Crypto_Mohit.ipynb

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        "# Import the warnings module and set it to ignore any warnings\n",
        "import warnings \n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# Import necessary data science libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Import Plotly libraries for more interactive and visually appealing plots\n",
        "import plotly.express as px\n",
        "import plotly.figure_factory as ff\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Import necessary machine learning libraries\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, ExtraTreesClassifier"
      ],
      "metadata": {
        "id": "tFIz_wjmjNYq"
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      "execution_count": 295,
      "outputs": []
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        "# Reading data from the file named as \"dataset.csv\"\n",
        "# The value inside the read_csv can be modified to match the path to the datafile being used.\n",
        "data=pd.read_csv(\"/content/dataset.csv\")\n",
        "data.head()"
      ],
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      "execution_count": 296,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
              "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
              "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
              "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
              "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
              "\n",
              "   ca  thal  num  \n",
              "0   0     1    1  \n",
              "1   0     2    1  \n",
              "2   0     2    1  \n",
              "3   0     2    1  \n",
              "4   0     2    1  "
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              "      <td>172</td>\n",
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              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "          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",
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      "cell_type": "code",
      "source": [
        "# Droping the null values from the dataframe \"data\"\n",
        "data.dropna(inplace = True)\n",
        "\n",
        "# Shuffling data so that we can remove any dependicies on the ordinal number of dataframe\n",
        "from sklearn.utils import shuffle\n",
        "data = shuffle(data)\n",
        "\n",
        "# Printing the dataframe information for reference\n",
        "data.info()"
      ],
      "metadata": {
        "id": "PilhVP89jNTs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24273a62-b436-49bd-8c49-326363175c9e"
      },
      "execution_count": 297,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 303 entries, 137 to 185\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  num       303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 35.5 KB\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "# Create a new figure and axis object with a custom size\n",
        "fig, ax = plt.subplots(figsize=(15,6))\n",
        "\n",
        "# Plot a histogram of the 'age' column in the 'data' DataFrame using the specified axis\n",
        "data[\"age\"].hist(ax=ax)\n",
        "\n",
        "# Add an x-label to the plot\n",
        "plt.xlabel(\"Age\")\n",
        "\n",
        "# Add a y-label to the plot\n",
        "plt.ylabel(\"Entries in Dataset\")\n",
        "\n",
        "# Add a title to the plot\n",
        "plt.title(\"Age Distribution\");"
      ],
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          "height": 403
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        "outputId": "82ea09f2-b372-4273-9b8e-9b3514938ffa"
      },
      "execution_count": 298,
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          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# For getting the info of age distribution based on sex\n",
        "fig = go.Figure()\n",
        "\n",
        "# For age distribution independent of the sex in green color\n",
        "fig.add_trace(go.Box(y=data['age'].values , name='Age', marker_color = 'blue',boxmean=True))\n",
        "\n",
        "# For age distribution dependent of the male in blue color\n",
        "fig.add_trace(go.Box(y=data[data['sex']==1]['age'].values, name ='Male only', marker_color = 'green', boxmean = True))\n",
        "\n",
        "# For age distribution dependent of the male in red color\n",
        "fig.add_trace(go.Box(y=data[data['sex']==0]['age'].values, name ='Female only', marker_color = 'red', boxmean = True))\n",
        "\n",
        "fig.update_layout(title = 'Age Distribution(all)', yaxis_title = 'Age', title_x = 0.5)\n",
        "fig.update_xaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\n",
        "fig.update_yaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "VEjvOpbvjNOe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "2382080e-229f-4388-fb0b-8036e18fead6"
      },
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      "source": [
        "# Create a new figure and axis object with a custom size\n",
        "fig, ax = plt.subplots(figsize=(15,6))\n",
        "\n",
        "# Plot a histogram of the 'trestbps' column in the 'data' DataFrame using the specified axis\n",
        "data[\"trestbps\"].hist(ax=ax)\n",
        "\n",
        "# Add an x-label to the plot\n",
        "plt.xlabel(\"Resting Blood Pressure\")\n",
        "\n",
        "# Add a y-label to the plot\n",
        "plt.ylabel(\"Entries in Dataset\")\n",
        "\n",
        "# Add a title to the plot\n",
        "plt.title(\"Resting Blood Pressure (trestbps) Distribution\");"
      ],
      "metadata": {
        "id": "nDIiad9CjNMP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
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        "outputId": "0ebad3b7-5986-4688-f54b-d6b44fde58af"
      },
      "execution_count": 300,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Similar plot for attribute \"thal\"\n",
        "feg,ax=plt.subplots(figsize=(15,6))\n",
        "data[\"thal\"].hist(ax=ax)\n",
        "plt.xlabel(\"Defect Type\")\n",
        "plt.ylabel(\"Entries in Dataset\")\n",
        "plt.title(\"Defect Type (thal) Distribution\");"
      ],
      "metadata": {
        "id": "A_dSiNMqjNJz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "1b577534-a4be-4dcc-f7c8-2e9b0216d58d"
      },
      "execution_count": 301,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Similar plot for attribute \"chol\"\n",
        "feg,ax=plt.subplots(figsize=(15,6))\n",
        "data[\"chol\"].hist(ax=ax)\n",
        "plt.xlabel(\"Serum Cholestoral\")\n",
        "plt.ylabel(\"Entries in Dataset\")\n",
        "plt.title(\"Serum Cholestoral (chol) Distribution\");"
      ],
      "metadata": {
        "id": "30l-EGgkjNHM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "59b86b9a-ade9-4d37-ca7a-27d7bbd76c4c"
      },
      "execution_count": 302,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Similar plot for attribute \"fbs\"\n",
        "feg,ax=plt.subplots(figsize=(15,6))\n",
        "data[\"fbs\"].hist(ax=ax)\n",
        "plt.xlabel(\"fasting blood sugar\")\n",
        "plt.ylabel(\"Entries in Dataset\")\n",
        "plt.title(\"fasting blood sugar (fbs) Distribution\");"
      ],
      "metadata": {
        "id": "WBugYe3yjNFA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "7a59aa33-77ab-410c-8d1a-7145244e3c79"
      },
      "execution_count": 303,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Similar plot for attribute \"oldpeak\"\n",
        "feg,ax=plt.subplots(figsize=(15,6))\n",
        "data[\"oldpeak\"].hist(ax=ax)\n",
        "plt.xlabel(\"ST depression induced by exercise relative to rest\")\n",
        "plt.ylabel(\"Entries in Dataset\")\n",
        "plt.title(\"ST depression induced by exercise relative to rest (oldpeak) Distribution\");"
      ],
      "metadata": {
        "id": "1a1WasAYjNCZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "5eba809f-48fb-4a35-9fad-ca1258a818ab"
      },
      "execution_count": 304,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Similar plot for attribute \"oldpeak\"\n",
        "feg,ax=plt.subplots(figsize=(15,6))\n",
        "data[\"slope\"].hist(ax=ax)\n",
        "plt.xlabel(\"Slope of the peak exercise ST segment\")\n",
        "plt.ylabel(\"Entries in Dataset\")\n",
        "plt.title(\"Slope of the peak exercise ST segment (slope) Distribution\");"
      ],
      "metadata": {
        "id": "jpWnkM9BjM_7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "a98de998-c94d-410e-b47e-f61dada60687"
      },
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Similar plot for attribute \"cp\"\n",
        "feg,ax=plt.subplots(figsize=(15,6))\n",
        "data[\"cp\"].hist(ax=ax)\n",
        "plt.xlabel(\"Chest Pain Type\")\n",
        "plt.ylabel(\"Entries in Dataset\")\n",
        "plt.title(\"chest pain type (cp) Distribution\");"
      ],
      "metadata": {
        "id": "DwAtl_tXjM9S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "f34b8821-bd64-45c8-e7f6-a55011fe6dc7"
      },
      "execution_count": 306,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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4cKEaN25s32/+/PmKj4+Xh4eH7rvvPvXq1Ut+fn7OKg0AAAAAjOW0mTxPT08tWrRIgYGBxdrfeustRUVFydPTU5K0Z88ehYSEyMfHR/Xq1VOHDh20a9cuZ5UFAAAAAEZzWshzd3dXvXr1irX99ttvOnDggPr06WNvS09Pl7+/v/21v7+/0tLSnFUWAAAAABjNpY9QePXVVzVp0qQy97HZbBU6VlJSUlWUhGqUmJhY3SVUKdOuBxVH39dd9H3dRL/XXfR93VXb+t5lIS81NVWHDx/W008/LUk6efKkhgwZotGjRys9Pd2+38mTJ3XTTTeVe7zg4GB5eXk5q9zK4Rl5DgkNDa3uEqpMYmKiUdeDiqPv6y76vm6i3+su+r7uqol9n5ubW+akl8tCXlBQkDZv3mx/HR4ermXLlunChQuaNGmSsrOzZbVatWvXLk2cONFVZQEAAACAUZwW8pKSkhQbG6uUlBS5u7srISFBc+fOLXHXzHr16ik6OlojRoyQxWLRqFGj5OPj46yyAAAAAMBoTgt5wcHBiouLK3X7V199Zf85MjJSkZGRzioFAAAAAOoMpz4MHQAAAADgWoQ8AAAAADAIIQ8AAAAADELIAwAAAACDEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIMQ8gAAAADAIIQ8AAAAADAIIQ8AAAAADELIAwAAAACDEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIMQ8gAAAADAIIQ8AAAAADAIIQ8AAAAADELIAwAAAACDEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAM4tSQl5ycrJ49e2rZsmWSpOPHj+uRRx7RkCFD9MgjjygtLU2StHbtWg0cOFCDBg3Sxx9/7MySAAAAAMBoTgt5586d05QpU9S5c2d726xZs3T//fdr2bJl6tWrlxYvXqxz585p/vz5WrJkieLi4rR06VJlZmY6qywAAAAAMJrTQp6np6cWLVqkwMBAe1tMTIwiIiIkSY0aNVJmZqb27NmjkJAQ+fj4qF69eurQoYN27drlrLIAAAAAwGjuTjuwu7vc3Ysfvn79+pKkwsJCLV++XKNGjVJ6err8/f3t+/j7+9uXcZYlKSmpaguGyyUmJlZ3CVXKtOtBxdH3dRd9XzfR73UXfV931ba+d1rIK01hYaHGjx+vTp06qXPnzvrss8+KbbfZbBU6TnBwsLy8vJxRYuUt31/dFdQqoaGh1V1ClUlMTDTqelBx9H3dRd/XTfR73UXf1101se9zc3PLnPRy+d01n332WTVv3lyPP/64JCkwMFDp6en27SdPniy2xBMAAAAAUHEuDXlr166Vh4eHxowZY29r37699u7dq+zsbJ09e1a7du1Sx44dXVkWAAAAABjDacs1k5KSFBsbq5SUFLm7uyshIUEZGRny8vLS0KFDJUktW7bU5MmTFR0drREjRshisWjUqFHy8fFxVlkAAAAAYDSnhbzg4GDFxcVVaN/IyEhFRkY6qxQAAAAAqDNc/p08AAAAAIDzEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIMQ8gAAAADAIIQ8AAAAADAIIQ8AAAAADELIAwAAAACDEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIMQ8gAAAADAIIQ8AAAAADAIIQ8AAAAADELIAwAAAACDEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIMQ8gAAAADAIE4NecnJyerZs6eWLVsmSTp+/LiGDh2qqKgoPfHEE8rLy5MkrV27VgMHDtSgQYP08ccfO7MkAAAAADCa00LeuXPnNGXKFHXu3NneNmfOHEVFRWn58uVq3ry54uPjde7cOc2fP19LlixRXFycli5dqszMTGeVBQAAAABGc1rI8/T01KJFixQYGGhv27Fjh+644w5JUlhYmLZv3649e/YoJCREPj4+qlevnjp06KBdu3Y5qywAAAAAMJq70w7s7i539+KHP3/+vDw9PSVJAQEBSktLU3p6uvz9/e37+Pv7Ky0tzVllAQAAAIDRnBbyymOz2Rxq/6OkpKSqLAfVIDExsbpLqFKmXQ8qjr6vu+j7uol+r7vo+7qrtvW9S0Ne/fr1deHCBdWrV0+pqakKDAxUYGCg0tPT7fucPHlSN910U7nHCg4OlpeXlxOrrYTl+6u7glolNDS0ukuoMomJiUZdDyqOvq+76Pu6iX6vu+j7uqsm9n1ubm6Zk14ufYRCly5dlJCQIEnatGmTunfvrvbt22vv3r3Kzs7W2bNntWvXLnXs2NGVZQEAAACAMZw2k5eUlKTY2FilpKTI3d1dCQkJmjFjhiZMmKCVK1fq6quv1oABA+Th4aHo6GiNGDFCFotFo0aNko+Pj7PKAgAAAACjOS3kBQcHKy4urkT74sWLS7RFRkYqMjLSWaUAAAAAQJ3h0uWaAAAAAADnIuQBAAAAgEEIeQAAAABgEEIeAAAAABiEkAcAAAAABiHkAQAAAIBBCHkAAAAAYJByQ96ECRNKtI0YMcIpxQAAAAAA/pxSH4a+du1arVixQocOHdJDDz1kb8/Pz1d6erpLigMAAAAAOKbUkNevXz/deuutevrppzV69Gh7u5ubm66//nqXFAcAAAAAcEyZyzWDgoIUFxcnPz8/ZWdn65ZbblHr1q3l5+fnovIAAAAAAI4odSbvkiVLlmjdunXKy8tTz549tWDBAjVs2FAjR450RX0AAAAAAAeUe+OVdevW6aOPPpKvr68kafz48dqyZYuz6wIAAAAAVEK5Ic/b21tubv9/Nzc3t2KvAQAAAAA1R7nLNa+99lrNmzdP2dnZ2rRpk9avX6+WLVu6ojYAAAAAgIPKnZJ74YUXdNVVVykoKEhr165V+/btFRMT44raAAAAAAAOKncmz8PDQw888IBGjBihtLQ0HTlyRO7u5b4NAAAAAFANyp3JmzJlijZs2KDMzExFRUVp2bJlmjx5sgtKAwAAAAA4qtyQt3//fg0aNEgbNmzQgAEDNGvWLB05csQVtQEAAAAAHFRuyLPZbJKkLVu2KDw8XJKUl5fn3KoAAAAAAJVSbshr0aKF7rzzTp09e1Y33HCDVq9ebX9mHgAAAACgZin3Diovv/yykpOT7Y9NuP766zVy5EinFwYAAAAAcFyFbpN58uRJHTx4UNLFpZpvvfWWvvrqK6cWBgAAAABwXLkhb9y4ccrKytLBgwfVoUMH7dmzR6NHj3ZFbQAAwBDW6LjqLqHqLN/v9FMUvj7U6ecAYK5yv5N34sQJvfvuu2rRooXmzJmj5cuXa+/eva6oDQAAAADgoHJD3iUFBQXKzc1V06ZN9csvvzizJgAAAABAJZW7XLNTp05atGiRevbsqXvvvVdNmzZVUVGRK2oDAAAAADio3JA3ZswYFRYWymq16qabbtKpU6fUuXNnV9QGAAAAAHBQucs1R4wYIavVKkkKDQ1Vr169NGzYMKcXBgAAAABwXKkzeWvXrtX8+fP1+++/6/bbb7e3FxQUKCAgwBW1AQAAAAAcVGrI69evn+666y4999xzxR6Z4ObmpqCgIJcUBwAAAABwTJnLNa1Wq1577TX5+fnJYrHIYrEoNzdX999/v6vqAwAAAAA4oNwbr7zzzjt66623lJeXp/r16ys3N1d33323K2oDAAAAADio3BuvbNy4Ud9//73at2+vH374QTNmzFCrVq1cURsAAAAAwEHlhjxvb295enoqPz9fknTHHXfoyy+/dHphAAAAAADHlbtc09fXV2vXrlXr1q317LPPqmXLljp58mSlTnb27Fk988wzysrKUn5+vkaNGqXGjRtr8uTJkqQ2bdroxRdfrNSxAQAAAAAVCHmxsbHKyMhQr169tHTpUp04cUIzZ86s1Mk+/fRTtWjRQtHR0UpNTdWwYcPUuHFjTZw4Ue3atVN0dLS2bt2qHj16VOr4AAAAAFDXlRvyfv/9dyUnJ8tqtapv375q1qxZpU/WqFEjHTx4UJKUnZ0tPz8/paSkqF27dpKksLAwbd++nZAHAAAAAJVUasi7cOGCoqOj9fPPPys4OFhnzpzRzz//rG7duumVV16Rp6enwye76667tGrVKvXq1UvZ2dl688039dJLL9m3BwQEKC0trULHSkpKcvj8qFkSExOru4QqZdr1oOLo+7qLvoez8NmqmeiXuqu29X2pIW/BggUKCgrS7Nmz5e5+cbfz58/rlVde0RtvvKFnnnnG4ZOtWbNGV199td59910dOHBAo0aNko+Pj327zWar8LGCg4Pl5eXlcA1OtXx/dVdQq4SGhlZ3CVUmMTHRqOtBxdH3dRd97yD+jHQIn62ahzFfd9XEvs/NzS1z0qvUu2vu3LlTEyZMsAc8SbrqqqsUExOjbdu2VaqYXbt2qVu3bpKk//qv/1Jubq5Onz5t356amqrAwMBKHRsAAAAAUEbIs1qtV1yS6eHhoYYNG1bqZM2bN9eePXskSSkpKfL29lbLli21c+dOSdKmTZvUvXv3Sh0bAAAAAFDGck2LxVLqm6xWa6VO9sADD2jixIkaMmSICgoKNHnyZDVu3FgvvPCCioqK1L59e3Xp0qVSxwYAAAAAlBHydu/erdtvv71Eu81mK7bE0hHe3t6aPXt2ifbly5dX6ngAAAAAgOJKDXkbN250ZR0AAAAAgCpQashr2rSpK+sAAAAAAFSBUm+8AgAAAACofQh5AAAAAGCQUpdrXu7AgQPKzMws9rDyzp07O60oAAAAAEDllBvyRo8erQMHDqhJkyb2NovFQsgDAAAAgBqo3JCXkpKiL774whW1AAAAAAD+pHK/k9eiRQvl5eW5ohYAAAAAwJ9U7kyem5ub7rrrLrVr105Wq9XePm3aNKcWBgAAAABwXLkhr0uXLurSpYsragEAAAAA/EmlhryTJ08qMDBQHTt2dGU9AAAAAIA/odSQFxsbq9dff13Dhg2TxWIp9vgEi8WiL7/80iUFAgAAAAAqrtSQ9/rrr0uSvvrqK5cVAwAAAAD4c8q9uyYAAAAAoPYg5AEAAACAQQh5AAAAAGCQckPe1q1btWbNGklSdHS0evfurU2bNjm9MAAAAACA48oNeQsWLFD37t21detWFRUV6dNPP1VcXJwragMAAAAAOKjckFevXj35+/tr69at6t+/v7y9veXmxipPAAAAAKiJyk1rubm5euedd/TNN9+oc+fO+r//+z/l5OS4ojYAAAAAgIPKDXlTpkxRamqqXnvtNXl5eWnbtm16+umnXVEbAAAAAMBBpT4M/ZJWrVrpvvvu09GjRyVJ/fr1U8OGDZ1eGAAAAADAceWGvCVLlmjdunXKy8tTz549tWDBAjVs2FAjR450RX0AAAAAAAeUu1xz3bp1+uijj+Tr6ytJGj9+vLZs2eLsugAAAAAAlVBuyPvj3TTd3Ny4uyYAAAAA1FDlLte89tprNW/ePGVnZ2vTpk1av369WrZs6YraAAAAAAAOKndK7oUXXtBVV12loKAgrV27Vu3bt1dMTIwragMAAAAAOKjUmTybzSaLxSKr1arhw4dr+PDhrqwLAAAAAFAJpYa8YcOG6f3331fbtm1lsVjs7ZfC388//+ySAgEAAAAAFVdqyHv//fclSfv37+dGKwAAAABQS5Sb3oYNG+aKOgAAAAAAVaDcu2vecMMNmj17tm6++WZ5eHjY2zt37uzUwgAAAAAAjis35F367t3OnTvtbRaLhZAHAAAAADVQuSFv1KhR6tSpU7G2zZs3O60gAAAAAEDllRryjh07pqNHjyo2NlYTJkyQzWaTJBUUFGjq1Knq2bNnpU64du1avfPOO3J3d9eYMWPUpk0bjR8/XoWFhWrcuLGmT58uT0/Pyl0NAAAAANRxpYa8tLQ0rV+/XikpKZo/f7693c3NTYMHD67UyU6fPq358+frk08+0blz5zR37lwlJCQoKipKffr00cyZMxUfH6+oqKhKHR8AAAAA6rpSQ97NN9+sm2++WT169Kj0rN0fbd++XZ07d1aDBg3UoEEDTZkyReHh4XrxxRclSWFhYXrvvfcIeQAAAABQSeV+J6958+aaOnWqsrKy7Es2JWnatGkOn+zYsWO6cOGCHnvsMWVnZ2v06NE6f/68fXlmQECA0tLSKnSspKQkh8+PmiUxMbG6S6hSpl0PKo6+r7voezgLn62aiX6pu2pb35cb8p588kn16dNHN9xwQ5WcMDMzU/PmzdPvv/+uhx9+uFhwvPzn8gQHB8vLy6tKaqoyy/dXdwW1SmhoaHWXUGUSExONuh5UHH1fd9H3DuLPSIfw2ap5GPN1V03s+9zc3DInvcoNeX/5y1/0+OOPV0kxAQEBuvnmm+Xu7q5rr71W3t7eslqtunDhgurVq6fU1FQFBgZWybkAAAAAoC5yK2+H2267Tdu2bVNeXp6Kiors/1VGt27d9MMPP6ioqEinT5/WuXPn1KVLFyUkJEiSNm3apO7du1fq2AAAAACACszkvfnmmzpz5oykiw9Bt9lsslgs9oekOyIoKEgRERG6//77JUmTJk1SSEiInnnmGa1cuVJXX321BgwY4PBxAQAAAAAXlRvydu7cWaUnHDx4cIlHMCxevLhKzwEAAAAAdVWpyzXfe++9Yq/37t1r/3nixInOqwgAAAAAUGmlhrwtW7YUez19+nT7z0ePHnVaQQAAAACAyis15P3xcQaXv7ZYLM6rCAAAAABQaaWGvLKCnCPPswMAAAAAuE65j1C45PLQx0weAAAAANRMpd5dc/fu3br99tvtrzMyMnT77bfLZrPp9OnTrqgNAAAAAOCgUkPexo0bXVkHAAAAAKAKlBrymjZt6so6AAAAAABVoMLfyQMAAAAA1HyEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIOU+jB0AKjNrNFxrjvZ8v2uO5eTFL4+tLpLAAAAVYSZPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIMQ8gAAAADAIIQ8AAAAADAIIQ8AAAAADELIAwAAAACDEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMEi1hLwLFy6oZ8+eWrVqlY4fP66hQ4cqKipKTzzxhPLy8qqjJAAAAAAwQrWEvDfffFO+vr6SpDlz5igqKkrLly9X8+bNFR8fXx0lAQAAAIARXB7yfv31V/3yyy+6/fbbJUk7duzQHXfcIUkKCwvT9u3bXV0SAAAAABjD5SEvNjZWEyZMsL8+f/68PD09JUkBAQFKS0tzdUkAAAAAYAx3V55s9erVuummm3TNNddccbvNZqvwsZKSkqqqLFSTxMTE6i6hSpl2Pahb+PxWDr83OAufrZqJfqm7alvfuzTkbdmyRUePHtWWLVt04sQJeXp6qn79+rpw4YLq1aun1NRUBQYGVuhYwcHB8vLycnLFDlq+v7orqFVCQ0Oru4Qqk5iYaNT1GIHx6BA+v45j3DuIMekQPls1D2O+7qqJfZ+bm1vmpJdLQ96sWbPsP8+dO1dNmzbV7t27lZCQoP79+2vTpk3q3r27K0sCAAAAAKNU+3PyRo8erdWrVysqKkqZmZkaMGBAdZcEAAAAALWWS2fyLjd69Gj7z4sXL66uMgAAAADAKNU+kwcAAAAAqDqEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIMQ8gAAAADAIIQ8AAAAADAIIQ8AAAAADELIAwAAAACDEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIMQ8gAAAADAIIQ8AAAAADAIIQ8AAAAADELIAwAAAACDEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIO4V3cBAAAAAFzHGh1X3SXUKv+KalvdJTiMmTwAAAAAMAghDwAAAAAM4vLlmtOmTVNiYqIKCgr06KOPKiQkROPHj1dhYaEaN26