Traditional-UnreducedCleveland-Heart_Disease_Classification.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "lbOytv4mz_lx"
      },
      "source": [
        "# Heart Disease Classification using Cleveland Dataset - Not Reduced"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook applies traditional machine learning approach to Cleveland Heart dataset for classifying heart disease. The steps do not include feature enginering, specifically does not include the selected the most correlated features with the label. Hence, the dataset is **_not reduced_**.\n",
        "\n",
        "\n",
        "\n",
        "**The steps performed on the notebook is as follows:**\n",
        "\n",
        "1) To clean the data, remove the rows that have missing information on columns ca and thal.\n",
        "\n",
        "2) Converted all fields to numeric data types\n",
        "\n",
        "3) Scaled the data\n",
        "\n",
        "4) Calculated cross validation (k=5) accuracy scores for 10 different machine learning algorithms:\n",
        "\n",
        "- Stochastic Gradient Descent (SGD)\n",
        "- LogisticRegression\n",
        "- Support Vector Machine (kernel='linear' parameter)\n",
        "- Support Vector Machine (kernel = 'rbf' parameter)\n",
        "- DecisionTreeClassifier\n",
        "- RandomForestClassifier\n",
        "- ExtraTreesClassifier\n",
        "- AdaBoostClassifier\n",
        "- GradientBoostingClassifier\n",
        "- XGBoost\n",
        "\n",
        "\n",
        "5) Identified top-performing algorithms (adaboost, random forest, gradient boosting, XGB) that can be fine-tuned by adjusting hyperparameters. Improved accuracy scores by performing Grid Search with the following hyperparameters:\n",
        "\n",
        "- n_estimators = [100, 200, 300, 400, 500]\n",
        "- learning_rate = [0.3, 0.1, 0.05]\n",
        "- max_features = [1, 0.7, 0.5, 0.4, 0.3]\n",
        "- subsample = [1, 0.5, 0.3]\n",
        "- max_samples = [1, 0.5, 0.3, 0.2]\n",
        "- bootstrap = [True,False]\n",
        "\n",
        "6) Using the fine-tuned estimators (adaboost, random forest, gradient boosting, XGB) and top-performing other estimators (SVC, SGD, Logistic regression), formed an ensemble voting classifier.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Zf7D3iTaXvSr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Import the data and perform exploratory data analysis:"
      ],
      "metadata": {
        "id": "LRGzH50vHuoI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "import pandas as pd\n",
        "import io\n",
        "import os"
      ],
      "metadata": {
        "id": "Q1DTLK9Ujl4e"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "train_csv_path = \"processed.cleveland.data\"\n",
        "#Upload the file to the files section of colab before running the code\n",
        "\n",
        "train_data = pd.read_csv(train_csv_path, sep = '\\t')"
      ],
      "metadata": {
        "id": "2KsZwnt8jouB"
      },
      "execution_count": 6,
      "outputs": []
    },
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        "outputId": "01b6dbc2-1c4e-42db-f565-55844b7e2d12"
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              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   63    1   1       145   233    1        2      150      0      2.3      3   \n",
              "1   67    1   4       160   286    0        2      108      1      1.5      2   \n",
              "2   67    1   4       120   229    0        2      129      1      2.6      2   \n",
              "3   37    1   3       130   250    0        0      187      0      3.5      3   \n",
              "4   41    0   2       130   204    0        2      172      0      1.4      1   \n",
              "\n",
              "  ca thal  num  \n",
              "0  0    6    0  \n",
              "1  3    3    2  \n",
              "2  2    7    1  \n",
              "3  0    3    0  \n",
              "4  0    3    0  "
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    object \n",
            " 12  thal      303 non-null    object \n",
            " 13  num       303 non-null    int64  \n",
            "dtypes: float64(1), int64(11), object(2)\n",
            "memory usage: 33.3+ KB\n"
          ]
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              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    54.438944    0.679868    3.158416  131.689769  246.693069    0.148515   \n",
              "std      9.038662    0.467299    0.960126   17.599748   51.776918    0.356198   \n",
              "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
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              "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak       slope         num  \n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000  \n",
              "mean     0.990099  149.607261    0.326733    1.039604    1.600660    0.937294  \n",
              "std      0.994971   22.875003    0.469794    1.161075    0.616226    1.228536  \n",
              "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000  \n",
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              "50%      1.000000  153.000000    0.000000    0.800000    2.000000    0.000000  \n",
              "75%      2.000000  166.000000    1.000000    1.600000    2.000000    2.000000  \n",
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              "      <td>131.689769</td>\n",
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              "      <td>17.599748</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>56.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "train_data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Identify non-numeric data in the object fields (thal and ca)"
      ],
      "metadata": {
        "id": "CJ4nJaosR7vw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "WojYLTpZjQPC",
        "outputId": "7931bacc-e2cb-449a-a80f-97a4f3d3e036"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0.0\n",
              "1      3.0\n",
              "2      2.0\n",
              "3      0.0\n",
              "4      0.0\n",
              "      ... \n",
              "298    0.0\n",
              "299    2.0\n",
              "300    1.0\n",
              "301    1.0\n",
              "302    NaN\n",
              "Name: ca, Length: 303, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "ca = pd.to_numeric(train_data.ca, errors='coerce') #Convert to numeric using 'coerce' which fills bad values with 'nan'\n",
        "ca"
      ]
    },
    {
      "cell_type": "code",
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              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
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              "302   38    1   3       138   175    0        0      173      0      0.0   \n",
              "\n",
              "     slope ca thal  num  \n",
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      ],
      "source": [
        "train_data[ca.isna()] # Use isna to return a boolean index. Then apply that index to the data frame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
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        "id": "cvA91pXIntxP",
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              "266   52    1   4       128   204    1        0      156      1      1.0   \n",
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              "     slope ca thal  num  \n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-75356a02-8384-4177-9f26-d822e7448760 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-75356a02-8384-4177-9f26-d822e7448760');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "thal = pd.to_numeric(train_data.thal, errors='coerce') #Convert to numeric using 'coerce' which fills bad values with 'nan'\n",
        "idx = thal.isna()\n",
        "train_data[idx] # Use isna to return a boolean index. Then apply that index to the data frame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XgCle_eopQB7",
        "outputId": "ef020dd2-0802-407b-d96b-92f783f28e16"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         303\n",
              "sex         303\n",
              "cp          303\n",
              "trestbps    303\n",
              "chol        303\n",
              "fbs         303\n",
              "restecg     303\n",
              "thalach     303\n",
              "exang       303\n",
              "oldpeak     303\n",
              "slope       303\n",
              "ca          303\n",
              "thal        303\n",
              "num         303\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "train_data.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fddsONkboOe2",
        "outputId": "131d61d2-5186-43b8-a133-3b57a25c52ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         301\n",
              "sex         301\n",
              "cp          301\n",
              "trestbps    301\n",
              "chol        301\n",
              "fbs         301\n",
              "restecg     301\n",
              "thalach     301\n",
              "exang       301\n",
              "oldpeak     301\n",
              "slope       301\n",
              "ca          301\n",
              "thal        301\n",
              "num         301\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "update_df = train_data[(train_data.thal != '?')]\n",
        "update_df.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CAsl7CK-ptjI",
        "outputId": "6e3f6d68-cc20-4461-f720-e5145d418592"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         297\n",
              "sex         297\n",
              "cp          297\n",
              "trestbps    297\n",
              "chol        297\n",
              "fbs         297\n",
              "restecg     297\n",
              "thalach     297\n",
              "exang       297\n",
              "oldpeak     297\n",
              "slope       297\n",
              "ca          297\n",
              "thal        297\n",
              "num         297\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "update_df = update_df[(update_df.ca != '?')]\n",
        "update_df.count()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### After cleaning the non-mumeric data, convert the fields to int64 type"
      ],
      "metadata": {
        "id": "9nAxg3mfSGly"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "VDPfLcJApqWD"
      },
