PRML Course Project Code.ipynb

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        "# Preprocessing and visualization"
      ],
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      "cell_type": "markdown",
      "source": [
        "### Importing necessary libraries"
      ],
      "metadata": {
        "id": "6H-ALXMW272-"
      }
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      "cell_type": "code",
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      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from collections import Counter\n",
        "import seaborn as sns\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "import lightgbm as lgb\n",
        "from sklearn.metrics import auc, roc_auc_score, roc_curve, precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.graph_objects as graph_objects\n",
        "from collections import Counter\n",
        "from sklearn.neighbors import KNeighborsClassifier as kNN\n",
        "import seaborn as sns\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import make_scorer\n",
        "from sklearn.linear_model import LogisticRegression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xcUqQE2hSBV8",
        "outputId": "49cab100-649d-4b91-ad94-da754032b2a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/DataSet/Project/healthcare-dataset-stroke-data.csv\") "
      ],
      "metadata": {
        "id": "G_G6UMBnymYQ"
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      "outputs": []
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              "         id  gender   age  hypertension  heart_disease ever_married  \\\n",
              "0      9046    Male  67.0             0              1          Yes   \n",
              "1     51676  Female  61.0             0              0          Yes   \n",
              "2     31112    Male  80.0             0              1          Yes   \n",
              "3     60182  Female  49.0             0              0          Yes   \n",
              "4      1665  Female  79.0             1              0          Yes   \n",
              "...     ...     ...   ...           ...            ...          ...   \n",
              "5105  18234  Female  80.0             1              0          Yes   \n",
              "5106  44873  Female  81.0             0              0          Yes   \n",
              "5107  19723  Female  35.0             0              0          Yes   \n",
              "5108  37544    Male  51.0             0              0          Yes   \n",
              "5109  44679  Female  44.0             0              0          Yes   \n",
              "\n",
              "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
              "0           Private          Urban             228.69  36.6  formerly smoked   \n",
              "1     Self-employed          Rural             202.21   NaN     never smoked   \n",
              "2           Private          Rural             105.92  32.5     never smoked   \n",
              "3           Private          Urban             171.23  34.4           smokes   \n",
              "4     Self-employed          Rural             174.12  24.0     never smoked   \n",
              "...             ...            ...                ...   ...              ...   \n",
              "5105        Private          Urban              83.75   NaN     never smoked   \n",
              "5106  Self-employed          Urban             125.20  40.0     never smoked   \n",
              "5107  Self-employed          Rural              82.99  30.6     never smoked   \n",
              "5108        Private          Rural             166.29  25.6  formerly smoked   \n",
              "5109       Govt_job          Urban              85.28  26.2          Unknown   \n",
              "\n",
              "      stroke  \n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5110 entries, 0 to 5109\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   id                 5110 non-null   int64  \n",
            " 1   gender             5110 non-null   object \n",
            " 2   age                5110 non-null   float64\n",
            " 3   hypertension       5110 non-null   int64  \n",
            " 4   heart_disease      5110 non-null   int64  \n",
            " 5   ever_married       5110 non-null   object \n",
            " 6   work_type          5110 non-null   object \n",
            " 7   Residence_type     5110 non-null   object \n",
            " 8   avg_glucose_level  5110 non-null   float64\n",
            " 9   bmi                4909 non-null   float64\n",
            " 10  smoking_status     5110 non-null   object \n",
            " 11  stroke             5110 non-null   int64  \n",
            "dtypes: float64(3), int64(4), object(5)\n",
            "memory usage: 479.2+ KB\n"
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              "                 id          age  hypertension  heart_disease  \\\n",
              "count   5110.000000  5110.000000   5110.000000    5110.000000   \n",
              "mean   36517.829354    43.226614      0.097456       0.054012   \n",
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              "min       67.000000     0.080000      0.000000       0.000000   \n",
              "25%    17741.250000    25.000000      0.000000       0.000000   \n",
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              "75%    54682.000000    61.000000      0.000000       0.000000   \n",
              "max    72940.000000    82.000000      1.000000       1.000000   \n",
              "\n",
              "       avg_glucose_level          bmi       stroke  \n",
              "count        5110.000000  4909.000000  5110.000000  \n",
              "mean          106.147677    28.893237     0.048728  \n",
              "std            45.283560     7.854067     0.215320  \n",
              "min            55.120000    10.300000     0.000000  \n",
              "25%            77.245000    23.500000     0.000000  \n",
              "50%            91.885000    28.100000     0.000000  \n",
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              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5110.000000</td>\n",
              "      <td>5110.000000</td>\n",
              "      <td>5110.000000</td>\n",
              "      <td>5110.000000</td>\n",
              "      <td>5110.000000</td>\n",
              "      <td>4909.000000</td>\n",
              "      <td>5110.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36517.829354</td>\n",
              "      <td>43.226614</td>\n",
              "      <td>0.097456</td>\n",
              "      <td>0.054012</td>\n",
              "      <td>106.147677</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>0.048728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>21161.721625</td>\n",
              "      <td>22.612647</td>\n",
              "      <td>0.296607</td>\n",
              "      <td>0.226063</td>\n",
              "      <td>45.283560</td>\n",
              "      <td>7.854067</td>\n",
              "      <td>0.215320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>67.000000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.120000</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17741.250000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>77.245000</td>\n",
              "      <td>23.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>36932.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>91.885000</td>\n",
              "      <td>28.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>54682.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>114.090000</td>\n",
              "      <td>33.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>72940.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>271.740000</td>\n",
              "      <td>97.600000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "id": "Zhbd5Kp7yuWR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcefcfde-3664-4715-e6f3-63bd41a52893"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                     0\n",
              "gender                 0\n",
              "age                    0\n",
              "hypertension           0\n",
              "heart_disease          0\n",
              "ever_married           0\n",
              "work_type              0\n",
              "Residence_type         0\n",
              "avg_glucose_level      0\n",
              "bmi                  201\n",
              "smoking_status         0\n",
              "stroke                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# filling missing values in the dataset\n",
        "avg1 = dataset[dataset['stroke'] == 1]['bmi'].mean()\n",
        "df2 = dataset[dataset['stroke'] == 1].fillna(avg1)\n",
        "avg0 = dataset[dataset['stroke'] == 0]['bmi'].mean()\n",
        "df1 = dataset[dataset['stroke'] == 0].fillna(avg0)\n",
        "dataset = pd.concat([df1,df2], axis = 0)"
      ],
      "metadata": {
        "id": "-yoZKZt_uCnx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = dataset.copy(deep = True)"
      ],
      "metadata": {
        "id": "hhEDNFvjq2tA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Discriminant Analysis"
      ],
      "metadata": {
        "id": "xxI9QNjo3JSN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "counts0 = Counter(df[df['stroke']==0]['work_type'])\n",
        "counts1 = Counter(df[df['stroke']==1]['work_type'])\n",
        "fig = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]])\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts0.items())],\n",
        "     labels=[item[0] for item in sorted(counts0.items())],\n",
        "     domain=dict(x=[0, 0.5]),\n",
        "     name=\"Stroke Negative\",title='Stroke Negative'), \n",
        "     row=1, col=1)\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts1.items())],\n",
        "     labels=[item[0] for item in sorted(counts1.items())],\n",
        "     domain=dict(x=[0.5, 1.0]),\n",
        "     name=\"Stroke Positive\",title='Stroke Positive'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.update_layout(\n",
        "    title={'text':'Stroke vs Worktype','xanchor':'left','yanchor': 'top','y':0.9,'x':0.35},\n",
        "    xaxis_title=\"X Axis Title\",\n",
        "    yaxis_title=\"Y Axis Title\",\n",
        "    legend_title=\"Worktype\",\n",
        "    font=dict(size=18)\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s41_NPTh4w7e",
        "outputId": "3e9ad901-eb94-4c4a-a3f6-35be6b82c0ac"
      },
      "execution_count": null,
      "outputs": [
        {
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "counts0 = Counter(df[df['stroke']==0]['gender'])\n",
        "counts1 = Counter(df[df['stroke']==1]['gender'])\n",
        "fig = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]])\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts0.items())],\n",
        "     labels=[item[0] for item in sorted(counts0.items())],\n",
        "     domain=dict(x=[0, 0.5]),\n",
        "     name=\"Stroke Negative\",title='Stroke Negative'), \n",
        "     row=1, col=1)\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts1.items())],\n",
        "     labels=[item[0] for item in sorted(counts1.items())],\n",
        "     domain=dict(x=[0.5, 1.0]),\n",
        "     name=\"Stroke Positive\",title='Stroke Positive'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.update_layout(\n",
        "    title={'text':'Stroke vs Gender','xanchor':'left','yanchor': 'top','y':0.9,'x':0.35},\n",
        "    xaxis_title=\"X Axis Title\",\n",
        "    yaxis_title=\"Y Axis Title\",\n",
        "    legend_title=\"Gender\",\n",
        "    font=dict(size=18)\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7T8qR0J_yNB",
        "outputId": "f24f36cd-f834-4b2b-9b01-2e938773a9c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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    {
      "cell_type": "code",
      "source": [
        "counts0 = Counter(df[df['stroke']==0]['Residence_type'])\n",
        "counts1 = Counter(df[df['stroke']==1]['Residence_type'])\n",
        "fig = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]])\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts0.items())],\n",
        "     labels=[item[0] for item in sorted(counts0.items())],\n",
        "     domain=dict(x=[0, 0.5]),\n",
        "     name=\"Stroke Negative\",title='Stroke Negative'), \n",
        "     row=1, col=1)\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts1.items())],\n",
        "     labels=[item[0] for item in sorted(counts1.items())],\n",
        "     domain=dict(x=[0.5, 1.0]),\n",
        "     name=\"Stroke Positive\",title='Stroke Positive'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.update_layout(\n",
        "    title={'text':'Stroke vs Residence type','xanchor':'left','yanchor': 'top','y':0.9,'x':0.35},\n",
        "    xaxis_title=\"X Axis Title\",\n",
        "    yaxis_title=\"Y Axis Title\",\n",
        "    legend_title=\"Residence type\",\n",
        "    font=dict(size=18)\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6XZ9-lm8RVI",
        "outputId": "3eaac311-6951-40ba-907f-bb97d0189ccd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "counts0 = Counter(df[df['stroke']==0]['hypertension'])\n",
        "counts1 = Counter(df[df['stroke']==1]['hypertension'])\n",
        "fig = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]])\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts0.items())],\n",
        "     labels=[item[0] for item in sorted(counts0.items())],\n",
        "     domain=dict(x=[0, 0.5]),\n",
        "     name=\"Stroke Negative\",title='Stroke Negative'), \n",
        "     row=1, col=1)\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts1.items())],\n",
        "     labels=[item[0] for item in sorted(counts1.items())],\n",
        "     domain=dict(x=[0.5, 1.0]),\n",
        "     name=\"Stroke Positive\",title='Stroke Positive'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.update_layout(\n",
        "    title={'text':'Stroke vs hypertension','xanchor':'left','yanchor': 'top','y':0.9,'x':0.35},\n",
        "    xaxis_title=\"X Axis Title\",\n",
        "    yaxis_title=\"Y Axis Title\",\n",
        "    legend_title=\"hypertension\",\n",
        "    font=dict(size=18)\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_GOb2U2PANnO",
        "outputId": "f5cd6878-2ac0-49f1-e913-bec7606d935f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "counts0 = Counter(df[df['stroke']==0]['ever_married'])\n",
        "counts1 = Counter(df[df['stroke']==1]['ever_married'])\n",
        "fig = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]])\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts0.items())],\n",
        "     labels=[item[0] for item in sorted(counts0.items())],\n",
        "     domain=dict(x=[0, 0.5]),\n",
        "     name=\"Stroke Negative\",title='Stroke Negative'), \n",
        "     row=1, col=1)\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts1.items())],\n",
        "     labels=[item[0] for item in sorted(counts1.items())],\n",
        "     domain=dict(x=[0.5, 1.0]),\n",
        "     name=\"Stroke Positive\",title='Stroke Positive'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.update_layout(\n",
        "    title={'text':'Stroke vs marriage status','xanchor':'left','yanchor': 'top','y':0.9,'x':0.35},\n",
        "    xaxis_title=\"X Axis Title\",\n",
        "    yaxis_title=\"Y Axis Title\",\n",
        "    legend_title=\"marriage status\",\n",
        "    font=dict(size=18)\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfnB8cYv8yYn",
        "outputId": "5b96c71f-0919-4cb1-f01e-ec1caccaa784"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "counts0 = Counter(df[df['stroke']==0]['heart_disease'])\n",
        "counts1 = Counter(df[df['stroke']==1]['heart_disease'])\n",
        "fig = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]])\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts0.items())],\n",
        "     labels=[item[0] for item in sorted(counts0.items())],\n",
        "     domain=dict(x=[0, 0.5]),\n",
        "     name=\"Stroke Negative\",title='Stroke Negative'), \n",
        "     row=1, col=1)\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts1.items())],\n",
        "     labels=[item[0] for item in sorted(counts1.items())],\n",
        "     domain=dict(x=[0.5, 1.0]),\n",
        "     name=\"Stroke Positive\",title='Stroke Positive'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.update_layout(\n",
        "    title={'text':'Stroke vs Heart Disease','xanchor':'left','yanchor': 'top','y':0.9,'x':0.35},\n",
        "    xaxis_title=\"X Axis Title\",\n",
        "    yaxis_title=\"Y Axis Title\",\n",
        "    legend_title=\"Heart disease\",\n",
        "    font=dict(size=18)\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HhLM7Hc3AslR",
        "outputId": "4d6a14ca-828b-4ba8-e472-4abebfb5a78c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "counts0 = Counter(df[df['stroke']==0]['smoking_status'])\n",
        "counts1 = Counter(df[df['stroke']==1]['smoking_status'])\n",
        "fig = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]])\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts0.items())],\n",
        "     labels=[item[0] for item in sorted(counts0.items())],\n",
        "     domain=dict(x=[0, 0.5]),\n",
        "     name=\"Stroke Negative\",title='Stroke Negative'), \n",
        "     row=1, col=1)\n",
        "\n",
        "fig.add_trace(graph_objects.Pie(\n",
        "     values=[item[1] for item in sorted(counts1.items())],\n",
        "     labels=[item[0] for item in sorted(counts1.items())],\n",
        "     domain=dict(x=[0.5, 1.0]),\n",
        "     name=\"Stroke Positive\",title='Stroke Positive'),\n",
        "    row=1, col=2)\n",
        "\n",
        "fig.update_layout(\n",
        "    title={'text':'Stroke vs Smoking Status','xanchor':'left','yanchor': 'top','y':0.9,'x':0.35},\n",
        "    xaxis_title=\"X Axis Title\",\n",
        "    yaxis_title=\"Y Axis Title\",\n",
        "    legend_title=\"Smoking Status\",\n",
        "    font=dict(size=18)\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DS9Iil-LvvuJ",
        "outputId": "ebc7792b-dce3-4336-e163-c1a99f089b29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<head><meta charset=\"utf-8\" /></head>\n",
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              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "glucose_bins = np.linspace(0,280,29)\n",
        "bmi_bins = np.linspace(0,100,51)\n",
        "age_bins = np.linspace(0,90,10)\n",
        "df['binned_glucose'] = pd.cut(df['avg_glucose_level'], glucose_bins, labels=glucose_bins[:-1],right=False)\n",
        "df['binned_bmi'] = pd.cut(df['bmi'], bmi_bins, labels=bmi_bins[:-1],right=False)\n",
        "df['binned_age'] = pd.cut(df['age'], age_bins, labels=age_bins[:-1],right=False)\n",
        "df['binned_glucose'] = df['binned_glucose'].astype('int')\n",
        "df['binned_bmi'] = df['binned_bmi'].astype('int')\n",
        "df['binned_age'] = df['binned_age'].astype('int')"
      ],
      "metadata": {
        "id": "k52wHin890aI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(9,5)})\n",
        "ax = sns.countplot(x='binned_age',hue='stroke',data=df)\n",
        "ax.set_title('Stroke/Non stroke cases at various age groups').set_fontsize(22)\n",
        "ax.set_xlabel('Age',fontsize=14)\n",
        "ax.set_ylabel('Count',fontsize=14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lJU8RiJ9-oN",
        "outputId": "72972ac5-33c5-4b91-be3e-ac9f72a0b28d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(9,5)})\n",
        "ax = sns.countplot(x='binned_glucose',hue='stroke',data=df)\n",
        "ax.set_title('Stroke/Non stroke cases at various glucose Levels').set_fontsize(22)\n",
        "ax.set_xlabel('Glucose level',fontsize=14)\n",
        "ax.set_ylabel('Count',fontsize=14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6ftDY1I-mmn",
        "outputId": "51602339-82d2-4607-a00f-e14a89ce5575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(9,5)})\n",
        "ax = sns.countplot(x='binned_bmi',hue='stroke',data = df)\n",
        "ax.set_title('Stroke/Non stroke cases at various bmi Levels').set_fontsize(22)\n",
        "ax.set_xlabel('bmi level',fontsize=14)\n",
        "ax.set_ylabel('Count',fontsize=14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9LwwfqF0u9S9",
        "outputId": "2c22c67b-078e-4c7e-eaab-6d73ef08e0c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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KFWRmZuqUd+rUCQ4ODjrTy7djVlaWyfaWz9+wYUPs27cPy5Yt046nqQ51eb/rc+jQIZSWliIoKAjPPPOMyfnLlZWV4cCBA4iLi8OMGTMwdepUTJkyBevXrwcAnXEG5V+gH330EY4ePWpwXA7wYDxEdnY2nnnmGTz99NN65ynvuykpKVWqQwpz1/FRlZ/mj4+PR1lZmXa6Wq3Wfinou9wlhMCRI0ewbNkyxMTEaNu5dOlSk+3U9xgIY1JTU3H37l14eXnh+eef1yl3c3PTjiNJSkoya9n6VPc+BYCwsLAKP6DKlSeXR48erbD9y5VfZn6Ys7Oz9jJz165ddcqbNWsGQPpnV3Uy9Vnh7e2NZs2aIS8vr0IfKe9jlZPsgoIC7N69G7a2tggPD9dOr+rxakj5Pp8/fz527dqFu3fvmoypTLZjbh4WHByM4OBgAA/GmqSkpGDJkiU4cOAANm3ahG7duqFPnz5mL9fQ7ZHlX9r6vgDLNWnSBMePH9f7BX/s2DEkJSXBwcEBa9asga+vr95lXLt2DQDw+uuvm2xrfn6+9hfW6dOn8ffffyMiIqJCJm+Im5sb3n33XcTFxWHhwoX4/vvvDc5rat3t7e3h5eWFzMxMZGZmVvjVB/zfWKnKys+S3L9/32R7y+efO3cuPvnkE3z55Zf48ssv4e3tjaCgILz44ot4+eWXDY5BMKWu7ndDyhM/fQmZIenp6Xj//fdx8eJFg/Pcvn27wt8TJkzAX3/9hd9//x2///47nnjiCQQEBKBTp04YMGBAhfEZ5et4/vx5k4NxyweGm1tHTazjo2rRogXatWuH48eP4/fff9cmCQcPHkR2djZat26tM37w1q1bGDt2LI4fP16ldhrqZ4aUf0mb6ucA9PZzc1XnPq3cvsqUSiUUCgVKSkqQn5+vc9bXx8dHb1zDhg2Rn5+vt7x8zJ7Uz67qVH4cjR8/3uS8ubm5eOqppwAAzz//PHx8fJCWloazZ89qj8Ft27bh/v37CAsLqzBusKrHqyEDBgzAgQMHkJCQgPfffx/W1tZ45pln0KFDB/Tt21f7fW5MvUhuHqZQKBAcHIwVK1bgjTfewOnTp7Fr164qJTf6zjA8zNSgPEOefvppWFtb4/Tp05gzZw4WLVqk91dG+S+Yvn37mvyifvgsSvmD+/QNFjPk3Xffxdq1a3HgwAEcPnxYZ5Btdal8av1R9O7dG126dMFvv/2G5ORkHDt2DImJiUhMTERcXBzWrFljMJkypq7u9+ps7wcffICLFy8iNDQUw4cPR8uWLeHo6Ahra2ukp6fr/aXo6emJDRs24PDhw/jjjz9w7NgxnDhxAsnJyVi6dCk+++wzbcKm0WgAPPhl2aVLF6NteTgpM6eOmljH6hAREYHjx49j06ZN2uRm8+bNACpeCig3ffp0HD9+HMHBwRg3bhyeffZZODo6wtbWFvfv30ebNm2M1meqPxtS1X5uiDBwh1917tNHZerzqbq3yaMq/6x48cUXTQ5mfjhZUSgUGDBgAJYvX45NmzZpr3CU98PKZw+rerwaolAosHDhQrz33nvYu3cvjh07hmPHjuHnn3/Gzz//jNdeew1z5841uox6l9yUs7a2RseOHXH69Gm9ty0+ivJfyuXZrD7ltxfr+1Xt5OSEb775BiNGjMBvv/2G0aNHY8mSJTofQkqlEleuXMGYMWPMupywa9cuNGjQQO8pVEMaNWqEUaNGYe7cuVi4cCE+/fRTvfOVr4+hp/yWlJRof/mZOqNQHZycnBAREaE9GK9evYrp06fj8OHDWLBggd4nVFfV477fDSk/E5Weni5p/osXL+LcuXNwd3dHXFyczjOkrl69ajBWoVCgc+fO2rtb7t69i9WrV2PhwoWYOXMmevfujUaNGml/AXt6euKLL74wa32k1lFT6/io+vbti7lz52LPnj3Iy8uDjY0Ndu3apXMpAHiwbvv27YO1tTWWL19eYZxXTbXTy8sLgOFj/OGyh/t5eaJ9584dvTHGLh1Xxz6VUldGRgY0Gg3s7e0l3dH4uFMqlUhPT8c///lPvPjii2bFRkREYPny5YiPj8fEiRNx9epVpKSkwNPTU+e741GOV2NUKpX2TKVGo8G+ffvw0UcfYcOGDejbt6/ey4TlZDvmxtCvgIeVD+qs/EVTfhBW9dpuSEgIrKyscOLECb1fGBcvXsSJEye0DxLUx9HREd9//z06duyIAwcOYMSIETofCuUD7MwZ1Jieno4LFy6ga9euZl+W+ec//wlfX1+cOHFCOyC5svL1SUhI0HvNetOmTRBCoFmzZrWS3FT25JNPYvTo0QAe3EL7MDnvd2M6deoEW1tbHD9+3OglmHIFBQUAHnzJVf7SByBpwGC5Bg0aYOTIkfDx8UFJSQkuXboE4ME1dxcXF5w5cwZXrlyRvDxz6jDmUdaxvB/p6/9SODo6agd0bt26Fdu2bUNJSQlCQ0N1vnCLioqg0WjQsGFDncQGgMHnSj2K8jFdmZmZegf45+XlYffu3QCA5557Tjv94eS/8rNVAGDfvn2S21CVffqwxMREvZeJyrdXcHCwpEv2j7tH+ax46qmn0K5dO9y6dQv79+/XnrWpPKAdqN7j1RCFQoHu3btrb26o/PmtM3+NtOIxsHbtWkydOhUnT57UKSsrK8O///1v7R0Effv2rVBe/stEyge9Pr6+vnjppZeg0WgQHR2NoqIibVlhYSGio6Oh0WjQp08fo5dFGjZsiBUrVuAf//gHkpKSMGzYsArLGjZsGBo1aoTly5djzZo1ej9Mz58/r70MBRh/l5QpdnZ22oeH/fTTT3rnKV+n69evY+HChdrTlQBw4cIFLF68GAAwdOhQs+s3R1paGrZt26b3AWblH7yVx87Ieb8b4+7ujsjISGg0GowbN04nMVOr1dptBgDNmzeHQqHA+fPndQZGb9iwAVu3btVbz/fff6/3LrFTp04hOzsbCoVCu11sbW0xZswYqNVqvP/++3qP4/v37+O3336rsL/MqcOYqq4j8Oj9CPi/y08bN240+twcDw8PODs7o7CwUCeR+f3337Fy5coqt8EQBwcHREZGAgDmzJlTYaBsSUkJYmJicPfuXQQFBaF9+/baMl9fXzz55JMoLCzUadeuXbt0HhRZrrr26cNu3ryp8/l08uRJbbuGDBli1vIeVwMHDoRSqcSmTZuwePFivQ8RvXbtGrZs2aI3vrwfbtiwQTuPvn5Y1ePVkM2bN+P06dM60/Py8rQDkk29EqTup6YGlJaWYuPGjdi4cSM8PT3x7LPPwtnZGQUFBTh79qz2gBw+fLjOKbZevXohKSkJEydOxAsvvKB9r9LEiRMlP4QpJiYG6enpSEpKQs+ePbW/YA4fPoyCggI8++yziI6ONrkcBwcHLFmyBBMmTMDOnTvx9ttv4/vvv4erqyuUSiWWLFmC8ePHY+bMmVi6dCmeeeYZuLm5oaioCOfOnUNGRgb69u2rveV7x44dsLW1Rffu3SVvy4f1798f33//Pc6dO6e33N7eHosWLcKIESPwww8/YNeuXWjTpg0KCgpw+PBhlJaWYsCAAaYfwPSIbty4gQ8//BBPPPEEWrVqBaVSidLSUqSlpeHatWto2LBhhae8AvLe76ZMnjwZV69exb59+xAeHo6goCD4+PggJycH586dQ05OjvYJo25uboiKisLq1asxZMgQdOjQAZ6enjh37hzOnTuH9957D8uXL9epY+nSpZg3bx5atmyJli1bws7ODhkZGTh+/Dg0Gg1GjhwJT09P7fxvv/02bty4gR9//BFvvPEG/Pz88OSTT8LW1haZmZk4c+YM7t69i2+//VZ766m5dRhS1XUEHvSjH3/8Ee+88w46deqkHZ82Z84cSfsCADp37gylUqn9gNd3KQB4cHl91KhRiI2NxcSJE7UD0a9evYqTJ09i1KhRZj34Uqp//etfSE1NRVJSEsLCwrQP8Tty5Aiys7PRuHFjLFiwQCduwoQJ+PDDD7Fw4UJs374dTZo0wZUrV3D27Fm89957ettaXfv0YZGRkVi7di327NmDgIAA5ObmIjk5GWVlZYiKiqqVpwY/Kn2Po3hYXFwcvLy8sHz5cowaNQpxcXFYvXo1VCoVvLy8cOfOHVy6dAlXrlxBYGCg9k6xh/Xt2xdz5szRnqnXN6C9XFWOV0N27NiBjz/+GN7e3vD394ejoyPy8/Nx9OhR3L17FyEhISZ/oMs2uXn99dfRpEkT/PHHHzh58iTOnTuH3Nxc2NjYwNvbGxEREXj99dcrPJOg3KBBg3D79m3Ex8djz5492tOXo0ePlvwl5+bmhvXr12PVqlX43//+p70lr1mzZhg6dCiGDBkieVCunZ0dFi1ahI8//hgJCQkYMmQIVq5cCQ8PD3Tq1AkJCQlYvXo19u7di5SUFJSVlcHT0xNNmzZFVFSUduBjRkYGUlNT0bVrV7OuTz9MoVBgwoQJRt/JFRQUhM2bN2PFihXYv38/duzYAQcHBwQFBWHgwIHo169fjQ+8CwwMxEcffYSkpCRcunQJp0+fhq2tLZRKJYYOHYpBgwbp3CUi1/0uta5ly5YhPj4eGzduxJkzZ3DixAm4ubnBz89PZ/D5tGnT4Ofnh3Xr1uHUqVOwsbFB69at8e2336JFixZ6v/ijo6Pxxx9/IDU1FYcPH0ZxcTE8PT3RvXt3REVF6b1+PnXqVPTs2RPr1q3DsWPHsHfvXjg4OMDT0xMvvvgiQkNDKxzDVanDkKqsI/DgNQpWVlbYuXMndu7cqb0EY05yUz6gs/zLXt+lgHJDhw6Fr68vfvjhB5w/f177RPb58+ejf//+NZLc2Nvb44cffsD69euxZcsWHD58GGVlZfD19cWAAQMwfPhwvcdMnz59YGdnhxUrVuCvv/7C5cuXtdv0qaee0tvW6tyn5QIDAzFw4EAsXrwYBw4cQHFxMVQqFaKiomptcPKjMnUGpPzzy8/PD//973+xdu1a/Pbbb0hLS8Px48fh5uYGpVKJl19+GWFhYXqX0ahRI/Tq1Ut7VlDfgPaHmXu8GlLep48fP47U1FQUFBTA1dUVrVq1QkREBPr372/yZgkrIWVwCsnCqlWrMHfuXMyaNQsDBw60dHOIiIhqhGzH3JAuT09PjB071qxbwImIiOoanrkhIiIiWeGZGyIiIpIVJjdEREQkK0xuiIiISFZkeyv4w/Ly7kCj4dAiIiKiukyhsIKra0OT89WL5EajEUxuiIiI6gleliIiIiJZYXJDREREslIvLksR0eNBrS5DXl42ysp038j8uLKxsYOrqyesrflxSVRX8GglolqTl5cNB4cGaNjQp8bfL1YdhBC4c6cQeXnZ8PAw783TRGQ5tZbcXL9+He+//77276KiIty+fRtJSUlIT0/HlClTkJ+fDxcXF8TGxqJ58+YAYLSMiOqWsrL7dSaxAQArKys0bOiE27fzLd0UIjJDrY25adKkCbZs2aL916NHD4SHhwMAZsyYgaioKCQmJiIqKgrR0dHaOGNlRFT31JXEplxday8RWWhA8f379xEfH4/XXnsNOTk5SEtL0yY64eHhSEtLQ25urtEyIiIiIn0sMuZm9+7d8Pb2RuvWrZGamgpvb29YW1sDAKytreHl5YWMjAwIIQyWubm5Sa7P3b1RjawHEZknK0sBG5va+001evQI9O7dFwMGRDzSchQKBTw9HaupVURU0yyS3GzYsAGvvfZardWXk3ObD/EjegxoNBqUlWkkzfv998vx99/XER09q8r1CfHgAZ5S6zREo9EgO7vokZZBRI9OobCSdMKi1pObzMxMJCcnY968eQAApVKJzMxMqNVqWFtbQ61WIysrC0qlEkIIg2VE1cnRyQEO9rY604tLSlFUWGyBFpEpQggIIaBQ8HFdRFRRrSc3mzZtQrdu3eDq6goAcHd3h7+/PxISEjBgwAAkJCTA399fe9nJWBlRdXGwt0XU5DU609fOewtFYHJT01av/hH/+c8vuHPnDjw8PDBu3AT8/PNKCCGwf/9eNG7cBKtWrcPYsSPRpk0gUlKO4uzZs/jpp/XIy8vFV18txLVrV9C0aTOMH/8R2rQJ1Knj1q1b+OijsQgL64uoqCFITT2FuLgvcfnyJXh7KzF+/EyB7icAACAASURBVEcIDg6xwNoTUXWzSHIzbdq0CtNiYmIwZcoUfPPNN3ByckJsbKykMiKq+65evYyNG3/Fd9/9BA8PT2Rk3IBGo8Hgwe/qvSyVmLgNCxZ8jSefbIbCwgIMHz4E//rXRPTsGYY9e3Zh0qR/4ZdfNsHZ2UUbc+PG35gwYRz++c9BGDDgVWRnZ2Hy5H/h008/Q8eOXXD0aBKmT/8Ya9b8R/vDi4jqrlpPbhITE3WmtWzZEr/++qve+Y2VEVHdp1BY4/79+0hPvwQXF1colY2Nzt+3bz+0aNESAJCUdAhNmzZF794vAwB69eqN//znFxw8uB99+/YDAFy+fAmrVn2P9957H7169QbwIEHq3LkLOnd+AQDQoUMnPPusPw4dOog+fcJralWJqJbwCcVEZFFNmjTFBx98hB9+WIH09Evo2LETxo2bYHB+Ly9v7f9v3cqGt3fFMXg+Pj7Izs7S/r1jx3Y0adIE3bv31E67efMm9uz5DQcP7tdOKysrQ7t2vCxFJAcciUdEFvfSS72xdOn32LAhHoAVli792uDD8x6e7uHhiczMjArlmZmZ8PT00v49dOhIODu7ICZmGtRqNQDA29sbYWF9sX37Xu2/XbsOYPDgd6p93Yio9jG5ISKLunr1Mo4eTcb9+/dhZ2cPe3t7WFkp4Obmph1/Y0jnzs/j2rWr2LFjO8rKyvDbbztw+fIldOnSVTuPjY0NZs2KRXHxPcyePQMajQYvvdQHBw/ux+HDf0KtVqOkpATHjh1BVlZmbawyEdUwJjdEZFH375di2bLFePnlnujfPwz5+XkYNWqs9jJS3749MHToW3pjnZ1dEBu7COvXr8bLL/fAmjU/ITZ2EVxcXCrMZ2trizlz5iM3Nxeffz4Tnp5e+Pzzhfj555UID++F1157GevW/cznYRHJhJUQQvZHMx/iR6Z4ejoavBWcD2+rPjdvXoGPTzNLN8NsdbXdRHIj9SF+PHNDREREssLkhoiIiGSFyQ0RERHJCpMbIiIikhUmN0RERCQrTG6IiIhIVpjcEBERkazw3VJEZDGOTg5wsLet9uUWl5SiqLC42pdLRHUDkxsishgHe1u9D098VGvnvYUimE5url69gjlzYlBQUABnZ2dMn/4ZmjZ9strbQ0S1i5eliKjeWrDgc7z66htYv34jXn31DcyfP9fSTSKiasDkhojqpby8XJw79xd69gwDAPTsGYZz5/5CXl6ehVtGRI+KyQ0R1UuZmZnw8PCCtbU1AMDa2hoeHp58MziRDDC5ISIiIllhckNE9ZK3tzdu3cqCWq0GAKjVaty6lQ0vL28Lt4yIHhWTGyKql1xd3fD00yrs2pUIANi1KxHPPOMHV1dXC7eMiB4VbwUnIospLinF2nlv1chypZg06RPMnj0DK1d+B0dHR3z66WfV3hYiqn1MbojIYooKiyU9j6amNGvWHN9+u8pi9RNRzeBlKSIiIpIVJjdEREQkK0xuiIiISFaY3BAREZGsMLkhIiIiWWFyQ0RERLJSq7eCl5SUYO7cufjzzz9hb2+PoKAgzJo1C+np6ZgyZQry8/Ph4uKC2NhYNG/eHACMlhFR3ebqbAcbO/tqX27Z/RLkFdw3OV9c3CLs27cbGRk38NNP69GixdPV3hYiqn21mtzMnz8f9vb2SExMhJWVFW7dugUAmDFjBqKiojBgwABs2bIF0dHR+Omnn0yWEVHdZmNnj6Pzhlf7cttP/g6A6eSma9cX8cYbkXj//RHV3gYispxauyx1584dbN68GePHj4eVlRUAwMPDAzk5OUhLS0N4eDgAIDw8HGlpacjNzTVaRkT0qAIDg+Dt7WPpZhBRNau1MzfXrl2Di4sL4uLicPjwYTRs2BDjx4+Hg4MDvL29YW1tDQCwtraGl5cXMjIyIIQwWObm5ia5bnf3RjWyTlQ/eHo6WroJspGVpYCNTe38pjK3Hmtrw21TKBTsB0R1SK0lN2q1GteuXUOrVq3w8ccf48SJExg1ahS++uqrGq87J+c2NBpR4/VQ3WXsiys7u6gWWyJvGo0GZWWaWqnL3HrUasNt02g07AdEjwGFwkrSCYtaS26USiVsbGy0l5gCAwPh6uoKBwcHZGZmQq1Ww9raGmq1GllZWVAqlRBCGCwjIiIi0qfWxty4ubmhY8eOOHjwIIAHd0Hl5OSgefPm8Pf3R0JCAgAgISEB/v7+cHNzg7u7u8EyIiIiIn2shBC1dr3m2rVr+OSTT5Cfnw8bGxv861//Qrdu3XDx4kVMmTIFhYWFcHJyQmxsLFq0aAEARsuk4mUpMsXT0xFRk9foTF877y1ejqhGN29egY9PM+3flr4VfNGi+di3bw9yc3Pg7OwCJydnrF79b535KrebiCxD6mWpWk1uLIXJDZnC5KZ21NUkoa62m0hupCY3fEIxERERyQqTGyIiIpIVJjdEREQkK0xuiKhW1bVhfnWtvUTE5IaIapGNjR3u3CmsMwmDEAJ37hTCxsbO0k0hIjPU6osziah+c3X1RF5eNm7fzrd0UySzsbGDq6unpZtBRGZgckNEtcba2gYeHnzCOBHVLF6WIiIiIllhckNERESywstSJBuOTg5wsLfVmV5cUoqiwmILtIiIiCyByQ3JhoO9rcFXKBSByQ0RUX3By1JEREQkK0xuiIiISFaY3BAREZGsMLkhIiIiWWFyQ0RERLLC5IaIiIhkhckNERERyQqTGyIiIpIVJjdEREQkK0xuiIiISFaY3BAREZGsMLkhIiIiWWFyQ0RERLLC5IaIiIhkhckNERERyQqTGyIiIpIVJjdEREQkKza1WVloaCjs7Oxgb28PAJg4cSK6du2KlJQUREdHo6SkBL6+vpg/fz7c3d0BwGgZERERUWW1fubm66+/xpYtW7BlyxZ07doVGo0GkyZNQnR0NBITExESEoIFCxYAgNEyIiIiIn0sflkqNTUV9vb2CAkJAQBERkZi+/btJsuIiIiI9KnVy1LAg0tRQgi0b98eEyZMQEZGBho3bqwtd3Nzg0ajQX5+vtEyFxeX2m46ERER1QG1mtysWbMGSqUS9+/fx5w5czBz5kz06tWrxut1d29U43XQ483T09EisUREVPtqNblRKpUAADs7O0RFRWH06NEYMmQIbty4oZ0nNzcXCoUCLi4uUCqVBsvMkZNzGxqNqJ6VoMeWsSQkO7uoxmKJiKh2KBRWkk5Y1NqYm7t376Ko6MGXhBAC27Ztg7+/PwICAlBcXIwjR44AANavX4/evXsDgNEykidHJwd4ejrq/HN0crB004iIqI6otTM3OTk5GDduHNRqNTQaDVq2bIkZM2ZAoVBg3rx5mDFjRoXbvQEYLSN5crC3RdTkNTrT1857C0UotkCLiIiorqm15KZp06bYvHmz3rLg4GDEx8ebXUZERERUmcVvBSciIiKqTkxuiIiISFaY3BAREZGsMLkhIiIiWWFyQ0RERLLC5IaIiIhkhckNERERyQqTGyIiIpIVJjdEREQkK0xuiIiISFaY3BAREZGsMLkhIiIiWWFyQ0RERLLC5IaIiIhkhckNERERyQqTGyIiIpIVJjdEREQkK0xuiIiISFaY3BAREZGsMLkhIiIiWWFyQ0RERLJiY+kGkPw4OjnAwd5WZ3pxSSmKCost0KKaVd/Wl4joccfkhqqdg70toiav0Zm+dt5bKIL8vuzr2/oSET3ueFmKiIiIZIXJDREREckKkxsiIiKSFSY3REREJCuSk5vk5GSUlZXpTC8rK0NycnK1NoqIiIioqiQnN0OGDEFBQYHO9KKiIgwZMqRaG0VERERUVZKTGyEErKysdKbn5+fjiSeeMKvSuLg4+Pn54dy5cwCAlJQU9O/fH2FhYRg6dChycnK08xorIyIiIqrM5HNuRo0aBQCwsrLCpEmTYGv7fw8r02g0OH/+PNq1aye5wtOnTyMlJQW+vr7aZUyaNAmff/45QkJC8M0332DBggX4/PPPjZYRERER6WPyzI2rqytcXV0hhICTk5P2b1dXV/j4+CAyMhLz58+XVNn9+/cxc+ZMxMTEaKelpqbC3t4eISEhAIDIyEhs377dZBkRERGRPibP3JSfJfH19cXQoUPRoEGDKlf21VdfoX///mjSpIl2WkZGBho3bqz9283NDRqNBvn5+UbLXFxcJNfr7t6oym2m6uXp6chYIiKqUZJfvzB27NhHquj48eNITU3FxIkTH2k5VZGTcxsajaj1eusrY1/o2dlFjCUioipRKKwknbCQnNzk5+fjyy+/xKFDh5CTkwONRlOh/NixY0bjk5OTcfHiRfTo0QMAcPPmTQwbNgyDBw/GjRs3tPPl5uZCoVDAxcUFSqXSYBkRERGRPpKTm2nTpuHMmTMYOHAgvLy89N45ZczIkSMxcuRI7d+hoaFYtmwZnn76afz73//GkSNHEBISgvXr16N3794AgICAABQXF+stIyIiItJHcnLz559/YuXKlQgMDKzWBigUCsybNw8zZsxASUkJfH19tQOUjZURERER6SM5uXF3d3+kwcSV7d69W/v/4OBgxMfH653PWBkRERFRZZIf4vfhhx/i66+/xp07d2qyPURERESPRPKZm6VLl+L69evo0qULGjduDBubiqE8u0JERESPA8nJTVhYWE22g4iIiKha1NpzboiIiIhqg+QxN0RERER1geQzN+3atTP6bBtTD/EjIiIiqg2Sk5vo6OgKf5eVlSEtLQ07duzQvjmciIiIyNIkJzcRERF6p7dq1QqHDh3C4MGDq61RRERERFUlObkxpFOnTpg7d251tIWoXnJ0coCDva3O9OKSUhQVFlugRUREddsjJzdbt26Fq6trdbSFqF5ysLdF1OQ1OtPXznsLRWByQ0RkLsnJTb9+/XSm3bp1CwUFBYiJianONhERERFVWZUf4mdlZQU3Nzc899xzaNmyZbU3jIiIiKgq+BA/IiIikhWzx9z8+eefuHjxIqysrPD000+jY8eONdEuIiIioiqRnNxkZmbi/fffx+nTp+Hl5QUAyMrKQkBAAOLi4uDt7V1jjaTaxbt3iIioLpOc3MyePRvW1tbYsWMHmjZtCgC4du0aJk2ahDlz5uDrr7+usUZS7eLdO0REVJdJfrfUwYMHER0drU1sAKBp06aYNm0aDh48WCONIyIiIjKXWS/O1PduKWPvmyIiIiKqbZKTm86dO2PWrFnIyMjQTrtx4wbmzp2Lzp0710jjiIiIiMwleczN9OnTMXr0aPTs2bPCgGKVSoXp06fXWAOJiIiIzCE5uVEqldi0aRP++OMPXLp0CQDQsmVLdOnSpcYaR0RERGQuk5el9u3bh9DQUNy+fRtWVlZ4/vnnMXjwYAwePBht2rRBaGgoBxQTERHRY8NkcrNmzRoMGzYMjRo10ilzdHTE8OHDsWrVqhppHBEREZG5TCY3Z8+eNTpguFOnTvjrr7+qtVFEREREVWUyucnNzYVCYXg2Kysr5OfnV2ujiIiIiKrKZHLj4+ODs2fPGiw/e/YsX71AREREjw2TyU23bt3w1VdfobhY97H79+7dw9dff41u3brVSOOIiIiIzGXyVvDRo0cjMTERYWFheOutt9CiRQsAwKVLl7B69WoIITBq1KgabygRERGRFCaTG3d3d6xfvx4xMTH48ssvIYQA8GCszQsvvIDo6Gh4eHhIqmzMmDG4fv06FAoFGjRogE8//RT+/v5IT0/HlClTkJ+fDxcXF8TGxqJ58+YAYLSMiIiIqDJJD/Hz9fXFt99+i4KCAly5cgUA0KxZMzg7O5tVWWxsLBwdHQEAu3btwieffIJNmzZhxowZiIqKwoABA7BlyxZER0fjp59+AgCjZURERESVmfXiTGdnZ7Rt2xZt27Y1O7EBoE1sAGgfCpiTk4O0tDSEh4cDAMLDw5GWlobc3FyjZURERET6SH79QnWZNm0aDh48CCEEvvvuO2RkZMDb2xvW1tYAAGtra3h5eSEjIwNCCINlbm5ukut0d9d9ACFVjaeno+mZaii+vsVWRzwRUX1U68nNnDlzAACbN2/GvHnzMH78+BqvMyfnNjQaUeP1yIWxL9Ts7KIaja9vsdURT0RUXygUVpJOWJh1Wao6vfLKKzh8+DB8fHyQmZkJtVoNAFCr1cjKyoJSqYRSqTRYRkRERKRPrSU3d+7cQUZGhvbv3bt3w9nZGe7u7vD390dCQgIAICEhAf7+/nBzczNaRkRERKRPrV2WunfvHsaPH4979+5BoVDA2dkZy5Ytg5WVFWJiYjBlyhR88803cHJyQmxsrDbOWBlRfefo5AAHe1ud6cUlpSgq1H3wJhFRfVBryY2Hhwf+/e9/6y1r2bIlfv31V7PLiOo7B3tbRE1eozN97by3UAQmN0RUP1lszA0RERFRTWByQ0RERLLC5IaIiIhkhckNERERyQqTGyIiIpIVJjdEREQkK0xuiIiISFaY3BAREZGsMLkhIiIiWWFyQ0RERLLC5IaIiIhkhckNERERyQqTGyIiIpIVJjdEREQkK0xuiIiISFZsLN0AqhmOTg5wsLfVmV5cUoqiwmILtIiIiKh2MLmRKQd7W0RNXqMzfe28t1AEJjdERCRfvCxFREREssLkhoiIiGSFyQ0RERHJCpMbIiIikhUmN0RERCQrTG6IiIhIVpjcEBERkawwuSEiIiJZYXJDREREssLkhoiIiGSFyQ0RERHJSq0lN3l5eRgxYgTCwsLQr18/jB07Frm5uQCAlJQU9O/fH2FhYRg6dChycnK0ccbKiIiIiCqrteTGysoKw4cPR2JiIuLj49G0aVMsWLAAGo0GkyZNQnR0NBITExESEoIFCxYAgNEyIiIiIn1qLblxcXFBx44dtX8HBQXhxo0bSE1Nhb29PUJCQgAAkZGR2L59OwAYLSMiIiLSxyJjbjQaDdatW4fQ0FBkZGSgcePG2jI3NzdoNBrk5+cbLSMiIiLSx8YSlc6aNQsNGjTAoEGDsHPnzhqvz929UY3XUZd4ejpaJNaSddfFWEvXTURUV9V6chMbG4srV65g2bJlUCgUUCqVuHHjhrY8NzcXCoUCLi4uRsvMkZNzGxqNqLZ1qAuMfbFlZxfVWKwl666LsZaum4ioLlEorCSdsKjVy1L/7//9P6SmpmLJkiWws7MDAAQEBKC4uBhHjhwBAKxfvx69e/c2WUZERESkT62duTl//jyWL1+O5s2bIzIyEgDQpEkTLFmyBPPmzcOMGTNQUlICX19fzJ8/HwCgUCgMlhERERHpU2vJzTPPPIOzZ8/qLQsODkZ8fLzZZXLn6OQAB3tbnenFJaUoKiy2QItITti/iEiuLDKgmKRxsLdF1OQ1OtPXznsLReCXDz0a9i8ikiu+foGIiIhkhckNERERyQqTGyIiIpIVJjdEREQkK0xuiIiISFaY3BAREZGsMLkhIiIiWWFyQ0RERLLC5IaIiIhkhckNERERyQpfv0BEZuN7qYjoccbkhojMxvdSEdHjjJeliIiISFaY3BAREZGsMLkhIiIiWWFyQ0RERLLC5IaIiIhkhckNERERyQqTGyIiIpIVJjdEREQkK0xuiIiISFaY3BAREZGsMLkhIiIiWeG7pYioVhl66SbAF28SUfVgckNEtcrQSzcBvniTiKoHL0sRERGRrDC5ISIiIllhckNERESyUmvJTWxsLEJDQ+Hn54dz585pp6enp+PNN99EWFgY3nzzTVy+fFlSGREREZE+tZbc9OjRA2vWrIGvr2+F6TNmzEBUVBQSExMRFRWF6OhoSWVERERE+tRachMSEgKlUllhWk5ODtLS0hAeHg4ACA8PR1paGnJzc42WERERERli0VvBMzIy4O3tDWtrawCAtbU1vLy8kJGRASGEwTI3Nzez6nF3b1Ttbbc0T0/HOhdrybrrYqwl666r60xEBNST59zk5NyGRiMs3QyzGfuQz84ueixjLVl3XYy1ZN2PY6yUeCKqvxQKK0knLCya3CiVSmRmZkKtVsPa2hpqtRpZWVlQKpUQQhgsIyIiIjLEoreCu7u7w9/fHwkJCQCAhIQE+Pv7w83NzWgZEdVfjk4O8PR01Pnn6ORg6aYR0WOi1s7czJ49Gzt27MCtW7fw7rvvwsXFBVu3bkVMTAymTJmCb775Bk5OToiNjdXGGCsjovrJ0Osb+OoGIipXa8nN9OnTMX36dJ3pLVu2xK+//qo3xlgZERERkT58QjERERHJCpMbIiIikhUmN0RERCQrTG6IiIhIVpjcEBERkazUiycUE1mCpqzU4NN4y+6XIK/gfi23iIiofmByU8McnRzgYG+rM724pBRFhXwmh5wpbGxxdN5wvWXtJ38HgMkNEVFNYHJTw/jAMSIiotrFMTdEREQkK0xuiIiISFaY3BAREZGsMLkhIiIiWeGAYiKqN3j3IlH9wOSGiOoN3r1IVD/wshQRERHJCpMbIiIikhUmN0RERCQrTG6IiIhIVjigmGTP0Ass+fJKIiJ5YnJDsmfoBZZ8eSURkTwxuSEygmd9iIjqHiY3REbwrA+V4wMAieoOJjdERBI8ygMAmRgR1S4mN0RENYxPRiaqXUxuqE7g2Beqzyx15odnnKiuYnJDdUJ9HPvChI7KWerMD884UV3F5MYEQ79cAP56MRe/rM3zKAndo2xr7id54dkXqo+Y3Jhg6JcLwF8v5qqPZ18s5VG2NfeTvPDsC9VHdSK5SU9Px5QpU5Cfnw8XFxfExsaiefPmlm5WvcNf9EQkFc8YkSXVieRmxowZiIqKwoABA7BlyxZER0fjp59+snSz6h3+oqeaZqkEmol79bPkGSMmVvTYJzc5OTlIS0vDypUrAQDh4eGYNWsWcnNz4ebmJmkZjRrZw15PRy8pKcXt2yUm4z1cGxosUyisqhxfF2PtnNwlLfNR4utDrCXrrslYY0lCQVGpyXoVNrY4texjneltRsVCoTAe/yh1P0q9QM0fj5as25LrXFUO9rb44PPNOtO/nvoK7ihMf+bT40tq37ESQogabssjSU1Nxccff4ytW7dqp/Xt2xfz589H69atLdgyIiIiehwpLN0AIiIiour02Cc3SqUSmZmZUKvVAAC1Wo2srCwolUoLt4yIiIgeR499cuPu7g5/f38kJCQAABISEuDv7y95vA0RERHVL4/9mBsAuHjxIqZMmYLCwkI4OTkhNjYWLVq0sHSziIiI6DFUJ5IbIiIiIqke+8tSREREROZgckNERESywuSGiIiIZIXJDREREcnKY//6heoSGxuLxMRE/P3334iPj4dKpQIg7aWc+mLz8vIwefJkXL16FXZ2dmjWrBlmzpypc4u6oXrLxcXFYfHixXrLDMWWlJRg7ty5+PPPP2Fvb4+goCDMmjVLUuyePXvw1VdfQQgBIQTGjh2Ll156qUKssXVLSUlBdHQ0SkpK4Ovri/nz58Pd3V1SfEFBAaKjo5GdnQ0bGxu0adMGM2bMgIODg6S6y02dOhUbN27EsWPH0LBhQ0mx+fn5mDlzJk6fPg0bGxv06dMHY8eOlRT7n//8B6tWrYJCoYC1tTU++eQThISEVFjnMWPG4Pr161AoFGjQoAE+/fRT+Pv7S37pq754Hx8fSX3MUN1S+pihWCl9zFCslD5mqG1S+pe+WFtbW5N9y1Td5Qz1L2OxpvqXsVgp/QsAQkNDYWdnB3t7ewDAxIkT0bVrV0nbTF9skyZNJG0zQ/VK2V6GYqVsL0OxUreXoT4s5ZjUFzthwgRJx6OpY8fY8Wgo1tQyr1+/jvfff1/7d1FREW7fvo2kpCRJ62ssXsrxvHfvXnz11VcoKyuDs7MzPv/8czRq1EjS9tIX27RpU0mfQUaJeiI5OVncuHFDdO/eXZw9e1Y7ffDgwWLz5s1CCCE2b94sBg8eLCk2Ly9PHDp0SDvPF198IaZOnSq5XiGESE1NFcOGDdNbZix21qxZYs6cOUKj0QghhMjOzpYUq9FoREhIiPbvM2fOiKCgIKFWqyvEGlo3tVotevbsKZKTk4UQQixZskRMmTJFp25D8deuXROnT58WQgihVqvF+PHjRVxcnKTYcr/99puYOnWqUKlU4vbt25Jj33vvPbFy5UptWVZWlqTY3Nxc0a5dO+023rVrl+jTp4/OOhcWFmr/v3PnTvHKK68IIaT1L0PxUvuYobqFMN3HDMVK6WP6YqX2MX1tk9q/9MVK6VvG4ssZ61/GYk31L0OxUvuXEELvPpS6zfTFSt1mhvqOEKa3l6FYKdtLX6w528tQH5ZyTOqLlXo8Gjt2TB2PhmKlHI8Pmz17tvjss88kr6+heCnHc35+vnjuuefEpUuXtHUMHTpU0vYyFFuVda6s3lyWCgkJ0XmqcflLOcPDwwE8eClnWloacnNzTca6uLigY8eO2r+DgoJw48YNSfUCwP379zFz5kzExMSY1eY7d+5g8+bNGD9+PKysHrxAzMPDQ3K9CoUCRUVFAB5k515eXlAoKnYDQ+uWmpoKe3t77a+kyMhIbN++XacOQ/FNmjRBq1attO1o27atzjYztl3z8vIQFxeHqVOn6tRpLPby5cs4d+4c3n77bW2Zp6enpFjx//9auXPnjnab+fj46NTt6Ph/L2y8ffs2rKysJPcvQ/FS+5i+WEBaH9MXK7WPGapXSh/T1zap/UtfrJS+ZSweMN2/DMVK6V+GYqX2L0OkbjN9zNlm+kjZXvpI3V76SN1ehvqwlGPSUKyU49HYsWPqeDQUK/V4LHf//n3Ex8fjtddeM+szSF88YPp4vnLlCjw8PPDUU08BALp164YDBw5Ao9GY3F6GYm/evGnWOutTby5L6ZORkQFvb29YW1sDAKytreHl5YWMjAyznoCs0Wiwbt06hIaGSo756quv0L9/fzRp0sSsNl+7dg0uLi6Ii4vD4cOH0bBhQ4wfP17vadnKrKyssGjRIowZMwYNGjTAnTt3sGLFCqMxD69bRkYGGjdurC1zc3ODLovJ5gAAC8pJREFURqPRnu40Ff+w4uJibNiwARMmTJBUNwDMnDkTH3zwQYUvVSmxFy5cgLe3N6ZNm4YzZ87Aw8MDkydPxjPPPGMy1s3NDTNnzkRERAScnJyg0Wjw888/642bNm0aDh48CCEEvvvuO7P7V+V4Y9tCSqzUPlY51pw+VjlWah/T1zap/cvUepnqW4bipfQvfbFS+5e+WHP6F/Dg0owQAu3bt8eECRPMOiYrxzo5OUneZvpipR6PlWPNOR4rx0rdXob6sIODg8ljUkr/N3Q8Gos11W8NxTZq1Misz/zdu3fD29sbrVu3RmpqqtnfcQ/HAzB5PD/11FO4desWTp48ibZt2yI+Ph4AKtRhaHsZir169WqVv+e0zDrPIwMPnw48deqU6Nu3b4XyPn36iNTUVJOxD4uJiRGjR4/We+pdX+yxY8fEkCFDtKfbjJ32rVyempoqVCqV+O9//yuEECIlJUV06tRJFBUVmYwtLS0Vb7/9tjhy5IgQQogjR46Ibt26GTz9Xnndtm/fLkaMGFGhvG3btiIvL09SfLnS0lIxatQoMXPmTINxlWO3bt0qJk2apC0zdtmgcmxiYqLw9/fXnrpPTEwUPXr0kBRbVFQk3nzzTXHx4kUhhBBbt24V4eHh2n2nz6ZNm8Tw4cPN7l+V4w21SUqsuX3s4Vhz+9jDsVL6mKG2SelfptbLVN8yFC+lfxmKldK/DMWa079u3LghhBCipKREREdHi48++kjyMakvVuo20xcr9XjUFyv1eNQXK3V7GerDhw8fNnlMSun/ho5HY/WaOh4NxSYlJZl1PA4fPlysWrVKCGH+d1zleKnfGQcPHhSRkZEiIiJCfPnllyIkJEScOXPG5PYyFGvuOutTby5L6VMdL+WMjY3FlStXsGjRIp1T74YkJyfj4sWL6NGjB0JDQ3Hz5k0MGzYMBw4ckNRmGxsb7WnGwMBAuLq6Ij093WTsmTNnkJWVhfbt2wMA2rdvjyeeeAIXL16UtG5KpbLCacXc3FwoFAqDZ230bRu1Wo2JEyfC2dkZ06dPN9jWyrFJSUk4dOgQQkNDtdl/eHg4Lly4IKndSqVSm/W/9NJLyM7O1ntqtnLsgQMH4OjoqH3dR9++fXH16lXk5eUZbPsrr7yCw4cPw8fHp0r9qzy+vA5z+lh57KFDh8zuY+Wx3t7eZvex8tjTp0+b7GOG+v+VK1dM9i9jx46UvmUoPi4uzmT/MtZuU/3LUOzvv/8uuX+V9xs7OztERUXh2LFjko9JfbGAtONRX6zU49FQm6Ucj/pipR6Phj4nHRwcTB6Tpj5jjR2PhmKPHDli8ng01mapx2NmZiaSk5PRr18/7TLN+QyqHC/1O6NLly5Yt24dNm7ciEGDBqG4uBhPPvmkye1lKLZx48ZV/p7TkpwGyUTljHnQoEEVBlsNGjRIcuzChQvFoEGDxN27d82uV2qZvvJ3331X7N+/XwghxKVLl8Rzzz0nCgoKTMZmZWWJdu3aaX/1XLhwQXTo0EHvmRd966ZWq0WPHj0kDfg0FD9x4kQxYcIEUVZWZnB9pWxXQ78U9cVqNBoRHh4uzp07J4QQIikpSXTt2lXn156+2FOnTokuXbqIW7duCSGE+PPPP0WXLl0qxN6+fVv7K1OIB4MsX3jhBaHRaCT1L2PxpraFsdiH6etjxmJN9TFDsZmZmZL7WOW2mdO/9MVK6VuG4v+/9u42pKn2jwP4d2ZbkmCDGmgNjUViomI4SpItTFaZk6Ks6AGiJHGhGBFTp01jUinFgmKC1CgIRKQH14R8U1BvLBBDCrFeWQiWmIQZztz5v7hxuNrZjmP37n/z+3m5ne8517m4du3HeeD6Xagrg4uzUsdXoKyU8SUIgvDjxw/fA9xer1e4ceOGYDKZJPVZsGyoPhPLSukvsayU/hLLSu0vQRCfJ6X8JsWyUuYmKfOz2LgTy0qd8x0Oh1BdXe332VL+437PS/3PWHggfH5+XqirqxNsNpsgCNLmcrHsUv7nAlk2a0vZbDb09fVhYmICSqUSa9asgdvtlrQoZ6Cs3W5HSUkJ0tLSfK9ObtiwAbdv35Z03MUKCwvR3t7+x2uBYtlPnz6hvr4eU1NTiI+PR01NDfR6vaRsT08POjo6fA9pVVdXo6ioyC/74cMH0XMbGBiA1Wr1e+309we9xPJlZWWoqKjA5s2bfRX81q1bYbVaJR17sfT09D9ePQ2WHRoaQnNzMzweDxISEmCxWJCdnS0p63Q60dXVhZUrV0Iul6O2ttbv3u/ExARMJhN+/vyJuLg4JCUlwWw2IzMzU9L4EsvL5fKQfRHs2IsFGmPBsqHGWLCslDEm1jYp4ytQdmxsLOTYCnXsxQKNr2DZUOMrWDbU+AL+eR6jqqoK8/Pz8Hq90Gg0aGhogEqlCtlnYtn379+H7LNgxw3VX8GyoforWFZKfy3sI9AYlvKbDJRNSUmRNDdJmZ/Fxp1YVso+AWD37t2wWCzQ6XS+z5ay8HSgvJTfs8ViwcDAAObm5rBjxw7U19djdHRUUn8FyioUCsnnLGbZFDdERES0PCzrZ26IiIgo9rC4ISIiopjC4oaIiIhiCosbIiIiiiksboiIiCimsLghIiKimMLihogi5uTJk7h8+XLE9/v582ekp6djaGhIdJva2lpUVFRE/NjBSGkXEUXfsl44k4j+DsnJyXj16hWUSuV/3RQi+guwuCGi/3srVqzAunXr/utmENFfgreliCiifv36BZvNBq1WC61Wi2vXrsHr9fq+LywsxK1bt1BbW4vc3Fzo9Xr09vbi+/fvOH/+PHJzc2EwGPwWFQzn9o8gCOjo6EBRURGys7NhNBrx5MkT3/dHjx7F1atX/TLT09PIzs5GX18fAMDj8aCtrQ06nQ45OTk4ePAgXr58GW7XEFGUsLghoohyuVwQBAGdnZ1obm5GV1cX7t2757fN/fv3kZWVhUePHmHv3r0wm824cOEC9Ho9Hj9+jLy8PFy8eBGzs7Nht8Nut6O7uxuXLl2C2+3G2bNnYbVa8eLFCwCA0WiE2+32K7yePXsGhUKBnTt3AgDq6urw5s0bXL9+HU+fPsWBAwdQWVmJ4eHhsNtFRP8+FjdEFFEqlQoNDQ3QaDQoLi7GmTNn4HQ6/bYpKCjA8ePHkZaWhqqqKng8HqSmpmL//v1ITU2FyWTC5OQkRkZGwmrDzMwMnE4nWlpaoNPpoFarYTQaUVZWhgcPHgAAiouL8e3bN/T39/tyLpcLe/bsgVwux+joKNxuN+x2O7RaLdRqNU6cOAGdTofOzs7wO4iI/nV85oaIIionJ8e3gjAA5Obm4ubNm5ienkZiYiKAf1aRXrB69WokJCT4rZC8sKr15ORkWG34+PEjZmdnUV5e7teWubk5rF+/HgCgVCpRUFCAnp4e5OfnY3x8HP39/Th37hwA4N27dxAEAfv27fPbt8fjwfbt28NqFxFFB4sbIoq6+Hj/qUcmk/l9tlCQLL5ltBSCIAAAHA4HUlJSRI9dWlqKxsZGNDU1obe3F8nJycjLy/PtQyaTobu7+4/2rlq1Kqx2EVF0sLghooh6+/atrzAAgMHBQahUKt9Vm2jQaDSQy+UYGxtDfn6+6Ha7du1CY2Mjnj9/DpfLhZKSEl+7MzIyIAgCvn79yis1RH8ZFjdEFFFfvnxBS0sLjh07hpGREdy5cweVlZVRbUNiYiJOnz6N1tZWCIIArVaLmZkZDA4OIi4uDkeOHAEAKBQKGAwGOBwODA8Po7W11bePjRs3wmg0oq6uDmazGZmZmZiamsLr16+hVqthMBiiek5EJB2LGyKKKKPRCK/Xi8OHD0Mmk+HQoUM4depU1NtRU1ODtWvX4u7du2hqakJiYiIyMjJQXl7ut11paSkePnyILVu2YNOmTX7fXblyBe3t7Whra8P4+DiSkpKQlZWFbdu2RfNUiGiJZMLCzWkiIiKiGMBXwYmIiCimsLghIiKimMLihoiIiGIKixsiIiKKKSxuiIiIKKawuCEiIqKYwuKGiIiIYgqLGyIiIoop/wPmxBHbv9UD5AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stroke_cases = Counter(df['binned_glucose'][df['stroke']==1])\n",
        "total_cases = Counter(df['binned_glucose'])\n",
        "print(stroke_cases.items())\n",
        "print(total_cases.items())\n",
        "print(np.array([item[1] for item in sorted(stroke_cases.items())]).shape)\n",
        "stroke_pct = 100*np.array([item[1] for item in sorted(stroke_cases.items())])/np.array([item[1] for item in sorted(total_cases.items())])\n",
        "labels = [item[0] for item in sorted(total_cases.items())]\n",
        "plt.style.use('seaborn')\n",
        "plt.figure(figsize=(9,5))\n",
        "plt.ylim(0,30)\n",
        "plt.title('Stroke risk vs Glucose level (All age groups)',fontsize=20)\n",
        "plt.ylabel('% of strokes',fontsize=15)\n",
        "plt.xlabel('Average glucose level',fontsize=15)\n",
        "plt.plot(labels[:-1],stroke_pct[:-1],'b')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dznhwiu__nZ8",
        "outputId": "d10ac98d-7b74-46ce-ff1e-37ad44422fcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_items([(220, 10), (200, 12), (100, 20), (170, 4), (180, 7), (70, 36), (90, 24), (50, 8), (80, 25), (120, 4), (210, 16), (160, 6), (190, 16), (230, 7), (250, 3), (60, 19), (140, 3), (240, 6), (110, 15), (130, 5), (260, 1), (150, 1), (270, 1)])\n",
            "dict_items([(90, 729), (80, 872), (110, 354), (60, 534), (160, 47), (210, 110), (70, 776), (240, 30), (200, 119), (50, 220), (120, 198), (190, 96), (100, 506), (140, 90), (130, 100), (220, 86), (230, 64), (180, 53), (250, 16), (170, 39), (150, 62), (260, 8), (270, 1)])\n",
            "(23,)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc8f276b850>]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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RcHNzA/BP947wywsAFixYgCVLlmDixIno378/BgwYgAEDBsg0P4ewa0FYG/G8hoYG9O3bV2J7hw4dJPqsW6K0tBQFBQVSz2FjYwMbGxsAjUlSYWEhPD09pRbrdenSBZcvX0ZaWhrs7e1lfj2ePHkCAAgICGgyxlctABe+F6dNm/bSY/bs2ROdOnXC+fPnsXr1anA4HFH3DpfLRY8ePcSOuWLFiiaPKev7W6iyshL19fUwMzOT+riwOLZ9+/bw8fERbQ8KCsI333yDU6dOYeXKla02si0yMhK7d+/Go0ePJOrQnv8yFSYrDg4OYvtwOBx4eHiIvQ6tcV8Qns/e3l5su6GhIbp37y7240FIWo2W8L4n/FHxvObue7JydHSU+GwIf3QI/wY/Pz94enri3Llz8Pf3R0BAAHx8fODr69vkjzILCwvU1dWhqqoKhoaGLY5PXVGCoqJsbW0xefJkUV/zkydPcPDgQezevRuffPIJoqKiYGpq2uwxdHR0JFpJhDcfaTc2YV9wTU2N2PbExETRr86YmBiJm39VVRUAYN68eRg4cKDUWGSpPwEg04fQ3Nxc9FqcPn0aYWFhCAsLg5GREWbPno2FCxeK1RS8LP6m9OnTB3Z2djh37hyWLVsGoPHXtaamJkaNGiXab9iwYThw4ADCwsIQHR0tKmrjcrlYs2YN+vXr1+Q5hEnA879ShTQ1NfH777+LbRPG0RqExZ3a2trN7id8zxgYGEh9XJgUCt83sr4eVVVV4HA42LVrV5M1T7IkrM8Tvhc3bNgAS0tLqfsI6xKAxmR369atuH37Njw9PREXF4fS0lK89957Esf87LPP4OzsLPWYL/ssNhVnU19M4eHh4PP58PPzQ1ZWlthj3t7euHz5Ms6cOdPk6J9XER4ejtWrV8POzg6LFi1Ct27doKenh7KyMixevFhsX+E1lvZ5NjY2Fvv/1rgv1NTUQFtbW+r8MC+eT0jaPaS6uhra2tpS6/qauu+9CmmfDeG1ra+vB9B4Pw4LC8OBAwdw9OhR7Nu3D/v27YOuri7effddrFixQiI+4d9YWVlJCQpRXV27dsXnn3+OkpISnDhxAnfu3GmyuK45wg+StNE6wg/oix+E2tparFy5Eunp6Thw4AD27NkjNmOlcH9TU1OFTRbXrl07LFmyBEuWLEFGRgYuX76MvXv3IjQ0FBoaGmLFlS+LvykcDgdBQUEICwvDo0eP4ODggMuXL8PHx0eiS8bDwwMbN25EfX09bt++jbNnz+LgwYOYO3cuTp8+LfELUMjGxgYdOnTA3bt3UV5eDhMTE7Hzv/h6NtVCJIva2lqx/zczMwOHw3lpt2Fz7xlA+vtGltfD0NAQjDFwudxWm4RPGEOnTp2aLJR+XnBwMLZu3Soqhj5z5gw0NDRE3T/PH9Pa2rrV3t/CY0rrrhMWxwKNXTlNdeccOnSoVRKUsLAwaGpqYteuXaLWBOCfVofnCb9ApY1cevFvaY37go6ODhoaGiAQCCSS2Ffp6jQwMEB9fT3q6uqa/NEmSwLQ1IgtaclNRUUFgMZ71fNxzJkzB3PmzBG1ku3btw979uxBTU0NvvnmG6nHeFOHlVMNioqoq6tDaGgovvvuu2b3E/6ifPHLRlbCJs5Hjx5JPCZskhXWHQj16dMHs2fPxmeffQZHR0esW7dONCQa+Ke7qKl6AXlP2ezg4IBZs2bh0KFD0NbWRlRUlNjjL4u/OcIvqgsXLkjt3nmRtrY2+vbtizVr1mDFihWoq6uTWtPzvIkTJ4LH42Hnzp0yxfQyWlpaEkOEAckmbB0dHVhZWSEjI0Ni/6dPnyIiIgIpKSkwMTGBlZUVnjx5ItbcLyR833Tt2lXiseZeD+F7Udr7pry8vEXTvjd3zKqqKokvmO7du8PJyQkXLlwQde/07dsXtra2Yvs0dcy6uroW1QQZGRlBW1sbpaWlEo/98ccfyMvLg6enJzZt2iT1X+fOnZGQkCB67V9HdnY2bG1txZITABLDx4F/Wvxe7HpjjEkMSW6N+4KNjQ0YYxLnq66uRkpKykufL9TcfU/Y1Sh8/wpba2T5DAmlpaWBMSa2TTgUuan6MltbW7z77rsIDw+HlZWVxH0LaHyNdHR03sjWE4ASFJWho6ODy5cv47fffmtynoOioiKcOnUKurq66NOnD4B/Cq9knYvB398f2traOHLkiMQHUFiU92Kxl5Cenh7Wr18PgUCATz75RJQk9enTBxYWFoiOjhZ92IUiIyPh5+fXqnM3fPHFFxgzZozE32xgYAANDY0m55RoKv7muLi4wNHREdHR0bhw4QL09fVFhZFA4xDN0aNHY+PGjRLPFf7qaSoeoZkzZ8LR0RE7duxocqZQHo+HjRs34unTpy9tRbGyskJhYaFY/359fT0OHDggsW9AQAAqKiokCkV37NiBzz77TPQFKixkPX36tNh+6enpiIuLg5ubG9q3b/9Kr4ew4Hb37t1iNTjs7+nfBw0a9Mpf/sJj7t+/X+L6rlu3Dj4+PqJiXqHg4GBkZmbiyJEjKC0tlUhA/f39oaOjg5MnT4qKmoV2796N/v374/r1668UJ9CYWGdkZEh8sQk/h++//z4CAwOl/hO2ALZGsaylpSVKSkrEWgFyc3OxZ88eAOI/hoTF5cJCYqETJ05IJBGtcV9o6ny//fbbK/1IEw4WePEzUFdXh6NHj0JXV1dUmybsAnyxvuXMmTMoKCiQevySkhJcvHhRbJsw4RDeq7ds2SJ1jR0tLS3o6upK3CcEAgGysrKabH19E1AXjwr5/vvvMXv2bCxfvlw00ZC5uTmqqqrw+PFjnDp1CqWlpfjyyy9hbm4OoLEgjMPh4OTJkzAzM4OTk1OzBbRWVlZYunQp1q1bhxkzZmD06NHQ1tZGbGwsIiMjMXz4cAwePLjJ57u4uIie/9133+E///kPdHR0sHbtWixduhQzZszArFmzYGVlhcTERBw8eBBdunTBkCFDWu118vHxwaFDhzBp0iSMHTsWlpaWKCsrw/Hjx8Hj8TB16tRXiv9lRo0ahV9++QVpaWkICAgQ+zVjY2MDOzs7bN26FU+fPkW/fv2gq6uLtLQ07NmzB1ZWVk0mfEIGBgYICwvDwoULsXr1ahw5cgQBAQGwtbUVXfuzZ8+ioKAAnp6eWLduXbPHGzp0KG7cuIFFixaJ5l04duwYunXrhuTkZLF9P/zwQ1y6dAlr1qxBeno67O3tcfPmTRw6dAgjRowQFdAuXLgQFy9exJo1a/Dw4UN0794dubm52L9/P7S0tEQTnr3K6xEQEIBhw4bh/PnzmDVrFsaMGYOGhgacPn0acXFxWLBgwSs3bffo0UM04m3y5MmYNGkStLS08Mcff4jmF3rxhj9q1Chs2LABmzZtgq6ursTIN+HU499++y1CQkIwffp0GBsbIz4+HseOHUOfPn1eOipMGh8fH+zduxcPHz4UFeTm5uYiJiYG9vb2zX4Ox44diw0bNuD48eOiuWZaKigoCDt37sTixYsRHByMZ8+e4ffff8fnn3+O7777DsnJydi/fz8GDx6MkSNHIjQ0FAcOHACHw4G7uztSUlIQGRmJgIAAsS/p1rgvTJo0Cbt378bmzZtRWlqKrl274vbt27h+/Tr69u0rc2I4dOhQDB48GOHh4eDxePD29kZVVRVOnz6N1NRUrFq1SlSw7OXlBXNzcxw7dgxmZmbo2rUrHj58iJMnT2LgwIGi+Wue5+Ligm+++Qa3b99Gt27d8PDhQ/z+++/o0KEDxowZA6Dxem/duhWTJk3CxIkTYWdnh+rqakRFRSE7OxtLly4VO2ZSUhJKS0vFuhvfOEob4EykKioqYps2bWLjx49nvXv3Zs7Ozqxnz54sMDCQrVmzhiUnJ0s856effmJeXl6sV69ebOfOnYyxxrkw3NzcmjzPqVOn2MSJE1mvXr2Yu7s7Cw4OZjt27GD19fVi+0mb00EgELAZM2YwLpfLzp8/L9oeHx/P5s6dy/r06cNcXFzY4MGD2ZdffsmKiope+ncL5x7Ytm2bxGMvzoPCGGNXrlxhc+bMYT4+PszFxYX17duXzZo1i12+fLnF8TclLS1NNNfBlStXJB7n8Xhsy5YtLDg4mHl6ejIXFxc2ZMgQtnr1avb06dOXHl+ooaGBRUREsHnz5rEBAwYwV1dX1qdPHxYUFMTWrFnDrl69KvEcafOg1NfXs59//pkNHTqUubm5sYCAALZt2zb29OlTiXlQGGMsNzeXrVy5UnTOIUOGsG3btknMv5Ofn89WrVrFBg4cyFxcXJi3tzdbvHixxHvyVV6P+vp6tmPHDhYcHMzc3d2Zh4cHGzduHDt06JBMr9mL86AIHTp0iI0fP555eHgwNze3Jt/fQpMmTWJcLpd99NFHTZ7r/PnzbOrUqczT05O5urqy4cOHsw0bNrCqqiqx/WSdB+XKlSuMy+Wy7du3i7Zt2rRJbO6M5gjncRHOX9LSeVCqq6vZV199xQYOHMh69erFJk6cyC5cuMAYYyw8PJz17duX9enTh8XHxzPGGEtNTWVz585lvXr1Yn379mXz589njx8/ZmvWrGFcLpddv35d7Pivc19gjLE7d+6wyZMnM3d3d+bj48OWLVvG8vLyRHOSCN9TwjlJmppTSPi+HDFiBHN1dWWenp5s6tSpUu8BycnJbNasWaxv377My8uLzZkzhz169Ih9/vnnUudBmTNnDktOTmazZ89mvXr1Yp6enuyDDz5gmZmZYsdNSEhgH374oeiz5unpyUJCQsTmbhLaunUr43K5Eve0NwmHsRfaFwkhhMidQCDA6NGjUVVVhaioqNdqBVEFixcvRlRUFCIjI6XWI7W2cePGISkpCbdu3WpyhJm8ZWdnIyAgAH5+fq1WQwY0vjcCAwNFdVG0Fg8hhBCF0dDQwIIFC5Cbm6s2q1Tn5+fjo48+wvr168W2FxYW4urVqzA3N29yiYqWePDgARYsWIDdu3eLbX/48CGSk5Ph4uKitOREniIiIpCRkYGlS5e+sckJoMAalJqaGqxcuRJFRUXg8XhYuHAhevTogRUrVoDP58PKygrr1q1T+18RhBAiq1GjRuHo0aPYsGED3nrrrWZnHVYF1tbWKCgowLlz51BYWAhvb2+Ul5dj7969qK6uxvLly1v1C9XBwQFPnjxBdHQ0cnJy4Obmhvz8fFHC8tFHH7XauVRFUVER/u///g8DBw5sdsTgm0BhXTyRkZHIycnBvHnzkJOTgzlz5sDLywtvvfUWgoKCsGHDBtja2mLKlCmKCIcQQlRCcXExJk6cCCsrK+zdu1fqpGSqpLKyUjR3TF5eHjQ1NeHk5IQZM2Zg5MiRrX6+goIC/Pzzz4iOjsazZ8+gp6cHNzc3vP/++02u/q0ord3FI1yMMTMzE4cPHxYNhnhTKaUG5caNG9i8eTOys7Nx9uxZ6OjoICEhAWFhYQgNDVV0OIQQQghRMQpP1UNCQpCXl4etW7di9uzZoi4dCwuLJseYE0IIIeTNovAE5cCBA0hOTsby5cvFJiiSpSGnoYEPLa03t2CIEEIIeVMoLEFJTEyEhYUF7Ozs4OzsDD6fD0NDQ9TW1kJPTw/5+flNTgksVFIifS2Q1mBlZYyCggq5HZ+0Prpm6oeumXqh66V+1PGaWVlJX/hRYcOMb9y4gbCwMACNQ9Kqq6vh6+srmmI7KiqqyRUvCSGEEPJmUVgLSkhICFatWoUpU6agtrYWX3zxBdzc3PDpp5/i4MGDaN++PcaOHauocAghhBCiwhSWoAgXanvRrl27FBUCIYQQQtQEzSRLCCGEEJVDCQohhBBCVA4lKIQQQghROZSgEEIIIUTlUIJCCCGEEJVDCQohhBBCVA4lKIQQQghROZSgEEIIIUTlUIJCCCGEEJVDCQohhBBCVA4lKIQQQghROZSgEEIIIUTlUIJCCCGEEJVDCQohhBBCVA4lKIQQQghROZSgEEIIIUTlUIJCCCGEEJVDCQohhBBCVA4lKIQQQghROZSgEEIIIUTlUIJCCCGEEJVDCQohhBBCVA4lKIQQQghROZSgEEIIIUTlUIJCCCGEEJVDCQohhBBCVA4lKIQQQghROZSgEEIIIUTlUIJCCCGEEJVDCQohhBBCVA4lKIQQQsikn0oAACAASURBVAhROZSgEEIIIUTlUIJCCCGEEJVDCQohhBBCVA4lKIQQQghROZSgEEIIIUTlUIJCCCGEEJWjpciT/fjjj7h58yYaGhowf/58XLp0Cffv30e7du0AAHPnzsXgwYMVGRIhhBBCVJDCEpRr164hJSUFBw8eRElJCd555x34+Phg2bJlGDJkiKLCIIQQQogaUFiC0rdvX3h4eAAATExMUFNTAz6fr6jTE0IIIUSNcBhjTNEnPXjwIG7cuAFNTU0UFBSgvr4eFhYWWLNmDczNzZt8XkMDH1pamgqMlBBCCCHKoPAE5cKFC9i2bRvCwsKQmJiIdu3awdnZGdu3b0deXh6++OKLJp9bUFAht7isrIzlenzS+uiaqR+6ZuqFrpf6UcdrZmVlLHW7QkfxxMTEYOvWrdixYweMjY3Rv39/ODs7AwD8/f3x6NEjRYZDCCGEEBWlsASloqICP/74I7Zt2yYatbN48WJkZWUBAOLi4tC9e3dFhUMIIYQQFaawItnIyEiUlJRg6dKlom3jxo3D0qVLoa+vDwMDA3z33XeKCocQQgghKkwpRbItRTUo5Hl0zdQPXTP1QtdL/ajjNVOJGhRCCCGEEFlQgkIIIYQQlUMJCiGEEEJUDiUohBBCCFE5lKAQQgghROVQgkIIIYQQlUMJCiGEEEJUDiUohBBCCFE5lKAQQgghROVQgkIIIYQQlUMJCiGEEEJUDiUohBBCCFE5lKAQQgghROVQgkIIIYQQlUMJCiGEEEJUDiUohBBCCFE5lKAQQgghROVQgkIIIYQQlUMJCiGEEEJUDiUohBBCCFE5lKAQQgghROVQgkIIIYQQlUMJCiGEEEJUDiUohBBCCFE5lKAQQgghROVQgkIIIYQQlUMJCiGEEEJUDiUohBBCCFE5lKAQQgghROVQgkIIIYQQlUMJCiGEEEJUDiUohBBCCFE5lKAQQgghROVQgkIIIYQQlUMJCiGEEEJUDiUohBBCCFE5Woo82Y8//oibN2+ioaEB8+fPh7u7O1asWAE+nw8rKyusW7cOOjo6igyJEEIIISpIYQnKtWvXkJKSgoMHD6KkpATvvPMO+vfvjylTpiAoKAgbNmzA4cOHMWXKFEWFRAghhBAVpbAunr59+2LTpk0AABMTE9TU1CAuLg4BAQEAgCFDhiA2NlZR4RBCCCFK0cAXIKegEowxZYei0hSWoGhqasLAwAAAcPjwYbz11luoqakRdelYWFigoKBAUeEQQgghSnHqr3Ss2RmPr3+/iaT0YmWHo7IUWoMCABcuXMDhw4cRFhaG4cOHi7bLkkmamRlAS0tTbrFZWRnL7dhEPuiaqR+6ZuqFrlfrS0wvAYcDpOWW4/8O3IZHN0tMD3JGj87mrXL8tnLNFJqgxMTEYOvWrfj1119hbGwMAwMD1NbWQk9PD/n5+bC2tm72+SUl1XKLzcrKGAUFFXI7Pml9dM3UD10z9ULXq/VVVNchNacMPezb4V3/boiITsXdx4VYHhoDj64WGPeWI+xtWp5gqOM1ayqhUlgXT0VFBX788Uds27YN7dq1AwD4+vri3LlzAICoqCgMHDhQUeEQQgghCvcgsxQA4NLZHJ1tTbDs3V5YOdUL3I6muPukCGt3Xcd/jyUit6hKyZEqn8JaUCIjI1FSUoKlS5eKtn3//fdYvXo1Dh48iPbt22Ps2LGKCocQQghROGHNictz3TncTu3w6VQv3E8rxpHoVFx/8Aw3Hj7DADc7vO3XGZam+soKV6k4rIVlxOXl5TAxMWnteJolz2YrdWwWe9PRNVM/dM3UC12v1vfp1r9QWdOA0CUDoaHBkXicMYZbjwpxNCYVTwuroKnBwaBe7RHs2xntjHRfenx1vGav1cXz5MkTsflJli9fjn79+sHPzw/3799vnQgJIYSQNuxZaQ0KSmvRw76d1OQEADgcDno7WeHLOf0wL9gF5ia6uHQrByu3xiL88mNU1tQrOGrlkSlB+fbbb+Hi4gIAiI2NxcWLF7Fr1y5MnToVGzZskGuAhBBCSFuQLKV7pykaGhz0d7PFN/N8MCPQCYb62jgTl4lPt/6FE1fTUMNrkHe4SidTDcq9e/cQGhoKADh//jxGjBiB/v37o3fv3ti9e7dcAySEEELaguSMEgCAS2czmZ+jpamBwb06wNfVFlcScnAqNgPHrqbhws1sjPRxgL9XB+hoy2/6DWWSqQVFIBCIJlm7du2aaLSNlpYWeDye/KIjhBBC2gABY0hKL4GZsS5szQ1e+fk62poY3s8eP3zQH+8M7AK+gOHQ5cf4dFssLt/KRgNfIIeolUumFhRHR0eEh4dDW1sb2dnZ8PPzA9DY3WNnZyfXAAkhhBB1l/2sEpU19RjgZgsOR3r9iSz0dbUwekAXDPHqiHPxmTh/Iwt7oh7hTFwmxvh1wejBRq0YtXLJlKAsXboUixYtQl1dHT7++GOYmJigpKQEixYtwueffy7vGAkhhBC1lpQu7N5pndlijfS1MX5QVwzt3RGnYzNw5XYOdp5ORtyDZ/h4gsdrJUGqQqYExdfXF/Hx8airqxN19ZiZmWHnzp3w8vKSa4CEEEKIuhPOf+L8CvUnsjA10sWUYVyM6GePX08lIfFJEVKyy8Dt1K5Vz6MMMs8kq6mpibt37+LIkSOibU5OTnIJihBCCGkr6hsEeJRdig6WhjLNZdISFqZ6eNuvCwAg5u5TuZxD0WRKUNLT0xEYGIg5c+bg3//+NwAgOzsbAQEBePDggVwDJIQQQtRZ6tMy1NULWr315EVO9u1gY26A6w+etYlhyDLPg+Ll5YVr165BQ6PxKR07dkRISAjWrVsn1wAJIYQQdXZfWH/i0Dr1J03R4HAwrJ896uoFuP7gmVzPpQgyJSg3b97EqlWrYGJiIlZ4M3/+fCQmJsotOEIIIUTdJacXQ4PDgZO9/OtC/PvYg4O20c0jU4Kira0NHR0die08Hg91dXWtHhQhhBDSFlTXNiAttwKO7U2gryv/9XmtzPTh2sUcT3LK8bRQvVdElilBcXV1xfbt28W2VVdX44cffkDPnj3lEhghhBCi7h5mlUDA2CvNHvu6BvZsDwC4ejdXYeeUB5nSueXLl2PmzJk4dOgQ6urqMHbsWGRkZMDAwAC//vqrvGMkhBBC1JJw/hNnB8UlKL26WcJQTwt/JeZi3CBHaGnKPGBXpciUoPTo0QNnzpzByZMnkZaWBj09PYSEhCA4OBhGRm1n1jpCCCGkNSWlF0NHWwNdO5gq7JzaWhro72qLCzezce9JETy5Vgo7d2uSKUFJSEiAp6cnZs6cKfHYrl27MHv27FYPjBBCCFFnJRU85BZVw93RQuGtGH4edrhwMxsxd3PVNkGR6RWbO3cuYmNjxbaVlZXhgw8+wM8//yyXwAghhBB1lpzROHusIutPhOxtjOFga4y7T4pQWqmei/rKlKB89dVXWLRoES5cuACgcdjx22+/jaKiIhw9elSuARJCCCHqqLXX33lVAz3sIGAMsYl5Sjn/65Kpi2fUqFEwNzfH0qVLERUVhcjISEyfPh2ffPIJtLTkP2yKEEIIUSeMMSSlF8PYQBsdrAyVEoO3iw0OXHyMmLu5CPS2V7sFBGXuFOvfvz92796N2NhYjBs3Dp9++iklJ4QQQogUuUXVKK2sg7ODGTSUlBgY6mmjj5MV8oqr8TinTCkxvI4mM4wVK1ZI3e7g4ICIiAhUVVVBU1MTAPDjjz/KJzpCCCFEDSVnKLd7R8jPww7XkvIRczcX3Tuq1wrHTSYoubnSJ3jhcDjw9PTEs2fqP88/IYQQIg9J6corkH1eDwczWJrq4XryM0wZ2h16OurT89FkpHv27FFkHIQQQkibwBcI8CCzBNbt9GFpqq/UWDQ4HPi52+HY1TRcT34mmmVWHcicSuXm5iIyMhKZmZngcDhwdHTEqFGjYGFhIc/4CCGEvARfIICmhnrOFtoWpedWoIbHh7ezcltPhHzdbXH8ahpi7uWqVYIi0zs6Pj4eQUFB2L59O5KTk5GYmIgtW7YgMDAQjx8/lneMhBBCmpCSXYoPf4rGlYQcZYdC/vZP945y60+ELE314dLZDI+zy5BbpD4LCMqUoGzYsAEzZ87En3/+iUOHDuHw4cO4evUq3nnnHfzwww/yjpEQQogUNbwG7DiZhLp6AS7ezAZjTNkhETQWyHLQWP+hKvw8/l5A8J76LCAoU4Ly8OFDfPjhh2LDinV0dLBkyRIkJibKLThCCCFNO3gpBYVltdDV0UROYRUy8yuVHdIbj1fHx+OcMtjbGMNIX1vZ4Yh4cf9eQPBeHvgCgbLDkYlMCYqhoSFqa2sltgsEArWb+IUQQtqChJQCRN/JRSdrI8wZ6QwA+EtNZwxtS1KyS9HAZ0ofvfMibS1N+LjYoqyqDveeFCs7HJnIlKB4enriP//5D0pKSkTbCgsLsXr1anh4eMgtOEIIIZLKq+qw+8wDaGlyMG+0Czy7W8JIXxtxyflq8+u4rVL29PbN8fOwAwDE3H2q5EhkI9Mons8++wyzZ8/GgAEDYG5uDsYYSkpKYGtri7CwMHnHSAgh5G+MMew++wDl1fWY5N8NHa2MAAD9nK1x6VYO7qeVwKMrja5UlqT0YmhpaqB7R1NlhyLBwdYY9jZGuPukCGVVdTA11FF2SM2SKUFp3749IiMjER0djczMTPB4PHTp0gWDBg2Cjo5q/4GEENKWXL2bi4SUQvSwb4dhfTuJtvd3s8WlWzn4KzGXEhQlqaiuQ+azSjg7mEFHW1PZ4Ug10KM99p1/hNjEPAR62ys7nGbJlKCsXr0aX3/9NYYMGSLveAghhDShoLQG/7uYAn1dTcwZ5Sy2xoujnQlszPSRkFKIGl4D9HXVZ8bQtkI4vb2zCo3eeZG3iw0OXnqMmLtPMaJfJ5WuI5WpBuXPP/+kqe0JIUSJBAKGX08lgVfHx9RhXIkZSjkcDnzdbFHfIMCNh3S/VgZVrj8RMtLXhhfXErlF1Uh9Wq7scJolU4q9YMECLFu2DEFBQejUqRO0tcWHTvXv318uwRFCCGl0Lj4TKdll6O1khf6utlL36e9qi6MxaYhNzMNAD/WZMbStSEovhr6uFjrbGis7lGYN9GiP+ORniLn7FF07qF6tjJBMCcoXX3wBALhx44bEYxwOB8nJya0bFSGEEJHM/ApERKfC1FAHM0Y4Ndksb9lOH9yOpniQWYqislpYmOopONI317PSGhSW1cKLawUNDdXtNgEau6AsTHQRl/wMkwO40NVRzXoZmRKUixcvyjsOQgghUtQ38LHjVBL4AobZI51hbND8wIT+brZ4lF2Ga0l5GNW/s2KCJEj+e3p7Va4/EdLQ4GCAux1O/JmOGw+fYYC7nbJDkkqmGpRdu3ahQ4cOEv9MTU2xfv16mU/26NEjDB06FHv37gUArFy5EqNHj8b06dMxffp0XLlypUV/BCGEtFVHo9OQU1CFwZ4dZBqd07eHNbQ0NfBXYh5Nfa9A/9SfqH6CAgB+fyclMXdUd06UZltQysvLUVZWhkOHDmHmzJkSjz9+/Bjnz5+X6UTV1dX46quvJOpVli1bRqODCCFEioeZJTgXnwlrM31MGtJNpucY6GmjV3dL3HjwDOl5FehiZyLnKImAMSRnlMDMWBe25gbKDkcmlu304exghuSMEuQVV6tk3M0mKCdPnsS3334LgUCA4cOHSzzOGJO5QFZHRwc7duzAjh07WhYpIYS8QaprG/DrqSSAA8wLdnmlOgFfV1vcePAMsYl5lKAoQFZ+JSpr6jHA3Valh+2+aKCHHZIzSvDnvVyMH9RV2eFIaDZBmTp1KkaPHg1fX1+pM8bq6+vD2dlZthNpaYktNii0d+9e7Nq1CxYWFlizZg3MzVV3eBYhhCjK/guPUFTOw2jfzq880sLN0Vw09f27/t2gpSlTbz5pIeH8J6o8vFgaL64VDHS1cPVeLsYO7AJNDdV6n7y0SNbExATHjx9H167i2VV5eTlMTF4vMx8zZgzatWsHZ2dnbN++HVu2bBGNGJLGzMwAWlryqza2slLtoWFEEl0z9UPX7OX+uvsUfybmoVtHU8wZ696iBGNw7444dTUNWcU16OcifViyLOh6vdzjv+cT8fPqBHMT5Y+cepVrNrh3R0T+lY6sohr0fY33iTzIPNXglClT8L///Q8AsHz5cpw8eRKWlpbYtm0bXF1dW3Ty57uH/P39sXbt2mb3LympbtF5ZGFlZYyCggq5HZ+0Prpm6oeu2cuVVfIQeug2tLU0MCuwB0qKq1p0HM+uFjh1NQ1n/0xDFyvDFh2DrtfL1TcIkPikEB0sDcHn1aOgoF6p8bzqNevDtUTkX+k4HZOKzi18n7yuphIqmdLyb7/9VtSVExsbi4sXL2LXrl2YOnUqNmzY0OKgFi9ejKysLABAXFwcunfv3uJjEUKIumOMYdeZB6isqcfEwV3R3rLlXxidbY1hZ2GAhJRCVNcq90uzLXuSU4a6BgGc1WT0zoscbIzRydoItx8Xory6TtnhiJGpBeXevXsIDQ0FAJw/fx4jRoxA//790bt3b+zevVumEyUmJuKHH35ATk4OtLS0cO7cOUybNg1Lly6Fvr4+DAwM8N1337X8LyGEEDX3x52nuPukCC6dzeDfu+NrHYvD4aC/qy0iolNx42EB3uqpGjPLRsVnIj2vArOCeqjsgnqvIimjcf4Tdas/EeJwOPDzsMP+CymITczDiH6qs4CgTAmKQCCAgUHjEKRr165h0aJFjU/W0gKPx5PpRG5ubtizZ4/E9hEjRsgaKyGEtFn5JdU4cDEFBrpamDNSfCHAlvJxtUFEdCr+SsxTiQQlt6gKhy4/gYAxCBjD/Ldd1WrUizTJ6SXQ4HDg1KmdskNpsf6utgi//Bgxd3MxvK/qLCAoUxePo6MjwsPDcezYMWRnZ8PPzw9AY3ePnZ1qzkBHCCHqgi8Q4NeTSairF2D6CKdWK7S0NNVHD/t2eJRVisLSmlY55uuIiE6FgDGYm+giPvkZIq9lKDuk11Jd24DU3HI4tjdR69WjjfS14dndCk8Lq5CWqzo1RzIlKEuXLsV3332H1atXY8mSJTAxMUFJSQkWLVqE2bNnyztGQghp0yKvZeLJ03L0c7aGt4tNqx5buLBg7P28Vj3uq3rytAw3Hxaga3sTrJnRB2bGuoj4IxUJKQVKjet1PMwsAWPqM3tscwZ6/D2z7F3VmVlWpgTF19cX8fHxiI+Px9y5cwEAZmZm2LlzJyZOnCjXAAkhpC1LzyvHiatpMDPWxbThTq1+/D49rKGtpYG/7ucrbep7xhgOX34CAJgwuCtMjXTx0XgPaGtpYPvJJGQXVColrtf1z/T26ll/8jyXzuYwM9ZFXFI+ePV8ZYcDQMYEBWisNxHWoQh5eXm1ekCEEPKmqKvnY8fJxoUA54x0hpG+dqufQ19XC57dLZFfXK205vt7qcV4mFUKj64WcLJvbG1wsDXG3GAX8Or42Hz4LipUbASJLJIyiqGrrQnH9uo/W69wAcHaOj5uPnym7HAAvEKCQgghpHUd/uMJcouqEdC7I1y7yO9XuK9bYzfPX4m5cjtHUwSM4fCVJ+AAmPDCdOp9e1hjtG9nFJbV4r/HEtHAFyg8vpYqqeAht6ga3E7t2sxMvX7Cbp47in+fSNM2XlVCCFEz99OLceFGNuwsDDBhsHzXQXHtYg4TA23EJz9TeBIQdz8f2QWV6O9mi47WRhKPjxnYBV5cKzzILMX+CykKje11JKULhxerf/2JkHW7xqLqh1mlyJfjxKiyogSFEEIUrKq2HmGnk6GpwcF7wS7QlfN8IJoaGvB2sUVlTT3upRbJ9VzPq28QICI6FVqaHIwd2EXqPhocDt4LdkZHKyNcTsjB5VvZCovvdbSl+pPnDfRoHI7+5z3lt6I0maB4enqK/rtnz54KCYYQQt4E+6IeoaSCh9EDOitsteF/unkUN5rnSkIOispr4e/VEZam+k3up6ejhY/Gu8NIXxv/u5CCB38vvqeqGGNIyiiGiYE2Oihpenh56e1kBX1dLfx5Lw8CgXKKqoWaHLhtbGyMjz/+GJ07d0ZDQwM2b97cZAX4kiVL5BYgIYS0JfHJ+biWlA/H9iYY1d9BYee1tzFCe0tD3HlciKraehjqtX5B7vOqaxtw8q906OtqIti380v3t2ynj0Xj3LFufwJ+OZaI1TP7wLpd00mNMuUWVaOssg79nK1bZUI9VaKjrQlvFxtcSchBYloxPLpaKC2WJltQVq9ejYyMDJw4cQICgQDHjh3D8ePHJf6dOHFCkfESQojaKqngYc+5h9DR1sB7wS4KXd6+cep7GzTwGa4/kP8ojbPxmaisqUeQt4PMo5O4ndph2nAuKmvqEXrkLmp4DXKOsmX+qT9pW907QsI5Ua4qeU6UJltQhg8fjuHDhwNo7O65dOmSwoIihJC2RiBgCDudhKraBkwf4QRbc4OXP6mV9Xe1RcQfjVPfD+7VQW7nKa3kIep6JkyNdDCsT6dXeu6gXh2QXVCFizezseNkEhaNd1e5Vop/6k/aToHs8zrbGqOjlSESUgpRUV0HYwMdpcQhU/qekJAg+u/CwkIUFSmuyIoQQtqCozGpuJ9eAo+uFhjcSznr4pib6KGHgxkeZ5fhmRynvj/5Zzrq6gUY49cFujqvXgAcEtANzg5muP24EEejU+UQYcvxBQI8yCyBtZl+s3U16qxxAcH24AsYYu/nKy0OmRKUuro6fPnll+jduzcGDhwIPz8/eHt7Y/369RAI1GfcOiGEKEN8cj5Ox2bA2kwf80a7KHUxNuHU99fkVCybV1yNP24/hY25gair4FVpamhgwVg3WLfTx+nYDFxLUu40/c9Ly61AbR2/zXbvCPm42kBTg4Ord58qbQZimRKUdevW4cqVK1i0aBG2bduGrVu3Yu7cuTh+/Dh27twp7xgJIURtZeRVIOx0MvR0NLF4vIfci1NfpreTFXS0NPBXYp5cvniECwKOf8vxtWpsjPS1sXiCB/R0NLEr8gHScstbMcqWSxbWnzi0ze4dIRMDHfTqbonsgiqk5ylnBmKZ3j1nz57F1q1bMXv2bLz11lsYNGgQ3n//ffzyyy+IiIiQd4yEECLV08Iq7Dh5HwUqsFKvNOVVdQiNuIv6BgHeH+2KDpbKH5Kqr6sFL64VnpXW4MnT1v3ST8stx40Hz9DFzgS9naxe+3gdLA3xwRhXNDQIsCXiHkorea0Q5etJSi8BB0CPNp6gAP/MiXL1rnLmRJEpQamsrES3bt0ktru4uCA/X3n9U4SQN1v0naeIvZ+PdfsTUFxeq+xwxDTwBfjl6D0Ul/Mw9i1H9OpuqeyQRPr/PSdKbCt28zDGEH75MQBg4uCurdaN5dHVEhOGdEVJBQ9bIu6hvkF5C9nx6vh4nFMGe1tjuaybpGrcujQuIHgtKR91SlhAUKYEpWPHjoiLi5PYHhcXBzu7lvUxEkLI60r9uwWgsKwW/3fgNsqqVGfBuf9dSMGj7DL06WGNYAXOdyILl85mMDXUQXxyPuobWqeO8H5aMR5klsLd0aLVWxcC+9mjv6stUp+W47czD5VWE/EouxR8AWuzo3depKHBga+bLWp4Dbj5qEDx55dlp6lTp2LRokX4+uuvceDAAezfvx9r167FwoULMX78eHnHSAghEhr4AmTkV8De2ghB3vbIK67G+gMJqKypV3ZouJyQgysJOehoZYS5I52VWhQrTePU9zaoqm3A3SevPyrz+QUBxw9yfP0AX8DhcDAryAld7EwQez8P5+KzWv0cskgWDi92aNsFss8b6GEHDv752xWpyXlQnhcSEgIdHR3s27cPx48fB4/HQ+fOnbFixQpMnjxZ3jESQoiE7IJK1DcI4NjeBBMGdwWvno9Lt3Lw06Hb+FeIJ/R1Zbq9tbpHWaX43/lHMNLXxkfj3Vs0zFYRfN1sEXU9C7H38167XiQ+KR+ZzyrR39UG9jbGrRShOG0tTSwe744vf7uO8MuP0d7SAB5dFdttlpReDC1NDXTvaKrQ8yqTtZkBVkzxhIWpnsLPLfMneNy4cRg3bpw8YyGEEJkJu3e6tDcBh8PBlGFc8Or5+PNeHjaF38HH7/ZSeHJQVFaLn4/eAwAsHOsGSxWdqh0AOlkboaNV49T3lTX1La6peH5BwHcGtn7ryfPaGeli8XgPfL/vFraduI9V0/ugvYIKj8ur65D5rBLODmbQkfPijqrGyV45XVq0mjEhRC0JExTH9o2/ZjU4HMwOckY/Z2s8yi7Dloi7Ci2o5NXzERpxFxXV9Zg8tLvKj/LgcDjo72YLvoDhenLLBztcuZ2DwrJaDPHsqJCErIudCWYH9UANj4/NR+4qrEtPuIDhm1J/ogooQSGEqKXUp+XQ19WEncU/U8ZraHDwXrALenWzxP30Evz32H008OU/mSRjDLsik5GZX4m3erbHEE/5TSPfmnxcbMEB8Nf9lo3mqeE14OSf6dDT0USwr+IKgX1cbTGqvwOeldRg6/FE8BUwYWhbX39HFVGCQghRO1W19cgrrkZnWxOJdVq0NDWwYKwrXDs3TpX+66kkuS8bH3ktA/HJz9CtoymmDeeqXFFsU8yMdeHS2QxPcsqRX1L9ys8/J1oQ0F7h67W885YjenWzRFJ6CQ5efCz38yWll8BAVwsOcqqxIZIoQSGEqJ20v7t3unYwkfq4tpYmFo3zQPeOpohPfoZdZ5IhkNPQ1DuPCxHxRyrMjHXx4Tvu0NJUr9tqS+dEKauqw7n4LJgY6mB4X3t5hNYsDQ4H80a7oIOlIS7czEb0HfmtvPustAaFZbXo4WAGDQ31SD7bApk/SfX19di3bx8+/vhjzJkzBx999BF+//131NWpzrwDhJA3g6j+xK7p0RS6OppYOrEnutgZ4897efjf+UetPn9GblEVtp+8Dy0tDSwa5w5TQ+Ws+vo6vLhW0NHWdLgGEwAAIABJREFUQOz9V5v6/uSfaeDV81u8IGBr0NfVwuIJHjDU08Kecw8RJ6cJxf7p3qH6E0WSOUH597//jUuXLsHLywsjR46Eu7s7IiMj8dlnn8kzPkIIkZCaKyyQld6CIqSvq4WP3+2FjlZGuHQrB4evPGm1JKW6th6bj9xDDY+PWUE90MWu+VhUlZ6OFnpzrVFQWovHOWUyPSe/5O8FAc30W7wgYGuxbqePhe+4AwC2nbiPjzbFYGP4HVy8md1qSyAkCec/ofoThWp2mPGNGzfQp08fAEBiYiJOnDgh9viMGTMwYMAA+UVHCCEvYIwh9Wk5LE31YCJDi4WRvjY+CemFH/bdwpm4TOhqa+Jtvy6vFYNAwLD1xH3kF1cj0NtetEKwuvJ1s0Xs/Tz8lZiH7h3bvXT/o9Gp4AsYxg3qqhJdWs4OZlg1ozfik57hbmoR7j5p/LfvPGBnYQB3Rwu4d7UAt2M7aGu9WrwCxvAgowRmxrqwMVPdYeNtUbMJyldffQVfX198/PHH6NixI9asWQN/f38YGBigvLwc586dg4uLi6JiJYQQFJTWoLKm/pWa200NdfCvkF74ft8tHLuaBh1tTQR6t7xu4sgfT5CYWgw3R3NMGNS1xcdRFc4OZmhnpIPryc8wZWh3aGs13WWTnleO+ORn6GJnjD6tsCBga+lsa4LOtiZ4178bCstqcC+1GPeeFCEpoxhR17MQdT0LutqacOls1piwOFrINPlYVn4lKmvqMcDdVm2Kn9uKZhOU8PBwbNq0CRMmTMCKFSsQHR2N0NBQVFRUwMjICF5eXtTFQwhRqH/qT16tS8XcRA/LJ3vi+323cOjyY+jqaLZoOPC1+3k4E5cJGzN9fPC2a5somtTQ4MDHxRZn4zNx53ER+vSwbnLfw1eeAAAmDGq9BQFbm6WpPoZ4dsAQzw6ob+DjYVYp7j0pxt3UIiSkFCIhpRBA42rJ7l0t4OFogW4dTaW2BiVl0PBiZWk2QdHR0cHy5csxdOhQrF69GiNGjEB4eDg0Nd+sWfQIIarjxQnaXoVVO3386+/unj3nHkJHSwMD3GWvoUjPK8euMw+gr6uJjyZ4wECv7axo6+vWmKD8lZjXZIJyP60YSeklcOtiDmc1+cLW1tKEWxcLuHWxwGR0x7OSatxLLcbdJ0V4kFmCs3GZOBuXCT0dTbh2Nod718bWFTNjXQDP1Z+o+MR7bZFMU917enri8OHDWL9+Pd599138+OOP6NpV/Zs1CSHqJzW3HJoaHNjbGLXo+XYWhvgkxBM//u8WwiKToaOtib7NtBgIlVXVIfTIPTQ0CLBgrAfsLBQzxbqidLQ2QidrI9xLLUJFdZ3EvCYCxhB+pXG+kQmD1ff+b21mgIDeBgjo3RF19Xw8yCzFvSdFuJtaiJuPCkSr9nayNoK7owVSskrRwdIQpka6So78zaO5du3atS/bqaioCA0NDRgxYgTs7e2xYsUK8Hg8eHp6KrSJr7pafkOaDQ115Xp80vromqmf171m9Q0C7L/wCB2tjRDQu2OLj2NqqANnBzPEJeXjevIz2NsYw9bcoMn9G/gCbAy/g6eFVRg/yBFv9Wzf4nOrMl4dH4lpxbAw0YNjexOx6xWf/AyXbuXAx8UG/q/x2qsSTU0N2JgbwKOrBYb27ggfV1tYtdMHQ2Nr2cOsUvAFDN4uNnB3tFB2uDJRx/uioaH05K/ZcuY///wT/v7+GDBgAHx9fTFixAhoa2vjyJEjSE1NxdSpU5GVpZxlrwkhb56sZ5Vo4LOXDi+WRRc7Eyyd2BOaGhz8cjRRNNfFixhj2Bv1CI+zy9DP2RojfRQ3pbui+bjagMMBYl+Y+r6B///t3XlcVPX+P/DXMAwgq4IDyqaIbMomiqaIC7iEBuZW7mVpab/Me6t7NW9lVt/bzTbTurcyswwrvGZqXjfMsizALU1AJQUB2US2YWeWz+8PdBTFDYFzgNfz8eAhzJw55z18nJkXn/M5n48BW34+B6WJAg8Oa9kFAaWiUCjQzd4SY0Ld8NzDwVizeBiemRKI6CE9ETWo/ba5nN0yoCxfvhzPP/88jh8/jiNHjmDhwoV4+eWXYWVlhf/7v//DE088gXnz5rVWrUTUwaXn1s/TcbcDZG/G260zFk0OBCCw+ts/8OeF0hu2+fH3HPx8IhfujtaYG+Un24GhzaGztTn69rRHeq4GeUWVxtsPHM9FYWkNRvZzgaOMV2huTuZmSgT37oqJw3oZx6NQ67plQCkrK0NYWBgsLCxgbW2NYcOGobj46l8ZI0aMwKZNm1q8SCIi4M4naLsbfT3s8dSDAdDrBVb99wQyLh8DqF/B9ut9f8LGUoVFkwMlmzG1NRmnvk+pX+G4fkHADJibKfHAkJ4SVkYdzS0HyU6YMAExMTEICQkBABw7dgwPPfRQg23s7O5+JD0RUVOk52hgZWEKp1uMF2mKYK+umB/dBx9vT8G7ccexZEYILMyU+PfWZADAUw/639GcGe1BiJca5mZKJKbkY/7EQOw9nA1NlRYPDvW4o4nxiJrLLQPKiy++iMjISJw8eRIKhQIzZ840zizbFGlpaXjqqafw6KOPYtasWcjLy8Pf//536PV6qNVqvPXWWzAz4wuAiG5UXlWHi6XV8Pewv2EF4+Yw0M8JdVoDPtt5Cm/HHYeNpQoV1VrMHusDH/eOc4mpuZkSA7zV+DU5Hwkn87D7UBZsLVUYM9BN6tKog7ntZcaDBw/G4MGD7/lAVVVVeO211xrsa/Xq1ZgxYwaioqLw7rvvYvPmzZgxY8Y9H4uI2p+MFji9c72hgd1Rp9Mjdm8aNJV1GBHs3KTJ3Nq6wf7d8GtyPt79+hjqtHpMHeEJC7M7mpWCqNm02iIKZmZmWLt2LRwdr843kJSUhMjISADAyJEjkZCQ0FrlEFEbc3WCtpZdlC8ixBVzo3wRGeKKGaO9W/RYcuXr3gVdbMxRp9XDsXOndntZNclbq0ViU1NTmJo2PFx1dbXxlI6DgwMKCwtbqxwiamOuBJTWWDU4vIN/IJuYKBAW0B07fjuPScN7yWJBQOp4ZNNndydLoHfpYgnTWyxida/UapsW2ze1DLZZ29OUNhNC4Hx+Obo7WKFXj7YxYVZb99gEf4wMdYd3Bxp/0160l/dFSQOKpaUlampqYGFhgYKCgganfxpTUlLVYrWo1TYoLCxvsf1T82ObtT1NbbP84ipUVGvh72HPNm9F3u5d+PtuY9ri++LNApWk/XZDhgzBnj17AAB79+5FeHi4lOUQkUxdmaDNo4XHnxCRfLRaD0pycjLefPNN5OTkwNTUFHv27MHbb7+NpUuXIi4uDs7OznjwwQdbqxwiakNaa4AsEclHqwUUf39/fPnllzfcvn79+tYqgYjaqPRcDUyVCrg7to9z60R0exyaTUSyptXpkX2xAm6ONlCZ8i2LqKPgq52IZC2zoAJ6Q/OsYExEbQcDChHJGsefEHVMDChEJGtXruDxZEAh6lAYUIhI1tJzNbDupIK6cyepSyGiVsSAQkSypamsw6WyGvRytoWiBVYwJiL5YkAhItkyjj9phfV3iEheGFCISLbS8+rHn3CALFHHw4BCRLJlXMGYAYWow2FAISJZMgiBjDwNnOwtYWWhkrocImplDChEJEv5RVWortVz/AlRB8WAQkSyxAnaiDo2BhQikqX0PAYUoo6MAYWIZCk9twymShO4OVpLXQoRSYABhYhkp1arx4WLlejRzRqmSr5NEXVEfOUTkexk5pfDIAQ8ne2kLoWIJMKAQkSywwGyRMSAQkSyc2UFY15iTNRxMaAQkeyk52lga6mCg52F1KUQkUQYUIhIVkoralGsqUUvZzuuYEzUgTGgEJGscP0dIgIYUIhIZjhAlogABhQikpn03DIoAHh0Y0Ah6sgYUIhINgwGgYz8cnRzsISlhanU5RCRhBhQiEg2cosqUVun5+kdImJAISL5uDr+hDPIEnV0DChEJBvGgMIJ2og6PAYUIpKN9NwymJmawNXRSupSiEhiDChEJAs1dTrkXKpEz242UJrwrYmoo+O7ABHJwvm8cgjB8SdEVI8BhYhkIT2PE7QR0VUMKEQkC5xBloiuxYBCRLKQnlsGO2szdLExl7oUIpIBBhQiklyxpgalFXXo1d2WKxgTEQAGFCKSAZ7eIaLrMaAQkeSuDpDlFTxEVI8BhYgkl56rgQJAz242UpdCRDIh6XKhSUlJWLx4Mby8vAAA3t7eeOmll6QsiYhamd5gwPl8DZzVVuhkzhWMiaie5O8GAwcOxOrVq6Uug4gkklNYiTqtgevvEFEDPMVD1EaVVdTiTFYJhBBSl3JPrgyQ9XTh+BMiukryHpSzZ89iwYIFKCsrw9NPP42wsDCpSyKSvcoaLd6IPYaLpdUY4OuIOWN9YN1JJXVZTcIVjImoMQoh4Z9fBQUFOHr0KKKiopCdnY05c+Zg7969MDMza3R7nU4PU1NlK1dJJC96g8CKtQn4Pa0QDnYWKCqrgb2tORY/HIIQX0epy7trT63cj0ulVfj69fFQmnAOFCKqJ2kPipOTE8aNGwcAcHd3R9euXVFQUAA3N7dGty8pqWqxWtRqGxQWlrfY/ptTbZ0eZiqTDj+hVVtqs+b03x/P4ve0QgR6OuDpSQHYcygLW3/JwPK1CYgIccHUkb1hrpJnkL++zaprdbhQUA4f984oLqqQsDJqTEd9jbVlbbHN1OrGr96TNKBs374dhYWFePzxx1FYWIiioiI4OTlJWZKsZV+swN5DWUhMLYCnsy2eiOkLe1sLqcuiVpSUWoBdSVlwsrfEE9F9YKo0wfjBPRHQywFrv0/F/mM5SDlfgvkP9GkTk55l5GkgAHi0gVqJqHVJGlAiIiLw/PPP44cffoBWq8Urr7xy09M7HZUQAinni7EnKQsp50sAADaWKqRdKMMr6w/jsfF+CO7dVeIqqTVkFZRj/c5TsDBTYtGkAFhaXB1z4u5kg5cfHYBvD6Rj7+Fs/PPLo4gO64nxg3vAVCnfsfBXx59wgCwRNSRpQLG2tsZHH30kZQmypdUZkJRagL2Hs3ChsBIA4OveGWMHuiPA0wEHjufi631/YvXmPzAm1A1TRnjK+oOI7k15VR3WfHsSdToDFk0OgHNXqxu2UZkqMS3SC0GeDli38xS2HczAH+eKMD+6D7rZW0pQ9e1xinsiuhnJr+KhhiqqtThwPAf7jl5AWUUdTBQK3NfHCWMHuqPHNbNsjuznAk9nW3y0LQV7D2cjLbsUCyb0hWMXeX4QyZUQApoqLYo1NSgqq4Gmqg6Bng7oatdJ6tKMdHoD/rM1GUWaGjw41AP9vNS33N6vpz1efWwgNsanISGlAK98dggPR/TGiH4ushq3JIRAep4GXWzMuYIxEd2AAUUmLpZWI/5wNn75Ixd1WgMszJQYO9ANo/q7wcGu8XEm7k42WP5oKGLjz+DXk/l4Zf1hPBrli4F+HMdzhVanR3F5LYrKalCkqUGx5trva1CkqYVOb2jwGBtLFZ59KLhBIJTSph/P4nRWKUK81XggrOcdPcbSQoX50X0R7KXGht2n8eXeNPx+9hIeG+eHztbyCANFmhpoKuvQ3+fWgYuIOiYGFImdyynD7kNZOJZWCCEAe1tzjBrqhmFBzrC0uH3zmJsp8fj4PvDr0QVf7knDR9tSkHq+BNNHecn2So7mIoRARbXWGDiMoaOsPnhc+QC8GVtLFVzVVnCws4CDrQXsbS1QU6vDtoMZePOrY1g8JRA+7l1a8Rnd6NeTedh35AKcu1rh8fF+MLnLHpBQX0f0drHD+p2nkJxejJc+TcIj9/tigAwuR+bpHSK6FQYUCRgMAr//WYg9h7JxNqcMANDDyQZjB7phgK9jk8aSDPHvDo/u9ad8fj6Ri3M5ZVjwoD9cGhmr0Fbp9AacyylDyvlipGSUIK+oEjV1+ka3NVUqYG9jAWf3zg0CiPF7G3OY3STAdXOwxNrvU/FO3AksfLDvbU+ptJSMPA2+2H0GluamWDQ5oMnr1HSxMcdfHwrCj7/nYNP+s/j31mQM7tsNM0d731EIbimcoI2IboUBpRXV1ulx8GQe4g9n42JpNQAg0NMB9w90h49753seH9DdwQovzumPTfvP4YdjF/Da54cxY7Q3wgO7y2rswd0oKKlCSkYxktOLcTqrxBhIlCYKuHezgZ2lmTF0ONhZwN7WHA62FrC1Mrvr3oYrBvo5wdLcFB98dxIfbknG3HG+CAvo3pxP67bKKuvwwZaT0OsNeHJyAJzucWyRQqFARIgr/Hp0wac7UpGQko8z2SWYN74PfHtI00uUnqeBiUKBnt0YUIjoRpLOJHu3WnLyGQcHaxS10ERRZRW1+OHYBfx4LAeVNTqYKk0wxL8bxoS6NXo1RnM4euYi1u88japaHQb1ccKcsT5tYqXYqhodTmeVIDmjGCkZRSgsrTHe59ilE/w97NHXwx6+7l3g7tqlRf9PnMspw6r/nkBljQ4PR/TG2IHuLXasa+n0Bqz8+necvVCGKSM8Me6+Hs2+/x2/nceO3zIhhMDoUDdMHt4LqlaYpfnKJFI6vQH/772f0d3eEq88NrDFj0tN0xYn/ero2mKbyXKiNrn46XgOYvecAaCASmUCldIEKlMTmJnW/1v/pWx4m9IEKpXyhm1Nr/1eaYLk9GIkpuZDpxew7qRCTFhPjAxxhZ1Vy8730t/HET2cbPDx9hQkpRYgI0+DBRP6yu6vVYNB4Hx+OZIzipCSUYxzORoYLmfmTuZKhHir4e9hjz4e9nDs3LpX1ni62GHJzBC8E3cccfvPoqJai0nDerV4b9RX+/7E2QtlCPV1RNSg5g9FpkoTPBjeCwGeDvj0+1TsPZyNlIxizI/uA3en1hkYfKGwAlqdgeNPiOim2IMCIC27FLsPZ6Oisg5anQF1Oj20OgO0egO02sv/6gy339FNOHXphDED3THEv1urD1zV6Q347pd07ErMgtJEgYdG9saoAa6SnvIp1tRc7iEpRur5YlTW6AAACgXg0d3W2EvSy9kWSpObj8dprb8UCkur8U7ccVwsqcaIYGfMGuMDkxZaM+an4znYsPsM3BytsWxWf5ibtez/l9o6PTb9eBY//p4DpYkCE4f1wv0D3Vvs+V1ps/3HLiB2bxrmjvNFeKBzixyL7l1b/Gu8o2uLbcYelFvwduuMsBC3WzaqEAK6y0GlPsRc/b7+Sw+t3oC6awKNVmdAVzsLBHg6NHk8xL0yVZpg6oje8HPvgrU7UvH1D3/iVGYJHhvv12qr39bW6XEmu9TYS5JXdHVNJXtbc/T3UaOvhwP8enSR5Yq86s6d8MKs/ng37jh+Op6Lyhod5l+eZr45nb1Qho1702DdSYVFkwJaPJwA9VeBzR7rg6DeXbF+5yls/ukcTpy9hHkP9IG6BXusrl7Bwxlkiahx7EG5rC2mzrtVWlGLtd+n4lRmCextzfFEdF94u3Vu1mNoquqQc7ECFworkV1YgZzCCmRfrIBOX//fzExlAl/3LujrYQ9/D3t0s7dscm9Oa7dZVY0W72/+A39eKENfD3v8v4n+sDBrnoxfUl6LVz8/jPIqLZ59OAh9eto3y37vRnlVHTbsOYOjZwphYabE7DE+GOzfrVmPcaXNln2SiLLKWqz5yzDJwjvdXkd4X2xv2mKb3awHhQHlsrbYqE1hMAj8L+E8th7MgAIKPBjugXGDe9z1h4RWp0fupSpcKKyo/7ocSsqum3dEaaKAq9oafTy6wL+nPXq7dobKtHl6HqRos1qtHv/Zmow/zhXB09kWi6cG3XOvj1anx782/o6MPA2mRXphTGjjq3m3BiEEfkvOR2x8Gmrr9LivrxNmjfZptsuR1WobnM8uxqJVv8CvRxf8bXq/ZtkvtYyO8r7YnrTFNuMpHgIAmJgoEB3mAR/3Lvh4ewq2/JyO01n1q9/aNTLDqEEIFJXVXA4gFcgurEROYQUKiquNg1mvcLA1R6CnA9wcreGitoKb2hpO9pbtao0gc5UST08KwPqdp5CQUoA3Nx7Dsw8HN3mqdiEENuw5g4w8DQb37YbRA1ybueK7o1AoEBbQHV6udvjk+1QkphTg7IUyPBHTF71dmud0TEYeJ2gjottjD8plbTF13quKai3W7UjFiXNFsLVU4dEoP3QyV+JCYeXVXpFLlai9bjK0TuZKuKit4aa2hqvaCi6X/712dd3WIGWbGYTANz/8iX1HLqCrnQWeezgYTk1YkO+HoxewMT4NPbrZ4IWZITedPE4KOr0B23/NwP9+y4RCoUDM0J54YHDPexpAq1bbYN3WP7D1lwwsmhwg2SR4dGc64vtiW9cW24w9KHQD604qPDMlEPFHLuC/P57F6m//aHC/0kSBbvaW9b0hjtbGUGJva95mJ35rLiYKBaZHesG6kwpbf8nAG7FH8ezDwXd1me7pzBJ8ve9P2FrWD4qVUzgB6gdYTxrmib497fHJ96nY+kuG8XLke1lMkQNkiehOMKB0cAqFAmNC3eDlaocfjl6AnZUZXNX1p2i6O1g123iR9kihUCAmzAPWnVTYuDft8vo9QXc08LiorAb/3poMhQJ4amIA7G0bXxBSDnzcu+DVxwfii12nceRMIZZ/dhiP3O/TpEUphRBIz9XAwdaixecCIqK2jZ8+BKB+/pF5D/TB1JG9Mdi/G9ydbBhO7lBEiCvmx/RBndaAd+KO4/jZS7fcvlarx5otf6CiWosZo7ya/UqqlmBlocLCB/0xN8oXeoMBH21Lwbr/paK6VndX+ykorkJFtZbjT4jotvgJRNQM7uvTDc9MCYQCwAffnkRCcn6j2wkh8MWu08gqqMCwoO4Y0c+ldQu9BwqFAuFBznhl7kD06GaDX0/mY8X6w8ZTNnfiTGYJAA6QJaLbY0AhaiYBvRzw/LR+sDBTYu2OVMQfyb5hmz2HspGYWgBPZ1vMHO3TJsfydLO3xD9m90fUIHcUllbjjdij+F/CeRgMtx9vn5bFgEJEd4YBhagZ9XatX7/HzsoMX+/7E1t/SceVC+VSMorx35/Ows7aDE9NDGjTp9BMlSaYOrI3np8WDBtLFb49kI63vv4dxZqaWz7uTGYJlCYK9GilNX+IqO1qu++QRDLl5miNF2b3h7qzBbb/eh4b49NQUFKFj7YlQ2miwNMTA5o8b4rc+PW0x6uPD0I/r644k12K5Z8dwpHTFxvdVqsz4FxOGVwdrWV3xRIRyQ8DClELcLy8fo+r2gr7j+Vg+WeHUFmjw+wxPvBspgnP5MK6kwpPTwrAnPt9oNUZ8O+tyfh816kb5s+pX/KAKxgT0Z1hQCFqIZ2tzbFkZgh6u9qhTmtARIgLwoPa58q9CoUCI4Jd8PKjoXB3tMbPJ/LwyueHkZl/dcKo9NwyAECv7gwoRHR7DChELcjKQoXnHw7G89OCMX2Ul9TltDjnrlb4x5wBGBPqhoLiKry+4Qh2J2XBIATSOcU9Ed0FTtRG1MLMVEpJVieWisrUBNMiveDfyx6f7jiFTT+eRXJGEQqKq2DVSdWkJQGIqONhDwoRtQh/Dwe8+vhABHk6IPV8CYo0tfB263zXK2cTUcfEgEJELcbW0gzPTAnEzNHesDQ3RXhw25mYjoikxVM8RNSiFAoFIvu7IiLEBY6Otm1upVUikgZ7UIioVbTFWXOJSDoMKERERCQ7DChEREQkOwwoREREJDsMKERERCQ7DChEREQkOwwoREREJDsMKERERCQ7DChEREQkO5LPJPvPf/4TJ06cgEKhwLJlyxAYGCh1SURERCQxSQPKoUOHkJmZibi4OJw7dw7Lli1DXFyclCURERGRDEh6iichIQGjRo0CAHh6eqKsrAwVFRVSlkREREQyIGlAuXTpErp06WL82d7eHoWFhRJWRERERHIg+RiUawkhbnm/Wm3Tosdv6f1T82ObtT1ss7aF7dX2tJc2k7QHxdHREZcuXTL+fPHiRajVagkrIiIiIjmQNKCEhYVhz549AICUlBQ4OjrC2tpaypKIiIhIBiQ9xRMSEoK+ffti2rRpUCgUWL58uZTlEBERkUwoxO0GfhARERG1Ms4kS0RERLLDgEJERESyI6vLjFtLUlISFi9eDC8vLwCAt7c35s2bh7///e/Q6/VQq9V46623YGZmJnGllJaWhqeeegqPPvooZs2ahby8vEbbafv27fjiiy9gYmKChx56CFOnTpW69A7r+jZbunQpUlJS0LlzZwDA448/jhEjRrDNZGTlypU4evQodDodnnzySQQEBPB1JmPXt9f+/fvb52tMdECJiYli0aJFDW5bunSp2LlzpxBCiHfeeUds3LhRitLoGpWVlWLWrFnixRdfFF9++aUQovF2qqysFGPGjBEajUZUV1eL8ePHi5KSEilL77Aaa7MlS5aI/fv337Ad20weEhISxLx584QQQhQXF4vhw4fzdSZjjbVXe32N8RTPZUlJSYiMjAQAjBw5EgkJCRJXRGZmZli7di0cHR2NtzXWTidOnEBAQABsbGxgYWGBkJAQHDt2TKqyO7TG2qwxbDP5CA0Nxfvvvw8AsLW1RXV1NV9nMtZYe+n1+hu2aw/t1WEDytmzZ7FgwQJMnz4dv/76K6qrq42ndBwcHDjlvgyYmprCwsKiwW2NtdOlS5dgb29v3IZLJkinsTYDgNjYWMyZMwd//etfUVxczDaTEaVSCUtLSwDA5s2bMWzYML7OZKyx9lIqle3yNdYhx6D07NkTTz/9NKKiopCdnY05c+Y0SKCCV163CTdrJ7afvEyYMAGdO3eGn58fPvnkE3zwwQfo169fg23YZtLbt28fNm/ejM8++wxjxowx3s7XmTxd217Jycnt8jXWIXtQnJycMG7cOCgUCri7u6Nr164oKytDTU0NAKCgoOC2XdQkDUtLyxvaqbElE9h+8jF48GD4+fkBACIiIpCWlsY2k5lffvkFH330EdauXQsbGxu+zmRE1LEkAAAMWklEQVTu+vZqr6+xDhlQtm/fjnXr1gEACgsLUVRUhEmTJhmn3d+7dy/Cw8OlLJFuYsiQITe0U1BQEE6ePAmNRoPKykocO3YMAwYMkLhSumLRokXIzs4GUD+GyMvLi20mI+Xl5Vi5ciU+/vhj41UgfJ3JV2Pt1V5fYx1yJtmKigo8//zz0Gg00Gq1ePrpp+Hn54clS5agtrYWzs7OeOONN6BSqaQutUNLTk7Gm2++iZycHJiamsLJyQlvv/02li5dekM77d69G+vWrYNCocCsWbMQExMjdfkdUmNtNmvWLHzyySfo1KkTLC0t8cYbb8DBwYFtJhNxcXFYs2YNPDw8jLf961//wosvvsjXmQw11l6TJk1CbGxsu3uNdciAQkRERPLWIU/xEBERkbwxoBAREZHsMKAQERGR7DCgEBERkewwoBAREZHsMKAQkeQiIiLw3nvvSV1GkyQlJcHHxweZmZmtcrylS5di+vTprXIsIikxoBBJqLq6GqGhoQgKCkJpaanU5RARyQYDCpGEtm/fDkdHR3h6emLLli1Sl0NEJBsMKEQS2rhxI6KjoxETE4NvvvnGuKBXRUUFAgMD8d133zXYfsuWLQgKCkJFRQX0ej0++OADjB07FkFBQYiMjMSnn37aYNtBgwZh48aNGDBggHFfW7duRXR0NPr164ehQ4fi5ZdfNq67AgAHDhzA2LFjERgYiKlTpyIxMRE+Pj5ISkoCgNsetzFbtmzByJEjERQUhLlz5yI+Ph4+Pj64cOHCDduuWbMGw4YNa3Dbe++9h4iICOPPmZmZePLJJxESEoKhQ4filVdeQXV1tfH+b775BtHR0QgODsbQoUPx+uuvo7a2FkD9ommrVq0y1hMeHo433ngDWq22yc/vWjU1NXj99dcRERGBwMBAREVFYevWrQCAgwcPwsfHBzk5OQ0ec+1pm5KSEixZsgTDhw9HUFAQJk6ciAMHDtzx8YnaDUFEkjh8+LDw8/MT+fn5oqioSPTt21ccOHDAeP/ixYvF/PnzGzzmscceE88++6wQQohVq1aJyMhIcfr0aaHT6cThw4dFSEiI+O6774QQQnz77bciKChIvPDCC6KiokIYDAbxxx9/CG9vb7F//34hhBCZmZkiLCxMvP/++0IIIcrLy0VwcLBYsWKFqKqqEqdOnRITJkwQ3t7eIjEx8Y6Oe72srCzh6+srPv74Y1FTUyMSExNFZGSk8Pb2FtnZ2UIIIUaOHCneffddIYQQq1evFuHh4Q328e6774qRI0cKIYSora0VERER4tVXXxXl5eUiPz9fREdHi2XLlhmfd3BwsDh48KDQarXi1KlTYsSIEWL58uVCCCF27NghwsLCRGZmphBCiPT0dDFmzBjx9ddfN+n5JSYmCm9vb3H+/HkhhBB/+9vfxJQpU0RWVpbQarVi7969ok+fPuLQoUNCp9OJsLAwsXbtWuPja2trRUhIiIiLixNCCDFjxgzx5JNPisLCQlFbWytiY2NFnz59RFZWlhBCiCVLlohp06Y1WgtRe8IeFCKJbNy4EeHh4XBycoK9vT0iIyPx1VdfGe+PiYnBb7/9Bo1GAwAoLi5GYmIiJk6cCIPBgK+++grz58+Hj48PlEolBgwYgKlTp2LTpk3GfVRXV+ORRx6BlZUVFAoF/P39kZCQgJEjRwIA3N3d0b9/f5w4cQIA8PPPP6O6uhrPPPMMOnXqBF9f3wYDMu/0uNfavXs3bG1t8dhjj8Hc3ByDBg3CuHHjmvx7+/nnn5Gbm4vFixfD2toaTk5OWLlyJcaMGQMAiI2NxcSJExEWFgZTU1P4+vpi9uzZ2LZtGwwGAzQaDRQKBczNzQEAHh4e2L17N6ZNm9ak53et0tJSfP/991i8eDHc3NxgamqK0aNHIyIiAps2bYJSqcQDDzyAXbt2GR9z4MABaLVaREVF4fTp0zhy5AiWLFmCrl27wszMDDNnzoSPjw++/fbbJv/OiNoiU6kLIOqILl68iPj4eKxatcp420MPPYR58+YhJycHLi4uCA8Ph7W1NeLj4zF58mTs2rULDg4OGDJkCIqLi1FaWorXXnsNr7/+unEfQgio1eoGx3J3dzd+bzAYsGHDBuzYsQMXL16EEAI6nc64yml+fj5sbW2Nq6QCQHBwsPH7uznuFfn5+XB2doap6dW3m2v3ebcyMzNha2sLW1tb422+vr7w9fUFAGRlZWHKlCkNHuPp6YmqqipcunQJDzzwAHbv3o3IyEiEhIRgyJAhiI6OhouLS5Oe3/W1GQwGLFiwAAqFosHjg4KCAAATJkzA+vXrkZ2dDTc3N+zYsQOjRo2CjY0N0tPTAeCGRd2EEOjdu/dd/qaI2jYGFCIJxMXFQavVYsmSJQ0+yAwGA7755hs899xzUKlUGDduHHbt2oXJkydj586diImJgYmJCSwsLADUj80YPXr0LY917arc//nPf7Bhwwa8//77uO+++6BSqfDss8+isLDQePzrV/G+tr67Oe61z+lW+7zTfVyhVCqNY3UaU1tbe8P9V35WKBSwsbHBF198gT///BMHDx7EDz/8gA8++ABr1qxBaGgogLt7fte60iuzadMm9OnTp9Ft/Pz84OXlhV27dmHmzJn46aefsGbNmgaPP3jwIOzs7O76+ETtCU/xELUyrVaLuLg4zJ07F9u2bcPWrVuNXwsWLMDmzZtRV1cHoP6v7cTERJw9exZHjx7FxIkTAQDW1tbo2rUrUlNTG+y7oKDA+NjG/P777xg4cCDCw8OhUqlgMBiQnJxsvF+tVqOkpAQVFRXG244fP278vinHVavVyMnJaRAyrt3n9czNzRsM2gXQYI6Rnj17QqPRGEMVAKSmphpPj/Xs2RNnzpxp8Pi0tDTY2tqia9euqKurQ0VFBby8vDB37lzExsYiKioKcXFxTf69XuHm5galUnnD43Nzc6HT6Yw/x8TEID4+Hvv374eNjQ3CwsKMtV95PtfKzs6+ZSgjao8YUIhaWXx8PIqLi/HII4/A1dW1wdecOXNQXl5uHKMQFBQEFxcXvPbaa/D394enp6dxP4888gg2btyIhIQE6PV6nD59GjNmzMC6detuemx3d3ekp6ejpKQEly5dwooVK2BjY4OLFy9Cp9Nh2LBhUCqV+PDDD1FbW4vTp0/fMPbibo87atQoXLp0CbGxsairq8OhQ4ewe/fum9bYq1cvlJWV4cCBA9Dr9fjxxx9x7Ngx4/1Dhw6Fq6sr3n77bWNQWb58uTFoTZ8+Hdu2bcNvv/0GvV6P5ORkfPnll5g6dSoUCgVeffVVLFy4ELm5uQDqw8f58+fRq1evJv9er7CyssKUKVPw4YcfIjU1FXq9HocPH8bEiROxc+dO43bR0dFITU3Fpk2bEBMTA6VSCaD+VNTQoUPx5ptvIjMzE3q9HvHx8Rg/fjyOHj162+MTtStSjc4l6qhmzJghFi5ceNP7//KXv4iHH37Y+POaNWuEt7e3iI2NbbCdTqcTq1atEsOHDxf+/v4iIiJCrFmzRuj1eiFE/dUs3t7eQqvVGh+Tn58vZs+eLYKCgkRERIT47rvvxNGjR8WgQYPE/fffL4QQ4vvvvxfDhw8XQUFBYvbs2eLAgQPC29tbHDp06I6O25j169eLwYMHi+DgYLFw4UKxfft24e3tLXJycoQQDa/i0ev1YtmyZSI0NFT0799f/OMf/xDr1q0zXsUjhBA5OTli7ty5IigoSAwZMkS89NJLoqKiQgghhMFgEGvXrhVRUVEiODhYjBkzRnz00UfG34NGoxFLly4V9913nwgICBDDhw8XK1asENXV1U16ftdfxVNVVSVWrFghBg8eLAIDA8X9999/Q9sJIcTs2bOFt7e3SEtLa3B7UVGReO6550RoaKgIDg4WEyZMEDt37jTez6t4qKNQCMF+QyK6SqfTQaFQGP+qT0pKwpw5c7Bv3z64ubk1aZ91dXVQqVTGsSdbtmzBiy++iBMnTtwwPoWICOApHiK6Rm1tLYYOHYqVK1eitrYWxcXF+OSTT+Dn5wcXF5cm7bOgoAAhISH4/PPPodPpkJubiw0bNmD48OEMJ0R0U+xBIaIGjhw5grfeegtpaWmwsLBAv3798MILLzS59wQA9u3bh9WrVyMrKwvW1tYYMmQIlixZAgcHh2asnIjaEwYUIiIikh2e4iEiIiLZYUAhIiIi2WFAISIiItlhQCEiIiLZYUAhIiIi2WFAISIiItn5/7FdpIZVN1WaAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stroke_cases = Counter(df['binned_age'][df['stroke']==1])\n",
        "total_cases = Counter(df['binned_age'])\n",
        "stroke_cases[20] = 0\n",
        "stroke_pct = 100*np.array([item[1] for item in sorted(stroke_cases.items())])/np.array([item[1] for item in sorted(total_cases.items())])\n",
        "labels = [item[0] for item in sorted(total_cases.items())]\n",
        "plt.style.use('seaborn')\n",
        "plt.figure(figsize=(9,5))\n",
        "plt.ylim(0,30)\n",
        "plt.title('Stroke risk vs age level (All age groups)',fontsize=20)\n",
        "plt.ylabel('% of strokes',fontsize=15)\n",
        "plt.xlabel('Average age level',fontsize=15)\n",
        "plt.plot(labels[:-1],stroke_pct[:-1],'b')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AuDk-16QwnBy",
