Logistic_Regression.ipynb

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      "source": [
        "# Data Processing\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import mean\n",
        "\n",
        "# Modelling\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, ConfusionMatrixDisplay\n",
        "from sklearn.model_selection import RandomizedSearchCV, train_test_split\n",
        "from scipy.stats import randint\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Tree Visualisation\n",
        "from sklearn.tree import export_graphviz\n",
        "from IPython.display import Image\n",
        "import graphviz\n",
        "\n",
        "# Graphing\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import loguniform\n",
        "\n",
        "# for data balancing\n",
        "from collections import Counter\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "# Cross-validation\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
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      "source": [
        "# Data Cleaning\n",
        "best = pd.read_csv('BEST.csv')\n",
        "best = best.drop(['PARTICIPANT.NUMBER', 'GENDER', 'HANDEDNESS'], axis = 1)\n",
        "\n",
        "best['MAXIMUM.EDUCATIONAL.LEVEL'] = best['MAXIMUM.EDUCATIONAL.LEVEL'].map({'High School':0,'University':1,'Postgraduate':2, 'Professional Training':3})\n",
        "\n",
        "best.head(10)\n",
        "\n",
        "# print(best['MAXIMUM.EDUCATIONAL.LEVEL'].unique())\n",
        "# Create future datasets - untangle english and other languages, as well as look at\n",
        "# the best number of features to look at"
      ],
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              "   AGE.IN.YEARS  MAXIMUM.EDUCATIONAL.LEVEL  PERCENTAGE.EXPOSED.SPANISH  \\\n",
              "0            22                          0                          40   \n",
              "1            21                          1                          70   \n",
              "2            30                          2                          20   \n",
              "3            21                          1                          90   \n",
              "4            39                          3                          50   \n",
              "5            20                          1                         100   \n",
              "6            23                          1                          60   \n",
              "7            24                          1                          50   \n",
              "8            36                          2                          30   \n",
              "9            25                          1                          30   \n",
              "\n",
              "   PERCENTAGE.EXPOSED.BASQUE  PERCENTAGE.EXPOSED.ENGLISH  \\\n",
              "0                         40                          20   \n",
              "1                         20                          10   \n",
              "2                         70                          10   \n",
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              "4                         40                          10   \n",
              "5                          0                           0   \n",
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              "   SELF.PERCEIVED.LEVEL.SPANISH  SELF.PERCEIVED.LEVEL.BASQUE  \\\n",
              "0                            10                            9   \n",
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              "2                             9                           10   \n",
              "3                             9                            7   \n",
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              "6                            10                           10   \n",
              "7                             9                            9   \n",
              "8                             9                            9   \n",
              "9                             8                           10   \n",
              "\n",
              "   SELF.PERCEIVED.LEVEL.ENGLISH  INTERVIEW.MARK.SPANISH  \\\n",
              "0                             5                       5   \n",
              "1                             7                       5   \n",
              "2                             5                       5   \n",
              "3                             3                       5   \n",
              "4                             7                       5   \n",
              "5                             2                       5   \n",
              "6                             7                       5   \n",
              "7                             7                       5   \n",
              "8                             7                       5   \n",
              "9                             7                       5   \n",
              "\n",
              "   INTERVIEW.MARK.BASQUE  INTERVIEW.MARK.ENGLISH  AGE.OF.ACQUISITION.SPANISH  \\\n",
              "0                      5                       3                           0   \n",
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              "7                      5                       4                           0   \n",
              "8                      5                       4                           5   \n",
              "9                      5                       3                           6   \n",
              "\n",
              "   AGE.OF.ACQUISITION.BASQUE  AGE.OF.ACQUISITION.ENGLISH  \\\n",
              "0                          3                           5   \n",
              "1                          0                           5   \n",
              "2                          0                           8   \n",
              "3                          3                           3   \n",
              "4                          0                          12   \n",
              "5                          6                           6   \n",
              "6                          0                           6   \n",
              "7                          0                           8   \n",
              "8                          0                          11   \n",
              "9                          0                           6   \n",
              "\n",
              "   PICTURE.NAMING.TEST.SPANISH  PICTURE.NAMING.TEST.BASQUE  \\\n",
              "0                           63                          52   \n",
              "1                           65                          57   \n",
              "2                           65                          65   \n",
              "3                           65                          45   \n",
              "4                           65                          64   \n",
              "5                           65                          57   \n",
              "6                           64                          64   \n",
              "7                           65                          63   \n",
              "8                           65                          65   \n",
              "9                           62                          65   \n",
              "\n",
              "   PICTURE.NAMING.TEST.ENGLISH  LEXTALE.TEST.SPANISH  LEXTALE.TEST.BASQUE  \\\n",
