{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":569,"status":"ok","timestamp":1649014192694,"user":{"displayName":"Kyle Masiak","userId":"03455281970866158381"},"user_tz":240},"id":"cZeDLqvDHdCD","outputId":"78ef67a7-e980-4dd2-fb83-6aeb615c3721"},"outputs":[{"output_type":"stream","name":"stdout","text":["         BELNR WAERS BUKRS KTOSL PRCTR BSCHL HKONT       DMBTR       WRBTR  \\\n","0       288203    C3   C31    C9   C92    A3    B1   280979.60        0.00   \n","1       324441    C1   C18    C7   C76    A1    B2   129856.53   243343.00   \n","2       133537    C1   C19    C2   C20    A1    B3   957463.97  3183838.41   \n","3       331521    C4   C48    C9   C95    A2    B1  2681709.51    28778.00   \n","4       375333    C5   C58    C1   C19    A3    B1   910514.49      346.00   \n","...        ...   ...   ...   ...   ...   ...   ...         ...         ...   \n","533004  446818    C1   C18    C3   C32    A1    B2  2501589.15        0.00   \n","533005  455564    C8   C80    C1   C11    A1    B1   390076.18    12065.45   \n","533006  156896    C1   C10    C1   C19    A1    B1   192147.38   326823.09   \n","533007  455245    C1   C14    C4   C40    A1    B3   870539.68        0.00   \n","533008  281395    C8   C80    C7   C78    A1    B2   532106.87    32467.08   \n","\n","          label  \n","0       regular  \n","1       regular  \n","2       regular  \n","3       regular  \n","4       regular  \n","...         ...  \n","533004  regular  \n","533005  regular  \n","533006  regular  \n","533007  regular  \n","533008  regular  \n","\n","[533009 rows x 10 columns]\n"]}],"source":["# data and metric imports\n","import sklearn.model_selection\n","import sklearn.metrics\n","\n","import pandas as pd\n","df = pd.read_csv('Dataset.csv')\n","print(df)"]},{"cell_type":"code","source":["#Convert all categorical variables to number (for example C3 will be converted to 3)\n","#print(df)\n","#print(df.dtypes)\n","df['WAERS'] = df['WAERS'].str[1:]\n","df['BUKRS'] = df['BUKRS'].str[1:]\n","df['KTOSL'] = df['KTOSL'].str[1:]\n","df['PRCTR'] = df['PRCTR'].str[1:]\n","df['BSCHL'] = df['BSCHL'].str[1:]\n","df['HKONT'] = df['HKONT'].str[1:]\n","#print(df.dtypes)"],"metadata":{"id":"tvF1iMFt4H2K"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","# Shuffle the Dataset.\n","shuffled_df = df.sample(frac=1,random_state=4)\n","\n","# Put all the fraud class in a separate dataset.\n","fraud_df_global = shuffled_df.loc[shuffled_df['label'] == 'global']\n","fraud_df_local = shuffled_df.loc[shuffled_df['label'] == 'local'] \n","\n","#Randomly select 100 observations from the non-fraud (majority class)\n","non_fraud_df = shuffled_df.loc[shuffled_df['label'] == 'regular'].sample(n=100,random_state=42)\n","\n","# Concatenate both dataframes again\n","normalized_df = pd.concat([fraud_df_global, non_fraud_df])\n","normalized_df = pd.concat([normalized_df, fraud_df_local])\n","#normalized_df = normalized_df.append([normalized_df] * 100)\n","normalized_df = normalized_df.sample(frac=1)\n","\n","#plot the dataset after the undersampling\n","plt.figure(figsize=(8, 8))\n","sns.countplot('label', data=normalized_df)\n","plt.title('Balanced Classes')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":568},"id":"6wJGFQmOCVbz","executionInfo":{"status":"ok","timestamp":1649014196526,"user_tz":240,"elapsed":855,"user":{"displayName":"Kyle Masiak","userId":"03455281970866158381"}},"outputId":"3189c38c-3537-4e4a-8bd8-cb57f3cfb2aa"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  FutureWarning\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 576x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["#import matplotlib.pyplot as plt\n","#\n","## Pie chart, where the slices will be ordered and plotted counter-clockwise:\n","#labels = 'Regular', 'Global', 'Local'\n","#sizes = [532909, 70, 30]\n","#explode = (0, 0.1, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n","#\n","#fig1, ax1 = plt.subplots()\n","#ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n","#        shadow=True, startangle=90)\n","#ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n","#\n","#plt.show()\n","\n","#plot