzf5t9if361xWL1tN3uY8pr82nU4nbN1y/WX9L0WPa21fq+heLb9u+pWC/ODQSM3taqatRVLVtW/Tip132jHdYaRNpnc1RDm4NhB4gOAOmyuBScBllRFUZdmr/p7krlxdNL8qdw1t2h1cavmz+p7GlP+9A4Akbrj1KoYOvJYjOnes9First332p21uWuL5teuWkuLNi22Kde9X3dOvfhUqteozrV9ri1a3qJNC6649wr2bLgn915xb1Eidi5Rmf6FnXDiNbO1wLmVGEtGOfXM7hx8SEt6d78QgH79+zBl8tuszl2T5siyx7hnxzHu2XF06dmFPpf3YeSVIwFYOHshlx53KY33a8yVI69k1tRZbP51c5qjdVVJ1lwyLOnBEhb/CMwys1dLKF90y+Rdd2hAne12K3eQybJ61Tc0aLhX0Xz9BvVYs2rbRHpE544M/svF9O5+Ib/9Fnzg23ZozaGd2nJe/7OoXXsHatSswcYNG7lz+N9SFn9VsW71Ouo2KLr/H3Xr12XdmnWllp82dhqX3bbtj6fli5azacMmmrZoypdzv6yUWF12SubpZJK6EvzarwY8YWZ3Fls/kODXfz6wHhhgZgvKqjNKH28toA3wZTgdTHCr4/6Stsk+ZjbKzNqbWftMSLoAcz7+lGb7NKHx3g2pUaM6PU7rxuQ3pm5V5qBWB3DHyGH0P2cI69Z+V7R86CXX0OngEzi8TVdGDLuPl0e/5km3FF/M+YIGzRpQr3E9qteozlE9juKDyR9sVaZB0wZFjzsc24Hcr3MBqNe4XtHBtD0b7kmj/RqxZrn/ynDRFJglPJVFUjXgEaAb0BI4W1LLYsX+Y2atzKwNcDcwMl58Ufp4DwYON7P8MKDHgLeBI4B5EepJm/z8fG786+08N+ZxqlWrxgv//i9ffL6YK6+9jHmfzGfyG1O5/pa/sEPtHXjsqfsAyF2xiv7nXp7myKuWgvwCHrvxMUY8N4KcajlMemESy75YRt8r+/LlvC+ZMXkGPS7oQZsj2pC3OY/1P67nviuD9/ugDgdx5qAzyduchxUYj17/KD99/1OaX1HVc/VNdzLzk7n88MNPHNurL4P6n8fpPU5Md1gpk8SzFQ4FFpnZEgBJo4GeQFGL1sxid9DaEP/JlWgntKSFwKHhfeSRtDPwoZm1kPSJmR1S2rZ779YqsztcssAf6jSOX8hVyKsfP5zuEH4XatTdRxWto1PDoxPOOR/kTr2EsFs0NMrMRgFIOgPoamYXhfPnAYeZ2eDYOiRdBlwJ1ASOMbMy+8aitHjvBmZLmgqI4OKJ2yXVBv4XoR7nnKtUUc5qCJPsqAo+3yPAI5LOAW4Azi+rfJSzGv4paQJwHsH5u5OAFWa2Abi6/CE751xyJfGshpVA7M/JRuGy0owGHotXaZSzGi4ChoZPPBvoCLwPHJNoHc45lwpJPKthJtBcUjOChNsHOCe2gKTmMV0LJxOcfFCmKGc1DAU6AEvN7GjgEOCHCNs751xKJGusBjPLAwYDEwl+6b9oZvMlDZd0SlhssKT5kmYT9POW2c0A0fp4N5nZJklI2s7MPpe07eVIzjmXZskcCN3MxgPjiy0bFvN4aNQ6oyTeFZJ2AV4BJkv6Hlga9Qmdc66yZc2Va2Z2avjwZklTgJ2BNyolKuecq4BMHwi9XLf+MbNpyQ7EOeeSJd4Vaenm91xzzmWdrGzxOudcJvO7DDvnXIp5V4NzzqWYdzU451yKeYvXOedSzFu8zjmXYuYH15xzLrX8rAbnnEuxrLlk2Dnnqoqsub27c85VFX5Wg3POpZif1eCccynmXQ3OOZdimX5WQ5Rb/zjnXJVQYJbwFI+krpIWSlok6ZoS1l8paYGkuZLelNQkXp2eeJ1zWSdZ91yTVA14BOgGtATOltSyWLFPgPZmdjAwBrg7XnyeeJ1zWacAS3iK41BgkZktMbPfCG7f3jO2gJlNMbON4ewHBHdiL5MnXudc1onS4pU0QNKsmGlATFUNgeUx8yvCZaXpD0yIF58fXHPOZZ0oB9fMbBQwqqLPKakv0B7oHK+sJ17nXNZJ4gUUK4HGMfONwmVbkXQccD3Q2cx+jVepJ17nXNZJ4nm8M4HmkpoRJNw+wDmxBSQdAvwd6Gpm3yRSqSde51zWSdaVa2aWJ2kwMBGoBjxpZvMlDQdmmdlY4B6gDvCSJIBlZnZKWfV64nXOZZ1kXrlmZuOB8cWWDYt5fFzUOj3xOueyTqZfMqxMDzBdJA0Ij3a6SuLvceXz9zgz+Xm8pRsQv4irIH+PK5+/xxnIE69zzqWYJ17nnEsxT7yl836xyufvceXz9zgD+cE155xLMW/xOudcinnidc65FPPE6yqNpKaSPi3ntg0kjUl2TM5lgqxNvJK6SBqX7jhiVSQRpbLOTGBmuWZ2RrrjcK4yZG3idRmjuqR/S/pM0hhJO0j6WtIdkmaHA0+3lTRR0mJJAyF7v1Aqg6RXJH0kaX7hIN6S+kv6QtKHkv4h6eFw+R6SXpY0M5wOT2/0v08pS7zFdw5JAyXdE7P+gpid48bw5nLvSHpe0lVl1NshvMncbEn3lPRhlXRzbB2SPpXUNHzcL9x+jqTnwmVNJb0Vc/O6vcPlZ4bbzpE0PVxWLXzemWH5SxJ8P0rcTtJoSSfHlHta0hnlfZ4M0AJ41MwOBH4CBoXLl5lZG+Bt4GngDKAjcEs6gqzi/mRm7QgG4b5cUkPgRoL383DggJiyDwD3m1kH4HTgiVQH61I7SM6fzOw7SdsTjHF5LPAucHW4/izgNkmFO0RroAbwMfBRGfU+BVxsZu9LujNKQJIOAm4A/mhmayXtFq56CHjGzJ6R9CfgQaAXMAw40cxWStolLNsf+NHMOkjaDnhX0iQz+yrO05e4HfAC0Bt4XVJNgvfp0jLKZ/r5gMvN7N3w8b+Ay8PHY8P/5wF1zOxn4GdJv8a8ty4xl0s6NXzcGDgPmGZm3wFIegnYP1x/HNAyHL4QYCdJdcxsfSoD/r1LZVfD5ZLmENwMrjHQDFgiqaOk3Qm+ld8l+IZ+1cw2hR/G10qrMPyA7mhm74eL/hMxpmOAl8xsLUDhjgp0iqnrOeCI8PG7wNOSLiYYmxPgBKCfpNnADGB3oHkCz13adhOAo8Pk2g2Ybma/VOB50q34F0PhfOEo/QUxjwvnfdS8BEnqQpBMO5lZa4I73n5exiY5QEczaxNODT3ppl5KdvBiO8dGSVOBWgR37OxNsKP818ws5ps4mfLY+kumVnkqMbOBkg4DTgY+ktQOEDDEzCZGrK7U7cL350SCXwGjyypf2GWSwfaW1Cn8cjwHeAc4JM0xZZOdge/Dz9UBBN0LtYHOknYFfib4BTkvLD8JGEIweDeS2pjZ7NSH/fuWqhZvSTsHwH8JbpV8NlsSzLtAD0m1JNUBupdWqZn9QPDz9LBwUZ9Sin4NtAWQ1JagtQ3wFnBm2OImpqvhvZi6ziXoh0TSvmY2IxwE+VuClvtE4FJJNcIy+0uqHef9IM52LwAXAkcCbyRQPpMtBC6T9BmwK/BYmuPJNm8QHMD8DLiT4BflSuB24EOCz9PXwI9h+cuB9uFxggXAwJRH7FL2k+4NYGC4cywk2Dkws+/DZS3N7MNw2UxJY4G5wBqCb+ofS64WCPo+/yGpAJhWStmXCX6mzyf4mf5F+FzzJd0GTJOUT/Az7QKCFsFTkq4mSLAXhvXcI6k5QevzTWBOGGdT4GMFzfVvCfqD43mijO0mEXRxvGpmvyVQPiOZ2ddsfWCnUNOYMk8THFwrnC9ctxb4Q2XFli3CGyt2K75c0iwzGyWpOkED55Ww/FqCX1IujTJyrIbCzn5JOwDTgQFm9nFZZcPH1wD1zWxoCsN1LuNIupege68WwRf5UMvED/vvVKYm3v8ALQl2mmfM7I4yyp4FXEvQel8KXGBm36YkUOecK4eMTLwlkfQIwRkPsR4ws6fSEU9ZJLUi6CqI9auZHVZSeefc70uVSbzOOZct/JJh55xLMU+8zjmXYp54nXMuxTzxOudciv1/dw7ip7YmkxUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Missing values"
      ],
      "metadata": {
        "id": "V8nZOrKhZO1l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def arr_is_nan(arr):\n",