              "0                           33                 88.33                   89   \n",
              "1                           59                 85.00                   92   \n",
              "2                           50                 93.33                   96   \n",
              "3                           39                 92.50                   88   \n",
              "4                           54                 84.17                   73   \n",
              "5                           31                 90.83                   83   \n",
              "6                           57                 98.33                   89   \n",
              "7                           56                 98.33                   98   \n",
              "8                           60                 98.33                   96   \n",
              "9                           50                 94.17                   97   \n",
              "\n",
              "   LEXTALE.TEST.ENGLISH  \n",
              "0                 58.75  \n",
              "1                 72.50  \n",
              "2                 78.75  \n",
              "3                 67.50  \n",
              "4                 60.00  \n",
              "5                 61.25  \n",
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              "7                 76.25  \n",
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              "9                 71.25  "
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              "          const element = document.querySelector('#df-c5ec722b-7715-41c0-8e74-b0b89e6bb87c');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create initial binary mapping of early vs. late stage\n",
        "def earlyClassifier1(n):\n",
        "  if n <= 1:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier2(n):\n",
        "  if n <= 2:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier3(n):\n",
        "  if n <= 3:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier4(n):\n",
        "  if n <= 4:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier5(n):\n",
        "  if n <= 5:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier6(n):\n",
        "  if n <= 6:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier7(n):\n",
        "  if n <= 7:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier8(n):\n",
        "  if n <= 8:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier9(n):\n",
        "  if n <= 9:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier10(n):\n",
        "  if n <= 10:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "binaryAcquisition1 = list(map(earlyClassifier1, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition2 = list(map(earlyClassifier2, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition3 = list(map(earlyClassifier3, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition4 = list(map(earlyClassifier4, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition5 = list(map(earlyClassifier5, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition6 = list(map(earlyClassifier6, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition7 = list(map(earlyClassifier7, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition8 = list(map(earlyClassifier8, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition9 = list(map(earlyClassifier9, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition10 = list(map(earlyClassifier10, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "\n",
        "\n",
        "#best['AGE.OF.ACQUISITION.BASQUE'] = list(map(earlyClassifier, list(best['AGE.OF.ACQUISITION.BASQUE'])))\n",
        "#best['AGE.OF.ACQUISITION.SPANISH'] = list(map(earlyClassifier, list(best['AGE.OF.ACQUISITION.SPANISH'])))\n",
        "\n",
        "#binaryAcquisition = best['AGE.OF.ACQUISITION.ENGLISH']\n",
        "#binaryAcquisitionBasque = best['AGE.OF.ACQUISITION.BASQUE']\n",
        "#binaryAcquisitionSpanish = best['AGE.OF.ACQUISITION.SPANISH']\n",
        "\n",
        "#allVariables = best.drop(['AGE.OF.ACQUISITION.ENGLISH'], axis = 1)\n",
        "\n",
        "# Create an English only dataframe \n",
        "EnglishOnly = pd.DataFrame()\n",
        "EnglishOnly['AGE'] = best['AGE.IN.YEARS']\n",
        "EnglishOnly['EDUCATION'] = best['MAXIMUM.EDUCATIONAL.LEVEL']\n",
        "EnglishOnly['EXPOSURE'] = best['PERCENTAGE.EXPOSED.ENGLISH']\n",
        "EnglishOnly['SELF.PERCEIVED'] = best['SELF.PERCEIVED.LEVEL.ENGLISH']\n",
        "EnglishOnly['INTERVIEW'] = best['INTERVIEW.MARK.ENGLISH']\n",
        "EnglishOnly['PICTURE.NAMING'] = best['PICTURE.NAMING.TEST.ENGLISH']\n",
        "EnglishOnly['LEXTALE.TEST'] = best['LEXTALE.TEST.ENGLISH']\n",
        "\n",
        "oversample = RandomOverSampler(sampling_strategy='minority')\n",
        "# fit and apply the transform\n",
        "X_over1, y_over1 = oversample.fit_resample(EnglishOnly, binaryAcquisition1)\n",
        "X_over2, y_over2 = oversample.fit_resample(EnglishOnly, binaryAcquisition2)\n",
        "X_over3, y_over3 = oversample.fit_resample(EnglishOnly, binaryAcquisition3)\n",
        "X_over4, y_over4 = oversample.fit_resample(EnglishOnly, binaryAcquisition4)\n",
        "X_over5, y_over5 = oversample.fit_resample(EnglishOnly, binaryAcquisition5)\n",
        "X_over6, y_over6 = oversample.fit_resample(EnglishOnly, binaryAcquisition6)\n",
        "X_over7, y_over7 = oversample.fit_resample(EnglishOnly, binaryAcquisition7)\n",
        "X_over8, y_over8 = oversample.fit_resample(EnglishOnly, binaryAcquisition8)\n",
        "X_over9, y_over9 = oversample.fit_resample(EnglishOnly, binaryAcquisition9)\n",
        "X_over10, y_over10 = oversample.fit_resample(EnglishOnly, binaryAcquisition10)\n",
        "\n",
        "#print(Counter(y_over))\n",
        "\n",
        "#print(sum(binaryAcquisition6))\n",
        "#print(sum(y_over))\n",
        "\n",
        "X_train1, X_test1, y_train1, y_test1 = train_test_split(X_over1, y_over1, test_size=0.2, random_state = 1, stratify = y_over1)\n",
        "X_train2, X_test2, y_train2, y_test2 = train_test_split(X_over2, y_over2, test_size=0.2, random_state = 1, stratify = y_over2)\n",
        "X_train3, X_test3, y_train3, y_test3 = train_test_split(X_over3, y_over3, test_size=0.2, random_state = 1, stratify = y_over3)\n",
        "X_train4, X_test4, y_train4, y_test4 = train_test_split(X_over4, y_over4, test_size=0.2, random_state = 1, stratify = y_over4)\n",
        "X_train5, X_test5, y_train5, y_test5 = train_test_split(X_over5, y_over5, test_size=0.2, random_state = 1, stratify = y_over5)\n",
        "X_train6, X_test6, y_train6, y_test6 = train_test_split(X_over6, y_over6, test_size=0.2, random_state = 1, stratify = y_over6)\n",
        "X_train7, X_test7, y_train7, y_test7 = train_test_split(X_over7, y_over7, test_size=0.2, random_state = 1, stratify = y_over7)\n",
        "X_train8, X_test8, y_train8, y_test8 = train_test_split(X_over8, y_over8, test_size=0.2, random_state = 1, stratify = y_over8)\n",
        "X_train9, X_test9, y_train9, y_test9 = train_test_split(X_over9, y_over9, test_size=0.2, random_state = 1, stratify = y_over9)\n",
        "X_train10, X_test10, y_train10, y_test10 = train_test_split(X_over10, y_over10, test_size=0.2, random_state = 1, stratify = y_over10)\n",
        "\n",
        "#allVariables.head()\n"
      ],
      "metadata": {
        "id": "4hklxC_vcAC-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "space = dict()\n",
        "space['solver'] = ['newton-cg', 'lbfgs', 'liblinear']\n",
        "space['penalty'] = ['l2']\n",
        "space['C'] = [100, 10, 1.0, 0.1, 0.01]\n",
        "\n",
        "# Create a random forest classifier\n",
        "lr = LogisticRegression(max_iter = 3000)\n",
        "cv = KFold(n_splits=5, random_state=1, shuffle=True)\n",
        "\n",
        "# Use random search to find the best hyperparameters\n",
        "rand_search = RandomizedSearchCV(lr, space, \n",
        "                                 n_iter=15, \n",
        "                                 cv=5)\n",
        "\n",
        "# Fit the random search object to the data\n",
        "lr.fit(X_train1, y_train1)\n",
        "y_pred1n = lr.predict(X_test1)\n",
        "y_pred1nK = cross_val_predict(lr, X_test1, y_test1, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train1, y_train1)\n",
        "best_lr = rand_search.best_estimator_\n",
        "y_pred1noK = best_lr.predict(X_test1)\n",
        "y_pred1 = cross_val_predict(best_lr, X_test1, y_test1, cv=cv)\n",
        "\n",
        "\n",
        "lr.fit(X_train2, y_train2)\n",
        "y_pred2n = lr.predict(X_test2)\n",
        "y_pred2nK = cross_val_predict(lr, X_test2, y_test2, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train2, y_train2)\n",
        "best_lr = rand_search.best_estimator_\n",
        "y_pred2noK = best_lr.predict(X_test2)\n",
        "y_pred2 = cross_val_predict(best_lr, X_test2, y_test2, cv=cv)\n",
        "\n",
        "\n",
        "lr.fit(X_train3, y_train3)\n",
        "y_pred3n = lr.predict(X_test3)\n",
        "y_pred3nK = cross_val_predict(lr, X_test3, y_test3, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train3, y_train3)\n",
        "best_lr = rand_search.best_estimator_\n",
        "y_pred3noK = best_lr.predict(X_test3)\n",
        "y_pred3 = cross_val_predict(best_lr, X_test3, y_test3, cv=cv)\n",
        "\n",
        "lr.fit(X_train4, y_train4)\n",
        "y_pred4n = lr.predict(X_test4)\n",
        "y_pred4nK = cross_val_predict(lr, X_test4, y_test4, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train4, y_train4)\n",
        "best_lr = rand_search.best_estimator_\n",
        "y_pred4noK = best_lr.predict(X_test4)\n",
        "y_pred4 = cross_val_predict(best_lr, X_test4, y_test4, cv=cv)\n",
        "\n",
        "\n",
        "lr.fit(X_train5, y_train5)\n",
        "y_pred5n = lr.predict(X_test5)\n",
        "y_pred5nK = cross_val_predict(lr, X_test5, y_test5, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train5, y_train5)\n",
        "best_lr = rand_search.best_estimator_\n",
        "y_pred5noK = best_lr.predict(X_test5)\n",
        "y_pred5 = cross_val_predict(best_lr, X_test5, y_test5, cv=cv)\n",
        "\n",
        "lr.fit(X_train6, y_train6)\n",
        "y_pred6n = lr.predict(X_test6)\n",
        "y_pred6nK = cross_val_predict(lr, X_test6, y_test6, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train6, y_train6)\n",
        "best_lr = rand_search.best_estimator_\n",
        "y_pred6noK = best_lr.predict(X_test6)\n",
        "y_pred6 = cross_val_predict(best_lr, X_test6, y_test6, cv=cv)\n",
        "\n",
        "\n",
        "lr.fit(X_train7, y_train7)\n",
        "y_pred7n = lr.predict(X_test7)\n",
        "y_pred7nK = cross_val_predict(lr, X_test7, y_test7, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train7, y_train7)\n",
        "best_lr = rand_search.best_estimator_\n",
        "y_pred7noK = best_lr.predict(X_test7)\n",
        "y_pred7 = cross_val_predict(best_lr, X_test7, y_test7, cv=cv)\n",
        "\n",
        "\n",
        "lr.fit(X_train8, y_train8)\n",
        "y_pred8n = lr.predict(X_test8)\n",
        "y_pred8nK = cross_val_predict(lr, X_test8, y_test8, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train8, y_train8)\n",
        "best_lr = rand_search.best_estimator_\n",
        "y_pred8noK = best_lr.predict(X_test8)\n",
        "y_pred8 = cross_val_predict(best_lr, X_test8, y_test8, cv=cv)\n",
        "\n",
        "lr.fit(X_train9, y_train9)\n",
        "y_pred9n = lr.predict(X_test9)\n",
        "y_pred9nK = cross_val_predict(lr, X_test9, y_test9, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train9, y_train9)\n",
        "best_lr = rand_search.best_estimator_\n",
        "y_pred9noK = best_lr.predict(X_test9)\n",
        "y_pred9 = cross_val_predict(best_lr, X_test9, y_test9, cv=cv)\n",
        "\n",
        "\n",
        "lr.fit(X_train10, y_train10)\n",
        "y_pred10n = lr.predict(X_test10)\n",
        "y_pred10nK = cross_val_predict(lr, X_test10, y_test10, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train10, y_train10)\n",
        "best_lr = rand_search.best_estimator_\n",
        "y_pred10noK = best_lr.predict(X_test10)\n",
        "y_pred10 = cross_val_predict(best_lr, X_test10, y_test10, cv=cv)\n",
        "\n",
        "# Print the best hyperparameters\n",
        "# #print('Best hyperparameters:',  rand_search.best_params_)"
      ],
      "metadata": {
        "id": "KH-jtyJzi18C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating Performance of the Models\n",
        "\n",
        "# With hyperparameter tuning and kfold validation - REDUNDANT\n",
        "accuracy1 = accuracy_score(y_test1, y_pred1)\n",
        "precision1 = precision_score(y_test1, y_pred1)\n",
        "recall1 = recall_score(y_test1, y_pred1)\n",
        "\n",
        "accuracy2 = accuracy_score(y_test2, y_pred2)\n",
        "precision2 = precision_score(y_test2, y_pred2)\n",
        "recall2 = recall_score(y_test2, y_pred2)\n",
        "\n",
        "accuracy3 = accuracy_score(y_test3, y_pred3)\n",
        "precision3 = precision_score(y_test3, y_pred3)\n",
        "recall3 = recall_score(y_test3, y_pred3)\n",
        "\n",
        "accuracy4 = accuracy_score(y_test4, y_pred4)\n",
        "precision4 = precision_score(y_test4, y_pred4)\n",
        "recall4 = recall_score(y_test4, y_pred4)\n",
        "\n",
        "accuracy5 = accuracy_score(y_test5, y_pred5)\n",
        "precision5 = precision_score(y_test5, y_pred5)\n",
        "recall5 = recall_score(y_test5, y_pred5)\n",
        "\n",
        "accuracy6 = accuracy_score(y_test6, y_pred6)\n",
        "precision6 = precision_score(y_test6, y_pred6)\n",
        "recall6 = recall_score(y_test6, y_pred6)\n",
        "\n",
        "accuracy7 = accuracy_score(y_test7, y_pred7)\n",
        "precision7 = precision_score(y_test7, y_pred7)\n",
        "recall7 = recall_score(y_test7, y_pred7)\n",
        "\n",
        "accuracy8 = accuracy_score(y_test8, y_pred8)\n",
        "precision8 = precision_score(y_test8, y_pred8)\n",
        "recall8 = recall_score(y_test8, y_pred8)\n",
        "\n",
        "accuracy9 = accuracy_score(y_test9, y_pred9)\n",
        "precision9 = precision_score(y_test9, y_pred9)\n",
        "recall9 = recall_score(y_test9, y_pred9)\n",
        "\n",
        "accuracy10 = accuracy_score(y_test10, y_pred10)\n",
        "precision10 = precision_score(y_test10, y_pred10)\n",
        "recall10 = recall_score(y_test10, y_pred10)\n",
        "\n",
        "x_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "accuracies = [accuracy1, accuracy2, accuracy3, accuracy4, accuracy5, accuracy6, accuracy7, accuracy8, accuracy9, accuracy10]\n",
        "precisions = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
        "recalls = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"Accuracy:\", accuracies[i])\n",
