Machine_Learning_Algorithms.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Modules"
      ],
      "metadata": {
        "id": "SrnvNmtn-ja1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from sklearn import datasets\n",
        "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
        "from sklearn.ensemble import IsolationForest\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import silhouette_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBClassifier\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.losses import CategoricalCrossentropy\n",
        "from tensorflow.keras.metrics import CategoricalAccuracy\n",
        "from tensorflow.keras.utils import to_categorical"
      ],
      "metadata": {
        "id": "24KIz5qf-mxY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BUnydooH-P6v"
      },
      "source": [
        "#Linear Regression with Diabetes Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cLhLOAzm_h9N"
      },
      "outputs": [],
      "source": [
        "diabetes_data=datasets.load_diabetes(as_frame=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8sIHBEGv_0sE",
        "outputId": "8d17a5dc-98b3-43cb-e163-56ac088b37c4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "sklearn.utils._bunch.Bunch"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(diabetes_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5qRSMGC_3fc",
        "outputId": "1dbdafe2-42da-4402-a695-a0048c6e4f55"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename', 'data_module'])"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diabetes_data.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fYXKYZqIAVpR"
      },
      "outputs": [],
      "source": [
        "#X data\n",
        "x=diabetes_data.data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XZEUUBzAZRw",
        "outputId": "cfafdd2f-fa70-4032-edc1-6901ef2f7ca5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diabetes_data.feature_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W3xOObyZAdOs"
      },
      "outputs": [],
      "source": [
        "#Y label\n",
        "y=diabetes_data.target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fZsxHybPAnCU"
      },
      "outputs": [],
      "source": [
        "X_train,x_test,Y_train,y_test=train_test_split(x,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DNPbgWZtCdlc"
      },
      "outputs": [],
      "source": [
        "#Cross Validation Set and Test set\n",
        "X_cv,x_test,Y_cv,y_test=train_test_split(x_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "zRh9byDHCm1r",
        "outputId": "36ac2e5a-bca6-48cf-8036-372b725070a7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7a9c91809390>"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Plot Graph on bases of Training set\n",
        "fig,axis=plt.subplots(2,2)\n",
        "axis[0,0].set_title(\"Age\")\n",
        "axis[0,0].scatter(X_train['age'],Y_train)\n",
        "axis[0,1].set_title(\"Sex\")\n",
        "axis[0,1].scatter(X_train['sex'],Y_train)\n",
        "axis[1,0].set_title(\"BMI\")\n",
        "axis[1,0].scatter(X_train['bmi'],Y_train)\n",
        "axis[1,1].set_title(\"BP\")\n",
        "axis[1,1].scatter(X_train['bp'],Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_wCZSuSzGSCo"
      },
      "outputs": [],
      "source": [
        "#Initializing a Linear Regression Model\n",
        "linear_regression=LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "tDozZcNXIEKV",
        "outputId": "acc5ddbf-9509-4cb9-c5cf-93f056240df4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Training the model\n",
        "linear_regression.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_xK5m2vfIKrz"
      },
      "outputs": [],
      "source": [
        "#Test with Cross Validation set\n",
        "y_pred_cv=linear_regression.predict(X_cv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m_HI4F_HO2K_",
        "outputId": "2f64658d-d829-4cbf-c079-4979bb197bd1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2916.678959465798"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mean_squared_error(Y_cv,y_pred_cv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Q9poMheO__w",
        "outputId": "399d84d2-d996-41ae-fcd6-0255d49982ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[   8.41475062 -265.66597008  537.46926265  294.10055988 -796.99177946\n",
            "  496.49270526   88.95785808  139.23921897  758.18035319   81.61905209]\n",
            "151.26942430213987\n"
          ]
        }
      ],
      "source": [
        "#Weight and Bias\n",
        "print(linear_regression.coef_)\n",
        "print(linear_regression.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "Edme1B6hP7TH",
        "outputId": "44a935e6-7b04-4bf5-ee37-2dbae7e885bc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7a9c91794e80>"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Plotting Cross Validation\n",
        "fig,axis=plt.subplots(2,2)\n",
        "axis[0,0].set_title(\"Age\")\n",
        "axis[0,0].scatter(X_cv['age'],Y_cv,c=\"#FD7F20\")\n",
        "axis[0,0].scatter(X_cv['age'],y_pred_cv,c=\"g\")\n",
        "axis[0,1].set_title(\"Sex\")\n",
        "axis[0,1].scatter(X_cv['sex'],Y_cv,c=\"#FD7F20\")\n",
        "axis[0,1].scatter(X_cv['sex'],y_pred_cv,c=\"g\")\n",
        "axis[1,0].set_title(\"BMI\")\n",
        "axis[1,0].scatter(X_cv['bmi'],Y_cv,c=\"#FD7F20\")\n",
        "axis[1,0].scatter(X_cv['bmi'],y_pred_cv,c=\"g\")\n",
        "axis[1,1].set_title(\"BP\")\n",
        "axis[1,1].scatter(X_cv['bp'],Y_cv,c=\"#FD7F20\")\n",
        "axis[1,1].scatter(X_cv['bp'],y_pred_cv,c=\"g\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4wB0pDyjRtEP"
      },
      "outputs": [],
      "source": [
        "#Predicting with Test data\n",
        "y_pred=linear_regression.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OS2nZvRaUNIh",
        "outputId": "8a88edd7-a26c-41f6-e061-00e1a95330dc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1475.7918103180368"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mean_squared_error(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "MUouJjZPUVHG",
        "outputId": "4f582f4a-b819-4d2a-b76c-66ff489279d4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7a9c91549690>"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Plotting Test data\n",
        "fig,axis=plt.subplots(2,2)\n",
        "axis[0,0].set_title(\"Age\")\n",
        "axis[0,0].scatter(x_test['age'],y_test,c=\"r\")\n",
        "axis[0,0].scatter(x_test['age'],y_pred,c=\"g\")\n",
        "axis[0,1].set_title(\"Sex\")\n",
        "axis[0,1].scatter(x_test['sex'],y_test,c=\"r\")\n",
        "axis[0,1].scatter(x_test['sex'],y_pred,c=\"g\")\n",
        "axis[1,0].set_title(\"BMI\")\n",
        "axis[1,0].scatter(x_test['bmi'],y_test,c=\"r\")\n",
        "axis[1,0].scatter(x_test['bmi'],y_pred,c=\"g\")\n",
        "axis[1,1].set_title(\"BP\")\n",
        "axis[1,1].scatter(x_test['bp'],y_test,c=\"r\")\n",
        "axis[1,1].scatter(x_test['bp'],y_pred,c=\"g\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZykYP2sZeA_5"
      },
      "source": [
        "# Logistic Regression with E-mail Spam Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w9m8Ig2RUvIs"
      },
      "outputs": [],
      "source": [
        "email_data=pd.read_csv('email_spam.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "C1bdFa38eWd7",
        "outputId": "9d34b8fd-5d94-48f5-b09b-d0ecb58c78e1"
      },
      "outputs": [
        {
          "data": {
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ],
            "text/plain": [
              "      Unnamed: 0 label                                               text  \\\n",
              "0            605   ham  Subject: enron methanol ; meter # : 988291\\r\\n...   \n",
              "1           2349   ham  Subject: hpl nom for january 9 , 2001\\r\\n( see...   \n",
              "2           3624   ham  Subject: neon retreat\\r\\nho ho ho , we ' re ar...   \n",
              "3           4685  spam  Subject: photoshop , windows , office . cheap ...   \n",
              "4           2030   ham  Subject: re : indian springs\\r\\nthis deal is t...   \n",
              "...          ...   ...                                                ...   \n",
              "5166        1518   ham  Subject: put the 10 on the ft\\r\\nthe transport...   \n",
              "5167         404   ham  Subject: 3 / 4 / 2000 and following noms\\r\\nhp...   \n",
              "5168        2933   ham  Subject: calpine daily gas nomination\\r\\n>\\r\\n...   \n",
              "5169        1409   ham  Subject: industrial worksheets for august 2000...   \n",
              "5170        4807  spam  Subject: important online banking alert\\r\\ndea...   \n",
              "\n",
              "      label_num  \n",
              "0             0  \n",
              "1             0  \n",
              "2             0  \n",
              "3             1  \n",
              "4             0  \n",
              "...         ...  \n",
              "5166          0  \n",
              "5167          0  \n",
              "5168          0  \n",
              "5169          0  \n",
              "5170          1  \n",
              "\n",
              "[5171 rows x 4 columns]"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "email_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ikq1oVCje8w-"
      },
      "outputs": [],
      "source": [
        "X=email_data.text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tEMswDTLfC-O",
        "outputId": "48ed86c9-9e4a-44df-c4e1-a3b982aba504"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0       Subject: enron methanol ; meter # : 988291\\r\\n...\n",
              "1       Subject: hpl nom for january 9 , 2001\\r\\n( see...\n",
              "2       Subject: neon retreat\\r\\nho ho ho , we ' re ar...\n",
              "3       Subject: photoshop , windows , office . cheap ...\n",
              "4       Subject: re : indian springs\\r\\nthis deal is t...\n",
              "                              ...                        \n",
              "5166    Subject: put the 10 on the ft\\r\\nthe transport...\n",
              "5167    Subject: 3 / 4 / 2000 and following noms\\r\\nhp...\n",
              "5168    Subject: calpine daily gas nomination\\r\\n>\\r\\n...\n",
              "5169    Subject: industrial worksheets for august 2000...\n",
              "5170    Subject: important online banking alert\\r\\ndea...\n",
              "Name: text, Length: 5171, dtype: object"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Mails\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iFK7CL0WkrTj",
        "outputId": "6f2aae19-32c5-4bd4-b075-bd362caf5838"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['00', '000', '0000', ..., 'zzocb', 'zzso', 'zzsyt'], dtype=object)"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Vectorizing text data\n",
        "vect=TfidfVectorizer()\n",
        "X=vect.fit_transform(X)\n",
        "vect.get_feature_names_out()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bjryS18QfJ7u"
      },
      "outputs": [],
      "source": [
        "Y=email_data.label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3F_7bzQjfNQt",
        "outputId": "a8c7e541-a33b-4009-ff69-da5b07212d7a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        ham\n",
              "1        ham\n",
              "2        ham\n",
              "3       spam\n",
              "4        ham\n",
              "        ... \n",
              "5166     ham\n",
              "5167     ham\n",
              "5168     ham\n",
              "5169     ham\n",
              "5170    spam\n",
              "Name: label, Length: 5171, dtype: object"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Label\n",
        "Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uxzZ0z6ffOUK"
      },
      "outputs": [],
      "source": [
        "X_train,x,Y_train,y=train_test_split(X,Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tRo8ed08f9dL"
      },
      "outputs": [],
      "source": [
        "x_cv,x_test,y_cv,y_test=train_test_split(x,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rZoEyk9hg27A"
      },
      "outputs": [],
      "source": [
        "logistic_regression=LogisticRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "4cdwBAgRjiGe",
        "outputId": "f9a90016-b843-4520-b283-8db2249c14d0"
      },
      "outputs": [
        {
          "data": {
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              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "logistic_regression.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bse2Y63wjyWa"
      },
      "outputs": [],
      "source": [
        "#Cross Validation\n",
        "y_cv_pred=logistic_regression.predict(x_cv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9koFEfilcom",
        "outputId": "2452ad6e-2c53-4b3b-b9b6-cd6ea71f7aed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "961"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(y_cv_pred,y_cv,normalize=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwqW7oQKl4_L",
        "outputId": "a0a91510-ca86-4dd9-9d63-b8f73a1cde8c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(969,)"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_cv.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ODh9gzeAl61z"
      },
      "outputs": [],
      "source": [
        "#Test data set\n",
        "y_pred=logistic_regression.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vaaCiUYFmm_K",
        "outputId": "3a340219-2564-430f-eeb0-3b306302cb5c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "315"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(y_pred,y_test,normalize=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1p837AzEmt8i",
        "outputId": "d1632f1f-f8df-4c9f-87cf-89d6f1013d4d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(324,)"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q5osNnL7vr4W"
      },
      "source": [
        "# Tensorflow Softmax and ReLu function using Iris Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z4ghcQtyvrAw"
      },
      "outputs": [],
      "source": [
        "iris=pd.read_csv('IRIS.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "d2UT2PrOmxM7",
        "outputId": "61e9203e-e1d5-49b2-e8d1-57064c51630c"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "\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",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e198b93f-eced-487f-b9da-0d91199aecc5')\"\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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              "\n",
              "    <div id=\"df-dd77da9b-9580-4816-bca7-129004cd3c2e\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dd77da9b-9580-4816-bca7-129004cd3c2e')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
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              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-dd77da9b-9580-4816-bca7-129004cd3c2e button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
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              "      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-e198b93f-eced-487f-b9da-0d91199aecc5 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-e198b93f-eced-487f-b9da-0d91199aecc5');\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"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width         species\n",
              "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
              "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
              "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
              "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
              "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
              "..            ...          ...           ...          ...             ...\n",
              "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
              "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
              "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
              "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
              "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "iris"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LnNC5lu5yWeO"