        "outputId": "cc353cef-5dc6-4324-cdf2-5f1c88b523fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc8f26d6bd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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31gnnGsWfU4Cnzyua0P6s0p5XjNWgQQNtANHQnBLX/AwhISEIDAzE/v37ERoairCwMHTo0AHBwcElvunx8vKCUqlETk4OqlSp8tL1UREGlEqiRo0aGD58uPZc+M2bN7FlyxZs2LAB7733HmJjY+Hm5lbqNmQymd5REs2To6EnXs256ry8PJ3lCQkJ2nfFx44d03txysnJAQC89dZb6Ny5s8FajOk/AWDUk4Snp6d2LPbu3Yt169Zh3bp1cHZ2xvjx4zF16lSdnofn1V+SNm3awMfHB/v378fs2bMBFL37t7W1RZ8+fbTrdevWDZs3b8a6detw9OhRbdOdXC7HggUL0K5duxL3IYTA5MmTcerUKYSEhGD69OmoVq0abG1tsWfPHp138vn5+QAM/+6cnJz0nsBzcnIgkUiwfv36EnuKjAmExbcJAEuWLEHVqlUNrqPpSwCKwuTKlStx7tw5BAYGIj4+Hunp6TpHfDTb/PDDDw2GLwDP/Vsvqc6SXpiio6NRWFiIkJAQ3Lt3T+e29u3b4/Dhw9i3b1+JV/+8iOjoaERERMDHxwfTpk1Do0aN4ODggIyMDEyfPl1nXc1jz9DjxcXFRef7snjc5eXlwc7OzuD8MMX3p2HoMZqbmws7OzuDfXMlPa+8CCcnJ71lmt9tQUEBgKLnu3Xr1mHz5s3YsWMHNm3ahE2bNsHe3h5Dhw7F3Llz9erT/IzZ2dkMKGWAAaWSatiwIebPn4+0tDTs2rUL58+fL7H5rzSaB7qhq3U0TyDFH6j5+fmYN28e/vzzT2zevBlRUVE6M2pq1ndzc9M2PZqau7s7ZsyYgRkzZuDOnTs4fPgwNm7ciMjISNjY2Og0fz6v/pJIJBL06tUL69atw7Vr1+Dr64vDhw+jQ4cO2ndvGgEBAfj2229RUFCAc+fO4ZdffsGWLVswceJE7N27V+8dqsaFCxdw6tQptGvXDmvWrNEJEsWvLtA8uRq6qiUvL0/vcskqVapACAG5XF5mk9xpftd16tQpsRH5WX379sXKlSu1zcb79u2DjY2N9vTPs9usVq1amf39aLZp6JJhTXMsUHQqp6TTOVu3bi2TgLJu3TrY2tpi/fr12qMJwNOjDs8q7Xdc/Gcpi8edTCaDSqWCWq3WC7Evcrm1k5MTCgoKoFQqS3xTZEwAKOmKLUPhJisrC0DRc8GzdUyYMAETJkzQHiXbtGkToqKikJeXh88++8zgNp53WpmMwx6UCkqpVCIyMhJffPFFqetp3vFq3k2/KM0h2GvXrundpjlk3KBBA53lbdq0wfjx4/Hhhx+iQYMGWLx4sc6MtprTRSX1M5h6SmlfX1+MGzcOW7duhZ2dHWJjY3Vuf179pdG8kB48eNDg6Z3i7Ozs0LZtWyxYsABz586FUqk02NOjobmcsn379novEL///rvO9x4eHpDJZAZPy5w7d05vmeZ3bej3kpmZ+VLTvpe2zZycHL0XmMaNG8PPzw8HDx7Unt5p27YtatSoobNOSdtUKpUvNS+Js7Mz7OzskJ6ernfbb7/9hqSkJAQGBmLp0qUG/9WrVw9nz57VPib+jsTERNSoUUMnnABPeyeeVb16dQD6p96EEHqXJJfF46569eoQQujtLzc3F9evX3/u/TVKe17RnGps2LAhAGiP1iiVSr11SzoNdPv2bQghdJZpHjvF3yxo1KhRA0OHDkV0dDS8vb31nheAojGSyWQ8elJGGFAqKJlMhsOHD+OHH34ocR6G1NRU7NmzB/b29mjTpg2Ap41hxs4VERoaCjs7O2zfvl3vCULTNFi8GU3DwcEBX3/9NdRqNd577z1tSGrTpg28vLxw9OhR7ZORRkxMDEJCQsp0bomPP/4YAwYM0PuZnZycYGNjU+KcFyXVX5pmzZqhQYMGOHr0KA4ePAhHR0dt4yZQdAlpv3798O233+rdV/OurKR6AGiPbDzbKAkUzRWheYetqdPGxgYtW7bE/fv3dV6s1Go1vv/+e71t9+rVCwCwYcMG7fwkwNPp31999dUXfvHXbPPHH3/UG7/FixejQ4cOuHv3rs7yvn374u7du9i+fTvS09P1Al5oaChkMhl2796N1NRUnds2bNiAjh076oU1Y/j6+uLOnTt6L2yav/NJkyahZ8+eBv9pjrCVRbNs1apVkZaWpnMU4MGDB4iKigKg+2ZD07ytaSTW2LVrl16IKIvHXUn7++GHH17oTZCmGX/z5s06y5VKJXbs2AF7e3tt75fmFGDx/pZ9+/YhJSXF4PbT0tLw66+/6izTBA7Nc+Hy5csNfsaOVCqFvb293uNQrVbj3r17JR7dpBfHUzwV2Jdffonx48djzpw52omQPD09kZOTgxs3bmDPnj1IT0/HP//5T3h6egIoaliTSCTYvXs3PDw84OfnV2oDrbe3N2bOnInFixdjzJgx6NevH+zs7BAXF4eYmBh0794dXbp0KfH+zZo1097/iy++wD/+8Q/IZDIsXLgQM2fOxJgxYzBu3Dh4e3sjISEBW7ZsQf369dG1a9cyG6cOHTpg69atGDZsGAYOHIiqVasiIyMDO3fuhEKhwMiRI1+o/ufp06cPVqxYgdu3byMsLEzn3Vb16tXh4+ODlStX4q+//kK7du1gb2+P27dvIyoqCt7e3iUGPqDoBcLHxwe7d+9G9erVUb9+fZw8eRJxcXH45JNPMHv2bOzYsQMeHh7o1asXJkyYgN9//x3Tpk3D6NGj4ebmhv3798PT01PvCTgsLAzdunXDgQMHMG7cOAwYMAAqlQp79+5FfHw8pkyZ8sKHtps0aaK9omz48OEYNmwYpFIpfvvtN+38PcWf8Pv06YMlS5Zg6dKlsLe317uyTDP1+Oeff47w8HCMHj0aLi4uOHnyJH7++We0adPmuVddGdKhQwds3LgRV69e1TbkPnjwAMeOHUPdunVL/TsfOHAglixZgp07d2rntnlZvXr1wtq1azF9+nT07dsXDx8+xH/+8x/Mnz8fX3zxBS5fvowff/wRXbp0Qe/evREZGYnNmzdDIpHA398f169fR0xMDMLCwnRepMvicTds2DBs2LABy5YtQ3p6Oho2bIhz587h999/R9u2bY0Ohq+99hq6dOmC6OhoKBQKtG/fHjk5Odi7dy9u3bqFjz76SNuwHBQUBE9PT/z888/w8PBAw4YNcfXqVezevRudO3fWzl/zrGbNmuGzzz7DuXPn0KhRI1y9ehX/+c9/UKtWLQwYMABA0e975cqVGDZsGIYMGQIfHx/k5uYiNjYWiYmJmDlzps42//jjD6Snp+ucbqS/yVLXN5N5pKamiqVLl4rXX39dtG7dWjRt2lS0bNlS9OzZUyxYsEBcvnxZ7z7ffPONCAoKEq1atRJr164VQhTNg9KiRYsS97Nnzx4xZMgQ0apVK+Hv7y/69u0r1qxZIwoKCnTWMzTnhFqtFmPGjBFyuVwcOHBAu/zkyZNi4sSJok2bNqJZs2aiS5cu4p///KdITU197s+tmRth1apVercVnwdFCCGOHDkiJkyYIDp06CCaNWsm2rZtK8aNGycOHz780vWX5Pbt29q5GI4cOaJ3u0KhEMuXLxd9+/YVgYGBolmzZqJr164iIiJC/PXXX8/d/pUrV8SoUaNEYGCg6Nixo5g9e7b466+/hEqlEpMnTxb+/v6if//+2vV37NghevbsKZo3by66du0qvv76a6FUKkXz5s1FWFiYzrYLCgrEmjVrRN++fYW/v78ICAgQgwcPFlu3bn1uXULoz4OisXXrVvH666+LgIAA0aJFixL/fjSGDRsm5HK5ePfdd0vc14EDB8TIkSNFYGCgaN68uejevbtYsmSJyMnJ0VnP2HlQjhw5IuRyuVi9erV22dKlS3XmziiNZh4XzfwlLzsPSm5urvj0009F586dRatWrcSQIUPEwYMHhRBCREdHi7Zt24o2bdqIkydPCiGEuHXrlpg4caJo1aqVaNu2rZg8ebK4ceOGWLBggd68OEL8vcedEEKcP39eDB8+XPj7+4sOHTqI2bNni6SkJO2cJJq/Yc2cJBMmTDC4Hc3joEePHqJ58+YiMDBQjBw50uBj7PLly2LcuHGibdu2IigoSEyYMEFcu3ZNzJ8/3+A8KBMmTBCXL18W48ePF61atRKBgYHi7bffFnfv3tXZ7tmzZ8U777wjOnXqpK0hPDxcZ24kjZUrVwq5XK73nEEvTyJEseOVRFTpZWdno3Xr1ggICOAH3j2hVqvRr18/5OTkIDY29m8dBbEG06dPR2xsLGJiYrT9HKY0ePBg/PHHHzhz5ozBq2jMITExEWFhYQgJCcHatWvLbLtqtRo9e/bU9kUVvwKOXg57UIgqsZiYGEyYMEGvwXLnzp0AoDeBVmVmY2ODKVOm4MGDBwY/98UaJScn491339X5QFCg6PN5jh8/Dk9PzxI/AuJlXLlyBVOmTMGGDRt0ll+9ehWXL19Gs2bNLBZOTOmnn37CnTt3MHPmTIaTMmS2HpS8vDzMmzcPqampUCgUmDp1Kpo0aYK5c+eisLAQ3t7eWLx4cbl/V0JUntSvXx9nzpzBu+++i7Fjx6JGjRq4cuUKoqKi4O7ujnHjxlm6RKvSp08f7NixA0uWLMErr7zy3Fl9La1atWpISUnB/v378ejRI7Rv3x6ZmZnYuHEjcnNzMWfOnDJ9QfX19cXNmzdx9OhR3L9/Hy1atEBycrI2sLz77rtlti9rkZqain//+9/o3LlzqVfk0Ysz2ymemJgY3L9/H2+99Rbu37+PCRMmICgoCK+88gp69eqFJUuWoEaNGhgxYoQ5yiGiJxISErBixQpcuHABaWlpcHd3R4cOHfDuu+/qzUBKRZeSDhkyBN7e3ti4caPBScmsSXZ2tnbumKSkJNja2sLPzw9jxoxB7969y3x/KSkp+O6773D06FE8fPgQDg4OaNGiBSZNmqT9tHRLKetTPJoPY7x79y62bdumvdiAyoZFelBOnTqFZcuWITExEb/88gtkMhnOnj2LdevWITIy0tzlEBERkZUxe/QPDw9HUlISVq5cifHjx2tP6Xh5eZV4zToRERFVLmYPKJs3b8bly5cxZ84cnQmPjDmQo1IVQiplAxIREVFFZ7aAkpCQAC8vL/j4+KBp06YoLCxElSpVkJ+fDwcHByQnJ5c4xbBGWpr+572UFW9vF6SkZJls++UBx6AIx4FjAHAMAI6BBsfBtGPg7W34gyTNdpnxqVOnsG7dOgBFl7jl5uYiODgY+/fvB1A0zXBJn6BJRERElYvZjqCEh4fjo48+wogRI5Cfn4+PP/4YLVq0wAcffIAtW7agZs2aGDhwoLnKISIiIitmtoCi+WC14tavX2+uEoiIiKic4EyyREREZHUYUIiIiMjqMKAQERGR1WFAISIiIqvDgEJERERWhwGFiIiIrA4DChEREVkdBhQiIiKyOgwoREREZHUYUIiIiMjqMKAQERGR1WFAISIiIqvDgEJERERWhwGFiIiIrA4DChEREVkdBhQiIiKyOgwoREREZHUYUIiIiMjqMKAQERGR1WFAISIiIqvDgEJERERWhwGFiIiIrA4DChEREVkdBhQiIiKyOgwoREREZHUYUIiIiMjqMKAQERGR1WFAISIiIqvDgEJERERWhwGFiIiIrA4DChEREVkdBhQiIiKyOgwoREREZHUYUIiIiMjqMKAQERGR1WFAISIiIqvDgEJERERWR2rOnX311Vc4ffo0VCoVJk+ejEOHDuHSpUtwd3cHAEycOBFdunQxZ0lERERkhcwWUE6cOIHr169jy5YtSEtLw6BBg9ChQwfMnj0bXbt2NVcZREREVA6YLaC0bdsWAQEBAABXV1fk5eWhsLDQXLsnIiKickQihBDm3umWLVtw6tQp2NraIiUlBQUFBfDy8sKCBQvg6elZ4v1UqkJIpbZmrJSIiIgswewB5eDBg1i1ahXWrVuHhIQEuLu7o2nTpli9ejWSkpLw8ccfl3jflJQsk9Xl7e1i0u2XBxyDIhwHjgHAMQA4BhocB9OOgbe3i8HlZr2K59ixY1i5ciXWrFkDFxcXdOzYEU2bNgUAhIaG4tq1a+Ysh4iIiKyU2QJKVlYWvvrqK6xatUp71c706dNx7949AEB8fDwaN25sro9kRaQAACAASURBVHKIiIjIipmtSTYmJgZpaWmYOXOmdtngwYMxc+ZMODo6wsnJCV988YW5yiEiIiIrZraAMmzYMAwbNkxv+aBBg8xVAhEREZUTnEmWiIiIrA4DChEREVkdBhQiIiKyOgwoREREZHUYUIiIiMjqMKAQERGR1WFAISIiIqvDgEJERERWhwGFiIiIrA4DChEREVkdBhQiIiKyOgwoREREZHUYUIiIiMjqMKAQERGR1WFAISIiIqvDgEJERERWhwGFiIiIrA4DChEREVkdBhQiIiKyOgwoREREZHUYUIiIiMjqMKAQERGR1WFAISIiIqvDgEJERERWhwGFiIiIrA4DChEREVkdBhQiIiKyOgwoREREZHUYUIiIiMjqMKAQERGR1WFAISIiIqvDgEJERERWhwGFiIiIrA4DChEREVkdBhQiIiKyOgwoREREZHWk5tzZV199hdOnT0OlUmHy5Mnw9/fH3LlzUVhYCG9vbyxevBgymcycJREREZEVMltAOXHiBK5fv44tW7YgLS0NgwYNQseOHTFixAj06tULS5YswbZt2zBixAhzlURERERWymyneNq2bYulS5cCAFxdXZGXl4f4+HiEhYUBALp27Yq4uDhzlUNERERWzGwBxdbWFk5OTgCAbdu24ZVXXkFeXp72lI6XlxdSUlLMVQ4RERFZMbP2oADAwYMHsW3bNqxbtw7du3fXLhdCPPe+Hh5OkEptTVabt7eLybZdXnAMinAcOAYAxwDgGGhwHMw/BmYNKMeOHcPKlSvx/fffw8XFBU5OTsjPz4eDgwOSk5NRrVq1Uu+flpZrstq8vV2QkpJlsu2XBxyDIhwHjgHAMQA4BhocB9OOQUnBx2yneLKysvDVV19h1apVcHd3BwAEBwdj//79AIDY2Fh07tzZXOUQERGRFTPbEZSYmBikpaVh5syZ2mVffvklIiIisGXLFtSsWRMDBw40VzlERERkxV46oGRmZsLV1dXo9YcNG4Zhw4bpLV+/fv3LlkBEREQVlFGneG7evKkzP8mcOXPQrl07hISE4NKlSyYrjoiIiConowLK559/jmbNmgEA4uLi8Ouvv2L9+vUYOXIklixZYtICiYiIqPIx6hTPxYsXERkZCQA4cOAAevTogY4dO6J169bYsGGDSQskIiKiyseoIyhqtVo7ydqJEye0V9tIpVIoFArTVUdERESVklFHUBo0aIDo6GjY2dkhMTERISEhAIpO9/j4+Ji0QCIiIqp8jAooM2fOxLRp06BUKjFr1iy4uroiLS0N06ZNw/z5801dIxEREVUyRgWU4OBgnDx5EkqlUnuqx8PDA2vXrkVQUJBJCyQiIqLKx+iZZG1tbXHhwgVs375du8zPz88kRREREVHlZlRA+fPPP9GzZ09MmDABn3zyCQAgMTERYWFhuHLlikkLJCIiosrH6HlQgoKCcOLECdjYFN2ldu3aCA8Px+LFi01aIBEREVU+RvWgnD59Gr/99hucnZ0hkUi0yydPnowuXbqYqjYiIiKqpIw6gmJnZweZTKa3XKFQQKlUlnlRREREVLkZFVCaN2+O1atX6yzLzc3FokWL0LJlS5MURkRERJWXUad45syZg7Fjx2Lr1q1QKpUYOHAg7ty5AycnJ3z//femrpGIiIgqGaMCSpMmTbBv3z7s3r0bt2/fhoODA8LDw9G3b184OzubukYiIiKqZIwKKGfPnkVgYCDGjh2rd9v69esxfvz4Mi+MiIiIKi+jelAmTpyIuLg4nWUZGRl4++238d1335mkMCIiIqq8jAoon376KaZNm4aDBw8CKLrsuH///khNTcWOHTtMWiARERFVPkad4unTpw88PT0xc+ZMxMbGIiYmBqNHj8Z7770HqdSoTRAREREZzejP4unYsSM2bNiAuLg4DB48GB988AHDCREREZlEiQlj7ty5Bpf7+vrip59+Qk5ODmxtbQEAX331lWmqIyIiokqpxIDy4MEDg8slEgkCAwPx8OFDkxVFRERElVuJASUqKsqcdRARERFpGd1E8uDBA8TExODu3buQSCRo0KAB+vTpAy8vL1PWR0RERJWQUQHl5MmTmDRpEuzt7eHr6wu1Wo2YmBhERkbixx9/RKNGjUxdJxEREVUiRgWUJUuWYOzYsZg+fbr2yh2lUol///vfWLRoEdasWWPSIomIiMj8ClRqbD50HR0DaqFRDfN+tI1RlxlfvXoV77zzjs5lxTKZDDNmzEBCQoLJiiMiIiLLEEJg04GrOHzmPq7eeWz2/RsVUKpUqYL8/Hy95Wq1GhKJpMyLIiIiIss6cu4vHD3/AHWrOyO8u5/Z929UQAkMDMQ//vEPpKWlaZc9evQIERERCAgIMFlxREREZH7X7qXjvweuwdnRDtMG+8NBZv6JWY3a44cffojx48ejU6dO8PT0hBACaWlpqFGjBtatW2fqGomIiMhMHmfmY8WOixACmDqwBaq6OVqkDqMCSs2aNRETE4OjR4/i7t27UCgUqF+/Pl599VXIZDJT10hERERmUKAqxPKfLiIztwAjXmuMJr4eFqvFqIASERGBf/3rX+jataup6yEiIiILEELgP79cxZ9JWejkXwNhrWtbtB6jelD+97//cWp7IiKiCuzg6UT8LyEJ9X1cMKaHn8UvgjHqCMqUKVMwe/Zs9OrVC3Xq1IGdnZ3O7R07djRJcURERGR6V+6kYcuvN+DqZId3BvnDTmpr6ZKMCygff/wxAODUqVN6t0kkEly+fLlsqyIiIiKzeJSRhxU/J0AiAaYO8oenq4OlSwJgZED59ddfTV0HERERmZmioKgpNjuvAKN7+EFex93SJWkZ1YOyfv161KpVS++fm5sbvv76a6N3du3aNbz22mvYuHEjAGDevHno168fRo8ejdGjR+PIkSMv9UMQERHRixFCYMO+K7ibnI1XWtZEl1Y1LV2SjlKPoGRmZiIjIwNbt27F2LFj9W6/ceMGDhw4YNSOcnNz8emnn+r1q8yePZtXBxEREZnZ/pP3cOKPZDSs5YqR3eQWb4otrtSAsnv3bnz++edQq9Xo3r273u1CCKMbZGUyGdasWcMPFiQiIrKwS7cfI/rIDbg5y540xRp1QsWsSg0oI0eORL9+/RAcHGxwxlhHR0c0bdrUuB1JpTofNqixceNGrF+/Hl5eXliwYAE8PT2NLJ2IiIhe1MP0PKzcmQBbGwmmDfKHu7O9pUsySCKEEM9b6ebNm2jYsKHOsszMTLi6ur7wDiMjI+Hh4YFRo0YhLi4O7u7uaNq0KVavXo2kpCTtFUOGqFSFkFrBpU9ERETlUb5ChTmRx/Dng0xMG9IKPTr4WrqkEhn96T8jRozAf//7XwDAnDlzsHv3blStWhWrVq1C8+bNX2rnz54eCg0NxcKFC0tdPy0t96X2YwxvbxekpGSZbPvlAcegCMeBYwBwDACOgUZFGQchBP5v5yX8+SATXYNqIaihp9E/lynHwNvbxeByo046ff7559pTOXFxcfj111+xfv16jBw5EkuWLHnpoqZPn4579+4BAOLj49G4ceOX3hYRERGVLObEHZy68hDy2m4YHmb9r7dGHUG5ePEiIiMjAQAHDhxAjx490LFjR7Ru3RobNmwwakcJCQlYtGgR7t+/D6lUiv3792PUqFGYOXMmHB0d4eTkhC+++OLlfxIiIiIy6MLNVPz02y14uNhjyiB/SG2trym2OKMCilqthpOTEwDgxIkTmDZtWtGdpVIoFAqjdtSiRQtERUXpLe/Ro4extRIREdELSn6ci1W7LsHW1gbTBvvDrYrM0iUZxaiA0qBBA0RHR8POzg6JiYkICQkBUHS6x8fHx6QFEhER0cvJU6iwbPsF5ClUmNinKer7vPjFLZZiVECZOXMmpk2bBqVSiVmzZsHV1RVpaWmYNm0a5s+fb+oaiYiI6AWphcD3e/7Ag9RcdGtTB538y9cBBaMCSnBwME6ePAmlUqk91ePh4YG1a9ciKCjIpAUSERHRi9vz//7E2euP0KSuO4aGNnz+HayM0ZcZG5pojeGEiIjI+py9noKfj92Gl6sD3h7YArY21t8UW1z5q5iIiIhK9CA1B2t2/wGZtKgp1tWpfDTFFseAQkREVEHk5quwbPtF5CsLMa53E/jWMDwJWnnAgEJERFQBqIXA6t2XkPw4Fz3b10WHZjUsXdLfUmJACQwM1H7dsmVLsxRDREREL+fnY7dx4WYqmtf3xBuvlr+m2OJKbJJ1cXHBrFmzUK9ePahUKixbtgwlfa7gjBkzTFYgERERle701YfY8//+hLe7Ayb3bw4bG4mlS/rbSgwoERERWLlyJS5cuAC1Wo2ff/7Z4HoSiYQBhYiIyEISU7Lx/Z7LsLezxfTBAXB2tLN0SWWixIDSvXt3dO/eHUDR6Z5Dhw6ZrSgiIiJ6vpz8AizffhGKgkJMHdgCtas5W7qkMmNUk+zZs2e1Xz969AipqakmK4iIiIieT60WWLXzEh6m56FPR1+0aVLN0iWVKaMmalMqlfjyyy+xc+dO5ObmAgBcXV0xdOhQzJo1CzblcAIYIiKi8mz70ZtIuP0YAQ29MKhzA0uXU+aMCiiLFy/GkSNHMG3aNDRs2BBCCFy9ehUbN26Eq6sr3nrrLVPXSURERE+cvJyMfSfuorqHIyb1a1YhmmKLMyqg/PLLL1i7di3kcrl22auvvorg4GDMmTOHAYWIiMhM7iZnYd3ey3CQ2WL66wFwcqgYTbHFGXVuJjs7G40aNdJb3qxZMyQnJ5d5UURERKQvO68Ay3+6CKVKjbf6NkPNqlUsXZLJGBVQateujfj4eL3l8fHx8PEpXx/fTEREVB4VqtX4v58T8CgjHwNC6iNQ7m3pkkzKqFM8I0eOxLRp0zBo0CA0atRI24Oyc+dOTJ8+3dQ1EhERVXrRh2/i8p00BDauin6d6lm6HJMzKqCEh4dDJpNh06ZN2LlzJxQKBerVq4e5c+di+PDhpq6RiIioUotLSELs7/fg4+WEN/s2g42k4jXFFmdUQAGAwYMHY/DgwaashYiIiIr5MykTP/xyBY72RU2xjvZGv3SXa5zAhIiIyEpl5iix/KeLUKnUmNSvOWp4Olm6JLNhQCEiIrJCqkI1VvycgMeZCgx6pQFaNqpq6ZLMigGFiIjICm359Qau3UtHGz9v9Onoa+lyzI4BhYiIyMocu/AXfj2TiFreVTChT1NIKkFTbHFGd9oUFBRg69atOHXqFDIyMuDs7Iw2bdpor/AhIiKiv+/mXxmI2n8VVRykmD7YHw6yytEUW5zRP/Unn3yC5ORkdOnSBY6OjkhLS0NMTAzOnz+Pr7/+2pQ1EhERVQoZ2Qp899NFFKoFJvdvjmoelacptrhSA8qpU6fQpk0bAEBCQgJ27dqlc/uYMWPQqVMn01VHRERUSagK1fhuRwLSs5UY0rUhWjTwsnRJFlVqQPn0008RHByMWbNmoXbt2liwYAFCQ0Ph5OSEzMxM7N+/H82aNTNXrURERBXWfw9cw437GWjXtBp6tqtr6XIsrtQm2ejoaNjY2OCNN97AiBEj4OjoiMjISERERGDFihVwc3PDN998Y65aiYiIKqQjZ+/jyLm/ULeaM8b3rpxNscWVegRFJpNhzpw5eO211xAREYEePXogOjoatra25qqPiIioQruemI5NB67B2dEO0wb7w96Or7GAkZcZBwYGYtu2bcjMzMTQoUNx8+ZNU9dFRERU4aVlKfDdjgQIAUwZ0BxV3R0tXZLVMCqgpKamIi8vDxEREXj//ffxzjvvYO3atRBCmLo+IiKiCqlAVYjlP11EZo4Sw0IboWk9T0uXZFVKDSj/+9//EBoaik6dOiE4OBg9evSAnZ0dtm/fjlu3bmHkyJG4d++euWolIiKqEIQQiIq9htsPMhHcogZea1Pb0iVZnVIDyieffIL3338f586dw6lTpzBlyhR8/PHHqFKlCj777DNMmjQJb775prlqJSIiqhAOnbmP4xcewLeGC8b08GNTrAGlBpSMjAx06tQJDg4OcHZ2xiuvvILHjx9rb+/SpQu2bt1q8iKJiIgqiqt307D51+twcbLD9MH+kLEp1qBSr+IZMGAA+vfvj6CgIADAmTNnMHToUJ113NzcTFcdERFRBZKakY8VPycAAKYObAFPVwcLV2S9Sg0oERERCAsLw8WLFyGRSDBy5EjtzLIv49q1a5g6dSrGjRuHUaNG4cGDB5g7dy4KCwvh7e2NxYsX83N9iIioQlIWFDXFZuUWYFR3Ofzqeli6JKv23M/i6dixIzp27Pi3d5Sbm4tPP/1UZ1vLli3DiBEj0KtXLyxZsgTbtm3DiBEj/va+iIiIrIkQAht+uYI7yVnoHOCDroG1LF2S1TPqMuOyIJPJsGbNGlSrVk27LD4+HmFhYQCArl27Ii4uzlzlEBERmc2BU4mIu5SMhjVdMao7m2KNYbbPcJZKpZBKdXeXl5enPaXj5eWFlJQUc5VDRERkFn/8+RhbD92AWxUZpg7yh53UbMcGyjWzBZTnMWbSNw8PJ0ilput29vZ2Mdm2ywuOQRGOA8cA4BgAHAONlx2HpNQcrNr1B2xsgI/Gt4e8fvmdjM3cfwsWDShOTk7Iz8+Hg4MDkpOTdU7/GJKWlmuyWry9XZCSkmWy7ZcHHIMiHAeOAcAxADgGGi87DgplIT7feBpZuUqM7emHqs525XY8Tfm3UFLwsehxpuDgYOzfvx8AEBsbi86dO1uyHCIiojIhhMD6fZdx72E2ugTWwqut2BT7osx2BCUhIQGLFi3C/fv3IZVKsX//fvz73//GvHnzsGXLFtSsWRMDBw40VzlEREQm80v8XZy8/BCNarthxGuNLV1OuWS2gNKiRQtERUXpLV+/fr25SiAiIjK5hFup2HbkJjxc7PHOwBaQ2rIp9mVw1IiIiMpIclouVu68BFtbG0wb7A83Z3tLl1RuMaAQERGVgXylCsu3X0SuQoUxPfxQ38fV0iWVawwoREREf5MQAmv3XMb9RzkIa10bIQE+li6p3GNAISIi+pv2xN3B6Wsp8KvjjmGhjSxdToXAgEJERPQ3nLvxCD8fvQUvV3tMGcSm2LLCUSQiInpJD1JzsGb3JUilNpg2OACuTjJLl1RhMKAQERG9hDyFCst/uog8RSHG9WoC3xr8WICyxIBCRET0gtRCYM3uP/AgNRc92tVBx+Y1LF1ShcOAQkRE9IJ2Hb+NczceoVk9D7zRpaGly6mQGFCIiIhewJlrKdj1vz9R1c0Bbw9oAVsbvpSaAkeViIjISPcf5WDNnj8gsyuaKdbZ0c7SJVVYDChERERGyM0vQOT2C1AoCzGhd1PUrc6mWFNiQCEiInoOtVpg1a4/8DAtD707+KJd0+qWLqnCY0AhIiJ6jh3HbuHirVS0aOCJwa80sHQ5lQIDChERUSmOn7+PvXF3UM3DEZP7N4eNjcTSJVUKDChEREQluPcwG99uPgt7mS2mD/ZHFQc2xZqL1NIFEBERWRMhBB6m5eHy3TTExN2BQlmIdwb5o5a3s6VLq1QYUIiIqFITQiAlPQ9X7qbjyt00XL2bjrQshfb2ET2aoLWftwUrrJwYUIiIqFIRQiAlIx9X76Thyt00XCkWSFyd7NC2STU0qeuOJr4eCGhSAykpWRasuHJiQCEiogrvkc4RkjSkZj4NJM6Odmjj540mvh7wq+uBml5OkEjYCGtpDChERFThpGbkPzk6UnTK5lFGvvY2Z0c7tJZrAok7alatAhsGEqvDgEJEROXe48x8XL2bjstPjpCkpD8NJFUcpAhsXBVNfD3QpK4HankzkJQHDChERFTupGUpcPXu0x6Sh2l52tuc7KVo1UgTSNxRu5ozA0k5xIBCRERWLz1bgatPekiu3E1H8uNc7W2O9rZo2dBLe4SkTjVnTqZWATCgEBGR1cnIUT45QpKOq3fT8CD1aSBxkNkioKEXmtQt6iHxre7CQFIBMaAQEZHFZeYqnx4huaMbSOxltvBv4KW97LdudWfY2nAi9IqOAYWIiMwu60kg0YSS+49ytLfZ29miRX1P+D0JJL7VXSC1ZSCpbBhQiIjI5LLzCp4EkqLG1sSUp4FEJrVB83pFc5A08fVAvRoMJMSAQkREJpCTX4Brd9O1k6MlPsyGeHKbndQGTZ9cYdPE1wP1fVwZSEgPAwoREf1tufkFuHYvQzs52r3kp4FEamtTdLrmyRGS+j6usJMykFDpGFCIiOiF5earcD3x6WW/d5OzIJ4kEqmtBPI67vCr646mvh5oUNMVdlJbyxZM5Q4DChERPVeeQoXriRnaz7L5M+lpILG1kaBxLTdtD0nDmq6Q2TGQ0N/DgEJERHpUhWqcufIQJy7ex9W76fjzQRbUTxKJrY0EDWu5FZ2yqeuOhrXcYM9AQmWMAYWIiHRcuJmKLYeua+cisbWRoH5NlyeBxAONarnBXsZAQqbFgEJERACAvx7lYMuhG7h4KxUSCdCtXV341/NAo9pucJDx5YLMi39xRESVXHZeAXYev43DZ+5DLQSa1HXH8NfkCGrug5SULEuXR5UUAwoRUSWlKlTjyNn72Hn8NnLyVajm7ohhoY3QqnFVSPjpv2RhFg0o8fHxmDFjBho3bgwAkMvlWLBggSVLIiKqFJ7tM3G0t8XQro0Q1ro25ychq2HxIyjt2rXDsmXLLF0GEVGlcP9RDrYcuo6EW48hkQBdWtXEwM4N4FpFZunSiHRYPKAQEZHpFe8zaerrgfCwxqhTzdnSpREZZPGAcuPGDbz99tvIyMjAtGnT0KlTJ0uXRERUYagK1Th89j52sc+EyhmJEJq5AM0vOTkZp0+fRq9evXDv3j2MGTMGsbGxkMkMH2pUqQoh5XTJRERGOXU5GWt3JSDxYTacHKQI7+aHviH1Oe08lQsWPYJSvXp19O7dGwBQt25dVK1aFcnJyahTp47B9dPSck1Wi7e3S6W/nI5jUITjwDEAyvcY6PWZBNbCwM714eokQ/oLPI+W5zEoSxwH046Bt7eLweUWDSi7du1CSkoKJk6ciJSUFKSmpqJ69eqWLImIqNzKzivAzmO3cfjs0z6T4WGNUZt9JlQOWTSghIaG4v3338evv/6KgoICLFy4sMTTO0REZJhen4nHkz6TRuwzofLLogHF2dkZK1eutGQJRETl2oWbj7D51xtIepwLR3sphoUWzWciteV8JlS+WfwqHiIienH3H+Vgy6/XkXBbv8+EqCJgQCEiKkfYZ0KVBQMKEVE5oCpU4/CZos/NyVWoUN3DEcNCG6NlIy/2mVCFxIBCRGTFhBC4eCuVfSZU6TCgEBFZqeJ9Jl0Da2EA+0yokmBAISKyMsX7TJrVK/rcnNre7DOhyoMBhYjISrDPhOgpBhQiIgsTQuDCzVRsOfS0zyQ8tBFC2WdClRgDChGRBd1PycbmQzdwSdNnElQLA0LYZ0LEgEJEZAFZuUrsPH4bR87+xT4TIgMYUIiIzMhgn0lYY7RsyD4TomcxoBARmQH7TIheDAMKEZGJGeozGRhSHy7sMyEqEQMKEZGJZOUq8fPx2/jtSZ9J83oeGMY+EyKjMKAQEZUxVaEah87cxy5Nn4mnE4aFNmKfCdELYEAhIiojmj6TzYduIPlxLpzspQgPa4zQoFrsMyF6QQwoRERlgH0mRGWLAYWI6G/Q9JkcOXsfQoB9JkRlhAGFiOglqArVOHQ6ETv/9yfynvSZhIc2QgD7TIjKBAMKEdELEELg/JP5TNhnQmQ6DChEREa6n5KNzb9ex6U/02AjkSD0yefmsM+EqOwxoBARPYehPpPwsMaoxT4TIpNhQCEiKgH7TIgshwGFiKgYIQROXkrC6h0XkJyWByd7KYaHNUZX9pkQmQ0DChFVKmq1QGauEunZCqRnPflf+0+J9CwF0rIVyMot0PaZDOzcAM6OdpYunahSYUAhogpBLQSycguQUSxsaL5Oe7I8M0cJIUrejszOBh7O9vBvVBW92tZhnwmRhTCgEJFVE0IgO68A6dlKZGQrngQNzRGQp19n5ihRqC45edhJbeDuLEOjWm5wd7Yv+ucie/q1c9HXDjJbSCQSeHu7ICUly4w/KRE9iwGFiCxCCIFchUonZKRnF/s6S4mMHAVUhSUHD6mtBG5V7FHPx0UvbLi7PP3eyV7KxlaicoQBhYjKlBAC+cpC3SMcOYb7PQpU6hK3Y2sjgZuzDHWquRQFjmfChof2CIg9qjgweBBVRAwoRGQ0hSZ4aE61PDnCUbzfQ1FQWOI2JBLArYoMtapWMXC04+kpF2cnO9gweBBVWgwoRAQhBHLyVXiUkYdH6flQXn6IxKTMJw2nT4985ClKCR4AXKrIUN3TUTd4FOv3cHWSwcaGwYOISseAQlRJ5ClUSEnPQ2pGPlIy8rVh5NGTr/OVJYcPZ0c7eLk6PtNUWix8OMvgWkXGOUKIqMwwoBBVEAplIR5l5uNRep42dDzKyH8SQvKQk68yeD97mS283RxQ1c0RVd0cUNXdEfVqucNGqOHuLINbFXvYSRk8iMi8GFCIyokClRqpmfpHPlLS85GakYfM3AKD97OT2qCqmwMa1HRDVXcHVHVzgLebI7zcHODt7miwyZSX2BKRpTGgEFkJVaEaaVkK7RGQlIyi4JGSUXRUJCNbCUMX29raSODl5oDa1ZxR1c0R3u4OReHjyRER1yoyXuVCROUOAwqRmajVAunZiqLwoe0FeXo0JC1LAbWBKU4lEsDTxQF+dd2fBg/3p6dk3J3t2XRKRBWOxQPK559/jvPnz0MikWD+/PkICAiwdElEL0UIgcwcpcEG1Efp+UjNzDc406kEgLuLPRrUcoW3mwO83ByLekLciwKIh4s9m0+JqNKxaEA5efIk7ty5gy1btuDmzZuYP38+tmzZYvY6jp7/C1H7r+q8eOi8H5U8++XTb0o6ai4xcGeJpPiS0rddwpcGt136Os8uN3yDZrGt55o3GAAAEKFJREFUjQ0kEsDO1gZ2UhtIbW0glUpgZ1v0tXaZrQ3spBKdZXa2NpBKn13v6f2k0qJl2u9tJU+/17m/BLY21vtCrJlyvSh0GAghGfklTjzm6mQH3xouRU2o2mbUoqMhnq4ObEIlIirGogElLi4Or732GgCgYcOGyMjIQHZ2NpydzfvhXNXcHdG8gRfyFU+ucnjmMPuz73d13vuKZ78UJSzX25zOCsLw3V5o26Vuv8RtPnvfp9/Z2NhAoVRBqSpETn4BVIUCqkJ1qZ9vUtZsJBKdUKQTgp6EIs2ypwHHQIjSBiLJM/e3MXB/iTYg2UltYGsjQZZSjet/pj5zNcyTZtSMfChKuBS3ioMUNb2qaJtQn70ipqqbA+ztbM02hkREFYFFA8qjR4/QvHlz7feenp5ISUkxe0Bp4uuBzm3qVvqrFkq6ckOtLgoqBYVqqFRP/i8U2q8LVGqoCov+FWhuVxm6T9FyQ/dRFQoUqAqL/teuW3S/3PwCFDwJSyqV2mCjqKk5yGy1Taea/o+i0zFFXzs5WPxsKRFRhWJVz6qitM9AR9ELqCmZevvlAceANPi3wDEAOAYaHAfzj4FFT3xXq1YNjx490n7/8OFDeHt7W7AiIiIisgYWDSidOnXC/v37AQCXLl1CtWrVzH56h4iIiKyPRU/xBAUFoXnz5ggPD4dEIsEnn3xiyXKIiIjISkjE8xo/iIiIiMyMky8QERGR1WFAISIiIqtjVZcZW0plnm7/2rVrmDp1KsaNG4dRo0bhwYMHmDt3LgoLC+Ht7Y3FixdDJpNZukyT+uqrr3D69GmoVCpMnjwZ/v7+lWoM8vLyMG/ePKSmpkKhUGDq1Klo0qRJpRoDjfz8fPTt2xdTp05Fx44dK9UYxMfHY8aMGWjcuDEAQC6X480336xUYwAAu3btwvfffw+pVIp3330Xfn5+lW4MoqOjsWvXLu33CQkJ+PHHH7Fw4UIAgJ+fH/7xj3+YvhBRycXHx4tJkyYJIYS4ceOGGDp0qIUrMp+cnBwxatQoERERIaKiooQQQsybN0/ExMQIIYT4+uuvxaZNmyxZosnFxcWJN998UwghxOPHj8Wrr75a6cZg7969YvXq1UIIIRITE0X37t0r3RhoLFmyRAwePFhs37690o3BiRMnxPTp03WWVbYxePz4sejevbvIysoSycnJIiIiotKNQXHx8fFi4cKFYtSoUeL8+fNCCCFmz54tjhw5YvJ9V/pTPCVNt18ZyGQyrFmzBtWqVdMui4+PR1hYGACga9euiIuLs1R5ZtG2bVssXboUAODq6oq8vLxKNwa9e/fGW2+9BQB48OABqlevXunGAABu3ryJGzduoEuXLgAq32PBkMo2BnFxcejYsSP+f3v3HtPU/f4B/A3l4ri4TQTmGERnUkCR1smQOQpaghccMAbMSwJsZhpkZm5OJm7GjGCiMOfQYpybsmjAAILgZZCA22RG7t6JmszoJiAXBTootAg9z/cPv5wv9bLhbz8Qd55XQtKe03Penz6IfdLPudjZ2cHJyQkpKSmSq8GDdu/ejZUrV6KpqUmcXRitOki+Qbl79y5efPFF8fng5falwMLCAuPGjTNZptfrxa8vHRwc/vW1kMlksLGxAQDk5+cjICBAcjUYtHTpUqxfvx6ff/65JGuQmpqKpKQk8bkUa3D9+nXEx8dj2bJlOHPmjORq0NjYCIPBgPj4eCxfvhyVlZWSq8FQly5dwqRJkyCTyTB+/Hhx+WjVgY9BeQDxWdciKdXi5MmTyM/PR2ZmJubPny8ul1INcnJycPXqVSQmJpq8bynUoKioCEqlEq6uro9cL4UaTJ48GWvWrMGiRYvQ0NCA2NhYGI3/uzmmFGoAAFqtFhkZGbh9+zZiY2Ml97cwVH5+PiIiIh5aPlp1kHyDwpfbN2VjYwODwYBx48ahtbXVZPrn3+r06dP49ttvsW/fPtjb20uuBvX19XBwcMCkSZPg6ekJo9EIW1tbSdXg1KlTaGhowKlTp9DS0gIrKyvJ/TtwdnZGSEgIAMDNzQ0TJ07E5cuXJVUDBwcHzJw5ExYWFnBzc4OtrS1kMpmkajBUdXU1Nm3aBDMzM2i1WnH5aNVB8lM8fLl9U3PmzBHrUVpaCpVK9ZRHNLK6u7uRlpaGvXv34oUXXgAgvRrU1dUhMzMTwP0pz97eXsnVID09HQUFBcjLy0N0dDQSEhIkV4Njx45h//79AIA7d+6gvb0d77zzjqRq4O/vj6qqKgiCgM7OTkn+LQxqbW2Fra0trKysYGlpiVdffRV1dXUARq8OfCVZANu3b0ddXZ14uX0PD4+nPaRRUV9fj9TUVDQ1NcHCwgLOzs7Yvn07kpKS0NfXh5dffhlbt26FpaXl0x7qiMnNzYVGo8GUKVPEZdu2bcOmTZskUwODwYAvvvgCzc3NMBgMWLNmDby8vLBhwwbJ1GAojUYDFxcX+Pv7S6oGOp0O69evR1dXF/r7+7FmzRp4enpKqgbA/anO/Px8AMDq1asxY8YMydUAuP/5kJ6ejn379gG4f3zS5s2bIQgCFAoFNm7cOOJj4AaFMcYYY2OO5Kd4GGOMMTb2cIPCGGOMsTGHGxTGGGOMjTncoDDGGGNszOEGhTHGGGNjDjcojDH2X42NjXB3d0dFRcWo5Gk0GgQEBIxKFmPPGm5QGHsG6fV6vP7661AoFCZXeGSMsX8LblAYewYdO3YMTk5OmDp1Ko4cOfK0h8MYY//vuEFh7BmUnZ2N0NBQhIWFIScnR7x5l06ng7e3NwoLC01ef+TIESgUCuh0OhiNRmRkZGDBggVQKBQICgoSrxY5+NrZs2cjOzsbPj4+4r6KiooQGhqKmTNnwt/fH5s3b4bBYBC3Ky8vx4IFC+Dt7Y3o6GhUVVXB3d0d1dXVAPC3uY9SXl6OqKgozJo1C35+fvjkk0/Q0dEhrr906RIiIiLg7e2N0NBQ1NTUQKlUmjRtg7VSKpUICAjAV199hYGBgWHV+a/GfPPmTbi7u6OmpsZkG41Gg7lz50IQBBgMBmzZsgVqtRre3t5YtGgRioqKhpXNmOQRY+yZUltbS56entTS0kLt7e00ffp0Ki8vF9evXbuWVq5cabLNihUraN26dURElJ6eTkFBQXTt2jUaGBig2tpaeu2116iwsJCIiAoKCkihUNDGjRtJp9ORIAh06dIlksvl9PPPPxMR0R9//EFvvvkm7dy5k4iIuru7SalUUnJyMvX29tLVq1cpPDyc5HI5VVVVDSv3Qa2trTR9+nTKysoio9FIbW1t9NZbb9Fnn31GRESCIJBaraaEhATq6uqiW7duUUxMDMnlciooKCAiosOHD5Ovry/V1taS0Wikq1ev0ty5c0mj0Twys6GhgeRyOZ05c2ZYY46KiqIvv/zSZB8LFiygHTt2EBFRYmIiRUVF0a1bt6i/v59KS0tp2rRpVFNTQ0REu3btIpVK9de/cMYkir9BYewZk52dDZVKBWdnZ0yYMAFBQUE4dOiQuD4sLAwVFRXo6uoCAHR0dKCqqgoREREQBAGHDh3CypUr4e7uDplMBh8fH0RHRyMvL0/ch16vR1xcHGxtbWFmZgYvLy9UVlZi3rx5AO7f7XbWrFm4ePEiAODXX3+FXq/HRx99hOeeew4eHh5YtmyZuL/h5g7l5OSE06dPY+nSpTA3N4ejoyNUKpWYefnyZTQ2NuLDDz+Evb09XF1dsWrVKpN9ZGVlYcmSJfDx8YG5uTk8PDywYsUKHD58+G/rPJwxh4WFobS0FIIgAACuXLmCmzdv4u2334ZWq8Xx48exdu1auLq6wsLCAsHBwVCr1Y99z4yx/7F42gNgjA1fW1sbysrKkJ6eLi5799138cEHH6CpqQkuLi5QqVSws7NDWVkZIiMjUVJSAgcHB8yZMwcdHR3QarVISUnBli1bxH0QERwdHU2y3NzcxMeCIODgwYM4ceIE2traQEQYGBiAj48PAKClpQXjx48X7wgNAEqlUnz8JLlDHT16FHl5ebh9+zaMRiOMRiNeeuklAEBzc/ND4xyaCQA3btzAb7/9hh9++MEkEwDu3bsHKyurx2YPZ8yLFy/Gtm3bUFNTAz8/P5w4cQJKpRJTpkzBxYsXIQgC4uPjYWZmZrK9QqF4bC5j7D5uUBh7huTm5qK/vx8bNmww+dATBAE5OTn49NNPYWlpiZCQEJSUlCAyMhLFxcUICwuDubk5xo0bBwD45ptvEBwc/JdZQ+/YumfPHhw8eBA7d+6En58fLC0tsW7dOty5c0fMf/AOr0PH9yS5gwoLC5GWlobU1FTMnz8f1tbW+Prrr/Hjjz+KmQ+Oc2jmYG5CQgLee++9YWU+uO3fjXnChAnw9/dHSUkJZs+ejZKSEvFbHGtrawBAXl4epk2b9sT5jEkdT/Ew9ozo7+9Hbm4u3n//fRw9ehRFRUXiT3x8PPLz83Hv3j0AQHh4OKqqqnD9+nWcPXsWERERAAA7OztMnDgRV65cMdl3a2uruO2jnD9/Hr6+vlCpVLC0tIQgCKivrxfXOzo6orOzEzqdTlx24cIF8fH/Jff8+fOYOnUqQkNDxQ/7wemdwUwAaGhoeGQmAEyePPmhzPb2dvT09Dz2vT7pmMPDw/HTTz/h3LlzuHv3LkJCQgAArq6ukMlkD21/+/btYR+ky5iUcYPC2DOirKwMHR0diIuLwyuvvGLyExsbi+7ubpSUlAAAFAoFXFxckJKSAi8vL0ydOlXcT1xcHLKzs1FZWQmj0Yhr165h+fLl2L9//2Oz3dzccOPGDXR2duLu3btITk6Gvb092traMDAwgICAAMhkMuzevRt9fX24du3aQ8dZPGmum5sbWlpa0NTUhD///BMZGRno7e2FVqtFb28vlEolHB0dsWfPHvT09KCxsRGZmZkPZRYXF6OkpAT9/f1oaGjAqlWrsHXr1mHVfDhjVqvV0Ov12LVrF9RqNZ5//nkAgK2tLaKiorB7925cuXIFRqMRtbW1iIiIQHFx8bDyGZO0p3mELmNs+JYvX06rV69+7PqPP/6YlixZIj7XaDQkl8spKyvL5HUDAwOUnp5OgYGB5OXlRWq1mjQaDRmNRiK6fxaPXC6n/v5+cZuWlhaKiYkhhUJBarWaCgsL6ezZszR79mxauHAhEREdP36cAgMDSaFQUExMDJWXl5NcLhfPWPm73AfpdDpKSEggpVJJ/v7+9P3339Pvv/9OgYGB5OvrS3q9nioqKmj+/Pk0Y8YMioyMpHPnzpFcLjc5M+jAgQMUHBxMXl5eFBAQQCkpKaTX6x+Z+eBZPMMdc1JSEsnlcvrll19Mlvf29lJycjK98cYb5O3tTQsXLjT5ffBZPIw9nhnRf48YY4yxf2BgYABmZmaQyWQAgOrqasTGxuLkyZNwdXUdkUxBEGA0GsXjUBobGxEUFIQDBw7Az89vRDIZY6ODp3gYY/9YX18f/P39kZaWhr6+PnR0dOC7776Dp6cnXFxcRiw3PDwciYmJ0Ol06OnpQUZGBpycnODt7T1imYyx0cENCmPsH7O2tkZGRgYuXLgAPz8/LF68GNbW1tBoNDA3H7n/Znbs2AGtVovAwEDMmzcPzc3N2Lt3L2xsbEYskzE2OniKhzHGGGNjDn+DwhhjjLExhxsUxhhjjI053KAwxhhjbMzhBoUxxhhjYw43KIwxxhgbc7hBYYwxxtiY8x8oMb7XzrmyxAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stroke_cases = Counter(df['binned_bmi'][df['stroke']==1])\n",
        "total_cases = Counter(df['binned_bmi'])\n",
        "for key in total_cases:\n",
        "    if(key not in stroke_cases.keys()):\n",
        "        stroke_cases[key] = 0\n",
        "stroke_pct = 100*np.array([item[1] for item in sorted(stroke_cases.items())])/np.array([item[1] for item in sorted(total_cases.items())])\n",
        "labels = [item[0] for item in sorted(total_cases.items())]\n",
        "plt.style.use('seaborn')\n",
        "plt.figure(figsize=(9,5))\n",
        "plt.ylim(0,30)\n",
        "plt.title('Stroke risk vs BMI level (All age groups)',fontsize=20)\n",
        "plt.ylabel('% of strokes',fontsize=15)\n",
        "plt.xlabel('BMI level',fontsize=15)\n",
        "plt.plot(labels[:-1],stroke_pct[:-1],'b')"
      ],
      "metadata": {
        "id": "dHkYUIUPw9hQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64e0251c-c6a6-47c2-ad45-6452df188e10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc8f26cc290>]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "id": "H2N8PahGy_bd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "572b55eb-169a-498e-ab08-07dd7b1fadda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                   0\n",
              "gender               0\n",
              "age                  0\n",
              "hypertension         0\n",
              "heart_disease        0\n",
              "ever_married         0\n",
              "work_type            0\n",
              "Residence_type       0\n",
              "avg_glucose_level    0\n",
              "bmi                  0\n",
              "smoking_status       0\n",
              "stroke               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.drop(['id'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "9U08KKX2zCy3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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            "text/plain": [
              "     gender   age  hypertension  heart_disease ever_married     work_type  \\\n",
              "249    Male   3.0             0              0           No      children   \n",
              "250    Male  58.0             1              0          Yes       Private   \n",
              "251  Female   8.0             0              0           No       Private   \n",
              "252  Female  70.0             0              0          Yes       Private   \n",
              "253    Male  14.0             0              0           No  Never_worked   \n",
              "\n",
              "    Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
              "249          Rural              95.12  18.0          Unknown       0  \n",
              "250          Urban              87.96  39.2     never smoked       0  \n",
              "251          Urban             110.89  17.6          Unknown       0  \n",
              "252          Rural              69.04  35.9  formerly smoked       0  \n",
              "253          Rural             161.28  19.1          Unknown       0  "
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    {
      "cell_type": "code",
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        "dataset.info()"
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      "metadata": {
        "id": "-3-DLm6AzGyw",
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        },
        "outputId": "b64c52a0-cb24-4139-def7-c3545bb08f30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5110 entries, 249 to 248\n",
            "Data columns (total 11 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   gender             5110 non-null   object \n",
            " 1   age                5110 non-null   float64\n",
            " 2   hypertension       5110 non-null   int64  \n",
            " 3   heart_disease      5110 non-null   int64  \n",
            " 4   ever_married       5110 non-null   object \n",
            " 5   work_type          5110 non-null   object \n",
            " 6   Residence_type     5110 non-null   object \n",
            " 7   avg_glucose_level  5110 non-null   float64\n",
            " 8   bmi                5110 non-null   float64\n",
            " 9   smoking_status     5110 non-null   object \n",
            " 10  stroke             5110 non-null   int64  \n",
            "dtypes: float64(3), int64(3), object(5)\n",
            "memory usage: 608.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# normalizing continous feature columns\n",
        "scaler = StandardScaler()\n",
        "for feature in dataset.columns:\n",
        "    if(feature == 'age' or feature == 'avg_glucose_level' or feature == 'bmi'):\n",
        "        dataset[[feature]] = scaler.fit_transform(dataset[[feature]])\n",
        "    else:\n",
        "        continue\n",
        "dataset"
      ],
      "metadata": {
