RizzitelloSVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Useful packaeges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.model_selection import  GridSearchCV, KFold\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.model_selection import  GridSearchCV, KFold, train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.utils import resample\n",
    "from sklearn import model_selection \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(r\"C:\\Users\\Nicola\\Desktop\\MASTER_BIG_DATA\\Machine Learning\\Consglio\\5 (3)\\Python\\default of credit card clients.xls\")\n",
    "new_header = df.iloc[0]\n",
    "df = df[1:]\n",
    "df.columns = new_header\n",
    "df = df.drop(columns=['ID'],axis=1)\n",
    "df = df.iloc[:,0:25].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "marr = df.groupby(['MARRIAGE']).count()\n",
    "edu = df.groupby(['EDUCATION']).count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Check valori nulli variabile Marriage')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = ['0','1','2','3'] # the x locations for the groups\n",
    "width = 0.35\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(ind, marr.iloc[:,0], width, color='b')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('Marriage')\n",
    "ax.set_xticks(ind)\n",
    "for p in ax.patches:\n",
    "   ax.annotate('{:.0f}'.format(p.get_height()), (p.get_x()+0.05, p.get_height()+0.01))\n",
    "ax.set_yticks(np.arange(0, 20000, 5000))\n",
    "plt.title('Check valori nulli variabile Marriage')\n",
    "#ax.legend(labels=['Training', 'Test'],loc='upper right')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Check valori nulli variabile Education')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = ['0','1','2','3','4','5','6'] # the x locations for the groups\n",
    "width = 0.35\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(ind, edu.iloc[:,0], width, color='y')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('Education')\n",
    "ax.set_xticks(ind)\n",
    "for p in ax.patches:\n",
    "   ax.annotate('{:.0f}'.format(p.get_height()), (p.get_x()-0.02, p.get_height()+0.01))\n",
    "ax.set_yticks(np.arange(0, 20000, 5000))\n",
    "plt.title('Check valori nulli variabile Education')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CHECK MISSING VALUES: all'interno del dataset per le colonne education e marriage ci sono delle categorie uguali a 0 che non sono esistono della descrizione del dataset. \n",
    "considerando che sono 68 righe, decido di eliminarle pioichè non riduce dui molto la numerosità campionaria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_missing = df.loc[(df['EDUCATION'] != 0) & (df['MARRIAGE'] != 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CHECK BALANCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_df, testing_df = model_selection.train_test_split(df_no_missing, test_size=0.2, random_state=1)\n",
