CA_2.ipynb

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        "<a href=\"https://colab.research.google.com/github/HosnawHb/KNN_and_Naive_Bayes/blob/main/CA_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Importing Libraries"
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      "execution_count": 434,
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      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score,cross_validate\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, mean_squared_error\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import f1_score\n",
        "import numpy as np\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "import warnings\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import KFold"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Loading"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data'"
      ],
      "metadata": {
        "id": "-Dj_gszUltYo"
      },
      "execution_count": 435,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "column = ['Sex', 'Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell_weight','Rings']"
      ],
      "metadata": {
        "id": "v6gJxxE0jK0N"
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      "execution_count": 436,
      "outputs": []
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      "source": [
        "df = pd.read_csv(url,names=column)"
      ],
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        "id": "6n_Mkn7jn3X7"
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      "outputs": []
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        "df.head()"
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              "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
              "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
              "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
              "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
              "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
              "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
              "\n",
              "   Shell_weight  Rings  \n",
              "0         0.150     15  \n",
              "1         0.070      7  \n",
              "2         0.210      9  \n",
              "3         0.155     10  \n",
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        "categorical = ['Sex']\n",
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              "   Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
              "0    2   0.455     0.365   0.095        0.5140          0.2245   \n",
              "1    2   0.350     0.265   0.090        0.2255          0.0995   \n",
              "2    0   0.530     0.420   0.135        0.6770          0.2565   \n",
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              "4    1   0.330     0.255   0.080        0.2050          0.0895   \n",
              "\n",
              "   Viscera weight  Shell_weight  Rings  \n",
              "0          0.1010         0.150     15  \n",
              "1          0.0485         0.070      7  \n",
              "2          0.1415         0.210      9  \n",
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 440
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Confusion Matrix"
      ],
      "metadata": {
        "id": "pRGsV_CVUg2y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusionmatrix(y_train_pred,y_train,dom):\n",
        "    print(f'{dom} Confusion matrix')\n",
        "    cf = confusion_matrix(y_train_pred,y_train)\n",
        "    sns.heatmap(cf,annot=True,yticklabels=classes, xticklabels=classes,\n",
        "                cmap='Blues', fmt='g')\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "ukBOBOPNUgJ0"
      },
      "execution_count": 441,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1."
      ],
      "metadata": {
        "id": "OkZKgLcvE4uJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PreProcess"
      ],
      "metadata": {
        "id": "m3ossKfKFg8b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['Sex']\n",
        "X = df.drop(['Sex'],axis=1)"
      ],
      "metadata": {
        "id": "QS3Y_RCxpEGj"
      },
      "execution_count": 442,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "CrSuhk2ntMIJ",
        "outputId": "3a0b2306-5ecd-425e-9a99-dd366891b61d"
      },
      "execution_count": 443,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
              "0   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
              "1   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
              "2   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
              "3   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
              "4   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
              "\n",
              "   Shell_weight  Rings  \n",
              "0         0.150     15  \n",
              "1         0.070      7  \n",
              "2         0.210      9  \n",
              "3         0.155     10  \n",
              "4         0.055      7  "
            ],
            "text/html": [
              "\n",
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              "    <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",
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              "\n",
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              "        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>Length</th>\n",
              "      <th>Diameter</th>\n",
              "      <th>Height</th>\n",
              "      <th>Whole weight</th>\n",
              "      <th>Shucked weight</th>\n",
              "      <th>Viscera weight</th>\n",
              "      <th>Shell_weight</th>\n",
              "      <th>Rings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.455</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.095</td>\n",
              "      <td>0.5140</td>\n",
              "      <td>0.2245</td>\n",
              "      <td>0.1010</td>\n",
              "      <td>0.150</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.350</td>\n",
              "      <td>0.265</td>\n",
              "      <td>0.090</td>\n",
              "      <td>0.2255</td>\n",
              "      <td>0.0995</td>\n",
              "      <td>0.0485</td>\n",
              "      <td>0.070</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.530</td>\n",
              "      <td>0.420</td>\n",
              "      <td>0.135</td>\n",
              "      <td>0.6770</td>\n",
              "      <td>0.2565</td>\n",
              "      <td>0.1415</td>\n",
              "      <td>0.210</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.440</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.5160</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.1140</td>\n",
              "      <td>0.155</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.330</td>\n",
              "      <td>0.255</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.0895</td>\n",
              "      <td>0.0395</td>\n",
              "      <td>0.055</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7845c34-3408-4e5e-97d0-8b6c2e46399f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b7845c34-3408-4e5e-97d0-8b6c2e46399f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b7845c34-3408-4e5e-97d0-8b6c2e46399f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 443
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1)"
      ],
      "metadata": {
        "id": "V_V5Sn4QtpCq"
      },
      "execution_count": 444,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "\n",
        "X_train = scaler.transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "9oUW4EpJ-oiW"
      },
      "execution_count": 445,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KNN Classification"
      ],
      "metadata": {
        "id": "xNgFtRdkBY3p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Finding The Best K For KNN Classification"
      ],
      "metadata": {
        "id": "8aQ0Y2aX__Qw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f1s=[]\n",
        "for i in range(1, 50):\n",
        "    knn = KNeighborsClassifier(n_neighbors=i)\n",
        "    knn.fit(X_train, y_train)\n",
        "    pred_i = knn.predict(X_test)\n",
        "    f1s.append(f1_score(y_test, pred_i, average='weighted'))\n"
      ],
      "metadata": {
        "id": "atu0sWxP_ZOo"
      },
      "execution_count": 446,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(range(1, 50), f1s, color='red', linestyle='dashed', marker='o',\n",
        "         markerfacecolor='blue', markersize=10)\n",
        "plt.title('F1 Score K Value')\n",
        "plt.xlabel('K Value')\n",
        "plt.ylabel('F1 Score')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "33tWcVfhAqhm",
        "outputId": "5b251e69-ff20-434b-de9c-d6a97e51a7ae"
      },
      "execution_count": 447,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'F1 Score')"
            ]
          },
          "metadata": {},
          "execution_count": 447
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k = [i for i in range (0,50)]\n",
        "maxF1 = np.argmax(f1s)\n",
        "BestK = k[maxF1]\n",
        "print(BestK)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4Orymp_eURh",
        "outputId": "8147ae4d-d930-47db-cc85-32b2ccd82888"
      },
      "execution_count": 448,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "48\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier48 = KNeighborsClassifier(n_neighbors=BestK)\n",
        "classifier48.fit(X_train, y_train)\n",
        "y_pred48 = classifier48.predict(X_test)\n",
        "print(classification_report(y_test, y_pred48))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdmQGfLoAwiM",
        "outputId": "a28143dd-7d95-4840-bc96-f65ad3476dbc"
      },
      "execution_count": 449,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.49      0.40      0.44       131\n",
            "           1       0.68      0.81      0.74       126\n",
            "           2       0.52      0.52      0.52       161\n",
            "\n",
            "    accuracy                           0.57       418\n",
            "   macro avg       0.56      0.58      0.57       418\n",
            "weighted avg       0.56      0.57      0.56       418\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K-Fold"
      ],
      "metadata": {
        "id": "8Grqw4nwgg1B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "XF = X.to_numpy()\n",
        "yF = y.to_numpy()"
      ],
      "metadata": {
        "id": "RuiF_RDfmdn1"
      },
      "execution_count": 450,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kf = KFold(n_splits = 10)\n",
        "cnt =1\n",
        "report = []\n",
        "for train, test in kf.split(XF,yF):\n",
        "   X_trainF, X_testF, y_trainF, y_testF = XF[train], XF[test], yF[train], yF[test]\n",
        "   knn = KNeighborsClassifier(n_neighbors=BestK)\n",
        "   knn.fit(X_trainF, y_trainF)\n",
        "   y_predF = knn.predict(X_testF)\n",
        "   print('Fold',cnt,':')\n",
        "   print(classification_report(y_testF, y_predF))\n",
        "   print('-----------------------------------------------')\n",
        "   report.append(classification_report(y_testF, y_predF, output_dict=True))\n",
        "   cnt = cnt+1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SZiJNnRgzGz",
        "outputId": "049c38fb-6c90-4b47-95c7-f6368ca83f3b"
      },
      "execution_count": 451,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold 1 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.45      0.32      0.37       164\n",
            "           1       0.42      0.85      0.56        78\n",
            "           2       0.47      0.38      0.42       176\n",
            "\n",
            "    accuracy                           0.44       418\n",
            "   macro avg       0.44      0.51      0.45       418\n",
            "weighted avg       0.45      0.44      0.43       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 2 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.39      0.29      0.33       144\n",
            "           1       0.44      0.51      0.47       114\n",
            "           2       0.38      0.42      0.40       160\n",
            "\n",
            "    accuracy                           0.40       418\n",
            "   macro avg       0.40      0.41      0.40       418\n",
            "weighted avg       0.40      0.40      0.40       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 3 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.45      0.42      0.43       113\n",
            "           1       0.78      0.97      0.86       163\n",
            "           2       0.53      0.42      0.47       142\n",
            "\n",
            "    accuracy                           0.63       418\n",
            "   macro avg       0.59      0.60      0.59       418\n",
            "weighted avg       0.61      0.63      0.61       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 4 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.37      0.39      0.38        97\n",
            "           1       0.87      0.69      0.77       197\n",
            "           2       0.43      0.55      0.48       124\n",
            "\n",
            "    accuracy                           0.58       418\n",
            "   macro avg       0.56      0.54      0.54       418\n",
            "weighted avg       0.62      0.58      0.59       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 5 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.39      0.39      0.39       121\n",
            "           1       0.73      0.73      0.73       132\n",
            "           2       0.49      0.49      0.49       165\n",
            "\n",
            "    accuracy                           0.54       418\n",
            "   macro avg       0.54      0.54      0.54       418\n",
            "weighted avg       0.54      0.54      0.54       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 6 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.44      0.37      0.40       158\n",
            "           1       0.44      0.62      0.52        93\n",
            "           2       0.41      0.38      0.39       167\n",
            "\n",
            "    accuracy                           0.43       418\n",
            "   macro avg       0.43      0.46      0.44       418\n",
            "weighted avg       0.43      0.43      0.42       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 7 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.41      0.25      0.31       113\n",
            "           1       0.87      0.87      0.87       171\n",
            "           2       0.48      0.63      0.54       134\n",
            "\n",
            "    accuracy                           0.63       418\n",
            "   macro avg       0.59      0.58      0.57       418\n",
            "weighted avg       0.62      0.63      0.61       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 8 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.46      0.36      0.41       160\n",
            "           1       0.59      0.69      0.64        91\n",
            "           2       0.47      0.52      0.50       166\n",
            "\n",
            "    accuracy                           0.50       417\n",
            "   macro avg       0.51      0.53      0.51       417\n",
            "weighted avg       0.49      0.50      0.49       417\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 9 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.35      0.34      0.34       111\n",
            "           1       0.81      0.78      0.80       169\n",
            "           2       0.43      0.45      0.44       137\n",
            "\n",
            "    accuracy                           0.56       417\n",
            "   macro avg       0.53      0.53      0.53       417\n",
            "weighted avg       0.56      0.56      0.56       417\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 10 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.44      0.39      0.41       126\n",
            "           1       0.72      0.76      0.74       134\n",
            "           2       0.49      0.51      0.50       157\n",
            "\n",
            "    accuracy                           0.55       417\n",
            "   macro avg       0.55      0.55      0.55       417\n",
            "weighted avg       0.55      0.55      0.55       417\n",
            "\n",
            "-----------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Acc=[]\n",
        "Precision=[]\n",
        "Recall=[]\n",
        "F1Score=[]\n",
        "for r in report:\n",
        "  Acc.append(r['accuracy'])\n",
        "  Precision.append(r['weighted avg']['precision'])\n",
        "  Recall.append(r['weighted avg']['recall'])\n",
        "  F1Score.append(r['weighted avg']['f1-score'])\n",
        "print('Average Accuracy cross-validation score:',np.mean(Acc))\n",
        "print('Average Precision cross-validation score:',np.mean(Precision))\n",
        "print('Average Recall cross-validation score:',np.mean(Recall))\n",
        "print('Average f1_score cross-validation score:',np.mean(F1Score))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8HycK7RssMjm",
        "outputId": "27a16c32-e679-4c95-b2a9-22d104ade559"
      },
      "execution_count": 452,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Accuracy cross-validation score: 0.5257438068683808\n",
            "Average Precision cross-validation score: 0.526666285271771\n",
            "Average Recall cross-validation score: 0.5257438068683808\n",
            "Average f1_score cross-validation score: 0.5206245145635493\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model_Eval"
      ],
      "metadata": {
        "id": "ouirlO2vTtmX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=BestK)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)"
      ],
      "metadata": {
        "id": "3yw9S-Ou7dpk"
      },
      "execution_count": 453,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classes = ['0','1','2']\n",
        "print(f'Test score {accuracy_score(y_pred,y_test)}')\n",
        "plot_confusionmatrix(y_pred,y_test,dom='Test')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "lt5rHW7HUC28",
        "outputId": "474133f9-18fd-4dc2-dae6-2cd3f578121f"
      },
      "execution_count": 454,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test score 0.569377990430622\n",
            "Test Confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acm2qDXk8aXw",
        "outputId": "2ad07ce8-6e41-423f-beb9-625f338f3ec7"
      },
      "execution_count": 455,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.49      0.40      0.44       131\n",
            "           1       0.68      0.81      0.74       126\n",
            "           2       0.52      0.52      0.52       161\n",
            "\n",
            "    accuracy                           0.57       418\n",
            "   macro avg       0.56      0.58      0.57       418\n",
            "weighted avg       0.56      0.57      0.56       418\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## NB"
      ],
      "metadata": {
        "id": "vo35vJ1hQs2t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kf = KFold(n_splits = 10)\n",
        "cnt =1\n",
        "report = []\n",
        "for train, test in kf.split(XF,yF):\n",
        "   X_trainF, X_testF, y_trainF, y_testF = XF[train], XF[test], yF[train], yF[test]\n",
        "   gnb = GaussianNB()\n",
        "   gnb.fit(X_trainF, y_trainF)\n",
        "   y_predF = gnb.predict(X_testF)\n",
        "   print('Fold',cnt,':')\n",
        "   print(classification_report(y_testF, y_predF))\n",
        "   print('-----------------------------------------------')\n",
        "   report.append(classification_report(y_testF, y_predF, output_dict=True))\n",
        "   cnt = cnt+1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcd860cb-eccc-4608-c162-7d9df6494030",
        "id": "Wd9cOfF92Utp"
      },
      "execution_count": 456,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold 1 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.48      0.53      0.51       164\n",
            "           1       0.41      1.00      0.59        78\n",
            "           2       0.44      0.12      0.19       176\n",
            "\n",
            "    accuracy                           0.45       418\n",
            "   macro avg       0.45      0.55      0.43       418\n",
            "weighted avg       0.45      0.45      0.39       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 2 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.52      0.38      0.44       144\n",
            "           1       0.39      0.75      0.51       114\n",
            "           2       0.42      0.26      0.32       160\n",
            "\n",
