Soil_Fertility.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Soil Fertility\n",
        "Use elemetal soil analysis to determine the fertility of the soil.\n",
        "\n",
        "**Input**\n",
        "* N - ratio of Nitrogen (NH4+) content in soil\n",
        "* P - ratio of Phosphorous (P) content in soil\n",
        "* K - ratio of Potassium (K) content in soil\n",
        "* ph - soil acidity (pH)\n",
        "* ec - electrical conductivity\n",
        "* oc - organic carbon\n",
        "* S - sulfur (S)\n",
        "* zn - Zinc (Zn)\n",
        "* fe - Iron (Fe)\n",
        "* cu - Copper (Cu)\n",
        "* Mn - Manganese (Mn)\n",
        "* B - Boron (B)\n",
        "\n",
        "**Output**\n",
        "* Class fertility (0 \"Less Fertile\", 1 \"Fertile\", 2 \"Highly Fertile\")\n"
      ],
      "metadata": {
        "id": "1lZ7vFNW6f_x"
      },
      "id": "1lZ7vFNW6f_x"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Connect colab to google drive."
      ],
      "metadata": {
        "id": "Hyiwmcm06YIF"
      },
      "id": "Hyiwmcm06YIF"
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQPw5O_p6_U3",
        "outputId": "91820717-53b9-4370-a1ca-5c4cba116370"
      },
      "id": "hQPw5O_p6_U3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd '/content/drive/MyDrive/AgriWise/ML/Soil Quality - Fertility'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YjF8OVPn7FfN",
        "outputId": "aa237522-7244-4877-e0ca-379530e35402"
      },
      "id": "YjF8OVPn7FfN",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1nNpHsxhVob0qpN6DIX6h02v09VRuwzHw/AgriWise/ML/Soil Quality - Fertility\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Liberaries and Modules"
      ],
      "metadata": {
        "id": "H9O-G83_KsZ-"
      },
      "id": "H9O-G83_KsZ-"
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import tree, ensemble\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "VRYd3KoPKvhF"
      },
      "id": "VRYd3KoPKvhF",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Naive Approch \n",
        "Before Data Modification"
      ],
      "metadata": {
        "id": "QGb7ztTYrj0L"
      },
      "id": "QGb7ztTYrj0L"
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('data1.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xgEB_0HEulOc",
        "outputId": "986fcd5c-0f2a-4049-b0da-1a9eb1b6cd06"
      },
      "id": "xgEB_0HEulOc",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     N    P    K    ph    ec    oc     S    zn    fe    cu    Mn     B  \\\n",
              "0  138  8.6  560  7.46  0.62  0.70   5.9  0.24  0.31  0.77  8.71  0.11   \n",
              "1  213  7.5  338  7.62  0.75  1.06  25.4  0.30  0.86  1.54  2.89  2.29   \n",
              "2  163  9.6  718  7.59  0.51  1.11  14.3  0.30  0.86  1.57  2.70  2.03   \n",
              "3  157  6.8  475  7.64  0.58  0.94  26.0  0.34  0.54  1.53  2.65  1.82   \n",
              "4  270  9.9  444  7.63  0.40  0.86  11.8  0.25  0.76  1.69  2.43  2.26   \n",
              "\n",
              "   fertility  \n",
              "0          0  \n",
              "1          0  \n",
              "2          0  \n",
              "3          0  \n",
              "4          1  "
            ],
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting data into inputs and targets"
      ],
      "metadata": {
        "id": "QynXGd9qu7jE"
      },
      "id": "QynXGd9qu7jE"
    },
    {
      "cell_type": "code",
      "source": [
        "targetData = data['fertility']\n",
        "inputData = data.drop('fertility', axis=1)"
      ],
      "metadata": {
        "id": "BQCHbyLxvBtI"
      },
      "id": "BQCHbyLxvBtI",
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting raw data into train and validation sets"
      ],
      "metadata": {
        "id": "gxdndzxCuU8v"
      },
      "id": "gxdndzxCuU8v"
    },
    {
      "cell_type": "code",
      "source": [
        "naiveTrainInput, naiveValidationInput, naiveTrainTarget, naiveValidationTarget = train_test_split(inputData, targetData, test_size = 0.2, shuffle=True, random_state = 42)\n",
        "print(\"Naive Train Data Shape: \", naiveTrainInput.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uf0ycNZuufk9",
        "outputId": "0ee87f25-98d7-40cd-e72c-4984b8d28f2b"
      },
      "id": "Uf0ycNZuufk9",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Naive Train Data Shape:  (704, 12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "naiveTrainTarget = naiveTrainTarget.values.ravel()"
      ],
      "metadata": {
        "id": "Wvv33WNTu4lH"
      },
      "id": "Wvv33WNTu4lH",
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "naiveClf = ensemble.RandomForestClassifier()\n",
        "naiveClf.fit(naiveTrainInput, naiveTrainTarget)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "AAevCQQ3vX_m",
        "outputId": "e3f5fb42-a1a7-4fe6-cf77-8dec188f798c"
      },
      "id": "AAevCQQ3vX_m",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "naivePred = naiveClf.predict((naiveValidationInput))"
      ],
      "metadata": {
        "id": "DrJE5_07vVYk"
      },
      "id": "DrJE5_07vVYk",
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "naiveAcc = accuracy_score(naiveValidationTarget, naivePred)\n",
        "\n",
        "print(\"Naive Accuracy : \", naiveAcc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M9qnw5FEvcQL",
        "outputId": "4ec93597-d4f5-43af-af0a-541a7b9c17b0"
      },
      "id": "M9qnw5FEvcQL",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Naive Accuracy :  0.8806818181818182\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a209efea",
      "metadata": {
        "id": "a209efea"
      },
      "source": [
        "# Get the modified data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "973d7180",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "973d7180",
        "outputId": "367de594-5da3-43bf-81ef-9058a914501c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     N    P    K    ph    ec    oc     S    zn    fe    cu    Mn     B  \\\n",
              "0  138  8.6  560  7.46  0.62  0.70   5.9  0.24  0.31  0.77  8.71  0.11   \n",
              "1  213  7.5  338  7.62  0.75  1.06  25.4  0.30  0.86  1.54  2.89  2.29   \n",
              "2  163  9.6  718  7.59  0.51  1.11  14.3  0.30  0.86  1.57  2.70  2.03   \n",
              "3  157  6.8  475  7.64  0.58  0.94  26.0  0.34  0.54  1.53  2.65  1.82   \n",
              "4  220  8.6  444  7.43  0.65  0.72  11.7  0.37  0.66  0.90  2.19  1.82   \n",
              "\n",
              "   fertility  \n",
              "0          0  \n",
              "1          0  \n",
              "2          0  \n",
              "3          0  \n",
              "4          0  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>N</th>\n",
              "      <th>P</th>\n",
              "      <th>K</th>\n",
              "      <th>ph</th>\n",
              "      <th>ec</th>\n",
              "      <th>oc</th>\n",
              "      <th>S</th>\n",
              "      <th>zn</th>\n",
              "      <th>fe</th>\n",
              "      <th>cu</th>\n",
              "      <th>Mn</th>\n",
              "      <th>B</th>\n",
              "      <th>fertility</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>138</td>\n",
              "      <td>8.6</td>\n",
              "      <td>560</td>\n",
              "      <td>7.46</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.70</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.77</td>\n",
              "      <td>8.71</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>213</td>\n",
              "      <td>7.5</td>\n",
              "      <td>338</td>\n",
              "      <td>7.62</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.06</td>\n",
              "      <td>25.4</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.86</td>\n",
              "      <td>1.54</td>\n",
              "      <td>2.89</td>\n",
              "      <td>2.29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>163</td>\n",
              "      <td>9.6</td>\n",
              "      <td>718</td>\n",
              "      <td>7.59</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1.11</td>\n",
              "      <td>14.3</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.86</td>\n",
              "      <td>1.57</td>\n",
              "      <td>2.70</td>\n",
              "      <td>2.03</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>157</td>\n",
              "      <td>6.8</td>\n",
              "      <td>475</td>\n",
              "      <td>7.64</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.94</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.54</td>\n",
              "      <td>1.53</td>\n",
              "      <td>2.65</td>\n",
              "      <td>1.82</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>220</td>\n",
              "      <td>8.6</td>\n",
              "      <td>444</td>\n",
              "      <td>7.43</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.72</td>\n",
              "      <td>11.7</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.90</td>\n",
              "      <td>2.19</td>\n",
              "      <td>1.82</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d363a3cf-f138-472a-ab53-77a77993dcb2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d363a3cf-f138-472a-ab53-77a77993dcb2 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-d363a3cf-f138-472a-ab53-77a77993dcb2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "rawData = pd.read_csv('Soil Fertility Data.csv')\n",
        "rawData.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Exploration"
      ],
      "metadata": {
        "id": "XO3qR-S36y47"
      },
      "id": "XO3qR-S36y47"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fed46b4c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fed46b4c",
        "outputId": "bb9fe271-c1ad-4bac-8acf-ed184c8d2725"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1288 entries, 0 to 1287\n",
            "Data columns (total 13 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   N          1288 non-null   int64  \n",
            " 1   P          1288 non-null   float64\n",
            " 2   K          1288 non-null   int64  \n",
            " 3   ph         1288 non-null   float64\n",
            " 4   ec         1288 non-null   float64\n",
            " 5   oc         1288 non-null   float64\n",
            " 6   S          1288 non-null   float64\n",
            " 7   zn         1288 non-null   float64\n",
            " 8   fe         1288 non-null   float64\n",
            " 9   cu         1288 non-null   float64\n",
            " 10  Mn         1288 non-null   float64\n",
            " 11  B          1288 non-null   float64\n",
            " 12  fertility  1288 non-null   int64  \n",
            "dtypes: float64(10), int64(3)\n",
            "memory usage: 130.9 KB\n"
          ]
        }
      ],
      "source": [
        "rawData.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7618ca98",
      "metadata": {
        "id": "7618ca98"
      },
      "source": [
        "No null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1dfc56b2",
      "metadata": {
        "id": "1dfc56b2",
        "outputId": "3dd89498-1dce-457f-8c02-5a8cfe1c4fa9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 N            P            K           ph           ec  \\\n",
              "count  1288.000000  1288.000000  1288.000000  1288.000000  1288.000000   \n",
              "mean    269.061335    17.643478   497.135093     7.549130     0.535893   \n",
              "std      80.542499    25.303343   120.887088     0.507163     0.134156   \n",
              "min       6.000000     2.900000    11.000000     0.900000     0.100000   \n",
              "25%     207.000000     7.200000   422.000000     7.340000     0.430000   \n",
              "50%     289.000000     9.200000   465.000000     7.500000     0.530000   \n",
              "75%     333.000000    13.800000   570.000000     7.700000     0.630000   \n",
              "max     383.000000   125.000000   887.000000    11.150000     0.950000   \n",
              "\n",
              "                oc            S           zn           fe           cu  \\\n",
              "count  1288.000000  1288.000000  1288.000000  1288.000000  1288.000000   \n",
              "mean      0.632174     7.122888     0.442811     4.119185     0.911281   \n",
              "std       0.720488     4.026723     1.568333     3.028314     0.448782   \n",
              "min       0.100000     0.640000     0.070000     0.210000     0.090000   \n",
              "25%       0.390000     4.520000     0.280000     1.360000     0.600000   \n",
              "50%       0.620000     6.330000     0.360000     3.740000     0.890000   \n",
              "75%       0.832500     8.450000     0.480000     6.320000     1.250000   \n",
              "max      24.000000    31.000000    42.000000    44.000000     3.020000   \n",
              "\n",
              "                Mn            B    fertility  \n",
              "count  1288.000000  1288.000000  1288.000000  \n",
              "mean      8.601530     0.611957     1.035714  \n",
              "std       4.466209     0.560781     0.810938  \n",
              "min       0.110000     0.060000     0.000000  \n",
              "25%       6.045000     0.290000     0.000000  \n",
              "50%       8.540000     0.420000     1.000000  \n",
              "75%      11.560000     0.650000     2.000000  \n",
              "max      31.000000     2.820000     2.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0080f422-2993-4f33-bf0d-04b3acba09f9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>N</th>\n",
              "      <th>P</th>\n",
              "      <th>K</th>\n",
              "      <th>ph</th>\n",
              "      <th>ec</th>\n",
              "      <th>oc</th>\n",
              "      <th>S</th>\n",
              "      <th>zn</th>\n",
              "      <th>fe</th>\n",
              "      <th>cu</th>\n",
              "      <th>Mn</th>\n",
              "      <th>B</th>\n",
              "      <th>fertility</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "      <td>1288.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>269.061335</td>\n",
              "      <td>17.643478</td>\n",
              "      <td>497.135093</td>\n",
              "      <td>7.549130</td>\n",
              "      <td>0.535893</td>\n",
              "      <td>0.632174</td>\n",
              "      <td>7.122888</td>\n",
              "      <td>0.442811</td>\n",
              "      <td>4.119185</td>\n",
              "      <td>0.911281</td>\n",
              "      <td>8.601530</td>\n",
              "      <td>0.611957</td>\n",
              "      <td>1.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>80.542499</td>\n",
              "      <td>25.303343</td>\n",
              "      <td>120.887088</td>\n",
              "      <td>0.507163</td>\n",
              "      <td>0.134156</td>\n",
              "      <td>0.720488</td>\n",
              "      <td>4.026723</td>\n",
              "      <td>1.568333</td>\n",
              "      <td>3.028314</td>\n",
              "      <td>0.448782</td>\n",
              "      <td>4.466209</td>\n",
              "      <td>0.560781</td>\n",
              "      <td>0.810938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>0.210000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.110000</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>207.000000</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>422.000000</td>\n",
              "      <td>7.340000</td>\n",
              "      <td>0.430000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>4.520000</td>\n",
              "      <td>0.280000</td>\n",
              "      <td>1.360000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>6.045000</td>\n",
              "      <td>0.290000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>289.000000</td>\n",
              "      <td>9.200000</td>\n",
              "      <td>465.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>0.530000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>6.330000</td>\n",
              "      <td>0.360000</td>\n",
              "      <td>3.740000</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>8.540000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>333.000000</td>\n",
              "      <td>13.800000</td>\n",
              "      <td>570.000000</td>\n",
              "      <td>7.700000</td>\n",
              "      <td>0.630000</td>\n",
              "      <td>0.832500</td>\n",
              "      <td>8.450000</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>6.320000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>11.560000</td>\n",
              "      <td>0.650000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>383.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>11.150000</td>\n",
              "      <td>0.950000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>3.020000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.820000</td>\n",
              "      <td>2.000000</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0080f422-2993-4f33-bf0d-04b3acba09f9')\"\n",
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              "\n",
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              "          const element = document.querySelector('#df-0080f422-2993-4f33-bf0d-04b3acba09f9');\n",
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              "            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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "rawData.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4975b46b",
      "metadata": {
        "id": "4975b46b",
        "outputId": "70ad751f-36e9-464a-dd40-2559db23a4a3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    447\n",
              "1    440\n",
              "0    401\n",
              "Name: fertility, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "rawData['fertility'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4fe6b1d0",
      "metadata": {
        "id": "4fe6b1d0"
      },
      "source": [
        "- 0 describes low-fertility\n",
        "- 1 describes medium-fertility\n",
        "- 2 describes high-fertility"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ced5db8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1ced5db8",
        "outputId": "4db9ff03-31e5-42cf-8204-5ae36cad25a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     N    P    K    ph    ec    oc     S    zn    fe    cu    Mn     B  \\\n",
              "0  138  8.6  560  7.46  0.62  0.70   5.9  0.24  0.31  0.77  8.71  0.11   \n",
              "1  213  7.5  338  7.62  0.75  1.06  25.4  0.30  0.86  1.54  2.89  2.29   \n",
              "2  163  9.6  718  7.59  0.51  1.11  14.3  0.30  0.86  1.57  2.70  2.03   \n",
              "3  157  6.8  475  7.64  0.58  0.94  26.0  0.34  0.54  1.53  2.65  1.82   \n",
              "4  220  8.6  444  7.43  0.65  0.72  11.7  0.37  0.66  0.90  2.19  1.82   \n",
              "\n",
              "   fertility  \n",
              "0          0  \n",
              "1          0  \n",
              "2          0  \n",
              "3          0  \n",
              "4          0  "
            ],
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              "      <td>0.72</td>\n",
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              "      <td>0.37</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.90</td>\n",
              "      <td>2.19</td>\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-ac22d53c-f951-4c30-a5db-13d6c2b93cf7 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ac22d53c-f951-4c30-a5db-13d6c2b93cf7');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "rawDataCopy = rawData.copy()\n",
        "rawDataCopy.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation is a statical technique to show the relation between variables and if they depend on each other."