    "train_dim=training_df.groupby('default payment next month').count()\n",
    "test_dim=testing_df.groupby('default payment next month').count()\n",
    "\n",
    "ind = ['Defaulted','Did not default'] # the x locations for the groups\n",
    "width = 0.35\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(ind, train_dim.iloc[:,0], width, color='r')\n",
    "ax.bar(ind, test_dim.iloc[:,0], width, bottom=train_dim.iloc[:,0],color='b')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('Default')\n",
    "ax.set_xticks(ind)\n",
    "#ax.set_yticks(np.arange(0, 30000, 10000))\n",
    "ax.legend(labels=['Training', 'Test'],loc='upper right')\n",
    "\n",
    "fig.savefig('fig1.png', dpi = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_default = df_no_missing[df_no_missing['default payment next month']==0]\n",
    "default = df_no_missing[df_no_missing['default payment next month']==1]\n",
    "df_no_default_downsampled = resample(df_no_default,\n",
    "                                    replace = False,\n",
    "                                    n_samples=1000,\n",
    "                                    random_state=42)\n",
    "default_downsampled = resample(default,\n",
    "                                    replace = False,\n",
    "                                    n_samples=1000,\n",
    "                                    random_state=42)\n",
    "df_downsampled = pd.concat([df_no_default_downsampled,default_downsampled])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_df, testing_df = model_selection.train_test_split(df_downsampled, test_size=0.2, random_state=1)\n",
    "train_dim=training_df.groupby('default payment next month').count()\n",
    "test_dim=testing_df.groupby('default payment next month').count()\n",
    "\n",
    "ind = ['Defaulted','Did not default']  # the x locations for the groups\n",
    "width = 0.35\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(ind, train_dim.iloc[:,0], width, color='r')\n",
    "ax.bar(ind, test_dim.iloc[:,0], width, bottom=train_dim.iloc[:,0],color='b')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('Default')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_yticks(np.arange(0, 1000, 300))\n",
    "ax.legend(labels=['Training', 'Test'],loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalizing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_df, testing_df = model_selection.train_test_split(df_downsampled, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_df = pd.get_dummies(training_df, columns=['SEX',\n",
    "                        'EDUCATION',\n",
    "                        'MARRIAGE',\n",
    "                        'PAY_0',\n",
    "                        'PAY_2',\n",
    "                        'PAY_3',\n",
    "                        'PAY_4',\n",
    "                        'PAY_5',\n",
    "                        'PAY_6',])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(df):\n",
    "    result = df.copy()\n",