        "  print(\"Precision:\", precisions[i])\n",
        "  print(\"Recall:\", recalls[i])\n",
        "  print(\"----------\")\n",
        "\n",
        "plt.plot(x_values, accuracies, label = \"Accuracy\")\n",
        "plt.plot(x_values, precisions, label = \"Precision\")\n",
        "plt.plot(x_values, recalls, label = \"Recall\")\n",
        "plt.legend()\n",
        "plt.xlabel(\"Binary Cutoff\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.title(\"Results with Optimization and Cross Fold Validation\")\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QwEYQ_bKsD2C",
        "outputId": "dc1064a9-2c6d-47f0-9c03-89d8a727bbe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6614785992217899\n",
            "Precision: 0.6693548387096774\n",
            "Recall: 0.6434108527131783\n",
            "----------\n",
            "Accuracy: 0.609375\n",
            "Precision: 0.6076923076923076\n",
            "Recall: 0.6171875\n",
            "----------\n",
            "Accuracy: 0.5739130434782609\n",
            "Precision: 0.5825242718446602\n",
            "Recall: 0.5217391304347826\n",
            "----------\n",
            "Accuracy: 0.5476190476190477\n",
            "Precision: 0.5520833333333334\n",
            "Recall: 0.5047619047619047\n",
            "----------\n",
            "Accuracy: 0.6890243902439024\n",
            "Precision: 0.7066666666666667\n",
            "Recall: 0.6463414634146342\n",
            "----------\n",
            "Accuracy: 0.7073170731707317\n",
            "Precision: 0.7428571428571429\n",
            "Recall: 0.6341463414634146\n",
            "----------\n",
            "Accuracy: 0.7058823529411765\n",
            "Precision: 0.7294117647058823\n",
            "Recall: 0.6595744680851063\n",
            "----------\n",
            "Accuracy: 0.7183098591549296\n",
            "Precision: 0.7373737373737373\n",
            "Recall: 0.6822429906542056\n",
            "----------\n",
            "Accuracy: 0.7212389380530974\n",
            "Precision: 0.7403846153846154\n",
            "Recall: 0.6814159292035398\n",
            "----------\n",
            "Accuracy: 0.8\n",
            "Precision: 0.839622641509434\n",
            "Recall: 0.7416666666666667\n",
            "----------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Displaying the results of Random Forest Classifier\n",
        "# With hyperparameter tuning only\n",
        "\n",
        "accuracy1 = accuracy_score(y_test1, y_pred1noK)\n",
        "precision1 = precision_score(y_test1, y_pred1noK)\n",
        "recall1 = recall_score(y_test1, y_pred1noK)\n",
        "\n",
        "accuracy2 = accuracy_score(y_test2, y_pred2noK)\n",
        "precision2 = precision_score(y_test2, y_pred2noK)\n",
        "recall2 = recall_score(y_test2, y_pred2noK)\n",
        "\n",
        "accuracy3 = accuracy_score(y_test3, y_pred3noK)\n",
        "precision3 = precision_score(y_test3, y_pred3noK)\n",
        "recall3 = recall_score(y_test3, y_pred3noK)\n",
        "\n",
        "accuracy4 = accuracy_score(y_test4, y_pred4noK)\n",
        "precision4 = precision_score(y_test4, y_pred4noK)\n",
        "recall4 = recall_score(y_test4, y_pred4noK)\n",
        "\n",
        "accuracy5 = accuracy_score(y_test5, y_pred5noK)\n",
        "precision5 = precision_score(y_test5, y_pred5noK)\n",
        "recall5 = recall_score(y_test5, y_pred5noK)\n",
        "\n",
        "accuracy6 = accuracy_score(y_test6, y_pred6noK)\n",
        "precision6 = precision_score(y_test6, y_pred6noK)\n",
        "recall6 = recall_score(y_test6, y_pred6noK)\n",
        "\n",
        "accuracy7 = accuracy_score(y_test7, y_pred7noK)\n",
        "precision7 = precision_score(y_test7, y_pred7noK)\n",
        "recall7 = recall_score(y_test7, y_pred7noK)\n",
        "\n",
        "accuracy8 = accuracy_score(y_test8, y_pred8noK)\n",
        "precision8 = precision_score(y_test8, y_pred8noK)\n",
        "recall8 = recall_score(y_test8, y_pred8noK)\n",
        "\n",
        "accuracy9 = accuracy_score(y_test9, y_pred9noK)\n",
        "precision9 = precision_score(y_test9, y_pred9noK)\n",
        "recall9 = recall_score(y_test9, y_pred9noK)\n",
        "\n",
        "accuracy10 = accuracy_score(y_test10, y_pred10noK)\n",
        "precision10 = precision_score(y_test10, y_pred10noK)\n",
        "recall10 = recall_score(y_test10, y_pred10noK)\n",
        "\n",
        "x_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "accuracies = [accuracy1, accuracy2, accuracy3, accuracy4, accuracy5, accuracy6, accuracy7, accuracy8, accuracy9, accuracy10]\n",
        "precisions = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
        "recalls = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"Accuracy:\", accuracies[i])\n",
        "  print(\"Precision:\", precisions[i])\n",
        "  print(\"Recall:\", recalls[i])\n",
        "  print(\"----------\")\n",
        "\n",
        "plt.plot(x_values, accuracies, label = \"Accuracy\")\n",
        "plt.plot(x_values, precisions, label = \"Precision\")\n",
        "plt.plot(x_values, recalls, label = \"Recall\")\n",
        "plt.legend()\n",
        "plt.xlabel(\"Binary Cutoff\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.title(\"Results with Optimization Only\")\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9iCZtXVJIOgm",
        "outputId": "1da823e8-70ae-4b87-fe98-2a2b71a93e5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6381322957198443\n",
            "Precision: 0.6451612903225806\n",
            "Recall: 0.6201550387596899\n",
            "----------\n",
            "Accuracy: 0.64453125\n",
            "Precision: 0.635036496350365\n",
            "Recall: 0.6796875\n",
            "----------\n",
            "Accuracy: 0.6217391304347826\n",
            "Precision: 0.6458333333333334\n",
            "Recall: 0.5391304347826087\n",
            "----------\n",
            "Accuracy: 0.5428571428571428\n",
            "Precision: 0.5463917525773195\n",
            "Recall: 0.5047619047619047\n",
            "----------\n",
            "Accuracy: 0.7378048780487805\n",
            "Precision: 0.8\n",
            "Recall: 0.6341463414634146\n",
            "----------\n",
            "Accuracy: 0.6890243902439024\n",
            "Precision: 0.7246376811594203\n",
            "Recall: 0.6097560975609756\n",
            "----------\n",
            "Accuracy: 0.732620320855615\n",
            "Precision: 0.8142857142857143\n",
            "Recall: 0.6063829787234043\n",
            "----------\n",
            "Accuracy: 0.7276995305164319\n",
            "Precision: 0.7474747474747475\n",
            "Recall: 0.6915887850467289\n",
            "----------\n",
            "Accuracy: 0.7389380530973452\n",
            "Precision: 0.7647058823529411\n",
            "Recall: 0.6902654867256637\n",
            "----------\n",
            "Accuracy: 0.8\n",
            "Precision: 0.8333333333333334\n",
            "Recall: 0.75\n",
            "----------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the performance of the models, no optimization and kfold cross validation\n",
        "\n",
        "accuracy1 = accuracy_score(y_test1, y_pred1nK)\n",
        "precision1 = precision_score(y_test1, y_pred1nK)\n",
        "recall1 = recall_score(y_test1, y_pred1nK)\n",
        "\n",
        "accuracy2 = accuracy_score(y_test2, y_pred2nK)\n",
        "precision2 = precision_score(y_test2, y_pred2nK)\n",