      },
      "outputs": [],
      "source": [
        "X=iris.iloc[:,:-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "hcFQUE1fyctW",
        "outputId": "26afc459-5834-4669-ddd3-4489b4deb805"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "      <div>\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
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              "\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      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",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f53a412b-687c-441c-8feb-650308fa3c94 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-f53a412b-687c-441c-8feb-650308fa3c94');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "0             5.1          3.5           1.4          0.2\n",
              "1             4.9          3.0           1.4          0.2\n",
              "2             4.7          3.2           1.3          0.2\n",
              "3             4.6          3.1           1.5          0.2\n",
              "4             5.0          3.6           1.4          0.2\n",
              "..            ...          ...           ...          ...\n",
              "145           6.7          3.0           5.2          2.3\n",
              "146           6.3          2.5           5.0          1.9\n",
              "147           6.5          3.0           5.2          2.0\n",
              "148           6.2          3.4           5.4          2.3\n",
              "149           5.9          3.0           5.1          1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ruHq6jGLydgQ"
      },
      "outputs": [],
      "source": [
        "Y=iris.species"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AIOvb8nP6YOI",
        "outputId": "0301b31f-c547-4dda-f089-d02d55063093"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "label_encoder=LabelEncoder()\n",
        "y_encoded=label_encoder.fit_transform(Y)\n",
        "y_encoded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cdKhtoX5yjhl"
      },
      "outputs": [],
      "source": [
        "y_cat=to_categorical(y_encoded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UsDN6E8-L-yJ",
        "outputId": "2501e9ab-cd67-4d08-91cc-c81d79a1f904"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.]], dtype=float32)"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_cat[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W28nezJVyu3_"
      },
      "outputs": [],
      "source": [
        "#Split Train data\n",
        "X_train,x,Y_train,y=train_test_split(X,y_cat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CzgjPjsxy5AM"
      },
      "outputs": [],
      "source": [
        "#Cross Validataion data\n",
        "x_cv,x_test,y_cv,y_test=train_test_split(x,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8v7s1vRdzB1Y"
      },
      "outputs": [],
      "source": [
        "#Creating Neural Network\n",
        "model=Sequential(\n",
        "    [\n",
        "        Input(shape=X_train.shape[1:]),\n",
        "        Dense(1000,'relu'),\n",
        "        Dense(500,'relu'),\n",
        "        Dense(300,'relu'),\n",
        "        Dense(3,'softmax')\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UWOyqXGlJEoc",
        "outputId": "d29c0af9-4cff-40e6-9ff8-38f83b639791"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 1000)              5000      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 500)               500500    \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 300)               150300    \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 3)                 903       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 656,703\n",
            "Trainable params: 656,703\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NBNofObKzZrq"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=Adam(learning_rate=0.01),loss=CategoricalCrossentropy(),metrics=CategoricalAccuracy())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLC1vYNwCJxi",
        "outputId": "2f60b3c6-fe1c-450f-97b6-2a0ed7517eb3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "4/4 [==============================] - 1s 94ms/step - loss: 4.0718 - categorical_accuracy: 0.3036 - val_loss: 0.9102 - val_categorical_accuracy: 0.4643\n",
            "Epoch 2/50\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 0.6836 - categorical_accuracy: 0.5982 - val_loss: 0.5768 - val_categorical_accuracy: 0.5714\n",
            "Epoch 3/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.3870 - categorical_accuracy: 0.8214 - val_loss: 0.2543 - val_categorical_accuracy: 1.0000\n",
            "Epoch 4/50\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 0.1705 - categorical_accuracy: 0.9554 - val_loss: 0.1118 - val_categorical_accuracy: 1.0000\n",
            "Epoch 5/50\n",
            "4/4 [==============================] - 0s 26ms/step - loss: 0.4660 - categorical_accuracy: 0.8214 - val_loss: 0.4232 - val_categorical_accuracy: 0.8571\n",
            "Epoch 6/50\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.3689 - categorical_accuracy: 0.8571 - val_loss: 0.7357 - val_categorical_accuracy: 0.6786\n",
            "Epoch 7/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.3096 - categorical_accuracy: 0.8393 - val_loss: 0.1955 - val_categorical_accuracy: 0.9286\n",
            "Epoch 8/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.2556 - categorical_accuracy: 0.8661 - val_loss: 0.1608 - val_categorical_accuracy: 1.0000\n",
            "Epoch 9/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1588 - categorical_accuracy: 0.9464 - val_loss: 0.1643 - val_categorical_accuracy: 0.9643\n",
            "Epoch 10/50\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 0.0983 - categorical_accuracy: 0.9732 - val_loss: 0.0913 - val_categorical_accuracy: 0.9286\n",
            "Epoch 11/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1015 - categorical_accuracy: 0.9464 - val_loss: 0.0798 - val_categorical_accuracy: 1.0000\n",
            "Epoch 12/50\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 0.0813 - categorical_accuracy: 0.9554 - val_loss: 0.2402 - val_categorical_accuracy: 0.9286\n",
            "Epoch 13/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1077 - categorical_accuracy: 0.9464 - val_loss: 0.2456 - val_categorical_accuracy: 0.8214\n",
            "Epoch 14/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.2051 - categorical_accuracy: 0.9286 - val_loss: 0.0859 - val_categorical_accuracy: 0.9286\n",
            "Epoch 15/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1191 - categorical_accuracy: 0.9464 - val_loss: 0.0816 - val_categorical_accuracy: 1.0000\n",
            "Epoch 16/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.0662 - categorical_accuracy: 0.9821 - val_loss: 0.3181 - val_categorical_accuracy: 0.9286\n",
            "Epoch 17/50\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 0.1903 - categorical_accuracy: 0.9196 - val_loss: 0.2218 - val_categorical_accuracy: 0.8214\n",
            "Epoch 18/50\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 0.1192 - categorical_accuracy: 0.9554 - val_loss: 0.2247 - val_categorical_accuracy: 0.9286\n",
            "Epoch 19/50\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 0.1595 - categorical_accuracy: 0.9107 - val_loss: 0.1232 - val_categorical_accuracy: 1.0000\n",
            "Epoch 20/50\n",
            "4/4 [==============================] - 0s 26ms/step - loss: 0.1088 - categorical_accuracy: 0.9554 - val_loss: 0.0753 - val_categorical_accuracy: 0.9643\n",
            "Epoch 21/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1415 - categorical_accuracy: 0.9286 - val_loss: 0.0783 - val_categorical_accuracy: 1.0000\n",
            "Epoch 22/50\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 0.1811 - categorical_accuracy: 0.9375 - val_loss: 0.0515 - val_categorical_accuracy: 1.0000\n",
            "Epoch 23/50\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 0.1043 - categorical_accuracy: 0.9643 - val_loss: 0.0606 - val_categorical_accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1040 - categorical_accuracy: 0.9643 - val_loss: 0.0515 - val_categorical_accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 0.0746 - categorical_accuracy: 0.9643 - val_loss: 0.0944 - val_categorical_accuracy: 0.9286\n",
            "Epoch 26/50\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 0.0754 - categorical_accuracy: 0.9554 - val_loss: 0.0182 - val_categorical_accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 0.0587 - categorical_accuracy: 0.9911 - val_loss: 0.1221 - val_categorical_accuracy: 0.9286\n",
            "Epoch 28/50\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 0.0992 - categorical_accuracy: 0.9643 - val_loss: 0.2688 - val_categorical_accuracy: 0.8214\n",
            "Epoch 29/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.2051 - categorical_accuracy: 0.9464 - val_loss: 0.1147 - val_categorical_accuracy: 0.9286\n",
            "Epoch 30/50\n",
            "4/4 [==============================] - 0s 24ms/step - loss: 0.0881 - categorical_accuracy: 0.9554 - val_loss: 0.0807 - val_categorical_accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 0.1442 - categorical_accuracy: 0.9464 - val_loss: 0.0426 - val_categorical_accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1545 - categorical_accuracy: 0.9375 - val_loss: 0.0509 - val_categorical_accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1142 - categorical_accuracy: 0.9464 - val_loss: 0.1507 - val_categorical_accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 0.1237 - categorical_accuracy: 0.9464 - val_loss: 0.1063 - val_categorical_accuracy: 0.9286\n",
            "Epoch 35/50\n",
            "4/4 [==============================] - 0s 27ms/step - loss: 0.0929 - categorical_accuracy: 0.9643 - val_loss: 0.0239 - val_categorical_accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.0748 - categorical_accuracy: 0.9821 - val_loss: 0.0715 - val_categorical_accuracy: 0.9643\n",
            "Epoch 37/50\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 0.0698 - categorical_accuracy: 0.9821 - val_loss: 0.0134 - val_categorical_accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 0.0680 - categorical_accuracy: 0.9732 - val_loss: 0.0086 - val_categorical_accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "4/4 [==============================] - 0s 25ms/step - loss: 0.1121 - categorical_accuracy: 0.9554 - val_loss: 0.0120 - val_categorical_accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 0.0731 - categorical_accuracy: 0.9643 - val_loss: 0.0298 - val_categorical_accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1627 - categorical_accuracy: 0.9286 - val_loss: 0.2580 - val_categorical_accuracy: 0.8214\n",
            "Epoch 42/50\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 0.3212 - categorical_accuracy: 0.8750 - val_loss: 0.0848 - val_categorical_accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.2462 - categorical_accuracy: 0.8839 - val_loss: 0.1912 - val_categorical_accuracy: 0.9286\n",
            "Epoch 44/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1944 - categorical_accuracy: 0.9107 - val_loss: 0.3221 - val_categorical_accuracy: 0.7143\n",
            "Epoch 45/50\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 0.1920 - categorical_accuracy: 0.9018 - val_loss: 0.0764 - val_categorical_accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1155 - categorical_accuracy: 0.9554 - val_loss: 0.0299 - val_categorical_accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 0.1164 - categorical_accuracy: 0.9554 - val_loss: 0.0959 - val_categorical_accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.1187 - categorical_accuracy: 0.9554 - val_loss: 0.1178 - val_categorical_accuracy: 0.9286\n",
            "Epoch 49/50\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 0.0648 - categorical_accuracy: 0.9821 - val_loss: 0.0569 - val_categorical_accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 0.1018 - categorical_accuracy: 0.9464 - val_loss: 0.0275 - val_categorical_accuracy: 1.0000\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7a9c91a239d0>"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(X_train,Y_train,epochs=50,verbose=1,validation_data=(x_cv,y_cv))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEIOCoCkJ-vS",
        "outputId": "00a07b1d-e253-4308-ca3a-bf97d31efc1e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 27ms/step - loss: 0.0275 - categorical_accuracy: 1.0000\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.027486665174365044, 1.0]"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate(x_cv,y_cv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZUbmgktKZKe",
        "outputId": "8fe8903c-e22f-4d2b-ee17-cae7aa9062eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 102ms/step\n"
          ]
        }
      ],
      "source": [
        "y_prediction=model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ip5TObBhKkn6",
        "outputId": "ac2fa0f4-06b8-47fc-e051-938325708eb7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.],\n",
              "       [0., 0., 1.],\n",
              "       [1., 0., 0.],\n",
              "       [0., 0., 1.],\n",
              "       [1., 0., 0.],\n",
              "       [0., 1., 0.]], dtype=float32)"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gy7kXzVEKnJl",
        "outputId": "ecd8e723-f92f-47b9-fc61-5b2a2ad3bb06"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0\n",
            "2\n",
            "1\n",
            "1\n",
            "2\n",
            "2\n",
            "0\n",
            "2\n",
            "0\n",
            "1\n"
          ]
        }
      ],
      "source": [
        "for pred in y_prediction:\n",
        "  print(np.argmax(pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKyoITUmKzK8",
        "outputId": "9a91d101-9397-4f67-b492-03720f2bbb09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 28ms/step - loss: 0.1154 - categorical_accuracy: 0.9000\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.11539934575557709, 0.8999999761581421]"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate(x_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGokkiUec9w7"
      },
      "source": [
        "# K-means Clustering with News Article"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "URClOas4gP2G"
      },
      "outputs": [],
      "source": [
        "articles=pd.read_excel('Articles.xls')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "hv6Am2phlP-X",
        "outputId": "09e68a04-3c00-45de-eba4-33d3c3712979"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Article                 Date  \\\n",
              "0     KARACHI: The Sindh government has decided to b...  2015-01-01 00:00:00   \n",
              "1     HONG KONG: Asian markets started 2015 on an up...  2015-02-01 00:00:00   \n",
              "2     HONG KONG:  Hong Kong shares opened 0.66 perce...  2015-05-01 00:00:00   \n",
              "3     HONG KONG: Asian markets tumbled Tuesday follo...  2015-06-01 00:00:00   \n",
              "4     NEW YORK: US oil prices Monday slipped below $...  2015-06-01 00:00:00   \n",
              "...                                                 ...                  ...   \n",
              "2687  strong>DUBAI: Dubai International Airport and ...            3/25/2017   \n",
              "2688  strong>BEIJING: Former Prime Minister, Shaukat...            3/26/2017   \n",
              "2689  strong>WASHINGTON: Uber has grounded its fleet...            3/26/2017   \n",
              "2690  strong>BEIJING: The New Development Bank plans...            3/27/2017   \n",
              "2691  strong>KARACHI: Karachi-based technology incub...            3/27/2017   \n",
              "\n",
              "                                                Heading  NewsType  \n",
              "0     sindh govt decides to cut public transport far...  business  \n",
              "1                       asia stocks up in new year trad  business  \n",
              "2              hong kong stocks open 0.66 percent lower  business  \n",
              "3                asian stocks sink euro near nine year   business  \n",
              "4                    us oil prices slip below 50 a barr  business  \n",