    "    for feature_name in df.columns:\n",
    "        max_value = df[feature_name].max()\n",
    "        min_value = df[feature_name].min()\n",
    "        result[feature_name] = 2*(df[feature_name] - min_value) / (max_value - min_value)-1\n",
    "    return result\n",
    "training_df = normalize(training_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nested cross validation with linear kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_features = np.array(training_df.drop(columns = ['default payment next month'],axis = 1).to_numpy())\n",
    "y_target = np.array(training_df[['default payment next month']].to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridHyperPar = {'C':[0.5,1,10,100]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "outerCV = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "outerScores_1=np.array([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.738, Best Hyper Parameter = 1.000\n",
      "Accuracy = 0.666, Best Hyper Parameter = 1.000\n",
      "Accuracy = 0.703, Best Hyper Parameter = 0.500\n",
      "Accuracy = 0.691, Best Hyper Parameter = 0.500\n",
      "Accuracy = 0.666, Best Hyper Parameter = 0.500\n",
      "Accuracy = 0.693 (0.027)\n"
     ]
    }
   ],
   "source": [
    "for trainOuter, testOuter in outerCV.split(X_features):\n",
    "    \n",
    "    current_X_Outer_TrainingSet = X_features[trainOuter]\n",
    "    \n",
    "    current_X_Outer_TestSet = X_features[testOuter]\n",
    "    \n",
    "    current_y_Outer_TrainingSet = y_target[trainOuter]\n",
    "    \n",
    "    current_y_Outer_TestSet = y_target[testOuter]\n",
    "\n",
    "    innerCV = KFold(n_splits=3, shuffle=True, random_state=1)\n",
    "    svmModel = LinearSVC(random_state=0, \n",
    "                     tol=1e-5,dual=False,\n",
    "                     max_iter = 5000)\n",
    "    gridSearch = GridSearchCV(svmModel, \n",
    "                              gridHyperPar, \n",
    "                              scoring='accuracy', \n",
    "                              cv=innerCV, refit=True)\n",
    "    searchResult = gridSearch.fit(current_X_Outer_TrainingSet,\n",
    "                                  current_y_Outer_TrainingSet.ravel())\n",
    "    best_model = searchResult.best_estimator_\n",
    "    \n",
    "    bestHyperParameter = gridSearch.best_params_['C']\n",
    "    current_y_Predictions = best_model.predict(current_X_Outer_TestSet)\n",
    "    currentScore = accuracy_score(current_y_Outer_TestSet, \n",
    "                                 current_y_Predictions)\n",
    "    outerScores_1 = np.append(outerScores_1, currentScore)\n",
    "    print('Accuracy = %.3f, Best Hyper Parameter = %.3f' % (currentScore, bestHyperParameter))\n",
    "\n",
    "print('Accuracy = %.3f (%.3f)' % (mean(outerScores_1), std(outerScores_1))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM with linear kernel using training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_downsampled_def = df_downsampled\n",