s6dOny9PT09VlAQAAAIARXBryfvjhBx06dEgrV67U6dOndc8996hz586KiopSnz59NHPmTMXHxysqKsqVZQEAAACAMVy6XPPvf/+7Zs+eLUlq2LChzp8/rx07duiOO+6QJIWFhWn79u2uLAkAAAAAjOLSmTyr1ar69etLkuLj43Xbbbdp27Zt9uWZAQEBSktLq9CxkpKSnFYnXCMxMbG6S6hSpl0P6hY+v5XD7w3OwmerZqJf6q7a1vfV8giFzZs3Kz4+Xu+995569+5tb7fZbBU+RnBwsLy8vJxRXuUt31/dFdQqoaGh1V1ClUlMTDTqeozAeHQIn1/HMe4dxJh0CJ+tmseoMc94dFhN6/vc3NwyJ71cfnfNb7/9Vm+99ZYWLVokHx8f1a9fXxcuXJAkpaamKjAw0NUlAQAAAIAxXBrycnJyNG3aNL399tvy8/OTJHXp0kUJCQmSpE2bNql79+6uLAkAAAAAjOLS5Zrr16/X6dOn9eSTT9rbXnvtNU2aNEkrV67U1VdfrQEDBriyJAAAAAAwiktD3gMPPKAHHnigRPvixYtdWQYAAAAAGMvl38kDAAAAADgPIQ8AAAAADELIAwAAAACDEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIMQ8gAAAADAIIQ8AAAAADAIIQ8AAAAADELIAwAAAACDEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAMQsgDAAAAAIMQ8gAAAADAIIQ8AAAAADAIIQ8AAAAADELIAwAAAACDEPIAAAAAwCCEPAAAAAAwCCEPAAAAAAxCyAMAAAAAgxDyAAAAAMAghDwAAAAAMAghDwAAAAAM4l7dBVwydepU7dmzRxaLRRMnTlS7du2quyQAAAAAqHVqRMj717/+pSNHjmjlypX69ddfNXHiRK1cubK6ywIAAACAWqdGLNfcvn27evbsKUlq2bKlsrKydObMmWquCgAAAABqnxoxk5eenq4bb7zR/trf319paWlq0KBBiX1tNpskKS8vz2X1VdRfvT2qu4RaJTc3t7pLqFKmXU9tx3h0DJ/fyuH3VnGMScfw2aqZTOkXxqPjalrfX8pCl7LRH1lspW1xoeeff149evSwz+Y9+OCDmjp1qlq0aFFi35ycHCUnJ7u6RAAAAACoUVq3bi0fH58S7TViJi8wMFDp6en21ydPnlTjxo2vuK+3t7dat24tDw8PWSwWV5UIAAAAADWCzWZTfn6+vL29r7i9RoS8rl27au7cuRo8eLD27dunwMDAKy7VlCQ3N7crplUAAAAAqCvq1atX6rYaEfI6dOigG2+8UYMHD5bFYlFMTEx1lwQAAAAAtVKN+E4eAAAAAKBq1IhHKAAAAAAAqgYhDwAAAAAMUiO+k1fbTJ06VXv27JHFYtHEiRPVrl07+7bvv/9eM2fOlNVq1W233aZRo0ZVY6WoamX1fXh4uJo0aSKr1SpJmjFjhoKCgqqrVFSx5ORkjRw5Uo888oiGDBlSbBvj3mxl9T3j3mzTpk1TYmKiCgoK9Oijj6p37972bYx7s5XV94x7M50/f14TJkxQRkaGcnNzNXLkSIWFhdm317oxb4NDduzYYfuf//kfm81ms/3yyy+2+++/v9j2Pn362H7//XdbYWGh7cEHH7QdOnSoOsqEE5TX92FhYbYzZ85UR2lwsrNnz9qGDBlimzRpki0uLq7Edsa9ucrre8a9ubZv32777//+b5vNZrOdOnXK1qNHj2LbGffmKq/vGfdm+vzzz20LFy602Ww227Fjx2y9e/cutr22jXmWazpo+/bt9oe2t2zZUllZWTpz5owk6ejRo/L19dVf//pXubm5qUePHtq+fXt1losqVFbfw2yenp5atGiRAgMDS2xj3JutrL6H2f7+979r9uzZkqSGDRvq/PnzKiwslMS4N11ZfQ9z3XnnnfrnP/8pSTp+/Hix2dnaOOZZrumg9PR03XjjjfbX/v7+SktLU4MGDZSWliZ/f/9i244ePVodZcIJyur7S2JiYpSSkqLQ0FBFR0fLYrFUR6moYu7u7nJ3v/L/Lhn3Ziur7y9h3JvJarWqfv36kqT4+Hjddttt9uV5jHuzldX3lzDuzTV48GCdOHFCb731lr2tNo55Qt6fZOMJFHXWH/t+zJgx6t69u3x9fTVq1CglJCQoMjKymqoD4AqMe/Nt3rxZ8fHxeu+996q7FLhYaX3PuDfbihUr9PPPP2vcuHFau3ZtrQ3wLNd0UGBgoNLT0+2vT548qcaNG19xW2pqKkt8DFJW30vSgAEDFBAQIHd3d912221KTk6ujjLhYoz7uo1xb7Zvv/1Wb731lhYtWiQfHx97O+PefKX1vcS4N1VSUpKOHz8uSbrhhhtUWFioU6dOSaqdY56Q56CuXbsqISFBkrRv3z4FBgbal+s1a9ZMZ86c0bFjx1RQUKCvv/5aXbt2rc5yUYXK6vucnByNGDFCeXl5kqR///vfatWqVbXVCtdh3NddjHuz5eTkaNq0aXr77bfl5+dXbBvj3mxl9T3j3lw7d+60z9qmp6fr3LlzatSokaTaOeYtNtYbOmzGjBnauXOnLBaLYmJitH//fvn4+KhXr17697//rRkzZkiSevfurREjRlRztahKZfX90qVLtXr1anl5ealt27Z6/vnna+0UP4pLSkpSbGysUlJS5O7urqCgIIWHh6tZs2aMe8OV1/eMe3OtXLlSc+fOVYsWLextt956q9q0acO4N1x5fc+4N9OFCxf03HPP6fjx47pw4YIef/xxZWZm1tq/4xPyAAAAAMAgLNcEAAAAAIMQ8gAAAADAIIQ8AAAAADAIIQ8AAAAADELIAwAAAACDuFd3AQAAXMnJkyc1bdo0JScny9vbW5I0evRodenSRatWrdL3339vv511ZaWmpurw4cPq3LlzsfZVq1ZpxowZatmypaSLt9bu3r27xowZU+bxhg4dqiVLlshqtZa5X2ZmpkaPHi1JSklJkc1mU7NmzSRJs2fPlr+/f2UvCQAAQh4AoOax2WwaNWqUBgwYYA9yBw8e1D/+8Q99+OGHVXaeHTt26Ndffy0R8iSpS5cu9nPn5+dr6NChCgkJUVhYWKnHi4uLq9B5/fz87PvOnTtXBQUFeuqppypxBQAAlETIAwDUONu3b5fFYtFDDz1kb2vTpo3Wr18vX19f7dy5U2fOnNHTTz+tX3/9VVdffbXmzZsni8WiuLg4bdiwQYWFhbruuusUExOjwsJCRUdHKzs7WwUFBQoLC1Pfvn01a9Ys2Ww2+fn5afjw4aXW4+HhoZtuukmHDx9WWFiYZs+ere3bt0uSmjRpounTp8vDw0Nt2rTRvn379OabbyozM1MnTpzQkSNHdOutt+r5558v97pzcnIUERGhL774Qt7e3srLy1NYWJg+//xzdenSRSNHjtSOHTt09uxZvfbaa2rdurUOHDig2NhYFRQUKD8/Xy+88ILatm375zsBAFBr8Z08AECNc+jQIYWEhJRo9/X1tf/8yy+/aMqUKVq1apUOHTqkffv26aefftIXX3yhDz74QCtXrpSPj48+/vhjff/99yooKNDy5cu1YsUK1a9fX02bNtU999yjfv36lRnwpIvh67vvvlNoaKgKCgp01VVX2Y+Vk5Ojbdu2lXjP/v37NWfOHMXHx2vVqlXKysoq97p9fHzUo0cPJSQkSJK2bdumTp06yc/PT4WFhWrVqpXi4uL04IMPas6cOZKkcePG6cUXX1RcXJwmT56sSZMmlXseAIDZmMkDANQ4VqtVhYWFZe4TEhKiq666SpIUFBSknJwcJSUl6T//+Y8efvhhSdK5c+fk7u6uO++8U3PmzNETTzyhHj16aNCgQXJzK/vfOb///nsNHTpUkmSxWDRixAjddNNNkiQ3NzdFRUXJ3d1dhw8f1unTp0u8PzQ0VFarVVarVY0aNVJWVlaxkFqawYMHa8aMGbr33nu1YcMG3XffffZt3bp1kyR16NBB7777rjIyMvTbb7/pueees+9z5swZFRUVlXt9AABzEfIAADVO69at9fHHH5doP3jwoK655hpJKnFzE5vNJk9PT4WHh+uFF14o8d41a9Zo9+7d+vLLLzVw4EB9+umnZdZw+XfyLpeYmKhPPvlEn3zyierXr1/qzViuVF9FtG/fXjk5OTp8+LAOHTqkTp06XfEYFotFnp6e8vDwqPB3AQEAdQP/zAcAqHFuueUWeXt7a+HChfa2Q4cO6X//93914sSJUt/XoUMHffPNNzp79qwk6YMPPtDu3bu1bds2bdmyRaGhoRo/frzq16+vjIwMWSwWFRQUOFRbRkaGmjZtqvr16yslJUU//vij8vLyKnehpbj//vv13HPPqXfv3rJYLPb2H374QdLFoNmmTRv5+PioWbNm2rp1qyTpt99+07x586q0FgBA7cNMHgCgRlq4cKFeffVV9e3bV35+fvLy8tKsWbN03XXX6ccff7zie0JCQvTQQw9p6NCh8vLyUmBgoO69916dOnVKEyZM0DvvvCOr1apu3bqpadOm6tixo5566il5eHjoySefrFBdXbt21XvvvacHH3xQrVq10ujRozV//nzdeuutVXbt/fr106uvvqpZs2YVa9+/f78+/PBDZWVlKTY2VpIUGxurl19+WQsXLlRBQYEmTJhQZXUAAGoni62i60cAAIBLbNiwQZs3b9brr79ub7t05053d/59FgBQNv6kAACgBhk9erQyMjLsd88EAMBRzOQBAAAAgEG48QoAAAAAGISQBwAAAAAGIeQBAAAAgEEIeQAAAABgEEIeAAAAABiEkAcAAAAABvl/GV+Xgxxt1GMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Similar plot for attribute \"thalach\"\n",
        "feg,ax=plt.subplots(figsize=(15,6))\n",
        "data[\"thalach\"].hist(ax=ax)\n",
        "plt.xlabel(\"Maximum Heart Rate Achieved\")\n",
        "plt.ylabel(\"Entries in Dataset\")\n",
        "plt.title(\"Maximum Heart Rate Achieved (thalach) Distribution\");"
      ],
      "metadata": {
        "id": "swZC2GHNjM7F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "f8ade037-dd0e-4ed1-ffae-24a70d297cbb"
      },
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Similar plot for attribute \"exang\"\n",
        "feg,ax=plt.subplots(figsize=(15,6))\n",
        "data[\"exang\"].hist(ax=ax)\n",
        "plt.xlabel(\"Exercise Induced Angina\")\n",
        "plt.ylabel(\"Entries in Dataset\")\n",
        "plt.title(\"Exercise Induced Angina (exang) Distribution\");"
      ],
      "metadata": {
        "id": "lhAwODV2jM4l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "0ab13c39-7bed-4916-dafa-433ed64c28c2"
      },
      "execution_count": 308,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Similar plot for target attribute \"num\"\n",
        "feg,ax=plt.subplots(figsize=(15,6))\n",
        "data[\"num\"].hist(ax=ax)\n",
        "plt.xlabel(\"Diagnosis of heart disease\")\n",
        "plt.ylabel(\"Entries in Dataset\")\n",
        "plt.title(\"diagnosis of heart disease (num) Distribution\")"
      ],
      "metadata": {
        "id": "z5yMt1SwjM2X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "4ef219f3-9e2f-4383-ec91-fdfa58af8c6a"
      },
      "execution_count": 309,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'diagnosis of heart disease (num) Distribution')"
            ]
          },
          "metadata": {},
          "execution_count": 309
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new empty figure object using the Plotly library\n",
        "fig = go.Figure()\n",
        "\n",
        "# Add a violin plot of cholesterol levels for all patients to the figure with a custom name and color\n",
        "fig.add_trace(go.Violin(y=data['chol'].values , name='All Patient', marker_color = 'green'))\n",
        "\n",
        "# Add a violin plot of cholesterol levels for patients with no disease to the figure with a custom name and color\n",
        "fig.add_trace(go.Violin(y=data[data['num']== 0]['chol'].values, name ='No Disease', marker_color = 'blue'))\n",
        "\n",
        "# Add a violin plot of cholesterol levels for patients with heart disease to the figure with a custom name and color\n",
        "fig.add_trace(go.Violin(y=data[data['num'] ==1]['chol'].values, name ='Heart Disease', marker_color = 'red'))\n",
        "\n",
        "# Update the layout of the figure with a custom title and y-axis label\n",
        "fig.update_layout(title = 'Cholesterol Level Distribution', yaxis_title = 'Cholesterol Level', title_x = 0.5)\n",
        "\n",
        "# Update the x-axis of the figure with custom styling options\n",
        "fig.update_xaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\n",
        "\n",
        "# Update the y-axis of the figure with custom styling options\n",
        "fig.update_yaxes(showline=True, linewidth=2, linecolor='black', mirror=True)\n",
        "\n",
        "# Display the figure object\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "1WBhYNJ3jM0O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "16db220f-4173-4433-af54-75e0879c98f9"
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    {
      "cell_type": "code",
      "source": [
        "# continuous and categorical variables\n",
        "cont_var = ['age','trestbps','chol','thalach','oldpeak']\n",
        "cat_var = ['sex','cp','fbs','restecg','exng','slp','caa','thall']"
      ],
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      },
      "execution_count": 311,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Concatenate 'data' and 'num' columns and compute the Pearson correlation matrix\n",
        "corrmat = pd.concat([data,data['num']],axis=1).corr(method='pearson')\n",
        "\n",
        "# Create a new figure with a size of 10x7 inches\n",
        "plt.subplots(figsize=(10,7))\n",
        "\n",
        "# Create a heatmap with correlation matrix 'corrmat'\n",
        "# Set 'annot' to True to display correlation coefficients in each cell\n",
        "# Set 'vmax' to 0.9 to limit the color scale range to -0.9 to 0.9\n",
        "# Set 'square' to True to force square cells\n",
        "# Set 'annot_kws' to modify the size of the annotations\n",
        "sns.heatmap(corrmat, annot=True, vmax=0.9, square=True, annot_kws={'size':7})\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
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5++23q4wtdvUzjtjVzzhiVz/jiF39jCN29TOO6PwfXZ3e7f/ss88ydepUtm/fzgsvvMDWrVv517/+xQsvvICnpyctW7ZEJpPx9NNPExUVxahRo9DpdEyZMqUuX6YgCIIgVBCL/DyiVq1a8ccff1Q87tOnD9u3b2f16tXY2dkxceJEGjZsiEKhYN68eXX50gRBEATh3p7ARX7+9r/zV6vVjBs3DnNzc3x9fWnXrt3f/ZIEQRAE4RYx8q95wcHBBAcH/90vQxAEQRD+Mf72zl8QBEEQHmuP0eI8NUV0/oIgCIJQlSdw2l9s7CMIgiAI/zBi5C8IgiAIVanju/3nz5/P6dOnkSSJ6dOn06rVrb0Q1qxZw2+//YZMJqNly5bMmDHjoXKIzl8QBEEQqlKH0/5Hjx7lypUrrF+/nqSkJKZPn8769euBss3xli5dyp9//olCoWDChAmcOnWKNm3aGJ1HTPsLgiAIwmPi8OHDBAWV7dHh4+NDfn4+hYWFACiVSpRKJcXFxZSWlqJSqbC1tX2oPE/MyL8ult59/8THtZ4D4Nt2H9V6jvXX3Gs9R6ysbpYS7S+Z1HoOe33tL7k8waRRrecAsCpR1XqOqzdsaj0HQHOT2n8LS0p2qPUcTW3yaj0HQJ5GjPceSh1O+2dlZeHn51fx2MHBgczMTKysrDA1NWXy5MkEBQVhamrKM888g7e390PlEWeCIAiCIFTBYNDV2I/xuSvt5FpYyA8//MD27dvZtWsXp0+fJi4u7qHKJDp/QRAEQXhM1KtXj6ysW5tJZWRk4OxctkVzUlISDRo0wMHBARMTEzp06MC5c+ceKo/o/AVBEAShKgZ9zf08QEBAADt27AAgJiaGevXqYWVlBYCHhwdJSUmo1WVfqZ47dw4vL6+HKtIT852/IAiCINSKOvzOv127dvj5+TFq1CgkSWLmzJmEh4djbW1N3759mThxImPHjkUul9O2bVs6dOjwUHlE5y8IgiAIj5F33nnntsfNmzev+PeoUaMYNWrUI+cQnb8gCIIgVOUJXN5XdP6CIAiCUJUncGMfccOfIAiCIPzDiJG/IAiCIFRFTPs/mrS0NN59911kMhk6nY7PP/+cxYsXc/XqVUpLS3nzzTdp3749o0aNYvHixTg7O/P888/zzTff0KBBA6NySTKJZxZMRFukJiPuKqc2RAJgamPBgJkhlKi0JOw6yY1rOXQc2xdJJuPk2t3o9Xraj+mNtlhD/I7jJB+Lf6iyZmbl8Mmi7+nSsR3Dhwx4qBiVy9JnwURKitVkxSYTs2FfRVkCZ4VQotJwcWc0l/ecRmFmwphtH/PrhK+wbeCET7/2KMxNOfJNBDeuZlaZo++CiWjLc5yrlKNXpRwWjjZ4925DQWoWF3efwtLZDhd/L8wdbchJSuPo4i1GlEvG2Pmvoi5WkxKbzMGNuwGwtLVi5EcvoSooJiX2CvvX7zKqvuzbNMInpA/IJM5/GU5xShZufdvi1qcNcjMToj9YRWmhCpee/jSbPIh9IxbQ69ePyD6RiL5Ux7n566vx2svqq6RYTeYd9dW7vL6SyuurUe823CivL4WpEq/A1kiSRPTKv8i9eK3KHL0rtfv58hwmNhb0nBVCqUrDpUrtPmrbx2yZ8BX5V65j4WzLmD/msbTTmxh01XvjsmrTGJeQfkgyiatfbkCTkol5Yw+8504kdXEE+fvPAGDbszUek4M5P2J2teIC2LT1of7YICRJIumLTahTsnDu1w6nPm2RmZkQN2MFHmMCMXN1QG5lRtz7y7F7qhnOAzogN1WSMGcNumLNA/NIMonATyZSUqQmOy6Z2PW36qzHrBBK1Bou/xVN3qV0Or07HH2JjsQtUaRGxdF1+ijUeYUc+XxTtcpk3bYx7mP7giRx+YsNaFKysGjsTuP5E7n6bQS5+87iPrYvFk08UDrakPDuj+iKjFv10qxVU+xHPQ0yGZnfrKE0LQOroC5YdW+HzNqSjM+WoXRzxmXGq6S+/Rklyfc/n6oik8mYvGAyqmI1l2Mvs3PDXwBY2Vrx8sxXKC4o5nLsJZLOJTHghQGoitRE/RnF+aMxj1WOWlPHG/vUhTqd9t+xYwddu3YlNDSUGTNmEBERgbOzM6GhoSxevJj58+djYmLCe++9x1dffcW6devo37+/0R0/gHdAS5KPxvPnnNV4d2tZ8bzfM504tSGS3z9Yjn9wAD49/Dn0wzb+/Hg1HV/qh1dnX05t3Meh77fc9nvGkmQSw4c8/dC/X1mDAD/SjsUTOXs1DSu9pqaDOnFufSS7Z6ygeXBXANq/NpCUw7EAePdpy765azn58x/4jehZZY6GAX6kHItn731y7JqxAt/yHDptCXITJQXXcoj77TCR89ZRlJFH9Iq/jCqXb4A/F47Fsn7OClp08694Xm6iIOLLMNbNWkbjDs2MigngM7YPJ99fTuzCzXiPCQSgQXAXTk5bzuWN+6k/+CkU1uY4dW6OOiMPAINOj75Uhza7oFo5Ggb4kXosnj2zV+NZqb6aDerE2fWR7KxUX6WV6su7V2uOLP6N85sP4tG+SZU5brb7vnu0yfn1keyZsYJm5TnavTaQ1PJ2B2j7ytOkRMXeFbMqLmP7cfH9H7m6cBP1xvQBQFeoIjN8f8UxcmsLbDq3oCQj16jYDcYFETt1KRcXhuPxQm8AXIO7Ejt1Kdc27sN1SGcsG7uTMGcNhfEp2HdtgevQrmgz8tBm30Cn0lYrT/1uflw7Gs+B2atpUKnOmgzqxPkNkUROX0HToV0x6PXsm7GSgx+vpWFgK2QKGWdXGnf+uo/rS8LUn7iycBNuL5TVV2mhmoxfbtVXwblLJH64goJTiZh7uRoVH8B+9EDSZy0ma/E67Ef0L8uRkU36zMXc+H0/Zi2bUHItg8LI40bHrqx1QGtijsXw8+yfaNOtdcXzChMFa75YzY8zf8C3gy8tO/uzc8NOwr//5bbjHpccQvXV6cg/ICCAKVOmUFBQQP/+/cnIyODEiROcPHkSAI1Gg1arpXPnzoSHh/Pbb7+xdu3ah8pl4WhNUVY+wG0jHwtHG64cLVsO0aA3cCb8AF1eeQZ1QTEyuYyEXdE8PXsc6oJiDv+w7aHL6uRgz8XLVx/69yuzcLShKLO8LJU+gZo7WFN8pHxpR4OB+p19yU5IRZKVfaY7E7qLru8MR51XhIWjdZU5zB1tKL5HDgsHa1LLcxgMBpJ2RnNhx3EMegM9PxjDzveXYe/tSnHWDUqMHNVYO9iQX95G+kptdCMzD4Ahb40gcq1xb8gAMqUCvbYUVUYeZs7lm17oy5bIVGfk4di+Mc0mDSJhyTbazh8HwLH//EBRciYt/jsMq0auFF5MrzJH5TbR36dN7qyvwA/GELv5IH3nj0eSydj1wYoqc1RukztzVG4Tj86+5FRq92ZDA0j64xj+L/apTnVVkJQKDNpSSjJyUTrbAaBNz7ntGPdJz5K25FcazX/ZuNiKstia67mYlreJobxNNNdzsW3fhKyd0TR+fyRKeytKsm9gXt+Z2PeW4hHSB4fuLcnZd/aBecwdbCi+xzll5lipXfQGbiRn4tLWh07vDOfoV+Fo8osxsbZ4qDJpr+dhcp/6KjiZiEUTD8waulD44+9Gxa+cozQjB7ljWQ71mQQU9Ryw6tmB6x8vwaAtNTrunWwcbckrv+4qn2s3nxvz9hi2r9lOQV4Br815neKCIsK/D3/sctSaJ3Dav05H/k2bNuXXX3+lQ4cOfPXVV+zfv5/XX3+d0NBQQkND+fPPPzExKdukJS8vD51Oh0pl/CYkbUf1otukIVi72AO3r41ckJ6LVfmFqtfpkcnlRC39gxNrdqHKL6Lj2L5smfoT4W98S+sRPR690I+o5ehAOk4ejJWLHXDHOs/puVhWKkvDbn44NW9A/c6+NBvSGYPBwJFvfuVy5Bnyq5jy9x8dSKcqclhUymHn5QJAqUqDJCvb7KZ5cFfitx4xqlzdRwUxcNJQ7Oo5lOe8/f+fm/Yip/48xsXoC0bFvfnaZKZKLNwcKE7NLotf/mZj7uaA6loOFu4ONHllALa+DXHs2BSLBmXLZ5bcKEZmoqwy/p31xX3axHCP+mr7Uj9+e/0b/np/Gf6jA++bw290IB0mD8byPjksKuVo2M0Px/J2bzqkM25tffAMbI1zC0+aDulSjRoro1dpkEyVmLg5ok29+3xR2Flh6u6I+yuDsPD1wtqIWRldeZuYuTmiSi1furS8TUzdHFGnZKHJyCNxwXqKkq6hSs5Em32j7HeL1MgtHrx5U4vRgbSfcu86K6rcLno9Ds3qkxOfwpaxn+M/Nqja5ahMX14mUzcHNPeoLwArPy9cRwaS+NHKh8uhViOZKFG4OlFyrSyHwtUJx9dHcn3ejzXS8fcb3Z/nJz+Pg4sjcPe1OO79lzjy5xHio+N5Ztwg/vfeN3w+5XOCRlS/3uoiR63S62vu5zEhGQx3NkPt2bZtGw0aNKBVq1YcP36cGTNm0KJFCxYuXEh2djYrV67k7bffZtu2bZw7d45WrVoRFRXF7NkP/m5xjucLtz2WZBID545Hr9OTGp1IYuQZWg0N4GTYXgbMGou+VMf5rVFkJqYxYFYI2mIN+/8XgV19Z5r374C2SE3KqUTi/jhWEdOYXf2ijkezfvM2CgqLGTXsGYJ6BlT7d+/c1U+SSfSeNx59qY70U0lc3nsG36EBnAvbS+DsEPSlOhK2HiF5f9kaz53fGkZs+EFMrc3p8K9B6LQl7Pt4LaqcW1Pad56Ckkyiz7zxGEp1XDuVxKW9Z2gxNICzYXvpVSmHOr+Yjq8NRFOoImbjPtKOX2DAwtfY/tYPd5XjQbv6STIZL859GV2pnkunLnAuMprOwT1ITbjKoCnPcSXmIvkZeWz/PqLKOP01prc9tvP3ovH4vkgKOYWXrnP118NYebngPqA9clMlJ99fUfH98VPf/oujU5bQdt449LqyP+c5/dHqu3JcUd6+q58kkwgqb5NrldrkbNheepfXV/zWI2jyi+nw2kC0hSrObdyHnacL9Vp4IlPIiNsSRdrxWx9u7vzIIckkelVq9yt7z9B8aAAxYXvpWZ7jQqV27/TWMOLCD5J/5ToAfb98lZ3v/XzXd/4d77Orn6W/N67jn0ZSyFFfSifr1wNYtvDC5cUgdIVqri3dxo3D5wFo8u2bXJjyzX3bpFB/e2ms/b1oOLE/klxO8aV00iMOY+HlgvPTZd/px05bhuuwAGxaeWMo1RE3fQVOQW1x7t8eubkp59/5Eb265K48CXfs6ifJJHrOG49ep+N6dBLJe8/QbFgAMev20mNOSNl3/FuPoM4tpNX4fhj0eq4dTyDz3BVaje+HQ1MPYtbsIXZ9ZEXMltp732tg5e+Nx8QBSHI5qkvpZEQcxMrPE7cXg9AVqkj5+Q+83n2eovPJYDBwbd0eis5fuWcsF5uiez5v1sIH+5DBIJdTkpzGja37cHpjDPoiFQaNloKdUZh4e2D3XF80SVfJWbYZTcLl+7bLe/fZ1U8mk/Gvef9CV6oj/lQCJ/eeIHBoL5LjrzDizZEknUsiNyOHi+cu0nlAF1RFKhKi4zn0x6H75qrNHFuSt1Y7b01QH1xTY7HMAl548EF1oE47/5iYGGbOnImFhQVyuZxp06axatUqkpKS0Ol0TJkyhfbt2/PCCy+wevVqrK2tCQkJ4d1336VVq1ZVxr6z868NT9KWvnXx+bPOtvS9o/OvDXd2/rWh6vmGmnO/zr8m3dn515Y7O//acL/Ovybdr/Ovaffr/P+/qfPOf39ojcUy6x5SY7EeRZ1+5+/n58emTbffSTtv3ry7jvv1118r/h0aWnOVLgiCIAjGepiteB93T8bHQEEQBEEQqk0s8iMIgiAIVXmMbtSrKaLzFwRBEISqiD/1EwRBEATh/zsx8hcEQRCEqohpf0EQBEH4hxHT/oIgCIIg/H8nRv6CIAiCUBUx7f/4MqP2V2Cri5X3AKacnFPrOX71/7DWc2hNzGo9B4Ct/tHXN38QzxJ5refo5pNW6zkABiTW/op1fcwb1noOgCGq2i/LevPaf28xKbGp9RwAawfl10meJ46Y9hcEQRAE4f+7J2bkLwiCIAi1Qkz7C4IgCMI/zBPY+Ytpf0EQBEH4hxEjf0EQBEGoyhN4w5/o/AVBEAShKmLaXxAEQRCE/+/EyF8QBEEQqiKm/QVBEAThH+YJnPZ/Yjt/SSbRf8FEtEVqMuOSObthHwCmNhb0mRlCiUpD0q5okg/H0uPd50GSSNoVDUCT/u1RmJty+JsI8q9mVpmjz4KJlBSryYpNJqZSjsBZZTku7ozm8p7TKMxMGLPtY36d8BW2DZzw6VeW48g3EdyoIseDZGbl8Mmi7+nSsR3Dhwx46Dj2bRrRKKQPkkzi/JfhFKdk4da3La592iA3M+HUB6soLVTh0tOfZpMHsW/EAlp9NAbtjWJMbC05M3vNA3NIMokBCyZSUqQmIy6ZM5Xqq295myTuisagN+DVzQ+FqRJJLufqkThc/b2wcLAhOymNw99tqTKPTVsf6o8NQpIkkr7YhDolC+d+7XDq0xaZmQlxM1bgMSYQM1cH5FZmxL2/HINOj2PPVni9MYQTw+dWq758QvrAHfXlVl5f0feoLwDPET1w6eHH0SlLqtEqt1P6Nsfi2cEgSRQuW4nu+nXMAntg2rkTkqUlBd8uQXf9utFxb5LJZHz4+XsUF6m4EJtIxLptANja2zB9wTsUFRYTcyqWfX8dZPLUV9CoNRzac4TIvw5WO4ckk3h+/itoi9WkxSZzdOPestxyGUFThuLk6cratxfTcXhPfAPbkJuWTeyekyQePm9UWazbNsZ9bF+QJC5/sQFNShYWjd1pPH8iV7+NIHffWdzH9sWiiQdKRxsS3v0RXZHaqBySTOKF+a+iKVaTEpvM4Y17Ksry9JRhOHu6suLtb2ng5033F4LQFKk5/ecxEo/FGZVj5PxX0JTX15FK9dWvvL5Wv70YpZkJg94dBRLE7DpJwsFzRpVF5tkUZfeBSJKEZtsaDDkZZc+7eWL2ynSK57yGMnAIMtcGSFa2qFd/Derixy6HUH2PxXf+JSUl/Pe//2XUqFGMGzeOJUuW8NZbb/HKK68wePBgfvnlF6Njegb4cfVoPLvnrMazW8uK55s/04mzGyL564MVtAjuStOnO6It1qDX6biRmoVPn7bsmbuW4z//gf+InlXmaBDgR9qxeCJnr6ZhpRxNB3Xi3PpIds9YQfPgrgC0f20gKYdjAfDu05Z9c9dy8uc/8HtAjgeRZBLDhzz9SDEAGo3tQ/T7y4lduBnvMYEANAjuQvS05VzZuJ/6g59CYW2OU+fmqDLyALCo70Tc1xHIlHIkxYOXv/UK8CPlaDw756zGq1J9+T7TidMbItnxwQr8grtyMfIMu+et40ZaDocX/8b53w6ze946CjPyOLHyrwfmaTAuiNipS7m4MByPF3oD4BrcldipS7m2cR+uQzpj2didhDlrKIxPwb5rCxTW5th3aY7mem616stnbB9O3qO+Tk5bzuU76ktdqb5M7a2qFf9eLIKHkP/l1xSuDMV88EAAdOnXyf/kc9R79qL0bfbQsQE6de/AySOn+fyjRXTq3rHieYVCwcKPFzP3vc/oGNCO1h39+WPzXyyY/hXBowcZlaNJgD8Xj8URMWcVTbv5VzwvyWUcDP0TqdJKuqXaUhQmCvKu5RhdFvdxfUmY+hNXFm7C7YU+ZfEK1WT8sr/imIJzl0j8cAUFpxIx93I1OkfzAH8Sj8Wxcc5KfCuVRSaXERm6g5uFadq5BYc37uXPH36jeaXjqqNpgD9Jx+LYPGcVze6or/2V6qv1gKfQFKnRl+rJTc0yuiwm3Z9BE7YYze/rUHbtX14QGcoufdEnJwGguxSHJmwx+isJyOp5PJY5ao1BX3M/j4nHovOPiIjAycmJsLAwRowYga2tLYmJiSxZsoSVK1fy9ddfozdy2sXC0YbirLJ1rA26W79r7mhN0c3n9Qas3Ry4cuAch77eTKfJQzi1ehfd/jscn95tsXCwfmCOosybsSrlcLCuyI3BQP3OvmQnpFKcfQOAM6G76PrOcBr1aYuFY9U5HsTJwR65/NHXnZcpFei1pagy8jB1ti176XoDAOqMPMycbWk2aRAJS7ZV/I46M5/Ws19EZmaCoVT3wBwWjja36r5Sm1jc0SYAZjYWmNlaciMtGwAHb1eKsm+grcboTFIoMGhL0VzPvassmuu5mDjbkrUzmsbvj8SqWX1MHK3xmjyYy99tfWDsmyrXl1l5Dh5QX40n9ufC0h3VznEXuRxKStBlZSN3cACgJC4e8wH9sBgyGM2Jkw8fG7B3tCM7s6yj1Vdqn+zMHExNTfh6xSf8tXUPB3cfplP3Drz23wlIMuPeQqwcrCksb+vKOXTaUopyCioex+w8QfjMZfz+eRi9XjXuAwbcOge01/MwcbYDQJt++4eIgpOJWDTxwKyhC4Uxl43OYeVgw42svLvKUqotpbBSWc7uPsmgt55n5KwJnN1lXBtZOVhTUI36snN3JP7gWbYv2kTQpGeNLgtyOZSWYLiRg2RjD4CyVzDayC1A2Xmtv5KAsvdQ5I1bok+99HjmqC16fc39PCYei84/JiaGdu3aAfDMM89gZmZGx44dUSgUODg4YGtrS25u9UZkAK1GBdJp0mCsXOwAMBgMFf9XmJ6LZfmbgV6nL+ukJYkStRaFiQKDwcDh//3KpX1nyKtiOr7l6EA6Tq5ejobd/HBq3oD6nX1pNqQzBoOBI9/8yuXIM1V+rVCXdCoNMlMlFm4OFKeWdbg3P9CYuzlQfC0Hc3cHmrwyAFvfhjh2aILcVMnpmaspupKBRQPnKuO3HhVIl/u0SUF6LlaV6gugSb/2JJZ/DQPQ4tmuxG09YlRZzNwcUd0cBZWXxdTNEXVKFpqMPBIXrKco6Rqq5EzM3B3xfHUg1i0aYtuh6QNzlD6gvlTXcrC4o76UVmb4/vtZbH0bYt3U+FGNQaMBEyXyes4V0/tKP19U2/+kYMkPWAwaaHTMm4a9MISJb46lnqtTWS5utY97A1cKbhTy5tj36De4NwqlkvA1W/j565UUF1Z/WrbzqN70mRSMTb2yN/7K58CdnMpH4lqVBkkyfmMdfXn7mLo5oEm99zVm5eeF68hAEj9aaXT8gFF9GDApGNt6ZR/CqipLz7H9WT31B5a++TVdng+sdo4uo3rTd1IwttWor4LMfKSK9zFltXPcZNBqQKFEsnOqmI6XN/BB2TkImYc3cr+OyLyaU7J7MyVRO1G06vxY5hCq77H4zl8ul981sq/82GAwGPUGcCZsL2c3RNJ37nhc/L25ciAGC0cbWgwN4EzYXvrMCqFFcFfORxzi2ukk+swMoeVz3TkTthcTSzO6/3c4pdoS9sxde98c59btJWZ9JL3njaeevzfJB2Iwd7TBd2gA58L2Ejg7hOZDuxIXcYjk/WXfv3V+axjxv0Vham1O13eGo9OWsO/j++eojqjj0azfvI2CwmLsbK0J6hnwUHEuhu6m7YKXkCnkFF66jlUjV1J+jaLdZxOQmSqJfn8FyRsPAGDp7UL2iUQ8R3SnxX+HYVrPDtUdo6o7nQ7by5kNkfSfOx5Xf28ul7eJ39AAToftpe+sEPyCuxITcQgAFz8vLvx5ouL37Ro6U/CAHDelrNqJ76cTkORyii+lY9HIjfSIw/h+/jJyUyWx05bhOiwAjzG9MJTqSP7hd/JPJgJg7u1C/vGEatVXuwUvIVWqr6vl9SU3VXLy/RVcKa8vK28Xso9fIPv4hYrHBQmp1SpLZcW/bsH2v2+BXI4uJRV5g/ooPD0x7xuEzMGBwtUPfy6Fr/mNiHVbmfHJO/i2ak7UvmM4ONnzzHP9+WvrHt6eOQVVsYr4mAuUlpTynw8nUVxYzPoV1f9KLipsN0c27GH43InU9/fmwsGzWDna0D64G2e2H6XnxIHU929Er1cHkRh1nucXvIqmoJijmyKNLk/aqr9o8unLSHI5qkvpmDdyw8rPk3rDuqErVKEv0eH17vMUnU+m8eyxXFu3h6LzV6od/2DYLg5t2M3ouS/j6d+IuINnsXa04ang7kRvP0Kfic/Q0N+bvq8O5tzukwyYPBR1oYp4I76LPxy2m6gNe3h+7kQa+HuTUF5fHYK7cXr7UQLL66v3q4M4tG4Xw2a+xFPP9eDw+t1G11fJgd8xGzUF5DL0GdeQ6nmgXvE5ADJnd3QxxzDp+zxS+x5I9s5ofvnhscxRax6jEXtNkQxVfZysIxEREURHRzN79mz27NlDfHw827dv55dffiE/P59hw4axe/duZFVMMX7m+WKtv05lHdXUk7Kl7wWT2t8KFaC9uva39M2XiS19jdHHrK629H3w102Pqk629K2jSdi5A5+MLX2tv/ujTvOp1s+usVjmI2fWWKxH8ViM/AcOHMihQ4d48cUXUSgUdOrUCQ8PD/79739z5coV/vOf/1TZ8QuCIAiCUH2PRedvYmLCZ599VvE4PDycGzduMHXq1L/xVQmCIAgCT+S0/2PR+QuCIAjCY0t0/nVj2LBhf/dLEARBEIQn1mPZ+QuCIAjCY+MxWpynpojOXxAEQRCqIqb9BUEQBOEf5u//i/gaJzp/QRAEQXiMzJ8/n9OnTyNJEtOnT6dVq1YV/3ft2jXefvttSkpKaNGiBXPmPNy6MOKP5wVBEAShKnW4tv/Ro0e5cuUK69evZ968ecybN++2///kk0+YMGECmzZtQi6Xk5b2cIuDic5fEARBEKpSh53/4cOHCQoKAsDHx4f8/HwKCwvLX4aeEydO0Lt32W6lM2fOxN3d/aGK9MRM+49xrf2lUddfe7hKNlZdLL377NmPaz3H4ZZ1s0iTtam21nPsk1nWeo6U5LrZwnQOtX/zUiOp4MEH1YAw80fbFbM6htXBEsJFUt2Mw2b+blsneWrbV3/3C6hFWVlZ+Pn5VTx2cHAgMzMTKysrcnJysLS0ZMGCBcTExNChQwf++9//PlQeMfIXBEEQhKoY9DX3Y2zqSjcbGgwGrl+/ztixY1m9ejXnz59n7969D1Uk0fkLgiAIQhUMekON/TxIvXr1yMrKqnickZGBs3PZlun29va4u7vTsGFD5HI5Xbp04cKFCw9VJtH5C4IgCMJjIiAggB07dgAQExNDvXr1sLKyAkChUNCgQQMuX75c8f/e3t4PleeJ+c5fEARBEGpFHS7y065dO/z8/Bg1ahSSJDFz5kzCw8Oxtramb9++TJ8+nWnTpmEwGGjatGnFzX/GEp2/IAiCIFSljpf3feedd2573Lx584p/e3p6sm7dukfOIab9BUEQBOEfRoz8BUEQBKEq1bhR7/+bB478b9548DDS0tI4c+YMACEhISQkJDx0LEEQBEH4W9ThIj91pcqRf0pKCtu2baN///4PFTwqKori4uLb1iWua8oWzbEaOghkMm78tBJd+nXMegRg1rkjMitL8r/9EYung5CUJsjsbclb+B2UllYrtiST6LtgItpiNVmxyZzbsA8AUxsLes0KoUSl4eLOaCwcbfDu3YaC1Cwu7j6FpbMdLv5emDvakJOUxtHFW6rMY9+mEY1C+iDJJM5/GU5xShZufdvi2qcNcjMTTn2witJCFS49/Wk2eRD7Riyg1Udj0N4oxsTWkjOz1zxSHWZm5fDJou/p0rEdw4cMeKRYANZtG+MW0hdkEle+2IAmJQvzxu40njeRq99GkLf/LG5j+2Le2AMTRxsuvPcjuiK10XksWjfB6YX+IJNxbeE6SlIzMfXxoP6cV8lYEk7BgdO4TH4OyUSJwsGW1DlLMZRUr+1vkmQSz85/GW2xmvTYZE5ujARAJpfRc0owjp4ubHp7CSaWZvSfNhpVXiE7v9xY7dh9FkykpPz8iql0fgVWOr8u7zmNwsyEMds+5tcJX+HauhH1/L2wKD+/jj3g/LJr64NXSG8kmYy4L35BlZKFS992uAS1QW6m5OyMVdj6e+LavwNyMyUxc9bS/N3nKMkvRmlnScys6p9f5q2b4DB6AMhkZCxaW9YmjerjNvtVMr//haKDp3EcPwSlqyMyS3PSZn4POuPfMCWZRPD8l9EUq7kem8yJSu0SOCUYh/J2cfB0odebQ9FpS4n98zjxe04ZncumrQ/1xwaBJOPiFxtRp2Rh0did5vPHc/nb38jZd9bomHZtffAO6Q2V2sS1UpucKW8Tt/4dkJW3SePXB6KwNsfE3prot340ajMaSSbx/PxX0BarSYtN5ujGvUBZfQVNGYqTpytr314MgNLMhLe2zGfZy5+TdeX6Y5VDqL4qR/5z5szh6NGjNG/enPfee48xY8ag0+lYuHAhL7zwAqNGjWLr1q0AHDhwgOHDh/Piiy/y73//m5ycHL799ltWrVrFrl27ANi0aRMhISEMHz6c1NRUjhw5wssvv8ykSZMIDg5myZIlAERERDB8+HBGjx7N7NmzH6mAVsMGk/vZIm4sDcXy2YEA6LKyyfvsa4p37kXZvCnKZk258fNKdOkZyJ2dqh27YYAfKcfi2Tt7NQ27tax4vumgTpxbH8muGSvwDe5allNbgtxEScG1HOJ+O0zkvHUUZeQRveKvB+ZpNLYP0e8vJ3bhZrzHBALQILgL0dOWc2XjfuoPfgqFtTlOnZujysgDwKK+E3FfRyBTypEU8mqX6V4kmcTwIU8/UozK3Mb25cK0n0j+ahOuY/oAoCtUkxG+v+KYwrOXuPjRCgpOJWLm5fpQeZxeHMDVD74n/Zv1OI3qC4C+UEXu5siKY8xb+pC+MAxtWiZKVwejc/gEtOTKsTh+nxOKT6VzQJLLOBL6F0gSADK5nCOr/jQqdoMAP9KOxRN5n/Nr94wVNC8/v9q/NpCUw7EAxP92mP3l59epapxfXmP7cGbacuK/2oznmF4AeAR34czUZVzdsB/3wZ3wCO6KJjMPTfYNdCot5vWdSfg6AplSYdT55TDmadI+WkLG/8JwGNkPAF1RMXkRt9rE1Kc+6QuWo7mQjGWnlvcLVSWfgJZcvk+7RIX+hVTeLhgM/P7xarYvWItPd/+HylV/XF9ipy7l0sJf8Hih7M5rXaGKa78ceKh4AN5j+3C6vE28KrXJ6anLSN6wH4/Bnagf3BV1Zh7a8jbJ2HOGmNlrKckvQmljYVS+JgH+XDwWR8ScVTTtdqseJLmMg6F/3jyNAej16iCSos4bXaa6yFFrnsCRf5Wd/8SJE3nqqaeYPHkyJSUlrF27lujoaFJTU1mzZg2rVq1iyZIlqNVqVq9ezbRp01i9ejXPPPMMOp2OoUOHMnbsWPr0KXuDd3JyIjQ0lODgYEJDQwE4d+4cn3/+OevXr2fjxo3k5uaydOlS/ve//7Fu3TpatmyJWm38qK+CQgElJeiys5E52ANQcj4OmZMjZgGdUB8+SklcAnZT/4PStxm69Op/yjR3tKE4Mx8AQ6VGtXCwpjir/HmDgaSd0ez6cCX7P9tAh9eeAcDe25XirBuUVGNEK1Mq0GtLUWXkYepsW56v7FO9OiMPM2dbmk0aRMKSbRW/o87Mp/XsF5GZmWAofbTlSZ0c7JHLH+0DRGWSQoFBW4o2Iw8TZzsAtOk5tx1TEJ2IeRMPzBq6UBRz+eHyKOUYtKWUXM9FUZ6n5PrteVRnk2gw/19YtGqMNiXT6ByWDjYU3mzrSiNUnbaU4pxbS9yqbxShVWmMim3haEPRPc4v80rnFwYD9Tv7kp2QSnH2jYpj7Iw5vxRy9NpS1Bm5FefXzTcpdfk5Z9HAicTFW1Ffy8W5ux+azHxazn4RmanSqPNLUpa1fWlGLgonOwBK72iTgj3HcHknBNMmDVE4PNxytJXbRV9Fu+QkZ6AtUtHnreFErXi4rzglRdl5prmei0l5/WnScx8q1k3GtImqvE1yo5NweKoZJflFlOQXGZXPysH6vvVVVKm+fDq3ID0hhcJK59rjlKPWGAw19/OYqPbd/jen7k+ePMnp06cJCQlh4sSJ6PV6MjMzGTBgADNnzuT777/H19e3YkWiyjp16lQR69KlSwC0bt0aS0tLTE1NadKkCVevXmXQoEFMnjyZFStW0LNnT8zMzB66gAa1GkyUKOo5o0vPAEBezxmbl14g/6vFUFKCiZ8veZ9+jWrvfkzbta5WXP/RgXSaPBgrF7uyPJUatTA9F4vyzkav02Pn5QJAqUqDJCv7eNs8uCvxW49UK5dOpUFmqsTCzYHi1OyyfOVvBOZuDhRfy8Hc3YEmrwzA1rchjh2aIDdVcnrmaoquZGDR4O62+DvpVRokUyUmbg5oUu/d4Vr6eeEyIpCkmStrII8j2vvksWjTlKvTl5D/x2GsOhs3yuwwqhc9Jg3Bul7Zh8qavK5bjg6kYxXnl2Wl86thNz+cmjegfmdfmg3pDJSdXwnVPr+0yEyVmLs5okrNui2fuZsDqtQstNllb86lRWrMXO2Rmyo5N3M1xVcysGhY/fNLr9IgmShRujmiTbt3m5Rm5nH9i1A0F1PRphg/5dthVC96ThqCTXm7UEW7yBRyBn4YwqGlf5CTnGF0LgB9ef2ZuTmiLr8+H1VppTYpvkebFKdmoSlvE12RGrm5KfUCW+HQvjHxX202KlfnUb3pMym4or4MVZzITQNa4ta8IT6dW9BmcNfHKodgnGrf7a9UKgEwMTFh+PDhvPbaa7f9f4MGDejevTs7d+7kX//6F4sWLborhlRpXufmv/WVRjQ3T4jXXnuNwYMHs2PHDsaNG8fq1auxt7c3oli3FG3eiv17/wG5nNKUVBQN6mPz8lj0xcXYTHoZ9b6DlF5NwebVl5A5OnJj8U/Vint23V7OrY+kz7zxuPh7k3wgBnNHG1oMDeBs2F56zQ7Bd2hX4iIOYdAb6PfJRDSFKmI2ln1va9vQmcI7Rrv3czF0N20XvIRMIafw0nWsGrmS8msU7T6bgMxUSfT7K0jeWDbFaOntQvaJRDxHdKfFf4dhWs8OVTXz3E/U8WjWb95GQWExdrbWBPUMeKR410L/osknLyMp5KgupWPeyA1LP0/qDe1GaaEKQ6kOz3eep+h8Mo1mjeV62B6Kzl8xOk/Wmh00mPs6kkKO5vI1TL3dMW/hhX1wD/SFKgwlpWgup+H69hiULvakLVhlVPzjYXs4sWEvQ+ZOwN3fm6SD57B0tKF1cDfObz9K14lP4+7vTbdXB5F08Cydx/WnXtP65F7N5MSGvVXGPrduLzHrI+k9bzz1Kp1fvkMDOBe2l8DZITQvP7+S958DoPNbw4j/LQow7vy6HLqLVp+MLz+/0rFs5EpqxGFafzYRmZmSM9OWU5JfROvPJiI3N+H01GU4dGhKs3eew9TZFtW16p9fOWu34/7xv5AUcrSXr2Hi7Y6Zrzd2z/ZEX6iCUh0m3u7Yj+iLobSU7GW/Vjv2TXe2S2J5u7QJ7kbMHe2iKSjGzsOJLhMGkJGQwvGwPUbnS1m1k+afTkSSl53PFo3csPLzxG1YAKWFagwlpeSWfyVTXZdDd9H6k/FICjlF5W2SUt4mcjMlpyu1icLchOh3f6bn7x+TdSAG/zkhJH6/DVVa9dolKmw3RzbsYfjcidT39+bCwbNYOdrQPrgbZ7YfpefEgdT3b0SvVwfxx5cbAOj/n+Gc2nKo2uWpixy16jGarq8pkqGKj2DHjh1j+fLl+Pr6Ym9vz4svvsjJkyf57LPPWLt2LSUlJXz22Wd8+OGHLF68mBdffBFbW1u+++47GjduTEJCAlZWVrz00kuEhITQq1cvJkyYwKpVq0hPT6dnz568/fbb7Ny5E5lMxtNPP014eDjLly9nypQpKJVKZsyYwZgxY27b5eheUjo93CpHxqirXf08S2p/auiJ2tXPpPZ39fulDnb1c9TXzbIbjbR1sKufeR3t6meo/V39AutkV7+a+1qtKvvMnoxO7KvLYXWar/iLl2sslsU7P9dYrEdR5cjfx8eH8+fPU79+/YqRd7t27ejUqRMjR47EYDAwZswYANzd3Rk/fjw2NjbY2Ngwfvx4LC0tmTp1Kg4OZTdSZWdn8/LLL3Pjxg2++eYbrly5go+PD9OnT+fy5cuMGjUKOzs7LC0tGTlyJNbW1jRo0ABfX99argZBEARB+OeocuRf244cOcKaNWv45ptvHjmWGPkbR4z8jSNG/kbmECN/o4iRv3HqfOT/+YQai2Xx7rIai/UoxAp/giAIglCVJ3CFv7+18+/UqVPFXwAIgiAIglA3xMhfEARBEKpgeALv9hedvyAIgiBU5Qmc9hdb+gqCIAjCP4wY+QuCIAhCVQxi2l8QBEEQ/lnEtL8gCIIgCP/fPTEj/8Lch9/8p7piZY+wu6ARtCa1X5a6WICny7lPaz0HwLrWH9V6jiJZaa3n8KyjRX7qwgVV7S++A9BIUft1ppZqf9Rnaaj9hYQAHA1PzFt+3RJ3+wuCIAjCP4yY9hcEQRAE4f87MfIXBEEQhKqIu/0FQRAE4R9GTPsLgiAIgvD/nRj5C4IgCEIVxNr+giAIgvBPI6b9a8aRI0d48803a/xYQRAEQRAeTIz8BUEQBKEqT+DIv046/5KSEqZNm0ZqaiqmpqY899xzFBUV8c477xAfH0///v2ZMmUK8fHxzJkzB5lMhqWlJZ988skj5zZr1RS7kU+DTEbW/9ZQmpaBVVAXLLu1Q2ZtSebny1C6OVNv+quk/fczSpKvPXQuSSZj7PxXURerSYlN5uDG3QBY2lox8qOXUBUUkxJ7hf3rdxkZV2LAgomUFKnJiEvmzIZ9AJjaWNB3ZgglKg2Ju6Ix6A14dfNDYapEksu5eiQOV38vLBxsyE5K4/B3Wx6Yy7ptY9xC+oJM4soXG9CkZGHe2J3G8yZy9dsI8vafxW1sX8wbe2DiaMOF935EV1QzKx9mZuXwyaLv6dKxHcOHDHjoOI5tGtH0xd5IMhmnv/yFotRs6vdti0fv1sjNTDj2YSh2zevTYeYLHHhjCQWXr9P6necwtbdCaW3Oof/8gKGaF7skkxgx/xU0xWrSYpM5unEvADK5jL5ThuLk6cqatxfTcXhPWgS2ITctm/N7TpJ4+HyVMbt9WtbeObHJJKwva28TGwu6zA6hVKUheWc0eRfSaPf2MAw6HedX7sTE1hKvAe1RmJkSvSgCa896tz0uuJp5z3x2bX3wCimrr7gvfkGVkoVL33a4BLVBbqbk7IxV2Pp74tq/A3IzJefnhtHsv0PL6rqzL0cnLER9LafKerJv64N3eY7z5Tlc+7bDtTzH6Rmr6PjdZAqS0gCI+yKc0iI1DUf2oF73lhyf8l212yOgUt1dqFR3ne9Rd3qdjtiVO8k+exlzZ1uCt88j7Kk3Meju/x2vXVsfPEP6gEwioVJ91Qtqg9zMhHMzVuL98gCUthbY+nsTt2A9Jg7W+H44hshe71UZ+yabtj7UHxsEkoyLX2xEnZKFU792OPVpi9zMhLgZK/AY0wtTV3sUVubEvb+MBhMH3Pa4OnkkmcTABRPRFqnJiLvK6Q2RQNl7S7+ZIZSotFzYdZKizHzajumNtlhDwo7jeAX4YePmgPpGMec2H+T6+St/a45a9wT+qV+dTPtHRETg5OREWFgYI0aMoLCwkKSkJD7++GPCwsJYvXo1APPmzeO9994jNDSUjh07smrVqkfObTdqINdnLSb7u3XYPd8fgNKMbK7PWkzBH/sxa9mEkrQMivYdf+RcvgH+XDgWy/o5K2jRzb/iebmJgogvw1g3axmNOzQzOq5XgB8pR+PZOWc1Xt1a3sr3TCdOb4hkxwcr8AvuysXIM+yet44baTkcXvwb5387zO556yjMyOPEyr+qlcttbF8uTPuJ5K824TqmDwC6QjUZ4fsrjik8e4mLH62g4FQiZl6uRpfnfiSZxPAhTz9ynGYhfTgyfQVnvt5MkzG9APAa0pkj76/g4qYDeA56iqLUbNL2nKn4nZSd0RydsZKSAhUmdlbVztU0wJ+Lx+KImLOKppXaXJLLOBD6J0i3ji3VlqIwUZD3gI7SvZsf6UfiiZq1Go/ut9q70eBOxK+P5OD0FTQe2pVmY3px4otNHJyxkhbjgmgY1JaoOWs5+/MfNB3Z867H9+M1tg9npi0n/qvNeJbXl0dwF85MXcbVDftxH9wJj+CuaDLz0GTfoLRITczstSQs+pWMPacf2PEDeI/tw6lpy4n9ajPe5TnqB3fh1NRlJG/Yj8fgThj0egxaHTqVltJiDeb1nTCxr35bVK67I3fUnffgTiSsj+TQ9BX4lNfdyS82cXjGSnzHBQHQ8tWnSY+KfWAOz7FBnJ22jAtfbaZheVncg7twtry+3AZ34sLXm4mdF8aN88nknrhA/rnL5EUnVrsc9cf1JXbqUi4t/AWPF3oD4BocQNzUpaRt3IfLkM5YNnbnwpw1FManYN+1xV2Pq8MroCXJR+P56473lhbl7y3bP1hOy+AAGnb25fTGfUR9vwXv8uNK1FpkCjkF13P/9hyC8eqk84+JiaFdu3YAPPPMM/j4+NCiRQvMzc2xtLTEYCgbZSUlJdG6dWsAOnXqxPnz9x8dVZekVGAoKaU0IweFkx0A6jMJKOo5YNWzA0WRxyi9nv3IeQCsHWzIz8oHQF/pU/eNzDxy0rIY8tYIItdWrxOuzMLRhqLyuJU/zVs4Wt96vnykamZjgZmtJTfSysrk4O1KUfYNtNUcnUsKBQZtKdqMPEyc7QDQpt/+5l4QnYh5Ew/MGrpQFHPZ6PLcj5ODPXK5/JHjSEo5em0pqow8zJxsACrOMdX1PMycbSm+o8PKPnWRdtNHorQ2R5NTUO1clg7WFNyjbXTaUooqxYnZeYJfZi5j2+dh9Hp1UJUxzRxtUN0jppmDNarMW+1t7miNKusGOk0JMqWC2FW76PDucBr2aYu5o/Vdj+9HpiirL3VGLqbOtmVPlt/drM7Iw9TZFosGTiQu3or6Wi7O3f0A8J7Qj8srd1arnh6Uw8zZljMfrSZmwXqKrmRQr6c/jV/uT9LPO6oVv6KOqlF33FF3cqUCn2EBXP7jGKWakocsS9n5pan0XIMRPUgJP1BWxmp8QKpMUsgxaEvRXM/FpDzezTvOtdfzMHG2JXPnSXzeH4lVs/ooHW3uelwdt72H3PbeYnPbe0virmh6vPUc/WaP48KuaKLX7mbX/HUcXfoHT02oepauLnLUOr2h5n4eE3XS+cvlcvR3/KmEQlH1Nw4lJSXIZI/+8vQqNZKJEoWrEyVpZdOeClcnHF8fyfV5P2IoqZkNW7qPCmLgpKHY1XMAwHBHGz837UVO/XmMi9EXjIrbelQgXSYNxsrFrjzurcAF6blYlXfQNz9sNOnXnsRd0RXHtHi2K3Fbj1Q7n16lQTJVYuLmgCb13tPEln5euIwIJGnmSqPKUld0Ki0yUyUWbg4UlX8IunnRWbg5UJR694c9p7Y+nJy/ntyYZBxbeVcrT+dRvQmaFIxNPXvg9ra5K375DIlWpUGSpPse12xMIG0mD8aivL0rn0hF6blY1Ct73qDTU5Sei7mzLQpzU0oK1WAwEL3oV1L2nimb4r/z8X3crC9zN0dUqVm3lcXczQFVahba7LIPMqVFauTmpgBY1HdCfT3v/hV0nxzF98hRnJqFlbcLACX5xVg3dkdhaU7z/wRj26Ih1k09Hpij2ZhAWlequ8rtUZyei3l53envUXf12vlQP7A1jn6eNHq2ywPKoqlUX9m35TKr9Jx9hybkn7pYrfq5k768vszcHFGn3n4Om7o5oE7JQpuRT9KC9RQlpaFKzrjr8YO0GdWLrpOGYO1y9/lbkJ6LZaX3lvZj+7Jt6k9EvPEtrUf0wKmJB/qSUtQ3ipGbKv/WHHXBoDfU2M/jok6+8/f39ycqKoqnn36aPXv2EB8ff8/jmjRpQnR0NG3btuXYsWO0bNnynscZIy/sD1xmTUZSyNFeSUPp5YHTlDHoi1Q4vz2Owl1RmHh5YNm9PQr3euQu34wm4bLRefaH7eTAht28OPdlPP0bEXvwDNaONnQO7kFqwlV82jZFYaKgRffWbP8+otpxT4ft5cyGSPrPHY+rvzeXD8Rg4WiD39AAToftpe+sEPyCuxITcQgAFz8vLvx5ouL37Ro6U5Be/VHHtdC/aPLJy0gKOapL6Zg3csPSz5N6Q7tRWqjCUKrD853nKTqfTKNZY7ketoeiGvouLup4NOs3b6OgsBg7W2uCegY8VJyE0N10nv8SkkJGwaXrWDdy5dKvh+n86QTkpkqOTF9Bkxd7496rFZb1nTj/4+94PvMUXs92xsLFnoTVu6v3esN2c2TDHobPnUgDf28SDp7FytGG9sHdOLP9KD0nDqSBfyN6vTqIxKjzjFjwKuqCYo5tirxvzPi1e0kIiyRg/nic/L1J3R+DmaMNTYYFELduL13nhNB4aFcSNx8iL+kaHd4dDgYDZ3/ajsLSjO7vDkevLSFqzlos3R1ve3w/l0N30eqT8cgUcgovpWPZyJXUiMO0/mwiMjMlZ6YtpyS/iNafTURubsKpd39GaWtJaZGm2m1yKXQXbT4Zj6SQU3QpHatGrqREHKZteY5T05bT5PWBuPVvj8xEwenpK9Bryz6YW3q5UJCQ+sAcN+uua3ndpZXXXeNhAcSv20vn8rpL2nyI/KRrtH93OAaDgZiftpMTmwyAVX0nLv56uMo8V0J34f/JBCSFjKJL17Fs5EpaxCFafTYRmZkJZ6ctA8pmoG5q/Maz2LX1ocWsF0lavAV1etXT2CmrdtL804lI8rLr0KKRG+kRh/D9/BVkpkripi3FdVgAHmN6oS/VkfzD73i82Pu2xw9yKmwPpzfsZUDFe8s5LBxtaDk0gFNhe+k3aywtgwOIiTgIkkTA5GfRFqm5dDAGhakJg798HX1pKYequJeoLnIID0cyVDVcqSFarZYPPviAtLQ0FAoFw4YNY+fOnXzzzTdA2RT/kSNHSExMZPbs2UiShK2tLQsWLCAmJoY1a9ZUHHs/cU0H1nYx+EJrUes5AHwMtb+lb4Cm+m/cD+tJ2tI3Wln7W/q2KK2bP76pV1r7Ny/VzQa1kFsHW/rWK6390pjV0Q1lR82ejD/wmnFlTZ3mK3iz6q/qjGH9zdYai/Uo6uRMMDEx4bPPPrvtuSFDhlT8+8iRsmnpxo0bExoaettxnTp1olOnTrX/IgVBEAThXp7AFf7E2v6CIAiC8A/zZMwBCYIgCEJteYxu1KspovMXBEEQhKo8gZ2/mPYXBEEQhH8YMfIXBEEQhCrUwR/F1TnR+QuCIAhCVcS0vyAIgiAI/9+Jzl8QBEEQqlLHa/vPnz+fkSNHMmrUKM6cOXPPY7788ktCQkIeukhPzLR/xg3LWs/RXzKp9RwAtvraX03O2lRb6znqYuU9gNGn59R6jqvtP6z1HHWlsAb2zHiQkvtvX1Cjcutg+NKcB2/286iiTWp/VU8A+ydvrZo6UZdr8h89epQrV66wfv16kpKSmD59OuvXr7/tmMTERI4dO4ZS+fB7HoiRvyAIgiBUpQ5H/ocPHyYoqGybaR8fH/Lz8yksLLztmE8++YS33nrrkYokOn9BEARBeExkZWVhb29f8djBwYHMzFu7coaHh/PUU0/h4fHgnS6rIjp/QRAEQaiKvgZ/jFT5zwzz8vIIDw9n/PjxD12Um56Y7/wFQRAEoTbU5Xf+9erVIysrq+JxRkYGzs7OAERFRZGTk8MLL7yAVqslOTmZ+fPnM336dKPziJG/IAiCIDwmAgIC2LFjBwAxMTHUq1cPKysrAAYMGMDvv//Ohg0b+Pbbb/Hz83uojh/EyF8QBEEQqlaHI/927drh5+fHqFGjkCSJmTNnEh4ejrW1NX379q2xPKLzFwRBEISq1PGfSL7zzju3PW7evPldx9SvX5/Q0NCHziGm/QVBEAThH+ZvG/mXlJQwZswYGjVqhL+/Py+++OLf9VIEQRAE4b7q8oa/uvK3df6ZmZlotVrq169fq3ms2zbGLaQvyCSufLEBTUoW5o3daTxvIle/jSBv/1ncxvbFvLEHJo42XHjvR3RF6mrFtm/TCJ+QPiCTOP9lOMUpWbj1bYtbnzbIzUyI/mAVpYUqXHr602zyIPaNWECvXz8i+0Qi+lId5+avf3ASwKatD/XHBiFJEklfbEKdkoVzv3Y49WmLzMyEuBkr8BgTiJmrA3IrM+LeX45Bp8exZyu83hjCieFzq11fFq2b4PRCf5DJuLZwHSWpmZj6eFB/zqtkLAmn4MBpXCY/h2SiROFgS+qcpRhKqrcioWObRjR9sTeSTMbpL3+hKDWb+n3b4tG7NXIzE459GIpd8/p0mPkCB95YQsHl67R+5zlM7a1QWptz6D8/PPJFmJmVwyeLvqdLx3YMHzLgoeNIMomBCyaiLVKTEXeV0xsiATC1saDfzBBKVFou7DpJUWY+bcf0RlusIWHHca4ei8fS2ZaXf5/HN53fxKC793yiJJPo9ulESorU5MQmk7B+HwAmNhZ0mR1CqUpD8s5o8i6k0e7tYRh0Os6v3ImZkw0NAlsjySRilv+FJJfwDemDXKng5KIIitNz78rl2KYRjUPK2uXsF2Xt4tG3Le7l7XL8w1C8h3bBprE7po7WHJ26HLdAf5zaNUaSyzg5ey3cZ9czp0ptfqpSm9fv3RqFmQlHPgzFa0gn7JvVR25mwuFpy3k6/AMyTyaiL9ER88Pv+E8ZDIBbgB/bBs9EX6KrVvv0XTCRkmI1mbHJnNuwr6J9es8KoUSlIWlnNFcPx9LtvefLrq1d0SQfiHlw45ezatsY1/L3lquV3lsazZtIyrcR5O8/C4Bdz9Z4TAkm5vnZ1Y4tySQCPylr/+y4ZGIrtX+PWSGUqDVc/iuavEvpdHp3OPoSHYlboshNukbHN4PRlZRycccJruw+9cA8PSvliauUp9usEEor5enwZjD6klIu7TiBQa+nYWBrkEmcW/EXeRev/a05at0TuDLi3zbtv2DBApKTk0lLS+Ps2bNMmDCBQYMGsW9f2Ykxd+5cRo0axfPPP094ePhD53Eb25cL034i+atNuI7pA4CuUE1G+P6KYwrPXuLiRysoOJWImZdrtWP7jO3DyfeXE7twM95jAgFoENyFk9OWc3njfuoPfgqFtTlOnZujzsgDwKDToy/Voc0uqHaeBuOCiJ26lIsLw/F4oTcArsFdiZ26lGsb9+E6pDOWjd1JmLOGwvgU7Lu2QGFtjn2X5miu3/1mXxWnFwdw9YPvSf9mPU6jym4u0ReqyN0cWXGMeUsf0heGoU3LROnqUO3YzUL6cGT6Cs58vZkmY3oB4DWkM0feX8HFTQfwHPQURanZpO25tZZ1ys5ojs5YSUmBChM7K6PKci+STGL4kKcfOY5XQEuSj8bz15zVeHVrWfF8i2c6cXpDJNs/WE7L4AAadvbl9MZ9RH2/Be/y4zq9PJArUXFVxnfv5kf6kXiiZq3Go/ut+I0GdyJ+fSQHp6+g8dCuNBvTixNfbOLgjJW0GBdEg16tOfXtb1wIP4hLhyY0GxXIjSsZaAuK0eYX3zNXk7F9OPb+Cs4t3IxPebt4PtuZY++v4NKmA3gOfoqcc1c4MXM12acuYu1VjwZPd0CbV0RxWs59O34oa/Oo6Ss4/fVmmpbH9h7Smaj3V5C46QBeg57CtYsvR2euJvNkIm4BLcqukRIdmtwCNLmFHP94HQlr9pC4cX+1On6AhgF+pB6LZ8/s1XhWap9mgzpxdn0kO2eswDe4K02e7khJkQZ9qY4bKVlVRLyb69h+JE37iatfbcJlTNmKbLpCNZmV3lvk1hbYdPZFa+R1WL+bH9eOxnNg9moaVHr9TQZ14vyGSCKnr6Dp0K4Y9Hr2zVjJwY/X0jCwFQaDgQNz1nBw3joaVDpvHpTn4OzV1K+Up/GgTsRtiGRfeR4MBg7OWcOheeuo370lDXu15sTi30gIP4hrhyZ/ew7BeH9b5z916lS8vb1xd3cnOzubZcuW8dVXX/H111+Tl5fH3r17CQsLY+3atZSWPvxa95JCgUFbijYjDxNnOwC06Tm3HVMQnYh5Ew/MGrpQFHO52rFlSgV6bSmqjDzMnG3LniwfmarLn2s2aRAJS7ZV/M6x//zAufnrUViZYdWoeh80bpZBcz0X0/I8N0fAmuu5mDjbkrUzmsbvj8SqWX1MHK3xmjyYy99trXZZKnIp5Ri0pZRcz0VRXl8l12+vL9XZJBrM/xcWrRqjTcm8R5T7x66oLyebsnKUdxyq62X1VXzt9lzZpy7SbvpIlNbmaHKq/4Hpfpwc7JHL5Y8cx8LRmqKsfIDbRu8Wjja3ntcbSNwVTY+3nqPf7HFc2BVNy6EBxG0/Rqmm6vXizRxtUN0jvpmDNarMW/HNHa1RZd1ApylBplRwadtRui0YT9s3niV1/zlsPOsRt2Y3qQdi8Bna5Z65ZIpK7eJc1i43z2PV9TzMnGzJjk7CprE7Vg3rkRuTjKmDNTH/+w1TeyusvV3uWw7ZPdqcO9q84nH5MQfe/pGTn2xAaWmOTfk10mxsHxLW7K6yziqzcLShqLye9Ppb9WfuYE3xzXo1GLB2d+DKgXMc+noznSYPqXZ8AElRfq1k5KEsvy7vfG/xmDSE1CW/GRW37HXaVLxOfeX2d7SmuFL730jOxNbbhb7f/IuEiMNlH/SKVHR6+znOrPjzgXnMKuWpfJ6Z35nnSgYlRSqeevs5zq74k6Tfj9JzwXjav/ksKfvP/e05aptBb6ixn8fFY3HD31NPPQVA06ZNuXbtGnZ2dnh5efGvf/2L33//neDg4IeOrVdpkEyVmLg5oEm9d0dl6eeFy4hAkmauNCp2qUqDzFSJhZsDxanZABjK32jM3RxQXcvBwt2BJq8MwNa3IY4dm2LRoGyxhpIbxchMqrcpg648j5mbI6rU8tFJeR5TN0fUKVloMvJIXLCeoqRrqJIzMXN3xPPVgVi3aIhth6bVLtOt+nJEe5/6smjTlKvTl5D/x2GsOj94dHGrHNqK+ipKyy5PWHYxWLg5UFReh5U5tfXh5Pz15MYk49jKu9q5alObUb3oOmkI1i5lS3BWXoGrID0Xy/IPTXqdnvZj+7Jt6k9EvPEtrUf0wKNtY3x6tsK1hSd+Q+7dGTcbE0ibyYOxcCmLU3lkXZSei0W9sucNOj1F6bmYO9uiMDelpFCN30v92PnaN+yftoxmYwJRZd8AA5QUaVCYm94zX2mldrl1Hpe3i3tZu9j7NaTRiO6cnLUaAHX5m3ZpsQb5feLejC2/o80Nd7R55cfF6blYlV8j2htFyEwUyM1MkMnl6NTV22DHf3QgnSYPxuoe9VdYqX0MOj3FmflIkkSpWovcxLhvQW9/b7l71kBhZ4WphxPurw7CsoUn1h2aVStui9GBtJ8yGMv7tH/F69frcWhWn5z4FLaM/Rz/sUHIFHK6zXyRU0u3c+NKRpV5fEcH0u4+eQrTc7GolEemkBMw80VOl8f1H9ePHa99Q+S0ZfiODvxbc9SJv3GFv9oiGQxVzNnVopSUFN5880169eqFmZkZr7zyCgBdu3bl0KFDQNkCB1u3biU+Pp5ly5ZVGW+f6/P3fN7K3xv3CQOQFHJUl9LJjDiIpZ8nbi8EUVqoIm3pH3i+8zxF55MxGAxcD9tD0fkr94x1/Y5d/ez8vWg8vi+SQk7hpetc/fUwVl4uuA9oj9xUycn3V6Ar1gDw1Lf/4uiUJbSdNw69rmzq8vRHq++Zx1Z/+9Smtb8XDSf2R5LLKb6UTnrEYSy8XHB+ugNyUyWx05bhOiwAm1beGEp1xE1fUfG7LRdP5tzkxXflcDZV3TO3ectGOI97BkkhR3P5Grm/7ce8hReOo/uhL1SRuXwrNn06oFeXoHSxJ23BKnR59x6Rn9VZ3/bYoaUXzcf3RVLIKLh0nUu/RWHtWY+GA8rKcWT6CryHBdB4ZA/yL6Rx/sff8RneHUkhw8LFnkPv/EzpPe7HMGZXv6jj0azfvI2CwmJGDXuGoJ4B1fq9z+7Y1U+SSQyYOx69Tk9adCJJkWdoOTSAU2F76TdrLPpSHbFbo0CSaNa/A9oiNamnEon/4xgAg754jW1Tf7ptJFRPJ90WP2D+ePSlOjKjk7i69wxNhgUQt24vXeeEoC/VcXHLEfKSrtHh3eFgMHD2p+04tmiIo58nkkLGxd+i0GlKaPFSP+SmCqLmrEWVkYfFHW9A9v5eNBvfF0kuo+Dyda78WtYu9cvb5dj0FfRc+V/yYq+CwUDS+n04tmmEXfMGyM2UHH3v7mvz5q5+Di298K2izaOmr8BrUCcc/b2QmSg4PHUZneaOraiXozNX4+DniWtXX87/tP2uPFn3mcSRZBJB88rq79qpJC7vPYPv0ADOhu2l9+yy+ovfeoRrp5LoPSsEDBDzy36uHo69K1YX7b3vAbL098ZtwtNICjnqS9fK31u8cHkhCF2himtLf+fG4fMANPn2TS5M+ebeLxaIVt6+q58kk+g5bzx6nY7r0Ukk7z1Ds2EBxKzbS485IWXf8W89gjq3kFbj+2HQ67l2PAGZXI5333bkXb5OTkIK59ftvS3unaM9SSbRozxPRnmepsMCOL9uL90r5bFp4IxX33bkl8fVa0tx9PNEJpeRuCWK9OMX7lu22sgx6eq93ztrS86zPWsslsOvkQ8+qA48Fp1/TEwM33//PXFxcXzwwQd8/fXX7N69m7FjxwIwbNiwB37vf7/Ovybd2fnXljs7/9pwv86/Jt3Z+deWutjS987OvzZU7vxr052df22oqy1979f516T7df416c7Ov7Y8FlO9NaCuO//swTXX+TtueTw6/8dikR9HR0def/11UlJSmDFjBvXq1SM6Oprff/8dpVLJc88993e/REEQBOGf6jGarq8pf1vnX79+/SpH8wsXLqzDVyMIgiAI/xyPxchfEARBEB5XBjHyFwRBEIR/mCew839S7v8QBEEQBKGaxMhfEARBEKogpv0FQRAE4R/mSez8xbS/IAiCIPzDiJG/IAiCIFThSRz5i85fEARBEKpiqKMlK+vQE9P57zCr3iY5j8JeXzcngGdJ7a9Zuk9mWes5imQPvxujMa7WwdK77534uNZzRPjXfjkAkkxq/zxW1NGi4a00tX+O7Ter/aV37Wt/RW8AWpbW/lLFwv8PT0znLwiCIAi1QUz7C4IgCMI/jKGOZn3rkrjbXxAEQRD+YcTIXxAEQRCqIKb9BUEQBOEfxvAE3u0vpv0FQRAE4R9GjPwFQRAEoQpP4rR/rY/8CwsLOXDgQG2nEQRBEIRaYdBLNfbzuKj1zj8mJoaDBw/WdhpBEARBEKrJqGn/8PBw9u3bR0ZGBt27dycyMhKZTEZQUBATJkzg/PnzzJ49GxMTE0xMTFi4cCFz5syhsLAQLy8vAgMDmTFjBiUlJcjlcubOnYu7uzsRERGEhoYik8kYP348AwcO5Mcff2Tbtm00aNCA0tJSxo8fT6dOnYwuoCSTCJ7/MppiNddjkzmxMRIAmVxG4JRgHDxd2PT2Ehw8Xej15lB02lJi/zxO/J5T1Yrdd8FESorVZMYmc27DPgBMbSzoPSuEEpWGpJ3RWDja0Kh3G26kZnFx9ykUpkq8AlsjSRLRK/8i9+K1KvPYt2mET0gfkEmc/zKc4pQs3Pq2xa1PG+RmJkR/sIrSQhUuPf1pNnkQ+0YsAMBzRA9cevhxdMoSo+rr2fkvoy1Wkx6bzMlK9dVzSjCO5fVlYmlG/2mjUeUVsvPLjdWOfzPHiPmvoClWkxabzNGNeyty9J0yFCdPV9a8vZiOw3vSIrANuWnZnN9zksTD56sdf+CCiWiL1GTEXeX0hrIymNpY0G9mCCUqLRd2naQoM5+2Y3qjLdaQsOM4V4/FY+lsy8u/z+Obzm9i0D3cXF9mVg6fLPqeLh3bMXzIgIeKcVNdtL0kkxhQXl+ZccmcqXQeB80sO48Td0Vj0Bvw6uaHwlSJTC7nwKLNBM0M4dL+s5wJ2/vAHA9zrUhyGU36tUdhbkrUNxHkX82sMo9NWx/qjw1CkiSSvtiEOiUL537tcOrTFpmZCXEzVuAxJhAzVwfkVmbEvb+c+i/1xaqJB0pHG2Le+gFdoapW6uvsL/tpPSoQmVzO8eU7uH7ucpU5un86kdIiNdmxycSvL8thYmNB19khlKo0XNkZTfqReDpNH4Umr5Bjn2/CpV1jmo8JRJLLObdsB1ln75+jMqu2jXEN6QsyiatfbECTkoV5Y3cazZtIyrcR5O8/C4Bdz9Z4TAkm5vnZ1Ypb1zlqi6GOVqysS0aP/K9du8ann37KoUOHWLduHWvWrOHPP/8kLS2N8PBwRo8eTWhoKC+//DKZmZlMnDiRgQMHMnLkSBYtWsSECRNYuXIl48aN47vvvqOwsJDvvvuONWvWsHTpUrZs2UJeXh5r1qxh/fr1zJo1i6NHjz50AX0CWnL5WBy/zwnFp1vLiucluYyo0L+QpPJpGIOB3z9ezfYFa/Hp7l+t2A0D/Eg9Fs+e2avxrBS72aBOnF0fyc4ZK/AN7gpAqbYEuYmSgms5ePdqzZHFv3F+80E82jd5cBnG9uHk+8uJXbgZ7zGBADQI7sLJacu5vHE/9Qc/hcLaHKfOzVFn5AFgUd8JU3urapXjtlwBLblyn/o6EvoXlNeXTC7nyKo/jY4P0DTAn4vH4oiYs4qm3W7VtSSXcSD0T6g0M1aqLUVhoiDvWk6143sFtCT5aDx/zVmNV6UytHimE6c3RLL9g+W0DA6gYWdfTm/cR9T3W/AuP67TywO5EhX3UOWqKIdMYviQpx8pxk110faeAX5cPRrPrjm3n8fNn+nEmQ2R/PnBCvyCu3Ip8gx75q3jRloOhxf/hkGv59Ta3dXK8bDXik+ftuydu5YTP/9ByxE9H5inwbggYqcu5eLCcDxe6A2Aa3BXYqcu5drGfbgO6YxlY3cS5qyhMD4F+64tyD+ZSOy0Zdw4lYSlj1ut1VdJsYY/P1zJsaV/0OCpZlXm8OjmR/qReA7NWo1H91s5fAZ3In59JAemr6DJ0K5IChkxK/+q+P8SlYYDM1Zy9qc/cHtAjspcx/YjadpPXP1qEy5jggDQFarJDN9fcYzc2gKbzr5or+dWO25d56gtYtof8Pf35+zZs1y5coWxY8cyduxYioqKSE1NpU+fPixZsoSvv/4aR0dHfHx8bvvd6Oho/ve//xESEsIPP/xAXl4eFy9epFGjRpiZmWFjY8OSJUtITk6madOmmJmZ4eTkRKtWrR66gJYONhRm5QOgrzSS02lLKc4pqHick5yBtkhFn7eGE7ViR7ViWzjaUJRZHlt/K7a5gzXF5TkNBgNJO6PZ9eFK9n+2gY6vPUPCtqP0nT+ezm88y5UD5x6YR6ZUoNeWosrIw8zZtuxJfdlHUXX5c80mDSJhybaK32k8sT8XllavHJVVri9DFfWlvlGEVqUxOn5ZDmsK7pOjqFKOmJ0n+GXmMrZ9HkavVwdVO76FozVF94hv4Whz63m9gcRd0fR46zn6zR7HhV3RtBwaQNz2Y5RqSh6qXDc5Odgjl9fM/gx10fa31ctt9WV9W31B2ejWzNaSG2nZFGfdqPbsyMNeK6dCdxHwznAa9WmLuaP1A/NICgUGbSma67mYltfXzdeuuZ6LibMtWTujafz+SKya1cfE0Zob0Ul4vjYQ+y6+FJy/Ur2yPER9ZcZdRa5U0C4kiHO/VH0flJmjDap75DBzsEaVeSuHNr+YkuJb12FO7FVkSgV+44JIeECOyiSFHIO2lJKMPJTl9aZNv/0Dt8ekIaQu+a3aMf+OHEL1Gd35K5VKlEolgYGBhIaGEhoaypYtW+jYsSNdunRh06ZNNGrUiGnTphEVFXXX7y5atIjQ0FDWrl3Lt99+i0wmu+3NAMreBGSyWy+tYnRupA6jetFz0hBs6tmXB77/sTKFnIEfhnBo6R/kJGc8MLb/6EA6TR6MlYvdzRdd8X+F6blYOpc9b9DpsfNyAaBUpUGSSbR9qR+/vf4Nf72/DP/RgQ/MVarSIDNVYuHmQHFqdlnc8jozd3NAdS0HC3cHmrwyAFvfhjh2aILSygzffz+LrW9DrJt6PDAHlNVXj0lDsC6vr9qY6uo8qjdBk4Ir2sRQRRInL1cAtCpNtc+BNqN60XXSEKxd7o5fUKld9Do97cf2ZdvUn4h441taj+iBR9vG+PRshWsLT/yGdHmY4tW42m771qMC6TJpMNb3OY+tKp3HAE37tSdpV7RRZXiUawWDgahvfuVy5BluPGDKH0BXXl9mbo6oUrPKniyvL1M3R9QpWWgy8khcsJ6ipGuokjOxbd+YKz/8TlpYJPX6d6gy/qPUl6m1Ob3eH8XeTzegzi+6b47mYwJpO3kwFvfIUZSei3m923NUZmJtTucZoziyYAOavPvnuJNepUEyVWLi5oDmZr1VorCzwtTDCfdXB2HZwhPrDtWfVajLHLXlSRz5P9Sf+vn5+fHFF1+gUqkwMzNj3rx5vPPOO2zatImePXsyZMgQDAYDsbGx2NvbU1patvNW69at2blzJ2PGjOHw4cNkZWXRp08fLl26RFFREQqFgtdff53PP/+cCxcuUFJSQkFBAefOPXh0fC/Hw/ZwYsNehsydgLu/N4kHz2HpaEOb4G7EbD9K14lP4+7vTbdXB6EpKMbOw4kuEwaQkZDC8bA9VcY+u24v59ZHEjRvPC7+3lw5EIOFow2+QwM4G7aX3rND8B3aldiIQ6A30O+TiWgLVZzbuA87Txd6Th+NTCEjbktUlXkALobupt2Cl5AUcgovXceqkStXf42i3WcTkJsqOfn+Cq5sLPuUb+XtQvbxC2Qfv1DxuCAh9aHqK6m8vloHd+P8HfWVdPAsncf1p17T+uRezeTEhr3VyhEVtpsjG/YwfO5EGvh7k3DwLFaONrQP7saZ7UfpOXEgDfwb0evVQSRGnWfEgldRFxRzbFNkteKfCtvD6Q17GTB3PK7+3lw+cA4LRxtaDg3gVNhe+s0aS8vgAGIiDoIkETD5WbRFai4djCH+j2MA2NZ3Jua3w9XKd88yHo9m/eZtFBQWY2drTVDPgIeOVdttfzpsL2c2RNJvbtl5fLn8PPYbGsDpsL30nRVCi+CuxEQcAsDFz4uEP08AZdPfbV/sg6m1OercQhJ2HL9njke5VpSWZvR7Zzg6bQl7P177wPpKWbUT308nIMnlFF9Kx6KRG+kRh/H9/GXkpkpipy3DdVgAHmN6YSjVkfzD73hNGYLrs10xc3ck7qNVtVZfnV4fjIm1OQFvBpNyLL7ifLtT3Nq9xIdF0m3+eJz9vUnZH4OZow1NhgUQt24vAXNCaDK0Kxc2H8LRz5OW4/th38yDG1czsfV0QWltTvv/BHPtaDyXfr93jjulh/6FzyevICnkqC9dw6yRG5Z+XjgN7Y6uUMW1pb9z4c1vATDzcqXgeHy14tZ1jtryJH7nLxmqGnrdITw8nAsXLjB16lTWrFnDL7/8glwuJygoiNdee419+/bx9ddfY21tjYmJCQsWLCAnJ4cJEyYwfvx4Bg0axPTp01Gr1UiSxIIFC2jQoAFbtmwhNDQUgJdeeomBAwfy1VdfsW/fPnx8fMjLy+Nf//oXHTrc/1P5DK8xj14bD2Cvr5s1kTxLav9MO21a+zmKqJs/jnUy1P4WyGJLX+M8SVv6njSr/eVQxJa+xglI31Sn+S617ltjsbxP//Xgg+qAUZ1/XQoPD2fQoEEoFAoGDx7M0qVLcXV1ve/xovM3juj8jSM6f+OIzt84ovM3Tl13/hf9+9VYrEZnH+5G6Zr22K7wl5WVxYgRIzAxMWHw4MFVdvyCIAiCUFuexLX9H9vO/9VXX+XVV1/9u1+GIAiCIDxxHtvOXxAEQRAeB0/i2v6i8xcEQRCEKuifwGl/saWvIAiCIPzDiJG/IAiCIFRB3PAnCIIgCP8wj9PKfDVFdP6CIAiC8BiZP38+p0+fRpIkpk+fftv+NlFRUXz11VfIZDK8vb2ZN2/ebcvhV9cT0/lvVV+u9RwTTBrVeg6Abj5ptZ4jJbl66/0/Cs86WhSpLtTFAjzBZ2t/ISGALv7jaj2Hucyk1nMAvBxU+6sJrTtoX+s5vOVmtZ4DYPQzBQ8+SLhLXS6Fd/ToUa5cucL69etJSkpi+vTprF+/vuL/P/roI1atWoWrqytvvvkm+/fvp2fPB+94eacnpvMXBEEQhNpQl9P+hw8fJiiobMtjHx8f8vPzKSwsxMqqbJvu8PDwin87ODiQm/tw2x8/OUMzQRAEQfh/LisrC3v7W7NNDg4OZGbe2tHyZsefkZHBwYMHH2rUD2LkLwiCIAhV+jv/zv9e2+9kZ2fz+uuvM3PmzNs+KBhDdP6CIAiCUIW6/FO/evXqkZWVVfE4IyMDZ2fniseFhYW88sor/Oc//6Fbt24PnUdM+wuCIAhCFQyGmvt5kICAAHbs2AFATEwM9erVq5jqB/jkk08YN24cPXr0eKQyiZG/IAiCIDwm2rVrh5+fH6NGjUKSJGbOnEl4eDjW1tZ069aNiIgIrly5wqZNZdsaDxo0iJEjRxqdR3T+giAIglCFuv7O/5133rntcfPmzSv+fe7cuRrJYfS0/44dOwgPD+fTTz+t1vHGHHvT//73P1avXm3sSxMEQRCEGmcwSDX287gwqvNPSUlh27ZttfVaBEEQBEGoA0ZN+8+ZM4czZ87QtGlTMjIyeOONN0hMTGTixIkMHz6c3377jdWrVyOTyWjSpAkff3z7imULFizgzJkzaDQaRo8ezfPPP09qairTpk1Dp9Ph7u5eMUuQkJDAa6+9xuXLl5kxY8Yj39wgk8n48PP3KC5ScSE2kYh1tz7ESJLEjxu/4edFKzmy/3i1Y0oyid4LJlJSrCYrNpnzG/YBYGJjQc9ZIZSqNFzaGc3lPadRmJkwatvHbJnwFflXrmPhbMuYP+axtNObGHTGbRat9G2OxbODQZIoXLYS3fXrmAX2wLRzJyRLSwq+XYLu+nWjYkoyiT6VyhJTXhZTGwsCZ4VQotJwsVJZxmz7mF8nfIVr60bU8/fCwtGGnKQ0ji3ecs/Y3T6dSEmRmpzYZBLW36qnLrPL6il5ZzR5F9Jo9/YwDDod51fuxMTWEq8B7VGYmRK9KAJrz3q3PS64mlkjecycbGgQ2BpJJhGz/C8kuYRvSB/kSgUnF0VQnH73Ihr2bRrhE9IHZBLnvwynOCULt75tcevTBrmZCdEfrKK0UIVLT3+aTR7EvhELAPAc0QOXHn4cnbLEqPa5l8ysHD5Z9D1dOrZj+JABjxxPJpMx/bN3KS4uJvF8Er+F/Q6Arb0NU+e/TVFRMedPxXJozxFe++8EsjKyiT93gV1b9xqV491P36K4WEXS+Yv8vn47ADb2Nrw9702KC4uJPR3PljXbkCSJhes/J/R/azmx/6RRZZE3ao5Jn0EgyVCHr8SQdR3lUz1QtH4KycIS1ZrvUTTzR+7VBMnCEvWaJRiKi4zKIclkvDT/NdTFalJir7B/424ALG2tGP3RS6gKVFyNvczRrQd5floIRXmFhH+5rpqxJZ5ZMBFtkZqMuKuc2hAJlF2PA2aGUKLSkrDrJDeu5dBxbF8kmYyTa3eTkZBC73dHgAQXdkVz6WBMtfLJvJph0n0gyGRotoRiyMkoe97dE/NXP6Bo1isoezyDzLUBMitbVGu+AY3KqPqqixy1pS5X+KsrRo38J06cyFNPPYW7uztXr17l66+/ZvHixYSGhgKgUqn4+eefCQsL4+LFi8THx1f8rkajwcPDg3Xr1rF27VoWLVoEwMKFC3nppZdYu3Yt9erVq/g+Iy8vjx9++IEPPviAsLCwRy5op+4dOHnkNJ9/tIhO3Tve9n/PjxtKzKlYo2M2CPAj7Vg8+2avpmG3lhXPNx3UifPrI9kzYwXNgrsC0O61gaQevpWj7StPkxJlfE4Ai+Ah5H/5NYUrQzEfPBAAXfp18j/5HPWevSh9mz10WSLvUZZz6yPZPWMFzcvL0v61gaSUlyX+t8Psn7eOoow8Tq34656x3bv5kX4knqhZq/Hofit2o8GdiF8fycHpK2g8tCvNxvTixBebODhjJS3GBdEwqC1Rc9Zy9uc/aDqy512PaypPg16tOfXtb1wIP4hLhyY0GxXIjSsZaAuK0eYX37NMPmP7cPL95cQu3Iz3mMCyOgzuwslpy7m8cT/1Bz+Fwtocp87NUWfkAWBR3wlTe6t7xnsYkkxi+JCnayxex+7tiT5ymq8++h9Pde9Q8bxCoWDR3O9Y8N4XdOjajhatmxMVeYwln/5Mh4B2RuVo360tp4+e5X8zv6ND91u/q1DI+W7uj3wx7WvadW0DwLNjBxN3Kv4+kapm0mcwquWL0GwOxTSw7BrRZ11H9dMXlETtRdGoGYqmLVGvWULJ8YMoOxo/uGgR4E/CsVjWzVlOi2631l6XmygI/zKMNbOW0rhDc2RyObtWbTcqtndAS5KPxvPnnNV4V7oe/Z7pxKkNkfz+wXL8gwPw6eHPoR+28efHq+n4Uj98n+6ItliNQacnPzWrigy3M+nxDOp136LZtgaTbuUfJGUylF37o09OBECfnIhmw/foriQgc3Yzqjx1laO26A1Sjf08Lh76T/1at26NXC7HxcWFgoKy9aJtbW2ZNGkSL774IklJSeTl5VUcb2pqSn5+PqNGjeKVV16pWJLw/PnztGtX9ibw3nvv0bp1a4CK5yrHfxT2jnZkZ+YAoK800vb0aYhMJnHxwmWjY5o72lCcmV8WU38rprmDNcVZZc8bDAY8OvuSk5BKcfYNAJoNDSDpj2PoNCUPVxi5HEpK0GVlI3dwAKAkLh7zAf2wGDIYzQnjRkkAFo42FJWXxXCfsmAwUL+zL9mVygJg5+1KcdYNSorU94xt5miD6mZ9VKp7MwdrVBU5DZg7WqPKuoFOU4JMqSB21S46vDuchn3aYu5ofdfjmspzadtRui0YT9s3niV1/zlsPOsRt2Y3qQdi8Bna5Z5lkikV6LWlqDLyMHO2LXtSXzY8UJc/12zSIBKW3JphajyxPxeW7rhnvIfh5GCPXC6vsXj2jnbkZJVfI5XOgezMHExNTfli+Xx2bdvLiUPRPDtmEB8tfJ+ovUeNymHnaEdOZu5dOXIyczE1M2H+sjns3baPBj71kUkSVxKTH6oskkIBpSXo87KRbMsWQdFdjEfZrS8mvQdTeu4k2v1/YjbqFRRNWyLZ2Bmdw9rBhvysvLKyVDrfbmTmkZOWRfBbI9m79k+KbxShUd372rgfC0driu5xLls42lCYdetcPhN+gPZjevPU+P7I5DJs3By5dOAckV+HEzB5SPUTyuVQWoIhPwfJpqy+THoPRbvnVwyUnde6y/HIXBsgObqgT7loVHnqLIdQbQ/d+SsUt39joNVqmTNnDgsXLmT16tUVnfhNR48eJSoqitDQUEJDQzExKdv4Qy6X33MFozvjP4phLwxh4ptjqefqBFBxogF079MFRycHeg3owYDgoGrH9BsdSIfJg7F0sSt7olIZCtNzsXAue96g09Owmx+OzRtQv7MvTYd0xq2tD56BrXFu4UnTIffuXKpi0GjARIm8nnPF9L7SzxfV9j8pWPIDFoMGGhWv5ehAOk4ejFV5WQx3lMWyvCz68rI4lZel2ZDOADQP7krC1iP3jN1sTCBtJg/G4h71VJSei0W98pw6PUXpuZg726IwN6WkUA0GA9GLfiVl75myKf47H9dQHr+X+rHztW/YP20ZzcYEosq+AQYoKdKgMDe9Z7lKVRpkpkos3BwoTs0ui13emZm7OaC6loOFuwNNXhmArW9DHDs0QWllhu+/n8XWtyHWTWt/YyVjBL8wmPFvvIizS/k1Uqn+3Oq7UnijkLfHTSNoUC8GjXian75azuz/zCcgqPrn7+AxA3lxymicXBzvyuFa34XCG0VMe+kDeg3qSZfenbB3tqd7/wCChvQyujwGjRqUSmQOzuizy6aX5Y19KTnwF+qwHzEJHAglWtTrl6JLikWfec2o+D1GBfHMpGHY13O4qywAz097keg/j3Ix+oLRr73tqF50mzQEaxf7u2IXpOdiVel6lMnlRC39gxNrdqHKLyr7wCBJlKi1KEyU1U+q1YBCiczeCf3N6fiGPph06YvcwxtFy47I6jdC2aUvmo0/Gl2mOstRS57EG/6M6mFlMhmlpaX3/L+ioiLkcjnOzs5cu3aNc+fOUVJya2Sbm5uLq6srSqWSXbt2odPp0Gq1tGzZkqioKAYOHMiiRYvo2LHjPeM/ivA1vxGxbiszPnkH31bNidp3DAcne555rj+hP5R9pTBk5ECup2VUO2bMur2cXx9Jr3njqefvTfKBGMwdbWg+NICYsL30nB1C86FdiY84RPL+8j/NeGsYCb9FkX+lrMO2qe9Ewm+HjS5P8a9bsP3vWyCXo0tJRd6gPgpPT8z7BiFzcKBw9Vqj4p1bt5eY9ZH0vqMsvkMDOBe2l8DyssRVKkvnt4YR/1sUALYNnSlMz7ln7Pi1e0kIiyRg/nic/L1J3R+DmaMNTYYFELduL13nhNB4aFcSNx8iL+kaHd4dDgYDZ3/ajsLSjO7vDkevLSFqzlos3R1ve1xTeRxbNKTTjNFIChkXf4tCpykhYP545KaKu/LcdDF0N+0WvISkkFN46TpWjVy5+msU7T6bgNxUycn3V3Bl4wEArLxdyD5+gezjFyoeFySkGtVG9xJ1PJr1m7dRUFiMna01QT0DHjpWxJot/LZuG9M+eZvmrZpxdP9x7B3tGDi8Pzu37uHfH01CVawm4Xwixw+eZOSE5+gS+BTxZ6vfuW1Z+zvbwrbz9oJ/06xVU47vP4mdox39nwtiz9Z9TPrwVdTFahLPJ7Hhp18AeHpEfzKuZT4g8t20u7di/tJ/kORydNdTkbnWR+7uiUnXICRbezRb1iHZO2Ix4DkMej2q5V8bFX9f2E72b9jN2Lmv4OnfiPMHz2DtaEOX4J6kJiTj07YZChMlft1bc27/aYLGPY170wZkXs1g/4ZdVcaODtvDqQ17GTh3PG7+3lw6cA4LRxtaDQ3gZNheBswai39wAOciDoIk8fTssWiLNez/XwRFWfn0nzmW1s91Jzpsb/Xra9/vmI15A2Ry9JlpyOp5oF72GQBSPXdKzx3D4u3P0KVcxPT5Vyk59KfRI/O6yFFbHqfp+poiGe417L6PnJwchg0bRlFREcOHD2fq1KkUFRUxePBgdu/ezbRp07hw4QLNmzencePGbNq0iXHjxnHp0iUmTZrE+PHjMTMzIygoiJMnT2JlZcUbb7zB+++/T2lpKW5ubnzyySd899132Nvb8+KLL5KQkMDHH39ccV/B/bR27frIlfEgdbWl74iGj94xPEhYHWzpa2XcfYyPNTtd7d/xI7b0Nd6WwNpvl7fqYktfQ91s6fufAdW/D+BxZvNDzX19Vh1H3IfVWKxOaeE1FutRGNX5P85E528c0fkbR3T+xhGdv3FE52+cuu78o2qw8+/8mHT+YoU/QRAEQajCkzjtLzb2EQRBEIR/GDHyFwRBEIQqPE536dcU0fkLgiAIQhWeoNuXKohpf0EQBEH4hxEjf0EQBEGoggEx7S8IgiAI/yj6J+IP4m8npv0FQRAE4R9GjPwFQRAEoQp6Me3/+Prc0LDWc1iV1M3e0gMSNbWeY84TdP9qoaz2J7CSTGr/4q+LlfcADp9dWes5ModMrPUcAC8fuHt3x5o2RV3751c9q7xazwHQPvzJWOHvwg91m+9J/M5fTPsLgiAIwj/MEzPyFwRBEITa8OTMk94iOn9BEARBqIKY9hcEQRAE4f89MfIXBEEQhCqIaX9BEARB+Id5Ejt/Me0vCIIgCP8wYuQvCIIgCFV4Em/4e2I7f5u2PtQfG4QkSSR9sQl1ShbO/drh1KctMjMT4maswGNMIGauDsitzIh7fzlebz6L0sYSm1ZeXFiwnvzjF6qVy6pNY1xC+iHJJK5+uQFNSibmjT3wnjuR1MUR5O8/A4Btz9Z4TA7m/IjZRpdHJpPx4efvUVyk4kJsIhHrtpXFtLdh+oJ3KCosJuZULPv+Osjkqa+gUWs4tOcIkX8drFZ8u7Y+eIX0RpLJiPviF1QpWbj0bYdLUBvkZkrOzliFrb8nrv07IDdTEjNnLc3ffY6S/GKUdpbEzFpT4znOzw2j2X+HAuDY2ZejExaivpbzwDyObRrRuDzP2S9+oSg1G4++bXHv3Rq5mQnHPwzFe2gXbBq7Y+pozdGpy3EL9MepXWMkuYyTs9eCoerFvCWZxIAFE9EWqcmMS+bMhn0AmNpYEDQzhBKVhsRd0Rj0Bry6+aEwVSKTyzmwaDNBM0O4tP8sZ8L2PrhhKGv76Z+9S3FxMYnnk/gt7HegrO2nzn+boqJizp+K5dCeI7z23wlkZWQTf+4Cu7ZWL35VMrNy+GTR93Tp2I7hQwY8cjylb3MsggcjySQKlq5El34dsx4BmD7VEcnKihvf/YB5YA/kzk5IFhbkf7UIdMZPuspkMl5dMAl1kZrkuMvs3rATACtbK1766GWKC4u5EnuJ/RGRjHk3BCSJE7uOcfbg6WrnsG7bGPexfUGSuPzFBjQpWVg0dqfx/Ilc/TaC3H1ncR/bF4smHigdbUh490d0RWqjymHWqin2o59GkklkLlpDSVom1n07Y9m9HXJrS65/thylmzOuM14h5e3PKblyzaj4N8lkMj7+cgbFRcXEn7/AprW/VfyfJEms/GUJ33+9jEP7jmJmbsbmv0J5LeQtki+lPFY5aov+yev7H33a///au++wKI43gOPfqyAgHQFFxd5QjDWCvcZeYlcsMRpjjEYTfxpNYo8aTTSxJJrYYteIxhZ7b9iwIyqKiHQRkYPj6u+PQwQLcnoQo/Px4Xlg73zfm929nd3Z2Rm9Xs/YsWMJCAigR48eHD16lE6dOhEdHY1Op6Njx47cvXuX48eP061bN3r37s2QIUPQaDQEBQUxbNgwhg8fTtu2bZk3bx4Ax48fp02bNvTr14+JEycyd+5csz9X0b5NCRm9mFuzAynSqzEAHh38CBm9mOgNh/Fo9z62pQtzfdIqUkIjcfKryO3Zm7gxdQ2PrkbkuuIHcO/TnFtfL+Lu7L8o1LOJab2kpBEfeCTzPbKCNti/XxFt3AOzywJQu14NzgVdYOZ3P1O7Xs3M5XK5nNmT5zPlfz9Q078avjUr88+mPUwb+xMderTJdXzvPk24OGYpoT9tonjPRgAU6VCHi6OXcHf9EQq3rU2RDn6kxyeRfj8ZfZqGAl5uXJ+zGalCjkQus3gOnUrNlYmruf7z38QduJCrih+gTJ8mnP56GZdnb6JURp7i7d/n9NfLuP3XUYq3rUXi5TucHb+S++dvUdC7EEVb1kCTpCI1KvGlFT9Acf9K3D0Vyr5JKyle1ydzefnWtbm4/hC7v1lGpQ5+3D50kQNT15AclciJ+VswGgycX70/V+V4rGa96gQHXeCn7+ZSq16NzOVyuZyfpyxg2v9mUcOvGhV9y3Py0Gl+nfEHNfyrmZXjRSRSCZ3btbRILADbju14OGsOj5auwKZtKwD08fd5OGsO6v0HUJYvi7x4MZLnL0R3Oxxl1aqvlKeyfxVCTl1l2aQ/qFzXN3O5XCln7Y8rWTJ+EeWqV6B2Sz/UqWoMej3x9+LMylG4bzOuj/6dO7P/wrOX6XuvS1ETt/HJ9/7R5dvc/HYZj87fpIC3h9nlcOrZkpjx84mftxbHbi0A0MYmEvPdApJ3HKGAT2m0UXGkHDpjduys/OrX4szJYKZ+8yN+9Wtne61Hv85cCr6S+ffHQwIIOnb2jcwh5N5rV/5bt27Fzc2NFStWMH/+fGbMmMH//vc/fvrpJ9asWUOLFi0oWrQoDx8+ZNasWaxcuRI7OzuOHj0KwMWLF5k+fTpr165lxYoVAMyaNYsffviBxYsXExIS8kqfSyKXY9ToSI99gJWbAwDGjKmZ0mMfoHRzIGFvMKW/7oZdOS+ULqZhQgt3q0/0xqPm5VKYcmnjHqBwcwRAE5O9oio8pD1Rv/79SmUBcHJx5H68KaYhy5XQ/fhErKyUzFk2nT3bDnBs/wlq16vBJ19+hMSMYW+lchkGjQ513JP1hcGURx2XhJWbAzZFXbk5fxvq6Ae41atEevxDfCb2RmqlwKjT50kOgBIfNSd8+V6zy5IWl4S1m31GHtO2T4tNwtrVgfvBYdiXLoxdsUI8uBKBlXNBrszdgpWTHQVLuL80h42LPaqEhwAYs2wPG5eCT5Zn5LSyt8HawZbkqPukJiRne39uOLk4kpiQse0NT297K2Yt/Z592w9y9ngw7Xu24bvZX3Py4CmzcryIq7MTMtnLT+xyTS4DrRZ9wn2kzs4AaEOuIXV1warO+6hPnEJ9/CQFPxmAvIQ3UieHV0pj7+LAw4QkIPv3JSk+iYSoBLqO6MGe1btw9XTl4tHzrJ+zlk6fdTErx+NjjCY2CeULvvePzt3EpkwRrIu5k3Il3OxyPM6hi0tE7uoEgPrideSFnLFrWJOUQ2fQxdw3O+7TnF2cSIg3xdHrn3yXS5QqjlQqIeyG6bPX8qvOjdAw7ifk7kQ8v3PkFQMSi/28KV678g8ODmbfvn0EBAQwfPhw0tPTqVatGhKJhC1btjBggGmMb2dnZ7755ht69+5NUFAQSUlJAFSsWJECBQpga2ubGfPevXtUrFgRmUxG/fr1X+lz6dPSkVopsPZ0Ie1exnjWGQdOK08X1JEJpMclcXPaOlRh0aRFxAPgUKMsyedvmZXLkJaOxEqB0tMFzb34Z16XO9phVdiFwgPbYFPBm4I1ypkVv1OvdgwY1odCHq4AGHlyZVq4qAePklMY1ud/NG/bGLlCQeCqrfwxZzmpKam5zqFP0yC1UlAgy/oyZlwBF/B0Ju1eApr7jwDQqdRYezghs1JwefxKUu/EYVPMzeI5ZAWsALDxckUdm5Trsugy8th4OpN6z3SweVwR2xR2RnXvPk6VilGyaz3OTVgJgDreVGHrUtMz876Ib/eG1BnSloLujmQUIvO1lJgH2GVUBI8r+bLNqxO2LzjXnz+rDr3a0v/z3ri5Z2z7LLk8vTxISU5hZN8xNG3TiDZdW/L7T0uZ+MX3+Det80r58ppRnQ5KBbJCbuhjYwGQFnKjYJ9eJM+ZB1othsREHi1cjC7iLvqoGLNzNO3enI5DOuPs7pyRNHtLTu8xfTm9J4gbwaE8TEhCIpGgUaejUJp3F9SQcYyx8nQm/TnfewC7St54dGvIze9ebT4FozodiVKBwtMVbZSpZULu6Yrrp12JmbwIo0b3SnGz6tq7I58M74e7h+k7nHUfa9DUH1c3F5q2bEjrji3wb1CLshVKU9u/Om06tnijcuQlowV/3hSvfc9foVAwePBg2rTJ3sSclJSEXq8nLS0NhULB2LFjWbRoEaVKlWLSpElPPoA8548gkbzamVLkn3upMOMjJDIZqbdjsCnpSczmE1SY+TEyKwUhY5bg0cmfIj0bYdTpiVhouo8qzUXz9dNiV+ym5LSBSOQy1LdjsC7piW1Fb9w61UWfoiZ68XZuDjfd0rAu4cGjM6FmxQ9ctYXNa7YxbvpXVKhSnpOHT+Ps6kTrD1uwZ9sBRo4fSlpqGqFXbqDT6vji2yGkpqSybtnGXOcIX7GPKtP7I5XLSLkdg21JD+5tPoHvDwOQWiu4OGYp2ocqfH8YgKyAkgujl+BcoyzlvvoQKzcH0nLRJG9ujvOj/kDhYItOZd5ERzdW7qfWtH5IZFIehcdSsKQHd/4+Qa0ZHyGzUnB67DIaLP+SpJC7VBvfi7B1h4kLCqX6pABk1gqSrkbkGP/C2oNcXH+I5lP64165BOFHr2DjYk+ljv5cWHuQZhMCqNjBjyubjwPgXsmb67tNTZjF/SvxXu8mWBUsgPpBCtd35dxcu3nVVras2c6Y6SMpX6Ucp46cwcnFkVadW7B32wGGfzeEtFQ116/e5Myxc3T76EPqNKxF6KXc37bKyckzwazbtJ1HKak4OhSkaQP/14qXunkrjl+NAJkMXeQ9ZEW9KPhRX4ypqRQc/DHqI8eQexXBpk0rjDod2nV/mZ1j79rd7F+/l4+nDKZk5VJcPHoBexcH6ndsyN3QO5StVh65UoFv3arsXrWT/uMH0uDDxuxdu8esPFF/7qHMjI+RyGSk3Y6hQElP7CoVp1CnuuhT0jBo9XiP6oLqagSlJ/Yhes0BVFfvmJXjwZp/8Jg0BIlchiY8GqV3YdyG9USvSqPQV314tDcIZYnC2NavjqJIIe4v3kT6dfNyrF+5ib9W/83EH8ZQybcCxw+fwtnVifadW7H0N1Nfnk7d2xITFcvxw49blAaxbdOuNyqHYB6J0ZiLG5w52Lp1K/v372f27Nncv3+f5cuXU65cOS5fvkyVKlU4efIkEydOpHbt2hw4cACdTkePHj3o06cP3t7erFq1il9++QWA2rVrExQURJs2bfj555/x9vamb9++1K5dm88//zzHz7HbvfvrFCNX7KTaPM8B8Knx1TrtmGMS3nmeI7/kx6x+dxV5noK/NOYdtF/V2zSr37B7+TGrX84tQZZQyC73rXSvo32y+S0pb6Ib8fnbHyDQo6fFYnWKWW2xWK/jta/8W7ZsycmTJ+nevTt6vZ5PPvmEuXPnsnLlSgoWLMjq1au5ePEiPXv2pEePHnh7e/Pxxx8zd+5cRo4c+dyYX3zxBZ9//jleXl6ULFkSaT4c3AVBEATheQyv2AL9Jnvtyl8ulzN16tRsy5o2bZr5++NOfFWqVGH48OGZyzt2ND3ClfV2QVBQEADW1tYsWrQILy8vvvvuO4oVK/a6H1MQBEEQhAxv5HP+RqORoUOHYmtri4uLCy1avBmdPgRBEIR3z5vUUc9S3sjKv169etSrV+/f/hiCIAiCIMb2FwRBEAThv++NvPIXBEEQhDfF2zi8r6j8BUEQBCEHb9LIfJYimv0FQRAE4R0jrvwFQRAEIQeit78gCIIgvGPEPf83WFH75DzPcTfZPs9zADQpkPeDGpWUPMrzHDfS8n7oVQBtPnwx5flw6l9Aqsz7JOTP0LtuWxbneQ6AzYXz/pHgqSWr5HkOgz5/ahfJW3jv+m30/fffc+HCBSQSCWPHjqVKlSf74PHjx/npp58yJ7777LPPXimHuOcvCIIgCDkwWPDnZU6dOsWdO3dYt24dU6dOfWYE3SlTpjB37lzWrFnDsWPHuHnz5iuVSVT+giAIgpCD/JzS98SJE5lD5JcqVYqHDx+SkpICwN27d3FwcMDT0xOpVEqDBg04ceLEK5VJVP6CIAiC8IZISEjAyckp829nZ2fi4+MBiI+Px9nZ+bmvmeutuecvCIIgCHnh3+zwZzTmTYcjUfkLgiAIQg7yc2z/QoUKkZCQkPl3XFwcbm5uz30tNjaWQoUKvVIe0ewvCIIgCG8If39/du3aBcCVK1coVKgQdnZ2AHh5eZGSkkJkZCQ6nY4DBw7g7+//SnnElb8gCIIg5CA/r/yrVatGpUqV6N69OxKJhPHjxxMYGEjBggVp1qwZEyZM4MsvvwSgVatWlChR4pXyvFLl37hxY7Zu3YqtrW3msgMHDrBr1y6mT5/+Sh8kq7lz5+Lk5ETv3r1fO5YgCIIgvA5jPt/z/+qrr7L9Xb58+czfa9asybp16147h7jyFwRBEIQc5OeVf355aeWv1Wr57rvvuHv3LhqNhmHDhmW+FhoayujRo3FwcKBYMdOodJGRkQwfPhxvb2/Cw8OpXLkyEyZMIDY2lnHjxqHVapHJZEyZMoXChQuzZMkSdu3ahcFgoEGDBgwdOjRb/i+//JJ69erRoUOHVyqgdZWyOHVvCVIp8b+sQhcVh13TOtjVq4a0oC1xPyxB4emG+7hB3Bv5A9qI6FzHtn+vFF59miKRSAib9RfqyATcmlfDtcl7SK2VXBu3jCI9G2Lt4YzMzpprXy/FsVY53D6ogcxKwfVJq9Cnpuc6n0Qqocv3A9GkqokKieDUhoMASGVSmg7tiGtxD1aPnE/Nzg2o0LAqD6LuE3LgHDdPXM11jgK+ZXDu8QFIpcT9vBrtvXisSnrhOXEQ8b9tRHXsAi7926HwcEFqW4Co8b+BPndfDaf3SlEioDESqZSrszaSFpmAR7NqeDStisxawYVxf1JzwWc8CosC4NqsQHQqNcW61adQPR/ODF2QY3zXqiUp29sU//yPG1Hdu49Xs/fwauyL3FpJ0Lcr8G5XG6dyXsislZwYs5SWgd8Qf+4mBq2eKwt3UHloWwA8/Suxve14DFr9c3NJpBKaTRuANlVNfEgEl9cfBsDK3obGEwLQpqUTtjcYGxd7SjauSvK9BG7tP49EJqVM8+rIC1hx8pfNPLz78sd0pFIpo2aMIDU1jbCrt9ixbicA9k72jJw6jNSUVEIuhLJ11XYkEgmz181kxdzVnD1yLlfb5TFFhfLYdGiLRCrh0eLl6GNisa7vj1Wtmkjs7EhesJACDesjc3NFYmPDw59+zvW2z0l8QiLTf/6NOjWr0bndB68dD0zr7NcFM0hJSeXSpassW266UmpQvw6j/zeUy5evcSLoLOG3Ixg4MABVioq/t+zk6LFTuc5hXbksDt1aIZFKSJiXcWxpUgfbutWRFrQhftYSbBvUQlmyKHJnB2ImzMWoSjOrHHl5/MpKKpUy+cexqFSphF69ycbVWzJfk0gkLNu4gIVzlnIvMpovxnyKTqtj++bdHNxz9I3KIeTeSzv8bd++HaVSycqVK5k7dy6TJ0/OfG3BggUMHTqU5cuXI5U+CRUaGspXX33FX3/9xaVLl7h27Ro///wzH330EcuXL6dv374sWPDkQL569WrWr19PYGBg5mAGAIsXL6ZIkSKvXPEDOPVoRcyE+STMX4NT1xYA6OLuEzN+Psk7jmDtUwZtdBwph86YHbto36aEjF7MrdmBFOnVGACPDn6EjF5M9IbDeLR7H9vShbk+aRUpoZE4+VXEo6MfmrgkNPeT0adpzMpXxr8yt05fY/OkPylbt3LmcolMyrEVu5FkaZrSaXTIlXKSohPNyuHcsyVR3/1K3Ny1OHdrDoBelUrS5kOZ77Eq5UXMtKWk34jAtrZPrmOX6NOE82OWEvLTJkr0bASAV4c6nB+9hIj1RyjStjZGgwGjRo8+TYMuNZ0CXq4onexyFb9cQBNOjl3GhTmbKJsRv0S79zn59TJu/nUU7za18KhTgVPjVxJ/7iae/hUx6g0YtHrSHzwi/UEKZyav4fqqA9zccOSFFT9AMf9K3DsdyoGJKyle98k6KNemNpfWHWLvuGVU6OAHgE6jRaZU8Cg6kVJN3uPglNWc/eMffLo2yFW5qtd9jwunLjF3/AJq1KuWuVwul7FgyiJmjZlDNb+qALTv05Zr50NzFfdpth3b8XDWHB4tXYFN21YA6OPv83DWHNT7D6AsXxZ58WIkz1+I7nY4yqpVXynP0yRSCZ3btbRIrMeaNK7L0WOn+PKr8TRpkn0IYI1Wi1wh515kNA0a+LF8+Tpm/rjgmfe9jGP31sRNnM/9BWtw6Pzk2BI7cR6P/jmCdaUypIeEET9tIepL11EW9TS7HHl5/MqqTv2anDkZzPff/IRf/VrZXuvR70MuBZsuIAx6AxNHz2Da+NnUb+L3xuXIK/k5wl9+eemV/+XLl6lduzYA7u7uKJXKzEEFwsLCqFbNdDCqXbs2hw+brn68vb3x9DTt6L6+vty6dYvg4GBu377Nr7/+il6vzxyowNramt69eyOXy3nw4AFJSUmAaZSj6OhoNm7c+FoFlMjlGDU6dHGJyFwcAVBfvI68kDN2DWoQO/lXjBrda8VOj32AlZsDAEaD6ZnM9NgHOFQvQ8LeYEp/3Q2Fkx3a+8kU8HIj5H+LKRLQBOd6PiQevpTrfHbOBUlJeAiYviCP6TU6VIlPxuq/svcsl3adwmgw0m5cbzaM/SP3ZVI8Xl8PkLs6AqCLzX4C8ejAady/CkDmWBDd/Ye5ji2VyzBodKjjnqwvDKZyqOOScK5ehovfrSQ1Is50td+gMu4NK3N5ylqqz/nk5fEVpvhpcUlYu2bMw5DxjGxabBJu1Uo/+TvjPUdHLiIlIp6qIzthX9KD5FsxlOvThHPT1+eYy8bFHlV8xrYwPNkWBZwLkhp0LSO1kbC9wdzYdQajwUjDb3py5vd/8P+qM+okFQVccjf3gaOLI4nxD57JlRj/gKIlvfh+yST+Wb+LoqW8kEok3LkZkau4z5DLQKtFn3Afacb3UxtyDamrC1Z13ufhT79g1Bso+MkApPb26DO+q6/L1dmJW+F3LRIrM6abC3GxpuOUPst35ey5i3w8cCSJiUksWTyHqd/P4efZk3mY/Igff/zVvCRyGUatFl18InJX06As6kvXkRVyxrZBTeIm/4pRq0VZsigKL3fS/7xldjny8viVlbOLEwnxpu951mNLiVLFkUqlhN24DcDdO/fwre7D8NGDmTtz0RuXI6+8jbP65epRv6yDDGg0msyrfKPRiCTjcjPrQSnr74/fo1Ao+Pnnn1mxYgWrV69m3rx53Lt3j2XLlvHHH3+wYsUKihQpkvn/Hjx4gFKp5OzZs69VQINajUSpQO7hijbadDCQe7jiMrgbsVMXvdYXR5+WjtRKgbWnC2n3Mp69zCi7lacL6sgE0uOSuDltHaqwaNIi4tHcN01ApFepkdnkfiKX97s3psmQDtgXMh1kchr4wdXbAwBNWnrm9sktQ1o6EqUChacLmqjnN0nr4pOInbWC9Fv30ETG5jq2Pk2D1EpBAU8XUjPW1+NyFPB0JvVeAnYl3AHQPkylYOnCyG0LUP6LDjhULEbBskVeGBtAl6ZBZqXAxtMZVdR9U/yMkzEbT2dU9+5n+zs15gF2RU3Pz2qSVUiVcmTWSqQyGXq19oV5KvdoSO3P2mLn7khGITJfS4l5gK2bablRb8DR2z3js6UjkUrAaOTkL38Tfugiyblo8m/bsxW9h/bA1d0l2/oC8PByJyVZxZh+39CoTQPqNK6Nk5sT9Vr407Rdo5fGfppRnQ5KBbJCbuhjTdtVWsiNgn16kTxnHmi1GBITebRwMbqIu+ijYszOkR8GfNST0f8biqenad1nXWdeXp5YW1tjMBjQarV8OrgvgwaPolfvIfTt282sPEZ1+pNjS1QckHFs+aQbcd8vxKjVYlW+JPYdmhI3/fdXKkteHr8e69q7A58M708hD9N3Iev6atDUHxc3Z5q1bEjrjs0pU74UN66FMbDHcHr27/xG5RDM89Ir/8qVKxMUFETr1q2Jjo5GKpVib2+6qipRogSXL1+mXr16BAUFZf6fiIgI4uLicHV15cKFC/Ts2RNfX1/27t1Lz549OXHiBAkJCZQoUQJnZ2dsbW25cuUK9+7dQ6s1HXRbtWpFnTp1+OKLL9iwYQPW1tavVMCkNf/gMfEzkMnQRkSh9C6C6+c9MajSKPRlXx7tPYmyRBHs6ldHUaQQiUs2kX49PFexI//cS4UZHyGRyUi9HYNNSU9iNp+gwsyPkVkpCBmzBI9O/hTp2QijTk/Ewh0onAuaXi9gxdWvcn9We3LtfoLWH6DzlAF4VS7BjWOXsHOxp3qHulzceYoGA1rhVbkkjQa14ebJq3SZNoj0R6mc+uvQy4Nnkbh6J4Unf4pELkMTHo2yRGGsK5TAsX0DDClpoNOjLFEYp67NMOp03F/yd65j316xj6rT+yORy1DdjsGupAeRm0/w3g8DkForOD9mKWUGt8KzRXWkSjkXxi7DkHFws/V259H1eznGD12xn/e/74dELuXR7VjsS3pw++8T1JnxETIrBSfHLkMql1F7ch+kSjk3Vh+k9pQ+FGtuar1KuhaJc6XiPIqIyzHPpTUHubzuEE2n9se9cgnuHL2CjYs9FTr6c2ntQRpPDKBCRz9CNh8Hg5Hm0wegSUnj8obDKGytaf5VZ/QaLQcnr37pOtu6egfb1+5k5LThlKtSljNHzuHo4kiLD5tyYNthhnw7CHWqmptXw1j/u6mVrGXXFsRFmz/kZ+rmrTh+NQJkMnSR95AV9aLgR30xpqZScPDHqI8cQ+5VBJs2rTDqdGjX/WV2juc5eSaYdZu28yglFUeHgjRt8GrPLT+2eMlqli5by7y506hWrQr79h/Bzc2FXj0/ZNPmHfw4awJxcQls37EXVYqKMaM/JyUlhf0HzLu3nLRuB+7jh4JMijYiGoV3EVw/64VBlYrriH6k7D+B65CepIfexm30QJIDd5Meetu8HHl4/Hps/crN/LV6CxN+GIOPb3mOHz6Fs6sT7Tu3YulvqwDo2L0NsVFxKJRyvp02CoPByMmjub/VkB858tLbOKWvxPiSsQN1Oh3jx48nIiICrVbLl19+yejRo9m6dSvh4eF8/fXXuLu7U7RoUVJTUxk6dCiffvopPj4+3Lx5E19fX7755htiY2MZO3YsarUaiUTCtGnTKFy4MIMGDUKlUlG9enUMBgMhISFUr14981G/RYsWkZCQwNixY3MsSEiZVhZdMc+TX1P67iyQ9zn6S1R5niO/pvRNluX9WFUJsjxPwd/6V+usZa51xfP+zmN+TelbIB+m9L3yFk3p2z4p5xPb/4rr8fl7UjC7mOUeOx8RsdJisV7HS6/85XL5M1MK7t+/H4BKlSqxZcuWbK9FRkaiUCiYNm1atuXu7u4sXvzsAeF5y7IaNGjQyz6iIAiCIAhmEM/5C4IgCEIO3qRe+pZi8crfy8uLwMBAS4cVBEEQhH/FO9vbXxAEQRCEt4do9hcEQRCEHLyNvf1F5S8IgiAIOXgb7/mLZn9BEARBeMeIK39BEARByMHb2OFPVP6CIAiCkAPDW1j9vzWV/3xt3o8mV16ZP6urXVrup/l9VWsL5P36KinPn7tKD/IhTZX01x9D/WU+bpo/B5iPj+b9tt+cDyPvAaRFHcnzHLOqf5fnOdIk+bPtT9TK+5E9hf+Gt6byFwRBEIS88DZ2+BOVvyAIgiDk4O1r9Be9/QVBEAThnSOu/AVBEAQhB6LZXxAEQRDeMW/jCH+i2V8QBEEQ3jHiyl8QBEEQciCe8xcEQRCEd8zbV/XncbN/YGAgM2bMyMsUgiAIgiCY6a2/8pdIJfT4fhDpqWruhdzh5IaDAEhlUloM7YRbcXf+HDmfCg18Ke9fGbmVAplcxtpxf+QqdsPpA9Cq1Ny/FkHIusMAKO1tqD8hAK06nfA9wSTdjqH2qM4YtHpubj3JvZPX8BvbHXVSCkEz/8p1WQq+V5rCfZqBREL4rPWkRyZgU7owpb8fwN15m3lw+BKF+zTDpkwRFC72XB+1CL1Kbfb66vD9x6SnqokNieDshkOZ66vh0A44F3fnr5G/4lzcnUbDOqLX6AjZfYbQA+dzFdt/hml9JYZEcCPL+np/YgC6tHQi9gaTdCOKaiM7YdDrCVm+l/uXwing5kCHnVNZW2sYRn3u+t5KpBKaTRuANlVNfEgEl9eb8lnZ29B4QgDatHTC9gZz90QIdf/XBYlEQti+YCKOXnlpbPv3SuHVp6np/8z6C3VkAm7Nq+Ha5D2k1kqujVtGkZ4NsfZwRmZnzbWvl+LVrxl2GdvmyoiF6FPSclUOWcnyKJu0AYkUdeByjAmxKGrVR+5bC4mNLWmrfkNerjIy7zJIbGxRr/oVY6r5I7lJpVIGTRuCWqUm4lo4+9fvBcDOwY5+331Makoqd0Juc2TzIXqOCgCJhLP7TnPp2AWzcvy6YAYpKalcunSVZcvXAdCgfh1G/28oly9f40TQWcJvRzBwYACqFBV/b9nJ0WOnzC7P0+ITEpn+82/UqVmNzu0+eO14EqmED6aZ9ue4axFczLJ/NRtv2r9u7gsmJf4h7/VsjCZVzfVdZ4k8HfpKudp9PwBNqpqYkLsEZ/le1s/4XgaO/PWVyyIvWx6rlu1AIiVt1VIM8bEo/RugqFEbiY0tqX8soEDv/hiTkwEj6i0bMcTHvXE58srb2Nvfolf+UVFR9OrVi4CAAHr27ElUVFTma8uXL6dbt25069aNRYsWATBmzBgmTZpEv379aN++PVevXgVg1apVdO/enZ49e7JkyZLX+kzl/CsTdjqEjZOWU75u5czlUpmUIyt2gcTUjTPk0AU2fb+SB9H32TV/U65ie9WtRPSpUI5OXEnRuj6Zy8u0qc3V9Yc4NHYZZTv6YTQYODxuOccmr6ZYwypI5VIuLd9jdlkK923G9dG/c2f2X3j2agKALkVN3MYnQ5w+unybm98u49H5mxTw9jA7Ryl/H8JPX2PHpBWUylImiUzKyRV7kGSsL4xGdkxeyc5pqylVr/ILoj31+etWIiYolKAJKylS70nsEm1rc33dIY6PXUapjn6U69mIc7P+4sS45VTo2xQAn0EtiTkZYlZZivlX4t7pUA5MXEnxLGUp16Y2l9YdYu+4ZVTo4EeZljXRqtIx6PQkRybkKnbRvk0JGb2YW7MDKdKrMQAeHfwIGb2Y6A2H8Wj3PralC3N90ipSQiNx8qvIw3M3CRmzhOTzYdiW8sx1OZRN2pK29GfSN63AqmErAAwJsaT9PgvtyYPIS5ZDXtYH9apf0Z45hqJmfTPW0hOV/asQcuoqyyb9QeW6vpnL5Uo5a39cyZLxiyhXvQK1W/qhTlVj0OuJv2fewblJ47ocPXaKL78aT5Mm2YcA1mi1yBVy7kVG06CBH8uXr2Pmjwueed+rkkgldG7X0iKxALz9KxF5KpS9k1binWX/qtC6NhfWH2LXN8uo1MGPYu9X4OKGQ5z8bRsl6lZ6pVwl/X24czqUfyatfOZ7eWrFnseHsVdm1bI9qvmzSVu7HKsWrQHQx8Wi+vkHNEcOIi9bHgCjVgOAIfnhG5kjrxgwWuznTWHRyn/Xrl34+fmxYsUKxo0bh1KpBODu3bts2rSJVatWsWrVKv755x8iIiIA0Ol0LFu2jOHDhzN//nzu3r3Lzp07WbNmDatWrWL37t3ZTiLMZedsT3KCaScyZLli1Gl0pCQ+yvbeAva22DjY8iDqfq5iF3C2J/U5sa1dCpIab1puNBhJjojHoYQ7zX75lOubT5D+MBVtqvnj90vkcowaHZrYJJRujgBoYhKzvefRuZvYlCmCdTF3Uq6Em53D1tmelOeUSa/RkZplfSVGxKFRpdFkRGdOLtuVq9jWLvakZcTOevVu7VyQtIz1hcFIAZeCpCUko0/XIlPIKdXJn/B/TqNL15pVFhsXe1QZcQ2GJ/kKOBfM3G5Go5GChZ25c/Qyx+dsovZn7XIV+/G2SI99gJWbgymWwfTFTo99gNLNgYS9wZT+uht25bxQuhQkOTiM4p+0wqlOBR5dvZPrckjkctBpMSTdR+LgBID+ViiKus1QNm6L7vI5NEd2Y919IPKyPkjsHXMdOyt7FwceJiQB2bd9UnwSCVEJdB3Rgz2rd+Hq6crFo+dZP2ctnT7rYlYOVzcX4mLjTWXIkuPsuYt8PHAkX42ayJAh/dm+Yy/jv/uSn+dMYfv2va9UnmdyOzshk8ksEgsy9q/n7M82LgWfLDcYCdsXTL0RH9J8Yl9u7jv/SrlsnQvm6nv5qjL3scT7SJ2cTbFvXEPZuAXWLduivXCWtFXLSFuxGM2pE1g1av5G5hByz6KVv7+/P3///TfTp09Ho9Hg6uoKQEhICL6+vsjlcuRyOdWqVePatWsA+Pn5AVC1alVu377NpUuXuHPnDn369KFPnz6oVCru3bv3Sp/Hr3tjmg/pgEMh0wHT+JKTrirNa3B537lcxa7YoyHVh7bF1t2Rp4OrYh5gm1E5Gw0GnMt5kRgaydY+M6ncp6m5xchkSEtHaqXAytOZ9Hvxz32PXSVvPLo15OZ3y82OX6N7IxoMaYd9xvrK6SRVKpfR6tsAji/+h8SIl1/9levZEN/P2mKTsb6MWdZXaswDChQyLTfoDahiHlDAzQF5ASu0KWoKVSuFV0NfXCoVp2T7OrkqS+UeDan9WVvsnrN9UrJuH72B1PiHSCQSdGoNslxO3qTP2BbWni6k3ctoLcg4wbDydEEdmUB6XBI3p61DFRZNWkQ8DtVLc2fhDqLWHqJQixq5ygNgTFeDQoHU2Q3DfdO6lpWugPboHtRrF6Fs2Aq0GtTrFqMPC8EQH53r2I817d6cjkM64+zunJE0+8bvPaYvp/cEcSM4lIcJSUgkEjTqdBRmTHY14KOejP7fUDw93TNSPMnh5eWJtbU1BoMBrVbLp4P7MmjwKHr1HkLfvt3MLk9e8+3ekDpDnuxfWcvyKOYBdhn7l0FvoFqfZuwY/Qd/fz6PKl3Nb5Wp3r0R9Ya0o2Dm99LyV4+mfUyJ1NUNQ1wsAPJyFdHs34Vq6UKsmrdGVsQLjEaMqhQkCsUbmSOvGC3486aQGI2W3ZPS0tI4duwYy5cvp3bt2qhUKqpVq8aRI0eYNGkSAN999x3169dn79691K9fn1atWpGYmEhAQABffPFFtvfm1lDv5x8gJFIJ3aZ8jEGnJ/z8Ta4eOk+tDvUI3hlE4wGtqVDflxPrD7Bv0VY+HN+XHbP/Ii35+fdLy+sVz8RuMLU/Br2e2OAwIg5epFwnf66sOUj9SQGme/zbglA/SKFK/+YYDQaiz1wn/vIdqvRvjnPZIlxZdYCQdYeyxfXRPL9VwK5yCYoM+ACJTEba7RjiNh/DrlJxPHs3RZ+SRuQf/+A9qguqqxFgNBK95gCqF1xh7inw/C+WRCqh3ZSPMOj03D0fxo1DF6jaoS5Xdp7Cb0BLytSvwtn1h0h/lEr5ptW4fyeWuOuRnFl74JlYJXXSZ2L7fd8fg05PfHAYkQcvUrqTP6FrDvL+pACMOj23tgbxMCya6qM6YzQaufL7ThJDTK1E9X4axNFRfzxzz/9Fs/pJpBKaTjXliz4fRvjBi1To6M+ltQdpPDEAg05P6LYgos+H0XhCABjhysYj3D3x7O2Fp2f1K1jZm2IDWiCRyUi9HUPM5hPYeLvj1rIGMisFIWOW4NHJH/sqJTDq9Fwbuwzvoe2wKuSIdWEXrn33J+lPtTDVavr8kyiZdxmUzTogkcnQx95De+IA8rI+pnv8Dk6kb12DxNEZZe2GGA0G0pbOAd2LW0k+Pmr33OVSqZSPpwzGoNdzI/g6wYfOUb9jQ+6G3uHDz7tx63IYSXGJ7F61k/7jB2I0GjkUeIArJy49E2tz9NkX5pg3dxo6nY5Tp4PZtesAvXp+yKbNO/hhxnfExSWwb/8RVCkq2rdvSUpKCkGngtm0acdz45kzq9/JM8Gs27SdRympdO/UmqYN/HP1/140q59EKqHFFNP3Pyo4jFuHLlKpoz8X1h6k2QTT/hWyLQiJBMq2qIFGpSbqfBih/5x+thwvmdVPIpXQZkp/DDoDkedvcvPQRXw7+HNl52n8BnxA6fpVOLf+EMcWbc8xzvBKkc9dLitVBus2nUz7WPQ90g/tQ1GxMrLSZZE6OpG2YRXy0uWQl60AMhmpS37FaGazvCVzuGw/9NzleeUr7x4WizUrfI3FYr0Oi1b+27dvp2jRolSpUoUzZ84waNAgunXrRq9evRgyZAiBgYEAfPjhhyxYsIC5c+dia2vLt99+y759+/j7778ZM2YM/fv3Z/PmzVhbWzN16lS++uorrK2tc8z9osrfkp6u/PPKiyp/S3pR5W9JT1f+eeVtmdL3RZW/pb2o8rekF1X+liam9DXPiyr//xpR+b8+i/b29/b2Zvz48djY2CCTyfjqq6+4e/cuXl5edOvWjd69e2M0GunSpQtFihQBID09nU8++YTo6GhmzpxJ4cKF6dOnD7169UImk9G0adOXVvyCIAiCkFfepI56lmLRyr9SpUr89dfzH13r1asXvXr1emZ5kyZNaNSoUa7eKwiCIAj57e2r+sXY/oIgCILwzvlXB/mZPn36v5leEARBEF7qbRzk560f4U8QBEEQXofxLWz4F83+giAIgvCOEVf+giAIgpAD0ewvCIIgCO+Yt/FRP9HsLwiCIAjvGHHlLwiCIAg5ePuu+9+iyn+0W+6mYn0dYRHOeZ4DYF2B15yfMxc6penzPIc6n4YsLY95s/29iiP5MMrkmmNOeZ4DYKg67xv8ppaskuc5IH+G3v3qrHnzjLyKc1W+yvMcAE3P5v0w1fkhOJ/z/dvN/lqtljFjxhAVFYVMJmPatGkULVo023t27NjBkiVLkEql1KlThxEjRuQYUzT7C4IgCMIbbNu2bdjb27NmzRoGDx7Mjz/+mO31tLQ0Zs2axbJly1i3bh3Hjx/n5s2bOcYUlb8gCIIg5MBgwZ9XceLECZo1awaAn58f585ln3q+QIECbNmyBTs7OyQSCY6OjiQlJeUY861p9hcEQRCEvPBvD/KTkJCAs7PptrNUKkUikaDRaFAqlZnvsbMzzdYZGhrKvXv38PX1zTGmqPwFQRAE4Q2xYcMGNmzYkG3ZhQsXsv1tND7/ZCQ8PJyvvvqKH3/8EYUi52nbReUvCIIgCDnIz0F+unTpQpcuXbItGzNmDPHx8ZQvXx6tVovRaMx21Q8QExPDZ599xg8//ECFChVemkfc8xcEQRCEHBgt+O9V+Pv7s3PnTgAOHDhA7dq1n3nPuHHjmDBhApUqVcpVTHHlLwiCIAhvsFatWnH8+HF69OiBUqnMnBF30aJF1KxZE0dHR86cOcMvv/yS+X/69etHkyZNXhhTVP6CIAiCkIN/e2z/x8/2P23QoEGZvz/dL+Bl3vrKX1mxHLad2iKRSni4aDn6mDgKNPDH+v0aSOxseTjvd2xaNkWiUCB1ciTppwWgM28gjILvlaZwn2YgkRA+az3pkQnYlC5M6e8HcHfeZh4cvkThPs2wKVMEhYs910ctQq9Sm10WiVRCr+8HkZ6qJjIkghMbDgAglUlpObQTbsU9WDZyHkUrlaBer6akq9Rc2H2am6evmZ3L/r1SePVpChIpt2ZtQJ1RpvLf9yd83hYSD18yO6bje6UoHtAEpBKuz9pIWmQC7s2qUahpVWTWSi6PW06Jjz9A4WCDQ+USXJu2DqVzQSp825NDjf6HUW/eV9DuvdJ4BDQDqYS7GdulQOnClJw6gMh5m3l4xFQGxwa+FBnagStdJuY6tkQq4YNpA9Co1MRfi+Di+sMAWNnb0HR8ANq0dG7uC8ZoMOJdtxJyKwVSmYxLG4/g270hUpmMM0t3EXs5PBe5pPT7/hPUqWoiQ+5wZMN+AGwd7OjxXT/SHqVxNyScU9uO0WVMAKqkFAJ/XGPWuoL82Y+tK5fFoVsrJFIJCfNWoYuKw65JHWzrVkda0Ib4WUuwbVALZcmiyJ0diJkwF6MqzeyyPN4+WpWauKe2T7Ms2ycl/iHv9WyMJlXN9V1niTwdanaurOITEpn+82/UqVmNzu0+eK1YALZVS1MooDkSqZTIWevQ3IvHunQRvKd8TNT8TSQfuUjZ5V+jDosCIPLHdRjM3CZSqZRvZv6PVFUaN0Ju8vea7QA4ONnz9bQvUaWkcuV8CEf2HGfI6I9RqzWcOBDE4T3H3sg8ecHwgg52/2VvxD1/rVbLl19+Sffu3enbty/37t3jk08+ISAggC5dunDx4sVXjm37YVsezPiZh4tXYtuhFQD6+AQezPiZtL2HUJYvi7JcGZJ//xN9TByyQq5m5yjctxnXR//Ondl/4dnL1MyiS1ETt/FI5nseXb7NzW+X8ej8TQp4e7xSWcr7V+bm6WtsmLScCnUrZy6XyqQcWrELJKaRAcu+X5ETGw6ye+EWymd5nzm8+jYjZPRibs/eSJFejQHQp6QRvfHoK8UDKN6nKZfGLOHGT5so1rMRAIU71OHS6CXcXX8Ez7a1uTFnEyFT15J8NYIHZ2/w8HI4ScE5D1bxIh59mhM25nfu/vQX7j2bZpRBTXzgk+0iK2iD/fsV0MQ+MK8s/pW4eyqUfZNWUryuT+by8q1rc3H9IXZ/s4xKHfy4fegiB6auITkqkRPzt6BNTWf3t8s5vfgfitYql6tcFf0rc/10CGsmLaVi3Scj58mUcgJ/XMuqCYspXaM8UpmMfX/uNKscWeXHfuzYvTVxE+dzf8EaHDq3MOWIu0/sxHk8+ucI1pXKkB4SRvy0hagvXUdZ1POVyuLtX4nIU6HsnbQS7yzbp0Lr2lxYf4hdGdun2PsVuLjhECd/20aJurm7V5oTiVRC53YtXzvOY4X6tCD860Xcm72BQpn7cBoJgYefvMlgxKDRoU9Lx5CabnaOWvVqEBx0gVnf/UztejUyl8vlcuZMXsDU/82kpn81fGv6sHPTXmaM/Yn2PVq/sXmE3HkjKv/Nmzfj6urK2rVr6dq1K3v37qVLly6sWLGCkSNH8vvvv79ybIlcDlothoT7yJxNw6dqroYidXXB2r8WacdPobl2HacxX6CsUBZ9dOwr5TBqdGhik1C6OZpyxCRme8+jczexKVME62LupFwJf6Wy2Dnbk5yQBIAhy1WwTqMjJfFR5t+X9p+jzYgudJvwEZf2nXs6TK5I5DKMGh3psQ9QujkAkB5jXgX5NKlchkGjQx33AKuMmBhMZ9TpWZYV7VqfyEDTSYY6OvG5sXLjcRm0cUkoMmI/vV2KDGnHvV+3mB3bxsUeVcJDgGwtEjYuBZ8szyiblb0N1g62JEfdJ/7aXWQKOdUCmnI5lydSBZ3tefic7Z4cn0RiVAIdRnTj4OrdpCarSE8zv0XpsXzZj+UyjFotuvhE5K6m76P60nVkhZyxbVAT1eEzqC+GoixZFIWXO+nXbr1SWXK7fcL2BVNvxIc0n9iXm/vOv1KurFydnZDJZK8d5zFpxj6siXuQuQ9rn9omd8YvJXL6KtLvxOJQ3/xhlZ1cHLkfb4qpz7Ku7scnorRS8tOyaezddpCj+09Sq14NBn3ZH6nU/Kojv/LkBaMFf94Ub8SavXLlCtWqVQOgdevWdOrUiV27dtGjRw9mzZr10pGKcmJUp4NSgayQG/qYOABk7m7Y9+9J0o8LQKtFWakCD6bPIe3AEayq5TwwwvMY0tKRWimw8nQm/V78c99jV8kbj24Nufnd8lcqh3/3JnwwpAMOhUwDPbzoOU+ABn1asHL0QhYPm0OdLg1fKZ8hTYPUSoG1pwvqe/dfKcbT9BnrqYCnC2kZMR+XwzrLMqcaZXh4/tUO+lkZ0tKRWClQejqTfu/ZuR/kjnZYFXGl8KA22FYsTsEaubsS9+3ekDpD2lLQ3ZGMQmS+lhLzALuMivNxpVO2eXXC9plGI7cqWIBGX3fn4Iz1qB+qXpqrfvemtB7SCacXbPcuY3oTvPsUt4Jv5Oqz5yQ/9mOjOh2JUoHcwxVtlOn7KPdwxeWTbsR9vxCjVotV+ZLYd2hK3PRXO+l/vH3sMrZP1nX2KMv2MegNVOvTjB2j/+Dvz+dRpWv9V8qXl/Rpmox92OW5+zCAdQlTC4w+WYVEmfOz3U/r2KstHw0LwM3jcYvnk3XlWdSDlOQUvugzmmZtG6FQKNi0aiuL5/yJKiX1jcyTVwwYLfbzpngj7vnLZDIMhidngsuXL8fd3Z2ZM2dy6dIlfvjhh1eOnRK4DafRw5HIZOgio5AX88J+YABGVRoOnw0g7dBxdHfvYf9JP2Suzjyc94fZOaL+3EOZGR8jkclIux1DgZKe2FUqTqFOddGnpGHQ6vEe1QXV1QhKT+xD9JoDqK7eMSvHsbX7OL5+Pz2mfEzxyiW5duwSBV3sqdWhHsE7g2gyoDXFKpeg2aC2XN5/jg8+64g6JY3QY5fNLg9A5J97KT9jQGaZbDLK5NnJH12KGqNWx4MTIWbFvLNiH5Wnf4RELkV1Oxbbkh5EbT5OlR8GILVWcmnMEgAkiidXTqU/b4/je6WoOKE3YfO3ojaj9SFmxR5KTR+IRC5DfTsa65Ke2FbyxrVjPdMtjMU7uDFsHgDW3h48OpO7+70X1h7k4vpDNJ/SH/fKJQg/egUbF3sqdfTnwtqDNJsQQMUOflzZfBwA90reXN99FoDag9uiLFgA/2EdiDwdSug/p3PMdXjtXo6s30+fKQMpXrkkV49dpKCLPXU6NODe9QhKvVcOuVJBpXq+XD5ygaZ9W1K4bFHi78ZxZP2+XK8ryJ/9OGndDtzHDwWZFG1ENArvIrh+1guDKhXXEf1I2X8C1yE9SQ+9jdvogSQH7iY99LZZOR5vnxZT+uPxgu1TKWP7SCTg91k7NCo14ceumJXneU6eCWbdpu08SknF0aEgTRv4v1a8uJW78Z42CIlMRnq4aR+2qfhkHzZqddhVK4tj85pIlQrCx5l3wrRp1Vb+XrOdr6d/SYUq5Qg6fAYnV0daf9iCvdsOMmL8Z6Slqrl+5SY6rY7h336KKiWVDcsC38g8eeXfHuEvL0iMOV1C5pPNmzcTHBzMxIkTOXDgAEOHDmX8+PF07dqV2bNnExQUxNq1a3OMcbfmix9psJS3a1a/vM+hluRPw5JzfszqZ5X3s/qFSszv2PYq+qbl/XbxdErJ8xwAgWkueZ7jbZrVb7DB/Nuab6LgmPztBNijeAeLxVpzZ7PFYr2ON6LZv1WrVqSlpdG7d2+WL1/O0qVLWbp0KR999BFVqlQhPj6ejRs3/tsfUxAEQXgH/dsT++SFN6LZX6lUPtO0/88//2T+ntNABYIgCIKQl96ke/WW8kZc+QuCIAiCkH/eiCt/QRAEQXhTvY0d/kTlLwiCIAg5eJPu1VuKaPYXBEEQhHeMuPIXBEEQhBy8AU/EW5yo/AVBEAQhB6K3vyAIgiAI/3niyl8QBEEQcvA2dvh7ayr/c3fd8zxHWfukPM8BoNTa53kOVT4MvWtr1Od5DoBgZd4PveuUD0UpIcv7cgAUskvK8xwGfd4PHw2QJsn75tj8GHq32sVZeZ4DwM53YL7kedu8jY/6iWZ/QRAEQXjHvDVX/oIgCIKQF97GDn+i8hcEQRCEHLyNj/qJZn9BEARBeMeIK39BEARByIHo7S8IgiAI7xjR218QBEEQhP+8fL/y37VrFyqVihs3bjB69OiXvj8wMDDX7xUEQRAES3sbe/vn65V/ZGQk27dvz8+UgiAIgvBajEajxX7eFPl65T9p0iQuXrxI2bJliYuL4/PPP+fmzZsMGDCAzp07s2XLFlauXIlUKqVMmTJMnjz5lXM5vlcK74DGSKRSrs3aSFpkAu7NquHetCoyawWXxv2JQ+XieLSogcxawZVJqyk/6kO0D1NRONpyZcKqXOeyrlIWp+4tQSol/pdV6KLisGtaB7t61ZAWtCXuhyUoPN1wHzeIeyN/QBsR/UplkkgldPt+IOmpaqJCIgjacBAAqUxK86EdcS3uwcqR81FYK2kzqjtI4Mq+c1w/djlX66tEQGPIsr48sqyvixnry7NFDaQZ66v04FbICxZA6VSQ4BGL4CU7tv17pfDq0xQkUm7N2oA6MgHX5tVwbfIeMmsl18Yto0jPRlh5OCG3K8C1r5dQdMAH2f426l/e9UYildBw+gC0KjX3r0UQsu4wAEp7G+pPCECrTid8TzBJt2OoPaozBq2em1tP8iAsmprDOqDX6ri16yx39p/PMUe9GQPQqdTcD4kgNEsOv4kB6NLSubM3mJigUGqP7U56UgqnZ/6Fe7XSlO/ZEIlMxuUlu0i4FJ5jjtbTBqBRqYm7dpfz6w8BYGVvwwfjA9Cmabi+7xzJ0YnU7NMMiVTKudX7ibseSeNRXUECN/YFc/vYlZeuM8jYj3u0RCKVEP/zKrRR8RRs9j629aohK2hL7A9LUXi64TFuIJEjZ6K9Y/5+nF/fFTCtv3bfD0CTqiYm5C7BG0zrTyqTUn9oB5yLuxM48tdXjm9btTSFApojkUqJnLUOzb14rEsXwXvKx0TN30TykYuUXf416rAoACJ/XIdBpX7lfFnFJyQy/effqFOzGp3bffDa8aRSKaNmjCA1NY2wq7fYsW4nAPZO9oycOozUlFRCLoSyddV2JBIJs9fNZMXc1Zw9cu6NyiHkXr5e+Q8YMIBatWpRuHBh7t69y5w5c5g/fz4rVqwAIC0tjT/++IO1a9dy69YtQkNDXzmXd58mXByzlNCfNlG8ZyMAinSow8XRS7i7/giF29amSAc/0uOTSL+fjD5NQwEvN67P2YxUIUcil+U6l1OPVsRMmE/C/DU4dW0BgC7uPjHj55O84wjWPmXQRseRcujMK5cHoKx/ZcJOX2PTpD8pV7dy5nKJTMqRFbuRZIyo6vtBLdJVagw6Aw/uJeQqdok+TbiQsb68s6yvC6OXELH+CEXa1sargx/q+CQ0Gesr7sBFrkxcjfahCoW9zUtzePVtRsjoxdyevZEivRoD4NHBn2ujFxO14TDu7d7HtnRhbkxaRUpoJE5+FZ/5Oze86lYi+lQoRyeupGhdn8zlZdrU5ur6Qxwau4yyHf0wGgwcHrecY5NXU6xhFYxGI0cnreLY1DUUreeTQwYoUrcSMUGhHJ+wkiJZ3luqbW1C1x3i6NhllOnoh0Qu5cryPZmva9PSOTpuOZd+/wfPWuVyzFHC34eIU6HsnrSSElnKUal1bc6vP8SOb5ZSuYM/pepX5vjC7eyevJKa/ZpToWVNNKlqjHoDD3O5/QGcerYkZvx84uetxbGbaT/WxiYS890CknccoYBPabRRr7cf59d3BaCkvw93Tofyz6SVlMqy/iQyKadW7Mn8vryqQn1aEP71Iu7N3kChnk0B0KekkRB4+MmbDEYMGh36tHQMqemvlzALiVRC53YtLRavet33uHDqEnPHL6BGvWqZy+VyGQumLGLWmDlU86sKQPs+bbl23vxjc37kyCsGjBb7eVP8ax3+fH19kclkuLu78+jRIwAcHBwYMmQIvXv3JiwsjKSkpFeOL5XLMGh0qOMeYOXmYFpoMF01quOSsHJzwKaoKzfnb0Md/QC3epVIj3+Iz8TeSK0UGHW5H8xdIpdj1OjQxSUic3E05bh4HXkhZ+wa1EB1+DS6mPuvXJbH7JwL8ijhoakoWa6A9RodqsRHmX87FnYh9Ngldv78F02HtM9VbHPWV1rG+noQHIZzrXJoH6rQPlS9NIdELsOo0ZEe+wBlRg5jRg5NbBJKNwfi956j1NfdsCvnhcLF/pm/c6OAsz2pz1lP1i4FSY1/mJHXSHJEPA4l3Gn2y6dc33yC5DtxaFRp1B75IReX7c4xh7WLPWkZObK2Rlg7FyQtSw7Nw1S0WQ76iSF3kSrkVOrblOsbj+aYw8alIKrn5LBxsScl4UmOi4FHqd6zMbX6t0Aqk2Lv6cLto5c5NCcQ/8/a5byyssi6H8tdnYAs+3HDmqQcOvPa+3F+fVcAbJ0LZq6np78vqVm+L69KmrE/a+IeoMjYn7Uxidnec2f8UiKnryL9TiwO9au8ds7HXJ2dkMlyf4HyMo4ujiTGPwDAYHiyrhLjH2BlreT7JZM4uP0wRUt5IZVIuHMz4o3MkVeMFvz3pvjXKn+5PPsdB41Gw6RJk5g9ezYrV67E19f3teLr0zRIrRQU8HQhLePq5/H9lgKezqTdS0Bz33QA0KnUWHs4IbNScHn8SlLvxGFTzC3XuQxqNRKlArmHK9roeFP5PFxxGdyN2KmLMGp0r1UWgDrdG9NsSAccCjllK8vzPIp/iEQiQavWIFcqchVfl2V9pT5nfaXeSyA9Y33pVWpkBawo1LAKztVLE/rTplzlMGTksPZ0QX0v4wBvMOWw8nRGHZmAJu4hYdPWoQqLIi0i7pm/X6Zij4ZUH9oWW3dHMgqR+Zoq5gG2bqblRoMB53JeJIZGsrXPTCr3aYpULqPu+N6cX7yT5DsvzlW+Z0Pe+6wtNi/IUaBQRo7n3KJQFizA++O6EzRtPelJLz5heq97I+oOaUdB92e396OYB9hllMOgNyCVyTi5+B/OrtpH2kOV6YTBzO0PYFSnI1EqUHi6oo0ylV/u6Yrrp12JmWyZ/Tg/visA1bs3ot6QdhTM+L687JbUq9CnaZBYKVB6upD+ghYW6xIepvcmq5CYsS3yU9uereg9tAeu7i5A9n3Nw8udlGQVY/p9Q6M2DajTuDZObk7Ua+FP03aN3qgcgnkkxnzsgXD69GmWLl1K06ZNM3vwq1Qq2rZty8aNG2nXrh1HjhwhOjqazp07M2PGDOLi4nLV2/9vj57Z/nao7E2Jj5ojlctIuR3Dvc0nsPV2x/MD0z3ri2OW4upXAY/m1ZEVUHJh9BJ8JvTOvMq99M1yjNrsV/9lCyY9N7d1xVI4BbQFmQxtRBTJ2w7j+nlPDKo0jOkaHu09ibJEERw/bEZ62F0Sl2wi/Xr4C8vy2wtm9ZNIJXSZMgCDTs+d8zcJOXSBGh3qcmHnKRoOaEX5+r4ErT/A8TX76DS+HxiNnAo8zM0TV5+J1Uid/bzPobI3JT9qjkQuQ3U7hsgs60tmreBClvUlL6AkeNQfNNgxmYSjpvvJN3/bTlpU9quep2f1K1i5BEUHtEAik5F2O4aYzccp4O1OoZY1kVopuDZmMR6d/LGvUhKDTk/o2KUU6d0429/PE/rUQVUildBgan8Mej2xwWFEHLxIuU7+XFlzkPqTAkz3+LcFoX6QQpX+zTEaDESfuY5UJqNEs2okhceSeD2Sq2sOZsZUPPUtkUgl1P2+P0adKcfdgxcp08mfa2sO4j8pAINOT9jWINSJj/Dp3xynckUIWXUAh+Lu2BV1RZ2QTPSpUG7vOJ0ZM1ZmfCZHqyn9MegN3Au+yc1DF6nS0Z9zaw/ywYQ+GHR6rm47SfzNKD6YEIAmNZ0jczejSnhIi/F9wGhqFQh/avt3lic9dz1aVyqFU0AbJHIZmvBokrcdwm1YT/SZ+3EQyhKFcfywGZqwu9xfvIn063eeG0siff5hxdLflTVapxe+JpFKaDOlPwadgcjzpvXn28GfKztP4zfgA0rXr8K59Yc4tijnTsitNWnPXW5TuSTu/VsikclID4/m/t9HsanoTaFezdCnpBGzeDv2dSohd3VAqlQQPu73F57cmDur38kzwazbtJ1HKal079Sapg38c/X/Gr9gVj+pVMrIacPR6/RcDQ4h6MBpWnzYlAPbDjNwdH/UqWpi78Wxct4aAFp2bUFcdLzZ9/wtlePIvX25zmsJ9Ys0sVisw/n82V8kXyv/xMREOnXqhEqlonPnztkq//379zNmzBhu3LhB+fLlKV26NH/99Rd9+/bl9u3bZlf+eeFFlb+lvajyt6SnK/+8kF9T+j5d+eeFpyv/vPB05Z9XXlT5W9KLKn9Ly6nyt5QXVf6WlF9T+r6o8v+vye/Kv54FK//8/uwvkq+9/Z2dnTl48GC2Zba2tuzfvx+A6dOnZ3utf//++fXRBEEQBOGdIYb3FQRBEIQcvEm99C1FVP6CIAiCkIO3sfIXY/sLgiAIwjtGXPkLgiAIQg7epGF5LUVU/oIgCIKQA9HsLwiCIAjCf5648hcEQRCEHLxJw/Jaiqj8BUEQBCEH4p7/G+wvK8tMlZmTpPT8uUuyus3DPM8xfodDnudwMebP7uX08ll+X5uPLu/3rx6tX3+ymdyoHpj7mf5elYTXnDIvl07UevmEUq+r6VnLzDeQE7t8Gnlv/4Xf8yWP8OZ7ayp/QRAEQcgLb2OHP1H5C4IgCEIO/u1mf61Wy5gxY4iKikImkzFt2jSKFi363PeOHDkSpVL5zHD5TxO9/QVBEAThDbZt2zbs7e1Zs2YNgwcP5scff3zu+44dO0ZERESuYorKXxAEQRByYMBosZ9XceLECZo1awaAn58f5849O82xRqPh119/5dNPP81VTNHsLwiCIAg5+Lcf9UtISMDZ2RkAqVSKRCJBo9GgVCoz37Nw4UJ69OiBnZ1drmKKyl8QBEEQ3hAbNmxgw4YN2ZZduHAh299P90EIDw/n8uXLfP755wQFBeUqj6j8BUEQBCEHhnzs8NelSxe6dOmSbdmYMWOIj4+nfPnyaLVajEZjtqv+gwcPEhUVRdeuXUlJSSExMZHff/+dgQNf/AipqPwFQRAEIQf/drO/v78/O3fupF69ehw4cIDatWtne71fv37069cPgKCgIDZt2pRjxQ+iw58gCIIgvNFatWqFwWCgR48erFq1ii+//BKARYsWERwc/Eox3/orf4lUykfTBqNWqbl7LZzD6/cDYOtgR6/v+pOWkkZESDjXTl6h85c90Ol0BG09xvn9Z3OdQyqV8tm0z0hLVRMeEs7e9XsAsHOw4+PxA0l9lEp4yG3CLofxQa8PSFOpObn7JFdPXTGrLNLiZVHUa4VEIiF9+yqMiXGm5Z7FsR44ltRJn6Bo2A6pR1Ekdg6oV84BdapZOSRSCV2+H4gmVU1USASnNhw05ZBJaTq0I67FPVg9cj4ACmslI7Z+z5KPZ5JwJzZXsVtNG4BGpSbu2l0urD8EgJW9Dc3HB6BN03Bj3zlU8Q95r2djNKnpXN91Bm//Sth7OqNOTuXypmPEXr3z0jwNpg9Aq1Jz/1oE19YdBkBpb0PdCQHo1OmE7wkm6XYMNYZ1wKDVcXvXWYwGA8Ua+oJUwuVle0i6Ff3SMtm9VxqPgGYglXB31nrSIxMoULowJacOIHLeZh4euQSAYwNfigztwJUuE18a83mk3uVQ1msFUinpW1c82faFi1Ng0DeoJgxEUb81Uo+iSO0cSFv1C6SnmZdDKmXyj+NIVaUSevUGf63ekvmaRCJh+cZf+W3OEo4fPoV1AWs27VnBJwEjiLgdaWaOsahUqYRevcnGp3Is27iAhXOWci8ymi/GfIpOq2P75t0c3HPUrLLIy5bHqmU7kEhJW7UUQ3wsSv8GKGrURmJjS+ofCyjQuz/G5GTAiHrLRgzxcWblkEqlfDPzf6Sq0rgRcpO/12wHwMHJnq+nfYkqJZUr50M4suc4Q0Z/jFqt4cSBIA7vOWZWjlEzRpCamkbY1VvsWLcTAHsne0ZOHUZqSiohF0LZumo7EomE2etmsmLuas4eebY3uLniExKZ/vNv1KlZjc7tPnjteP9WDkvIz2b/53n8bP/TBg0a9Myy2rVrP9My8Dx5fuUfGBjIuHHj+PTTT/nggw/YsGEDjRs3RqUyDcs5Y8YMAgMDCQwM5Ouvv2bw4ME0adKEbdu2MXjwYJo1a/ZMZwdzVPKvTOipq6yatIRKdatkLpcr5Wz8cQ0rxv9B2erlMegNLP92EWumLKdKw/fMyuHr78uV01f4Y+LvVK3rmy3HqlkrWTR+IRVqVMDn/crsXb+XwN82ZntfbinrtSZ97XzSd6xB4dfCtFAqRVGnGYaIMAD0t6+RvnY+hjvXkRYqYnaOMv6VuXX6Gpsn/UnZupUzl0tkUo6t2I0ky6itjQa1Iezk1VzH9vb3IeJUKHsmrcS7rk/m8oqta3Nh/SF2frMUnw7+FHu/Ahc2HObkb1spkfE+rVqDVC7jUeyDl+bxqluJ6FOhHJu4Eq8seUq3qc219Yc4PHYZZTv6gdHIsUmrOD51DV71fCjWyJez87dwPfAYHjXK5KpMHn2aEzbmd+7+9BfuPZsCoE9REx94JPM9soI22L9fAU0uPvuLKOu3Rr1mHunbV6Gsm3GQlEpR+LXAEHETAEPETdLX/4b+znWkbp5m5/CrX4szJ4OZ+s2P+NXPfvDo0a8zl4KfnKx+PCSAoGO5P0F+rE79mpw5Gcz33/yEX/1aT+X4kEvBpv3JoDcwcfQMpo2fTf0mfmbnsWrZHtX82aStXY5Vi9YA6ONiUf38A5ojB5GXLQ+AUasx5Us2f0jtWvVqEBx0gVnf/UztejUyl8vlcuZMXsDU/82kpn81fGv6sHPTXmaM/Yn2PVqblaN63fe4cOoSc8cvoEa9allyyFgwZRGzxsyhml9VANr3acu186Fml+NFJFIJndu1tFi8fyuHJRgt+O9NkS9X/tevX2ft2rWEh4czcuTIF74vPDyc1atXs2HDBhYuXMjmzZsJDAxk27Zt+PqaX1kC2Ls48DDB9MU26J8MAv8wPgmATiO6sX/1buLvxlLqvbJ8OLI7m+asMztHUkY8g+FJjsfLeo7syc5VO3mU9IhPJg0m9ZGKwN8CzS+MTAY6LcbkRCT2TgAoGnVAc2grVm37mPLfuY6icUdkpX3Q7N9sdgo754KkPGd96TU6VIlPxp4v9X5FYq5HIpHm/vzRxqUgqozYxiyxbVzsiTh1zbTcYOTmvmCaT+xL+qNUTi7czqOYRNIeqrBzc6DWRx9wcOb6HPNYO9uT+pw8BVwKEhX0JE/ynTikChl+43pycekubD2caDCtPxKplCPjluWqTBK5DKNGhzYuCYWbab4ETUxitvcUGdKOe79uoeT3H+cq5nM93vYPn2x7ZeOOaA78jVX7vgDow0NNrT4u7hgib5mdwtnFiYT4+6ZYen3m8hKliiOVSgi7EQ5ALb/q3AgNQyI1f/x+Uw7T+sm6f5lySAm7cRuAu3fu4Vvdh+GjBzN35iKz80jkctBpMSTeR+pkekRKf+MaysYtsG7SgkfTvkN3IxRDfCxyH1+sGjUnfedWs3I4uThyP6Ms+ixluR+fSLGSRflp2TS2rvuHYwdOMvCLfvjWqozUjO8LgKOLI4nxppPGrMeWxPgHFC3pxfdLJvHP+l0ULeWFVCLhzs3cDfCSG67OTtwKv2uxeP9WDuH58uWef9WqVZHJZHh4ePDo0YsnL/Hx8UEikeDm5ka5cuWQyWS4urqSkpLySnkbdm9K2yGdcHI3HSyfPunqNiaAs3tOExZ8Ha+yxYgMjWBWvyk0Cch981PzHi3o8lkXnN1dTCmeytH3634E7Q4iNDiU1n3bMPd/vzBz6Eyadm1qdnmMmnSQK5A4umY2+8qKlkLxflOkRUogq1QTqXd5tPs3oT25F3mV982K/373xjQZ0gH7Qk4ZZXnxWWpZfx88yxej1PsVqdr25VdmVbs3wm9IOwq6Pxv7UcwDbN0cAVOFUL1PM7aP/p3Nn8/Dt2t9XMsUwaDVoU5ORWalyDFPhR4NqTa0LbbupnhZN0hKzANsMvIYDQakchn+43tzYfFOku/EUblvc3Z98guHxiyhQo+GLy0TgCEtHYmVAqWnM+n3np0wR+5oh1URVwoPaoNtxeIUrFEuV3GfkbHtpU6uGB43+RcrhbJOM2RFSiD3qYnUqySKOs1I32B+Zdm1d0c+Gd4Pdw83IPv2adDUH1c3F5q2bEjrji3wb1CLshVKU9u/Om06tjAjRwc+Gd6fQi/I4eLmTLOWDWndsTllypfixrUwBvYYTs/+nc0ujzFdDQolUlc3DHGmW1LychXR7N+FaulCrJq3RlbEC4xGjKoUJIqc96undezVlo+GBeDm4fo4Y+ZrnkU9SElO4Ys+o2nWthEKhYJNq7ayeM6fqFJyfxuubc9W9B7aA9fMY8uTHB5e7qQkqxjT7xsatWlAnca1cXJzol4Lf5q2a2RWWYSXMxiNFvt5U+TLlb9c/uI0Wq32ue/L+vurjqt8cO1eDq3fT78pgyhRuRSXj17E3sUBv471iQyNoHS1ssiVCnzq+nL56AX6TPwYo9HA1eOXcp1j95pd7F23h0+nfkrpyqU4f/Q8Di4ONOzYiIjQO5SvVh6FUkHVelU5s+80XYZ2JU2VxoVj5t/K0B7dgXX3oSCTYoiLRlKoCOplMwGQuhVGf+U0ymZdkFSvj8TJjfSNC82Kf3LtfoLWH6DzlAF4VS7BjWOXsHOxp3qHulzceYoGA1rhVbkkjQa14Z8fTVffLb7ozPmtx18a+/zaA1xYf5APpvTHo3IJwo9exsbFHp+O/pxfe5DmE/rg08GfK5uPgUSC/2ft0ajU3D52BbmVkrY/Dsag03F8Qc5XZyFrDnJt3SHqT+2PW+USRB69QgEXe8p28ufqmoPUmxRA2Y5+XN90nPJd61OwiCtVPmpB4vVI7uwLps64HkhlUm5uPZmrdRazYg+lpg9EIpehvh2NdUlPbCt549qxHvqUNKIX7+DGsHkAWHt78OjMqzXLag7vwLrn5yCVYYiPQlqoCOolPwAgKVQY3eXT2Iz8AX3kLay6DEJ7fLdZV//rV27ir9V/M/GHMVTyrcDxw6dwdnWifedWLP1tFQCdurclJiqW44dPZfyvQWzbtMuMHJv5a/UWJvwwBh/f8s/N0bF7G2Kj4lAo5Xw7bRQGg5GTR8/kOsdj6n+2YDtkBBKZDH30PaSFvZAVLY6yUTOkjk6kbViFvHQ5bD8fBTIZqUt+NSv+plVb+XvNdr6e/iUVqpQj6PAZnFwdaf1hC/ZuO8iI8aY+QNev3ESn1TH8209RpaSyYVnuW/y2rt7B9rU7GTltOOWqlOXMkXM4ujjS4sOmHNh2mCHfDkKdqubm1TDW/74RgJZdWxAXHW9WWV7k5Jlg1m3azqOUVBwdCtK0gb9F4uZ3Dkt4k5rrLUVizOMZCwIDA7lx4wajR49GpVLRtm1b7Ozs+OWXXyhatCjdu3enR48eAJnvO3DgALt27WL69OnZfs9JQPFOeVkMAJKMmjzPAbC6jf7lb3pN+TOlryzPcwA4GfJ++lhfbd5P6Vu5vZjS11wnatnmeY58mdJXZp3nOeDtmdJX4VoyX/OVcatusVg34s3vK5MX/pXe/r1792bw4MGUKFGC0qVL/xsfQRAEQRBy5U1qrreUPK/8O3V6ckVua2vL/v2mR+26du36wv/TqFEjGjVq9MzvgiAIgpDf3sZm/7f+OX9BEARBeB1Go+Hlb/qPESP8CYIgCMI7Rlz5C4IgCEIODKLZXxAEQRDeLXn8UNy/QjT7C4IgCMI7Rlz5C4IgCEIORLO/IAiCILxjRLO/IAiCIAj/eeLKXxAEQRByIEb4EwRBEIR3zNs4wp9o9hcEQRCEd4y48hcEQRCEHLyNHf5E5S8IgiAIOXgbH/UTzf6CIAiC8I4RV/6CIAiCkAPR7C8IgiAI75i38VE/0ewvCIIgCO8YceUvCIIgCDkQzf6vIDAwkLNnz5KYmMjt27cZMGAAv/76K1u3bsXW1pYZM2ZQpkwZAE6fPs2DBw+4ceMGI0aMYNu2bYSFhTFr1ix8fX1fKb9EKuWjaYNRq9TcvRbO4fX7AbB1sKPXd/1JS0kjIiScayev0PnLHuh0OoK2HuP8/rO5ziGVSvls2mekpaoJDwln7/o9ANg52PHx+IGkPkolPOQ2YZfD+KDXB6Sp1JzcfZKrp66YVRZp8bIo6rVCIpGQvn0VxsQ403LP4lgPHEvqpE9QNGyH1KMoEjsH1CvngDrVrBwSqYQu3w9Ek6omKiSCUxsOmnLIpDQd2hHX4h6sHjkfAIW1khFbv2fJxzNJuBObq9itpg1Ao1ITd+0uF9YfAsDK3obm4wPQpmm4se8cqviHvNezMZrUdK7vOoO3fyXsPZ1RJ6dyedMxYq/eeWmeBtMHoFWpuX8tgmvrDgOgtLeh7oQAdOp0wvcEk3Q7hhrDOmDQ6ri96yxGg4FiDX1BKuHysj0k3Yp+aZns3iuNR0AzkEq4O2s96ZEJFChdmJJTBxA5bzMPj1wCwLGBL0WGduBKl4kvjfk8Uu9yKOu1AqmU9K0rnmz7wsUpMOgbVBMGoqjfGqlHUaR2DqSt+gXS08zLIZUy+cdxpKpSCb16g79Wb8l8TSKRsHzjr/w2ZwnHD5/CuoA1m/as4JOAEUTcjjQzx1hUqlRCr95k41M5lm1cwMI5S7kXGc0XYz5Fp9WxffNuDu45alZZ5GXLY9WyHUikpK1aiiE+FqV/AxQ1aiOxsSX1jwUU6N0fY3IyYES9ZSOG+DizckilUr6Z+T9SVWncCLnJ32u2A+DgZM/X075ElZLKlfMhHNlznCGjP0at1nDiQBCH9xwzK8eoGSNITU0j7OotdqzbCYC9kz0jpw4jNSWVkAuhbF21HYlEwux1M1kxdzVnj5wzqyzPE5+QyPSff6NOzWp0bvfBa8f7t3JYgujt/4quX7/OvHnzmD9/PitXrnzh+8LDw/n111/55JNPWLhwIfPnz2fQoEFs27btlXNX8q9M6KmrrJq0hEp1q2QulyvlbPxxDSvG/0HZ6uUx6A0s/3YRa6Ysp0rD98zK4evvy5XTV/hj4u9UrfvkJEWulLNq1koWjV9IhRoV8Hm/MnvX7yXwt43Z3pdbynqtSV87n/Qda1D4tTAtlEpR1GmGISIMAP3ta6SvnY/hznWkhYqYnaOMf2Vunb7G5kl/UrZu5czlEpmUYyt2I5E8eW+jQW0IO3k117G9/X2IOBXKnkkr8a7rk7m8YuvaXFh/iJ3fLMWngz/F3q/AhQ2HOfnbVkpkvE+r1iCVy3gU++ClebzqViL6VCjHJq7EK0ue0m1qc239IQ6PXUbZjn5gNHJs0iqOT12DVz0fijXy5ez8LVwPPIZHjTK5KpNHn+aEjfmduz/9hXvPpgDoU9TEBx7JfI+soA3271dAk4vP/iLK+q1Rr5lH+vZVKOtmHCSlUhR+LTBE3ATAEHGT9PW/ob9zHambp9k5/OrX4szJYKZ+8yN+9Wtne61Hv85cCn5ysvrxkACCjuX+BPmxOvVrcuZkMN9/8xN+9Ws9leNDLgWb9ieD3sDE0TOYNn429Zv4mZ3HqmV7VPNnk7Z2OVYtWgOgj4tF9fMPaI4cRF62PABGrcaUL/mh2Tlq1atBcNAFZn33M7Xr1chcLpfLmTN5AVP/N5Oa/tXwrenDzk17mTH2J9r3aG1Wjup13+PCqUvMHb+AGvWqZckhY8GURcwaM4dqflUBaN+nLdfOh5pdjheRSCV0btfSYvH+rRzC8+VL5V+1alVkMhkeHh48evTohe/z8fFBIpHg5uZGuXLlkMlkuLq6kpKS8sq57V0ceJhg+mIb9IbM5Q/jk7gflUCnEd3Yv3o38XdjcS9RmMFzhnPi7yMvCvfCHEnxSaYchic5kuKTiI+Kp+fInuxctZPT+07Rc2QvPpn0Caf3nja/MDIZ6LQYkxOR2DsBoGjUAc2hrZBxZmq4cx1F447ISvtguHfb7BR2zgVJec760mt0qBKfbLtS71ck5nokKfeTcx3bxqUgqozYxiyxbVzsnyw3GLm5L5j6Iz6k+cS+3NgXTPDq/ez7fg2nFv9DrY9efnVg7WxP6nPyFHApSGr8kzzJd+LQqtKoNfJDLi3bTdiOUzSY1p/qw9oTeeRyrsokkcswanRo45JQuDkAoIlJzPaeIkPace/XLc/777n3eNs/fLLtlY07ojnwd+bQo/rwUFOrj4s7hshbZqdwdnEiIf6+KZZen7m8RKniSKUSwm6EA1DLrzo3QsO4n5D4vDC5yGH6f1n3L1MOKWE3TPvs3Tv3KF6yKLMWTGbrxp1m55HI5aDTYki8j9TJ2VSmG9dQNm6Bdcu2aC+cJW3VMtJWLEZz6gRWjZqbncPJxZH7GWXRZynL/fhElFZKflo2jb3bDnJ0/0lq1avBoC/7I5Wad8h1dHEkMd500pj12JIY/wArayXfL5nEwe2HKVrKC6lEwp2bEWaX40VcnZ2QyWQWi/dv5bAEo9FosZ83Rb5U/nL5i+8uaLXa574v6++vusIadm9K2yGdcHJ3ygiU/fVuYwI4u+c0YcHX8SpbjMjQCGb1m0KTgNw3PzXv0YIun3XB2d0l47Nmf73v1/0I2h1EaHAorfu2Ye7/fmHm0Jk07drU7PIYNekgVyBxdM1s9pUVLYXi/aZIi5RAVqkmUu/yaPdvQntyL/Iq75sV//3ujWkypAP2hZwyyvLi9V7W3wfP8sUo9X5FqrZ9+ZVZ1e6N8BvSjoLuz8Z+FPMAWzdHwFQhVO/TjO2jf2fz5/Pw7Vof1zJFMGh1qJNTkVkpcsxToUdDqg1ti627KV7WDZIS8wCbjDxGgwGpXIb/+N5cWLyT5DtxVO7bnF2f/MKhMUuo0KPhS8sEYEhLR2KlQOnpTPq9hGdelzvaYVXElcKD2mBbsTgFa5TLVdxnZGx7qZMrhsdN/sVKoazTDFmREsh9aiL1KomiTjPSNywyO3zX3h35ZHg/3D3cgOzbp0FTf1zdXGjasiGtO7bAv0EtylYoTW3/6rTp2MKMHB34ZHh/Cr0gh4ubM81aNqR1x+aUKV+KG9fCGNhjOD37dza7PMZ0NSiUSF3dMMSZbknJy1VEs38XqqULsWreGlkRLzAaMapSkChy3q+e1rFXWz4aFoCbh+vjjJmveRb1ICU5hS/6jKZZ20YoFAo2rdrK4jl/okrJ/W24tj1b0XtoD1wzjy1Pcnh4uZOSrGJMv29o1KYBdRrXxsnNiXot/GnarpFZZRFezmA0WuznTfGvdPizs7MjPj4ea2trLly4QMWKFfMkz8G1ezm0fj/9pgyiROVSXD56EXsXB/w61icyNILS1coiVyrwqevL5aMX6DPxY4xGA1ePX8p1jt1rdrF33R4+nfoppSuX4vzR8zi4ONCwYyMiQu9Qvlp5FEoFVetV5cy+03QZ2pU0VRoXjl0wuzzaozuw7j4UZFIMcdFIChVBvWwmAFK3wuivnEbZrAuS6vWROLmRvnGhWfFPrt1P0PoDdJ4yAK/KJbhx7BJ2LvZU71CXiztP0WBAK7wql6TRoDb88+N6AFp80ZnzW4+/NPb5tQe4sP4gH0zpj0flEoQfvYyNiz0+Hf05v/YgzSf0waeDP1c2HwOJBP/P2qNRqbl97ApyKyVtfxyMQafj+IKtOeYJWXOQa+sOUX9qf9wqlyDy6BUKuNhTtpM/V9ccpN6kAMp29OP6puOU71qfgkVcqfJRCxKvR3JnXzB1xvVAKpNyc+vJXK2zmBV7KDV9IBK5DPXtaKxLemJbyRvXjvXQp6QRvXgHN4bNA8Da24NHZ16tWVZzeAfWPT8HqQxDfBTSQkVQL/kBAEmhwugun8Zm5A/oI29h1WUQ2uO7zbr6X79yE3+t/puJP4yhkm8Fjh8+hbOrE+07t2Lpb6sA6NS9LTFRsRw/fCrjfw1i26ZdZuTYzF+rtzDhhzH4+JZ/bo6O3dsQGxWHQinn22mjMBiMnDx6Jtc5HlP/swXbISOQyGToo+8hLeyFrGhxlI2aIXV0Im3DKuSly2H7+SiQyUhd8qtZ8Tet2srfa7bz9fQvqVClHEGHz+Dk6kjrD1uwd9tBRow39QG6fuUmOq2O4d9+iiollQ3LAnOdY+vqHWxfu5OR04ZTrkpZzhw5h6OLIy0+bMqBbYcZ8u0g1Klqbl4NY/3vGwFo2bUFcdHxZpXlRU6eCWbdpu08SknF0aEgTRv4WyRufucQnk9izON2iMDAQG7cuMHo0aNRqVS0bduWwYMHs2TJEkqUKIGjoyM1a9YEyHzfgQMH2LVrF9OnT8/2e04CinfKy2IAkGTU5HkOgNVt9C9/02sav8Mhz3O4GPOnOc/JIHn5m16Tr1ad5zkqt3/xLTFLqh74bAuFpUnI+20CcKKWbZ7naHpWl+c57GTWeZ4DYP+F3/MlT15TuJbM13y2Nt4Wi6VKDbdYrNeR51f+nTo9qZRtbW3Zv9/U275r164v/D+NGjWiUaNGz/wuCIIgCPntTWqutxQxyI8gCIIgvGPEID+CIAiCkIM3qZe+pYjKXxAEQRByYBSD/AiCIAiC8F8nrvwFQRAEIQei2V8QBEEQ3jFvY+Uvmv0FQRAE4R0jrvwFQRAEIQdv33V/PozwJwiCIAjCm0U0+wuCIAjCO0ZU/oIgCILwjhGVvyAIgiC8Y0TlLwiCIAjvGFH5C4IgCMI7RlT+giAIgvCOEZW/IAiCILxjROUvCIIgCO+Yd7Ly1+l0eRY7MjLymWWXLl3Ks3yJiYk8ePAgz+JrNBoAHj58SEhISJ7lEYS3zaZNm/7tjyAIL/ROjfB38uRJvv/+ezQaDTt37mT27NnUqFGDevXqWSxHp06d+OCDDxgwYABpaWnMmjWL27dvs3z5covlAAgMDGTOnDk4ODhgNBpJTU1lxIgRtG3b1mI5Jk+ejI+PD/Xr16dv375UrVoVqVTKpEmTXjv2sGHDkEgkL3z9559/fu0cj23evBmtVkv79u359NNPSUpK4sMPP6Rnz54WywFw+vTpZ5ZJpVK8vLxwd3e3SI6jR4+yZs0aUlJSsk028ueff1ok/mPz5s17ZplMJqNYsWK0aNECufz1RwbfsmUL7dq1y/xbo9Hw008/MWbMmNeOnVVMTAyRkZHUqFEDjUaDUqm0aHwwneD//vvvJCUlAaDVaklISGDPnj2vHXvGjBk5flf+97//vXaOrC5evMj27dt59OhRtn1s2rRp/6kcQs7eqbH9586dy/Llyxk2bBgAffr0YciQIRat/Dds2MCaNWvo2bMnOp2OQYMGMWHCBIvFf2z58uX8/fffODk5AaYWgP79+1u08r927Rrffvsty5cvp3PnzvTr14/+/ftbJHbv3r0tEic31qxZw6pVq9ixYwflypXjf//7H3379rV45b948WJOnz6Nr68vAJcvX6ZKlSrExMTQrl07Bg0a9No5pk6dyrhx4yx2MvEiiYmJXL16lQYNGiCRSDh27BilSpUiOjqaPXv2MGfOnNfOcfjwYcLCwhgxYgRnzpxh4sSJ2U4GLGHZsmXs3LmT1NRUtmzZwsyZM3Fzc7PItshqypQpjBgxglmzZjFhwgT27NlD1apVLRK7bNmyL3wtL1oxR40axcCBA3F1dbV47PzMIeTsnar85XI5Tk5OmWfRLi4uOZ5Rv4qEhAQuXryIt7c3SUlJXLhwgbp162Jra2vRPO7u7jg6Omb+7eTkRLFixSyaQ6PREBsby5YtW5g/fz46nY7k5GSLxK5VqxYAycnJLF++nJCQEKRSKT4+PgQEBFgkx2NSqRS5XM6uXbsYOnQoAOnp6RbNAaBQKNi9ezcuLi6AqQL9/vvv+f333+nRo4dFKpzixYtTt27d147zMuHh4axZsybz+zFw4EA+++wzfvvtN4uduM2aNYslS5bw4YcfYmVlxS+//EKJEiUsEvuxvXv3snbt2sx9auzYsXTv3t3ilb+1tTXvv/8+SqUSHx8ffHx8GDBgAI0aNXrt2B07dsz8/caNG5mtCxqNhunTp9OlS5fXzpFVyZIl+fDDDy1+bMzvHELO3qnK38vLi59//pkHDx6wY8cO9u7dS+nSpS2a49NPP2XMmDGZldv27dvp3bu3xe//2dnZ0b59e2rVqoXBYOD8+fMUKVKEH374AbBMU2CvXr0YOHAgbdq0wcPDg9mzZ9OiRYvXjpvV6NGjqVWrFp999hlarZZTp07x9ddf88svv1gsR6VKlWjWrBklSpSgQoUKrFixgsKFC1ss/mN3796lYMGCmX87ODhw69Yt9Hr9a59srFq1CjCd9A0fPpzq1asjk8kyX+/Vq9drxX9afHw8oaGhlC9fHoCIiAju3r1LVFQUKpXqtWI/LguAlZUVnp6eJCUlcfz4cY4fP27Rsuj1eoDMSiY9PT1PrpYLFCjAvn378PLy4qeffqJo0aJER0dbNMd3333HrVu3uHXrFlWqVOHy5ct8/PHHFs0B0KZNGzp06EC5cuWy7WOWbJLPjxxCzt6pe/4Gg4GtW7cSHByMUqmkSpUqtGrVCqnUcv0etVotCoUCnU6XeV80JSUFOzs7i+UA+O2333BwcMDa2hqAuLg4AAoVKgRkv1p4HUlJSdy9exeJREKxYsWwt7e3SNzH+vTp88z96n79+rFs2TKL5nn48CEODg4AREVF4ebmhkKhsGiORYsWsXr1asqVK4dEIuHGjRu0adMGb29v4uPjX+tq83n34LN63KJhKcePH2fWrFlERUUB4ObmxvDhwylQoABGo/G1Wh/ysyyrVq1i165d3Llzh4YNG3Ly5En69etHjx49LJYDTPvUvn376NixI8uWLeO3335j2bJl1KhRw2I5evbsyerVqwkICGDFihVER0ezYMECJk+ebLEcAM2aNWPQoEG4ubllW96wYcP/VA4hZ+/Ulf+WLVsAMu/F6XQ6tm3bRrFixSx2f+7cuXNMnTo1W6fCmjVrWryp9uTJk3Tt2pVWrVoBcPDgQf7880+WLFlisRy//fYbGzZsoEyZMhiNRsLCwujRowcDBgywWA6DwcClS5eoXLkyABcuXMBgMFgsPpg6yf3000/ExsYikUgoXLgwX375JbVr17ZonkGDBtGtWzfu3LkDQJEiRTJvAbyuxxWiwWDI7EsAcOLECd5//32L5MjKz8+PwMBAi8eF7JW7SqXi4cOHgKkZ2xKdSbPq1asXDRo04OLFiyiVSgYPHoynp6dFcwCMGzeOLl26YGdnx9ChQ6lUqRILFiyw6PdRr9eTkpICmG4peXp6cu3aNYvFf6xUqVIWv5Xwb+QQcvZOVf4nTpzgzJkz+Pn5AXDq1Cl8fHxISkrC29ubb7/99rVz/PLLL8/tVGjpyj89PT2z4gfTGfPixYstmmP37t38888/mb2j09PTLV75f/fdd0ydOpWwsDDA1Llp/PjxFosPpt7SP/30E2XKlAFMHRlHjRrF1q1bLZrnn3/+Ydu2bcyfPx+Ajz76iK5du/LBBx9YLMeYMWMoVKhQZuV/+vRpNm/ezIwZMyyWA0xX51mb5x87ceKExXLMnz+fwMBAkpKSKFy4MFFRUXTr1s1i8cG0refNm8ft27eRSCSULl2azz77LHNfsBS1Wp3t+9ioUSOLVvxg6iT7zz//0Lt3b9q2bYtcLs88llmSk5MTvXr1wsfHJ1uTvCWfKsiPHELO3qnKPykpiW3btlGgQAHA9IUdNWoUixcvtljP7/zoVAhQuHBhZsyYQbVq1TAYDJw8edLi97E9PT2fuQq3dIessmXLWvwxyKcVKlQo28G+fPnyeHl5WTzPsmXL+OOPPzL//vXXX+nbt69FK/+oqKjMfh1gemTS0h0kwXTit2/fPmxsbCwe+7HDhw+zb9++zGbsK1eusHPnTovm+Prrrxk2bBhVq1bFaDQSHBzMqFGj2Lx5s0Xz5Mf3MeuTPI0bN0alUmXr9GsptWrVyuyzlFfyI4eQs3eq8o+KiiItLS2z8tdqtYSHh5OcnExqaqpFcjzdqXDfvn0Wv8oA09Xspk2bOH78ODKZDF9fX1q3bm3RHBqNhsaNG1OlShUMBgNXr16lVKlSDB8+HLDMs/jz589n1apVPN31xBJXmI+vXB8/2lWrVi0kEglnz57Nk0eM9Ho9VlZWmX8bDIZnyvW6JBIJBw8e5L333susZCzxzP3TSpYsmSdxs5JIJBiNRvR6PWq1mkqVKjF16lSL5nB0dMzW475JkyZs2LDBojkgf76PgYGB/Pnnn8+M8bBv3z6L5gHypRe+6On/73qnKv8BAwbQqVMn7OzskEgkJCUl8emnn3LixAn69etnkRyfffYZmzdvpnr16vz4448oFArmzp1rkdhZyeXyPL9nNnDgwDyND7Bz50727t2bJ1eYly5dwsvLC6lUSpUqVVCr1QBUrFjR4rmAzObYkiVLYjAYCA8Pz7z9YykzZsxg9uzZzJw5M7Nc06dPt2gOMJ24fPDBB1SsWDFbs6wlB19q0aIFy5cvp23btrRv3x4XF5fME3NLKVmyJBMmTMDPzw+DwcCZM2coVKgQhw4dAqBBgwYWyZMf38fFixczb968PB/j4fr165m/63Q6Lly4QJkyZejQocN/KoeQs3eqt//FixdZtGgRt2/fxmg0Ym9vz/379y0yCtdjvXv3Zty4caSnp/Pjjz/yxRdfsGDBAovfj88P0dHR/Pnnn5n3S0uVKkVAQIBFDz6jRo1i6tSpeTLqWteuXdFoNNy9exdvb+9sr0kkEv766y+L51SpVISFhSGTyShZsqTFK7OnabVaJk6cyJQpUywa99SpU88sS0hIyHZf25KioqJ48OABFSpUsOjTN19//XWOr/+XHi0bNmyYRR+BzS29Xs+wYcMy+7L8V3MI2b1TV/5Tp05lxIgR/Pjjj4wfP96io3A9JpPJqFChAjNmzKBfv35Ur149T+cSyEvDhw+nffv2tGzZEjD1xB8+fDhr16597diPh/dVqVR5doW5evVq4uLimD59OqNHj37teC8TExPD/PnzefjwIb/88gvbt2+natWqFClSxGI5NmzYwC+//MKDBw9QKpUYDIY8eTyqWrVqHD16NNtwtQsXLrRo5f/0+goODsbR0dGi62vatGncvXuXa9euIZVKqVixYp709s9Lj4f3VSgUdO/eHV9f3zztJJeWlpbt77i4OG7duvWfyyHk7J2q/B+PwqVQKCw+Ctdjer2eX3/9lf379/PFF19w8eJFi/UnyG9WVlbZBlypUqUKhw8ftkjsx6PExcbGkpKSQqlSpQAIDg622MFfLpdTuHDhfLtaGjduHH369OH3338HwNnZmTFjxrBixQqL5Vi3bh179+7l448/ZsWKFezbt++5k0m9ri+++AJbW1tOnTpF48aNCQoKsvhYAvmxvv744w927NhBtWrV0Gg0zJs3jy5dulh8aOe89Hh4Xzs7u8ynhh73l5g+fbrFK//WrVtn3o+XSCTY2dnx0Ucf/edyCDl7p2b1e3oUrg0bNlh8FK6ZM2dSoEAB5s2bh5WVFZGRkUycONGiOfLazZs3uXnzJhUrVuT333/n4sWLXLp0iWXLlmWO+Pa6Hvf2DQwMxMHBIfPvcuXK5dnz5XnNYDBkjoUPUKdOHYt3+LOyssLKygqtVovBYKBJkybs3bvXojnANCjSjBkz8PLy4ttvv2X16tWZ98ktJT/W1969e9mwYQPffPMNkyZNYsOGDZnjffxX2NracuDAAVauXMnBgwc5cOAABw4cyLO+MkOGDMkcqdJgMJCcnMxvv/32n8sh5OyduvKfNWsWCQkJfPfddyxbtozQ0FCLPx/t6emZrfNgXt0jzUtPn6xkvdq3dA/dp5+PzovxCvKLXC7nxIkTGAyGzBndsvb+t4TKlSuzcuVK6tatS9++ffHw8MjsyGhJWq2We/fuIZPJuH37Np6enty+fduiOfJjfQHZ+hBIpdL/XC/z5s2bU7FiRSZPnpytJU4qlVKyZEmL51uyZAnz58/P046F+ZFDyNk71eFPMM+BAweeuSWybds22rRpY7EcX375JYUKFcr2fHRqaqrFT8ryQ1xcHD///DPBwcEoFAp8fX35/PPPnxnC9HU9npb29OnTPHjwAD8/P4sPH33ixAkePnyIs7MzY8eOJSUlhV69evH5559bLEfW9fV4uO2hQ4dmDlFtCcuXL2fz5s1UrVoVg8HAhQsX6Nq163+q2T+/DRkyhAULFvzncwg5E5W/8IxLly5x8eJF/vzzT/r06ZO5XK/X88cff1jsvj+YHvPZtGkTV69eRSaT4ePjQ+vWrS0+7n5+2LBhwzOPey1dutQi0yDn95zukZGRzwyElHUYZku5du1a5tMkZcqUyez7YUmRkZGEhIQgkUioUKGCRTsUvo2++eYbbty4QdWqVfOsY2F+5BBy9k41+wu54+rqio2NDVqtlgcPHmQul0gkFn+mPD+ej85rx44d4+jRo+zcuTNb07hOp+Off/6xSOWf33O6Dxs2jA8++IABAwaQlpbGrFmzuH37tkVHY5w4cSKXL1/G19cXg8HA77//TvXq1Rk7duxrx37RydK5c+cAUcnkpHr16lSvXv0/n0PImbjyF14oMTERo9GIi4tL5lSi9erVy5P7sv9lKSkpXLlyhcmTJ2eb90AikVCpUiWLj/D4vDndLT1PgV6vZ82aNWzduhWdTsegQYMsPp1z586ds421YDAY6N69O+vXr3/t2I+n0L537x5KpRI3NzeioqJYsmQJo0aNonv37q+dQxD+y96p3v6CeSZPnkxwcDCRkZEMHz6cGzdu5Mvz8v81dnZ21K5dmzVr1lC1alU6duxIkSJFSEpKstisfo999913TJw4keHDh7N48WJGjx7Nhx9+aNEcYBrQ5+LFi3h7e+Pq6sqFCxdQqVQWzeHt7U1sbGzm34mJiZQuXdoisTt27EjHjh05efIk9erVo0SJEgQFBbFo0SKLDuolCP9VovIXXighIYGmTZuyY8cOAgIC+PTTTzOnXxWeNWLECOLi4rhx4wYzZszA2dn5pSPMmevmzZusXLmSUqVKZU65/HhGREv69NNP6dy5M1OnTmXhwoX4+Phkjs1gKeHh4TRt2pS2bdvSunVrGjVqxOXLl/nwww/p3LmzRXI8HnRr165d9O3bl+rVq6PX6y0SWxD+y8Q9f+GF1Go1Z8+eZcuWLfz5558kJyeLyj8HGo2G2rVr88svv9CvXz/atm1r8TEL8mtO9y+//JIpU6ag0WjYuXMnoaGhFu3pD5adJ+BFnjfolqVbMAThv0hU/sILDR8+nD/++IOBAwfi7OzMggULsvX+F7LTaDRs2bKF7du3s3HjRiIjI3n06JFFc+TXnO4LFixg+fLlmRMT9enThyFDhtC4cePXjp2f4+3PnDmTXbt2/acH3RKEvCAqf+GF6tatS82aNYmPjwdMz+YKLzZ+/Hg2btzIhAkTsLOz4++//+aLL76waI78mtNdLpfj5OSU2WPexcXFYoPjPO44uH//fqRSKbVq1cJoNBIUFGTxCZ7ehkG3BCEviMpfeKEdO3ZkDsSxbds2pkyZgo+Pj5h28wUqVKjAgAEDuHfvHgBdunSxeGWWX3O6e3l58fPPP/PgwQN27NjB3r17LfbUwuOJiJYvX87SpUszl7du3ZpPPvnEIjkEQciZqPyFF1q5ciWBgYGZj6+NGjWKgIAAUfm/wLJly9i5cyepqals2bKFmTNn4ubmxqBBgyyWI7/mdJ88eTJbt26levXqBAcH07hx48zZHS0lKSmJAwcOULVqVaRSKZcuXSImJsaiOQRBeD5R+QsvJJPJUCqVmc29lr6Kfdvs3buXtWvXEhAQAMDYsWPp3r27RSv/UqVKUaJECYvFexGpVEr79u1p3759nuWYMWMGCxYs4KeffsJoNFKyZEmL3u8XBOHFROUvvFC1atUYNWoUsbGxLFq0iAMHDuRJ57K3xeNHyB6fLKWnp1ts9L38ntM9Lz2ey71o0aJMmzYt8/bFf23CHUH4LxMj/AkvZDQaOXv2bLaJat57771/+2O9sX777TeOHz/OnTt3aNiwIUFBQfTt25cePXq8duzHI9ZFR0fj6ekJZJ/TPSgo6LVz5JfGjRtnq+gfPnyIVCrNnOLV0v0XBEF4lrjyF14oICCAlStXUqNGjX/7o/wnHD16lBkzZnDhwgWUSiWDBw/OrKhfl62tLdu2bePMmTPUrFkTo9GIRCJBp9PlyZzueWn//v2AaU6ESZMm4enpiVarRSqVMmnSpH/50wnCu0Fc+QsvNHr0aHQ6HZUrV842y17WOcWFJ0aMGEF0dPQz68tSTfKRkZHPzB/weE53Z2dni+TIT927d+eXX37JnMI3OjqaL7/8ktWrV//Ln0wQ3n7iyl94oS1btvDZZ59ljigHZPtdyK5+/fp5Gt/Ly4uFCxfmaY78pFAoMit+MD2TL5eLQ5Ig5Adx5S88Y/fu3Wzbto3Tp09nDsACpg5tISEhmc22gvA6vv76a6ytrbMN8qPX65kyZcq//dEE4a0nKn/hud62JmbhzaPT6di2bRuXL19GIpFQuXJlWrdune0pBkEQ8oao/AVBEAThHSOm9BUEQRCEd4yo/AVBEAThHSMqf0EQBEF4x4jKXxAEQRDeMaLyFwRBEIR3zP8BAAGR0MxwpSMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Re-reading the dataset for training and testing various models\n",
        "data = pd.read_csv('/content/dataset.csv')\n",
        "data.dropna()"
      ],
      "metadata": {
        "id": "QRBsylvaxGez",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "d5239eec-ff2f-4dc8-e81e-e71091f3afdb"
      },
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0     63    1   3       145   233    1        0      150      0      2.3   \n",
              "1     37    1   2       130   250    0        1      187      0      3.5   \n",
              "2     41    0   1       130   204    0        0      172      0      1.4   \n",
              "3     56    1   1       120   236    0        1      178      0      0.8   \n",
              "4     57    0   0       120   354    0        1      163      1      0.6   \n",
              "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
              "298   57    0   0       140   241    0        1      123      1      0.2   \n",
              "299   45    1   3       110   264    0        1      132      0      1.2   \n",
              "300   68    1   0       144   193    1        1      141      0      3.4   \n",
              "301   57    1   0       130   131    0        1      115      1      1.2   \n",
              "302   57    0   1       130   236    0        0      174      0      0.0   \n",
              "\n",
              "     slope  ca  thal  num  \n",
              "0        0   0     1    1  \n",
              "1        0   0     2    1  \n",
              "2        2   0     2    1  \n",
              "3        2   0     2    1  \n",
              "4        2   0     2    1  \n",
              "..     ...  ..   ...  ...  \n",
              "298      1   0     3    0  \n",
              "299      1   0     3    0  \n",
              "300      1   2     3    0  \n",
              "301      1   1     3    0  \n",
              "302      1   1     2    0  \n",
              "\n",
              "[303 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9a7e1b97-015a-42d2-abac-46e741e71da4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>123</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 14 columns</p>\n",
              "</div>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply logarithmic transformation to 'trestbps', 'chol', and 'thalach' columns in the 'data' DataFrame\n",
        "data['trestbps'] = np.log(data['trestbps'])\n",
        "data['chol'] = np.log(data['chol'])\n",
        "data['thalach'] = np.log(data['thalach'])\n",
        "\n",
        "# Calculate the variance of the 'trestbps', 'chol', and 'thalach' columns in the 'data' DataFrame\n",
        "np.var(data[[\"trestbps\",'chol','thalach']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnIvkXYHxPNj",
        "outputId": "3c16fb0b-2259-41ed-fa0d-196bda659766"
      },
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "trestbps    0.016894\n",
              "chol        0.041401\n",
              "thalach     0.027054\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 314
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assign all columns except for 'num' to the 'x' variable\n",
        "x = data.drop(['num'], axis=1)\n",
        "\n",
        "# Assign only the 'num' column to the 'y' variable\n",
        "y = data['num']"
      ],
      "metadata": {
        "id": "FW0rnk3xxTOw"
      },
      "execution_count": 315,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets with a 80/20 split, a random state of 807, and assign to respective variables\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=807)\n",
        "\n",
        "# Initialize empty dictionaries for the accuracy, sensitivity, specificity, and ROC_AUC metrics\n",
        "accuracy_dictionary = {}\n",
        "sensitivity_dictionary = {}\n",
        "specificity_dictionary = {}\n",
        "ROC_AUC_dictionary = {}"
      ],
      "metadata": {
        "id": "GYaI5X0DxVbK"
      },
      "execution_count": 316,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest"
      ],
      "metadata": {
        "id": "Rffz7CHOvdi-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the 'RandomForestClassifier' class from the 'ensemble' module in the 'sklearn' library, and the 'accuracy_score' function from the 'metrics' module in the 'sklearn' library\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Instantiate a 'RandomForestClassifier' object with a random state, and assign to the 'model' variable\n",
        "model = RandomForestClassifier(random_state=323)\n",
        "\n",
        "# Fit the 'model' object to the training data\n",
        "model.fit(x_train,y_train)\n",
        "\n",
        "# Predict the target variable for the test data using the trained 'model', and assign to the 'y_pred_RF' variable\n",
        "y_pred_RF = model.predict(x_test)\n",
        "\n",
        "# Calculate the accuracy score between the predicted and actual target variable values, and assign to the 'accuracy' variable\n",
        "accuracy = accuracy_score(y_test, y_pred_RF)\n",
        "\n",
        "# Calculate the ROC AUC score between the predicted and actual target variable probabilities, and assign to the 'roc_auc' variable\n",
        "roc_auc = roc_auc_score(y_test, model.predict_proba(x_test)[:, 1])\n",
        "\n",
        "# Calculate the number of true negatives (tn), false positives (fp), false negatives (fn), and true positives (tp) in the confusion matrix for the predicted and actual target variable values, and assign to their respective variables\n",
        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_RF).ravel()\n",
        "\n",
        "# Calculate the sensitivity (i.e., true positive rate) by dividing the number of true positives by the sum of true positives and false negatives, and assign to the 'sensitivity' variable\n",
        "sensitivity = tp / (tp + fn)\n",
        "\n",
        "# Calculate the specificity (i.e., true negative rate) by dividing the number of true negatives by the sum of true negatives and false positives, and assign to the 'specificity' variable\n",
        "specificity = tn / (tn + fp)\n",
        "\n",
        "# Print the accuracy, ROC AUC, sensitivity, and specificity metrics for the 'RandomForestClassifier' model\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"ROC AUC:\", roc_auc)\n",
        "print(\"Sensitivity:\", sensitivity)\n",
        "print(\"Specificity:\", specificity)\n",
        "\n",
        "# Add the accuracy, sensitivity, specificity, and ROC AUC metrics for the 'RandomForestClassifier' model to their respective dictionaries\n",
        "accuracy_dictionary[\"Random Forest\"]= accuracy\n",
        "sensitivity_dictionary[\"Random Forest\"]= sensitivity\n",
        "specificity_dictionary[\"Random Forest\"]= specificity\n",
        "ROC_AUC_dictionary[\"Random Forest\"]= roc_auc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3yJMAFyxXjh",
        "outputId": "316ee37d-b96f-4c38-b8c9-4a58bcef4068"
      },
      "execution_count": 317,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9508196721311475\n",
            "ROC AUC: 0.9707792207792209\n",
            "Sensitivity: 0.9393939393939394\n",
            "Specificity: 0.9642857142857143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gradient Boost"
      ],
      "metadata": {
        "id": "fFm4kIl-4D56"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libraries and load the data\n",
        "# Assuming x_train, x_test, y_train, y_test are already loaded and split from the original dataset\n",
        "\n",
        "# Define the Gradient Boosting Classifier model with the following hyperparameters\n",
        "# 1. Random state for reproducibility of the results\n",
        "# 2. Number of trees in the forest = 500\n",
        "# 3. Maximum number of nodes allowed in each tree = 3\n",
        "# 4. Loss function to be optimized = 'exponential'\n",
        "# 5. Minimum number of samples required to be at a leaf node = 20\n",
        "model = GradientBoostingClassifier(random_state=1, n_estimators=500, max_leaf_nodes=3, loss='exponential', min_samples_leaf=20)\n",
        "\n",
        "# Fit the Gradient Boosting Classifier model on the training data\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# Predict the target variable on the test data using the trained model\n",
        "y_pred_GB = model.predict(x_test)\n",
        "\n",
        "# Calculate the accuracy of the model using the predicted and actual values of the target variable\n",
        "accuracy = accuracy_score(y_test, y_pred_GB)\n",
        "\n",
        "# Calculate the ROC AUC score of the model using the predicted probabilities of the target variable on the test data\n",
        "roc_auc = roc_auc_score(y_test, model.predict_proba(x_test)[:, 1])\n",
        "\n",
        "# Calculate the confusion matrix values of the model using the predicted and actual values of the target variable\n",
        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_GB).ravel()\n",
        "\n",
        "# Calculate the sensitivity of the model using the confusion matrix values\n",
        "sensitivity = tp / (tp + fn)\n",
        "\n",
        "# Calculate the specificity of the model using the confusion matrix values\n",
        "specificity = tn / (tn + fp)\n",
        "\n",
        "# Print the values of the metrics\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"ROC AUC:\", roc_auc)\n",
        "print(\"Sensitivity:\", sensitivity)\n",
        "print(\"Specificity:\", specificity)\n",
        "\n",
        "# Store the metric values in separate dictionaries for each model for comparison purposes\n",
        "accuracy_dictionary[\"Gradient Boost\"]= accuracy\n",
        "sensitivity_dictionary[\"Gradient Boost\"]= sensitivity\n",
        "specificity_dictionary[\"Gradient Boost\"]= specificity\n",
        "ROC_AUC_dictionary[\"Gradient Boost\"]= roc_auc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDuQj_V9ywD3",
        "outputId": "6ff0fd2b-020e-4742-9c14-c11f44936ee9"
      },
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9016393442622951\n",
            "ROC AUC: 0.9577922077922078\n",
            "Sensitivity: 0.9393939393939394\n",
            "Specificity: 0.8571428571428571\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Adaboost"
      ],
      "metadata": {
        "id": "TXIPa_CW5tp9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libraries and load the data\n",
        "# Assuming x_train, x_test, y_train, y_test are already loaded and split from the original dataset\n",
        "\n",
        "# Define the AdaBoost Classifier model with the following hyperparameters\n",
        "# 1. Number of estimators (weak learners) in the ensemble = 3\n",
        "model = AdaBoostClassifier(n_estimators=3)\n",
        "\n",
        "# Fit the AdaBoost Classifier model on the training data\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# Predict the target variable on the test data using the trained model\n",
        "y_pred_AB = model.predict(x_test)\n",
        "\n",
        "# Calculate the accuracy of the model using the predicted and actual values of the target variable\n",
        "accuracy = accuracy_score(y_test, y_pred_AB)\n",
        "\n",
        "# Calculate the ROC AUC score of the model using the predicted probabilities of the target variable on the test data\n",
        "roc_auc = roc_auc_score(y_test, model.predict_proba(x_test)[:, 1])\n",
        "\n",
        "# Calculate the confusion matrix values of the model using the predicted and actual values of the target variable\n",
        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_AB).ravel()\n",
        "\n",
        "# Calculate the sensitivity of the model using the confusion matrix values\n",
        "sensitivity = tp / (tp + fn)\n",
        "\n",
        "# Calculate the specificity of the model using the confusion matrix values\n",
        "specificity = tn / (tn + fp)\n",
        "\n",
        "# Print the values of the metrics\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"ROC AUC:\", roc_auc)\n",
        "print(\"Sensitivity:\", sensitivity)\n",
        "print(\"Specificity:\", specificity)\n",
        "\n",
        "# Store the metric values in separate dictionaries for each model for comparison purposes\n",
        "accuracy_dictionary[\"Adaboost\"]= accuracy\n",
        "sensitivity_dictionary[\"Adaboost\"]= sensitivity\n",
        "specificity_dictionary[\"Adaboost\"]= specificity\n",
        "ROC_AUC_dictionary[\"Adaboost\"]= roc_auc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gesd4hz43o8s",
        "outputId": "1cc9b959-fc2b-4010-cd35-0e46b198185b"
      },
      "execution_count": 319,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9016393442622951\n",
            "ROC AUC: 0.9231601731601732\n",
            "Sensitivity: 0.9393939393939394\n",
            "Specificity: 0.8571428571428571\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Extra Trees Classifier"
      ],
      "metadata": {
        "id": "V_HK0Ors50sM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libraries and load the data\n",
        "# Assuming x_train, x_test, y_train, y_test are already loaded and split from the original dataset\n",
        "\n",
        "# Define the Extra Trees Classifier model with the following hyperparameters\n",
        "# 1. Number of estimators (decision trees) in the ensemble = 15\n",
        "# 2. Random state for reproducibility of results = 47\n",