      "outputs": [],
      "source": [
        "update_df.ca = update_df.ca.astype('int64')\n",
        "update_df.thal = update_df.thal.astype('int64')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrmFaWgUp1b2",
        "outputId": "a0c6ced1-b7e4-442a-b1ca-8e44c691dc09"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 297 entries, 0 to 301\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       297 non-null    int64  \n",
            " 1   sex       297 non-null    int64  \n",
            " 2   cp        297 non-null    int64  \n",
            " 3   trestbps  297 non-null    int64  \n",
            " 4   chol      297 non-null    int64  \n",
            " 5   fbs       297 non-null    int64  \n",
            " 6   restecg   297 non-null    int64  \n",
            " 7   thalach   297 non-null    int64  \n",
            " 8   exang     297 non-null    int64  \n",
            " 9   oldpeak   297 non-null    float64\n",
            " 10  slope     297 non-null    int64  \n",
            " 11  ca        297 non-null    int64  \n",
            " 12  thal      297 non-null    int64  \n",
            " 13  num       297 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 34.8 KB\n"
          ]
        }
      ],
      "source": [
        "update_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "oEvgtYKbr3ZC"
      },
      "outputs": [],
      "source": [
        "train_data = update_df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Partition data as X and Y"
      ],
      "metadata": {
        "id": "4h23onguS398"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_data.drop(columns = ['num'])\n",
        "Y_train = train_data['num']"
      ],
      "metadata": {
        "id": "ml6tk1dVS3TJ"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Scale all columns"
      ],
      "metadata": {
        "id": "SM3UCGuzSh0T"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "3TQfVeo0tFvM"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "X_train = pd.DataFrame(data = StandardScaler().fit_transform( X ),\n",
        "                       index = X.index, columns= X.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "ygtdKh3yNhXy",
        "outputId": "e8cc6588-3af1-4275-8714-7d3f11ed8f5f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                age           sex            cp      trestbps          chol  \\\n",
              "count  2.970000e+02  2.970000e+02  2.970000e+02  2.970000e+02  2.970000e+02   \n",
              "mean  -1.226105e-16 -7.177199e-17 -4.037175e-17  4.904420e-16 -1.958777e-16   \n",
              "std    1.001688e+00  1.001688e+00  1.001688e+00  1.001688e+00  1.001688e+00   \n",
              "min   -2.827176e+00 -1.446980e+00 -2.240629e+00 -2.125634e+00 -2.337704e+00   \n",
              "25%   -7.241238e-01 -1.446980e+00 -1.642895e-01 -6.594306e-01 -7.002541e-01   \n",
              "50%    1.613719e-01  6.910947e-01 -1.642895e-01 -9.550637e-02 -8.380217e-02   \n",
              "75%    7.148067e-01  6.910947e-01  8.738802e-01  4.684179e-01  5.519138e-01   \n",
              "max    2.485798e+00  6.910947e-01  8.738802e-01  3.851964e+00  6.099981e+00   \n",
              "\n",
              "                fbs       restecg       thalach         exang     oldpeak  \\\n",
              "count  2.970000e+02  2.970000e+02  2.970000e+02  2.970000e+02  297.000000   \n",
              "mean  -4.186700e-17  4.784800e-17  4.784800e-16 -3.289550e-17    0.000000   \n",
              "std    1.001688e+00  1.001688e+00  1.001688e+00  1.001688e+00    1.001688   \n",
              "min   -4.114503e-01 -1.003419e+00 -3.431849e+00 -6.964194e-01   -0.906712   \n",
              "25%   -4.114503e-01 -1.003419e+00 -7.247694e-01 -6.964194e-01   -0.906712   \n",
              "50%   -4.114503e-01  3.389928e-03  1.484822e-01 -6.964194e-01   -0.219520   \n",
              "75%   -4.114503e-01  1.010199e+00  7.160957e-01  1.435916e+00    0.467672   \n",
              "max    2.430427e+00  1.010199e+00  2.287949e+00  1.435916e+00    4.419026   \n",
              "\n",
              "              slope            ca          thal  \n",
              "count  2.970000e+02  2.970000e+02  2.970000e+02  \n",
              "mean  -1.136390e-16 -7.775299e-17 -1.196200e-16  \n",
              "std    1.001688e+00  1.001688e+00  1.001688e+00  \n",
              "min   -9.765832e-01 -7.219761e-01 -8.942201e-01  \n",
              "25%   -9.765832e-01 -7.219761e-01 -8.942201e-01  \n",
              "50%    6.437811e-01 -7.219761e-01 -8.942201e-01  \n",
              "75%    6.437811e-01  3.448244e-01  1.172577e+00  \n",
              "max    2.264145e+00  2.478425e+00  1.172577e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a4faf55-2c71-4b5e-9c1a-547bcb829c3b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "      <td>2.970000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-1.226105e-16</td>\n",
              "      <td>-7.177199e-17</td>\n",
              "      <td>-4.037175e-17</td>\n",
              "      <td>4.904420e-16</td>\n",
              "      <td>-1.958777e-16</td>\n",
              "      <td>-4.186700e-17</td>\n",
              "      <td>4.784800e-17</td>\n",
              "      <td>4.784800e-16</td>\n",
              "      <td>-3.289550e-17</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.136390e-16</td>\n",
              "      <td>-7.775299e-17</td>\n",
              "      <td>-1.196200e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "      <td>1.001688e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.827176e+00</td>\n",
              "      <td>-1.446980e+00</td>\n",
              "      <td>-2.240629e+00</td>\n",
              "      <td>-2.125634e+00</td>\n",
              "      <td>-2.337704e+00</td>\n",
              "      <td>-4.114503e-01</td>\n",
              "      <td>-1.003419e+00</td>\n",
              "      <td>-3.431849e+00</td>\n",
              "      <td>-6.964194e-01</td>\n",
              "      <td>-0.906712</td>\n",
              "      <td>-9.765832e-01</td>\n",
              "      <td>-7.219761e-01</td>\n",
              "      <td>-8.942201e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-7.241238e-01</td>\n",
              "      <td>-1.446980e+00</td>\n",
              "      <td>-1.642895e-01</td>\n",
              "      <td>-6.594306e-01</td>\n",
              "      <td>-7.002541e-01</td>\n",
              "      <td>-4.114503e-01</td>\n",
              "      <td>-1.003419e+00</td>\n",
              "      <td>-7.247694e-01</td>\n",
              "      <td>-6.964194e-01</td>\n",
              "      <td>-0.906712</td>\n",
              "      <td>-9.765832e-01</td>\n",
              "      <td>-7.219761e-01</td>\n",
              "      <td>-8.942201e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.613719e-01</td>\n",
              "      <td>6.910947e-01</td>\n",
              "      <td>-1.642895e-01</td>\n",
              "      <td>-9.550637e-02</td>\n",
              "      <td>-8.380217e-02</td>\n",
              "      <td>-4.114503e-01</td>\n",
              "      <td>3.389928e-03</td>\n",
              "      <td>1.484822e-01</td>\n",
              "      <td>-6.964194e-01</td>\n",
              "      <td>-0.219520</td>\n",
              "      <td>6.437811e-01</td>\n",
              "      <td>-7.219761e-01</td>\n",
              "      <td>-8.942201e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.148067e-01</td>\n",
              "      <td>6.910947e-01</td>\n",
              "      <td>8.738802e-01</td>\n",
              "      <td>4.684179e-01</td>\n",
              "      <td>5.519138e-01</td>\n",
              "      <td>-4.114503e-01</td>\n",
              "      <td>1.010199e+00</td>\n",
              "      <td>7.160957e-01</td>\n",
              "      <td>1.435916e+00</td>\n",
              "      <td>0.467672</td>\n",
              "      <td>6.437811e-01</td>\n",
              "      <td>3.448244e-01</td>\n",
              "      <td>1.172577e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.485798e+00</td>\n",
              "      <td>6.910947e-01</td>\n",
              "      <td>8.738802e-01</td>\n",
              "      <td>3.851964e+00</td>\n",
              "      <td>6.099981e+00</td>\n",
              "      <td>2.430427e+00</td>\n",
              "      <td>1.010199e+00</td>\n",
              "      <td>2.287949e+00</td>\n",
              "      <td>1.435916e+00</td>\n",
              "      <td>4.419026</td>\n",
              "      <td>2.264145e+00</td>\n",
              "      <td>2.478425e+00</td>\n",
              "      <td>1.172577e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a4faf55-2c71-4b5e-9c1a-547bcb829c3b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0a4faf55-2c71-4b5e-9c1a-547bcb829c3b 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-0a4faf55-2c71-4b5e-9c1a-547bcb829c3b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "X_train.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSGEMPMusgVk",
        "outputId": "bde66f06-ca18-4ad2-f87d-4f7a4343c7f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 297 entries, 0 to 301\n",
            "Data columns (total 13 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       297 non-null    float64\n",
            " 1   sex       297 non-null    float64\n",
            " 2   cp        297 non-null    float64\n",
            " 3   trestbps  297 non-null    float64\n",
            " 4   chol      297 non-null    float64\n",
            " 5   fbs       297 non-null    float64\n",
            " 6   restecg   297 non-null    float64\n",
            " 7   thalach   297 non-null    float64\n",
            " 8   exang     297 non-null    float64\n",
            " 9   oldpeak   297 non-null    float64\n",
            " 10  slope     297 non-null    float64\n",
            " 11  ca        297 non-null    float64\n",
            " 12  thal      297 non-null    float64\n",
            "dtypes: float64(13)\n",
            "memory usage: 32.5 KB\n"
          ]
        }
      ],
      "source": [
        "X_train.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ozzPXGvnuVQO"
      },
      "source": [
        "### For each of the algorithms, use Scikit-Learn's cross-validation feature to measure the performance and print out mean and std for each algoritm:\n",
        "\n",
        "1. Stochastic Gradient Descent (SGD)\n",
        "2. LogisticRegression\n",
        "3. Support Vector Machine (kernel='linear' parameter)\n",