    "df_downsampled_def = normalize(df_downsampled_def)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_features_def = np.array(df_downsampled_def.drop(columns = ['default payment next month'],axis = 1).to_numpy())\n",
    "y_target_def = np.array(df_downsampled_def[['default payment next month']].to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Train, X_Test, y_Train, y_Test = model_selection.train_test_split(X_features_def, y_target_def,test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy = 0.610, Best Hyper Parameter = 1.000\n",
      "\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "Did not default       0.59      0.67      0.62       195\n",
      "      Defaulted       0.64      0.56      0.59       205\n",
      "\n",
      "       accuracy                           0.61       400\n",
      "      macro avg       0.61      0.61      0.61       400\n",
      "   weighted avg       0.61      0.61      0.61       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gridHyperPar = {'C':[0.5,1,10,100]}\n",
    "\n",
    "crossValidationModel = KFold(n_splits=10)\n",
    "\n",
    "svmModel = LinearSVC(random_state=0, \n",
    "                     tol=1e-5,dual=False,\n",
    "                     max_iter = 5000)\n",
    "\n",
    "gridSearch = GridSearchCV(svmModel, \n",
    "                          gridHyperPar, \n",
    "                          scoring='accuracy', \n",
    "                          cv=crossValidationModel, \n",
    "                          refit=True)\n",
    "\n",
    "\n",
    "searchResult = gridSearch.fit(X_Train,\n",
    "                              y_Train.ravel())\n",
    "\n",
    "\n",
    "best_model = searchResult.best_estimator_\n",
    "    \n",
    "bestHyperParameter = gridSearch.best_params_['C']\n",
    "\n",
    "y_Predictions = best_model.predict(X_Test)\n",
    "\n",
    "score = accuracy_score(y_Test, y_Predictions)\n",
    "\n",
    "print('\\nAccuracy = %.3f, Best Hyper Parameter = %.3f\\n' % (score, bestHyperParameter))\n",
    "\n",
    "\n",
    "target_names = ['Did not default', 'Defaulted']\n",
    "\n",
    "print(classification_report(y_Test, y_Predictions,\n",
    "                            target_names = target_names\n",
    "                            ))\n",
    "\n",
    "#df_class_report = pandas_classification_report(y_true=y_true, y_pred=y_pred)\n",
    "np.set_printoptions(precision=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x24a66769e50>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(gridSearch,\n",
    "                        X_Test,\n",
    "                        y_Test,\n",
    "                        values_format='d',\n",
    "                        display_labels = ['Did no default', 'Defaulted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nested cross validation with non linear kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridHyperPar_2 =  [\n",