              "...                                                 ...       ...  \n",
              "2687    Laptop ban hits Dubai for 11m weekend traveller  business  \n",
              "2688  Pak China relations not against any third coun...  business  \n",
              "2689         Uber grounds self driving cars after accid  business  \n",
              "2690  New Development Bank plans joint investments i...  business  \n",
              "2691  Google powered Startup Weekend energizing prou...  business  \n",
              "\n",
              "[2692 rows x 4 columns]"
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              "      <div>\n",
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              "      <td>HONG KONG: Asian markets started 2015 on an up...</td>\n",
              "      <td>2015-02-01 00:00:00</td>\n",
              "      <td>asia stocks up in new year trad</td>\n",
              "      <td>business</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HONG KONG:  Hong Kong shares opened 0.66 perce...</td>\n",
              "      <td>2015-05-01 00:00:00</td>\n",
              "      <td>hong kong stocks open 0.66 percent lower</td>\n",
              "      <td>business</td>\n",
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              "      <th>3</th>\n",
              "      <td>HONG KONG: Asian markets tumbled Tuesday follo...</td>\n",
              "      <td>2015-06-01 00:00:00</td>\n",
              "      <td>asian stocks sink euro near nine year</td>\n",
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              "      <td>3/25/2017</td>\n",
              "      <td>Laptop ban hits Dubai for 11m weekend traveller</td>\n",
              "      <td>business</td>\n",
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              "      <th>2688</th>\n",
              "      <td>strong&gt;BEIJING: Former Prime Minister, Shaukat...</td>\n",
              "      <td>3/26/2017</td>\n",
              "      <td>Pak China relations not against any third coun...</td>\n",
              "      <td>business</td>\n",
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              "      <th>2689</th>\n",
              "      <td>strong&gt;WASHINGTON: Uber has grounded its fleet...</td>\n",
              "      <td>3/26/2017</td>\n",
              "      <td>Uber grounds self driving cars after accid</td>\n",
              "      <td>business</td>\n",
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              "    <tr>\n",
              "      <th>2690</th>\n",
              "      <td>strong&gt;BEIJING: The New Development Bank plans...</td>\n",
              "      <td>3/27/2017</td>\n",
              "      <td>New Development Bank plans joint investments i...</td>\n",
              "      <td>business</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2691</th>\n",
              "      <td>strong&gt;KARACHI: Karachi-based technology incub...</td>\n",
              "      <td>3/27/2017</td>\n",
              "      <td>Google powered Startup Weekend energizing prou...</td>\n",
              "      <td>business</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2692 rows × 4 columns</p>\n",
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              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-feb6e35e-e92d-4fc5-a3e8-d4eebde54eab button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-1d85d913-fa38-49ae-b03f-a30fcd041dbc 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-1d85d913-fa38-49ae-b03f-a30fcd041dbc');\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": 296
        }
      ],
      "source": [
        "articles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q_BrxtJfmWRL"
      },
      "outputs": [],
      "source": [
        "X=articles['Article']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MraYWV89mpHc"
      },
      "outputs": [],
      "source": [
        "Y=articles['NewsType']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3L_gsfOMmt8w",
        "outputId": "3378591c-cbb0-4a08-9be4-4e9cd4dcd790"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       KARACHI: The Sindh government has decided to b...\n",
              "1       HONG KONG: Asian markets started 2015 on an up...\n",
              "2       HONG KONG:  Hong Kong shares opened 0.66 perce...\n",
              "3       HONG KONG: Asian markets tumbled Tuesday follo...\n",
              "4       NEW YORK: US oil prices Monday slipped below $...\n",
              "                              ...                        \n",
              "2687    strong>DUBAI: Dubai International Airport and ...\n",
              "2688    strong>BEIJING: Former Prime Minister, Shaukat...\n",
              "2689    strong>WASHINGTON: Uber has grounded its fleet...\n",
              "2690    strong>BEIJING: The New Development Bank plans...\n",
              "2691    strong>KARACHI: Karachi-based technology incub...\n",
              "Name: Article, Length: 2692, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 299
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JE7pUPBFmv1J",
        "outputId": "33c27f9a-7524-4108-907a-96e632054345"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 300
        }
      ],
      "source": [
        "type(X)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vect=TfidfVectorizer(lowercase=True)\n",
        "X=vect.fit_transform(X)"
      ],
      "metadata": {
        "id": "nWU9eizP0nbp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmean=KMeans(n_clusters=2,max_iter=1000)"
      ],
      "metadata": {
        "id": "_bcsXJnX2jkC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmean.fit(X,Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "rrZIej0G-AUD",
        "outputId": "684c902b-dd58-478e-d8b1-cd71ca28b2dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(max_iter=1000, n_clusters=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(max_iter=1000, n_clusters=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(max_iter=1000, n_clusters=2)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 303
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=kmean.predict(X)"
      ],
      "metadata": {
        "id": "UwtHG9y_BNkI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "silhouette_score(X,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w0uehvwCBT-2",
        "outputId": "1fc882ac-bbc6-4b91-a2b8-2488ddb021b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.007647726385680743"
            ]
          },
          "metadata": {},
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca_composition=PCA(n_components=2)\n",
        "X_pca=pca_composition.fit_transform(X.toarray())"
      ],
      "metadata": {
        "id": "3M8AmolkBkbs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Business and Sports\")\n",
        "plt.scatter(X_pca[:,0],X_pca[:,1],c=y_pred,cmap='bwr')\n",
        "plt.xlabel(\"Princial composition 1\")\n",
        "plt.ylabel(\"Princial composition 2\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "_RMXGyMcCySy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "outputId": "33ae0402-2c22-464a-fe75-bcfe12542fc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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fpyAIghCUxMbGonbt2li6dGmghyIIuQaJERIEQRAEIc8ihpAgCIIgCHkWMYQEQRAEQcizSIyQIAiCIAh5FvEICYIgCIKQZxFDSBAEQRCEPIsIKjrBZDLh3LlzKFSokFPJeUEQBEEQggMiwo0bN1CmTBmHIqJiCDnh3LlziI6ODvQwBEEQBEFwgzNnzqBcuXJ23xdDyAmFChUCwF+kuWSBIAiCIAjBTVJSEqKjozPu4/YQQ8gJ5umwiIgIMYQEQRAEIYfhLKxFgqUFQRAEQciziCEkCIIgCEKeRQwhQRAEQRDyLGIICYIgCIKQZxFDSBAEQRCEPIsYQoIgCIIg5FnEEBIEQRAEIc8ihpAgCIIgCHkWMYQEQRAEQciziCGUA1i5EujYEShSBChWDOjTB9i9O9CjEgRBEIScT44zhD777DPExsYiPDwcTZs2xbZt2+y2XbhwIRo1aoSoqCgUKFAAcXFxmD17th9H6zlvvQW0bw+sWAFcuwZcuQLMmQM0asR/BUEQBEFwnxxlCM2bNw/Dhg3DqFGjsGvXLtSrVw/t27fHxYsXbbYvUqQI/ve//2HLli3Yt28f+vXrh379+mHFihV+Hrl7rFgBvP8+/280WtanpwMmE3uGTp4MyNAEQRAEIVegEBEFehB6adq0KRo3boypU6cCAEwmE6Kjo/HSSy9hxIgRuvpo0KABOnbsiPfee09X+6SkJERGRiIxMdHvRVc7dABWrbI2gjKjacBrrwEffODXYQmCIAhC0KP3/p1jPEKpqanYuXMn2rZtm7FOVVW0bdsWW7Zscbo9EWHNmjU4cuQI7rnnHrvtUlJSkJSUZLUEij//tG8EAfzepk3+G48gCIIg5DZyjCF0+fJlGI1GlCxZ0mp9yZIlkZCQYHe7xMREFCxYEKGhoejYsSOmTJmC+++/3277sWPHIjIyMmOJjo722mdwFU1z3iYkxPfjEARBEITcSo4xhNylUKFC2LNnD7Zv344xY8Zg2LBhWL9+vd32I0eORGJiYsZy5swZ/w02Cw88ABgM9t9XVaBdO/+NRxAEQRByGw5us8FFsWLFoGkaLly4YLX+woULKFWqlN3tVFVF5cqVAQBxcXE4fPgwxo4di3vvvddm+7CwMISFhXlt3J7w8svA3Lm231NVoEAB4Jln/DsmQRAEQchN5BiPUGhoKBo2bIg1a9ZkrDOZTFizZg2aN2+uux+TyYSUlBRfDNHrNG4MTJ/ORk/maTJVBfLnB5YtY10hQRAEQRDcI8d4hABg2LBh6NOnDxo1aoQmTZrg008/xa1bt9CvXz8AQO/evVG2bFmMHTsWAMf7NGrUCJUqVUJKSgqWLVuG2bNn44svvgjkx3CJPn2AFi2AL78ENm/mmKAOHdgTVLx49vYXLgBTpwKzZgHXrwMVKwLPPw/07QvodXRdvAjcvg2ULg2Ehnrz0wiCIAhCcJGjDKFevXrh0qVLePvtt5GQkIC4uDj8/vvvGQHUp0+fhqpanFy3bt3Ciy++iLNnzyJfvnyoXr06vv/+e/Tq1StQH8EtKlUCPvrIebu//wZatmThRXO22d69wAsvALNns0J1/vz2t1+yBHjvPWD7dn4dFQU89xzw5ptAwYIefwxBEARBCDpylI5QIAikjpArEAG1awNHjthOudc04KWXgE8+sb39558DAwfytJvJZL1dvXrAhg0ckyQIgiAIOQG9928xhJyQUwyhP/4A7MR/Z1CgAE+dZTVozp8HoqPtaxapKvDOO5zFZhZ4bNYMaNOG3xMEQRCEYEPv/TtHTY0J9vnrL/beOBJgvHULOHSIg7Az89137FGyh8kEjBoFvP02Gz6qymU+qlQBFi0CatXyzmcQBEEQBH8jhlAuQdMcGzNmbOkSbdxoPR1mC3PfJpOl7YkTQKtWwIEDQKlSQHIysGABT89FRADdunF8kyAIgiAEKzI15oScMjW2dy8QF+e4TbFiwLlz1mrUV65wdlhamnv71TRg5EiOI3rmGSApifs3mdg79eSTwDffAOHh7vUvCIIgCO4gU2N5CCIgJYXjfM6ete0ZUhRg2LDsJTlmzOBpLncxGtnQuXjRsi6zUTVnDhtFP/zg/j4EQRAEwVdIqGsOJykJaN8eaNqUvT1ZjSDzVFjv3lypPisrVuibUnPE5ctsaNnqx2RiY+joUc/2IQiCIAi+QAyhHE7PnsDatfy/rUDpli2B9etZodpWEVdPRbYVhffrKMZI04CffvJsP4IgCILgC2RqLAezcyd7dOyhqsCdOxzQbI9z5zwbgx5vkqoCiYme7UcQBEEQfIF4hHIwixY5rk5vMgFbtljH72Rm717g+HHPxhAWlj3uKCvmVHtBEARBCDbEEMrB3LrFU1N62tli4ULHhpQeQkKcZ4SFhwOPPurZfgRBEATBF4ghlIOpWdN5xle+fMCIEVx8tUYN4PXXgVOn+L2kJM8DpW/eBG7ccNzmiy9YV0gQBEEQgg3REXKCL3SEjEbgzz95yqpcOaBJE32enazcuMEaQMnJ9lPmidjrYzaYNI0ryn/1FafTX77s2WfRw9GjMjUmCIIg+Be992/xCPmZefOA2FjgnnuA7t25Zlf16lzDy1UKFWIdIEXJnhGmqhbjKLPXyGjkTLE+ffxjBAFs9AmCIAhCMCKGkB+ZM4djZc6etV5/7BjQoQOwerXrfXbvzunzrVtb1kVGsqfJVro8wEHUrvoBo6LYk+QOUphVEARBCFbkFuUnUlOBIUNsv0fExsmQIe7F7LRqxR6lM2eAPXu4mvz5844LsLpKmTLA+PGuGzWKAtx7r/fGIQiCIAjeRAwhP7FypeOpKCKuDL9nj+t9r18P3H8/l9iIiwPKlnVeRNVVEhO5f1f61TQuvBod7d2xCIIg5DkSE4FPPgEaNADKlwfuuw+YO9ezGkkCABFU9Bt6hQvj44H69fX3O28e8Nhj1p6aa9dcG5seypcHOnYEChd23r+qssHUsCHXIRMEQRA84PRpDiw9fdoybRAfD6xbx4Giv/zCom6CW4hHyE+UKuXddgA/IPTrx/97cxrMFs89x7+zyZNtv68ovFSqxN6pH38ENm7k2CJBEATBA7p3Z8Mnc+yE2T2/ahXw9tuBGVcuQQwhP9G+PXtT7KEoQNWq7EXRyw8/cAkNZ3FFnoomligB9OrF/z/5JDB/Pme+ZaZ+fWDzZlaq/v13Dgp3N7haEARB+I/t23mxNwVmMrFYW3Kyf8eVixBDyE+EhQETJth+z6wh9MknrukJHTyoL3i5fXvnZTDsERoK/PWXtXp09+7AP//w+qVLgf37ue5Z8+bu7UMQBEGwwx9/OL/Q37gBHDjgn/HkQiRGyI88/TQbOq+9Zh04Xa4cMHUq8OCDrvWXP7++KbGrV93LRitalDWAKlTI/p6qAk2but6nIAiC4ALmuAM97QS3EEPIz/Trx9NLq1ezsnR0NKeXu6O1U6aM8zaKwoVXHREWxsrPhw5ZZ4UVK8ZxSCkp7BmS35kgCIKfufde50+8ERFA7dp+GU5uRAyhABASwgKKnmKvmGpmiCxZXPZISeFptqxeo6NHufwHwEbR889zWQ5HsU6CIAiCF2nYkOMO7MUJqSowaBAXlhTcQmqNOcEXtca8wfr1wIABrErtDGeGkF40jYu3bt7MnqLNm9lLdM892YOnBUEQBC9x9ix7hk6c4NdEfEE2GoFOnYAFCyQ7xQZ679/iEcqBzJoF9O2rbzqteHHg0iXv7Ndo5CDpuDhrXSRFAbp0Ab79FihSxLL+xg3W+zp2jMt+9OjBmXGCIAiCC5Qrx2q7s2cDM2dykGmVKvw03Lmz/XpKgi7EI+SEYPMImSvWp6Xpaz90KKezHztmf5rZGx4jTQPq1uV4pLAw4PvvWXvo9m1O3zeZeP+PP84GU+YsNEEQBEHwNuIRyqVMn+6aeOKnn3JAdoECHFNk3tZs/MTEZC8C6w5GI7B7N3uASpQAeve2xBxlNtrmzmUP0vffe75PQRAEQfAUMYSCGCKuLP/FF+wVjYhwz3NjNnQee4zT4a9dYwXoRx7haeURI7wzXlVlQy05mY0dW75Gk4mFIN99l8cgCIIgCIFEDKEghQgYOJCNIIPBs7p6RGyYbNvGsXYXLgB9+gBvveW98QJs5Jw+Dfz7r/O2zz3HEgKCIAg5mr//5uwVIs4cqVUr0CMSXESUpYOUb79lIwjwTnFhIi5/sXYt0LIl//U2mgaULKmv7Zo1XDBWEAQhR3LxItCuHVCjBvDii/zkWrs20Lq1/irbQlAghlAQQsTlOHwhYDh7NnuFvGFcZcVoZI0hvSKoH37I/1++DIwdC9Spw1Xu27UDFi3yTsq/IAiC17l9mw0e8xMlkSUWYNMm9gzduBG48QkuIVljTghE1tjly5z2rgdzQVWjUV8ZjdhY4ORJ++97S3NIL1u3Ag89xJ/ZvF+zPEb37lzF3tOisYIgCF5l2jSgf3/77ysKMGkS8NJL/htTTiM1FVi8mC/yV66wtsqzzwLNmnltF3rv32IIOSEQhtClS5x55YiQEPaclC3LFehr1QI++si6hllmFIWNoMRErj3mCLMh4g8qVOC4Ilv7UxTggw+8F8wtCILgFVq25MwTe0+NigLUr8/VqIXsXLoEtG0L7Ntnefo2B8M+9xzw+efu1Z3Kgt77t0yNBSHFirFWlqMpprQ0TlH/6ivW13rtNU6VtwcR8MYbnErvqF9NAypX9so56BBF4Xiif/+1b3QR8UOVL6bxBEEQ3ObiRceucyJuI9imZ0+u6wRYvkfzhf6rr4BPPvHrcMQQCkIUBRg+3P5Ul6YBpUsDDz9svf6JJzg13VbAsqqyCGnx4o6n0IxG4P33gbvv9r0xVKcOe7YckZDgeCpPEATB78TGOlZzVlWpO2SPPXs4y87RtMOECX59AhZDKEgZMICnSwHrGBlVZT2h336zXVrm8ceBwYOzrzeZ2ABavZoNJVu/YVVlb+UjjwArVwLjx/PUlS/o0QNo1EhfXJNUvRcEIajo39/xjdxk4ou4kJ0VK5yXBElIAA4f9s94IIZQ0KIowNdfc3mMBx9kBejatYF33uHzo35929vdvm3JxrLHzZts7GQ+F0ND+Xf766+8PjycvVInTnB1+sqVvfO5ChQA5szh+Lg2bZwb/ZoG/PGHd/YtCILgFR5+mJ8abbnNNY1jiHr18vuwcgTp6fqebvXWkfICko8TxCgK0L49L3pZtw5ISnLc5tYt4KmngMmTgR07+LfcvLl1wdTMhIYC993HOkSecusWK1urKvdZvbrjOmhGI/DMM7zN8OGe718QBMFjDAZgyRJg5EiOabl9m9eHhwNPP82ZK1IN3jZNmzp/Ai5QAKhWzT/jgWSNOSXYiq4646ef9D2I/PADT6PZ4vJl4Lvv2BuVlsbn7YIF3ovVURTg6FH2Mh0/DrRq5Vx/zGDgNnplBQRBEPzCjRucHUYENGgAREYGekTBjcnERo69TBlNY9kBLwRMS9HVPEr16p6127wZ6NCBPTfmYP5Nm7wztsx8+SXHw1WuzFpCsbHOp9x/+AEYOtT7YxEEQXCbQoWAe+91f3sivggePcpG1P33A/nze214QYeqAgsX8hNwUpLlwm+eLmvShDN2/Dkkv+5N8Dl16wKNG9uPRdM0IC6OH1yycuVKdiPIFxBxiY0RI1gqIDrauW6RpgGnTvluTIIgCD7jzBng559ZMv/SJcv6LVuAmjU5NqFPH449KlUKGDdOXyZJTqVOHWD/fo53KF2aDb+aNYEpU1itu0ABvw5HPEK5kGnTgBYtuAp8ZgND04B8+Xjayxbffed7I8jM/v3A3r36f+smE1C0qG/HJAiC4FUuX+YslMWLLRc7g4FF4J5+mgOuU1Ott7lxg2OPkpOB0aP9PmS/UbYsZ/Y4y+7xA+IRyoXUrQts384lKsyeIU0DunXjCvT2Ms5+/907RpCehAC9JUEyt3/sMffHJAiC4Fdu3uSaY7/+an2xS09nFdzOnTkI095Fd+xY4MIF/4w1jyMeoVxKtWrA3Ln8cHH5MntTnMV6eytbUVW9W6JDUThzrFIl7/UpCILgU779Fvj7b9tPfEaj81pHJhNfxIcM8c34hAzEI5TLKVSIRRH1JLzddZd3xAu9bQQNHMilZwRBEHIM337rWZyPprGwoOBzxCMkZHDtmuu/W18XaA0N5fg5QRCEHIUzTRBnGI0cRyP4HPEICQBYI+ibb1zbpmhRDv73JVJwVRCEHEnp0s5d7M4qYD/6qHfHJNhEDCEBADB7tutFVq9cAQ4c8M14AL5G1Krlu/4FQRB8xjPPOG+jKPaNoXfeYX0RweeIISQAAOLjXTeEFMW3Hhsi4NAhVrXOTGoqZ5W2aMElfcaO5Wk9QRCEgJCczBpB337LOjgmExtC1apZV83OiqJkj0coWpTrH40c6dsxCxlIiQ0n5LQSG84wGlm/59w54K+/gOXLWTtI0zjBwR8aQq6gKGygbdvGIpBz53KdtKwGmMHA73XrFphxCoKQS7l4kZeSJbPX+CECPv0UGDWKU3TNlC/PsQZxcVypfskS5wGYqsoFH48eBQoX9vanyJPovX+LRyiPQMRlLcqXBxo2BB56CBgzBti1CzhyhJdgM4IAHreiAB9/DPz5J2sJ2fJCpaezbtKGDd7Z76lTwLBhfO3Llw+oXZsz11JSvNO/IAhBzq5dwAMPsNJznTp8MejUidVgzXz0EV8oMhtBACtJP/ggu7R/+QU4cQL48UegXTv7sv8mE8cbzJ/vu88k2IZyGFOnTqWYmBgKCwujJk2a0NatW+22/frrr6lFixYUFRVFUVFR1KZNG4ftbZGYmEgAKDEx0dOhB5S33yZis8J7i6IQ1a1LpKre7zvrUrAg78tZO1UlGjyYKDnZ/e9q506iiAgiTbP+rIpCdPfdRLduee+4CIIQhPz5J1F4uPVFAODX+fPzReL6dW7j6GLUuLF1v8WLO7+otm8fmM+cC9F7/85RHqF58+Zh2LBhGDVqFHbt2oV69eqhffv2uHjxos3269evx2OPPYZ169Zhy5YtiI6ORrt27RAfH+/nkQeWkyeB997T316vllBEBHD33f7xJKWmAvv2OW9nMgFTp/LDmDsCkUYj0LUrTxdmlgUwX6n++otjGAVByKUQcfmL1NTs2iBGI7uF+/fnmCBHLmKTiSX+jx+3rLt92/m+d+3iC5DgP/xkmHmFJk2a0MCBAzNeG41GKlOmDI0dO1bX9unp6VSoUCGaOXOm7n3mBo/Q229nf7BxtsycycukSfyQ4muPjzMvT506rm83e7br39VvvznvNyLCM4+TIAhBzJ9/6rvADBlCZDA4b7dpk6Xv5s2du9AVhah1a6L09IB9BbmFXOcRSk1Nxc6dO9G2bduMdaqqom3bttiyZYuuPpKTk5GWloYiRYrYbZOSkoKkpCSrJafz77+ub9O5M9cFXLLE9WwyM+btPFWrNplYXdqVflRVvy7SnTvArFkcDtC/v/P9JCUB//yjfyyCIOQQjEZg61Z9bYn0pc1mFkUcONC5C50IWLcOWLFC3zgEj8kxhtDly5dhNBpRsmRJq/UlS5ZEgk4Z8tdffx1lypSxMqayMnbsWERGRmYs0dHRHo07GChaVL8RoShAjRpAZCTXKFu92n3laJMJCAkBKle2Xu8om9QWTz3FBkrHjq7t+8QJ5+3On+citH36AKtWcTYdkfPtQkL0j0UQhCAnLY11OKKjgZdf1rdNmzZAgQL239c0LroaG2tZ99hjQI8ezvvWNC7MKviFHGMIecq4ceMwd+5cLFq0COHh4XbbjRw5EomJiRnLmTNn/DhK3/D44/r1fog4CUJRnNcE1ENaGmdg3XMP8P77nJ32yiv2Eyey0rw5MGMGe3g++YQzuPSiacDrrwNvv83p97YMnG7dLFP4emOdoqOBKlX0j0MQhCAmPR14+GHgzTf5yUgPRYqwC3niRNvvqyo/8WV9X1U5e8zZk5TRqH8sgsfkGEOoWLFi0DQNFy5csFp/4cIFlCpVyuG2EyZMwLhx47By5UrUrVvXYduwsDBERERYLTmdxo15qsvRFJf5vQEDLIKopUt7x/ORmspp7W++yf2/8AI/JOnxDG3ZYvHsVK7McYRxcfr2e+oUG09jxwJNmwL33steLjPbt3P/ropCvv66+9OFgiAEGbNmAcuWuZb18e67bKwsW2b7/WLFeHorPZ0veI88Arz4Ij+RqSrrmDjCYABiYvSPR/AMP8UseYUmTZrQoEGDMl4bjUYqW7asw2DpDz/8kCIiImjLli1u7TM3BEsTcXDvU09Z0sDNwdMGA1GJEkSdOhEtW0ZkMllv9+ST+uIB9S6aRvTYY0QXLhB17aqv/Zgx2T/PwYNEZcu6FgSuaUQNGxIZjdzHmDH6tzd/B4MHZ/+OBEHIwTRo4DyAWVV5CQkh+uADvgj06eN4O7Peh/niYf77+ON88XG2zzVrAv3N5Hj03r9zlCE0d+5cCgsLoxkzZtChQ4dowIABFBUVRQkJCURE9NRTT9GIESMy2o8bN45CQ0Pp559/pvPnz2csN27c0L3P3GIImTl1iuizz4gmTCBascJiFNjj5EmiYsXsGwyNGrluDCkK0ZkzRIcPO28bEkL02mu2x5aQwMkV5j71ZrctW8bbjx6tz8hr2JDoueeIduzw7rEQBCEIyJ/f+UUgOppo6lSiS5d4mzNn3BdQUxR+oqpWzfYFSFWJHn5Ynri8QK40hIiIpkyZQuXLl6fQ0FBq0qQJ/fXXXxnvtWrVivr06ZPxOiYmhgBkW0aNGqV7f7nNEHKHf/4h6tzZ2tCoUIHo+++JvvnGvWvB9OlESUlEoaHOrxlff+14fPv3E02Zwqn+ISHOPTt9+/J2q1Y5H6ekygtCLuDyZX6SOXYsu4HhTORQVdllnhl3L3zmJTycL6zdu1sbVPnyEQ0bRpSS4r/vRi+3bhH9/DPRV18RrVyZI9L7c60h5G/EELIQH0+0YQPRnj0WT1J6untTZ2bjpm9fx9vny8cGkx5SU/U9jN1/P9GdO/wZqlSx7+1SVfveKEEQcgCnThH16mV9kalTh2jxYkub555z7t3Jqj03ZYrnAmu//sp9xccTLVlCtHw5UTDeZ0wmoo8/