        "recall2 = recall_score(y_test2, y_pred2nK)\n",
        "\n",
        "accuracy3 = accuracy_score(y_test3, y_pred3nK)\n",
        "precision3 = precision_score(y_test3, y_pred3nK)\n",
        "recall3 = recall_score(y_test3, y_pred3nK)\n",
        "\n",
        "accuracy4 = accuracy_score(y_test4, y_pred4nK)\n",
        "precision4 = precision_score(y_test4, y_pred4nK)\n",
        "recall4 = recall_score(y_test4, y_pred4nK)\n",
        "\n",
        "accuracy5 = accuracy_score(y_test5, y_pred5nK)\n",
        "precision5 = precision_score(y_test5, y_pred5nK)\n",
        "recall5 = recall_score(y_test5, y_pred5nK)\n",
        "\n",
        "accuracy6 = accuracy_score(y_test6, y_pred6nK)\n",
        "precision6 = precision_score(y_test6, y_pred6nK)\n",
        "recall6 = recall_score(y_test6, y_pred6nK)\n",
        "\n",
        "accuracy7 = accuracy_score(y_test7, y_pred7nK)\n",
        "precision7 = precision_score(y_test7, y_pred7nK)\n",
        "recall7 = recall_score(y_test7, y_pred7nK)\n",
        "\n",
        "accuracy8 = accuracy_score(y_test8, y_pred8nK)\n",
        "precision8 = precision_score(y_test8, y_pred8nK)\n",
        "recall8 = recall_score(y_test8, y_pred8nK)\n",
        "\n",
        "accuracy9 = accuracy_score(y_test9, y_pred9nK)\n",
        "precision9 = precision_score(y_test9, y_pred9nK)\n",
        "recall9 = recall_score(y_test9, y_pred9nK)\n",
        "\n",
        "accuracy10 = accuracy_score(y_test10, y_pred10nK)\n",
        "precision10 = precision_score(y_test10, y_pred10nK)\n",
        "recall10 = recall_score(y_test10, y_pred10nK)\n",
        "\n",
        "x_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "accuracies = [accuracy1, accuracy2, accuracy3, accuracy4, accuracy5, accuracy6, accuracy7, accuracy8, accuracy9, accuracy10]\n",
        "precisions = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
        "recalls = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"Accuracy:\", accuracies[i])\n",
        "  print(\"Precision:\", precisions[i])\n",
        "  print(\"Recall:\", recalls[i])\n",
        "  print(\"----------\")\n",
        "\n",
        "plt.plot(x_values, accuracies, label = \"Accuracy\")\n",
        "plt.plot(x_values, precisions, label = \"Precision\")\n",
        "plt.plot(x_values, recalls, label = \"Recall\")\n",
        "plt.legend()\n",
        "plt.xlabel(\"Binary Cutoff\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.title(\"Results with Cross Fold Validation and No Optimization\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fsdJlQ_p8G4V",
        "outputId": "3fbfa28e-cd56-455b-958a-cda89101dc5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6614785992217899\n",
            "Precision: 0.6615384615384615\n",
            "Recall: 0.6666666666666666\n",
            "----------\n",
            "Accuracy: 0.60546875\n",
            "Precision: 0.6046511627906976\n",
            "Recall: 0.609375\n",
            "----------\n",
            "Accuracy: 0.5826086956521739\n",
            "Precision: 0.594059405940594\n",
            "Recall: 0.5217391304347826\n",
            "----------\n",
            "Accuracy: 0.5619047619047619\n",
            "Precision: 0.5844155844155844\n",
            "Recall: 0.42857142857142855\n",
            "----------\n",
            "Accuracy: 0.6890243902439024\n",
            "Precision: 0.7066666666666667\n",
            "Recall: 0.6463414634146342\n",
            "----------\n",
            "Accuracy: 0.7073170731707317\n",
            "Precision: 0.7428571428571429\n",
            "Recall: 0.6341463414634146\n",
            "----------\n",
            "Accuracy: 0.7219251336898396\n",
            "Precision: 0.7333333333333333\n",
            "Recall: 0.7021276595744681\n",
            "----------\n",
            "Accuracy: 0.7136150234741784\n",
            "Precision: 0.7346938775510204\n",
            "Recall: 0.6728971962616822\n",
            "----------\n",
            "Accuracy: 0.7831858407079646\n",
            "Precision: 0.8137254901960784\n",
            "Recall: 0.7345132743362832\n",
            "----------\n",
            "Accuracy: 0.825\n",
            "Precision: 0.8421052631578947\n",
            "Recall: 0.8\n",
            "----------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the performance of the model, with no optimization - REDUNDANT\n",
        "\n",
        "accuracy1 = accuracy_score(y_test1, y_pred1n)\n",
        "precision1 = precision_score(y_test1, y_pred1n)\n",
        "recall1 = recall_score(y_test1, y_pred1n)\n",
        "\n",
        "accuracy2 = accuracy_score(y_test2, y_pred2n)\n",
        "precision2 = precision_score(y_test2, y_pred2n)\n",
        "recall2 = recall_score(y_test2, y_pred2n)\n",
        "\n",
        "accuracy3 = accuracy_score(y_test3, y_pred3n)\n",
        "precision3 = precision_score(y_test3, y_pred3n)\n",
        "recall3 = recall_score(y_test3, y_pred3n)\n",
        "\n",
        "accuracy4 = accuracy_score(y_test4, y_pred4n)\n",
        "precision4 = precision_score(y_test4, y_pred4n)\n",
        "recall4 = recall_score(y_test4, y_pred4n)\n",
        "\n",
        "accuracy5 = accuracy_score(y_test5, y_pred5n)\n",
        "precision5 = precision_score(y_test5, y_pred5n)\n",
        "recall5 = recall_score(y_test5, y_pred5n)\n",
        "\n",
        "accuracy6 = accuracy_score(y_test6, y_pred6n)\n",
        "precision6 = precision_score(y_test6, y_pred6n)\n",
        "recall6 = recall_score(y_test6, y_pred6n)\n",
        "\n",
        "accuracy7 = accuracy_score(y_test7, y_pred7n)\n",
        "precision7 = precision_score(y_test7, y_pred7n)\n",
        "recall7 = recall_score(y_test7, y_pred7n)\n",
        "\n",
        "accuracy8 = accuracy_score(y_test8, y_pred8n)\n",
        "precision8 = precision_score(y_test8, y_pred8n)\n",
        "recall8 = recall_score(y_test8, y_pred8n)\n",
        "\n",
        "accuracy9 = accuracy_score(y_test9, y_pred9n)\n",
        "precision9 = precision_score(y_test9, y_pred9n)\n",
        "recall9 = recall_score(y_test9, y_pred9n)\n",
        "\n",
        "accuracy10 = accuracy_score(y_test10, y_pred10n)\n",
        "precision10 = precision_score(y_test10, y_pred10n)\n",
        "recall10 = recall_score(y_test10, y_pred10n)\n",
        "\n",
        "x_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "accuracies = [accuracy1, accuracy2, accuracy3, accuracy4, accuracy5, accuracy6, accuracy7, accuracy8, accuracy9, accuracy10]\n",
        "precisions = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
        "recalls = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"Accuracy:\", accuracies[i])\n",
        "  print(\"Precision:\", precisions[i])\n",
        "  print(\"Recall:\", recalls[i])\n",
        "  print(\"----------\")\n",
        "\n",
        "plt.plot(x_values, accuracies, label = \"Accuracy\")\n",
        "plt.plot(x_values, precisions, label = \"Precision\")\n",
        "plt.plot(x_values, recalls, label = \"Recall\")\n",
        "plt.legend()\n",
        "plt.xlabel(\"Binary Cutoff\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.title(\"Results with No Optimization\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "32lzf6PGuMnO",
        "outputId": "01d6b6f1-fe91-4530-d397-c7766fca08e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.642023346303502\n",
            "Precision: 0.648\n",
            "Recall: 0.627906976744186\n",
            "----------\n",
            "Accuracy: 0.64453125\n",