        "  return arr is np.nan\n",
        "\n",
        "missing_val = stroke.isnull().sum()\n",
        "missing_percent = (stroke.isnull().sum()) / stroke.isnull().count()\n",
        "\n",
        "cols = stroke.columns\n",
        "\n",
        "location = 0.5\n",
        "locations = []\n",
        "labels = []\n",
        "fig , ax = plt.subplots()\n",
        "ax.set_title(\"Amount of NaN Values Found in Each Column\")\n",
        "for i in range(len(missing_val)):\n",
        "  ax.bar(location, missing_val[i], width=0.4)\n",
        "  labels.append(f\"{cols[i]}\")\n",
        "  locations.append(location)\n",
        "  location += 1\n",
        "\n",
        "fig.set_size_inches(20, 10)\n",
        "ax.set_xticks(locations)\n",
        "ax.set_xticklabels(labels)\n",
        "plt.savefig(\"nans.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "AKN53PjxZOfi",
        "outputId": "4643d7fd-8580-42ee-ea29-f41fca41be4f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Distribution of Data Before and After Scaling"
      ],
      "metadata": {
        "id": "665dh1NkE2QF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Before Scaling\")\n",
        "stroke.boxplot(column=list(set(numerical).union({'bmi'})))\n",
        "plt.ylabel(\"Range\")\n",
        "plt.show()\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "stroke['avg_glucose_level'] = scaler.fit_transform(stroke['avg_glucose_level'].to_numpy().reshape(-1,1))\n",
        "stroke['bmi'] = scaler.fit_transform(stroke['bmi'].to_numpy().reshape(-1,1))\n",
        "stroke['age'] = scaler.fit_transform(stroke['age'].to_numpy().reshape(-1,1))\n",
        "\n",
        "plt.title(\"After Scaling\")\n",
        "stroke.boxplot(column=list(set(numerical).union({'bmi'})))\n",
        "plt.ylabel(\"Range\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "SKJIssVmE8ZU",
        "outputId": "c2beec7c-86ff-4b0e-e29b-06966ef1db48"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tweaking individual paramaters for each model"
      ],
      "metadata": {
        "id": "ETKN5fsmG7sJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X.drop(columns='bmi')\n",
        "\n",
        "np_numerical = [stroke.columns.get_loc(name) for name in numerical]\n",
        "np_categorical = [stroke.columns.get_loc(name) for name in categorical]\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = (20, 14)\n",
        "\n",
        "def knn_params(i, metric, alg):\n",
        "  return {\"n_neighbors\" : i, \"algorithm\" : alg, \"metric\" : metric}\n",
        "\n",
        "def dtc_params(i, criterion, splitter):\n",
        "  return {\"criterion\" : criterion, \"splitter\" : splitter, \"max_depth\" : i}\n",
        "\n",
        "def mlpc_params(i, activation, solver):\n",
        "  return {\"activation\" : activation, \"solver\" : solver}\n",
        "\n",
        "def svc_params(i, kernel, gamma):\n",
        "  return {\"degree\" : i, \"kernel\" : kernel, \"gamma\" : gamma}\n",
        "\n",
        "def indiv_param(X, y, model, param_func, ax, x_axis, first_l, second_l, M, L, x_ticks, i):\n",
        "  classif_X = X.to_numpy()\n",
        "  classif_y = y.to_numpy()\n",
        "  skf = StratifiedKFold(n_splits=10)\n",
        "  for m in first_l:\n",
        "    for l in second_l:\n",
        "      scores = []\n",
        "      for i in range(1, i+1):\n",
        "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
        "        params = param_func(i, m, l)\n",
        "        transformer = ColumnTransformer(transformers=[('scl', StandardScaler(), np_numerical), ('enc', OneHotEncoder(), np_categorical)])\n",
        "        pipeline = Pipeline(steps=[('t', transformer), ('m', model(**params))])  \n",
        "  \n",
        "        y_pred = pipeline.fit(X_train, y_train).predict(X_test)\n",
        "        score = f1_score(y_test, y_pred)\n",
        "        scores.append(score)\n",
        "      ax.plot(range(1, i+1), scores, label = f\"{M} = {m}, {L} = {l}\")\n",
        "      ax.set_xlabel(x_axis)\n",
        "      ax.set_ylabel(\"F1 score\")\n",
        "      ax.set_xticks(x_ticks)\n",
        "      ax.legend()\n",
        "  \n",
        "def mlpc_analysis(X, y, model, param_func, ax ,first_l, second_l, M, L):\n",