        "4. Support Vector Machine (kernel = 'rbf' parameter)\n",
        "5. DecisionTreeClassifier\n",
        "6. RandomForestClassifier\n",
        "7. ExtraTreesClassifier\n",
        "8. AdaBoostClassifier\n",
        "9. GradientBoostingClassifier\n",
        "10. XGBoost\n",
        "-- You can import XGBoost library as follows:  \"from xgboost import XGBClassifier\"\n",
        "\n",
        "Parameters:\n",
        "- Set the scoring parameter of cross-validation to 'accuracy'\n",
        "- Use the default cv = 5 parameter of cross-validation\n",
        "- Use random_state = 42 for initializing model algorithms\n",
        "- Use the kernel parameter indicated above for SVM algorithms\n",
        "- For others, use the default parameters (no need to specify explicitly)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcdX8i3buO7Z",
        "outputId": "33e62d31-bfd4-4bf0-ced8-42a92095fd19"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 1, 0, 0, 0, 3, 0, 2, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       1, 3, 4, 0, 0, 0, 0, 3, 0, 2, 1, 0, 0, 0, 3, 1, 3, 0, 4, 0, 0, 0,\n",
              "       1, 4, 0, 4, 0, 0, 0, 0, 2, 0, 1, 1, 1, 1, 0, 0, 2, 0, 1, 0, 2, 2,\n",
              "       1, 0, 2, 1, 0, 3, 1, 1, 1, 0, 1, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0,\n",
              "       0, 0, 3, 0, 0, 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 3, 0, 2, 1, 2, 3, 1,\n",
              "       1, 0, 2, 2, 0, 0, 0, 3, 2, 3, 4, 0, 3, 1, 0, 3, 3, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 4, 3, 1, 0, 0, 1, 0, 1, 0, 1, 4, 0, 0, 0, 0, 0, 0, 4, 3,\n",
              "       1, 1, 1, 2, 0, 0, 4, 0, 0, 0, 0, 0, 1, 0, 3, 0, 1, 0, 4, 1, 0, 1,\n",
              "       0, 0, 3, 2, 0, 0, 1, 0, 0, 2, 1, 2, 0, 3, 2, 0, 3, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 3, 3, 3, 0, 1, 0, 4, 0, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       3, 1, 0, 0, 0, 3, 2, 0, 2, 1, 0, 0, 3, 2, 1, 0, 0, 0, 0, 0, 2, 0,\n",
              "       2, 2, 1, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 4, 2, 2,\n",
              "       1, 0, 1, 0, 2, 0, 1, 0, 0, 0, 1, 0, 2, 0, 3, 0, 2, 4, 2, 0, 0, 1,\n",
              "       0, 2, 2, 1, 0, 3, 1, 1, 2, 3, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn import utils\n",
        "\n",
        "#convert y values to categorical values\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "Y_train = label_encoder.fit_transform(Y_train)\n",
        "Y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PpEU_sKXuLdL",
        "outputId": "67ef84d7-7db3-4668-c5ea-3041b0c24c1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 297 entries, 0 to 301\n",
            "Data columns (total 13 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       297 non-null    float64\n",
            " 1   sex       297 non-null    float64\n",
            " 2   cp        297 non-null    float64\n",
            " 3   trestbps  297 non-null    float64\n",
            " 4   chol      297 non-null    float64\n",
            " 5   fbs       297 non-null    float64\n",
            " 6   restecg   297 non-null    float64\n",
            " 7   thalach   297 non-null    float64\n",
            " 8   exang     297 non-null    float64\n",
            " 9   oldpeak   297 non-null    float64\n",
            " 10  slope     297 non-null    float64\n",
            " 11  ca        297 non-null    float64\n",
            " 12  thal      297 non-null    float64\n",
            "dtypes: float64(13)\n",
            "memory usage: 32.5 KB\n"
          ]
        }
      ],
      "source": [
        "X_train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "RfTMfTvZulhK"
      },
      "outputs": [],
      "source": [
        "random_seed = 42\n",
        "scoring_parameter = \"accuracy\"\n",
        "k_fold = 5\n",
        "from sklearn.model_selection import cross_val_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wam6an3CX_E5",
        "outputId": "1d6f55e9-3065-47a9-aa8c-878cc1c79184"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SGD_Classifier: \n",
            "- mean():  0.5787005649717514\n",
            "- std():  0.056292737460961466\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "SGD_classifier = SGDClassifier(random_state = random_seed)\n",
        "\n",
        "SGD_classifier_scores = cross_val_score(SGD_classifier, X_train, Y_train, scoring=scoring_parameter)\n",
        "\n",
        "print(\"SGD_Classifier: \")\n",
        "print(\"- mean(): \" , SGD_classifier_scores.mean())\n",
        "print(\"- std(): \" , SGD_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jK9hoUVgYGvB",
        "outputId": "866b37c9-c3b7-4e6d-ac25-2955faec7bd0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic_Regression: \n",
            "- mean():  0.5854802259887005\n",
            "- std():  0.061477026717872675\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logistic_regression = LogisticRegression(random_state = random_seed)\n",
        "\n",
        "logistic_regression_scores = cross_val_score(logistic_regression, X_train, Y_train, scoring=scoring_parameter, cv=k_fold)\n",
        "\n",
        "print(\"Logistic_Regression: \")\n",
        "print(\"- mean(): \" , logistic_regression_scores.mean())\n",
        "print(\"- std(): \" , logistic_regression_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6y0o-aqoYRZc",
        "outputId": "6f7a408b-0703-4a95-aa8d-a32921186257"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC kernel = linear: \n",
            "- mean():  0.5719774011299434\n",
            "- std():  0.05374880069285337\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "SVC_linear = SVC(random_state = random_seed, kernel = 'linear')\n",
        "\n",
        "SVC_linear_scores = cross_val_score(SVC_linear,\n",
        "                                    X_train,\n",
        "                                    Y_train,\n",
        "                                    scoring=scoring_parameter,\n",
        "                                    cv=k_fold)\n",
        "\n",
        "print(\"SVC kernel = linear: \")\n",
        "print(\"- mean(): \" , SVC_linear_scores.mean())\n",
        "print(\"- std(): \" , SVC_linear_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FdKgKNGHYX7T",
        "outputId": "2b63bc65-40a0-4178-8195-a083729877c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC kernel = rbf: \n",
            "- mean():  0.5587570621468926\n",
            "- std():  0.02930019978913075\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "SVC_rbf = SVC(random_state = random_seed, kernel = 'rbf')\n",
        "\n",
        "SVC_rbf_scores = cross_val_score(SVC_rbf,\n",
        "                                    X_train,\n",
        "                                    Y_train,\n",
        "                                    scoring=scoring_parameter,\n",
        "                                    cv=k_fold)\n",
        "\n",
        "print(\"SVC kernel = rbf: \")\n",
        "print(\"- mean(): \" , SVC_rbf_scores.mean())\n",
        "print(\"- std(): \" , SVC_rbf_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5M3wDyfXYdN2",
        "outputId": "4f3ebe7f-1bf4-45a0-aee2-26a6b4aab40e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "decision_tree: \n",
            "- mean():  0.4848022598870057\n",
            "- std():  0.037826282762864316\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "decision_tree = DecisionTreeClassifier(random_state = random_seed)\n",
        "\n",
        "decision_tree_scores = cross_val_score(decision_tree,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"decision_tree: \")\n",
        "print(\"- mean(): \" , decision_tree_scores.mean())\n",
        "print(\"- std(): \" , decision_tree_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6AWlOonYh0L",
        "outputId": "18cdcbe9-24f0-4367-de53-14a6850e1549"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "random_forest_classifier: \n",
            "- mean():  0.5755367231638417\n",
            "- std():  0.027984984779427555\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "random_forest_classifier = RandomForestClassifier(random_state = random_seed)\n",
        "\n",
        "random_forest_classifier_scores = cross_val_score(random_forest_classifier,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"random_forest_classifier: \")\n",
        "print(\"- mean(): \" , random_forest_classifier_scores.mean())\n",
        "print(\"- std(): \" , random_forest_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XlvnLPU1YlKx",
        "outputId": "623f1c0c-afa5-40cd-8b75-3c1451ae0ffc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "extra_trees_classifier: \n",
            "- mean():  0.5654237288135593\n",
            "- std():  0.03523431278195498\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "extra_trees_classifier = ExtraTreesClassifier(random_state = random_seed)\n",
        "\n",
        "extra_trees_classifier_scores = cross_val_score(extra_trees_classifier,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"extra_trees_classifier: \")\n",
        "print(\"- mean(): \" , extra_trees_classifier_scores.mean())\n",
        "print(\"- std(): \" , extra_trees_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUld2qs1Yo38",
        "outputId": "9aa1e02f-15e5-422c-a658-923951657705"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ada_boost_classifier: \n",
            "- mean():  0.5488135593220339\n",
            "- std():  0.034472259642510716\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "ada_boost_classifier = AdaBoostClassifier(random_state = random_seed)\n",
        "\n",
        "ada_boost_classifier_scores = cross_val_score(ada_boost_classifier,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"ada_boost_classifier: \")\n",
        "print(\"- mean(): \" , ada_boost_classifier_scores.mean())\n",
        "print(\"- std(): \" , ada_boost_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zh-9eXCCYtbb",
        "outputId": "3e44bde6-22a2-494a-953e-8356e0b3e458"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gradient_boosting_classifier: \n",
            "- mean():  0.5586440677966101\n",
            "- std():  0.037477093560393616\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "gradient_boosting_classifier = GradientBoostingClassifier(random_state = random_seed)\n",
        "\n",
        "gradient_boosting_classifier_scores = cross_val_score(gradient_boosting_classifier,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"gradient_boosting_classifier: \")\n",