            "Precision: 0.635036496350365\n",
            "Recall: 0.6796875\n",
            "----------\n",
            "Accuracy: 0.6217391304347826\n",
            "Precision: 0.6458333333333334\n",
            "Recall: 0.5391304347826087\n",
            "----------\n",
            "Accuracy: 0.5428571428571428\n",
            "Precision: 0.5463917525773195\n",
            "Recall: 0.5047619047619047\n",
            "----------\n",
            "Accuracy: 0.7378048780487805\n",
            "Precision: 0.8\n",
            "Recall: 0.6341463414634146\n",
            "----------\n",
            "Accuracy: 0.6890243902439024\n",
            "Precision: 0.7246376811594203\n",
            "Recall: 0.6097560975609756\n",
            "----------\n",
            "Accuracy: 0.7112299465240641\n",
            "Precision: 0.7857142857142857\n",
            "Recall: 0.5851063829787234\n",
            "----------\n",
            "Accuracy: 0.7183098591549296\n",
            "Precision: 0.7326732673267327\n",
            "Recall: 0.6915887850467289\n",
            "----------\n",
            "Accuracy: 0.7433628318584071\n",
            "Precision: 0.7722772277227723\n",
            "Recall: 0.6902654867256637\n",
            "----------\n",
            "Accuracy: 0.8083333333333333\n",
            "Precision: 0.8490566037735849\n",
            "Recall: 0.75\n",
            "----------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizations for logistic regression classifier\n",
        "\n",
        "X_train5, X_test5, y_train5, y_test5 = train_test_split(X_over5, y_over5, test_size=0.2, random_state = 1, stratify = y_over5)\n",
        "\n",
        "lr = LogisticRegression(max_iter=3000)\n",
        "cv = KFold(n_splits=5, random_state=1, shuffle=True)\n",
        "\n",
        "lr.fit(X_train5, y_train5)\n",
        "y_pred5 = cross_val_predict(lr, X_test5, y_test5, cv=cv)\n",
        "accuracy = accuracy_score(y_test5, y_pred5)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "\n",
        "cm = confusion_matrix(y_test5, y_pred5)\n",
        "ConfusionMatrixDisplay(confusion_matrix=cm).plot()\n",
        "plt.show()\n",
        "\n",
        "feature_importances = pd.Series(lr.coef_[0], index=X_train5.columns).sort_values(ascending=False)\n",
        "feature_importances.plot.bar()\n",
        "plt.show()\n",
        "\n",
        "space = dict()\n",
        "# space['solver'] = ['newton-cg', 'lbfgs', 'liblinear']\n",
        "# space['penalty'] = ['none', 'l1', 'l2', 'elasticnet']\n",
        "# space['C'] = loguniform(1e-5, 100)\n",
        "\n",
        "space['solver'] = ['newton-cg', 'lbfgs', 'liblinear']\n",
        "space['penalty'] = ['l2']\n",
        "space['C'] = [100, 10, 1.0, 0.1, 0.01]\n",
        "\n",
        "#Create a based model\n",
        "#Instantiate the grid search model\n",
        "rand_search = RandomizedSearchCV(lr, space, \n",
        "                                 n_iter=15, \n",
        "                                 cv=5)\n",
        "\n",
        "# Fit the random search object to the data\n",
        "rand_search.fit(X_train5, y_train5)\n",
        "best_lr = rand_search.best_estimator_\n",
        "# Fit the random search object to the data\n",
        "y_pred5 = best_lr.predict(X_test5)\n",
        "accuracy = accuracy_score(y_test5, y_pred5)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "\n",
        "cm = confusion_matrix(y_test5, y_pred5)\n",
        "ConfusionMatrixDisplay(confusion_matrix=cm).plot()\n",
        "plt.show()\n",
        "\n",
        "feature_importances = pd.Series(lr.coef_[0], index=X_train5.columns).sort_values(ascending=False)\n",
        "feature_importances.plot.bar()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7TB0SkevCJFJ",
        "outputId": "62f6f456-ca0d-4435-bf3c-a8d147e058b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6646341463414634\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7073170731707317\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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AAIxHYQEAAMa7rsIyadIkBQcHy8fHRyEhIdq0aVO2x3/66aeqWbOmfHx8dMcdd2jJkiUZ9vfu3VsOhyPDo23bttcTDQAAFEC5Lizz5s1TRESEhg4dqi1btqh+/foKCwvTsWPHMj1+3bp1Cg8PV79+/bR161Z17NhRHTt21I8//pjhuLZt2+ro0aOux5w5c67vOwIAAAWOw7IsKzdfEBISoiZNmmjixImSpPT0dJUvX14DBw5UZGTkVcd369ZNKSkp+uKLL1zbmjVrpgYNGmjy5MmSLo2wJCQkaOHChTnKcP78eZ0/f971PCkpSeXLl1diYqL8/f1z8+1cJTjyyxv6+pvtYHR7uyMAAJAnkpKSFBAQkKPf37kaYUlNTdXmzZsVGhr61ws4nQoNDdX69esz/Zr169dnOF6SwsLCrjp+1apVuv3221WjRg0988wzOnnyZJY5Ro8erYCAANejfPnyufk2AACAm8lVYTlx4oQuXryo0qVLZ9heunRpxcXFZfo1cXFx1zy+bdu2mjVrlpYvX64333xT3377rdq1a6eLFy9m+pqvvvqqEhMTXY/Dhw/n5tsAAABuppDdASSpe/furn/fcccdqlevnqpUqaJVq1apdevWVx3v7e0tb2/v/IwIAABslKsRllKlSsnLy0vx8fEZtsfHxyswMDDTrwkMDMzV8ZJUuXJllSpVSvv3789NPAAAUEDlqrAUKVJEjRo10vLly13b0tPTtXz5cjVv3jzTr2nevHmG4yVp6dKlWR4vSb///rtOnjypMmXK5CYeAAAooHJ9WXNERISmTJmimTNn6qefftIzzzyjlJQU9enTR5LUs2dPvfrqq67jn3/+ecXGxurtt9/Wzz//rNdff10//PCD/vWvf0mSTp8+rZdfflkbNmzQwYMHtXz5cj3yyCOqWrWqwsLCbtK3CQAA3Fmu57B069ZNx48fV1RUlOLi4tSgQQPFxsa6JtYeOnRITudfPeiuu+7Sxx9/rNdee02DBw9WtWrVtHDhQtWtW1eS5OXlpR07dmjmzJlKSEhQUFCQHnjgAY0YMYJ5KgAAQNJ13IfFRLm5jvtauA8LAAD5I8/uwwIAAGAHCgsAADAehQUAABiPwgIAAIxHYQEAAMajsAAAAONRWAAAgPEoLAAAwHgUFgAAYDwKCwAAMF6u1xKC5zJp2QKWLAAAz8IICwAAMB6FBQAAGI/CAgAAjEdhAQAAxqOwAAAA41FYAACA8SgsAADAeBQWAABgPAoLAAAwHoUFAAAYj8ICAACMR2EBAADGo7AAAADjUVgAAIDxKCwAAMB4FBYAAGA8CgsAADAehQUAABiPwgIAAIxHYQEAAMajsAAAAONRWAAAgPEoLAAAwHgUFgAAYDwKCwAAMB6FBQAAGI/CAgAAjEdhAQAAxqOwAAAA4xWyOwBQEARHfml3BJeD0e3tjgAANx0jLAAAwHgUFgAAYDwKCwAAMB6FBQAAGI/CAgAAjEdhAQAAxqOwAAAA41FYAACA8SgsAADAeBQWAABgPAoLAAAwHoUFAAAYj8ICAACMR2EBAADGo7AAAADjUVgAAIDxKCwAAMB4hewOAKBgC4780u4ILgej29sdAcB1uq4RlkmTJik4OFg+Pj4KCQnRpk2bsj3+008/Vc2aNeXj46M77rhDS5YsybDfsixFRUWpTJkyKlq0qEJDQ7Vv377riQYAAAqgXBeWefPmKSIiQkOHDtWWLVtUv359hYWF6dixY5kev27dOoWHh6tfv37aunWrOnbsqI4dO+rHH390HTNmzBhNmDBBkydP1saNG1W8eHGFhYXp3Llz1/+dAQCAAiPXhWXcuHF66qmn1KdPH9WuXVuTJ09WsWLFFBMTk+nx7777rtq2bauXX35ZtWrV0ogRI3TnnXdq4sSJki6NrowfP16vvfaaHnnkEdWrV0+zZs3SkSNHtHDhwkxf8/z580pKSsrwAAAABVeu5rCkpqZq8+bNevXVV13bnE6nQkNDtX79+ky/Zv369YqIiMiwLSwszFVGDhw4oLi4OIWGhrr2BwQEKCQkROvXr1f37t2ves3Ro0dr2LBhuYmeY5zjzhrvTdZ4b7LGe4PcMmnek2TWZ9iT35tcjbCcOHFCFy9eVOnSpTNsL126tOLi4jL9mri4uGyPv/y/uXnNV199VYmJia7H4cOHc/NtAAAAN+OWVwl5e3vL29vb7hgAACCf5GqEpVSpUvLy8lJ8fHyG7fHx8QoMDMz0awIDA7M9/vL/5uY1AQCAZ8lVYSlSpIgaNWqk5cuXu7alp6dr+fLlat68eaZf07x58wzHS9LSpUtdx1eqVEmBgYEZjklKStLGjRuzfE0AAOBZcn1KKCIiQr169VLjxo3VtGlTjR8/XikpKerTp48kqWfPnipbtqxGjx4tSXr++efVsmVLvf3222rfvr3mzp2rH374QR9++KEkyeFw6IUXXtAbb7yhatWqqVKlShoyZIiCgoLUsWPHm/edAgAAt5XrwtKtWzcdP35cUVFRiouLU4MGDRQbG+uaNHvo0CE5nX8N3Nx11136+OOP9dprr2nw4MGqVq2aFi5cqLp167qOeeWVV5SSkqIBAwYoISFB99xzj2JjY+Xj43MTvkUAAODuHJZlWXaHuFFJSUkKCAhQYmKi/P397Y4DALgBnnzp7rUUtPcmN7+/WfwQAAAYj8ICAACMR2EBAADGo7AAAADjUVgAAIDxKCwAAMB4FBYAAGA8CgsAADAehQUAABiPwgIAAIxHYQEAAMbL9eKHAADkJZPW7oE5GGEBAADGo7AAAADjUVgAAIDxKCwAAMB4FBYAAGA8CgsAADAelzUDAOAmPPmSb0ZYAACA8SgsAADAeBQWAABgPAoLAAAwHoUFAAAYj8ICAACMR2EBAADGo7AAAADjUVgAAIDxKCwAAMB4FBYAAGA8CgsAADAehQUAABiPwgIAAIxHYQEAAMYrZHeAm8GyLElSUlKSzUkAAEBOXf69ffn3eHYKRGFJTk6WJJUvX97mJAAAILeSk5MVEBCQ7TEOKye1xnDp6ek6cuSI/Pz85HA47I6jpKQklS9fXocPH5a/v7/dcYzCe5M13pvM8b5kjfcma7w3WTPpvbEsS8nJyQoKCpLTmf0slQIxwuJ0OlWuXDm7Y1zF39/f9g+DqXhvssZ7kznel6zx3mSN9yZrprw31xpZuYxJtwAAwHgUFgAAYDwKSx7w9vbW0KFD5e3tbXcU4/DeZI33JnO8L1njvcka703W3PW9KRCTbgEAQMHGCAsAADAehQUAABiPwgIAAIxHYQEAAMajsAAAAONRWAAAgPEoLDfgwQcfVGJiout5dHS0EhISXM9Pnjyp2rVr25AMcF9eXl46duyY3TGMlJKSoqioKNWtW1e+vr7y8/NTvXr1NHz4cJ05