the dataset after the undersampling\n","plt.figure(figsize=(8, 8))\n","sns.countplot('label', data=df)\n","plt.title('Balanced Classes')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":568},"id":"aOYD62qHI-Ot","executionInfo":{"status":"ok","timestamp":1649019780276,"user_tz":240,"elapsed":533,"user":{"displayName":"Kyle Masiak","userId":"03455281970866158381"}},"outputId":"6d16aabb-63c8-4065-9c85-3a84399ff074"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  FutureWarning\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 576x576 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# select categorical attributes to be \"one-hot\" encoded]\n","categorical_attr_names = ['label']\n","\n","# encode categorical attributes into a binary one-hot encoded representation \n","y = pd.get_dummies(normalized_df[categorical_attr_names])\n","\n","y"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"kwG_J7WlE4z9","executionInfo":{"status":"ok","timestamp":1649014200755,"user_tz":240,"elapsed":188,"user":{"displayName":"Kyle Masiak","userId":"03455281970866158381"}},"outputId":"d61588fb-675c-42e3-8b5a-692f33870753"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        label_global  label_local  label_regular\n","142689             0            0              1\n","514174             0            0              1\n","281431             0            0              1\n","26584              0            0              1\n","198583             1            0              0\n","...              ...          ...            ...\n","20188              0            0              1\n","126093             0            0              1\n","463973             0            1              0\n","515689             0            0              1\n","502056             0            1              0\n","\n","[200 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-3f413c52-5651-4351-833d-af53ab203659\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>label_global</th>\n","      <th>label_local</th>\n","      <th>label_regular</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>142689</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>514174</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>281431</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>26584</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>198583</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>20188</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>126093</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>463973</th>\n","      <td>0</td>\n","      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Masiak","userId":"03455281970866158381"}},"outputId":"8b846156-b2c1-44d8-e401-0447dab62a60"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["BELNR      int64\n","WAERS     object\n","BUKRS     object\n","KTOSL     object\n","PRCTR     object\n","BSCHL     object\n","HKONT     object\n","DMBTR    float64\n","WRBTR    float64\n","label     object\n","dtype: object"]},"metadata":{},"execution_count":167}]},{"cell_type":"code","source":["normalized_df.columns"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ES8CJe9bIm-G","executionInfo":{"status":"ok","timestamp":1648940230238,"user_tz":240,"elapsed":257,"user":{"displayName":"Kyle Masiak","userId":"03455281970866158381"}},"outputId":"900bc4d9-4de0-4ed5-afc4-1f050fdad8ff"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['KTOSL_A19', 'KTOSL_B06', 'KTOSL_B12', 'KTOSL_B83', 'KTOSL_C1',\n","       'KTOSL_C2', 'KTOSL_C3', 'KTOSL_C39', 'KTOSL_C4', 'KTOSL_C5',\n","       ...