fraud-detection-analysis-using-ml-tc.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":1940,"sourceType":"datasetVersion","datasetId":1069}],"dockerImageVersionId":30626,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n#Visualization libraries\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom plotly.offline import plot, iplot, init_notebook_mode\ninit_notebook_mode(connected=True)\n%matplotlib inline\n\n#preprocessing libraries\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\n\n#ML libraries\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.neighbors import KNeighborsClassifier\n#Metrics Libraries\nfrom sklearn import metrics\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix\n#Misc libraries\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-01-01T02:20:44.793053Z","iopub.execute_input":"2024-01-01T02:20:44.794195Z","iopub.status.idle":"2024-01-01T02:20:44.828170Z","shell.execute_reply.started":"2024-01-01T02:20:44.794155Z","shell.execute_reply":"2024-01-01T02:20:44.826992Z"},"trusted":true},"execution_count":118,"outputs":[{"name":"stdout","text":"/kaggle/input/paysim1/PS_20174392719_1491204439457_log.csv\n","output_type":"stream"},{"output_type":"display_data","data":{"text/html":"        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-2.25.2.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "},"metadata":{}}]},{"cell_type":"code","source":"# read the data\ndf = pd.read_csv(\"/kaggle/input/paysim1/PS_20174392719_1491204439457_log.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:20:44.830459Z","iopub.execute_input":"2024-01-01T02:20:44.831262Z","iopub.status.idle":"2024-01-01T02:21:00.030799Z","shell.execute_reply.started":"2024-01-01T02:20:44.831220Z","shell.execute_reply":"2024-01-01T02:21:00.029566Z"},"trusted":true},"execution_count":119,"outputs":[]},{"cell_type":"code","source":"#ML libraries\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.neighbors import KNeighborsClassifier\n#Metrics Libraries\nfrom sklearn import metrics\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix\n#Misc libraries\nimport warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:00.032174Z","iopub.execute_input":"2024-01-01T02:21:00.032511Z","iopub.status.idle":"2024-01-01T02:21:00.039022Z","shell.execute_reply.started":"2024-01-01T02:21:00.032481Z","shell.execute_reply":"2024-01-01T02:21:00.037786Z"},"trusted":true},"execution_count":120,"outputs":[]},{"cell_type":"code","source":"# read the data\ndf = pd.read_csv(\"/kaggle/input/paysim1/PS_20174392719_1491204439457_log.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:00.041665Z","iopub.execute_input":"2024-01-01T02:21:00.042099Z","iopub.status.idle":"2024-01-01T02:21:15.054541Z","shell.execute_reply.started":"2024-01-01T02:21:00.042008Z","shell.execute_reply":"2024-01-01T02:21:15.053352Z"},"trusted":true},"execution_count":121,"outputs":[]},{"cell_type":"code","source":"# checking the first 5 rows of the data\ndf.head(5)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:15.055738Z","iopub.execute_input":"2024-01-01T02:21:15.056035Z","iopub.status.idle":"2024-01-01T02:21:15.074409Z","shell.execute_reply.started":"2024-01-01T02:21:15.056008Z","shell.execute_reply":"2024-01-01T02:21:15.073424Z"},"trusted":true},"execution_count":122,"outputs":[{"execution_count":122,"output_type":"execute_result","data":{"text/plain":"   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n\n      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n0  M1979787155             0.0             0.0        0               0  \n1  M2044282225             0.0             0.0        0               0  \n2   C553264065             0.0             0.0        1               0  \n3    C38997010         21182.0             0.0        1               0  \n4  M1230701703             0.0             