        "id": "xUVNEbRczJEt",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "895b099a-afe5-4542-8922-f49e28d5e819"
      },
      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "     gender       age  hypertension  heart_disease ever_married  \\\n",
              "249    Male -1.779117             0              0           No   \n",
              "250    Male  0.653388             1              0          Yes   \n",
              "251  Female -1.557980             0              0           No   \n",
              "252  Female  1.184116             0              0          Yes   \n",
              "253    Male -1.292616             0              0           No   \n",
              "..      ...       ...           ...            ...          ...   \n",
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              "245  Female -1.292616             0              0           No   \n",
              "246  Female  1.405253             0              0          Yes   \n",
              "247    Male  1.228344             1              0          Yes   \n",
              "248  Female  1.537935             0              0          Yes   \n",
              "\n",
              "         work_type Residence_type  avg_glucose_level       bmi  \\\n",
              "249       children          Rural          -0.243549 -1.416293   \n",
              "250        Private          Urban          -0.401679  1.337478   \n",
              "251        Private          Urban           0.104735 -1.468251   \n",
              "252        Private          Rural          -0.819532  0.908825   \n",
              "253   Never_worked          Rural           1.217610 -1.273408   \n",
              "..             ...            ...                ...       ...   \n",
              "244        Private          Rural          -0.467935  1.012741   \n",
              "245       children          Rural          -1.064899  0.259351   \n",
              "246  Self-employed          Rural          -0.603980  0.051519   \n",
              "247  Self-employed          Rural          -0.405213  0.203664   \n",
              "248        Private          Rural          -0.603759 -1.208461   \n",
              "\n",
              "      smoking_status  stroke  \n",
              "249          Unknown       0  \n",
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              "251          Unknown       0  \n",
              "252  formerly smoked       0  \n",
              "253          Unknown       0  \n",
              "..               ...     ...  \n",
              "244          Unknown       1  \n",
              "245          Unknown       1  \n",
              "246  formerly smoked       1  \n",
              "247          Unknown       1  \n",
              "248          Unknown       1  \n",
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              "[5110 rows x 11 columns]"
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              "      <td>Female</td>\n",
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              "<p>5110 rows × 11 columns</p>\n",
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              "\n",
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              "    }\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a21622ea-1618-4f4f-9cb6-070012be3a57');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Label Encoding of Categorical features\n",
        "for feature in dataset.columns:\n",
        "    if(dataset[feature].dtype == 'object'):\n",
        "        dataset[feature] = LabelEncoder().fit_transform(dataset[feature])"
      ],
      "metadata": {
        "id": "-F7qOXukzLYu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p4qVnYXhg9Qf",
        "outputId": "ea58498f-71cc-47a1-c26c-62f80f35d738"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      \n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7fd6bbe6-7018-4391-95ec-bcc92ce934e8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the dataset into train and test\n",
        "dataset_train, dataset_test = train_test_split(dataset, random_state = 42, test_size = 0.2, stratify = dataset.stroke)"
      ],
      "metadata": {
        "id": "Q-DN_IE4zNTh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# splitting the train and test data into inputs(features) and outputs(targets)\n",
        "X_train = dataset_train.iloc[:,:10]\n",
        "y_train = dataset_train.iloc[:,-1]\n",
        "X_test = dataset_test.iloc[:,:10]\n",
        "y_test = dataset_test.iloc[:,-1]"
      ],
      "metadata": {
        "id": "g6BKixpuzOyQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sampling of the dataset since, number of positive samples are very low in the original dataset\n",
        "counter = Counter(y_train)\n",
        "print('Before', counter)\n",
        "oversampler = RandomOverSampler(sampling_strategy='minority')\n",
        "x_sm, y_sm = oversampler.fit_resample(X_train, y_train)\n",
        "counter = Counter(y_sm)\n",
        "print(\"After\", counter)"
      ],
      "metadata": {
        "id": "ViM6h6lKzQWt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cf166d6-0957-4cca-87ea-252c4862715f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before Counter({0: 3889, 1: 199})\n",
            "After Counter({0: 3889, 1: 3889})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Models"
      ],
      "metadata": {
        "id": "mq-b194Ez6d2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# function for specificity and sensitivity\n",
        "def calc_sens_spec(y_true,y_pred):\n",
        "    conf_matrix = confusion_matrix(y_true,y_pred)\n",
        "    TP = conf_matrix[1][1]\n",
        "    TN = conf_matrix[0][0]\n",
        "    FP = conf_matrix[0][1]\n",
        "    FN = conf_matrix[1][0]\n",
        "    # calculate the sensitivity\n",
        "    sensitivity = TP / (TP + FN)    \n",
        "    # calculate the specificity\n",
        "    specificity = TN / (TN + FP)\n",
        "    return sensitivity,specificity"
      ],
      "metadata": {
        "id": "GQwFdAnmFToB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = []\n",
        "models_accuracy = []\n",
        "models_sensitivity = []\n",
        "models_specificity = []\n",
        "models_f1_score = []\n",
        "classifiers = []"
      ],
      "metadata": {
        "id": "2fB6yEM8MR7m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# custom loss function to prioritize both sensitivity and specificity  \n",
        "def custom_loss_function1(y_test,pred):\n",
        "    sens = calc_sens_spec(y_test,pred)[0]\n",
        "    spec = calc_sens_spec(y_test,pred)[1]\n",
        "    return 2*sens*spec/(sens + spec)"
      ],
      "metadata": {
        "id": "dfRIi5VbD4Xx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def append_scores(model,y_test,y_pred):\n",
        "    models.append(model)\n",
        "    models_accuracy.append(accuracy_score(y_test, y_pred))\n",
        "    models_sensitivity.append(calc_sens_spec(y_test,y_pred)[0])\n",
        "    models_specificity.append(calc_sens_spec(y_test,y_pred)[1])\n",
        "    models_f1_score.append(f1_score(y_test, y_pred))\n",
        "    return\n",
        "\n",
        "def show_analysis(model,y_test,pred,y_prob):\n",
        "    print(\"Classification report for {}: \\n{}\".format(model,classification_report(y_test,pred)))\n",
        "    print(\"Confusion matrix for {}: \\n{}\".format(model,confusion_matrix(y_test,pred)))\n",
        "    print(\"Accuracy score for {}: {:.2f}\".format(model,accuracy_score(y_test,pred)))\n",
        "    # calculate sensitivity, specificity, and auc\n",
        "    sens_dtree,spec_dtree = calc_sens_spec(y_test,pred)\n",
        "    fpr, tpr, _ = roc_curve(y_test,  y_prob[:,1])\n",
        "    auc_dtree = roc_auc_score(y_test, y_prob[:,1])\n",
        "    print(\"Sensitivity score for {}: {:.2f}\".format(model,sens_dtree))\n",
        "    print(\"Specitivity score for {}: {:.2f}\".format(model,spec_dtree))\n",
        "    print(\"AUC score for {}: {:.2f}\".format(model,auc_dtree))\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.plot(fpr, tpr, color='blue', label='ROC curve (area = %0.2f)' % auc_dtree)\n",
        "    ax.plot([0, 1], [0, 1], color='green', linestyle='--')\n",
        "    ax.set_xlim([-0.05, 1.0])\n",
        "    ax.set_ylim([0.0, 1.05])\n",
        "    ax.set_xlabel('False Positive Rate')\n",
        "    ax.set_ylabel('True Positive Rate')\n",
        "    ax.set_title('Receiver Operating Characteristic {}'.format(model))\n",
        "    ax.legend(loc=\"lower right\")\n",
        "    plt.show()\n",
        "    return"
      ],
      "metadata": {
        "id": "nnEVucTs578e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##DT"
      ],
      "metadata": {
        "id": "Udimz69IDjtl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training Decision Tree Classifier\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit(x_sm, y_sm)\n",
        "y_pred = model.predict(X_test)\n",
        "y_prob = model.predict_proba(X_test)\n",
        "classifiers.append(model)\n",
        "\n",
        "append_scores('DT',y_test,y_pred)\n",
        "show_analysis('DT',y_test,y_pred,y_prob)"
      ],
      "metadata": {
        "id": "waVSYeu2DjH8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "outputId": "0e938c86-320c-4e46-ba79-fe260e9ab631"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for DT: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.98      0.97       972\n",
            "           1       0.23      0.14      0.17        50\n",
            "\n",
            "    accuracy                           0.93      1022\n",
            "   macro avg       0.59      0.56      0.57      1022\n",
            "weighted avg       0.92      0.93      0.93      1022\n",
            "\n",
            "Confusion matrix for DT: \n",
            "[[948  24]\n",
            " [ 43   7]]\n",
            "Accuracy score for DT: 0.93\n",
            "Sensitivity score for DT: 0.14\n",
            "Specitivity score for DT: 0.98\n",
            "AUC score for DT: 0.56\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Grid Search for Decision tree Classifier\n",
        "params = {\n",
        "        'criterion': ['gini','entropy'],\n",
        "        'min_samples_leaf': range(1,3),\n",
        "        'max_depth': range(2,6)\n",
        "        }\n",
        "scorer = make_scorer(custom_loss_function1,greater_is_better=True)\n",
        "clf = GridSearchCV(DecisionTreeClassifier(), params, scoring = scorer)\n",
        "clf.fit(x_sm,y_sm)\n",
        "# clf.cv_results_\n",
        "print(clf.best_score_)\n",
        "print(clf.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k7C6-RWIGW70",
        "outputId": "917bb056-af1e-467d-b730-b4b3eba1d12f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7926921645494159\n",
            "{'criterion': 'gini', 'max_depth': 5, 'min_samples_leaf': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = DecisionTreeClassifier(criterion = 'gini', max_depth = 5, min_samples_leaf = 1)\n",
        "clf.fit(x_sm,y_sm)\n",
        "y_pred = clf.predict(X_test)\n",
        "y_prob = clf.predict_proba(X_test)\n",
        "show_analysis('Grid search DT',y_test,y_pred,y_prob)\n",
        "append_scores('Grid search DT', y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "B6AnxOwGER9I",
        "outputId": "2e31b4bd-f387-43d8-e329-33b87b874e12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for Grid search DT: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.79      0.87       972\n",
            "           1       0.14      0.68      0.23        50\n",
            "\n",
            "    accuracy                           0.78      1022\n",
            "   macro avg       0.56      0.73      0.55      1022\n",
            "weighted avg       0.94      0.78      0.84      1022\n",
            "\n",
            "Confusion matrix for Grid search DT: \n",
            "[[765 207]\n",
            " [ 16  34]]\n",
            "Accuracy score for Grid search DT: 0.78\n",
            "Sensitivity score for Grid search DT: 0.68\n",
            "Specitivity score for Grid search DT: 0.79\n",
            "AUC score for Grid search DT: 0.77\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##XGB"
      ],
      "metadata": {
        "id": "vMflk6eODiHe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training XGBoost Classifier\n",
        "model = XGBClassifier()\n",
        "model.fit(x_sm, y_sm)\n",
        "y_pred =model.predict(X_test)\n",
        "y_prob = model.predict_proba(X_test)\n",
        "classifiers.append(model)\n",
        "# Evaluation of the model\n",
        "append_scores('XGB',y_test,y_pred)\n",
        "show_analysis('XGB',y_test,y_pred,y_prob)"
      ],
      "metadata": {
        "id": "thOeL2jWz7sZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "outputId": "e7a43f74-8199-4f25-f61f-1f98a3f59925"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for XGB: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.80      0.88       972\n",
            "           1       0.16      0.74      0.27        50\n",
            "\n",
            "    accuracy                           0.80      1022\n",
            "   macro avg       0.57      0.77      0.57      1022\n",
            "weighted avg       0.94      0.80      0.85      1022\n",
            "\n",
            "Confusion matrix for XGB: \n",
            "[[780 192]\n",
            " [ 13  37]]\n",
            "Accuracy score for XGB: 0.80\n",
            "Sensitivity score for XGB: 0.74\n",
            "Specitivity score for XGB: 0.80\n",
            "AUC score for XGB: 0.85\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# grid search for XGBoost model\n",
        "params = {\n",
        "        'min_child_weight': [1, 5, 10],\n",
        "        'gamma': [0.5, 1.5, 2.5],\n",
        "        'subsample': [0.6, 0.8, 1.0],\n",
        "        'colsample_bytree': [0.6, 0.8],\n",
        "        'max_depth': [3, 4, 5]\n",
        "        }\n",
        "scorer = make_scorer(custom_loss_function1,greater_is_better=True)\n",
        "clf = GridSearchCV(XGBClassifier(), params, scoring = scorer)\n",
        "clf.fit(x_sm,y_sm)\n",
        "# print(clf.cv_results_)\n",
        "print(clf.best_score_)\n",
        "print(clf.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jX7O_yRk2ThQ",
        "outputId": "86a7e919-cf11-40e4-f889-0da93eb70515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9419546656343118\n",
            "{'colsample_bytree': 0.8, 'gamma': 0.5, 'max_depth': 5, 'min_child_weight': 1, 'subsample': 0.6}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = XGBClassifier(probability = True,colsample_bytree = 0.8, gamma = 0.5, max_depth = 5, min_child_weight = 1, subsample = 0.6)\n",
        "clf.fit(x_sm,y_sm)\n",
        "y_pred = clf.predict(X_test)\n",
        "y_prob = clf.predict_proba(X_test)\n",
        "show_analysis('Grid search XGB',y_test,y_pred,y_prob)\n",
        "append_scores('Grid search XGB', y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "UdkrewENNmQU",
        "outputId": "e5c2dee8-0eef-4319-fe8a-6cd499bacb51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for Grid search XGB: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.91      0.94       972\n",
            "           1       0.23      0.56      0.33        50\n",
            "\n",
            "    accuracy                           0.89      1022\n",
            "   macro avg       0.60      0.73      0.63      1022\n",
            "weighted avg       0.94      0.89      0.91      1022\n",
            "\n",
            "Confusion matrix for Grid search XGB: \n",
            "[[880  92]\n",
            " [ 22  28]]\n",
            "Accuracy score for Grid search XGB: 0.89\n",
            "Sensitivity score for Grid search XGB: 0.56\n",
            "Specitivity score for Grid search XGB: 0.91\n",
            "AUC score for Grid search XGB: 0.84\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Random Forest"
      ],
      "metadata": {
        "id": "GFMKzvmWDonQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training Random Forest Classifer\n",
        "model = RandomForestClassifier()\n",
        "model.fit(x_sm, y_sm)\n",
        "y_pred = model.predict(X_test)\n",
        "y_prob = model.predict_proba(X_test)\n",
        "classifiers.append(model)\n",
        "\n",
        "# Evaluation of the model\n",
        "\n",
        "append_scores('RFC',y_test,y_pred)\n",
        "show_analysis('RFC',y_test,y_pred,y_prob)"
      ],
      "metadata": {
        "id": "fIBDZYiRDu3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "outputId": "2c09ec08-2b17-4f6d-b6ee-608de4df99af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for RFC: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.99      0.97       972\n",
            "           1       0.27      0.06      0.10        50\n",
            "\n",
            "    accuracy                           0.95      1022\n",
            "   macro avg       0.61      0.53      0.54      1022\n",
            "weighted avg       0.92      0.95      0.93      1022\n",
            "\n",
            "Confusion matrix for RFC: \n",
            "[[964   8]\n",
            " [ 47   3]]\n",
            "Accuracy score for RFC: 0.95\n",
            "Sensitivity score for RFC: 0.06\n",
            "Specitivity score for RFC: 0.99\n",
            "AUC score for RFC: 0.82\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "params = {\n",
        "\n",
        "'max_depth': [3,5,7],\n",
        "\n",
        "'max_features': ['auto', 'sqrt'],\n",
        "\n",
        "'min_samples_leaf': [1, 2, 4],\n",
        "\n",
        "'min_samples_split': [2, 5, 10],\n",
        "\n",
        "'n_estimators': [50,100,150]\n",
        "\n",
        "}\n",
        "scorer = make_scorer(custom_loss_function1,greater_is_better=True)\n",
        "clf = GridSearchCV(RandomForestClassifier(), params, scoring = scorer)\n",
        "clf.fit(x_sm,y_sm)\n",
        "# print(clf.cv_results_)\n",
        "print(clf.best_score_)\n",
        "print(clf.best_params_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xaqwVbsCSKiE",
        "outputId": "db2a5ed4-9439-458e-eb9a-b6245edb7848"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8534260719114531\n",
            "{'max_depth': 7, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RandomForestClassifier(max_depth = 7, max_features = 'sqrt', min_samples_leaf = 1, min_samples_split = 2, n_estimators = 50)\n",
        "clf.fit(x_sm,y_sm)\n",
        "y_pred = clf.predict(X_test)\n",
        "y_prob = clf.predict_proba(X_test)\n",
        "\n",
        "show_analysis('Grid search RFC',y_test,y_pred,y_prob)\n",
        "append_scores('Grid search RFC', y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "Jx7mSIYmSRSC",
        "outputId": "65f381ea-22d6-48a3-8135-63aac50d7932"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for Grid search RFC: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.77      0.87       972\n",
            "           1       0.15      0.80      0.26        50\n",
            "\n",
            "    accuracy                           0.77      1022\n",
            "   macro avg       0.57      0.79      0.56      1022\n",
            "weighted avg       0.95      0.77      0.84      1022\n",
            "\n",
            "Confusion matrix for Grid search RFC: \n",
            "[[749 223]\n",
            " [ 10  40]]\n",
            "Accuracy score for Grid search RFC: 0.77\n",
            "Sensitivity score for Grid search RFC: 0.80\n",
            "Specitivity score for Grid search RFC: 0.77\n",
            "AUC score for Grid search RFC: 0.84\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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rTU5Ohq2tLerVqwe1Wo3NmzcDALKysmBpaQmtVlvsDkwA0KJFC+zduxdarRYPHz7EkSNHnjmfh4cH6tWrhxkzZuiW/+jRI0yePBlqtfq560hOToazszOqV6+OnJwcbNu2DdnZ2c880pCUlIQaNWrAyckJKSkp2LNnj+6Hh7e3N3755Rfk5+cjOzsbQ4YMwbVr16BQKJCZmQkhBFq1aoWEhARcunQJABATE4Pp06c/96iGSqVCSEiI7ohEs2bNoFAoIJfLoVAoChXXx7y9vXWnE27cuIEBAwY81VqtXbs2+vXrh/fee0932VZ+fj6+++47XLhwodBh6Gdp06YNzp8/j+TkZGg0GuzcufOZ8/30009YvXo1gIIiXK9ePQAF79m5c+d0p2r++OMPLFiwQLefmzRpAgsLCxw/fhx3794t8hLT4mxrSSgUCkyYMAGffPKJ7v/K8/Tr1w8qlQoLFy5Efn6+Lsf06dN1/2+p9GBhp6fY2dnh7bffRnh4OIQQGDJkCKpXr44+ffogICAAN2/eRLt27SCTybBy5UqsW7cOPXv2xM8//4x58+ZBJpNh+fLl2LhxIwICAjB06FB07tz5mYdt/f39sX///kKH7SdPnoyMjAz4+/ujT58+0Gq1aNSokd7cFhYW+Prrr5GWloZevXohICAA7777Lt544w2MHj1aN1+bNm3wzTffwNvbG9HR0Zg+fXqJ1tu4cWN069YN/v7+GDx4MLy9vdG6dWuEhITAxcUF7dq1Q48ePXDhwoVi7e833ngDVlZW8PX1xYcffog+ffoUahU9JpPJsG7dOri6uqJfv3667RsyZIiuP0RRPD094erqCl9fX4waNQrDhw+Hvb09Jk6c+NS8ffv2RWpqKvz8/DB16lRMnjwZjx49wpIlS9C7d294eHigZ8+e6N+/PwYOHIi2bduiXbt2iI+Ph6enJywtLbFq1SrMnz8fvXr1wrhx4xAQEPDMbXrM0tISAwcOxIgRI9C7d2+EhIRg9uzZsLGxQY8ePbBp06ansk6fPh2PHj2Ct7c33nvvPXzyySdPHT0AgA8++ABeXl4YPny4rlV96tQpfPfdd+jQocNz91uTJk0QHByM/v37Y8CAAWjbtu0z5/Px8cGVK1fQs2dP9OrVCzdu3MDIkSPh6uqK+fPnY9y4cejVqxc++ugj9O7dGwDw7rvvIjw8HH379sWZM2cwfvx4RERE4Pz5808tv7jbWhJ9+/aFlZWV7sfs81hbWyMyMlJ3xUevXr3w/vvvIywsTNdHgUoPmSjOyUEiMxEVFYWdO3fqzjWWFkIIXeELDw+HRqNBWFiYxKmIqCxii51IYgcOHMBrr72G/Px8ZGVl4fDhw7pe+0REJcXOc0QS6969Ow4fPoxevXpBLpeje/fuz+0NTkT0PDwUT0REZEZ4KJ6IiMiMsLATERGZkTJzjj0h4enrWF+Wo6MtUlIMf2vS8oz71Di4Xw2P+9TwuE8Nz8XFXv9M/1KuW+wKBQdWMDTuU+PgfjU87lPD4z4tHcp1YSciIjI3LOxERERmhIWdiIjIjLCwExERmREWdiIiIjPCwk5ERGRGWNiJiIjMCAs7ERGRGTFqYb927Rp8fX3x3XffPfXciRMnMHDgQAwePBirV682ZgwiIqJyw2iFPTs7G/Pnz0fnzp2f+fyCBQsQERGBH374AcePH8eNGzeMFYWIiKjcMFphVyqV+OKLL+Dq6vrUczExMahYsSKqVasGuVwOLy8vnDx50lhRiIiIyg2j3QRGoVBAoXj24hMSEuDk5KSbdnJyQkxMjLGiEBEZ3EcfKREdXWbuo2USCgWgVttKHcMsZLruh7VFBVzd71Hi15aZT6Wjo61RbjDwInfOoefjPjUO7lfDe5l9+v33QEoKULGiAQOZBd4I5mUJRTbS+wyHPN8RwN8lfr0khd3V1RWJiYm66bi4uGcesn+SMW4F6OJib5TbwZZn3KfGwf1qeC+7T4WwQ+PGWhw+zNuUPsbP6ctJzU1BJWtHAMChmPVwsnbS84pnk+Ryt5o1ayIzMxOxsbFQq9U4ePAgunbtKkUUIiIiSWWqMjHzyBR0/aE9knKSAADda3mjpUvrF1qe0Vrsly9fRnh4OO7fvw+FQoHffvsN3t7eqFmzJvz8/DBv3jxMnToVANC7d2+4ubkZKwoREVGpdPz+UUw6OA730u+gsVMTpOQmw9nG+aWWabTC3rx5c0RGRhb5fPv27bF582ZjrZ6IqMRUKuDRI1mx5tVojByGzFqWKgsLTs3Fhj8/h1wmx6S2UzGtfSisLKxeetllpvMcEZGxDRlig8OHi/+1WKuWEcOQWZscPQ47bkahoWMjrPJei7ZV3A22bBZ2IqL/untXDhsbgcBAdbHm79OnePMRAYBWaCGXFXRtm9Y+FLUd6mB6+1mwVlgbdD0s7ERET6hUSeCzz3KljkFm5tSDE5h6aCLW+W1AC5dWaOTUGHM6f2iUdfEmMEREREaSrcrGnGOhCNreCzdSr+P0Q+OPssoWOxERkRGceXgaE6PH4FbaTdSv9ApW9liLDtU6Gn29LOxEVCrExsrw228KaLWmWZ+dHZCZaVnosfR0wOrlOyUTYeeNbXhr7wgAwJhW4zGr4xzYKGxMsm4WdiIqFRYssEJUlKX+GQ3q6U5LjRvzOjZ6eV61eqBrDU/M6PA+OlV79l1OjYWFnYhKhcfXj2/YkANZ8S4lfykVK9ogLS3nqcdbtGBhp5LLVefi47OL0My5OV5rOAgVrSohKuhnSbKwsBNRqZCSIkPFisW/1OxlubgACQm8XI1e3oW4c5gY/S6upVxFG9e2GNDgdchM8eu0COwVT0SlQlKSDE5OQuoYRMWWp8nDgpPz0DvKF9dSruI/Ld5BVNAvkhZ1gC12IioFhACSk2WoVctEPeeIXlJc1iMM3Pkqrqb8g9oOdbGyx2p0reEpdSwALOxEZGS//KLA119bQjynMS4EoFazxU5lh4utK5xtKmNUjbcwu/OHsLO0kzqSDgs7ERnVt99a4siR4n3VdO7Mc95Uel2Kv4gzj07hrZbvQi6TY0vgdigtlFLHegoLOxEZ1eOW+u3bGXqvEVfwG4lKoXxNPpafC8fKC8sBAD3r9kIdh7qlsqgDLOxEZCIKBQs3lT1/JPyOCQfexd/JV1DTrhZW9PgMdRzqSh3rufjfjIiI6F+EEPjk3BKsOL8Uaq0aIU1HYl6X+bBXOkgdTS8WdiIqRAgg5+lxW16YhuO9UBkkk8mQnJuEKrZVsbx7BHrU9pE6UrGxsBNRIWPGWGPbNlMP7UokPZVGhajrWzGo0RuQyWSY3elDzOowBw5WFaWOViIs7ERUyJ9/ymFpKdCtm+Ga2q1aaXhzFSrV/kq6ggkHxuDPxEvQCi3eaDIUFSwrSB3rhbCwE9FTKlUS+OEHAx6PJyql1Fo1Ii6swCfnlkClVeGNxkPRu15fqWO9FBZ2IiIql/5O+gsTo9/FpYSLqFqhGpZ3XwXfOv5Sx3ppLOxERFQunY87i0sJFzGo0RtY0HUJKlk7Sh3JIFjYiUgnPl6GGzcsULkyx2wn83Qt+Sqq29eAnaUd3mwyDA0cG6FjtU5SxzIo3t2NiHTCwgp6uNnYSByEyMDUWjVWXVgBn60emH/yAwAFl7SZW1EH2GInoidkZBTcbnLdOnacI/NxPeUaJkaPwfm4c3CxcUX3WmXnmvQXwcJORE9p0YKH4qns02g1WHdpNZacmY88TR4GNHgdizw/hpO1s9TRjIqFnYiIzNLfyX9h/qkP4GTtjHVen6JPvUCpI5kECzsREZkNjVaDtPxUOFk7o3nlFljr+yW61ewBZxvzbqU/iYWdqJzbu9cCUVEFQ8heucL+tFR23Uq9gYnRYyGTybCj3x7IZXL0bzBQ6lgmx8JOVM59+qkVzp2z0E3XrKmFJYeKpzJEK7T44o+1WHT6I+Soc/Bq/f7IVmfDztJO6miSYGEnKueEAJRKgXPnsgAUDCdrYaHnRUSlxK20m5gcPQ6nHp6As7UzIrzX4dVX+ksdS1Is7EQEmQyoWlVIHYOoRFQaFV7bEYj7mbHoWy8I4d2Ww8XWRepYkmNhJyKiMkWtVUMhV8DSwhIfdV0MtVaFfq+8BplMJnW0UoE9ZYiIqEzQCi02/Pk5PDd1QGpuCgAgsH4Q+jcYyKL+BBZ2IiIq9e6m38FrOwIx6+g0JOUk4mrKVakjlVo8FE9ERKWWVmjxf1e+wocn5iBbnYWAur2x1OtTVKlQVepopRYLOxERlVqzj83El3+uR0WrSljt9TkGNhzMw+56sLATEVGpNaTJMNzPvI/wbstQtUI1qeOUCTzHTkREpUZsRgyG/jII/yT/DQBoXrkF/q/X9yzqJcAWO5ER3L4tQ2ys4X43V6oEpKYaZ9SY9HSjLJaoRIQQ2Pj3t/jgeBgyVRl4xbEh5nVZIHWsMomFncjAsrIAL68KyM019HlAWwMv73/s7Tk4DUnnQeZ9vHdwPA7GHIC90gGf9liNNxoPlTpWmcXCTmRgOTky5ObK0KiRBkFBaoMss0IFK2Rl5RlkWc/Spo3GaMsmep7j949i2J43kJGfjh61fLC8ewRq2NeUOlaZxsJOZCQNG2oxbVq+QZbl4mKFhATDLIuoNGni3BSutq74sMtCvNlkGHu8GwALOxERmYwQApuvfo+KVpXQy60PnKydcTT4DBRyliND4Z4kIiKTeJT1ENMOTcLeu7+ijkNd+NXxh0KuYFE3MO5NkpwQwBdfWOLePfO4+jInR+oERKWLEAJbr23C+8dmIi0vFZ41u+PTHp+xoBsJ9ypJ7uFDGWbPtpY6hsHxNqhEQHpeGsYfeAe/3tkNW0UFfNxtBYY3G8Vz6UbEwk6SU6kK/vb3V2PGDOP1/DYluRxo1EgrdQwiyVWwtENCTgI8anTDih6foY5DXakjmT2jFvZFixbh0qVLkMlkCAsLQ8uWLXXPbdy4ETt37oRcLkfz5s3x/vvvGzMKlQGOjgItWrAYEpV18dnxOHH/KPo1eA0Wcgts7LMFlawcIZeZx+m20s5ohf3MmTO4e/cuNm/ejJs3byIsLAybN28GAGRmZmLDhg3Yu3cvFAoFRo0ahd9//x2tW7c2VhwiIjIyIQS2Xf8Rs45OQ1peGho7N0VjpyZwsnaWOlq5YrSfTydPnoSvry8AoH79+khLS0NmZiYAwNLSEpaWlsjOzoZarUZOTg4qVqxorChERGRkCdkJeH3r63hn3yjkqnOxwGMJGjo2kjpWuWS0FntiYiKaNWumm3ZyckJCQgLs7OxgZWWFcePGwdfXF1ZWVujTpw/c3NyMFYVKgf37LTB7tjXUzxiI7fE5diIqm3bd3I4Zh99DUm4SOlbrjJXea1CvYn2pY5VbJus8J8T/eghnZmZi/fr1+PXXX2FnZ4fhw4fjn3/+QePGjYt8vaOjLRQKw98Ew8XF3uDLLO+etU9PnQJu3QKqVgWUysLPWVoCbm5A//6WcHGxNFHKsoefVcPjPjWMkyePIFudjRX+KzCx40SeS5eY0Qq7q6srEhMTddPx8fFwcXEBANy8eRO1atWCk5MTAMDd3R2XL19+bmFPSck2eEYXF3skJGQYfLnlWVH7NCfHCoAS33+fhebNi+4gl5BgxHBlGD+rhsd9+nJOPTiBjtU6QyaTYVa7eRjd5F10atCW+9TAXuTHp9F+VnXt2hW//fYbAODKlStwdXWFnZ0dAKBGjRq4efMmcnNzAQCXL19G3bp1jRWFiIgMJCknCW/vHYFXtwdgy9UfAAD2SgfUr9RA4mT0mNFa7G3btkWzZs0QHBwMmUyGuXPnIioqCvb29vDz88Po0aMxbNgwWFhYoE2bNnB3dzdWFCIiMoBfbu3C9MOTkZiTgHZV2qNdlfZSR6JnMOo59mnTphWafvJQe3BwMIKDg425eipF7tzhOTeisio5NwlhR2cg6vpWWFlYYW7nBRjTahws5Ibv90QvjyPPkdHt2KHAvn0FHzUFP3FEZc7PN3ci6vpWtKvijlXe69DAsaHUkeg5+DVLRvfwYcGY0O3aadCwIUeWIyoLUnNTYKWwho3CBkObDoeNwgb9GwzkjVvKAB4fJZOZODEfcn7iiEq9vXf2wHNTRyw+PR8AINqxaG0AACAASURBVJfJ8XqjYBb1MoLvEhERAShopc8+HootV3+ApdwSlW0qSx2JXgALOxERYf/d3zDl0EQ8ynqIVi