    "    {'C':[0.5,1,10,100],\n",
    "     'gamma':[1,0.1,0.01,0.001,0.0001],\n",
    "     'kernel':['rbf']},\n",
    "     ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "outerCV_2 = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "outerScores_2=np.array([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.731, C = 1.000, gamma = 0.0100\n",
      "Accuracy = 0.694, C = 1.000, gamma = 0.0100\n",
      "Accuracy = 0.700, C = 10.000, gamma = 0.0100\n",
      "Accuracy = 0.688, C = 0.500, gamma = 0.1000\n",
      "Accuracy = 0.688, C = 0.500, gamma = 0.1000\n",
      "Accuracy = 0.700 (0.016)\n"
     ]
    }
   ],
   "source": [
    "for trainOuter, testOuter in outerCV_2.split(X_features):\n",
    "    current_X_Outer_TrainingSet = X_features[trainOuter]\n",
    "    \n",
    "    current_X_Outer_TestSet = X_features[testOuter]\n",
    "    \n",
    "    current_y_Outer_TrainingSet = y_target[trainOuter]\n",
    "    \n",
    "    current_y_Outer_TestSet = y_target[testOuter]\n",
    "    \n",
    "    innerCV = KFold(n_splits=3, shuffle=True, random_state=0)\n",
    "    clf_svm = SVC(random_state=0, kernel = 'rbf')\n",
    "    gridSearch = GridSearchCV(clf_svm, \n",
    "                              gridHyperPar_2, \n",
    "                              scoring='accuracy', \n",
    "                              cv=innerCV, refit=True)\n",
    "    searchResult = gridSearch.fit(current_X_Outer_TrainingSet,\n",
    "                                  current_y_Outer_TrainingSet.ravel())\n",
    "    best_model = searchResult.best_estimator_\n",
    "    \n",
    "    bestHyperParameter_C = gridSearch.best_params_['C']\n",
    "    bestHyperParameter_gamma = gridSearch.best_params_['gamma']\n",
    "    current_y_Predictions = best_model.predict(current_X_Outer_TestSet)\n",
    "    currentScore = accuracy_score(current_y_Outer_TestSet, \n",
    "                                 current_y_Predictions)\n",
    "    outerScores_2 = np.append(outerScores_2, currentScore)\n",
    "    print('Accuracy = %.3f, C = %.3f, gamma = %.4f' % (currentScore, bestHyperParameter_C, bestHyperParameter_gamma))\n",
    "\n",
    "print('Accuracy = %.3f (%.3f)' % (mean(outerScores_2), std(outerScores_2))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM with non linear kernel using train and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy = 0.650, C = 100.000, gamma = 0.100\n",
      "\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "Did not default       0.62      0.71      0.67       195\n",
      "      Defaulted       0.68      0.59      0.63       205\n",
      "\n",
      "       accuracy                           0.65       400\n",
      "      macro avg       0.65      0.65      0.65       400\n",
      "   weighted avg       0.65      0.65      0.65       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gridHyperPar_2 =  [\n",