      ],
      "metadata": {
        "id": "ru8hTTuTHVZT"
      },
      "id": "ru8hTTuTHVZT"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "507e7cec",
      "metadata": {
        "id": "507e7cec"
      },
      "outputs": [],
      "source": [
        "corr = rawDataCopy.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "40dbdd02",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "40dbdd02",
        "outputId": "c6475ec2-256c-40e2-c535-c7a6bae6f3e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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17t1bCQkJ+uCDDxQSEuLusgAAcJjBgwfrxIkTmjZtmvLz8xUbG6sNGzbYLl7Nzc2Vj89/1iN+/vlnpaSkKD8/X/Xr11fHjh316aefKjo62tZn8uTJKikp0ahRo3Tq1Cl1795dGzZsqPQgNkcyVVRUVDhtdA/2RM6H7i7B6ZJa93D5nJmHtrh0vj15OS6dz106RsS6uwSnuxK+llfC19Ed/h7b12ljb8o76rSxb45o4bSxjczrT+UAAADPQTABAACGQTABAACGQTABAACGQTABAACGQTABAACGQTABAACGQTABAACGQTABAACGcUU8kh5Vc/VTWCX3PG3W1a6EJ5ReCcfojqewuuPPlafNwmhYMQEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIbhdcEkMjJS8+fPd3cZAADgEnhdMAEAAJ6LYAIAAAzD44JJr169NGbMGI0ZM0ahoaEKDw/X1KlTVVFRYetz+vRpjRgxQsHBwWrWrJmWLFnixooBAMDF8rhgIkkrV66Un5+fdu7cqeeff17z5s3Tyy+/bNv/7LPPqlOnTvrss8/0wAMPaPTo0Tp48KAbKwYAABfDIz/Er2nTpnruuedkMpl07bXX6vPPP9dzzz2nlJQUSVL//v31wAMPSJIeffRRPffcc/r444917bXXVjme1WqV1Wq1aztbWiq/gADnHggAALDjkSsmN954o0wmk+11XFycDh06pHPnzkmSYmJibPtMJpMsFouOHz9e7Xjp6ekKDQ212zYvX+e8AwAAAFXyyGByIf7+/navTSaTysvLq+2fmpqqwsJCu63niMHOLhMAAPyBR57K2bFjh93rf/3rX2rdurV8fX0vaTyz2Syz2WzXxmkcAABczyNXTHJzczVx4kQdPHhQa9as0cKFCzVu3Dh3lwUAAC6TR66YJCUl6cyZM+rcubN8fX01btw4jRo1yt1lAQCAy+SRwcTf31/z58/Xiy++WGnfN998U6ktJyfH+UUBAIDL5pGncgAAgHcimAAAAMPwuFM52dnZ7i4BAAA4iccFEwAAjGJ7wSGnjX1zRAunjW1knMoBAACGQTABAACGQTABAACGQTABAACGQTABAACGwV051diTl+PS+TpGxLp0Psn1x+gOSa17uLsE4KK54/uAq7nl+05sX9fPiUvGigkAADAMggkAADAMggkAADAMggkAADAMggkAADAMggkAADAMggkAADAMggkAADAMggkAAF5i0aJFioyMVGBgoLp06aKdO3dW23fp0qW66aabVL9+fdWvX1/x8fGV+icnJ8tkMtltiYmJTj0Gjw0m5eXlSk9PV4sWLRQUFKT27dvr9ddft+3fv3+/br31VoWEhCg4OFg33XSTjhw54saKAQBwnnXr1mnixIlKS0vT3r171b59eyUkJOj48eNV9s/Oztbdd9+tjz/+WNu3b1fTpk3Vt29f/fDDD3b9EhMTlZeXZ9vWrFnj1OPw2EfSp6en69VXX9XixYvVunVrbdmyRcOGDVPDhg0VFRWlHj16qFevXtq0aZNCQkK0bds2nT171t1lAwDgFPPmzVNKSoqGDx8uSVq8eLHee+89LV++XFOmTKnUf9WqVXavX375Zb3xxhvKyspSUlKSrd1sNstisTi3+N/xyGBitVr15JNP6qOPPlJcXJwkqWXLltq6dateeuklRUZGKjQ0VGvXrpW/v78k6U9/+pM7SwYAoFasVqusVqtdm9lsltlsrtS3tLRUe/bsUWpqqq3Nx8dH8fHx2r59+0XNd/r0aZWVlalBgwZ27dnZ2WrUqJHq16+vm2++WbNnz9ZVV111CUd0cTzyVM7hw4d1+vRp3XLLLapXr55ty8zM1JEjR5STk6ObbrrJFkouxGq1qqioyG47V8bqCgDAfdLT0xUaGmq3paenV9n3xx9/1Llz59S4cWO79saNGys/P/+i5nv00UfVpEkTxcfH29oSExOVmZmprKwszZkzR5s3b1a/fv107ty5Sz+wC/DIFZPi4mJJ0nvvvaerr77abp/ZbNb48eNrNV56erpmzJhh19bmr/FqO+yWy6oTAIBLlZqaqokTJ9q1VbVa4ghPPfWU1q5dq+zsbAUGBtrahwwZYvv/du3aKSYmRq1atVJ2drb69OnjlFo8csUkOjpaZrNZubm5ioqKstuaNm2qmJgYffLJJyorK7uo8VJTU1VYWGi3/WlwbycfBQAA1TObzQoJCbHbqgsm4eHh8vX1VUFBgV17QUHBBa8PeeaZZ/TUU0/pww8/VExMTI19W7ZsqfDwcB0+fLh2B1MLHhlMgoODNWnSJE2YMEErV67UkSNHtHfvXi1cuFArV67UmDFjVFRUpCFDhmj37t06dOiQXnnlFR08eLDK8ar64vv6e+RiEgDgChQQEKCOHTsqKyvL1lZeXq6srCzbtZhVmTt3rmbNmqUNGzaoU6dOF5zn+++/108//aSIiAiH1F0Vj/3pO2vWLDVs2FDp6en6+uuvFRYWpuuvv16PPfaYrrrqKm3atEmPPPKIevbsKV9fX8XGxqpbt27uLhsAAKeYOHGi7r33XnXq1EmdO3fW/PnzVVJSYrtLJykpSVdffbXtOpU5c+Zo2rRpWr16tSIjI23Xopy/brO4uFgzZszQoEGDZLFYdOTIEU2ePFlRUVFKSEhw2nF4bDAxmUwaN26cxo0bV+X+mJgYffDBBy6uCgAA9xg8eLBOnDihadOmKT8/X7GxsdqwYYPtgtjc3Fz5+PznRMmLL76o0tJS/eUvf7EbJy0tTdOnT5evr6/27dunlStX6tSpU2rSpIn69u2rWbNmOe1aF8mDgwkAALA3ZswYjRkzpsp92dnZdq+/+eabGscKCgpyyy/4HnmNCQAA8E4EEwAAYBgEEwAAYBgEEwAAYBgEEwAAYBgEEwAAYBgEEwAAYBgEEwAAYBg8YK0aHSNiXTrfnrwcl87nLlfCcSa17uHyOTMPbXHpfGNi73LpfJK0veCQy+d0NXf8+3D19zpXzwfPw4oJAAAwDIIJAAAwDIIJAAAwDIIJAAAwDIIJAAAwDIIJAAAwDIIJAAAwDIIJAAAwDIIJAAAwDIIJAAAwDI8NJlarVQ899JAaNWqkwMBAde/eXbt27bLt379/v2699VaFhIQoODhYN910k44cOeLGigEAwIV4bDCZPHmy3njjDa1cuVJ79+5VVFSUEhISdPLkSf3www/q0aOHzGazNm3apD179mjEiBE6e/asu8sGAAA18MgP8SspKdGLL76ojIwM9evXT5K0dOlSbdy4UcuWLdPPP/+s0NBQrV27Vv7+/pKkP/3pT+4sGQAAXASPDCZHjhxRWVmZunXrZmvz9/dX586ddeDAAeXn5+umm26yhZILsVqtslqtdm1nS0vlFxDg0LoBAEDNPPZUTk2CgoJq1T89PV2hoaF22+bl65xUHQAAqI5HBpNWrVopICBA27Zts7WVlZVp165dio6OVkxMjD755BOVlZVd1HipqakqLCy023qOGOys8gEAQDU88lRO3bp1NXr0aD3yyCNq0KCBmjVrprlz5+r06dMaOXKkysvLtXDhQg0ZMkSpqakKDQ3Vv/71L3Xu3FnXXnttpfHMZrPMZrNdG6dxAABwPY8MJpL01FNPqby8XPfcc49++eUXderUSR988IHq168vSdq0aZMeeeQR9ezZU76+voqNjbW7JgUAABiPxwaTwMBALViwQAsWLKhyf0xMjD744AMXVwUAAC6HR15jAgAAvBPBBAAAGAbBBAAAGAbBBAAAGAbBBAAAGAbBBAAAGAbBBAAAGAbBBAAAGAbBBAAAGIbHPvnV2fbk5bi7BKfrGBHr7hK8UuahLS6fM6l1D5fON25L1U9c9iZXyr+PuMatXTrfCzmvuXQ+SVJsX9fPiUvGigkAADAMggkAADAMggkAADAMggkAADAMggkAADAMggkAADAMggkAADAMrwwmJ06c0OjRo9WsWTOZzWZZLBYlJCRo27Zt7i4NAADUwCsfsDZo0CCVlpZq5cqVatmypQoKCpSVlaWffvrJ3aUBAIAaeN2KyalTp/TJJ59ozpw56t27t5o3b67OnTsrNTVVt99+u7vLAwDAaRYtWqTIyEgFBgaqS5cu2rlzZ439X3vtNbVp00aBgYFq166d3n//fbv9FRUVmjZtmiIiIhQUFKT4+HgdOnTImYfgfcGkXr16qlevnt5++21ZrVZ3lwMAgEusW7dOEydOVFpamvbu3av27dsrISFBx48fr7L/p59+qrvvvlsjR47UZ599poEDB2rgwIH64osvbH3mzp2rBQsWaPHixdqxY4fq1q2rhIQE/frrr047Dq8LJn5+fsrIyNDKlSsVFhambt266bHHHtO+ffvcXRoAAE4zb948paSkaPjw4YqOjtbixYtVp04dLV++vMr+zz//vBITE/XII4+obdu2mjVrlq6//nq98MILkn5bLZk/f74ef/xx3XHHHYqJiVFmZqaOHTumt99+22nH4XXBRPrtGpNjx47pnXfeUWJiorKzs3X99dcrIyOjyv5Wq1VFRUV227mys64tGgCA36nqZ1N1ZwJKS0u1Z88excfH29p8fHwUHx+v7du3V/me7du32/WXpISEBFv/o0ePKj8/365PaGiounTpUu2YjuCVF79KUmBgoG655Rbdcsstmjp1qu677z6lpaUpOTm5Ut/09HTNmDHDrq3NX+PVdtgtLqoWAOCJnPlJ9Olvf1rpZ1NaWpqmT59eqe+PP/6oc+fOqXHjxnbtjRs31ldffVXl+Pn5+VX2z8/Pt+0/31ZdH2fwyhWTqkRHR6ukpKTKfampqSosLLTb/jS4t4srBADgP6r62ZSamuruspzO61ZMfvrpJ911110aMWKEYmJiFBwcrN27d2vu3Lm64447qnyP2WyW2Wy2a/P197o/GgCAB6nqZ1N1wsPD5evrq4KCArv2goICWSyWKt9jsVhq7H/+vwUFBYqIiLDrExsbe7GHUWtet2JSr149denSRc8995x69Oih6667TlOnTlVKSortgh4AALxJQECAOnbsqKysLFtbeXm5srKyFBcXV+V74uLi7PpL0saNG239W7RoIYvFYtenqKhIO3bsqHZMR/C6ZQGz2az09HSlp6e7uxQAAFxm4sSJuvfee9WpUyd17txZ8+fPV0lJiYYPHy5JSkpK0tVXX237+Thu3Dj17NlTzz77rAYMGKC1a9dq9+7dWrJkiSTJZDJp/Pjxmj17tlq3bq0WLVpo6tSpatKkiQYOHOi04/C6YAIAwJVo8ODBOnHihKZNm6b8/HzFxsZqw4YNtotXc3Nz5ePznxMlXbt21erVq/X444/rscceU+vWrfX222/ruuuus/WZPHmySkpKNGrUKJ06dUrdu3fXhg0bFBgY6LTjIJgAAOAlxowZozFjxlS5Lzs7u1LbXXfdpbvuuqva8Uwmk2bOnKmZM2c6qsQL8rprTAAAgOcimAAAAMMgmAAAAMMgmAAAAMMgmAAAAMMgmAAAAMMgmAAAAMPgOSbV6BgR69L5nPkJlUZyJRznmNjqnwngLOO2LHDpfM/3eMil80nSoaI8l873Qs5rLp3PXbYXHHJ3CYAdVkwAAIBhEEwAAIBhEEwAAIBhEEwAAIBhEEwAAIBhEEwAAIBhEEwAAIBhEEwAAIBhEEwAAIBhEEwAAIBheGQw+eabb2QymSptvXr1UkZGhsLCwvTBBx+obdu2qlevnhITE5WX59rHWQMAgNrzyGDStGlT5eXl2bbPPvtMV111lXr06CFJOn36tJ555hm98sor2rJli3JzczVp0iQ3Vw0AAC7EI4OJr6+vLBaLLBaLwsLCdP/99ysuLk7Tp0+XJJWVlWnx4sXq1KmTrr/+eo0ZM0ZZWVnuLRoAAFyQx3+68IgRI/TLL79o48aN8vH5LWfVqVNHrVq1svWJiIjQ8ePHqx3DarXKarXatZ0tLZVfQIBzigYAAFXyyBWT82bPnq0PPvhA77zzjoKDg23t/v7+dv1MJpMqKiqqHSc9PV2hoaF22+bl65xWNwAAqJrHBpM33nhDM2fO1P/+7//arY5citTUVBUWFtptPUcMdlClAADgYnnkqZwvvvhCSUlJevTRR/XnP/9Z+fn5kqSASzz1YjabZTab7do4jQMAgOt55IrJ7t27dfr0ac2ePVsRERG27c4773R3aQAA4DJ4ZDBJTk5WRUVFpS07O1vJyck6deqUXf+BAwfWeI0JAAAwBo8MJgAAwDsRTAAAgGEQTAAAgGEQTAAAgGEQTAAAgGEQTAAAgGEQTAAAgGEQTAAAgGEQTAAAgGF45GfleKOOEbEun3NPXo7L57wSbC845O4SnO5QUZ7L52wdEuHS+dzxb9Id1n66xKXzDek6yqXzwfOwYgIAAAyDYAIAAAyDYAIAAAyDYAIAAAyDYAIAAAyDYAIAAAyDYAIAAAzD44NJRUWFRo0apQYNGshkMiknJ8fdJQEAgEvk8Q9Y27BhgzIyMpSdna2WLVsqPDzc3SUBAIBL5PHB5MiRI4qIiFDXrl3dXQoAALhMHn0qJzk5WWPHjlVubq5MJpMiIyNVXl6u9PR0tWjRQkFBQWrfvr1ef/11d5cKAIAhnDx5UkOHDlVISIjCwsI0cuRIFRcX19h/7NixuvbaaxUUFKRmzZrpoYceUmFhoV0/k8lUaVu7dm2t6/PoFZPnn39erVq10pIlS7Rr1y75+voqPT1dr776qhYvXqzWrVtry5YtGjZsmBo2bKiePXu6u2QAANxq6NChysvL08aNG1VWVqbhw4dr1KhRWr16dZX9jx07pmPHjumZZ55RdHS0vv32W91///06duxYpV/8V6xYocTERNvrsLCwWtfn0cEkNDRUwcHB8vX1lcVikdVq1ZNPPqmPPvpIcXFxkqSWLVtq69ateumll6oNJlarVVar1a7tbGmp/AICnH4MAAC4yoEDB7Rhwwbt2rVLnTp1kiQtXLhQ/fv31zPPPKMmTZpUes91112nN954w/a6VatWeuKJJzRs2DCdPXtWfn7/iRJhYWGyWCyXVaNHn8r5o8OHD+v06dO65ZZbVK9ePduWmZmpI0eOVPu+9PR0hYaG2m2bl69zYeUAANizWq0qKiqy2/74S3Rtbd++XWFhYbZQIknx8fHy8fHRjh07LnqcwsJChYSE2IUSSXrwwQcVHh6uzp07a/ny5aqoqKh1jR69YvJH58+Rvffee7r66qvt9pnN5mrfl5qaqokTJ9q1PffVFscXCADARUpPT9eMGTPs2tLS0jR9+vRLHjM/P1+NGjWya/Pz81ODBg2Un59/UWP8+OOPmjVrlkaNGmXXPnPmTN18882qU6eOPvzwQz3wwAMqLi7WQw89VKsavSqYREdHy2w2Kzc3t1bXk5jN5krBhdM4AAB3quqX5up+yZ4yZYrmzJlT43gHDhy47JqKioo0YMAARUdHVwpIU6dOtf1/hw4dVFJSoqeffvrKDibBwcGaNGmSJkyYoPLycnXv3l2FhYXatm2bQkJCdO+997q7RAAALkpVvzRX5+GHH1ZycnKNfVq2bCmLxaLjx4/btZ89e1YnT5684LUhv/zyixITExUcHKy33npL/v7+Nfbv0qWLZs2aJavVetHHIXlZMJGkWbNmqWHDhkpPT9fXX3+tsLAwXX/99XrsscfcXRoAAE7RsGFDNWzY8IL94uLidOrUKe3Zs0cdO3aUJG3atEnl5eXq0qVLte8rKipSQkKCzGaz3nnnHQUGBl5wrpycHNWvX79WoUTygmAyfvx4jR8/3vbaZDJp3LhxGjdunPuKAgDAgNq2bavExESlpKRo8eLFKisr05gxYzRkyBDbHTk//PCD+vTpo8zMTHXu3FlFRUXq27evTp8+rVdffdV2Ia70WyDy9fXV+vXrVVBQoBtvvFGBgYHauHGjnnzySU2aNKnWNXp8MAEAABdv1apVGjNmjPr06SMfHx8NGjRICxYssO0vKyvTwYMHdfr0aUnS3r17bXfsREVF2Y119OhRRUZGyt/fX4sWLdKECRNUUVGhqKgozZs3TykpKbWuj2ACAMAVpEGDBtU+TE2SIiMj7W7z7dWr1wVv+01MTLR7sNrl8KrnmAAAAM9GMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIbBc0yqsScvx90leKWOEbHuLsErufrP9YWc11w6n+T6Y0xq3cOl80lS5iHXf6r5kK6jLtzJgdzyvTW2r+vnxCVjxQQAABgGwQQAABgGwQQAABgGwQQAABgGF78CAHCJDh3d6e4SvA4rJgAAwDAIJgAAwDAIJgAAwDAIJgAAwDA8NpiUl5dr7ty5ioqKktlsVrNmzfTEE08oOztbJpNJp06dsvXNycmRyWTSN99847Z6AQDAhXnsXTmpqalaunSpnnvuOXXv3l15eXn66quv3F0WAAC4DB4ZTH755Rc9//zzeuGFF3TvvfdKklq1aqXu3bsrOzvbvcUBAIBL5pHB5MCBA7JarerTp49DxrNarbJarXZt58rOytffI/94AADwWB55jUlQUFC1+3x8fjukiooKW1tZWVmN46Wnpys0NNRu+791HzumWAAAcNE8Mpi0bt1aQUFBysrKqrSvYcOGkqS8vDxbW05OTo3jpaamqrCw0G770+DeDq0ZAABcmEeeqwgMDNSjjz6qyZMnKyAgQN26ddOJEye0f/9+JSUlqWnTppo+fbqeeOIJ/d///Z+effbZGsczm80ym812bZzGAQDA9Tz2p+/UqVPl5+enadOm6dixY4qIiND9998vf39/rVmzRqNHj1ZMTIxuuOEGzZ49W3fddZe7SwYAABfgscHEx8dHf//73/X3v/+90r5u3bpp3759dm2/v+YEAAAYk0deYwIAALwTwQQAABgGwQQAABgGwQQAABgGwQQAABgGwQQAABgGwQQAABgGwQQAABgGwQQAABiGxz751dk6RsS6dL49eTkunU9y/TFKrj/OK+EY4RyZh7a4fM6k1j1cPmf/lcNcOt+QrqNcOh88DysmAADAMAgmAADAMAgmAADAMAgmAADAMAgmAADAMAgmAADAMAgmAADAMAgmAADAMDwimCQnJ8tkMun++++vtO/BBx+UyWRScnKy6wsDAAAO5RHBRJKaNm2qtWvX6syZM7a2X3/9VatXr1azZs3cWBkAAHAUjwkm119/vZo2bao333zT1vbmm2+qWbNm6tChg62tV69eeuihhzR58mQ1aNBAFotF06dPd0PFAAAYz8mTJzV06FCFhIQoLCxMI0eOVHFxcY3v6dWrl0wmk932x7MYubm5GjBggOrUqaNGjRrpkUce0dmzZ2tdn8cEE0kaMWKEVqxYYXu9fPlyDR8+vFK/lStXqm7dutqxY4fmzp2rmTNnauPGja4sFQAAQxo6dKj279+vjRs36t1339WWLVs0atSFP8MoJSVFeXl5tm3u3Lm2fefOndOAAQNUWlqqTz/9VCtXrlRGRoamTZtW6/o8KpgMGzZMW7du1bfffqtvv/1W27Zt07BhlT+AKiYmRmlpaWrdurWSkpLUqVMnZWVluaFiAACM48CBA9qwYYNefvlldenSRd27d9fChQu1du1aHTt2rMb31qlTRxaLxbaFhITY9n344Yf68ssv9eqrryo2Nlb9+vXTrFmztGjRIpWWltaqRo8KJg0bNtSAAQOUkZGhFStWaMCAAQoPD6/ULyYmxu51RESEjh8/Xu24VqtVRUVFdtvZWv5BAgBgdNu3b1dYWJg6depka4uPj5ePj4927NhR43tXrVql8PBwXXfddUpNTdXp06ftxm3Xrp0aN25sa0tISFBRUZH2799fqxr9atXbAEaMGKExY8ZIkhYtWlRlH39/f7vXJpNJ5eXl1Y6Znp6uGTNm2LX1/tsw9bn/nsusFgCAS2O1WmW1Wu3azGazzGbzJY+Zn5+vRo0a2bX5+fmpQYMGys/Pr/Z9f/3rX9W8eXM1adJE+/bt06OPPqqDBw/arvvMz8+3CyWSbK9rGrcqHrViIkmJiYkqLS1VWVmZEhISHDJmamqqCgsL7baeIwY7ZGwAAC5Fenq6QkND7bb09PQq+06ZMqXSxal/3L766qtLrmXUqFFKSEhQu3btNHToUGVmZuqtt97SkSNHLnnM6njciomvr68OHDhg+39HqCqB+gUEOGRsAAAuRWpqqiZOnGjXVt1qycMPP3zB53m1bNlSFoul0qUNZ8+e1cmTJ2WxWC66ti5dukiSDh8+rFatWslisWjnzp12fQoKCiSpVuNKHhhMJNldcAMAgDeqzWmbhg0bqmHDhhfsFxcXp1OnTmnPnj3q2LGjJGnTpk0qLy+3hY2LkZOTI+m3azjPj/vEE0/o+PHjtlNFGzduVEhIiKKjoy96XMlDgklGRkaN+99++23b/2dnZ9e4HwCAK1Xbtm2VmJiolJQULV68WGVlZRozZoyGDBmiJk2aSJJ++OEH9enTR5mZmercubOOHDmi1atXq3///rrqqqu0b98+TZgwQT169LDdbNK3b19FR0frnnvu0dy5c5Wfn6/HH39cDz74YK2vifG4a0wAAMClW7Vqldq0aaM+ffqof//+6t69u5YsWWLbX1ZWpoMHD9ruugkICNBHH32kvn37qk2bNnr44Yc1aNAgrV+/3vYeX19fvfvuu/L19VVcXJyGDRumpKQkzZw5s9b1ecSKCQAAcIwGDRpo9erV1e6PjIxURUWF7XXTpk21efPmC47bvHlzvf/++5ddHysmAADAMAgmAADAMAgmAADAMAgmAADAMAgmAADAMAgmAADAMAgmAADAMHiOSTX25OW4uwSv1DEi1t0lOJ07jjGucWuXzre94JBL55OktZ8uuXAnBxrSdZRL55Ok/iuHuXzO9+991aXzZR7a4tL54HlYMQEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIbhdcEkOTlZJpPJtl111VVKTEzUvn373F0aAAC4AK8LJpKUmJiovLw85eXlKSsrS35+frr11lvdXRYAALgArwwmZrNZFotFFotFsbGxmjJlir777judOHHC3aUBAIAaeGUw+b3i4mK9+uqrioqK0lVXXeXucgAAQA288kP83n33XdWrV0+SVFJSooiICL377rvy8ak6h1mtVlmtVru2c2Vn5evvlX88AAAYlleumPTu3Vs5OTnKycnRzp07lZCQoH79+unbb7+tsn96erpCQ0Pttv9b97GLqwYAAF4ZTOrWrauoqChFRUXphhtu0Msvv6ySkhItXbq0yv6pqakqLCy02/40uLeLqwYAAFfEuQqTySQfHx+dOXOmyv1ms1lms9mujdM4AAC4nlf+9LVarcrPz5ck/fzzz3rhhRdUXFys2267zc2VAQCAmnhlMNmwYYMiIiIkScHBwWrTpo1ee+019erVy72FAQCAGnldMMnIyFBGRoa7ywAAAJfAKy9+BQAAnolgAgAADINgAgAADINgAgAADINgAgAADMPr7soBAMBVDh4+7O4SvA4rJgAAwDAIJgAAwDAIJgAAwDBMFRUVFe4uwog25R116XzbCw65dL4rxZ68HJfP2TEi1uVzuuM4Xc3Vf65Xyt8dV0tq3cPlczatG+i0sQMmxjpt7NJ5OU4b28hYMQEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMAEAAIZRq2BSUVGhUaNGqUGDBjKZTMrJyXFoMSaTSW+//bYk6ZtvvrGbIzs7WyaTSadOnXLonAAAwDhqFUw2bNigjIwMvfvuu8rLy9N11113SZNOnz5dsbGxldrz8vLUr1+/Kt/TtWtX5eXlKTQ0VJKUkZGhsLCwS5ofAAAYU60+xO/IkSOKiIhQ165dL2myiooKnTt3rtr9Foul2n0BAQE17gcAAJ7voldMkpOTNXbsWOXm5spkMikyMlLl5eVKT09XixYtFBQUpPbt2+v111+3vef86Zd//vOf6tixo8xms1599VXNmDFD//73v2UymWQymZSRkSHJ/lTOH/3+VE52draGDx+uwsJC2xjTp0/XzJkzq1zFiY2N1dSpU2v3JwMAAFzuooPJ888/r5kzZ+qaa65RXl6edu3apfT0dGVmZmrx4sXav3+/JkyYoGHDhmnz5s12750yZYqeeuopHThwQLfccosefvhh/fnPf1ZeXp7y8vI0ePDgWhXdtWtXzZ8/XyEhIbYxJk2apBEjRujAgQPatWuXre9nn32mffv2afjw4bWaAwAAb3Ty5EkNHTpUISEhCgsL08iRI1VcXFxt//PXfFa1vfbaa7Z+Ve1fu3Ztreu76FM5oaGhCg4Olq+vrywWi6xWq5588kl99NFHiouLkyS1bNlSW7du1UsvvaSePXva3jtz5kzdcsstttf16tWTn5/fJZ+aCQgIUGhoqEwmk90Y9erVU0JCglasWKEbbrhBkrRixQr17NlTLVu2vKS5AADwJkOHDlVeXp42btyosrIyDR8+XKNGjdLq1aur7N+0aVPl5eXZtS1ZskRPP/10petCV6xYocTERNvrS7kWtFbXmPze4cOHdfr0abvAIUmlpaXq0KGDXVunTp0udZpaS0lJ0YgRIzRv3jz5+Pho9erVeu6552p8j9VqldVqtWsrtVoVYDY7s1QAAFzqwIED2rBhg3bt2mX72bxw4UL1799fzzzzjJo0aVLpPecXJH7vrbfe0n//93+rXr16du1hYWGXfT3oJT/H5Pyyz3vvvaecnBzb9uWXX9pdZyJJdevWvawia+O2226T2WzWW2+9pfXr16usrEx/+ctfanxPenq6QkND7bY1C190UcUAAFRmtVpVVFRkt/3xl+ja2r59u8LCwuwWDOLj4+Xj46MdO3Zc1Bh79uxRTk6ORo4cWWnfgw8+qPDwcHXu3FnLly9XRUVFrWu85BWT6Ohomc1m5ebm2p22uRgBAQE13p1zOWP4+fnp3nvv1YoVKxQQEKAhQ4YoKCioxrFSU1M1ceJEu7ZPTx67rPoAALgc6enpmjFjhl1bWlqapk+ffslj5ufnq1GjRnZtfn5+atCggfLz8y9qjGXLlqlt27aV7tCdOXOmbr75ZtWpU0cffvihHnjgARUXF+uhhx6qVY2XHEyCg4M1adIkTZgwQeXl5erevbsKCwu1bds2hYSE6N577632vZGRkTp69KhycnJ0zTXXKDg4WOZanjaJjIxUcXGxsrKy1L59e9WpU0d16tSRJN13331q27atJGnbtm0XHMtsNleaP6Dkp1rVAwCAI1X1S3N1PyunTJmiOXPm1DjegQMHLrumM2fOaPXq1VXe6fr7tg4dOqikpERPP/2064KJJM2aNUsNGzZUenq6vv76a4WFhen666/XY489VuP7Bg0apDfffFO9e/fWqVOntGLFCiUnJ9dq7q5du+r+++/X4MGD9dNPP9mlyNatW6tr1646efKkunTpcolHBwCA+1T1S3N1Hn744Qv+HG3ZsqUsFouOHz9u13727FmdPHnyoq4Nef3113X69GklJSVdsG+XLl00a9YsWa3WWi0+mCou5QSQwVVUVKh169Z64IEHKqXNi7Up76iDq6rZ9oJDLp3vSrEnL8flc3aMiHX5nO44Tldz9Z/rlfJ3x9WSWvdw+ZxN6wY6beyAibFOG7t0Xo7Dxzxw4ICio6O1e/dudezYUZL04YcfKjExUd9//32VF7/+Xq9evRQeHl7pWtKqPPHEE3r22Wd18uTJWtV4WSsmRnTixAmtXbtW+fn5PLsEAIDfadu2rRITE5WSkqLFixerrKxMY8aM0ZAhQ2yh5IcfflCfPn2UmZmpzp072957+PBhbdmyRe+//36lcdevX6+CggLdeOONCgwM1MaNG/Xkk09q0qRJta7R64JJo0aNFB4eriVLlqh+/fruLgcAAENZtWqVxowZoz59+sjHx0eDBg3SggULbPvLysp08OBBnT592u59y5cv1zXXXKO+fftWGtPf31+LFi3ShAkTVFFRoaioKM2bN08pKSm1rs8rT+U4AqdyvMOVshzPqRzHu1L+7rgap3IunjNO5XiCS36OCQAAgKMRTAAAgGEQTAAAgGEQTAAAgGEQTAAAgGEQTAAAgGFwu7ADWa1WpaenKzU1tdaf/eMpc14Jx+iOOTlG5vSU+a6kOS8Gtws7HsHEgYqKihQaGqrCwkKFhIR45ZxXwjG6Y06OkTk9Zb4rac6LQTBxPE7lAAAAwyCYAAAAwyCYAAAAwyCYOJDZbFZaWppLL8xy9ZxXwjG6Y06OkTk9Zb4raU64Bxe/AgBwibj41fFYMQEAAIZBMAEAAIZBMAEAAIZBMAEAAIZBMMFlKy4udncJAAAvQTBBjZ577rka9//yyy9KSEhwUTXOc/ToUR06dKhS+6FDh/TNN994/Jzbt2/Xu+++a9eWmZmpFi1aqFGjRho1apSsVqtD5wQuR1pamr799lt3lwE3IJh4kPLycs2ZM0fdunXTDTfcoClTpujMmTNOnfOxxx5TZmZmlftKSkqUmJion376yak1uEJycrI+/fTTSu07duxQcnKyx885c+ZM7d+/3/b6888/18iRIxUfH68pU6Zo/fr1Sk9Pd+icklRQUKB77rlHTZo0kZ+fn3x9fe02Z/jkk080bNgwxcXF6YcffpAkvfLKK9q6datT5svNza1x8xa//3f+3Xffadq0aXrkkUf0ySefOGW+f/zjH2rVqpX69Omj1atXE5yvIDzH5DL4+PjIZDLV2MdkMuns2bMOmW/WrFmaPn264uPjFRQUpA8++EB33323li9f7pDxq/L666/rnnvu0bp163T77bfb2ktKSpSQkKDjx49r8+bNioiIcPjchw4d0scff6zjx4+rvLzcbt+0adMcOldISIj27t2rqKgou/bDhw+rU6dOOnXqlEPnc/WcERERWr9+vTp16iRJ+vvf/67Nmzfbfli/9tprSktL05dffumwOSWpX79+ys3N1ZgxYxQREVHp38sdd9zh0PneeOMN3XPPPRo6dKheeeUVffnll2rZsqVeeOEFvf/++3r//fcdOp904e8D586dc/ic52VlZSkrK6vKfyOO+r7w+eef67bbbtN3332n1q1ba+3atUpMTFRJSYl8fHxUUlKi119/XQMHDnTIfL/32WefacWKFVqzZo3Onj2rIUOGaMSIEbrhhhscPtel4jkmjufn7gI82VtvvVXtvu3bt2vBggWVvllcjszMTP3P//yP/va3v0mSPvroIw0YMEAvv/yyfHycs/j1l7/8RadOndLdd9+t9957T7169bKtlBQUFDgtlCxdulSjR49WeHi4LBaL3Td+k8nk8GBiMpn0yy+/VGovLCx02g8WV875888/q3HjxrbXmzdvVr9+/Wyvb7jhBn333XcOnVOStm7dqk8++USxsbEOH7sqs2fP1uLFi5WUlKS1a9fa2rt166bZs2c7Zc7PPvvM7nVZWZk+++wzzZs3T0888YRT5pSkGTNmaObMmerUqVOVoc9RJk+erHbt2mnVqlV65ZVXdOutt2rAgAFaunSpJGns2LF66qmnnBJMOnTooA4dOujZZ5/V+vXrtWLFCnXr1k1t2rTRyJEjlZycrNDQUIfPC/dixcTBDh48aFsaHzp0qGbOnKnmzZs7ZGyz2azDhw+radOmtrbAwEAdPnxY11xzjUPmqM7cuXP1xBNP6B//+IemTZumH374QZs3b3bavM2bN9cDDzygRx991Cnj/9Ftt92moKAgrVmzxnaK4dy5cxo8eLBKSkr0z3/+06PnbN68uV555RX16NFDpaWlCgsL0/r169WnTx9Jv/1W3LNnT508edJhc0pSdHS0Vq1apQ4dOjh03OrUqVNHX375pSIjIxUcHKx///vfatmypb7++mtFR0fr119/dUkdkvTee+/p6aefVnZ2tlPGj4iI0Ny5c3XPPfc4ZfzzwsPDtWnTJsXExKi4uFghISHatWuXOnbsKEn66quvdOONNzplVfG80tJSvfXWW1q+fLk2bdqkrl276tixYyooKNDSpUs1ePBgp819IayYOB4rJg5y7NgxpaWlaeXKlUpISFBOTo6uu+46h85x9uxZBQYG2rX5+/urrKzMofNUZfLkyTp58qT69OmjyMhIZWdnOzUM/fzzz7rrrrucNv4fzZkzRz169NC1116rm266SdJv1yoUFhbq448/dumcRUVF2rRpk0Pn6t+/v6ZMmaI5c+bo7bffVp06dWxzStK+ffvUqlUrh84pSfPnz9eUKVP00ksvKTIy0uHj/5HFYtHhw4crzbV161a1bNnS6fP/3rXXXqtdu3Y5bfzS0lJ17drVaeOfd/LkSVksFklSvXr1VLduXdWvX9+2v379+lWu/DnCnj17bKdyzGazkpKStGjRItvpz4ULF+qhhx5yazCB4xFMLlNhYaGefPJJLVy4ULGxscrKyrL7hu9IFRUVSk5OtvsQq19//VX333+/6tata2t78803HTbnnXfeaffa399f4eHhGjdunF27I+eUpLvuuksffvih7r//foeOW53o6Gjt27dPixYtUk5OjoKCgpSUlKQxY8aoQYMGHj/nrFmzdOedd6pnz56qV6+eVq5cqYCAANv+5cuXq2/fvg6dU5IGDx6s06dPq1WrVqpTp478/f3t9jt6hSYlJUXjxo3T8uXLZTKZdOzYMW3fvl2TJk3S1KlTHTrXeUVFRXavKyoqlJeXp+nTp6t169ZOmVOS7rvvPq1evdppx/V7fzxN5KzTRr/Xrl07ffXVV+rbt6+WLVum2267rdIF03fffXel70XwfASTyzB37lzNmTNHFotFa9ascfiFfH907733VmobNmyYU+f84/nbu+++22lzLViwwPb/UVFRmjp1qv71r3+pXbt2lX6gPfTQQw6f/8iRI/rmm2908uRJvf7667r66qv1yiuvqEWLFurevbvD55N+O/XQoEED23U69erVc8rdKuHh4dqyZYsKCwurnOO1115TvXr1HD7v/PnzHT7mH+3bt0/XXXedfHx8lJqaqvLycvXp00enT59Wjx49ZDabNWnSJI0dO9Yp84eFhVX6QV1RUaGmTZtqzZo1TplT+u2XkiVLluijjz5STExMpX8j8+bNc9hcv/+F6I+/DDnrbpn//u//1ogRI3T11VdX2yc8PNyh1/HBGLjG5DL4+PgoKChI8fHxNf4wcfRqgrdq0aLFRfUzmUz6+uuvHTq3O+7m2L17txISEhQUFKTOnTtLknbt2qUzZ87oww8/1PXXX+/wOV3t5ptvVs+ePZWWlmbX/vPPP2vQoEEOOWXl6+urvLw8NWrUSC1bttSuXbsUHBysw4cPq7i4WNHR0U4JXedt3rzZ7rWPj48aNmyoqKgo+fk573e/3r1717jfUacghw8fflH9VqxY4ZD5zps5c6YmTZqkOnXq2LWfOXNGTz/9tMMvgL9UXGPieASTy5CcnHxRS5qO/gd7pTn/V9SZy8cdOnTQhAkTlJSUZHfR5GeffaZ+/fopPz/f4XPedNNNioqK0tKlS20/wM6ePav77rtPX3/9tbZs2eLwOV3Nx8dHV111lbp166ZVq1bZfssuKChQkyZNHHL30VVXXaX3339fXbp0kY+PjwoKCtSwYcPLHvdipaenq3HjxhoxYoRd+/Lly3XixAmXXcDtbX4fOH/vp59+UqNGjZx6G3ZtEEwcj1M5lyEjI8PdJXi1ZcuW6bnnnrM9HbV169YaP3687rvvPofPdfDgQfXo0aNSe2hoqNPuNti9e7ddKJEkPz8/TZ482fa8EW/w0Ucf6W9/+5tuvPFGrV+/3uEXwQ4aNEg9e/a03TLbqVOnalcwHb3SJkkvvfSSVq9eXan9z3/+s4YMGeLwYPLHAFQVk8mkZcuWOXReV6uoqKjyl5F///vfTrvuC8ZAMIEhTZs2TfPmzdPYsWMVFxcn6bdnw0yYMEG5ubmaOXOmQ+dzx90cISEhys3NVZs2bezav/vuOwUHBztlTneIiIjQ5s2bNXz4cN1www167bXX1LZtW4eNv2TJEt155506fPiwHnroIaWkpLj0zy8/P7/KZ/k0bNhQeXl5Dp8vIyNDzZs3V4cOHeSNC97169eXyWSSyWTSn/70J7twcu7cORUXF7vsoni4B8EEhvTiiy9q6dKldhfb3n777YqJidHYsWMdHkzccTfH4MGDNXLkSD3zzDO22z63bdumRx55xKkXGbvS+R8qZrNZq1ev1uzZs5WYmOjwVYTExERJv91eOm7cOJcGk6ZNm2rbtm2VrpHatm2bmjRp4vD5Ro8erTVr1ujo0aMaPny4hg0b5lUrCPPnz1dFRYVGjBihGTNm2F2AHxAQoMjISNsvK/BOBBMYUllZWZWnMzp27OiwR/z/3pQpU1x+N8czzzwjk8mkpKQk2zH5+/tr9OjReuqpp5wyp6v98Tf6xx9/XG3btq3yDjNHcMf1XCkpKRo/frzKysp08803S/rtUfGTJ0/Www8/7PD5Fi1apHnz5unNN9/U8uXLlZqaqgEDBmjkyJHq27evS27ldabzfzdatGihrl27VrrbCN6Pi19hSGPHjpW/v3+lWx4nTZqkM2fOaNGiRU6Zt7S01GV3c5x3+vRpHTlyRJJsz/vwFt9++62aNWtW6Yfl/v37tXv3bqcFFFeqqKjQlClTtGDBApWWlkr67YnMjz76qEvuHPn222+VkZGhzMxMnT17Vvv373fJ31tnKCoqUkhIiO3/a3K+n7tx8avjEUxgSGPHjlVmZqaaNm2qG2+8UdJvn7qbm5urpKQku9+iHPm8BuBSFRcX68CBAwoKClLr1q3tHoToTN99951WrFihjIwMlZaW6quvvvLYYPL7O3Gq+3DE8xfFcleO9+JUDgzpiy++sD3H4/xqQnh4uMLDw/XFF1/Y+nn6sjW8R7169Vz2qbdWq9V2Kmfr1q269dZb9cILLygxMdFpH+jpCps2bbJdL+Osj4KA8bFiAgAe5IEHHtDatWvVtGlTjRgxQkOHDlV4eLi7y7pisWLieAQTAPAgPj4+atasmTp06FDjiqEnPnF63759F903JibGiZVcPIKJ43EqBwA8SFJSkteewoyNjZXJZLrg81mMdI0JHI9gAgAexJufOH306FF3lwADIJgAAAyhefPm7i4BBkAwAQAYwjvvvKN+/frJ399f77zzTo19b7/9dhdVBVfj4lcAgCH4+PgoPz/f9hyT6hjpGhMufnU8VkwAAIZQXl5e5f/jyuK5T+IBAHitzMxMWa3WSu2lpaXKzMx0Q0VwFU7lAAAM5/ePp/+9n376SY0aNeJUjhdjxQQAYDjnPxPnj77//nuFhoa6oSK4CteYAAAM4/wTbU0mk/r06SM/v//8mDp37pyOHj2qxMREN1YIZyOYAAAMY+DAgZKknJwcJSQk2H1SckBAgCIjIzVo0CA3VecdnnjiCb333nvKyclRQECATp06dcH3VFRUKC0tTUuXLtWpU6fUrVs3vfjii2rdurWtz8mTJzV27FitX79ePj4+GjRokJ5//vlaf9o1wQQAYBhpaWk6d+6cIiMj1bdvX0VERLi7JK9TWlqqu+66S3FxcVq2bNlFvWfu3LlasGCBVq5cqRYtWmjq1KlKSEjQl19+qcDAQEnS0KFDlZeXp40bN6qsrEzDhw/XqFGjtHr16lrVx8WvAADDCQwM1IEDB9SiRQt3l1IjT774NSMjQ+PHj7/giklFRYWaNGmihx9+WJMmTZIkFRYWqnHjxsrIyNCQIUN04MABRUdHa9euXerUqZMkacOGDerfv7++//57NWnS5KLr4uJXAIDhXHfddfr666/dXYZbWa1WFRUV2W1V3ULtbEePHlV+fr7i4+NtbaGhoerSpYu2b98uSdq+fbvCwsJsoUSS4uPj5ePjox07dtRqPk7lAAAMZ/bs2Zo0aZJmzZqljh07qm7dunb7Q0JC3FSZPWeuakyfPl0zZsywa0tLS9P06dOdNmdV8vPzJUmNGze2a2/cuLFt3/kn9v6en5+fGjRoYOtzsQgmAADD6d+/v6TfPhPn97cNn7+N2CjPMXGm1NRUTZw40a7NbDZX2XfKlCmaM2dOjeMdOHBAbdq0cVh9zkIwAQAYzscff+zuEtzObDZXG0T+6OGHH1ZycnKNfVq2bHlJdVgsFklSQUGB3cXIBQUFio2NtfU5fvy43fvOnj2rkydP2t5/sQgmAADD6dmzp7tL8CgNGzZUw4YNnTJ2ixYtZLFYlJWVZQsiRUVF2rFjh0aPHi1JiouL06lTp7Rnzx517NhRkrRp0yaVl5erS5cutZqPi18BAIb0ySefaNiwYeratat++OEHSdIrr7yirVu3urkyz5abm6ucnBzl5ubq3LlzysnJUU5OjoqLi2192rRpo7feekvSb5/mPH78eM2ePVvvvPOOPv/8cyUlJalJkya25860bdtWiYmJSklJ0c6dO7Vt2zaNGTNGQ4YMqdUdORLBBABgQG+88YYSEhIUFBSkvXv32u5GKSws1JNPPunm6jzbtGnT1KFDB6Wlpam4uFgdOnRQhw4dtHv3blufgwcPqrCw0PZ68uTJGjt2rEaNGqUbbrhBxcXF2rBhg+0ZJpK0atUqtWnTRn369FH//v3VvXt3LVmypNb18RwTAIDhdOjQQRMmTFBSUpKCg4P173//Wy1bttRnn32mfv361fpOD3gOVkwAAIZz8OBB9ejRo1J7aGjoRT1CHZ6LYAIAMByLxaLDhw9Xat+6desl310Cz0AwAQAYTkpKisaNG6cdO3bIZDLp2LFjWrVqlSZNmmS7EwTeiduFAQCGsG/fPl133XXy8fFRamqqysvL1adPH50+fVo9evSQ2WzWpEmTNHbsWHeXCifi4lcAgCH4+voqLy9PjRo1UsuWLbVr1y4FBwfr8OHDKi4uVnR0tOrVq+fuMuFkrJgAAAwhLCxMR48eVaNGjfTNN9+ovLxcAQEBio6OdndpcCGCCQDAEAYNGqSePXsqIiJCJpNJnTp1kq+vb5V9r/RPHvZmBBMAgCEsWbJEd955pw4fPqyHHnpIKSkpCg4OdndZcDGuMQEAGM7w4cO1YMECgskViGACAAAMg+eYAAAAwyCYAAAAwyCYAAAAwyCYAAAAwyCYAAAAwyCYAAAAwyCYAAAAwyCYAAAAw/h/tpmS3+HBKJYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "f, ax = plt.subplots(figsize=(6, 6))\n",
        "sns.heatmap(corr,\n",
        "    cmap=sns.color_palette(\"BuGn_r\"),\n",
        "    vmin=-1.0, vmax=1.0,\n",
        "    square=True, ax=ax)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Divide data into X (features) and y (labels/targets)."