0.0        0               0  ","text/html":"<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>step</th>\n      <th>type</th>\n      <th>amount</th>\n      <th>nameOrig</th>\n      <th>oldbalanceOrg</th>\n      <th>newbalanceOrig</th>\n      <th>nameDest</th>\n      <th>oldbalanceDest</th>\n      <th>newbalanceDest</th>\n      <th>isFraud</th>\n      <th>isFlaggedFraud</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>9839.64</td>\n      <td>C1231006815</td>\n      <td>170136.0</td>\n      <td>160296.36</td>\n      <td>M1979787155</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>1864.28</td>\n      <td>C1666544295</td>\n      <td>21249.0</td>\n      <td>19384.72</td>\n      <td>M2044282225</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>TRANSFER</td>\n      <td>181.00</td>\n      <td>C1305486145</td>\n      <td>181.0</td>\n      <td>0.00</td>\n      <td>C553264065</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>CASH_OUT</td>\n      <td>181.00</td>\n      <td>C840083671</td>\n      <td>181.0</td>\n      <td>0.00</td>\n      <td>C38997010</td>\n      <td>21182.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>11668.14</td>\n      <td>C2048537720</td>\n      <td>41554.0</td>\n      <td>29885.86</td>\n      <td>M1230701703</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# checking the shape of the data\ndf.shape","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:15.075817Z","iopub.execute_input":"2024-01-01T02:21:15.076128Z","iopub.status.idle":"2024-01-01T02:21:15.082380Z","shell.execute_reply.started":"2024-01-01T02:21:15.076100Z","shell.execute_reply":"2024-01-01T02:21:15.081378Z"},"trusted":true},"execution_count":123,"outputs":[{"execution_count":123,"output_type":"execute_result","data":{"text/plain":"(6362620, 11)"},"metadata":{}}]},{"cell_type":"code","source":"# checking the overall info about the data\ndf.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:15.083515Z","iopub.execute_input":"2024-01-01T02:21:15.083841Z","iopub.status.idle":"2024-01-01T02:21:15.095376Z","shell.execute_reply.started":"2024-01-01T02:21:15.083792Z","shell.execute_reply":"2024-01-01T02:21:15.094316Z"},"trusted":true},"execution_count":124,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 6362620 entries, 0 to 6362619\nData columns (total 11 columns):\n #   Column          Dtype  \n---  ------          -----  \n 0   step            int64  \n 1   type            object \n 2   amount          float64\n 3   nameOrig        object \n 4   oldbalanceOrg   float64\n 5   newbalanceOrig  float64\n 6   nameDest        object \n 7   oldbalanceDest  float64\n 8   newbalanceDest  float64\n 9   isFraud         int64  \n 10  isFlaggedFraud  int64  \ndtypes: float64(5), int64(3), object(3)\nmemory usage: 534.0+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"# checking for null values\ndf.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:15.096852Z","iopub.execute_input":"2024-01-01T02:21:15.097200Z","iopub.status.idle":"2024-01-01T02:21:16.034466Z","shell.execute_reply.started":"2024-01-01T02:21:15.097171Z","shell.execute_reply":"2024-01-01T02:21:16.033394Z"},"trusted":true},"execution_count":125,"outputs":[{"execution_count":125,"output_type":"execute_result","data":{"text/plain":"step              0\ntype              0\namount            0\nnameOrig          0\noldbalanceOrg     0\nnewbalanceOrig    0\nnameDest          0\noldbalanceDest    0\nnewbalanceDest    0\nisFraud           0\nisFlaggedFraud    0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# descriptive statistics of the data\ndf.describe().round(2)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:16.038028Z","iopub.execute_input":"2024-01-01T02:21:16.038352Z","iopub.status.idle":"2024-01-01T02:21:17.871698Z","shell.execute_reply.started":"2024-01-01T02:21:16.038325Z","shell.execute_reply":"2024-01-01T02:21:17.870618Z"},"trusted":true},"execution_count":126,"outputs":[{"execution_count":126,"output_type":"execute_result","data":{"text/plain":"             