c8bueECe4j4sN8DLy0tHjx7V7bffLunSugzbtm1T5cqVJUnx8fEKCgrSxYsX7Yxpm4oVK+r+++9Xq1at1KpVK1bT/psHH3xQc+bMca2hER0draefflq33HKLpEtlt0WLFtq9e7eNKe3hdDoVFxfn+v8VLklNTdVdd92lH3/8Ue3atVPNmjVlWZZ++uknxcbG6s4779Tq1atVuHBhu6Pa4tChQzk6rkKFCnmcxDxJSUmuNYOWLFmitLQ01z4vLy+1b9/ermi5UiAWP7TLlV2P7pdRnz59tGrVKs2dO1epqamqVKmSWrVq5SoxgYGBdke0zddff63z58+7no8aNUpdu3Z1FZa0tDTt2bPHpnQw0X/+8x/9/vvv2r59u2rUqJFh388//6z77rtPkydP1sCBA21KaK/g4GA5HI6rtluW5drucDgy/LL2BF988YWGDBmirVu3SpK6deumlJQU136Hw6F58+apS5cudkXMMQoL8szrr78uSTp//rzWrl2rb7/9VqtWrdJHH32kCxcuqHr16rr//vs1adIke4PagLKbvalTp8rX1zfbY5577rl8SmOGBQsWaMiQIVeVFUmqWbOm/u///k+fffaZxxaWy7+Qr2RZlubOnasJEyZc8zNVEH344YdXfSb279/vOhMwZswYxcTEuEVh4ZTQDfDy8lJcXJxuu+02SZKfn5927NihSpUqSeKUUFZOnTqlt99+W++9955Onz7tke/Plac9/Pz8tH37dk4n6tJ7U65cOXl5eWV5jMPh0K+//pqPqex32223adWqVapTp06m+3/88Ue1atVKx48fz+dk5lq2bJkiIyO1d+9eRURE6MUXX5Sfn5/dsfJVpUqVFBsb6yq6V/6s2blzp1q3bu0W88YYYbkBlmWpd+/ergWkzp07p6efflrFixeXpAxD/p4sNTVV69ev16pVq7Rq1Spt3LhRZcuWVZcuXdSyZUu749nC4XBcNXyd2XC2p/rhhx+Yw3KFhIQE3XrrrVnuv/XWWzNcBODJtmzZokGDBmnNmjXq37+/lixZ4rGfp6NHj2ZY5HDlypUZ5hP6+vq6zeeGwnIDevXqleH5E088cdUxPXv2zK84xhk+fLiroFSsWFH33nuvBgwYoP/+978KCgqyO56tKLtZo7hlLj09PdtRJ6fT6ZEjcn/3yy+/aPDgwZo/f766du2q3bt3u0YSPFXJkiW1f/9+BQcHS5IaN26cYf++fftUsmRJG5LlHqeEkGecTqcqVKigyMhIPfbYY9n+dehp+vTpk6Pjpk+fnsdJzMNVQplzOp2qW7euChXK/O/MtLQ07dq1y2NLy7PPPqtp06apVatWio6OVoMGDeyOZITu3bvrzJkzWrRoUab7H3roIRUvXlzz5s3L52S5R2FBnvn666+1cuVKrVq1Slu3blX16tV13333qWXLlmrZsqVr7g/wd8OGDdPLL7+sYsWK2R3FKMOGDcvRcUOHDs3jJGZyOp3y8fFRzZo1sz1uy5Yt+ZTIDFu3blXz5s318MMP65VXXlH16tUlSXv27NGbb76pL7/8UuvWrdOdd95pc9Jro7DcgKNHj2rixIkaOXKkJOmee+7JcPMmLy8vLVy4UGXLlrUrojGSk5O1Zs0affvtt1q5cqW2b9+uqlWrqlWrVpo4caLd8Yz02WefucXM/ZvtxIkTSklJUcWKFV3bdu3apbFjxyolJUUdO3bU448/bmNCmIhCl7X//e9/6t+/v/78888M20uUKKGpU6eqY8eO9gTLJQrLDRgyZIhOnjyp999/X9Kl2dd9+/Z1nQ/86quvdM8992js2LF2xjTKxYsXtWnTJi1atEjvv/++x14lJF0awv/5559VpEgR11890qUfLlFRUfr55589ci5LeHi4goKC9Pbbb0uSjh07ppo1ayooKEhVqlTRV199pWnTpunJJ5+0OWn+OnbsWLanydLS0rRlyxY1bdo0H1PBXZw5c0Zff/219u3bJ0mqVq2aHnjgAde8Obdg4bo1aNDAWr16teu5r6+v9csvv7iex8bGWrVr17YjmjEuXrxobdy40YqOjrbatm1r+fn5WU6n06pQoYLVq1cva8aMGXZHtMXOnTutihUrWk6n03I6ndajjz5qxcXFWffee69VsmRJa9CgQdbhw4ftjmmL4OBga9WqVa7nb731llWlShXrwoULruchISF2xbON0+m04uPjXc/r1q1rHTp0yPU8Li7OcjqddkQzzvbt261PP/3U+vTTT63t27fbHQc3CVcJ3YCDBw+67rkiSW3atMnQVmvUqKEDBw7YEc0I7dq107p165ScnKygoCC1atVK77zzjlq1auXxM/cHDRqkqlWrauLEiZozZ47mzJmjn376Sf369VNsbKyKFi1qd0TbxMXFua5okKQVK1aoU6dOrsmmHTp00OjRo21KZx/risHwgwcP6sKFC9ke42k2bdqkfv36affu3a73wuFwqE6dOpo2bZqaNGlic8L8N2vWrBwd5w5XtFJYbsCFCxd0/PhxlStXTtKlO1H+3alTp+R0eu76krfccoveeusttWrVStWqVbM7jlG+//57ffPNN2rQoIFatGihOXPmaPDgwR53miMz/v7+SkhIcM1hufxL6DKHw+GRp8pywpMvCd+9e7dat26tWrVqafbs2apVq5Zr+zvvvKPWrVtrw4YNHrcg7fPPP5/lPofDoZSUFKWlpVFYCroaNWpo3bp1atiwYab716xZk2FugqeZM2eO3RGMdeLECde9aAICAlS8eHE1a9bM5lRmaNasmSZMmKApU6ZowYIFSk5O1v333+/av3fvXhbSxFVef/11tWnTRvPnz89Q3Bo0aKDw8HB16tRJr7/+uj755BMbU+a/U6dOZbr96NGjGjZsmGJiYtSmTZt8TnV9PPfP/5uge/fuioqK0o4dO67at337dg0fPlzh4eE2JDPDgw8+mOEOitHR0UpISHA9P3nypMf9tXOZw+FQcnKykpKSlJiYKIfDobNnzyopKSnDwxONGDFCixYtUtGiRdWtWze98sorKlGihGv/3LlzPfIOyZl9Zk6fPu3xn5fLVq5cqcGDB2c6yuRwODR48GCtXLnShmRmSU5O1muvvabq1atr27Zt+vrrrxUbG2t3rBzhKqEbcOHCBYWGhmrdunVq06aNa62GPXv2aOnSpWrWrJlWrFjhscu9X3kDMH9/f23bto31cnTpvfn7D1brbyvK/v25J7430qURqLVr1yowMFAhISEZ9n355ZeqXbt2hvljnoDPTPZ8fHy0b9++LEffDh8+rGrVquncuXP5nMwMFy5c0HvvvadRo0bp1ltv1ciRI93utgmcEroBhQsX1tKlSzVu3DjNnTtXq1atknTpcrERI0YoIiJCe/bsUd26de0Nagi68V/4Sy97pUqV0iOPPJLpvvbt2+dzGjPwmclexYoVtWnTpiwLy+UlQjyNZVmaNWuWoqKilJaWplGjRqlfv37ZLvNgLJuuTirQEhMTrQ8++MBq2rSpR19m6HA4MlyGeeVl31yGicy0a9fOSkhIcD0fPXq0derUKdfzEydOWLVq1bIhmb1OnjxpdwSjRUVFWRUqVLB27tx51b4dO3ZYFStWtIYMGWJDMnvVrVvXKlasmDVo0CDr6NGjVmJiYqYPd8ApoZto9erVmjZtmubPn6+goCB16tRJnTt39shL6aRLd/qNi4tz3YLfz89PO3bscA3le/IpoczmPUmXJuBWqFDBo6/28PLy0tGjRzmVeAUfHx917NhR/fr1c5tJkvnp3Llzat26tTZu3Kg2bdqoVq1asixLP/30k5YtW6amTZtqxYoV8vHxsTtqvvr7laqZ/Vyx3OhUIqeEblBcXJxmzJihadOmKSkpSV27dtX58+e1cOFCj51QepnFisRZatCggRwOx1WnyRwOh3x8fPTCCy9o+PDh7jlse4OufE/4m+qSKVOmaMaMGWrbtq3Kly+v3r17q3fv3hnuWePJfHx8tHLlSr3zzjuaM2eOvv32W0lS9erV9cYbb+jf//6362eRJylIpxIZYbkBDz/8sFavXq327durR48eatu2rby8vFS4cGFt377d4wsLKxJn7bfffst0e0JCgjZv3qwhQ4bo3//+t1566aV8Tma/Kydr+/n5afv27R4/wnLZgQMHNGPGDM2aNUuHDx9Wq1at1L9/fz366KMqUqSI3fGAPENhuQGFChXSc889p2eeeSbDjdEoLLhRn332mYYNG6adO3faHSXfcSox55YtW6bp06dr4cKF8vHxUY8ePTRhwgS7Y8EgOb3c3d/fP4+T3DgKyw3YsGGDpk2bpnnz5qlWrVp68skn1b17d5UpU4bCghty4MAB3XHHHTp9+rTdUfKd0+lUu3btXMP3ixcv1v3335/hVGJsbCyF5W/mz5+vAQMGKCEhwWPflxIlSuRo7teVKxYXdFdeDn8l5rB4iGbNmqlZs2YaP3685s2bp5iYGEVERCg9PV1Lly5V+fLl5efnZ3dM23Tq1ClHx125pAGUYYTB0/Ts2TPDD9gnnngi02M83W+//abp06dr5syZrlNDf1/CwNOMHz/e7ghGYg4LsrRnzx5NmzZNH330kRISEtSmTRstWrTI7li26N27d47+4vHEOSzZOX78uMLDw1WhQgXFxMTYHQcGOX/+vObPn6+YmBitWrVKZcuWVe/evdWnTx+Pn3y7evVq3XXXXa5FMlHwUFjyyMWLF7V48WLFxMR4bGFB1ho2bJhpmUtMTNTvv/+uGjVq6JtvvlFgYKAN6ex15WXNuOTZZ5/V3LlzdebMGT3yyCOuy5s9+RL4v+NzU/BRWJBnunTpov79+yssLIwfqlcYNmxYptv9/f1Vo0YNhYWFeeQlzdLVVwnhknr16qlfv3564okndOutt9odxzh8bjKX058jzGGBRzt16pTat2+voKAg9enTR71793Zdmurphg4dancEuJmsbjaIv/CH0dUsy1LFihXVq1cvNWzY0O44N4QRFuSpyxMDZ82apd9++00tW7ZU//791blzZ4+8iVN2zp07p3nz5iklJUVt2rTJcKm8J3E6nXrjjTfk6+ub7XHPPfdcPiUyQ0RERI6OGzduXB4nMdOVV5dlxdMm+f/www+aNm2a5s6dq0qVKqlv377q0aNHhhXQ3QWFBflmxYoViomJ0eeffy5vb2+Fh4erb9++atSokd3R8l1ERIRr9VRJSk1NVdOmTbV7924VK1ZMaWlpWrp0qZo3b25z0vzndDpVrly5bIeyHQ6Hfv3113xMZb9WrVpd8xiHw6EVK1bkQxrzOJ1Ode3aVUWLFs32OE+d5H/u3Dl99tlnmj59ujZs2KCHH37Y7ZZ5oLAg3yUnJ+vjjz/W4MGDlZiYqLS0NLsj5bu6detq1KhR6tChg6RLP0RffPFFbd26VRUqVFDfvn117NgxffnllzYnzX/MRcD14HOTcwcOHFC/fv307bff6vjx4ypZsqTdkXKEOSzIV5dvKz5jxgwlJiYqNDTU7ki2OHToUIYbC37zzTfq0qWLKlasKEl6/vnn9eCDD9oVz1bMQ8D14HNzbb///rvr5++ZM2f08ssvu8Udbi9zXvsQ4MacO3dOs2fP1v33369q1app1qxZ6tevnw4cOKDY2Fi749nC6XRmWNRvw4YNatasmev5LbfcolOnTtkRzXYM+maudu3aGe7S+uyzz+rEiROu58eOHVOxYsXsiGaEa31ufvrpJ49cmys1NVXz5s3TAw88oGrVqmnLli0aP368Dh8+rOjoaLe6bw2FBXlm06ZNevrpp1WmTBk99dRTCgwMVGxsrH799VdFRUWpfPnydke0Ta1atbR48WJJ0q5du3To0KEMcxR+++03lS5d2q54tho6dOg1J9x6op9//jnD6dPZs2dnWCfGsiydO3fOjmhGWLly5VWnNlJSUjRt2jTdddddqlOnjkf+gVSmTBkNGjRIzZs3186dOzVjxgzde++9SklJUVJSkuvhDpjDgjzjdDpVv3599evXz21npeeVzz//XN27d9c999yjXbt2qUmTJq4CI0mDBg3SgQMH9Mknn9iYEiZhFeucW7t2raZNm6ZPPvlEZ8+e1b///W/1799fNWvWtDtavnM6/xqXyOy0GWsJAbp0Od2dd96Z5f6jR49q5MiRmjhxYj6mMsOjjz6qJUuW6IsvvtADDzyggQMHZthfrFgxPfvsszalM1utWrW0d+9et/gBi/xz7NgxzZgxQzExMUpMTFR4eLhWrVql5s2bq2/fvh5ZViTWEgJybNeuXVq5cqWKFCmirl276pZbbtGJEyc0cuRITZ48WZUrV9auXbvsjgk3snDhQiUmJqpXr152R8lXXl5eGRbF9PPz044dO1SpUiVJjLAULVpUXbp00RNPPKE2bdq4RhYKFy6s7du3Z5jkjoz+/PNPt7hSiBEW5JlFixapS5curvPuY8aM0ZQpU9S1a1c1atRIn3/+udq2bWtzSnt9//33mjNnjvbu3StJql69uh5//HE1btzY5mTm6tixo90RbGFZllq3bu2aJHn27Fk9/PDDKlKkiCR55O0B/q5ixYr67rvvVKFCBVWsWNFjR1Ry45tvvtHUqVO1ePFinT171u4418QIC/JM06ZNdffdd2vEiBGaOnWqIiIiVKdOHcXExKhJkyZ2x7PdK6+8orFjx8rX19c1D+GXX37RmTNn9NJLL+nNN9+0OaE9jh07lu29NNLS0rRlyxY1bdo0H1PZL6v1p67kycs+XJ678umnn6p69ep64okn9Morr2jHjh2qVauW3fGM8NtvvykmJkYzZ87UqVOn1K5dO3Xu3FmPPfaY3dGuzQLyiL+/v7Vv3z7LsiwrLS3N8vLyspYuXWpzKjPMmDHD8vHxsd577z0rNTXVtT01NdV69913LR8fH2vmzJk2JrSP0+m04uPjXc/r1q1rHTp0yPU8Li7OcjqddkSDm0hOTrY+/PBDq3nz5pbD4bDuu+8+68MPP7SOHTtmdzRbnD9/3pozZ47VunVry8fHx3rooYcsLy8va8eOHXZHyxUua0aeSU5Odt2UyMvLS0WLFmXxw/9v0qRJGjVqlP71r3+pcOHCru2FCxfWc88957GTkaWr76dx8OBBXbhwIdtjPMG5c+e0aNEiJScnX7UvKSlJixYt0vnz521IZh5fX1899dRTWrdunXbt2qVGjRrptddeU1BQkN3R8t3AgQMVFBSkd999V48++qh+//13LV68WA6Hw+1WhOeUEPKM0+nUzJkzFRAQIEkKDw/X+PHjr7q/yOXb03uS4sWLa+fOnVkWuF9//VV33HGHUlJS8jmZ/bh8N3PvvvuuFi1apOXLl2e6PzQ0VB07dtS//vWvfE7mHtLS0rRo0SJ16tTJ7ij5qlChQho0aJAiIyPl5+fn2u6Ok5GZdIs8deWVHP/4xz8yPHeX6/9vNi8vL6Wmpma5/8KFC2731w/y1n//+18NGTIky/0vvPCChg8f7rGF