JipUyHr8ZcsSLV0a6NE5RO/9W4quOiG3FV31lKNHga++4sKtBQqwCvPRo8AHH7jWz+7dHPR87hwHc1+8aB20rKocuzhzJqfPb9nCCtlFiwKtWwOhobb7jYkBTp92vn9VZYmAe+/lYtE3b1pkAsz7vvdeLkrrIMlQEIRg5cwZvrhcuWJ9cTFXfP/uO1ZwHjmSLwC2MBg4sPngQesLwcaNnArrCfPnc4HEYOejj4DXXsu+XlF4WbECcCBJE0h037/9YpblYMQjZOGjj/ghyPxwZX6IKl+eaMAAfQ9BBgNRs2bc36VLRKNGEZUpk90rU78+PyStXctem8zvFS3K3llbjBvn3tR927ZEMTFEUVEcEzRtWnB6pwVB0ImzTA9n8+gAUc2aHCiZFZOJY3xclezPvBw75v/vxFUSE9ktb+8zqCpfrIMU8Qh5CfEIMb/+CnTpYvs9g4HT4VevZjXqTZvsZ6KGhLBwa+HCQIsWQEKCtWCjogAVKnCbv/9m74/JZLu/KVOAQYOs1yUnsydn1y7XhSBXr2aNNEEQcjiJiUDx4u4VHDRToQJw7Jh90bM9e4BWrfii46oGR9267FYPdmbNYrVZZxw+DFSv7vvxuIje+7eooQi6GDfOvnZOejqLEu7dC/zxBzBihP1+jEbgrbd4uuvChezGChFPgQ0YwMKL9owggPeT1aOdPz9PdQ0bxlNfruBo3IIg5CDi4z0zggCuTXTkiP334+L4iatfP4vSqyOl6cw8/bRnY/MXFy/qU7+1U/g8pyAeISfkVY/Qvn3AL7/wFHqVKsCzzzpubzBwm08+AUqV4gcyfzB7NvDkk7bfS0kB7rqLr1V6UFX3y4kIghAAkpPZ4ImIsK4jdO6cdY0vd1m7lt3SzjCZuGjhP/+wt8cZu3ZxbZ/MnD0LHDgAhIWxpH4wBCfOmwc8+qjzdv/8A1Ss6PvxuIh4hAS3SEriml716rF46scfOzeCADYgfv4ZaNLEf0aQpjlWoQ8LA8qU0a8CLY8EgpBDWLKE59YLFACiolh2fvJkyxRVmTL8FOSpBHy5cvraqSq7o+/c4YwOe2gaG0CZjaD4eI47KF8e6NABuO8+fpocM8Y1tWtf0LkzG5n20DQ+DkFoBLmCGEJCBkSsBG8uemw06vcuE3H5iv37fTe+rBiNfL1zRK9e+q8lhQp5PiZBEHzMxx/zDfqvvyzr/v0XGDqUf/Bmt+577/FfWxWnFcVx/SBVBZo1sy4qSMRlM4YN43IZX30F3LhheX/2bK7lc/267T4NBp5C++47y7pLl9j7s2yZ9ZNYYiLXJBo40P4Y/UG+fMCnn9p+z149tZyIHwK3czR5KWts0ybPZDH8vYSGEt286fgz3blDVL26PsmP55/3z/csCIKbHDni/Mc8ZYql/cKFnApqzhJTVd6+f3/7oojmUhp//mnpJyHBIqNvMPD7ikJUoADRL79wZpmzDLKWLVlOPzOvveZ8u/37/fPdOmLOHE4PzjyuBg2I3Cxd5S/03r91lL0U8grz57OB7ywBwqyzE2hMJuceq7AwYM0aoFMn1i6yR1QUMH68V4cnCIK3+eorno5xdJF6+WWgfXvOxti/n70qRiPH3ERGsvhZ+fL2f/BE7HVq3pxfG41cad7s7s687+RkoFs34IknHI9bVdkDlTmzigj45hvHgYkGAzB9euC9Lo89xt62rVtZlyk2FqhdO7Bj8iJiCAkZ6I3tKVdOn2ihrzHHJTmLYSpTBti5E1i4EHj+eesK9ABQrRqwapVMjQlC0LN3r/MntfR0Dli+c4cNCfO6ypU5A6R8eWDlSuD11+33MXIkGzeFC3OswJ49ttuZp7N++82xQWMyceX5zKSmAteuOf4sJlNwXGwBNubMxmEuQwwhIYOqVZ17egoXZsMhGH6bBgMnh+hBUfjBrWtXVqnesIHX3XsvB3jHxwNvvw2sX8/r27QB+vcHSpf25ScQBMEl9Kan37nDfzMbTf/+y7o/Bw+yx0fTbBsvRJwuO2MGe5cWLnTsKk9Pz/50ZYusaeihofx5bt2yv42qAiVKcCzS3Lks4x8ZCfTowRdiwSuIISRk0Lcva/zYQ1U5RvHHH/02JIekpfF1gch2PKQtFIWTSe66y7Lul1+Anj35mmi+Lm7aBIwdy9fADh28P3ZBENzg4YdZ3dUdjEb2wHz5JT/xONPKWLeODaFbtzyPBTAYOB03M4rCYoVffWV/LOnpLAxZqhRw+zb3YzLxhfrRR3naLBjS7HM4kjUmZFC6ND8oAbYNC3Ptr9RU/47LET/8AAwZ4v72R49yuZ+0NOtrkcnEOkSPPAKcOuX5OAVB8AKPPspGgbsYjcD33zs3gogsxk/Nmp4bQiYTZ7Vl5dVXHQsW1qsHjB7NsUhE1heqn34CnnnGs3EJAMQQErIweDAHTWeNg9PrcfE2muZ831Om8HSXO3z2Gf+1pSFExA9kX3zhXt+CIHiZfPlY5NATfaDjx/VlhJiLqrZo4f6+FIUvYjNncgHYrKxebf/JUlGAQ4fsXwBNJmDOHC4DIniEGEJCNrp355jEf/5hbTBzseZAUKSIc4V3gwH4+mv3+v/tN8fXRKOR2wiCECTUqAGMGuX+9s4uZorC003mMhh//un+k6CmsUy/Lfl7Iq7sbq9vswfIkTdK04AFC9wbm5CBxAgJNjE/jFy54r0+q1ZlJegbN/Sn4F+65LxNejrHP7qDnlqJrtZTFATBx7zxBhf6nDvXEsjsLV2PsDBg0SKgWDG+WH39tftPgunp1qKLmbl0iefmPUFVuRyAJ5w/DyxdyrFQNWsCbdt6rsidw8hbn1ZwiePHvft7OHqUp7p9oUN08iQLvtoTdbVHixaWDFtbGAwWD7kgCEGCwcDTQsuXAw89xHP5LVpw4LEzTR9nvP466+UMGQLcfTdw5oxn/T3zDE+NZX2i8oabPS2NnzDdITUVeOEFIDqaq1wPH876SxUrcrZIHkKKrjohrxZdBTh7tF+/QI/Cdfr31z9VtnUrK+nbQ1FYiLFePe+MTRAEH2MycfXniRMtxQgjI10rgmgOTvSGO9j85NepE6ehmkt7ELG20b//OjaK7D05Kgqn3yck6JcVyEzv3pxtkrVvVeXU/q1b9RWQDWKk6KrgMQ89xL+HnMY333DWqx6aNgUmTOD/M3uGzHFJkyeLESTkQkwmvgk2b84ByCEh/AMoWBB4/HFgx45Aj9A90tN57A0bsoDh4cM8b37ggGv9GI3emxM3GxrLlnFMkBlFYTe2PSNI01hDyByomRmzq37aNPeMoIMHuTaaLQPLLNk/erTr/eZQxBAS7FK0KHtLHVG6dHCKDn76KStFP/gg8PvvjtsOH86yIm3bWgw/c4bqsmX8YCQIuQajkUsmPPkkn9x37vBN32jkOJF58/gJwR3BsORkYPNmnlqxFxvjC4iAqVNZ9r5pU6B1ayAmhvV2oqJ4vSPXryeUL89/nQVUm0yc4prZwHrhBdYSAqyzQlSVjdJy5WwHSlaqxOrYvXpZ1qWm8rHr1Im/g8cf5ww7W4bWnDmOYwKMRo6TciT2mJvwS+WzHExeKrpqC6OR6NVXuS6guRahohDly0f01Vfc5vXXA1+A1d5irmf45puOP+fZs0RlynA9xazbGwxEK1b4/rsWBL8wdaq+KsQhIfzD0MPBg0QNG1r3GxZG9NJLRLduuT/WU6eIFi0iWrKE6No1++3+9z/bn8FgIIqOJrpwgei333xzkalQgahHD96Pnu/15Enrsa9YQVSlSvZ2YWF80bXVh6IQrVpl6ePyZaL69fk98zbmi9mjjxKlpVnv87nn+Pg6G2t8vPvHLgjQe/8WQ8gJed0QMnPuHNGkSURvvUU0bRqR+es4cCDwxo7eZeVK+5+vVy/7RaAVhahECaLUVP9814LgM0wmosqV9d2wNY1o1CjnfS5aZL8/ReGq6ykpro3z/Hmizp2t+w0PJxo8OHtfJ044/jyaRhQX59uLi6oSFSqU/UnK1pLZuPzmG/f2p2lErVoRXbxING4cUfHijo9B1uP4wQfOq97ny0d0545rxy3IEEPIS4gh5JihQ/X99r21GAxERYq4d53q1Cn7+E0mos2bnV8TAKKFC4muXyeaM4foiy+I1q5lj5kg5Bj27nXth9Ohg+P+zp7VdwGYPl3/GK9dI6pY0faPUlWJunThH66Zt97S9wP29WLPe5PZIClXjuj0aR73uXOeXzzDwvQZtZGRRLdvWx83R+M1GIief17/MQtS9N6/JUZI8IgjR/yrs1O/PrB9u+tp/SYTlw7KzC+/cNbt3Xc7V9w3GHh6v1Qpnnp/4QXgvvs46eOPP1wbiyD4nSNHOAjOlch/RcmeLbFvH/Dss5xyXaYMp1vruQC4onj62Wesh2HrR2ky8Q93/XrLumCpgeNME4QIOHuWY5cefJADpz3VEUlJ4X6dkZgI7NljeV22rP1gaE1jDSVHhSdzGS4bQmfPnsXNmzezrU9LS8OGDRu8Migh51C4sHPlZ29SoADLeowY4fq2t24B+/fz/99/z/UbDx/Wt63RyIaUuai1mVOngHbt2DgDgBMngJEjucp9nz7AkiXOjSxB8CknTnB2WGbjQS8PPGD5f84cfhKZOZNv6OfP61cyPXFC/z4nTXKupjxjhuW1rayqYIaIA50/+0yfEeMtshqsb7zBuktly1rWKQoHW2/dyoZuXkGvi+ncuXPUuHFjUlWVNE2jp556im7cuJHxfkJCAqmq6r4PK0iRqTHH/PKLf73PisLLTz8RvfsuUWioa9s+9xzHbhYq5L0xaRpRu3Y8Va8o/FpRLF7vevU4VlMQAsITT7g3BaNpRA8+SDR/PtHff3s2/VS7tr6xzpihr7/WrS3b7Njh34uQNy9m/tpXeDjP69siPZ1o2zaidetyfHB0Vrw+NTZixAioqoqtW7fi999/x6FDh9C6dWtcu3Yts1HlA1NNCFaSkvhBs359/3mFzL/snj2BDz+01CssXFjftqtXs9aaK5m9zh42jUZ+wBsxgvdhNPJf8wPYgQPsfZKfh+B3btzglGp35q+NRmDFCqBHD+Deez07gc0p4o5ITOQ5Z2domrW3omFDoFu3nFcWwt3vU9Ncu+BqGivjRkbaf79xYz7GeckLlAndZ87q1asxefJkNGrUCG3btsXmzZtRunRp3Hfffbh69SoAQMlJ7knBbRYuZJmKyEjW+7p0CYiN9f84kpMt/2eyxx3yzz/Am2/qa2u+1lSv7lhywxlGI7BlCy+C4DdOn+Z5W0+C+MzzugkJ7sezlCzJcUXOmDMHuH1b35iyGlbff883+9x4DzLrFAH8xPfaaxzvpccYUhQ2FMeP9934cgG6DaHExEQUzvTYHRYWhoULFyI2NhatW7fGxYsXfTJAIbgYN44fvjILz549ywZGbuPFF7nw7MCBnsf5GAwcLyQIfuHyZc4CMAevBYrYWFZ4jopy3lavgGNICNCokfW68HBWWb73XhcH6AH+cIMrCl+AEhM5JuviReCDD1g639lFqWpVFplcv57FGX3N4cP8lPn88zzGs2d9v08vodsQqlixIvbt22e1zmAwYP78+ahYsSI6derk9cEJwcWhQxwIDHi/aGowoar8EDV5MlCjBvDoo555hAC+nqWkeGd8guCUyZP5xumvSH1N4xTMSpXYTdysGZfw+Pdfa4+GPdLSgJ079e0rLY09QABPLx08yMG9V65wtpM/vELly7N6tSfomeIyz+dHRHDKqvlC1K4dl+cw95O5T4Dn/48c4ae5fPk8G6cz0tLYG1ezJscrfPcd8PbbnB337rs5IiZAtyHUoUMHfG0jBdJsDMXFxXlzXEIQMnhwoEfgH4j4gcZM0aLA2LGe9ZmWBjRo4FkfgqCbb7/1b7qiyQTMmgUcPw5cuMDzwI8/rn/7w4et57odoSjAggVcK6tKFTbAmjVjQ+HsWd/feDWNgxSfecb9PlSVP4e5AKsjbB1HReEiiT//zIGaBgP3df/9wKpVwNCh7o/NVYYP50xCgKdh09J4zCYT8M47wBdf+G8s7qI3+jotLc1h5HVaWhqdzCodngvIi1lj164RzZxJ9N13lmynBQsCn2ThjyUkhOjnn21/L998Q1S6tOt9qipR4cLWemaC4FP0lE9w9weSOXvMYOATfMYMz8a7a5dr4yhXLnAXCYOB6J9/uOyHu32ULcslMu6/33E2nqrqU/fOLDDpTy5ccJ6RWKpU9hIffkLv/Vu3w99gMDgsY28wGBATE+MF00wIFMnJQIcOQFY5qAYN/Fs/MZBMmMAxULZ49lmgb1+gc2dg+XJ9/RkM/AA5fz6HMQiCXyhRAoiP915/BgPPF8+fz9ozv//OT/+tWvHcceXKfNtzd1qqenWe/klKct5W07z72VylRw+gYkX2QBUsCNjQ1bNCUfi7KVaMLyItW7IQpaax92TVKvvbaRrQv7/zMQUqSHzZMufB+AkJHCd2113+GZMb5LB8Q8FXpKezl9mWJuauXcCxY/4fk7/Jl4+lAIYNY6/+8OEWAUYzR47oN4LCwrjI9/btQJs23h+vINjl2We9F8yrqmxY/fgjK0q//z7f2J59ljMA7rqL3y9ZEqhbF+jYEZg4kX88ixdzG2dpneHhQJcu+sbjrSm/QoVc30ZVgVq1WJ31q6+cP900bMiBzUuWsEEwdiyrSpuPTYcOFgXnzIGI5ieouXOtBQ+DiTt32CDWQ7BXsfeThyrHklemxt55x3OPsaIQ9e0bHGV/PFk0jT3SZo/vM8+w5hgR0ZAh+rTpXnwxcN5qQaDLl4liY71TCPDVV4kuXbL0bTIRPfWU5Uevp4+QEKJu3YgyifBmcPgwTxU568NcG6tBA8+n/po3t13xXc/y4INccd7RBURRiIYN01+McM0arqFWogRRmTKs/HrggFdOBZ9w8yZ/h3q+L0Wx1FfzM1J01UvkFUOoVCnPr5eNGnFhaF8aKZmX8HD/7EdRiN54g7+n++/X197VYtuC4BWuXWPZ9RkziJYv56KpnigYqyrRhx9a72PRIvf7Cw0lmjzZ8pRw5ox+efhq1YjmziUaMcJzA0/T3DemHH2fisKBhP/84+8j718GDdL3xGtWJw8QUnRVcAm9goSOqFMHGDDA8370krXul68g4mzUmzc5jMGZgG1ERPZalYLgU9LTgddfB0qX5oymvn152uX8ec8yiDSNU+DNpKUBr77qfn+pqZx+OmoUv378cYs8vDOOHGGNmogIzys9G438WdyJrSFy/N7586zjZOu9jRuBb77haUZvXHQDwY0b+rISNY1TbqdO9c+4PMAtdZTr169j27ZtuHjxIkxZBGV69+7tlYEJ/iU83HOdmyZNgDFjXNtGVbmQqqYB1697tn9fcvs232ceeYQzd+1hMHBckCD4lRde4JtT1pv0/v0sAGYwuGc8EPHNDOAbX/funCLvKe+/zwbbpk2ubXf9OhcLbdCAq6l7Imimqr4RRFMUjqFq0sSybutWVsM+csSyLiyM44fef9+zeK4bN7gUyo0bLKJ4112+CZ7eu5fT9Y8c0acA3qgRty9Xzvtj8Tauupp+/fVXKlSoECmKQpGRkRQVFZWxFC5c2G0XVrCSV6bG+vTxbOqoQAGipCSi6GjXt69Wjb3lkZGWMIBgXZ58kqhqVfue+ZAQor/+CvTRFPIUe/b49qQ3x6r88IP3+tQ0oocecn/70FAuJuvJtJ+mERUrZn+KJybGvX4VheiLLyzHZ98+onz5bO9HUTig0BnHj3Os1t13E7VqxRWeExI4uDNfPus+q1Uj+vNP751ft24RPfww920w6JuWVBSiTz7x3hjcxGcxQlWqVKEhQ4bQrVu33B5cTiKvGEJXrjieMldVopYtLdXfzesNBt5u6VLup39/964bAP/OmzXz7TXdG0vhwvavnYrCsUtLlgT2eAp5iAYNfHOiKwrRAw/wPkwmojp1/Fsx3dkyYwbRkSPub6+qRO++SzRgAFFYmGV9gQJEr71GtGWL+30fP245Po884jieRlEcxxTNmMFjzdyHqtq/YKsqX4R27fLO+dW1q3sZMPv3e2f/HuAzQyh//vz0T24PBMtEXjGEiIh27yaKiMh+PufLR7RuHVFqKtFnnxHVqMG/tXz52EOye7elj/R0z65LZ87w7+fLLwOrmebJoih8jTp4MEAHUgg+TCb23Kxf750MGqOR+7x82fdu1ObN+Yce6B9W1mX4cP4u6tRx7zswGNirQsRB5uvWEf3xB2dEERElJrqekaFp7D0xk5jo3HjUNKLRo20f523b3DM+NY2oY0fPz7ODB937Xtu08XzfXsBnhtAjjzxC8+bNc3tgOY28ZAiZmTuXqHNn/h19843tDFBHqeGzZ7t3XdM09vSa+eOPwF9r3V0MBqLnn/f+sRFyID/9RFS5suXkUBSidu04bdwVjEai6dOJ4uIs1najRr4/mYN1vnrMGP5epk937/N89pnz73zAAP3ZUQC7tK9ds2y/d6++i8WgQbb3/9hj7mfIKQobyp7w3nv6vUHm77VuXaKLFz3br5fwurK0mY4dO+LVV1/FoUOHUKdOHYRkqZXSuXNnj+OWhMDSqxcvjnAUi/fkk0BcHMcK6ompy8zRo5b/77kH+PhjS23BnER6OrBokf0yOwcPcrHsf/7hwtyPPmoRmxVyEd9+y8KDmX8wRMCaNVwfa+tWoFo15/2YTMBTTwFz5nCQLxFnPe3a5buxZ953MDJzJvD33yzWV6IEV2bXQ1wcFwXt3JlTT8PC7F/Qxo0DNm/mWmiZvweDgZf77uN00uLF+fh07GgtjOgos8KM0chClbZYvtz9DDkizl4zB7u7w82bfL45yxCrU4c/w1NPAV275ry0WVctLEVR7C6qqrptuQUredEj5C1mzXLtASarF8VoJHr8cf88XJoFFL0ZAmErd8BkInr9dcvnzfowefWq/46P4GOSkojy53fsRejSRV9fM2b454eQm5eSJYnOnWOPzeDBlmOjaUTt29v30CUmciyRudBgvnyssnrkiPPjFhurb2zx8ba3L1jQ/c+radbeKXfQ420LD7dMJwYZIqjoJcQQch+jkahHj+wB1o6WNWss23/+uW+viwYDTwNu3kz08ssc6D1mDFHx4p6rY2saT5Nfu2ZRpSbiZBJH27Rv7/fDJPiK775zfuKrqr5phAYNgneKKqcs5crxVFHx4vaPxapVjo9DaqprkvGODGHzEh1te9sLF7JnhLlyAereXf84zRw/zmmvZ8/y61u3iAoVsn8eaxqrYAcpYgh5CTGEPCM9nafinanZaxpnhpqvMXfuuJeK785y113Wgc0HD3pn32ZjqlAhoqFD+WFUT7/79gXmWAle5u239akX68nuESPI8yVfPudlIcLDrZ9c3MFo5GO6YQNR+fKO96eqRI8+arufp55y77hrGl90XIlBW7OGqHFj637ateOL0YIF2bPWzPupWDFo4oFs4VNl6T/++AMPPfQQKleujMqVK6Nz587YuHGj9+brhFyDpgEvvsgaXDduAL/+ChQuzO+FhFim0++9l+sSmkzABx+wQO6ZM/4Z49atwN13c7wOwCEbHTq415dZdVpRLNPqN24AU6ZwQVdnn0nTuKCzkAsoWlRfgVBHMRy//84nZ7DG6eQkbt8Gtmxx3ObOHY4LunCBb/euMmMGUKECCz7ec4/zH7zJBPTrl3391ausPu3suLdty1XtM9O0Kcc1Va+ub8xLlwL33w/s3Gm9fs0aoHlzID4eePhhLqprJn9+FvHcupXjo3I6rlpYs2fPJoPBQD179qRJkybRpEmTqGfPnhQSEkI//PCD25ZbsCIeIe9z+zbR99/zNPs99/D02fjx7DHp1y8wUiUGAz+AEXEMj7tjCAlx7EXWM4533w3s8RG8xLlzjg+6qrKHwh7ffWdpF0hPCsBqp1nHEgzj8uVSrx6RKxnS48e71r+qci04W2m5f/6pb/sPP+TChmvXEi1eTHTokGvnaFoaF5p0dsEzC8YBrLGyYoVr+wkQPpsaq169On388cfZ1k+cOJGqV6/uandBjxhC3ic1lQ0ewFL7UFXdv64qineuySEhPDWeWVstEEvHjiy627cvhyzoLWAtBCHDh9u+ySgKn/zr1tne7tIlzyuse3O55x6iOXOIWrTgG2H+/Bxrk5uNIfNnGzvW+XFOSHAtzd2sal2+PFdy/vln6ym53bud96EoRJMmeXZ+Ll3q3veSLx/R0aOe7dsP+MwQCg0NpWPHjmVbf+zYMQoLC3O1u6BHDCHvM3BgcAnUZl6mTvXf9dXWdc18jQQs19V77yW6cSPQR01wyK1bLH63fTu7PM2kp7OL0ewqNB/cEiWIfv3Vfn8TJwaXkVG8OJ+ELVpYn6R5YVEU5zf9jz7Sd7xCQzmdNPM683fZqRN7d8znTYkSzsd14oRn5+3kye5djA0GvpAHOT6LEYqOjsaaNWuyrV+9ejWi7WkhCMJ/JCQAX33Fv6Zgw2CwLrTtKypXzl7B3iwNA1jCSszyIRs3Ak8/7ftxCW6QksLVeEuVYuGsxo35/7feYp0fTeOYk/PnWThq/Hhg8WLg7FngoYfs93v4cPaTJJCEhwMDB1pibPTEPnkTVQWGD+cCpZ4UFM2Xz719f/ON4zYnT+oTAUtNzV5d2vxdLlsGvPsu///3346rUCsKi71VqOB8n44oXNi9i3F6OvDTT57tO5hw1cL6/PPPKTQ0lJ5//nmaNWsWzZo1i5577jkKCwujL7/80m3LTS9Tp06lmJgYCgsLoyZNmtDWrVvttj1w4AB17dqVYmJiCAB94kYROPEIeZdp0wL/gGfv4UpPpqunS4kSRNevs7fdXNOxYEHn03GZH/6MRqJFi4jatiUqU4aLwL79NtH584E8snmQtDSe1rDlCVAU1ghyd15z6FD3FYV9sfTqFZjxmMW9pk3j78Wdkg+Zl59+sp8+72jp1Mnx8Xr7be94ySIj2aP40EOOPUyKwllpc+cSLVzovgDZ9euulxExLwUKuLdPP+LT9PmFCxfS3XffTUWKFKEiRYrQ3XffTYsXL3ZroK4wd+5cCg0Npe+++44OHjxI/fv3p6ioKLpw4YLN9tu2baNXXnmFfvzxRypVqpQYQkHAp58Gl8ff38t777G20JUrLBVgMumr7agorKuUns7ZtkD2GoyFC3M5K8FPzJnj/MC5e13cuDHwJ2vmk+/LL33Tt6Y5zi7o2pVo0ybL9+LuODLH0yQmEr30kv4nH02zn+JORLR1K4/TW9/JqlWuT1eFhfFnMk+tZeXOHTaa3n6bA6wzi0GOHu36GFWV0+2DnFypI9SkSRMamGle0mg0UpkyZWisjmC2mJgYMYSCgBUrAn9d9/di75oWE8OG4dq1+q47n3xC9PHHju8b5cqxo0LwA61aOfYCeFL40mTiAOVgiMVp357ohx8Cs+/Molr79mXXurG31K7NrtJatViu3uwxuX2baOZMogce4L4ee4z/d+bt+vln28folVf4fW96y+bOdW87VeWCr1kFH5cvJypShNuEhFjOqZ49ObbNZLJoXimK/s8yY4btc/eff9ijWa8e6xkVKsTn8k8/uSZG6QVynSGUkpJCmqbRokWLrNb37t2bOnfu7HR7vYbQnTt3KDExMWM5c+aMri9S0IfRyIkSecUrpEchv1Mnfdeedev0CTIuWBDoo5xHKFfO+cGoWtX9/q9e5Uj5QJ/EQPYAX2eLN1IvDQaikSP5u/jjD57C0WMYqqrtqaJz54iqV7e0Me/D/NrWRUlV+Ud8zz0sP//335b+vv3W+99zZCTRv/961seGDZYxbt1qv3aQqlqXeLl8meirr4jef5/rI/3vf9bfVealZs3sc/GpqSzPb2tM5v337evXNFivGkKFCxemS5cuERFRVFQUFS5c2O7iK+Lj4wkA/fnnn1brX331VWrSpInT7fUaQqNGjSIA2RYxhLzHhg18nQzWzDFvLa58vrvvtn+N1zSiGjWITp923k9ICF+vBR9x6xbfAPv1szxlO1oc6QTpwWgMrh+Ks7FoGse3eGNfBgNXf09N5Tphrjw9Va3KkvbmlHSTiYv5OXriMJezCAmx3c5sUEyYwP1Vq+bascmaFpp1UVU2Pog4ANAdb6CqEsXFEY0axcZjx47O+3GkbD53ru16aarKMULr11vaDhqkb4zffuvZb8IFvFp9/pNPPkGhQoUy/lc8idoPckaOHIlhmcqdJyUlSTacl2nZkpNPhgzhjKjcCpG+dqrKoreVKwPHjmUvcl2wIDBvnv4kolz88wwsW7YAnTqx6q/BoC9zqmxZz/apqlwd/c4dz/rxFs5OaiKWiPcGJhNnNT36KCs9u8KxY8CgQcDq1cD8+cCOHcBffzneJjIS+PxzVlieNYsrr2f+MZrTOF95hdWcjxxxbUzlywNjxwIjRwKnT/M6Is42MxqBBx8E3n6b13/wAdCiBb/viqq4yQTs3QscOGDJQHOEwQDMncuy97aoWZPVsRXF+tibTHzR6tgROH6c3/viC+f7U1Xgk0+CLg1WlyHUp0+fjP/79u3rq7E4pFixYtA0DRey/CAuXLiAUqVKeW0/YWFhCAsL81p/gm3q1wc2bAD69+es4ryMycTZsufOAZMns7xAfDxfl/v0AYYNA2Ji+FpTqRJw4oT9+1FaGtC6tWv7v3ABmD6dr5/h4ZzV3bmzpfyJAL4ZtGsHJCfza/NN0RmbNvFNTk9qtT3atePaNDkBb5cCmTSJJQpcxfwDWbQI+PZb4OJFi8Fhj4QELk9RoAAwdar9dprGBpMzQkK4BEbnzkCVKvzDVFU2HmbOZGPryhV+77nngC5dLOdJ48ZsxPXrZ6n9A2Q3SOx9dr3np6IAiYn235882f4+zcbQtGlsGOo59iYTG2kpKWzgBwuuuppUVbWZpXX58mVSVdXV7lyiSZMmNGjQoIzXRqORypYtK8HSORijkQOGS5b0nTc/JyyKQnTmjOV7sRdT6Kx6faVKrtWMnD3bWtnbPCNQtSrRyZOeHdtcxYgR7gcuHz7MWTtXr7oXH9GmTeBP0EAsoaGeBxMqCgdOv/OOvkC8ffu49o+etqVLO2/jSokOW5hMPMX17bccsP3aa94NoFdVLg1iDz2fsXlz7sOVcaWmeva96MRnwdKKotg0hOLj4yk8PNzV7lxi7ty5FBYWRjNmzKBDhw7RgAEDKCoqihISEoiI6KmnnqIRI0ZktE9JSaHdu3fT7t27qXTp0vTKK6/Q7t27bSpj20MMIf9gLrmRVxdFIXrxRcffkcnEISoDBvA2ma/VisIlgzLHcjpjwwb7IQ4GAxtDkoH2H5Uru39w27a13NCLFCF64w2i+Hi+eVSsyO9FRXGxux9/tD6IO3YE/uTM6YuicOaUs3ZmDZ/evfXF/rz7ruMUzpIl7aezu8ulS5wx4a0stZAQIjvyM0SkT3OpUSOWidCzP03jwHM/4XVDyFxgVVVVGjNmTMbrSZMm0ccff0wPP/wwxcXFeTxwZ0yZMoXKly9PoaGh1KRJE/rrr78y3mvVqhX16dMn4/W///5LQPbA51atWunenxhC/uGBBwJ/vQz0ki+fbcMjNZVT5ytWtLRt3JiodWtO3mjWjN+/ds2171xPHOXChV44uLmBsmXdP7BZvRqaxhlQjm62FSpwunGFCoE/MXPDMnIk/4AcBSqbH6K7d3feX5Uq/MPs0iX7MdY0Thl3IPbrEadPs3HtyfdhPvcmT3a8r4cfdmx0GQxEQ4ZYAtr17HvpUt98LzbwuiEUGxtLsbGxpCgKRUdHZ7yOjY2lqlWrUrt27ayMktyCGEL+4cUXA3+tDIYla9ZvairRgw/ydSvzfdOsQ2dPysMZ6enOZx0MBs52zTOkpRHNn89WeY0abGnOmMHTWnqsRlmCd1EUzmYrUiS7EinAMgXz5rHmkJ7puKefZhdtejrR99/z9FBUFBvMr7xCdOqU78/Xo0c5q6tWLcdj1jTWLAkNtayrUUPftN2aNY6/B1W1eDCXL+eLhqOxfPSRb7+TLPhsauzee++lq+7KeedAxBDyD5cuBf5a6e5Ss6b3rtXJydbfi7OaiAaDe6U17txxPh5ngrq5iuRkSyyO+UZpvqDHxfFNI9AnmiyeL8uX89RkdDRPhTVoQDRmjMXz5kpMUvnyrNKcuchuINAjwPj771xOY88eomPHXBM2HDWK+8hsQJqlBL7+2rrt5s3WMW2KQlSsGFGfPkQHDnjzU+si1wkqBgoxhPxH796BuTbGxnom1dKxo/fGctddRI88wl76f/5hD7yjsakq65+5Q6VKzvseN867xzhoefFF+zdBszbO88+7frOUJXgWWxXTb9xgYUx3vX2qyjEvgTSG0tP5/LT1Y1YUoief9FzRecUKog4deMovMpJVqbNo+llx8SKX8UhK8my/HqL3/q0QETnLLBs2bBjee+89FChQwEpjxxYff/yxuwlsQUlSUhIiIyORmJiIiIiIQA8n1/P005zKnZdRFM6yNZn4auaM++8HVq50fT+TJgEvv2x/HyEhXCS9RAnX+85RXL/OFeMdpWkrCmvTbNwITJgAHDzot+G5TKFCwI0bgR6Fb9CTPu6ILl2AxYstr7/6CnjhBc/6VFVg9Gjgf/9zvw9PSUtjjaLJkzklH+Bz+uWXgeHDPZNvyMHovX/rkmjbvXs30tLSMv63t+zZs8crgxfyLt99x3p1PXqwNEWBAkCNGvrFBHMDRCx3ovfavHYt4M5P78UXgQce4HtLZhFGg4FfT5+eB4wgANi2zblWDRHwxx9A374szhesFC6cc4ygqCgWGXSFUqXYgncHgwEoWdJ63cKF7vWVGZOJdYe8raHkCiEhLMZ4/jxw6BBw+DBrX732Wp41glxBl2TaunXrbP4vCL6gcGHgp5+s15nVqPWI+eY1jEbgvvuAo0fZeNRLSAjwyy8sCDt5Muu2qSobR6+/zsK2eYKEBH3tzJZpWBiQLx+LyQUb164FegT6UFW2xJ95BqhTxyJU6Yzz51mS3h3S04Heva3X3bjhmTfITEICf/dFi3rWz9697KXatYsl5bt2BZ56ir18eggJ4SdHwSU8fs5OSkrC4sWL8ffff3tjPIJgk6FDxQhyxLVrwF13uX5vDgkBBg9mlfw7d4DUVK6QkGeMoFOnuGSCHsxfiqIAjz/unvS2ovATesOGrm+bW1BVftoZNAioWJFLPPhjn1268I8kM/XqeU9C3VOl5PfeA+LigG++AbZuZVfvoEFA1aosPS/4DJcNoZ49e2Lqf/Ljt2/fRqNGjdCzZ0/UqVMHCxYs8PoABQHgB6OhQ/l/8fTa5tgxYM4c97cPC/Pwu711i+X2e/UCunXjWBpzvEKw8tprPBfrCE3jQKxq1SzrRozgeiSuFHaLjARefRVo0IDrWeWl+V6zAQjw97hxI1C6NL9evdq3P2qDARgwgAv2ZT1ezz2nvxyFPTQNaNWKPTjp6e55mBYssNQZM4/HHPJ86RLQvj3HAXmLf/5hA3TBAuDyZe/1m1NxNQq7ZMmStGfPHiIi+uGHH6hy5cp069Yt+vzzz/0iqOhvJGsseDCZWIurfXuiwoWJSpRg6Q6Rd7EkiLRoEaCDs2uXRYVWVXkwqsrCgUuWBGhQTrh8Wd/JU6pUdo2C27eJqld37QDNmsWpenkp6yw6mmvoTJzIqebr12fPYKpVyzf7HjaMaM4czmByxDvvWM7bzD8mReGU0EOHiOrWdSws+OyzlirtYWFETzzB5Tr00rix8/Ni/nz9/dkjPp6oXTvrfkNCWK4+q3ZHLsBn6fPh4eF0+vRpIuKSFq+//joREZ06dYoKFCjgxlCDGzGE/M/x4yzrkT8/UdGirFdmj/PnWVk50Nf7YFnCwoimTWOdIL9x9SofKFtGhaLwhfbgQT8OSCd6yleoKuvOZOXbb10/OPnyBf4E8ceiqkT16xOtW6evtlrt2t4fg6a5Vtdt7lwes3n72FiiSZMshfvOnSOqU4ffM4sGqqqlwF/W1HWDgQUMV61yvu8bN5x/HoOBqH9/532ZTGxsPv44l75o355FQW/fJrpyhT+XLYNOVVlI1J1aeEGMzwyhKlWq0Lx58+jmzZtUvHhxWrNmDRER7dmzh4oWLereaIMYMYT8i72KAiEhrAdmi9RUfqApUcJ55YK8sjRvztdXv/DJJ85VHwcM8NNgXODYMX03dVvFmu+6S040R4seA8DMsGHedesqCitFu8P16+wptKW7k57OLulnniF67DEW8BoyxL4nx1xDzpmn5fp155/JYGA1a0cYjZaijWZjxzy2atW4YKszr9OKFe59b0GKzwyhzz77jAwGA0VFRVG9evXI+J8FOXnyZLrX3ZMviBFDyH/ExTn+jYaEZN9m2zb2ggT6uh9si6a5ZnskJ7OTo1UrVt/v2JFo0SKdlezvu8/5gIoX1z8Yf2Ey8VO+M4PmPw+4FTExgT/IwbBkrbEFEL39tmvH4dgxi1Kxt8bVvTt7UEaPJjp50iunSzZSU3mO3tlYZs503I/J5Fw5Fciu4pyV8eMdXxCcXSg1jT1JuQifKktv376dFi5cSDcyPXIuXbqUNm3a5E53QY0YQv5Dz/Utc22tW7fYOAr0vSBYl7Cw7LXLbHHhgiVMw3xfM9/TOnTQMc3WvLnzwURGenJq+I5ffnH+JX75Zfbt7r47b8X62FoaN2aF4agoVhzu0IFLObjD/PlsDGX2DGUtdeLKoml8cTAX5Rs50nN15aycPOl8HCEhREOHOu/rs8/s96Gq/Pu5edP+9mlp+oueOlr8WBneH/ilxIbJZCKTt0+uIEMMIf/w2mv6fqcGA4cU3Hdf9pg/WbIv/81cO+T+++3Hgaoq15B0yODBjgNJNY0PWLAyc6bzp+WpU623cSdGKKctUVH231MULjDq6LpoMrGrcdMmoueeI+rcmf9u2cLv7dnDUzklSnAx1HvvJerala3ymjWJBg0i2r6dqEcP73yeiRMdnweJiRw3tn+/vliZ8+ed79NgIPovjtYhRiNPt5l/L5m3Dw8nWrvW8fb793v+/WgaB3nnInxqCM2cOZNq165NYWFhFBYWRnXq1KFZs2a5NdBgRwwh/9Cpk2u/2bz+MK53cXb9PHTIeR8FCjiJNzp0yLlbf9Eib54u3sVk4huvo88QEcEuSDO3b3PBzkAfYF8ukZHOj2vFimwQVanCN/xTp7i+1LvvWnsozP2YDebmzflHnNmANhsAb75p+Z5Pn+YT0Bufp0gRopSU7Mf/6lU20MLDLW2jo9kT6OhB32TibDJnF6ONG/Wdh0YjF/dt2ZKN0FKliF56iavMO2PPHuef35zN6ahN+fL8ZPTjjzz1l8PxmSE0ceJEyp8/P7322mv0yy+/0C+//EKvvvoq5c+fnz7++GO3BxysiCHkH77+OvDX/dy25M/vvObhZ5/pC8344w8nB3DSJG6Y+WnWfIN4/nnvT0t4k3379H2hWR/2TpzgG2agD3SwLKrKJ13Fip4HPy9fzt/x/fd7d4xVqvDFxmwQJSayB8reeG1lDGbmp5/s78tgIGra1D/nfnIyG+vOPn/p0o69t+bjCLCxGuCiqZ7iM0MoNjaWZtoI/poxYwbFxsa62l3QI4aQ/wj0dVzPkj9/4MegZ1EUopdfdv6dT52qzxBav17HAVy5ktN1zXEZjRsT/fBDcBtBRDxuPV9qWBhPib3xhuObZ15evBHwrGmcyn3unO/Gd889bDy8+65zj87ffzs+fz76iPs1n/dmQyMujgPw/IWjrDBNI6palXWEOnbUd5xywVSZzwyhsLAwOnbsWLb1R48epbCwMFe7C3rEEPIfgwcH/jqeW5aICE7GcYYej3p4uH3pApuYTDlLj0TP/GDmReZlfb8ULOhbN7E5+K10acftDAaiV191fg6dOMEGcteuRL17s4iorpRLL3L7NsdZZT1HNY2oWDFrLa8TJ/j7dXYuaxobpDkUvfdvlzXeK1eujJ+yVsQEMG/ePFSpUsVlZWtBMDNpEjBwYKBHkTtITubiqc5qWdarxyW0bJdbItyr/IEFtUchcuLbXApBT4VtRclZ5SNq1OCyF3rHHMgq43kFTeMyEK7gSo0Ykwn48ksu4uoIoxE4ccJ5fxUqAGPGcMmKmTOBTp38XwsoPBxYsYLL3DRowPXcYmOBN94A9u8Hata0Hm+RIs7PZaMR2LTJp8MOBlyuNvfuu++iV69e2LBhA+6++24AwObNm7FmzRqbBpIguMLUqbx8+imwbBlQvjwwY4bzgqv58+svYJ0XSE/n+8iPP3KBbzNEQEoK3zPMZZd+/BG45x7g5ElLm4rKv1hEXVCX9oP2GIA94KKQVatyyfrq1f38iXzMp58C990nRk4wYDBwba2KFfW1r1oVmDgRKFGCj2FKir76YTdv8g8hJcV+G00DoqL0jSMYCA3lH3zmH709iPT1qbddTsYdd9OOHTvoiSeeoAYNGlCDBg3oiSeeoF27drnlugp2ZGos8AwcaD8cw2DgmD4iTljZvj3wXv1gWRSFqE0b/m6OHWNBXHOVh6JFWVrlyhV+//p1oo8/5iSYKiUT6XxoNBlVG0GVmsbpzpcuBeZkyEpaGk9DjB3LAdv//ut+X+vXO04Zl8V/J+6WLTx946ytwWCdzXfoEIsC6p2+fOQR58HDq1d7fJoGJWfOOI8VUhTbgqI5BL/oCOUFxBAKPDdvckUDwPr6pqqc7XnqlHV7uZdZlrg4lkYpVCj79d5cJikhIcsXPnmy4wukqhJ98IHfjr9d/viDqEwZyw3RnB78xBOuF5D880+iLl0Cf8DywqJpbInnz589lkVVuViemW7dHJ+Lr71m+3hu2qRvLDNmcHq+rSctTeOgam8G+1+7xlISc+c6D8L2B9262X/K1DSOecrB+NQQSk9Pp/nz59Po0aNp9OjR9PPPP1NaWppbAw12xBAKDu7cIfruO85GLVmSk3Y++oivK1nJ7fIurizlyhFVruzYo2ZW1b9zh7OKb9dvTiY4eVKsWdOvxz8be/ZwFpetJ39VJXr4Yf19zZ2bXdNGFt8sRYqw3lBCAmdUffABUYsW/MN++WWiI0esj8316/ar0xcvTvTbb0RTpvCTUo0a7OH5/XcO1nfm7VBVLsPx118WGQSzQQ0QPfSQY8FIV0hNJRo+3FqrCODg5hMnvLMPd7hyxVJmxvx9mf+vXZvrruVgfGYIHThwgCpWrEj58+en+vXrU/369alAgQIUGxtL+/fvd3vAwYoYQjmPuXOdX48jIzlr+pln2FjIy/UzVdX6XnMY1Z1vVLZsYA9y9+7O09d37rS97Z07nNY/dCjPu4oB5J8lJIQ1pW7f1n+c33pLX9/mH7D5nHjiCX3bdevG+0lPZ6Pq/feJJkwgOnzY83M0M48+avsio2ksnHj+vHf35wq3bhF98QVRw4Y8loYN+XXmKcccis8MoWbNmtFDDz1EVzMVMbp69Sp17tyZmpuDNXIRYgjlTIoXd3z9+/BDS9vkZA4vqVGDH9jyYnZ05mv0AjxMqQjikhm3bzs3ggwG27VB/viDU4nNbQL9xefURVH4h+Lqj0VVWcdGz3TTr796Nr6CBd07R7zNtm2Ox6Fp/hlHHsRnhlB4eDgdOHAg2/r9+/dTeHi4q90FPWII5Uzi4zmm19Z158UXHW975gzXjwz0vSZQS3ssd95o/nz/HEhbXLzofHy2KmkfOsTR4nnR0vXFknk6xdXFWe0XIp428kS0smBBz8USvcGLLzo3uqOifD+OPIjPdISqVq2KCxcuZFt/8eJFVK5c2e3sNUHwJmXKABcuAPPmsU5OXBzwxBNAfDzw2WeOty1XjlP316+3pJjnJVagPb7HEzBBAWV5zwQFCc26AI88EpCxAWB9lEKFHLcxGlkXoGdPYMIEFkyqWRO4fVtS5L2F+TbuKgYDa2I4Y/Nm57oZjrh5E4iJsSeSxTz+OPD77+7vQw/nzzv/HNev60v5F3yDqxbWb7/9RrVq1aL58+fTmTNn6MyZMzR//nyqU6cO/fbbb5SYmJix5AbEI5S3OXuWU8oD/fDt70VFOr2C8XQOpTJWXkQxehOjae2KICjGOHSoPm+B2WMhXiDfLe54hcy6Do4IDfV8bFu2EPXt6/hcURQuMuorxCMUMPTevxUiIlcMJzWT+qry3+OyuYvMrxVFgdETaz5ISEpKQmRkJBITExERERHo4QgB5I03gI8+yv0PblVwFD3xEyKRiOOohO1ojGQUwD+ohAKRITh/HsiXL0CDu3wZ+OYbdtdt2gTcueN7D09oKJCa6tt95GRCQvhHofdWYjCwe9aZV6h9e2DNGve9QsWKAefOcR8dOjhuGxEBJCT45sTevh1o0sT++5oGvPwyX1wEr6L3/u2ysvS6des8Gpgg+BuTCTh1iq835ctnf+/HH3m6bP9+vg727AkMGQJkrRjTpQswdqz/xu1LFAXo0weYPdtynwnDHUzDM3gSc5AODSaoMCAdt5EPz+NLHEF1DB8eQCPok0+AV17x/9RWbrd8PSUtzbX26elAv37O2w0fDqxc6d6YFAUYPJiNruHDnbdPSgIWLmQDzds0bgw8+ijP02c1Fg0GNtiGDfP+fgX9+ME7laORqbGcS1oaZ62GhFg80OHhRC+8wDIj6emchQ1Yz5wYDBxTu26ddX8mExdwDvRMhLeWrVutY4dn4QlKR/YpJCNARig0pcNvgaul+uWXgf/CctsSiOlCVWXhSr0ihePHu9a/eQqsc2fW7jlyRP9277zju/M3WHWEcjl6798ue4QA4M6dO9i3bx8uXrwIU5ans86dO3vBPBMEzzCZgGrVstdLvHMH+OILYPdufkhbsMDS3kx6Or9+5BHg7FmgQAFeT8SxtrkDwqIHp+FVNRHjaAiicRJP4QebLVUApCgYdGkUoD7o32ECfNAGD/b/fnM7BQoAN274d58vvMB1wTJnIRAB27YBe/eyu7F9e64bBgDR0ezKdTY9VqwYT2FWrcr76NaNt7t8Wd+4TCbf1hQLCeGg/bfe4mndO3c4g6NaNd/tU9CPqxbW8uXLqXjx4qQoSrZFVVW3LbdgRTxCOZNXX3X+EFi0qPM4z2nTWFx28mSi+vUD/xDv+WKicjhNX+EZOoAalAaVklCQVqAtpUFH8HEg6g59/32gv7TcuYSGcnkJf+2vbt3sx3bfPlY2ztzOYGDhxe3b9afPFypkWwDw7Fl9wdyKwroZQq7CZ+nzL730Enr06IHz58/DZDJZLbkhOFrIHUyb5rzNlSt8FbSHwQCsWgXUqsUxQ3v26N+/6vIvy/eEIBXv4m2cRCz6YSZq4G8YYEIh3ERbrIYKHb/fpCTfDzQr+/b5f595AbPr019MmGD9+sQJoGVL4NCh7OP6+msO1tOrX3HjBvDnn9nXly0LtGvn/AfZvz/rZgh5Epenxi5cuIBhw4ahZMmSvhiPIHiF69c974OIYzWTkhwbTFkJDweeegpITAR++snzcXgHE04gFqVwERpM0GB9A9Rlt4WG8lSFvwlYdHYux2RyPdDZVcyGzOefA/ffb/3euHHArVu2p71MJuDff13bV0qK7fUTJwLNmvG+bP2Q27QBnnkGeO45NroLFQJ69GCNIfO8uJCrcdkQ6t69O9avX49KlSr5YjyC4BVU1XlYgfkh0d5DsdEIXLumf5+aBpQuDZw8yf8TAcePA7t26e/Dd6j4HANxESURilQ8iGXogOVWBpH5FmHzGdxg4BtDICQkunQB3n3X//vNCygK/xC87RmqUAGoXZuFLPv3z56uaTQC33/vvYw8RQHq1rX9Xq1awJYtwNChnEpvpkQJ4P33+QfbtCmf4+np3NeqVfzeunVAxYreGaMQtLisI5ScnIwePXqgePHiqFOnDkJCQqzeH5zLghpFRyhn0qQJy3c4a7Ntm+33DAagYEEWp9VzrTYY2BO0Zo21ZMiQIfwwHAwZ2ApM0P6b/kpHCKrjMH7HA4jBaQCwUpHOZgwVL87BrKVL+36gRHwDWruW/7/7bk6dX73a9/vOaxQoABQtCpw545rb0xEGA/dnMABLl/K0VdWqQNu2/IQA8A/LmTq4K/t78EHgl1+ctz11ig2fIkXYUPvhB3bf2uu3YkXg8OHgnOsWnKL7/u1q8NG0adPIYDBQwYIFKSYmhmJjYzOWChUquBnSFLxIsHTOZNMm57GRp04RvfeeJT7TvB7givT9+1un3jtaYmOJfvop+zj27w98TCwvpmzrDEilSjhGd2BR8DXZ6yAsjGjPHtcPxKlTnAL92mtEn31GdOWK4/YnThDVqmU5KOYDExNDVL58oL/E3Lm89Raf8N7oy2DgOmKvvGL58ZjT9MuVI1qzho+z0UgUEeH5/lSVz434eNfPTZOJzzVnwdS//eZ630JQ4LOiqyVLlqQxY8aQMWCCIv5FDKGcy1df2b7GaRrRL79Y2u3bxyr4LVoQtW9P9PXXRDdvEi1Z4tr1X9Ns1yJ96aXA3+scLXPwqL4bTvfu+r/89HT+4OYq5SEh/H9YGNGkSba3SUoiio62XY5ASmT4blEUohEjPO9HVYnOnycaOND2D898Hvz1Fx/vl1/2vKjqG28QXbqk/7zMzIULzvcREsLlXIQcic8MocKFC9Px48fdHlhOQwyhnM2VK0TPPktUvTpRzZpEw4bZzrK1RXo6e3r0XqsVhfXSrl2z9JGQwH24W6Tb14uKNOqJufpvdEOGEJUqxWnX9eqxtZmSkv3Le/VVxx965szs20ydGrxfVG5fChf28ERSiXr3Jjp50vEx1DSidu0sP46yZd0zhtq2dfFKYINz55zvJySEaNAgz/clBASfpc/36dMH8+bNc2/CThD8TJEiXJrq8GHg4EFOIMmfX9+2mgYsWcI6a3pCBIg4cWXWLMu6V17xbviFtzFBwy3o/EJMJmDqVK7JdOsWZ9g8/zzwwAMsEGfmyhXg008df+jhw7NHs8+d6/L4BS9x7RrQoIH72+fLx3Fcc+c6/rEYjZyKeekSULIkBzF36KA/TR7g2B1vpLqXLOm8n7Q0zjgTcjUuZ40ZjUaMHz8eK1asQN26dbMFS3/88cdeG5wgBJratdmA+uorrst1/Ljj9ppm0Ru6epXvC4GS11JhRH7cwi0UBNlJkNdgRB0c0N9p5g9jNnT++AN47z1gzBh+vXSp87Tsy5c5RXnBAr4JEgHx8cFrMeYF9u9n4+DCBde3jY3lp47Ll/WlbF69ygH40dH8tHH6NP/QwsP5aWXFCvsZBunpwMMPuz7GrKgqZzO89prt805V+TN17+75voSgxmWP0P79+1G/fn2oqooDBw5g9+7dGcseVxTnBCGHULIk8Pbb+gUVzbI3x48HLlusMK7iT9yFCXjVYTuCgv74xrOdmUycGmfWcUlM1OdCW7QImD+fv6TevV3XjRG8S1oaG0FPPMHZUqGh+rbTNDaEAE6Td2YEGQxAqVLW68qXZ89Q69bAG2/wOWXLS2QwANWrAx076hubM4YOZXkGwPqc1TT+If/yCxAW5p19CcGLn6bqciwSI5S32bKFqFcvoiJFOIyiWDHncbu//87bBjJjbAXupzRolAaNOmIJKTASYLSEaiCNAKJPMThjI7sZY3qX+fOJLl8mWr5cf1zJXXcR/e9/EhsUTIvBwBl6xYvrD1JfuJBP+suXuXSHo7bh4UR79zr+4c2bx4H1isIxROYA+po1vV8KIz2daPZsombNOJOtdGkOkJZiqDkevfdvl3WEMnP27FkAQLlcLE0uOkJ5l6++4vqNmmbx7DjSnjMYgBo12HNkblepEsuW+A9CXezDXsRlrEmDAZMxGJMxGKcRAwC4G5swAuPQCb9l2dqOoKJe0T1F4WKShw9bxw3ZQ1X5i0tNdd5WCD5UlctkrF7NxxHg+LCXX3a8TZEiwLFjjgudXrkCzJzJ+lXh4UDnzhyPZtYiEgQn+ExHyGg00rvvvksRERGkqiqpqkqRkZE0evToXJlSLx6hvMe5c5zZq8ehoaqWh9VatbjGY2a++87/D/Q/oielI7uHxQTQFRSmG3Ch0Gb+/EQvvBB4L4UsjhdVJerb1//7bdbMdhrmffc53k5R7MsoCIKX8FnW2P/+9z9MnToV48aNy4gN+uCDDzBlyhS89dZb7ptughAEfPghx29+8onjdprGgdQ9e3J4y6+/8oNr2bLW7fr1Az74gB+CXUmMcR/CI1gEzUonmlEAFME1FMQtfV2FhgLz5gGffcYfVggeFAXInKgSEsLH69tvgchI/42jb1/baZhZC6naYvFib49GENzC5amxMmXK4Msvv0Tnzp2t1v/yyy948cUXER8f79UBBhqZGss7fPcd117US7lynBqvh7NngV69OFvYtV+caxiQhjToDHLVQ758XCW8e3dg82bv9St4H00DypQBnnwSGDvW9/tTVS5ZYSs0onBh55WPmzQBtm71ydAEAdB//3bZI3T16lVUr1492/rq1avj6tWrrnYnCEGByQS8845r2xhcEJ8oV46lc3xpBAFcQ+wiinmvw9u3gQkTxAjKCRiNwPnzXKPN16gqu0LtxYfWr+84lsdgABo18s3YBMFFXDaE6tWrh6lTp2ZbP3XqVNSrV88rgxIEf7Nnj37vDsDX8Q4dXNtH5848debrWM+xGInp6I0Z6IPjqOR5h99953kfgn9ITwd27fJ9kdAuXYAvvrD//qBBjtPo09OB557z/rgEwQ1cFlQcP348OnbsiNWrV6N58+YAgC1btuDMmTNYtmyZ1wcoCP7gls6wGcAS6/PSS67tw2Bg7bg2bYCkJN8JLX6Kl5E596sDfsNM9EVxXHavw2vXvDMwwT+kpbFY5c8/+8YF+eabLKDpiEceAZ5+mo3ozBmHmsYn/rhxQN263h+bILiBy48NrVq1wpEjR/DII4/g+vXruH79Orp27YojR46gZcuWvhijIPicKlX0PUSbs73nzuVUeVepX58FdN94AyhY0PXt9WEdlb0S7dEKf+gvpSHkfN5/nyP1vYmqciD2yJHO2yoKMG0aG0K1alnW33UXZxa8/rp3xyYInuCXHDYvMnXqVIqJiaGwsDBq0qQJbd261WH7n376iapVq0ZhYWFUu3Zt+u2331zan6TP5x26dnVc/zE0lIt0nzrl+b6OHPFvlrMCI32B5wKf5i2Lf5bYWKJOnbzXn6axEOLate6d8HfuEKWmev7DEQQX8Fn6/PTp0zF//vxs6+fPn4+ZM2d6wTSzz7x58zBs2DCMGjUKu3btQr169dC+fXtcvHjRZvs///wTjz32GJ555hns3r0bDz/8MB5++GEcOOBCbSUhz/Dpp0CJEtmDoDWNVfZXruRknPLl9fe5bx9PobVvz1ljCxdyeEQg6ot+h6f9v1MhMJw8yTXfvEGhQqwsum8fl8Bwh7Aw63R/QQgmXLWwqlSpQmttPBWsX7+eqlat6mp3LtGkSRMaOHBgxmuj0UhlypShsWPH2mzfs2dP6tixo9W6pk2b0nPPPad7n+IRylvExxM99xxRvnz8IKyqRF26EO3a5Vo/JhPR8OHch7l6hLlaQVwcUb16rj2QV8XfVAwXPHqoj8apwHsqZAnexWBgAU3z67p1iX7+2Rc/M0HwCz7zCJ0+fRoVKlTItj4mJganT5/2gmlmm9TUVOzcuRNt27bNWKeqKtq2bYstW7bY3GbLli1W7QGgffv2dtsDQEpKCpKSkqwWIe9Qpgzw5ZccH3z2LNcPXbyYY3tc4e23uYg2wHcVwBIvuncvP1zrpToOYyuaoi9mQIOjKq5k9x0VRsTglP6dCnkLTeMiuJcuAX//zdXg9+wBunUL9MgEwee4bAiVKFEC+2xcxffu3YuiRYt6ZVC2uHz5MoxGI0qWLGm1vmTJkkhISLC5TUJCgkvtAWDs2LGIjIzMWKKjoz0fvJDjCAvjVHd3AprPnWM1aXuYH7n1MgWDUBA3MQSTURA37RhDBDtVwgAAJmieV5l3hkx95EyaNAH++gvo1IlVoqtVY3n1CxeAjz8GXn2V/54/H+iRCoJPcNkQeuyxxzB48GCsW7cORqMRRqMRa9euxZAhQ/Doo4/6Yox+ZeTIkUhMTMxYzrgiLiMIYCNIT31SPcTgJNpiLQwwohzisQZtUBIXAAAhSIUBaQCA4riExvgLKrLn5GtIRzNsQU/M886g7JGW5tv+Be/z66+s7pxZ3JAIGDWKxRJffRWYNIn/RkcDb73lmhUvCDkAl3WE3nvvPZw8eRJt2rSB4b+oUpPJhN69e+MDR4/BHlKsWDFomoYLFy5Yrb9w4QJKlSplc5tSpUq51B4AwsLCEBYW5vmAhTyLt2JUAaAS/rF63RC7cBKx+BWdsQXNocGI1liLkriAqjiGl/ExZqIvUsHncAhS8SS+xyQMQTikwruQiZIluXzK5s2c1m4WyJo4ERg92tIus1X//vscPP3aa/4dqyD4EJdrjZk5duwY9uzZg3z58qFOnTqIiYnx9tiy0bRpUzRp0gRTpkwBwAZY+fLlMWjQIIwYMSJb+169eiE5ORlLlizJWHfXXXehbt26+PLLL3XtU2qNCa5SqhTPKniDBtiJnXBeiiAdKgzgG9ZVFMZWNAVBQRNsQzFc8c5ghNyFovBiMnFR3XnzgIoV+QROTLS/XUQEkJDAdegEIYjRff/2S+i2l5g7dy6FhYXRjBkz6NChQzRgwACKioqihIQEIiJ66qmnaMSIERntN2/eTAaDgSZMmECHDx+mUaNGUUhICO3fv1/3PiVrTHCV1q0tmWKeLgqMdAKxZHTQyJRlCXj2kSzBu6iq7ZNTVYkKFSKaMUNfP0uWBPpnJghO0Xv/dnlqLJD06tULly5dwttvv42EhATExcXh999/zwiIPn36NNRM8sB33XUX5syZgzfffBNvvPEGqlSpgsWLF6N27dqB+ghCHuCFF4B16/S3DwmxH15DUPE/jMEcPGHnfUuIdOb/BSEbmUtdZMVkAm7c4JNXD55m06anszDXyZNA0aJAx46+lFoXBIe4PTWWV5CpMcFVTCagZ08WT9Tz64qLA/r0AV5+mWcqMm9jfv08vsBEDEc47iAdBoT8FyQtho8QEHbsABo2dG/bX3/lgqsJCZYTPH9+DtB+9VVLrJIgeIje+7ePSxQLQt5DVVk5etw4TsN3hMHAIRd//AHUqQNERVm/99hjLOvy9LYXcHzDeYwu+zXOoTQUiBEkBABN42KpDRq4t/3KlcDDD1uC6MxWf3Iy1x/78EOvDFMQXEE8Qk4Qj5DgCb/+CnTp4ryd+cHYXJw7NJQ9S1WrAgMHAs8+Cxw+zN6j64hABG6IIST4FvPJmPl1aChb7Y0bu94fEZ/A+/fbd5WGh7OnKDLSrSELQmb03r91GUK2BBTtUbduXd1tcwJiCAmeQMSGzBdfWIdomO8xWafCsmMCoKBsWQUPPgh88w2QhIIoiFtiCAm+5b77gPXr+aRVVRZcfP99dl26w+HDQM2aztvNmMFzxYLgIXrv37qCpePi4qAoCuzZTOb3FEWB0Zhd0E0Q8iqKAnz2Gcu0fPwxsHs3r69Shae8nD+GqAAI5+JN+OYbBaFIwSq0Q1cssruFuUsxlAS3UVXAXFz70iWgeHGgSBHP+rx0yXkbTdPXThC8iC5D6N9///X1OAQh16IowJNP8pKWxq+feQY4dsx65sFBD6D/zJo0hKInfsJGtERz/GV/i0KFOAtIELLSqhVPb9lD0ziOx2z4eGoAmSlXznkboxEoX947+xMEnegyhPwhligIeQFzOa7UVH0ZZVkhqACMGIuR+BW2g4+UkBCOxdi40e1xCrkERWHDhoiNjKFDWTl6wgQOTs6KpgEFCgBjxnh/LBUrAi1bAn/+af8JIDIS6NzZ+/sWBAe4rSN06NAhnD59Gqmp1rL9neUkFgSnNG4M/PSTe9saEYLf0BEpCEVYprIZBADly0OZMIHz9wWhc2ee1ipTBujdG6hUide/9hrr97z9NlcJNtOiBTB1KhstvuDTT3kfqam2jaHJkzlgWhD8iMtZYydOnMAjjzyC/fv3W8UNKf9pP+S2GCEJlhYccesWsG8fP3jXrctyKHq4epWr26ek2PMMOZdHvIYoRCFTKQRN4/m3S5eAFSv0zrsJuRFN40C03bsdGxZGIxddTUxkQcOff+Zg5aQkoHBhoH9/4JVX2JjyFjt2AIMG8X7NVKjAqfM9enhvP0Kex2c6QkOGDEGFChVw8eJF5M+fHwcPHsSGDRvQqFEjrF+/3pMxC0KO4c4dYPhwoEQJDoRu3pzvI1WrAsuXO5/2KlKEtYY0jfWCLOh7LimJBEQiSz0ooxFo2pTVesUIyntoGi8Anwdr1zr3rmgan8AVKwKPPAJ8/rlFNfraNZ5Ga9AAOHvWe+Ns1Aj46y/OIlu+HNi2DTh+XIwgIWC47BEqVqwY1q5di7p16yIyMhLbtm1DtWrVsHbtWgwfPhy7zWkxuQTxCAlZSU8HOnQA1qyxb/AMHsyzAM5Ecvfs4WyyxYuBOzfToFE67iAfHOV+qTBiFN7F23gve4dVqnAxTBckL4Qczrffskflzz/ZsGnblo0NV2jcmL1HtgxogwFo3x5YutQ74xUEP+HV9PnMGI1GFCpUCAAbRefOnUO1atUQExODI0eOuD9iQcgh/PQTsHq14zaTJ/ND+eOPO24XFwfMmvXfi1PnkNqkBRZdvAsf4VXsRCNoSIcx089UQzoaYBeGY6LtDo8d0/05hFxCairQujUv7rB7N09X2SM9HVi2DDh1CvBX4szevfx0kJzMukXdu0vskOAzXJ4aq127Nvbu3QsAaNq0KcaPH4/Nmzdj9OjRqOirADtBCCK++kpfOaSJdmwVu8TEIHT2t+iOBdiBxliGDmiWKUW+CK5gBMZhHVqjAJJd7FzItfxXdNouJhN7i37+Gdi0KXvh1T17nO+DyD9exsREdrfGxbF44yefAE89BZQuzcaYIPgAlz1Cb775Jm7dugUAGD16NDp16oSWLVuiaNGimDdvntcHKAjBxokT+lLfd+3iYGhn9cYA8M1p4EDgyy8znk464Hd0wO+4isK4g3CUwEUYYARBKs0L/6GqbDjY47ffeJ72xAnLupgYNjAeeYRf6zpB4XuPDBHQtatF4yg93fJeYiLXqvnzT/fKewiCA7xSa+zq1asoXLhwRuZYbkJihISsxMWx514Pd+7ovM+MGQO8+aauPvl5XoGqM7BayMVUqsSBxrb47TfgoYf4/6yXeUXhOd6YGGD7djaWHAXYFyzIU2PLlvHJHx7OfTdpYrt9WhrHFrlyT9i8mVPr7WEwAB078pSZIOjAr9XnixQpkiuNIEGwRe/ezq/visL3CF1G0J07wEcf6dp3OjSkIAxQvPLTFXI6L75oe73JBLz0Ev9v61mXCHjsMT5JBw50nmWoKGx0PfUUMGkSMG4cB8Hdey9w5Qq3uX2bU+BjYrg4a1gYB8npfWqYPz9rCqU16enAkiX8exEEL6Jraqxr166YMWMGIiIi0LVrV4dtFy5c6JWBCUKw8swzfC84fdp+GyKWX3EIEd9gzDouTmBPEEEFQSVJj8/zhIYCffvafu+vvwBnpZEyTz0548YNi/WflmZZv2kTe2lWrwbateNz2RyDlJbGxs2CBWzAtGvneB86fgMwmTiAWgKnBS+i67EyMjIyw+MTGRnpcBGE3E5kJFevqF3bfpuRI+3Iovz1F78RHs5Pvw0a6EpLNscEqSArNWkhDzNihP06YJnVor2FLc+SWZCxf39rI8hMejovvXqxx8gRVatm3z4rhQvzD1AQvIhXYoRyMxIjJDhixQr2Du3bx3bN3XfzbMXdd2dpuGsXW0crV/KTtflnp6rOL/6CYIvTp4HoaNvvbdrEdb38gcHA53GqEwN9xgygTx/7758/z5/H3jSdpnF9NF/UQRNyJXrv3y4bQv/++y/S09NRpUoVq/XHjh1DSEgIYmNj3RpwsCKGkOARRiPw3HMseicI3kBVWTSxdm02rIlYQ+jFF4EaNbiNycQii47mb705HmfGfEgIxyJ98onjdlOncmxT1j41DahWjbPGxCMk6MRnwdJ9+/bFn3/+mW391q1b0dfefLUg5FXeew/47rtAj0LITRQvzuUzJk0CDhwADh4EvvySDSOzOqeq6pM29wZ69kGkL65n0CDWO8o871ygABt5mzeLEST4BJc9QhEREdi1axcqV65stf748eNo1KgRrl+/7s3xBRzxCAluk5wMlCrFgaaC4A3M06qZp1czo6qsFF23Lr9esAAYMgSIj7e0CQvjQGZvTcmqKlC9OvD334773LyZ65rpgYjHnJzM02X58nlnrEKewmceIUVRcMPGhT0xMTHXVZ4XBI/YssV/RpDIV+QNzMaPvedXVeXpJTPdurH+z+rVwMyZPJW2cyd7WcwFWm2haTydBbAXpnLl7O3V/24fH34IjB5t3wgyGLgqcfPmzj+fGUUBypXjAGoxggQf47IhdM8992Ds2LFWRo/RaMTYsWPRwpEYliDkNVJS9Lc131RcNWg0jTPPChd2bTshd5KezhH8mdE0oE0bFsC6/36gVi0Ops5amDUqivWs9u4FRo0Chg8HZs/mIObdu4Fhw6ynpmrVAubNY52Ibt24poyq8v4UxaIJVLcu8MsvYqwLQYvLU2OHDh3CPffcg6ioKLT8Lyth48aNSEpKwtq1a1HbUU5xDkSmxgS3OXsWKF9eXz0ORQHGjwc+/9xa/6VMGQ4enT0bOHQo+9RIXBywahWQPz8wZQqnVAt5m+ho9gJt2sQGTGgol+GwVTD1wAGe0ipUCGjVynkcT0oKn9fh4XxuZjVuTp0Cpk2z9Nm9O/DAAxZDXxD8iM+yxgDg3LlzmDp1Kvbu3Yt8+fKhbt26GDRoEIrY07TIwYghJHhEly5clsCReJ3BwFllvXvz9MKWLXyzKVGCheg++YTbZO0jf37ghRfY+ClWjA2jqCggKcmnH0kIch54gA2Sw4fZADFf4nv2ZCOlYMHAjk8Q/IRPDaG8hBhCgkecO8exEfHx1voo5qmDAQNYX6hs2ezbfvGF/RIKZjSNPQBbtnBgtp5thJxLZKRzBebQUDaas8bsaBqn2Zu1rAQhl+NTQ+j69evYtm0bLl68CFOWH1vv3r1dH20QI4aQ4DGXL7NX5+uv+f+ICKBfP47BsCeI54oOjMHAVbvnzePXmiYijbkRRQGKFuVzyBPWrmWDyFUOHAA++wzYsIHPuQceYI9kLtOOE3IPPjOElixZgieeeAI3b95ERESEVbFVRVFw9epV90cdhIghJHiV9HTHhSXNHD3KAnJ60TSeTjt3DmjY0P3xCcGNvbR5vRgMrO48bZpr2331FRs9mmaZotU0XhYtAh580P0xCYKP8Fn6/PDhw/H000/j5s2buH79Oq5du5ax5DYjSBC8jh4jCHBeriArRiPHhKxZI4GpuRkix2nvzkhPt1SL18vWrWwEEVnHqRmNrEfUtau1TpEg5DB0XpUtxMfHY/DgwcifP78vxiMIeYtbt4DffweuXQMqVWIDaNo0zhDTNPt1l2wxfjzHf8i0WO4lIsKzYHhVtZ095ohJk6w9QZkxG0dffw28+6774xKEAOKyIdS+fXvs2LEDFStW9MV4BCFvQMSGy/vvAzdvWr/nTiFWReE0ejGCci+axiUoTp0C5sxxb4rMZAKeftq1bVatcpz1aDSyYKMYQkIOxWVDqGPHjnj11Vdx6NAh1KlTByFm9dH/6Ny5s9cGJwi5lvffB95+2/Z77hozouyeu6lYkcULIyK4pMWkSZbA6WLF9AVRN2xoKb+hFz0GlyQfCzkYl4OlVQfxB4qi5LoyGxIsLXidK1eA0qU5vsIdFIUXVXX8pC7kLvbu5SzDN94AZswA7tzh9eXKsVH93XfAtm2ODemdO1mJ3BV69AAWL7Z/rmka8NprwAcfuNavIPgYvfdvlz1CWdPlBUFwkZ9/dt2AMRj4Znf//cD33/PTf0wMq/sOHeqTYQpBRuPGlpiczM+vZ8+yHtWTT3Jgsy00jc8dV40ggIu2/vyz7ffMBvlzz7neryAECZJeIgj+5sIF/dljmSECmjXjoppz53KxyypVvD8+IThJTWUvoj0n/g8/AOPGcXV5ReGiqebzrG1bi86Uq7RowR4fwFqI0WBgA+vHH10PwBaEIELX1Xjy5MkYMGAAwsPDMXnyZIdtBw8e7JWBCUKupWxZ1z1C6enA3XdnX3/vvVzTyV9V7oXgRVVZdfrcOTaK/v6bDReTCbh9mz2KvXqxMa1XWfrGDfY0/fqrdbkOVWXtoHHjgBo1fPeZBMEP6IoRqlChAnbs2IGiRYuiQoUK9jtTFJw4ccKrAww0EiMkeJ3ERC6HYY7xcIbBAFSubCm6mpUxY4A33/TuGAX/kT8/kJzsnb46dgSWLuX/P/2Ug6sBy3mTng60a8dTXYUKOe6LCGjfnpWos8Z+qip7nP76iwv/CkIQIrXGvIQYQoJPmDIF0OM9VVXOCNqwwb7S9J49QJMm7gdfC4FB07gq/Ny5wLp1XDz34EHP+9y0iVPsH33UfpuOHYFffnHc1+bNPC1mj6ylXQQhyPCJsnRaWhoqVaqEw4cPezxAQcjTvPQSV5wvXdp6fcWKQL16QMmSQNWqrM1y4IDjchvPPy/6QTkFTbPE7TRvDkyfDhQoAHTqxNNMnmI08nTpW2/Zn/4yGnmq69Ahx33Nm+c4li09HViwwHUVdEEIMlyK2AwJCcEdve58QRAc8/TTQO/eXDn++nU2gmrVcq2PgwftZwoJwUWJEsBddwHHj3MtuU2bOF7s8ceB//2PvUM1a3JsjyeGbUoKcOyY4zaaxh6hmjXZkFm8GFi2jP9v3JjrkV2/7lwfyGjk+KPQUPfHKwgBxuWpsQ8++ABHjx7FtGnTYHAn8yWHIVNjQtBCxIGsc+YEeiSCXszp5pljbgwGjhPauBEoUgSoXZvjyHxJSAjw+utsiLdrB5w8