5tsMp7LZo4N5U6Fr0AFnYyqrw84KuvlPpnJCLJ3Ey9jjd/GQSFXIFZHeZgfJvJsLTgKJBlFQs7GdWxYxa6S90cHYWeuYnIlPI1+VBaKFG/UgPM77oYHjW90NS5mf4XUqnGwk5GlZtb0CP+tddU6NhRI3EaIgKA9Lw0zD3xPmIyYrA1cDtkMhnebjVW6lhkIOyjTCbRpo0GMpnUKYjo4L0D6LapEzb+/S2SchKRlJskdSQyMLbYiYjKgYz8dMw7MRuRf30DhVyBae6hmNxuGpQW7ANjbljYiYjMnFZoEbgtAH8lXUYTp2aI8FmLli6tpY5FRsLCTka1bRs/YkRSk8vkeLfVeNxOu4kp7jPZSjdz/NYlo4qPLzix3rIlh5IlMqWjsYex/NzHiOy9CXZKewxuPETqSGQi7DxHRvW4w1ynTuwRT2QKmapMzDwyBa/tDMTJh8dx9P4RqSORibHFTkRkJk7cP4aJB8fiXvodNHJsjFXea9GmSjupY5GJsbATEZmBzy6uxEcn50Auk2NCm/cwvf0sWCuspY5FEmBhpyJlZgLZ2SW7+FyrBRIT//ea/HxevE5kCl2re6CJU1Ms7xGBdlXaSx2HJMTCTs90964MHh4VkJf3IoXZrtCUXM6hZIkMLVuVjfAzCzGs2QjUr9QAbaq0w8HBJyCXsetUecfCTs8UGytHXp4MjRpp0Lhx8Xu0W1lZIi9PVeixtm3ZcY7IkE4/PIVJ0e/iVtpNJOYkYLXv5wDAok4AWNhJj8BANWbMyC/2/C4ulkhIyDViIqLyK0edg8Wn52P9pdUAgHdbTUBox9kSp6LShoWdiKgM+DvpL4z6bShupt5AvYr1sdJ7LTpW6yR1LCqFWNiJiMoAZ5vKSMtLwzutxmFWhzmwtbSVOhKVUizs9JRz5+TYu5cfDSKpnY87i2xVNjxresHV1hUnh5xHRatKUseiUo7f3vSUgQNtdZe52duzRzuRqeWqc/Hx2UVY8/sqVLGtitNv/g5rhTWLOhULCzs9JTtbhgYNNAgNzYePj1rqOETlyoW4c5gY/S6upVxFHYe6WOW9lgPNUImwsNMzubgIBAayqBOZSr4mH0vPLkbExRXQCi1Gt3gbszt9iAqWFaSORmUMCzsRUSkggwzR9/ajpl0tfOq9Gh41ukkdicooFnYiIonkafJw7tEZdK3hCUsLS3wd8B2cbJxhZ2mn/8VERWBhLwfi4mSYO9cKmZkct52otPgj4XdMODAGN1KvY+/Aw2hWuTlqO9SROhaZARb2cuDQIQtERVmW6DVNmxZ/GFkiKr58TT5WnF+KT89/Ao3QYFjTUajDgk4GpLew379/H+Hh4UhJSUFkZCS2bNmCDh06oG7duiaIR4Yg/nvF2pIluRg0SPX8mf/LjkcCiQzuz8Q/MPHAu7iS9Cdq2tXCih6fwatWD6ljkZnRe8eAOXPmICgoCOK/1cHNzQ1z5swxejAyPBsbATs7FOsPERne55fW4ErSnwhpOgKHg0+yqJNR6C3sKpUKPj4+kMkKzs+2b8/7/BIRFde99Lu6f3/UdRE2992GZd1XwV7pIGEqMmfFusdfenq6rrBfv34deXl5Rg1FJSdE0X+IyPRUGhWWnQtHp+/bYMeNKACAo7UTetT2kTgZmTu959jHjRuHQYMGISEhAYGBgUhJScHSpUtNkY2K6eFDGXr0sEVyMu/FTFQa/JV0BROj38UfCb+jaoVqcFBWlDoSlSN6C3vTpk2xfft2XLt2DUqlEm5uboiPjzdFNiqmO3fkSE6Wo2ZNLerUeXZvdjs7oFs3jYmTEZUvaq0aERdW4JNzS6DSqhDc+E3M77qYY7yTST23sGu1WowbNw7ffvstmjdvDgBQq9UYO3Ysdu3aZZKAVHyDBqkQGpovdQyicuvHa5ux+Mx8VLGtiuXdV8GvboDUkagcKrKw//zzz4iIiMDdu3fRpEkTyGQyCCEgl8vh4eFhyoxERKWWWquGVmihtFDi9YbBeJB5H6Oav4VK1o5SR6NyqsjC3rdvX/Tt2xcRERGYMGFCoecyMjKMHoyIqLS7lnwVE6PHwLNmd7zfaS4s5BaY4j5D6lhUzuk9xz5hwgTcuHEDKSkpAID8/HwsWLAAe/bsMXo4er7792X44w8LXL3KTnNEpqTRarDmUgQ+PrMQeZo8NHBsBCGE7uohIinpLewLFy7EsWPHkJiYiNq1ayMmJgajRo0yRTbSY9gwG/z5p4Vu2tZWwjBE5cT1lGuYGD0G5+POwcXGFZ90X4lebn2kjkWko7ew//HHH9izZw9CQkIQGRmJy5cvY9++fabIRnqkpcng4CAwbVoelEpgwIDiDRdLRC/mQeZ9+GzxQK4mFwMaDMQiz6VwsnaWOhZRIXoLu1KpBFAwAp0QAs2bN0d4eLjRg1Hx2NsLjBnDgk5kTI8Ps1e3q4ExrcajpUtr9K3/qtSxiJ5Jb2F3c3PDxo0b4e7ujpEjR8LNzY2d54ioXNBoNfjiz7U4++gMvuz5f5DJZAjr9IHUsYieS29h//DDD5GWlgYHBwf88ssvSEpKwjvvvGOKbEREkrmVdhOTosfi9MOTcLZ2RmxmDGrZ15Y6FpFezy3s6enpuHfvHurXrw+5XI7AwEAAwKVLl1C1alWTBCyvtFrg44+VePiw6B7vSUkyVKrEweCJDEkrtNjw53osODUPOeocBNbvhyWey+Bi6yJ1NKJiKbKw79u3Dx9++CFcXFyQkJCAdevWoWHDhlixYgX279/PDnRGdu2aHMuXW+mdr2VLDhNLZChCCITsHox9d3+Dk7UTVnmvRdArA6SORVQiRRb2DRs2YMeOHXB2dsbly5fxwQcfIC8vDx4eHtixY4cpM5ZLmv/W60GDVJg2rei76VWvzhY7kaHIZDL41vGHpVyJj71WwNXWVepIRCVWZGG3tLSEs3PBZRzNmzdHbm4uwsPD0aJFi2IvfNGiRbh06VJBh5OwMLRs2VL33MOHDzFlyhSoVCo0bdoUH3300UtshvlycBCoW5fFm8hY7qTdxrJz4Qjvthy2lrYY0Ww0RjQbzcFmqMwq8gTuvz/Uzs7OJSrqZ86cwd27d7F582YsXLgQCxcuLPT8kiVLMGrUKPz444+wsLDAgwcPShidiOjFaYUWX13+At03d8Hmq98j6vpWAAXffSzqVJYVWdiFEBBCQKvVQqvV6h57cvp5Tp48CV9fXwBA/fr1kZaWhszMTAAFd407f/48vL29AQBz585F9erVX3pjiIiK4176XfhF+iH0yFQoLSyxxvcLvNlkmNSxiAyiyEPxZ8+eRdOmTXXTQgg0bdpUN1DD33///dwFJyYmolmzZrppJycnJCQkwM7ODsnJyahQoQIWL16MK1euwN3dHVOnTjXA5piPnTsL3hrBo/BEBrX9+k9479AEZKky4V+3Fz7xWokqFXiVD5mPIgv7P//8Y9AViScqlBACcXFxGDZsGGrUqIG3334bhw4dQvfu3Yt8vaOjLRQKiyKff1EuLvYGX6YhpKYW/N2+vRIuLkppw5RQad2nZR33q2E0yXkF1gorrO2zBkNbDuVhdwPj51R6egeoeVGurq5ITEzUTcfHx8PFpeA6UEdHR1SvXh21axcM9tC5c2dcv379uYU9JSXb4BldXOyRkFA6R9HLzbUGYIm2bTORkFB2mu2leZ+WZdyvL04IgY1/fwvPml6o41AXjWxa4ezQP+FWvRr3qYHxc2p4L/JDyWj3++zatSt+++03AMCVK1fg6uoKOzs7AIBCoUCtWrVw584d3fNubm7GikJE5dT9jFgM/rk/phyagDnHZ+ket7O0kzAVkXEZrcXetm1bNGvWDMHBwZDJZJg7dy6ioqJgb28PPz8/hIWFITQ0FEIINGzYUNeRjojoZQkh8P3fkfjgRBgy8tPhXdsXSzw/kToWkUnoLez5+fnYunUrHj58iGnTpuHSpUto3LgxrKz0j4o2bdq0QtONGzfW/btOnTr44YcfXiCyecvMBP76S46EBJ73I3oRj7IeYvLBcYi+tx/2Sgd82mM13mjMc+lUfugt7PPmzYO9vT0uXLgAoOCw+TfffIMVK1YYPVx59J//2CA6+n9vi7Js9ZsjkpwQAucenUWPWj5Y3j0CNexrSh2JyKT0FvZbt25h06ZNCAkJAQAMGTIEv/zyi9GDlVeJiTJYWgqMHZuPWrUEh4wlKoaHmQ/wKOsh2lRph2p21bH39UNwc6jHVjqVS3oLu0JRMMvj/yDZ2dnIzc01bqpyTqkE3n8/X+oYRKWeEAKbr36P2cdCYa+0x9Hg07BT2qNexfpSRyOSjN7CHhAQgOHDhyM2NhYLFizAkSNHMGTIEFNkIyIq0qOsh5h2aBL23v0VFSztMLndNFRgb3ci/YV96NChaNmyJc6cOQOlUonly5ejefPmpshGRPQUIQS2XtuE94/NRFpeKjxreGFFj89Q26GO1NGISgW9hX3QoEEICgrCwIEDUalSJVNkIiIqkkqrwuqLK6HSqLCk2zKMaDYacpnRhuQgKnP0FvaZM2diz5496N+/Pxo3boygoCB4e3tDye7aRGQiQgjcTL2BVxwbQGmhxDq/r2CjsEHdihzYiujf9P7MbdeuHWbPno3o6GiMGDECR48eRbdu3UyRjYgI8dnxGPnrUPTY0gXXU64BAJo4N2VRJypCsUaeS09Px/79+/Hrr78iJiYGgwcPNnYuIirnhBDYcSMKoUenIjk3GZ2qdYHSgkcKifTRW9hHjx6N69evw9fXF2PGjEHbtm1NkYuIyrGE7ATMPDIFP9/aARuFDRZ6hGN0i3d4Lp2oGPQW9mHDhsHT0xNyOf9DEZFpLDr9IX6+tQMdq3XGyh6rUa/SK1JHIioziizsCxYswOzZs7F+/Xp8/vnnTz2/ceNGowYrL+LiZBg1ygZpaQXTd+/KoTDarXmISq9MVaburmvvd5qH5pVbYESz/8BCbiFxMqKypcgSMnDgQADA5MmTTRamPPr9dznOnrWAra2Ara2Avb1At24aqWMRmdTPN3dixpH3sLx7BALceqOyTWWMbvGO1LGIyqQiC/vjO7FFRUVhyZIlhZ4bPXo0OnToYNxk5cyMGXkYO1YldQwik0rOTULY0emIuv4jrC2skZATL3UkojKvyMK+c+dObNq0CdevX8ebb76pe1ylUiEpKckk4YjIfO2+9TOmH56MhJx4tKvSHhHe6/CKYwOpYxGVeUUW9ldffRUdO3bEtGnTMGHCBN3jcrkcr7zCjixE9OJ23/oZI34dAisLK3zQeT7ebTWe59KJDKTIwh4fH48qVapg0aJFTz2XkZHB4WUN4P59GR494tUGVH4IISCTydCzbgBCmo7AOy3HoaFTI6ljEZmVIgt7eHg4li1bhuHDh0Mmk0GI/90XXCaT4cCBAyYJaK42b1ZgwgQb3bQFGytkxlJzU/D+sZlwq1gP09qHQiFXYFn3VVLHIjJLRRb2ZcuWAQCio6NNFqY8iY0taKl7e6vh5qZFUJBa4kRExrH3zh5MPTQJcdmP0L5qR7zXbjoPuxMZkd7jwIcPH8aOHTsAAFOnTkXPnj2xd+9eowcrL8aOzcfixXmoWlXon5moDEnLS8WEA2MwdPfggt7vHT/Ajn57WNSJjExvYV+zZg08PT1x+PBhaLVabNu2DZGRkabIRkRlVEpuMrpt6oTNV79HS5fW2Pf6EUxuNw0KOUdfIjI2vf/LrK2t4eTkhMOHDyMoKAgVKlTg8LJE9FyO1k7wrdMTNexqYkKb92BpYSl1JKJyQ2+FzsvLw5dffokjR46gc+fOuHPnDjIyMkyRzWzl5gIbN/KLjsxL9L39CD0yVdfR9hOvlZjiPoNFncjE9Bb2+fPnIy4uDkuWLIGVlRWOHTuGadOmmSKb2dq3T6HrPFepEs+tU9mWkZ+OKQcnIPjnAfj2r69xNeUfAAVXzxCR6ek9FN+gQQMMHz4cf/31F/bt2wdvb29Ur17dFNnMVk5Owd9DhuSjRQuttGGIXsKhmGi8d3A87mfGoqlzc0T4rENjpyZSxyIq1/S22H/44QcMGzYMv/zyC3bt2oWQkBBs27bNFNnMXvv2WrBRQ2XVnGOhGLSrH+KyH2Gq+0zsHXgILSq3lDoWUbmnt8W+Y8cO7NmzB1ZWVgCA7OxsjBw5Ev379zd6OCIqvWrY10QTp2aI8FmLli6tpY5DRP+lt8WuUCh0RR0AbG1tYWnJzjBE5U1mfgaWnl2MXHUuAOCtFu9i7+uHWNSJShm9LfaqVati/vz56NKlCwDg2LFjqFatmtGDmbOffuIPIypbjt0/gsnR43Av4y5sFRUwrs1EWMgtYAEONkNU2ugt7PPnz0dkZCSioqIgk8nQqlUrhISEmCKb2UpIKDix3qSJRuIkRM+XqcrEgpNz8dXlLyCXyTGp7VT8p+U7UscioufQW9jz8vLw9ttvmyJLuSGXAxUqCLRtyx7xVHqdeXga4w68hbvpd9DQsRFWea9F2yruUsciIj2KPMd+7tw5eHh4wN/fH3369MG9e/dMmYuIJJaryUFsRgwmtHkP+18/yqJOVEYU2WJfsWIFvv76azRo0AAnT57EsmXLsHLlSlNmIyITO/XwJGrZ1UIN+5roVrM7zgy9hFr2taWORUQlUGSLXS6Xo0GDBgCAzp07Izk52WShzF1+PiA44ByVItmqbMw5FoqgbQGYfniy7nEWdaKyp8gW+7+Hg+TwkIZx/74M//xjARsbVnYqHU4/PIVJ0e/iVtpN1K/0Ct5zny51JCJ6CUUW9rS0NJw8eVI3nZ6eXmi6c+fOxk1mpmJiCg6SODiwsJO0ctQ5WHx6PtZfWg0AeLfVBIR2nA0bhY3EyYjoZRRZ2B0cHLBmzRrdtL29vW5aJpOxsL+kN99USR2ByrnEnARE/vUN6lZ0wyrvdehYrZPUkYjIAIos7JGRkabMQUQmkKvOxYPMWNSr9Apq2dfG5r7b0LxyC9ha2kodjYgMRO+QskRkHs7HnYXPFg8M/nkAslRZAIAO1TqyqBOZGRZ2E8rIAI4e5RCcZFp5mjwsODkPfaL8cD31GnrWCYAM7AxLZK70jjxHhrN2rRKffFJwQx1bNpLIBC7GncfE6HdxNeUf1HGoi5U91qBLDQ+pYxGREeltsd+/fx8TJ07UjQ+/ZcsW3Llzx9i5zFJmZkErKTQ0D6NG5UuchsydVmh1RX1U87dwcPAJFnWickBvYZ8zZw6CgoIg/juiipubG+bMmWP0YObMx0cNOzupU5C5Ss1NAQDIZXKs9F6DqKCfsaTbMthZ8kNHVB7oLewqlQo+Pj66AWrat29v9FBEVHL5mnwsOT0f7t+1xO20WwCAtlXc4VGjm8TJiMiUitV5Lj09XVfYr1+/jry8PKOGIqKS+SPhd/ht9cLy80vhoHRAcm6S1JGISCJ6O8+NGzcOgwYNQkJCAgIDA5GSkoKlS5eaIhsR6ZGvyceK80ux8sIyqLVqhDQdiXld5sNe6SB1NCKSiN7C3qlTJ2zfvh3Xrl2DUqmEm5sbrKysTJGNiPSYf2ou1l9ajRp2NbG8ewR61PaROhIRSUxvYS/qVq2TJk0yeBgi0k8rtJDLCs6ijW89CRqtGqEdZsPBqqLEyYioNNB7jt3CwkL3R6vV4vTp08jIyCjWwhctWoTBgwcjODgYf/zxxzPnWbZsme5SOiJ6viuJl9Hzx+6IvrcfAFClQlUs8lzKok5EOnpb7OPHjy80rdFoMGHCBL0LPnPmDO7evYvNmzfj5s2bCAsLw+bNmwvNc+PGDZw9exaWlpYljE1Uvqg0Kiw/9zGWnQuHSqvCifvH4F3bV+pYRFQKlXhIWbVajXv37umd7+TJk/D1LfjiqV+/PtLS0pCZmVloniVLluC9994raQSicuXvpL/QeUNnLDmzAM42lfF9n62Y3Xme1LGIqJTS22L38vLSXeoGFNynvX///noXnJiYiGbNmummnZyckJCQALv/jswSFRWFDh06oEaNGi+Su0xat04pdQQqY04+OI7XdwYhX5uPwY2GYH7Xxahk7Sh1LCIqxfQW9u+//173b5lMBjs7Ozg4lPxSmscj1wFAamoqoqKi8PXXXyMuLq5Yr3d0tIVCYfgbqLi42Bt8mfp4eFSAtbXJV2syUuxTcxXg5A3vy94Y3348+jTsI3Ucs8PPquFxn0pPb2FfunQpPv300xIv2NXVFYmJibrp+Ph4uLi4AABOnTqF5ORkvPnmm8jPz8e9e/ewaNEihIWFFbm8lJTsEmfQx8XFHgkJxesIaAhyuR3at9cgIyMHxex/WOaYep+aG7VWjTW/R8DKQol3Wo0DAHzbcwv3qxFwnxoe96nhvcgPJb2FvWbNmvjxxx/Rpk0bKJX/O5Rcq1at576ua9euiIiIQHBwMK5cuQJXV1fdYfiAgAAEBAQAAGJjYzFr1qznFnWi8uBa8lVMjB6DC/HnUdu+DkY0/w+sLDhmBBGVjN7Cvnv37qcek8lkOHDgwHNf17ZtWzRr1gzBwcGQyWSYO3cuoqKiYG9vDz8/vxdPTGRmNFoN1l76DOFnFiBPk4cBDV7HIs+PWdSJ6IXIxJMnv5+wc+dOvPrqq6bOUyRjHN4x5WEjjQaoVs0eHTuqsWtXjknWKQUeiiuZTFUmXt8ZhPNxZ1HZxgWfeK1E73p9n5qP+9XwuE8Nj/vU8F7kUHyRl7v9+OOPLxWGCtu2reDgSFaWTM+cVJ7YWdqhln0t9H/lNRwNPvPMok5EVBJ6D8WTYaSmFhT0Xr3UEichqd1MvY6dN7bjPffpAIDPfD6H0oKXQhKRYRRZ2C9evIju3bs/9bgQAjKZDIcOHTJiLPPVuLFW6ggkEa3Q4os/1mLhqQ+Rq8mFZ00vuFftwKJORAZVZGFv2rQpli9fbsosRGbrVtpNTIoei9MPT8LZ2hkRPuvgXrWD1LGIyAwVWdiVSmW5GhWOyFi+vvwl5p14HznqHPStF4TwbsvhYusidSwiMlNFFvaWLVuaModZy84GwsN56VJ5lZGfDhuFDT7tsRr9Xnmt0BDNRESGVmSv+OnTp5syh1k7edICaWkFX+bVqvEcu7nTCi22Xt2EfE0+AGBs64k4GnwW/RsMZFEnIqMr8d3dqOTU/+0I/847+XB3Z2E3Z3fT7+C1HYEYd+BtrL64EgCgkCt46J2ITIaXu5kQW+vmSyu0+L8rX+HDE3OQrc5CgFsfDGk6TOpYRFQOsbATvaSYjHuYfHA8jsYeQiWrSljq9TkGNhzMw+5EJAkWdqKXdC35HxyNPYSedQLwSfeVqFqhmtSRiKgcY2E3sqwsICTEVuoYZGCxGTFQWljB1dYVPnV6Ylf/vehQtSNb6UQkOXaeM7K7d/+3i7t00UiYhAxBCIHIv75Bt02dMO3wJDy+h1LHap1Y1ImoVGCL3URGj85H69bsPFeW3c+IxZRDE3Aw5gDslQ7oVbeP1JGIiJ7Cwk6khxACP/zzHeYcn4WM/HR41/bF8u4RqG7HkRmJqPRhYSfS42HWA8w6Og0WMgVWdP8MQ5qE8LA7EZVaLOxGlJEBXL/ObgxlkRACSblJqGxTGdXtamCd31doUbklatrXkjoaEdFzsbAbUZ8+tvjnHwsAgIJ7usx4lPUQUw9NxO20Wzgw6BhsFDbo5cbz6URUNrA5aURxcXI4OgqMHZuPUaPypY5DegghsOXqD/Dc1BH77v6GanY1kJmfKXUsIqISYTvSyKpW1WLevDypY5AecVmPMO3wJPx2Zw9sFRXwcbcVGN5sFM+lE1GZw8JO5Z4QAkN3D8alhIvwqNENK3p8hjoOdaWORUT0QljYqdxSa9VQyBWQyWT4sMtC/JPyN0Y0Gw25jGeoiKjsYmE3sL17LXDpUkGHuZwcicPQMwkhsO3Gj1h06iNs77cbNe1roUsND3Sp4SF1NCKil8bCbmBvv22D7Oz/nZd1chISpqF/i8+Ox4zD72H37V2wVdjicuKfvISNiMwKC7uB5eUBjRtrsGhRQYe55s05PnxpseNGFGYemYLk3GR0qtYFK73XwK1iPaljEREZFAu7ETg4CHh4sKCXJqsurMCCU3Nho7DBgq5L8J+WY3gunYjMEgs7lQsDGgzEyQfHsNAjHPUqvSJ1HCIio2GThcxSUk4S3tk7EsfuHwEA1LSvhR/6/sSiTkRmjy12A1q5UgmNhgOaSO2XW7sw/fBkJOYkQCaTwaNGN6kjERGZDAu7AW3caAkA8PTk+XUpJOcmIezodERd/xFWFlaY23kBxrQaJ3UsIiKTYmE3sGrVtJg5k+PCm9qVxMsYtKsfEnLi0a6KO1Z5r0MDx4ZSxyIiMjkWdjIL9SrVR5UKVTGm9XiMbTUBFnILqSMREUmChZ3KrN/u7EFyThLeaDIUNgob7B14CAo5P9JEVL7xW9BAhADi4mSoWJEjzRlbam4KZh8PxZarP6CiVSUE1g+CndKeRZ2ICLzczWCmT7dCTo4MFjwCbFT77vyKbps7YcvVH9DKpQ129f8Ndkp7qWMREZUabOIYyO3bBb+RZs7kvdeNIV+Tj2mHJ2HTPxthKbdEWMcPML7NZLbSiYj+hd+KBjZwoFrqCGbJUm6JtLw0tHRpjVXea9HUuZnUkYiISiUWdiq10vPSsPv2zwhu/CZkMhlWea+BraICLC0spY5GRFRqsbBTqXTw3gG8d3A8HmTdRw27mvCs6YWKVpWkjkVEVOqxsBvI0aPclYaQkZ+OeSdmI/Kvb6CQKzDNPRQdq3WWOhYRUZnBamQAd+78b3x4Oa8zeGFHYg9hcvQ4xGbGoKlzc0T4rEOLyi2ljkVEVKawsBtATk5BYe/USc3C/hJOPTiBh1kPMMV9Bqa0mwGlhVLqSEREZQ4LuwE1baqVOkKZc/bRbRn6WwAAHkBJREFUabRxbQeFXIHJ7aahT71X0axyc6ljERGVWWxfkiQyVZmYeWQK+kT5YfXFlQAApYWSRZ2I6CWxxU4md/z+UUw6OA730u+gkWNjeNXqIXUkIiKzwcJuAKdPcxzZ4shSZWHBqbnY8OfnkMvkmNR2Kqa1D4WVhZXU0YiIzAYLuwFcvFhQ2J2ceAOY5zlx/yg2/Pk5Gjo2wirvtWhbxV3qSEREZoeF3QDk8oKCPnCgSuIkpU+2KhsqbT4qWlWCX90ArPPbgN5ugbBWWEsdjYjILBm189yiRYswePBgBAcH448//ij03KlTpzBo0CAEBwdj1qxZ0GrZo9zcnHp4Ej22dMH0w5N1jw1o8DqLOhGRERmtsJ85cwZ3797F5s2bsXDhQixcuLDQ8x988AFWrVqFTZs2ISsrC0ePHjVWFDKxbFU25hwLRdC2ANxJu41qFWpAo9VIHYuIqFww2qH4kydPwtfXFwBQv359pKWlITMzE3Z2dgCAqKgo3b+dnJyQkpJirCgGp1YDTx5g0GhkRc9czpyIOYGQn4bhVtpN1KtYH6u816FDtY5SxyIiKjeMVtgTExPRrNn/bq3p5OSEhIQEXTF//Hd8fDyOHz+OSZMmGSuKQR0/boHgYBvk5T1dzGXlvL4n5STBN9IXuepcvNNqHGZ1mANbS1upYxERlSsm6zwnxNM9xpOSkjBmzBjMnTsXjo6Oz329o6MtFArDX1bm4mJfovljY4G8PKBlS6BKlf89Xrcu4O5uVy6HlM3X5ENpoYQL7PFZ78/Q0LkhPGp7SB3L7JT0s0r6cZ8aHvep9IxW2F1dXZGYmKibjo+Ph4uLi246MzMTb731FiZPngwPD/1FICUl2+AZXVzskZCQUaLXZGZaArDGpEk5CAxUF3ouKen/27vz+Jju/Y/jr8mORJCVhCq1K409ixC10yo/JKmgKNcuaC2VNrHva6zVomgtdXPbcm21xBaJfQttiDYSIrKgSSSRyZzfH6mpuQghMSb5PB8Pj4fMOXPOZz5h3nPO98z3FGBxBiBTncmckzMIjT3A7v87gJmxGf1d+pOYmJrvvoq8vcy/VZE36WnBk54WvJf5oFRox5fu7u7s2bMHgMjISOzt7bWn3wFmzZpF37598fT0LKwSRCE6k3CK1j82Z+nZRaQ+/Iu4tFh9lySEEIJCPGJv0KABderUwcfHB5VKRWBgICEhIVhZWeHh4cFPP/1ETEwM27ZtA6Bz5854e3sXVjmigGTlZDH3xEyWnluERtEw4N1BBDSbTCnTUvouTQghBIU8xv7ZZ5/p/FyzZk3t3y9dulSYuy5wqalw+LAJFy8Ww0H0x/Td5cuBG/uoZPUWi1stx92pub5LEkII8RiZee4FzZ1rzsqV/9wfvESJ4jl97KB6Q6lk9RZfuU3F0tTy+U8QQgjxWhXvw898SP37epCxY7NYtiyDFi2Kx4QrFxLP8dFPHbmdHg9Aq0qtmdNioYS6EEK8oSTY86lHj2x69FBjaqrvSgrXw5yHzDoxjXbbvAi7dZQ9f+7Sd0lCCCFegJyKF0+4mHSBEfsHczn5Es6WFVnotVTumS6EEAZCgl3o2Pr7JvwPDkOtUdO79icEuU3Dyqy0vssSQgjxgiTYn2HNGlNOn/5npruTJwt+1rs3URPHZlSxrspU91l4VXpf3+UIIYTIJwn2ZwgMNH9iPnhLSwVb26J1NXx2TjZLzi6guVNLmpRvSmXrtznsE4GRSi6/EEIIQyTB/gw5OVC/fg5r12ZoHytTRsGyCF0Mfjk5kpEHhnAh8RxhTkf5d5ftABLqQghhwCTY82BmBs7OResIHUCtURN8ZiHzTs0iW5ONb00/prjP0HdZQgghCoAEezETlxpLv91+nE88i2Op8ixouYTWb7XTd1lCCCEKiAR7MVPWohz3s+7Rs4Yv09xnUcYi79vlCiGEMCwS7E8xfrw5arXq+SsaiN9TfiP63jU6VulMKdNS/NrjENbmZfRdlhBCiEIgwf4UoaG5benZM1vPlbwatUbN8nPBzDkxHVNjM06Vv4hNCRsJdSGEKMIk2J/B0VFD376GG+xX70Yx8sBgTiecwq6EPfNaLsamhI2+yxJCCFHIJNiLGI2iYeX5ZcyMmEJWThbdqvVgRvM5lLOQUBdCiOJAgr2IUaHicNxBrMxKs7LFIjpV+UDfJQkhhHiNJNj/lpio4urV3IlZMjKes/IbJkeTw7FbR/B0bolKpWKx13JMjEzl1LsQQhRDEux/++ijEly9+s988JUqafRYzYu7fu8aIw8M5cTtcLZ9+Auezi1xKOWo77KEEELoiQT731JSVNjYaPjkk9wL5tzccvRcUd40ioZvLqxkesRkMtQZfFi1K7Vt6uq7LCGEEHomwf4YW1uF8eMf6ruM57p+Pxr/A8MIjw+jnEU5lrRaQZd3uum7LCGEEG8ACXYDtPnK94THh9G5Shdmey7ArqSdvksSQgjxhpBgNxBxqbGUL1UBYyNjxjQah4tDQ9pX7ohKVXRmyBNCCPHq5P6cQFoaJCe/ma3QKBq+vfg1HpuasPriCgAsTCzo8HYnCXUhhBBPkCN2YP/+3DbcufNmhfuNv2LwPziMozcPU8a8DI4ly+u7JCGEEG84CXYg+++ZY8eMydJvIX/TKBq+i1zD5LAveaBOp13lDsxrsVi+xiaEEOK5JNgfU7KkvivIdTgulPGHx2BtXoalLVbRo7qPnHYXQgjxQiTY3xCKopCVk4WFiQUtnL340nUKPap741hKTr8LIYR4cW/WoHIxFZcaS8/tH/HZoVEAqFQqRrj4S6gLIYTINwl2PVIUhe8vr8dzczMOxR0kOSOJrJw3Y5xfCCGEYZJT8XpyK+0mY0JHcODGPqzMSrPIaxm+Nf1kLF0IIcQrkWDXg7TsNFr/2JykjCRaVmzFwpZLcbJy1ndZQgghigAJ9tdIURRUKhWWppYMdxlNabPS9KrVR47ShRBCFBgJ9tdAURS2/P4D26K2sqnTNkyNTRn63gh9lyWEEKIIKtbBnpoKUVFGxMcX3jWEt9Pj+Sx0FHtjdlPK1JIrKZHUs3uv0PYnhCh+4uNv0aePDzVq1AQgOzubKlXe4bPPJmBsbExmZibBwQu4fPkSJiYmlC1rw9ix43FwyJ30Kjb2BkuWzOfevbvk5Gh49916DBvmj5mZmd5eU05ODuPHj2b06HE4OelvqDItLY3JkyeRlpZGiRIlCQqaRunS1jp1zp07g9jYG2RnZ9OtWw/at++kXR4RcZyxY0dw9Ogpjh07wqlTJxg1amyh1lysr4qvXx88PEoxdao5ACYmSoFtW1EUfvx9M56bm7I3ZjfNnVpwyPu4hLoQolBUqvQWS5d+zdKlX7Nq1VrU6mx+/XU3AMHBC7C1tWPt2h9YvXo9fn59GTt2JGq1mpycHAICxvHxx31YvXo93367AYC1a1fr8+Xw00/bqF/fRa+hDrB16w+4uDRkxYpvadHCi40bv9NZHh4eRkZGBsuWrSY4eCUrVgSj0WgAyMrKYsOGtdjY2ALg7t6c27dvceVKZKHWXKyP2GNjwc5OQ8eOakqVgnbt1AW27c8O+bPh8lpKmpRitucC+tbpj5GqWH+OEkK8RrVr1yUuLpYHD9IJDw9jy5aftMvq1XuP2rXrcORIKCVKlKRSpcq4uDQEcufRGDp0JKr/eb9Sq9VMmxZIQkI8ZmbmBARM5uTJCK5fj2b4cH8ePHhAq1Zd2LLlZ3x8utKsmTtly5Zl167/snlzCAC7du3g2rUofH17M3PmVNTqbIyMjBg//kscHXWnzN62bQurVq0FYO/eXWzbtgVjYyMqV67K+PGT2LlzO+HhYSQlJTJ58gwOHw5l377dqFRGNG/eEl9fP+7cSWDq1K+09QcETNb5oBAWdpQfflivs98PP+xG27bttT+fPn2SiRNzt+Hu7sm4cf4661tblyEtLQ2NRsODBxmULFkSI6Pc3m3YsJZu3XqyfPli7frduvXkxx8389VXU1/0V5lvxTrYASpXVpg7t+C/O97mrXb8cT+ahV5Leat05QLfvhDizRQUZM727QX71vrBB2qCgl78fUqtVnPkyCE++uj/uHkzjrfeqoyJiW5N1arV4MaNGEqUKEG1atV1lpmbWzyxzV27dmBjY0NQ0HT27dvD0aOHMTc3f+b+mzVzo1kzN86cOcX169FUqVKVI0cO4evrx+rVK/Dx6UXjxk05fvwo3333DePHB2iff/v2bczMzLSnvDMyMpg/PxgrKyuGDRtIdPQ1ABISbrNy5Rri428RGrqf5cu/BWDIkAF4ebXm7t1k+vUbSIMGjdix42dCQn5kxIjR2v24uXng5uaRZy+Tk5MpU6YsAGXLliU5OUlned267+Lg4ECPHh+Snp6u/RBw40YM165F8emng3WCvV69+sycOSXPfb6qYh/sBeXOgztMDw8ioNlk7Era0f7tjrSr3EGueBdCvBY3bsQwfPggAKKjr9GrVx88PVty9WoUOTmaJ9ZXFAUjI2NApT11nJfff/+NRo0aA9C6dTsAdu7c/sz1a9euA4CnpxfHjh3BycmZP/6Ipm7desyaNZUbN2L47rtv0Wg02uB8JCkpETs7e+3PpUuXZuLE3HHpmJg/uH//HgC1atVGpVJx5UokcXGxjBjxLwAePEjn9u1blC9fgUWL5vHtt6tITf2LGjVqPfd15kVRnhyuPX/+LHfuJLBly0/cvZvCyJGDcXPzIDh4Af7+nz+xvrm5hXYIxNjY+JXqeRYJ9lekKAo/XwthwpGxpGSm4GTpzLgmXwBIqAtRDAUFZeXr6LqgPBpjBwgIGEfFim8B4OTkRGxsDNnZ2ZiammrXv3YtCk/PlpiamvHvf2/V2dbDhw+Ji7tBlSrvaB8zNjZCo9ENtsff49Rq3aFME5PcfbVo4cWXX06gSpWqNG3qikqlwsTElKlTZ2Nra/vM1/No29nZ2SxYMId1637AxsZW51T4o32YmJji6urOuHGTdLYxY8ZkmjZtxkcfdefgwX2EhR3VWf4ip+JtbW1JSUnC0tKSpKREbG3tdNa/ePE8DRs2wcTEBDs7e0qXtubKlUhiYv5k8uTcsxDJyUkMHz5I+/spbDLo+woSHyQyYE8fBv3ajwx1BtM9ZvNZ4wn6LksIUcwNHTqKlSuDyczMpGTJUri5NWfNmn9C5eLF80RF/Y6rqweNGzclISGeo0cPA6DRaFixIpj9+3/V2WbNmrU5c+YkAMeOHWH9+jWULFlKe2r6woVzT63F1tYOlUrFvn17aNnyfSB3/P/IkVAgdwx7797dTzznzp07QO7Rt7GxMTY2tiQk3Oa336488SGiRo1anDlzmszMTBRFYdGieWRlZXLv3j2cnJxRFIWjRw+R/ege3X9zc/PQXnD46M/joQ7QpEkzDhzYB0Bo6H6aNnXVWe7sXFF7MVx6ehqJiXeoUaMmW7f+zNdfr+Prr9dhY2OrDfWsrExMTEwK7WgdJNhf2qHYg3hubsKO6z/TtLwrB73DGFhviFwgJ4TQuwoVnGjZ8n2++y53zHnUqLE8fJhF376+DBzYh/Xr1zB16iyMjY0xMjJi/vyl/PLLfxgwoDdDh36KpaUlAwb8S2ebrVu3IyMjg+HDB7F16yY6dOhMo0aNtUMAN278+cyzlB4enpw7d4Z69XK/FTRgwCCOHAll2LCBrF27mrp139VZ39HRkaysLP766y+srcvQuHFTPv20D2vXrubjj3uzZMkCnXB3dHSkZ09fhg0byKBBn2BjY4O5uQVdunRj4cK5jB07kvffb8e5c2c4cSI8X73s3t2H33+/wtChn3LmzGk+/rgPAIsXz+fWrZt4enphaWnJkCEDGDNmBEOHjnzqNQqPXLx4gfr1XfJVQ36plKcNGryBEhNTC3ybFSpY4eKSw3//+yDfz7129yqdQlozptE4CfTH2NlZFcrvqriTvhY86WnBK8ie/vjjZrKyMvHz+6RAtvem+OKLz/Hz60vt2nVfaH07O6t870PSKB+2R//M+TtnAXinbDVO94nkX/WHSagLIUQB69q1O+fOneHmzTh9l1JgwsKOYm/v8MKh/rLk4rkXkJyRzMQjY/npWgjv2bmwp3uods53IYQQBc/ExIR585bou4wC9SJfrysIEuzP8d/r2/n8kD9JGYk0dGhMcKuVcrW7EEKIN1axDfbr11Wo85ho7n7WPcYfHkvI1R8xNzYn0HUag+sPw9io8K5kFEIIIV5VsQ32gwdzX/qzLh00UhlxIj6cBvYNWdJqJdXL1XiN1QkhhBAvp9gG+yODBz/U/v1e5l2upFzGtYI7VmalCemyA2eripgYFfs2CSGEMBCFejn3jBkz8Pb2xsfHhwsXLugsCwsLo3v37nh7e7Ns2bLCLOOF7P1zF803N6X3Th9up8cDUNn6bQl1IYQQBqXQgv3EiRPExMSwZcsWpk+fzvTp03WWT5s2jeDgYDZt2sSxY8e4du1aYZWSpweae4zYPxi/nd6kZCYzwsUf2xJ2z3+iEEII8QYqtMPR48eP07p1awCqVq3K/fv3SUtLw9LSktjYWKytrSlfvjwALVq04Pjx47zzzjt5bbLgVdvJV7c/5d6teOrbubCk1Qpq2dR+vTUIIYQQBajQjtiTkpIoW/afO/aUK1eOxMREABITEylXrtxTl70uKpUCrvNJ1SQxoUkAO7vtk1AXQghh8F7bAPKrzlz7MtPq5WX8eBjP/gLdpshV0L8rkUv6WvCkpwVPeqp/hXbEbm9vT1LSPzekv3PnDnZ2dk9dlpCQgL29/RPbEEIIIUT+FFqwu7u7s2fPHgAiIyOxt7fH0jJ3ClZnZ2fS0tKIi4tDrVZz8OBB3N3dC6sUIYQQotgo1Lu7zZs3j1OnTqFSqQgMDOTy5ctYWVnRpk0bTp48ybx58wBo27YtAwYMKKwyhBBCiGLDYG7bKoQQQojnk/uNCiGEEEWIBLsQQghRhBSbYDek6W0NRV49DQ8Pp2fPnvj4+DBx4kQ0Go2eqjQsefX0kfnz59O7d+/XXJnhyqun8fHx+Pr60r17d7766is9VWh48urp999/j7e3N76+vk/MOCqeLSoqitatW7Nx48YnluU7o5RiICIiQhk0aJCiKIpy7do1pWfPnjrLO3TooNy6dUvJyclRfH19latXr+qjTIPyvJ62adNGiY+PVxRFUUaMGKGEhoa+9hoNzfN6qiiKcvXqVcXb21vx8/N73eUZpOf1dOTIkcrevXsVRVGUoKAg5ebNm6+9RkOTV09TU1MVLy8vJTs7W1EURenXr59y9uxZvdRpSNLT0xU/Pz8lICBA2bBhwxPL85tRxeKI/VnT2wI609saGRlpp7cVecurpwAhISE4OjoCuTML3r17Vy91GpLn9RRg1qxZjB49Wh/lGaS8eqrRaDh9+jStWrUCIDAwkAoVKuitVkORV09NTU0xNTXlwYMHqNVqMjIysLa21me5BsHMzIzVq1c/dT6Xl8moYhHsb/r0toYor54C2jkL7ty5w7Fjx2jRosVrr9HQPK+nISEhNGnSBCcnJ32UZ5Dy6mlKSgqlSpVi5syZ+Pr6Mn/+fH2VaVDy6qm5uTnDhg2jdevWeHl5Ub9+fd5++219lWowTExMsLCweOqyl8moYhHs/0uRb/gVuKf1NDk5mcGDBxMYGKjzRiBezOM9vXfvHiEhIfTr10+PFRm+x3uqKAoJCQn06dOHjRs3cvnyZUJDQ/VXnIF6vKdpaWmsWrWK3bt3s3//fs6fP89vv/2mx+qKp2IR7DK9bcHLq6eQ+x984MCB+Pv74+HhoY8SDU5ePQ0PDyclJYVevXoxfPhwIiMjmTFjhr5KNRh59bRs2bJUqFCBSpUqYWxsjKurK1evXtVXqQYjr55GR0dTsWJFypUrh5mZGY0aNeLSpUv6KrVIeJmMKhbBLtPbFry8egq5Y8F9+/bF09NTXyUanLx62r59e3bu3MnWrVtZunQpderU4YsvvtBnuQYhr56amJhQsWJF/vzzT+1yOW38fHn11MnJiejoaDIzMwG4dOkSlStX1lepRcLLZFSxmXlOprcteM/qqYeHB40bN8bFxUW7bufOnfH29tZjtYYhr3+nj8TFxTFx4kQ2bNigx0oNR149jYmJYcKECSiKQvXq1QkKCsLIqFgc77ySvHq6efNmQkJCMDY2xsXFhXHjxum73DfepUuXmD17Njdv3sTExAQHBwdatWqFs7PzS2VUsQl2IYQQojiQj6ZCCCFEESLBLoQQQhQhEuxCCCFEESLBLoQQQhQhEuxCCCFEEWKi7wKEKA7i4uJo3769zlcAAb744gtq1ar11OcEBwejVqtfaW74iIgIhg4dSu3atQHIysqidu3aTJo0CVNT03xt6/Dhw0RGRjJkyBDOnDmDnZ0dFStWZPr06XTp0oW6deu+dJ3BwcGEhITg7OwMgFqtxtHRkSlTpmBlZfXM5yUkJHD9+nVcXV1fet9CFDUS7EK8JuXKldPLd8+rV6+u3a+iKIwePZotW7bg5+eXr+14enpqJxwKCQmhY8eOVKxYkUmTJhVInR9++KHOh5i5c+eycuVKPv/882c+JyIigujoaAl2IR4jwS6EnkVHRxMYGIixsTFpaWn4+/vTvHlz7XK1Wk1AQAB//PEHKpWKWrVqERgYyMOHD5kyZQoxMTGkp6fTuXNn+vfvn+e+VCoVDRs25Pr16wCEhoaybNkyLCwsKFGiBFOnTsXBwYF58+YRHh6OmZkZDg4OzJ49mx07dhAWFka7du3YvXs3Fy5cYOLEiSxfvpwhQ4Ywf/58Jk2aRIMGDQD45JNP6NevH9WqVWPy5MlkZGTw4MEDxowZg5ub23P74uLiwtatWwE4deoU8+bNw8zMjMzMTAIDAyldujSLFi1CURTKlClDr1698t0PIYoiCXYh9CwpKYlRo0bRuHFjzp49y9SpU3WCPSoqivPnz7Nr1y4Atm7dSmpqKlu2bMHe3p5p06aRk5NDz549cXNzo2bNms/cV1ZWFgcPHqR79+5kZGQQEBDAtm3bcHR0ZOPGjSxatIgJEybw/fffc+rUKYyNjdm5c6fOXNVt2rRh/fr1DBkyBFdXV5YvXw7ABx98wJ49e2jQoAHJyclER0fj4eHBkCFD6N+/P82aNSMxMRFvb2/27t2Licmz337UajU7duzgvffeA3JvghMUFETNmjXZsWMHq1atYsmSJXTt2hW1Wk2/fv345ptv8t0PIYoiCXYhXpOUlBR69+6t89jixYuxs7Njzpw5LFy4kOzsbO7du6ezTtWqVSlbtiwDBw7Ey8uLDh06YGVlRUREBLdv3+bkyZMAPHz4kBs3bjwRZFFRUTr79fLyomPHjly5cgUbGxscHR0BaNKkCZs3b8ba2prmzZvj5+dHmzZt6Nixo3advHTq1AlfX18mTpzI7t27ad++PcbGxkRERJCens6yZcuA3Dnak5OTcXBw0Hn+L7/8wpkzZ1AUhcuXL9OnTx8GDRoEgK2tLXPmzCErK4vU1NSn3uP7RfshRFEnwS7Ea/KsMfaxY8fSqVMnunfvTlRUFIMHD9ZZbm5uzg8//EBkZKT2aHvTpk2YmZkxbNgw2rdvn+d+Hx9jf5xKpdL5WVEU7WNLliwhOjqaQ4cO4efnR3Bw8HNf36OL6S5cuMCuXbuYMGECAGZmZgQHB+vcU/ppHh9jHzx4ME5OTtqj+nHjxjF58mRcXV05ePAga9aseeL5L9oPIYo6+bqbEHqWlJREtWrVANi5cycPHz7UWX7x4kX+85//UKdOHYYPH06dOnX4888/adiwofb0vEajYebMmU8c7eelcuXKJCcnc+vWLQCOHz9O/fr1iY2NZd26dVStWpX+/fvTpk2bJ+6prVKpyM7OfmKbH3zwAdu2beP+/fvaq+QfrzMlJYXp06c/t7bAwECCg4O5ffu2To9ycnLYvXu3tkcqlQq1Wv3Efl6mH0IUFRLsQuhZ//79GTduHAMGDKBhw4ZYW1sza9Ys7fJKlSqxZ88efHx86NOnD6VLl6ZBgwb06tWLkiVL4u3tTc+ePbGysqJMmTIvvF8LCwumT5/O6NGj6d27N8ePH8ff3x8HBwcuX75M9+7d6du3Lzdv3qRt27Y6z3V3dycwMJC9e/fqPN62bVu2b99Op06dtI9NmjSJffv28fHHHzNo0CCaNWv23NrKly/PwIED+fLLLwEYOHAgffv2ZfDgwXTt2pX4+HjWrVtHo0aNCAkJYdGiRa/cDyGKCrm7mxBCCFGEyBG7EEIIUYRIsAshhBBFiAS7EEIIUYRIsAshhBBFiAS7EEIIUYRIsAshhBBFiAS7EEIIUYRIsAshhBBFyP8DDlcUEw/9xdMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##LGBM"
      ],
      "metadata": {
        "id": "xCUAfTmjD0TH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = lgb.LGBMClassifier()\n",
        "model.fit(x_sm, y_sm)\n",
        "y_pred =model.predict(X_test)\n",
        "y_prob = model.predict_proba(X_test)\n",
        "classifiers.append(model)\n",
        "\n",
        "append_scores('LGBM',y_test,y_pred)\n",
        "show_analysis('LGBM',y_test,y_pred,y_prob)"
      ],
      "metadata": {
        "id": "8deQv9aDDv07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "outputId": "d5708d7d-55c0-452e-d244-b161eebcc6ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for LGBM: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.96      0.96       972\n",
            "           1       0.21      0.22      0.22        50\n",
            "\n",
            "    accuracy                           0.92      1022\n",
            "   macro avg       0.59      0.59      0.59      1022\n",
            "weighted avg       0.92      0.92      0.92      1022\n",
            "\n",
            "Confusion matrix for LGBM: \n",
            "[[931  41]\n",
            " [ 39  11]]\n",
            "Accuracy score for LGBM: 0.92\n",
            "Sensitivity score for LGBM: 0.22\n",
            "Specitivity score for LGBM: 0.96\n",
            "AUC score for LGBM: 0.83\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params = {\n",
        "\n",
        "        'bagging_fraction': [0.8,0.9,1.0],\n",
        "        'max_depth': [ 5, 7, 9, 11],\n",
        "        'num_leaves': [200, 300, 400],\n",
        "        'n_estimators': [150,200,300]\n",
        "}\n",
        "scorer = make_scorer(custom_loss_function1,greater_is_better=True)\n",
        "clf = GridSearchCV(lgb.LGBMClassifier(), params, scoring = scorer)\n",
        "clf.fit(x_sm,y_sm)\n",
        "# print(clf.cv_results_)\n",
        "print(clf.best_score_)\n",
        "print(clf.best_params_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4TtB03o5UTey",
        "outputId": "7d899bdb-48e3-462c-8c67-efafbf294d08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9903900790165979\n",
            "{'bagging_fraction': 0.8, 'max_depth': 11, 'n_estimators': 300, 'num_leaves': 200}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = lgb.LGBMClassifier(bagging_fraction= 0.8, max_depth= 11, n_estimators= 300, num_leaves= 200,probability = True)\n",
        "clf.fit(x_sm,y_sm)\n",
        "y_pred = clf.predict(X_test)\n",
        "y_prob = clf.predict_proba(X_test)\n",
        "\n",
        "show_analysis('Grid search LGBM',y_test,y_pred,y_prob)\n",
        "append_scores('Grid search LGBM', y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "zawBlR_BVH_D",
        "outputId": "3d8534bf-2ccb-4b2a-c9b8-e24619aee72e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for Grid search LGBM: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.99      0.97       972\n",
            "           1       0.27      0.08      0.12        50\n",
            "\n",
            "    accuracy                           0.94      1022\n",
            "   macro avg       0.61      0.53      0.55      1022\n",
            "weighted avg       0.92      0.94      0.93      1022\n",
            "\n",
            "Confusion matrix for Grid search LGBM: \n",
            "[[961  11]\n",
            " [ 46   4]]\n",
            "Accuracy score for Grid search LGBM: 0.94\n",
            "Sensitivity score for Grid search LGBM: 0.08\n",
            "Specitivity score for Grid search LGBM: 0.99\n",
            "AUC score for Grid search LGBM: 0.82\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##LinearSVC"
      ],
      "metadata": {
        "id": "F5omeK_tD5WO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = SVC(kernel='linear', probability=True)\n",
        "model.fit(x_sm, y_sm)\n",
        "y_pred =model.predict(X_test)\n",
        "y_prob = model.predict_proba(X_test)\n",
        "classifiers.append(model)\n",
        "\n",
        "append_scores('Linear SVC',y_test,y_pred)\n",
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "id": "sBiHPWv4D13q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19d3091a-bf30-48a0-d950-836e90d70a56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[703 269]\n",
            " [ 10  40]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_analysis('Linear SVC',y_test,y_pred,y_prob)"
      ],
      "metadata": {
        "id": "VEQzMFuMHAse",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "outputId": "a3cc8b41-6ed4-4c31-b5af-4e0600269be8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for Linear SVC: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.72      0.83       972\n",
            "           1       0.13      0.80      0.22        50\n",
            "\n",
            "    accuracy                           0.73      1022\n",
            "   macro avg       0.56      0.76      0.53      1022\n",