            "    accuracy                           0.43       418\n",
            "   macro avg       0.45      0.46      0.42       418\n",
            "weighted avg       0.45      0.43      0.41       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 3 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.48      0.75      0.58       113\n",
            "           1       0.81      0.98      0.89       163\n",
            "           2       0.58      0.18      0.27       142\n",
            "\n",
            "    accuracy                           0.65       418\n",
            "   macro avg       0.62      0.64      0.58       418\n",
            "weighted avg       0.64      0.65      0.60       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 4 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.39      0.75      0.51        97\n",
            "           1       0.87      0.67      0.76       197\n",
            "           2       0.28      0.18      0.22       124\n",
            "\n",
            "    accuracy                           0.54       418\n",
            "   macro avg       0.51      0.53      0.50       418\n",
            "weighted avg       0.58      0.54      0.54       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 5 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.40      0.83      0.54       121\n",
            "           1       0.77      0.74      0.76       132\n",
            "           2       0.44      0.10      0.17       165\n",
            "\n",
            "    accuracy                           0.51       418\n",
            "   macro avg       0.53      0.56      0.49       418\n",
            "weighted avg       0.53      0.51      0.46       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 6 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.47      0.46      0.46       158\n",
            "           1       0.36      0.81      0.50        93\n",
            "           2       0.46      0.16      0.23       167\n",
            "\n",
            "    accuracy                           0.41       418\n",
            "   macro avg       0.43      0.47      0.40       418\n",
            "weighted avg       0.44      0.41      0.38       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 7 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.44      0.74      0.55       113\n",
            "           1       0.87      0.85      0.86       171\n",
            "           2       0.35      0.16      0.22       134\n",
            "\n",
            "    accuracy                           0.60       418\n",
            "   macro avg       0.56      0.59      0.55       418\n",
            "weighted avg       0.59      0.60      0.57       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 8 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.68      0.58       160\n",
            "           1       0.49      0.78      0.60        91\n",
            "           2       0.51      0.16      0.25       166\n",
            "\n",
            "    accuracy                           0.50       417\n",
            "   macro avg       0.50      0.54      0.47       417\n",
            "weighted avg       0.50      0.50      0.45       417\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 9 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.44      0.74      0.55       111\n",
            "           1       0.76      0.80      0.78       169\n",
            "           2       0.46      0.18      0.26       137\n",
            "\n",
            "    accuracy                           0.58       417\n",
            "   macro avg       0.56      0.58      0.53       417\n",
            "weighted avg       0.58      0.58      0.55       417\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 10 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.46      0.70      0.55       126\n",
            "           1       0.64      0.74      0.69       134\n",
            "           2       0.36      0.16      0.22       157\n",
            "\n",
            "    accuracy                           0.51       417\n",
            "   macro avg       0.49      0.53      0.49       417\n",
            "weighted avg       0.48      0.51      0.47       417\n",
            "\n",
            "-----------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Acc=[]\n",
        "Precision=[]\n",
        "Recall=[]\n",
        "F1Score=[]\n",
        "for r in report:\n",
        "  Acc.append(r['accuracy'])\n",
        "  Precision.append(r['weighted avg']['precision'])\n",
        "  Recall.append(r['weighted avg']['recall'])\n",
        "  F1Score.append(r['weighted avg']['f1-score'])\n",
        "print('Average Accuracy cross-validation score:',np.mean(Acc))\n",
        "print('Average Precision cross-validation score:',np.mean(Precision))\n",
        "print('Average Recall cross-validation score:',np.mean(Recall))\n",
        "print('Average f1_score cross-validation score:',np.mean(F1Score))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQL9HC9E4KBS",
        "outputId": "4025a644-8718-4223-ae51-92ba19c7d6dd"
      },
      "execution_count": 457,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Accuracy cross-validation score: 0.5183223755923491\n",
            "Average Precision cross-validation score: 0.5250662853998318\n",
            "Average Recall cross-validation score: 0.5183223755923491\n",
            "Average f1_score cross-validation score: 0.48236509050621673\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model_Eval"
      ],
      "metadata": {
        "id": "znMPO-k9WEXJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gnb = GaussianNB()\n",
        "gnb.fit(X_train, y_train)\n",
        "y_pred = gnb.predict(X_test)"
      ],
      "metadata": {
        "id": "-LZ_Umi6WHU4"
      },
      "execution_count": 458,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classes = ['0','1','2']\n",
        "print(f'Test score {accuracy_score(y_pred,y_test)}')\n",
        "plot_confusionmatrix(y_pred,y_test,dom='Test')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "znvBxoPtXh1O",
        "outputId": "54066f49-19ae-4b0d-fb81-5b9ca079fcfe"
      },
      "execution_count": 459,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test score 0.5119617224880383\n",
            "Test Confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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zjJFeSVLbtm31448/KiEhQYMHD1ZwcLAkKS0tTSEhIcbz0tLS1L59e1vrZiwIAAAsuxpjwaLIycmRn593W1OhQgUVFBRIksLCwhQcHKw1a9YYx7OyspSUlKSIiAjL3/9/I7kCAACl3l133aUXXnhBjRo10nXXXadvvvlGL7/8soYOHSrpYrIWGxur559/Xs2aNVNYWJjGjh2r0NBQ9evXz9ZaaK4AAIBlvt6KYdasWRo7dqyeeOIJnTx5UqGhoXr00Uc1btw445xRo0YpOztbw4YNU0ZGhrp3766VK1fK39/f1locnv/eutSH6j++1NcloBz5YVZ/X5eAcqRmn3/7ugSUM7mfjij212wxepXt10yZEmX7NYsDyRUAALDMx8FViUJzBQAALPPzo7v6BZ8WBAAAsBHJFQAAsIyxoInkCgAAwEYkVwAAwDJfb8VQktBcAQAAy+itTIwFAQAAbERyBQAALGMsaCK5AgAAsBHJFQAAsIzkykRzBQAALKO3MjEWBAAAsBHJFQAAsIyxoInkCgAAwEYkVwAAwDKCKxPNFQAAsIyxoImxIAAAgI1IrgAAgGUEVyaSKwAAABuRXAEAAMu458pEcwUAACyjtzIxFgQAALARyRUAALCMsaCJ5AoAAMBGJSa5+uwff/J1CShHanaJ8XUJKEeWvDPW1yUAVx3BlanENFcAAKD0YixoYiwIAABgI5IrAABgGcGVieQKAADARiRXAADAMu65MtFcAQAAy+itTIwFAQAAbERyBQAALGMsaCK5AgAAsBHJFQAAsIzkykRzBQAALKO3MjEWBAAAsBHJFQAAsIyxoInkCgAAwEYkVwAAwDKCKxPNFQAAsIyxoImxIAAAgI1IrgAAgGUEVyaSKwAAABuRXAEAAMv8iK4MNFcAAMAyeisTY0EAAAAbkVwBAADL2IrBRHIFAABgI5IrAABgmR/BlYHmCgAAWMZY0MRYEAAAwEYkVwAAwDKCKxPJFQAAgI1IrgAAgGUOEV39guYKAABYxqcFTYwFAQAAbERyBQAALGMrBhPJFQAAgI1IrgAAgGUEVyaaKwAAYJkf3ZWBsSAAAICNSK4AAIBlBFcmkisAAAAbkVwBAADL2IrBRHMFAAAso7cyMRYEAACwEckVAACwjK0YTDRXAADAMlorE2NBAAAAG5FcAQAAy/i0oInkCgAAwEYkVwAAwDI/gisDzRUAALCMsaCJsSAAAICNSK4AAIBlBFcmkisAAAAbkVwBAADLuOfKRHMFAAAs49OCJsaCAACgTDh27Jj+/ve/q3bt2goICFDbtm21bds247jH49G4ceMUEhKigIAARUZGav/+/bbXQXMFAAAsczgctj+K4syZM+rWrZsqVaqkzz77TLt379ZLL72kmjVrGudMnTpVM2fO1Lx585SUlKSqVasqKipKeXl5tr4XjAUBAECpN2XKFDVs2FDz58831sLCwoz/93g8mjFjhsaMGaO+fftKkhYuXKigoCAtW7ZMAwcOtK0WkisAAGCZ4yo8imLFihXq3Lmz7rnnHtWrV08dOnTQ66+/bhw/ePCgUlNTFRkZaay5XC6Fh4crMTHxD3zHl0ZzBQAALPNzOGx/uN1uZWVleT3cbnehr3/gwAG98soratasmVatWqXHH39cTz75pN5++21JUmpqqiQpKCjI63lBQUHGMdveC1uvBgAAYJOEhAS5XC6vR0JCQqHnFhQUqGPHjpo0aZI6dOigYcOG6ZFHHtG8efOKuWqaKwAAYAOHw/5HfHy8MjMzvR7x8fGFvn5ISIhat27ttdaqVSsdPnxYkhQcHCxJSktL8zonLS3NOGYXmisAAFAiOZ1OVa9e3evhdDoLPbdbt25KSUnxWtu3b58aN24s6eLN7cHBwVqzZo1xPCsrS0lJSYqIiLC1bj4tCAAALPP1Du0jR45U165dNWnSJN17773asmWLXnvtNb322mtGfbGxsXr++efVrFkzhYWFaezYsQoNDVW/fv1srYXmCgAAWObr337TpUsXLV26VPHx8Zo4caLCwsI0Y8YMDRo0yDhn1KhRys7O1rBhw5SRkaHu3btr5cqV8vf3t7UWmqsSbunit5S0cZ2OHTmkyk6nWrS+XoMeeVL1GzYxzkk9fkQLX52hvTu363x+vtp3jtDQ4aNUo2Zt3xWOUqNbx6Ya+UCkOrZupJC6Lt078jV9/OW3XueMfby3hvTvqhqBAUrccUBPTvpAPxxO/821KleqqA3vPK12LRoo/K8J+nbfseL6NlBGrPmfd/XpotfUo/df1G/ok5Kk/HNurXh7jrZvXKvz5/PVol0XDRgWp8AatXxcLUqaO++8U3feeecljzscDk2cOFETJ068qnVwz1UJt+vbrxXV9x5NmrVAY6fM1fnz5/X86Gjl5eZKkvJyc/X86Gg5HA6Nf3Ge/jXjTZ0/n6/JY0aqoKDAx9WjNKga4NR3+44pNuGDQo8/9WCknrivp56c9L5uemCasnPP6eM50XJW/u2/zSbF9tWJ9MyrXTLKqMPf79Hm1SsU0rip1/ry+bO1e9smPfD0BD0xcaayzvykBVPH+KhKXMrV2IqhtKK5KuHGTJ6tW6L6qGGTpmrStLmiR03QqZOpOrB/jyQpZdd2nUw7oehnnlPja5qp8TXNFD1qgg7s262d32z1cfUoDT7/arcmzP1EK9Z9W+jx6L/doimvr9InX36nnfuP6+GxCxVS16U+t7TzOu+2bq3V68ZWip++tDjKRhnjzs3Rohn/0j2PjVKVaoHGem72WW1Z+7/q82CMmrXtpIZNW+iv0c/qUMpO/bhvlw8rBi6N5qqUyck+K0mqFlhdkpSfny+HHKpUqbJxTuXKTjkcftq7c7svSkQZ0qR+bYXUdWlt0l5jLetsnrbuPKTw65sYa/VqBWru2Pv00NiFysk954NKUdr9zxvT1bpThJq36+y1fvRAii6cP6/m13cy1oIaNFbNOkE6lEJzVZJcja0YSiuaq1KkoKBAC+ZOU4vr2qlR2LWSpGat2srp769335gpd16u8nJztfDVGSoouKAzp0/5uGKUdsF1LjbxJ0//7LV+8qefFVS7uvH1axP/rtc/2qivdx8u1vpQNnyzcY2OHtinPw8a9ptjP2ecVoWKlRRQNdBrvVqNmvo546fiKhFXwNe/uLkksb25OnLkiIYOHfq75xS2nf25S2xnD9MbMyfryKEfNHKMuTutq0ZNPTVuipITN+j+u3pocN+eys7+WWHNWpbqH0yUHk/c11OBVfz14luf+7oUlEJnTqVp2VszNWjEOFWqXPj+RUBpY/unBU+fPq23335bb7311iXPSUhI0IQJE7zWHouN1+Nx/7C7nDLjjVlT9HXSRk14+XXVruv9e5HadY7Q7HdWKCvzjCpUqKiq1QL18D23KejmBj6qFmVF6qksSRfHfr/8vyTVqx2ob1OOSpJu7tJc4deHKTNphtdzv1o0Su9/tk2PjHun2OpF6XP0h306m3lG05952FgrKLigA7t36KvPluqRsdN04Xy+crN/9kqvzmacUWANPhFdkjAKMxW5uVqxYsXvHj9w4MBlrxEfH6+4uDivtX0n84taSrng8Xj05uyp2rJxnSa89JqCQupf8tzqrpqSpO++2aKsjNPq3PWm4ioTZdShYz/pRHqmbglvYWyrEFjVX13aNNHrSzZKkp6a+pGem/OJ8ZyQui598kqM7n92vrZ+d8gXZaMUaXZ9Jz09fYHX2gezJ6te/Ua6pf/fVKN2PVWoWFH7v03W9RE3S5JOHjusM6fS1KTFdcVfMHAFitxc9evXTw6HQx6P55LnXG4c5XQ6f7N9feXMs0UtpVx4Y+ZkbVy7UqMmviz/KlWM+6iqVK0mp/PipmfrVq5Q/UZhql6jhvbt/k7z50xT7wF/89oLC7iUqgGV1bRhXePrJvVr6/rm9XUmK0dHUs9ozuJ1Gv3w7fr+cLoOHftJ45/orRPpmVqxbock6UjqGa/rnc25OOI/cCRdx05mFNv3gdLJP6CKQhpd47VW2d9fVQKrG+s33NpbKxbMUZVq1eWsUlVL35yhxi2uU+PmNFclCbeimIrcXIWEhGju3Lnq27dvoce3b9+uTp06FXoMRff5xx9Jkp57yvtGzyeeGa9bovpIko4dOaRFb87W2Z8zVS8oVHcPGqo7Bwz6zbWAwnRs3VifvzHC+Hrq0wMkSe+s2Kxh49/VSwu+UJUAp2aPuU81AgO0afsP6hM9V+5z531VMsqZvkNi5PBzaMG0sbqQn68W7bvo7kfiLv9EFCs/eiuDw/N7EVQh+vTpo/bt219yd9MdO3aoQ4cORd7A8tsjJFcoPuF9nvV1CShHlrwz1tcloJy5s03Q5U+yWezyvZc/qYhm9G1p+zWLQ5GTq2eeeUbZ2dmXPH7ttddq3bp1looCAAClC8mVqcjNVY8ePX73eNWqVdWzZ88/XBAAAEBpxi9uBgAAlnFDu4nmCgAAWMZY0MSeXwAAADYiuQIAAJYxFTSRXAEAANiI5AoAAFjmR3RloLkCAACWMQoz8V4AAADYiOQKAABYxlTQRHIFAABgI5IrAABgGTe0m2iuAACAZfRWJsaCAAAANiK5AgAAlvG7BU0kVwAAADYiuQIAAJZxQ7uJ5goAAFhGb2ViLAgAAGAjkisAAGAZN7SbSK4AAABsRHIFAAAsc4jo6hc0VwAAwDLGgibGggAAADYiuQIAAJaRXJlIrgAAAGxEcgUAACxzsIuogeYKAABYxljQxFgQAADARiRXAADAMqaCJpIrAAAAG5FcAQAAy/yIrgw0VwAAwDJuaDcxFgQAALARyRUAALCMqaCJ5AoAAMBGJFcAAMAyPxFd/YLmCgAAWMZY0MRYEAAAwEYkVwAAwDK2YjCRXAEAANiI5AoAAFjGDu0mmisAAGAZvZWJsSAAAICNSK4AAIBljAVNJFcAAAA2IrkCAACWEVyZaK4AAIBljMJMvBcAAAA2IrkCAACWOZgLGkiuAAAAbERyBQAALCO3MtFcAQAAy9jnysRYEAAAwEYkVwAAwDJyKxPJFQAAgI1IrgAAgGXccmWiuQIAAJaxz5WJsSAAAICNSK4AAIBlpDUm3gsAAAAbkVwBAADLuOfKRHMFAAAso7UyMRYEAACwEckVAACwjLGgqcQ0V0Eup69LQDlyZutsX5eAcuRQeo6vSwBQjEpMcwUAAEov7jMy0VwBAADLGAuaaDQBAABsRHIFAAAsI7cykVwBAIAyZ/LkyXI4HIqNjTXW8vLyFB0drdq1a6tatWoaMGCA0tLSbH9tmisAAGCZw2H/44/aunWrXn31VV1//fVe6yNHjtTHH3+sJUuWaP369Tp+/Ljuvvtui9/5b9FcAQAAy/zksP3xR5w9e1aDBg3S66+/rpo1axrrmZmZevPNN/Xyyy/r1ltvVadOnTR//nxt2rRJmzdvtuttkERzBQAASii3262srCyvh9vt/t3nREdHq3fv3oqMjPRaT05OVn5+vtd6y5Yt1ahRIyUmJtpaN80VAACw7GqMBRMSEuRyubweCQkJl6zh/fff19dff13oOampqapcubJq1KjhtR4UFKTU1FRb3ws+LQgAAEqk+Ph4xcXFea05nYX/RpcjR45oxIgRWr16tfz9/YujvEuiuQIAAJY5rsJmDE6n85LN1K8lJyfr5MmT6tixo7F24cIFbdiwQbNnz9aqVat07tw5ZWRkeKVXaWlpCg4OtrVumisAAGCZrzdo79Wrl7777juvtSFDhqhly5YaPXq0GjZsqEqVKmnNmjUaMGCAJCklJUWHDx9WRESErbXQXAEAgFIvMDBQbdq08VqrWrWqateubaw/9NBDiouLU61atVS9enUNHz5cERERuvHGG22theYKAABY9ke3TihO06dPl5+fnwYMGCC3262oqCjNnTvX9tdxeDwej+1X/QPSsvJ9XQLKEVeVSr4uAeXIofQcX5eAcqZlSJVif82Vu9Jtv+bt19W1/ZrFgeQKAABY5ut7rkoSmisAAGAZzZWJTUQBAABsRHIFAAAsuxr7XJVWJFcAAAA2IrkCAACW+RFcGWiuAACAZYwFTYwFAQAAbERyBQAALGMrBhPJFQAAgI1IrgAAgGXcc2WiuQIAAJbxaUETY0EAAAAbkVwBAADLGAuaSK4AAABsRHIFAAAsYysGE80VAACwjN7KxFgQAADARiRXAADAMj/mggaSKwAAABuRXAEAAMvIrUw0VwAAwDq6KwNjQQAAABuRXAEAAMvYod1EcgUAAGAjkisAAGAZOzGYaK4AAIBl9FYmxoIAAAA2IrkCAADWEV0ZSK4AAABsRHIFAAAsYysGE80VAACwjE8LmhgLAgAA2IjkCgAAWEZwZSK5AgAAsBHJFQAAsI7oykBzBQAALOPTgibGggAAADYiuQIAAJaxFYOJ5AoAAMBGJFcAAMAygisTzRUAALCO7srAWBAAAMBGJFcAAMAytmIwkVwBAADYiOQKAABYxlYMJporAABgGb2VibEgAACAjUiuAACAdURXBpqrUmD719v0/jvzlbJ3t346la4XXvy3etzcyziek5OjV2dP18b1a5WZmaGQ0Pr6y18Hqe+Av/qwapRWydu2asFbb2rP7p1KT0/X9JlzdGuvSOP4F6s/15IP39eeXbuUmZmhDz5appatWvmwYpRmHy16U4kb1uro4UNyOp1qeV07PfDoCDVo1MTrvL27dujdN+Zo357v5OdXQWHXNtdzL86V0+nvm8KB38FYsBTIy81V0+YtNHLUPws9Pmf6VG1J3KgxExP0zocrdM/A+zXjxUnauH5dMVeKsiA3N0ctWrRQ/JjxlzzeoUNHxcY9XcyVoSzauf1r/bnfX/Xi3IWaMO0Vnb9wXs8987jycnONc/bu2qEJo2LUvvONmvbKu5o271317j9Qfg7+CitJHFfhv9KK5KoUuLFbD93Yrcclj+/8drtu791XHTrdIEnqc/c9WrF0ifbs/k7de95SXGWijOjeo6e69+h5yeN39eknSTp27GgxVYSy7LkX53h9PeLZCXqgXy/9sG+3rmvXSZL05uyXdOfdA/WXQUON836dbMH3+LSgiba/DGhzfXt9tWGd0k+myePx6OttW3Tk8CF1Ce/q69IAoEhyzp6VJFULdEmSMs6c1r4938lVs5ZGRQ/WA/176R8jHtLub7/xZZnA76K5KgNGPPMPNb6mqQb07qVbIzromScf1chR/1T7jp19XRoAXLGCggK9MXuaWrVpr8bXXCtJSjt+MSF9f8Gruu3Ou/Xc1Dm6plkrjX3qUR0/+qMvy8WvOK7Co7Qq8lgwNzdXycnJqlWrllq3bu11LC8vTx9++KEeeOCB372G2+2W2+3+1ZqfnE5nUcuBpP98sEi7v/tWCS/NVnBIiLZ/k6zpU19QnTr11Dk8wtflAcAVeXVGgg4f/F4Js+YbawWeAklS1F0DFHlHX0nSNc1a6tuvt+iLT5frgWFP+qRW4PcUKbnat2+fWrVqpZtuuklt27ZVz549deLECeN4ZmamhgwZctnrJCQkyOVyeT1mvjyl6NVD7rw8vT7334oZ+Yy63XSzmjZroQH3/k23/ul2vf/uAl+XBwBX5NUZk7U18f/0/IzXVadekLFeq3ZdSVLDxtd4nd+gcZjST6YWa424DKIrQ5Gaq9GjR6tNmzY6efKkUlJSFBgYqG7duunw4cNFetH4+HhlZmZ6PZ6MG12ka+Ci8+fP6/z583L86lMzfn4VjH/xAUBJ5fF49OqMydq8ca2en/6qgkLqex2vFxyqWnXq6tiRQ17rx4/8qHpBIcVYKS6HTwuaijQW3LRpk7744gvVqVNHderU0ccff6wnnnhCPXr00Lp161S1atUruo7T6fzNCDA3K78opZQrOTk5OnbEbGBPHD+m/Sl7Vd3lUlBwiNp37KxXZr4kp79TQcGh2vH1Nq36dIViYp/xYdUorXKys73+wXTs6FHt3bNHLpdLIaGhyszI0IkTJ5SeflKSdOjQQUm6+OdC3bo+qRml16szErThi8/0jxemKyCgqs78dEqSVKVaNTmd/nI4HOr/18F6b8E8NWnaXNdc20JrV32sY4cPafSEF31cPVA4h8fj8VzpydWrV1dSUpJa/WrDwJiYGC1fvlyLFy/WzTffrAsXLhS5kDSaq0v6JnmLRjw29Dfrt/fuq38894J+OnVKr82Zoa1Jm5SVlang4FDd1f8vuvdvD8jBZ2ML5apSydcllFhbtyTp4SG/vW+yT9/++tekyVq+9H80bkz8b44/9kSMHo8eXhwlljqH0nN8XUKJ1ffmDoWuPzl6gnrd0cf4+qNFb+nTZR/q7M+ZatK0uR58NFatry/8uZBahlQp9tdMSbX/57xFcPF/H3YoUnN1ww03aPjw4br//vt/cywmJkaLFi1SVlYWzRVKPJorFCeaKxQ3mivfKtI9V/3799d7771X6LHZs2frvvvuUxF6NQAAUEZwP7upSMnV1URyheJEcoXiRHKF4uaL5Gpfmv0/582DykFyBQAAgN/H7xYEAACWleatE+xGcgUAAGAjkisAAGAZO/+YaK4AAIBl9FYmxoIAAAA2IrkCAADWEV0ZSK4AAABsRHIFAAAsYysGE80VAACwjE8LmhgLAgAA2IjkCgAAWEZwZSK5AgAAsBHJFQAAsI7oykBzBQAALOPTgibGggAAADYiuQIAAJaxFYOJ5AoAAMBGJFcAAMAygisTyRUAALDM4bD/URQJCQnq0qWLAgMDVa9ePfXr108pKSle5+Tl5Sk6Olq1a9dWtWrVNGDAAKWlpdn4LlxEcwUAAEq99evXKzo6Wps3b9bq1auVn5+v2267TdnZ2cY5I0eO1Mcff6wlS5Zo/fr1On78uO6++27ba3F4PB6P7Vf9A9Ky8n1dAsoRV5VKvi4B5cih9Bxfl4BypmVIlWJ/zaNnztl+zQY1K//h56anp6tevXpav369brrpJmVmZqpu3bpavHix/vKXv0iS9u7dq1atWikxMVE33nijXWWTXAEAgJLJ7XYrKyvL6+F2u6/ouZmZmZKkWrVqSZKSk5OVn5+vyMhI45yWLVuqUaNGSkxMtLVumisAAGDZ1bjnKiEhQS6Xy+uRkJBw2VoKCgoUGxurbt26qU2bNpKk1NRUVa5cWTVq1PA6NygoSKmpqba+F3xaEAAAWHY1Pi0YHx+vuLg4rzWn03nZ50VHR2vnzp3auHHjVajq8miuAABAieR0Oq+omfpvMTEx+uSTT7RhwwY1aNDAWA8ODta5c+eUkZHhlV6lpaUpODjYrpIlMRYEAAA28PVWDB6PRzExMVq6dKnWrl2rsLAwr+OdOnVSpUqVtGbNGmMtJSVFhw8fVkREhB1vgYHkCgAAlHrR0dFavHixli9frsDAQOM+KpfLpYCAALlcLj300EOKi4tTrVq1VL16dQ0fPlwRERG2flJQYisGlFNsxYDixFYMKG6+2IohNdP+v8eDXVf+Z7XjElHX/Pnz9eCDD0q6uInoU089pffee09ut1tRUVGaO3eu7WNBmiuUSzRXKE40VyhuPmmursLf48HVS+ef1dxzBQAAYCPuuQIAAJbxi5tNJFcAAAA2IrkCAACWFXXrhLKM5goAAFjmYDBoYCwIAABgI5IrAABgHcGVgeQKAADARiRXAADAMoIrE80VAACwjE8LmhgLAgAA2IjkCgAAWMZWDCaSKwAAABuRXAEAAMu458pEcgUAAGAjmisAAAAbMRYEAACWMRY0kVwBAADYiOQKAABYxlYMJporAABgGWNBE2NBAAAAG5FcAQAAywiuTCRXAAAANiK5AgAA1hFdGWiuAACAZXxa0MRYEAAAwEYkVwAAwDK2YjCRXAEAANiI5AoAAFhGcGWiuQIAANbRXRkYCwIAANiI5AoAAFjGVgwmkisAAAAbkVwBAADL2IrB5PB4PB5fF4E/xu12KyEhQfHx8XI6nb4uB2UcP28obvzMobSiuSrFsrKy5HK5lJmZqerVq/u6HJRx/LyhuPEzh9KKe64AAABsRHMFAABgI5orAAAAG9FclWJOp1Pjx4/nRk8UC37eUNz4mUNpxQ3tAAAANiK5AgAAsBHNFQAAgI1orgAAAGxEcwUAAGAjmqtSas6cOWrSpIn8/f0VHh6uLVu2+LoklFEbNmzQXXfdpdDQUDkcDi1btszXJaEMS0hIUJcuXRQYGKh69eqpX79+SklJ8XVZQJHQXJVCH3zwgeLi4jR+/Hh9/fXXateunaKionTy5Elfl4YyKDs7W+3atdOcOXN8XQrKgfXr1ys6OlqbN2/W6tWrlZ+fr9tuu03Z2dm+Lg24YmzFUAqFh4erS5cumj17tiSpoKBADRs21PDhw/Xss8/6uDqUZQ6HQ0uXLlW/fv18XQrKifT0dNWrV0/r16/XTTfd5OtygCtCclXKnDt3TsnJyYqMjDTW/Pz8FBkZqcTERB9WBgD2y8zMlCTVqlXLx5UAV47mqpQ5deqULly4oKCgIK/1oKAgpaam+qgqALBfQUGBYmNj1a1bN7Vp08bX5QBXrKKvCwAAoDDR0dHauXOnNm7c6OtSgCKhuSpl6tSpowoVKigtLc1rPS0tTcHBwT6qCgDsFRMTo08++UQbNmxQgwYNfF0OUCSMBUuZypUrq1OnTlqzZo2xVlBQoDVr1igiIsKHlQGAdR6PRzExMVq6dKnWrl2rsLAwX5cEFBnJVSkUFxenwYMHq3Pnzrrhhhs0Y8YMZWdna8iQIb4uDWXQ2bNn9f333xtfHzx4UNu3b1etWrXUqFEjH1aGsig6OlqLFy/W8uXLFRgYaNxL6nK5FBAQ4OPqgCvDVgyl1OzZs/Xiiy8qNTVV7du318yZMxUeHu7rslAGffnll7rlllt+sz548GAtWLCg+AtCmeZwOApdnz9/vh588MHiLQb4g2iuAAAAbMQ9VwAAADaiuQIAALARzRUAAICNaK4AAABsRHMFAABgI5orAAAAG9FcAQAA2IjmCgAAwEY0VwAAADaiuQIAALARzRUAAICNaK4AAABs9P8AzKCEgn1SMNYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uEvMKaRHXmps",
        "outputId": "5b0d5412-3401-4f01-bae4-8e76ab3c27a9"
      },
      "execution_count": 460,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.44      0.64      0.52       131\n",
            "           1       0.60      0.83      0.70       126\n",
            "           2       0.47      0.16      0.24       161\n",
            "\n",
            "    accuracy                           0.51       418\n",
            "   macro avg       0.51      0.54      0.49       418\n",
            "weighted avg       0.50      0.51      0.47       418\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cost_Sensitive"
      ],
      "metadata": {
        "id": "-TEaL-SG4QUp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class NaiveBayesCostSensitive:\n",
        "    def __init__(self, alpha=1., cost_ratio=5.):\n",
        "        self.alpha = alpha\n",
        "        self.cost_ratio = cost_ratio\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        self.classes, self.class_counts = np.unique(y, return_counts=True)\n",
        "        self.n_classes = len(self.classes)\n",
        "        self.n_features = X.shape[1]\n",
        "        self.feature_counts = np.zeros((self.n_classes, self.n_features))\n",
        "\n",
        "        for i, c in enumerate(self.classes):\n",
        "            X_c = X[y==c]\n",
        "            self.feature_counts[i, :] = np.sum(X_c, axis=0)\n",
        "\n",
        "        self.prior = self.class_counts / np.sum(self.class_counts)\n",
        "        self.feature_probs = (self.feature_counts + self.alpha) / \\\n",
        "            (np.sum(self.feature_counts, axis=1, keepdims=True) + self.alpha * self.n_features)\n",
        "\n",
        "    def predict(self, X):\n",
        "        n_samples = X.shape[0]\n",
        "        log_probs = np.zeros((n_samples, self.n_classes))\n",
        "\n",
        "        for i in range(self.n_classes):\n",
        "            class_prob = self.prior[i]\n",
        "            for j in range(self.n_features):\n",
        "                feature_prob = self.feature_probs[i, j]\n",
        "                log_likelihood = np.log(feature_prob) * X[:, j] + \\\n",
        "                                 np.log(1 - feature_prob) * (1 - X[:, j])\n",
        "                log_prob_feature = np.sum(log_likelihood)\n",
        "                log_probs[:, i] += log_prob_feature\n",
        "\n",
        "            log_probs[:, i] += np.log(class_prob) * self.cost_ratio\n",
        "            log_probs[:, i] -= np.log(1 - class_prob)\n",
        "\n",
        "        y_pred = self.classes[np.argmin(log_probs, axis=1)]\n",
        "\n",
        "        return y_pred\n"
      ],
      "metadata": {
        "id": "IN7mZUhLVr57"
      },
      "execution_count": 461,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = NaiveBayesCostSensitive(alpha=1., cost_ratio=5.)\n",
        "clf.fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "print(classification_report(y_test, y_pred,zero_division=0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKpCrSfHV7QK",
        "outputId": "67c9a9d6-13d8-4bcd-92b1-c89e974f07c5"
      },
      "execution_count": 462,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       131\n",
            "           1       0.00      0.00      0.00       126\n",
            "           2       0.39      1.00      0.56       161\n",
            "\n",
            "    accuracy                           0.39       418\n",
            "   macro avg       0.13      0.33      0.19       418\n",
            "weighted avg       0.15      0.39      0.21       418\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2."