      ],
      "metadata": {
        "id": "CN9nIAbWHCWE"
      },
      "id": "CN9nIAbWHCWE"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1910015c",
      "metadata": {
        "id": "1910015c"
      },
      "outputs": [],
      "source": [
        "labels = rawDataCopy[['fertility']]\n",
        "features = rawDataCopy.drop('fertility', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4ea4fb8c",
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          "data": {
            "text/plain": [
              "     N    P    K    ph    ec    oc     S    zn    fe    cu    Mn     B\n",
              "0  138  8.6  560  7.46  0.62  0.70   5.9  0.24  0.31  0.77  8.71  0.11\n",
              "1  213  7.5  338  7.62  0.75  1.06  25.4  0.30  0.86  1.54  2.89  2.29\n",
              "2  163  9.6  718  7.59  0.51  1.11  14.3  0.30  0.86  1.57  2.70  2.03\n",
              "3  157  6.8  475  7.64  0.58  0.94  26.0  0.34  0.54  1.53  2.65  1.82\n",
              "4  220  8.6  444  7.43  0.65  0.72  11.7  0.37  0.66  0.90  2.19  1.82"
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              "      <th>0</th>\n",
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              "      <td>7.46</td>\n",
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              "      <td>7.5</td>\n",
              "      <td>338</td>\n",
              "      <td>7.62</td>\n",
              "      <td>0.75</td>\n",
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              "      <td>25.4</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>163</td>\n",
              "      <td>9.6</td>\n",
              "      <td>718</td>\n",
              "      <td>7.59</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1.11</td>\n",
              "      <td>14.3</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.86</td>\n",
              "      <td>1.57</td>\n",
              "      <td>2.70</td>\n",
              "      <td>2.03</td>\n",
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              "      <th>3</th>\n",
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              "      <td>7.64</td>\n",
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              "      <td>2.65</td>\n",
              "      <td>1.82</td>\n",
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              "      <td>7.43</td>\n",
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              "      <td>0.72</td>\n",
              "      <td>11.7</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.90</td>\n",
              "      <td>2.19</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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      "id": "5b9f9710",
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        "colab": {
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          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fertility\n",
              "0          0\n",
              "1          0\n",
              "2          0\n",
              "3          0\n",
              "4          0"
            ],
            "text/html": [
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-762a8caf-dc51-4762-8b73-1f1b5ea5514e');\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",
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              "  "
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          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "labels.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dae013bf",
      "metadata": {
        "id": "dae013bf",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "features.hist(bins=50, figsize=(10, 10), color ='green', grid=False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b9ccfed5",
      "metadata": {
        "id": "b9ccfed5"
      },
      "source": [
        "# Prepare the Data for Machine Learning Algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "81754cb4",
      "metadata": {
        "id": "81754cb4"
      },
      "source": [
        "### Feature Scaling"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use Log Transform to convert skewed distibution to a normal distribution"
      ],
      "metadata": {
        "id": "fTPAWSANI8OC"
      },
      "id": "fTPAWSANI8OC"
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "id": "18389af6",
      "metadata": {
        "id": "18389af6"
      },
      "outputs": [],
      "source": [
        "transformedFeatures = features.apply(lambda x: np.log10(x) if np.issubdtype(x.dtype, np.number) else x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "id": "f5a164e2",
      "metadata": {
        "id": "f5a164e2",
        "outputId": "17c66e61-9465-4e32-d8e9-453d83d216b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 12 Axes>"
            ],
            "image/png": 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XQECA/TGLFi1Senq6Ro4caZ8EZu7cuc1eFwAA4HlIjAC0CMOHDz/rBC8Wi0UzZ8486wyWwcHBysvLc0V4AADAy3GNEQAAAADTIzECAAAAYHokRgAAAABMj8QIAAAAgOmRGAEAAAAwPWalAwC4lSXL4rBuzKh/dkIAAFyFxAgAAAAwMX6g+hVD6QAAAACYHokRAAAAANMjMQIAAABgeiRGAAAAAEyPxAgAAACA6ZEYAQAAADA9pusGAAAA0GjeMt03iREAAAAAu9MTHbNgKB0AAAAA0yMxAgAAAGB6JEYAAAAATI/ECAAAAIDpMfkCAMClzHoRLwDAs5AYAV7MW6bPBAAAcDWG0gEAAAAwPRIjAAAAAKZHYgQAAADA9LjGCAA8UEu6fqwlxQIAcD9P/VwgMQIAAABw3rx1tlGG0gEAAAAwPRIjAAAAAKZHYgQAAADA9EiMAAAAAJgeky8AzchTZ2kBAADwdvQYAQAAADA9EiMAAAAApkdiBAAAAMD0SIwAAIBXW7NmjcaOHauIiAhZLBYtW7bMYb9hGJo+fbrCw8PVpk0bxcbGaseOHQ5lysrKlJycLKvVqqCgIKWmpqqqqqoZawHA1UiMAACAV6uurtaAAQOUk5NT5/5Zs2Zp7ty5mj9/voqLi9WuXTvFxcXpyJEj9jLJycnaunWrCgoKtHz5cq1Zs0ZTpkxprioAaAbMSgd4kNNntQMAnFtCQoISEhLq3GcYhubMmaNHHnlE48aNkyQtXLhQoaGhWrZsmZKSkrRt2zbl5+dr/fr1GjJkiCRp3rx5Gj16tGbPnq2IiIhmqwsA16HHCAAAmNbu3btls9kUGxtr3xYYGKjo6GgVFRVJkoqKihQUFGRPiiQpNjZWPj4+Ki4ubvaYAbhGgxMjxukCAABvYbPZJEmhoaEO20NDQ+37bDabQkJCHPa3atVKwcHB9jJ1qampUWVlpcMCoOVqcGLEOF0AMBdLlsVhAXB+srOzFRgYaF8iIyPdHRKAs2hwYpSQkKAnnnhCN9988xn7Th+n279/fy1cuFD79++39yydHKf7r3/9S9HR0Ro2bJjmzZunxYsXa//+/U2uEADv9dhjj8lisTgsvXr1su8/cuSI0tLS1LFjR7Vv316JiYkqLS11Y8QAWrqwsDBJOqOtKC0tte8LCwvTwYMHHfYfP35cZWVl9jJ1yczMVEVFhX3Zu3evk6MH4ExOvcbIVeN06YoGcFLfvn114MAB+/Lpp5/a9z3wwAN69913tWTJEq1evVr79+/XhAkT3BgtgJYuKipKYWFhKiwstG+rrKxUcXGxYmJiJEkxMTEqLy9XSUmJvczKlStVW1ur6Ojoep/b399fVqvVYQHQcjl1VjpXjdPNzs5WVlaWM0MF4KFatWpV5y+0FRUVevHFF5WXl6frr79ekrRgwQL17t1b69at05VXXtncoQJoIaqqqrRz5077+u7du7Vp0yYFBwerS5cumjp1qp544gldeumlioqK0qOPPqqIiAiNHz9ektS7d2/Fx8dr8uTJmj9/vo4dO6b09HQlJSUxIx1wHk4fhm3MMNwUydl5xKx0dEUDOGnHjh2KiIhQt27dlJycrD179kiSSkpKdOzYMYce6169eqlLly72HmsA5rRhwwYNGjRIgwYNkiRlZGRo0KBBmj59uiRp2rRpuv/++zVlyhRdccUVqqqqUn5+vgICAuzPsWjRIvXq1UsjR47U6NGjNWzYML3wwgtuqQ8A13Bqj9Gp43TDw8Pt20tLSzVw4EB7mYaO0/X395e/v78zQwXggaKjo5Wbm6uePXvqwIEDysrK0jXXXKMtW7bIZrPJz89PQUFBDo85tce6LjU1NaqpqbGvM1QX8D7Dhw+XYdT/C7XFYtHMmTM1c+bMessEBwcrLy/PFeEBaCGcmhidOk73ZCJ0cpzuvffeK8lxnO7gwYMlnd84XcCMmAHM0ak3aOzfv7+io6PVtWtXvf7662rTpk2jnpOhugAAQGpEYsQ4XQAtRVBQkHr06KGdO3fqhhtu0NGjR1VeXu7Qa3TqzFJ1yczMVEZGhn29srKSKXWbiIQeAOCJGnyNEeN0AbQUVVVV2rVrl8LDwzV48GC1bt3aYWap7du3a8+ePfaZperCrFEAAEBqRI8R43QBuMuDDz6osWPHqmvXrtq/f79mzJghX19fTZw4UYGBgUpNTVVGRoaCg4NltVp1//33KyYmhhnpAADAOTn1GiMAcKV9+/Zp4sSJOnTokDp37qxhw4Zp3bp16ty5syTpmWeekY+PjxITE1VTU6O4uDg9++yzbo4aAAB4AhIjAB5j8eLFZ90fEBCgnJwc5eTkNFNELYen3CMCAICWyiPuYwQAAAAArkRiBAAAAMD0SIwAAAAAmB6JEQAAAADTIzECAAAAYHrMSgcAXuj0WeoAADiJz4i6kRgBAM7AhyYAwGwYSgcAAADA9EiMAAAAAJgeQ+kAF2I4EgAAgGegxwgAAACA6ZEYAQAAADA9htIBAAAAaDanXmpgzDDcGIkjeowAAAAAmB49RkALwmQNAAAA7kGPEQAAAADTo8cIANAg9GxCarnXCABAY5EYAedw+pdAZ34B4AsmAABAy0BiBADwKvRkAAAag2uMAAAAAJgeiREAAAAA0yMxAgAAAGB6XGMEAC2QKyf9AAAAZyIxAgAAALwYs+CeH4bSAQAAADA9eowAAB6NX0LrxnBMAGgYEiMAAEAiBcD0GEoHAAAAwPRIjAAAAACYHkPpAMADcB0NAACuRY8RAAAAANOjxwgAAA/U0MkSmFwBAM6OxAgAAACAW5xrqHhz/ojDUDoAAAAApkePEdBADEcBAADwPiRGQBOdmiiRJAHwFMx0CHgvzu/GITGC13Fnjw4NEQBvQe84ALPhGiMAAAAApkePEQC0APQ2AgDgXiRGAIAWhSFcAAB3cOtQupycHF188cUKCAhQdHS0Pv/8c3eGgxbKkmVxWIBzoW2BNzpXW+jqtpK2+Fe0L4D3clti9NprrykjI0MzZszQxo0bNWDAAMXFxengwYPuCgleqqEf5nz4ezbaFgCuQvsCeDe3DaV7+umnNXnyZE2aNEmSNH/+fL333nt66aWX9PDDD7srLHggkhecqjnbloYM+WJ4mPNwzsNd+O6CloS20PnckhgdPXpUJSUlyszMtG/z8fFRbGysioqKzihfU1Ojmpoa+3pFRYUkqbKy8px/KzA70GG9IrOisWG7XVPqcq7HNvS5m1r+dKc+/lxlLZlNawia+nhPdq5z5uR+w/DML+0NbVukprUvOuK4etbHnKvsafvxX005Z8/rOHqKFvweOZ/Xmfalge0LcJpzfT/yVs3avhhu8P333xuSjLVr1zpsf+ihh4yhQ4eeUX7GjBmGJBYWlmZa9u7d21zNgVM1tG0xDNoXFpbmXmhfWFhYXLU0tX3xiFnpMjMzlZGRYV+vra1VWVmZOnbsKIvFfb/+V1ZWKjIyUnv37pXVanVbHM5GvTyPs+pmGIYOHz6siIgIJ0bXsjmrffHm91ddqK93c0V9aV/qbl+87b3lTfWhLi3X6fVxVvvilsSoU6dO8vX1VWlpqcP20tJShYWFnVHe399f/v7+DtuCgoJcGWKDWK1Wr3iTnY56eR5n1C0wMNBJ0TS/hrYtkvPbF29+f9WF+no3Z9eX9qX+9sXb3lveVB/q0nKdWh9ntC9umZXOz89PgwcPVmFhoX1bbW2tCgsLFRMT446QAHgB2hYArkL7Ang/tw2ly8jIUEpKioYMGaKhQ4dqzpw5qq6uts/0AgCNQdsCwFVoXwDv5rbE6LbbbtMPP/yg6dOny2azaeDAgcrPz1doaKi7Qmowf39/zZgx44xuck9HvTyPN9etodzVtpjtGFBf72a2+p4vV7Qv3vZae1N9qEvL5ar6WAzDQ+fNBAAAAAAnccs1RgAAAADQkpAYAQAAADA9EiMAAAAApkdiBAAAAMD0SIzqsWbNGo0dO1YRERGyWCxatmzZOR+zatUqXX755fL399cll1yi3Nxcl8fZGA2t26pVq2SxWM5YbDZb8wR8HrKzs3XFFVeoQ4cOCgkJ0fjx47V9+/ZzPm7JkiXq1auXAgIC1K9fP61YsaIZom2YxtQtNzf3jOMVEBDQTBF7r5ycHF188cUKCAhQdHS0Pv/883rLevox8OY2sC7e2C7Wx5vby5bIm84lbzpPvOk88LbvCc8995z69+9vv3lrTEyM3n///bM+xlnHhcSoHtXV1RowYIBycnLOq/zu3bs1ZswYjRgxQps2bdLUqVP129/+Vh988IGLI224htbtpO3bt+vAgQP2JSQkxEURNtzq1auVlpamdevWqaCgQMeOHdOoUaNUXV1d72PWrl2riRMnKjU1VV988YXGjx+v8ePHa8uWLc0Y+bk1pm7Sr3eDPvV4fffdd80UsXd67bXXlJGRoRkzZmjjxo0aMGCA4uLidPDgwXof48nHwJvbwLp4Y7tYH29uL1sibzqXvOk88abzwNu+J1x00UV68sknVVJSog0bNuj666/XuHHjtHXr1jrLO/W4GDgnScbSpUvPWmbatGlG3759HbbddtttRlxcnAsja7rzqdvHH39sSDJ++umnZonJGQ4ePGhIMlavXl1vmVtvvdUYM2aMw7bo6Gjjd7/7navDa5LzqduCBQuMwMDA5gvKBIYOHWqkpaXZ10+cOGFEREQY2dnZdZb3pmPgzW1gXby1XayPN7eXLY03nUvedp5403ngjd8TLrjgAuNf//pXnfuceVzoMXKSoqIixcbGOmyLi4tTUVGRmyJyvoEDByo8PFw33HCDPvvsM3eHc1YVFRWSpODg4HrLeOoxO5+6SVJVVZW6du2qyMjIs/7SgnM7evSoSkpKHN4vPj4+io2NPev7xUzHwFPPp6bypHaxPt7cXnoib3ytPeE88abzwJu+J5w4cUKLFy9WdXW1YmJi6izjzONCYuQkNpvtjDtfh4aGqrKyUr/88oubonKO8PBwzZ8/X2+++abefPNNRUZGavjw4dq4caO7Q6tTbW2tpk6dqquvvlqXXXZZveXqO2YtYexzfc63bj179tRLL72kt99+W6+88opqa2t11VVXad++fc0Yrff48ccfdeLEiQa9X8x2DLy5DayLp7WL9fHm9tJTedO55CnniTedB97yPWHz5s1q3769/P399fvf/15Lly5Vnz596izrzOPSqlHRwlR69uypnj172tevuuoq7dq1S88884z+/e9/uzGyuqWlpWnLli369NNP3R2K051v3WJiYhx+WbnqqqvUu3dvPf/883r88cddHSbEMfB2ntYu1seb20u4n6ecJ950HnjL94SePXtq06ZNqqio0BtvvKGUlBStXr263uTIWegxcpKwsDCVlpY6bCstLZXValWbNm3cFJXrDB06VDt37nR3GGdIT0/X8uXL9fHHH+uiiy46a9n6jllYWJgrQ2y0htTtdK1bt9agQYNa5DHzBJ06dZKvr2+T3i/efgzM1gbWpaW2i/Xx5vbSk3n7udTSzhNvOg+86XuCn5+fLrnkEg0ePFjZ2dkaMGCA/t//+391lnXmcSExcpKYmBgVFhY6bCsoKKh3PKSn27Rpk8LDw90dhp1hGEpPT9fSpUu1cuVKRUVFnfMxnnLMGlO30504cUKbN29uUcfMk/j5+Wnw4MEO75fa2loVFhae9/vF24+Bp5xPrtTS2sX6eHN76Q28/bVuKeeJN50HZvieUFtbq5qamjr3OfW4NHi6BpM4fPiw8cUXXxhffPGFIcl4+umnjS+++ML47rvvDMMwjIcffti488477eW/+eYbo23btsZDDz1kbNu2zcjJyTF8fX2N/Px8d1WhXg2t2zPPPGMsW7bM2LFjh7F582bjj3/8o+Hj42N89NFH7qrCGe69914jMDDQWLVqlXHgwAH78vPPP9vL3HnnncbDDz9sX//ss8+MVq1aGbNnzza2bdtmzJgxw2jdurWxefNmd1ShXo2pW1ZWlvHBBx8Yu3btMkpKSoykpCQjICDA2Lp1qzuq4BUWL15s+Pv7G7m5ucbXX39tTJkyxQgKCjJsNpthGN53DLy5DayLN7aL9fHm9rIl8qZzyZvOE286D7zte8LDDz9srF692ti9e7fx1VdfGQ8//LBhsViMDz/80DAM1x4XEqN6nJxi8vQlJSXFMAzDSElJMa677rozHjNw4EDDz8/P6Natm7FgwYJmj/t8NLRuTz31lNG9e3cjICDACA4ONoYPH26sXLnSPcHXo676SHI4Btddd529jie9/vrrRo8ePQw/Pz+jb9++xnvvvde8gZ+HxtRt6tSpRpcuXQw/Pz8jNDTUGD16tLFx48bmD97LzJs3z/66Dh061Fi3bp19n7cdA29uA+vije1ifby5vWyJvOlc8qbzxJvOA2/7nnDPPfcYXbt2Nfz8/IzOnTsbI0eOtCdFhuHa42IxDMNoeD8TAAAAAHgPrjECAAAAYHokRgAAAABMj8QIAAAAgOmRGKHZWCwWpaenuzsMAAAA4AwkRgAAAABMj8QIAAAAgOmRGAEAAAAwPRIjNNljjz0mi8Wi//znP7r11ltltVrVsWNH/fGPf9SRI0fOKL9s2TJddtll8vf3V9++fZWfn++GqAF4ku+//1733HOPQkND7W3HSy+95FDmyJEjeuyxx9SjRw8FBAQoPDxcEyZM0K5du9wUNQBP8MUXXyghIUFWq1Xt27fXyJEjtW7dOocy5eXleuCBB3TxxRfL399fF110ke666y79+OOPbooartDK3QHAe9x66626+OKLlZ2drXXr1mnu3Ln66aeftHDhQnuZTz/9VG+99Zbuu+8+dejQQXPnzlViYqL27Nmjjh07ujF6AC1VaWmprrzySvsELp07d9b777+v1NRUVVZWaurUqTpx4oRuvPFGFRYWKikpSX/84x91+PBhFRQUaMuWLerevbu7qwGgBdq6dauuueYaWa1WTZs2Ta1bt9bzzz+v4cOHa/Xq1YqOjlZVVZWuueYabdu2Tffcc48uv/xy/fjjj3rnnXe0b98+derUyd3VgLMYQBPNmDHDkGTcdNNNDtvvu+8+Q5Lx5ZdfGoZhGJIMPz8/Y+fOnfYyX375pSHJmDdvXrPGDMBzpKamGuHh4caPP/7osD0pKckIDAw0fv75Z+Oll14yJBlPP/30GY+vra1trlABeJjx48cbfn5+xq5du+zb9u/fb3To0MG49tprDcMwjOnTpxuSjLfeeuuMx9O+eBeG0sFp0tLSHNbvv/9+SdKKFSvs22JjYx1+ue3fv7+sVqu++eab5gkSgEcxDENvvvmmxo4dK8Mw9OOPP9qXuLg4VVRUaOPGjXrzzTfVqVMne7tzKovF4obIAbR0J06c0Icffqjx48erW7du9u3h4eG6/fbb9emnn6qyslJvvvmmBgwYoJtvvvmM56B98S4MpYPTXHrppQ7r3bt3l4+Pj7799lv7ti5dupzxuAsuuEA//fSTq8MD4IF++OEHlZeX64UXXtALL7xQZ5mDBw9q165d6tmzp1q14mMNwPn54Ycf9PPPP6tnz55n7Ovdu7dqa2u1d+9e7dq1S4mJiW6IEM2NTxC4TF2/ovj6+tZZ1jAMV4cDwAPV1tZKku644w6lpKTUWaZ///7NGRIAwEuRGMFpduzYoaioKPv6zp07VVtbq4svvth9QQHwaJ07d1aHDh104sQJxcbG1luue/fuKi4u1rFjx9S6detmjBCAp+rcubPatm2r7du3n7HvP//5j3x8fBQZGanu3btry5YtbogQzY1rjOA0OTk5Duvz5s2TJCUkJLgjHABewNfXV4mJiXrzzTfr/GLyww8/SJISExP1448/6h//+McZZeiRBlAXX19fjRo1Sm+//bbDsP/S0lLl5eVp2LBhslqtSkxM1JdffqmlS5ee8Ry0L96FHiM4ze7du3XTTTcpPj5eRUVFeuWVV3T77bdrwIAB7g4NgAd78skn9fHHHys6OlqTJ09Wnz59VFZWpo0bN+qjjz5SWVmZ7rrrLi1cuFAZGRn6/PPPdc0116i6ulofffSR7rvvPo0bN87d1QDQAj3xxBMqKCjQsGHDdN9996lVq1Z6/vnnVVNTo1mzZkmSHnroIb3xxhu65ZZbdM8992jw4MEqKyvTO++8o/nz5/M9x4uQGMFpXnvtNU2fPl0PP/ywWrVqpfT0dP397393d1gAPFxoaKg+//xzzZw5U2+99ZaeffZZdezYUX379tVTTz0l6ddfflesWKG//vWvysvL05tvvqmOHTtq2LBh6tevn5trAKCl6tu3rz755BNlZmYqOztbtbW1io6O1iuvvKLo6GhJUvv27fXJJ59oxowZWrp0qV5++WWFhIRo5MiRuuiii9xcAziTxaAPEE302GOPKSsrSz/88AM3OQMAAIBH4hojAAAAAKZHYgQAAADA9EiMAAAAAJge1xgBAAAAMD16jAAAAACYHokRAAAAANMjMQIAAABgeh55g9fa2lrt379fHTp0kMVicXc4gNcwDEOHDx9WRESEfHzM+bsJ7QvgGrQvtC+AqzirffHIxGj//v2KjIx0dxiA19q7d69p7+ZN+wK4Fu0L7QvgKk1tXzwyMerQoYOkXytvtVrdHA3gPSorKxUZGWk/x8yI9gVwDdoX2hfAVZzVvnhkYnSy+9lqtdKwAC5g5iEetC+Aa9G+0L4ArtLU9sWcg3wBAAAA4BQkRgAAAABMj8QIAAAAgOmRGAEAAAAwPY+cfAHwVJYsx4sCjRmGmyIB4GloP4AzcV7AmegxAgAAAGB6JEYAAAAATI/ECAAAAIDpkRgBAAAAMD0SIwAAAACmR2IEAAAAwPRIjAAAAACYHokRAADwamvWrNHYsWMVEREhi8WiZcuWOey/++67ZbFYHJb4+HiHMmVlZUpOTpbValVQUJBSU1NVVVXVjLUA4GokRgAAwKtVV1drwIABysnJqbdMfHy8Dhw4YF9effVVh/3JycnaunWrCgoKtHz5cq1Zs0ZTpkxxdegAmlErdwcAAADgSgkJCUpISDhrGX9/f4WFhdW5b9u2bcrPz9f69es1ZMgQSdK8efM0evRozZ49WxEREU6PGUDzIzECAKAFsmRZ3B2CqaxatUohISG64IILdP311+uJJ55Qx44dJUlFRUUKCgqyJ0WSFBsbKx8fHxUXF+vmm292V9he7/TzwJhhuCkSmAGJEQAAMLX4+HhNmDBBUVFR2rVrl/7yl78oISFBRUVF8vX1lc1mU0hIiMNjWrVqpeDgYNlstnqft6amRjU1Nfb1yspKl9UBQNORGAEuxC++ANDyJSUl2f/fr18/9e/fX927d9eqVas0cuTIRj9vdna2srKynBEigGbA5AsAAACn6Natmzp16qSdO3dKksLCwnTw4EGHMsePH1dZWVm91yVJUmZmpioqKuzL3r17XRo3gKYhMQIAADjFvn37dOjQIYWHh0uSYmJiVF5erpKSEnuZlStXqra2VtHR0fU+j7+/v6xWq8MCoOViKB0AAM2EC8ndo6qqyt77I0m7d+/Wpk2bFBwcrODgYGVlZSkxMVFhYWHatWuXpk2bpksuuURxcXGSpN69eys+Pl6TJ0/W/PnzdezYMaWnpyspKYkZ6QAvQo8RAADwahs2bNCgQYM0aNAgSVJGRoYGDRqk6dOny9fXV1999ZVuuukm9ejRQ6mpqRo8eLA++eQT+fv7259j0aJF6tWrl0aOHKnRo0dr2LBheuGFF9xVJQAuQI8RAADwasOHD5dh1N8798EHH5zzOYKDg5WXl+fMsEyJXlO0ZPQYAQAAADA9EiMAAAAApkdiBAAAAMD0SIwAAAAAmB6JEQAAAADTIzEC0CKsWbNGY8eOVUREhCwWi5YtW+aw3zAMTZ8+XeHh4WrTpo1iY2O1Y8cOhzJlZWVKTk6W1WpVUFCQUlNTVVVV1Yy1AAAAnorECECLUF1drQEDBignJ6fO/bNmzdLcuXM1f/58FRcXq127doqLi9ORI0fsZZKTk7V161YVFBRo+fLlWrNmjaZMmdJcVQAAAB6M+xgBaBESEhKUkJBQ5z7DMDRnzhw98sgjGjdunCRp4cKFCg0N1bJly5SUlKRt27YpPz9f69ev15AhQyRJ8+bN0+jRozV79mzuTg8AXuD0+yABzkSPEYAWb/fu3bLZbIqNjbVvCwwMVHR0tIqKiiRJRUVFCgoKsidFkhQbGysfHx8VFxfX+9w1NTWqrKx0WAAAgPmQGAFo8Ww2myQpNDTUYXtoaKh9n81mU0hIiMP+Vq1aKTg42F6mLtnZ2QoMDLQvkZGRTo4eAAB4AhIjAKaWmZmpiooK+7J37153hwQAANyAxAhAixcWFiZJKi0tddheWlpq3xcWFqaDBw867D9+/LjKysrsZeri7+8vq9XqsAAAAPMhMQLQ4kVFRSksLEyFhYX2bZWVlSouLlZMTIwkKSYmRuXl5SopKbGXWblypWpraxUdHd3sMQMAAM/SpMToySeflMVi0dSpU+3bjhw5orS0NHXs2FHt27dXYmLiGb/y7tmzR2PGjFHbtm0VEhKihx56SMePH29KKAA8XFVVlTZt2qRNmzZJ+nXChU2bNmnPnj32duaJJ57QO++8o82bN+uuu+5SRESExo8fL0nq3bu34uPjNXnyZH3++ef67LPPlJ6erqSkJGakg1eyZFkcFgBA0zR6uu7169fr+eefV//+/R22P/DAA3rvvfe0ZMkSBQYGKj09XRMmTNBnn30mSTpx4oTGjBmjsLAwrV27VgcOHNBdd92l1q1b629/+1vTagPAY23YsEEjRoywr2dkZEiSUlJSlJubq2nTpqm6ulpTpkxReXm5hg0bpvz8fAUEBNgfs2jRIqWnp2vkyJHy8fFRYmKi5s6d2+x1AQAAnqdRiVFVVZWSk5P1z3/+U0888YR9e0VFhV588UXl5eXp+uuvlyQtWLBAvXv31rp163TllVfqww8/1Ndff62PPvpIoaGhGjhwoB5//HH9+c9/1mOPPSY/Pz/n1AyARxk+fLgMw6h3v8Vi0cyZMzVz5sx6ywQHBysvL88V4QEAAC/XqKF0aWlpGjNmjMM9RSSppKREx44dc9jeq1cvdenSxeFeI/369XOYdjcuLk6VlZXaunVrY8IBAAAAgCZpcI/R4sWLtXHjRq1fv/6MfTabTX5+fgoKCnLYfvq9Ruq6F8nJfXWpqalRTU2NfZ0bMAIAAABwpgb1GO3du1d//OMftWjRIodx/a7GDRgBAAAAuFKDEqOSkhIdPHhQl19+uVq1aqVWrVpp9erVmjt3rlq1aqXQ0FAdPXpU5eXlDo87/V4jdd2L5OS+unADRgAAAACu1KDEaOTIkdq8ebN9St1NmzZpyJAhSk5Otv+/devWDvca2b59u/bs2eNwr5HNmzc73IixoKBAVqtVffr0qfPvcgNGAAAAAK7UoGuMOnTooMsuu8xhW7t27dSxY0f79tTUVGVkZCg4OFhWq1X333+/YmJidOWVV0qSRo0apT59+ujOO+/UrFmzZLPZ9MgjjygtLU3+/v5OqhYAAAAAnL9G38eoPs8884z9/iE1NTWKi4vTs88+a9/v6+ur5cuX695771VMTIzatWunlJSUs07BCwAAAACu1OTEaNWqVQ7rAQEBysnJUU5OTr2P6dq1q1asWNHUPw0AAAAATtGo+xgBAAAAgDchMQIAAABgeiRGAAAAAEyPxAgAAACA6ZEYAQAAADA9EiMAAAAApuf0+xgBAIDzY8my2P9vzDDcGAkAgB4jAAAAAKZHYgQAAADA9EiMAAAAAJgeiREAAAAA0yMxAgAAXm3NmjUaO3asIiIiZLFYtGzZMof9hmFo+vTpCg8PV5s2bRQbG6sdO3Y4lCkrK1NycrKsVquCgoKUmpqqqqqqZqwFAFcjMQIA4DxZsiwOCzxDdXW1BgwYoJycnDr3z5o1S3PnztX8+fNVXFysdu3aKS4uTkeOHLGXSU5O1tatW1VQUKDly5drzZo1mjJlSnNVAUAzYLpuAADg1RISEpSQkFDnPsMwNGfOHD3yyCMaN26cJGnhwoUKDQ3VsmXLlJSUpG3btik/P1/r16/XkCFDJEnz5s3T6NGjNXv2bEVERDg95tMTb6ZzB1yPHiMAAGBau3fvls1mU2xsrH1bYGCgoqOjVVRUJEkqKipSUFCQPSmSpNjYWPn4+Ki4uLje566pqVFlZaXDAqDloscIAGBa/CoPm80mSQoNDXXYHhoaat9ns9kUEhLisL9Vq1YKDg62l6lLdna2srKynBwxAFehxwgAAMAFMjMzVVFRYV/27t3r7pAAnAWJEQAAMK2wsDBJUmlpqcP20tJS+76wsDAdPHjQYf/x48dVVlZmL1MXf39/Wa1WhwVAy0ViBAAATCsqKkphYWEqLCy0b6usrFRxcbFiYmIkSTExMSovL1dJSYm9zMqVK1VbW6vo6OhmjxmAa3CNEQAA8GpVVVXauXOnfX337t3atGmTgoOD1aVLF02dOlVPPPGELr30UkVFRenRRx9VRESExo8fL0nq3bu34uPjNXnyZM2fP1/Hjh1Tenq6kpKSXDIjHQD3IDECAABebcOGDRoxYoR9PSMjQ5KUkpKi3NxcTZs2TdXV1ZoyZYrKy8s1bNgw5efnKyAgwP6YRYsWKT09XSNHjpSPj48SExM1d+7cZq8LANchMQIAAF5t+PDhMoz6Zxy0WCyaOXOmZs6cWW+Z4OBg5eXluSI8AC0EiREAAE7C9N8A4LlIjAAAqMfpiQ4AwHsxKx0AAAAA06PHCAAAAE5zak8rw0nhSegxAgAAAGB6JEYAAAAATI+hdAAAeAFmxAOApqHHCAAAAIDpkRgBAAAAMD0SIwAAAACmR2IEAAAAwPRIjAAAAACYHrPSAQAAoEU4fXZFoDnRYwQAAADA9OgxAgAAgFfi/l5oCHqMAAAAAJgeiREAAAAA0yMxAgAAAGB6JEYAAAAATI/ECIDHeOyxx2SxWByWXr162fcfOXJEaWlp6tixo9q3b6/ExESVlpa6MWIAAOApSIwAeJS+ffvqwIED9uXTTz+173vggQf07rvvasmSJVq9erX279+vCRMmuDFaAADgKZiuG4BHadWqlcLCws7YXlFRoRdffFF5eXm6/vrrJUkLFixQ7969tW7dOl155ZXNHSoAAPAg9BgB8Cg7duxQRESEunXrpuTkZO3Zs0eSVFJSomPHjik2NtZetlevXurSpYuKiorcFS4AAPAQ9BgB8BjR0dHKzc1Vz549deDAAWVlZemaa67Rli1bZLPZ5Ofnp6CgIIfHhIaGymaz1fucNTU1qqmpsa9XVla6KnzgrE6/ESUAoHk1qMcoOztbV1xxhTp06KCQkBCNHz9e27dvdyhzPhc/79mzR2PGjFHbtm0VEhKihx56SMePH296bQB4tYSEBN1yyy3q37+/4uLitGLFCpWXl+v1119v9HNmZ2crMDDQvkRGRjoxYgAA4CkalBitXr1aaWlpWrdunQoKCnTs2DGNGjVK1dXV9jLnuvj5xIkTGjNmjI4ePaq1a9fq5ZdfVm5urqZPn+68WgEwhaCgIPXo0UM7d+5UWFiYjh49qvLycocypaWldV6TdFJmZqYqKirsy969e10cNQAAaIkalBjl5+fr7rvvVt++fTVgwADl5uZqz549KikpkfTfi5+ffvppXX/99Ro8eLAWLFigtWvXat26dZKkDz/8UF9//bVeeeUVDRw4UAkJCXr88ceVk5Ojo0ePOr+GALxWVVWVdu3apfDwcA0ePFitW7dWYWGhff/27du1Z88excTE1Psc/v7+slqtDgsAADCfJk2+UFFRIUkKDg6WdH4XPxcVFalfv34KDQ21l4mLi1NlZaW2bt1a59+pqalRZWWlwwLAfB588EGtXr1a3377rdauXaubb75Zvr6+mjhxogIDA5WamqqMjAx9/PHHKikp0aRJkxQTE8OMdAAA4JwaPflCbW2tpk6dqquvvlqXXXaZJJ3Xxc82m80hKTq5/+S+umRnZysrK6uxoQLwEvv27dPEiRN16NAhde7cWcOGDdO6devUuXNnSdIzzzwjHx8fJSYmqqamRnFxcXr22WfdHDUAAPAEjU6M0tLStGXLFoebK7pKZmamMjIy7OuVlZVcIA2Y0OLFi8+6PyAgQDk5OcrJyWmmiADA85w+A6Ixw3BTJEDL0qjEKD09XcuXL9eaNWt00UUX2befevHzqb1Gp178HBYWps8//9zh+U7OWlffBdL+/v7y9/dvTKgAAAAAcE4NusbIMAylp6dr6dKlWrlypaKiohz2n8/FzzExMdq8ebMOHjxoL1NQUCCr1ao+ffo0pS4AAAAA0CgN6jFKS0tTXl6e3n77bXXo0MF+TVBgYKDatGnjcPFzcHCwrFar7r//foeLn0eNGqU+ffrozjvv1KxZs2Sz2fTII48oLS2NXiEAAAAAbtGgxOi5556TJA0fPtxh+4IFC3T33XdLOvfFz76+vlq+fLnuvfdexcTEqF27dkpJSdHMmTObVhMAAAAAaKQGJUaGce6L887n4ueuXbtqxYoVDfnTAAAAAOAyTbqPEQAAaJksWRaHBWf32GOPyWKxOCy9evWy7z9y5IjS0tLUsWNHtW/fXomJifbJowB4BxIjAAAASX379tWBAwfsy6m3JHnggQf07rvvasmSJVq9erX279+vCRMmuDFaAM7W6PsYAQAAeJNWrVrVeeuQiooKvfjii8rLy9P1118v6dfrq3v37q1169bZJ5jyVtz3CGZBYgQAACBpx44dioiIUEBAgGJiYpSdna0uXbqopKREx44dU2xsrL1sr1691KVLFxUVFdWbGNXU1Kimpsa+XllZ6fI64OxI8nA2DKUDAACmFx0drdzcXOXn5+u5557T7t27dc011+jw4cOy2Wzy8/NzuHm9JIWGhtpvXVKX7OxsBQYG2pfIyEgX1wJAU9BjBADA/49JCswrISHB/v/+/fsrOjpaXbt21euvv642bdo06jkzMzOVkZFhX6+srCQ5AloweowAAABOExQUpB49emjnzp0KCwvT0aNHVV5e7lCmtLS0zmuSTvL395fVanVYALRcJEYAAACnqaqq0q5duxQeHq7BgwerdevWKiwstO/fvn279uzZo5iYGDdGCcCZGEoHAABM78EHH9TYsWPVtWtX7d+/XzNmzJCvr68mTpyowMBApaamKiMjQ8HBwbJarbr//vsVExPj9TPSAWZCYgQAAExv3759mjhxog4dOqTOnTtr2LBhWrdunTp37ixJeuaZZ+Tj46PExETV1NQoLi5Ozz77rJujBuBMJEYAAMD0Fi9efNb9AQEBysnJUU5OTjNFBKC5cY0RAAAAANOjxwhwI240BwAA0DLQYwQAAADA9OgxAgB4NHf2vHJDWADwHvQYAQAAADA9EiMAAAAApsdQOgAAwGQwAEyPxAgAABMg8QGAsyMxAgDAhJg4AgAccY0RAAAAANOjxwgA0KIxBAzwXu7uuaR9wanoMQIAAABgeiRGAAAAAEyPoXQAAK9y6tAYdw+LcfcwIQDA+SMxAgAAgEvw4wA8CYkR4ER8AAAAAHgmrjECAAAAYHokRgAAAABMj6F0AACvxT1KAADni8QIAGAaXAcIAKgPQ+kAAAAAmB49RgAAADhvLXmIKr3CaAp6jAAAAACYHokRAAAAANMjMQIAAABgelxjBAAAztCSryMBAFegxwgAAACA6dFjBLQg/EILAADgHiRGAAAAJsaPcsCvSIwAAB6F+5S4B1+eAXg7EiMAAAAv5uofE/ixAt6CyRcAAAAAmB49RgAAALCjBwhmRY8RAAAAANNza2KUk5Ojiy++WAEBAYqOjtbnn3/uznAAeAnaFs9mybI4LEBL0hLbF84ZwDnclhi99tprysjI0IwZM7Rx40YNGDBAcXFxOnjwoLtCAuAFaFsAuIo72xcSH8D13JYYPf3005o8ebImTZqkPn36aP78+Wrbtq1eeukld4UEeB0z/opI2+J6Znxf4UxmfA/QvgDezS2TLxw9elQlJSXKzMy0b/Px8VFsbKyKiorcERIAL9DcbYtZ7uvS0C++5yp/+utkpi/W8Fx8dwG8n1sSox9//FEnTpxQaGiow/bQ0FD95z//OaN8TU2Nampq7OsVFRWSpMrKynP+rcDsQIf1isyKxoQMnJ8jzn06S6ZzvzCe65w5ud8wPPMLfkPbFqlp7cvpx/u8HuOJzvG+PqPeTi6Plu983vu0L01rX051rnOIc6zxvLYd9yCN+e7urPbFI6brzs7OVlZW1hnbIyMjG/xcgU8GnrsQ4KXO9/1/+PBhBQaa41yhfWm6htbbrK+TN2vIMaV9aVz7cqpzvd6cY43Ha9fyNGf74pbEqFOnTvL19VVpaanD9tLSUoWFhZ1RPjMzUxkZGfb12tpalZWVqWPHjrJYGv+LemVlpSIjI7V3715ZrdZGP09zImbX87R4JefFbBiGDh8+rIiICCdG13wa2rZIjW9fPPF9Uhfq0bJ4cz1oX8zXvrgbr6NzeMLr6Kz2xS2JkZ+fnwYPHqzCwkKNHz9e0q+NRWFhodLT088o7+/vL39/f4dtQUFBTovHarW22ANdH2J2PU+LV3JOzJ78S25D2xap6e2LJ75P6kI9WhZvrQftiznbF3fjdXSOlv46OqN9cdtQuoyMDKWkpGjIkCEaOnSo5syZo+rqak2aNMldIQHwArQtAFyF9gXwbm5LjG677Tb98MMPmj59umw2mwYOHKj8/PwzLmoEgIagbQHgKrQvgHdz6+QL6enp9XY/Nwd/f3/NmDHjjG7uloyYXc/T4pU8M2ZXao62xVtec+rRslCPlo/2xXPwOjqHmV5Hi+Gp82YCAAAAgJP4uDsAAAAAAHA3EiMAAAAApkdiBAAAAMD0vC4xysnJ0cUXX6yAgABFR0fr888/P2v5JUuWqFevXgoICFC/fv20YsUKh/2GYWj69OkKDw9XmzZtFBsbqx07drgl3n/+85+65pprdMEFF+iCCy5QbGzsGeXvvvtuWSwWhyU+Pt5p8TY05tzc3DPiCQgIcCjj6te4oTEPHz78jJgtFovGjBljL+PK13nNmjUaO3asIiIiZLFYtGzZsnM+ZtWqVbr88svl7++vSy65RLm5uWeUaei5YXZ//etfddVVV6lt27bnfd+R83kvl5WVKTk5WVarVUFBQUpNTVVVVZULatC4v/ftt9/W+f63WCxasmSJvVxd+xcvXtxi6iHVfS7//ve/dyizZ88ejRkzRm3btlVISIgeeughHT9+vMXUo6ysTPfff7969uypNm3aqEuXLvrDH/6giooKh3KuPh6e9tnqTRrTFoHPPGdozPcRj2d4kcWLFxt+fn7GSy+9ZGzdutWYPHmyERQUZJSWltZZ/rPPPjN8fX2NWbNmGV9//bXxyCOPGK1btzY2b95sL/Pkk08agYGBxrJly4wvv/zSuOmmm4yoqCjjl19+afZ4b7/9diMnJ8f44osvjG3bthl33323ERgYaOzbt89eJiUlxYiPjzcOHDhgX8rKypoca2NjXrBggWG1Wh3isdlsDmVc+Ro3JuZDhw45xLtlyxbD19fXWLBggb2MK1/nFStWGP/zP/9jvPXWW4YkY+nSpWct/8033xht27Y1MjIyjK+//tqYN2+e4evra+Tn59vLNPQ1gGFMnz7dePrpp42MjAwjMDDwvB5zPu/l+Ph4Y8CAAca6deuMTz75xLjkkkuMiRMnuqgWDf97x48fd3hfHzhwwMjKyjLat29vHD582F5OkrFgwQKHcs46Z51RD8MwjOuuu86YPHmyQ4wVFRUOdb3sssuM2NhY44svvjBWrFhhdOrUycjMzGwx9di8ebMxYcIE45133jF27txpFBYWGpdeeqmRmJjoUM6Vx8PTPlu9TWPaIrPjM885Gvp9xBt4VWI0dOhQIy0tzb5+4sQJIyIiwsjOzq6z/K233mqMGTPGYVt0dLTxu9/9zjAMw6itrTXCwsKMv//97/b95eXlhr+/v/Hqq682e7ynO378uNGhQwfj5Zdftm9LSUkxxo0b1+TY6tPQmBcsWHDWhtzVr3FjYj7dM888Y3To0MGoqqqyb3P163zS+TRE06ZNM/r27euw7bbbbjPi4uLs6019DczsXO/hk87nvfz1118bkoz169fby7z//vuGxWIxvv/+e6fH7qy/N3DgQOOee+5x2NacH5KNrcd1111n/PGPf6x3/4oVKwwfHx+HH2uee+45w2q1GjU1NU6J/VTOOh6vv/664efnZxw7dsy+zZXHw9M+W73V+bZF4DPPFcySGHnNULqjR4+qpKREsbGx9m0+Pj6KjY1VUVFRnY8pKipyKC9JcXFx9vK7d++WzWZzKBMYGKjo6Oh6n9OV8Z7u559/1rFjxxQcHOywfdWqVQoJCVHPnj1177336tChQ02KtakxV1VVqWvXroqMjNS4ceO0detW+z5XvsZNiflUL774opKSktSuXTuH7a56nRvqXO9jZ7wGOLfzeS8XFRUpKChIQ4YMsZeJjY2Vj4+PiouLnR6TM/5eSUmJNm3apNTU1DP2paWlqVOnTho6dKheeuklGS66+0NT6rFo0SJ16tRJl112mTIzM/Xzzz87PG+/fv0cbs4ZFxenyspKh3aqJdTjVBUVFbJarWrVyvFWhK44Hp722QrwmYemcOsNXp3pxx9/1IkTJ864+3RoaKj+85//1PkYm81WZ3mbzWbff3JbfWWaM97T/fnPf1ZERITDyR8fH68JEyYoKipKu3bt0l/+8hclJCSoqKhIvr6+zR5zz5499dJLL6l///6qqKjQ7NmzddVVV2nr1q266KKLXPoaNzbmU33++efasmWLXnzxRYftrnydG6q+93FlZaV++eUX/fTTT01+r+Hczue9bLPZFBIS4rC/VatWCg4Odsr7va6Ymvr3XnzxRfXu3VtXXXWVw/aZM2fq+uuvV9u2bfXhhx/qvvvuU1VVlf7whz84Lf6TGluP22+/XV27dlVERIS++uor/fnPf9b27dv11ltv2Z+3ruN1cp+zOeN4/Pjjj3r88cc1ZcoUh+2uOh6e9tkKOOP7FczLaxIjs3nyySe1ePFirVq1ymEyg6SkJPv/+/Xrp/79+6t79+5atWqVRo4c2exxxsTEKCYmxr5+1VVXqXfv3nr++ef1+OOPN3s8DfXiiy+qX79+Gjp0qMP2lvY64/w8/PDDeuqpp85aZtu2berVq1czRdQ451uPpvrll1+Ul5enRx999Ix9p24bNGiQqqur9fe//71BX8RdXY9Tk4d+/fopPDxcI0eO1K5du9S9e/dGP+/pmut4VFZWasyYMerTp48ee+wxh33OOB5oPt7SFgHexmsSo06dOsnX11elpaUO20tLSxUWFlbnY8LCws5a/uS/paWlCg8PdygzcODAZo/3pNmzZ+vJJ5/URx99pP79+5+1bLdu3dSpUyft3LmzyV/YmxLzSa1bt9agQYO0c+dOSa59jZsac3V1tRYvXqyZM2ee8+8483VuqPrex1arVW3atJGvr2+Tj5u3+NOf/qS77777rGW6devWqOc+n/dyWFiYDh486PC448ePq6ysrEHH4nzr0dS/98Ybb+jnn3/WXXfddc6y0dHRevzxx1VTUyN/f/9zlpearx6nxihJO3fuVPfu3RUWFnbGTFUnz5OWdjwOHz6s+Ph4dejQQUuXLlXr1q3PWr4xx6MunvbZ6ilc2RaZnTO+q8C8vOYaIz8/Pw0ePFiFhYX2bbW1tSosLHTosThVTEyMQ3lJKigosJePiopSWFiYQ5nKykoVFxfX+5yujFeSZs2apccff1z5+fkO49Trs2/fPh06dMjhw6e5Yz7ViRMntHnzZns8rnyNmxrzkiVLVFNTozvuuOOcf8eZr3NDnet97Izj5i06d+6sXr16nXXx8/Nr1HOfz3s5JiZG5eXlKikpsZdZuXKlamtr7V/anVmPpv69F198UTfddJM6d+58zrKbNm3SBRdc0KAv4c1Vj1NjlGQ/T2NiYrR582aHZKWgoEBWq1V9+vRpMfWorKzUqFGj5Ofnp3feeeeMWx7UV9eGHo+6eNpnq6dwZVtkdnzmoUncPfuDMy1evNjw9/c3cnNzja+//tqYMmWKERQUZJ9x6M477zQefvhhe/nPPvvMaNWqlTF79mxj27ZtxowZM+qcUjQoKMh4++23ja+++soYN26cU6frbki8Tz75pOHn52e88cYbDlOynpxC9/Dhw8aDDz5oFBUVGbt37zY++ugj4/LLLzcuvfRS48iRI02OtzExZ2VlGR988IGxa9cuo6SkxEhKSjICAgKMrVu3OtTLVa9xY2I+adiwYcZtt912xnZXv86HDx82vvjiC+OLL74wJBlPP/208cUXXxjfffedYRiG8fDDDxt33nmnvfzJ6bofeughY9u2bUZOTk6d03Wf7TXAmb777jvjiy++sE9VffKYnDpldc+ePY233nrLvn4+7+X4+Hhj0KBBRnFxsfHpp58al156qcun6z7b39u3b5/Rs2dPo7i42OFxO3bsMCwWi/H++++f8ZzvvPOO8c9//tPYvHmzsWPHDuPZZ5812rZta0yfPr3F1GPnzp3GzJkzjQ0bNhi7d+823n77baNbt27Gtddea3/Myem6R40aZWzatMnIz883Onfu7PLpuhtSj4qKCiM6Otro16+fsXPnToe2//jx44ZhuP54eNpnq7c5n7YIjvjMc45zfR/xRl6VGBmGYcybN8/o0qWL4efnZwwdOtRYt26dfd91111npKSkOJR//fXXjR49ehh+fn5G3759jffee89hf21trfHoo48aoaGhhr+/vzFy5Ehj+/btbom3a9euhqQzlhkzZhiGYRg///yzMWrUKKNz585G69atja5duxqTJ092ekPQkJinTp1qLxsaGmqMHj3a2Lhxo8Pzufo1bmjMhmEY//nPfwxJxocffnjGc7n6df7444/rPM4nY0xJSTGuu+66Mx4zcOBAw8/Pz+jWrZvDPZdOOttrgDOlpKTUeRw+/vhjexn9//eOOel83suHDh0yJk6caLRv396wWq3GpEmTXPoF51x/b/fu3WfUyzAMIzMz04iMjDROnDhxxnO+//77xsCBA4327dsb7dq1MwYMGGDMnz+/zrLuqseePXuMa6+91ggODjb8/f2NSy65xHjooYcc7mNkGIbx7bffGgkJCUabNm2MTp06GX/6058cpsF2dz3qaw8kGbt37zYMo3mOh6d9tnqT82mLcCY+85ruXN9HvJHFMFw0vyoAAAAAeAivucYIAAAAABqLxAgAAACA6ZEYAQAAADA9EiMAAAAApkdiBAAAAFNbv369rrrqKrVr104Wi8V+3zOYC4kRXGbz5s36zW9+o65duyogIEAXXnihbrjhBs2bN8/doQEAAEiSjh07pltuuUVlZWV65pln9O9//1tdu3Z1d1hwA6brhkusXbtWI0aMUJcuXZSSkqKwsDDt3btX69at065du7Rz5053hwgAAKD//Oc/6t27t/75z3/qt7/9rbvDgRu1cncA8E5//etfFRgYqPXr1ysoKMhh38GDB90TFAAAwGlOfi85/fsKzIehdHCJXbt2qW/fvnU2MiEhIc0fEACP9O2338pisdS7SNLw4cN12WWX6euvv9aIESPUtm1bXXjhhZo1a5abowfQ0t1999267rrrJEm33HKLLBaLhg8fLunXnqTf/OY3Cg4OVkBAgIYMGaJ33nnHjdHC1RhKB5eIi4tTUVGR1q5dq8suu8zd4QDwUNXV1Vq6dKnDtmPHjumBBx6Qn5+fDh48qOHDh2vHjh3y9fXVhAkT1LNnT73xxhtauXKlVqxYoYSEBDdFD6ClKyoq0vLly/W3v/1Nf/jDH3TFFVcoNDRUERERuvrqq3XhhRcqJSVF7dq10+uvv65PPvlEb775pm6++WZ3hw4XIDGCSxQUFNi/jAwdOlTXXHONRo4cqREjRqh169Zujg6AJ0tLS9Pzzz+vgoICjRgxQsOHD9fq1au1cOFC3XnnnZKko0ePqmvXrrr66qv1xhtvuDliAC3ZqlWrNGLECC1ZskS/+c1vJEmxsbE6ePCg1q9fL39/f0mSYRgaNmyYfvjhB/3f//2fO0OGizCUDi5xww03qKioSDfddJO+/PJLzZo1S3FxcbrwwgvphgbQaAsXLtSzzz6rWbNmacSIEfbt7du31x133GFf9/Pz09ChQ/XNN9+4I0wAHqysrEwrV67UrbfeqsOHD+vHH3/Ujz/+qEOHDikuLk47duzQ999/7+4w4QIkRnCZK664Qm+99ZZ++uknff7558rMzNThw4f1m9/8Rl9//bW7wwPgYTZt2qTf//73mjhxojIyMhz2XXTRRfZrjk664IIL9NNPPzVniAC8wM6dO2UYhh599FF17tzZYZkxY4YkJpLyVsxKB5fz8/PTFVdcoSuuuEI9evTQpEmTtGTJEnvjAgDn8tNPPykxMVE9evTQv/71rzP2+/r61vk4RosDaKja2lpJ0oMPPqi4uLg6y1xyySXNGRKaCYkRmtWQIUMkSQcOHHBzJAA8RW1trZKTk1VeXq6PPvpIbdu2dXdIALxYt27dJEmtW7dWbGysm6NBc2IoHVzi448/rvOX2hUrVkiSevbs2dwhAfBQWVlZ+uCDD/Tqq68qKirK3eEA8HIhISEaPny4nn/++Tp/yP3hhx/cEBWaAz1GcIn7779fP//8s26++Wb16tVLR48e1dq1a/Xaa6/p4osv1qRJk9wdIgAPsHnzZj3++OO69tprdfDgQb3yyisO+0+dcAEAnCUnJ0fDhg1Tv379NHnyZHXr1k2lpaUqKirSvn379OWXX7o7RLgAiRFcYvbs2VqyZIlWrFihF154QUePHlWXLl1033336ZFHHuHu0gDOy6FDh2QYhlavXq3Vq1efsZ/ECIAr9OnTRxs2bFBWVpZyc3N16NAhhYSEaNCgQZo+fbq7w4OLcB8jAAAAAKbHNUYAAAAATI/ECAAAAIDpkRgBAAAAMD0SIwAAAACmR2IEAAAAwPRIjAAAAACYnkfex6i2tlb79+9Xhw4dZLFY3B0O4DUMw9Dhw4cVEREhHx9z/m5C+wK4Bu0L7QvgKs5qXzwyMdq/f78iIyPdHQbgtfbu3auLLrrI3WG4Be0L4Fq0L7QvgKs0tX3xyMSoQ4cOkn6tvNVqdXM0gPeorKxUZGSk/RwzI9oXwDVoX2hfAFdxVvvikYnRye5nq9VKwwK4gJmHeNC+AK5F+0L7ArhKU9sXcw7yBQAAAIBTkBgBAAAAMD0SIwAAAACmR2IEAAAAwPQ8cvIF4FSWLMcL7YwZhpsiATwX5xEANC/a3ZaHHiMAAAAApkdiBAAAAMD0SIwAAAAAmB6JEYAWITs7W1dccYU6dOigkJAQjR8/Xtu3b3coc+TIEaWlpaljx45q3769EhMTVVpa6lBmz549GjNmjNq2bauQkBA99NBDOn78eHNWBQAAeCASIwAtwurVq5WWlqZ169apoKBAx44d06hRo1RdXW0v88ADD+jdd9/VkiVLtHr1au3fv18TJkyw7z9x4oTGjBmjo0ePau3atXr55ZeVm5ur6dOnu6NKAADAgzArHYAWIT8/32E9NzdXISEhKikp0bXXXquKigq9+OKLysvL0/XXXy9JWrBggXr37q1169bpyiuv1Icffqivv/5aH330kUJDQzVw4EA9/vjj+vOf/6zHHntMfn5+7qgaAADwAPQYAWiRKioqJEnBwcGSpJKSEh07dkyxsbH2Mr169VKXLl1UVFQkSSoqKlK/fv0UGhpqLxMXF6fKykpt3bq1zr9TU1OjyspKhwUAAJgPiRGAFqe2tlZTp07V1Vdfrcsuu0ySZLPZ5Ofnp6CgIIeyoaGhstls9jKnJkUn95/cV5fs7GwFBgbal8jISCfXBgAAeAKG0sEtuKkZziYtLU1btmzRp59+6vK/lZmZqYyMDPt6ZWUlyREAACZEYgSgRUlPT9fy5cu1Zs0aXXTRRfbtYWFhOnr0qMrLyx16jUpLSxUWFmYv8/nnnzs838lZ606WOZ2/v7/8/f2dXAsAAJqGH5GbH0PpALQIhmEoPT1dS5cu1cqVKxUVFeWwf/DgwWrdurUKCwvt27Zv3649e/YoJiZGkhQTE6PNmzfr4MGD9jIFBQWyWq3q06dP81QEAAB4JHqMALQIaWlpysvL09tvv60OHTrYrwkKDAxUmzZtFBgYqNTUVGVkZCg4OFhWq1X333+/YmJidOWVV0qSRo0apT59+ujOO+/UrFmzZLPZ9MgjjygtLY1eIQAAcFb0GAFoEZ577jlVVFRo+PDhCg8Pty+vvfaavcwzzzyjG2+8UYmJibr22msVFhamt956y77f19dXy5cvl6+vr2JiYnTHHXforrvu0syZM91RJQAe4sSJE3r00UcVFRWlNm3aqHv37nr88cdlGP8dumQYhqZPn67w8HC1adNGsbGx2rFjhxujBuBs9BgBaBFO/QJSn4CAAOXk5CgnJ6feMl27dtWKFSucGRoAL/fUU0/pueee08svv6y+fftqw4YNmjRpkgIDA/WHP/xBkjRr1izNnTtXL7/8sqKiovToo48qLi5OX3/9tQICAtxcAwDOQGIEACZ0+kW9gJmtXbtW48aN05gxYyRJF198sV599VX7ZC6GYWjOnDl65JFHNG7cOEnSwoULFRoaqmXLlikpKcltsQNwHobSAQAAU7vqqqtUWFio//u//5Mkffnll/r000+VkJAgSdq9e7dsNpvDDaYDAwMVHR1tv8F0XbiBNOBZ6DECAACm9vDDD6uyslK9evWSr6+vTpw4ob/+9a9KTk6W9N8bRNd1A+n6bh4t/XoD6aysLNcFjhbF1dNrM32369FjBAAATO3111/XokWLlJeXp40bN+rll1/W7Nmz9fLLLzfpeTMzM1VRUWFf9u7d66SIAbgCPUYAAMDUHnroIT388MP2a4X69eun7777TtnZ2UpJSbHfILq0tFTh4eH2x5WWlmrgwIH1Pi83kAY8Cz1GAADA1H7++Wf5+Dh+JfL19VVtba0kKSoqSmFhYQ43mK6srFRxcbH9BtMAPB89RgDghRiLDpy/sWPH6q9//au6dOmivn376osvvtDTTz+te+65R5JksVg0depUPfHEE7r00kvt03VHRERo/Pjx7g0egNOQGAEAAFObN2+eHn30Ud133306ePCgIiIi9Lvf/U7Tp0+3l5k2bZqqq6s1ZcoUlZeXa9iwYcrPz+ceRjhv3Cah5SMxAgAAptahQwfNmTNHc+bMqbeMxWLRzJkzNXPmzOYLDECz4hojAAAAAKZHjxEAAABwGq7VNB96jAAAAACYHokRAAAAANMjMQIAAABgeiRGAAAAAEyPxAgAAACA6TU4MVqzZo3Gjh2riIgIWSwWLVu2zGG/YRiaPn26wsPD1aZNG8XGxmrHjh0OZcrKypScnCyr1aqgoCClpqaqqqqqSRUBAAAAgMZqcGJUXV2tAQMGKCcnp879s2bN0ty5czV//nwVFxerXbt2iouL05EjR+xlkpOTtXXrVhUUFGj58uVas2aNpkyZ0vhaAAAAAC5kybI4LPA+Db6PUUJCghISEurcZxiG5syZo0ceeUTjxo2TJC1cuFChoaFatmyZkpKStG3bNuXn52v9+vUaMmSIJGnevHkaPXq0Zs+erYiIiCZUBwAAAAAazqnXGO3evVs2m02xsbH2bYGBgYqOjlZRUZEkqaioSEFBQfakSJJiY2Pl4+Oj4uJiZ4YDAHACfiUFAJhBg3uMzsZms0mSQkNDHbaHhoba99lsNoWEhDgG0aqVgoOD7WVOV1NTo5qaGvt6ZWWlM8MGAAAAYHIeMStddna2AgMD7UtkZKS7QwIAAADgRZyaGIWFhUmSSktLHbaXlpba94WFhengwYMO+48fP66ysjJ7mdNlZmaqoqLCvuzdu9eZYQMAAAAwOacmRlFRUQoLC1NhYaF9W2VlpYqLixUTEyNJiomJUXl5uUpKSuxlVq5cqdraWkVHR9f5vP7+/rJarQ4LAAAAADhLgxOjqqoqbdq0SZs2bZL064QLmzZt0p49e2SxWDR16lQ98cQTeuedd7R582bdddddioiI0Pjx4yVJvXv3Vnx8vCZPnqzPP/9cn332mdLT05WUlMSMdICJneseaXfffbcsFovDEh8f71CGe6QBAIDGavDkCxs2bNCIESPs6xkZGZKklJQU5ebmatq0aaqurtaUKVNUXl6uYcOGKT8/XwEBAfbHLFq0SOnp6Ro5cqR8fHyUmJiouXPnOqE6ADzVyXuk3XPPPZowYUKdZeLj47VgwQL7ur+/v8P+5ORkHThwQAUFBTp27JgmTZqkKVOmKC8vz6WxAwAAz9fgxGj48OEyDKPe/RaLRTNnztTMmTPrLRMcHMwXFQAOznaPtJP8/f3rvRaRe6QBAICm8IhZ6QBAklatWqWQkBD17NlT9957rw4dOmTfxz3SAADNiXu8eR+n3scIAFwlPj5eEyZMUFRUlHbt2qW//OUvSkhIUFFRkXx9fRt1jzSJ+6QBAIBfkRgB8AhJSUn2//fr10/9+/dX9+7dtWrVKo0cObLRz5udna2srCxnhAgAQKPR6+R+DKUD4JG6deumTp06aefOnZIad480ifukAQCAX5EYAfBI+/bt06FDhxQeHi6pcfdIk7hPGoBfff/997rjjjvUsWNHtWnTRv369dOGDRvs+w3D0PTp0xUeHq42bdooNjZWO3bscGPEAJyNxAhAi3C2e6RVVVXpoYce0rp16/Ttt9+qsLBQ48aN0yWXXKK4uDhJ3CMNQOP99NNPuvrqq9W6dWu9//77+vrrr/W///u/uuCCC+xlZs2apblz52r+/PkqLi5Wu3btFBcXpyNHjrgxcgDOxDVGcInTx8kaM+qf4v308ucqC+90tnukPffcc/rqq6/08ssvq7y8XBERERo1apQef/xxh3sZcY80AI3x1FNPKTIy0uE+aVFRUfb/G4ahOXPm6JFHHtG4ceMkSQsXLlRoaKiWLVvmcA0kAM9FYgSv19AkDe5xrnukffDBB+d8Du6RBqAx3nnnHcXFxemWW27R6tWrdeGFF+q+++7T5MmTJf3ag22z2RQbG2t/TGBgoKKjo1VUVFRvYsSsl4BnYSgdAAAwtW+++UbPPfecLr30Un3wwQe699579Yc//EEvv/yyJNmn/A8NDXV4XGho6FlvB5Cdna3AwED7EhkZ6bpKAGgyEiMAAGBqtbW1uvzyy/W3v/1NgwYN0pQpUzR58mTNnz+/Sc/LrJeAZyExAgAAphYeHq4+ffo4bOvdu7f27NkjSfYp/0tLSx3KlJaWnvV2AMx6CXgWEiMAAGBqV199tbZv3+6w7f/+7//UtWtXSb9OxBAWFqbCwkL7/srKShUXFysmJqZZYwXgOky+AAAATO2BBx7QVVddpb/97W+69dZb9fnnn+uFF17QCy+8IEmyWCyaOnWqnnjiCV166aWKiorSo48+qoiICI0fP969wQNwGhIjAABgaldccYWWLl2qzMxMzZw5U1FRUZozZ46Sk5PtZaZNm6bq6mpNmTJF5eXlGjZsmPLz8xUQEODGyAE4E4kRAAAwvRtvvFE33nhjvfstFotmzpypmTNnNmNUAJoT1xgBAAAAMD0SIwAAAACmR2IEAAAAwPS4xggex5JlcXcIAADAC5z+ncKYYbgpkobz5NhbKnqMAAAAAJgeiREAAAAA0yMxAgAAAGB6XGOEZsF1QQAAAGjJ6DECAAAAYHokRgAAAABMj8QIAAAAgOlxjRFaPK5PAgAAgKvRYwQAAADA9OgxglPQqwMAAABPRo8RAAAAANMjMQIAAABgeiRGAAAAAEyPa4wAwAS4DhAAgLOjxwhAi7BmzRqNHTtWERERslgsWrZsmcN+wzA0ffp0hYeHq02bNoqNjdWOHTscypSVlSk5OVlWq1VBQUFKTU1VVVVVM9YCAAB4KhIjAC1CdXW1BgwYoJycnDr3z5o1S3PnztX8+fNVXFysdu3aKS4uTkeOHLGXSU5O1tatW1VQUKDly5drzZo1mjJlSnNVAQAAeDCG0gFoERISEpSQkFDnPsMwNGfOHD3yyCMaN26cJGnhwoUKDQ3VsmXLlJSUpG3btik/P1/r16/XkCFDJEnz5s3T6NGjNXv2bEVERDRbXQAAgOehxwhAi7d7927ZbDbFxsbatwUGBio6OlpFRUWSpKKiIgUFBdmTIkmKjY2Vj4+PiouL633umpoaVVZWOiwAADSVJcvisKDlIzEC0OLZbDZJUmhoqMP20NBQ+z6bzaaQkBCH/a1atVJwcLC9TF2ys7MVGBhoXyIjI50cPQBP8+STT8pisWjq1Kn2bUeOHFFaWpo6duyo9u3bKzExUaWlpe4LEoDTMZQOpneuX3GMGUYzRQJ3yMzMVEZGhn29srKS5EjMYgfzWr9+vZ5//nn179/fYfsDDzyg9957T0uWLFFgYKDS09M1YcIEffbZZ26KFK5A22du9BgBaPHCwsIk6YxfZ0tLS+37wsLCdPDgQYf9x48fV1lZmb1MXfz9/WW1Wh0WAOZUVVWl5ORk/fOf/9QFF1xg315RUaEXX3xRTz/9tK6//noNHjxYCxYs0Nq1a7Vu3To3RgzAmUiMALR4UVFRCgsLU2FhoX1bZWWliouLFRMTI0mKiYlReXm5SkpK7GVWrlyp2tpaRUdHN3vMADxPWlqaxowZ43A9oySVlJTo2LFjDtt79eqlLl262K9zrIu3XsPItTPwVgylA9AiVFVVaefOnfb13bt3a9OmTQoODlaXLl00depUPfHEE7r00ksVFRWlRx99VBERERo/frwkqXfv3oqPj9fkyZM1f/58HTt2TOnp6UpKSmJGOgDntHjxYm3cuFHr168/Y5/NZpOfn5+CgoIctp96nWNdsrOzlZWV5exQAbgIiRGAFmHDhg0aMWKEff3kdT8pKSnKzc3VtGnTVF1drSlTpqi8vFzDhg1Tfn6+AgIC7I9ZtGiR0tPTNXLkSPn4+CgxMVFz585t9roA8Cx79+7VH//4RxUUFDi0KU3FNYyAZyExgtehW98zDR8+XIZR/0QXFotFM2fO1MyZM+stExwcrLy8PFeEB8CLlZSU6ODBg7r88svt206cOKE1a9boH//4hz744AMdPXpU5eXlDr1Gp17nWBd/f3/5+/u7MnQATkRiBABokNN/fGDmRni6kSNHavPmzQ7bJk2apF69eunPf/6zIiMj1bp1axUWFioxMVGStH37du3Zs8d+nSMAz0diBAAATK1Dhw667LLLHLa1a9dOHTt2tG9PTU1VRkaGgoODZbVadf/99ysmJkZXXnmlO0IG4AJOn5Xusccek8VicVh69epl388N0gAAgKd55plndOONNyoxMVHXXnutwsLC9NZbb7k7LABO5JIeo759++qjjz767x9p9d8/ww3SAABAS7dq1SqH9YCAAOXk5CgnJ8c9AQFwOZckRq1atarzYsSTN0jLy8vT9ddfL0lasGCBevfurXXr1tEdDQAAYGJcwwh3cskNXnfs2KGIiAh169ZNycnJ2rNnj6TG3yANAAAAAFzJ6T1G0dHRys3NVc+ePXXgwAFlZWXpmmuu0ZYtWxp9g7SamhrV1NTY173lztEA4CxMUw8AQNM4PTFKSEiw/79///6Kjo5W165d9frrr6tNmzaNek7uHA0AAOAZTv2h5lxD4fhRBy2JS4bSnSooKEg9evTQzp07FRYWZr9B2qnOdYO0zMxMVVRU2Je9e/e6OGoAAAAAZuLyxKiqqkq7du1SeHi4Bg8ebL9B2knnc4M0f39/Wa1WhwUAAADnZsmyOCwA6ub0oXQPPvigxo4dq65du2r//v2aMWOGfH19NXHiRAUGBnKDNAAAAAAtjtMTo3379mnixIk6dOiQOnfurGHDhmndunXq3LmzpF9vkObj46PExETV1NQoLi5Ozz77rLPDAAAAAIDz5vTEaPHixWfdzw3SAAAAALQ0Lr/GCAAAAABaOhIjAAAAAKbn9KF08E6nz2JzrvsSAAAAAJ6EHiMAAAAApkdiBAAAAMD0SIwAAAAAmB6JEQAAAADTIzECAAAAYHokRgAAAABMj+m6AQAA4BLc7gOehB4jAAAAAKZHYgTAYzz22GOyWCwOS69evez7jxw5orS0NHXs2FHt27dXYmKiSktL3RgxAADwFAylQ4tzerc7cKq+ffvqo48+sq+3avXfZuyBBx7Qe++9pyVLligwMFDp6emaMGGCPvvsM3eECgAAPAg9RgA8SqtWrRQWFmZfOnXqJEmqqKjQiy++qKefflrXX3+9Bg8erAULFmjt2rVat26dm6MG0NJlZ2friiuuUIcOHRQSEqLx48dr+/btDmXolQa8G4kRAI+yY8cORUREqFu3bkpOTtaePXskSSUlJTp27JhiY2PtZXv16qUuXbqoqKjIXeEC8BCrV69WWlqa1q1bp4KCAh07dkyjRo1SdXW1vcwDDzygd999V0uWLNHq1au1f/9+TZgwwY1RA3AmhtIB8BjR0dHKzc1Vz549deDAAWVlZemaa67Rli1bZLPZ5Ofnp6CgIIfHhIaGymaz1fucNTU1qqmpsa9XVla6KnwALVh+fr7Dem5urkJCQlRSUqJrr73W3iudl5en66+/XpK0YMEC9e7dW+vWrdOVV17pjrABOBGJERqF64DgDgkJCfb/9+/fX9HR0eratatef/11tWnTplHPmZ2draysLGeFCMBLVFRUSJKCg4MlnbtX2psSI6bYhlkxlA6AxwoKClKPHj20c+dOhYWF6ejRoyovL3coU1paqrCwsHqfIzMzUxUVFfZl7969Lo4aQEtXW1urqVOn6uqrr9Zll10mSY3qla6pqVFlZaXDAqDloscIpkNvl/eoqqrSrl27dOedd2rw4MFq3bq1CgsLlZiYKEnavn279uzZo5iYmHqfw9/fX/7+/s0VMgAPkJaWpi1btujTTz9t0vPQI93y8B0AZ0OPEQCP8eCDD2r16tX69ttvtXbtWt18883y9fXVxIkTFRgYqNTUVGVkZOjjjz9WSUmJJk2apJiYGK8a4gLAtdLT07V8+XJ9/PHHuuiii+zbG9MrTY804FnoMQLgMfbt26eJEyfq0KFD6ty5s4YNG6Z169apc+fOkqRnnnlGPj4+SkxMVE1NjeLi4vTss8+6OWrz4foEeCLDMHT//fdr6dKlWrVqlaKiohz2N6ZXmh5pwLOQGAHwGIsXLz7r/oCAAOXk5CgnJ6eZIgLgLdLS0pSXl6e3335bHTp0sF83FBgYqDZt2jj0SgcHB8tqter++++nV1ote3haS44NLQ+JEQAAML3nnntOkjR8+HCH7QsWLNDdd98tiV5pwNuRGAEAANMzjHMP+aRXuuWjhwhNQWIEnAPXSwAAAHg/EiMA8ED8KgoAgHORGAEAAMAjMIoDrsR9jAAAAACYHj1GAAAAaBYNHQbMsGE0JxIjAAAAEyP5AH7FUDoAAAAApkePEQAAgAdjQgLAOegxAgAAAGB69BgBDcQvcwAAT8YECEDd6DECAAAAYHr0GAEAmoRfkwEA3oDECABaIJINAACaF0PpAAAAAJgePUZAM3L2xA1MBAEAAOAc9BgBAAAAMD0SIwAAAACmx1A6FzrXMKfmHAZ1rgu5GYLlGbggHwCA/+JzEc5EYgQ4Edf8AAAAeCYSI6CJ+LUKANCS8LkENA6JESTRiAIAAHiyU7/LMWKlcUiMABdq7oSToXwtF8cGAICWjcQIAACgheHHFKD5uXW67pycHF188cUKCAhQdHS0Pv/8c3eGA8BL0LYAcBXaF8B7ua3H6LXXXlNGRobmz5+v6OhozZkzR3Fxcdq+fbtCQkLcFRbgVlzr1XTubFv4hRfwbnx3Abyb2xKjp59+WpMnT9akSZMkSfPnz9d7772nl156SQ8//LC7wgK8ihm/qHtK22LGYwN4One2L/xwBm9ytoki3Pn56JbE6OjRoyopKVFmZqZ9m4+Pj2JjY1VUVHRG+ZqaGtXU1NjXKyoqJEmVlZXn/FuB2YEO6xWZFY0Nu+GOOK6eEe9p+y2ZDWv0GlSXI+cuguZ3rveEy/9ePfsNwzO/pDe0bZGc276czuE5znFsG3r+e5LT69as7TBc5tT3//kcU9qXhrUvfG6jKc7rPeZOp7y/z/Vd6Hzq4rT2xXCD77//3pBkrF271mH7Qw89ZAwdOvSM8jNmzDAksbCwNNOyd+/e5moOnKqhbYth0L6wsDT3QvvCwsLiqqWp7YtHzEqXmZmpjIwM+3ptba3KysrUsWNHWSzN/ytrZWWlIiMjtXfvXlmt1mb/+83FLPWUzFPXc9XTMAwdPnxYERERbojOPZqjfTHL+6uheF3q542vDe2L+7+/tCTe+B53J7O/ns5qX9ySGHXq1Em+vr4qLS112F5aWqqwsLAzyvv7+8vf399hW1BQkCtDPC9Wq9UUbz6z1FMyT13PVs/AwMBmjsZ5Gtq2SM3bvpjl/dVQvC7187bXhvalZXx/aUm87T3ubmZ+PZ3Rvrhlum4/Pz8NHjxYhYWF9m21tbUqLCxUTEyMO0IC4AVoWwC4Cu0L4P3cNpQuIyNDKSkpGjJkiIYOHao5c+aourraPtMLADQGbQsAV6F9Abyb2xKj2267TT/88IOmT58um82mgQMHKj8/X6Ghoe4K6bz5+/trxowZZ3SPexuz1FMyT13NUM+W2LaY4XVvDF6X+vHatEwtsX3xVLzHnYvX0zkshuGh82YCAAAAgJO45RojAAAAAGhJSIwAAAAAmB6JEQAAAADTIzECAAAAYHokRvXIycnRxRdfrICAAEVHR+vzzz8/a/klS5aoV69eCggIUL9+/bRixYpmirRpGlLP3NxcWSwWhyUgIKAZo22cNWvWaOzYsYqIiJDFYtGyZcvO+ZhVq1bp8ssvl7+/vy655BLl5ua6PM6mamg9V61adcbxtFgsstlszROwSf31r3/VVVddpbZt25r+Ro8NbWfNoDHtFeBpOPedhzbDuUiM6vDaa68pIyNDM2bM0MaNGzVgwADFxcXp4MGDdZZfu3atJk6cqNTUVH3xxRcaP368xo8fry1btjRz5A3T0HpKv95R+cCBA/blu+++a8aIG6e6uloDBgxQTk7OeZXfvXu3xowZoxEjRmjTpk2aOnWqfvvb3+qDDz5wcaRN09B6nrR9+3aHYxoSEuKiCCFJR48e1S233KJ7773X3aG4VWPaHzNo7HkMeArOfeeizXAyA2cYOnSokZaWZl8/ceKEERERYWRnZ9dZ/tZbbzXGjBnjsC06Otr43e9+59I4m6qh9VywYIERGBjYTNG5hiRj6dKlZy0zbdo0o2/fvg7bbrvtNiMuLs6FkTnX+dTz448/NiQZP/30U7PEBEfecD41RUPbHzM6n/MY8DSc+65Dm9F09Bid5ujRoyopKVFsbKx9m4+Pj2JjY1VUVFTnY4qKihzKS1JcXFy95VuCxtRTkqqqqtS1a1dFRkZq3Lhx2rp1a3OE26w88Xg2xcCBAxUeHq4bbrhBn332mbvDgQk0tv0B4Nk499HSkRid5scff9SJEyfOuIt1aGhovdde2Gy2BpVvCRpTz549e+qll17S22+/rVdeeUW1tbW66qqrtG/fvuYIudnUdzwrKyv1yy+/uCkq5wsPD9f8+fP15ptv6s0331RkZKSGDx+ujRs3ujs0eLnGtD8APB/nPlq6Vu4OAJ4jJiZGMTEx9vWrrrpKvXv31vPPP6/HH3/cjZGhMXr27KmePXva16+66irt2rVLzzzzjP7973+7MTLP8/DDD+upp546a5lt27apV69ezRQRAABoKBKj03Tq1Em+vr4qLS112F5aWqqwsLA6HxMWFtag8i1BY+p5utatW2vQoEHauXOnK0J0m/qOp9VqVZs2bdwUVfMYOnSoPv30U3eH4XH+9Kc/6e677z5rmW7dujVPMB7AGe0PAM/DuY+WjqF0p/Hz89PgwYNVWFho31ZbW6vCwkKH3pJTxcTEOJSXpIKCgnrLtwSNqefpTpw4oc2bNys8PNxVYbqFJx5PZ9m0aZPXHc/m0LlzZ/Xq1eusi5+fn7vDbDGc0f4A8Dyc+2jp6DGqQ0ZGhlJSUjRkyBANHTpUc+bMUXV1tSZNmiRJuuuuu3ThhRcqOztbkvTHP/5R1113nf73f/9XY8aM0eLFi7Vhwwa98MIL7qzGOTW0njNnztSVV16pSy65ROXl5fr73/+u7777Tr/97W/dWY1zqqqqcujV2r17tzZt2qTg4GB16dJFmZmZ+v7777Vw4UJJ0u9//3v94x//0LRp03TPPfdo5cqVev311/Xee++5qwrnpaH1nDNnjqKiotS3b18dOXJE//rXv7Ry5Up9+OGH7qqCKezZs0dlZWXas2ePTpw4oU2bNkmSLrnkErVv3969wTWjc7U/ZnWu8xjwdJz7zkWb4WTunhavpZo3b57RpUsXw8/Pzxg6dKixbt06+77rrrvOSElJcSj/+uuvGz169DD8/PyMvn37Gu+9914zR9w4Dann1KlT7WVDQ0ON0aNHGxs3bnRD1A1zclrq05eTdUtJSTGuu+66Mx4zcOBAw8/Pz+jWrZuxYMGCZo+7oRpaz6eeesro3r27ERAQYAQHBxvDhw83Vq5c6Z7gTSQlJaXO4/Txxx+7O7Rmd7b2x6zOdR4D3oBz33loM5zLYhiG0WxZGAAAAAC0QFxjBAAAAMD0SIwAAAAAmB6JEQAAAADTIzECAAAAYHokRgCAFic3N1cWi0UWi6XOmw4bhqHIyEhZLBbdeOONbogQgLc5td05uYSEhGjEiBF6//333R0emgH3MQIAtFgBAQHKy8vTsGHDHLavXr1a+/btk7+/v5siA+CtZs6cqaioKBmGodLSUuXm5mr06NF69913+SHGy5EYAQBarNGjR2vJkiWaO3euWrX670dWXl6eBg8erB9//NGN0QHwRgkJCRoyZIh9PTU1VaGhoXr11VdJjLwcQ+ngNN9//71SU1MVEREhf39/RUVF6d5779XRo0f12GOPyWKxnPGYk93W3377bfMHDKDFmzhxog4dOqSCggL7tqNHj+qNN97Q7bff7lD222+/lcVi0ezZs/XCCy+oe/fu8vf31xVXXKH169c3d+gAvERQUJDatGnj8OMMvBNHGE6xf/9+DR06VOXl5ZoyZYp69eql77//Xm+88YZ+/vlnd4cHwENdfPHFiomJ0auvvqqEhARJ0vvvv6+KigolJSVp7ty5ZzwmLy9Phw8f1u9+9ztZLBbNmjVLEyZM0DfffKPWrVs3dxUAeJiKigr9+OOPMgxDBw8e1Lx581RVVaU77rjD3aHBxUiM4BSZmZmy2WwqLi526H6eOXOmDMNwY2QAPN3tt9+uzMxM/fLLL2rTpo0WLVqk6667ThEREXWW37Nnj3bs2KELLrhAktSzZ0+NGzdOH3zwAcNgAJxTbGysw7q/v79eeukl3XDDDW6KCM2FoXRostraWi1btkxjx451SIpOqmsIHQCcr1tvvVW//PKLli9frsOHD2v58uVnDKM71W233WZPiiTpmmuukSR98803Lo8VgOfLyclRQUGBCgoK9Morr2jEiBH67W9/q7feesvdocHF6DFCk/3www+qrKzUZZdd5u5QAHihzp07KzY2Vnl5efr555914sQJ/eY3v6m3fJcuXRzWTyZJP/30k0vjBOAdhg4d6vBD78SJEzVo0CClp6frxhtvlJ+fnxujgyvRY4RmUV+v0YkTJ5o5EgCe6Pbbb9f777+v+fPnKyEhQUFBQfWW9fX1rXM7w3oBNIaPj49GjBihAwcOaMeOHe4OBy5EYoQm69y5s6xWq7Zs2VJvmZO/2JaXlzts/+6771wZGgAvcfPNN8vHx0fr1q076zA6AHCF48ePS5KqqqrcHAlcicQITebj46Px48fr3Xff1YYNG87YbxiGunfvLklas2aNfXt1dbVefvnlZosTgOdq3769nnvuOT322GMaO3asu8MBYCLHjh3Thx9+KD8/P/Xu3dvd4cCFuMYITvG3v/1NH374oa677jpNmTJFvXv31oEDB7RkyRJ9+umnGjVqlLp06aLU1FQ99NBD8vX11UsvvaTOnTtrz5497g4fgAdISUlxdwgATOD999/Xf/7zH0nSwYMHlZeXpx07dujhhx+W1Wp1c3RwJRIjOMWFF16o4uJiPfroo1q0aJEqKyt14YUXKiEhQW3btlXr1q21dOlS3XfffXr00UcVFhamqVOn6oILLtCkSZPcHT4AAIAkafr06fb/BwQEqFevXnruuef0u9/9zo1RoTlYDK5GBQAAAGByXGMEAAAAwPRIjAAAAACYHokRAAAAANMjMQIAAABgeiRGAADAq61Zs0Zjx45VRESELBaLli1b5rDfMAxNnz5d4eHhatOmjWJjY7Vjxw6HMmVlZUpOTpbValVQUJBSU1O52SfgZUiMAACAV6uurtaAAQOUk5NT5/5Zs2Zp7ty5mj9/voqLi9WuXTvFxcXpyJEj9jLJycnaunWrCgoKtHz5cq1Zs0ZTpkxprioAaAYeOV13bW2t9u/frw4dOshisbg7HMBrGIahw4cPKyIiQj4+5vzdhPYFcI2W0r5YLBYtXbpU48ePt8cVERGhP/3pT3rwwQclSRUVFQoNDVVubq6SkpK0bds29enTR+vXr9eQIUMkSfn5+Ro9erT27duniIiI8/rbtC+AazirffHIG7zu379fkZGR7g4D8Fp79+7VRRdd5O4w3IL2BXCtlta+7N69WzabTbGxsfZtgYGBio6OVlFRkZKSklRUVKSgoCB7UiRJsbGx8vHxUXFxsW6++eY6n7umpkY1NTX29e+//159+vRxXWUAk2tq++KRiVGHDh0k/Vp5q9Xq5mgA71FZWanIyEj7OWZGtC+Aa7TU9sVms0mSQkNDHbaHhoba99lsNoWEhDjsb9WqlYKDg+1l6pKdna2srKwzttO+AM7lrPbFIxOjk93PVquVhgVwATMP8aB9AVzLTO1LZmamMjIy7Osnv7zRvgCu0dT2xZwXEQAAAEgKCwuTJJWWljpsLy0tte8LCwvTwYMHHfYfP35cZWVl9jJ18ff3tydBJENAy0diBAAATCsqKkphYWEqLCy0b6usrFRxcbFiYmIkSTExMSovL1dJSYm9zMqVK1VbW6vo6OhmjxmAa3jkUDoAAIDzVVVVpZ07d9rXd+/erU2bNik4OFhdunTR1KlT9cQTT+jSSy9VVFSUHn30UUVERNhnruvdu7fi4+M1efJkzZ8/X8eOHVN6erqSkpLOe0Y6AC0fiREAAPBqGzZs0IgRI+zrJ6/7SUlJUW5urqZNm6bq6mpNmTJF5eXlGjZsmPLz8xUQEGB/zKJFi5Senq6RI0fKx8dHiYmJmjt3brPXBYDreOR9jCorKxUYGKiKigrG6wJOxLnFawC4CucWrwHgKs46txp8jdGaNWs0duxYRUREyGKxaNmyZQ777777blksFoclPj7eoUxZWZmSk5NltVoVFBSk1NRUVVVVNboSQENYsiwOCwC0ROdqq2jLAPfiHPQ+DU6MqqurNWDAAOXk5NRbJj4+XgcOHLAvr776qsP+5ORkbd26VQUFBVq+fLnWrFmjKVOmNDx6AAAAAHCCBl9jlJCQoISEhLOW8ff3r3f6ym3btik/P1/r16+330F63rx5Gj16tGbPns1FjAAAAACanUum6161apVCQkLUs2dP3XvvvTp06JB9X1FRkYKCguxJkSTFxsbKx8dHxcXFdT5fTU2NKisrHRYAAAAAcBanJ0bx8fFauHChCgsL9dRTT2n16tVKSEjQiRMnJEk2m00hISEOj2nVqpWCg4Nls9nqfM7s7GwFBgbal8jISGeHDQAAAMDEnD5dd1JSkv3//fr1U//+/dW9e3etWrVKI0eObNRzZmZm2qfWlH6deYLkCAAAAICzuGQo3am6deumTp062W+sFhYWpoMHDzqUOX78uMrKyuq9Lsnf319Wq9VhAQAAAABncXlitG/fPh06dEjh4eGSpJiYGJWXl6ukpMReZuXKlaqtrVV0dLSrwwEAAACAMzR4KF1VVZW990eSdu/erU2bNik4OFjBwcHKyspSYmKiwsLCtGvXLk2bNk2XXHKJ4uLiJEm9e/dWfHy8Jk+erPnz5+vYsWNKT09XUlISM9IBAAAAcIsG9xht2LBBgwYN0qBBgyRJGRkZGjRokKZPny5fX1999dVXuummm9SjRw+lpqZq8ODB+uSTT+Tv729/jkWLFqlXr14aOXKkRo8erWHDhumFF15wXq0AAAAAoAEa3GM0fPhwGYZR7/4PPvjgnM8RHBysvLy8hv5pAAAAAHAJl19jBAAAAAAtHYkRgBZhzZo1Gjt2rCIiImSxWLRs2TKH/XfffbcsFovDEh8f71CmrKxMycnJslqtCgoKUmpqqqqqqpqxFgAAwFORGAFoEaqrqzVgwADl5OTUWyY+Pl4HDhywL6+++qrD/uTkZG3dulUFBQVavny51qxZoylTprg6dAAA4AWcfoNXAGiMhIQEJSQknLWMv79/vfc727Ztm/Lz87V+/XoNGTJEkjRv3jyNHj1as2fPZtZLAIBbWbIsDuvGjPqv2Yd70GMEwGOsWrVKISEh6tmzp+69914dOnTIvq+oqEhBQUH2pEiSYmNj5ePjo+Li4nqfs6amRpWVlQ4LAAAwHxIjAB4hPj5eCxcuVGFhoZ566imtXr1aCQkJOnHihCTJZrMpJCTE4TGtWrVScHCwbDZbvc+bnZ2twMBA+xIZGenSegAAgJaJoXQAPEJSUpL9//369VP//v3VvXt3rVq1SiNHjmz082ZmZiojI8O+XllZSXIEAB7q9OFqp2P4Gs6GHiMAHqlbt27q1KmTdu7cKUkKCwvTwYMHHcocP35cZWVl9V6XJP163ZLVanVYAACA+ZAYAfBI+/bt06FDhxQeHi5JiomJUXl5uUpKSuxlVq5cqdraWkVHR7srTAAA4CEYSgegRaiqqrL3/kjS7t27tWnTJgUHBys4OFhZWVlKTExUWFiYdu3apWnTpumSSy5RXFycJKl3796Kj4/X5MmTNX/+fB07dkzp6elKSkpiRjoAAHBO9BgBaBE2bNigQYMGadCgQZKkjIwMDRo0SNOnT5evr6+++uor3XTTTerRo4dSU1M1ePBgffLJJ/L397c/x6JFi9SrVy+NHDlSo0eP1rBhw/TCCy+4q0oAAMCD0GMEoEUYPny4DKP+i2I/+OCDcz5HcHCw8vLynBkWAAAwCRIjAAAAmAI3WcXZMJQOAAAAgOmRGAEAAAAwPRIjAAAAAKZHYgQAAADA9EiMAACA6V188cWyWCxnLGlpaZJ+nTnz9H2///3v3Rw1AGdiVjoAAGB669ev14kTJ+zrW7Zs0Q033KBbbrnFvm3y5MmaOXOmfb1t27bNGiMA1yIx+v/au//YqOs7juOvK9AriG1TKL02lgpMgfA7IGcdApPOCo7IbHSURgEJnQuQQYdiF6BCttQ4owRTYUuwxAhTTPixgWOByiBAKVIhILiGNoyCenWTtKU4WqGf/cE4OegP2t7d9+6+z0fyTfr9cd++P3ff+9zn/f1+P98PAACwvcTERJ/51157TYMGDdKkSZO8y3r16iWXyxXs0AAECbfSAQAA3KKpqUnvv/++XnjhBTkcP4x7s2nTJvXt21fDhw9Xfn6+vvvuuzb309jYqPr6ep8JQOjiihEAAMAttm/frtraWs2ZM8e7bNasWUpLS1NKSopOnjypZcuWqaKiQlu3bm11P4WFhVq1alUQIgbgDyRGAAAAt9iwYYOmTp2qlJQU77Lc3Fzv3yNGjFBycrKmTJmiqqoqDRo0qMX95OfnKy8vzztfX1+v1NTUwAUOoEtIjAAAAP7v/Pnz2rt3b5tXgiTJ7XZLkiorK1tNjJxOp5xOp99jRGhyrHL4zJsCY1Ek6Cz6GAEAAPxfcXGx+vXrpyeffLLN7U6cOCFJSk5ODkJUAIKBK0YAAACSmpubVVxcrNmzZ6t79x+aSFVVVdq8ebOmTZumPn366OTJk1qyZIkmTpyokSNHWhgxAH8iMQIAAJC0d+9eVVdX64UXXvBZHh0drb1792rNmjW6cuWKUlNTlZWVpeXLl1sUKYBAIDECAACQ9Pjjj8uYO/uFpKamav/+/RZEBCCY6GMEAAAAwPZIjAAAAADYHokRAAAAANsjMQIAAABgezx8ARGHAdYAAADQUVwxAgAAAGB7XDECAMAit17h5uo2AFiLxAgAAAC4Dbfm2w+JEdAOKkYAAIDI1+E+RgcOHND06dOVkpIih8Oh7du3+6w3xmjlypVKTk5Wz549lZGRobNnz/psc+nSJeXk5Cg2Nlbx8fGaN2+eGhoaulQQAAAAAOisDidGV65c0ahRo1RUVNTi+tdff11r167V+vXrVVZWpnvuuUeZmZm6evWqd5ucnBydPn1ae/bs0c6dO3XgwAHl5uZ2vhQAAAAA0AUdvpVu6tSpmjp1aovrjDFas2aNli9frqeeekqS9N577ykpKUnbt2/XzJkz9cUXX2j37t369NNPNW7cOEnS22+/rWnTpumNN95QSkpKF4oDAAAAAB3n18d1nzt3Th6PRxkZGd5lcXFxcrvdKi0tlSSVlpYqPj7emxRJUkZGhqKiolRWVubPcICAcKxy+EwAAAAIf359+ILH45EkJSUl+SxPSkryrvN4POrXr59vEN27KyEhwbvN7RobG9XY2Oidr6+v92fYAAAAAGwuLAZ4LSwsVFxcnHdKTU21OiQAAAAAEcSviZHL5ZIk1dTU+CyvqanxrnO5XPrmm2981l+7dk2XLl3ybnO7/Px81dXVeacLFy74M2wAAAAANufXxGjAgAFyuVwqKSnxLquvr1dZWZnS09MlSenp6aqtrVV5ebl3m08++UTNzc1yu90t7tfpdCo2NtZnAgAAAAB/6XBi1NDQoBMnTujEiROSbjxw4cSJE6qurpbD4dDixYv1u9/9Tn/5y1906tQpPf/880pJSdGMGTMkSUOHDtUTTzyh+fPn6+jRozp06JAWLlyomTNn8kQ6wMYYIw0AAFipw4nRsWPHNGbMGI0ZM0aSlJeXpzFjxmjlypWSpJdfflmLFi1Sbm6uHnroITU0NGj37t2KiYnx7mPTpk0aMmSIpkyZomnTpmnChAn605/+5KciAQhHjJEGAACs1OGn0k2ePFnGmFbXOxwOrV69WqtXr251m4SEBG3evLmj/xpABGOMNAAAYKWweCodAHtjjDQAsAfGCoSV/DqOEQAEQqDGSJMYJw2hg0YgAFiLK0YAbI1x0gAAgERiBCAMBGqMNIlx0hBY3BYEAOGDxAhAyAvUGGkS46QBAIAb6GMEICQ0NDSosrLSO39zjLSEhAT179/fO0baAw88oAEDBmjFihWtjpG2fv16ff/994yRBgAA7hqJEYCQcOzYMf3kJz/xzufl5UmSZs+erY0bN+rll1/WlStXlJubq9raWk2YMKHFMdIWLlyoKVOmKCoqSllZWVq7dm3QywJY4fZb9UxB60NrAADuRGIEICQwRhoAALASfYwAAIDtvfrqq3I4HD7TkCFDvOuvXr2qBQsWqE+fPurdu7eysrLueCAMgo8HnMCfSIwAAAAkDRs2TF9//bV3OnjwoHfdkiVL9Ne//lUfffSR9u/fr6+++kpPP/20hdEC8DdupQMAANCNQaFberx/XV2dNmzYoM2bN+uxxx6TJBUXF2vo0KE6cuSIHn744WCHCiAAuGIEAAAg6ezZs0pJSdHAgQOVk5Oj6upqSVJ5ebm+//57ZWRkeLcdMmSI+vfvr9LS0lb319jYqPr6ep8JQOgiMQIAALbndru1ceNG7d69W+vWrdO5c+f06KOP6vLly/J4PIqOjlZ8fLzPa5KSkuTxeFrdZ2FhoeLi4rxTampqgEsBoCu4lQ4AANje1KlTvX+PHDlSbrdbaWlp2rJli3r27Nmpfebn53uHHpBuDExNcgSELq4YAQAA3CY+Pl4PPvigKisr5XK51NTUpNraWp9tampqWuyTdJPT6VRsbKzPBCB0kRgBAADcpqGhQVVVVUpOTtbYsWPVo0cPlZSUeNdXVFSourpa6enpFkYJwJ+4lQ4AANje0qVLNX36dKWlpemrr75SQUGBunXrpuzsbMXFxWnevHnKy8tTQkKCYmNjtWjRIqWnp/NEOiCCkBgBAADbu3jxorKzs/Xtt98qMTFREyZM0JEjR5SYmChJeuuttxQVFaWsrCw1NjYqMzNT77zzjsVRA/AnEiMAAGB7H3zwQZvrY2JiVFRUpKKioiBFBCDY6GMEAAAAwPZIjAAAAADYHokRAAAAANsjMQIAAABgeyRGAAAAAGyPxAgAAACA7ZEYAQAAALA9EiMAAAAAtkdiBAAAAMD2ulsdAAAAAGA3jlUOn3lTYCyKBDeRGAEAEAZub0QBdsT3IDyFSxLIrXQAAAAAbI8rRgAAALClcLmSgeAgMUJIuLViolICAABAsJEYAQAAAO2gf1Pko48RAAAAANsjMQIAAABgeyRGAAAAAGyPxAgAAACA7fk9MXr11VflcDh8piFDhnjXX716VQsWLFCfPn3Uu3dvZWVlqaamxt9hAJZwrHL4TAAAAAgPAXkq3bBhw7R3794f/kn3H/7NkiVLtGvXLn300UeKi4vTwoUL9fTTT+vQoUOBCAUAAAAhgnGDEMoCkhh1795dLpfrjuV1dXXasGGDNm/erMcee0ySVFxcrKFDh+rIkSN6+OGHAxEOAAAAENJIGq0XkMTo7NmzSklJUUxMjNLT01VYWKj+/furvLxc33//vTIyMrzbDhkyRP3791dpaWmriVFjY6MaGxu98/X19YEIGxGKW9oAAADQHr/3MXK73dq4caN2796tdevW6dy5c3r00Ud1+fJleTweRUdHKz4+3uc1SUlJ8ng8re6zsLBQcXFx3ik1NdXfYQMIA/RhBAAAgeL3K0ZTp071/j1y5Ei53W6lpaVpy5Yt6tmzZ6f2mZ+fr7y8PO98fX09yRFgU/RhRDjjCjYAhK6A3Ep3q/j4eD344IOqrKzUT3/6UzU1Nam2ttbnqlFNTU2LfZJucjqdcjqdgQ4VYYqGhr3QhxEAAARCwMcxamhoUFVVlZKTkzV27Fj16NFDJSUl3vUVFRWqrq5Wenp6oENBhOCR2PZ2sw/jwIEDlZOTo+rqaklqtw9jaxobG1VfX+8zAQAA+/F7YrR06VLt379f//rXv3T48GH9/Oc/V7du3ZSdna24uDjNmzdPeXl52rdvn8rLyzV37lylp6dzNhdAu+jDCAAAAsXvidHFixeVnZ2twYMH69lnn1WfPn105MgRJSYmSpLeeust/exnP1NWVpYmTpwol8ulrVu3+jsMABFo6tSpeuaZZzRy5EhlZmbq448/Vm1trbZs2dLpfebn56uurs47XbhwwY8RAwgXhYWFeuihh3TvvfeqX79+mjFjhioqKny2mTx58h0PgHnxxRctihiAv/m9j9EHH3zQ5vqYmBgVFRWpqKjI3/8agM3QhxGAv+zfv18LFizQQw89pGvXrum3v/2tHn/8cZ05c0b33HOPd7v58+dr9erV3vlevXpZES4ChFv07S3gD18AgEC52Yfxueee8+nDmJWVJYk+jLA3BovsmN27d/vMb9y4Uf369VN5ebkmTpzoXd6rV682T7YACF8kRgg5/JijNUuXLtX06dOVlpamr776SgUFBS32YUxISFBsbKwWLVpEH0Z0CPUPbqqrq5MkJSQk+CzftGmT3n//fblcLk2fPl0rVqxo9aoRA9QjXFD33UBihKDgCwd/uNmH8dtvv1ViYqImTJhwRx/GqKgoZWVlqbGxUZmZmXrnnXcsjhpAuGlubtbixYv14x//WMOHD/cunzVrltLS0pSSkqKTJ09q2bJlqqioaLWvdGFhoVatWhWssAF0EYlRBCH5QKSjDyOAYFiwYIE+//xzHTx40Gd5bm6u9+8RI0YoOTlZU6ZMUVVVlQYNGnTHfhigHggvJEawPRJKAMBNCxcu1M6dO3XgwAHdd999bW7rdrslSZWVlS0mRjzcBQgvJEaAhUjKACA0GGO0aNEibdu2Tf/4xz80YMCAdl9z4sQJSVJycnKAowMQDCRGAADA9hYsWKDNmzdrx44duvfee70DQ8fFxalnz56qqqrS5s2bNW3aNPXp00cnT57UkiVLNHHiRI0cOdLi6AH4A4kRAACwvXXr1km6MYjrrYqLizVnzhxFR0dr7969WrNmja5cuaLU1FRlZWVp+fLlFkQLdE174zXZ9Y4WEiMAAGB7xrTd8EtNTdX+/fuDFA0AK5AYAQAiyq1nOv19ltOuZ1EBwA5IjAAAAAA/a+92tUgWrmUnMQphnJkEAP+iXgUAtIbECAAAAAhxnNgJPBIjAAAAdBoNdnRUIPuCdkWU1QEAAAAAgNW4YgQAQCvCtQMx4E9cEYJdcMUIAAAAgO1xxQiW6MhZ2FA/Yxvq8QFoXVe/v5xJB4DIwRUjAAAAALbHFSMAAAAAnRYpd8+QGNkUt38AAAAAPyAxAgDAhjhBBgC+SIzQKfygAgAAIJKQGAEAANhIME9uRkrfE9gDiVEY4SoNAKCzaKDaF599eOJzCz4SI+A2VEQAAAD2Q2JkEx1t7HN1CgAAAHZCYhREdko2uOrSOXY6RgAAoYHfnshHu+zukBgBAcSPDQAAQHggMQIABBUnDICusdN3iCsdka+9zziYxzeJURgLpcoilGIBEF4C2cgLdt1EXQgA4YvEKISE8g9qKMcGAAAAdBWJEQAAQBiz0611QCDZLjGi8gAAoOtu/T3ltxRAJLBdYgQA4YiTOq3jVl8AgD+QGAFBRAMOABDu+C0LDcE8YWaXzzzK6gAAAAAAwGqWJkZFRUW6//77FRMTI7fbraNHj1oZTsRxrHJ4p45sa5ezAohc1C0AAoX6BYhclt1K9+GHHyovL0/r16+X2+3WmjVrlJmZqYqKCvXr18+qsIKKBATwP+oWAIESzPolnPsV0r5BuLIsMXrzzTc1f/58zZ07V5K0fv167dq1S++++65eeeUVv/2frn4523rqTjhXWkCkClbdgsBpr26l0QWrUL8Akc2SxKipqUnl5eXKz8/3LouKilJGRoZKS0vv2L6xsVGNjY3e+bq6OklSfX19+//satur293HLa+/Y9vb9n37+rjCuHaCA9p2V8d4G24/Buvy6+7q/xkTnkl+R+sWqWv1S0ff3y5pp74JJf6u+9qre+EfjnzfhLPd47mt38cWUL90rf3i85qOtm06+J0J5neO77f/tHlchfH7GtT6xVjgyy+/NJLM4cOHfZa/9NJLZvz48XdsX1BQYCQxMTEFabpw4UKwqgO/6mjdYgz1CxNTsCfqFyYmpkBNXa1fwuJx3fn5+crLy/PONzc369KlS+rTp48cjo7fUlFfX6/U1FRduHBBsbGx/gw1LFB+yt9a+Y0xunz5slJSUiyKLvjutn6x+3HTVbx/XRMJ7x/1S9fbL/4WCcfV3bBLOSX7lPX2cvqrfrEkMerbt6+6deummpoan+U1NTVyuVx3bO90OuV0On2WxcfHdzmO2NjYiD5o2kP5KX9L5Y+Li7MgGv/oaN0idbx+sftx01W8f10T7u8f9Yt/2i/+Fu7H1d2ySzkl+5T11nL6o36x5HHd0dHRGjt2rEpKSrzLmpubVVJSovT0dCtCAhABqFsABAr1CxD5LLuVLi8vT7Nnz9a4ceM0fvx4rVmzRleuXPE+6QUAOoO6BUCgUL8Akc2yxOgXv/iF/v3vf2vlypXyeDwaPXq0du/eraSkpID/b6fTqYKCgjsub9sF5af8kVz+QNUtkf6+BRrvX9fw/oUGK9sugWCX48ou5ZTsU9ZAldNhTJg+NxMAAAAA/MSSPkYAAAAAEEpIjAAAAADYHokRAAAAANsjMQIAAABge7ZIjH7/+9/rkUceUa9eve56YDVjjFauXKnk5GT17NlTGRkZOnv2bGADDaBLly4pJydHsbGxio+P17x589TQ0NDmayZPniyHw+Ezvfjii0GKuGuKiop0//33KyYmRm63W0ePHm1z+48++khDhgxRTEyMRowYoY8//jhIkQZGR8q/cePGOz7nmJiYIEYbnjpTr9hZR7+TuOHAgQOaPn26UlJS5HA4tH37dqtDQhizU3sokts9dmnjWNGWsUVi1NTUpGeeeUa/+tWv7vo1r7/+utauXav169errKxM99xzjzIzM3X16tUARho4OTk5On36tPbs2aOdO3fqwIEDys3Nbfd18+fP19dff+2dXn/99SBE2zUffvih8vLyVFBQoM8++0yjRo1SZmamvvnmmxa3P3z4sLKzszVv3jwdP35cM2bM0IwZM/T5558HOXL/6Gj5pRsjR9/6OZ8/fz6IEYenztQrdtWZYxI3XLlyRaNGjVJRUZHVoSAC2Kk9FKntHru0cSxryxgbKS4uNnFxce1u19zcbFwul/nDH/7gXVZbW2ucTqf585//HMAIA+PMmTNGkvn000+9y/72t78Zh8Nhvvzyy1ZfN2nSJPPrX/86CBH61/jx482CBQu889evXzcpKSmmsLCwxe2fffZZ8+STT/osc7vd5pe//GVA4wyUjpb/br8XaBnvX/s6ekyiZZLMtm3brA4DESDS20OR3O6xSxvHqraMLa4YddS5c+fk8XiUkZHhXRYXFye3263S0lILI+uc0tJSxcfHa9y4cd5lGRkZioqKUllZWZuv3bRpk/r27avhw4crPz9f3333XaDD7ZKmpiaVl5f7fHZRUVHKyMho9bMrLS312V6SMjMzw/Kz7kz5JamhoUFpaWlKTU3VU089pdOnTwcjXNhAZ49JANYL1/ZQpLZ77NLGsbIt071TEUc4j8cjSXeMZJ2UlORdF048Ho/69evns6x79+5KSEhoszyzZs1SWlqaUlJSdPLkSS1btkwVFRXaunVroEPutP/85z+6fv16i5/dP//5zxZf4/F4Iuaz7kz5Bw8erHfffVcjR45UXV2d3njjDT3yyCM6ffq07rvvvmCEjQjWmWMSQGgI1/ZQpLZ77NLGsbItE7ZXjF555ZU7OlndPkX6j26g34Pc3FxlZmZqxIgRysnJ0Xvvvadt27apqqrKj6WA1dLT0/X8889r9OjRmjRpkrZu3arExET98Y9/tDq0oKNeARBu7FRv0e5Ba/zVlgnbK0a/+c1vNGfOnDa3GThwYKf27XK5JEk1NTVKTk72Lq+pqdHo0aM7tc9AuNv3wOVy3dFZ7dq1a7p06ZK3rHfD7XZLkiorKzVo0KAOxxsMffv2Vbdu3VRTU+OzvKamptWyulyuDm0fyjpT/tv16NFDY8aMUWVlZSBCDGmBrFfsyh/HJIDW2ak9ZPd2j13aOFa2ZcI2MUpMTFRiYmJA9j1gwAC5XC6VlJR4v/j19fUqKysLqSdQ3e17kJ6ertraWpWXl2vs2LGSpE8++UTNzc3eL/3dOHHihCT5VI6hJjo6WmPHjlVJSYlmzJghSWpublZJSYkWLlzY4mvS09NVUlKixYsXe5ft2bNH6enpQYjYvzpT/ttdv35dp06d0rRp0wIYaWgKZL1iV/44JgG0zk7tIbu3e+zSxrG0LdPlxzeEgfPnz5vjx4+bVatWmd69e5vjx4+b48ePm8uXL3u3GTx4sNm6dat3/rXXXjPx8fFmx44d5uTJk+app54yAwYMMP/973+tKEKXPfHEE2bMmDGmrKzMHDx40DzwwAMmOzvbu/7ixYtm8ODBpqyszBhjTGVlpVm9erU5duyYOXfunNmxY4cZOHCgmThxolVFuGsffPCBcTqdZuPGjebMmTMmNzfXxMfHG4/HY4wx5rnnnjOvvPKKd/tDhw6Z7t27mzfeeMN88cUXpqCgwPTo0cOcOnXKqiJ0SUfLv2rVKvP3v//dVFVVmfLycjNz5kwTExNjTp8+bVURwsLd1Cu4ob1jEq27fPmy99iSZN58801z/Phxc/78eatDQxiyU3soUts9dmnjWNWWsUViNHv2bCPpjmnfvn3ebSSZ4uJi73xzc7NZsWKFSUpKMk6n00yZMsVUVFQEP3g/+fbbb012drbp3bu3iY2NNXPnzvWpCM+dO+fznlRXV5uJEyeahIQE43Q6zY9+9CPz0ksvmbq6OotK0DFvv/226d+/v4mOjjbjx483R44c8a6bNGmSmT17ts/2W7ZsMQ8++KCJjo42w4YNM7t27QpyxP7VkfIvXrzYu21SUpKZNm2a+eyzzyyIOrzcTb2CH7R1TKJ1+/bta/E4u70OA+6GndpDkdzusUsbx4q2jMMYYzp2jQkAAAAAIkvYPpUOAAAAAPyFxAgAAACA7ZEYAQAAALA9EiMAAAAAtkdiBAAAAMD2SIwAAAAA2B6JEQAAAADbIzECAAAAYHskRgAAAABsj8QIAAAAgO2RGAEAAACwPRIjAAAAALb3P3pq0Gh7DICNAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "transformedFeatures.hist(bins=50, figsize=(10, 10), color='green', grid=False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b196b17b",
      "metadata": {
        "id": "b196b17b"
      },
      "source": [
        "#Choose a Model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting prepared data into train and validation sets"
      ],
      "metadata": {
        "id": "Diam6b-6sNe-"
      },
      "id": "Diam6b-6sNe-"
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "id": "1b8f3d04",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1b8f3d04",
        "outputId": "dd26ffbd-932e-4ea0-d7ce-ab398dc78f90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Data Shape:  (1030, 12)\n"
          ]
        }
      ],
      "source": [
        "trainInput, validationInput, trainTarget, validationTarget = train_test_split(transformedFeatures, labels, test_size = 0.2, shuffle=True, random_state = 42)\n",
        "print(\"Train Data Shape: \", trainInput.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "id": "936b80ae",
      "metadata": {
        "id": "936b80ae"
      },
      "outputs": [],
      "source": [
        "trainTarget = trainTarget.values.ravel()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "id": "b2ed2a69",
      "metadata": {
        "id": "b2ed2a69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "ebd675ce-5f9b-4d2a-9898-d59449455da2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 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-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 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-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 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-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 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-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</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-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ],
      "source": [
        "svcClf = SVC()\n",
        "svcClf.fit(trainInput, trainTarget)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "id": "2e95fc38",
      "metadata": {
        "id": "2e95fc38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "484f86b7-90ef-4869-a31f-19c3e454a44f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 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-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 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-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 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-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 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-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" 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-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" 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": 123
        }
      ],
      "source": [
        "forestClf = ensemble.RandomForestClassifier()\n",
        "forestClf.fit(trainInput, trainTarget)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "id": "18a8e58a",
      "metadata": {
        "id": "18a8e58a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "db35c3f2-347d-4979-e7b4-a26d64850e3b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 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-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 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-17 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-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 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-17 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-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 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-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 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-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 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-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ],