        "  location = 0.5\n",
        "  locations = []\n",
        "  labels = []\n",
        "  for m in first_l:\n",
        "    for l in second_l:\n",
        "      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state=42)\n",
        "      params = param_func(0, m, l)\n",
        "      transformer = ColumnTransformer(transformers=[('scl', StandardScaler(), np_numerical), ('enc', OneHotEncoder(), np_categorical)])\n",
        "      pipeline = Pipeline(steps=[('t', transformer), ('m', model(**params))]) \n",
        "\n",
        "      y_pred = pipeline.fit(X_train, y_train).predict(X_test)\n",
        "      score = f1_score(y_test, y_pred)\n",
        "      ax.bar(location, score)\n",
        "      locations.append(location)\n",
        "      labels.append(f\"{M} = {m}\\n{L} = {l}\")\n",
        "      location += 1\n",
        "  ax.set_ylabel(\"F1 Score\")\n",
        "  ax.set_xticks(locations)\n",
        "  ax.set_xticklabels(labels)\n",
        "\n",
        "fig1, ax1 = plt.subplots(1, 1)\n",
        "fig2, ax2 = plt.subplots(1, 1)\n",
        "fig3, ax3 = plt.subplots(1, 1)\n",
        "fig4, ax4 = plt.subplots(1, 1)\n",
        "\n",
        "fig.set_size_inches(20,20)\n",
        "ax1.set_title(\"K-Nearest Neighbours\")\n",
        "indiv_param(X, y, \n",
        "            KNeighborsClassifier, \n",
        "            knn_params, \n",
        "            ax1, \n",
        "            \"Number of Neighbours\", \n",
        "            [\"euclidean\", \"manhattan\", \"minkowski\"], \n",
        "            [\"auto\", \"ball_tree\", \"kd_tree\", \"brute\"],\n",
        "            \"Metric\", \n",
        "            \"Algorithm\",\n",
        "            [i for i in range(1, 11)],\n",
        "            10)\n",
        "\n",
        "fig1.savefig('knn.png')\n",
        "\n",
        "ax2.set_title(\"Decision Tree\")\n",
        "indiv_param(X, y, \n",
        "            DecisionTreeClassifier, \n",
        "            dtc_params, \n",
        "            ax2, \n",
        "            \"Depth of Tree\", \n",
        "            [\"gini\", \"entropy\"], \n",
        "            [\"best\"],\n",
        "            \"Criterion\", \n",
        "            \"Splitter\",\n",
        "            [i for i in range(1, 21)],\n",
        "            20)\n",
        "\n",
        "fig2.savefig('dtc.png')\n",
        "ax3.set_title(\"Multi-Layer Perceptron\")\n",
        "mlpc_analysis(X, y,\n",
        "            MLPClassifier,\n",
        "            mlpc_params, \n",
        "            ax3, \n",
        "            [\"logistic\" , \"tanh\"], \n",
        "            [\"lbfgs\", \"sgd\"], \n",
        "            \"Activation function\", \n",
        "            \"Solver\",)\n",
        "\n",
        "fig3.savefig('mlpc.png')\n",
        "\n",
        "ax4.set_title(\"Support Vector\")\n",
        "indiv_param(X, y,\n",
        "            SVC,\n",
        "            svc_params,\n",
        "            ax4,\n",
        "            \"Degree\", \n",
        "            [\"linear\", \"poly\", \"sigmoid\"], \n",
        "            [\"scale\", \"auto\"],\n",
        "            \"Kernel\",\n",
        "            \"Gamma\", \n",
        "            [i for i in range(1, 11)],\n",
        "            10)\n",
        "fig4.savefig('svc.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_qSYgHX1HCwc",
        "outputId": "baeb94f6-4dee-468a-d2c3-b687c07ab883"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1008 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1008 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1008 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1008 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Creating the model pipeline"
      ],
      "metadata": {
        "id": "SyO4KUBHyReO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models = [KNeighborsClassifier(n_neighbors=1, algorithm='brute'), \n",
        "          DecisionTreeClassifier(max_depth=20), \n",
        "          MLPClassifier(activation='tanh', solver='lbfgs'), \n",
        "          SVC(kernel='poly', degree=10,gamma='scale')]\n",
        "names = [\"KNN Classifier\", \"Decision Tree\", \"MLP Classifier\", \"SV Classifier\"]\n",