      ],
      "metadata": {
        "id": "NNzWROTHFN1N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PreProcess"
      ],
      "metadata": {
        "id": "MhScgxJ4FzGo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['Rings']\n",
        "X = df.drop(['Rings'],axis=1)\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Hve_EW02FSWQ",
        "outputId": "bd99adab-d64d-469c-c552-2062270d91ca"
      },
      "execution_count": 463,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
              "0    2   0.455     0.365   0.095        0.5140          0.2245   \n",
              "1    2   0.350     0.265   0.090        0.2255          0.0995   \n",
              "2    0   0.530     0.420   0.135        0.6770          0.2565   \n",
              "3    2   0.440     0.365   0.125        0.5160          0.2155   \n",
              "4    1   0.330     0.255   0.080        0.2050          0.0895   \n",
              "\n",
              "   Viscera weight  Shell_weight  \n",
              "0          0.1010         0.150  \n",
              "1          0.0485         0.070  \n",
              "2          0.1415         0.210  \n",
              "3          0.1140         0.155  \n",
              "4          0.0395         0.055  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e6f7a88d-d5f2-47a6-8226-0204c04b7284\">\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",
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              "        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>Sex</th>\n",
              "      <th>Length</th>\n",
              "      <th>Diameter</th>\n",
              "      <th>Height</th>\n",
              "      <th>Whole weight</th>\n",
              "      <th>Shucked weight</th>\n",
              "      <th>Viscera weight</th>\n",
              "      <th>Shell_weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>0.455</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.095</td>\n",
              "      <td>0.5140</td>\n",
              "      <td>0.2245</td>\n",
              "      <td>0.1010</td>\n",
              "      <td>0.150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.265</td>\n",
              "      <td>0.090</td>\n",
              "      <td>0.2255</td>\n",
              "      <td>0.0995</td>\n",
              "      <td>0.0485</td>\n",
              "      <td>0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.530</td>\n",
              "      <td>0.420</td>\n",
              "      <td>0.135</td>\n",
              "      <td>0.6770</td>\n",
              "      <td>0.2565</td>\n",
              "      <td>0.1415</td>\n",
              "      <td>0.210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>0.440</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.5160</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.1140</td>\n",
              "      <td>0.155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.330</td>\n",
              "      <td>0.255</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.0895</td>\n",
              "      <td>0.0395</td>\n",
              "      <td>0.055</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6f7a88d-d5f2-47a6-8226-0204c04b7284')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e6f7a88d-d5f2-47a6-8226-0204c04b7284 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e6f7a88d-d5f2-47a6-8226-0204c04b7284');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 463
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = np.unique(df[\"Rings\"])"
      ],
      "metadata": {
        "id": "ybnQbUBqlxn-"
      },
      "execution_count": 464,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1)"
      ],
      "metadata": {
        "id": "_hZcc746GMDN"
      },
      "execution_count": 465,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "\n",
        "X_train = scaler.transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "VwFxAHcaGdXn"
      },
      "execution_count": 466,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KNN"
      ],
      "metadata": {
        "id": "ba4KshhMGVgB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finding The Best K For KNN Classification 2"
      ],
      "metadata": {
        "id": "g3cCmV6nH6gE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f1s=[]\n",
        "for i in range(1, 50):\n",
        "    knn = KNeighborsClassifier(n_neighbors=i)\n",
        "    knn.fit(X_train, y_train)\n",
        "    pred_i = knn.predict(X_test)\n",
        "    # using average='weighted' to calculate a weighted average for the 4 classes\n",
        "    f1s.append(f1_score(y_test, pred_i, average='weighted'))"
      ],
      "metadata": {
        "id": "iW6x0mfZHMph"
      },
      "execution_count": 467,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(range(1, 50), f1s, color='red', linestyle='dashed', marker='o',\n",
        "         markerfacecolor='blue', markersize=10)\n",
        "plt.title('F1 Score K Value')\n",
        "plt.xlabel('K Value')\n",
        "plt.ylabel('F1 Score')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "a9Vkk-uiICEi",
        "outputId": "4f6c5a3f-06c6-4290-d757-6d8e3fa958b2"
      },
      "execution_count": 468,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'F1 Score')"
            ]
          },
          "metadata": {},
          "execution_count": 468
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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39YEZiwwTAX9/YMwYFzWQiIiImsJAnoiIqCneGPSuXAnjY3/HECUPqwzDEYGCBneLQAFWGYZjiJIHY85yIDjYte0kIiKiBjVdEJeIiKilCw6GMTsTQxKHYpVhOMZbFqIQnS7YLQIFWGSYiCFKHgw5a9w36P32W+D++wGzGYY5cxA/41nkV3RFDkYiSyShBCEIQQmSlWyMRA7g5y9fT3y83i0nIiKisxQh3LHIrb7Ky8sRFBSEsrIyBAYG6t0cIiJyB3l5MCelAhUVFwa9WI6RWAG0bStHrt016C0oAK66Sm6TkoDly4GyMmDpUpjmvS5L5p1lio2TywPGjgWCgnRsNBERUctgSxzKQL4BDOSJiKhB33wDJCXBVFUD37LjtT82wQhfpQbYtg24/HIdG9iE6mrg5puBTZuASy4BvvuuflZ9IYC33gL+9jfguuvka3XXNf5EREReyJY4lGvkiYiIrLVtG3D0KHz7XgkUFwP79gHFxfC9eZAMhN9/X+8WNm7aNBnEBwUBK1deWBpPUYDeveXj/HwG8URERG6MgTwREZG1Nm6U20GDgNBQIDpabh96SP584UKgokK35jXqnXeA11+XwfkHHwA9ejS8X0yM3B46BFRVua59REREZBMG8kRERNYQoi6QHziw/nOJiUC3bkBJiQyU3c0vv8jts8/KtjYmLExm3BcCOHjQNW0jIiIimzGQJyIissb+/cCRI4CvL3DNNfWf8/EBHnxQPp43TwbC7uQ//wFyc4F//rPp/RSlblR+717nt4uIiIjswkCeiIjIGupo/FVXyVHr840bJ3/+229yLb3eTCbAfE7d+4QEwGDFx/4llwA9e8rjiYiIyC2xjjwREZE1vvlGbs+fVq8KDpZr0Xv3BuLiXNYsCAEcPw6cOgW0ayfX7CsK8OijwPbtwLJl8mfWyshwXluJiIhIE24xIj9//nxER0ejTZs26NevH3744YdG9124cCEGDhyIkJAQhISEYPDgwfX2N5lMePzxx3H55Zejbdu26Ny5M8aMGYMjR4644qUQEZG36twZuPjixgN5AEhNdV0QX1oKpKfD1KOXXNverRsQFia/v+ceID0d+PxzWWaOiIiIvIrugXxGRgYeeeQRzJw5E1u2bEHv3r2RkJCAo0ePNrj/l19+idGjR2PDhg349ttv0aVLF8THx+Pw4cMAgIqKCmzZsgVPP/00tmzZgpycHOzcuRPDhg1z5csiIiJv88wzwM6dwNCh1u3vzOz1eXkwR0bBPG06cvb2QQoyMRjrkIJM5OztA/MHy2CGAbjrrqaT2xEREZFHUoTQNyNPv3790LdvX7z22msAAIvFgi5dumDKlCl44oknmj2+pqYGISEheO211zBmzJgG99m8eTOuueYaHDhwAF27dm32nOXl5QgKCkJZWRkCAwNte0FERNSylZQAf/sbsGGDrDPftq2258/LgyVxKHJFAsZZ3kYRIi7YJRyFWIxxGOKzDoY1q+X6eGsdPgwMGyan6+/bx3ryRERELmJLHKrriHx1dTV++uknDB48uPZnBoMBgwcPxrfffmvVOSoqKmAymdC+fftG9ykrK4OiKAgODm7w+aqqKpSXl9f7IiIiqnXkSP3EcU0JDAR+/BE4dkz7UnSlpTAnpSJXJGCYZWWDQTwAFCECw7AKuSIB5qRUOQ3fWiEhwJYtwIEDwIkT2rSbiIiINKVrIF9cXIyamhqEh4fX+3l4eDgKCwutOsfjjz+Ozp071+sMONeZM2fw+OOPY/To0Y32arz44osICgqq/erSpYttL4SIiLzbiBEymd26dc3v68xSdEuWABUVGGd5GzXN5KutgRHjLQvlFP+lS62/hr8/0KmTfMwSdERERG5J9zXyjnjppZewbNkyrFixAm3atLngeZPJhNTUVAghsGDBgkbP8+STT6KsrKz2Kz8/35nNJiIiT3L6tByhPn1aJruzxn33ySn127fLKfZaEAKm/y5ANpIaHYk/XyE6IQcjYZr3um0dCmot+X377GgoEREROZuugXyHDh3g4+ODoqKiej8vKipCRETTNylz5szBSy+9hLVr1+KKK6644Hk1iD9w4ADWrVvX5BqD1q1bIzAwsN4XERERAOD77+W0+shIwIo8KwDk6P3YsfLxvHnatOP4cfju2YkskWTTYVkiCb57dto2TV4N5DkiT0RE5JZ0DeRbtWqFq666CuvXr6/9mcViwfr169G/f/9Gj5s9ezZmzZqF3NxcXH311Rc8rwbxu3btwueff45QW+rnEhERnWvjRrkdONC2xG/q9PpVq7QZ2T51CgBQghCbDqvd/+RJ6w9iIE9EROTWdJ9a/8gjj2DhwoVYsmQJ/vjjD0yaNAmnT5/GfffdBwAYM2YMnnzyydr9X375ZTz99NNYvHgxoqOjUVhYiMLCQpw6e4NjMpmQnJyMH3/8ER988AFqampq96murtblNRIRkQc7N5C3Ra9eQHy8nNLexPIuq7VrBwAIQYlNh9XuHxBg/UEM5ImIiNxa05lyXCAtLQ3Hjh3DjBkzUFhYiD59+iA3N7c2Ad7BgwdhMNT1NyxYsADV1dVITk6ud56ZM2fimWeeweHDh7Fq1SoAQJ8+ferts2HDBtx4441OfT1ERORFTCbgu+/k4wEDbD/+ySeBIUPkmnlHhYbCFBuH5L3ZyBIpVh+WrGTDFBMH3yaqu1ygRw+ZD8DapQRERETkUrrXkXdHrCNPREQAgM2bgWuukSXZiosBg84T2dLTYZ42HZEi36qEdxEoQL7SFca5rwBTp7qggURERGQvj6kjT0RE5NbCw4FZs4ApUxwP4oVwvBTd2LGA0YjFGAcfNF3X3gdmLDJMlOXkxoxx7LpERETkVhjIExERNaZrV+Cpp4Bnn3XsPMuXA/36Aeckd7XLkiUwms5gCHKxSrkTEShocLcIFGCVYTiGKHkw5iyXWfTtxYl7REREboeBPBERkbN9/bWcpu9IKTohgJ9/BgAYxt2HeP9vkK90RYaShhRkYjDWIQWZyFDSkK90RbzfRhg+XSMT7tnj4YeB0FBg4UL720xEREROwUCeiIioIUeOAFlZQGGh4+dSS9GtXm1/JnhFAd55B/j4Y+Dtt2E8dADGua9gRMw2ZCIN6xCPTKRhRMw2GOe+AuPhg/YH8QBQUyNrz2tROo+IiIg0xUCeiIioIZ99BqSkAKmpjp8rLk5mrxcCmD/ftmMLCmRQDchgftgwuQ0OBqZOhe+uP2Qivn37gOJi+f3UqUBQkGNtZgk6IiIit8VAnoiIqCH21o9vjJo1ftEi4NQp644pKgKuv152JlRWNryPosgp8NHRcqsomjSXgTwREZH7YiBPRETUEK0D+YQEWZ+9rAx4773m9z99GrjjDjnSvnWr9cG/VhjIExERuS0G8kREROc7ckQGsIoC9O+vzTkNhrq18vPmNZ0N3mwGRo+WCfJCQ+U0/7AwbdphrW7d5PbECaC01LXXJiIioiYxkCciIjrfN9/Ibe/ejq81P9e99wK33w68+CJgsci17fv3y60a2Ashp+F/8gnQpg2wahVw8cXatcFa7doBHTvKx0x4R0RE5FaMejeAiIjI7Wg9rV4VGAh88AGwZAlMcZfCd8/O2qdMsXHwnTIJKCkBFiyQswE++AC47jpt22CLG2+USwGIiIjIrTCQJyIiOp86Ij9ggLbnzcuDOSkVqKhADpKQhVkoQQhCUILkvdlImjYdEEJ+OP/nP8DIkdpe31YZGfpen4iIiBrEQJ6IiOh8K1fKUfmbb9bunHl5sCQOxVqRgHHibRQhot7TWSIF4SjEYmU8hiAPhp49tbs2EREReRVFiKay7bRM5eXlCAoKQllZGQIDA/VuDhERebrSUpgjo7C2ciCGWVaipol+dB+YscowHPF+G2E8dEDWi9fbmTNyvT4RERE5jS1xKJPdEREROduSJUBFBcZZ3m4yiAeAGhgx3rIQqKgAli51UQMb8dNPMlv+FVfo2w4iIiKqh4E8ERHRuZ58Evj3v4GiIm3OJwRM/12AbCRdMJ2+MYXohByMhGne602XqXO2sLC6zPo1Nfq1g4iIiOphIE9ERKSqqpJJ5h57TLts7cePw3fPTmSJJJsOyxJJMqv9iRPatMMeF10E+PoCJhNw+LB+7SAiIqJ6GMgTERGpfvxRBvMdOwI9emhzzlOnAAAlCLHpsNr9T57Uph328PEBoqPl47179WsHERER1cNAnoiI3J8QdVO8i4udN91crR8/YICs466Fdu0AACEosemw2v0DArRph71iYuSWgTwREZHbYCBPRETuq7QUSE+HqUcvuV67WzcgLEx+n54un9eSWj9+4EDtzhkaClNsHJKVbJsOS1ayYYqNA9q3164t9mAgT0RE5HYYyBMRkXvKy4M5MgrmadORs7cPUpCJwViHFGQiZ28fmKdNhzkyCsjL0+Z6FguwaZN8PGCANucEAEWB75RJSEI2wlFo1SERKMBI5MB36mTtZgbYq1s3uWUgT0RE5DYYyBMRkfvJy4MlcSjWVg5EpMjHKLEMWUjBegxGFlIwSixDpMjH2sqBsCQO1SaY375djvC3bQv06eP4+c41dizg74/FhgnwgbnJXX1gxiLDRMDfHxgzRtt22KN3byA+XvvfCREREdmNgTwREbmX0lKYk1KRKxIwzLKy0ZJtRYjAMMtK5IoEmJNSHZ9mv2sX0Lo10L8/YGy61rvNgoNhzM7EECUPqwzDEYGCBneLQAFWGYZjiJIHY85yIDhY23bYIz5edpQ89pjeLSEiIqKzFCH0LFDrnsrLyxEUFISysjIEBgbq3RwiopYlPR3madMRKfKtqrsegQLkK11hnPsKMHWqY9euqpLJ9C66yLHzNCYvT3Y6VFQgByORJZJQghCEoATJSjZGIgfw95dBfHy8c9pAREREbsmWOJSBfAMYyBMR6UQImHr0Qs7ePhgllll9WIaShhEx2+C76w/915Q3p7QUWLoUpnmvyzrxZ5li4+Sa+LFjgaAg/drXmJMnZU35Nm30bgkREZFXsiUO5dR6IiJyH8ePw3fPTmSJJJsOyxJJMig+ccJJDdNQcDAwdarsdCguBvbtA4qL5fdTp7pnEH/rrUBgoHaJBYmIiMghDOSJiMh9nDoFAChBiE2H1e5/8qR91/3gA+CKK4BXX7XveHsoChAaCkRHy607zyRQS+Axcz0REZFbYCBPRETuo107AEAISmw6rHb/gAD7rvv118CvvwIFDSeha/FYS56IiMitMJAnIiL3ERoKU2wckpVsmw5LVrJhio2rGzm21caNcqtl/Xhvogby+/bp2w4iIiICwECeiIjciaLAd8okJCEb4Si06pAIFGAkcmSiOHumpxcXA3/8IR8zkG9Yt25yyxF5IiIit8BAnoiI3MvYsYC/PxYbJsAH5iZ39YEZiwwTAX9/YMwY+663aZPcXnKJXKtOFzp3RN5i0bctRERExECeiIjcTHAwjNmZGKLkYZVhOCLQ8Lr1CBRgFYZhiPhM1l0PDrbvepxW37wuXQAfH+DMGaDQupkSRERE5DxGvRtARER0gYQEGNasRnxSKvJPd0UOhiMLKShBCEJQgmQlGyORAwgBg7AAZWX2X0sN5AcO1Kbt3sjXFxg9Ws58EELv1hAREbV4ihD8RD5feXk5goKCUFZWhsDAQL2bQ0TUcpWWAgMHwvTbDvieM83eFBsn18Tv3w/85z+y9vrWrbKUmy2EACZOBL74Qn7ZejwRERGRRmyJQzkiT0RE7is4GFiwAL4ffwzceScQGQkEBMC3fXuZ2M5kAr77Drj8cqBjR9vPryjA229r3mwiIiIiZ+KIfAM4Ik9E5EEqKwE/P71b0TKcOiWXMVx0kd4tISIi8jq2xKFMdkdERJ7t3CDeYgH27LH+2H37gJoa7dvkjVasAAICgFGj9G4JERFRi8dAnoiI3NesWUBuLlBd3fy+x48DCQnAdddZl1ndbAauuEKWnNu/3+Gmer3ISLllLXkiIiLdMZAnIiL3tH8/MGMGMHQoUFHR/P7+/kBREXD0qKwp31y9861b5VRxRQG6dtWixd5NrSV/5IhczkBERES6YSBPRETu6bPP5Pa666yrEe/nB2RkyIB+3Trg3/9uev9vvpHb668HDPw4bFb79oC6Xo8zGIiIiHTFOxciInJPaiB/223WH9OrF/Df/8rH//wn8O23je+r1o8fMMC+9rU0ilI3Ks/p9URERLpiIE9ERO7nzBlg/Xr5+PbbbTv2vvuA0aNlErvRo2Ut+vMJURfIDxzoUFNblG7d5JaBPBERka4YyBMRkfvZuFGui+/cWSaks4WiAG+8IUePDxwAJk+ue04IoLgY2LABOHYMaNUKuPpqbdvuzdQR+X379G0HERFRC8dAnoiI3M+nn8rtkCEyMLdVYKBcL3/ZZcBjj8lR+fR0mHr0AsLCgFtuAQCYDK1k0N/QqD1daNAgYMIEmVeAiIiIdKMIIYTejXA35eXlCAoKQllZGQLVxD5EROQ6t9wCfPEFkJUFJCXZfx6LBVi3DuakVKCiAtlIQpZIQglCEIISJCtZSEIO4O8PY3amLF9HREREpANb4lAG8g1gIE9EpDMhgN9/B6KigHbt7D9PXh4siUORKxIwzvI2ihBxwS7hKMRiwwQMUfJgWLOawTwRERHpgoG8gxjIExF5gdJSmCOjsLZyIIZZVqIGxkZ39YEZqwzDEe+3EcZDB6wrd9dSVVTINfIxMbLkHxEREWnCljiUa+SJiMi9aNW/vGQJUFGBcZa3mwziAaAGRoy3LJRB6tKl2lzfW/XsKXMPbNumd0uIiIhaLAbyRETkPiorgS5dgFGjgJMn7T+PEDD9dwGykdTgdPqGFKITcjASpnmva9eZ4I1Ygo6IiEh3DOSJiMh9fPUVcPgwsGmTY2vjjx+H756dyBK2JcrLEknw3bMTOHHC/mt7O7UEHQN5IiIi3TCQJyIi9/HZZ3J72232lZ1TnToFAChBiE2H1e7vyGwAb8dAnoiISHdNLxokInIWIYDjx2XA1a4dEBrqWOBG3kGtH3/77Y6d5+xofghKbDqsdv+AAMeu783UQH7fPn3b0RLxfZOIiM7iiDwRuVZpKZCeDlOPXkBYmFxvGxYmv09Pl89Ty7RrF7B7N+DrK+vIOyI0FKbYOCQr2TYdlqxkwxQbB7Rv79j1vRlH5F2P75tERHQeBvJE5Dp5eTBHRsE8bTpy9vZBCjIxGOuQgkzk7O0D87TpMEdGAXl5ereU9KBOqx8wwPERcUWB75RJSEI2wlFo1SERKMBI5MB36mSOcjZFDeTz84Hqan3b0hLwfZOIiBrAqfVE5Bp5ebAkDsVakYBx4u0LMolniRSEoxCLKydgSOJQGNasBhISdGos6UIN5B2dVq8aOxb45wwsrpxgVR35RYaJgJ8/MGaMNtf3Vh07An/9K9C1K2AyAa1a6d0i78X3TSIiaoQiBGvsnK+8vBxBQUEoKytDYGCg3s0h8nylpTBHRmFt5UCrAqpVhuGI99sI46EDQHCw69pJ+nrhBWDFCuDdd4FLL9XmnGcDoVyRgPGWhShEpwt2iUABFhkmYoiSB8Ona4D4eG2uTeQIvm8SEbU4tsShnFpPRM63ZAlQUYFxlrebvBkFgBoYMd6yEKioAJYudVEDyS384x/A5s3aBfEAkJAAw5rViPfbiHylKzKUtHpTkzOUNOQrXRHvt5FBPLkXvm8SEVETOCLfAI7IE2lICJh69ELO3j4YJZZZfViGkoYRMdvgu+sPrlcmx5WWAkuXwjTvdVkn/ixTbJxcEz92LBAUpF/7PM2ZMzJrvaIAPXvq3Rrvw/dNIqIWiSPyROQ+jh+H756dyBJJNh2WJZJkwHXihJMaRm5DCFl2zpm124ODgalTZYBTXCyD0OJi+f3UqQzibfXWW8AllwD//KfeLfFOfN8kIqJmMJAnIuc6dQoAUIIQmw6r3d+ZwR25hz//BBITgc6dgaoq515LUWTt7eho1uB2BEvQORffN4mIqBkM5InIudq1AwCEoMSmw2r3d7QMGbk/NVv9tdcCrVvr2xayzrmBPFfoaY/vm0RE1AwG8kTkXKGhMMXGIVnJtumwZCUbptg4oH17JzWM3Mann8qtVmXnyPmio+W2vBwosS3YJCvwfZOIiJrBQJ6InEtR4DtlEpKQjXAUWnVIBAowEjkyCRmnPnu306eBr76Sj2+7Td+2kPX8/YFOZ0v5cXq99vi+SUREzWAgT0TON3Ys4O+PxYYJ8IG5yV19YMYiw0QZKIwZ46IGkm6++AKorpYjvHFxereGbMF18s7F900iImoCA3kicr7gYBizMzFEycMqw3BEoKDB3SJQgFWG4Rii5MGYs1xmGifvpq6Pv/12jiJ6GgbyzmXL+6ZyJ983iYhaGKPeDSCiFiIhAYY1qxE/MgX5FV2QgxHIQgpKEIIQlCAZWRiprAD8/GHIWQPEx+vdYnI2tewcwGn1nigpSc6iuOkmvVvivdT3zaRU5J9u4n3Tn++bREQtjSIE082er7y8HEFBQSgrK0NgYKDezSHyLosWARMmwNS6LXyrTtf+2AQjfF96Hvjb31jTuyXZs0eOyt93H9C2rd6tIXJPxcVAZCRMVTXwPWeavSk2Tq6JHzuW75tERF7AljiUI/JE5FqXXgpMmADf7t2BCRNkveOhQ+HbtSuQksKb0ZYmNhZ48EG9W0Hk3nbvBqqq4BscDOzcCVRUAAEB8G3fvm5JitkMGHlbR0TUUvAdn4hc69pr5ZcqNBT45RfAwJQdRB5FCBlU7t0rp3QziHSezz+X21tuATp2rP+cxQL861/A668D338PdO3q+vYREZHL8c6ZiPTHIL7lOXUKGDkSePNNoKZG79aQPYQA+vQBEhOBgwf1bo13GzAAuP9+OWvpfAYD8OWXQGEh8O9/u7xpRESkD66RbwDXyBM5yb59wPHj8ua/odG7gwflTWlkpMubRi728cfA8OFyav3u3Xq3huzVqxewY4ccMb7lFr1b03J98YX8/bdpA+zfD4SH690iIiKygy1xKIfBiMh1Fi8G+vaVI0vn+8c/gKgoYO5clzeLdKBmq7/9dn3bQY5hCTr3cNNNQL9+wJkzfA8lImohGMgTkets2iS3566RV115pdx+8onr2kP6EKKufjzLznk2BvLO9/nnwHffyWR2jVEU2RkKAPPnAyUlrmkbERHphoE8EbmGySRvRgHg+usvfD4hAfD1Bf78UybQIu+1fTuQny+nAd94o96tIUcwkHe+6dOB/v2BrKym9xs6FLj8clkJZP5817SNiIh0w0CeiFzj55+BykogJESuqz1fYGBdUMdRee+mjsbfdBPg56dvW8gxDOSd6+hRYNs2+fjmm5ve12AAnnxSPp43D6iudm7biIhIVwzkicg11Gn111/feJb6YcPkdtUq17SJ9KGuj+e0es/HQN65vvhCbnv3vrDsXENSU+UI/tdfA61aObdtRESkK7cI5OfPn4/o6Gi0adMG/fr1ww8//NDovgsXLsTAgQMREhKCkJAQDB48+IL9c3JyEB8fj9DQUCiKgq1btzr5FRBRs775Rm4HDGh8nzvukNtNm2R2e/I+FovsyPHxYSDvDWJigFmz5Agwi+BoT60fP3iwdfv7+MgSdD17Oq9NRETkFnQP5DMyMvDII49g5syZ2LJlC3r37o2EhAQcPXq0wf2//PJLjB49Ghs2bMC3336LLl26ID4+HocPH67d5/Tp0xgwYABefvllV70MImqKEHWBfEPr41VRUcAVV8hgTx21Je9iMADr18uOmu7d9W4NOaptW+Cpp4C775YJ10g7QgDr1snH1gby5ztzRrv2EBGRW9G9jny/fv3Qt29fvPbaawAAi8WCLl26YMqUKXjiiSeaPb6mpgYhISF47bXXMGbMmHrP7d+/H926dcPPP/+MPn36WN0m1pEn0pjFAmzcKEfaH3lEJjlrTHY2UFMjk98FBbmujURE7mT3bqBHD5kEtKREdppYq6gI+Pvfgc2bZXJJo9F57SQiIs3YEofq+s5eXV2Nn376CU+qyVkAGAwGDB48GN9++61V56ioqIDJZEL79u3tbkdVVRWqqqpqvy8vL7f7XETUAIMBuOEG+dWcpCTnt4f0IYQMSBx4vyY3dPgw8MsvQEREXRlJctz69XJ73XW2BfEA0K4dkJsrZ75kZgJ33aV9+4iISFe6Tq0vLi5GTU0NwsPD6/08PDwchYWFVp3j8ccfR+fOnTHY3mlnAF588UUEBQXVfnXp0sXucxER0XmEAIqLZWDRoQNwyy1cT+1NFiwAbr8dWLhQ75Z4l3Hj5Cym556z/di2bYGHH5aPX3xRzooiIiKvovsaeUe89NJLWLZsGVasWIE2TU3VbcaTTz6JsrKy2q/8/HwNW0lEeP55OSp0+rR1++/bJxNo/ec/zm0XOVdpKZCeDlOPXkBYmAz2hIDpux9lcrTSUr1bSFpg5nrn8PWVo/GDBtl3/IMPAgEBwG+/AatXa9s2IiLSna6BfIcOHeDj44OioqJ6Py8qKkJERESTx86ZMwcvvfQS1q5diyuuuMKhdrRu3RqBgYH1vohII8XFwNNPA2lp1ide+vVXYMYMID2dI7eeKi8P5sgomKdNR87ePkhBJgZjHVKQiZyKITBPmw5zZBSQl6d3S8lRDOTdU3Aw8MAD8vG//sX3UiIiL6NrIN+qVStcddVVWK+uA4NMdrd+/Xr079+/0eNmz56NWbNmITc3F1dffbUrmkpE9vrf/+T2kkuA0FDrjhk8WCbEO3BABvXkWfLyYEkcirWVAxEp8jFKLEMWUrAeg5GFFIxCBiJFPtZWDoQlcSiDeU+nBvL798tEleS4RYuAv/2t7v3TXg8/LN9Lf/ihriY9ERF5Bd2n1j/yyCNYuHAhlixZgj/++AOTJk3C6dOncd999wEAxowZUy8Z3ssvv4ynn34aixcvRnR0NAoLC1FYWIhTp07V7nPixAls3boVv//+OwBg586d2Lp1q9Xr7olIQ9aUnTufv39duaVPPtG+TeQ8paUwJ6UiVyRgmGUlitDw7KoiRGCYZSVyRQLMSamcZu/JLrpITgM3mYAjR/RujXf46CPgzTeBLVscO094ODBxonycnu54u4iIyG3oHsinpaVhzpw5mDFjBvr06YOtW7ciNze3NgHewYMHUVBQULv/ggULUF1djeTkZHTq1Kn2a86cObX7rFq1CldeeSUSExMBAKNGjcKVV16JN954w7UvjojqAvkBA2w7btgwuWUg71mWLAEqKjDO8jZqmimMUgMjxlsWAhUVwNKlLmogac7HB4iKko85vd5xlZV175sOJPKtNX068MILwHvvOX4uIiJyG7rXkXdHrCNPpJHKSlkL3mQC9uypm4JrjYICoHPnusfN5M0gNyAETD16IWdvH4wSy6w+LENJw4iYbfDd9QegKE5sIDlNQgKwdi2weDFwdkadQ4SQpdNOnZKl1EJDW87/jc8/B269Vc50yM9vOa+biIhsikN1H5EnIi/2448yiI+IALp1s+3YTp2Avn3l4zVrtG8bae/4cfju2YkskWTTYVkiCb57dgInTjipYeR0U6fKWRU33eTYec6vdNCtGxAWJr9PT28ZSzA+/1xuBw/WPogXQlYPUUtC7