        "model = ExtraTreesClassifier(n_estimators=15, random_state=47)\n",
        "\n",
        "# Fit the Extra Trees Classifier model on the training data\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# Predict the target variable on the test data using the trained model\n",
        "y_pred_ET = model.predict(x_test)\n",
        "\n",
        "# Calculate the accuracy of the model using the predicted and actual values of the target variable\n",
        "accuracy = accuracy_score(y_test, y_pred_ET)\n",
        "\n",
        "# Calculate the ROC AUC score of the model using the predicted probabilities of the target variable on the test data\n",
        "roc_auc = roc_auc_score(y_test, model.predict_proba(x_test)[:, 1])\n",
        "\n",
        "# Calculate the confusion matrix values of the model using the predicted and actual values of the target variable\n",
        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_ET).ravel()\n",
        "\n",
        "# Calculate the sensitivity of the model using the confusion matrix values\n",
        "sensitivity = tp / (tp + fn)\n",
        "\n",
        "# Calculate the specificity of the model using the confusion matrix values\n",
        "specificity = tn / (tn + fp)\n",
        "\n",
        "# Print the values of the metrics\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"ROC AUC:\", roc_auc)\n",
        "print(\"Sensitivity:\", sensitivity)\n",
        "print(\"Specificity:\", specificity)\n",
        "\n",
        "# Store the metric values in separate dictionaries for each model for comparison purposes\n",
        "accuracy_dictionary[\"Extra Trees Classifier\"]= accuracy\n",
        "sensitivity_dictionary[\"Extra Trees Classifier\"]= sensitivity\n",
        "specificity_dictionary[\"Extra Trees Classifier\"]= specificity\n",
        "ROC_AUC_dictionary[\"Extra Trees Classifier\"]= roc_auc\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIW2lzMe5yq8",
        "outputId": "6f3afeff-d1db-4446-b2b3-37428395150e"
      },
      "execution_count": 320,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9016393442622951\n",
            "ROC AUC: 0.9707792207792207\n",
            "Sensitivity: 0.9696969696969697\n",
            "Specificity: 0.8214285714285714\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decision Trees"
      ],
      "metadata": {
        "id": "86H1PF6a6J40"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libraries and modules\n",
        "\n",
        "# Create an instance of the Decision Tree Classifier (DTC) model with maximum depth=3, minimum samples per leaf=5, entropy criterion, minimum samples for splitting=5, random splitter, and random_state=1 for reproducibility\n",
        "model = DecisionTreeClassifier(max_depth=3, min_samples_leaf=5, criterion='entropy', min_samples_split=5,splitter='random', random_state=1)\n",
        "\n",
        "# Train the DTC model using the training data (x_train, y_train)\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# Use the trained DTC model to make predictions on the test data (x_test)\n",
        "# The predicted values are stored in y_pred_DTC\n",
        "y_pred_DTC = model.predict(x_test)\n",
        "\n",
        "# Compute the accuracy score using the actual test labels (y_test) and the predicted labels (y_pred_DTC)\n",
        "accuracy = accuracy_score(y_test, y_pred_DTC)\n",
        "\n",
        "# Compute the ROC AUC score using the actual test labels (y_test) and the predicted class probabilities (model.predict_proba(x_test))\n",
        "roc_auc = roc_auc_score(y_test, model.predict_proba(x_test)[:, 1])\n",
        "\n",
        "# Compute the true negative (tn), false positive (fp), false negative (fn), and true positive (tp) rates using the confusion matrix\n",
        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_DTC).ravel()\n",
        "\n",
        "# Compute the sensitivity (true positive rate) and specificity (true negative rate) using the tn, fp, fn, and tp values from the confusion matrix\n",
        "sensitivity = tp / (tp + fn)\n",
        "specificity = tn / (tn + fp)\n",
        "\n",
        "# Print the computed evaluation metrics: accuracy, ROC AUC, sensitivity, and specificity\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"ROC AUC:\", roc_auc)\n",
        "print(\"Sensitivity:\", sensitivity)\n",
        "print(\"Specificity:\", specificity)\n",
        "\n",
        "# Store the computed evaluation metrics in dictionaries for later analysis or comparison with other models\n",
        "accuracy_dictionary[\"Decision Trees\"] = accuracy\n",
        "sensitivity_dictionary[\"Decision Trees\"] = sensitivity\n",
        "specificity_dictionary[\"Decision Trees\"] = specificity\n",
        "ROC_AUC_dictionary[\"Decision Trees\"] = roc_auc\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUsI0Z315_hf",
        "outputId": "eb642c41-bb90-4c9b-dea8-cb83b58cecea"
      },
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.819672131147541\n",
            "ROC AUC: 0.8571428571428572\n",
            "Sensitivity: 0.9393939393939394\n",
            "Specificity: 0.6785714285714286\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gaussian Naive Bayes"
      ],
      "metadata": {
        "id": "LT3o2PfY6YXF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libraries and load the data\n",
        "# Assuming x_train, x_test, y_train, y_test are already loaded and split from the original dataset\n",
        "\n",
        "# Define the GNB model with the following hyperparameters\n",
        "# 1. Smoothing parameter for variance regularization = 0.1\n",
        "model = GaussianNB(var_smoothing=0.1)\n",
        "\n",
        "# Fit the GNB model on the training data\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# Predict the target variable on the test data using the trained model\n",
        "y_pred_GNB = model.predict(x_test)\n",
        "\n",
        "# Calculate the accuracy of the model using the predicted and actual values of the target variable\n",
        "accuracy = accuracy_score(y_test, y_pred_GNB)\n",
        "\n",
        "# Calculate the ROC AUC score of the model using the predicted probabilities of the target variable on the test data\n",
        "roc_auc = roc_auc_score(y_test, model.predict_proba(x_test)[:, 1])\n",
        "\n",
        "# Calculate the confusion matrix values of the model using the predicted and actual values of the target variable\n",
        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_GNB).ravel()\n",
        "\n",
        "# Calculate the sensitivity of the model using the confusion matrix values\n",
        "sensitivity = tp / (tp + fn)\n",
        "\n",
        "# Calculate the specificity of the model using the confusion matrix values\n",
        "specificity = tn / (tn + fp)\n",
        "\n",
        "# Print the values of the metrics\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"ROC AUC:\", roc_auc)\n",
        "print(\"Sensitivity:\", sensitivity)\n",
        "print(\"Specificity:\", specificity)\n",
        "\n",
        "# Store the metric values in separate dictionaries for each model for comparison purposes\n",
        "accuracy_dictionary[\"GNB\"]= accuracy\n",
        "sensitivity_dictionary[\"GNB\"]= sensitivity\n",
        "specificity_dictionary[\"GNB\"]= specificity\n",
        "ROC_AUC_dictionary[\"GNB\"]= roc_auc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fydOGJn6QGs",
        "outputId": "28d3a4dd-d70a-448e-a543-51f872e0b3fd"
      },
      "execution_count": 322,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8688524590163934\n",
            "ROC AUC: 0.9199134199134199\n",
            "Sensitivity: 0.9393939393939394\n",
            "Specificity: 0.7857142857142857\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Support Vector Machines"
      ],
      "metadata": {
        "id": "cKl7Bj4j6mP6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libraries and modules\n",
        "\n",
        "# Create an instance of the Support Vector Machine (SVM) classifier model with linear kernel and C=10\n",
        "# Also, set probability to True to enable the calculation of ROC AUC score later\n",
        "model = SVC(kernel='linear', C=10, probability=True)\n",
        "\n",
        "# Train the SVM classifier model using the training data (x_train, y_train)\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "# Use the trained SVM classifier model to make predictions on the test data (x_test)\n",
        "# The predicted values are stored in y_pred_SVM\n",
        "y_pred_SVM = model.predict(x_test)\n",
        "\n",
        "# Compute the accuracy score using the actual test labels (y_test) and the predicted labels (y_pred_SVM)\n",
        "accuracy = accuracy_score(y_test, y_pred_SVM)\n",
        "\n",
        "# Compute the ROC AUC score using the actual test labels (y_test) and the predicted class probabilities (model.predict_proba(x_test))\n",
        "roc_auc = roc_auc_score(y_test, model.predict_proba(x_test)[:, 1])\n",
        "\n",
        "# Compute the true negative (tn), false positive (fp), false negative (fn), and true positive (tp) rates using the confusion matrix\n",
        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_SVM).ravel()\n",
        "\n",
        "# Compute the sensitivity (true positive rate) and specificity (true negative rate) using the tn, fp, fn, and tp values from the confusion matrix\n",
        "sensitivity = tp / (tp + fn)\n",
        "specificity = tn / (tn + fp)\n",
        "\n",
        "# Print the computed evaluation metrics: accuracy, ROC AUC, sensitivity, and specificity\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"ROC AUC:\", roc_auc)\n",
        "print(\"Sensitivity:\", sensitivity)\n",
        "print(\"Specificity:\", specificity)\n",
        "\n",
        "# Store the computed evaluation metrics in dictionaries for later analysis or comparison with other models\n",
        "accuracy_dictionary[\"SVM\"] = accuracy\n",
        "sensitivity_dictionary[\"SVM\"] = sensitivity\n",
        "specificity_dictionary[\"SVM\"] = specificity\n",
        "ROC_AUC_dictionary[\"SVM\"] = roc_auc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oA5Doh-06d_k",
        "outputId": "1dd4dc70-f84e-4a07-d712-a75f97dc8082"
      },
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9180327868852459\n",
            "ROC AUC: 0.9496753246753247\n",
            "Sensitivity: 0.9393939393939394\n",
            "Specificity: 0.8928571428571429\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a list of the accuracy values from the accuracy dictionary\n",
        "accuracy = accuracy_dictionary.items()\n",
        "\n",
        "# Sort the list of accuracy values by key (i.e., model name)\n",
        "accuracy = sorted(accuracy) \n",
        "\n",
        "# Unpack the keys and values from the sorted accuracy list into separate x and y lists\n",
        "x, y = zip(*accuracy) \n",
        "\n",
        "# Create a line plot of the accuracy measures for each model\n",
        "plt.plot(x, y)\n",
        "\n",
        "# Rotate the x-axis labels by 45 degrees for better readability\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "# Add x- and y-axis labels\n",
        "plt.xlabel(\"Models\")\n",
        "plt.ylabel(\"Accuracy Measure\")\n",
        "\n",
        "# Add a title to the plot\n",
        "plt.title(\"Accuracy Comparison\")\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "yYy75tTmlHDl",
        "outputId": "a63e3b41-ff53-4999-d37a-30aebb79f363"
      },
      "execution_count": 324,
      "outputs": [
        {
          "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": [
        "# Get a list of the sensitivity values from the sensitivity dictionary\n",
        "sstvy = sensitivity_dictionary.items()\n",
        "\n",
        "# Sort the list of sensitivity values by key (i.e., model name)\n",
        "sstvy = sorted(sstvy) \n",
        "\n",
        "# Unpack the keys and values from the sorted sensitivity list into separate x and y lists\n",
        "x, y = zip(*sstvy) \n",
        "\n",
        "# Create a line plot of the sensitivity measures for each model\n",
        "plt.plot(x, y)\n",
        "\n",
        "# Rotate the x-axis labels by 45 degrees for better readability\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "# Add x- and y-axis labels\n",
        "plt.xlabel(\"Models\")\n",
        "plt.ylabel(\"Sensitivity Measure\")\n",
        "\n",
        "# Add a title to the plot\n",
        "plt.title(\"Sensitivity Comparison\")\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "58PuyTMOmK_i",
        "outputId": "71f0cb3d-28ea-4585-d0f3-25c6f93293e9"
      },
      "execution_count": 325,
      "outputs": [
        {
          "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": [
        "# Get a list of the specificity values from the specificity dictionary\n",
        "sp = specificity_dictionary.items()\n",
        "\n",
        "# Sort the list of specificity values by key (i.e., model name)\n",
        "sp = sorted(sp) \n",
        "\n",
        "# Unpack the keys and values from the sorted specificity list into separate x and y lists\n",
        "x, y = zip(*sp) \n",
        "\n",
        "# Create a line plot of the specificity measures for each model\n",
        "plt.plot(x, y)\n",
        "\n",
        "# Rotate the x-axis labels by 45 degrees for better readability\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "# Add x- and y-axis labels\n",
        "plt.xlabel(\"Models\")\n",
        "plt.ylabel(\"Specificity Measure\")\n",
        "\n",
        "# Add a title to the plot\n",
        "plt.title(\"Specificity Comparison\")\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "8jXuU0H-mK8n",
        "outputId": "0effa705-4fe2-4774-d476-ff22dea7e84f"
      },
      "execution_count": 326,
      "outputs": [
        {
          "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": [
        "# Get a list of the ROC-AUC values from the ROC-AUC dictionary\n",
        "roc = ROC_AUC_dictionary.items()\n",
        "\n",
        "# Sort the list of ROC-AUC values by key (i.e., model name)\n",
        "roc = sorted(roc) \n",
        "\n",
        "# Unpack the keys and values from the sorted ROC-AUC list into separate x and y lists\n",
        "x, y = zip(*roc) \n",
        "\n",
        "# Create a line plot of the ROC-AUC measures for each model\n",
        "plt.plot(x, y)\n",
        "\n",
        "# Rotate the x-axis labels by 45 degrees for better readability\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "# Add x- and y-axis labels\n",
        "plt.xlabel(\"Models\")\n",
        "plt.ylabel(\"ROC-AUC Measure\")\n",
        "\n",
        "# Add a title to the plot\n",
        "plt.title(\"ROC-AUC Comparison\")\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "ANqgp-FhmK5s",
        "outputId": "2e1b9f83-2467-432e-dd54-efdfbdf0d549"
      },
      "execution_count": 327,
      "outputs": [
        {
          "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": [
        "# Final Funtion"
      ],
      "metadata": {
        "id": "XKwx1XSzDLi-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function for heart disease classification using a trained model and test data\n",
        "def heart_disease_classification(x_test, model):\n",
        "  \n",
        "  # Check that the test data has the correct number of features\n",
        "  if(len(x_test)==13):\n",
        "    \n",
        "    # Use the trained model to predict the class probabilities for the test data\n",
        "    y_prob_pred=model.predict_proba([x_test])\n",
        "    \n",
        "    # Print the predicted probabilities for each class\n",
        "    print(\"Probability of NOT GETTING a heart disease:\", y_prob_pred[0][0])\n",
        "    print(\"Probability of GETTING a heart disease:\", y_prob_pred[0][1])\n",
        "\n",
        "    return\n",
        "  \n",
        "  # If the test data has the wrong number of features, print an error message and return nothing\n",
        "  else:\n",
        "    print(\"List should contain 13 parameters. Please check!\")\n",
        "    return"
      ],
      "metadata": {
        "id": "QpYh0QwZB6uC"
      },
      "execution_count": 328,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample list containing 13 values corresponding to age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal respectively\n",
        "x_check=[70,1,0,4.976734,5.159055,0,1,4.828314,1,2.6,0,0,3]\n",
        "\n",
        "# Copy the model code for training and predicting\n",
        "# Currently the code is for our best performing model Random Forest\n",
        "model = RandomForestClassifier(random_state=323)\n",
        "model.fit(x_train,y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# Run the function by passing your list of attribute values and the model respectively\n",
        "heart_disease_classification(x_check,model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-acPdM8DNkh",
        "outputId": "f7bc863e-ff49-47de-f57a-ac728f48b9b4"
      },
      "execution_count": 329,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probability of NOT GETTING a heart disease: 0.94\n",
            "Probability of GETTING a heart disease: 0.06\n"
          ]
        }
      ]
    }
  ]
}