        "transformer = ColumnTransformer(transformers=[('scl', StandardScaler(), numerical), ('enc', OneHotEncoder(), categorical)])\n"
      ],
      "metadata": {
        "id": "z_DHzyiWyQ0c"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation of model's performance"
      ],
      "metadata": {
        "id": "WKG-08n42Plw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate(X, y, model):\n",
        "  cross_val = StratifiedKFold(n_splits=10)\n",
        "  score = cross_val_score(model, X, y, scoring = 'f1',  cv = cross_val)\n",
        "  return score\n",
        "\n",
        "results_no_us = []\n",
        "results_us = []\n",
        "\n",
        "for i in range(len(models)):\n",
        "  pipeline = Pipeline(steps = [('t', transformer),\n",
        "                               ('m', models[i])])\n",
        "  \n",
        "  no_us_score = evaluate(X, y, pipeline)\n",
        "  us_score = evaluate(rus_X, rus_y, pipeline)\n",
        "  results_no_us.append(no_us_score)\n",
        "  results_us.append(us_score)\n",
        "\n",
        "fig1, ax1 = plt.subplots(1,1)\n",
        "fig2, ax2 = plt.subplots(1,1)\n",
        "\n",
        "ax1.set_title(\"Distribution of Results for Each of the Models, Without Undersampling the Dataset\")\n",
        "ax1.boxplot(results_no_us, labels=names)\n",
        "fig1.savefig('result1.png')\n",
        "\n",
        "ax2.set_title(\"Distribution of Results for Each of the Models, After Undersampling the Dataset\")\n",
        "ax2.boxplot(results_us, labels=names)\n",
        "fig2.savefig('result2.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "X4UXsDk22Tyc",
        "outputId": "bb1c8017-d04f-4392-ef95-77cfc52bdef7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:775: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 762, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 103, in __call__\n",
            "    score = scorer._score(cached_call, estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
            "    y_pred = method_caller(estimator, \"predict\", X)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
            "    return getattr(estimator, method)(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/utils/metaestimators.py\", line 113, in <lambda>\n",
            "    out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)  # noqa\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\", line 469, in predict\n",
            "    Xt = transform.transform(Xt)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/compose/_column_transformer.py\", line 753, in transform\n",
            "    column_as_strings=fit_dataframe_and_transform_dataframe,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/compose/_column_transformer.py\", line 615, in _fit_transform\n",
            "    for idx, (name, trans, column, weight) in enumerate(transformers, 1)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 1046, in __call__\n",
            "    while self.dispatch_one_batch(iterator):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
            "    self._dispatch(tasks)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 779, in _dispatch\n",
            "    job = self._backend.apply_async(batch, callback=cb)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
            "    result = ImmediateResult(func)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
            "    self.results = batch()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in __call__\n",
            "    for func, args, kwargs in self.items]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
            "    for func, args, kwargs in self.items]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/utils/fixes.py\", line 211, in __call__\n",
            "    return self.function(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\", line 876, in _transform_one\n",
            "    res = transformer.transform(X)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_encoders.py\", line 513, in transform\n",
            "    warn_on_unknown=warn_on_unknown,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_encoders.py\", line 142, in _transform\n",