t8vtxzTISLyOAzkich51PWdAwbYd0N6xx1AYCBQUqJtu8g5zuYqKUGITYfV7n/ypNYtIldJTAT+7/+A6Gj7z9FUpYO9fVpOpYNzA3ktff898Je/AIMGteyOEiIiL8Gp9Q3g1HoiDR08KKfYx8XZfuzJk0Dr1kCrVtq3i7RXXAyEhSEFmchCitWHpSATmUiTx1tb2YC8y9lKB7kiAeMsbzeYJDEchVhsmIAhSh4Ma1bL6fze5vhxGWALIRMHduqk3bkXLIB58oMAFGQrycgSSShBCEJQgmQlG0nIBvz9YczO9M7fLRGRB7AlDmUg3wAG8kREdjhxAqZuPZBTfgtGIdPqw7hG3gucOQN89RVw+DAwbpxtx5aWwhwZhbWVAzHMsrLJJIk+MGOVYTji/TbCeOiArJXuTX7+GUhNlR2Yv/2m3XnVjpKaWzEOi1tuRwkRkZvjGnki8i5CAEeP6t2KlsfadbRCyHJZvXrBt/wEkpCDcFhX7jMCBRiJHPhOncwg3pNVVQFDhgDjx8s12LZgpYM6V14J7NrleP34c51bEhKrWBKSyFsx90WLw0CeiJzj/fdlCblM60dmG/Tnn0CPHkCfPoDFoknTqBm2JhwbNw646y7Z2XLxxUAbPyw2TIAPzE1exgdmLDJMBPz9gTFjnPZyyAWCgoD27eXjffusP04ImP67ANlIajTAPF8hOiEHI2Ga97r33qhqORuQHSVE3o1JQlssBvJE5Bx5ebIG/Pbtjp0nKgooKpIl6H76SZu2UePsSTh2220yj8GzzwK//ALjyiwMUfKwyjAcESho8DIRKMAqw3AMUfJgzFnufVOkWyK1vKQtteRZ6aDOmTOyyoeW2FFC5N2YJLRFYyBPRM7xzTdye/31jp2ndeu6tZqffOLYuahpZ9fRrq0ciEiRj1FiGbKQgvUYjCykYJRYhkiRj7UVA2C5PbHuxiAlBdi9G5gxo/bfy7BmNeL9NiJf6YoMJa3ezUWGkoZ8pSvi/TbC8OkaID5e39dN2lBLTNoSyLPSQZ3335ezGp54QrtzsqOEyHtZ+5ldORCWxKEM5r0QA3ki0t7hw3KNlsEAXHut4+cbNkxuGcg7z7nraC0rm15HKz5GriUe5pEpcsqeogBdutTfMSEBxkMHYJz7CkbEbEMm0rAO8ciETGxnnPsKjIcPMoj3JvaMyLdrBwAIgW0lJmv3Dwiw6Ti39vnnsmOjTRvtzsmOEiLvZMtnNnNfeC0G8kSkvU2b5LZ3b23Wet5+u+wU2LpVlrMj7dm6jhaLZFnBptbRBgcDU6fKbPTFxXLtdHGx/H7qVLmumryHPYF8aChMsXFIRpZNl0pWsmGKjatbl+/pLBZg/Xr5WMv68ewoIfJOzH1BYCBPRM6g1bR6VYcOwHXXycerV2tzTqrj7HW0iiLrw0dHyy2z03snewJ5RYHvpIlIQnbLrnTwyy+ys6tdO6BfP+3Oq3aUKNk2HeZ1HSVE3oS5L+gsBvJEpD01kB8wQLtz3nGH3K5apd05SeI6WtJCnz5ylGjRItuOGz8e8PfHYmV8y6108PnncnvDDYCvr3bnVRT4TpnEjhIib8LPbDqLgTwRaaumRk6pbtNGuxF5ABg+HPjb34BHHtHunCRxHS1poUMHGVz379/8vl9/LcsiAUBwMIw5yzHEsLblVjpwxrR61dixsqOEJSGJvAM/s+msphdVEBHZyscH+OILoLpaliTTysUXAwsWaHc+qsN1tKQVIYDjx+WNZrt2Fy6lEAJ4/XXg4Ydlp9+ll8rgVa10kJSK/IquyMFIZIkklCAEIShBMpZjpLIS8POHIcfLKh1UVcmODcA5gXxwMIzZmRiSOBSrDMMx3rIQheh0wW4RKMAiw0QMUfLk79ibOkqIvAk/s+ksjsgTkXNoGcSTc3EdLTmqtBRIT4epawwQFiZL0YWFwdSjlxx5Ly2VddInTAAefBAwm4G0tLrcF0DTlQ6wEsa/TfTOSgfV1cA//iFnHV16qXOu0VxJSKSyJCSRp+BnNp2lCMGMB+crLy9HUFAQysrKEKhFxm2ilqSiQk7LdAaLBfj2WyA3F3jmGTn6T9pIT4d52nREinyrkudEoAD5SlcY574iM9BTy5WXJ8saVVQgW4xEFpLrRtKVbCQhG/DzgzGyE/Dnn7ICxcsvA3//e+NrsIWQ6zhPngRmzADeew+YPBmYP9+1r83blJYCS5fCNO91uVb2LJNPa/i+8jJw772sJkENa262DbkWP7O9li1xKAP5BjCQJ7KT2Sx7eqOigHXrgAjrsqlazWQCOnaUN6MbN2qbTK+lKy2FOTIKaysHYphlZZPlbHxgxirDcMT7bYTx0AFOwW3J8vJgSRyKXJGAcZa3G7yhDEchFmMchiAXhnZtgexs20Z816wBhg4FIiNl+UkGD45TO0qKi2WSwjNngG3bgCuu0Ltl5G5KS4ElS2D674L6nT+xcfCdMknmYOBngOvxM9tr2RKHcmo9EWln2zY5gnbokAy4tebrK2vKA8Ann2h//pZMXUer5LXchGNkm9JSmJNSkSsSMMyystFRoSJEYBhWIRdDYLYowDXX2HadW24B2rYFjhyRI/repLwcyMyUAbUrqSUh4+KAm2+WP8vNdW0byP3l5cEcGQXztOnI2dun3nKMnL19YJ42HebIKCAvT++Wtjz8zCYwkCciLall5667Tk6fdYZhw+SWgbz2zl1Hiy7IQEr9dbRKGtfRUp0lS4CKCoyzvN3kaBAA1MCI8VgEVFYCS5fadp02beTf+9GjMvD0Jhs2yFwBAwfq14YhQ4BOnZz3nk2e6exsm7WVAxEp8jFKLEMWUrAeg5GFFIwSyxAp8rG2ciAsiUMZzOtB/cxu9SU/s1soTq1vAKfWE9kpNRVYvhx44QXgySedc42yMlnmymwGdu0Cund3znVaskOHgKgomCwG+J5TrsoUGydrS48dy3W0LZ0QMPXohZy9fTBKLLP6sAwlDSNitsF31x+cIg8AU6YAr70GPPCA3OqhulrOduK/B6k4bduzjBwJrFgBU2B7+JbX1Yg3dbwIvsFtgbfeAm64QccGki04tZ6IXE+IuhF5LevHny8oqO4DiaPyzvHzz4DFAt+YrnLK7759QHGxDL6mTmUQT8Dx4/DdsxNZIsmmw7JEklxne+JE8zu3BJ9/LrfOKDtnrVatGMRTfbbOtrEslIlubZ1tQ447flzmEQHgu2Fd/c/sG66Ty5FWrdK5keQsDOSJSBv79gEFBXJkp29f517rjjvklh9OzqGulR0yRK6jjY5mhmKq79QpAEAJQmw6rHb/kydtv+by5TLBpV4j11o7dAjYsUNOab/xRr1bI6uC5Ofr3QrSmxAw/XcBspFkVTZ0AChEJ+RgJEzzXped+uQ6H3wgZ9VceSXwl7/U/8y+6y65T0aG/Psmr8NAnoi0sWmT3F59NeDn59xrqYH8zp3A4cPA/v2yF5o3ENo4N5Anaki7dgCAEJTYdFjt/gEBtl/z0CH5PpOTY/ux7kgdje/bV//pyFu3yiojgwbxfbSl42wbzyEEsGiRfDx+/IXP33abnEF3+LCs9ENeh4E8EWkjOlqunR450vnXat8emD4dJv9AWZKqWzcgLAymHr2A9HRZLofsIwTw6qvApEnATTfp3RpyV6GhMMXGIVnJtumwZCUbptg4+TdsqzvvlNuvv/aOYMEdptWrevSQ+Uf27/e+ygBkGz1m25B9fvoJ+OUXoHXrutH3c7VuDSSd7ZD56CPXto1cgoE8EWlj4EDg3XeB6dOdex21HM4rc1kOxxkURQZMr79eO+pKdAFFge+USUhCNsJRaNUhESjASOTIhIn2LNOIiQEuuwyoqQE+/dT2492JEMD69fKxOwTybdvK0XiAZehaOj1m25B9duyQf7tJSUBIIx0vo0fL7fLlcgo+eRUG8kTkOVgOh8h9jB0L+PtjsWECfM6pbtAQH5ixyDAR8PcHxoyx/5rqqPzHH9t/DnegKMDmzcA77wD9++vdGkldSsNAvmXTY7YN2eeee2RuotmzG9/nppuA8HA5i2ndOte1jVyCgTwROW7fPrnGsqbGedcoLYU5KRW5IgHDLCsbTcJThAgMs6xErkiAOSnVddPshZDr9D15vX5VFTBzpqw+wMQ41JzgYBizMzFEycMqw3BEoKDB3SJQgFWG4Rii5MGYs9yx9eBqIJ+bK/+/uoKz/rYjI4F775XTX92BGsh/+SVQWalrU0hHesy2IfsFBAAXXdT48z4+clT+llvk6D15FQbyROS4N9+UGVMfeMB513DXcjilpUB6ulyfHxbm2ev1v/kGeO45IDWVN2NknYQEGNasRrzfRuQrXZGhpNVb7pKhpCFf6Yp4v40wfLoGiI937HpXXQV07izX8X7xhTavoTHe9LdtjUsuAbp0Ac6cAb76Su/WkJ70mG1Dttmxw/pOxVdflTk53KE6BmmKgTx5H28YGfU0av34fv2cc353LYejrtefNt071uurU2oTEhjIk/USEmA8dADGua9gRMw2ZCIN6xCPTKRhRMw2GOe+AuPhg44H8YAs1TZqlByZDwpy/HyNcebftskEjBgB/Oc/Mmh2F4rC6fUk6THbhqy3ezfQq5fs2LRmZhI/z72WIgSjnPOVl5cjKCgIZWVlCAwM1Ls5ZK3SUmDJEpj+u0CWQDnLFBsH3ymTZA8zP2S0d+aMvKGurpbZjnv00P4axcVAWBhSkIkspFh9WAoykYk0eXxoqLZtOrteP1ckYJzl7QY7GMJRiMWGCRii5MGwZrUMkN3Z5ZcDv/0GLFsGpKXp3RryRELItZgnT8opn+3be95NpLP/tr/5RiYH7dABKCqSnRPuYsMGuY52xAhZFo9atrw8uUStogI5YjiykIIShCAEJUhWsjESOYC/vwziteioI+v84x/Aiy/KjrfPPrP+uIICmZtj2DDntY0cZkscykC+AQzkPdA5HzbZSEKWSKr3YZOEbPlhk53p/sGUp9m0CRgwQE49LSpyzk37/v1At24YjHVYD+szPA/GOqxDvFzDHx2tXXtKS2GOjMLayoEYZlnZ5FR/H5ixyjAc8X4bYTx0wH07kw4flmt2FQU4dkz7jg8iT+DMv20hgOPHgeefl1PzU1OBjAxt20+ktdJSYNIkmJZlwfecafam2Di5Jn7sWOfOjqH6zGaga1cZlC9fDiQnW3fc/v1AbCxgNMp7NXe9FyGb4lA36gYmshMzmetLnVY/YIDzRt7crRyOu67Xd4T6d3HNNQziyTPs3Ss7ErXkjL/t89fap6cDAEwbNnrnWnvyLsHBQLduMogfN052jO/bB99vNwJTpzKId7XcXBnEd+hg28h6dLTMg1FdDeTkOK155FoM5MmzuXsm85ZAvZEeMMB513Cncjjuul7fUeqaWHWNLJE7+/hjObr0179qd05n/G03tda+eJB75tGoqABWrwbeeEPvlpC72LNHbi+9VJZM7NYNeOklfdvUUi1aJLf/939Aq1a2HavWlP/oI23bRLphIE+ezRtHRj2JxVIXyF9/vfOu407lcI4fh++encgSSTYdliWSZO6GEye0a4tWhAC2bJGPufSEPMGgQbKs0vbtdUGGo7T+2/bU2WI7dgB33AFMny5H74j27pXb2FhZ2QAAfv1Vv/a0VEVFspMNAMaPt/34UaPk9osvgELr7qXIvTGQJ8/lrSOjnkQIucbzmWdk+TlncpdyOKdOAQBKEGLTYbX7nzypbXu0oCjAzp3Ad98xwRV5hpAQ4IYb5OOPP9bmnFr8bat/3548W6xPHyA8HDh9WvulC+SZfHwAX18ZyF9+ufwZA3nXy8iQa+T79ZOzI2wVEyOPtViAzEzt20cux0CePJc3jox6Gh8fYPBgYOZM26d42cpdyuG423p9rfj4yA94Y9MzW4jcxp13yq1Wgbyjf9s+PnLd6mWXAYmJnjtbzGCom5ljS0Zs8l7ffQdUVso11pdcIn9WWCgrwpDrTJ4sR+RnzbL/HHfdJbecXu8VGMiT5/LGkVFqWkICDGtWI95vI/KVrshQ0uqtOc1QUpGProj32wjDp2ucUw7HndbrE7VkaiD/zTfaBBSO/m0fOiSnom/fDtP/fkC2GOm5s8VYT57O5+MjO3kCAuQaeYCj8q5mNMpOwltvtf8cqany3/Hnn4GjR7VrG+mCgTx5Lm8dGfUkzz8PrFwpe+pdJSEBxkMHYJz7CkbEbEMm0rAO8chEGkaIFTD6t4Jx/x7n1bR1p/X6Wjh+XGaznThRTtkj8hRRUXIauMUCrFnj+Pkc/dvu31/eGL/7LnxhRhasLAt1llvNFrv1Vvle9euvsjSlJxBCdujs3y+37tAh4q04vd71tPr/HBEBrFolZ1R07KjNOUk3DOTJc3FkVF+FhcDTTwMjR7o+IVJwMDB1Knx3/SFv2PbtA4qK4NuxvZye+sMPzr2+u6zX18K6dcCBA8D333NaPXkedVReTQDlKEf/tsPCatfue/RssQ4dZClKwH2S8DXm/PJ+3boBYWHye5b3c9x//yv/L8yfX/czBvKudfIk0LMn8I9/AGfOOH6+xETX1JFn55rTMZAnz+VtI6NNcac3Q7UtK1bI7y+/XL86sooia55HR8ue5dRU+XNnr/1yl/X6WlCnzjJbPXmiMWNkTeR339XmfOrftvgMqzDMvr9tb5ktpk6v37xZ33Y0panyfnv7uGd5P0+zbZv8P3DsWN3PbrpJzuJy1sw3qi8jA/jzT/le17q1tueuqdH2fAA711xJ0AXKysoEAFFWVqZ3U6g5JSXC1DZQrDEkCh+YhIwyG/7ygUmsMSQKU9tAIUpK9G65dUpKhJg7V1THxtV7MdWxcULMneva19FYWwLbu74tjdm0SbarXTshTp92/vVyc4XJr50wwUdkIFmkIEMMxlqRggyRgWRhgo/8/5aX5/y22MNiESIiQv7OPv9c79YQuYeffhICECYYhEkxigwltf7ftpIqTIqx8b9ti0VUx8aJZUpak59J539lKKny/dVicf1rbsihQ0Ls3Ok+7Tlfbq6o8TGKNYZEEY6CBn+n4SgQawyJosbHKERurt4t9kw33SR/mUuX6t2Sluvaa+W/wcsva3fOTz4R4pprhPjHP7Q7pxDyvqhtoDApRrFMSRPJyBS3YJ1IRqZYpqTVvXfy77FRtsShDOQbwEDew6gf5rhdROBIgx/mEThS92HurkHV+dzpzbDJtqS6zxuzxSJEVJT8R8/MdM01Dx4UYtQoUR0SVr+DQ/F1/wB561bZRn9/Ic6c0bs1RPqzWIS47jr5d5GcLER6esMdqenpQpSWNn6euXOFSTE2GmA29BllUozyvNQ8b+/Edyddu8pf5KZNerekZdq+/ex/ZB8hCgq0O++yZfK83bpp11nHzjVNMJB3EAN5D/TJJ8IEoxwZVVJsGz1xR+70ZuhObbHG44/LRo0Y4drrWixCFBcLsW+f3N51l2zHgw+6th22eOkl2cbERL1bQmS/8nIhZswQ4pZbhDCbHTvXe+/Jv4m2beWItBAX/m1bc9PLQNO52FHiGlVVQiiK/CWeH0RWVgqxZYvszCbneeQR+fu/805tz3v6tHyfA4T49lvHz8f3PM0wkHcQA3kP9O678t0hKEhUx1xs++iJO3GnN0N3aou11FHmPn0cv6l3xGefyXZERbnv1NQbb5Rt/O9/9W4Jkf2qqoQIDHR81LC8XIhOneR5XnzR8Xad0wnqsbPF/vxTzkwYPFjvltTxlqULnmDnzrqOrfN/b//3f/K5WbP0aVtLUFUlRIcO8ve8apX257/7bnnuqVMdPxc71zRjSxzKZHfk+YQA5s6Vj598Er67dwBHjsjkGjffDN8tPwBTp+qXkM1WS5YAFRUYZ3kbNWg6i3gNjBhvWSgztS9d6t1tsdYVV8hMulu2yLq3zvT448C8eUBJyYXPDR4MfPCBbIu7Jlbs21dmwmWiO/JkrVoBt98uH3/8sf3n2bNHvmd07w5Mm+Z4uxISYFizGvF+G5GvdEWGklYvGVuGkoZ8pSvi/TbC8Oka90wcFhAAZGUBn3/uPjWnjx+H756dyBJJNh3mVuX9PMXevXIbE3Ph59hll8ktM9c7zyefyOTCnToBt92m/flHj5bbzEzHkt4JAdN/FyAbSShChFWHFKITcjASpnmvy/t4sgsDefJ8p07JD5l27WQWVUWRb3p79wLr1wOBgXq30Hru9GboTm2xhaLIGwxnB89HjwKvvgo89JDsODqf0QjcdZf7ZKBuyOzZwB9/AD166N0SIseoZegcCeT79AF27JA1lrXKDJ2QAOOhAzDOfQUjYrYhE2lYh3hkIg0jYrbBOPcVGA8fdM8gHpA1p6+8Uj5eu1bftqhOnQLg4eX9PEV1tezY6tnzwudYgs75Lr0UmDwZmDLFOeVhb71VlmIuLAS+/NL+87BzTTcM5MnzBQQA2dlAfr7n14Z3pzdDd2qLvSor5QwBZ/jgA8BslqPal17a/P4Wi3PaQURytMrXF9i5U37Zq21boFcv7doFyNJ0U6fCd9cfcnRt3z6guFh+7wmzxdQydGqpSr15S3k/TzBsGLBrlyx/dj41kP/zT6CqyrXtail69gTmzweefNI552/VCkhJkY8//ND+87BzTTcM5Ml7NFaju7rapc1wiDu9GbpTW+wxa5asLa9VfelzCQG88458fN99Te+7dCnQu7e+yw3OJwSwcSNvvsh7BAUBN94oH9s6Kr92LbBokfM72xQFCA0FoqPl1l2X3JxPDeTz8tyjQzI0FKbYOCQr2TYdlqxkwxQb5/kd/npo6P/qRRfJ+66aGjmzizzTPfcAw4fXzWqyBzvXdMNAnjzbypVyXWNDMjOBDh2AUaNc2iSHuNOboTu1xR7+/rIz4qOPtD/3zz/L6YStWzf//ys/H/jlF+D997Vvh7127wYGDZJLUDypo4uoKfZMr6+qklNXJ0yQ+S7oQv37yyVqxcUy94jeFAW+UyYhCdkIR6FVh0SgACORA9+pkz2nA8XdKQqn1zuLxQI8+iiwaZPzlykOGACsWCFnX9iLnWu6YSBPnqu8HBgzRq7vbejmIiAAOH5cTgvzFO70ZuhObbFHWpq80fjmGxlMa0kdjR8+HAhpZsbCXXfJ7RdfAIcOadsOe+XlyW2fPnJqHZE3GDZMvu907279yPGrr8rO4E6dgPHjnds+T+XrK5N3AsBnn+nbFtXYsYC/PxYbJsAH5iZ39YEZiwwTZefumDEuaqAXEELOHrn2WqCoqOF9GMjL31NxMbB/v9zaG3ife55Vq4A5c2QSz8pKLVvrHIoC38n3s3NNBwzkyXO9+66cvt2zZ10ynnOpCbx273aP6YDWcKeRBndqiz0iI4GBA+Xjhtb32auqqm4tWXPT6gFZPWHAAPkh7YzZAfZQ17qqU2aJvEGXLjIJ5ZIlgMGK25tDh4Dnn5ePZ8/WfxaRO0tMlAFdly56t0QKDoYxOxNDRC5WYRgiUNDgbhEowCrDcAxR8mDMWd74Ejy6UGEhcOAAsHlz4x3WSUnAyy/XrbNuSUpLgfR0mHr0AsLC5Gd9WJj8Pj1dPm/veUaMgAlGmbjXVbPmdu+W74f2LIusqgI2bAAUhZ1rruaCcngeh3XkPYDZLERsrCxG+cYbDe9jMglhNMp9Dh50bfsc4U61292pLfZ4/XXZuL/8RbtzHjkixPDhQnTrZn2d+jfflO244grt2mGvykoh/P1le7Zu1bs1RPq56y75d3Dddawt7onOnBGiQwdhgkGYYBQZSqpIQYYYjLUiBRkiQ0kRJsUoP5Py8vRuref55hv59xEVpXdL3E9urjC1DRQmxSiWKWkiGZniFqwTycgUy5S0uv93ubn2nwcp1p/HURaLEBdfLP+933/ftmPLy4W45RZ5rNEoagxGscaQKCJwpNH68WsMiaLGx8i/y0bYEocykG8AA3kP8PHH8h0hJESI06cb369HD7nfF1+4rm1ayM0VNT5u8mboTm2x1dGjQvj4yEbu3KntuauqrN/3xAkhWrWS7di2Tdt22GrdurP/aBEMXsg7WSyyk6qpz4avv5Z/B4oixE8/ua5tpJ033qh7L5szR1THxtX7YKqOjRMiPV2I0lK9W+qZliyRv8ubb9a7Je7lnHuicBQ0eE8UjoK6e6LGgnCtzqOVGTPkRRMTrT+muFiIa66Rx7VtK8Tnn9frnLiwcy1Vdk4YfIWIiRGiosJ5r8eDMZB3EAN5D3DTTfKN4/HHm97vttvkfm++6Zp2aencN0MkN/xm6KqRBndqi62GDJH/B559Vt92jBgh2/HYY/q2Y/p02Y5779W3HUTOcuON8v/4ihUNP2+xCHHllXKf++93adM8XkmJEJs3690KIaqr5UgxIIN1IeS/a3GxEPv2ya3aUWky2dbxStLMmfL3O2FC0/vt3i3E8uVC7N3rkmbpSqtZiu442/GPP2pH1UVxcfP75+cL0auXPKZ9eyG+/77uuZISIdLTG+5ce/ZZIUJD+f7bBAbyDmIg7+a2bj377ubT/JT5qVPlvtOnu6ZtWispESI5WVTDqP9IQ0mJEBMnukdbbLFqlRDPPSfEnj2On+vrr4XYtcv+dqSmyh5rPV12mfy3++gjfdtB5CwPPdR8Z9X33wsRHy9n7ZB1vv9efu526qT/bJ7iYiFGjxaic+emR/Xee0+I6OjGl+BR4+65R/4dvfhi0/slJsr95s93Tbv0NHeuMCnGRkfQG5qtaFKMdZ1NWp9Ha336yAsuWCDEsWOyU+zYsQv/3v/8s64j7aKLhNi+veHzNda5tnatnA1lz1T+FsCWOJTJ7sjz/PmnrBmcnNx84p2+fYFbb61LfOdpgoOByEj4wgxMmgTs2wcUF8N31x/A1Kny9+DKtvTtC18jgJEj9W2LLe64A3j6aSAmxrHzCAHcf7/8v7RqlX3tyMgAbrnFsXY46r33gBdflH8XRN5ILUO3erWscd2Qa66R1RvCwlzXLk93xRWy5GZBgf5ZykNDZdLRnTsBP7/G9zt+XGYBnzOn8f8L1LC9e+U2Nrbp/VpK5nohYPrvAmQjCUWIsOqQQnRCDkbCNO91eQ+h5XmcYcQIAIBp2qNNJ/AzGIAzZ+T90KZNwCWXNHw+RZF/q9HRcqsmQb71VnlfBgB//SuwY4fzXpOXYyBPniclRWYbfvXV5ve95x5g7VoZgHmqP/+U2969L3wzdLWJE2UplLff1r8trvb99/LDxt8fuOkmvVtjvz59gCeekP92RN5o4ECZZbu4GPj00/ploU6c0Lt1nqtNm7r3PrXyhd7atWv6+fHj5f+F3buBlStd0iSvERUlO8C7d296v5YSyB8/Dt89O5Elkmw6LEskwXfPTuCNN+Q9aUqKY+dx1ntYXh7ML8+BGT7IOXMbUpCJwViHFGQiZ28fmKdNhzkySnaAxsYC69cDGzfK/yf2mDEDuPlm4PRpOTBXUaHt62khGMiTZ2rXDujcWe9WuMa+fXKrzio4dkz7uui2MBqbr53ubqqrgcxM2RFhbylCtXZ8UpJjZar++EP2RHtCbVgiT3TqFBAbK8s3DRtWN6rUNRaIiAD+/nfPKUnqbtSSlXoF8jU1wOOPA7t2Wbd/u3bAAw/Ix7NnO3c009t8+CGwZ0/D5X3PpQbyv/3m3b/fU6cAACWw7f6ndv/Nm4EPPgCysx07jz3l4ZqTlwdL4lCsPTMIkTiEUchEFlKwHoORhRSMEssQKfKxtmIALIlDZTB/6aVAeLj91/Txkf/HIiKA7duBxx7T7vW0IAzkyXNUVQFffGHfB0V5uTzeE/3yixxNuPZa4JVXgI4dgX/8Q+9WeZaaGmDCBDmT4LvvbD++ogJYtkw+tqZ2fGOEAG67TdZq/eQT+89jD7NZdmR88IHr6tISuVpeHsyRUTD/tBU5GFF/VOlQX5hNFpjnzQfWrdO7pZ5JDeS/+cY5AUVzMjNlQH799da/j02ZIpcE/PCDHEEkbcXFyQ7+sjJ9Bxmc7ezsjxCU2HRY7f533ilnkj73nGPncWQgoSGlpTAnpSJXJGCYZWWj0/2LEIFh4mPkWuJhTkqtm2bviPBw4KOPgP79genTHT9fC8RAnjyHur749tttO27AALl+21M/wI1GOY3J379urdrvv7u+Hfn5wFVXyamKntbr7ucHDB8uH3/0ke3Hr1ghO4Oio4EbbrC/HYoC3H23fPzee/afxx6bN8uOjClTZE84kbdRR5UqByJS5F84qoQMROIQ1loG140qkW26d5efQyYTsGGDa69tschOUEDmZWnVyrrjOnas64CdPds5bfM2tnzGt2oF9OwpH3vz9PrQUJhi45CsZNt0WLKSDVNsnJwdNG0a8NRTjp2nfXubjmvWkiVARQXGWd5GDYxN7loDI8aLt+XgxtKl2lz/xhvlOvvoaG3O18IwkCfPIAQwd658bGsgpU4Dt3YqnjtTE4rs2OH6qaHbtwNbtsgRbU9cFz96tNxmZsrRaVu8+67c3nuvTPLiiHvukdvcXLlMwlXUqbC33spAnryPLaNKlpXIFQnajSq1NLfdJreffeba6+bkyE7soCDZIWmLRx6Rn1tr1sjp4tS0//xHjpY+9ZR1+192mdx6cyCvKPCdMglJyEY4Cq06JAIFGIkc+E6dXHffpNV5tOAuiffOfU2ff67PbB8PxUCePMPGjcDPP8uR1YkTbTtWXVu+e7f27XK2zEw5gpuZKb+PiZG93xUVwMGDrm3LH3/IbWPZSd3d4MEywdvRo7aNJJWXAz/+KB+PGeN4O3r1kjMbzOa6f1dXUAN5dWoskTexdVTJslDbUaWWZMwYmbjriSdcd02LBZg1Sz5+6CHbq6T06CFH4//3v+azsJO8Xzp61PoBg8mT5drv//s/57ZLb2PHAv7+WGyYAB80PSDgAzMWGSbK2ZTn3ztodR5HOZrAT+vEe3PmyMGGv/2tXpZ/FBfXT1pKtRjIk2dQR+PHjLE927YayHviiPzXX8tkIFu2yO+NRrkeDXD99Hr1ep4ayPv6ysyogG3T6wMDgSNH5DTcbt20aYs6Ku+q6fXHj8up9QCQkOCaaxK5iruMKrUUffvKklH2Zqu2x6pVMl9MQIAM5O0xfbpci0vNs7b0nGrgQFmW9qKLnNcmdxAcDGN2JoYoeVhlGI4IFDS4WwQKsMowHEOUPBhzlsvyvc44j6McTeCn9ch5//51SfDmzgXS02Xpu6ZK4bVwDOTJ/e3bV1c2ZupU249XS6d4YiCvtlntjADqAmkG8rZTp9fn5NiW/NDPD4iP164do0bJKfrff++a/5fr1slg5fLLW061B2o53G1UqaVw1UiZELUJwjBlijZrhJnws2nq8gPOXrhQQgIMa1Yj3ncD8tEFGUitl1QzQ0lDvtIV8X4bYfh0TeP3Dup5/DYiX+mKDCXNvvM4wtEEflon3rv+euDFFwEA5kcehXnadOTs7dN0KbwWjoE8ub/XXpMf5AkJ9gWRahC8d6/MXu5J1BryF19c97NeveTWlYG8EHXXU6/viQYOBCIj5ayGw4eb3//0aefcnEZEyA/lkBCZ78DZOK2evJm7jSp5u9JS4F//gqljZ9eMlJlMMuN3ly4yWZgjKipkZ0BUlMyyTheqqZGdM4BczmetdeuAl14CDhxwSrPcSkICjFdfCSNqMCLkK2QiDesQj0ykYUTMNhjnvgLj4YPNB98JCTAeOgDj3FcwImab/eexl6MJ/LROvAcAl10Gi2LAWsTLpKViWcOl8CoHMmkpAEUIzik7X3l5OYKCglBWVobAwEC9m9OyCQFcd51MsPbZZ/YFIjU1cm1RdbUM5rWaHu1sVVWy3RYLUFAggz9ATrfPyQFuukne3LhCQYEcyTUYZHDbpo1rrusMZWXWr6+89145av6f/2gfBB86JG+CW7fW9rwNGTJEftitXw/cfLPzr0fkSsXFQFgYUs5mqbdWCjKRiTR5vK1LtlqqvDyZJLCiAtliBLKQghKEIAQlSFaykYRswN8fxuxM7ZfxmM1yeZkjLBbgiitk8taXX2bt6obs3y/vk9R8PNYmR73+epmD4MMP62a/eaudO2WmfoNB/r78/WWHYECADG7tSUgnhJwd5Oh5bJWeDvO06YgU+VYtTYpAAfKVrjDOfcW+WbJNKS2FOTIKaysHYphlZZP5TnxgxirDcMT7bYTx0AHtlx3oyJY4lCPy5N4URZalWL/e/h5JHx+ZMO5vf3M847gr7d0rbzratZPZY1WDBsm1Q64K4gGZXb1HDzkzwJODeMD6IP7UKSArS46Yaz19DJAzA1wRxANyRP7wYVmKkcjbuOOokjdqrryfs0fKHA3iAXkPoNarnjvXtiVWLYW6Pr5bN9sqnLSEzPWqhQvlNjFRzhQJDZXl00JD7Q++FUWb89jKXRLvAUxaagcPimqoxTIY5CiiI0H44sXAggWuTc7jqHPXx+td7u2KK+Q0/99+07cdWiotrbthacjy5XL2wcUXy1khziJE3TRGrc977vrVTp2sr7tM5EncqZyTt9KjvJ8QwIMPyo5ILSeP3nWXnGFWUAB88IF25/UWrVoBt9xie8fv5ZfLrTfdJzSkqqquJO399+vaFE24S+I9Ji21CwN5cl8HDgCVlXq3Qj+FhbI3/Nz18arSUuDbb61b560lb6k//v77cpbDww83vs8778jtvfc670Z/3z6ZjPHqq7VLvlRaykyv1PK406iSN9JjpGzDBmD+fGD4cDkrTCutWtWttZ8zx/oSay3FgAGylvfbb9t2nBrIe/uI/IoVshJMZKT35J1xh8R7TFpqF7sCebPZjM8//xxvvvkmTp5NEnPkyBGcOptwhsgu548g3neffKNUE3U56tQpGTh5ivvvlzdCr79+4XPjxslR4mzbppLSWVdeKQPn3NyG3/x37wY2bpSzQJxZF7dLF/lvfPy4NtNQ8/JgjoxipldqedxlVMkb6TVSpmaqv/9+oGNH+87RmPvvl6VF//gDWLNG23O3VGogv3+/dyeQXLxYbseP12a5h7vQO/Eek5baxeZA/sCBA7j88stx55134oEHHsCxs72kL7/8Mqar646IbNHYCOKGjUBJiTZ1STdulOucPa33tFWrhtdvuroEnTq9/OBB11zP2S69VN50mEwyceD51Glzt94qO5OcxWiU0zwBOUvAEeevX2WmV2pp3GFUyRvpMVL29dfAV1/Jz0BnJKQLDAQmTZKP//Mf7c/vyc6cse+49u3rypt68/T6Dz8EXn1VBvLeJjgYmDoVvrv+kANq+/YBxcXy+6lTrc8xZA93K4XnIWwO5B966CFcffXVKCkpgZ+fX+3PR4wYgfXr12vaOGoBmhpBxAiYhQHm/gMcDzqio+V2716Z+dbTuTKQP3ZMrtf/9lvvyuysZtVdtqz+z2tq5DRSQM4KcbZ77pHbVavsL4ekx/pVInek96iSN9JjpGzWLLkdP955nalTpwKPP+54J6o3EULmU4mIaDqHTGNawvT6Dh3k0owuXfRuifPokXiPSUvtYnMgv3HjRjz11FNodV7SpOjoaBx29Xpd8mzNjSAiE5E4pM0I4kUXyWzrZrNn1DitqAD695frNxtaO63Wcv/jD+e3Rb1GdDTQtq3zr+cqo0bJ7YYNMh+BSlHk1Ln77nNNZYA+fWTHzJkz9i+VYKZXojp6jip5I2ePlJ2/rG7TJrlG29cXeOIJOxpspc6dZd1zdRSZ5OyJ0lKgqKiu5K0tXnwR2LZN5qzwNi00mZrLMGmpXWwO5C0WC2pqai74+aFDhxDQQqc1kB1cPYJoMMikYkBdNnh3tns38N13wOrVDWcaj4uTb1rFxdomAWqIGsirswC8RbduwLXXykRHGRl1N5InTgCDB8tg3hWl9hSlbh2+PSNDzPRK1DC9yjl5G3tHyrAcpvYdGy+z2diyultvl8+PGgV07epY223RwL1ti6OOwnfqJJNB2urKK2WVG1eVVnWlzEzg+utlsjtyDluTlmJ8i09aanMgHx8fj7lz59Z+rygKTp06hZkzZ+L222+3qxHz589HdHQ02rRpg379+uGHH35odN+FCxdi4MCBCAkJQUhICAYPHnzB/kIIzJgxA506dYKfnx8GDx6MXZ4QvLUkeowgelIgf27puYb4+8ubHsD50+vV83tbIA/UjribHv+nvhne1XXyX34J5OfbdiwzvRKRM9k9UrYCvieOyhwrb71Vf1lbU8vqzgyBGT4wZ690TS6Pn3+W+XMmT3b+tdzdnj1yGxurbzvc0VtvAf/7H7B1q94t8V62JC3FMAxBLox3Dm3RSUttDuTnzJmDTZs24ZJLLsGZM2dw11131U6rf/nll21uQEZGBh555BHMnDkTW7ZsQe/evZGQkICjR482uP+XX36J0aNHY8OGDfj222/RpUsXxMfH15vWP3v2bMybNw9vvPEGvv/+e7Rt2xYJCQk4Y28CD9KWXiOIalDsDYE84Lp18t4ayOflwTzrBZhhRE71UH0zvHftKqd4fvGFnOZ57jTT5v6/M9MrETmbPeX9WreW64gLCoC//lUmGf3ySysSc2bIZXVnBrkmMefp0/IaS5bIttry/utt1BF5RwL5116TuQ2KirRpkzvYtUt+PiuKdya5cyfWJi1t/RUMkRfJ5RwtmbCDyWQS77//vnj00UfFpEmTxMKFC0VFRYU9pxLXXHONeOCBB2q/r6mpEZ07dxYvvviiVcebzWYREBAglixZIoQQwmKxiIiICPHvf/+7dp/S0lLRunVr8dFHH1l1zrKyMgFAlJWV2fBKyGrHjgkBiGRkCvkpad1XCjLkg+Ji+6771lvy+Ntu0/b1OMN998m2Pvts4/vk5Agxb54Qf/zh3LZ07izb8t13zr2OK+Xmihofo1hjSBThKGjw/1s4CsQaQ6Ko8TEKkZvr/DaVlAgxd66ojo2r15Dq2Dgh5s6Vzzdk3Tp9/p6IqGU5530zAkcafF+JwJG69828PCHOnBEiPV2IDh3kDmvXClPbQLHGkCh8YGryPcoHJrHGkChMbQMbf//TgsUiRN++8v02OMy2919vM25c8/cezenZU57DFZ+brvLYY/I13X673i1pOUpKhEhPb/ieKD1diNJSIaqq9G6lU9gSh9oUyFdXV4uYmBjx+++/2924c1VVVQkfHx+xYsWKej8fM2aMGDZsmFXnKC8vF23atBGffPKJEEKIPXv2CADi559/rrffoEGDxNSpUxs8x5kzZ0RZWVntV35+PgN5Z9q3TwhA3IJ1NgUeg7FWPti3z77r/vyzEBMmCPH22xq+GCcZMEC+1g8/1Lcd1dWy4yMqSr5peoOSEve7kczNFaa2gcKkGMUyJU0kI1PcgnUiGZlimZImTIpRtkG9MbJYhPj8cyEGD5YfbDCKZUix6e8pQ0mVH4gWi/NeFxF5l3PeqzKUVJGCDDEYa0UKMkSGklr3XpWXV/+4sjIhPvhAiLlzhUkxNtqB2lDHgEkxyht3Z76mNm2FCT5iGVKaf//1ZjfeKH/x779v/zlSUuQ5zhlQ82hVVUKEne3gWblS79a0PBaLHHDYt09uG7tnyc4W4rXXXNo0Z3FaIC+EEJ07d9YskD98+LAAIP73v//V+/mjjz4qrrnmGqvOMWnSJBETEyMqKyuFEEJs2rRJABBHjhypt19KSopITU1t8BwzZ84UAC74YiDvJHqNyHuS8HD5Wjdv1rsl3sfdbiRtnR3w1FO1o0eyt8FHiL593es1EZH3smakrCEWi6iOjRPLlDT36XR0x9lZenrySSFuvlmILVvsP8dzz8lf3Jgx2rVLT5mZ8vV06iSEyaR3a6ghW7cKYTDIf6d33tG7NQ6zJZC3eY38Aw88gJdffhlmN6jF/dJLL2HZsmVYsWIF2jiQXfrJJ59EWVlZ7Ve+rcmmyDasFdm0M2cAPz+Zab+pNfIAsGUL8N57XOdsLeFmGd5trd5giYf5+ReAzZvl/5EpU2RyorVrbV+/2sIzvRKRnewt7+duiTldXT3HE7zwArB+vcw+by9vqyX/1ltyO348YGw6OTPp5IorgIcflo/HjweysnRtjivZHMhv3rwZOTk56Nq1KxISEjBy5Mh6X7bo0KEDfHx8UHReQoyioiJENFO/cs6cOXjppZewdu1aXHHFFbU/V4+z5ZytW7dGYGBgvS9yIj1rRVZWyg+XQuuuq4s2beSNUUVF8/WO77xTBmPO+sD0tgSR7nYjaWv1BvE2AAVISAAOHADmzQOiomzL9GoYjiFKHow5y1t0plcicpCt5f3cLTGnHtVzWgI1kP/99/qVCjzVX/8qS9IyyZ37UhRgzhxgwgRZUviuu4DPPqu/jxBemcjS5kA+ODgYSUlJSEhIQOfOnREUFFTvyxatWrXCVVddhfXr19f+zGKxYP369ejfv3+jx82ePRuzZs1Cbm4urr766nrPdevWDREREfXOWV5eju+//77Jc5KL2ZMBV4sRxHvukT13GRmOnccVrKnD6uzM9SNHAh07Ah9/7Jzzu5o73UjaOztASYJp936gQ4f6T1qb6dVvIwyfrgHi47V7LUREzWnXDgAQghKbDqvdPyBAu7a42+wsd3DqlOyocFS3bkDbtkBVFbB7t+Pn01tyMrBuneywIvelKMAbbwCjRgEmk7x//eorOYMmPV2WFdazzLCzuGCqf5OWLVsmWrduLd59913x+++/i/vvv18EBweLwsJCIYQQ//d//yeeeOKJ2v1feukl0apVK5GVlSUKCgpqv06ePFlvn+DgYPHxxx+LX375Rdx5552iW7dutevom8Os9S5iTwZcRz3+uDzxOZUSPNrDD8vXM22ac84fFSXP//XXzjm/q7lTfgZntcXe9atERM7kTmvk3emzwF3Mmydf2333OX6ua66R5zqbiJrIZaqrhbjjDvn/r00bYfIPsD6RsJuwJQ61e7HHsWPHsHPnTgBAXFwcwsLC7DpPWloajh07hhkzZqCwsBB9+vRBbm4uwsPDAQAHDx6EwVA3cWDBggWorq5GcnJyvfPMnDkTzzzzDADgsccew+nTp3H//fejtLQUAwYMQG5urkPr6MkJ1BHEpFTkV3RFjhiOLKSgBCEIQQmSlWyMRA7g5w9DjkYjiJ5QS/6hh4CffgKeeAIYOrTpfdUR+T/+0L4dp07J6dvnXsfTqfkZ9mYjS6RYfViykg1TTBx8tczPoMXsgNDQC3dQ169OmSKXApw8CQQEyLY7siyFiMgR6rK6adMRjkKrRsLVZXXGqa9o+/7lrPdfT7Znj9xq8TmXkSFnjZ2dheGR1q4FfvwRuO8+oFMnvVtD1vL1BTIzgWuvheWXX7FWuQXjxNsXvN9kiRSEoxCLKydgSOJQGNaslssWPY2tvQSnTp0S9913n/Dx8RGKoghFUYTRaBTjxo0Tp0+ftqvnwd1wRN7FSkqEePVVUQ2j80cQv/pKnr9bN+3OqbV+/WQbly9vft+NG+W+Xbtq347Nm+W5O3bU/tx6cpes9RwRIqKWxl3Kf/L990JDh8rX9vrrerfEPcTHy9/Hk0/q3RKylbu8z9jJqVnrH3nkEXz11Vf45JNPUFpaitLSUnz88cf46quv8Pe//137ngbyfsHBwNCh8IVZJnrbs8e6DLj2UEfkDxwAqqu1O6+W1NkCzWWsB4BeveT24MHaEQbNqOvu1Wt4C73yM5yP1RuIqKVxl8ScfP+90N69chsbq2873MG+fXJEHgAmTtS3LWS7FpTI0uZAPjs7G4sWLcJtt91Wm+H99ttvx8KFC5HVgtL9k8YOHZLTYeLigJgY6zLg2iMiQk71sljqPrTcyYkTdZnRu3dvfv/QUODsMhTs2KFtW9Tp+t4yrV7lLjeSelZvICLSizsk5uT7b33n3hNpEcibTMCDDwI33gicPu34+Vxt4UK5jY+XydHIc7SwRJY2B/IVFRW169fP1bFjR1Roke2SWqabbpK9YWoPqLMoSl2A7I7r5NU2de4ss75aIz0dWLMGuPhibduijsh7WyAPuMeNJOA+swOIiFwpIQHGQwdgnPsKRsRsQybSsA7xyEQaRsRsg3HuKzAePujc6hp8/61TUCDLzfr4AF27On4+X19Zy/urr4Dt2x0/nyuZTMDixfLx/ffr2xaynbuVGXYymwP5/v37Y+bMmThzTn3pyspKPPvssyzvRo4xGmWpM2e7/37ghRfk6L+7+fNPubUlKE9LA26/HQgM1LYt11wD3HILcOWV2p7XXbjDjaS7zA4gInI1NTHnrj9kXed9+5y3rK6R6/P99yx1NL5rVxmEa0GtJ//rr9qc71zOrAn+ySdAUZGc7ThsmHbnJddwpzLDLmBz1vr09HQkJCQgMjISvXv3BgBs27YNbdq0QV5enuYNJNLcpEl6t6BxtqyPd7Z//lN+eTN3yPB+fvUGjESWSHJu9QYiInehKHKZmB5Z4Jt7/8VyjMQKwK+td7//BgUB48YBIbYFP026/HLg88+1DeRLS4ElS2D67wI5enqWKTYOvlMmyVkWjna0vPWW3N53n3adGuQ6ZyslhKDEpsNq9w8I0LpFTqUIYXs3VkVFBT744APsOLsmt1evXrj77rvh5+eneQP1UF5ejqCgIJSVlSFQ61FOalh8PBAWBrzyilzH3lL961/Am2/KEnTWJo88dQr49FPg8GFg2jTnto+cp7QUWLoUpnmvX3iDMnWyvEFx9ggVEVFL1Nj7r6EVfC3VQHY2MHKkfu3zRIsXA+PHAzffDKxf7/j58vJgTkoFKiqQjaQLOryTkA34+8OYnWl/GbGaGmDCBFm+7JdfmPjPEwkBU49eyNnbB6PEMqsPy1DkbEzfXX/ongPDljjUrkDe2zGQd7HS0rpe4PJy5/eGmc1yCnt+vvvWjBTC+jeSwkJZ41RRZFIZLTrUTpyQSx34/9/1hKg3OwCs/05E5Brnv/++8IKsCvPEE8BVV+ndOs+yebNcohcWBhw96ti58vJgSRyKXJGAcZYLa4IDkDXBDRMwRMlzvCb46dPW5yki95OeDvO06YgU+VYlvItAAfKVrjDOfUUu69GZUwP5F198EeHh4Rg3bly9ny9evBjHjh3D448/bnuL3UyLCuSFAI4fl6O67do5L1t8U77/Hrj2Wpng7fBh51/v6FG59klRZIK9Nm2cf01nEkL+u5WUAFu3AmeXvDhkxgxg1iw5K2DOHMfPR0RERO7pwAF5X6Tl/VBFhbyvFEKuObc3B1JpKcyRUVhbORDDLCubLCfmAzNWGYYj3m8jjIcOeGc+A2qeh/+fsSUOtTnZ3ZtvvomePXte8PNLL70Ub7zxhq2nI72UlgLp6TD16CV7S7t1A8LC5Pfp6fJ5V9l5dhqbq5LPhYXJkWYh3LMEna0UpS6zvJpp3lFq6bmLLtLmfEREROSe+vaVs/l++UW7c/r7y6npnTo5NkjjqprgP/8MbNtmfzvJfbSgRJY2B/KFhYXo1KnTBT8PCwtDQUHDvyhyM3l5MEdGwTxtOnL29qlXditnbx+Yp02HOTIKcFXyQlcH8u5agm7TJhk433WX7cdqHch7c+k5IiIiW+zcCbz2mt6tcI6TJ4Fjx+TjqChtz711K3DkiP3Vb5xdE/zc7PePPgr06SMHs8jzuUuZYSezOWt9ly5dsGnTJnTr1q3ezzdt2oTOnTtr1jBykrPrjNaKBIwTF64zyhIpcp1R5QQMSRzq+Doja5xNmogGZno4TY8ewJYt7hXI//mn/MBTP1BtoWUgbzLVlcFjIE9ERC3Z8ePApZfKRGiDB7v2XsUV9uyR29BQ7ROqOrrOXK0Jjlk2HZYlkpC6J1PmO2ioGkJj2e9hhO+xY/J5DxydpfOcLTOMpUsxYt7r8v/EWaaYOBinvuLxiYRtHpGfOHEiHn74Ybzzzjs4cOAADhw4gMWLF2PatGmYOHGiM9pIWikthTkpFbkiAcMsKxvt3SxCBIZZViJXJMgMoc6eZu/qEXmgrrybuwXygG015FW9esmtFoH87t0yIWC7dkBkpOPnIyIi8lShocBtt8nHS5bo2xZnUJcYumOGdmfUBG9qVipGwvzCy66dlUrOpZYZ3vWHnH2xbx9QXCy/nzrVo4N4wI5A/tFHH8X48eMxefJkxMTEICYmBlOmTMHUqVPx5JNPOqONpBVXrTOyhRCAwQD4+DCQd6SGvDpyvns3UF3tWDvUzoBevZgtnYiI6N575XbpUjky703UEXlnBPInTgBDhsj7GovF9uMdrQn+/vsykZ9KnZVaORCRIh+jxDJkIQXrMRhZSMEoZCBS5GNt5UBYEocymPcmiiI75aKj9Uns7SQ2B/KKouDll1/GsWPH8N1332Hbtm04ceIEZsyY4Yz2kVacvc7IXooik6tUVMg/LlfxtkA+MhJYsQLYvl2WjXOEmuiO0+qJiIiAoUNlKdAjR4DPP9e7NdpSA/mYGO3PHRQEfPWVHGSwJ7lwaChMsXFIVrJtOixZyYYprBPw9NPy3rJvX2DmTJhHprjfrFQiB9gcyKvatWuHvn37IiAgAHv27IHFnp42ch11nZFIsumwLJEk1w+dOOGkhp3VqpVre8d69ZLl1V55xXXXbIrFIj/oAPsCeUUBhg+X0/INdv9ZS1dfDdx/v/NzIxAREXmC1q3rEtG++66uTdGcM6fW+/jUDQr8+qvtxysKfKdMQhKyEY5Cqw6JQAFGIge+w4cCN9wg74l+/BF47jn3m5VK5CCr7/gXL16MV199td7P7r//fsTExODyyy/HZZddhvz8fM0bSBpxxjojTxYcDDz1FJCaqndLpCNH5AeGj48sBainIUOAN98ERo/Wtx1ERETu4r775HbFCu8apR0xQi4dsDezfHMuv1xu7QnkAZmMzN8fi5Xx8IG5yV19YMYiw0RZ+m72bODLL4GCAuCNN2DyC3C/WalEDrI6kH/rrbcQElIXBObm5uKdd97B0qVLsXnzZgQHB+PZZ591SiNJA46uMwoI0LpF0qOPAtdeC2TbNm3K61RUyJ7j/v0BX1/7zrFrF/Dii8D8+dq2jYiIqKW78koZlPr52R+UuqNJk4B33pGl15zB0UA+OBjGpe9giPgMqzDM9prgHTsCSUnwrTyJLCTbdGmXzUolspPVi2l37dqFq6++uvb7jz/+GHfeeSfuvvtuAMALL7yA+9TeSnI/6jqjvdnIEilWH5asZMMUEwff9u2d067Nm4Hvv5eBrKsdPgz8/DMQESGnk+vp4otlz7Ej/vwT+Mc/gCuuAB54wL5zlJXJjJ5xcfJmhYiIiOQStqwsoGtXoE0bvVvjORwN5AEgLw8GCMQr65CPrsjBSGSJJJQgBCEoQbKSjZHIAfz8YchpoCa4FrNSGypjR6Qzq0fkKysrERgYWPv9//73PwwaNKj2+5iYGBQWWrd+hXTgyDqjqZOdt35dj9JzqoULgTvukNPIvYG6Dm3nTlk+zh5ffSVHHa6/Xrt2EREReYOLL/auIL64WN4zVFU57xpqIL9rF1BZafvxX3wBvPUWAMC4agWMc1/BiJhtyEQa1iEemUjDiJhtMM59BcbDBy8M4gH3nZVK5CCrA/moqCj89NNPAIDi4mJs374d159zs19YWIggD6/F5/XUdUaGCbatMxozxjntKS8H1M4fPQL57t3l1h0y19sbeJ8rKkqOoldVyVF1e5xbeo6IiIguJATgDXmhsrOBnj2BZNumnNskIkLeb11/PXD8uG3Hnj4NTJggH0+aJKsH2FMT3JHs97FxsmIBkRuyOpAfO3YsHnjgAcyaNQspKSno2bMnrrrqqtrn//e//+Gyyy5zSiNJI8HBMGZnYoiSh1WG4bavM9KaOhofEdH4m68zuVMJuj595HS9H3+0/xwGg/xABupKyNlKDeRZeo6IiOhCf/4pR+avvdbza8o7s/ScSlHkfdbXX8tSubZ46ikZrHftCrz8cv1z2lIT3F1npRI5yOpA/rHHHsPEiRORk5ODNm3aYPny5fWe37RpE0Yzy7X7S0iAYc1qxPttRL7SFRlIRQoyMRjrkIJMZCAV+UpXxPtthOHTBtYZaUnPafVAXSB/5Ijs9dVLTY38kMvPd3wNlhqAqwG5rVhDnoiIqHHR0UBJibx3WLdO79Y4Rg3knVF6zlH/+x+Qni4fv/WW49Pb3W1WKpEGrA7kDQYDnnvuOfz888/47LPP0Ou8qbfLly/H+PHjNW8gOUFCAoyHDsh1Rm3X1l9nFPJl0+uMtLRjh9zqFci3b183XUqt4a6H/Hyguhpo1Ur2OjvCkUDeYmEgT0RE1JRWrYCziZ49vqa8WkPemSPy56qutn7f8HBZzWfsWCAhwfFru9usVCINWB3Ik5cJDpbrjHpfKr//97+BI0fge7yo6XVGWvLzkz3beq7HVkfl9Qzk//xTbmNjZR15R6gBuNpJYov8fDkzwdfXPXvniYiI3MG998rtypVydN4TCeG6Efldu+Q6eVs6DGJjgfXrgQULtGvH+bNSlbT6s1KVNNfNSiXSgNXl58hLDRokg/pbbgE6dXLttf/5T/klhGuve64ePWT5Oz3XyavXVjsVHHHTTbLEiz3nUkfx4+IAI98aiIiIGtSnjyz1+ssvwLJlMhGbpykulmXVAKBbN+deKyKirtPg+PGmlxFWVQGtW8vHBoP2pXDPzkrF0qUYMe91pO7JrH3KFBMH49RX5CwAJvAmD8C79ZbuxRf1boG+SUQmTABuvx3o10+/NmgZyAcF2f/hc/HFwOzZQNu2jreDiIjIWymKHJV/5BE5vd4TA3l1Wv1FFzm/pF5AgOws2LdPDjbceGPD+5lMwHXXya8XX6wtG6c5dVbqlCnAiROyQyMgAL7t2zOxHXkUTq2nOq++CvzlL8DSpc6/lp6j8Oe64QZg9GjXrQ9riJaBvCNiY4FHHwUmT9a3HURERO7u7rvl7LUffrC/UoyewsOBZ5913We+Wk/+118b32f2bGDLFuCjj1yThNjW7PdEboaBfEtWUwNUVtZ9f+QI8PPP8kPJ2fLygI4d5fSllu7KK4EBA+o+5By1ejUwfryc7kdERETa69hR5hfasEG/pL2OiI4GZswA/vEP11yvuUD+99+B556Tj9PTZUcDETVJs0A+Pz8f48aN0+p05Aq//ipLa3TvLr+/8kq5/fln5197507g2LG69Vl6EQL47DNg3jz9StA9/zywcaOcSqaFH38EFi8GPv/c+mOEAHJy5AepxaJNO4iIiLzZww/LaeIGjos1q6lAvqYGGDdOZrUfOhS46y7Xto3IQ2n2znPixAksWbJEq9ORKxw9Krfqmmg1kN+2Tb6pOpNaQ75nT+depzmKImuEPvRQXfZ4T2dPCbqCAiApSX7QmkzOaRcRERG5h++/l/c9rvrMv+wyuf3ttwsHDNLTZXsCA4E33uAUdyIrWZ3sbtWqVU0+v1dNmkGeQw3kw8LkNi5OZgc9fVqWY3PmVDG9a8ifq0cPmb111666zgxXOX1arrFTM7Rq4dxAXgjrPhDVoL97d23bQkRE5M327wfmzpXZ1rUsleZsSUnA4cMygL7mGudf7+KL5XViY2VOgfBwuS59zx7gqafkPnPmyOR7RGQVqwP54cOHQ1EUiCaSlCnsQfMsaiDfsaPc+vjIEdkffgC2bnVukK2OyLtLIP/tt/qUoFu4EPj734GJE2UvtBZ69JDT/MrK5Eh7587NH6MG8monABERETWvpESOKLdqBfzrX0D79nq3qHmVlTKIB1yT7Le0FFiyBKbjZfD94SOZzA6AKTYOvrfeJH93110nKwkRkdWsnlrfqVMn5OTkwGKxNPi1ZcsWZ7aTnOHYMblVA3nANevkT56UifUA9wnkATkLwdV27ZJTzEJCtDtn69Z1eQ+szaTLQJ6IiMh2ak356mrPSTK7b5/cBgY2XdNdC3l5MEdGwTxtOnL29kEKMjEY65CCTOTs7QPzm2/DbLLIcn4cECSyidWB/FVXXYWffvqp0eebG60nN3T+1HoAuOoqGQQGBjrvuupa9LAwbQNYe6lBrx4j8s4qPWfrOnk14GcgT0REZD21pjwga8p7AnU5bGysc4PnvDxYEodibeVARIp8jBLLkIUUrMdgZCEFo8QyRIp8rD0zCJZ775MVjYjIalYH8o8++iiuayKrdvfu3bFhwwZNGkUucv7UekBOa9q1y7nlSIQAbrlF1nB3B2oQ7Y2BvDrzoTkckSciIrKPWlN+82Zg+3a9W9O8PXvk1pnT6ktLYU5KRa5IwDDLShQhosHdihCBYZaVyBUJMCelymn4RGQVqwP5gQMHYsiQIY0+37ZtW9zgLoEZWadfPyAxUSYgUbliWtPVV8vSaMuXO/9a1lCD6KNHgfJy1123qgo4cEA+PvffQAvTpwOnTgEvvtj8vseOyWR/iuIeSx2IiIg8SceO8n4KADyhgpMayMfGOu8aS5YAFRUYZ3kbNc2k5KqBEeMtC4GKCmDpUue1icjLWB3I7927l1Pnvc1TTwGrVzc8Mi4EcOaM69ukh8BAua7t229l1n5X2bNH/p4DAurPitBCSEhdWcHm+PsD2dky666/v7btICIiagnU6fXvvQeYzbo2pVnq1HpnjcgLAdN/FyAbSY2OxJ+vEJ2Qg5EwzXtd3hsRUbOsDuR79OiBY2pyNABpaWkoKipySqNIZ//6FxAcDMye7ZzzV1Q457yOSEsDrr0W8PV13TXPnVavZ4KXtm2BkSOBqVP1awMREZEnu/12WSv9nntkadniYlmarrjY/QLTv/4VmDkTuP5655z/+HH47tmJLJFk02FZIgm+e3YCJ044p11EXsbqQP780fhPP/0Up0+f1rxB5CIWS+MBtb+/nGLujMz1FgvQoYMsiXbokPbn9yTh4cCYMcDQoc45/zPPyNkW333nnPMTERGR1KoV8PXXQGQkTFf1kwl9u3UDwsJg6tFLlqhzl/Xfd9wh7xEuu8w55z91CgBQAtsSGtfuf/Kk1i0i8kpW15EnL5OfD0RHyynYx4/XHxFWS9Bt3eqc61ZWymln4eHan99ee/cCn30mZyLcfbdrrnnttfLLWTZvljcVW7c2fZ0PPwTatQMGDZKvn4iIiGyTlyeTtVVUIAdJyMIslCAEIShB8t5sJE2bDvxzBozZmUBCgt6tda527QAAISix6bDa/QMCtG4RkVeyekReURQo503/Pf978iBqxvq2bS+c1t27t9zu3w+U2PYm3KydO+U2Nta109ib89NPwIMPAq+9pndLtGNtCbq//x248866fxsiIiKynrVl1ioHwpI4VN8ya4cPy+vv3++8a4SGwhQbh2Ql26bDkpVsmGLjgPbtndQwIu9i9Yi8EAL33nsvWrduDQA4c+YM/va3v6HteQm1cnJytG0hOUdDNeRVISFytH7/fjmae9NN2l1XDRbdLTu6HiXodu8GunaV0/GcwZpAvqQEKCyUj3v1ck47iIiIvNXZMmtrz5ZZayxDu1pmbZVhOOKTUmE8dECfWXBr1wLjxgHx8c7rUFAU+E6ZhKRp0xGOQqsS3kWgACORA+PUV/TNG0TkQawekR87diw6duyIoKAgBAUF4Z577kHnzp1rv1e/yEOoiQsby5auTq/Xep28uwby3bvL7fHj2s9CaMjp07LzwN/feWvm1ED+jz8a30d9LjJSZu8nIiIi63lamTVX1JAHgLFjAX9/LDZMgA+azuLvAzMWGSbKe6IxY5zbLiIvYvWI/DvvvOPMdpCrqSPyTQXyK1Y4L5Dv2VPb8zqqXTugUyegoECOyl9zjXOvt3u33AYFOa9HXv0dHzkiOwsauo46Wq8G/URERGSds2XWcuwoszZi3uvwnTLF9aPPrqghDwDBwTBmZ2JI4lCsMgzHeMtCFKLTBbtFoACLDBMxRMmDIWcNc/UQ2cDqEXnyMuqIfENT6wFZkuS224B+/bS9rruOyAOunV5/buk5ZwkKAi66SD5ubFReDeQ5rZ6IiMg2nlhmTa0h7+xAHgASEmBYsxrxfhuRr3RFhpKGFGRiMNYhBZnIUNKQr3RFvN9GGD5dI6f7E5HVmLW+pWpuRP7mm+WXliwWIDFRBpXuGsh//bX3BPKAHGkXovHlAhyRJyIiso8WZdZCQ7VuVdNcNbVelZAg8wEsXYoR815H6p7M2qdMMXFyTfzYsXLwgYhswkC+pbryStkTfOmlrrumwQAsWOC669lKDarVae/O5KpAfvXqppPpqSP1DOSJiIhs42ll1srKZC4gwHWBPCCny0+dKpcSnDghOzACAuDbvj0T2xE5gIF8S/Xww/KrOUVFgMkkk6F5u1GjgAEDXDNbQA3kL77YuddpLiP+J5/IUfkrrnBuO4iIiLyNWmZtbzayRIrVhyUr2TDFxMlA1pXUafVhYfrUalcUOQPB1bMQiLwU18hT42bNAiIigOef1+Z8R4/KTK3uKipK5gbo0MH51/rzT7l19oh8c664QnZgMGM9ERGRbdQya8hGOAqtOkQts+Y7dbLrR6O7dAHefVe7+zoi0hUD+ZZICFn+rDnqaLFWmesfegho2xaYP1+b83kqiwWYPBm4+27nB/JnzsjkMV261K7lIyIiIo14Upm1Dh1ke++/3/XXJiLNMZBviU6dkuu62rZteoRcrSX/yy+AuekPJ6uoGevdeZr+Rx8B06fXtdUZDAZg5kzg/fedPxLepg2wbRtw6BCwY0f953JygP/8B9i+3bltICIi8lZqmTUlD6sMwxGBggZ3i0ABVil3YoiSB2POcpZZIyKHMZBvidSM9YDsFW5M9+4y4D9zxvHAVoi66eTumLFe9eabwCuvAJs3690S7ail5dQM9ap33wUeeQT46iuXN4mIiMhrNFtmLRX56IJ4kQfD4kX6lVn75BNg3TqZ9I6IPB4D+ZaoudJzKoMB6N1bPnZ0ev3hw3I6v9Homtql9nJFLfk9e+SXFrMcrKFmpD+/ljxryBMREWnjbJk149xXMCJmGzKRhnWIRybSMCLmFxh7xMCIGmDFCv3a+NBDshPh11/1awMRaYaBfEt07JjchoU1v686vd7RQF4d0Y+JAXx9HTuXM7kikH/mGTnb4d//dt41zqUG8ueOyFdWAvv21X+eiIiI7KeWWdv1B1BcLD9ni4vl9ytWAD4+wMqVwNq1rm+byQQcPCgfu7L0HBE5DQP5lsjaEXkA6NNHbrUK5N15Wj3gmkDeVTXkVQ0F8n/+KZPutW9v3f8DIiIiso5aZi06Wm4VBbj0UmDKFPn81KlAdbVr23TgAFBTI3PndOrk2msTkVMwkG+J1BF5awK4AQOABx90PMOpmmjNkwJ5IZxzDVfVkFepgfzevXIkHqgL6i+5xPXlb4iIiFqiZ56R9147dwLz5rn22moN+ZgYfu4TeQkG8i2RLSPycXHAf/8ra407YtAgYNw44IYbHDuPs6nr98vK5LQ4rR0/Dpw4IR937679+RsSHi6vdf31ddc+N5AnIiIi5wsKAl56ST6ePVsmE3aVPXvk1p3zFBGRTYx6N4B0cMklwB131CWyc4XkZPnl7vz8ZM31/Hw5cm5NHgFbqKPxF13UdMUALSnKhUsFmOiOiIjI9caOlSPyf/2rnObuKgzkibwOA/mWaOJE+WWt06dlLXKjEbjmGue1y12sXi0D+IgI7c/t6vXxjVmyBHjySa6TIyIiciWDoW5U3pXOnVpPRF6BU+upeYsXy2nZs2bZd3xpKbB9O1BVpWmznOaKK2SA64w1ZK5eH38+teRdu3bA1VfLmQFERESkj59+kknonO2f/wTeeUe/GvZEpDkG8i1RRYVt+ztagm7tWuCyy4CbbrLveG9y663A00/LpQ2utGWL7IVX/y2JiIhIXw89JDvVFy503jWEkDl/QkOBoUP1G0ggIs0xkG9phJDJVvz9gYIC645R19IfPlyX8d4Wauk5T/nwOHgQeOwx4O9/1/7cAwcCzz0nP0xdKSxM1rPdsQNYv15WIsjJcW0biIiIqI66Xv2f/5TJcLVUWgqkp8PUo5e8B+jWDQgLk9+np8vnicijMZBvaUpL5fTqykpZQ9waAQF1Gda3brX9mmog37On7cfq4fRp4N//lj3kzipB52qRkUDbtvLffu5cYP584JNP9G4VERFRyzV5spyxeOKEnK2nlbw8mCOjYJ42HTl7+yAFmRiMdUhBJnL29oF52nSYI6OAvDztrklELsdAvqVRR9QDA4HWra0/zpHp9Z5SQ14VEyOT0Zw8WVeqTwsnTwLr1gEHDmh3TmuUlgLz5sF0dnk8Vq8GAJg++Yy98kRERHoxGmWJXwB48037BkvOl5cHS+JQrK0ciEiRj1FiGbKQgvUYjCykYJRYhkiRj7WVA2FJHMpgnsiDMZBvaWypIX8uewN5IepG5D0lkG/dGujaVT4+v2ybI7ZulUlmbrxRu3M259xe+arE+r3yJ25grzwREZGebrwRSEsDLBZgyhTHZgKWlsKclIpckYBhlpUoQsPVd4oQgWGWlcgVCTAnpbJDn8hDMZBvadRA3tb66PYG8gUFwKlTcoTbk2qXqksJtAzkXV167vxeeWSc1yufwV55IiIivc2ZI3MXffMN8OGH9p9nyRKgogLjLG+jppkK0zUwYrxloUyAvHSp/dckIt0wkG9p1Kn1to7I9+0r11a//bZtx6mj8TExtk3l15sayG/eLLO9arFW3pWBPHvliYiIPENkpEx417Ej0KqVfecQAqb/LkA2khr9zD9fITohByNhmve69+QEImpBGMi3NPZOrQ8NlWVSBgyw7bioKOBf/wImTbLtOL2oWV6Xr5DfL1igXZZXV9aQZ688ERGR5/j734E//wRSUup+ppaO27+/+UGF48fhu2cnskSSTZfNEknw3bNTJtwjIo/CQL6l6d4dGDYMuOoq11wvJgb4xz+ARx5xzfUcce568hM3aJ/l9c8/5dbZI/LslSciIvIsrVvL8sCAfaXjTp0CAJQgxKbL1u5/8qT9bSciXTQ9VEfeZ/Ro+WWPI0eAzz+XHzZpadq2S2/qenKRgHHi7QsC4CyRgnAUYnHlBAxJHArDmtVAQoL157dYgN275WNnB/Jqrzxm2XRYlkhC6p5M2SsfGuqkxhEREVGjcnNhHp4EVFUhR0lGFmahBCEIQQmS92Yjadp04J8zYMzOrLsPKS6unVEXghKbLle7f0CAlq+CiFyAgTxZ79tvgbFjgb/8xfpAfu1aOb2+e3fAx8e57bPX2fXka8+uJ29sKrq6nnyVYTjik1JhPHQACA627hpHjgCVlfJ3EB2tWdMbpEWvPAN5IiIi18rLgyXxDqy13IpxWIwi0cygwqznZEWcFSsAkwkmGJGMLGQhpeHzNyBZyYYpJg6+7dtr/GKIyNkYyLc0FRWAnx+gKLYfq2au/+03wGQCfH2b3r+yEhgyRE7VLiwEwsNtv6YrqOvJhfXryfMrusre76lTrbtGQADwzjuy17y535uj2rUDwF55IiIij6EOKiABw2DFoAKGIf4fT8EIi3ziqqvgGxODpKwchItCq5bWRaAAI5ED49RX7LsvJCJdcY18S9Otmyxx8vvv9h0bFARUV1t3/O7dMogPCrI9uZ6ruGo9eVAQcO+9wPTp9rfVWqGhMMXGIVnJtumwZCUbptg4gL3yRERErmVrklosAqDIJMRbtgA//gi89Rbg74/FhgnwgbnJc/jAjEWGifKecMwYDV8IEbkKA/mWpKZGjgifOWNfsKYoQJ8+8rE19eTV0nNxce7b0+uNWV4VBb5TJiEJ2QhHoVWHqL3yvlMnu++/FRERkTeyd1BBSYKp4FjdvVlwMIzZmRii5GGVYTgiUNDgsREowCrDcAxR8mDMWW79MkEiciu6B/Lz589HdHQ02rRpg379+uGHH35odN/t27cjKSkJ0dHRUBQFc+fOvWCfkydP4uGHH0ZUVBT8/Pxw3XXXYfPmzU58BR7kxAmZdA2wfw20Or3e1kDeXbkqy2tuLvDFF0BZmU3XsdvYseyVJyIi8gRaDiokJMCwZjXi/TYiX+mKDCWtXhWeDCUN+UpXxPtthOHTNUB8vMYvhohcRddAPiMjA4888ghmzpyJLVu2oHfv3khISMBRtdb5eSoqKhATE4OXXnoJEREN91hOmDAB69atw3vvvYdff/0V8fHxGDx4MA4fPuzMl+IZjh2T2/bt7V+nbU8g37OnfddyBWevJ1drwD74IHDLLcD339vTStuxV56IiMgzaD2okJAA46EDMM59BSNitiETaViHeGQiDSNitsE49xUYDx9kEE/k4XQN5F999VVMnDgR9913Hy655BK88cYb8Pf3x+LFixvcv2/fvvj3v/+NUaNGoXXr1hc8X1lZiezsbMyePRuDBg1C9+7d8cwzz6B79+5YsGCBs1+O+1M7SMLC7D+HGshv3Vo3ut8YTxiRt3s9eVbT68nPrwG7Zw8AwDRxcuM1YLXGXnkiIiL354xBheBgYOpU+O76Qw4o7NsHFBfL76dOratZT0QeS7dAvrq6Gj/99BMGDx5c1xiDAYMHD8a3335r1znNZjNqamrQpk2bej/38/PDN9980+hxVVVVKC8vr/flldRA3pHEcz17AmvWyCDd0MR/HyE8I5C3ez35irr15BkZwC+/1O2QlwdzZBTM06YjZ2+fesFzTv7VME+bDnNkFJCX56QXdQ72yhMREbk3ZyapVRS5nDI6Wm6ZB4fIa+gWyBcXF6Ompgbh55UkCw8PR2GhdQHV+QICAtC/f3/MmjULR44cQU1NDd5//318++23KChoeGoxALz44osICgqq/erSpYtd13d7WgTyvr7A7bcDnTo1vZ/FArz5JvDMM7KGvDtzZD350aPAhAky0cy99wLvvQdL4lCsrRyISJGPUWIZspCC9RiMLKRglFiGSJGPtZUDYUkc6ppgnr3yRERE7otJaonIDronu9Pae++9ByEELrroIrRu3Rrz5s3D6NGjYWhi9PjJJ59EWVlZ7Vd+fr4LW+xCXboAw4YB117r/Gv5+ABpacDMmcB5MyTcjiPryU0m2bEhBLBkCcxj7kNuTTyGWVY2mnlWrQGbKxJgTkp1zTR7gL3yRERE7opJaonIRroF8h06dICPjw+Kiorq/byoqKjRRHbWiI2NxVdffYVTp04hPz8fP/zwA0wmE2JiYho9pnXr1ggMDKz35ZXuvBP4+GPHa5nv3QvMmAE8+6w27XIH9q4nv+giObX+hx9qZx6MwyLrasBaFgIVFcDSpc5+dUREROTOmKSWiGykWyDfqlUrXHXVVVi/fn3tzywWC9avX4/+/fs7fP62bduiU6dOKCkpQV5eHu68806Hz0lnHT0KzJoFvPFG4/t8/TXw6ad10/k9gSPrya++GibhY3sNWIyEad7rckSfiIiIWi4mqSUiGyhC6BdBZGRkYOzYsXjzzTdxzTXXYO7cucjMzMSOHTsQHh6OMWPG4KKLLsKLL74IQCbI+/333wEAt99+O+6++27cfffdaNeuHbqfHQ3Ny8uDEAJxcXHYvXs3Hn30UbRp0wYbN26Er5Ul18rLyxEUFISysjLvGp2vrJTT3B2dUl1RITOkWixAQQHQ0AyKxEQZyL/xBvDXvzp2PT0IIeuynjwpX2v79k3/3oqLgbAwpCATWUix+jIpyEQm0uTxoaEaNJyIiIg8WmkpsHQpTPNel3XizzLFxsk18WPHMr8NkZeyJQ5tev6vk6WlpeHYsWOYMWMGCgsL0adPH+Tm5tYmwDt48GC9te1HjhzBlWr5MwBz5szBnDlzcMMNN+DLL78EAJSVleHJJ5/EoUOH0L59eyQlJeFf//qX1UG8V7vySmD/fuCLL4DrrrP/PP7+MhP9H3/IevK33XbhPp6Qsb4p6npya4NrLWrAMpAnIiIiNUntlCn1BhV8mxtUIKIWRddAHgAefPBBPPjggw0+pwbnqujoaDQ3gSA1NRWpqalaNc+7HD0KVFVps57qyisbD+SrqmRmdMBzA3lbOaMGLBEREbVctg4qEFGL4nVZ66kRJhNQcjZodKT8nKpPH7n9+ecLn9u9W067DwhoeNq9N3JmDVgiIiIiIqJzMJBvKYqL5dZg0CZoVJc4NBTInzutvqVMAWMNWCIiIiIichEG8i2Fmj2+QwcZzDtKDeSLimTyu3N5+vp4e7EGLBERERERuQAD+Zbi2DG51WJaPSDXa+3aJTOr+vvXf66lBvKsAUtERERERC6ge7I7chF1RF6rQB4Azpb8u8BjjwGDBwO9e2t3LU+h1oBNSkV+RVfkYCSyRBJKEIIQlCBZycZI5AB+/jDksAYsERERERHZjoF8SxEeDgwfDlxxhfOvdckl8qulSkiA8dABYOlSjJj3OlL3ZNY+ZYqJg3HqK6wBS0REREREdlNEc/XcWqDy8nIEBQWhrKwMgYGBejfHfe3ZAzz9NHDmDJCTo3dr3JMQ9WrAgjVgiYiIiIioAbbEoRyRJ/u1agV89BFgNMra8a1by9JzubkyGd711+vdQv2xBiwREREREWmMye5aispKOTqspchIOcJsNgO//SZ/9uWXwJQpwKxZ2l6LiIiIiIiIADCQbzluuQVo0wb49FPtzqkoF9aT37FDbltaxnoiIiIiIiIXYSDfUhw9ClRXa59g7fxAvqWWniMiIiIiInIRBvIthVpHPixM2/MykCciIiIiInIpBvItwZkzQHm5fKxlHXmgLpDftk1eZ+9e+X3Pntpeh4iIiIiIiAAwa33L8P/t3XtwVPXBxvFnN/eYZAMxJoZbAnIRlDBFiWm1oAYCgxdKwMJQiYB2+oq+QopW/8DUURvUSrnIxXl5KaEjxZCAtjCSF2MJQ72HxldRKaAWikDCJdmQvJDLnvePbbZGAhrYzdlz9vuZ2dnknLNnnwy/2ZmH32/PaZ+Nj4jw/9L6QYO85+zbV3r3XamtTYqLk9LS/Ps+AAAAAABJzMiHhm8uq/f3PczDwrzn/+gj6dAh77b+/f37HgAAAAAAH4p8KKip8T77e1m9JNXVSStXqmXgtVJ+vnfb//6v9/elS737AQAAAAB+Q5EPBYmJ0qRJ0q23+ve85eVq7d1PrfMXaPMXIzRVJcrRDk1ViTZ/MUKt8xeotXc/qbzcv+8LAAAAACHMYRiGYXaIYON2u+VyuVRfX6+EhASz4wSn8nJ5Jt6h7UauZnvW6LhSzzskRce01nm/xjvK5dy2VcrNNSEoAAAAAAS/rvRQZuTRdXV1as27R9uNXN3lea3TEi9Jx5WquzyvabuRq9a8e1hmDwAAAAB+QJEPBWfPSv5ceFFcLDU1abZnjdq+48YHbQrXHM9/SU1N0vr1/ssAAAAAACGKIh8KpkyRoqOlDRsu/1yGoZblq1SmvAvOxH/bMV2tzZqslmUr/fsfCgAAAAAQgijyoaCmRmpu9t7f/XKdPKmIg/tUauR16WWlRp4iDu6TTp26/AwAAAAAEMIo8qHAn7efO3NGknRaPbr0Mt/xDQ2XnwEAAAAAQhhFPhTU1nqf/VHk/zWr30Onu/Qy3/Hx8ZefAQAAAABCGEXe7hobvReak6Tk5Ms/X1KSWgYM1hRHWZdeNsVRppYBg6WePS8/AwAAAACEMIq83bUvq4+O9s935B0ORTz8H8pTmVJ07Hu9JFVHNVmbFfGfD0oOx+VnAAAAAIAQRpG3u29+P95fJTo/X4qN1Vrn/QpT60UPDVOr/tv5gBQbK82c6Z/3BwAAAIAQRpG3u9hYafJkaexY/50zMVHhZSUa7yjXn5yTlKqjnR6WqqP6k3OSxjvKFb55k5SY6L8MAAAAABCiHIbBjb2/ze12y+Vyqb6+XgkJCWbHCV7l5WrNu0dqatJmTVapkafT6qEeOq0pjjJN1mYpNtZb4seNMzstAAAAAAStrvRQinwnKPJdUFcnrV+vlmUrvfeJ/5eWAYO934nPz5dcLvPyAQAAAIAFUOQvk62K/LlzUmRk4C8yZxjSqVPe+8THx3uvTs+F7QAAAADge+lKD+U78nZ3//1SVJS0YkVg38fhkJKSpPR07zMlHgAAAAACgiJvdzU1UkuLf249BwAAAAAwHUXe7mprvc/JyebmAAAAAAD4BUXe7r55H3kAAAAAgOVR5O3MMP49I0+RBwAAAABboMjbmdstNTd7f2ZpPQAAAADYAkXeztqX1cfFSTEx5mYBAAAAAPhFuNkBEEBhYdLkyVI4/8wAAAAAYBc0PDvr318qKzM7BQAAAADAj1haDwAAAACAhVDk7ay52XvlegAAAACAbVDk7ezRR6XISOnZZ81OAgAAAADwE4q8ndXUSK2tUmys2UkAAAAAAH5Ckbez2lrv81VXmZsDAAAAAOA3FHk7a7+PPEUeAAAAAGyDIm9n7TPyycnm5gAAAAAA+A1F3q48HpbWAwAAAIANUeTt6vRpqa3N+/OVV5qbBQAAAADgN+FmB0CAtLZKeXlSY6P3FnQAAAAAAFugyNtVSopUWmp2CgAAAACAn7G0HgAAAAAAC6HI21Vzs/eCdwAAAAAAW6HI21VRkRQVJT36qNlJAAAAAAB+RJG3q5oa7wXvoqPNTgIAAAAA8COKvF1xD3kAAAAAsCWKvF3V1HifKfIAAAAAYCsUebtqL/LJyebmAAAAAAD4FUXerpiRBwAAAABbosjbUWurdOqU92dm5AEAAADAVsLNDoAAOHtWysvzXvAuKcnsNAAAAAAAP6LI21FcnLRpk9kpAAAAAAABwNJ6AAAAAAAshCJvRy0tksdjdgoAAAAAQABQ5O1o1SopMlK6/36zkwAAAAAA/Iwib0e1tVJbmxQdbXYSAAAAAICfUeTtqP0e8tx6DgAAAABshyJvR+1F/qqrzM0BAAAAAPA7irwd1dZ6nynyAAAAAGA7FHk7Ymk9AAAAANgWRd6OmJEHAAAAANsKNzsA/MzjkcaO9c7Kp6SYnQYAAAAA4GcUebtxOqWSErNTAAAAAAAChKX1AAAAAABYiOlFfsWKFUpPT1d0dLSysrL0/vvvX/DYvXv3Ki8vT+np6XI4HFqyZMl5x7S1tWnhwoXKyMhQTEyMBgwYoKefflqGYQTwrwgira3e5fUAAAAAAFsytci/+uqrKigoUGFhofbs2aPMzEzl5uaqpv2q69/S1NSk/v37a9GiRUpNTe30mOeee06rVq3SSy+9pM8++0zPPfecnn/+eS1fvjyQf0rw2LhRioyUpkwxOwkAAAAAIABMLfKLFy/WAw88oFmzZmno0KFavXq1YmNjtXbt2k6Pv/HGG/XCCy9o2rRpioqK6vSYt99+W3fffbcmTpyo9PR0TZkyRePGjbvoTL+t1NZKbW1SRITZSQAAAAAAAWBakW9ublZVVZVycnL+HcbpVE5Ojt55551LPu8Pf/hDVVRU6O9//7sk6aOPPtLu3bs1YcKEC77m3LlzcrvdHR6W1b6agVvPAQAAAIAtmXbV+hMnTqitrU0p37pFWkpKij7//PNLPu/jjz8ut9utIUOGKCwsTG1tbXr22Wc1Y8aMC76mqKhITz311CW/Z1ChyAMAAACArZl+sTt/Kykp0SuvvKINGzZoz549Ki4u1m9/+1sVFxdf8DVPPPGE6uvrfY/Dhw93Y2I/q631PlPkAQAAAMCWTJuRv/LKKxUWFqbjx4932H78+PELXsju+3j00Uf1+OOPa9q0aZKk66+/Xv/4xz9UVFSk/Pz8Tl8TFRV1we/cW077jHxysrk5AAAAAAABYdqMfGRkpEaOHKmKigrfNo/Ho4qKCmVnZ1/yeZuamuR0dvyzwsLC5AmVW7IxIw8AAAAAtmbajLwkFRQUKD8/XzfccINGjRqlJUuWqLGxUbNmzZIkzZw5U7169VJRUZEk7wXyPv30U9/PR44cUXV1teLi4nTNNddIku688049++yz6tu3r4YNG6a//e1vWrx4sWbPnm3OH9ndRo+W+vWTevUyOwkAAAAAIAAchmEYZgZ46aWX9MILL+jYsWMaMWKEli1bpqysLEnSmDFjlJ6ernXr1kmSvvrqK2VkZJx3jtGjR2vnzp2SpIaGBi1cuFBbtmxRTU2N0tLSNH36dD355JOKjIz8XpncbrdcLpfq6+uVkJDgl78TAAAAAIAL6UoPNb3IByOKPAAAAACgO3Wlh9ruqvUhra1NCpVrAQAAAABAiKLI20l5uRQRIeXkmJ0EAAAAABAgFHk7qa31zsiHm3oNQwAAAABAAFHk7aT9HvLceg4AAAAAbIsibyftRT452dwcAAAAAICAocjbCTPyAAAAAGB7FHk7qa31PlPkAQAAAMC2KPJ2wtJ6AAAAALA9Lm9uJ9nZUkKC1K+f2UkAAAAAAAFCkbeT5cvNTgAAAAAACDCW1gMAAAAAYCEUebtoa/M+AAAAAAC2RpG3i/fflyIipJEjzU4CAAAAAAggirxd1NZKhiGFhZmdBAAAAAAQQBR5u2i/9Rz3kAcAAAAAW6PI20VtrfeZe8gDAAAAgK1R5O2CGXkAAAAACAkUebugyAMAAABASKDI2wVL6wEAAAAgJISbHQB+MnKk9z7y/fubnQQAAAAAEEAUebsoKjI7AQAAAACgG7C0HgAAAAAAC6HI24HH411WDwAAAACwPYq8HezfL0VESAMGmJ0EAAAAABBgFHk7qK2VDENyOMxOAgAAAAAIMIq8HXAPeQAAAAAIGRR5O2i/hzxFHgAAAABsjyJvB8zIAwAAAEDIoMjbQXuRT042NwcAAAAAIOAo8nbA0noAAAAACBnhZgeAH1x/vXTihDRokNlJAAAAAAAB5jAMwzA7RLBxu91yuVyqr69XQkKC2XEAAAAAADbXlR7K0noAAAAAACyEIm91hiG1tpqdAgAAAADQTSjyVnfihBQR4b1ifVub2WkAAAAAAAFGkbe69ivWezxSWJi5WQAAAAAAAUeRt7r2e8hz6zkAAAAACAkUeatrn5FPTjY3BwAAAACgW1DkrY4ZeQAAAAAIKRR5q2sv8szIAwAAAEBIoMhbXfvSembkAQAAACAkUOStbvBgKSdHGjrU7CQAAAAAgG7gMAzDMDtEsHG73XK5XKqvr1dCQoLZcQAAAAAANteVHsqMPAAAAAAAFkKRt7rWVrMTAAAAAAC6EUXeqgxDOnpUioyUevSQTp40OxEAAAAAoBtQ5K2mrk5aulQtA6+V0tK8hb6uTi1ZP5KWLvXuBwAAAADYFkXeSsrL1dq7n1rnL9DmL0ZoqkqUox2aqhJt/mKEWucvUGvvflJ5udlJAQAAAAABEm52AHxP5eXyTLxD/2PkaraxRseV2mF3qTFVKTqmtf93v8ZPvEPObVul3FyTwgIAAAAAAoUZeSuoq1Nr3j3abuTqLs9r55X4dseVqrs8r2m7kavWvHtYZg8AAAAANkSRt4LiYqmpSbM9a9T2HYso2hSuOZ7/kpqapPXruykgAAAAAKC7UOSDnWGoZfkqlSnvgjPx33ZMV2uzJqtl2UrvxfAAAAAAALZBkQ92J08q4uA+lRp5XXpZqZGniIP7pFOnAhQMAAAAAGAGinywO3NGknRaPbr0Mt/xDQ3+TgQAAAAAMBFFPtjFxUmSeuh0l17mOz4+3t+JAAAAAAAmosgHu6QktQwYrCmOsi69bIqjTC0DBks9ewYoGAAAAADADBT5YOdwKOLh/1CeypSiY9/rJak6qsnarIj/fFByOAIcEAAAAADQnSjyVpCfL8XGaq3zfoWp9aKHhqlV/+18QIqNlWbO7KaAAAAAAIDuQpG3gsREhZeVaLyjXH9yTlKqjnZ6WKqO6k/OSRrvKFf45k1SYmL35gQAAAAABFy42QHwPeXmyrltq8bl3aPDTX21WZNVauTptHqoh05riqNMk7VZiomVc/M2adw4sxMDAAAAAALAYRiGYXaIYON2u+VyuVRfX6+EhASz43RUVyetX6+WZSu994n/l5YBg73fic/Pl1wu8/IBAAAAALqsKz2UIt+JoC7y7QxDOnXKe5/4+Hjv1em5sB0AAAAAWFJXeihL663K4ZCSkrwPAAAAAEDI4GJ3AAAAAABYCEUeAAAAAAALocgDAAAAAGAhFHkAAAAAACyEIg8AAAAAgIVQ5AEAAAAAsBCKPAAAAAAAFkKRBwAAAADAQijyAAAAAABYCEUeAAAAAAALCTc7QDAyDEOS5Ha7TU4CAAAAAAgF7f2zvY9eDEW+Ew0NDZKkPn36mJwEAAAAABBKGhoa5HK5LnqMw/g+dT/EeDweff3114qPj5fD4QjIe7jdbvXp00eHDx9WQkJCQN4DCBTGL6yM8QsrY/zCyhi/sLLuGL+GYaihoUFpaWlyOi/+LXhm5DvhdDrVu3fvbnmvhIQEPshgWYxfWBnjF1bG+IWVMX5hZYEev981E9+Oi90BAAAAAGAhFHkAAAAAACyEIm+SqKgoFRYWKioqyuwoQJcxfmFljF9YGeMXVsb4hZUF2/jlYncAAAAAAFgIM/IAAAAAAFgIRR4AAAAAAAuhyAMAAAAAYCEUeQAAAAAALIQib4IVK1YoPT1d0dHRysrK0vvvv292JKBTu3bt0p133qm0tDQ5HA699tprHfYbhqEnn3xSV199tWJiYpSTk6P9+/ebExb4hqKiIt14442Kj4/XVVddpUmTJmnfvn0djjl79qzmzp2rpKQkxcXFKS8vT8ePHzcpMdDRqlWrNHz4cCUkJCghIUHZ2dl64403fPsZv7CKRYsWyeFwaN68eb5tjF8Es1//+tdyOBwdHkOGDPHtD5bxS5HvZq+++qoKCgpUWFioPXv2KDMzU7m5uaqpqTE7GnCexsZGZWZmasWKFZ3uf/7557Vs2TKtXr1a7733nq644grl5ubq7Nmz3ZwU6KiyslJz587Vu+++qx07dqilpUXjxo1TY2Oj75j58+frz3/+szZt2qTKykp9/fXXmjx5sompgX/r3bu3Fi1apKqqKn344Ye67bbbdPfdd2vv3r2SGL+whg8++EAvv/yyhg8f3mE74xfBbtiwYTp69KjvsXv3bt++oBm/BrrVqFGjjLlz5/p+b2trM9LS0oyioiITUwHfTZKxZcsW3+8ej8dITU01XnjhBd+2uro6IyoqyvjjH/9oQkLgwmpqagxJRmVlpWEY3rEaERFhbNq0yXfMZ599Zkgy3nnnHbNiAhfVo0cPY82aNYxfWEJDQ4MxcOBAY8eOHcbo0aONRx55xDAMPn8R/AoLC43MzMxO9wXT+GVGvhs1NzerqqpKOTk5vm1Op1M5OTl65513TEwGdN2XX36pY8eOdRjPLpdLWVlZjGcEnfr6eklSz549JUlVVVVqaWnpMH6HDBmivn37Mn4RdNra2rRx40Y1NjYqOzub8QtLmDt3riZOnNhhnEp8/sIa9u/fr7S0NPXv318zZszQoUOHJAXX+A3v1ncLcSdOnFBbW5tSUlI6bE9JSdHnn39uUirg0hw7dkySOh3P7fuAYODxeDRv3jz96Ec/0nXXXSfJO34jIyOVmJjY4VjGL4LJxx9/rOzsbJ09e1ZxcXHasmWLhg4dqurqasYvgtrGjRu1Z88effDBB+ft4/MXwS4rK0vr1q3T4MGDdfToUT311FO65ZZb9MknnwTV+KXIAwBsbe7cufrkk086fL8NsILBgwerurpa9fX1Ki0tVX5+viorK82OBVzU4cOH9cgjj2jHjh2Kjo42Ow7QZRMmTPD9PHz4cGVlZalfv34qKSlRTEyMick6Yml9N7ryyisVFhZ23lUNjx8/rtTUVJNSAZemfcwynhHMHnroIW3dulV/+ctf1Lt3b9/21NRUNTc3q66ursPxjF8Ek8jISF1zzTUaOXKkioqKlJmZqaVLlzJ+EdSqqqpUU1OjH/zgBwoPD1d4eLgqKyu1bNkyhYeHKyUlhfELS0lMTNSgQYN04MCBoPr8pch3o8jISI0cOVIVFRW+bR6PRxUVFcrOzjYxGdB1GRkZSk1N7TCe3W633nvvPcYzTGcYhh566CFt2bJFb731ljIyMjrsHzlypCIiIjqM33379unQoUOMXwQtj8ejc+fOMX4R1G6//XZ9/PHHqq6u9j1uuOEGzZgxw/cz4xdWcubMGR08eFBXX311UH3+srS+mxUUFCg/P1833HCDRo0apSVLlqixsVGzZs0yOxpwnjNnzujAgQO+37/88ktVV1erZ8+e6tu3r+bNm6dnnnlGAwcOVEZGhhYuXKi0tDRNmjTJvNCAvMvpN2zYoNdff13x8fG+7625XC7FxMTI5XJpzpw5KigoUM+ePZWQkKCHH35Y2dnZuummm0xOD0hPPPGEJkyYoL59+6qhoUEbNmzQzp07VV5ezvhFUIuPj/ddj6TdFVdcoaSkJN92xi+C2YIFC3TnnXeqX79++vrrr1VYWKiwsDBNnz49qD5/KfLd7Kc//alqa2v15JNP6tixYxoxYoS2b99+3gXDgGDw4Ycf6tZbb/X9XlBQIEnKz8/XunXr9Nhjj6mxsVE///nPVVdXp5tvvlnbt2/nO3Ew3apVqyRJY8aM6bD997//ve677z5J0u9+9zs5nU7l5eXp3Llzys3N1cqVK7s5KdC5mpoazZw5U0ePHpXL5dLw4cNVXl6usWPHSmL8wtoYvwhm//znPzV9+nSdPHlSycnJuvnmm/Xuu+8qOTlZUvCMX4dhGEa3vysAAAAAALgkfEceAAAAAAALocgDAAAAAGAhFHkAAAAAACyEIg8AAAAAgIVQ5AEAAAAAsBCKPAAAAAAAFkKRBwAAAADAQijyAAAAAABYCEUeAAAE3Lp165SYmGh2DAAAbIEiDwBACLnvvvs0adKkDttKS0sVHR2tF1988bzjy8rKFBYWpiNHjnR6voEDB6qgoCAQUQEAwAVQ5AEACGFr1qzRjBkztGrVKv3yl788b/9dd92lpKQkFRcXn7dv165dOnDggObMmdMdUQEAwL9Q5AEACFHPP/+8Hn74YW3cuFGzZs3q9JiIiAjde++9Wrdu3Xn71q5dq6ysLA0bNkyLFy/W9ddfryuuuEJ9+vTRgw8+qDNnzlzwvTtbGTBv3jyNGTPG97vH41FRUZEyMjIUExOjzMxMlZaWXsqfCgCArVDkAQAIQb/61a/09NNPa+vWrfrJT35y0WPnzJmj/fv3a9euXb5tZ86cUWlpqW823ul0atmyZdq7d6+Ki4v11ltv6bHHHrusjEVFRVq/fr1Wr16tvXv3av78+frZz36mysrKyzovAABWF252AAAA0L3eeOMNvf7666qoqNBtt932nccPHTpUN910k9auXasf//jHkqSSkhIZhqFp06ZJ8s6mt0tPT9czzzyjX/ziF1q5cuUlZTx37px+85vf6M0331R2drYkqX///tq9e7defvlljR49+pLOCwCAHTAjDwBAiBk+fLjS09NVWFh40eXv3zR79myVlpaqoaFBkndZ/dSpUxUfHy9JevPNN3X77berV69eio+P17333quTJ0+qqanpkjIeOHBATU1NGjt2rOLi4nyP9evX6+DBg5d0TgAA7IIiDwBAiOnVq5d27typI0eOaPz48b5yfjHtM+8lJSXav3+//vrXv/qW1X/11Ve64447NHz4cJWVlamqqkorVqyQJDU3N3d6PqfTKcMwOmxraWnx/dz+Hwzbtm1TdXW17/Hpp5/yPXkAQMhjaT0AACGoX79+qqys1K233qrx48dr+/btvtn1zsTHx2vq1Klau3atDh48qEGDBumWW26RJFVVVcnj8ejFF1+U0+mdIygpKbno+ycnJ+uTTz7psK26uloRERGSvMv5o6KidOjQIZbRAwDwLczIAwAQovr06aOdO3eqpqZGubm5crvdFz1+zpw5evvtt7V69WrNnj3bt/2aa65RS0uLli9fri+++EJ/+MMftHr16oue67bbbtOHH36o9evXa//+/SosLOxQ7OPj47VgwQLNnz9fxcXFOnjwoPbs2aPly5d3eis8AABCCUUeAIAQ1rt3b+3cuVMnTpz4zjJ/8803a/DgwXK73Zo5c6Zve2ZmphYvXqznnntO1113nV555RUVFRVd9H1zc3O1cOFCPfbYY7rxxhvV0NDQ4ZyS9PTTT2vhwoUqKirStddeq/Hjx2vbtm3KyMi4vD8aAACLcxjf/oIaAAAAAAAIWszIAwAAAABgIRR5AAAAAAAshCIPAAAAAICFUOQBAAAAALAQijwAAAAAABZCkQcAAAAAwEIo8gAAAAAAWAhFHgAAAAAAC6HIAwAAAABgIRR5AAAAAAAshCIPAAAAAICF/D8ru4ZxTPNXDwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k = [i for i in range (0,50)]\n",
        "maxF1 = np.argmax(f1s)\n",
        "BestK = k[maxF1]\n",
        "print(BestK)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k9rOe07ncsci",
        "outputId": "6267752b-1860-4a8f-e50b-207f1a91e331"
      },
      "execution_count": 469,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier23 = KNeighborsClassifier(n_neighbors=23)\n",
        "classifier23.fit(X_train, y_train)\n",
        "y_pred23 = classifier23.predict(X_test)\n",
        "print(classification_report(y_test, y_pred23,zero_division=0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hljtwUrzLWue",
        "outputId": "e311e1ae-61c2-45e6-c58e-0b0a467b7fce"
      },
      "execution_count": 470,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         2\n",
            "           4       0.25      0.50      0.33         4\n",
            "           5       0.17      0.12      0.14         8\n",
            "           6       0.28      0.33      0.30        24\n",
            "           7       0.28      0.28      0.28        39\n",
            "           8       0.17      0.29      0.22        49\n",
            "           9       0.25      0.24      0.24        74\n",
            "          10       0.27      0.41      0.33        71\n",
            "          11       0.22      0.26      0.24        43\n",
            "          12       0.50      0.15      0.23        33\n",
            "          13       0.00      0.00      0.00        16\n",
            "          14       0.00      0.00      0.00        15\n",
            "          15       0.00      0.00      0.00        12\n",
            "          16       0.33      0.12      0.18         8\n",
            "          17       0.00      0.00      0.00         5\n",
            "          18       0.00      0.00      0.00         7\n",
            "          19       0.00      0.00      0.00         3\n",
            "          20       0.00      0.00      0.00         2\n",
            "          21       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.24       418\n",
            "   macro avg       0.14      0.14      0.13       418\n",
            "weighted avg       0.23      0.24      0.22       418\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K-Fold"
      ],
      "metadata": {
        "id": "-0x0H0VcTH7C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "XF = X.to_numpy()\n",
        "yF = y.to_numpy()"
      ],
      "metadata": {
        "id": "K7AdkQtlMJ0r"
      },
      "execution_count": 471,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kf = KFold(n_splits = 10)\n",
        "cnt =1\n",
        "report = []\n",
        "for train, test in kf.split(XF,yF):\n",
        "   X_trainF, X_testF, y_trainF, y_testF = XF[train], XF[test], yF[train], yF[test]\n",
        "   knn = KNeighborsClassifier(n_neighbors=BestK)\n",
        "   knn.fit(X_trainF, y_trainF)\n",
        "   y_predF = knn.predict(X_testF)\n",
        "   print('Fold',cnt,':')\n",
        "   print(classification_report(y_testF, y_predF,zero_division=0))\n",
        "   print('-----------------------------------------------')\n",
        "   report.append(classification_report(y_testF, y_predF, output_dict=True,zero_division=0))\n",
        "   cnt = cnt+1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MuTOE9Y1TX0D",
        "outputId": "ccfcfe4f-d6e2-4675-8a28-74d810eaa8c4"
      },
      "execution_count": 472,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold 1 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00         1\n",
            "           3       0.00      0.00      0.00         3\n",
            "           4       0.42      0.62      0.50         8\n",
            "           5       0.50      0.46      0.48        13\n",
            "           6       0.24      0.67      0.36        12\n",
            "           7       0.25      0.17      0.20        35\n",
            "           8       0.20      0.25      0.23        36\n",
            "           9       0.14      0.31      0.19        49\n",
            "          10       0.14      0.27      0.18        55\n",
            "          11       0.03      0.03      0.03        32\n",
            "          12       0.10      0.05      0.07        38\n",
            "          13       0.21      0.11      0.14        28\n",
            "          14       0.00      0.00      0.00        28\n",
            "          15       0.00      0.00      0.00        23\n",
            "          16       0.00      0.00      0.00        16\n",
            "          17       0.00      0.00      0.00         6\n",
            "          18       0.00      0.00      0.00         7\n",
            "          19       0.00      0.00      0.00         8\n",
            "          20       0.00      0.00      0.00        10\n",
            "          21       0.00      0.00      0.00         3\n",
            "          22       0.00      0.00      0.00         4\n",
            "          23       0.00      0.00      0.00         2\n",
            "          26       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.17       418\n",
            "   macro avg       0.10      0.13      0.10       418\n",
            "weighted avg       0.13      0.17      0.14       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 2 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           2       0.00      0.00      0.00         1\n",
            "           3       0.00      0.00      0.00         3\n",
            "           4       0.27      0.43      0.33         7\n",
            "           5       0.18      0.25      0.21        12\n",
            "           6       0.19      0.35      0.25        23\n",
            "           7       0.16      0.23      0.19        31\n",
            "           8       0.05      0.21      0.09        14\n",
            "           9       0.08      0.23      0.12        35\n",
            "          10       0.08      0.15      0.10        47\n",
            "          11       0.04      0.02      0.03        47\n",
            "          12       0.17      0.08      0.11        36\n",
            "          13       0.07      0.02      0.03        43\n",
            "          14       0.25      0.05      0.08        20\n",
            "          15       0.00      0.00      0.00        28\n",
            "          16       0.00      0.00      0.00        13\n",
            "          17       0.00      0.00      0.00        20\n",
            "          18       0.00      0.00      0.00        11\n",
            "          19       0.00      0.00      0.00         9\n",
            "          20       0.00      0.00      0.00         6\n",
            "          21       0.00      0.00      0.00         6\n",
            "          22       0.00      0.00      0.00         2\n",
            "          23       0.00      0.00      0.00         3\n",
            "          29       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.11       418\n",
            "   macro avg       0.07      0.09      0.07       418\n",