            "weighted avg       0.94      0.73      0.80      1022\n",
            "\n",
            "Confusion matrix for Linear SVC: \n",
            "[[703 269]\n",
            " [ 10  40]]\n",
            "Accuracy score for Linear SVC: 0.73\n",
            "Sensitivity score for Linear SVC: 0.80\n",
            "Specitivity score for Linear SVC: 0.72\n",
            "AUC score for Linear SVC: 0.84\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##RBF"
      ],
      "metadata": {
        "id": "V6XtEzfyEDYw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = SVC(kernel='rbf', probability=True) \n",
        "model.fit(x_sm, y_sm)\n",
        "y_pred =model.predict(X_test)\n",
        "y_prob = model.predict_proba(X_test)\n",
        "classifiers.append(model)\n",
        "\n",
        "append_scores('RBF',y_test,y_pred)"
      ],
      "metadata": {
        "id": "gHGHO1-lEEvc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_analysis('SVC RBF',y_test,y_pred,y_prob)"
      ],
      "metadata": {
        "id": "L6XKPOj3Jquh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "outputId": "3931bd86-7933-4c34-a765-214e1b7fe405"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for SVC RBF: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.72      0.83       972\n",
            "           1       0.11      0.68      0.19        50\n",
            "\n",
            "    accuracy                           0.72      1022\n",
            "   macro avg       0.54      0.70      0.51      1022\n",
            "weighted avg       0.94      0.72      0.80      1022\n",
            "\n",
            "Confusion matrix for SVC RBF: \n",
            "[[703 269]\n",
            " [ 16  34]]\n",
            "Accuracy score for SVC RBF: 0.72\n",
            "Sensitivity score for SVC RBF: 0.68\n",
            "Specitivity score for SVC RBF: 0.72\n",
            "AUC score for SVC RBF: 0.81\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params = {'C': [0.1, 1, 10],\n",
        "              'gamma': [1, 0.1, 0.01],\n",
        "              'kernel': ['rbf',]}\n",
        "scorer = make_scorer(custom_loss_function1,greater_is_better=True)\n",
        "clf = GridSearchCV(SVC(), params, scoring = scorer)\n",
        "clf.fit(x_sm,y_sm)\n",
        "# print(clf.cv_results_)\n",
        "print(clf.best_score_)\n",
        "print(clf.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XzZKO-_ilITV",
        "outputId": "753b9d6c-548f-44bd-a53a-620b22d0defa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9712569494220528\n",
            "{'C': 10, 'gamma': 1, 'kernel': 'rbf'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = SVC(probability = True,C = 10,gamma = 1,kernel = 'rbf')\n",
        "clf.fit(x_sm,y_sm)\n",
        "y_pred = clf.predict(X_test)\n",
        "y_prob = clf.predict_proba(X_test)\n",
        "\n",
        "show_analysis('Grid search SVC RBF',y_test,y_pred,y_prob)\n",
        "append_scores('Grid search SVC RBF', y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "MPKZLQ0klh9F",
        "outputId": "cf229e07-a248-4f7a-8f0d-e369b995c24c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for Grid search SVC RBF: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.94      0.95       972\n",
            "           1       0.07      0.08      0.07        50\n",
            "\n",
            "    accuracy                           0.90      1022\n",
            "   macro avg       0.51      0.51      0.51      1022\n",
            "weighted avg       0.91      0.90      0.91      1022\n",
            "\n",
            "Confusion matrix for Grid search SVC RBF: \n",
            "[[917  55]\n",
            " [ 46   4]]\n",
            "Accuracy score for Grid search SVC RBF: 0.90\n",
            "Sensitivity score for Grid search SVC RBF: 0.08\n",
            "Specitivity score for Grid search SVC RBF: 0.94\n",
            "AUC score for Grid search SVC RBF: 0.61\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##LDA"
      ],
      "metadata": {
        "id": "8L3oVE9CHvK3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearDiscriminantAnalysis() \n",
        "model.fit(x_sm, y_sm)\n",
        "y_pred = model.predict(X_test)\n",
        "y_prob = model.predict_proba(X_test)\n",
        "classifiers.append(model)\n",
        "\n",
        "append_scores('LDA',y_test,y_pred)"
      ],
      "metadata": {
        "id": "MRJC2OTnHulM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_analysis('LDA',y_test,y_pred,y_prob)"
      ],
      "metadata": {
        "id": "UXmtS19vIK8u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "outputId": "00efb391-7ed1-4549-ef5b-4232f090e792"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for LDA: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.73      0.84       972\n",
            "           1       0.13      0.82      0.23        50\n",
            "\n",
            "    accuracy                           0.73      1022\n",
            "   macro avg       0.56      0.77      0.54      1022\n",
            "weighted avg       0.95      0.73      0.81      1022\n",
            "\n",
            "Confusion matrix for LDA: \n",
            "[[709 263]\n",
            " [  9  41]]\n",
            "Accuracy score for LDA: 0.73\n",
            "Sensitivity score for LDA: 0.82\n",
            "Specitivity score for LDA: 0.73\n",
            "AUC score for LDA: 0.84\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##QDA"
      ],
      "metadata": {
        "id": "Ap52O4b3I5Mk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = QuadraticDiscriminantAnalysis() \n",
        "model.fit(x_sm, y_sm)\n",
        "y_pred = model.predict(X_test)\n",
        "y_prob = model.predict_proba(X_test)\n",
        "classifiers.append(model)\n",
        "\n",
        "append_scores('QDA', y_test,y_pred)"
      ],
      "metadata": {
        "id": "QhYSSpZFI6eO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_analysis('QDA',y_test,y_pred,y_prob)"
      ],
      "metadata": {
        "id": "4HBZtAuLJQeM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "outputId": "23c24f96-2239-4660-f466-4f829fcc5d65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for QDA: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.75      0.85       972\n",
            "           1       0.14      0.80      0.24        50\n",
            "\n",
            "    accuracy                           0.75      1022\n",
            "   macro avg       0.56      0.78      0.55      1022\n",
            "weighted avg       0.95      0.75      0.82      1022\n",
            "\n",
            "Confusion matrix for QDA: \n",
            "[[730 242]\n",
            " [ 10  40]]\n",
            "Accuracy score for QDA: 0.75\n",
            "Sensitivity score for QDA: 0.80\n",
            "Specitivity score for QDA: 0.75\n",
            "AUC score for QDA: 0.81\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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wcOFCdO7cGZ6enujYsSN69OiBXr16oVmzZvDw8MCzZ8/g5eUFU1NTrFy5EnPmzEGnTp0watQoBAUFvfI1FTI1NUWvXr0wePBgdO7cGQMGDMCMGTNQrlw5+Pj4YMuWLS9lnTJlCp48eQJfX19MmDABX3311UujB8DzIf6GDRuiR48e8Pf3R1BQECwtLREeHv7S9oKCguDl5YWlS5di3rx58PPzQ2pqKo4ePVrkkweF/Pz83urz+2S4ZOJN3pgjIo2JiIjA3r178Z///EfqKEUIIdTFt2jRIhQUFGD69OkSpzI8Y8eOhbu7Oz755BOpo5CB4hk7EeHIkSP44IMPkJubi4yMDBw/flw9e500q0uXLti5c2ep3iohKg0WOxGhQ4cOaNCgATp16oTu3bujXbt2r50NTm+vY8eOaN26NTp16vTSUDyRJnAonoiIyIDwjJ2IiMiAsNiJiIgMSJlZoCYu7uXPkL4rW1sLJCVp/rKgxozHVDt4XDWPx1TzeEw1z8HBuuQH/YtRn7ErFCZSRzA4PKbaweOqeTymmsdjqh+MutiJiIgMDYudiIjIgLDYiYiIDAiLnYiIyICw2ImIiAwIi52IiMiAsNiJiIgMCIudiIjIgGi12G/evAl/f3/873//e+m+M2fOoFevXujbty9Wr16tzRhERERGQ2vFnpmZiTlz5qBNmzavvH/u3LkIDw/H5s2bcfr0ady+fVtbUYiIiIyG1opdqVTi22+/haOj40v3xcTEoHz58qhSpQrkcjm8vb1x9uxZbUUhIiIyGlq7CIxCoYBC8erNx8XFwc7OTn3bzs4OMTEx2opCREQGJCzMDJGRZeYaZm8l2+kwZPmWeHqxXamfW2aOjK2thVYuMPA2V86h1+Mx1Q4eV83jMdU8XRzTffuA2FjAxUXru5KEyiQTCd4DIc+1BfBXqZ8vSbE7OjoiPj5effvp06evHLJ/kTYuBejgYK2Vy8EaMx5T7eBx1TweU83T1TFVqSzh5ARER2dofV+6lJydhArmtgCAYzHrYWduV8IzXk2Sj7u5uLggPT0dDx8+RH5+Po4ePYp27Uo/3EBERFTWpeelY+qJiWi3uQUSshIAAB1cfdHIoclbbU9rZ+xXr17FokWL8OjRIygUChw8eBC+vr5wcXFBQEAAwsLCMGnSJABA586dUaNGDW1FISIi0kunH53EuKOj8CD1HurZ1UdSdiLsy9m/0za1VuwNGjTAxo0bi72/RYsW2Lp1q7Z2T0REpLcy8jIw99wsfP/nN5DL5BjXbBImtwiFmYnZO2+7zEyeIyIqrdfNnpbLn79XS5qjq2MaGyuDk5PQ+n60aXzUKOy5E4E6tnWx0nctmlVqrrFts9iJyGBFRioMogSoKCcngW7d8qWOUWoqoYJc9nxq2+QWoahqUw1TWkyDucJco/thsRORQXNyErh06eXZ089ncBvWrGqp8ZgW71zsGUw6NhbrAr5HQ4fGqGtXDzPbzNbKvngRGCIiIi3JzMvEzFOhCN7dCbeTb+H8Y+2vssozdiIiIi248Pg8xkaNwN2UO6hV4T2s8FmLllVaaX2/LHYiKpPeZFlRvr9OUtl7exeGHxoMABjReDSmtZqJcopyOtk3i52IyqQ3mRhXVidZUdnn7eqDds5e+Kzl52hd5dVXOdUWFjsRlVnFTYwj0rXs/Gwsjp4Pd/sG+KBOH5Q3q4CI4J8lycJiJyIiege/Pb2IsVGf4mbSDTR1bIaetXtDJpNJloez4omIiN5CTkEO5p4NQ+cIf9xMuoH/a/gJIoL3SVrqAM/YiYiISu1pxhP02vs+biT9jao21bHCZzXaOXtJHQsAi52IJPAmM9pLwhnvJCUHC0fYl6uIoc7DMaPNbFiZWkkdSY3FTkQ6p4mlXjnjnXTtyrPLuPDkHIY3+hRymRzbuu2G0kQpdayXsNiJSBKc0U5lRW5BLpZdXIQVvy0DAHSs3gnVbKrrZakDLHYiIqJi/RH3O8Yc+RR/JV6Di5UrlvusQjWb6lLHei0WOxER0b8IIfDVxYVYfmkJ8lX5GOA2BGFt58BaaSN1tBKx2ImIiP5FJpMhMTsBlSwqY1mHcPhU9ZM60hvj59iJiIgA5BXkYevfP0GI55M6Z7SejeN9z5apUgd4xk5ERITrCdcw5sgI/Bl/BSqhwof1+8PS1FLqWG+FxU5EREYrX5WP8N+W46uLC5GnysOH9fqjc82uUsd6Jyx2IiIySn8lXMfYqE9xJe4yKltWwbIOK+FfLVDqWO+MxU5EREbp0tNoXIm7jD51P8TcdgtRwdxW6kgawWInIiKjcTPxBpysnWFlaoWP6g9Ebdu6aFWltdSxNIrFTkSlwnXeqSzKV+Vjze/hWBI9H/3qD8Ci9ssgk8kMrtQBFjsRlRLXeaey5lbSTYyNGoFLTy/CoZwjOriWrY+vlRaLnYhKjeu8U1lQoCrAuiursfDCHOQU5KBn7d6Y77UYdub2UkfTKhY7EREZpL8Sr2POuS9gZ26Pdd5fo0vNblJH0gkWOxERGYwCVQFScpNhZ26PBhUbYq3/d2jv4gP7coZ9lv4iFjuRkdDEpDeAE99If91Nvo2xUSMhk8mwp/sByGVy9KjdS+pYOse14omMROGkt3fFiW+kb1RChfVXVsNnWztceHIOlSwqIzM/U+pYkuEZO5ER4aQ3MjR3U+5gfNQonHt8Bvbm9gj3XYf33+shdSxJsdiJiKhMyivIwwd7uuFR+kN0rRmMRe2XwcHCQepYkmOxExFRmZKvyodCroCpiSm+bLcA+ao8dH/vA8hk7/5WkyHge+xERFQmqIQK3//5Dby2tERydhIAoFutYPSo3Yul/gIWOxER6b37qffwwZ5umHZyMhKy4nEj6YbUkfQWh+KJiEhvqYQK/732A2afmYnM/AwEVe+MJd5fo5JlZamj6S0WOxER6a0Zp6biuz/Xo7xZBaz2/ga96vTlsHsJWOxERKS3+tUfiEfpj7Co/VJUtqwidZwyge+xExGR3niYFoP++/rg78S/AAANKjbEfzv9xFIvBRY7ERFJTgiB/13/L9pvaY1D93/Blr83SR2pzOJQPJEBe3F9eK7xTvoqNv0RJhwdjaMxR2CttMHXPqvxYb3+Uscqs1jsRAascH14JyfBNd5JL51+dBIDD3yItNxU+Lj6YVmHcDhbu0gdq0xjsRMZOK4PT/qsvr0bHC0cMbvtPHxUfyBnvGsAi52IiHRGCIGtN35CebMK6FSjC+zM7XEy5AIUctaRpvBIEhGRTjzJeIzJx8bh0P1fUM2mOgKqBUIhV7DUNYxHk8iAvDhZDuCEOdIPQghsv7kFn5+aipScZHi5dMDXPqtY6FrCo0pkQF6cLAeAE+ZIcqk5KRh95BP8cm8/LBSWWNx+OQa5D+V76VrEYicyMJwsR/rE0tQKcVlx8HRuj+U+q1DNprrUkQyeVot9/vz5uHLlCmQyGaZPn45GjRqp79u0aRP27t0LuVyOBg0a4PPPP9dmFCIi0pFnmc9w5tFJdK/9AUzkJtjUZRsqmNlCLuOaaLqgtWK/cOEC7t+/j61bt+LOnTuYPn06tm7dCgBIT0/H999/j0OHDkGhUGDo0KH4/fff0aRJE23FISIiLRNCYNetHZh2cjJSclJQz94N9ezqw87cXupoRkVrfz6dPXsW/v7+AIBatWohJSUF6enpAABTU1OYmpoiMzMT+fn5yMrKQvny5bUVhYiItCwuMw69t/fGJ78ORXZ+NuZ6LkQd27pSxzJKWjtjj4+Ph7u7u/q2nZ0d4uLiYGVlBTMzM4waNQr+/v4wMzNDly5dUKNGDW1FoTLs37O8jZVcDqhUliU+jrPgSQqRd3bjs+MTkJCdgFZV2mCF7xrULF9L6lhGS2e/MYX4/3/ZpKenY/369fjll19gZWWFQYMG4e+//0a9evWKfb6trQUUChON53JwsNb4No2dJo/pvn1AbCzgwhUmIZeXPMDm4gL07i3jz/Ub4nHSjLNnTyAzPxPLA5djbKuxfC9dYlordkdHR8THx6tvP3v2DA4ODgCAO3fuwNXVFXZ2dgCA5s2b4+rVq68t9qSkTI1ndHCwRlxcmsa3a8w0fUxVKks4OQHR0cY9y7u0xzUuTothDAT//38352LPoFWVNpDJZJjmEYZh9T9F69rNeEw17G3++NTan1Xt2rXDwYMHAQDXrl2Do6MjrKysAADOzs64c+cOsrOzAQBXr15F9erVtRWFiIg0JCErAR8fGoz3dwdh243NAABrpQ1qVagtcTIqpLUz9mbNmsHd3R0hISGQyWSYNWsWIiIiYG1tjYCAAAwbNgwDBw6EiYkJmjZtiubNm2srChERacC+u5GYcnw84rPi4FGpBTwqtZA6Er2CTLz45rce08bwDofiNE/Tx9TD4/mEMWNfcIU/q5rHY/rmErMTMP3kZ4i4tR1mJmYIbTkTIxqPgom86LwnHlPNe5uheE43Jsm9buY7Z3kTSe/nO3sRcWs7PCo1x0rfdahtW0fqSPQaLHaS3L/XN38R1zonkkZydhLMFOYopyiH/m6DUE5RDj1q9+KFW8oAfodIL3B9cyL9cejeAUw6Ng49avfCl+3mQy6To3fdEKlj0RtisRMREYDnZ+kzTodi243NMJWbomK5ilJHorfAYiciIhy+fxATj43Fk4zHaOzQFCt916K+vZvUsegtsNhJ5/49WY4T5IikdSf5Fj7a1wcKuQLTWs7E6KbjYWpiKnUsekssdtK5f0+W4wQ5ImnkFuRCaaJErQq1MafdAni6eMPN3r3kJ5JeY7GTJDhZjkg6qTkpmHXmc8SkxWB7t92QyWT4uPFIqWORhnClfiIiI3L0wRG039Iam/7agISseCRkJ0gdiTSMZ+xEREYgLTcVYWdmYOP1/0AhV2By81CM95gMpYlS6mikYSx2IiIDpxIqdNsVhOsJV1Hfzh3hfmvRyKGJ1LFIS1jspBMvzoTnLHgi3ZLL5Pi08Wj8k3IHE5tP5Vm6geN77KQThTPhAc6CJ9KFkw+Po8fuLkjPfX5Rlr71+iG01UyWuhHgGTvpDGfCE2lfel465pz9Aj9e/Q5ymRwnH51ApxpdpI5FOsRiJyIyEGcencLYoyPxIPUe6trWw0rftWhayUPqWKRjLHYiIgOw6vIKfHl2JuQyOcY0nYApLabBXGEudSySAIudiMgAtHPyRH07NyzzCYdHpRZSxyEJsdjpnb04410uB1Qqy5cew5nwRJqVmZeJRRfmYaD7YNSqUBtNK3ngaN8zkMs4J9rYsdjpnf177fdX4Ux4Is05//gcxkV9irspdxCfFYfV/t8AAEudALDYSUMKZ7w7OFgjLo4z34m0ISs/CwvOz8H6K6sBAJ82HoPQVjMkTkX6hsVORFQG/JVwHUMP9sed5NuoWb4WVviuRasqraWORXqIxU5EVAbYl6uIlJwUfNJ4FKa1nAkLUwupI5GeYrHTOwkLM0NMjByuriqpoxAZnEtPo5GZlwkvF284WjjibL9LKG9WQepYpOdY7PROCmfDc2IckeZk52djcfR8rPl9JSpZVMb5j36HucKcpU5vhMVO78zVVYWwsBypYxAZhN+eXsTYqE9xM+kGqtlUx0rftVxohkqFxU5EpAdyC3KxJHoBwi8vh0qoMKzhx5jRejYsTV9eF4LodVjsRER6QAYZoh4chouVK772XQ1P5/ZSR6IyisVORCSRnIIcXHxyAe2cvWBqYoofg/4Hu3L2sDK1kjoalWFcpoiISAJ/xP2Ojtu90SeyO67FXwUAVLWpxlKnd8YzdiIiHcotyMXyS0vw9aWvUCAKMNBtKKrZVJM6FhmQEs/YHz16hLFjx2LAgAEAgG3btuHevXvazkVEZHD+jP8DgTt8sPTiIlSxdML2bnvwVYevYaW0ljoaGZASi33mzJkIDg6GEM8v8FGjRg3MnDlT68GIiAzNN1fW4FrCnxjgNhjHQ87C29VH6khkgEos9ry8PPj5+UEmkwEAWrTgdX6JiN7Ug9T76n9/2W4+tnbdhaUdVsJaaSNhKjJkbzR5LjU1VV3st27dQk4OFyMhInqdvII8LL24CK1/aoo9tyMAALbmdvCp6idxMjJ0JU6eGzVqFPr06YO4uDh069YNSUlJWFrLXJcAACAASURBVLJkiS6ykZ4JCzNTLyFbqKTrsBMZo+sJ1zA26lP8Efc7KltWgY2yvNSRyIiUWOxubm7YvXs3bt68CaVSiRo1auDZs2e6yEZ6JjJS8VKROzkJrhNP9P/kq/IR/ttyfHVxIfJUeQip9xHmtFvANd5Jp15b7CqVCqNGjcKGDRvQoEEDAEB+fj5GjhyJyMhInQQk/eLkJHDpUobUMYj00o6bW7HgwhxUsqiMZR1WIqB6kNSRyAgVW+w///wzwsPDcf/+fdSvXx8ymQxCCMjlcnh6euoyIxGR3spX5UMlVFCaKNG7Tghi0x9haIPhqGBuK3U0MlLFFnvXrl3RtWtXhIeHY8yYMUXuS0tL03owIiJ9dzPxBsZGjYCXSwd83noWTOQmmNj8M6ljkZEr8T32MWPG4Pbt20hKSgIA5ObmYu7cuThw4IDWwxER6aMCVQHWXAnH4gvzkFOQg9q2dSGEUH96iEhKJRb7vHnzcOrUKcTHx6Nq1aqIiYnB0KFDdZGN9EhYmBliYuRwdVVJHYVIUreSbmJs1AhcenoRDuUc8VWHFehUo4vUsYjUSvwc+x9//IEDBw6gXr162LlzJ3744QdkZWXpIhvpkcKPuXEGPBmz2PRH8NvmiUtPL6Jn7V44+eF5ljrpnRLP2JVKJYDnK9AJIdCgQQMsWrRI68FI/7i6qhAWxsWJyPgUDrM7WTljROPRaOTQBF1rvS91LKJXKrHYa9SogU2bNqF58+YYMmQIatSowclzRGQUClQF+PbPtYh+cgHfdfwvZDIZprf+QupYRK9VYrHPnj0bKSkpsLGxwb59+5CQkIBPPvlEF9mIiCRzN+UOxkWNxPnHZ2Fvbo+H6TFwta4qdSyiEr32PfbU1FRcu3YNZmZmkMvl6NatGwYPHoynT5/qKh9JLCzMDB4eloiN5WxfMg4qocK3f6yFz9a2OP/4LLrV6o4TIRdY6lRmFHvG/uuvv2L27NlwcHBAXFwc1q1bhzp16mD58uU4fPgwfv31V13mJIm8uIwsJ86RoRNCYMD+vvj1/kHYmdthpe9aBL/XU+pYRKVSbLF///332LNnD+zt7XH16lV88cUXyMnJgaenJ/bs2aPLjCQxLiNLxkImk8G/WiBM5Uos9l4ORwtHqSMRlVqxxW5qagp7e3sAQIMGDZCdnY1FixahYcOGb7zx+fPn48qVK88nnEyfjkaNGqnve/z4MSZOnIi8vDy4ubnhyy+/fIeXQUT0du6l/IOlFxdhUftlsDC1wGD3YRjsPoyLzVCZVex77P/+oba3ty9VqV+4cAH379/H1q1bMW/ePMybN6/I/QsXLsTQoUOxY8cOmJiYIDY2tpTRiYjenkqo8MPVb9Fha1tsvfETIm5tB/D8dx9LncqyYotdCAEhBFQqFVQqlfprL95+nbNnz8Lf3x8AUKtWLaSkpCA9PR3A86vGXbp0Cb6+vgCAWbNmwcnJ6Z1fDBHRm3iQeh8BGwMQemISlCamWOP/LT6qP1DqWEQaUexQfHR0NNzc3NS3hRBwc3NTL9Tw119/vXbD8fHxcHd3V9+2s7NDXFwcrKyskJiYCEtLSyxYsADXrl1D8+bNMWnSJA28HNKUsDCzV15/nais231rJyYcG4OMvHQEVu+Er7xXoJJlZaljEWlMscX+999/a3RHQogi/3769CkGDhwIZ2dnfPzxxzh27Bg6dOhQ7PNtbS2gUJhoNBMAODhYa3ybhmDfPiA2FnBxAXr3lpXqOPGYagePq2bUz3oP5gozrO2yBv0b9eewu4bx51R6JS5Q87YcHR0RHx+vvv3s2TM4ODgAAGxtbeHk5ISqVZ9/LrRNmza4devWa4s9KSlT4xkdHKwRF8dV9F5FpbKEkxMQHf18Nnxc3Js9j8dUO3hc354QApv+2gAvF29Us6mOuuUaI7r/n6jhVIXHVMP4c6p5b/OHUokXgXlb7dq1w8GDBwEA165dg6OjI6ysrAAACoUCrq6uuHfvnvr+GjVqaCsKERmpR2kP0ffnHph4bAxmnp6m/rqVqZWEqYi0S2tn7M2aNYO7uztCQkIgk8kwa9YsREREwNraGgEBAZg+fTpCQ0MhhECdOnXUE+mIiN6VEAI//bURX5yZjrTcVPhW9cdCr6+kjkWkEyUWe25uLrZv347Hjx9j8uTJuHLlCurVqwczM7MSNz558uQit+vVq6f+d7Vq1bB58+a3iExEVLwnGY8x/ugoRD04DGulDb72WY0P6/G9dDIeJQ7Fh4WF4cGDBzh//jyA58PmoaGhWg9Gule4LjzXhqeyTAiBi0+i4ePqhxN9z6Ff/QEsdTIqJRb73bt3MW3aNJibmwMA+vXrh2fPnmk9GOle4cfbAHBteCpTHqfH4vLTSwCAKlZOONT7GLZ0jYCztYvEyYh0r8SheIXi+UMK/+LNzMxEdna2dlORZLguPJUlQghsvfETZpwKhbXSGidDzsNKaY2a5WtJHY1IMiUWe1BQEAYNGoSHDx9i7ty5OHHiBPr166eLbERExXqS8RiTj43Dofu/wNLUCuM9JsOSs92JSi72/v37o1GjRrhw4QKUSiWWLVuGBg0a6CIbEdFLhBDYfnMLPj81FSk5yfBy9sZyn1WoalNN6mhEeqHEYu/Tpw+Cg4PRq1cvVKhQQReZSMe4fCyVJXmqPKy+vAJ5BXlY2H4pBrsPg1ymtSU5iMqcEot96tSpOHDgAHr06IF69eohODgYvr6+UCqVushHOvBiqXPCHOkjIQTuJN/Ge7a1oTRRYl3ADyinKIfq5bmwFdG/ycSLi7i/hhACFy5cwN69e3HkyBGcO3dO29mK0MYyhVz+8DkPD0sA0MikOR5T7TDm4/os8xk+Oz4BRx4cQlSf06htW0cj2zXmY6otPKaa9zZLyr7RynOpqak4fPgwfvnlF8TExKBv376l3hERUWkIIbDndgRCT05CYnYiWldpC6UJRwqJSlJisQ8bNgy3bt2Cv78/RowYgWbNmukiFxEZsbjMOEw9MRE/392DcopymOe5CMMafsL30oneQInFPnDgQHh5eUEu5/9QRKQb88/Pxs9396BVlTZY4bMaNSu8J3UkojKj2GKfO3cuZsyYgfXr1+Obb7556f5NmzZpNRjpRliYGWJi5HB1VUkdhYxcel66+qprn7cOQ4OKDTHY/f9gIjeROBlR2VJssffq1QsAMH78eJ2FId2LjHz+I8DZ8CSln+/sxWcnJmBZh3AE1eiMiuUqYljDT6SORVQmFVvshVdii4iIwMKFC4vcN2zYMLRs2VK7yUhnXF1VCAvLkToGGaHE7ARMPzkFEbd2wNzEHHFZvA4F0bsqttj37t2LLVu24NatW/joo4/UX8/Ly0NCQoJOwhGR4dp/92dMOT4ecVnP4FGpBcJ91+E929pSxyIq84ot9vfffx+tWrXC5MmTMWbMGPXX5XI53nuPE1mI6O3tv/szBv/SD2YmZviizRx82ng030sn0pBii/3Zs2eoVKkS5s+f/9J9aWlpXF6WiEpNCAGZTIaO1YMwwG0wPmk0CnXs6kodi8igFFvsixYtwtKlSzFo0CDIZDK8uECdTCbDkSNHdBKQNKNwPfh/4/rwpAvJ2Un4/NRU1ChfE5NbhEIhV2Bph5VSxyIySMUW+9KlSwEAUVFROgtD2lPcRV64Pjxp26F7BzDp2Dg8zXyCFpVbYYLHFA67E2lRiQvUHD9+HMnJyQgODsakSZPw559/YvLkyejYsaMu8pEGOTkJjawHT/QmUnKSMeNUKLbe+AmmclNMb/UFRjcdz1In0rISl5Nbs2YNvLy8cPz4cahUKuzatQsbN27URTYiKqOSshPRfktrbL3xExo5NMGvvU9gvMdkKORvdHkKInoHJf5fZm5uDjs7Oxw/fhzBwcGwtLTk8rJE9Fq25nbwr9YRzlYuGNN0AkxNTKWORGQ0Siz2nJwcfPfddzhx4gSmTp2Ke/fuIS2Nl+XTJ8VNjHsRJ8mRtkU9OIxD9w5ggddXkMlk+Mp7BWQymdSxiIxOiafec+bMwdOnT7Fw4UKYmZnh1KlTmDx5si6y0RsqnBj3OpwkR9qSlpuKiUfHIOTnnthw/UfcSPobAFjqRBIp8Yy9du3aGDRoEK5fv45ff/0Vvr6+cHJy0kU2KgVOjCMpHIuJwoSjo/Eo/SHc7Bsg3G8d6tnVlzoWkVEr8Yx98+bNGDhwIPbt24fIyEgMGDAAu3bt0kU2ItJjM0+Fok9kdzzNfIJJzafiUK9jaFixkdSxiIxeiWfse/bswYEDB2BmZgYAyMzMxJAhQ9CjRw+thyMi/eVs7YL6du4I91uLRg5NpI5DRP9PiWfsCoVCXeoAYGFhAVNTznAlMjbpuWlYEr0A2fnZAIDhDT/Fod7HWOpEeqbEM/bKlStjzpw5aNu2LQDg1KlTqFKlitaDUckKZ8Nzxjtp26lHJzA+ahQepN2HhcISo5qOhYncBCbgYjNE+qbEYp8zZw42btyIiIgIyGQyNG7cGAMGDNBFNirBi6XOGe+kDel56Zh7dhZ+uPot5DI5xjWbhP9r9InUsYjoNd7oc+wff/yxLrLQW+BseNKWC4/PY9SR4bifeg91bOtipe9aNKvUXOpYRFSCYt9jv3jxIjw9PREYGIguXbrgwYMHusxFRBLLLsjCw7QYjGk6AYd7n2SpE5URxZ6xL1++HD/++CNq166Ns2fPYunSpVixYoUusxGRjp17fBauVq5wtnZBe5cOuND/Clytq0odi4hKodgzdrlcjtq1awMA2rRpg8TERJ2FIiLdyszLxMxToQjeFYQpx8erv85SJyp7ij1j//dykFweUr+EhZkhJkYOV1eV1FGojDv/+BzGRX2Kuyl3UKvCe5jQfIrUkYjoHRRb7CkpKTh79qz6dmpqapHbbdq00W4yeq3Ci75wNjy9raz8LCw4Pwfrr6wGAHzaeAxCW81AOUU5iZMR0bsotthtbGywZs0a9W1ra2v1bZlMxmLXA66uKoSF5Ugdg8qo+Kw4bLz+H1QvXwMrfdehVZXWUkciIg0ottg3btyoyxxEpAPZ+dmITX+ImhXeg6t1VWztugsNKjaEhamF1NGISENKXFKWiAzDpafR8Nvmib4/90RG3vO1D1pWacVSJzIwLHYiA5dTkIO5Z8PQJSIAt5JvomO1IMjAybBEhqrEleeIqOy6/PQSxkZ9ihtJf6OaTXWs8FmDts6eUsciIi0q8Yz90aNHGDt2rHp9+G3btuHevXvazkVE70glVOpSH9pgOI72PcNSJzICJRb7zJkzERwcDCGeXz2sRo0amDlzptaDEdHbSc5OAgDIZXKs8F2DiOCfsbD9UliZWkmcjIh0ocRiz8vLg5+fn3qBmhYtWmg9FBGVXm5BLhaen4Pm/2uEf1LuAgCaVWoOT+f2EicjIl16o8lzqamp6mK/desWcnL42WkiffJH3O8I2O6NZZeWwEZpg8TsBKkjEZFESpw8N2rUKPTp0wdxcXHo1q0bkpKSsGTJEl1ko1cICzMrch12Mm65BblYfmkJVvy2FPmqfAxwG4KwtnNgrbSROhoRSaTEYm/dujV2796NmzdvQqlUokaNGjAzM9NFNnqFF0udy8nSnHOzsP7KajhbuWBZh3D4VPWTOhIRSazEYi/uUq3jxo3TeBh6M05OApcuZUgdgySiEirIZc/fRRvdZBwKVPkIbTkDNmblJU5GRPqgxPfYTUxM1P+pVCqcP38eaWlpb7Tx+fPno2/fvggJCcEff/zxyscsXbpU/VE6Inq9a/FX0XFHB0Q9OAwAqGRZGfO9lrDUiUitxDP20aNHF7ldUFCAMWPGlLjhCxcu4P79+9i6dSvu3LmD6dOnY+vWrUUec/v2bURHR8PU1LSUsYmMS15BHpZdXIylFxchT5WHM49Owbeqv9SxiEgPlXpJ2fz8fDx48KDEx509exb+/s9/8dSqVQspKSlIT08v8piFCxdiwoQJpY1AZFT+SriONt+3wcILc2FfriJ+6rIdM9qESR2LiPRUiWfs3t7e6o+6Ac+v096jR48SNxwfHw93d3f1bTs7O8TFxcHK6vkiGREREWjZsiWcnZ3fJrfR4Wx443Q29jR67w1GrioXfev2w5x2C1DB3FbqWESkx0os9p9++kn9b5lMBisrK9jYlP6jNIUr1wFAcnIyIiIi8OOPP+Lp06dv9HxbWwsoFCal3m9JHBysNb5Nbdi3D4iNBVxcgN69ZXqdW5+zlTVBdr7wveqL0S1Go0udLlLHMTj8WdU8HlPplVjsS5Yswddff13qDTs6OiI+Pl59+9mzZ3BwcAAAnDt3DomJifjoo4+Qm5uLBw8eYP78+Zg+fXqx20tKyix1hpI4OFgjLu7NJgJKTaWyhJMTEB39fDZ8XJzEgYpRlo6pPspX5WPN7+EwM1Hik8ajAAAbOm7jcdUCHlPN4zHVvLf5Q6nEYndxccGOHTvQtGlTKJVK9dddXV1f+7x27dohPDwcISEhuHbtGhwdHdXD8EFBQQgKCgIAPHz4ENOmTXttqRMZg5uJNzA2agR+e3YJVa2rYXCD/4OZCdeMIKLSKbHY9+/f/9LXZDIZjhw58trnNWvWDO7u7ggJCYFMJsOsWbMQEREBa2trBAQEvH1iIgNToCrA2iursOjCXOQU5KBn7d6Y77WYpU5Eb0UmXnzz+wV79+7F+++/r+s8xdLG8E5ZGjby8LAEAL1fmKYsHVN9kJ6Xjt57g3HpaTQqlnPAV94r0Llm15cex+OqeTymmsdjqnkaHYrfsWOHXhW7oSmc5f6mOBveMFmZWsHV2hVVrativtdXsC9nL3UkIirj3rxZSKNK+9E1rg1vOO4k38Le27sxofkUAMAqv2+gNFGW8CwiojdTbLFfvnwZHTp0eOnrQgjIZDIcO3ZMi7GMA9d8Ny4qocK3f6zFvHOzkV2QDS8XbzSv3JKlTkQaVWyxu7m5YdmyZbrMQmSw7qbcwbiokTj/+Czsze0R7rcOzSu3lDoWERmgYotdqVRyVTgiDfjx6ncIO/M5svKz0LVmMBa1XwYHCwepYxGRgSq22Bs1aqTLHAbv35PlOBnOeKTlpqKcohy+9lmN7u99UGSJZiIiTSv2IjBTpkzRZQ6DVzhZrhAnwxkulVBh+40tyC3IBQCMbDIWJ0Oi0aN2L5Y6EWkdZ8XrECfLGb77qfcwPmoUTseexMO0GExoPgUKuYJD70SkMyx2Ig1QCRX+e+0HzD4zE5n5GQiq0QX93AZKHYuIjBCLnegdxaQ9wPijo3Hy4TFUMKuAJd7foFedvhx2JyJJsNiJ3tHNxL9x8uExdKwWhK86rEBlyypSRyIiI8Zi17LC2fCcBW9YHqbFQGliBkcLR/hV64jIHofQsnIrnqUTkeSKnRVPmvFiqXMWfNknhMDG6/9B+y2tMfn4OBReQ6lVldYsdSLSCzxj1wHOhjcMj9IeYuKxMTgacwTWSht0qt5F6khERC9hsROVQAiBzX//DzNPT0Nabip8q/pjWYdwOFlxZUYi0j8sdqISPM6IxbSTk2EiU2B5h1XoV38Ah92JSG+x2IleQQiBhOwEVCxXEU5WzlgX8AMaVmwEF2tXqaMREb0WJ89pUViYGWJieIjLmicZj9F/fx+8vysQWflZAIBONbqw1ImoTGDraFHhRV84G75sEEJg243N8NrSCr/eP4gqVs5Iz02XOhYRUalwKF7LXF1VCAvLkToGleBpxhNMPj4OB+8dgIXCEovbL8cg96F8L52IyhwWOxk9IQT67++LK3GX4encHst9VqGaTXWpYxERvRUWOxmtfFU+FHIFZDIZZredh7+T/sJg92GQy/gOFRGVXfwNpmFhYWbw8LCEh4dlkeuvk/4QQiDi1na03tQUD9NiAABtnT0xtMFwljoRlXn8LaZhhUvIAuAysnroWeYzDPmlP0b8OgzxWXG4Gv+n1JGIiDSKQ/FawCVk9dOe2xGYemIiErMT0bpKW6zwXYMa5WtKHYuISKNY7GQUVv62HHPPzUI5RTnMbbcQ/9doBIfdicggsdjJKPSs3QtnY09hnuci1KzwntRxiIi0hqcsZJASshLwyaEhOPXoBADAxdoVm7vuZKkTkcHjGbsGFS4h6+qqkjqKUdt3NxJTjo9HfFYcZDIZPJ3bSx2JiEhnWOwaxCVkpZWYnYDpJ6cg4tYOmJmYYVabuRjReJTUsYiIdIrFrmFcQlYa1+Kvok9kd8RlPYNHpeZY6bsOtW3rSB2LiEjnWOxkEGpWqIVKlpUxoslojGw8BiZyE6kjERFJgsVOZdbBeweQmJWAD+v3RzlFORzqdQwKOX+kici48bcglTnJ2UmYcToU225sRnmzCuhWKxhWSmuWOhERWOxUxvx67xdMOj4OTzIeo7FDU4T7rYOV0lrqWEREeoPFTmVCbkEuJh8fhy1/b4Kp3BTTW32B0U3H8yydiOhf+FuRygRTuSlSclLQyKEJVvquhZu9u9SRiIj0Eoud9FZqTgr2//MzQup9BJlMhpW+a2ChsISpianU0YiI9BaLnfTS0QdHMOHoaMRmPIKzlQu8XLxR3qyC1LGIiPQei/0dhYWZqVeci42VwclJSJyobEvLTUXYmRnYeP0/UMgVmNw8FK2qtJE6FhFRmcFif0eRkQp1oTs5CS4n+w5OPDyG8VGj8DA9Bm72DRDutw4NKzaSOhYRUZnCYtcAJyeBS5cypI5R5p2LPYPHGbGY2PwzTPT4DEoTpdSRiIjKHBY7SSr6yXk0dfSAQq7AeI/J6FLzfbhXbCB1LCKiMovXYydJpOelY+qJiegSEYDVl1cAAJQmSpY6EdE74hk76dzpRycx7ugoPEi9h7q29eDt6iN1JCIig8FifwucCf92MvIyMPfcLHz/5zeQy+QY12wSJrcIhZmJmdTRiIgMBov9LXAm/Ns58+gkvv/zG9SxrYuVvmvRrFJzqSMRERkcFvtb4kz4N5OZl4k8VS7Km1VAQPUgrAv4Hp1rdIO5wlzqaEREBkmrk+fmz5+Pvn37IiQkBH/88UeR+86dO4c+ffogJCQE06ZNg0ql0mYUksC5x2fhs60tphwfr/5az9q9WepERFqktWK/cOEC7t+/j61bt2LevHmYN29ekfu/+OILrFy5Elu2bEFGRgZOnjyprSikY5l5mZh5KhTBu4JwL+UfVLF0RoGqQOpYRERGQWtD8WfPnoW/vz8AoFatWkhJSUF6ejqsrKwAABEREep/29nZISkpSVtRSIfOxJzBgJ0DcTflDmqWr4WVvuvQskorqWMRERkNrZ2xx8fHw9bWVn3bzs4OcXFx6tuFpf7s2TOcPn0a3t7e2opCOpKQlQD/Df74J+UuPmk8ClF9TrPUiYh0TGeT54R4+SNhCQkJGDFiBGbNmlXkj4BXsbW1gEJhovFcDg7WpX6OXP72zzVEuQW5UJoo4QBrrOq8CnXs68CzqqfUsQwOf940j8dU83hMpae1Ynd0dER8fLz69rNnz+Dg4KC+nZ6ejuHDh2P8+PHw9Cy5BJKSMjWe0cHBGnFxaaV+nkplCQCIizPuWfHZ+dlYHD0fx2Ki8MsHUVCaKDG06VDExaW91XGl4r3tzyoVj8dU83hMNe+tTj61kAMA0K5dOxw8eBAAcO3aNTg6OqqH3wFg4cKFGDRoENq3b6+tCKRFvz29CP/tXlh1+Wuk5abiYXqM1JGIiAhaPGNv1qwZ3N3dERISAplMhlmzZiEiIgLW1tbw9PTE7t27cf/+fezYsQMA0LVrV/Tt21dbcUhDcgpysOTCAqz6/WuohArDGn6MGa1nw9LUUupoREQELb/HPnny5CK369Wrp/731atXtblr0pJBBz5E1IPDqGpdDSt816Cds5fUkYiI6AVcea6UwsLMEBMjh6urcS6o83GjkahqXQ1ftJ0DK1Orkp9AREQ6xcu2llLhxV+MZX34P+J+R/fdnfEk4zEAwLeqPxZ7L2epExHpKRb7W3B1VSEsLEfqGFqVW5CLhRfmInCHD87EnsLBewekjkRERG+AQ/H0kj/j/8CYIyNwPeEqXKxcsdxnFa+ZTkRURrDYqYhtNzZj/NFRyFflY4DbYIS1nQtrpY3UsYiI6A2x2EvBGCbOtazcGjXL18KcdgvhU9VP6jhERFRKLPZSMMSJc3kFeVh5eRm8nDugZZVWqF6+Bk6EnIdcxukXRERlEYu9lAxp4tz1hGsYG/Up/oj7HWecT2FncCQAsNSJiMow/gY3QvmqfCy/uAQB29vjj7jf8WG9/vghaKPUsYiISAN4xm5kHqbFYMgv/XEl7jIqW1bBsg4r4V8tUOpYRESkISx2I2NrboeUnGT0qfsh5rZbiArmr79cLhERlS0s9mKEhZmpJ8sVio2Vwcnp5evK67sbiX/jTvJtdK7ZFZamlvi193GUN6sgdSwiItICFnsxIiMVLxW5k5MoUzPi81X5WPN7OBZfmAdTEyUuVvkT9uXsWepERAaMxf4aTk4Cly5lSB3jrdxKuomxUSNw6elFOJRzxFcdVsC+nL3UsYiISMtY7AZGJVRYd2U1Fpz/EjkFOehZuzfmey2GnTlLnYjIGLDYDYwMMpx4eBTWShus8/4aXWp2kzoSERHpEIvdABSoCnA69iTau3SATCbDCp81UMhNOfRORGSEuEDNKxSuCV8W3E2+jeDdndBr7/s48fAYAKCSZWWWOhGRkeIZ+yuUhTXhVUKF7/5Yh3nnZyMrPwvv1+oBN/sGUsciIiKJsdiLoc9rwt9NuYPxUaNw7vEZ2JnbYaXvWgS/11PqWEREpAdY7GXQlr824dzjM+haMxiL2i+Dg4WD1JGIiEhPsNjLiIdpMahi6QQTuQkmNv8MTSt5IKh6Z8hkMqmjERGRHikbM8SMmEqo8P2f38Bzc0t8++daAIC5whydanRhqRMR0Ut4xq7HHqTex/ijo3Dq0QlUMKuAyhZVpI5ERER6jsWuh1RCjBd12AAAFsNJREFUhf9e+wGzz8xEZn4GAqt3wlfeK1DJsrLU0YiISM+x2PXQiYfHMPXERJQ3q4BV3uvRu04Ih92JiOiNsNj1hBACOQU5MFeYw9vFBzPbfInedfqisiWH34mI6M1x8pweeJgWgz6R3TH5+DgAgEwmw5im41nqRERUaiz2f9HlcrJCCGy6vgHtt7TG8YdHkZAVj5wC/VwUh4iIygYOxf+LrpaTjU1/hInHxiDqwWFYK23wtc9qfFivP99LJyKid8JifwVtLyebnpcO/+1eiM+KRwdXXyzvsArO1i5a2x8RERkPFrsOCSEgk8lgZWqF0U0nwEZpg4/qD+RZOhERaQyLXQeEENh64yfsuLkNm7vsgKmJKUY2GSN1LCIiMkAsdi17kvEYk4+Nw6H7v8DS1Ap/JV5DI4cmUsciIgPy+HEsBg4MQd269QAAeXl5qFnzPUyeHAoTExNkZ2cjPHwZrl+/CoVCAVtbe0yaNBWVKj1f9Com5gFWrlyK5OQkFBSo0LBhI4waNR5KpVKy11RQUICpUydgwoTP4Ows3VuV6enpmD37c6Snp6NcOQuEhc2FjU35IjmXLJmPmJgHyMvLQ8+evREU1AUAsH37FqxatRwHDhyFhYUFTp8+iYsXL2DcuElazcxZ8f9PWJgZPDwsERurmWFxIQS239iC9lta4dD9X+Dl7I3jfc+y1IlIK6pWrYZVq77BqlXfYP36H5Gfn4dff/0FABAevgwVKzrgxx9/wrffbkD//oMwadJY5Ofno6CgADNmfIZ+/Qbi22//v/buPL6ma23g+O8kJwkhImQiiWqVEi6NCDKIRs2qrb65IhUUjSumUrepkDZRYmhpVWp61ayt4KaD3ASXK6aIImpuQ1QImYPKKEf2+0de5zo3HFPiNMnz/XzyR87ae+3nPOE8e5+19trrWbVqAwBr1qw05Nvhhx+20qGDi0GLOsDmzd/i4uLKsmWr6N7dh40b1+m0JyYmUFRUxJIlK4mMXM6yZZGUlZURFxdDXl4u1tb/efqmp2c3MjKuce7cmSqNWa7Y/9+2bWquXVPRtKlSKTPi/753MhvOrsFcXY/53p8zou0ojFRyHiWEeDacnduRlnaFwsICEhMTiIr6QdvWvv3LODu3Zf/+eOrWNadZs+a4uLgC5etojBs3CdV/fV5pNBpmzw4jMzMdU1MzQkNncuTIYS5eTGHChMkUFhbSo8cbREX9yJAhg+ja1RMrKyvi4v7Jpk3RAMTFxXDhQjL+/sOYO3cWGk0pRkZGfPjhR9jb6y6ZvXVrFCtWrAFg5844tm6NwtjYiObNW/DhhzOIjd1GYmICOTnZzJw5h3374tm1azsqlRHdur2Cv38AWVmZzJr1sTb+0NCZOicKCQkH+Pbb9TrHff31t+jdu6/292PHjhASUt6Hp6c3wcGTdba3tGxIfn4+ZWVlFBYWYW5ujpGREd27+2BuXk97cnXXW28NZsuWTXz88axH/Es+Pins92jaVOHYsYJK6avXc334/WYKX/h8xXMNmldKn0KIP7/wcDPtbbOVZeBAzWPdqaPRaNi/fy9vvvk/XL2axnPPNUet1o2pZcuXuHw5lbp169KyZSudNjOzOhX6jIuLoXHjxoSHR7Br1w4OHNiHmZnZA4/ftasHXbt6kJR0lIsXU3jhhRbs378Xf/8AVq5cxpAhQ3Fz68KhQwdYt+5rPvwwVLt/RkYGpqam2q+8i4qKWLgwEgsLC8aPDyQl5QIAmZkZLF++mvT0a8TH72bp0lUABAWNxsenJ9ev5zJyZCAdO3YiJuZHoqO3MHHiFO1xPDy88PDw0pvL3NxcGja0AsDKyorc3Byd9nbt/oKdnR1//evrFBQUaE8CzM3r3be/9u07MHfuJ3qP+bSksFeSrMIsIhLDCe06ExtzG/o+358+zfvJjHchxDNx+XIqEyaMASAl5QJDhw7H2/sVzp9P5s6dsgrbK4qCkZExoKKsrGL7f/vtt1/p1MkNgJ49+wAQG7vtgds7O7cFwNvbh4MH9+Pg4Mjvv6fQrl175s2bxeXLqaxbt4qysjJt4bwrJycbGxtb7e8NGjQgJKR8XDo19Xdu3rwBQJs2zqhUKs6dO0Na2hUmTvwbAIWFBWRkXKNJk6YsWrSAVatWcOvWH7z0UpuHvk99FEWp8NqJE8fJysokKuoHrl/PY9KksXh4eGFiYnLfPszM6miHQIyNjZ8qngeRwv6UFEXhxwvRTNs/lbziPBzqOxLceTqAFHUhaqHw8JIqXQfjQe6OsQOEhgbj5PQcAA4ODly5kkppaalOsblwIRlv71cwMTHlH//YrNPX7du3SUu7zAsvvKh9zdjYiLIy3cJ272ecRqM7hKlWlx+re3cfPvpoGi+80IIuXdxRqVSo1SbMmjUfa2vrB76fu32Xlpby+eefsnbttzRubK3zVfjdY6jVJri7exIcPEOnjzlzZtKlS1fefNOXPXt2kZBwQKf9Ub6Kt7a2Ji8vh/r165OTk60zZg5w6tQJXF07o1arsbGxpUEDS7KyMg06N6BWD/p+8AG4utZ74klz2YXZjN4xnDH/GkmRpogIr/n83W1aFUQqhBCPbty491i+PJLi4mLMzevh4dGN1av/V9t+6tQJkpN/w93dCze3LmRmpnPgwD4AysrKWLYskt27/6XTZ+vWziQlHQHg4MH9rF+/GnPzetqvpk+e/OW+sVhb26BSqdi1awevvPIqUD7+v39/PFA+hr1z5/YK+2RlZQHlV9/GxsY0bmxNZmYGv/56rsJJxEsvtSEp6RjFxcUoisKiRQsoKSnmxo0bODg4oigKBw7spbS0VGc/Dw8v7YTDuz/3FnWAzp278u9/7wIgPn43Xbq467Q7OjppJ8MVFOSTnZ2l94SlpKQYtVpdZVfrUMsL+5YtaAv6406a23tlD96bOhNz8Ue6NHFnj18Cge2DZIKcEMLgmjZ14JVXXmXduvIx5/fem8rt2yWMGOFPYOBw1q9fzaxZ8zA2NsbIyIiFC7/ip5++Z/ToYYwb9y7169dn9Oi/6fTZs2cfioqKmDBhDJs3f0e/fq/RqZObdgjg8uVLD/yW0svLm19+SaJ9+/K7gkaPHsP+/fGMHx/ImjUradfuLzrb29vbU1JSwh9//IGlZUPc3Lrw7rvDWbNmJW+/PYzFiz/XKe729vYMHuzP+PGBjBnzDo0bN8bMrA5vvPEWX3zxGVOnTuLVV/vwyy9J/Pxz4mPl0td3CL/9do5x494lKekYb789HIAvv1zItWtX8fb2oX79+gQFjeb99ycybtwkzMzqsG7dKiZMGENeXi5///skli79EoBTp07SoYPLY8XwuFTK/QYN/oSys29Vep9ubhaUlZU90YS5C9fPMyC6J+93CpaCfg8bG4sq+VvVdpLXyic5rXyVmdMtWzZRUlJMQMA7ldLfn8X06R8QEDACZ+d2j7S9jY3FYx9DqtFj2JbyIyeyjgPwolVLjg0/w986jJeiLoQQlWzQIF9++SWJq1fTDB1KpUlIOICtrd0jF/UnJZPnHkFuUS4h+6fyw4VoXrZxYYdvvHbNdyGEEJVPrVazYMFiQ4dRqR7l9rrKIIX9If55cRsf7J1MTlE2rnZuRPZYLrPdhRBC/GnV2sIeHm5Gaio4Od2//WbJDT7cN5Xo81swMzYjzH02YzuMx9io6mYyCiGEEE+r1hb2uytDPWgmvJHKiJ/TE+lo68riHstp1eilZxmeEEII8URqbWEHeO45dBaSuFF8nXN5Z3Fv6omFaQOi34jB0cIJtVGtTpMQQohqpEqnc8+ZMwc/Pz+GDBnCyZMnddoSEhLw9fXFz8+PJUuWVGUYj2TnpTi6berCsNghZBSkA9Dc8nkp6kIIIaqVKivsP//8M6mpqURFRREREUFERIRO++zZs4mMjOS7777j4MGDXLhwoapC0etmyQ0m7h5LQKwfecW5THSZjHVdm4fvKIQQQvwJVdnl6KFDh+jZsycALVq04ObNm+Tn51O/fn2uXLmCpaUlTZo0AaB79+4cOnSIF198UV+Xla7IMZZum94loyCdDjYuLO6xjDaNnZ9pDEIIIURlqrIr9pycHKys/vPEnkaNGpGdnQ1AdnY2jRo1um/bs6Kg8EfbheQW5TCtcyixb+2Soi6EEKLae2YDyE+7cu2TLKunz5XLALsrtU9RrrL/VqKc5LXySU4rn+TU8Krsit3W1pacnP88kD4rKwsbG5v7tmVmZmJra1uhDyGEEEI8nior7J6enuzYsQOAM2fOYGtrS/365UuwOjo6kp+fT1paGhqNhj179uDp6VlVoQghhBC1RpU+3W3BggUcPXoUlUpFWFgYZ8+excLCgl69enHkyBEWLFgAQO/evRk9enRVhSGEEELUGtXmsa1CCCGEeDh53qgQQghRg0hhF0IIIWqQWlPYq9PyttWFvpwmJiYyePBghgwZQkhICGVlZQaKsnrRl9O7Fi5cyLBhw55xZNWXvpymp6fj7++Pr68vH3/8sYEirH705fSbb77Bz88Pf3//CiuOigdLTk6mZ8+ebNy4sULbY9copRY4fPiwMmbMGEVRFOXChQvK4MGDddr79eunXLt2Tblz547i7++vnD9/3hBhVisPy2mvXr2U9PR0RVEUZeLEiUp8fPwzj7G6eVhOFUVRzp8/r/j5+SkBAQHPOrxq6WE5nTRpkrJz505FURQlPDxcuXr16jOPsbrRl9Nbt24pPj4+SmlpqaIoijJy5Ejl+PHjBomzOikoKFACAgKU0NBQZcOGDRXaH7dG1Yor9gctbwvoLG9rZGSkXd5W6KcvpwDR0dHY29sD5SsLXr9+3SBxVicPyynAvHnzmDJliiHCq5b05bSsrIxjx47Ro0cPAMLCwmjatKnBYq0u9OXUxMQEExMTCgsL0Wg0FBUVYWlpachwqwVTU1NWrlx53/VcnqRG1YrC/mdf3rY60pdTQLtmQVZWFgcPHqR79+7PPMbq5mE5jY6OpnPnzjg4OBgivGpJX07z8vKoV68ec+fOxd/fn4ULFxoqzGpFX07NzMwYP348PXv2xMfHhw4dOvD8888bKtRqQ61WU6dOnfu2PUmNqhWF/b8pcodfpbtfTnNzcxk7dixhYWE6HwTi0dyb0xs3bhAdHc3IkSMNGFH1d29OFUUhMzOT4cOHs3HjRs6ePUt8fLzhgqum7s1pfn4+K1asYPv27ezevZsTJ07w66+/GjC62qlWFHZZ3rby6csplP8HDwwMZPLkyXh5eRkixGpHX04TExPJy8tj6NChTJgwgTNnzjBnzhxDhVpt6MuplZUVTZs2pVmzZhgbG+Pu7s758+cNFWq1oS+nKSkpODk50ahRI0xNTenUqROnT582VKg1wpPUqFpR2GV528qnL6dQPhY8YsQIvL29DRVitaMvp3379iU2NpbNmzfz1Vdf0bZtW6ZPn27IcKsFfTlVq9U4OTlx6dIlbbt8bfxw+nLq4OBASkoKxcXFAJw+fZrmzZsbKtQa4UlqVK1ZeU6Wt618D8qpl5cXbm5uuLi4aLd97bXX8PPzM2C01YO+f6d3paWlERISwoYNGwwYafWhL6epqalMmzYNRVFo1aoV4eHhGBnViuudp6Ivp5s2bSI6OhpjY2NcXFwIDg42dLh/eqdPn2b+/PlcvXoVtVqNnZ0dPXr0wNHR8YlqVK0p7EIIIURtIKemQgghRA0ihV0IIYSoQaSwCyGEEDWIFHYhhBCiBpHCLoQQQtQgakMHIERtkJaWRt++fXVuAQSYPn06bdq0ue8+kZGRaDSap1ob/vDhw4wbNw5nZ2cASkpKcHZ2ZsaMGZiYmDxWX/v27ePMmTMEBQWRlJSEjY0NTk5ORERE8MYbb9CuXbsnjjMyMpLo6GgcHR0B0Gg02Nvb88knn2BhYfHA/TIzM7l48SLu7u5PfGwhahop7EI8I40aNTLIveetWrXSHldRFKZMmUJUVBQBAQGP1Y+3t7d2waHo6Gj69++Pk5MTM2bMqJQ4X3/9dZ2TmM8++4zly5fzwQcfPHCfw4cPk5KSIoVdiHtIYRfCwFJSUggLC8PY2Jj8/HwmT55Mt27dtO0ajYbQ0FB+//13VCoVbdq0ISwsjNu3b/PJJ5+QmppKQUEBr732GqNGjdJ7LJVKhaurKxcvXgQgPj6eJUuWUKdOHerWrcusWbOws7NjwYIFJCYmYmpqip2dHfPnzycmJoaEhAT69OnD9u3bOXnyJCEhISxdupSgoCAWLlzIjBkz6NixIwDvvPMOI0eOpGXLlsycOZOioiIKCwt5//338fDweGheXFxc2Lx5MwBHjx5lwYIFmJqaUlxcTFhYGA0aNGDRokUoikLDhg0ZOnToY+dDiJpICrsQBpaTk8N7772Hm5sbx48fZ9asWTqFPTk5mRMnThAXFwfA5s2buXXrFlFRUdja2jJ79mzu3LnD4MGD8fDwoHXr1g88VklJCXv27MHX15eioiJCQ0PZunUr9vb2bNy4kUWLFjFt2jS++eYbjh49irGxMbGxsTprVffq1Yv169cTFBSEu7s7S5cuBWDgwIHs2LGDjh07kpubS0pKCl5eXgQFBTFq1Ci6du1KdnY2fn5+7Ny5E7X6wR8/Go2GmJgYXn75ZaD8ITjh4eG0bt2amJgYVqxYweLFixk0aBAajYaRI0fy9ddfP3Y+hKiJpLAL8Yzk5eUxbNgwnde+/PJLbGxs+PTTT/niiy8oLS3lxo0bOtu0aNECKysrAgMD8fHxoV+/flhYWHD48GEyMjI4cuQIALdv3+by5csVCllycrLOcX18fOjfvz/nzp2jcePG2NvbA9C5c2c2bdqEpaUl3bp1IyAggF69etG/f3/tNvoMGDAAf39/QkJC2L59O3379sXY2JjDhw9TUFDAkiVLgPI12nNzc7Gzs9PZ/6effiIpKQlFUTh79izDhw9nzJgxAFhbW/Ppp59SUlLCrVu37vuM70fNhxA1nRR2IZ6RB42xT506lQEDBuDr60tycjJjx47VaTczM+Pbb7/lzJkz2qvt7777DlNTU8aPH0/fvn31HvfeMfZ7qVQqnd8VRdG+tnjxYlJSUti7dy8BAQFERkY+9P3dnUx38uRJ4uLimDZtGgCmpqZERkbqPFP6fu4dYx87diwODg7aq/rg4GBmzpyJu7s7e/bsYfXq1RX2f9R8CFHTye1uQhhYTk4OLVu2BCA2Npbbt2/rtJ86dYrvv/+etm3bMmHCBNq2bculS5dwdXXVfj1fVlbG3LlzK1zt69O8eXNyc3O5du0aAIcOHaJDhw5cuXKFtWvX0qJFC0aNGkWvXr0qPFNbpVJRWlpaoc+BAweydetWbt68qZ0lf2+ceXl5REREPDS2sLAwIiMjycjI0MnRnTt32L59uzZHKpUKjUZT4ThPkg8hagop7EIY2KhRowgODmb06NG4urpiaWnJvHnztO3NmjVjx44dDBkyhOHDh9OgQQM6duzI0KFDMTc3x8/Pj8GDB2NhYUHDhg0f+bh16tQhIiKCKVOmMGzYMA4dOsTkyZOxs7Pj7Nmz+Pr6MmLECK5evUrv3r119vX09CQsLIydO3fqvN67d2+2bdvGgAEDtK/NmDGDXbt28fbbbzNmzBi6du360NiaNGlCYGAgH330EQCBgYGMGDGCsWPHMmjQINLT01m7di2dOnUiOjqaRYsWPXU+hKgp5OluQgghRA0iV+xCCCFEDSKFXQghhKhBpLALIYQQNYgUdiGEEKIGkcIuhBBC1CBS2IUQQogaRAq7EEIIUYNIYRdCCCFqkP8D0A8ENPY+AJEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## knn"
      ],
      "metadata": {
        "id": "AlMRXFcEGorW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = kNN()\n",
        "model.fit(x_sm, y_sm)\n",
        "y_pred = model.predict(X_test)\n",
        "y_prob = model.predict_proba(X_test)\n",
        "classifiers.append(model)\n",
        "\n",
        "append_scores('kNN',y_test,y_pred)"
      ],
      "metadata": {
        "id": "WA5QvuP9uge-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_analysis('kNN',y_test,y_pred,y_prob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "YFqiXNipADK7",
        "outputId": "d9aa5ea4-77d6-49e8-97a0-22b7ec1cd690"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for kNN: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.88      0.92       972\n",
            "           1       0.09      0.22      0.12        50\n",
            "\n",
            "    accuracy                           0.85      1022\n",
            "   macro avg       0.52      0.55      0.52      1022\n",
            "weighted avg       0.91      0.85      0.88      1022\n",
            "\n",
            "Confusion matrix for kNN: \n",
            "[[855 117]\n",
            " [ 39  11]]\n",
            "Accuracy score for kNN: 0.85\n",
            "Sensitivity score for kNN: 0.22\n",
            "Specitivity score for kNN: 0.88\n",
            "AUC score for kNN: 0.63\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MLP"
      ],
      "metadata": {
        "id": "uN6qtXpXGsdq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = MLPClassifier(solver = 'sgd')\n",
        "model.fit(x_sm, y_sm)\n",
        "y_pred = model.predict(X_test)\n",
        "y_prob = model.predict_proba(X_test)\n",
        "classifiers.append(model)\n",
        "\n",
        "append_scores('MLP',y_test,y_pred)"
      ],
      "metadata": {
        "id": "s5fOI-KXxuao"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_analysis('MLP',y_test,y_pred,y_prob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "aQU6p4k_AQHS",
        "outputId": "50ef9ec9-7776-49a8-fcbd-2da23675703d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for MLP: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.73      0.84       972\n",
            "           1       0.13      0.80      0.23        50\n",
            "\n",
            "    accuracy                           0.74      1022\n",
            "   macro avg       0.56      0.77      0.54      1022\n",
            "weighted avg       0.94      0.74      0.81      1022\n",
            "\n",
            "Confusion matrix for MLP: \n",
            "[[714 258]\n",
            " [ 10  40]]\n",
            "Accuracy score for MLP: 0.74\n",
            "Sensitivity score for MLP: 0.80\n",
            "Specitivity score for MLP: 0.73\n",
            "AUC score for MLP: 0.84\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LR"
      ],
      "metadata": {
        "id": "vMVe6rsbGubw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression() \n",
        "model.fit(x_sm, y_sm)\n",
        "y_pred = model.predict(X_test)\n",
        "y_prob = model.predict_proba(X_test)\n",
        "classifiers.append(model)\n",
        "\n",
        "append_scores('LR',y_test,y_pred)"
      ],
      "metadata": {
        "id": "0u82nS9XtHpv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_analysis('LR',y_test,y_pred,y_prob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "DSJeWUyiBFW9",
        "outputId": "3b3e70d5-ff82-44d3-a3db-25bea46e52b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for LR: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.75      0.85       972\n",
            "           1       0.14      0.80      0.24        50\n",
            "\n",
            "    accuracy                           0.75      1022\n",
            "   macro avg       0.56      0.77      0.54      1022\n",
            "weighted avg       0.94      0.75      0.82      1022\n",
            "\n",
            "Confusion matrix for LR: \n",
            "[[726 246]\n",
            " [ 10  40]]\n",
            "Accuracy score for LR: 0.75\n",
            "Sensitivity score for LR: 0.80\n",
            "Specitivity score for LR: 0.75\n",
            "AUC score for LR: 0.84\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "params = {\n",
        "    'solver' : ['newton-cg', 'lbfgs', 'liblinear'],\n",
        "    'penalty' : ['none', 'l1', 'l2', 'elasticnet'],\n",
        "    'C' : [1e-5, 1e-3, 1e-1, 1]\n",
        "}\n",
        "\n",
        "scorer = make_scorer(custom_loss_function1,greater_is_better=True)\n",
        "clf = GridSearchCV(LogisticRegression(), params, scoring = scorer)\n",
        "clf.fit(x_sm,y_sm)\n",
        "# print(clf.cv_results_)\n",
        "print(clf.best_score_)\n",
        "print(clf.best_params_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74IbrzTvVGH-",
        "outputId": "a863f924-1d42-49f6-b3cb-4ad3fc038ec5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7708140929020271\n",
            "{'C': 0.001, 'penalty': 'l2', 'solver': 'newton-cg'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = LogisticRegression(C = 0.001, penalty = 'l2', solver = 'newton-cg')\n",
        "clf.fit(x_sm,y_sm)\n",
        "y_pred = clf.predict(X_test)\n",
        "y_prob = clf.predict_proba(X_test)\n",
        "\n",
        "show_analysis('Grid search LR',y_test,y_pred,y_prob)\n",
        "append_scores('Grid search LR', y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "MAbmxRt7VRni",
        "outputId": "49501252-a789-4590-bc5e-4c53432e5634"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for Grid search LR: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.73      0.84       972\n",
            "           1       0.13      0.80      0.22        50\n",
            "\n",
            "    accuracy                           0.73      1022\n",
            "   macro avg       0.56      0.76      0.53      1022\n",
            "weighted avg       0.94      0.73      0.81      1022\n",
            "\n",
            "Confusion matrix for Grid search LR: \n",
            "[[706 266]\n",
            " [ 10  40]]\n",
            "Accuracy score for Grid search LR: 0.73\n",
            "Sensitivity score for Grid search LR: 0.80\n",
            "Specitivity score for Grid search LR: 0.73\n",
            "AUC score for Grid search LR: 0.84\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Comparison"
      ],
      "metadata": {
        "id": "pR3OM0NDLm2z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'models': models, 'accuracy': models_accuracy, 'sensitivity': models_sensitivity, 'specificity': models_specificity})\n",
        "df"
      ],
      "metadata": {
        "id": "eNYOhnurLoeO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "c2171822-597d-49d5-f9fc-762a4a4b1cd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 models  accuracy  sensitivity  specificity\n",
              "0                    DT  0.934442         0.14     0.975309\n",
              "1        Grid search DT  0.781800         0.68     0.787037\n",
              "2                   XGB  0.799413         0.74     0.802469\n",
              "3       Grid search XGB  0.888454         0.56     0.905350\n",
              "4                   RFC  0.946184         0.06     0.991770\n",
              "5       Grid search RFC  0.772016         0.80     0.770576\n",
              "6                  LGBM  0.921722         0.22     0.957819\n",
              "7      Grid search LGBM  0.944227         0.08     0.988683\n",
              "8            Linear SVC  0.727006         0.80     0.723251\n",
              "9                   RBF  0.721135         0.68     0.723251\n",
              "10  Grid search SVC RBF  0.901174         0.08     0.943416\n",
              "11                  LDA  0.733855         0.82     0.729424\n",
              "12                  QDA  0.753425         0.80     0.751029\n",
              "13                  kNN  0.847358         0.22     0.879630\n",
              "14                  MLP  0.737769         0.80     0.734568\n",
              "15                   LR  0.749511         0.80     0.746914\n",
              "16       Grid search LR  0.729941         0.80     0.726337"
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              "      <th></th>\n",
              "      <th>models</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>sensitivity</th>\n",
              "      <th>specificity</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>DT</td>\n",
              "      <td>0.934442</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.975309</td>\n",
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              "      <th>1</th>\n",
              "      <td>Grid search DT</td>\n",
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              "      <td>0.68</td>\n",
              "      <td>0.787037</td>\n",
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              "      <th>2</th>\n",
              "      <td>XGB</td>\n",
              "      <td>0.799413</td>\n",
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              "      <th>3</th>\n",
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              "      <th>5</th>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LGBM</td>\n",
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              "      <td>0.22</td>\n",
              "      <td>0.957819</td>\n",
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              "      <th>7</th>\n",
              "      <td>Grid search LGBM</td>\n",
              "      <td>0.944227</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.988683</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Linear SVC</td>\n",
              "      <td>0.727006</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.723251</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>RBF</td>\n",
              "      <td>0.721135</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.723251</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Grid search SVC RBF</td>\n",
              "      <td>0.901174</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.943416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>LDA</td>\n",
              "      <td>0.733855</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.729424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>QDA</td>\n",
              "      <td>0.753425</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.751029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>kNN</td>\n",
              "      <td>0.847358</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.879630</td>\n",
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              "      <th>14</th>\n",
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              "      <td>0.737769</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.734568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>LR</td>\n",
              "      <td>0.749511</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.746914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Grid search LR</td>\n",
              "      <td>0.729941</td>\n",
              "      <td>0.80</td>\n",
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          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Bagging(input, models):\n",
        "    cumulative_pred = np.zeros((len(input)), dtype = int)\n",
        "\n",
        "    for i in range(len(models)):\n",
        "        cumulative_pred += np.array(models[i].predict(input))\n",
        "\n",
        "    cumulative_pred = cumulative_pred > len(models)//2\n",
        "    cumulative_pred = cumulative_pred*1\n",
        "\n",
        "    return cumulative_pred.tolist();\n",
        "\n",
        "pred = Bagging(X_test, classifiers)"
      ],
      "metadata": {
        "id": "-Gl7L6MbZKMq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Classification report for Bagging: \\n{}\".format(classification_report(y_test,pred)))\n",
        "print(\"Confusion matrix for Bagging: \\n{}\".format(confusion_matrix(y_test,pred)))\n",
        "print(\"Accuracy score for Bagging: {:.2f}\".format(accuracy_score(y_test,pred)))"
      ],
      "metadata": {
        "id": "DOd3V8mOiol4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed969b20-8809-412b-b0e2-0034fb01e67f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for Bagging: \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.78      0.87       972\n",
            "           1       0.15      0.74      0.24        50\n",
            "\n",
            "    accuracy                           0.77      1022\n",
            "   macro avg       0.56      0.76      0.56      1022\n",
            "weighted avg       0.94      0.77      0.84      1022\n",
            "\n",
            "Confusion matrix for Bagging: \n",
            "[[755 217]\n",
            " [ 13  37]]\n",
            "Accuracy score for Bagging: 0.77\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Machine Learning Model Deployment Code"
      ],
      "metadata": {
        "id": "PX_YQp7Cejae"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install streamlit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DTiVIF_cetMz",
        "outputId": "45b6ed3f-b0fc-4792-a0ed-8912bb75cd09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting streamlit\n",
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            "Requirement already satisfied: backcall in /usr/local/lib/python3.7/dist-packages (from ipython>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit) (0.2.0)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit) (2.6.1)\n",
            "Collecting prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0\n",
            "  Downloading prompt_toolkit-3.0.29-py3-none-any.whl (381 kB)\n",
            "\u001b[K     |████████████████████████████████| 381 kB 42.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.7/dist-packages (from ipython>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit) (0.18.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit) (0.7.5)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit) (57.4.0)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (5.3.0)\n",
            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (3.6.0)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (0.2.0)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (1.1.0)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from jedi>=0.16->ipython>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit) (0.8.3)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->altair>=3.2.0->streamlit) (2.0.1)\n",
            "Requirement already satisfied: pyzmq>=22.3 in /usr/local/lib/python3.7/dist-packages (from jupyter-client>=6.1.12->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit) (22.3.0)\n",
            "Requirement already satisfied: jupyter-core>=4.9.2 in /usr/local/lib/python3.7/dist-packages (from jupyter-client>=6.1.12->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit) (4.10.0)\n",
            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (2.15.3)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect>4.3->ipython>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit) (0.2.5)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (5.3.1)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (1.8.0)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (0.13.3)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (5.6.1)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (0.8.4)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (0.6.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (0.7.1)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (1.5.0)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (5.0.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (0.5.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->streamlit) (3.0.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->streamlit) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->streamlit) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->streamlit) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->streamlit) (1.24.3)\n",
            "Building wheels for collected packages: blinker\n",
            "  Building wheel for blinker (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for blinker: filename=blinker-1.4-py3-none-any.whl size=13478 sha256=46d045cbdd20e7028cc9d98d90feb9913a13fca5b452fc817589ed6d41eee7e1\n",
            "  Stored in directory: /root/.cache/pip/wheels/22/f5/18/df711b66eb25b21325c132757d4314db9ac5e8dabeaf196eab\n",
            "Successfully built blinker\n",
            "Installing collected packages: tornado, prompt-toolkit, jupyter-client, ipython, ipykernel, smmap, gitdb, watchdog, validators, toml, pympler, pydeck, gitpython, blinker, streamlit\n",
            "  Attempting uninstall: tornado\n",
            "    Found existing installation: tornado 5.1.1\n",
            "    Uninstalling tornado-5.1.1:\n",
            "      Successfully uninstalled tornado-5.1.1\n",
            "  Attempting uninstall: prompt-toolkit\n",
            "    Found existing installation: prompt-toolkit 1.0.18\n",
            "    Uninstalling prompt-toolkit-1.0.18:\n",
            "      Successfully uninstalled prompt-toolkit-1.0.18\n",
            "  Attempting uninstall: jupyter-client\n",
            "    Found existing installation: jupyter-client 5.3.5\n",
            "    Uninstalling jupyter-client-5.3.5:\n",
            "      Successfully uninstalled jupyter-client-5.3.5\n",
            "  Attempting uninstall: ipython\n",
            "    Found existing installation: ipython 5.5.0\n",
            "    Uninstalling ipython-5.5.0:\n",
            "      Successfully uninstalled ipython-5.5.0\n",
            "  Attempting uninstall: ipykernel\n",
            "    Found existing installation: ipykernel 4.10.1\n",
            "    Uninstalling ipykernel-4.10.1:\n",
            "      Successfully uninstalled ipykernel-4.10.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "jupyter-console 5.2.0 requires prompt-toolkit<2.0.0,>=1.0.0, but you have prompt-toolkit 3.0.29 which is incompatible.\n",
            "google-colab 1.0.0 requires ipykernel~=4.10, but you have ipykernel 6.13.0 which is incompatible.\n",
            "google-colab 1.0.0 requires ipython~=5.5.0, but you have ipython 7.33.0 which is incompatible.\n",
            "google-colab 1.0.0 requires tornado~=5.1.0; python_version >= \"3.0\", but you have tornado 6.1 which is incompatible.\u001b[0m\n",
            "Successfully installed blinker-1.4 gitdb-4.0.9 gitpython-3.1.27 ipykernel-6.13.0 ipython-7.33.0 jupyter-client-7.3.0 prompt-toolkit-3.0.29 pydeck-0.7.1 pympler-1.0.1 smmap-5.0.0 streamlit-1.8.1 toml-0.10.2 tornado-6.1 validators-0.18.2 watchdog-2.1.7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "IPython",
                  "ipykernel",
                  "jupyter_client",
                  "prompt_toolkit",
                  "tornado"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "znJQnonWe-fJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = classifiers[-1]"
      ],
      "metadata": {
        "id": "oJMjBAa8fyBO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'trained_model.sav'\n",
        "pickle.dump(model, open(filename, 'wb'))"
      ],
      "metadata": {
        "id": "IYk6fvEIfYmK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the saved model\n",
        "loaded_model = pickle.load(open('trained_model.sav', 'rb'))"
      ],
      "metadata": {
        "id": "c-rBVA8hfauT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (1,0.874525,0,0,1,2,0,-0.616568,0.765941,2)\n",
        "\n",
        "# changing the input_data to numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "# reshape the array as we are predicting for one instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "prediction = loaded_model.predict_proba(input_data_reshaped)[:,1][0]\n",
        "print(f\"You have {prediction*100}% chance of having a stroke.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iKd3Otpmfc_1",
        "outputId": "30521988-9293-45c6-fdc9-090da5140b35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You have 51.36114046468848% chance of having a stroke.\n"
          ]
        }
      ]
    }
  ]
}