    "    {'C':[0.5,1,10,100],\n",
    "     'gamma':[1,0.1,0.01,0.001,0.0001],\n",
    "     'kernel':['rbf']},\n",
    "     ]\n",
    "crossValidationModel = KFold(n_splits=10)\n",
    "\n",
    "# svmModel = SVC(kernel=\"linear\",cache_size=1000)\n",
    "\n",
    "svmModel = SVC(random_state=0)\n",
    "\n",
    "gridSearch = GridSearchCV(svmModel, \n",
    "                          gridHyperPar_2, \n",
    "                          scoring='accuracy', \n",
    "                          cv=crossValidationModel, \n",
    "                          refit=True)\n",
    "\n",
    "\n",
    "searchResult = gridSearch.fit(X_Train,\n",
    "                              y_Train.ravel())\n",
    "\n",
    "\n",
    "best_model = searchResult.best_estimator_\n",
    "    \n",
    "bestHyperParameter_c = gridSearch.best_params_['C']\n",
    "bestHyperParameter_gamma = gridSearch.best_params_['gamma']\n",
    "\n",
    "y_Predictions = best_model.predict(X_Test)\n",
    "\n",
    "score = accuracy_score(y_Test, y_Predictions)\n",
    "\n",
    "print('\\nAccuracy = %.3f, C = %.3f, gamma = %.3f\\n' % (score, bestHyperParameter_c, bestHyperParameter_gamma))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "\n",
    "target_names = ['Did not default', 'Defaulted']\n",
    "\n",
    "print(classification_report(y_Test, y_Predictions,\n",
    "                            target_names = target_names\n",
    "                            ))\n",
    "\n",
    "\n",
    "np.set_printoptions(precision=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x24a6bb28f40>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IuJt1v16YmfUe2VvywyWVTts+PSKmV1lqub8sFWuSpSX/JWAi8GBEfEPSFsBFOStnZlYcbZnPXBERk3Pm/pKkrdJW/FbAsjR9CTCq5LxtgRcqZZZlCOWbEdEGtEjaJC3QD0OZWe/U/ePkZwFHpK+PAK4vST9c0gBJY4BxwH2VMsvSkp8raVPgQpIRN6uzZGxmVlS1Gl0j6SqS7vDhkpYApwFnATMlHQk8CxwKEBGPSJoJLARagOMqjayBbHPXfCt9eb6kG4FNImJ+Fe/HzKwYaje65sudHNq/k/PPBM7MU0ZXC3nv0tWxiJiXpyAzM9vwumrJ/0cXxwL4eI3r0ustXDqCXc84tqerYTns+8C9PV0Fy+nWTpuv2dXwYahu19XDUPttyIqYmTWEINOUBfUi08NQZmZWoggteTMzK68Q3TVmZtaJBgryWVaGkqSvSvpxuv8+Sbt3f9XMzOpUwVaGOhfYC2gfz/k68F/dViMzszqmyL7VgyzdNXtExC6SHgSIiJcl9e/mepmZ1a+Cja5pltRE+uVD0gjyTM9jZlYw9dJKzyJLd81/AtcBIyWdSTLN8M+6tVZmZvWsgfrks8xdc4WkB0jmUhDwuYh4tNtrZmZWj+qovz2LLIuGvA94A/hjaVpEPNudFTMzq1tFCvLADaxd0HsjYAywiGQxWTOzXkcNdFcyS3fNh0r309kpj+62GpmZWc3kfuI1IuZJ2q07KmNm1hCK1F0j6bslu32AXYDl3VYjM7N6VrQbr8CQktctJH3013ZPdczMGkBRgnz6ENTgiDhpA9XHzKz+1TDIS/oO8E2SwS0XRsSvJA0DrgZGA08Dfx8RL1eTf6cPQ0nqmy4SW4N1VMzMikEko2uybBXzknYiCfC7AxOBz0gaB5wCzI6IccDsdL8qXbXk7yMJ8A9JmgVcA6xpPxgRf6i2UDOzhlXbPvkPAnMi4g0ASX8GPg8cAuybnjMDuAP4QTUFZOmTHwasJFnTtX28fAAO8mbWO2UP8sMlzS3Znx4R00v2FwBnStoceBP4FDAX2CIilgJExFJJI6utaldBfmQ6smYBa4N7uwa67WBmVmPZI+CKiJjcaTYRj0r6N+AWYDXwMMkAl5rpaoKyJmBwug0ped2+mZn1SrWcTz4iLo6IXSJiH2AVsBh4SdJWAOnPZdXWtauW/NKI+Em1GZuZFVZtR9eMjIhl6TxhXyBZpGkMcARwVvrz+mrz7yrIN86s+GZmG0rUfO6aa9M++WbguHRhprOAmZKOBJ4FDq02866C/P7VZmpmVmg1bMlHxMfKpK2kRjG40yAfEatqUYCZWdEUbVoDMzMr5