step       amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\ncount  6362620.00   6362620.00     6362620.00      6362620.00    6.362620e+06   \nmean       243.40    179861.90      833883.10       855113.67    1.100702e+06   \nstd        142.33    603858.23     2888242.67      2924048.50    3.399180e+06   \nmin          1.00         0.00           0.00            0.00    0.000000e+00   \n25%        156.00     13389.57           0.00            0.00    0.000000e+00   \n50%        239.00     74871.94       14208.00            0.00    1.327057e+05   \n75%        335.00    208721.48      107315.18       144258.41    9.430367e+05   \nmax        743.00  92445516.64    59585040.37     49585040.37    3.560159e+08   \n\n       newbalanceDest     isFraud  isFlaggedFraud  \ncount    6.362620e+06  6362620.00       6362620.0  \nmean     1.224996e+06        0.00             0.0  \nstd      3.674129e+06        0.04             0.0  \nmin      0.000000e+00        0.00             0.0  \n25%      0.000000e+00        0.00             0.0  \n50%      2.146614e+05        0.00             0.0  \n75%      1.111909e+06        0.00             0.0  \nmax      3.561793e+08        1.00             1.0  ","text/html":"<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>step</th>\n      <th>amount</th>\n      <th>oldbalanceOrg</th>\n      <th>newbalanceOrig</th>\n      <th>oldbalanceDest</th>\n      <th>newbalanceDest</th>\n      <th>isFraud</th>\n      <th>isFlaggedFraud</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>6362620.00</td>\n      <td>6362620.00</td>\n      <td>6362620.00</td>\n      <td>6362620.00</td>\n      <td>6.362620e+06</td>\n      <td>6.362620e+06</td>\n      <td>6362620.00</td>\n      <td>6362620.0</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>243.40</td>\n      <td>179861.90</td>\n      <td>833883.10</td>\n      <td>855113.67</td>\n      <td>1.100702e+06</td>\n      <td>1.224996e+06</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>142.33</td>\n      <td>603858.23</td>\n      <td>2888242.67</td>\n      <td>2924048.50</td>\n      <td>3.399180e+06</td>\n      <td>3.674129e+06</td>\n      <td>0.04</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>156.00</td>\n      <td>13389.57</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>239.00</td>\n      <td>74871.94</td>\n      <td>14208.00</td>\n      <td>0.00</td>\n      <td>1.327057e+05</td>\n      <td>2.146614e+05</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>335.00</td>\n      <td>208721.48</td>\n      <td>107315.18</td>\n      <td>144258.41</td>\n      <td>9.430367e+05</td>\n      <td>1.111909e+06</td>\n      <td>0.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>743.00</td>\n      <td>92445516.64</td>\n      <td>59585040.37</td>\n      <td>49585040.37</td>\n      <td>3.560159e+08</td>\n      <td>3.561793e+08</td>\n      <td>1.00</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# checking type column categories\ndf[\"type\"].unique()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:17.873070Z","iopub.execute_input":"2024-01-01T02:21:17.873384Z","iopub.status.idle":"2024-01-01T02:21:18.304377Z","shell.execute_reply.started":"2024-01-01T02:21:17.873355Z","shell.execute_reply":"2024-01-01T02:21:18.303131Z"},"trusted":true},"execution_count":127,"outputs":[{"execution_count":127,"output_type":"execute_result","data":{"text/plain":"array(['PAYMENT', 'TRANSFER', 'CASH_OUT', 'DEBIT', 'CASH_IN'],\n      dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"# storing the column ech categorie count \ntype = df[\"type\"].