            "weighted avg       0.09      0.11      0.08       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 3 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         2\n",
            "           4       0.60      0.30      0.40        10\n",
            "           5       0.35      0.27      0.31        22\n",
            "           6       0.42      0.26      0.32        57\n",
            "           7       0.37      0.41      0.39        64\n",
            "           8       0.36      0.27      0.31        81\n",
            "           9       0.31      0.43      0.36        76\n",
            "          10       0.30      0.40      0.34        50\n",
            "          11       0.24      0.32      0.28        37\n",
            "          12       0.29      0.14      0.19        14\n",
            "          14       0.00      0.00      0.00         2\n",
            "          15       0.00      0.00      0.00         1\n",
            "          17       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.33       418\n",
            "   macro avg       0.25      0.22      0.22       418\n",
            "weighted avg       0.34      0.33      0.33       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 4 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         1\n",
            "           4       0.00      0.00      0.00         1\n",
            "           5       0.25      0.11      0.15         9\n",
            "           6       0.20      0.22      0.21        18\n",
            "           7       0.32      0.38      0.35        42\n",
            "           8       0.34      0.38      0.36        89\n",
            "           9       0.34      0.36      0.35       101\n",
            "          10       0.40      0.36      0.38        91\n",
            "          11       0.37      0.44      0.40        39\n",
            "          12       0.00      0.00      0.00        18\n",
            "          13       0.00      0.00      0.00         6\n",
            "          14       0.00      0.00      0.00         2\n",
            "          15       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.34       418\n",
            "   macro avg       0.17      0.17      0.17       418\n",
            "weighted avg       0.32      0.34      0.33       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 5 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           4       0.50      0.67      0.57         3\n",
            "           5       0.27      0.43      0.33         7\n",
            "           6       0.44      0.35      0.39        20\n",
            "           7       0.38      0.32      0.34        38\n",
            "           8       0.40      0.41      0.40        56\n",
            "           9       0.32      0.38      0.34        88\n",
            "          10       0.29      0.42      0.34        84\n",
            "          11       0.27      0.24      0.26        70\n",
            "          12       0.38      0.09      0.15        33\n",
            "          13       0.00      0.00      0.00        12\n",
            "          14       0.00      0.00      0.00         4\n",
            "          15       0.00      0.00      0.00         2\n",
            "          16       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.32       418\n",
            "   macro avg       0.25      0.25      0.24       418\n",
            "weighted avg       0.32      0.32      0.31       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 6 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         2\n",
            "           4       0.43      1.00      0.60         3\n",
            "           5       0.35      0.54      0.42        13\n",
            "           6       0.36      0.33      0.35        24\n",
            "           7       0.16      0.18      0.17        28\n",
            "           8       0.13      0.23      0.17        30\n",
            "           9       0.10      0.24      0.14        42\n",
            "          10       0.09      0.26      0.13        43\n",
            "          11       0.14      0.14      0.14        35\n",
            "          12       0.17      0.05      0.08        37\n",
            "          13       0.22      0.05      0.08        39\n",
            "          14       0.00      0.00      0.00        34\n",
            "          15       0.00      0.00      0.00        20\n",
            "          16       0.00      0.00      0.00        15\n",
            "          17       0.00      0.00      0.00        19\n",
            "          18       0.00      0.00      0.00        12\n",
            "          19       0.00      0.00      0.00         7\n",
            "          20       0.00      0.00      0.00         5\n",
            "          21       0.00      0.00      0.00         3\n",
            "          23       0.00      0.00      0.00         4\n",
            "          25       0.00      0.00      0.00         1\n",
            "          27       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.14       418\n",
            "   macro avg       0.10      0.14      0.10       418\n",
            "weighted avg       0.12      0.14      0.12       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 7 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         1\n",
            "           4       0.67      0.22      0.33         9\n",
            "           5       0.36      0.45      0.40        11\n",
            "           6       0.31      0.28      0.29        29\n",
            "           7       0.46      0.38      0.42        58\n",
            "           8       0.48      0.44      0.46        91\n",
            "           9       0.37      0.44      0.40        93\n",
            "          10       0.33      0.32      0.32        66\n",
            "          11       0.20      0.32      0.24        38\n",
            "          12       0.11      0.09      0.10        11\n",
            "          13       0.00      0.00      0.00         9\n",
            "          14       0.00      0.00      0.00         1\n",
            "          15       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.36       418\n",
            "   macro avg       0.25      0.23      0.23       418\n",
            "weighted avg       0.37      0.36      0.36       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 8 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           4       0.25      0.25      0.25         4\n",
            "           5       0.27      0.30      0.29        10\n",
            "           6       0.08      0.07      0.08        14\n",
            "           7       0.15      0.27      0.19        22\n",
            "           8       0.18      0.23      0.20        40\n",
            "           9       0.22      0.33      0.26        60\n",
            "          10       0.16      0.43      0.23        54\n",
            "          11       0.28      0.15      0.19        75\n",
            "          12       0.07      0.03      0.04        39\n",
            "          13       0.00      0.00      0.00        29\n",
            "          14       0.00      0.00      0.00        20\n",
            "          15       0.00      0.00      0.00        17\n",
            "          16       0.33      0.08      0.13        12\n",
            "          17       0.00      0.00      0.00         7\n",
            "          18       0.00      0.00      0.00         7\n",
            "          19       0.00      0.00      0.00         2\n",
            "          20       0.00      0.00      0.00         2\n",
            "          21       0.00      0.00      0.00         1\n",
            "          24       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.18       417\n",
            "   macro avg       0.11      0.11      0.10       417\n",
            "weighted avg       0.16      0.18      0.15       417\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 9 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         2\n",
            "           4       0.40      0.40      0.40         5\n",
            "           5       0.36      0.33      0.35        12\n",
            "           6       0.46      0.41      0.44        29\n",
            "           7       0.23      0.33      0.27        43\n",
            "           8       0.34      0.34      0.34        71\n",
            "           9       0.32      0.36      0.34        78\n",
            "          10       0.23      0.28      0.25        72\n",
            "          11       0.34      0.38      0.36        53\n",
            "          12       0.29      0.12      0.17        16\n",
            "          13       1.00      0.06      0.11        18\n",
            "          14       0.00      0.00      0.00         4\n",
            "          15       0.00      0.00      0.00         2\n",
            "          16       0.00      0.00      0.00         2\n",
            "          17       0.00      0.00      0.00         2\n",
            "          18       0.00      0.00      0.00         3\n",
            "          19       0.00      0.00      0.00         4\n",
            "          20       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.30       417\n",
            "   macro avg       0.22      0.17      0.17       417\n",
            "weighted avg       0.32      0.30      0.29       417\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 10 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         1\n",
            "           4       0.67      0.57      0.62         7\n",
            "           5       0.20      0.33      0.25         6\n",
            "           6       0.37      0.21      0.27        33\n",
            "           7       0.22      0.30      0.25        30\n",
            "           8       0.43      0.40      0.41        60\n",
            "           9       0.22      0.39      0.28        67\n",
            "          10       0.28      0.39      0.33        72\n",
            "          11       0.36      0.28      0.31        61\n",
            "          12       0.36      0.16      0.22        25\n",
            "          13       0.22      0.11      0.14        19\n",
            "          14       0.00      0.00      0.00        11\n",
            "          15       0.00      0.00      0.00         8\n",
            "          16       0.00      0.00      0.00         8\n",
            "          17       0.00      0.00      0.00         2\n",
            "          18       0.00      0.00      0.00         2\n",
            "          19       0.00      0.00      0.00         2\n",
            "          20       0.00      0.00      0.00         2\n",
            "          21       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.29       417\n",
            "   macro avg       0.18      0.17      0.16       417\n",
            "weighted avg       0.29      0.29      0.28       417\n",
            "\n",
            "-----------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Acc=[]\n",
        "Precision=[]\n",
        "Recall=[]\n",
        "F1Score=[]\n",
        "for r in report:\n",
        "  Acc.append(r['accuracy'])\n",
        "  Precision.append(r['weighted avg']['precision'])\n",
        "  Recall.append(r['weighted avg']['recall'])\n",
        "  F1Score.append(r['weighted avg']['f1-score'])\n",
        "print('Average Accuracy cross-validation score:',np.mean(Acc))\n",
        "print('Average Precision cross-validation score:',np.mean(Precision))\n",
        "print('Average Recall cross-validation score:',np.mean(Recall))\n",
        "print('Average f1_score cross-validation score:',np.mean(F1Score))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mqeqInLYUER-",
        "outputId": "ef2c5ded-283b-47d0-bff3-dddc11e38858"
      },
      "execution_count": 473,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Accuracy cross-validation score: 0.2556894197560612\n",
            "Average Precision cross-validation score: 0.24523634603844607\n",
            "Average Recall cross-validation score: 0.2556894197560612\n",
            "Average f1_score cross-validation score: 0.23845617036967598\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model_Eval"
      ],
      "metadata": {
        "id": "ff8ljdElVm5o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=BestK)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)"
      ],
      "metadata": {
        "id": "2UDwihajVmng"
      },
      "execution_count": 474,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Test score {accuracy_score(y_pred,y_test)}')\n",
        "plot_confusionmatrix(y_pred,y_test,dom='Test')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "7zJ2Oy2uUvN-",
        "outputId": "2470d7c0-a107-4833-a28d-6e849c710e49"
      },
      "execution_count": 475,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test score 0.23923444976076555\n",
            "Test Confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## NB"
      ],
      "metadata": {
        "id": "47jwrccWaKi_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kf = KFold(n_splits = 10)\n",
        "cnt =1\n",
        "report = []\n",
        "for train, test in kf.split(XF,yF):\n",
        "   X_trainF, X_testF, y_trainF, y_testF = XF[train], XF[test], yF[train], yF[test]\n",
        "   gnb = GaussianNB()\n",
        "   gnb.fit(X_trainF, y_trainF)\n",
        "   y_predF = gnb.predict(X_testF)\n",
        "   print('Fold',cnt,':')\n",
        "   print(classification_report(y_testF, y_predF,zero_division=0))\n",
        "   print('-----------------------------------------------')\n",
        "   report.append(classification_report(y_testF, y_predF,zero_division=0,output_dict=True))\n",
        "   cnt = cnt+1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqvEzpNkcfpT",
        "outputId": "2111a48a-d498-4c80-ef84-9177720ddfeb"
      },
      "execution_count": 476,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold 1 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00         1\n",
            "           3       0.29      0.67      0.40         3\n",
            "           4       0.29      0.50      0.36         8\n",
            "           5       0.14      0.31      0.20        13\n",
            "           6       0.12      0.42      0.19        12\n",
            "           7       0.14      0.23      0.17        35\n",
            "           8       0.13      0.19      0.16        36\n",
            "           9       0.10      0.18      0.13        49\n",
            "          10       0.07      0.04      0.05        55\n",
            "          11       0.06      0.09      0.07        32\n",
            "          12       0.00      0.00      0.00        38\n",
            "          13       0.00      0.00      0.00        28\n",
            "          14       0.00      0.00      0.00        28\n",
            "          15       0.00      0.00      0.00        23\n",
            "          16       0.00      0.00      0.00        16\n",
            "          17       0.00      0.00      0.00         6\n",
            "          18       0.00      0.00      0.00         7\n",
            "          19       0.00      0.00      0.00         8\n",
            "          20       0.04      0.10      0.05        10\n",
            "          21       0.00      0.00      0.00         3\n",
            "          22       0.00      0.00      0.00         4\n",
            "          23       0.00      0.00      0.00         2\n",
            "          24       0.00      0.00      0.00         0\n",
            "          26       0.00      0.00      0.00         1\n",
            "          27       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.11       418\n",
            "   macro avg       0.05      0.11      0.07       418\n",
            "weighted avg       0.06      0.11      0.08       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 2 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           2       0.00      0.00      0.00         1\n",
            "           3       0.21      1.00      0.35         3\n",
            "           4       0.00      0.00      0.00         7\n",
            "           5       0.17      0.42      0.24        12\n",
            "           6       0.17      0.39      0.24        23\n",
            "           7       0.07      0.19      0.10        31\n",
            "           8       0.01      0.07      0.02        14\n",
            "           9       0.03      0.09      0.05        35\n",
            "          10       0.06      0.04      0.05        47\n",
            "          11       0.09      0.04      0.06        47\n",
            "          12       0.00      0.00      0.00        36\n",
            "          13       0.00      0.00      0.00        43\n",
            "          14       0.00      0.00      0.00        20\n",
            "          15       0.00      0.00      0.00        28\n",