yeMg4iU0FHj4YWD+fMdCnYULs5SD1LcTghCf6Qht374da9aswcqVK1GnTh0UKFDA6v2FCxe6PlpByGtcvMhxQt99xzeSMmVYC+bFF4HISH19fPGFGEE5DaLshkV6Otec690b2LGDjSJfG0JpaTz12qYNZ5mZx2EmJYWNIEfPyYrCsUZ6M9AEIUhx2RCKiopCt27dfDEWQcgb/Psvp8JfvGi5KZ48yZlfM2bwlEnx4o77SEuT2k65CaORlaMnTgTOn/dOn/aK9ioKe55SUoAzZ2xvS8Tt6tThYHxFyW4UEbG458GDwMKFQGysd8YtCH5GssacIFNjgtdp0YLjemxpCWkaZ+L89JPjPjZuBO65xzfjEwJHWBgbKN6geHHg6lVrY0jTeBpr6VLgq684NshRPFKBAtzunXc4tskWBgMH/e/fr9+bKQh+wOtZYyaTCR9++CHuvvtuNG7cGCNGjMDt27e9MlhByDPs28dpyfYEFY1GzsQxT1fYQwQUcyfeMoIAnmJ75RUgKopfqyrQuTMH57drx9pFzoKyU1OBJ57gqvf2SE/nMh/Tp3tt6ILgT3QbQmPGjMEbb7yBggULomzZspg0aRIGDhzoy7EJQu5jxw7nbUwmno5whKN0ekEAWEvonXc4Bu3KFY5DWriQK9ADQP367CGyh6py4DbAStWOYoGIgNmzvTZ0QfAnug2hWbNm4fPPP8eKFSuwePFiLFmyBD/88IMUYRUEV9CbaRkS4vj9SpVYL8bRjUzI26iqpR5YkSJAeLj1+/37Ow6GNplY7wpgQ8pZFMWVK56NVxAChG5D6PTp03jwwQczXrdt2xaKouCcMxe+IAgW2rRxnmqcPz+L7Tnjiy+4TIIYQzkTVfXdsTOrVjsyvCtU4AK+5vZmFIWX7t05kw3gDDNHY9U0KQAs5Fh0G0Lp6ekIz/JEERISgjSR9RcE/ZgF9OzdVBSF4zEKFnTeV/XqwPbtQKNG3h2j4B9MJg5479TJ+30bjawT5IwXXgBWrgRat7ZMfVWtCnz2GZf+MJ+nAwY41hMyGlnhXBByILqzxlRVRYcOHRAWFpaxbsmSJbjvvvustIRym46QZI0JXufWLQ5aXbvWkuJsMHDQaY8eHI/hbGosM3PmcECrkPMoWJA9gBcverfftm2BVatc2yY9nZesU2gAG21du3Jpjqy3DHMl+sWLxTspBBVeL7rar18/XTuenssyB8QQEnyCycQ3qlmzgIQEoHx5LrnRooXrAnVmQUbxzuZMIiKApCTv9pkvH5fI8Gbpi7Q04L33WAj0+nVeFxXFHsy33pIyG0LQkeuqz1+9ehUvvfQSlixZAlVV0a1bN0yaNAkFHUwhfP3115gzZw527dqFGzdu4Nq1a4gyp5LqRAwhIUcwaBDHDEnygj40jVPLZ83ynoChu0RH2xc29ITz54FSpbzfb0oKF2wl4lpltjxIghAE+KT6fCB54okncPDgQaxatQpLly7Fhg0bMGDAAIfbJCcn44EHHsAbb7zhp1EKghOIOIV+wQLgjz8cx13Y237dOuCNNzgGZPFins74+GOeVgOk5IEeIiOBsWNZTyfQ35cvEk4MBt+JG4aFcep9gwZiBAm5A8oBHDp0iADQ9u3bM9YtX76cFEWh+Ph4p9uvW7eOANC1a9dc3ndiYiIBoMTERJe3FQQr1q8nqlnTXNaSlzJliGbP1rf9yZNEderwdgYDUUgI/1+uHNHOndxmxw6i1q2t9yGL7aVfP6KHHgr8OBSFF2/22aiR785jQcgh6L1/5wiP0JYtWxAVFYVGmbJj2rZtC1VVsXXr1gCOTBB0smkTB7D+/bf1+nPngKeecq7Km5zMmT2HD/Pr9HRLTFB8PHDXXdxHXJxvspByIzNmAMuXB3oUFvPFm+zYwXXABEFwSo4whBISElCiRAmrdQaDAUWKFEFCQoJX95WSkoKkpCSrRRA85uWXOX7HXgzP0KHAnTv2t58zh4u12irNQcRxG08/zdMWw4d7Zci5HiL7pU6CGYPBIpToiBEjXJ96FYQ8SEANoREjRkBRFIfL31mfoH3M2LFjERkZmbFER0f7df9CLuTvv/kJ3VEgc1IS8OijXCTTFvPm6YtlkRtfzqRECX2q4wYD0KcPMGGC82MdH891xQRBcIhOvX/fMHz4cPTt29dhm4oVK6JUqVK4mEVnIz09HVevXkUpL2dFjBw5EsOGDct4nZSUJMaQ4Jzz54Eff+S/pUuzaKL53IyP19fHkiVAkyZ88ype3Pq9S5e8P30iBAeqyh7DVq1YYNGRl+r774FevYBvv9XXt5S9EASnBNQQKl68OIpnveDboHnz5rh+/Tp27tyJhv8VDFy7di1MJhOaNm3q1TGFhYVZiUYKgkOIgLff5gwkIotA4muvAf/7Hxe9LFlSX18mExfKHD6c07rN/Y8ZA+zd67OPIAQQTQNq1WL5gwMHuLTKxo3WbVTVUverZ09eFxurr3+97QQhD5MjYoRq1KiBBx54AP3798e2bduwefNmDBo0CI8++ijKlCkDAIiPj0f16tWxbdu2jO0SEhKwZ88eHD9+HACwf/9+7NmzB1ftTT8IgquMHw+8/z4bPyYTBzCbTPx69Ghg4kS+0dWt67zGGMDegB9/tEyRffUVi9UJuY/wcODZZ4ENG4Cff+aAd1tTWdHRwNKlwKRJlunR1q1ZhNPedKmqAvXq8SIIgmP8lMXmMVeuXKHHHnuMChYsSBEREdSvXz+6ceNGxvv//vsvAaB169ZlrBs1ahQByLZMnz5d934lfV6wy61bRBERjtOYIyOJbt8mWrWKSFX1pz9v2UKUlkZUunTg07tl8e4SFka0cSOR+Zpy4gSRpjneZvHi7Off8uW8XdbzSlV5H3/+6defgyAEG3rv3zlGWTpQiLK0YJelS4GHHnLebtkyrgS+fDnXBLt2zfk2u3dzJlizZp6PUwg+nnkG2LaNTZewMD7e9oLpNY1jh9auzf7eunXAq68CO3da1rVowZ7IJk18M3ZByCHovX8HNEZIEHI0N27oa2eWYOjQgW9+Vao4bl+uHFCnDitPC8FLhQocEO9OZtb06frLoRiNwPbttt9r3ZozEo8eBS5cAMqWBSpWdH08gpCHyRExQoIQlFStqq9dtWqW/ytX5qwfRxow//sfv1+tWuDLPwj2+fdf4N13gWPHgG7dXNvW1ZpwzlLrq1YFWrYUI0gQ3EAMIUFwlwYNOAjanlGjaVyTKS7Oev233wLt2/P/ZmE8TWOj5623gOee4/fKlgU6dhRjyJ/Urq2/rcHAAcyVK3Ow88yZvjFEDAY+DwRB8AkSI+QEiRESHLJzJz+Jp6ZaC9xpGsd+bNzIBlNWiIC//mLF6KtXeZrl6aez30h377a9fVZUlZecqJTsTcyp5gAbkObLW+b/HbF9O7BvH8fw6CEqyjrmi4ilDkaOBH7/ncejaZxNmHlsrqCqPKX6n3SIFYmJwE8/8bkEAI0bcxxaoUKu70cQchl6799iCDlBDCHBKfv3sydnyRK+0akq0Lkzp9XXquVZ39u2AXq1svLlA27f9mx/OZkXXgCqVwciIliPx+yluXKFBSqPHHG8fYECHGdToAAwZQoweLDzfRYtCly+bPu9f/4BFi0Cbt4EatRgvamjR533aTbaNI3PpVmzWHU8MydOsN7U4sXZty9UCJg/3+J1FIQ8ihhCXkIMIUE3V6+yAnSJEkDhwt7p89Ah/cZU4cL6MtJyIwUKsIJ3ZKTt94n4ezx61HZpCk1jwcJPPrGse+014KOP7O/TYOB4L73FTV98kXWhnHmFGjXiY9msGdC/P+sIXbzI51fp0kBCAr93/brt7RUFCAlhb2LNmvrGJgi5EL33b4kREgRvUaQIBzh7ywgC2JOgR5laUdgLlaU4cZ4gNBRYuNC+EQTw97NgAR+jzDFdisJLs2as4J2ZunUd79do5GK5ehk40LkRpGlAmTLAypUsyHnmDNCmDZ8DNWoAxYpxKr2jYtBEPLaPP9Y/NkHIw4ghJAjBjKKwR8AZRHxTvu8+nw8paMifH+jXj2N62rVz3r5GDW77xhtATAxPodWrB3zxBbBmDfdnZts27tsR06ez90YvtWplryGXFaORp1oBNoZatbKWUUhPZ++QM4PKaGTDTxAEp4iOkCAEO3pq34WFcXZagwYcPOtOUG5OonhxYPVqjouqXFn/dqVKsadl9GjH7caMcR5cHROjf79mypTh6VNHRESwwdO3Lxs07kYv3Lnj3naCkMcQj5AgBDuVKjnWkVEUvsGOGcPxI3kh7O/SJfbmVK3K05GzZ3uv75QUVg23FUtkxmDgYGxXefRRxzXnVJXbrFgBnD/v/rFUFNvxQUeOAEOGsNHcsCFnt5065d4+BCGXIIaQIAQ7zzzjOC2eiMX9Ro0CJk/OG4ZQZo4fB3r3BsaO9U5/d+7o86jpVRbPzLPPZo9TMqNp/N6zz7LB4kh00xlEXNE+MzNnsnH0+eec4r9rFweDV63Khp8g5FHEEBKEYKdlS+DJJ523MxotBpMtEcbcKsxoNvz+9z/g5El921y4AMyYAXz2GVd/z2w8FirkPJbHZOJUfVcpVgxYv57LqACc3RUSwv+XK8fvFSvGgd+OPFLOePhhNg7N7N7NMU8mk7VRbTSyxlG3bsDp0+7vTxByMGIICUKwoyjAyy+7to1Zh6ZrVxZt3LmTxff+/BMYNsw34ww0RJye7ojUVE5jL1eODYOXXuKA5OrVLfW8VJU1iRx5ZFTVeTC1PWrVYi/WL7/w/l96ifWAjh+3SCV07uy8rAZgyXozU6gQF1ydP996/JMn2/885iyzL7907/MIQg5HdIScIDpCQlDw4ovAN9+4rhxdqhTHmmTm3Dku35EbKVYMOHvWfoD5448D8+Zln/rSNCA8nAuYVq/OIogtW3IGV2bPjFkd+rPP+Jj4kmHDgE8/tT/VOWkSC2iePcsK1926cdyULc9f2bJ83B3RsCF/fkHIJUj1eUHIDSQl8ZTFjh3ulc+wFceybJnn4woEespkXL4M/PgjZ1xlZe9efs8WRiMHSY8Zw4HXBQvylNno0WyAJiZyu4YNgTffZI+Nrxk/nsf0xRfWJVRCQzm256WX9Pel53k3t2caCoIdxCPkBPEICQHhwgXO6PnhB57OcQdFAerUYQPATGoqqxNfveqdcfoLVc3+WWyhKFySZMuW7O+9/jqLDDoyKENC2HjM7FFKSWGvWr58+sQtvc3p08DcuVwqJCYGeOwx10U7H3uMs9zsfXZN42yyiRM9H68gBAniERKEnMrFi3wzj4/3vIjq889bv169OucZQQCLHU6aBLRu7di7QWQ/6NeZfg/AgcNZDaGwMCA21qXhepXy5bnchycMHszGlCOyniuCkEeQYGlBCDbeeYfjPjwxgjQNaNGCK9pnRo8xEIzMm8daSc6yuRTFfpmR8uWdTxHlz++4VEdOpXlzjjcCrIOwDQZLYdcqVQIyNEEINGIICUIwcfs2p3U7S51WVUvadf781gZCRAQH2q5YkT1oWE+5jmDkhx9Y70aPIZc5m+vcOTYsGzbk+CBn32vHjpbvNbcxZAjw119Az54cPB0dzd/Vnj0cRC4IeRSJEXKCxAgJfuXkSaBCBcdtDAaOl2nZEmjSBOjendcdPcpepMqVOZ7FFkYj93/mjNeHHnA0jT/brl2cRr5+PRs2KSkWA8hZwHV4OLB5M5cqEQQhRyPV5wUhJ6LX2O7UiWNmnniCvT6axkVF69SxbwQB3O6LL3KWuKKjkhSZadWKM70KFeLA4o4d2cOW2Qvk7LkvLQ149VX3xyoIQo5DDCFBCCaKFAHatHEs5peeDvTq5f4+OnYEli+3qBsHO3rSuqdM4QrypUvz6zffBJKTXS83YjQCa9eKyrIg5CHEEBKEYGPUKP5ry2ujqjwVZlYgdpf27dmr5Ek9q2CiaFHL/xcuAF9/7Vl/8fGebS8IQo5B0ucFIdho2RJYsIBFAa9f5+Bdk4m9FT17At9951p/p08DP/3EafMVKrA3qVAhFg70pJ5VMJHZMJw2zXNxwEDoBQmCEBDEEBKEYKRLFxbxW7AAOHyYDZeuXV1LcU5P50whc0yQplnWTZgA3Lrlu/F7C01jb8+VK7aNNk3jwOa6dS3rli93f3+qygHoFSu634cgCDkKMYQEIVgJD+dgaHd55RU2goh4MXtJbt8GBg4EChQIfmPIaAQ+/xzo35+FDjNrKxkMXApj+nTrbTzRX1IUYNw497cXBCHHITFCgpAbSUjgwqD2goUVhWtW6c3IChQvvsjFRHfsAJ56yqKLFBoKPPkkr88aL3X33e7FPqkq12Fr1crzcQuCkGMI8qugIAhu8csvjuN/iIBr15wrNQeS4sWB3r3ZOLl9m2OjEhM5GDopiT1BlSpl3+6FF1zPFgO4r3btPB+3IAg5CjGEBCG3cfYsxxXp0QoaPNj343EXoxFo1ozT/WvXZnXoXbu4hEZWxezMVK4MfPstf36Dztl/VRV1ZUHIo4ghJAi5hQ0buL5YdDSLLTrLnFIU5yrWgSRrcdg9e4B777VdWT4rffsCW7cCPXqwNlNEBBtPtqYCNY3rizkrOmoycZ+//QYcOKDzQwiCEOyIISQIuYEffuDK7Js362uvaawl9NRTvh2XNzGZWPlZb5X0xo2BOXM44ywxEdi711JFPiTEUlOsVCkWUSxVyn5fP//MU2fNmrH+Up067KHSY5QJghDUSK0xJ0itMSHo+fln9nzoxWDgMhw3bvhuTL6mSRM2cmzFCDnCaOSYo3XrOI6oZUvgoYccF1r9/nvbBqOq8nf5xx9sIAmCEFTovX+LIeQEMYSEoObQIfZO6BUQNBhYj+jPPzmWKKeiqqwvtGuXb0uFpKRw2Y5r1+yPo0kT8QwJQhAiRVcFIS8wZYr+tt98A1y8CMybF7xGUIsW+tqZTGyc+FrzZ+lS+0aQeRx//QUcPerbcQiC4DPEEBKEnMySJfq9QfXrA4UL+3Y8nqBp7GEpUEBf+/R0TqH3ZZmQs2f1aS2dOeO7MQiC4FPEEBKEnIxeFeXYWDaEfIWicNxR1nWuYDRyQPPo0fq3SU72baxT8eL6DM0SJXw3BkEQfIqU2BCEnEyzZpzO7cwgGjPGtyrSRCx6OHQoULMmkD8/qz/37OlaP5rGJTX0Eh7OZTZ8RefO/FmSk22/ryj8eWvX9t0YBEHwKeIREoSczEsvOTeC3n47u1hg+fK+Gc+nnwLFinGNtB49WL9HL5rG3p2TJ/W1NxhYeVqvaKI7FCwIvP++7ffMHq8JE1z3fgmCEDSIISQIOZk2bYC33uL/M9fXMt+YP/4YePfd7NvVqeOb8aiq9dTWtm36jARFsRSG1RPzo2lAoULAiBHuj1UvQ4eygVeokPX6UqWARYuABx7w/RgEQfAZkj7vBEmfF3IEy5fzzXrzZjYq7r8fGDbMfhZWs2askuwKYWGcTq6Hs2eBsmX5/61bWRH6zp3s7cxGkjm+yN4UVFYaNwZmzOBpKX+RnMzf8+XLQEwM0Latb71RgiB4hN77t/yKBcFXmEzs5XCnErqrdOjAi17KlePK7Xq8L2XLAvPnA9evs6qynuDhzAZN06acVfXtt8CCBcDNm5y9VqgQ/23enA2zpk2d9xsXx5licXHO23qb/PmBbt38v19BEHyKTI0JgrdZuZI9MiEh7DGoXx+YNcu9iui+ol8/50ZQ+/ZcU+vsWTZWOnSwHy+TmQIFsoscFisGvP46T5UdOsSV5KtV4+Kw337L5UGcYTAArVoFxggSBCHXIlNjTpCpMcElPv0UePll9gKZDQ1VZS/Ks88CX38dHIG1JhMbOmvXZvfwaBpQsSJ7jLKe87dvA2XKcO0uW5cOTQMGDuSir/aYNYsNMVXVn/5vZt8+38U3CYKQqxBlaUHwN3//zXE5gLW3xWxoTJsGLFzo/3HZQlWBX39l4yxznS1FATp2BDZtsp3xlS8f197StOxTfpoGVK9uOzjbzJ49XBneZHLdCHr5ZTGCBEHwOuIRcoJ4hATdDB0KfPaZ/Ru8pnGRz3Xr/Dosp1y+zIZPejoHIcfEWL9PxGUkvvsOOH2axQMbNwZWr2YNI5OJY32ef56nvyIj7e/r6aeB2bNdN4JKlgTOnw8Ob5ogCDkCCZYWBH+zY4fjG7zRyEVCg41ixYCHH7b9Xnq6xXgxGPi1prFX6L77uHaZorDxoycofOVK140gTeMYJTGCBEHwATI1JgjeIjzc+c06LMw/Y/EWo0ez0QNYDBjztN8ffwCDBwNFiujPjHPHAW00srdJEATBB4ghJAjeoksXx+8bDMAjj/hnLN4gOZmDnu0ZL0YjMHeua5XsW7VyTXtHUVgVu107/dsIgiC4gBhCguAtevcGiha17R1RFF6GDPH/uDJDBFy5wlNazvSAtm4FkpIctzGZgFWr9O9/8GD9U2MlSwJjx3KWmUyLCYLgI8QQEgRvERkJrFnDMTcAG0Sqyjfx8HAux+BPJeTMEAE//ADUq8fjK1kSqFAB+OQT+3pCqan6+tbbDmDhxE8/5f8ze4YMBv6uZsxg7aKDB9nT9Prr/hGkFAQhzyJZY06QrDHBZW7fBubNswQGN2vGKeNFigRuTG++aalAn9kTpChA16483qwGx/nzQHS0c+HFnTuBBg1cG8/WrcCUKcD69TymBx7gArKSHi8IgpfQe//OMYbQ1atX8dJLL2HJkiVQVRXdunXDpEmTULBgQbvtR40ahZUrV+L06dMoXrw4Hn74Ybz33nuIdJTemwUxhIQcz86dQKNGjtv88APH4qSmAl99BUydChw9ykYKke04IYOBVZ63b/fJsAVBEDwh1wkqPvHEEzh48CBWrVqFpUuXYsOGDRgwYIDd9ufOncO5c+cwYcIEHDhwADNmzMDvv/+OZ555xo+jFoQg4MsvHQcoqyrrH6WkcBmNIUOAY8f4PXO9tKxoGnu4fvzRN2MWBEHwEznCI3T48GHUrFkT27dvR6P/nmx///13PPjggzh79izKlCmjq5/58+fjySefxK1bt2DQmbkiHiEhx6On0nxkJPDaa8Bbb9kPolYUNpqKFGFtoSFDgNKlvT9eQRAEL5CrPEJbtmxBVFRUhhEEAG3btoWqqtjq7AKfCfOXodcIEoRcQaFCzrOu8uXjmB1HmWREHC+Umsp/CxTw7jgFQRACQI4whBISElCiRAmrdQaDAUWKFEFCQoKuPi5fvoz33nvP4XQaAKSkpCApKclqEYQcTffujt/XNKBzZ0DnbwmJiZxt1qKF8/R6QRCEICeghtCIESOgKIrD5e+///Z4P0lJSejYsSNq1qyJd955x2HbsWPHIjIyMmOJjo72eP+CEFCeeAIoW9Z2Grqqcmr/Cy+41qfRCBw6BIwb550xCoIgBIiAxghdunQJV65ccdimYsWK+P777zF8+HBcu3YtY316ejrCw8Mxf/58POJArffGjRto37498ufPj6VLlyI8PNzh/lJSUpCSkpLxOikpCdHR0RIjJORsjh/nQOjjxy3V5tPSWADyl1+Au+/mWKLt250LLWamcGEWZ/T2dDMRe5sUBZDfnSAIbpAjiq4WL14cxYsXd9quefPmuH79Onbu3ImGDRsCANauXQuTyYSmTZva3S4pKQnt27dHWFgYfv31V6dGEACEhYUhLKfVgxIEZ1SuDPz9N7BsGStBp6cDd93F02bm38XIkfaLr9rj2jXg6lWuSO8NiFhUceJEFlUEgFq1gFdeAfr0EYVpQRC8To7IGgOADh064MKFC/jyyy+RlpaGfv36oVGjRpgzZw4AID4+Hm3atMGsWbPQpEkTJCUloV27dkhOTsaiRYtQ4P/t3XlUVOf5B/DvDDADAYZNBC0IKopL0BgSkbiAEZdorHriRjxUiEdtUlEDWm20YrEmNCVVY40LTcF6ckSbYGpN1BI2U0oQEVQUET1qDIJGUdZEgXl/f8yPiROHZWAWYL6fc+acmXvfe+/zPjPBJ/e9975PXNjp6uoKi3Y+rZZ3jZFZ2b4diI5WFRxtPUgRULWrrgZaeJ6XToRQPVRx1y7Vfpv/NDW/j4xUzX3GYoiI2qFH3TUGAJ988gmGDBmCSZMmYfr06Rg3bhz27dunXt/Q0ICSkhLU19cDAM6ePYvc3FxcuHABPj4+6NOnj/p169YtU3WDqGtbvVo1fLZuHTB+fOttLSxUk6HqowgCVNOT7Nqlev/k/581v9+5E8jI0M+xiIj+X7c5I2QqPCNEZksI4OWXga+/fvrsUPMksllZqrvH9GHOHODYsZYnZbW0BGbNAj79VD/HI6IercedESIiI5NIVBPFBgWpPltaqi60lkhUzx06dEh/RRAAnDvX+sz0jY1AYaH+jkdEBBNfLE1EXZyjo2rIKi8PSEkB6utVFy+Hhqoe1KhP7XlAIx/iSER6xkKIiNr24ouqlyHNnat6NlFLt+9LpcC8eYaNgYjMDofGiKhrWL5c9cwgbXd0Wlio5kNbutT4cRFRj8ZCiIi6Bnd34KuvVA95BFTXJDU/qLFXL9U6NzfTxUdEPRKHxoio6/D3B779VnVnWFaWallwMPDaawAfdEpEBsDb59vA2+eJOujHH4E7d1TDXU5Opo6GiMwMb58nItP4/ntgxQrVEJe3N+DsDEyaBJw6ZerIiIiewkKIiPTn7l0gIADYs0d1q32zrCxg4kTg889NFhoRkTYshIhIfzZsAG7devpJ1E1NqidVL14M/PCDaWIjItKChRAR6UdNDfD3v7f8dGghVBO0cooMIupCWAgRkX7k5rb8MMRmVlZAcbFx4iEiagcWQkSkH3Pntt2moUF/s9UTEekBCyEi0o+qqva1e+01w8ZBRKQDFkJEZFw5OcDo0aoJXT08gN/+VnWBNRGRCfCBim3gAxWJ2kkiaX9bqfSn64ksLFTDZenpwPPPGyY2IjI7fKAiERmXVIc/J09eVN3UBNTWAr/8Zct3nBERGQgLISLSj5UrO75tUxNQVgYcO6a/eIiI2oGFEBHpx7ZtQFBQx7e3slJdP0REZEQshIhIfzIzgatXVRdBW1ioipuxY4ErV9reVgjA0tLgIRIRPYl/dYhIvwYOfPouMKUS6N8fuHFDVfBo09gITJ5s8PCIiJ7EM0JEZHhSqeo2+ZaKIEtLYOTIzg2tERF1AAshIjKO5ct/uqC6eQis+U4zLy/g6FHdbsEnItIDDo0RkXFIJMCOHUBoKLB3r2rOMUdHYMECYOFCwMbG1BESkRliIURExjVmjOpFRNQFcGiMiIiIzBYLISIiIjJbLISIiIjIbLEQIiIiIrPFQoiIiIjMFgshIiIiMlsshIiIiMhssRAiIiIis8VCiIiIiMwWCyEiIiIyW5xiow3i/2fLrq6uNnEkRERE1F7N/243/zveEhZCbaipqQEAeHp6mjgSIiIi0lVNTQ0cHBxaXC8RbZVKZk6pVOL27duwt7eHRCLR676rq6vh6emJW7duQaFQ6HXf3RHzoYn50MR8aGI+NDEfmpgP1Zmgmpoa9O3bF1Jpy1cC8YxQG6RSKTw8PAx6DIVCYbY/VG2YD03MhybmQxPzoYn50GTu+WjtTFAzXixNREREZouFEBEREZktFkImJJfLERMTA7lcbupQugTmQxPzoYn50MR8aGI+NDEf7ceLpYmIiMhs8YwQERERmS0WQkRERGS2WAgRERGR2WIhRERERGaLhZCRVVZWYtGiRVAoFHB0dMSSJUtQW1vb6jbLly/HwIEDYWNjA1dXV8yaNQuXL182UsSGpWs+KisrERkZCV9fX9jY2KBfv35YuXIlqqqqjBi14XTk97Fv3z4EBwdDoVBAIpHg4cOHxgnWAHbt2gVvb29YW1sjICAAp0+fbrX9P//5TwwZMgTW1tbw8/PDl19+aaRIjUOXfFy8eBGvvfYavL29IZFIsH37duMFaiS65CMhIQHjx4+Hk5MTnJycEBIS0ubvqTvRJRcpKSl44YUX4OjoCFtbWzz33HM4cOCAEaPt2lgIGdmiRYtw8eJFpKam4tixYzh16hSWLVvW6jb+/v5ITExEcXExTp48CSEEpkyZgqamJiNFbTi65uP27du4ffs24uPjUVRUhKSkJJw4cQJLliwxYtSG05HfR319PaZNm4Z33nnHSFEaxqFDhxAVFYWYmBicPXsWI0eOxNSpU3H37l2t7f/3v/8hNDQUS5YsQUFBAWbPno3Zs2ejqKjIyJEbhq75qK+vx4ABAxAXFwd3d3cjR2t4uuYjMzMToaGhyMjIQE5ODjw9PTFlyhSUlZUZOXL90zUXzs7O2LBhA3JycnD+/HlEREQgIiICJ0+eNHLkXZQgo7l06ZIAIPLy8tTLjh8/LiQSiSgrK2v3fs6dOycAiKtXrxoiTKPRVz4OHz4sZDKZaGhoMESYRtPZfGRkZAgA4sGDBwaM0nBGjx4tfvOb36g/NzU1ib59+4r33ntPa/v58+eLGTNmaCwLCAgQy5cvN2icxqJrPp7k5eUltm3bZsDojK8z+RBCiMbGRmFvby/2799vqBCNprO5EEKIUaNGiY0bNxoivG6HZ4SMKCcnB46OjnjhhRfUy0JCQiCVSpGbm9uufdTV1SExMRH9+/eHp6enoUI1Cn3kAwCqqqqgUChgadm9p87TVz66o8ePHyM/Px8hISHqZVKpFCEhIcjJydG6TU5OjkZ7AJg6dWqL7buTjuSjJ9NHPurr69HQ0ABnZ2dDhWkUnc2FEAJpaWkoKSnBhAkTDBlqt8FCyIgqKirQu3dvjWWWlpZwdnZGRUVFq9t+9NFHsLOzg52dHY4fP47U1FTIZDJDhmtwnclHs3v37mHLli1tDh91B/rIR3d17949NDU1wc3NTWO5m5tbi32vqKjQqX130pF89GT6yMe6devQt2/fp4rn7qajuaiqqoKdnR1kMhlmzJiBnTt3YvLkyYYOt1tgIaQH69evh0QiafXV2YubFy1ahIKCAmRlZWHw4MGYP38+fvzxRz31QL+MkQ8AqK6uxowZMzBs2DBs3ry584EbiLHyQUTaxcXFITk5GUeOHIG1tbWpwzEJe3t7FBYWIi8vD1u3bkVUVBQyMzNNHVaX0L3HErqI6OhohIeHt9pmwIABcHd3f+pitsbGRlRWVrZ5caODgwMcHBwwaNAgjBkzBk5OTjhy5AhCQ0M7G77eGSMfNTU1mDZtGuzt7XHkyBFYWVl1NmyDMUY+urtevXrBwsICd+7c0Vh+586dFvvu7u6uU/vupCP56Mk6k4/4+HjExcXhq6++wogRIwwZplF0NBdSqRQ+Pj4AgOeeew7FxcV47733EBwcbMhwuwUWQnrg6uoKV1fXNtsFBgbi4cOHyM/Ph7+/PwAgPT0dSqUSAQEB7T6eEAJCCDx69KjDMRuSofNRXV2NqVOnQi6X4+jRo13+//CM/fvojmQyGfz9/ZGWlobZs2cDAJRKJdLS0rBixQqt2wQGBiItLQ2rV69WL0tNTUVgYKARIjasjuSjJ+toPt5//31s3boVJ0+e1Lj2rjvT129DqVR22X9DjM7EF2ubnWnTpolRo0aJ3Nxc8d///lcMGjRIhIaGqtd/9913wtfXV+Tm5gohhLh27Zp49913xZkzZ8TNmzdFdna2mDlzpnB2dhZ37twxVTf0Rtd8VFVViYCAAOHn5yeuXr0qysvL1a/GxkZTdUNvdM2HEEKUl5eLgoICkZCQIACIU6dOiYKCAnH//n1TdKHDkpOThVwuF0lJSeLSpUti2bJlwtHRUVRUVAghhAgLCxPr169Xt8/OzhaWlpYiPj5eFBcXi5iYGGFlZSUuXLhgqi7ola75ePTokSgoKBAFBQWiT58+Ys2aNaKgoECUlpaaqgt6pWs+4uLihEwmE59++qnG34mamhpTdUFvdM3Fu+++K/7zn/+Ia9euiUuXLon4+HhhaWkpEhISTNWFLoWFkJHdv39fhIaGCjs7O6FQKERERITGf5jXr18XAERGRoYQQoiysjLxyiuviN69ewsrKyvh4eEhXn/9dXH58mUT9UC/dM1H8y3i2l7Xr183TSf0SNd8CCFETEyM1nwkJiYavwOdtHPnTtGvXz8hk8nE6NGjxTfffKNeFxQUJBYvXqzR/vDhw2Lw4MFCJpOJ4cOHiy+++MLIERuWLvlo/m38/BUUFGT8wA1El3x4eXlpzUdMTIzxAzcAXXKxYcMG4ePjI6ytrYWTk5MIDAwUycnJJoi6a5IIIYTRTj8RERERdSG8a4yIiIjMFgshIiIiMlsshIiIiMhssRAiIiIis8VCiIiIiMwWCyEiIiIyWyyEiIiIyGyxECIiNW9vb2zfvl1v+wsPD1dPA9AeN27cgEQiQWFhod5i6C6Cg4M1pgvRJikpCY6OjkaJh8hcsBAi6oHCw8PVM9vLZDL4+PggNjYWjY2NrW6Xl5eHZcuW6S2OHTt2ICkpSW/768lSUlKwZcsW9WdtRemCBQtw5coVo8QyZcoUuLi4mG1hSuaDhRBRDzVt2jSUl5ejtLQU0dHR2Lx5M/785z9rbfv48WMAqglin3nmGb3F4ODgwDMY7eTs7Ax7e/tW29jY2KB3794Gj6Wurg7jxo3Dn/70J4Mfi8jUWAgR9VByuRzu7u7w8vLCm2++iZCQEBw9ehTAT0NWW7duRd++feHr6wvg6bMQEokEf/vb3zBnzhw888wzGDRokHofzS5evIhXX30VCoUC9vb2GD9+PK5du6ZxnGYnTpzAuHHj4OjoCBcXF7z66qvqtu316NEjrFu3Dp6enpDL5fDx8cHHH3+sXp+VlYXRo0dDLpejT58+WL9+vcaZsODgYERGRmL16tVwcnKCm5sbEhISUFdXh4iICNjb28PHxwfHjx9Xb5OZmQmJRIIvvvgCI0aMgLW1NcaMGYOioiKN2D777DMMHz4ccrkc3t7e+OCDDzTWf/TRRxg0aBCsra3h5uaGuXPnasTVPDQWHByMmzdv4u2331af2QO0D43t3r0bAwcOhEwmg6+vLw4cOKCxvj3f4c+FhYVh06ZNCAkJabUdUU/AQojITNjY2KjP/ABAWloaSkpKkJqaimPHjrW43R/+8AfMnz8f58+fx/Tp07Fo0SJUVlYCAMrKyjBhwgTI5XKkp6cjPz8fb7zxRotDcHV1dYiKisKZM2eQlpYGqVSKOXPmQKlUtrsfv/rVr3Dw4EF8+OGHKC4uxt69e2FnZ6eOZ/r06XjxxRdx7tw57N69Gx9//DH++Mc/auxj//796NWrF06fPo3IyEi8+eabmDdvHl566SWcPXsWU6ZMQVhYGOrr6zW2W7t2LT744APk5eXB1dUVM2fORENDAwAgPz8f8+fPx8KFC3HhwgVs3rwZv//979VDg2fOnMHKlSsRGxuLkpISnDhxAhMmTNDax5SUFHh4eCA2Nhbl5eUoLy/X2u7IkSNYtWoVoqOjUVRUhOXLlyMiIgIZGRka7Vr7DonMnqlnfSUi/Vu8eLGYNWuWEEIIpVIpUlNThVwuF2vWrFGvd3NzE48ePdLYzsvLS2zbtk39GYDYuHGj+nNtba0AII4fPy6EEOJ3v/ud6N+/v3j8+HGbcWjz/fffCwDiwoULQoifZlAvKCjQ2r6kpEQAEKmpqVrXv/POO8LX11colUr1sl27dgk7OzvR1NQkhFDNzD1u3Dj1+sbGRmFrayvCwsLUy8rLywUAkZOTI4QQIiMjQwDQmLH7/v37wsbGRhw6dEgIIcTrr78uJk+erBHP2rVrxbBhw4QQQnz22WdCoVCI6upqrbEHBQWJVatWqT///LsQQojExETh4OCg/vzSSy+JpUuXarSZN2+emD59uvpzW99ha9r6Poh6Ap4RIuqhjh07Bjs7O1hbW+OVV17BggULsHnzZvV6Pz8/yGSyNvczYsQI9XtbW1soFArcvXsXAFBYWIjx48fDysqqXTGVlpYiNDQUAwYMgEKhgLe3NwDg22+/bdf2hYWFsLCwQFBQkNb1xcXFCAwMVA8lAcDYsWNRW1uL7777TmufLCws4OLiAj8/P/UyNzc3AFD3s1lgYKD6vbOzM3x9fVFcXKw+9tixYzXajx07FqWlpWhqasLkyZPh5eWFAQMGICwsDJ988slTZ5x01dIxm2Nq1tp3SGTuWAgR9VATJ05EYWEhSktL8cMPP2D//v2wtbVVr3/yfWt+XuRIJBL1UJaNjY1OMc2cOROVlZVISEhAbm4ucnNzAUBjyK41uh6vJdr69OSy5kJKlyG7ttjb2+Ps2bM4ePAg+vTpg02bNmHkyJF4+PCh3o7Rkta+QyJzx0KIqIeytbWFj48P+vXrB0tLS4McY8SIEfj666/V18m05v79+ygpKcHGjRsxadIkDB06FA8ePNDpeH5+flAqlcjKytK6fujQocjJyYEQQr0sOzsb9vb28PDw0OlY2nzzzTfq9w8ePMCVK1cwdOhQ9bGzs7M12mdnZ2Pw4MGwsLAAAFhaWiIkJATvv/8+zp8/jxs3biA9PV3rsWQyGZqamlqNp6VjDhs2TOe+EZkrFkJE1GErVqxAdXU1Fi5ciDNnzqC0tBQHDhxASUnJU22dnJzg4uKCffv24erVq0hPT0dUVJROx/P29sbixYvxxhtv4PPPP8f169eRmZmJw4cPAwDeeust3Lp1C5GRkbh8+TL+9a9/ISYmBlFRUZBKO//nLjY2FmlpaSgqKkJ4eDh69eqlvisuOjoaaWlp2LJlC65cuYL9+/fjr3/9K9asWQNANVT54YcforCwEDdv3sQ//vEPKJVK9R172vp66tQplJWV4d69e1rbrF27FklJSdi9ezdKS0vxl7/8BSkpKepjdlRlZSUKCwtx6dIlAEBJSQkKCwtRUVHRqf0SdUUshIiow1xcXJCeno7a2loEBQXB398fCQkJWq8ZkkqlSE5ORn5+Pp599lm8/fbbLT7XqDW7d+/G3Llz8dZbb2HIkCFYunQp6urqAAC/+MUv8OWXX+L06dMYOXIkfv3rX2PJkiXYuHFjp/sKAHFxcVi1ahX8/f1RUVGBf//73+rrrJ5//nkcPnwYycnJePbZZ7Fp0ybExsYiPDwcAODo6IiUlBS8/PLLGDp0KPbs2YODBw9i+PDhWo8VGxuLGzduYODAgXB1ddXaZvbs2dixYwfi4+MxfPhw7N27F4mJiQgODu5UP48ePYpRo0ZhxowZAICFCxdi1KhR2LNnT6f2S9QVScST55CJiOgpmZmZmDhxIh48eMAHRBL1MDwjRERERGaLhRARERGZLQ6NERERkdniGSEiIiIyWyyEiIiIyGyxECIiIiKzxUKIiIiIzBYLISIiIjJbLISIiIjIbLEQIiIiIrPFQoiIiIjMFgshIiIiMlv/B1l0JMfXeW9gAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Anomaly Detection with Credit Card Fraud Dataset"