            "    raise ValueError(msg)\n",
            "ValueError: Found unknown categories ['Never_worked'] in column 3 during transform\n",
            "\n",
            "  UserWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:775: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 762, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 103, in __call__\n",
            "    score = scorer._score(cached_call, estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
            "    y_pred = method_caller(estimator, \"predict\", X)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
            "    return getattr(estimator, method)(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/utils/metaestimators.py\", line 113, in <lambda>\n",
            "    out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)  # noqa\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\", line 469, in predict\n",
            "    Xt = transform.transform(Xt)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/compose/_column_transformer.py\", line 753, in transform\n",
            "    column_as_strings=fit_dataframe_and_transform_dataframe,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/compose/_column_transformer.py\", line 615, in _fit_transform\n",
            "    for idx, (name, trans, column, weight) in enumerate(transformers, 1)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 1046, in __call__\n",
            "    while self.dispatch_one_batch(iterator):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
            "    self._dispatch(tasks)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 779, in _dispatch\n",
            "    job = self._backend.apply_async(batch, callback=cb)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
            "    result = ImmediateResult(func)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
            "    self.results = batch()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in __call__\n",
            "    for func, args, kwargs in self.items]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
            "    for func, args, kwargs in self.items]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/utils/fixes.py\", line 211, in __call__\n",
            "    return self.function(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\", line 876, in _transform_one\n",
            "    res = transformer.transform(X)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_encoders.py\", line 513, in transform\n",
            "    warn_on_unknown=warn_on_unknown,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_encoders.py\", line 142, in _transform\n",
            "    raise ValueError(msg)\n",
            "ValueError: Found unknown categories ['Never_worked'] in column 3 during transform\n",
            "\n",
            "  UserWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:775: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 762, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 103, in __call__\n",
            "    score = scorer._score(cached_call, estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
            "    y_pred = method_caller(estimator, \"predict\", X)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
            "    return getattr(estimator, method)(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/utils/metaestimators.py\", line 113, in <lambda>\n",
            "    out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)  # noqa\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\", line 469, in predict\n",
            "    Xt = transform.transform(Xt)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/compose/_column_transformer.py\", line 753, in transform\n",
            "    column_as_strings=fit_dataframe_and_transform_dataframe,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/compose/_column_transformer.py\", line 615, in _fit_transform\n",
            "    for idx, (name, trans, column, weight) in enumerate(transformers, 1)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 1046, in __call__\n",
            "    while self.dispatch_one_batch(iterator):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