      "source": [
        "nbClf = GaussianNB()\n",
        "nbClf.fit(trainInput, trainTarget)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "id": "be871d00",
      "metadata": {
        "id": "be871d00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "f74b2fae-15a8-4683-fe84-7c76987fd746"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 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-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 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-18 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-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 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-18 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-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 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-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 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-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 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-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</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-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ],
      "source": [
        "knnClf = KNeighborsClassifier()\n",
        "knnClf.fit(trainInput, trainTarget)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "id": "9ed9e418",
      "metadata": {
        "id": "9ed9e418",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "fae20f0a-ea41-4935-8c96-1815fe4ce4e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 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-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 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-19 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-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 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-19 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-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 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-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 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-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 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-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ],
      "source": [
        "treeClf = tree.DecisionTreeClassifier()\n",
        "treeClf.fit(trainInput, trainTarget)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "id": "6c54ffb3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "6c54ffb3",
        "outputId": "34cff156-aa58-4477-931c-bf03b460d4ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC  accuarcy is  0.6511627906976745\n",
            "RandomForestClassifier  accuarcy is  0.9689922480620154\n",
            "GaussianNB  accuarcy is  0.5736434108527132\n",
            "KNeighborsClassifier  accuarcy is  0.810077519379845\n",
            "DecisionTreeClassifier  accuarcy is  0.9302325581395349\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 127
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "models = [svcClf, forestClf, nbClf, knnClf, treeClf]\n",
        "accs = []\n",
        "titles = []\n",
        "\n",
        "for model in models:\n",
        "    pred = model.predict((validationInput))\n",
        "    model_acc = accuracy_score(validationTarget, pred)\n",
        "    accs.append(model_acc)\n",
        "    titles.append(type(model).__name__)\n",
        "    print(type(model).__name__, \" accuarcy is \", model_acc)\n",
        "\n",
        "fig = plt.figure(figsize=(10, 5))\n",
        "sns.color_palette(\"crest\")\n",
        "sns.barplot(x = titles, y=accs)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1854bcaa",
      "metadata": {
        "id": "1854bcaa"
      },
      "source": [
        "### Pros & Cons\n",
        "**SVC**\n",
        "1. clear margin of separation between classes\n",
        "2. more effective in high dimensional spaces\n",
        "3. effective in cases where the number of dimensions is greater than the number of samples\n",
        "4. memory efficient                                                                                                          \n",
        "5. not suitable for large data sets                                                                                          \n",
        "\n",
        "**Random Forest Classifier**\n",
        "1. quite fast\n",
        "2. able to deal with unbalanced and missing data\n",
        "3. may over-fit data sets that are particularly noisy\n",
        "4. data-hungry\n",
        "\n",
        "**GaussianNB**\n",
        "1. very fast \n",
        "2. better than other models with less training data if the assumption of independence of features holds\n",
        "3. If you have categorical input variables, the Naive Bayes algorithm performs exceptionally well in comparison to numerical variables\n",
        "4. effectively works in Multi-class predictions.\n",
        "\n",
        "**KNeighborsClassifier**\n",
        "1. robust to noisy data\n",
        "2. effective if the training data is large\n",
        "\n",
        "**DecisionTreeClassifier**\n",
        "1. does not require normalization of data\n",
        "2. does not require scaling of data as well\n",
        "3. higher time to train the model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RandomForest Model"
      ],
      "metadata": {
        "id": "ux_h7Yra92p1"
      },
      "id": "ux_h7Yra92p1"
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "id": "afde4f80",
      "metadata": {
        "id": "afde4f80"
      },
      "outputs": [],
      "source": [
        "forestClassifier = ensemble.RandomForestClassifier(random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "id": "f7d06202",
      "metadata": {
        "id": "f7d06202"
      },
      "outputs": [],
      "source": [
        "paramGrid = { \n",
        "    'n_estimators': [200, 300, 500],\n",
        "    'max_features': ['auto', 'sqrt', 'log2'],\n",
        "    'max_depth' : [4, 5, 6 ,7 ,8, 9, 10],\n",
        "    'criterion' :['gini', 'entropy']\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "id": "b4449099",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "b4449099",
        "outputId": "b7e05e12-69b3-4831-85cb-18743a2887f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [4, 5, 6, 7, 8, 9, 10],\n",
              "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
              "                         'n_estimators': [200, 300, 500]})"
            ],
            "text/html": [
              "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 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-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 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-20 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-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 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-20 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-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 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-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 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-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 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-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [4, 5, 6, 7, 8, 9, 10],\n",
              "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
              "                         &#x27;n_estimators&#x27;: [200, 300, 500]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [4, 5, 6, 7, 8, 9, 10],\n",
              "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
              "                         &#x27;n_estimators&#x27;: [200, 300, 500]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "clf = GridSearchCV(estimator=forestClassifier, param_grid=paramGrid, cv= 5)\n",
        "clf.fit(trainInput, trainTarget)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "id": "4e0ff978",
      "metadata": {
        "id": "4e0ff978"
      },
      "outputs": [],
      "source": [
        "randomForestModel = ensemble.RandomForestClassifier(criterion = 'gini',\n",
        " max_depth = 10,\n",
        " max_features = 'auto',\n",
        " n_estimators = 300, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "randomForestModel.fit(trainInput, trainTarget)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "id": "K5aTxJRW-Q-P",
        "outputId": "348123df-de03-4afa-a0c3-8311c7571c6d"
      },
      "id": "K5aTxJRW-Q-P",
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=10, max_features='auto', n_estimators=300,\n",
              "                       random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-21 {color: black;background-color: white;}#sk-container-id-21 pre{padding: 0;}#sk-container-id-21 div.sk-toggleable {background-color: white;}#sk-container-id-21 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-21 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-21 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-21 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-21 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 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-21 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-21 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-21 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 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-21 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-21 div.sk-item {position: relative;z-index: 1;}#sk-container-id-21 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-21 div.sk-item::before, #sk-container-id-21 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-21 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-21 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-21 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-21 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-21 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-21 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-21 div.sk-label-container {text-align: center;}#sk-container-id-21 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-21 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, max_features=&#x27;auto&#x27;, n_estimators=300,\n",
              "                       random_state=42)</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-25\" type=\"checkbox\" checked><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10, max_features=&#x27;auto&#x27;, n_estimators=300,\n",
              "                       random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = randomForestModel.predict(validationInput)"
      ],
      "metadata": {
        "id": "VF5PKrJY-VqR"
      },
      "id": "VF5PKrJY-VqR",
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "report = classification_report(validationTarget, predictions)\n",
        "report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "0W5OlXTd68Ue",
        "outputId": "14580002-5e1e-4af0-fd42-85ea14e6d305"
      },
      "id": "0W5OlXTd68Ue",
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'              precision    recall  f1-score   support\\n\\n           0       0.95      0.97      0.96        86\\n           1       0.97      0.95      0.96        87\\n           2       1.00      1.00      1.00        85\\n\\n    accuracy                           0.97       258\\n   macro avg       0.97      0.97      0.97       258\\nweighted avg       0.97      0.97      0.97       258\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelAcc = accuracy_score(validationTarget, predictions)\n",
        "modelAcc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8TSzllTr-Z7o",
        "outputId": "f48826e0-e6ee-4274-aba9-1d19c9b7f06b"
      },
      "id": "8TSzllTr-Z7o",
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9728682170542635"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Naive approuch accuracy VS our model accuracy"
      ],
      "metadata": {
        "id": "N7ZZ4zjxw3fc"
      },
      "id": "N7ZZ4zjxw3fc"
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(5, 5))\n",
        "plt.ylim(.80, 1)\n",
        "sns.set_palette(\"GnBu\", 2)\n",
        "sns.barplot(x = ['Naive Approuch', 'Model'], y=[naiveAcc, modelAcc])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "qYRcOPhdw3DB",
        "outputId": "98a11a5e-8cbf-4e7c-fbd3-270a9041f71d"
      },
      "id": "qYRcOPhdw3DB",
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save the model"
      ],
      "metadata": {
        "id": "drZYjjl8Bu3H"
      },
      "id": "drZYjjl8Bu3H"
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('random_forest_pkl.pkl', 'wb') as file:\n",
        "  pickle.dump(model, file)"
      ],
      "metadata": {
        "id": "dxtuAuUZBLay"
      },
      "id": "dxtuAuUZBLay",
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load the model"
      ],
      "metadata": {
        "id": "i9dHq3j8BzT8"
      },
      "id": "i9dHq3j8BzT8"
    },
    {
      "cell_type": "code",
      "source": [
        "with open('random_forest_pkl.pkl', 'rb') as file:\n",
        "  model = pickle.load(file)"
      ],
      "metadata": {
        "id": "PssMt618BpHF"
      },
      "id": "PssMt618BpHF",
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "id": "3e4ed94e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3e4ed94e",
        "outputId": "e797927e-ed64-4067-e384-fbe0a9213ba1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'gini',\n",
              " 'max_depth': 10,\n",
              " 'max_features': 'auto',\n",
              " 'n_estimators': 300}"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ],
      "source": [
        "clf.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KdPi3l8-Br1-"
      },
      "id": "KdPi3l8-Br1-",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "1lZ7vFNW6f_x",
        "a209efea",
        "XO3qR-S36y47",
        "b9ccfed5"
      ]
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}