            "          16       0.00      0.00      0.00        13\n",
            "          17       0.33      0.05      0.09        20\n",
            "          18       0.00      0.00      0.00        11\n",
            "          19       0.00      0.00      0.00         9\n",
            "          20       0.00      0.00      0.00         6\n",
            "          21       0.00      0.00      0.00         6\n",
            "          22       0.00      0.00      0.00         2\n",
            "          23       0.00      0.00      0.00         3\n",
            "          29       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.08       418\n",
            "   macro avg       0.05      0.10      0.05       418\n",
            "weighted avg       0.06      0.08      0.05       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 3 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.25      0.50      0.33         2\n",
            "           4       0.31      0.40      0.35        10\n",
            "           5       0.40      0.45      0.43        22\n",
            "           6       0.40      0.39      0.39        57\n",
            "           7       0.52      0.38      0.44        64\n",
            "           8       0.42      0.27      0.33        81\n",
            "           9       0.39      0.30      0.34        76\n",
            "          10       0.20      0.20      0.20        50\n",
            "          11       0.26      0.73      0.38        37\n",
            "          12       0.00      0.00      0.00        14\n",
            "          14       0.00      0.00      0.00         2\n",
            "          15       0.00      0.00      0.00         1\n",
            "          17       0.00      0.00      0.00         2\n",
            "          24       0.00      0.00      0.00         0\n",
            "          27       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.34       418\n",
            "   macro avg       0.21      0.24      0.21       418\n",
            "weighted avg       0.36      0.34      0.34       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 4 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       1.00      1.00      1.00         1\n",
            "           4       0.50      1.00      0.67         1\n",
            "           5       0.44      0.44      0.44         9\n",
            "           6       0.14      0.28      0.19        18\n",
            "           7       0.21      0.26      0.23        42\n",
            "           8       0.37      0.27      0.31        89\n",
            "           9       0.35      0.40      0.37       101\n",
            "          10       0.43      0.20      0.27        91\n",
            "          11       0.27      0.64      0.38        39\n",
            "          12       0.00      0.00      0.00        18\n",
            "          13       0.00      0.00      0.00         6\n",
            "          14       0.00      0.00      0.00         2\n",
            "          15       0.00      0.00      0.00         1\n",
            "          17       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.31       418\n",
            "   macro avg       0.27      0.32      0.28       418\n",
            "weighted avg       0.32      0.31      0.30       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 5 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         0\n",
            "           4       0.20      0.33      0.25         3\n",
            "           5       0.20      0.57      0.30         7\n",
            "           6       0.39      0.35      0.37        20\n",
            "           7       0.38      0.39      0.39        38\n",
            "           8       0.35      0.36      0.35        56\n",
            "           9       0.44      0.32      0.37        88\n",
            "          10       0.34      0.20      0.25        84\n",
            "          11       0.29      0.63      0.39        70\n",
            "          12       0.00      0.00      0.00        33\n",
            "          13       0.00      0.00      0.00        12\n",
            "          14       0.00      0.00      0.00         4\n",
            "          15       0.00      0.00      0.00         2\n",
            "          16       0.00      0.00      0.00         1\n",
            "          17       0.00      0.00      0.00         0\n",
            "          20       0.00      0.00      0.00         0\n",
            "          24       0.00      0.00      0.00         0\n",
            "          27       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.33       418\n",
            "   macro avg       0.14      0.18      0.15       418\n",
            "weighted avg       0.31      0.33      0.30       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 6 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.20      1.00      0.33         2\n",
            "           4       0.00      0.00      0.00         3\n",
            "           5       0.19      0.38      0.25        13\n",
            "           6       0.12      0.21      0.15        24\n",
            "           7       0.14      0.36      0.21        28\n",
            "           8       0.11      0.27      0.15        30\n",
            "           9       0.06      0.12      0.08        42\n",
            "          10       0.06      0.07      0.07        43\n",
            "          11       0.09      0.11      0.10        35\n",
            "          12       0.00      0.00      0.00        37\n",
            "          13       0.00      0.00      0.00        39\n",
            "          14       0.00      0.00      0.00        34\n",
            "          15       0.00      0.00      0.00        20\n",
            "          16       0.17      0.07      0.10        15\n",
            "          17       0.00      0.00      0.00        19\n",
            "          18       0.00      0.00      0.00        12\n",
            "          19       0.00      0.00      0.00         7\n",
            "          20       0.00      0.00      0.00         5\n",
            "          21       0.00      0.00      0.00         3\n",
            "          23       0.00      0.00      0.00         4\n",
            "          25       0.00      0.00      0.00         1\n",
            "          27       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.10       418\n",
            "   macro avg       0.05      0.12      0.07       418\n",
            "weighted avg       0.06      0.10      0.07       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 7 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.50      1.00      0.67         1\n",
            "           4       0.78      0.78      0.78         9\n",
            "           5       0.27      0.55      0.36        11\n",
            "           6       0.31      0.31      0.31        29\n",
            "           7       0.42      0.43      0.43        58\n",
            "           8       0.51      0.25      0.34        91\n",
            "           9       0.34      0.34      0.34        93\n",
            "          10       0.33      0.29      0.31        66\n",
            "          11       0.25      0.63      0.36        38\n",
            "          12       0.00      0.00      0.00        11\n",
            "          13       0.00      0.00      0.00         9\n",
            "          14       0.00      0.00      0.00         1\n",
            "          15       0.00      0.00      0.00         1\n",
            "          24       0.00      0.00      0.00         0\n",
            "          27       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.35       418\n",
            "   macro avg       0.25      0.31      0.26       418\n",
            "weighted avg       0.37      0.35      0.34       418\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 8 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         0\n",
            "           4       0.25      0.50      0.33         4\n",
            "           5       0.36      0.50      0.42        10\n",
            "           6       0.24      0.36      0.29        14\n",
            "           7       0.14      0.41      0.21        22\n",
            "           8       0.12      0.15      0.13        40\n",
            "           9       0.21      0.27      0.24        60\n",
            "          10       0.16      0.17      0.16        54\n",
            "          11       0.28      0.41      0.34        75\n",
            "          12       0.00      0.00      0.00        39\n",
            "          13       0.00      0.00      0.00        29\n",
            "          14       0.00      0.00      0.00        20\n",
            "          15       0.00      0.00      0.00        17\n",
            "          16       0.00      0.00      0.00        12\n",
            "          17       0.25      0.14      0.18         7\n",
            "          18       0.00      0.00      0.00         7\n",
            "          19       0.00      0.00      0.00         2\n",
            "          20       0.00      0.00      0.00         2\n",
            "          21       0.00      0.00      0.00         1\n",
            "          24       0.00      0.00      0.00         2\n",
            "          27       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.20       417\n",
            "   macro avg       0.10      0.14      0.11       417\n",
            "weighted avg       0.14      0.20      0.16       417\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 9 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.40      1.00      0.57         2\n",
            "           4       0.50      0.60      0.55         5\n",
            "           5       0.25      0.58      0.35        12\n",
            "           6       0.41      0.45      0.43        29\n",
            "           7       0.28      0.42      0.33        43\n",
            "           8       0.31      0.23      0.26        71\n",
            "           9       0.30      0.26      0.28        78\n",
            "          10       0.32      0.24      0.27        72\n",
            "          11       0.34      0.68      0.45        53\n",
            "          12       0.00      0.00      0.00        16\n",
            "          13       0.00      0.00      0.00        18\n",
            "          14       0.00      0.00      0.00         4\n",
            "          15       0.00      0.00      0.00         2\n",
            "          16       0.00      0.00      0.00         2\n",
            "          17       0.00      0.00      0.00         2\n",
            "          18       0.00      0.00      0.00         3\n",
            "          19       0.00      0.00      0.00         4\n",
            "          20       0.00      0.00      0.00         1\n",
            "          27       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.32       417\n",
            "   macro avg       0.16      0.23      0.18       417\n",
            "weighted avg       0.28      0.32      0.28       417\n",
            "\n",
            "-----------------------------------------------\n",
            "Fold 10 :\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         1\n",
            "           4       0.12      0.14      0.13         7\n",
            "           5       0.15      0.50      0.23         6\n",
            "           6       0.41      0.39      0.40        33\n",
            "           7       0.25      0.43      0.32        30\n",
            "           8       0.31      0.28      0.30        60\n",
            "           9       0.23      0.34      0.27        67\n",
            "          10       0.20      0.15      0.17        72\n",
            "          11       0.30      0.41      0.35        61\n",
            "          12       0.00      0.00      0.00        25\n",
            "          13       0.00      0.00      0.00        19\n",
            "          14       0.00      0.00      0.00        11\n",
            "          15       0.00      0.00      0.00         8\n",
            "          16       0.00      0.00      0.00         8\n",
            "          17       0.00      0.00      0.00         2\n",
            "          18       0.00      0.00      0.00         2\n",
            "          19       0.00      0.00      0.00         2\n",
            "          20       0.00      0.00      0.00         2\n",
            "          21       0.00      0.00      0.00         1\n",
            "          24       0.00      0.00      0.00         0\n",
            "          27       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.25       417\n",
            "   macro avg       0.09      0.13      0.10       417\n",
            "weighted avg       0.21      0.25      0.23       417\n",
            "\n",
            "-----------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Acc=[]\n",
        "Precision=[]\n",
        "Recall=[]\n",
        "F1Score=[]\n",
        "for r in report:\n",
        "  Acc.append(r['accuracy'])\n",
        "  Precision.append(r['weighted avg']['precision'])\n",
        "  Recall.append(r['weighted avg']['recall'])\n",
        "  F1Score.append(r['weighted avg']['f1-score'])\n",
        "print('Average Accuracy cross-validation score:',np.mean(Acc))\n",
        "print('Average Precision cross-validation score:',np.mean(Precision))\n",
        "print('Average Recall cross-validation score:',np.mean(Recall))\n",
        "print('Average f1_score cross-validation score:',np.mean(F1Score))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PinJbwlRciZ3",
        "outputId": "2e0b8b5c-def1-453c-a6fb-8ab41e62118d"
      },
      "execution_count": 477,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Accuracy cross-validation score: 0.23846224455842027\n",
            "Average Precision cross-validation score: 0.21833174419731205\n",
            "Average Recall cross-validation score: 0.23846224455842027\n",
            "Average f1_score cross-validation score: 0.2148453197337902\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model_Eval"
      ],
      "metadata": {
        "id": "cc2p31OscjXt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gnb = GaussianNB()\n",
        "gnb.fit(X_train, y_train)\n",
        "y_pred = gnb.predict(X_test)"
      ],
      "metadata": {
        "id": "zPb8kQxUclCn"
      },
      "execution_count": 478,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Test score {accuracy_score(y_pred,y_test)}')\n",
        "plot_confusionmatrix(y_pred,y_test,dom='Test')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "59dp5xRKcmjA",
        "outputId": "76e72742-9f54-45d2-d090-ca6cfff8ba09"
      },
      "execution_count": 479,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test score 0.21291866028708134\n",
            "Test Confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred,zero_division=0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrj54cfwcpDq",
        "outputId": "1741b21e-28a4-4253-a375-afc5f3d2ab95"
      },
      "execution_count": 480,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       1.00      1.00      1.00         2\n",
            "           4       0.27      0.75      0.40         4\n",
            "           5       0.13      0.38      0.19         8\n",
            "           6       0.24      0.29      0.26        24\n",
            "           7       0.25      0.41      0.31        39\n",
            "           8       0.19      0.20      0.20        49\n",
            "           9       0.20      0.22      0.21        74\n",
            "          10       0.23      0.25      0.24        71\n",
            "          11       0.19      0.30      0.24        43\n",
            "          12       0.00      0.00      0.00        33\n",
            "          13       0.00      0.00      0.00        16\n",
            "          14       0.00      0.00      0.00        15\n",
            "          15       0.00      0.00      0.00        12\n",
            "          16       0.00      0.00      0.00         8\n",
            "          17       0.50      0.20      0.29         5\n",
            "          18       0.00      0.00      0.00         7\n",
            "          19       0.00      0.00      0.00         3\n",
            "          20       0.00      0.00      0.00         2\n",
            "          21       0.00      0.00      0.00         3\n",
            "          27       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.21       418\n",
            "   macro avg       0.16      0.20      0.17       418\n",
            "weighted avg       0.17      0.21      0.18       418\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RQrFJRaOc6x7"
      },
      "execution_count": 480,
      "outputs": []
    }
  ]
}