      ],
      "metadata": {
        "id": "TGGvqTu9Htfs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "creditcard=pd.read_csv('creditcard.csv')"
      ],
      "metadata": {
        "id": "_QTdVHw4FW0M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "creditcard"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "RW1Rh2ZyQ4_E",
        "outputId": "baeaaad2-6052-424f-cb92-da9a1c8b596f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Time        V1        V2        V3        V4        V5        V6  \\\n",
              "0          0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
              "1          0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
              "2          1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
              "3          1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
              "4          2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
              "...      ...       ...       ...       ...       ...       ...       ...   \n",
              "23318  32696  1.071505  0.187075  0.895129  2.656099 -0.452814  0.179717   \n",
              "23319  32696  1.329254  0.264064 -0.048885  0.320503  0.048595 -0.678869   \n",
              "23320  32697  1.323101 -1.393103  0.433789 -1.327880 -1.717583 -0.691175   \n",
              "23321  32697  1.582101 -1.197678 -1.147417 -2.504297  0.984772  3.234035   \n",
              "23322  32697  1.398222 -1.236821  0.333811 -1.597716 -1.295506 -0.096330   \n",
              "\n",
              "             V7        V8        V9  ...       V21       V22       V23  \\\n",
              "0      0.239599  0.098698  0.363787  ... -0.018307  0.277838 -0.110474   \n",
              "1     -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288   \n",
              "2      0.791461  0.247676 -1.514654  ...  0.247998  0.771679  0.909412   \n",
              "3      0.237609  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321   \n",
              "4      0.592941 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458   \n",
              "...         ...       ...       ...  ...       ...       ...       ...   \n",
              "23318 -0.247008  0.215500 -0.248462  ... -0.050902 -0.102234 -0.075050   \n",
              "23319  0.228248 -0.187475  0.008471  ... -0.560465 -1.689859  0.164856   \n",
              "23320 -1.001486 -0.132899 -1.601772  ... -0.338124 -0.900733  0.052258   \n",
              "23321 -1.492196  0.747692 -1.984488  ... -0.269321 -0.522610  0.001930   \n",
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              "            V24       V25       V26       V27       V28  Amount  Class  \n",
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              "4      0.141267 -0.206010  0.502292  0.219422  0.215153   69.99    0.0  \n",
              "...         ...       ...       ...       ...       ...     ...    ...  \n",
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              "      <td>-1.514654</td>\n",
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              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
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              "      <td>378.66</td>\n",
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              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
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              "      <td>-1.984488</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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          "metadata": {},
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      "cell_type": "code",
      "source": [
        "X=creditcard.iloc[:,:-1]"
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      "metadata": {
        "id": "MDLcKzDAQ76b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
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          "height": 476
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        "id": "L1avZiQiQ_2L",
        "outputId": "5a2a8ade-5054-4178-e24e-5967c1108e56"
      },
      "execution_count": null,
      "outputs": [
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              "        Time        V1        V2        V3        V4        V5        V6  \\\n",
              "0          0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
              "1          0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
              "2          1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
              "3          1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
              "4          2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
              "...      ...       ...       ...       ...       ...       ...       ...   \n",
              "23318  32696  1.071505  0.187075  0.895129  2.656099 -0.452814  0.179717   \n",
              "23319  32696  1.329254  0.264064 -0.048885  0.320503  0.048595 -0.678869   \n",
              "23320  32697  1.323101 -1.393103  0.433789 -1.327880 -1.717583 -0.691175   \n",
              "23321  32697  1.582101 -1.197678 -1.147417 -2.504297  0.984772  3.234035   \n",
              "23322  32697  1.398222 -1.236821  0.333811 -1.597716 -1.295506 -0.096330   \n",
              "\n",
              "             V7        V8        V9  ...       V20       V21       V22  \\\n",
              "0      0.239599  0.098698  0.363787  ...  0.251412 -0.018307  0.277838   \n",
              "1     -0.078803  0.085102 -0.255425  ... -0.069083 -0.225775 -0.638672   \n",
              "2      0.791461  0.247676 -1.514654  ...  0.524980  0.247998  0.771679   \n",
              "3      0.237609  0.377436 -1.387024  ... -0.208038 -0.108300  0.005274   \n",
              "4      0.592941 -0.270533  0.817739  ...  0.408542 -0.009431  0.798278   \n",
              "...         ...       ...       ...  ...       ...       ...       ...   \n",
              "23318 -0.247008  0.215500 -0.248462  ... -0.203401 -0.050902 -0.102234   \n",
              "23319  0.228248 -0.187475  0.008471  ... -0.089440 -0.560465 -1.689859   \n",
              "23320 -1.001486 -0.132899 -1.601772  ... -0.150140 -0.338124 -0.900733   \n",
              "23321 -1.492196  0.747692 -1.984488  ... -0.208870 -0.269321 -0.522610   \n",
              "23322 -1.096847 -0.008739 -1.990493  ... -0.143160 -0.330900 -0.760100   \n",
              "\n",
              "            V23       V24       V25       V26       V27       V28  Amount  \n",
              "0     -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62  \n",
              "1      0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69  \n",
              "2      0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66  \n",
              "3     -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50  \n",
              "4     -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99  \n",
              "...         ...       ...       ...       ...       ...       ...     ...  \n",
              "23318 -0.075050  0.317656  0.510180  0.034916 -0.005075  0.012346   24.56  \n",
              "23319  0.164856 -0.490515  0.175748  0.029118 -0.054453  0.007967    5.00  \n",
              "23320  0.052258 -0.037719  0.112664 -0.396570  0.017171  0.042853  118.75  \n",
              "23321  0.001930  0.981586  0.549879 -0.190893  0.033277  0.013703   10.00  \n",
              "23322       NaN       NaN       NaN       NaN       NaN       NaN     NaN  \n",
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              "      <th></th>\n",
              "      <th>Time</th>\n",
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              "      <th>2</th>\n",
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              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
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              "      <td>-0.139097</td>\n",
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              "      <td>378.66</td>\n",
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              "      <td>1.792993</td>\n",
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              "      <td>1.247203</td>\n",
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              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
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              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
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              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "    domScope.querySelector('#df-5b423e1c-76ea-40e2-ad23-709689eaf9aa button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-74478d8e-dd52-462c-b1f5-43e6a8313338 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-74478d8e-dd52-462c-b1f5-43e6a8313338');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 341
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y=creditcard['Class']"
      ],
      "metadata": {
        "id": "MrT5dHj3RCqS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6ObSM-6RLyM",
        "outputId": "986fd03f-f217-4246-e8f8-4263297cf494"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        0.0\n",
              "1        0.0\n",
              "2        0.0\n",
              "3        0.0\n",
              "4        0.0\n",
              "        ... \n",
              "23318    0.0\n",
              "23319    0.0\n",
              "23320    0.0\n",
              "23321    0.0\n",
              "23322    NaN\n",
              "Name: Class, Length: 23323, dtype: float64"
            ]
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          "metadata": {},
          "execution_count": 343
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,x,Y_train,y=train_test_split(X,Y)"
      ],
      "metadata": {
        "id": "qUUjxUd0RMPm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_cv,x_test,y_cv,y_test=train_test_split(x,y)"
      ],
      "metadata": {
        "id": "sETi6nGTRWl5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "isf=IsolationForest()"
      ],
      "metadata": {
        "id": "gBevJ2-CSUmH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "isf.fit(X_train[Y_train==0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "oMgslGptSulV",
        "outputId": "0831f455-6afa-4e1c-e4a9-ebe877c52fac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IsolationForest()"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>IsolationForest()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">IsolationForest</label><div class=\"sk-toggleable__content\"><pre>IsolationForest()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 352
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_cv_predt=isf.predict(x_cv)"
      ],
      "metadata": {
        "id": "YblulJyOTHf9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_anomoly=isf.decision_function(x_cv)"
      ],
      "metadata": {
        "id": "RNp0tdlYUIhM"
      },
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      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "cv_anomoly"
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        "id": "3eSTTXL2UUHa",
        "outputId": "cf5c7002-f3ff-4993-8767-48da1867f9d1"
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              "array([0.12620201, 0.11517392, 0.12617047, ..., 0.06717734, 0.08668893,\n",
              "       0.13866259])"
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    {
      "cell_type": "code",
      "source": [
        "y_predict=isf.predict(x_test)"
      ],
      "metadata": {
        "id": "BtKwF1RGUWH7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_anomoly=isf.decision_function(x_test)"
      ],
      "metadata": {
        "id": "xgBMqWXiUytA"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
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        "test_anomoly"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.11401934, 0.0806694 , 0.1454145 , ..., 0.11950623, 0.08901677,\n",
              "       0.13319574])"
            ]
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          "metadata": {},
          "execution_count": 361
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      "cell_type": "code",
      "source": [
        "y_test.shape"
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        "id": "CY6JCRdCXad5",
        "outputId": "26e22310-ca65-452e-cb4b-723714ec0054"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1458,)"
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          "metadata": {},
          "execution_count": 366
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      "cell_type": "code",
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        "y_predict.shape"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1458,)"
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          "metadata": {},
          "execution_count": 367
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    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,y_predict)"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9pCX4Ac7XH6h",
        "outputId": "43af43d0-da3a-4dda-9c39-2feb2fbff728"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   0,    0,    0],\n",
              "       [  61,    0, 1392],\n",
              "       [   5,    0,    0]])"
            ]
          },
          "metadata": {},
          "execution_count": 368
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classification_report(y_test,y_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "sJ1grcrKXwaW",
        "outputId": "f2a55292-9f3c-4a53-c390-2da1ff246065"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'              precision    recall  f1-score   support\\n\\n        -1.0       0.00      0.00      0.00       0.0\\n         0.0       0.00      0.00      0.00    1453.0\\n         1.0       0.00      0.00      0.00       5.0\\n\\n    accuracy                           0.00    1458.0\\n   macro avg       0.00      0.00      0.00    1458.0\\nweighted avg       0.00      0.00      0.00    1458.0\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 369
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_count=len(y_predict[y_predict==1])"
      ],
      "metadata": {
        "id": "DGPXZFVyX3fn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "normal_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBvH7Lo-ZG4a",
        "outputId": "86c256fd-a7b2-4417-b9cb-670efa432585"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1392"
            ]
          },
          "metadata": {},
          "execution_count": 375
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fraud_count=len(y_predict[y_predict==-1])"
      ],
      "metadata": {
        "id": "JSXuW8S4YHUS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fraud_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJEnOqWfZIm5",
        "outputId": "e3bc3c1c-0628-4ad3-8db0-6b28d26c9208"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "66"
            ]
          },
          "metadata": {},
          "execution_count": 376
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar([\"Normal\",\"Fraud\"],[normal_count,fraud_count],color=['blue','red'])\n",
        "plt.xlabel(\"Types of transaction\")\n",
        "plt.ylabel(\"Number of transaction\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "s3n_vQlxYtJo",