SBvZlZQdbS0XxYO8mZmOQh315iZFZqDvJlZkTnIm5kVmIO8mVlBFXBlKDMzK+Ugb2ZWXDWe1qBbOcibmeXk7hozs6Lyw1BmZgXnIG9mVkx+4tXMrODU1jhR3kHezCwP98mbmRVbI3XXdLWQt5mZlRMZtwwknSjpEUkLJF0laSNJwyTdImlx+nOzaqvqIG9mlpMi21YxH2kb4ARgckTsBDQBhwOnALMjYhwwO92vioO8mVleNWzJk3SbD5TUF9gYeAE4BJiRHp8BfK7aqjrIm5nlEcm0Blk2YLikuSXbUetkFfE88O/As8BS4NWIuBnYIiKWpucsBUZWW13feDUzyyHnOPkVETG507ySvvZDgDHAK8A1kr66nlVch1vyZmZ5RWTbKvsE8FRELI+IZuAPwN7AS5K2Akh/Lqu2qg7yZmY51erGK0k3zZ6SNpYkYH/gUWAWcER6zhHA9dXW1d01VnNf2fNhPrfzYwTwxEubc8b1+/JOa/JP7Wt7PcQ/f3IO+//8CF55c2CP1rM3W3JGG6/fBX2HwbiZSVvvxV+18dqdoH7Qf1vY9nTRNES0vBI8d3Lw5kLY9LOw9Q96eduwhg9DRcS9kn4PzANagAeB6cBgYKakI0n+EBxabRkN92lJapX0UDqu9GFJ35VU8X1IOju95uwqy12d/hwt6StVXH+ppC9VU3YjGTFkNYfvvoCvXfhFDjvvMJr6tDFlpycA2GKT1ewxdglLXxncw7W0zT4rRv9a66QN2kOMmynGXd2HAdvB8t8kkazPABh5rNjyn1Uuq14px43XiiLitIjYISJ2ioivRcTbEbEyIvaPiHHpz1XV1rXhgjzwZkRMiogdgQOATwGnZbjuaGCXiDhpPcsfDeQO8r1JU582BvRtoUltbNSvheWvDwLgu1P+yjm37tlIT4QX1qBdRNPQddOG7CXUNwnkG+8kml9K0vsMFIN2Fuq/gStZx2oZ5LtbIwb5d0XEMuAo4HglmtIW+/2S5ks6GkDSLGAQcK+kwyR9VtK9kh6UdKukLdLzTpf0/fb80yfQRnco9izgY+m3iRO7KFOSpklaKOkG1mMIVCNZ/vpgLr9nIjeceDk3fe8yVr/VnzlPjmKf7Z9m+esbs/il4T1dRcvg5VnBkI+45V5WUMsbr92u4fvkI+LJtLtmJMlQpFcjYjdJA4C/SLo5Ig6WtDoiJsG7w5b2jIiQ9E/AycD3MhZ5CvD9iPhMmtdR5coEdgbGAx8CtgAWApd0zCy9/iiAfoOrfnK5bgzZ6G3+bvzTfPacqax+qz//dugtfPrDi/j73R7huMs/3dPVswyWXRzQBEMP6uma1K9Gmrum4YN8qr3J8UngwyV930OBccBTHc7fFrg6HZrUv8zxPDorcx/gqohoBV6QdFu5iyNiOsmNFjYeOaqB/umUt8fYJTz/yia88kZyU/W2R8dw8KRFbL3Za1x1zDUAjNxkDVccfS1fv/ALrFyzcU9W1zp4+Y/B63cFY84TyWAPK6uB/qc2fJCXNBZoJRlHKuDbEXFThct+DfwiImZJ2hc4PU1vYd0urI2yVKFcmZI+RUP9U6iNF18dzIe2eYmN+jbzVktfdh/zPLc9NoajLzv43XP++J3L+dr0L3p0TZ15/a/BihnBmAtFn4EO8J3xoiEbkKQRwPnAtLTr5SbgWEm3RUSzpO2B5yNiTYdLhwLPp6+PKEl/GmjvhtmF5Cm0jl4HhpTsly0TuBM4WtJlJF1J+wFXrsfbbQgLnt+C2Y+O5Yqjr6WlTSxaOpw/PDChp6tlHTz3wzbWzIWWV+Cxg9oYebRY8ZugrRme/lYyRnDgh2CbHyZtnkWfaaNtDUQzvHZHG6P/S2w0tpf+IYjwoiHdbKCkh4B+JC3v3wK/SI9dRDL6ZV76YMFyyk/sczrJ48PPA3NYG8yvBb6e5n8/8HiZa+cDLZIeBi4FzumkzOuAjwN/S/P5czVvthFdcMduXHDHbp0e/+w5NX1q26ow6mfvHXMx7HOdB+3xf2roMRq11zgxvvGCfEQ0dXGsDfhhunU8Nrjk9fWUeYIsIt4k6WMvl/fg9GczyVNppcqWCRzfWV3NrHG5u8bMrKgCcHeNmVmBNU6Md5A3M8vL3TVmZgXm0TVmZkVVw1koNwQHeTOzHJKHoRonyjvIm5nlVSczTGbhIG9mlpNb8mZmRdVgffJ+VtnMLJdk7posWyWSxqdrU7Rvr0n6Z0nDJN0iaXH6s+p5yB3kzczyqtGiIRGxKF3pbhKwK/AGybxXpwCzI2IcMDvdr4qDvJlZHtFty//tD/xfRDxDsgDSjDR9BuUnWszEffJmZnllv/E6XNLckv3p6UJB5RwOXJW+3iIiliZFxVJJVS8f6iBvZpZX9huvKyJicqWTJPUHDgZOXY9aleUgb2aWk9pqPlD+IGBeRLyU7r8kaau0Fb8Vycp3VXGfvJlZHkHyMFSWLbsvs7arBmAWa1etO4Iy619k5Za8mVkOImr6MJSkjYEDgKNLks8CZko6EngWOLTa/B3kzczyqmGQj4g3gM07pK3kvSvQVcVB3swsL09rYGZWUO198g3CQd7MLKduGF3TbRzkzcxyyTZlQb1wkDczyyNwkDczK7TG6a1xkDczy8uLhpiZFZmDvJlZQUVAa+P01zjIm5nl5Za8mVmBOcibmRVUABnWb60XDvJmZrkEhPvkzcyKKfCNVzOzQnOfvJlZgTnIm5kVVWNNUOY1Xs3M8gigrS3bloGkTSX9XtJjkh6VtJekYZJukbQ4/blZtdV1kDczyysi25bNOcCNEbEDMBF4FDgFmB0R44DZ6X5VHOTNzHJJpzXIslUgaRNgH+BigIh4JyJeAQ4BZqSnzQA+V21tHeTNzPIIiGjLtAHDJc0t2Y7qkNtYYDnwG0kPSrpI0iBgi4hYCpD+HFltdX3j1cwsr+xPvK6IiMldHO8L7AJ8OyLulXQO69E1U45