        "print(\"- mean(): \" , gradient_boosting_classifier_scores.mean())\n",
        "print(\"- std(): \" , gradient_boosting_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xyeYVs-fY_tZ",
        "outputId": "971cd4fc-35f2-483b-a2a3-6d6573d6743b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGB_classifier: \n",
            "- mean():  0.5722598870056497\n",
            "- std():  0.024819177530746094\n"
          ]
        }
      ],
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "XGB_classifier = XGBClassifier(random_state = random_seed)\n",
        "\n",
        "XGB_classifier_scores = cross_val_score(XGB_classifier,\n",
        "                                       X_train,\n",
        "                                       Y_train,\n",
        "                                       scoring=scoring_parameter,\n",
        "                                       cv=k_fold)\n",
        "\n",
        "print(\"XGB_classifier: \")\n",
        "print(\"- mean(): \" , XGB_classifier_scores.mean())\n",
        "print(\"- std(): \" , XGB_classifier_scores.std())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98-GIi7vumU6"
      },
      "source": [
        "### Select the top best performing classifiers. List them along with their mean and std scores. For these top  performing classifiers, find the best hyperparameters using GridSearchCV.\n",
        "\n",
        "- Use estimator.get_params().keys() or the API description of the classifiers to find out which hyperparameters you can use.\n",
        "- Use the same scoring parameter ('accuracy') and use cv = 10 this time.\n",
        "- Print out the best_estimator_ and best_score_ results of your Grid Search\n",
        "- Use the following hyparparameter options if they fit for your top 3 classifiers:\n",
        "\n",
        "n_estimators = [100, 200, 300, 400, 500]\n",
        "\n",
        "learning_rate = [0.3, 0.1, 0.05]\n",
        "\n",
        "max_features =  [1, 0.7, 0.5, 0.4, 0.3]\n",
        "\n",
        "subsample = [1, 0.5, 0.3]\n",
        "\n",
        "max_samples = [1, 0.5, 0.3, 0.2]\n",
        "\n",
        "bootstrap = [True,False]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "f6q7KUMlZRc0"
      },
      "outputs": [],
      "source": [
        "n_estimators = [100, 200, 300, 400, 500]\n",
        "learning_rate = [0.3, 0.1, 0.05]\n",
        "max_features =  [1, 0.7, 0.5, 0.4, 0.3]\n",
        "subsample = [1, 0.5, 0.3]\n",
        "max_samples = [1, 0.5, 0.3, 0.2]\n",
        "bootstrap = [True,False]\n",
        "\n",
        "k_fold = 10\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ada_boost_classifier_scores:\n",
        "\n",
        "ada_grid_param = {'n_estimators' : n_estimators,\n",
        "              'learning_rate' : learning_rate\n",
        "              }\n",
        "\n",
        "ada_GridSearchCV = GridSearchCV(ada_boost_classifier,\n",
        "                    param_grid = ada_grid_param,\n",
        "                    cv = k_fold,\n",
        "                    scoring = scoring_parameter,\n",
        "                    verbose = 1)\n",
        "\n",
        "ada_GridSearchCV.fit(X_train, Y_train)\n",
        "\n",
        "ada_best_estimator = ada_GridSearchCV.best_estimator_\n",
        "ada_best_score = ada_GridSearchCV.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbZzqi5JPIKP",
        "outputId": "851edaec-fcf0-4961-dfc6-7ccf836f519c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 15 candidates, totalling 150 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ada_best_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9dyJ-k7PXKf",
        "outputId": "5e245db6-2359-4ce2-d55b-cd3251ecbe8d"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5726436781609195"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ada_best_estimator"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "ZzH5m93oPZrZ",
        "outputId": "afb5f5a7-b947-402e-aa1e-c21ad18f8d37"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(learning_rate=0.05, n_estimators=200, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(learning_rate=0.05, n_estimators=200, 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(learning_rate=0.05, n_estimators=200, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7E306FlKx68C",
        "outputId": "5f9b6f0b-e35d-4b70-b8c0-7627bbce0ef2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 200 candidates, totalling 2000 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
            "1000 fits failed out of a total of 2000.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "1000 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\", line 397, in fit\n",
            "    raise ValueError(\n",
            "ValueError: `max_sample` cannot be set if `bootstrap=False`. Either switch to `bootstrap=True` or set `max_sample=None`.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [0.53885057 0.53885057 0.53885057 0.53885057 0.53885057 0.55885057\n",
            " 0.55885057 0.57229885 0.56218391 0.56218391 0.57574713 0.57229885\n",
            " 0.57229885 0.56896552 0.56551724 0.56218391 0.56563218 0.57574713\n",
            " 0.57218391 0.56885057 0.53885057 0.53885057 0.53885057 0.53885057\n",
            " 0.53885057 0.57218391 0.56563218 0.56908046 0.56563218 0.55885057\n",
            " 0.57229885 0.56218391 0.57574713 0.57218391 0.57229885 0.57954023\n",
            " 0.56574713 0.5791954  0.56574713 0.56896552 0.53885057 0.53885057\n",
            " 0.53885057 0.53885057 0.53885057 0.56195402 0.56206897 0.57229885\n",
            " 0.57241379 0.57574713 0.57563218 0.57574713 0.56551724 0.57908046\n",
            " 0.57563218 0.56218391 0.56885057 0.56551724 0.56229885 0.56896552\n",
            " 0.53885057 0.53885057 0.53885057 0.53885057 0.53885057 0.55528736\n",
            " 0.56206897 0.55206897 0.5554023  0.55206897 0.56873563 0.5554023\n",
            " 0.55862069 0.55862069 0.55195402 0.56229885 0.56896552 0.56896552\n",
            " 0.55885057 0.56885057 0.53885057 0.53885057 0.53885057 0.53885057\n",
            " 0.53885057 0.57563218 0.56206897 0.56563218 0.56563218 0.55885057\n",
            " 0.57586207 0.57229885 0.56574713 0.56241379 0.55908046 0.58586207\n",
            " 0.57241379 0.56574713 0.56908046 0.56908046        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan]\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# random_forest_classifier_scores:\n",
        "\n",
        "rfc_grid_param = {'n_estimators' : n_estimators,\n",
        "              'bootstrap' : bootstrap,\n",
        "              'max_features': max_features,\n",
        "              'max_samples' : max_samples\n",
        "              }\n",
        "\n",
        "rfc_GridSearchCV = GridSearchCV(random_forest_classifier,\n",
        "                    param_grid = rfc_grid_param,\n",
        "                    cv = k_fold,\n",
        "                    scoring = scoring_parameter,\n",
        "                    verbose = 1)\n",
        "\n",
        "rfc_GridSearchCV.fit(X_train, Y_train)\n",
        "\n",
        "rfc_best_estimator = rfc_GridSearchCV.best_estimator_\n",
        "rfc_best_score = rfc_GridSearchCV.best_score_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlNADegQ19VH",
        "outputId": "c9f9bb57-0e7a-4968-dc22-8610dc9bad8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5858620689655172"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "rfc_best_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "TwF7b4RE2BHj",
        "outputId": "f9dee194-47cf-4100-970e-f3ed187560d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_features=0.3, max_samples=0.2, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_features=0.3, max_samples=0.2, 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_features=0.3, max_samples=0.2, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "rfc_best_estimator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I49e8t9Iu_Ho",
        "outputId": "da09b8e1-3c6a-4f38-8bcf-c6d4c52bc5fb"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 225 candidates, totalling 2250 fits\n"
          ]
        }
      ],
      "source": [
        "# gradient_boosting_classifier:\n",
        "\n",
        "gbc_grid_param = {'n_estimators' : n_estimators,\n",
        "              'learning_rate' : learning_rate,\n",
        "              'max_features': max_features,\n",
        "              'subsample' : subsample\n",
        "              }\n",
        "\n",
        "gbc_GridSearchCV = GridSearchCV(gradient_boosting_classifier,\n",
        "                    param_grid = gbc_grid_param,\n",
        "                    cv = k_fold,\n",
        "                    scoring = scoring_parameter,\n",
        "                    verbose = 1)\n",
        "\n",
        "gbc_GridSearchCV.fit(X_train, Y_train)\n",
        "\n",
        "gbc_best_estimator = gbc_GridSearchCV.best_estimator_\n",
        "gbc_best_score = gbc_GridSearchCV.best_score_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "i5t89rRdZj7R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d5a334a-f615-4e9d-8049-700e83e7edd9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.5893103448275862"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gbc_best_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "bh04ufFCZnp_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "8dfa1ed1-a365-46a8-953e-3f9cdc912fea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(learning_rate=0.05, max_features=0.3,\n",
              "                           random_state=42, subsample=0.3)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.05, max_features=0.3,\n",
              "                           random_state=42, subsample=0.3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.05, max_features=0.3,\n",
              "                           random_state=42, subsample=0.3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "gbc_best_estimator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "oBmcHrTCZ6T6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0a47632-8ce8-4645-e72b-dc3130131793"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 5 candidates, totalling 50 fits\n"
          ]
        }
      ],
      "source": [
        "# XGB_classifier:\n",
        "xgb_grid_param = {'n_estimators' : n_estimators\n",
        "              }\n",
        "\n",
        "xgb_GridSearchCV = GridSearchCV(XGB_classifier,\n",
        "                    param_grid = xgb_grid_param,\n",