        "outputId": "69fc91b0-6044-476a-c80e-e79918210851"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decision Tree with Random Forest and Wine Dataset"
      ],
      "metadata": {
        "id": "vH44YrUbvphf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wine=datasets.load_wine(as_frame=True)"
      ],
      "metadata": {
        "id": "nASrvIZDY_mn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y=wine.target"
      ],
      "metadata": {
        "id": "ZVihvV7Fwfer"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uG82voQdwhWF",
        "outputId": "d2bf1a26-3ce0-4a35-e6b5-6d48a5efa194"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      0\n",
              "4      0\n",
              "      ..\n",
              "173    2\n",
              "174    2\n",
              "175    2\n",
              "176    2\n",
              "177    2\n",
              "Name: target, Length: 178, dtype: int64"
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          "metadata": {},
          "execution_count": 383
        }
      ]
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      "cell_type": "code",
      "source": [
        "X=wine.data"
      ],
      "metadata": {
        "id": "dYLunh-nwuza"
      },
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      "outputs": []
    },
    {
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        "X"
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        "outputId": "2e2e9771-0ccd-4f1d-ba8b-c668d5430962"
      },
      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
              "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
              "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
              "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
              "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
              "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
              "..       ...         ...   ...                ...        ...            ...   \n",
              "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
              "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
              "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
              "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
              "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
              "\n",
              "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
              "0          3.06                  0.28             2.29             5.64  1.04   \n",
              "1          2.76                  0.26             1.28             4.38  1.05   \n",
              "2          3.24                  0.30             2.81             5.68  1.03   \n",
              "3          3.49                  0.24             2.18             7.80  0.86   \n",
              "4          2.69                  0.39             1.82             4.32  1.04   \n",
              "..          ...                   ...              ...              ...   ...   \n",
              "173        0.61                  0.52             1.06             7.70  0.64   \n",
              "174        0.75                  0.43             1.41             7.30  0.70   \n",
              "175        0.69                  0.43             1.35            10.20  0.59   \n",
              "176        0.68                  0.53             1.46             9.30  0.60   \n",
              "177        0.76                  0.56             1.35             9.20  0.61   \n",
              "\n",
              "     od280/od315_of_diluted_wines  proline  \n",
              "0                            3.92   1065.0  \n",
              "1                            3.40   1050.0  \n",
              "2                            3.17   1185.0  \n",
              "3                            3.45   1480.0  \n",
              "4                            2.93    735.0  \n",
              "..                            ...      ...  \n",
              "173                          1.74    740.0  \n",
              "174                          1.56    750.0  \n",
              "175                          1.56    835.0  \n",
              "176                          1.62    840.0  \n",
              "177                          1.60    560.0  \n",
              "\n",
              "[178 rows x 13 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
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              "      <td>2.76</td>\n",
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              "      <th>2</th>\n",
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              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735.0</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>2.45</td>\n",
              "      <td>20.5</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.06</td>\n",
              "      <td>7.70</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.74</td>\n",
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          "metadata": {},
          "execution_count": 385
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    {
      "cell_type": "code",
      "source": [
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        "id": "L6Mbuo-kwxnd"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "x_cv,x_test,y_cv,y_test=train_test_split(x,y)"
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      "execution_count": null,
      "outputs": []
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      "execution_count": null,
      "outputs": []
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    {
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        "id": "CydW5PP7yX2i",
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              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 389
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_cv_predict=rf.predict(x_cv)"
      ],
      "metadata": {
        "id": "Y0UNDGI4yb2X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_cv,y_cv_predict,normalize=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "isxMIjbRyl3S",
        "outputId": "817f0bd3-7ebd-4e5a-e8b3-2ecf6b351bcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {},
          "execution_count": 392
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_cv_predict.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZP_XxGayxKW",
        "outputId": "eeb3ec81-658b-4334-855a-00ca6553e4c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(33,)"
            ]
          },
          "metadata": {},
          "execution_count": 393
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_cv.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZl_I18Ny2ni",
        "outputId": "871e89c6-9821-46cb-aa84-54bbacde88d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(33,)"
            ]
          },
          "metadata": {},
          "execution_count": 394
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_cv,y_cv_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8InkVQfDy5GP",
        "outputId": "d53fbca0-770a-47ce-ae98-3480f88cac3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 9,  0,  0],\n",
              "       [ 0, 15,  1],\n",
              "       [ 0,  0,  8]])"
            ]
          },
          "metadata": {},
          "execution_count": 395
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict=rf.predict(x_test)"
      ],
      "metadata": {
        "id": "Pi6k2XpSy-IK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,y_predict,normalize=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SAPv___0Adb",
        "outputId": "5d40ac1b-efc3-41df-bac6-456c2b5492ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 397
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yIiCCUdJ0Y7z",
        "outputId": "fc85fc05-7440-44b4-9340-db073f56259a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12,)"
            ]
          },
          "metadata": {},
          "execution_count": 398
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,y_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9i31Ddp50bae",
        "outputId": "147fc382-4aa4-4220-a74e-2552e62b2ab7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2, 0, 0],\n",
              "       [0, 4, 0],\n",
              "       [0, 0, 6]])"
            ]
          },
          "metadata": {},
          "execution_count": 399
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# XGBoost with Breast Cancer Dataset"
      ],
      "metadata": {
        "id": "h-q6FZVT5Ik3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bc=datasets.load_breast_cancer()"
      ],
      "metadata": {
        "id": "WpVr4y4k3B6F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=bc.data"
      ],
      "metadata": {
        "id": "X3TPKLfj5jq8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y=bc.target"
      ],
      "metadata": {
        "id": "nEXTZK4k5lBR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,x,Y_train,y=train_test_split(X,Y)"
      ],
      "metadata": {
        "id": "yoCp1iZC5oW0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_cv,x_test,y_cv,y_test=train_test_split(x,y)"
      ],
      "metadata": {
        "id": "iv6oS2ff5qcL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xg=XGBClassifier(objective=\"binary:logistic\")"
      ],
      "metadata": {
        "id": "QlkVI0I15stE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xg.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "nqiVNIsm53Oj",
        "outputId": "e90c81ff-0896-4d1a-a340-e8f441d10a30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 422
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_cv_predict=xg.predict(x_cv)"
      ],
      "metadata": {
        "id": "HAL8qxlG56X7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_cv,y_cv_predict,normalize=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9PF_Blu36V6i",
        "outputId": "c3041289-3568-446f-a234-2accee20ad0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "102"
            ]
          },
          "metadata": {},
          "execution_count": 427
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_cv.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8nhdXpFp6bs0",
        "outputId": "640fe6e9-c77c-42ea-b14d-3cdf0aff2d24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(107,)"
            ]
          },
          "metadata": {},
          "execution_count": 428
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_cv,y_cv_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mh2hOH-s6ji_",
        "outputId": "53993284-a0ea-4691-e982-cc9a096ca1e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[41,  3],\n",
              "       [ 2, 61]])"
            ]
          },
          "metadata": {},
          "execution_count": 429
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict=xg.predict(x_test)"
      ],
      "metadata": {
        "id": "9Z8Lx7RV6o1Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,y_predict,normalize=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VhWSvYr27SZ1",
        "outputId": "6cca808d-5ba0-4824-9f88-8d7d6c4f3186"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34"
            ]
          },
          "metadata": {},
          "execution_count": 431
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wsDJDH4Y7Vpp",
        "outputId": "8a57f7b6-f3d6-470e-f554-b5287f2b43f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(36,)"
            ]
          },
          "metadata": {},
          "execution_count": 432
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,y_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VxesSWaE7Yd-",
        "outputId": "448a68ce-e68a-40f8-ea0f-a67ad524283d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[13,  1],\n",
              "       [ 1, 21]])"
            ]
          },
          "metadata": {},
          "execution_count": 433
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KJaltDI17dke"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recommendation System based on Collabrative flitering using Surprise Library SVD (Sigular value decomposition) with Movie Lens Dataset"
      ],
      "metadata": {
        "id": "oRCxCdLc-3Vb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install scikit-surprise"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5iz-Tt4m_Ngr",
        "outputId": "1a07ee3f-eccf-4ffe-bbb1-406614938b26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-surprise\n",
            "  Downloading scikit-surprise-1.1.3.tar.gz (771 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m772.0/772.0 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise) (1.3.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise) (1.10.1)\n",
            "Building wheels for collected packages: scikit-surprise\n",
            "  Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp310-cp310-linux_x86_64.whl size=2811645 sha256=3f178b3fda0e75fb832db0c016c9666fccd4da63450e45139db2e354cbcb1c22\n",
            "  Stored in directory: /root/.cache/pip/wheels/a5/ca/a8/4e28def53797fdc4363ca4af740db15a9c2f1595ebc51fb445\n",
            "Successfully built scikit-surprise\n",
            "Installing collected packages: scikit-surprise\n",
            "Successfully installed scikit-surprise-1.1.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from surprise import SVD,Dataset,accuracy\n",
        "from surprise.model_selection import train_test_split\n",
        "from surprise.dataset import DatasetAutoFolds"
      ],
      "metadata": {
        "id": "vejoPP1c_PaZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=Dataset.load_builtin(\"ml-100k\")"
      ],
      "metadata": {
        "id": "OhrWk1MIAGiX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac5190db-b60e-4ce1-eb56-cb5e8ac31eaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset ml-100k could not be found. Do you want to download it? [Y/n] Y\n",
            "Trying to download dataset from https://files.grouplens.org/datasets/movielens/ml-100k.zip...\n",
            "Done! Dataset ml-100k has been saved to /root/.surprise_data/ml-100k\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqLQsttZB75s",
        "outputId": "12d0cdec-c844-4447-842c-f5db58a0b707"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<surprise.dataset.DatasetAutoFolds at 0x7a9c6773dae0>"
            ]
          },
          "metadata": {},
          "execution_count": 444
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data,test_data=train_test_split(data)"
      ],
      "metadata": {
        "id": "QCGCwvHWB_lN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svd=SVD()"
      ],
      "metadata": {
        "id": "ZlfXm6WqCn52"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svd.fit(train_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asSoXknaDejc",
        "outputId": "800a0bad-0440-42b4-8a8a-9af2e4d93a99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7a9c7aca6170>"
            ]
          },
          "metadata": {},
          "execution_count": 454
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict=svd.test(test_data)"
      ],
      "metadata": {
        "id": "bxveoqrPDhP_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy.rmse(y_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eOmIPbvfDlgU",
        "outputId": "fcf0e460-143f-44c5-b656-d4c0cd083abd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 0.9310\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9309616747432026"
            ]
          },
          "metadata": {},
          "execution_count": 459
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting for a paticular user\n",
        "uid=str(21)\n",
        "iid=str(3)"
      ],
      "metadata": {
        "id": "SxDfWVycD9jk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svd.predict(uid,iid,r_ui=4,verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UBc25v4wEC1C",
        "outputId": "966329df-3d93-4e18-8047-96a519b666e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: 21         item: 3          r_ui = 4.00   est = 2.86   {'was_impossible': False}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid='21', iid='3', r_ui=4, est=2.858168358704596, details={'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 469
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}