\n","       'WAERS_W25', 'WAERS_W59', 'WAERS_X26', 'WAERS_Y59', 'WAERS_Z06',\n","       'WAERS_Z37', 'WAERS_Z54', 'label_global', 'label_local',\n","       'label_regular'],\n","      dtype='object', length=543)"]},"metadata":{},"execution_count":141}]},{"cell_type":"code","source":["X = normalized_df.iloc[:, :len(normalized_df.columns)-1]\n","\n","X_train, X_test, y_train, y_test = \\\n","        sklearn.model_selection.train_test_split(X, y)"],"metadata":{"id":"0Zw3UxKtEjOE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"FVroEbSY2hkf","executionInfo":{"status":"ok","timestamp":1649014263437,"user_tz":240,"elapsed":148,"user":{"displayName":"Kyle Masiak","userId":"03455281970866158381"}},"outputId":"2210e120-6118-4b86-8050-2ebae9b60c09"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         BELNR WAERS BUKRS KTOSL PRCTR BSCHL HKONT         DMBTR         WRBTR\n","142689  522596     2    21     1    17     1     1  4.810252e+05  4.033182e+04\n","514174  182654     7    71     1    19     3     1  2.973607e+05  0.000000e+00\n","281431  311738     2    20     2    22     1     3  9.809315e+04  1.010000e+02\n","26584   435757     1    19     4    44     1     3  9.316107e+04  0.000000e+00\n","198583  532927    11    00    19    99    24    10  9.244551e+07  5.958506e+07\n","...        ...   ...   ...   ...   ...   ...   ...           ...           ...\n","20188   337633     6    64     1    14     2     1  1.454566e+06  1.473480e+05\n","126093  385893     1    16     1    18     1     1  9.910422e+05  1.086100e+04\n","463973  532992     1    11     1    57     1     1  9.106499e+05  5.444288e+04\n","515689  507780     1    14     1    18     1     1  4.148723e+05  0.000000e+00\n","502056  532995     1    11     1    81     1     1  9.106594e+05  5.443292e+04\n","\n","[200 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-8d736527-de24-4696-b9ee-1af06a9bfce5\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>BELNR</th>\n","      <th>WAERS</th>\n","      <th>BUKRS</th>\n","      <th>KTOSL</th>\n","      <th>PRCTR</th>\n","      <th>BSCHL</th>\n","      <th>HKONT</th>\n","      <th>DMBTR</th>\n","      <th>WRBTR</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>142689</th>\n","      <td>522596</td>\n","      <td>2</td>\n","      <td>21</td>\n","      <td>1</td>\n","      <td>17</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>4.810252e+05</td>\n","      <td>4.033182e+04</td>\n","    </tr>\n","    <tr>\n","      <th>514174</th>\n","      <td>182654</td>\n","      <td>7</td>\n","      <td>71</td>\n","      <td>1</td>\n","      <td>19</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>2.973607e+05</td>\n","      <td>0.000000e+00</td>\n","    </tr>\n","    <tr>\n","      <th>281431</th>\n","      <td>311738</td>\n","      <td>2</td>\n","      <td>20</td>\n","      <td>2</td>\n","      <td>22</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>9.809315e+04</td>\n","      <td>1.010000e+02</td>\n","    </tr>\n","    <tr>\n","      <th>26584</th>\n","      <td>435757</td>\n","      <td>1</td>\n","      <td>19</td>\n","      <td>4</td>\n","      <td>44</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>9.316107e+04</td>\n","      <td>0.000000e+00</td>\n","    </tr>\n","    <tr>\n","      <th>198583</th>\n","      <td>532927</td>\n","      <td>11</td>\n","      <td>00</td>\n","      <td>19</td>\n","      <td>99</td>\n","      <td>24</td>\n","      <td>10</td>\n","      <td>9.244551e+07</td>\n","      <td>5.958506e+07</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>20188</th>\n","      <td>337633</td>\n","      <td>6</td>\n","      <td>64</td>\n","      <td>1</td>\n","      <td>14</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1.454566e+06</td>\n","      <td>1.473480e+05</td>\n","    </tr>\n","    <tr>\n","      <th>126093</th>\n","      <td>385893</td>\n","      <td>1</td>\n","      <td>16</td>\n","      <td>1</td>\n","      <td>18</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>9.910422e+05</td>\n","      <td>1.086100e+04</td>\n","    </tr>\n","    <tr>\n","      <th>463973</th>\n","      <td>532992</td>\n","      <td>1</td>\n","      <td>11</td>\n","      <td>1</td>\n","      <td>57</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>9.106499e+05</td>\n","      <td>5.444288e+04</td>\n","    </tr>\n","    <tr>\n","      <th>515689</th>\n","      <td>507780</td>\n","      <td>1</td>\n","      <td>14</td>\n","      <td>1</td>\n","      <td>18</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>4.148723e+05</td>\n","      <td>0.000000e+00</td>\n","    </tr>\n","    <tr>\n","      <th>502056</th>\n","      <td>532995</td>\n","      <td>1</td>\n","      <td>11</td>\n","      <td>1</td>\n","      <td>81</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>9.106594e+05</td>\n","      <td>5.443292e+04</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>200 rows × 9 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d736527-de24-4696-b9ee-1af06a9bfce5')\"\n","              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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":28}]},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.metrics import accuracy_score\n","\n","neigh = KNeighborsClassifier(n_neighbors=2)\n","neigh.fit(X_train, y_train)\n","\n","predicted = neigh.predict(X_test)\n","acc = accuracy_score(y_test, predicted)\n","acc"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kimDXRDF2PP7","executionInfo":{"status":"ok","timestamp":1649013438040,"user_tz":240,"elapsed":17599,"user":{"displayName":"Kyle Masiak","userId":"03455281970866158381"}},"outputId":"1756c349-1aa6-41a2-e9eb-ed74bcdfd180"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9999849909570516"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.metrics import accuracy_score\n","\n","neigh = KNeighborsClassifier(n_neighbors=5)\n","neigh.fit(X_train, y_train)\n","\n","predicted = neigh.predict(X_test)\n","acc = accuracy_score(y_test, predicted)\n","acc"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SlgProUf6Pgx","executionInfo":{"status":"ok","timestamp":1649014307988,"user_tz":240,"elapsed":133,"user":{"displayName":"Kyle Masiak","userId":"03455281970866158381"}},"outputId":"d4959eb3-3e4a-4899-cf27-3fb8a166a975"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["#OVERSAMPLING\n","from sklearn.metrics import confusion_matrix\n","confusion_matrix(y_test.values.argmax(axis=1), predicted.argmax(axis=1))\n","\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn import metrics\n","cm = metrics.confusion_matrix(y_test.values.argmax(axis=1), predicted.argmax(axis=1))\n","plt.figure(figsize=(9,9))\n","sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n","plt.ylabel('Actual label');\n","plt.xlabel('Predicted label');\n","all_sample_title = 'Accuracy Score: {0}'.format(acc)\n","plt.title(all_sample_title, size = 15);"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":526},"id":"bPamvMNt2-DD","executionInfo":{"status":"ok","timestamp":1649013639560,"user_tz":240,"elapsed":423,"user":{"displayName":"Kyle Masiak","userId":"03455281970866158381"}},"outputId":"6d1478e4-3370-4909-88c6-66e718d712ec"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 648x648 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["#UNDERSAMPLING\n","from sklearn.metrics import confusion_matrix\n","confusion_matrix(y_test.values.argmax(axis=1), predicted.argmax(axis=1))\n","\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn import metrics\n","cm = metrics.confusion_matrix(y_test.values.argmax(axis=1), predicted.argmax(axis=1))\n","plt.figure(figsize=(9,9))\n","sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n","plt.ylabel('Actual label');\n","plt.xlabel('Predicted label');\n","all_sample_title = 'Accuracy Score: {0}'.format(acc)\n","plt.title(all_sample_title, size = 15);"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":526},"id":"FBTOTMNZ6SJN","executionInfo":{"status":"ok","timestamp":1649014311856,"user_tz":240,"elapsed":355,"user":{"displayName":"Kyle Masiak","userId":"03455281970866158381"}},"outputId":"cbdd2e59-a84f-4d75-9f54-01a915aa3ed5"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 648x648 