        "                    cv = k_fold,\n",
        "                    scoring = scoring_parameter,\n",
        "                    verbose = 1)\n",
        "\n",
        "xgb_GridSearchCV.fit(X_train, Y_train)\n",
        "\n",
        "xgb_best_estimator = xgb_GridSearchCV.best_estimator_\n",
        "xgb_best_score = xgb_GridSearchCV.best_score_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "sGx4C6b8Z_h_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48273db2-998e-4dc1-bbd0-25ef6ca09ef2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5558620689655173"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "xgb_best_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "fu6f0fk8aCMz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "63d782fc-3ac7-4039-8ba9-02c808332dfb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=500, n_jobs=None, num_parallel_tree=None,\n",
              "              objective='multi:softprob', predictor=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=500, n_jobs=None, num_parallel_tree=None,\n",
              "              objective=&#x27;multi:softprob&#x27;, predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=500, n_jobs=None, num_parallel_tree=None,\n",
              "              objective=&#x27;multi:softprob&#x27;, predictor=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "xgb_best_estimator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "esoSP9yQaiuQ"
      },
      "source": [
        "### Plot Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "snY12Dhva-9l"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "# Plot learning curve\n",
        "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "\n",
        "# Generate a simple plot of the test and traning learning curve.\n",
        "# Parameters\n",
        "#----------\n",
        "#estimator : object type that implements the \"fit\" and \"predict\" methods An object of that type which is cloned for each validation.\n",
        "#title : string\n",
        "#Title for the chart.\n",
        "#X : array-like, shape (n_samples, n_features)\n",
        "#Training vector, where n_samples is the number of samples and n_features is the number of features.\n",
        "#y : array-like, shape (n_samples) or (n_samples, n_features), optional Target relative to X for classification or regression;\n",
        "#None for unsupervised learning.\n",
        "#ylim : tuple, shape (ymin, ymax), optional Defines minimum and maximum yvalues plotted.\n",
        "#cv : integer, cross-validation generator, optional\n",
        "#If an integer is passed, it is the number of folds (defaults to 3). Specific cross-validation objects can be passed, see sklearn.cross_validation module for the list of possible objects\n",
        "#n_jobs : integer, optional\n",
        "#Number of jobs to run in parallel (default 1).\n",
        "#x1 = np.linspace(0, 10, 8, endpoint=True) produces 8 evenly spaced points in the range 0 to 10\n",
        "\n",
        "  plt.figure()\n",
        "  plt.title(title)\n",
        "  if ylim is not None: plt.ylim(*ylim)\n",
        "\n",
        "  plt.xlabel(\"Training examples\")\n",
        "  plt.ylabel(\"Score\")\n",
        "  train_sizes, train_scores, test_scores = learning_curve(  estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "  train_scores_mean = np.mean(train_scores, axis=1)\n",
        "  train_scores_std = np.std(train_scores, axis=1)\n",
        "  test_scores_mean = np.mean(test_scores, axis=1)\n",
        "  test_scores_std = np.std(test_scores, axis=1)\n",
        "  plt.grid()\n",
        "\n",
        "  plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n",
        "  plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "  plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n",
        "  plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
        "\n",
        "  label=\"Cross-validation score\")\n",
        "\n",
        "  plt.legend(loc=\"best\")\n",
        "  return plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "gc1sKzsjbKZp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "b9b7a192-b29e-4fca-b523-76096dffe675"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Gradient boosting - Learning Curve\n",
        "plot_learning_curve(estimator = gbc_best_estimator,title = \"GBC learning curve\", X = X_train, y = Y_train, cv = k_fold);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "1ZkRTxdfbNlM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "c7d5d4d6-066f-4e5f-ad78-9203fbf52329"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Random Forest - Learning Curve\n",
        "plot_learning_curve(estimator = rfc_best_estimator,title = \"Random Forest learning curve\", X = X_train, y = Y_train, cv = k_fold);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "1WGPUfK6bRDY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "a5c730bc-50d2-4107-d897-3dee927a7a37"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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UKpw+fRrr1q3D8uXLbebEIaowp12nRVSDHTp0SCgUChEbG+tw/YEDB4RcLhfTpk2zWV5QUCDi4+NFjx49hEajEUqlUgQHB4t7771XrFmzRhiNRmlbR5eCK5VK0axZM/HMM8+InJwcu+ddsWKFaNWqlVCpVCIoKEhMnjzZ5vJyq7Vr14rbbrtNqNVq4e/vLx555BFx6dIlaf21a9fE1KlTRatWrYSXl5fw9fUVUVFR4ptvvrE5Tmpqqhg8eLDw8fERAKTLwou7zLlt27Z2ZRk7dqxo3LixzbJz586JwYMHCw8PD9GgQQMxa9Ys8e233woA4rfffnNY51aOLlW2likmJkb4+voKd3d3ERERIR577DFx6NAhaZtLly6J4cOHCz8/P+Hr6yseeOABceXKFQFAzJ8/X9rOepn01atX7Z6/cePGYvDgwXbL+/TpY3PZvDPrSAghLly4IMaMGSMaNGgg1Gq1aNasmZg6darQ6XTSNocPHxZRUVHCzc1NNGrUSCxbtqzYS8EdnXNRvXr1EgDEE088Uew2H3zwgejSpYvw8PAQPj4+on379uLZZ58VV65cueX5EJUF7y1FRDVCfHw8Zs6ciUuXLtnM/kw3sI6ISofhhoiqXUFBgd2cM7fddhtMJpPDu7DXRawjovLjmBsiqnYjRoxAo0aN0KlTJ2RnZ+OLL77AyZMni71UuC5iHRGVH8MNEVW7mJgYfPTRR1izZg1MJhPatGmDr7/+2u4qm7qMdURUfuyWIiIiIpfCeW6IiIjIpTDcEBERkUupc2NuzGYzrly5Ah8fnyqZWp6IiIgqnxACOTk5CA0NhVxecttMnQs3V65ckW5kSERERLXLxYsXER4eXuI2dS7cWKfKv3jxIjQajZNL43wGgwHbtm2TpkOnqsc6dw7We/VjnTuHq9a7VqtFw4YNpc/xktS5cGPtitJoNAw3sPwSeHp6QqPRuNQvQU3GOncO1nv1Y507h6vXe2mGlHBAMREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilMNwQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbmUOjdDcZUxmYBffwVSUoCQEODOOwGFwtmlotLi61e78fWr/fgauoYa8jo6teVm9+7dGDJkCEJDQyGTybBx48Zb7pOYmIjOnTtDrVajefPmWL16dZWX85Y2bACaNAHuvhsYPdryf5MmluVU8/H1q934+tV+fA1dQw16HZ0abvLy8tCxY0e88847pdo+KSkJgwcPxt13341jx45hxowZeOKJJ7B169YqLmkJNmwA7r8fuHTJdvnly5bl/OWs2fj61W58/Wo/voauoYa9jk7tlho4cCAGDhxY6u1XrlyJpk2b4o033gAAtG7dGnv27MGbb76JmJiYqipm8UwmYPp0QAj7dUIAMpllfXR0zW1eNRigKCwE8vIAF7zBWolMJmDatOp//epynVemsr5+rPfqd6s6d9bvoKur7p/10ryOM2YAQ4dW2+tYq8bc7N+/H9HR0TbLYmJiMGPGjGL30el00Ol00mOtVgvActdUg8FQofLIdu2C8uaUWpQQlhTr61uh56lKKgD3OrsQNVUVvX6s82py0+vHeq9+Fa7zWvAeWhPVuJ91IYCLF2H85ReIPn3KfZiyfGbXqnCTmpqKoKAgm2VBQUHQarUoKCiAh4eH3T5Lly7FwoUL7ZZv27YNnp6eFSpP2O7d6FqhIxAREdUNxzZvxuW8vHLvn5+fX+pta1W4KY+5c+ciLi5OeqzVatGwYUP0798fGo2mQseWeXkBy5bdcjvj+vUQPXtW6LmqisFoxM59+3BPz55QKV3+x8GGbN8+KO+//5bbVfbrV5frvDKV9fVjvVe/W9W5s34HXV11/6yX9nXsNHAgOlag5cba81Iateo3PDg4GGlpaTbL0tLSoNFoHLbaAIBarYZarbZbrlKpoKpoX+TddwPh4ZYBU476GmUyIDwcymHDam5/scEAk7s7VMHBFa+P2mbYMOe8fnW5zitTWV8/1nv1u1WdO+t30NVV9896aV/Hu++u0OtYlnOpVZP49ejRAzt27LBZlpCQgB49ejinQAoFsHy55XuZzHad9XF8PH8payq+frUbX7/aj6+ha6iBr6NTw01ubi6OHTuGY8eOAbBc6n3s2DEkJycDsHQpjRkzRtr+ySefxLlz5/Dss8/i5MmTePfdd/HNN99g5syZzii+xYgRwPr1QFiY7fLwcMvyESOcUy4qHb5+tRtfv9qPr6FrqGGvo1O7pQ4dOoS7775bemwdGzN27FisXr0aKSkpUtABgKZNm+Lnn3/GzJkzsXz5coSHh+Ojjz5yzmXgRY0YYbnErQbMykjlwNevduPrV/vxNXQNNeh1dGq4ueuuuyAc9c/9x9Hsw3fddReOHj1ahaUqJ4UCuOsuZ5eCyouvX+3G16/242voGmrI61irxtwQERER3QrDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilMNwQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilMNwQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilMNwQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilMNwQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilMNwQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilMNwQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilMNwQERGRS3F6uHnnnXfQpEkTuLu7IyoqCgcOHCh2W4PBgEWLFiEiIgLu7u7o2LEjtmzZUo2lJSIioprOqeFm7dq1iIuLw/z583HkyBF07NgRMTExSE9Pd7j9Cy+8gPfffx9vv/02Tpw4gSeffBLDhw/H0aNHq7nkREREVFM5NdwsW7YMEyZMwLhx49CmTRusXLkSnp6eWLVqlcPtP//8czz33HMYNGgQmjVrhsmTJ2PQoEF44403qrnkREREVFMpnfXEer0ehw8fxty5c6Vlcrkc0dHR2L9/v8N9dDod3N3dbZZ5eHhgz549xT6PTqeDTqeTHmu1WgCWLi6DwVCRU3AJ1jpgXVQf1rlzsN6rH+vcOVy13styPk4LN9euXYPJZEJQUJDN8qCgIJw8edLhPjExMVi2bBl69+6NiIgI7NixAxs2bIDJZCr2eZYuXYqFCxfaLd+2bRs8PT0rdhIuJCEhwdlFqHNY587Beq9+rHPncLV6z8/PL/W2Tgs35bF8+XJMmDABrVq1gkwmQ0REBMaNG1dsNxYAzJ07F3FxcdJjrVaLhg0bon///tBoNNVR7BrNYDAgISEB/fr1g0qlcnZx6gTWuXOw3qsf69w5XLXerT0vpeG0cBMQEACFQoG0tDSb5WlpaQgODna4T4MGDbBx40YUFhbi+vXrCA0NxZw5c9CsWbNin0etVkOtVtstV6lULvWiVxTro/qxzp2D9V79WOfO4Wr1XpZzcdqAYjc3N3Tp0gU7duyQlpnNZuzYsQM9evQocV93d3eEhYXBaDTi22+/xdChQ6u6uERERFRLOLVbKi4uDmPHjkXXrl3RvXt3xMfHIy8vD+PGjQMAjBkzBmFhYVi6dCkA4Pfff8fly5fRqVMnXL58GQsWLIDZbMazzz7rzNMgIiKiGsSp4WbUqFG4evUq5s2bh9TUVHTq1AlbtmyRBhknJydDLr/RuFRYWIgXXngB586dg7e3NwYNGoTPP/8cfn5+TjoDIiIiqmmcPqA4NjYWsbGxDtclJibaPO7Tpw9OnDhRDaUiIiKi2srpt18gIiIiqkwMN0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKXwnBDRERELoXhhoiIiFwKww0RERG5FIYbIiIicikMN0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKXwnBDRERELoXhhoiIiFwKww0RERG5FIYbIiIicikMN0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKXwnBDRERELoXhhoiIiFwKww0RERG5FIYbIiIicikMN0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKXwnBDRERELoXhhoiIiFwKww0RERG5FIYbIiIicikMN0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKXwnBDRERELoXhhoiIiFwKww0RERG5FIYbIiIicikMN0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKXwnBDRERELoXhhoiIiFwKww0RERG5FIYbIiIicikMN0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKXwnBDRERELoXhhoiIiFyK08PNO++8gyZNmsDd3R1RUVE4cOBAidvHx8ejZcuW8PDwQMOGDTFz5kwUFhZWU2mJiIiopnNquFm7di3i4uIwf/58HDlyBB07dkRMTAzS09Mdbv/ll19izpw5mD9/Pv755x98/PHHWLt2LZ577rlqLjkRERHVVE4NN8uWLcOECRMwbtw4tGnTBitXroSnpydWrVrlcPt9+/ahV69eGD16NJo0aYL+/fvj4YcfvmVrDxEREdUdSmc9sV6vx+HDhzF37lxpmVwuR3R0NPbv3+9wn549e+KLL77AgQMH0L17d5w7dw6bNm3Co48+Wuzz6HQ66HQ66bFWqwUAGAwGGAyGSjqb2staB6yL6sM6dw7We/VjnTuHq9Z7Wc7HaeHm2rVrMJlMCAoKslkeFBSEkydPOtxn9OjRuHbtGu644w4IIWA0GvHkk0+W2C21dOlSLFy40G75tm3b4OnpWbGTcCEJCQnOLkKdwzp3DtZ79WOdO4er1Xt+fn6pt3VauCmPxMRELFmyBO+++y6ioqJw5swZTJ8+HYsXL8aLL77ocJ+5c+ciLi5OeqzVatGwYUP0798fGo2muopeYxkMBiQkJKBfv35QqVTOLk6dwDp3DtZ79WOdO4er1ru156U0nBZuAgICoFAokJaWZrM8LS0NwcHBDvd58cUX8eijj+KJJ54AALRv3x55eXmYOHEinn/+ecjl9kOI1Go11Gq13XKVSuVSL3pFsT6qH+vcOVjv1Y917hyuVu9lORenDSh2c3NDly5dsGPHDmmZ2WzGjh070KNHD4f75Ofn2wUYhUIBABBCVF1hiYiIqNZwardUXFwcxo4di65du6J79+6Ij49HXl4exo0bBwAYM2YMwsLCsHTpUgDAkCFDsGzZMtx2221St9SLL76IIUOGSCGHiIiI6janhptRo0bh6tWrmDdvHlJTU9GpUyds2bJFGmScnJxs01LzwgsvQCaT4YUXXsDly5fRoEEDDBkyBC+99JKzToGIiIhqGKcPKI6NjUVsbKzDdYmJiTaPlUol5s+fj/nz51dDyYiIiKg2cvrtF4iIiIgqE8MNERERuRSGGyIiInIpDDdERETkUhhuiIiIyKUw3BAREZFLYbghIiIil8JwQ0RERC6F4YaIiIhcCsMNERERuRSGGyIiInIpDDdERETkUhhuiIiIyKVUKNzo9XqcOnUKRqOxsspDREREVCHlCjf5+fkYP348PD090bZtWyQnJwMAnnrqKbz88suVWkAiIiKisihXuJk7dy6OHz+OxMREuLu7S8ujo6Oxdu3aSiscERERUVkpy7PTxo0bsXbtWtx+++2QyWTS8rZt2+Ls2bOVVjgiIiKisipXy83Vq1cRGBhotzwvL88m7BARERFVt3KFm65du+Lnn3+WHlsDzUcffYQePXpUTsmIiIiIyqFc3VJLlizBwIEDceLECRiNRixfvhwnTpzAvn37sGvXrsouIxEREVGplavl5o477sDx48dhNBrRvn17bNu2DYGBgdi/fz+6dOlS2WUkIiIiKrUyt9wYDAZMmjQJL774Ij788MOqKBMRERFRuZW55UalUuHbb7+tirIQERERVVi5uqWGDRuGjRs3VnJRiIiIiCquXAOKIyMjsWjRIuzduxddunSBl5eXzfpp06ZVSuGIiIiIyqpc4ebjjz+Gn58fDh8+jMOHD9usk8lkDDdERETkNOUKN0lJSZVdDiIiIqJKUaG7ggOAEAJCiMooCxEREVGFlTvcfPbZZ2jfvj08PDzg4eGBDh064PPPP6/MshERERGVWbm6pZYtW4YXX3wRsbGx6NWrFwBgz549ePLJJ3Ht2jXMnDmzUgtJREREVFrlCjdvv/023nvvPYwZM0Zadt9996Ft27ZYsGABww0RERE5Tbm6pVJSUtCzZ0+75T179kRKSkqFC0VERERUXuUKN82bN8c333xjt3zt2rWIjIyscKGIiIiIyqtc3VILFy7EqFGjsHv3bmnMzd69e7Fjxw6HoYeIiIioupSr5WbkyJH4/fffERAQgI0bN2Ljxo0ICAjAgQMHMHz48MouIxEREVGplavlBgC6dOmCL774ojLLQkRERFRh5Wq52bRpE7Zu3Wq3fOvWrdi8eXOFC0VERERUXuUKN3PmzIHJZLJbLoTAnDlzKlwoIiIiovIqV7g5ffo02rRpY7e8VatWOHPmTIULRURERFRe5Qo3vr6+OHfunN3yM2fOwMvLq8KFIiIiIiqvcoWboUOHYsaMGTh79qy07MyZM5g1axbuu+++SiscERERUVmVK9y8+uqr8PLyQqtWrdC0aVM0bdoUrVq1Qv369fH6669XdhmJiIiISq1cl4L7+vpi3759SEhIwPHjx+Hh4YGOHTvizjvvrOzyEREREZVJmVpu9u/fj59++gkAIJPJ0L9/fwQGBuL111/HyJEjMXHiROh0uiopKBEREVFplCncLFq0CH///bf0+M8//8SECRPQr18/zJkzBz/++COWLl1a6YUkIiIiKq0yhZtjx46hb9++0uOvv/4a3bt3x4cffoi4uDi89dZbvLcUEREROVWZwk1mZiaCgoKkx7t27cLAgQOlx926dcPFixcrr3REREREZVSmcBMUFISkpCQAgF6vx5EjR3D77bdL63NycqBSqSq3hERERERlUKZwM2jQIMyZMwe//vor5s6dC09PT5srpP744w9ERERUeiGJiIiISqtMl4IvXrwYI0aMQJ8+feDt7Y1PP/0Ubm5u0vpVq1ahf//+lV5IIiIiotIqU7gJCAjA7t27kZ2dDW9vbygUCpv169atg7e3d6UWkIiIiKgsyj2JnyP+/v4VKgwRERFRRZXr9gtERERENRXDDREREbmUGhFu3nnnHTRp0gTu7u6IiorCgQMHit32rrvugkwms/saPHhwNZaYiIiIaiqnh5u1a9ciLi4O8+fPx5EjR9CxY0fExMQgPT3d4fYbNmxASkqK9PXXX39BoVDggQceqOaSExERUU3k9HCzbNkyTJgwAePGjUObNm2wcuVKeHp6YtWqVQ639/f3R3BwsPSVkJAAT09PhhsiIiIC4ORwo9frcfjwYURHR0vL5HI5oqOjsX///lId4+OPP8ZDDz0ELy+vqiomERER1SLluhS8sly7dg0mk8nmflWA5TYPJ0+evOX+Bw4cwF9//YWPP/642G10Oh10Op30WKvVAgAMBgMMBkM5S+46rHXAuqg+rHPnYL1XP9a5c7hqvZflfJwabirq448/Rvv27dG9e/dit1m6dCkWLlxot3zbtm3w9PSsyuLVKgkJCc4uQp3DOncO1nv1Y507h6vVe35+fqm3dWq4CQgIgEKhQFpams3ytLQ0BAcHl7hvXl4evv76ayxatKjE7ebOnYu4uDjpsVarRcOGDdG/f39oNJryF95FGAwGJCQkoF+/frzpaTVhnTsH6736sc6dw1Xr3drzUhpODTdubm7o0qULduzYgWHDhgEAzGYzduzYgdjY2BL3XbduHXQ6Hf7v//6vxO3UajXUarXdcpVK5VIvekWxPqof69w5WO/Vj3XuHK5W72U5F6d3S8XFxWHs2LHo2rUrunfvjvj4eOTl5WHcuHEAgDFjxiAsLAxLly612e/jjz/GsGHDUL9+fWcUm4iIiGoop4ebUaNG4erVq5g3bx5SU1PRqVMnbNmyRRpknJycDLnc9qKuU6dOYc+ePdi2bZszikxEREQ1mNPDDQDExsYW2w2VmJhot6xly5YQQlRxqYiIiKg2cvokfkRERESVieGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilMNwQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilMNwQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMilKJ1dAKKawGg2QmfUQW/SQylXQqVQwU3hBrmM+Z+IqLZhuKE6SQgBnUkHnVGHfEM+8vR50Jv1EEJAQEApswQcT5UnPFQecFO4wU3hBqWcvzJERDUd36mpzrC2zhQYCpCrz0WhsRBGYYRSroSbwg2+Kl/IZDJpW4PJgGxdNjIKMgAAbgo3qJVqeCo9oVaqpcBj3YeIiGoGhhtyWUVbZ3L1uSgwFEBn0gGwBBVPN89iW2KUciWUciU84CEdS2/So9BYiBxdjs02XiovqXVHpVCxdYeIyMn4LkwuxWg2otBYiEJDocPWGT+VX7laWmQyGdRKNdRQ2zyXwWRAZmEmrhVcgxxyqXXHy81LatlRyVVs3SEiqkYMN1SrldQ6o1aqS2ydqSipdUdlad0xCzMMJgMKjAXQ6rQQEFDJVVApVPBSecFd6W7pxjIz6BARVSWGG6p1irbO5OhzoDPqYDQboVRUrHWmouQyuV3rjsFkgMFswPWC6xBCQC6TQ262XIGVVZgFD+HB1h0iokrGcEM1nrV1ptBYiDx9HvIN+dCb9ACqvnWmolQKlXTVFWBp3SnQFQAAUnJSoChQQCVXSWOArK07vAydiKj8auYnAtV5BpPBEmiKts78N3ZGrVDDU+VZK1s65DI53JXuAIB6HvUgV8hhMBtgNBtxLf8azMIMhUwBN4Ub3JXu8FR53hi7o1A5ufRERLUDww3VCMW1zsggg5uy5CubajOZTCaFF2vrjslsgt6kR44+B1mFWQAgdV15u3lDrVRzkkEiohK43qcF1RrW1pkCQ4HUOmMSplrfOlNRCrkCHnIPm8vQDWYDDCYD0vLSOMkgEdEt8J2Qqo1ZmKEz6qAz6Ry2zni7eUMhVzi7mDVO0dYdL3gBsJ1k8HrBdUsdcpJBIiIADDdUxYprnZEG0dbR1pmKKs8kg9auLLbuEJGr47scVaqirTM5uhwUGguhM+ksE9yxdabKcJJBIqIbGG6owgwmAwqNhSgwFiBHlwO9Sc/WmRqgvJMMuincGECJqFZjuKEyK651RgYZ3JXubJ2poUo7yaBKroK70h1ebl5SVxZbd4ioNmG4oVJx1DpjFmbpnk1snamdHE0yqDfpkWfIQ5YuCzLIOMkgEdU6DDfkkLV1ptBYKN2zSW/Ws3XGxVknGbRONCiEsIzdMRs4ySAR1RoMNyTRm/TQGXUOx86olWp4yb3YOlPHyGQyu9admycZlMlkUMosLXjebt5wU7qxdYeInIrhpg4zCzMKDYUAgMvay9ALPQxmg+XKG4WarTPkECcZJKKaju80dYy1dSbfkI9cfS7yC/MBAPmGfHi6e8Jb4e3kElJt42iSQWvrDicZJCJnYLhxcWZhtlzNZNQhR5+DQkOhTeuMxl0DAPBR+0ChYCsNVQ5HrTvWSQZzdbmW1h1OMkhEVYTvIi7o5tYZnVEHM8yWv5SVbjatMyajyYklpbqCkwwSUXViuHEBJrMJOpPOpnVGb9Zb5jX5r3WGAzuppuEkg0RUVRhuaiHrAM5CYyEKDAW3bJ0hqg1KM8mgdeyOu9Idnm6ebN0hIocYbmoJa+uMdd4Zts5QXVCeSQZVcpXUKsTAQ1Q3MdzUUNYBmDqTDvn6fOQZ8qAz6ixN9QoVW2eoTirNJINyyKGUK6GQK6BWqC2BR6EC/hteZjKboAInHCRyZQw3NUjR1hnrPZsMZgNbZ4iK4WiSQbMww2g2wmQ2Ic+Qh2xdNgQEhFEAAJKzk+GudpfG8FhbeaxfRFT78TfZiYq2zuTp85BvyLdpnXFXusNH4ePsYhLVKnKZ5aor3DTm2GgwArAEIp3R8jtnFmYICChkCincuCvdoVaqoZQr2cVFVEsx3FQzk9kk3UW7aOuM9X49bJ0hqhrWcOKudIdCaZt8TGaTpbVHmJCty4apwNKHJYPMYRdX0ZYeXrlFVPMw3FQxR60zhcZCCAjpzZKtM0TOpZArHIYU65geo9lo08UFwKZVx3qLCZvQI1OwtYfISRhuqoC1dcY6dkZn0tm0zvi6+7J1hqgWKDqmpyghBEzC0tqjN+mRb8i36+JSyBXS4OeioYeXrRNVPYabSmQ0G3E17yry9HnQmXRsnSFyUdY7oTsagFy0i0ur0yKzINOyD7u4iKqN05sP3nnnHTRp0gTu7u6IiorCgQMHStw+KysLU6dORUhICNRqNVq0aIFNmzZVU2lLpjfpkVGQAZlMBl93X/h7+MPLzcvurz4icl0KucJyg1CVJzRqDep51EM9j3rwdfeFWmmZoDDfmI+0vDRc1F7E+azzSMpMQlJmEi5kXUBabhoyCzIt81kZC2E0GyGEcPJZEdUuTm25Wbt2LeLi4rBy5UpERUUhPj4eMTExOHXqFAIDA+221+v16NevHwIDA7F+/XqEhYXhwoUL8PPzq/7Cl8Bd6c5mZyKyUVwXFwBpXE/RLi7AcuVXSV1cSrmSXdzkdGZhtvtSyBRSmHcGp4abZcuWYcKECRg3bhwAYOXKlfj555+xatUqzJkzx277VatWISMjA/v27YNKZXmDaNKkSXUWmYio0hU3x471fltFu7gEhM1EhW4KN3goPdjFRRUmhICAcBhWin5Zw7j1Swj7fXzdfRGuCXfauTgt3Oj1ehw+fBhz586VlsnlckRHR2P//v0O9/nhhx/Qo0cPTJ06Fd9//z0aNGiA0aNHY/bs2VAoHP8i63Q66HQ66bFWqwUAGAwGGAyGSjwjyzwaZqMZJqOp1rTcWO8KzruDVx/WuXPU1npXQgmlTAm1Qi3N3SNdxWU0IteQiyxzlnTvLWtrj1KuhLvqxkSFKrnKclVYNV7FZX2Prez3WiqZtb51eh0MJoNt+IBtCDEJkzROzFFQcdQlKpNZfs6sX9bHSpkScrkc+Yb8KvmMLcvxnBZurl27BpPJhKCgIJvlQUFBOHnypMN9zp07h507d+KRRx7Bpk2bcObMGUyZMgUGgwHz5893uM/SpUuxcOFCu+Xbtm2Dp6dnxU/ERRzfd9zZRahzWOfOwXqvfgkJCc4uQp20Y/sOpz7/H/ijUo+Xn59f6m1r1dVSZrMZgYGB+OCDD6BQKNClSxdcvnwZr732WrHhZu7cuYiLi5Mea7VaNGzYEP3794dGo6nU8hUYCnAh6wJ83X1rVcvN8X3H0bFnR7uJzahqsM6do67Xe9HbUhjMBpjNlkvXi17F5aZwg1qptrT2yG50b5W3i8tgMCAhIQH9+vWThhLUdUVbRoprUZGuuCty5Z1diwpsW1RkkEktKMIkcObQGbTs3hIqlcqmlaU65OnzoFaqK71bytrzUhpOCzcBAQFQKBRIS0uzWZ6Wlobg4GCH+4SEhEClUtl0QbVu3RqpqanQ6/Vwc3Oz20etVkOtth/UpFKpKv2XzQAD5Eo5FMraN3mXQqmok2/4zsQ6d466Wu8KKBzeMLToRIV6oUe+Pl/qiijuthRKuRIqharUXVxV8X5bE9w8zsQ6/1HRIFK0y6fUQaVot49CDgUUNgHF2hVUHGvXq5e7l1N+1hVmy+9YZb/mZTme08KNm5sbunTpgh07dmDYsGEALC0zO3bsQGxsrMN9evXqhS+//BJmsxlyuSWB/vvvvwgJCXEYbIiIqGQlXcVlbeUxmA0oKCxw6au4bjWI9uaWL5PZZN/6cqugIpNDBhkUcgVUMlWpggqVj1O7peLi4jB27Fh07doV3bt3R3x8PPLy8qSrp8aMGYOwsDAsXboUADB58mSsWLEC06dPx1NPPYXTp09jyZIlmDZtmjNPg1yAyWzC75d/R3peOgK9AhEVFsWrTajOK65LqugHfY4uR7qKy9rFpZQrLV1b/33E5Ovz4S5zr7aruEobVG51xU/RoFK024dBpeZzargZNWoUrl69innz5iE1NRWdOnXCli1bpEHGycnJUgsNADRs2BBbt27FzJkz0aFDB4SFhWH69OmYPXu2s06BXMCm05sw75d5SMlNkZaFeIdg0d2LMChykBNLRlQzFXfn9aJdXAXGAuj0litVk7OToVKpiu3iKu7O69bAISBgMpts/i9rUHF0Djd386jkKpvgwqBSezl9QHFsbGyx3VCJiYl2y3r06IHffvutiktFdcWm05sw8ceJdk3JqbmpmPjjRHww5AMGHKJSurmLy0PuAQDw8/AD5JbJCq1dXCZhGRcih9wyfue/21Io5coSx6eUJ6jUti4yqjinhxsiZzGZTZj3yzy7YANAamKfnzgfMREx7KIiqiBrF5cathd4FO3iytXnwizMDCpUYQw3VOcIIZCWl4aNJzfadEXZbQeBKzlXsPHkRgxtNdThDLJEVDHFdXERVQTfrcllmcwmXNRexOmM0zh9/bT0/5mMM8jR55T6ONO2TMPTCU+jmV8zRPhHINI/Es39myOyfiSa1WsGTxUngyQiqkkYbqjW05v0SMpMwr8Z/+LM9TM4nXEa/17/F0mZSSg0FTrcRy6TI9AzEKl5qbc8vkqugt6kx8nrJ3Hyuv3s2eGacDSv1xzN6ze/EXz8I+Hv4c8BiURETsBwQ7VGnj4PZzIs4aVoa8yFrAvS4MSbqRVqNKvXDJH1I6Xg0aJ+CzT1awqlXImoj6KQmpvqcNyNDDKE+IRg3+P7kJKbYmn1yTyDM9fP4EzmGZy+fhqZhZm4pL2ES9pLSLyQaLO/n7uf9JzWr0j/SIR4hlRF9RAR0X8YbqjGySjIwJmMM/j3+r84nXFaao25nHO52H283bxtwos1SDTybVTiYOBFdy/CxB8nQgaZ3ZwWALDwroVQKVRo5NsIjXwboS/62pXV2tV1OsPy/5mMM7iovYiswiwcvHIQB68ctNlHrVAjRBWC9nntEVk/Es3rW4JPM79m8FB5lKfKiIioCIYbcgohBFJyUyyh4Pppmy6l6wXXi90vwDPApuvH2iIT7B1cri6gQZGD8MGQD+znufEJwcK7Ft7yMnB/D39EhUchKjzKZnmBoQBnM89KYed0xmmczTiLc5nnoDPpcN50HudPnwdO39hHBhka+jaUzs36f4R/BPw9/Mt8bkREdRXDDVUpk9mE5Oxkm24k64d9rj632P3CfMJswktkfcuHfVV8yA+KHISYiJhKnaHYQ+WBdoHt0C6wnc1yk9mE8xnnsfXXrRChAueyzkl1kq3LRnJ2MpKzk7EzaafNfvU96tt1bzX3b44wTRgvjSUiugnDDVUKnVGHc5k3Pqj/vf4vzmSckVoqHFHIFGji18TyQf3fYNwW9Vsgol4EvNy8qrX8CrkCPRv2rJbnaeLXBN18u6Fzl87STe2EELiWf82ue+t0xmlcybmC6wXXcf3ydfx++Xeb43koPRDhHyENaLYGn6Z+TaFW2t8wloioLmC4oTLJ1efahBdri8yF7AsOZw4FAHeFO5r5N0ML/xZSiIn0j0TTek0t81sQZDIZGng1QAOvBujRsIfNujx9ntTFVTT4JGUmocBYgL/S/8Jf6X/Z7COXydHIt9GN7rv/urci/SPh6+5bnadGRFTtGG7Ioev51+2uSjp9/XSJk95p1BqbD1Nrl1K4Jpwz/FaAl5sXOgR1QIegDjbLDSYDkrXJlqu3bgo+OfocnM86j/NZ57H93Hab/Rp4NrDp3oqsbwk+od6hvHSdiFwCw00dJoRlBt6j2qM4cvQIzmbdaB3IKMgodj/rh2OL+i1supSCvIL44ViNVAoVIupFIKJeBGIQIy0XQiA9L92ue+tMxhmk5qbiav5VXM2/iv2X9tscz1PlaTeuJ9I/Eo39GrOFjYhqFYabOsBoNuJC9gXpaqSiM/XmGfIsG52z36+hpqEUXop2Kfm5+1Vr+alsZDIZgryDEOQdhDsa3WGzLkeXg7OZZ6VL7K3B53zWeeQb8vFH2h/4I+0Pm32sY6OKXr1lHdDso/apzlMjIioVhhsXUmgslAb1SlcmXT+Dc1nnoDfpHe6jlCsRrApG+/D2iAyItBmfwdsKuB4ftQ86BXdCp+BONsv1Jj0uZF2w696yBuCzmWdxNvMstp7darNfsFewZSBzveZS9xZb8YjI2RhuaqEcXY7UAiO1xlw/jWRtcvGDepXuNpcQW7uUGno3xJ97/0Tn3jeu3KG6x03hZhkjVT8SAzFQWl50PqKbg096XjpS81KRmpeKPcl7bI7n4+aD5v7NbVp5mvs3RxO/JrwBKRFVOb7LVBKT2YTdF3bjz7Q/0cSvCW4Pv71Cg2iFELhecN1mMK810KTmFn8/JF+1r90svZH1LYN6Hc2HYjI6vm0BEWDp4gr1CUWoTyh6N+5tsy67MNumhccafC5kX0COPgdHU4/iaOpRm31UcpV0+f/Nwacil/+bzKZKnaeIiGo3hptKsOGfDZi+ZTouaS9Jy0K8Q7Do7kW3nOHWOqhXutVAxo0bP2YVZhW7X5BXkN0svZH1I9HAswG7A6ha+Lr7oktoF3QJ7WKzXGfU4XzWebsBzWczzqLAWCCF9JuFeIdIP8tFg8+tfqY3nd5kP8N0KX//iMg1MdxU0IZ/NuD+b+63u/Fiam4qJv44ER8M+QCDIgfBaDbifNZ5m/BifePPN+Q7PLZ1Ov6il1ZbAw3nKqGaSq1Uo2VAS7QMaGmz3CzMuJJzxbZ767+bkF7Lv4aU3BSk5KZg94XdNvv5qn3tWnki/SPR0Lchtp3dhok/Trzl7x8R1S0MNxVgMpswfct0h3eUti57avNTeG3va0jKSoLBbHB4HJVchab1mkpv2tYupYh6EbyRIrkMuUyOcE04wjXhuKvJXTbrMgoycDbDfqLC5OxkZOuycSTlCI6kHLHZRyVXQfz372YCAjLIMD9xPmIiYthFRVTHMNxUwK/Jv9p0RTlSaCzEvxn/ArBMlV+09cXaGtPYtzFUClV1FJmoRvL38Id/mD+6hXWzWW69AvDmcT3nMs6h0FRY4jEFLF2+j3//ODqHdkaoVyjycvMQqA1EqG8of+eIXBjDTQWk5BQ/W29RU7pOwdhOYxHqE8qbHBKVgbvSHW0atEGbBm1slpvMJnxy7BPMT5x/y2NsT9qO7UlFZmk+Y2lFCvIKQrgmHGE+YQjXhCNUE4pwn3CEacIQ5hPGOXyIajGGmwoI8Qkp1XZ3N70b4ZrwKi4NUd2hkCvsAk9x7m99P+RyOS5lX8K59HPIMGZAb9ZLY3wO4qDD/XzVvgj1CbULQNbvA70C+ccKUQ3FcFMBdza6E+GacFzWXnbY7y+DDCE+IYgKi3JC6YhcW1RYFEK8Q5Cam1ri79+ymGVQyBUwGU04svsIOt3ZCRm6DFzOuYxL2ku4knMFl7SXbjzWXkGWLgvZumxk67Lxz7V/HD6/Sq6SLpO3BqAwzX8hyMcSgjhmjuoak9mE3y/9jszCTLQPao87G93plDFvDDcVoJArsHzActz/zf2QQWbzBiuD5dLVhXct5GBGoiqgkCuw6O5FmPjjxDL9/sllcun2FJ1DOjs8dq4+F5e1l6XAcznnMq5or+BSziVc1l5Gam4qDGYDLmRfwIXsC8WWsb5HfSn4hGpsW4HCfMLg7+HPqRvIZTialiFcE47lA5ZjROsR1VoWhpsKGtF6BNY/uN5+nhufECy8ayEvQyWqQoMiB+GDIR/Yz3NTwd8/bzdvh5ezWxnNRqTlpknBp2irz6WcS7ikvYR8Qz6uF1zH9YLrOJ523OFx3JXuN1p8fMJtAlCYTxhCfEJ401KqFTad3uRwWobL2su4/5v7sf7B9dUacBhuKsGI1iMwtOVQJJxLqLQZiomodAZFDkJMREy1zlCslCstA481YQ7XCyGQrcu27fYq0hJ0JecK0vLSUGgslO7b5YgMMgR5BUnPZR3wXLQrjHNekTOYzCbk6HOQq89FZkEm5myfU+K0DDO2zMDQlkOr7XOR4aaSKOQK9G7cGw01DeHn7semZqJqpJAr0LNhT2cXQyKTyeDn7gc/dz+0C2zncBudUYeU3BTbbq+iY39yrkBn0kn37zqcctjhcXzcfKTWH+uVXkXHAAV5BfEPLbJRaCxEji4HOfoc5OhyoNVrbR6XZl2eIa/UzycgcFF7Eb8m/2o3x1VVYbghInICtVKNJn5N0MSvicP1Qghcy79m6fbSXpbG+1hbgC7nXEZGQQZy9Dk4ef0kTl4/6fA4SrkSwd7Bdt1eRQOQp8qzCs+UKotZmJGnz4NWr0WuLtdh8NDqtdAWanEh+QJUP6mQa8i1WZerz4XepK+0MqkVargp3JCjz7nltqWdPqUyMNwQEdVAMpkMDbwaoIFXA3QK7uRwm3xDfrHdXpe0l5CSmwKj2YhLWss4IFx2/Fz13OvZdHtZW4CsISjAM6DKW6Nd/eanBpPhRgjR50Cr09q1kNgElpvX6S0hxVHXT7EyS17t7eYNHzcfaNQaeLt5Q6PWwEftAx+3/77UN/7XqDU2y6z7qJVq7Lu4Dw+se+CWxSnt9CmVgeGGiKiW8lR5SvfbcsRkNiEtL63Ybq/LOZeh1WmRWZiJzMJM/JX+l8PjqBVqy+XtRQOQz40QFOoTCrVSXe7zqMk3PxVCoMBYAK3OEjCsoaSk1hNHXTqFxpJn1C4LpVwpBYyiAcS6zEvpBe1lLVq1agVfD1+HAcbbzbvS5mkqzbQM4Zpw3Nnozkp5vtJguCmGyWSCweD4XlDF0Rl0MBvMMMqNtWbMjcloglKphFFvhDCX4S8CKrfaXudypRxyOSevqw0UcoU0Fw9CHW+j1WmL7fa6pL2EtNw06Ew6JGUlISkrqdjnCvQKtAk84ZpwhHiGQJuvRbPCZvD3cnzZe3FX2VTGzU+tg17L1ELiYJ1JmMr1/I54qjztWkUcBRWNmyWMSK0mRVpU3JXuJX7GWOd06ty+MxTKqm/9Ks20DPED4qu1JY7h5iZCCKSmpiIrK6vM+5qFGcIsoJVppRe0phMQCA4ORu613FpT5tquNte59U3LzcsN3v7etSbEU/E0ag00DTRo3aC1w/UGkwEpuSkOu72sjwuNhUjPS0d6XjqOph61P8i/gJfKy2GLz6Jdi0q8yub5nc8j2CsYeca8Kh30eitymRwatxutHg7DiNr7lkFFKXfNj93ipmUI14QjfkA857lxNmuwCQwMhKenZ5nevE1mEwwmQ+36q1YAhfmFcPd0Ry37nK29anOdC6CgoABXr15FLnLhU5/3X3J1KoUKjXwboZFvI4frhRDILMy0tP4UCTzWgdDnr59HtjEbeYY8/Hv9X/x7/d9SP7eAQHpeOoZ8PaRC5+CucLeEjTKGkaLLPVVl+zyoi6zTMuw6v4szFNckJpNJCjb169cv+/5mE+Qmee0aBCcAs9EMd/da+EFbW9XyOnf3cAcApKenw1zPXLvCPFU6mUxmuau7hz/aB7W3WWftHmnTsw1SC1JxWXvZptXnaMpRnM44fcvn8HP3Q7BXMLzV3lLriaOgIq0rMgjW282bEyFWI4VcgajwKKiV6mIDcXVguCnCOsbG05OXRRKVxMPDcs8ks9EMuRvDDZXMXemOiHoRiKgXYbO8tFfZfDjkwxo1jxHVfHxXcoBNj0S3IEOtGy9ENY/1KpvifpZkkCHUJ5Q3H6YyY7ihYrWIaIG3lr9V6u13Je6CWqku12BsIqp7rFfZAPZhmTcfpopguKkqJhNkibsg/3otZIm7AFPlXUp4M7VSXeLX4oWLy3Xcvb/txRMTnij19j169sCFSxfg68t73RBR6Vivsgn2DrZZHuITUqHLwKlu45ibKiD7biOUM+Mgu3RjOlARHgbjm8sghg+r9Oe7cOmC9P26b9Zh0YJF+PPEn9Iyb2/vG+UQAiaTZZ6VW2nQoEGZyuHm5obg4OBbb1jLGAwGqFQqZxeDyGU54+an5NrYclPJ5N9thPLBh4BLN81zfvkKlA8+BNl3Gyv9OYODg6UvX19fyGQy6fGpk6dQ368+tmzegtu73w4fTx/s3bMXZ8+excjhI9EwrCHCw8PR8/ae2LF9h81xb+6WUivVWPXxKjww8gH4+fihTas2+PHHH6X1N3dLffbpZwisH4htW7ehQ7sO8Pf1x72D7kVKyo05EIxGI2bOmInA+oEICQzBc3Oew/hx43H/iPuLPd8LFy5g+NDhCAoIQj1NPXTq0AmbN22W1p/4+wSG3TcMAfUCUN+vPu7pcw/OnrXcddlsNuOlxS+hWeNm8PH0Qbcu3bB1y1Zp3/Pnz0OtVGPdN+sQfXc0NF4afPXlVwCAVR+vQod2HaDx0qB92/ZY+d7KcrxaROSI9eanw1oNQ8+GPRlsqEIYbm5FCCAvr3RfWi1UM58GhLAbHicTlkmqlDPiAK22dMcTlTd77QvPv4D/vfQ/HP/rONp3aI+83DwMGDgAW7Zuwa5du9A/pj9GDBuB5OTkEo/z0uKXcP8D9+PQ0UMYMHAAHnv0MWRkZBS7fX5+Pt5c9iY+Wf0JdvyyAxcvXsScZ+dI619/9XV8/eXX+PDjD/HL7l+gzdHih+9/KLEM05+aDp1Ohx2/7MDhY4fx0pKXpNapy5cvo+/dfaFWq7E1YSv2H9iPsePGwmg0AgDefuttxL8Zj5dffRmHjh5Cv379MHL4SJw+bXs56vPPPY/YabE4/tdx9OvfD199+RUWLViERYsX4fhfx7H4f4uxcP5CfP7Z5yWWlYiIqh+7pW4lPx8o0q1Tklv9nSETArh8GW7+pevu0WdnAF5epdr2VuYtmIfoftHSY39/f3To2AEQQH5uPhYsXIDvv/8eP/34E6ZMnVLscR4d8yhGPTQKALD4f4vxztvv4OCBg4gZEONwe4PBgBXvrkBEhOUS0MlTJmPJ/5ZI69995108M/sZDB02FACw/K3l2Lp5q8NjWV28eBHDhw9Hu/btAADNmjWT1q18dyU0vhp88eUXUldSixYtpPXxy+Lx9DNP48FRDwIAlry8BLsSd+Htt97GW2/faKV6atpTGFakC3HRwkV45bVXpGVNmzbFPyf+wUcffIRHxzxaYnmJiKh6MdzUEV26dLF5nJubi8ULF2Pz5s1IuZICk8mEgoICXEy+WOJx2ne4MUmXl5cXNBoNrl69Wuz2np6eUrABgJCQEKSnpwMAsrOzkZaWhm7duknrFQoFbut8G8xmc7HHnBo7FU9NfQrbE7bjnr73YPiI4VK5jh8/jjvuuMPhGBmtVosrV66gR88eNst79OyBP//402ZZ0frKy8vDubPnMGnCJEyeNFlabjQaOXiaiKgGYri5FU9PIDe3VJuadiVCMfjeW25n+OkHiDvvKN1zVxKvm1qAZj87Gzu278Arr7yC0JBQ1KtfDw8/9DD0Bn2Jx1GqbH9kZDJZiUHELmTILIOaK+Lx8Y+jX/9+2LxpM7YnbMerr7yKV157BVNjp0qTy1VU0frK/e/1f+/999Ctezeb7RQKjgsgIqppOObmVmQyS9dQab769YM5LAyimEkAhUwGER4O0S+6dMerwskE9+/bjzFjxmDosKFo27YtgoODceH8hVvvWIl8fX0RFBSEQ4cOSctMJhOOHT12y30bNmyIiZMm4pv132DGzBlY9fEqAED79u2xZ88eh3d012g0CA0Nxf59+22W79+3H63atCr2uYKCghAaGoqkc0lo3ry5zVfTpk1LebZERFRd2HJTmRQKGJa9BreHHoGQyaRBxACkwGN88w2gBvy137x5c2zcuBGDBw9GYUEhXnn1lRJbYKrKlKlT8NorryGieQRatmyJd995F5mZmSXOEj0rbhZiYmIQ2SISWZlZ2JW4C61aWcLJ5KmT8e477+L/Rv8fnp39LDS+Ghz4/QC6duuKli1bYuasmVi8cDGaRTRDh44d8Nnqz3D8+HGs/nx1ieV8cf6LiJsRB42vBv1j+kOv0+Pw4cPIzMzEjJkzKrFGiIioohhuKpl5+DAYv/kayplxtpeDh4XB+OYbVTLPTXm8+vqrmPTEJPTp3Qf+/v545tlnkKPNqfZyPP3s00hNS8X4x8ZDoVBg/BPj0a9/vxK7e0wmE6ZPm47Lly5Do7GEjdfeeA0AUL9+fWxN2Io5s+cg+p5oKBQKdOzYURpnE/tULLTZWsx+ZjbS09PRuk1rfPvdt4iMjCyxnI+Pfxyenp5Y9sYyzJ09F15eXmjXrh2emv5U5VUGERFVCpmo6ACIWkar1cLX1xfZ2dnQaDQ26woLC5GUlISmTZta7thcRiazCXqT3jI/g8kE2a97IEtNhQgOtoyxqQEtNnb+u1rK09uzRtyh2mw2o0O7Drj//vuxYNGCih9QAAL2P+KOllm3tbYaWad/l1lupFR5alidl0dhYSGSzydDE6yB0q12/I1kvUN1596doVDWwN9FF8Q6dw5n13uePq9K7gpe0uf3zWrHu1JtpFBA3NXH0UcoFQkcFy5cwPaE7ejduzcKdYVY+e5KnE86jwcfehBmYbbbvjyke9bIillu/d46Au2/57M+p1mYUdzTV3kQIiKiMmO4IQD/tVgIWZlbOcpLCgNyGT7/7HPMnT0XQgi0adsGm7ZuQuvWrW9sJ78piMgc32DPblkJYcbRcW4mxI2AU/R8BYR0xZc1gAnLBjDj1kHoxiEF76xNRFQFGG5qujJ2qxS7rrjDWz+kzWZY8kDpWzkqI3BENInAvr37bLerwqvEykImkxVbHzcrGoSK9vRag1DRQCS1CP1X53bPW6SepBBYQ+qEiKg2YLipAlJ3ilU1dKsA/30AysoYOgSghx5uSjebbcvaylHXlTYICWEJOYUohJvS7cbyIsHIJhBZW4Ru/pmyPi+DEBGRHYabSiSTySCX2U4dVJXdKo6OVVbWy79lsC87VT6ZTCa14Mggg1xefJ3f3NLj6DGDEBGRPYabSiSXyeGmcOMHBlWKmwcr36pFCGAQIiICasgMxe+88w6aNGkCd3d3REVF4cCBA8Vuu3r1aksXQJGv8ly2XVX4AUDOYP1dkMvkkMvkUMgVUMgVUMqVUMqVUClUcFO4Qa1UQ62wfLkpLY/dFG5wU7hBpVBBpVBBKVdCLpNLocYaikxmk/RlNpthFmbk6HKQq89FviEfBYYC6E16GM3GCt9ig4ioIpzecrN27VrExcVh5cqViIqKQnx8PGJiYnDq1CkEBgY63Eej0eDUqVPSYwYKotIrd4tQkbFjJoUJCrkCvmpfQAkYzUaYhAkGkwFmYYbJbJJaiKzPo5ArpPAlhTCZZRl/h4moMjk93CxbtgwTJkzAuHHjAAArV67Ezz//jFWrVmHOnDkO95HJZAgODq7OYhLVSTZBqEj+sLbuBHgFSC2nZmGWgo30vbB8bzQZYTRbvvRmPYQQMJgNKDQXWraF2WZMmTUIKWQKqUWKQYiISsup4Uavt9yfZ+7cudIyuVyO6Oho7N+/v9j9cnNz0bhxY5jNZnTu3BlLlixB27Ztq6PIVAOsXr0aM2bMQFZWFgBgwYIF2LhxI44dO1bsPo899hiysrKwcePGCj13ZR3HFVlbY5TyW7+t3ByErCHI2u1lNBthMBtgMBsghIDepJf2KTrfkKMWIOv3DEJEdZdTw821a9dgMpkQFBRkszwoKAgnT550uE/Lli2xatUqdOjQAdnZ2Xj99dfRs2dP/P333wgPD7fbXqfTQafTSY+1Wi0AwGAw2N052mCwvJGazeYK30TSZDbh1+RfkZKbghDvENzZ6E7LbRmqUGpqKpYsWYJNmzbh8uXLCAwMRMeOHTF9+nT07dvX4T5St8N/510bWMtp/T8uLg5Tp04tsfzWS7BLe47nz59HREQEDh8+jE6dOknL33zzzQrXVW2s85uZzWZL64vBUOJ9wG5F/t8/pUxpaRlyMApQ6uYSQgpBZtgHIaPJCLPZDJ3QSUHI5jgmy+OsvCwoVTfGFVnHKFkDEVUek9Fk8z9VD2fXu8loggkmu8/YiirL8ZzeLVVWPXr0QI8ePaTHPXv2ROvWrfH+++9j8eLFdtsvXboUCxcutFu+bds2eHp62ixTKpUIDg5Gbm4u9Hp9ucv445kfMWfXHFzJvSItC/UOxct9XsaQ5kPKfdySJCcnY8CAAfD19cWCBQvQpk0bGAwG7Ny5E1OnTi12kLbBYIBKpUJOTvXfNLO8CgsLIYSQgioAqFQqm8c3MxgMMBqNJW5TVG5uLgAgLy/PZh/rwN3SHqckNaXO9Xo93Nzcbr3hTfsUFBRg9+7dMBqNVVSyqnHu8DlnF6HOOb7vuLOLUCc5u97/wB+Verz8/PxSb+vUcBMQEACFQoG0tDSb5WlpaaUeU6NSqXDbbbfhzJkzDtfPnTsXcXFx0mOtVouGDRuif//+Dm+cefHiRXh7e5f7CqwN/2zA2J/H2k3al5KbgrE/j8U393+DEa1HlOvYJZk9ezbkcjkOHDgALy8vaXlUVBQmT54snatCocCKFSuwZcsW7Ny5E7NmzUJcXBzWrFmDZcuW4eLFi2jatCmee+45PProowAsLQyLFi3CJ598grS0NNSvXx8jR47E8uXLAQDvvfce4uPjcfHiRfj6+uKOO+7AunXr7MpoNpvRpEkTzJ07F5MnT5aWHz16FN26dcPZs2fRuHFjvPnmm1i9ejXOnTsHf39/3HvvvXjllVfg7e0NAHB3d4dMJpPOaeHChfj+++9x5MgRAJa7hj/77LP45JNPoFAo8Pjjj0OpVEKpVEr7bNmyBUuWLMFff/0FhUKB22+/HfHx8YiIiAAAdOzYEQDQu3dvAECfPn2wc+dOjBs3DllZWfjuu+8AWFoGn332WaxduxZarRZdu3bFG2+8gW7dugEAEhMT0bdvX2zbtg1z587FiRMn0LFjR7z11lvo3Lmzw24TvV6PWbNmYcOGDcjMzERQUBAmTZokjUHLysrCnDlz8P333yM7OxvNmzfHkiVLcO+99wIAvv32WyxYsABnzpxBSEgIYmNjbX4HmjVrhscffxynT5/G999/j+HDh+OTTz7Bnj178Pzzz+PQoUMICAjAsGHDsGTJEpufJ6vCwkJ4eHigd+/eNepqxZIYDAYkJCSgX79+UKlUAG50j5mFpSVKeowby41mI4wmIwwmA0zCZLudo0vpZbAZNC2DzG4wdV1hMppwfN9xdOzZkTfOrEbOrnfrjTPDNfa9KRVRlj8qnRpu3Nzc0KVLF+zYsQPDhg0DYPkA3LFjB2JjY0t1DJPJhD///BODBg1yuF6tVkOtVtstV6lU0htc0WPJZJaJ1ayTqwkhkG8oXVo0mU2YsXVGsbdLkEGGmVtnon9E/1J1UXmqPEs1ZiAjIwNbt27FSy+9BB8fH7v1/v7+No8XLVqEl19+GcuXL4dcLsdPP/2EmTNnIj4+HtHR0fjpp58wfvx4NGrUCHfffTfWr1+P+Ph4fP3112jbti1SU1Nx/PhxyOVyHDp0CNOnT8fnn3+Onj17IiMjA7/++qvDyenkcjkefvhhfP3115g6daq0/KuvvkKvXr3QtGlTAJYA9tZbb6Fp06Y4d+4cpkyZgjlz5uDdd9+VjlP0f2sdWR+//vrr+PTTT7Fq1Sq0bt0ab7zxBjZu3Ih77rlH2qagoABxcXHo0KEDcnNzMW/ePIwcORLHjh2TQmL37t2xfft2tG3bFm5ubpDL5Tcuuf7vOHPmzMGGDRvw6aefonHjxnj11VcxcOBAnDlzBv7+/tJ2L774It544w00aNAATz75JGJjY7F//36H9bRixQr8+OOP+Oabb9CoUSNcvHgRFy9ehFwuh9lsxuDBg5GTk4MvvvgCEREROHHiBBQKBeRyOQ4fPoyHHnoICxYswKhRo7Bv3z5MmTIFAQEBeOyxx6TneOONNzBv3jwsWLAAAJCUlIRBgwbhf//7H1atWoWrV68iNjYW06ZNwyeffOLwtZTJZA5/j2q6ipT55mDj6MtkNlm6yf7rLrPuYxRG6RL6m918FZmrjR1SKBUMN07grHpXmC3PW9nvDWU5ntO7peLi4jB27Fh07doV3bt3R3x8PPLy8qSrp8aMGYOwsDAsXboUgOWD+fbbb0fz5s2RlZWF1157DRcuXMATTzxRJeXLN+TDe6l3pRxLQOBSziX4vuJbqu1z5+bCy83+r+abnTlzBkIItGrVqlTHHT16tFS/ZrMZb7/9NsaOHYspU6YAsLwmv/32G15//XXcfffdSE5ORnBwMKKjo6FSqdCoUSN0794dgKU7zMvLC/feey98fHzQuHFj3HbbbcU+9yOPPII33ngDycnJaNSoEcxmM77++mu88MIL0jYzZsyQvm/SpAn+97//4cknn5TCza3Ex8dj7ty5GDHC0kK2cuVKbN261WabkSNH2jxetWoVGjRogBMnTqBdu3Zo0KABAKB+/frFtiLm5eXhvffew+rVqzFw4EAAwIcffoiEhAR8/PHHeOaZZ6RtX3rpJfTp0wcA8Oyzz2LIkCEoLCy06xoFLHUaGRmJO+64AzKZDI0bN5bWbd++HQcOHMA///yDFi1aALC0xFgtW7YMffv2xYsvvggAaNGiBU6cOIHXXnvNJtzcc889mDVrlvT4iSeewCOPPCLVfWRkJN566y306dMH7733Xq1pnalq1tChQOk/MG4VhqytQwaTQWodMpv/C0M3DaIuWg62DlFVsJvw08H/gP1984r+bzQboVbaNypUJ6eHm1GjRuHq1auYN28eUlNT0alTJ2zZskUaZJycnGzz121mZiYmTJiA1NRU1KtXD126dMG+ffvQpk0bZ52C05V1wrSuXbvaPP7333/x5JNP2izr1auX1O30wAMPID4+Hs2aNcOAAQMwaNAgDBkyBEqlEv369UPjxo2ldQMGDMDw4cPh6emJNWvWYNKkSdIxN2/ejDvvvBOtW7fGl19+iTlz5mDXrl1IT0/HAw88IG23fft2LF26FCdPnoRWq4XRaERhYSHy8/MdhoGisrOzkZKSgqioKGmZUqlE165dberp9OnTmDdvHn7//Xdcu3ZNGtibnJyMdu3alaoez549C4PBgF69eknLVCoVunfvjn/++cdm2w4dOkjfh4SEAADS09PRpEkTu+M+9thj6NevH1q2bIkBAwbg3nvvRf/+/QEAx44dQ3h4uBRsbvbPP/9g6NChNst69eqF+Ph4mEwmafDvzT8Dx48fxx9//IE1a9ZIy6wDnpOSkqS7tFPZlTVslKV1yNpCJISQ5hgqrrvs5tYhR61FVLOVJngICBgNlnFwObocKEwKCAip6/VW9zmU/ffP+jMhg8zmf+C/kA8F5Ir/LgmQ2/+vkju3Rdfp4QYAYmNji+2GSkxMtHn85ptv4s0336yGUll4qjyROze3VNvuvrAbg7503D1W1KbRm9C7ce9SPXdpREZGQiaTFXuF2c0cjaEoScOGDXHq1Cls374dCQkJmDJlCl577TXs2rULPj4+OHLkCBITE7Ft2zapq+PgwYO47777bEJGWFgYAEvrjTXcfPnllxgwYADq168PwHKV0r333ovJkyfjpZdegr+/P/bs2YPx48dDr9ffMtyU1pAhQ9C4cWN8+OGHCA0NhdlsRrt27So0kLwkRZtTrW8QxV0p1blzZyQlJWHz5s3Yvn07HnzwQURHR2P9+vXw8PColPLc/DOQm5uLSZMmYdq0aXbbNmrUqFKek0qnqlqHDCaD1EJkEpYrzQzCwNahSlAZrR2l+SP15qDhKHjIZTeChZfKCyo3lV3wKOk4RUMNcOPnoOg21uU1WY0INzWZTCYrVdcQAPSP6I9wTTguay87TMcyyBCuCS/1mJvS8vf3R0xMDN555x1MmzbN7oMrKysLfn5+xe7fokUL7Nu3T+qqAoC9e/fatIZ5eHhgyJAhGDJkCKZOnYpWrVrhzz//ROfOnaFUKhEdHY3o6GjMnz8ffn5+2LlzJ0aMGOFwDNDo0aPxwgsv4PDhw1i/fj1WrlwprTt8+DDMZjPeeOMNqcXum2++KXVd+Pr6IiQkBL///rs0GNhoNOLw4cPo3LkzAOD69es4deoUPvzwQ9x5550AgD179tgcx3r1kMlU/KWUERERcHNzw969e6WuI4PBgIMHD9p0rZWHRqPBqFGjMGrUKNx///0YMGAAMjIy0KFDB1y6dAn//vuvw9ab1q1bY+/evTbL9u7dixYtWpR4yXbnzp1x4sQJNG/evELlJueoqa1DwvTfjNZmE+Si+luHalLoKE1rx62OU9r/DQYD/sJfCNWE1roxcZWF4aYSKeQKLB+wHPd/cz9kkNkEHGvajR8QXyXz3bzzzjvo1asXunfvjkWLFqFDhw4wGo1ISEjAe++9Z9dNUtS0adMwbtw4dO7cGdHR0fjxxx+xYcMGbN++HYBl0jyTyYSoqCh4enriiy++gIeHBxo3boyffvoJ586dQ+/evVGvXj1s2rQJZrMZLVu2LPb5mjRpgp49e2L8+PEwmUy47777pHXNmzeHwWDA22+/jSFDhmDv3r024ac0pk+fjpdffhmRkZFo1aoVli1bJk34BwD16tVD/fr18cEHHyAkJATJycl2s2EHBgbCw8MDW7ZsQXh4ONzd3eHraztWysvLC5MnT8YzzzwDf39/NGrUCK+++iry8/Mxfvz4MpW5qGXLliEkJAS33XYb5HI51q1bh+DgYPj5+aFPnz7o3bs3Ro4ciWXLlqF58+Y4efIkZDIZBgwYgFmzZqFbt25YvHgxRo0ahf3792PFihW3HK80e/Zs3H777YiNjcUTTzwBLy8vnDhxAgkJCVixYkW5z4VqpupsHQIsV8/ITDK7P/pubh2yBjRXDR1UjUQdk52dLQCI7Oxsu3UFBQXixIkToqCgoELP8e2Jb0X4snCBBZC+Gi5rKL498W2FjnsrV65cEVOnThWNGzcWbm5uIiwsTNx3333il19+kbYBIL777jvpsclkEpmZmWLFihWiWbNmQqVSiRYtWojPPvtM2ua7774TUVFRQqPRCC8vL3H77beL7du3CyGE+PXXX0WfPn1EvXr1hIeHh+jQoYNYu3btLcv67rvvCgBizJgxduuWLVsmQkJChIeHh4iJiRGfffaZACAyMzOFEEJ88sknwtfXV9p+/vz5omPHjtJjg8Egpk+fLjQajfDz8xNxcXFizJgxYujQodI2CQkJonXr1kKtVosOHTqIxMREu7r58MMPRcOGDYVcLhd9+vQRQggxduxYm+MUFBSIp556SgQEBAi1Wi169eolDhw4IK3/5ZdfbMouhBCHDx8WAMTZs2cd1s0HH3wgOnXqJLy8vIRGoxF9+/YVR44ckdZfv35djBs3TtSvX1+4u7uLdu3aiZ9++klav379etGmTRuhUqlEo0aNxGuvvWZz/MaNG4s333zT7nkPHDgg+vXrJ7y9vYWXl5fo0KGDeOmllxyWsbJ+V6qTXq8XGzduFHq93tlFqRPMZrMoKCwQGzduFLn5uSJfny9ydblCW6gVWQVZIiM/Q6Tnposr2iviQtYFcTbjrDhz/Yw4fe20OHP9jDhz/Yw4m3FWJGUkiQtZF8Sl7EviivaKSM1JFem56eJa3jVxPf+6yMjPEFkFWSK7MFtoC7UiR5cjcnW5Ik+fJ/L1+aLAUCAKDYVCZ9QJvVEvDCaDMJqMwmQ2CbPZ7OxqqhKu+rNe0uf3zWRC1K3b92q1Wvj6+iI7O9vhPDdJSUlo2rRpha8OkWYozklBiE/1zFBcHmazGVqtFhqNxuFlyVT5XKHOK/N3pboYDAZs2rQJgwYNqrNN9dWtrHVuHfTK1o6KcdWf9ZI+v2/GbqkqopArcFeTu5xdDCKiWkMuk5d4l3qi0qqdfzYSERERFYPhhoiIiFwKww0RERG5FIYbIiIicikMNw7UsQvIiMqMvyNEVJMx3BRhvWQuP790dwEnqqusvyOudJkpEbkOXgpehEKhgJ+fH9LT0wEAnp6eLj/Pgtlshl6vR2FhYa2dc6W2qc11LoRAfn4+0tPT4efnV+ItHYiInIXh5ibBwcEAIAUcVyeEQEFBATw8PFw+yNUUrlDnfn5+0u8KEVFNw3BzE5lMhpCQEAQGBsJgMDi7OFXOYDBg9+7d6N27N7sYqkltr3OVSsUWGyKq0RhuiqFQKOrEG7hCoYDRaIS7u3ut/KCtjVjnRERVq3Z1+BMRERHdAsMNERERuRSGGyIiInIpdW7MjXXyMa1W6+SS1AwGgwH5+fnQarUc/1FNWOfOwXqvfqxz53DVerd+bpdmEtE6F25ycnIAAA0bNnRySYiIiKiscnJy4OvrW+I2MlHH5lE3m824cuUKfHx8au0cI5VJq9WiYcOGuHjxIjQajbOLUyewzp2D9V79WOfO4ar1LoRATk4OQkNDbzkBap1ruZHL5QgPD3d2MWocjUbjUr8EtQHr3DlY79WPde4crljvt2qxseKAYiIiInIpDDdERETkUhhu6ji1Wo358+dDrVY7uyh1BuvcOVjv1Y917hys9zo4oJiIiIhcG1tuiIiIyKUw3BAREZFLYbghIiIil8JwQ0RERC6F4aYOWLBgAWQymc1Xq1atpPWFhYWYOnUq6tevD29vb4wcORJpaWlOLHHttHv3bgwZMgShoaGQyWTYuHGjzXohBObNm4eQkBB4eHggOjoap0+fttkmIyMDjzzyCDQaDfz8/DB+/Hjk5uZW41nULreq88cee8zuZ3/AgAE227DOy2bp0qXo1q0bfHx8EBgYiGHDhuHUqVM225TmPSU5ORmDBw+Gp6cnAgMD8cwzz8BoNFbnqdQqpan3u+66y+7n/cknn7TZpq7UO8NNHdG2bVukpKRIX3v27JHWzZw5Ez/++CPWrVuHXbt24cqVKxgxYoQTS1s75eXloWPHjnjnnXccrn/11Vfx1ltvYeXKlfj999/h5eWFmJgYFBYWSts88sgj+Pvvv5GQkICffvoJu3fvxsSJE6vrFGqdW9U5AAwYMMDmZ/+rr76yWc86L5tdu3Zh6tSp+O2335CQkACDwYD+/fsjLy9P2uZW7ykmkwmDBw+GXq/Hvn378Omnn2L16tWYN2+eM06pVihNvQPAhAkTbH7eX331VWldnap3QS5v/vz5omPHjg7XZWVlCZVKJdatWyct++effwQAsX///moqoesBIL777jvpsdlsFsHBweK1116TlmVlZQm1Wi2++uorIYQQJ06cEADEwYMHpW02b94sZDKZuHz5crWVvba6uc6FEGLs2LFi6NChxe7DOq+49PR0AUDs2rVLCFG695RNmzYJuVwuUlNTpW3ee+89odFohE6nq94TqKVurnchhOjTp4+YPn16sfvUpXpny00dcfr0aYSGhqJZs2Z45JFHkJycDAA4fPgwDAYDoqOjpW1btWqFRo0aYf/+/c4qrstJSkpCamqqTT37+voiKipKquf9+/fDz88PXbt2lbaJjo6GXC7H77//Xu1ldhWJiYkIDAxEy5YtMXnyZFy/fl1axzqvuOzsbACAv78/gNK9p+zfvx/t27dHUFCQtE1MTAy0Wi3+/vvvaix97XVzvVutWbMGAQEBaNeuHebOnYv8/HxpXV2q9zp348y6KCoqCqtXr0bLli2RkpKChQsX4s4778Rff/2F1NRUuLm5wc/Pz2afoKAgpKamOqfALshal0XfVKyPretSU1MRGBhos16pVMLf35+vRTkNGDAAI0aMQNOmTXH27Fk899xzGDhwIPbv3w+FQsE6ryCz2YwZM2agV69eaNeuHQCU6j0lNTXV4e+CdR2VzFG9A8Do0aPRuHFjhIaG4o8//sDs2bNx6tQpbNiwAUDdqneGmzpg4MCB0vcdOnRAVFQUGjdujG+++QYeHh5OLBlR1XrooYek79u3b48OHTogIiICiYmJ6Nu3rxNL5hqmTp2Kv/76y2YMH1W94uq96Fix9u3bIyQkBH379sXZs2cRERFR3cV0KnZL1UF+fn5o0aIFzpw5g+DgYOj1emRlZdlsk5aWhuDgYOcU0AVZ6/LmK0aK1nNwcDDS09Nt1huNRmRkZPC1qCTNmjVDQEAAzpw5A4B1XhGxsbH46aef8MsvvyA8PFxaXpr3lODgYIe/C9Z1VLzi6t2RqKgoALD5ea8r9c5wUwfl5ubi7NmzCAkJQZcuXaBSqbBjxw5p/alTp5CcnIwePXo4sZSupWnTpggODrapZ61Wi99//12q5x49eiArKwuHDx+Wttm5cyfMZrP0JkUVc+nSJVy/fh0hISEAWOflIYRAbGwsvvvuO+zcuRNNmza1WV+a95QePXrgzz//tAmWCQkJ0Gg0aNOmTfWcSC1zq3p35NixYwBg8/NeZ+rd2SOaqerNmjVLJCYmiqSkJLF3714RHR0tAgICRHp6uhBCiCeffFI0atRI7Ny5Uxw6dEj06NFD9OjRw8mlrn1ycnLE0aNHxdGjRwUAsWzZMnH06FFx4cIFIYQQL7/8svDz8xPff/+9+OOPP8TQoUNF06ZNRUFBgXSMAQMGiNtuu038/vvvYs+ePSIyMlI8/PDDzjqlGq+kOs/JyRFPP/202L9/v0hKShLbt28XnTt3FpGRkaKwsFA6Buu8bCZPnix8fX1FYmKiSElJkb7y8/OlbW71nmI0GkW7du1E//79xbFjx8SWLVtEgwYNxNy5c51xSrXCrer9zJkzYtGiReLQoUMiKSlJfP/996JZs2aid+/e0jHqUr0z3NQBo0aNEiEhIcLNzU2EhYWJUaNGiTNnzkjrCwoKxJQpU0S9evWEp6enGD58uEhJSXFiiWunX375RQCw+xo7dqwQwnI5+IsvviiCgoKEWq0Wffv2FadOnbI5xvXr18XDDz8svL29hUajEePGjRM5OTlOOJvaoaQ6z8/PF/379xcNGjQQKpVKNG7cWEyYMMHmMlghWOdl5ai+AYhPPvlE2qY07ynnz58XAwcOFB4eHiIgIEDMmjVLGAyGaj6b2uNW9Z6cnCx69+4t/P39hVqtFs2bNxfPPPOMyM7OtjlOXal3mRBCVF87EREREVHV4pgbIiIicikMN0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKXwnBDRERELoXhhoiIiFwKww0RlVqTJk0QHx9f6u0TExMhk8ns7jNEtspar0RUMoYbIhckk8lK/FqwYEG5jnvw4EGbOw/fSs+ePZGSkgJfX99yPR8RUXkonV0AIqp8KSkp0vdr167FvHnzcOrUKWmZt7e39L0QAiaTCUrlrd8OGjRoUKZyuLm5udzdhomo5mPLDZELCg4Olr58fX0hk8mkxydPnoSPjw82b96MLl26QK1WY8+ePTh79iyGDh2KoKAgeHt7o1u3bti+fbvNcW/uPpHJZPjoo48wfPhweHp6IjIyEj/88IO0/uZuqdWrV8PPzw9bt25F69at4e3tjQEDBtiEMaPRiGnTpsHPzw/169fH7NmzMXbsWAwbNqzEc96zZw/uvPNOeHh4oGHDhpg2bRry8vIAAJ999hm8vb1x+vRpafspU6agVatWyM/PBwB8/vnn6Nq1K3x8fBAcHIzRo0fb3D3Zei5bt27FbbfdBg8PD9xzzz1IT0/H5s2b0bp1a2g0GowePVo6JgDcddddiI2NRWxsLHx9fREQEIAXX3wRJd35JisrC0888QQaNGgAjUaDe+65B8ePH5fWHz9+HHfffTd8fHyg0WjQpUsXHDp0qMT6IapLGG6I6qg5c+bg5Zdfxj///IMOHTogNzcXgwYNwo4dO3D06FEMGDAAQ4YMQXJyconHWbhwIR588EH88ccfGDRoEB555BFkZGQUu31+fj5ef/11fP7559i9ezeSk5Px9NNPS+tfeeUVrFmzBp988gn27t0LrVaLjRs3lliGs2fPYsCAARg5ciT++OMPrF27Fnv27EFsbCwAYMyYMVLZjEYjfv75Z3z00UdYs2YNPD09AQAGgwGLFy/G8ePHsXHjRpw/fx6PPfaY3XMtWLAAK1aswL59+3Dx4kU8+OCDiI+Px5dffomff/4Z27Ztw9tvv22zz6effgqlUokDBw5g+fLlWLZsGT766KNiz+eBBx6QQtPhw4fRuXNn9O3bV6rXRx55BOHh4Th48CAOHz6MOXPmQKVSlVhHRHWKc+/bSURV7ZNPPhG+vr7SY+udtDdu3HjLfdu2bSvefvtt6XHjxo3Fm2++KT0GIF544QXpcW5urgAgNm/ebPNcmZmZUlkA2NyV/p133hFBQUHS46CgIPHaa69Jj41Go2jUqJEYOnRoseUcP368mDhxos2yX3/9VcjlclFQUCCEECIjI0OEh4eLyZMni6CgIPHSSy+VeO4HDx4UAKQ7hFvPZfv27dI2S5cuFQDE2bNnpWWTJk0SMTEx0uM+ffqI1q1bC7PZLC2bPXu2aN26tfS4aL3++uuvQqPRiMLCQpvyREREiPfff18IIYSPj49YvXp1ieUnqsvYckNUR3Xt2tXmcW5uLp5++mm0bt0afn5+8Pb2xj///HPLlpsOHTpI33t5eUGj0dh059zM09MTERER0uOQkBBp++zsbKSlpaF79+7SeoVCgS5dupRYhuPHj2P16tXw9vaWvmJiYmA2m5GUlAQAqFevHj7++GO89957iIiIwJw5c2yOcfjwYQwZMgSNGjWCj48P+vTpAwB251/0fIOCguDp6YlmzZrZLLv5/G+//XbIZDLpcY8ePXD69GmYTCaH55Kbm4v69evbnE9SUhLOnj0LAIiLi8MTTzyB6OhovPzyy9JyIrLggGKiOsrLy8vm8dNPP42EhAS8/vrraN68OTw8PHD//fdDr9eXeJybu0NkMhnMZnOZthcljD8pjdzcXEyaNAnTpk2zW9eoUSPp+927d0OhUCAlJQV5eXnw8fEBAOTl5SEmJgYxMTFYs2YNGjRogOTkZMTExNidf9Hyy2SyMp9/ac4lJCQEiYmJduv8/PwAWLrGRo8ejZ9//hmbN2/G/Pnz8fXXX2P48OHlfl4iV8JwQ0QAgL179+Kxxx6TPiBzc3Nx/vz5ai2Dr68vgoKCcPDgQfTu3RsAYDKZcOTIEXTq1KnY/Tp37owTJ06gefPmxW6zb98+vPLKK/jxxx8xe/ZsxMbG4tNPPwUAnDx5EtevX8fLL7+Mhg0bAkClDtD9/fffbR7/9ttviIyMhEKhcHguqampUCqVaNKkSbHHbNGiBVq0aIGZM2fi4YcfxieffMJwQ/QfdksREQAgMjISGzZswLFjx3D8+HGMHj26Qi0Q5fXUU09h6dKl+P7773Hq1ClMnz4dmZmZNt06N5s9ezb27duH2NhYHDt2DKdPn8b3338vDSjOycnBo48+imnTpmHgwIFYs2YN1q5di/Xr1wOwtO64ubnh7bffxrlz5/DDDz9g8eLFlXZOycnJiIuLw6lTp/DVV1/h7bffxvTp0x1uGx0djR49emDYsGHYtm0bzp8/j3379uH555/HoUOHUFBQgNjYWCQmJuLChQvYu3cvDh48iNatW1daeYlqO7bcEBEAYNmyZXj88cfRs2dPBAQEYPbs2dBqtdVejtmzZyM1NRVjxoyBQqHAxIkTERMT47CVw6pDhw7YtWsXnn/+edx5550QQiAiIgKjRo0CAEyfPh1eXl5YsmQJAKB9+/ZYsmQJJk2ahB49eiAsLAyrV6/Gc889h7feegudO3fG66+/jvvuu69SzmnMmDEoKChA9+7doVAoMH369GInQ5TJZNi0aROef/55jBs3DlevXkVwcDB69+6NoKAgKBQKXL9+HWPGjEFaWhoCAgIwYsQILFy4sFLKSuQKZKKind1ERFXIbDajdevWePDBByu1NaW63HXXXejUqRNvr0BUjdhyQ0Q1yoULF7Bt2zb06dMHOp0OK1asQFJSEkaPHu3sohFRLcExN0RUo8jlcqxevRrdunVDr1698Oeff2L79u0cU0JEpcZuKSIiInIpbLkhIiIil8JwQ0RERC6F4YaIiIhcCsMNERERuRSGGyIiInIpDDdERETkUhhuiIiIyKUw3BAREZFLYbghIiIil/L/K2k/EELYAKgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# XG boosting - Learning Curve\n",
        "plot_learning_curve(estimator = xgb_best_estimator,title = \"XGBoosting learning curve\", X = X_train, y = Y_train, cv = k_fold);"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adaboost - Learning Curve\n",
        "plot_learning_curve(estimator = ada_best_estimator,title = \"AdaBoost learning curve\", X = X_train, y = Y_train, cv = k_fold);"
      ],
      "metadata": {
        "id": "CqR54SZsQWg0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "312f8ca7-a1f5-428b-bda8-16b64b0df589"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q1rf2O7pu_q8"
      },
      "source": [
        "### Use VotingClassifier of sklearn to create an ensemble learner that utilizes the top estimators you found above. Set the voting parameter to 'soft'. Use cross_val_score to calculate its score and print out its mean and std values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "0WukqonzvLfN"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "VotingPredictor = VotingClassifier(estimators =\n",
        "                           [('gbc', gbc_best_estimator),\n",
        "                            ('rfc', rfc_best_estimator),\n",
        "                            ('xgb', xgb_best_estimator),\n",
        "                            ('ada',ada_best_estimator),\n",
        "                            ('sgd',SGD_classifier),\n",
        "                            ('svc',SVC_linear),\n",
        "                            ('log',logistic_regression)\n",
        "                            ],\n",
        "                           voting='hard') #'soft' parameter cannot be used since probability estimates are not available\n",
        "\n",
        "VotingPredictor = VotingPredictor.fit(X_train, Y_train)\n",
        "\n",
        "emsemble_scores = cross_val_score(VotingPredictor, X_train, Y_train, cv = k_fold, scoring = scoring_parameter)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"emsemble: \")\n",
        "print(\"- mean(): \" , emsemble_scores.mean())\n",
        "print(\"- std(): \" , emsemble_scores.std())"
      ],
      "metadata": {
        "id": "-kyHB5FwQ8Lz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bdc4aa7-8570-4922-aa73-77291f3313d5"
      },
      "execution_count": 55,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "emsemble: \n",
            "- mean():  0.5822988505747128\n",
            "- std():  0.04415409186899065\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    },
    "nav_menu": {},
    "toc": {
      "navigate_menu": true,
      "number_sections": true,
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      "threshold": 6,
      "toc_cell": false,
      "toc_section_display": "block",
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