b8mZmedWuT34JsCQi7k33f08S9F+StBVA+nNZtVV1kDczyyOiZqNrIuJF4DlJ49Ok/YGFwCzgiDTtCOD6aqvr7hozs7xqO07+28AVkvoDTwLfIGmAz5R0JPAscGi1mTvIm5nlEkRra+1yi3gIKNdvv38t8neQNzPLw1MNm5kVnKcaNjMrpgDCLXkzs4IKLxpiZlZotbzx2t0UDTRlZtFJWg4809P16CbDgRU9XQnLrMif13YRMaLaiyXdSPL7yWJFRBxYbVm14CBvG4SkuRUe77Y64s+rOPzEq5lZgTnIm5kVmIO8bSjTe7oClos/r4Jwn7yZWYG5JW9mVmAO8mZmBeYgX2CSWiU9JOkRSQ9L+q6kPumxyZL+s5PrnpaUdRxwtXWrWIakQ9PV62+vsow7JE1OX/+wmjzqWVefb4Xrzk6vObvKclenP0dL+koV118q6UvVlG35+YnXYnszIiYBSBoJXAkMBU6LiLnA3B6sWxZHAt+KiKqCfAc/BH5Wg3zqSaefb4XrjgZGRMTb61n+aOArablWp9yS7yUiYhlwFHC8EvtK+hOApM0l3ZwuJHwBoHJ5SFot6cy01ThH0hZp+naSZkuan/58X5lrOy1D0lcl3Ze2Si+Q1CTpx8BHgfPTludoSXdJmpdue6fXvvs+0v1pkv6hQ9lnAQPT/K9Yv99kfSrz+Talv7f708/laABJs4BBwL2SDpP0WUn3pp/LrSWf6emSvt+ev6QFkkZ3KPYs4GPp7/XELspU+rkslHQD67EoteXnIN+LRMSTJJ95x/9kpwF3R8TOJMuOvSdIpwYBcyJiInAn8M00fRpwWUR8GLgCKNcNVLYMSR8EDgM+krZKW4GpEfETkm8aUyPiJJI1Lg+IiF3S88t2NXXyvk8hbfVGxNSs1zWaDp/vkcCrEbEbsBvwTUljIuJg1v4urgbuBvZMP5ffASfnKPIU4K40r192VibweWA88CGSfzN71+L9Wjburul9yrXS9wG+ABARN0h6uZNr3wHaW80PAAekr/dqvx74LfDzHGXsD+wK3C8JYCDlFy3uB0yTNInkD8H2ndSxt2v/fD8JfLik73soMA54qsP52wJXK1ksun+Z43l0VuY+wFUR0Qq8IOm29SjDcnKQ70UkjSUJkMuAD3Y4nOWBieZY+2BFK53/++ksr3LpAmZExKkVyj4ReAmYSNJafStNb2Hdb6QbVcinsDp8vgK+HRE3Vbjs18AvImKWpH2B09P0an6vZcuU9Cmy/fuybuDuml5C0gjgfGBaSaBudycwNT3vIGCznNn/FTg8fT2VpAugo87KmA18Kb1xiKRhkrYrc/1QYGlEtAFfA5rS9GeACZIGSBpK5+tiNkvql+9tNY4yn+9NwLHt71nS9pIGlbl0KPB8+vqIkvSngV3Sa3cBxpS59nVgSMl+Z2XeCRye9tlvBexX3bu0arglX2wDJT1E0tXRQtKV8osy550BXCVpHvBnktXh8zgBuETSScByktXmM5UREQsl/Qtws5Lhf83Acbx3yuVzgWslHQrcDqxJr39O0kxgPrAYeLCTOk4H5kuaV6B++a4+34tIRr/MU9IPthz4XJk8TgeukfQ8MIe1wfxa4Otp/vcDj5e5dj7QIulh4FLgnE7KvA74OPC3NJ8/V/NmrTqe1sDMrMDcXWNmVmAO8mZmBeYgb2ZWYA7yZmYF5iBvZlZgDvLWULR25sUFkq6RtPF65PXubIiSLpI0oYtz922fLydnGWVn2+wsvcM5q3OWtc58M2bgIG+Np33elZ1Iplk4pvSgpKbyl3UtIv4pIhZ2ccq+eM4Va0AO8tbI7gI+kLayb5d0JfC3amZD1Lpzzx+oZKbLh5XMqjma5I/Jiem3iI9JGiHp2rSM+yV9JL0204yepST9t6QHlMzxflSHY/+R1mV2+lQrkt4v6cb0mrsk7VCT36YVkp94tYYkqS9wEHBjmrQ7sFNEPJUGylcjYjdJA4C/SLoZ2Jm1syFuASwELumQ7wjgQmCfNK9hEbFK0vnA6oj49/S8K4FfRsTdSqZWvolkPqD22TZ/IunTJNP/VvKPaRkDSSZquzYiVpLM+jkvIr6nZOrl04DjSZ7ePSYiFkvag+Rp4I9X8Wu0XsBB3hpN+6P8kLTkLybpRrkvItpnUFyf2RD3BO5szysiVnVSj0+QzJnTvr+JpCFkn9Gz1AmSPp++HpXWdSXQBlydpl8O/EHS4PT9XlNS9oAMZVgv5SBvjebd1ZDapcFuTWkS1c+GqAznQNLVuVdEvFmmLpnnClEy8+Mn0rzekHQHnc/4GGm5r3T8HZh1xn3yVkTrMxviPcDfKVnsAknD0vSOMy7eTNJ1QnrepPRl3hk9hwIvpwF+B5JvEu36AO3fRr5C0g30GvBUOlFb+32GiRXKsF7MQd6K6CKS/vZ5khYAF5B8a72OZKbKvwHnUWY2xIhYTtKP/od0dsX27pI/Ap9vv/FKMvPm5PTG7kLWjvI5A9hHyWybn6TyjJ43An0lzQd+SjITZLs1wI6SHiDpc/9Jmj4VODKt3yPAIRl+J9ZLeRZKM7MCc0vezKzAHOTNzArMQd7MrMAc5M3MCsxB3syswBzkzcwKzEHezKzA/j95id7/rJbwbgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(gridSearch,\n",
    "                        X_Test,\n",
    "                        y_Test,\n",
    "                        values_format='d',\n",
    "                        display_labels = ['Did no default', 'Defaulted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}