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAgQAAAH9CAYAAABld2TaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3debzVVb3/8deHKRBkUBlVHNIcIhWn1LqFs+aAE5aZpnUv1c0S08oyM0tv1jXTn2Y3TNPKIc15uKLiQF6VRDAF0ZxQQQZFZg8xnPX74/sFD4fhHL6y99nny+vZYz84+zusvfZpu/fnvNf6rh0pJSRJ0vqtTUt3QJIktTwLAkmSZEEgSZIsCCRJEhYEkiQJCwJJkgS0a+kOSJLUWi1cQsWv3e/Yjqj0Y4AJgSRJwoRAkqTCyrS2nwmBJEkyIZAkqahU+SkEUJ0pBCYEkiTJhECSpOKcQyBJksrEhECSpIJKFBCYEEiSJBMCSZIKcx0CSZJUKiYEkiQVVKZ1CCwIJEkqyiEDSZJUJiYEkiQVVKKAwIRAkiSZEEiSVJiXHUqSpFIxIZAkqaDqXHZYHSYEkiTJhECSpKKcQyBJkkrFgkCSJFkQSJIk5xBIklSYcwikKoqI1yMiRcQ2Ld2XWhMRAyLijoiYGhF1+e/qpogY0NJ9W1sR0SEi/jsi/pY/l2a/1UbmhxHxVn7uqIjYpZL9lcrGgkA1LSL2BrbM757Qgl2pOXmB9BTQFTgNOAy4CNgE2KkFu1bUBsC/A+8DT6zluWcD5wK/AI4A5gMPRUSfddpDqZFUhf9VS6Qy5R0qnYj4f8BXgPFA15TSji3cJQAioi3QNqW0qAX7cCHwdaBfSulfjfZFqvB/3BHRKaVUt47bjJRSiojTgMtTSk1+EXxEdASmA79KKf0039YZmAT8LqX0o3XZR6mhmQuWVPxDdOPO7Zr872BdMCFQzco/dI8H7gKuAXaIiJ1XcdxnIuKRiJgfEXMi4tGIGNhg/xYRcWNEvBsR70fEcxHxxXzfoHw4YkCjNh+NiL82uH9tRIyJiKMiYgKwEPhkRPSNiGsi4rU8qv5nRFwQER0atdcpIn4ZEW9ExL/yaP/n+b5f5udHo3NOiYhFEdFzNb+i7sDsxsUAQONiICKOjoi/532cGRH3RcQWDfbvFxGjI2JhREyPiCsjokuD/ct+TwdHxF0RMR+4It/XPx+meC///Y6IiO1W0+c1KljE7EOWktzcoJ0FwN3AoUX6ITVXSpW/VYsFgWrZvkBv4Cbgr8BiGg0bRMQgYGS+78vA54G/AZvm+3sBTwJ7AGeRxclXA5sX6M+WwC+Bn5N90LxOFs+/B3wHOAT4b+BU4PIGfQzgTuAbwG+AzwHn5edCVuxsBXy20eOdCtydUnpnNf0ZC2wdEZdFxGqTk4g4CbgNeJWswDoV+CfQM9//ceB+4F3g2LxvXyT7nTd2NfAP4Ejg6ojYCHgc2I4srTge6EwW13dq0IdHI+LR1fXxQ9oeWAq83Gj7xHyfpGbwKgPVshOA2cD9KaVFEfEA8IWI+EGDvyR/TvYBdXCDbfc3aOMMoBuwW0ppar5tZMH+bAwckFJ6tsG2yWSFBgAR8X/AAuCaiPhWPqRwEHAgMDildFeDc/8IkFJ6MT/vVODRvJ2tgX8j++Bdnevytr8NfDsi3gPuAy5LKY3J22lDNq/g9pRSw2KqYT/OBd4AjkwpLc3Pew/4S0TsnVJ6ssGxt6SUzm3wfH9GVgDsklJ6r8HvYBLZUM9v8kOXruF5fFg9gPnL+t7ALGCDiOjQkkM7KrcyDbqbEKgm5ZH7MWQfZMvezG8CtgD2zo/pDHwSuG4NUfN+ZAXF1NXsXxtTGhUDy2a3D4uIFyKijiypuB74CNC/QR/ea1QMNHY1cGyDmP4UsnHx+1d3QkppSUrp88DOZB/qz5D9hf5kRByWH7Yd0A/4wxoee0+y33PDD9RbgSXApxsde2+j+wcADwJzI6JdRLQD5uV92b1BX/dPKe2/hj5IamEWBKpVh5KNkd8XEd0jojvZX8//4oNhgx5AAGv6sN+4if1rY/oqtg0DLgZuBwaTfbh+M9/XcS36cDNQDxyfDzF8GfhjSmlJU51KKT2XUrogpXQQWQEwFbigwWPTxOP3pdFzy4uDmcBGjY5t/DvYhGyYZnGj274UG5YpYhbQJZ9z0lAP4H3TAVVUqsKtShwyUK1a9qF/yyr2DYmIYWQfBPVkH2irM7OJ/Qvzfzs02t6DbEy9oVX9pzkE+GtK6ZxlG1Yxnt9UH0gpLYiIm8iSgTfI0oU1/VW/unYmRcQtwH82eGyaePypQK+GG/IP143J5kes8BCN7r9HNvzws1W0O685fV4HXgTaAtsALzXYvn2+T6oYv/5YqqB8KOAI4EayvzQb3r5DNtFwv3wm+Wjg5MYz9BsYCRwcEb1Xs39y/u8ODR5/c5o/Ga0TWWrR0Imr6MNGEXF4E21dTTZv4CfAUymlNX6Y5RMmV2VbPvhL/iVgClnisDqjgaMb/YV9DNkfDI830eeRwMeBCSmlMY1uLzVx7rryBDCXrDgDICI2IHsN/W+V+iC1eiYEqkWDyRapuSylNLrhjnzC2jlkCcKDZAvSPAT8b0QMJ5vQtzcwJqV0D/Br4GTgb5Fdt/8W2Yd/55TSL1NKkyNiDPCziHifrEj+ISv/Zbw6D5JN6BtNNov/RLK/VBsfMwK4ISJ+SnZ1QF/gMymlry07KKU0Or+k8dPA12jauZFdhnkD2Yz6zmQf5EeQT3RMKdVHxPeA6yPierIiK5HNa7gxn3x4ATAOuCMifgtsRrbAz4hGEwpX5RLgS8DDEXE5WfHRm+yKicdTSjcCRMTIvD9rnEcQEYfmz2OX/P5x+a6nU0pv5NuuBj6bUtomb3NhRFyU/z5mkaUC3yH7//JypAoq01I+FgSqRScALzcuBgBSSosj4mbgixHxjZTSqIg4kCyy/jOwiPzDLT/+nYj4FNnlgpeSTfZ7mezqhIaP9/v8/MnA98iuTmiOn5JdvrdszP42sln/dzfoc4qIo/M+DsuPf5vsg7yxO4CtySZQNuV6oAtwJtlllu+TXU54Qkpp+fkppRsiYiFZIfVXsqLpKeCdfP+E/IP4v/L+zyUrHL7XVAdSSu9GxF7AhWTFV3eyIYjHgecaHNp4fH91fks2cXSZZUNGpwLXNmir8XvXRWQFwA/IhjrGAAemlFY170PSKrhSoVRDIuLvwEsppZNaui+SmjZt7uKKf4j26dq+KisVmhBINSAidieL8ffgg6sUJKlqLAik2vA02SJMP0gpPd3SnZHUTCUK2S0IpBrQnC/xkaRKsiCQJKmgMq1DUMsFQXl+y5KklmDythZquSDgzLurta6JWoNfHZF9o26ngae1cE9US+rGXQH4utCKlr0uKq1MF+q5UqEkSarthECSpFpWooDAhECSJJkQSJJUmHMIJElSqZgQSJJUWHkiAhMCSZJkQiBJUlHOIZAkSaViQiBJUkElCggsCCRJKsohA0mSVComBJIkFVSmrz82IZAkSSYEkiQVVp6AwIRAkiSZEEiSVFiJAgITAkmSZEIgSVJhrkMgSZJaXERsHhGPRMQLETEhIk7Pt/8kIqZExLP57XNNtWVCIElSQTWwDsES4MyU0tiI2BB4JiIezPf9OqV0cXMbsiCQJKmVSilNBabmP8+LiInApkXacshAkqSiUuVvETE0IsY0uA1dVVciYktgIDA633RaRDwXEddERI+mnooFgSRJNSylNDyltHuD2/DGx0REF+BWYFhKaS7wW+CjwC5kCcKvmnochwwkSSqoxWcQABHRnqwYuD6ldBtASml6g/1XAfc01Y4JgSRJrVREBHA1MDGldEmD7X0bHHY0ML6ptkwIJEkqqAbWIfgUcBLwfEQ8m2/7IXBCROxCFmJMAr7WVEMWBJIktVIppceBWMWu+9a2LQsCSZIKqoF1CNYZ5xBIkiQTAkmSCitPQGBCIEmSTAgkSSqsRAGBBYEkSUXVwGWH64xDBpIkyYRAkqSivOxQkiSVigmBJElFlScgMCGQJEkmBJIkFVaigMCEQJIkmRBIklSY6xBIkqRSMSGQJKkg1yGQJEmlYkIgSVJR5QkITAgkSZIJgSRJhZUoIDAhkCRJJgSSJBXmOgSSJKlUTAgkSSrIdQgkSVKpmBBIklRUeQICCwJJkooqUT3gkIEkSTIhkCSpMC87lCRJpWJCIElSQV52KEmSSsWEQJKkosoTEFgQVMPnd+7DDr07M/9fS7n4sUkAHPSxjdmrfzfmL1oKwH0vvsuLMxasdO52PTfgqAG9aRMw+s05PPzKewBs1Kk9X9qtL507tGXy7IXcMG4qSxO0bRN8cZc+bNa9IwsWLeVPz7zNrLolVXuuKu7AfXbg4u8eR9s2bbj2jie4+A8PrrC/Q/t2XP2zkxi4Q3/em7OAL33/Gt6cmr0ezvrKQZwyeG+W1tdz5i//ykNPTmxWm6p9vi5ULQ4ZVMHTb83hqtGTV9o+6rVZXDLqDS4Z9cYqi4EAjvlEb64aPZlfPvI6A/ttSO8uHQA4bMdNGPXaLH7+8Ou8v7iePft3B+CTm3fj/cX1/Pzh1xn12iwO36FnRZ+b1o02bYJLzz6ewaddycBjL2DIIbux/dZ9VjjmlKP2Zta8OgYMPp/Lr3+EC08fDMD2W/dhyMG7sutxF3LkN6/ksh8cT5s20aw2Vdt8XdS+VIVbtVgQVMFr79Xxfp4ErI3+PToyc8Fi3nt/MUsTjHt7Hh/v0wWAbTfZgOemzgNgzOQ5fCLfPqBPF8ZMngPAc1PnsW3PDdbRs1Al7TFgS159610mTZnJ4iVLuWXEWA4ftNMKxxw+aCeuv3s0ALc9NI5Be263fPstI8ayaPES3nh7Jq++9S57DNiyWW2qtvm6UDVVbMggIrYHBgOb5pumAHellCZW6jFbm09t1YPdNu/G5NkLueuFGdQtrl9hf7eO7Zhdt3j5/TkLl9C/e0c6d2hL3eJ66vPScU7dErp2zP6v7NqxHbPzIYL6BHWL6+ncoS0LChQkqp5+vboxefqs5fenTJ/FngO2XPmYadkxS5fWM3d+HRt378ymPbsx+vlJH5w7Yxb9enUDaLJN1TZfF7XPdQiaEBHfB24iS73/nt8CuDEizl7DeUMjYkxEjBk+fHglulYznpg0m/8a+RqXPDaJuf9awpE79mrpLkmS1mOVSgi+Cnw8pbS44caIuASYAFy0qpNSSsOBZZVAOvPulyrUvZY3v8Ff7E+9MZuv7rnZSsfMWbiE7p3aL7/frWM75ixcwoJFS+nUvg1tIksBunVqx9yFWSowd+ESunfKjmsT0Kl9G9OBVuDtGXPYrHeP5fc37d2DKe/MWfmYPj2YMmM2bdu2oWuXTsycvYAp72Tbl5/bqwdvz8jObapN1TZfF7XPdQiaVg/0W8X2vvm+9d6GH2m7/OdP9N2QafP+tdIxb81eyCad27NRp/a0DRjYb0MmTJsPwCvv1rFT3w0B2H2zbozPt0+YPp/dN8tiwZ36bsjL775f6aeidWDMhDfYpn9Ptui3Me3btWXIwbty76PPrXDMvY89z4lHfBKAYw4YyGNP/zPb/uhzDDl4Vzq0b8cW/TZmm/49eXr8pGa1qdrm60LVVKmEYBgwMiJeBt7Kt/UHtgFOq9Bj1qwv7dqXj268AZ07tOXcA7ZmxEsz+egmndi0a0cSMOv9xdzy3DQAun6kLcfv3Iff/30K9QluGz+DoXttRgT8/a05TJ+/CIB7Jr7DSbv25dDtN2HKnH8x+q2swh/95hy+OLAvP9hvK95ftJQ/jZ3aUk9ba2Hp0nrO+MXN3H3lN2nbJrjuzqeY+No0zv3GYYx94U3ufex5rr3jCa654GTG33kes+Yu4KSz/wDAxNemcesD4xh36zksWVrPsItupr4+m5+8qjbVevi6aAXKExAQqUIzIiKiDbAnK04qfDql1Nz8utRDBlp7vzoimz3daeB6V1NqDerGXQH4utCK8tdFVPpxnn5tTsVLgj227lbx5wEVvMogpVQPPFWp9iVJamklCghch0CSJLl0sSRJhbkOgSRJKhUTAkmSCirTOgQWBJIkFVWeesAhA0mSZEIgSVJhJQoITAgkSZIJgSRJhdWX6LpDEwJJkmRCIElSUeXJB0wIJEkSJgSSJBVWoikEJgSSJMmEQJKkwsq0dLEJgSRJMiGQJKmo+vIEBCYEkiTJhECSpMKcQyBJkkrFhECSpIJch0CSJJWKCYEkSQU5h0CSJJWKCYEkSQWVaR0CCwJJkgpyyECSJJWKCYEkSQV52aEkSSoVEwJJkgoyIZAkSaViQiBJUkH1XmUgSZJaWkRsHhGPRMQLETEhIk7Pt28UEQ9GxMv5vz2aasuCQJKkglKq/K0JS4AzU0o7AnsB34yIHYGzgZEppW2Bkfn9NbIgkCSplUopTU0pjc1/ngdMBDYFBgPX5YddBxzVVFvOIZAkqaBqrFQYEUOBoQ02DU8pDV/FcVsCA4HRQO+U0tR81zSgd1OPY0EgSVINyz/8VyoAGoqILsCtwLCU0tyIaHh+iogmKxcLAkmSCqqFdQgioj1ZMXB9Sum2fPP0iOibUpoaEX2BGU214xwCSZJaqciigKuBiSmlSxrsugv4cv7zl4E7m2rLhECSpIJqYB2CTwEnAc9HxLP5th8CFwE3R8RXgTeA45tqyIJAkqRWKqX0OBCr2b3/2rRlQSBJUkG1MIdgXXEOgSRJMiGQJKmoEgUEJgSSJMmEQJKkwlKJJhGYEEiSJBMCSZKKqm/pDqxDFgSSJBXkkIEkSSoVEwJJkgoqTz5gQiBJkjAhkCSpMOcQSJKkUjEhkCSpoDJddmhCIEmSTAgkSSrKOQSSJKlUTAgkSSqoRAGBCYEkSYKo4fGPmu2YJKlViEo/wJ+fmVzxz6ov7bZZxZ8HmBBIkiRqfA5Bp4GntXQXVEPqxl0BQN+ht7ZwT1RLpg4/FvD9Qita9n5RafW1m7KvNRMCSZJU2wmBJEm1rDz5gAmBJEnChECSpMJq+Eq9tWZCIEmSTAgkSSqqTN92aEEgSVJBJRoxcMhAkiSZEEiSVJgLE0mSpFIxIZAkqaASBQQmBJIkyYRAkqTCnEMgSZJKxYRAkqSC6ssTEJgQSJIkEwJJkgor0RQCEwJJkmRCIElSYfWUJyIwIZAkSSYEkiQV5RwCSZJUKiYEkiQV5DoEkiSpVEwIJEkqyO8ykCRJpWJCIElSQSUKCCwIJEkqykmFkiSpVEwIJEkqKJVozMCEQJIkmRBIklSUcwgkSVKpmBBIklSQCYEkSSoVEwJJkgpKlCciMCGQJEkmBJIkFVWmOQSrLQgiYh4sz0Ii/zflP6eUUtcK902SJFXJaguClNKG1eyIJEmtTYkWKmzeHIKI+HREnJr/vElEbFXZbkmSpGpqcg5BRJwH7A5sB/wB6AD8GfhUZbsmSVJtqy9RRNCchOBo4EhgAUBK6W3A4QRJkkqkOVcZLEoppYhIABHRucJ9kiSpVSjTVQbNSQhujojfAd0j4j+Ah4CrKtstSZJUTU0mBCmliyPiQGAu8DHgxymlByveM0mSalyJphA0e2Gi54FOZOsQPF+57kiSpJbQ5JBBRPw78HfgGOA44KmI+EqlOyZJUq2rT6nit2ppTkLwXWBgSmkmQERsDDwBXFPJjkmSVOvKNGTQnEmFM4F5De7Py7dJkqSSWNN3GXwn//EVYHRE3Ek2h2Aw8FwV+iZJUk2rb+kOrENrGjJYtvjQq/ltmTsr1x1JktQS1vTlRudXsyOSJLU269XSxRHRMyL+OyLui4iHl92q0TlJkrRmEXFNRMyIiPENtv0kIqZExLP57XNNtdOcSYXXAy8CWwHnA5OApwv2W5Kk0kip8rdmuBY4ZBXbf51S2iW/3ddUI80pCDZOKV0NLE4pPZZS+gqwX7O6KEmSKiqlNAp478O205yCYHH+79SIOCwiBgIbfdgHliSptatPlb99CKdFxHP5kEKPpg5uTkFwQUR0A84EzgJ+D5zxobooSZKaJSKGRsSYBrehzTjtt8BHgV2AqcCvmjqhOV9udE/+4xxg32Z0QpKk9UKqwlUGKaXhwPC1PGf6sp8j4irgnjUcDqx5YaLLyRYiWt2DfXttOidJkqojIvqmlKbmd48Gxq/peFhzQjBmnfRKkqSS+pBj/OtERNwIDAI2iYjJwHnAoIjYhewP+0nA15pqZ00LE123TnqqlRy4zw5c/N3jaNumDdfe8QQX/+HBFfZ3aN+Oq392EgN36M97cxbwpe9fw5tTswmkZ33lIE4ZvDdL6+s585d/5aEnJzarTdW+rp3a86uTd2X7TbuRUuKM657hmddWnDj8s8/vzP6f6EPdoqUMu3YMz785G4Ahe/dn2Od2AODS+yZyy5NvArBT/+5ceurudGzflpHPT+Pcv/yjuk9KH5rvF2pKSumEVWy+em3bac6kQq1DbdoEl559PINPu5KBx17AkEN2Y/ut+6xwzClH7c2seXUMGHw+l1//CBeePhiA7bfuw5CDd2XX4y7kyG9eyWU/OJ42baJZbar2/ezzO/PIhOn8248fYP+fPsTLU+etsH+/AX3YuncX9vnRCL77p7FcdOJAALpv0J4zD9+Rw37+MJ/7+cOcefiOdNugPQAXnTiQs/44ln1+NIKte3dhvwG9q/68VJzvF7Wvxq8yWCsWBFW2x4AtefWtd5k0ZSaLlyzllhFjOXzQTiscc/ignbj+7tEA3PbQOAbtud3y7beMGMuixUt44+2ZvPrWu+wxYMtmtanatmGnduz1sU