5ciRIxo3bpyioqKuut18YmKi3njjDY+80+1HH32kmJgYlSlTRu3bt9eTTz6pdu3a2R3runBKCHkmPT39mg9PLCuSdOedd+q///1vlvs/+uijbO9hU5A5HA4lJycrKSlJiYmJcjgcOn36tNvdlfNm27dvn+rXr5/l/nr16mnfvn35mMgs48aNU1JSUqZr4wQEBCg5OVnjxo2zIZm9wsPDtXTpUu3cuVM1a9bUP//5TwUGBio9PV27d++2O17u2DuFBvBMixcvtry8vKyXX37ZiouLc20/evSo9dJLL1mFChWyFi9ebGNC+zgcDsvpdLoeWT33NL6+vtYPP/yQ5f4ffvjB8vX1zcdEZqlTp461Zs2aLPevXbvWql27dj4mMlN6eroVGxtrPfbYY5a3t7dVtmxZa+DAgXbHyhFOCSHPPPvssxozZoxrXZg5c+aoQ4cOKl68uCQpISFBjz/+uJYsWWJnTFs89NBDeuedd/TSSy/p7bffds3zSUxMVKFChTR27Fg99NBDNqe0R0G6M+fNVKdOHS1btkyNGjXKdP8333yjOnXq5HMqcxw4cEAVKlTIcn+5cuV08ODB/AtkKIfDobCwMIWFhenPP//UrFmzNH36dLtj5QiTbpFnvLy8dPToUdfkSX9/f23bts3jJ0/+3eHDh/XZZ5+5hvKrV6+uzp07e/TCkNdy5swZbdu2TXfddZfdUfLVhx9+qIiICM2dO/eqMrt48WKFh4dr3LhxGjBggE0J7VWqVCktWLBA9957b6b7V69erU6dOmVY4RruhREW5JkruzDd+Gq33367BgwY4Bp1wrXt27dPLVq08LiiO2DAAK1evVodOnRQzZo1VaNGDUmXVnHeu3evunbt6rFlRZJCQkL00UcfZVlYZs2a5XE3G5Sk4cOHX/MYh8OR7YRuU1BYABscP35cPXv21LJly5Senq4mTZpo9uzZqlq1qt3RYLDZs2erQ4cO+vjjj7V3715ZlqUaNWpo2LBh6tq1q93xbPXSSy+pTZs2CggI0Msvv+y6fUJ8fLzGjBmjGTNm6JtvvrE5Zf77/PPPs9zncDi0Z88enTt3zi0KC6eEkGe4n0bW+vbtq6+++krPPfecfHx89MEHH6hMmTLM38iB7du368477/TIz012zp07p4kTJ3rkpbuXffDBB3r++ed14cIF+fv7y+FwKDExUYULF9Y777yjZ555xu6Ixti2bZsiIyO1YsUK9e3bV5MnT7Y70jUxwoI8FRUVpWLFikmSUlNTNXLkSNcE0zNnztgZzVZLly7VjBkzFBYWJunSJNxatWrp/Pnz8vb2tjkdTHX8+HFt3LhRRYoUUevWreXl5aULFy7o/fff1+jRo5WWlubRheUf//iHHnroIX3yySfav3+/LMtS9erV1aVLF5UrV87ueEY4cOCAhgwZonnz5qlTp07atWuXqlWrZnesHGGEBXnmvvvuk8PhuOZxnjiq4OXlpT/++EOBgYGubcWLF9euXbsUHBxsXzADLFq0KNv9Bw4cUEREhMeNsHz33Xd66KGHlJSUJIfDocaNG2v69Onq2LGjChUqpOeee069evVS0aJF7Y4KA504cULDhg3Thx9+qHvuuUfR0dFutwgthQWwgZeXl+Li4nTbbbe5tvn7+2v79u2qVKmSjcns53Tm7H6W6enpeZzELPfdd5+CgoI0ePBgzZw5U2+//baqVaumkSNHqkuXLnbHs92ECRMy3R4QEKDq1aurefPm+ZzIDCkpKRo7dqzGjRunqlWravTo0XrggQfsjnVdKCzIM5UrV9b333+vW2+91e4oxnE6nQoICMgwApWQkCB/f/8Mv7D//PNPO+LBQLfeeqvWrFmj2rVr6+zZs/L19dWCBQv0yCOP2B3NCFkV/YSEBCUmJuquu+7SokWLVLJkyXxOZq/AwEAlJydr4MCBCg8Pz3LUu169evmcLPcoLMgzV066xV9mzpyZo+OuXIsJl0ZWlixZ4nE31stsEvu2bdtUpUoVm5OZ79dff9UTTzyhBg0a6P3337c7Tr76+x9ADocjw+0lLj93lzXdmHQL2IAiknv79+9XTEyMZsyYoePHj+vChQt2R8p3u3fvVlxcnKRL9zXas2fPVSt6u8NfyvmtcuXKio6OVt++fe2Oku8OHDhgd4SbhhEW5Bmn06mZM2e6rgrKSocOHfIpkTmWLVum0NDQLPenp6dr1KhReu211/IxlXnOnj2rTz/9VFOnTtXatWvVokULde/eXY8++qjrPhuewul0XvUX8mXu9peyHQ4ePKi6devq9OnTdkfBdaKwIM/kZPKkp/6ALVKkiAYMGKAxY8a4Lvu+7Mcff1SvXr0UFxenP/74w6aE9vr+++81depUzZ07V1WqVFGPHj00aNAg7dixQ7Vr17Y7ni1+++23HB1XsWLFPE7inhYvXqzIyEjt2rXL7ii4TpwSQp5iDkvm1qxZo969e6t+/fqaMWOG7r77bteoyogRI9S5c2ctW7bM7pi2qFevnpKSkvT4449r3bp1rgX9IiMjbU5mL4pI9pKSkjLdnpiYqM2bN+vFF1/kVGwmatWqpb1797rFH44UFuSZnNyDxVOFhIRo69atioyMVKtWrTRgwABt2LBBhw8f1pw5c9SpUye7I9pmz5496tatm1q1auWxoymZGTNmjAYOHOi6z8ratWvVuHFj140Gk5OTNWjQII+bVHrZLbfckuXPHIfDof79+3t86c3M6NGjlZiYaHeMHOGUEPIMVwldm2VZ6tGjh+bOnavixYvrhx9+cC1q56n++OMPzZgxQ9OnT9fZs2cVHh6uHj16KCQkRNu2bfPYEsPq59n79ttvM93u7++vatWqydfXN58T4WbL2R2agOvAXTez98svv+jee+/VihUrNHnyZNWtW1f33Xef/ve//9kdzVZly5bV//3f/2n//v366KOPFBcXp7vvvltpaWmaMWOG9u7da3dEW7D6efZatmyZ6aNhw4YeXVaOHTuW7f60tDRt2rQpn9LcGAoL8sz06dPl5+dndwwjTZw4UfXr19ftt9+unTt3asCAAVq7dq1eeOEFde/eXU8++aQSEhLsjmmL1atXKy0tTZJ0//33a/bs2Tp69KgmTpyoFStWqGbNmly6iyzt27dPY8eO1b/+9S8NHDhQ48aN06+//mp3LNuUKVMmQ2m54447dPjwYdfzkydPus9dgC0gjzgcDsvpdGb78PLysjumLUqUKGHNnj07030//vij1ahRIysoKCifU5nB6XRa8fHxWe7funWrNXDgwHxMZAaHw5HhffH19bV++eUX1/O4uDjL6XTaEc0Yo0aNsgoVKmQ5nU4rMDDQKl26tOV0Oq3ChQtbb731lt3xbJGTz43D4bAjWq4x6RZ5ZsGCBVlOglu/fr0mTJjgcevBXLZr1y6VKVMm03116tTRxo0bNWrUqHxOZQbrGqc6GjRokOW6MQXd1KlTXac3Lp8iK1WqlKRLk2492cqVK/Xaa69pyJAhev7551WiRAlJl5a3GD9+vCIjI9W0aVPde++9Nic1j7tcIMGkW+SrPXv2KDIyUosXL1aPHj00fPhwj7xc88EHH9ScOXNcN9WLjo7W008/rVtuuUXSpWHaFi1aaPfu3TamtIfT6VR8fHyGhSEhBQcH5+gXS0G6s2ludOvWTbfccos++OCDTPcPGDBAycnJmjNnTj4ns1dmSzps377dLSdrM8KCfHHkyBENHTpUM2fOVFhYmLZt26a6devaHcs2X3/9tc6fP+96PmrUKHXt2tVVWNLS0rRnzx6b0tmvd+/erst1s7JgwYJ8SmOGgwcP2h3BaJs2bdJHH32U5f4nn3xSPXv2zMdEZnA4HEpOTpaPj4/rbsinT5923bcmq/vXmIjCgjyVmJioUaNG6b333lODBg20fPlytWjRwu5YtrtyYJOBzoz8/Py4wiwTlmVp//79Sk1NVY0aNVSoED/CL4uPj1dwcHCW+ytVquRah8mTWJal6tWrZ3jesGHDDM/d5ZQQn3bkmTFjxujNN99UYGCg5syZo0ceecTuSHATEyZM4P49Vzhw4IA6dOjgOk1YtmxZzZ8/X02aNLE5mRnOnTunIkWKZLm/cOHCSk1NzcdEZli5cqXdEW4aCgvyTGRkpIoWLaqqVatq5syZmjlzZqbHedrQvnRpmPbKv2rc5a+cvMb7kLmXX35ZaWlpmj17tnx8fDR27Fg9/fTT2rx5s93RjPH3SclX8tRJyS1btrQ7wk1DYUGe6dmzJ798smBZVoZ5GufOndPTTz+t4sWLS1KG+S2ehtNjmfvuu+/02Wef6Z577pEkNWvWTOXKlVNKSorrc+PJKlSooClTplzzGGS0ZcsWRUVF6YsvvrA7yjVxlRBggz59+uTouOnTp+dxEvPMmzdPnTt3znZ+xpo1azxuLpTT6dTRo0dVunRp1zZfX1/t3LlTlSpVsjEZTPf1119r6dKlKlKkiPr376/KlSvr559/dl2xGRYWpiVLltgd85oYYUGeyckCfg6HQ/Pnz8+HNGbxxCKSUwMHDpSXl5e6dOly1b6zZ89q0KBBmjx5ssfNR7h8dcffJyM7nU4lJydnuNLD39/fjni241YBmZs2bZqeeuoplSxZUqdOndLUqVM1btw4DRw4UN26ddOPP/6oWrVq2R0zRxhhQZ5hFAHX4+2339aQIUP0yCOP6P3333fdAGzNmjXq06ePnE6npk+frrvvvtvmpPnL6XRedYr171d4XP63O9xPIy9ceb8RFoe8pF69enryySf18ssva/78+XrsscfUrFkzffLJJypXrpzd8XKFwgLAOLt371avXr30xx9/aMKECVqzZo3ef/99PfPMM3rzzTc98pLnrFYjvlJBmmSZGwXpBmk3U/HixbVr1y4FBwfLsix5e3tr5cqVbln4OSUEwDi1a9fWhg0b1KNHD3Xr1k3FihXTsmXLPPaXseS5RQQ35uzZsypWrJikS6cVvb29s1wWxHQUFgDGuXDhgoYOHaoFCxaoW7duio2N1ahRo1SlShW3G8ZG/uBWAVnLbg2qy5577jk7ouUKp4QAGGXbtm168sknlZKSomnTpqlVq1b6448/9NRTT2ndunV6++231a9fP7tjGic0NFS//vqrfv31V7uj2MLpdKpdu3auWwUsXrxY999/f4ZbBcTGxnrcKaGcrEHlcDjc4nNDYQFgFG9vb/Xq1Uvjxo276iZgU6dO1Ysvvqi7777bLS7DvJliYmLUo0ePLNdYmjRpkk6cOKGhQ4fmczIzMMm/4KOwADDKV199pXbt2mW5/7ffflP//v21dOnSfExlPy8vLx09etQ1qTQoKEjr1q3Ldv0coCChsACAG7jWVTBAZiIiIjLdHhAQoOrVq6tTp07XXBndFE67AwDA340ZM0Znz551PV+7dm2GpQqSk5P17LPP2hENcDtbt27N9LFw4UINGDBAderU0aFDh+yOmSOMsAAwypWnPrgB2CVeXl6Ki4vTbbfdJunS+7J9+3Zuy4/rlpSUpB49esjPz08ff/yx3XGuicuaARjlyr+h+JvqEsuyVL16ddcVH6dPn1bDhg3ldGYcKP/zzz/tiAc35O/vryFDhuixxx6zO0qOUFgAwA1wdQvyQqlSpdym5FJYAMAN9OrVK9v9aWlpOnbsWD6lQUGxYcMGValSxe4YOUJhAWCc7O7MmZycbGc0Y+3atUt33nmnx83tQfZ27NiR6fbExERt3rxZo0aNcpt79zDpFoBRCtKdOfPT9u3bKSy4yuVVvjP7VV+qVClFRERo0KBBbrGMASMsAIxy8ODBbPf//vvvGj58eP6EAdzcgQMHMt3u7++vEiVK5HOaG8N9WAC4lZMnT2ratGl2xwDcwrBhw1SyZElVrFgxw8PdyorECAsAuIWs5iJctmfPnnxKAncyc+ZMRUdHy8/Pz+4oN4zCAgBuoEGDBlnORbi83R3mISB/FaRpqhQWAHADWc1FAK4lOTlZPj4+2R7j7++fT2muH4UFgFE6deqU7f6EhIT8CWKYihUr2h0Bbqp69epZ7rs8MucOV5dRWAAYJSAg4Jr7e/bsmU9pzDFmzBgNHDhQRYsWlXRpUcjGjRu7VtpNTk7WoEGD9P7779sZEwb67LPPVLJkSbtj3DDuwwIAboBFIXE9nE6n4uLiXJ8bd8ZlzQDgBlgUEp6OwgIAQAFVsWJFeXl52R3jpqCwAABQQO3evVtr167NdA2upKQkLVq0SOfPn7chWe4x6RYA3ASLQiK3pkyZov/973/q0KHDVfv8/f01YcIEHT58WP/85z9tSJc7TLoFADeQk0UhJe7XgoyaNGmiqKgoPfzww5nu/+KLLzR8+HBt2rQpn5PlHoUFAIACqkSJEtq+fbsqVKiQ6f5Dhw6pfv36OnXqVD4nyz3msACAG1i/fr2++OKLDNtmzZqlSpUq6fbbb9eAAQPcZi4C8k9aWpqOHz+e5f7jx48rLS0tHxNdPwoLALiBYcOGadeuXa7nO3fuVL9+/RQaGqrIyEgtXrxYo0ePtjEhTFSnTh0tW7Ysy/3ffPON6tSpk4+Jrh+FBQDcwPbt29W6dWvX87lz5yokJERTpkxRRESEJkyYoE8++cTGhDBR3759NWLEiKtG5yRp8eLFGjlypPr27WtDstzjKiEAcAOnTp1S6dKlXc+//fZbtWvXzvW8SZMmOnz4sB3RYLABAwZo9erV6tChg2rWrKkaNWpIkn7++Wft3btXXbt21YABA2xOmTOMsACAGyhdurTrCqDU1FRt2bJFzZo1c+1PTk5W4cKF7YoHg82ePVtz585V9erVtXfvXu3Zs0c1atTQnDlzNGfOHLvj5RgjLADgBh588EFFRkbqzTff1MKFC1WsWDG1aNHCtX/Hjh2qUqWKjQlhsq5du6pr1652x7ghFBYAcAMjRoxQp06d1LJlS/n6+mrmzJkqUqSIa39MTIweeOABGxPCRElJSTk6zt/fP4+T3DjuwwIAbiQxMVG+vr5XrQ/z559/ytfXN0OJAZxOZ7Y3HLQsSw6Hwy1W+WaEBQDcSEBAQKbbS5Ysmc9J4A5Wrlxpd4SbhhEWAAAKqPT0dL311ltatGiRUlNT1bp1aw0dOlRFixa1O1qucZUQAAAF1MiRIzV48GD5+vqqbNmyevfdd91iocPMMMICAEABVa1aNb300kv6xz/+IUlatmyZ2rdvr7Nnz8rpdK8xCwoLAAAFlLe3t/bv36/y5cu7tvn4+Gj//v0qV66cjclyz73qFQAAyLG0tDT5+Phk2Fa4cGFduHDBpkTXj6uEAAAooCzLUu/eveXt7e3adu7cOT399NMqXry4a9uCBQvsiJcrFBYAAAqoXr16XbXtiSeesCHJjWMOCwAAMB5zWAAAgPEoLAAAwHgUFgAAYDwKCwAAMB6FBQAAGI/CAgAAjEdhAQAAxvt/T6ns3YdoRJ4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}