            "    self._dispatch(tasks)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 779, in _dispatch\n",
            "    job = self._backend.apply_async(batch, callback=cb)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
            "    result = ImmediateResult(func)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
            "    self.results = batch()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in __call__\n",
            "    for func, args, kwargs in self.items]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
            "    for func, args, kwargs in self.items]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/utils/fixes.py\", line 211, in __call__\n",
            "    return self.function(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\", line 876, in _transform_one\n",
            "    res = transformer.transform(X)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_encoders.py\", line 513, in transform\n",
            "    warn_on_unknown=warn_on_unknown,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_encoders.py\", line 142, in _transform\n",
            "    raise ValueError(msg)\n",
            "ValueError: Found unknown categories ['Never_worked'] in column 3 during transform\n",
            "\n",
            "  UserWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:549: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:775: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 762, in _score\n",
            "    scores = scorer(estimator, X_test, y_test)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 103, in __call__\n",
            "    score = scorer._score(cached_call, estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 258, in _score\n",
            "    y_pred = method_caller(estimator, \"predict\", X)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 68, in _cached_call\n",
            "    return getattr(estimator, method)(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/utils/metaestimators.py\", line 113, in <lambda>\n",
            "    out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)  # noqa\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\", line 469, in predict\n",
            "    Xt = transform.transform(Xt)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/compose/_column_transformer.py\", line 753, in transform\n",
            "    column_as_strings=fit_dataframe_and_transform_dataframe,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/compose/_column_transformer.py\", line 615, in _fit_transform\n",
            "    for idx, (name, trans, column, weight) in enumerate(transformers, 1)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 1046, in __call__\n",
            "    while self.dispatch_one_batch(iterator):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
            "    self._dispatch(tasks)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 779, in _dispatch\n",
            "    job = self._backend.apply_async(batch, callback=cb)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
            "    result = ImmediateResult(func)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
            "    self.results = batch()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in __call__\n",
            "    for func, args, kwargs in self.items]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
            "    for func, args, kwargs in self.items]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/utils/fixes.py\", line 211, in __call__\n",
            "    return self.function(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\", line 876, in _transform_one\n",
            "    res = transformer.transform(X)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_encoders.py\", line 513, in transform\n",
            "    warn_on_unknown=warn_on_unknown,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_encoders.py\", line 142, in _transform\n",
            "    raise ValueError(msg)\n",
            "ValueError: Found unknown categories ['Never_worked'] in column 3 during transform\n",
            "\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1008 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1008 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}