244fFJACxemphbt3iFYw7ZpS+3PPkGAGNff4+undrTq1tHBn28N6MmTmf2+4uZ8/5iRk2czr4f702vbh3ZsFN7xr6e/bV4y5NvcMgu/ar6vPTh+H6harIgqLJ+vboxefqs5fenTJ/Fpj27rXzMtOyYpUvrmTu/jo27d2bTnh9sB5gyYxb9enVrVpuqbf037szMef/i0lN244Ef7c/FJ+1Kpw5tVzimT/dOvD2rbvn9qbPq6Nu9Y7b9vRW39+neib7dO650fJ/unSr/ZLTO+H5R+1JKFb9VS9WvMoiIU1NKf1jNvqHAUIDf/e53RZqXWqV2bYNP9O/OOTc9y7jXZ/Gzz+/Mtw7Zjl/e9UJLd03SeqIlrjI4H1hlQdDoWst0+m9Pq1AXWs7bM+awWe8PFozatHcPprwzZ+Vj+vRgyozZtG3bhq5dOjFz9gKmvJNtX35urx68PSM7t6k2VdvenlXH1Fl1jHs9+8vtnmcmc9qh261wzLTZdfTr8cFf+H17dGLq7IVMm13HPtv1XGH7Ey+9w9TZC1c6ftrsOtR6+H5R++pbugPr0GqHDFJK163ptqZG86USV3V7HlivZzWNmfAG2/TvyRb9NqZ9u7YMOXhX7n30uRWOufex5znxiE8CcMwBA3ns6X9m2x99jiEH70qH9u3Yot/GbNO/J0+Pn9SsNlXb3pn7L96eVcdHe3cB4NM79OKfb684qXDEP6YyZO8tANh1q42YV7eYGXMW8uiE6Xx2x95026A93TZoz2d37M2jE6YzY85C5tUtZtetspXGh+y9Bfc/OxW1Hr5fqJqaXKkwInoC3wd2BDou255SWtMXHPUGDgZmNdoewBNr383yWLq0njN+cTN3X/lN2rYJrrvzKSa+No1zv3EYY194k3sfe55r73iCay44mfF3nsesuQs46ewsUJn42jRufWAc4249hyVL6xl20c3U1ycgrbJNtS7n3Pgsv/nqnrRv14Y3313AsGvHcPJntgLgj6NeZ+Tz09h/QB+evPBg6hYt5YxrsxBv9vuL+fW9E/nfH2b/SV5yz0Rmv59NSPzBDeO49JTd6dihLQ+Pn87D431dtCa+X9S+ao7xV1o09WQi4gHgL2TfY/B14MvAOyml76/hnKuBP6SUHl/FvhtSSl9sRt9Sp4HlGzJQcXXjrgCg79BbW7gnqiVThx8LgO8Xaih/v4hKP85/3vZCxSuCK4/ZseLPA5qREJB//XFEnJ5Segx4LCKeXtMJKaWvrmFfc4oBSZJqXokCgmYVBCt8/THwNn79sSRJ1JeoImhOQdDw648vB7ri1x9LklQqfv2xJEkFlSggaNZVBn9gFQsUpZS+UpEeSZKkqmvOkME9DX7uSPa9ym9XpjuSJLUeZbrssDlDBitc45V/7/JKlxNKkqTWqzkJQWPbAr3WdUckSWptShQQNGsOwTxWnEMwjWzlQkmSVBLNGTLYsBodkSSptSnTOgSr/XKjZSJiZHO2SZKk1mu1CUFEdAQ2ADaJiB58sCZ0V2DTKvRNkqSaVp58YM1DBl8DhgH9gGf4oCCYC1xR4X5JkqQqWm1BkFK6DLgsIr6VUrq8in2SJKlVKNM6BE3OIQDqI6L7sjsR0SMi/rOCfZIkSVXWnILgP1JKs5fdSSnNAv6jcl2SJKl1qE+Vv1VLcwqCthGxbP4AEdEW6FC5LkmSpGprzkqF9wN/iYjf5fe/lm+TJGm9VqY5BM0pCL4PDAW+kd9/ELiqYj2SJElV1+SQQUqpPqX0Pyml41JKxwEvAF51IEla76VU+Vu1NOvLjSJiIHACcDzwOnBbJTslSZKqa00rFX6MrAg4AXgX+AsQKaV9q9Q3SZJq2voyh+BF4G/A4SmlVwAi4oyq9EqSpFagmpcFVtqa5hAcA0wFHomIqyJifz5YvliSJJXImpYuvgO4IyI6A4PJvtegV0T8Frg9pfRAlfooSVJNKtOQQXOuMliQUrohpXQEsBkwjuxSREmSVBLNWalwuZTSrJTS8JTS/pXqkCRJrUWqwq1a1qogkCRJ5dSsdQgkSdLK6tenOQSSJKn8TAgkSSqoRAGBCYEkSTIhkCSpsPVqHQJJklR+JgSSJBVUooDAhECSJJkQSJJUmOsQSJKkUjEhkCSpoBIFBCYEkiTJhECSpMJch0CSJJWKCYEkSQXVlycgsCCQJKmoRHkqAocMJEmSCYEkSUWVaE6hCYEkSTIhkCSpMC87lCRJpWJCIElSQWW67NCEQJIkmRBIklSUcwgkSVKpmBBIklRQiQICEwJJkmRCIElSYfUlighMCCRJkgmBJDuq1vEAABDeSURBVElFlSggMCGQJEkmBJIkFeY6BJIkqVSihqubmu2YJKlViEo/wKBLn6j4Z9Wjw/ap+PMAEwJJkkSNzyHoNPC0lu6CakjduCsAXxda0bLXxcIlLdwR1ZSOVfp0q+GUfa3VdEEgSVItK1E94JCBJEkyIZAkqbAyDRmYEEiSJAsCSZKKSilV/NaUiLgmImZExPgG2zaKiAcj4uX83x5NtWNBIElS63YtcEijbWcDI1NK2wIj8/trZEEgSVJBKVX+1nQf0ijgvUabBwPX5T9fBxzVVDsWBJIk1bCIGBoRYxrchjbjtN4ppan5z9OA3k2d4FUGkiQVVI2rDFJKw4HhH+L8FBFNdtSEQJKk8pkeEX0B8n9nNHWCBYEkSQXVwhyC1bgL+HL+85eBO5s6wYJAkqRWLCJuBJ4EtouIyRHxVeAi4MCIeBk4IL+/Rs4hkCSpoFpYqTCldMJqdu2/Nu2YEEiSJBMCSZKKqoGAYJ0xIZAkSSYEkiQVVQtzCNYVEwJJkmRCIElSUSUKCEwIJEmSCYEkSYWVaQ6BBYEkSQWVqB5wyECSJJkQSJJUWJmGDEwIJEmSCYEkSUWVKCAwIZAkSSYEkiQV5hwCSZJUKiYEkiQVVKKAwIRAkiSZEEiSVJhzCCRJUqmYEEiSVJAJgSRJKhUTAkmSCipRQGBCIEmSTAgkSSrMOQSSJKlUTAgkSSqoRAGBCYEkSTIhkCSpsPr68kQEJgSSJMmEQJKkoso0h8CCQJKkgrzsUJIklYoJgSRJBZUoIDAhkCRJJgSSJBXmHAJJklQqJgSSJBVUooDAhECSJJkQSJJUmHMIJElSqZgQSJJUkAmBJEkqFRMCSZKKKk9AYEIgSZJMCCRJKsw5BJIkqVRMCCRJKsiEQB/KgfvswD9uP5fxd57HWaceuNL+Du3b8aeLTmX8necx6o9n0b/vRsv3nfWVgxh/53n84/ZzOWDvHZrdpmqfrwtNmzqVr55yEkcf8TmOPvIwrv/TdQC89OKLnPTFz3PsUUfwrf/8OvPnz1/l+f/3t1EcedjBHH7IgVx91fDl2ydPfosTvzCEww85kO+eOYzFixYBsGjRIr575jAOP+RATvzCEKZMmVz5J6maZUFQZW3aBJeefTyDT7uSgcdewJBDdmP7rfuscMwpR+3NrHl1DBh8Ppdf/wgXnj4YgO237sOQg3dl1+Mu5MhvXsllPzieNm2iWW2qtvm6EEDbdm0563tnc/vd9/HnG//CTTfewKuvvML5Pz6H0884k1vvuJv9DjiAa6/5/UrnLl26lP+68Kdc+T+/5/a77uX+++7h1VdeAeCySy7mSyefwj33P0jXrl25/ba/AnD7rbfQtWtX7rn/Qb508ilcesnFVX2+ZZBSqvitWiwIqmyPAVvy6lvvMmnKTBYvWcotI8Zy+KCdVjjm8EE7cf3dowG47aFxDNpzu+XbbxkxlkWLl/DG2zN59a132WPAls1qU7XN14UAevbsxQ47fhyAzp27sPXWWzNjxnTeeGMSu+2+BwB77/0pRj74wErnjn/+OTbffAs223xz2nfowCGfO4xHHxlJSom/j36KAw86GIAjBx/NwyNHAvDIww9z5OCjATjwoIP5+1NPlioC19qpWEEQEdtHxP4R0aXR9kMq9ZitQb9e3Zg8fdby+1Omz2LTnt1WPmZadszSpfXMnV/Hxt07s2nPD7YDTJkxi369ujWrTdU2XxdqbMqUybw4cSKf2GlnPrrNtjzycPYh/sCI+5k2bepKx8+YPp0+fT9IgHr17s306dOZPXsWG27YlXbtsiljvXv3YcaM6dk5M6bTp09fANq1a0eXDTdk9uxZK7Wt1TMhaEJEfBu4E/gWMD4iBjfY/V9rOG9oRIyJiDHDhw9f3WGSVGrvL1jAmcO+zXfP/iFdunTh/J9dyF9uuoEvDDmG999fQPv2HVq6i1omVeFWJZW6yuA/gN1SSvMjYkvgrxGxZUrpMiBWd1JKaTiwrBJIp//2tAp1r+W8PWMOm/Xusfz+pr17MOWdOSsf06cHU2bMpm3bNnTt0omZsxcw5Z1s+/Jze/Xg7RnZuU21qdrm60LLLF68mO8M+zafO+wIDjjwIAC22vqj/O6qawCYNOl1Rj326Ern9erdm2lTpy2/P2P6dHr37k337j2YN28uS5YsoV27dkyfPo1evXpn5/TqzbRpU+ndpw9Llixh/rx5dO/eY6W2tX6o1JBBm5TSfICU0iRgEHBoRFzCGgqC9cGYCW+wTf+ebNFvY9q3a8uQg3fl3kefW+GYex97nhOP+CQAxxwwkMee/me2/dHnGHLwrnRo344t+m3MNv178vT4Sc1qU7XN14Ugi59/8uNz2HrrrTn5lFOXb585cyYA9fX1XPW73zLk819Y6dyPD/gEb745icmT32LxokXcf9+9fHbf/YgI9tjzkzz4wAgA7rrzdvbdbz8ABu27H3fdeTsADz4wgj0/uRcR6/Vb9For05BBpRKC6RGxS0rpWYA8KTgcuAb4RIUes1VYurSeM35xM3df+U3atgmuu/MpJr42jXO/cRhjX3iTex97nmvveIJrLjiZ8Xeex6y5Czjp7D8AMPG1adz6wDjG3XoOS5bWM+yim6mvzzKlVbWp1sPXhQDGjX2Ge+66k20/9jGOPyYbaf3WsO/w5huTuOnGGwDY/4ADOeroY4FsDsD5P/4Rv/mfq2jXrh0/OOfHfGPov1Nfv5Sjjj6WbbbZFoBh3/ku3zvrDH7z/y5l+x124OhjhwBw9LHHcc7Z3+XwQw6ka7du/PLiX7fAs1atiEpUHxGxGbAkpbTSu09EfCql9H/NaCZ1Gli+IQMVVzfuCgB8XaihZa+LhUtauCOqKR2zP3crHnds+o3bK/4n/JTfHl2V2KYiCUFKabWrWzSzGJAkSVXk0sWSJBVUpnUbXJhIkiSZEEiSVFh5AgITAkmSZEIgSVJhziGQJEmlYkIgSVJBJgSSJKlUTAgkSSrIhECSJJWKCYEkSQWZEEiSpFIxIZAkqajyBAQmBJIkyYRAkqTCyjSHwIJAkqSCylQQOGQgSZJMCCRJKqoWEoKImATMA5YCS1JKuxdpx4JAkqTWb9+U0rsfpgELAkmSimr5gGCdcQ6BJEk1LCKGRsSYBrehjQ5JwAMR8cwq9jWbCYEkSQVVYw5BSmk4MHwNh3w6pTQlInoBD0bEiymlUWv7OCYEkiS1YimlKfm/M4DbgT2LtGNBIElSQSmlit/WJCI6R8SGy34GDgLGF3kuDhlIktR69QZujwjIPtNvSCndX6QhCwJJkgpq6XUIUkqvATuvi7YcMpAkSSYEkiQV1dIJwbpkQiBJkkwIJEkqrDwBgQmBJEkyIZAkqTDnEEiSpFIxIZAkqSATAkmSVComBJIkFVSmhMCCQJKkgspUEDhkIEmSTAgkSSqsPAGBCYEkSTIhkCSpMOcQSJKkUjEhkCSpIBMCSZJUKiYEkiQVZUIgSZLKxIRAkqSiUn1L92CdMSGQJEkmBJIkFeYcAkmSVCYmBJIkFeUcAkmSVCYmBJIkFeUcAkmSVCYmBJIkFeUcAkmSVCYmBJIkFWVCIEmSysSEQJKkokp0lYEFgSRJRZVoyKCmC4K6cVe0dBdUg3xdaFU61vS7mVT7avk/oWjpDtSKiBiaUhre0v1QbfF1oVXxdVFlJRoycFJh6zC0pTugmuTrQqvi60KF1HJCIElSbSvRHAITAkmSZELQSjgeqFXxdaFV8XVRTc4hUDU5QUir4utCq+LrQkWZEEiSVJRzCCRJUplYENS4iDgkIl6KiFci4uyW7o9aXkRcExEzImJ8S/dFtSMiNo+IRyLihYiYEBGnt3Sf1gspVf5WJRYENSwi2gK/AQ4FdgROiIgdW7ZXqgHXAoe0dCdUc5YAZ6aUdgT2Ar7p+4XWhnMIatuewCsppdcAIuImYDDwQov2Si0qpTQqIrZs6X6otqSUpgJT85/nRcREYFN8v6gs5xCoSjYF3mpwf3K+TZJWKy8YBwKjW7Ynak1MCCSpRCKiC3ArMCylNLel+1N6rkOgKpkCbN7g/mb5NklaSUS0JysGrk8p3dbS/VHrYkJQ254Gto2IrcgKgS8AX2zZLkmqRRERwNXAxJTSJS3dn/WGcwhUDSmlJcBpwAhgInBzSmlCy/ZKLS0ibgSeBLaLiMkR8dWW7pNqwqeAk4D9IuLZ/Pa5lu6UWg8TghqXUroPuK+l+6HakVI6oaX7oNqTUnociJbux3qn3jkEkiSpREwIJEkqqkRzCCwIJEkqqkQFgUMGkiTJhECSpMJcmEha/0TE0vxSrvERcUtEbPAh2ro2Io7Lf/79mr6EJiIGRcQ+BR5jUkRs0tztjY6Zv5aP9ZOIOGtt+yipdlgQSM1Xl1LaJaU0AFgEfL3hzogolLillP49pbSmL6AZBKx1QSCpClJ95W9VYkEgFfM3YJv8r/e/RcRdwAsR0TYi/jsino6I5yLia5CtIhcRV0TESxHxENBrWUMR8WhE7J7/fEhEjI2If0TEyPxLar4OnJGnE/8WET0j4tb8MZ6OiE/l524cEQ9ExISI+D3NuCY9Iu6IiGfyc4Y22vfrfPvIiOiZb/toRNyfn/O3iNh+XfwyJbU85xBIaylPAg4F7s837QoMSCm9nn+ozkkp7RERHwH+LyIeIPvmue2AHYHeZF9Je02jdnsCVwGfydvaKKX0XkT8DzA/pXRxftwNwK9TSo9HRH+ylSx3AM4DHk8p/TQiDgOas4LhV/LH6AQ8HRG3ppRmAp2BMSmlMyLix3nbpwHDga+nlF6OiE8CVwL7Ffg1SuVQojkEFgRS83WKiGfzn/9Gtm78PsDfU0qv59sPAnZaNj8A6AZsC3wGuDGltBR4OyIeXkX7ewGjlrWVUnpvNf04ANgxW7oegK75N9x9BjgmP/feiJjVjOf07Yg4Ov9587yvM4F64C/59j8Dt+WPsQ9wS4PH/kgzHkNSK2BBIDVfXUppl4Yb8g/GBQ03Ad9KKY1odNy6XFO+DbBXSmnhKvrSbBExiKy42Dul9H5EPAp0XM3hKX/c2Y1/B9J6zXUIJK3GCOAb+dfQEhEfi4jOwCjg8/kcg77Avqs49yngM/m3WxIRG+Xb5wEbNjjuAeBby+5ExLIP6FHk34YZEYcCPZroazdgVl4MbE+WUCzTBliWcnyRbChiLvB6RAzJHyMiYucmHkNSK2FBIK1bvyebHzA2IsYDvyNL4m4HXs73/ZHs2wpXkFJ6BxhKFs//gw8i+7uBo5dNKgS+DeyeT1p8gQ+udjifrKCYQDZ08GYTfb0faBcRE4GLyAqSZRYAe+bPYT/gp/n2E4Gv5v2bAAxuxu9EKq+UKn+rkkglmhAhSVI1ddrr+xX/EK176hdV+RZL5xBIklSUcwgkSVKZmBBIklRUiYbdTQgkSZIJgSRJhTmHQJIklYkJgSRJRTmHQJIklYkJgSRJRTmHQJIk1cLSxRFxSES8FBGvRMTZRZ+KBYEkSa1URLQFfgMcCuwInBAROxZpyyEDSZKKavkhgz2BV1JKrwFExE1kXzr2wto2ZEIgSVLrtSnwVoP7k/Nta82EQJKkgurGXVHxbyKMiKFkX42+zPCU0vB1/TgWBJIk1bD8w391BcAUYPMG9zfLt601hwwkSWq9nga2jYitIqID8AXgriINmRBIktRKpZSWRMRpwAigLXBNSmlCkbYilWjZRUmSVIxDBpIkyYJAkiRZEEiSJCwIJEkSFgSSJAkLAkmShAWBJEnCgkCSJAH/HwM8e9JVwxxZAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]}],"metadata":{"colab":{"name":"Fraud 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