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:18.305779Z","iopub.execute_input":"2024-01-01T02:21:18.306197Z","iopub.status.idle":"2024-01-01T02:21:18.826565Z","shell.execute_reply.started":"2024-01-01T02:21:18.306168Z","shell.execute_reply":"2024-01-01T02:21:18.825360Z"},"trusted":true},"execution_count":128,"outputs":[]},{"cell_type":"code","source":"# getting the categories in type column\ntransaction = type.index","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:18.827942Z","iopub.execute_input":"2024-01-01T02:21:18.828345Z","iopub.status.idle":"2024-01-01T02:21:18.835255Z","shell.execute_reply.started":"2024-01-01T02:21:18.828304Z","shell.execute_reply":"2024-01-01T02:21:18.833995Z"},"trusted":true},"execution_count":129,"outputs":[]},{"cell_type":"code","source":"quantity = type.values\nquantity","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:18.836832Z","iopub.execute_input":"2024-01-01T02:21:18.837807Z","iopub.status.idle":"2024-01-01T02:21:18.848755Z","shell.execute_reply.started":"2024-01-01T02:21:18.837759Z","shell.execute_reply":"2024-01-01T02:21:18.847396Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"array([2237500, 2151495, 1399284,  532909,   41432])"},"metadata":{}}]},{"cell_type":"code","source":"# visualizing the type column categories\npx.pie(df, values = quantity, names = transaction,hole = 0.4,title = \"distribution of transaction 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BClCAAhSgAAUoQAEGDM4BClCAAnoVcLvhOn4ErhNHPb+6xa/SP2edhdtRCBQWwl1YABQWwF3w76///vvoKt8izg7Y43DuV7vF82tcnPhnwG4DEhKAcmUtKFcWKFdG/Hrun8uWsSAlWa9wbDcFKEABCqgpwIChpi7LpgAFKKCAgOvYYTj374brwG44D+yB69BeuI4fhevksYhKFwEjko8IIp7AUQaoWd2CGtUsnl+rV7VEUizvpQAFKEABnQswYOh8ANl8ClDAOALuzDNw/PWHN0hIocKdn6dKJyMNGIEaZbEANatZUKO6pcivYjWEHwpQgAIUML4AA4bxx5g9pAAFNCrg3LcLzl3b4dixBc4dv3tWJ6L5UStgBOpDtSoW1LnAggsvsKBWDQvEv/NDAQpQgALGE2DAMN6YskcUoIAWBRyFcGzdCMeuHXCK//21Ba6Tx2Pa0mgHDN/OpqYAF2ZYUUusdNSweH4tUzqmJKycAhSgAAUUEGDAUACRRVCAAhTwJ+A6+g8cWzfAseVXOLZsgOtUbAOFbxtjHTD8mTVtaEGTRlaIXyulcYWDf7IoQAEK6FGAAUOPo8Y2U4ACmhVw7NjsCROOredChZY/WgwYci+GDS3PHraNAhSgQGABBgzODgpQgAIRCjj/+gMFa79F4a8/wHVoX4SlRe92rQcMho3ozQXWRAEKUEBJAQYMJTVZFgUoYBoBd3YWCtd+i4K1q+DYtE6X/dZTwJADt2ppxaUtLGjRzKpLdzaaAmYRWPKFM6SuZtSyoklDbo0MCU2jFzNgaHRg2CwKUECbAo5tmzzBonDtKs2dqQhVTK8BQ+qneOfGpS2taNXCggrl+aUk1PHn9RRQW2DaDAe2bHMHXc3Ie+0MGEFraftCBgxtjw9bRwEKaEDAXZCPgtUrUPjjN55D20b56D1gSOOQmACIVY1WLaxoVJ9Bwyjzk/3Qv0C0A0ZeXgHGvTAXy1au9eINHtATo+7u6/33U2cyMXTMNNSoloZnRg9CYmK892e79h7CPY+8iENHTpx3v1R2m5aN0adHe+/PFy1fg7Ubtp5XVnGjJ+55csocv22U2jfq3r5odVGD8+oZdXc/PPjU6/h9299+q5jwyOAi7YvVLGLAiJU866UABTQv4DpxFAWrv/CEC9c/+zXf3lAbaJSAIe93vToWtL7YirZtrLDbQhXh9RSggJIC0QwY0hfzVi0aFAkUs95dii7tL0ZGraqerq3ftB0Ll65CZlYOHr7vJu/v+/tiL0LF9Pmf4c7+3ZCUkOAJL5EGDBEuRP3TJ49EuTKpkIKLaJsIPLn5+Z4AFChgyENRoDCi5BiGWxYDRrhyvI8CFDCsgHPvTk+oEOFCvF3bqB8jBgxprKqmW9C2tRXt2liRXMqoI8h+UUDbAtEMGMGuJEydtRDtWzfFmnWbUbt6uvdv+0XwmDpjofeLv6+sEisYgQKB/PcvrF2NAUPb05qtowAFKBCagHhfhbRiEdqd+rzayAFDGpEK5Sxo2+Zc2BD/zA8FKBA9gWgFjEBf/n17Kr7IT3r1PTx2/y3YueegZyVBWhGQtkd179ymyAqIVIYSAaO4ECOCjwg8ndo2Z8CI3hRlTRSgAAXUE3Bs+hn5X3ziecysmT5mCBjSeIpVDLGaIYKGWN3ghwIUUF8g2gGjb6+ORc4t+PZQfMEXKxfiTIa/1QR/ZzDmvTzGU6a/8x1S+T27tAnqDIa0Pcv37IcoRwQM8RHbsbhFSv25yRooQAEKqCYgXopX8OUiFHz/lWp1aLlgMwUMaRzEuQxxPuPK9lZUq8KgoeX5ybbpXyDaAcP3fIRcUAoI8hAifamXHwKX3yO2Xb3x9mLMnPIQqlauGPEZjEhXMPYcOOz3wLrveQ0tzByewdDCKLANFKBAVAWc+/5G/heLULDys6jWq7XKzBgwpDEQT57q0tGKqzrYkJystZFheyhgDIFoBQyhVdIZDH+rE+K+Zg3rBDx3IV/laFo/I+KAEcwZDKke39UYf2GIh7yN8eeEvaAABXQu4Dr6D/K//MQTLlBYoPPeRN58MwcMSa9ymsUTNDq140v7Ip9RLIECRQWiGTBKeorUb1v/Pu9xsvJVDdFyafuU1Av5ikO0niIlHpvr+6QpKRxNHDukyBYwBgz+iaMABSgQY4G8hXOR/+UiuM+ejnFLtFM9A8Z/YyEeb3tVRyvfDq6d6cmWGEAgmgFDcAV6D8bQ23r7XX2QVj7E1iPp7IP8/RLy1Q0lDnlLQ1rcezCka8SKxZz3l3lngXQWRD4tGDAM8IeEXaAABfQp4PhjI0S4cGzdqM8OqNhqBozzccUL+67qYEVGbZ7PUHHqsWiTCEQ7YJiEVRfd5BkMXQwTG0kBCoQjIIKF+B8//gUYMALPjN7dbejVjdum+GeHApEILPnCGdLtGbWsaNJQn+FeWk0I9IZt3zeKhwSjw4sZMHQ4aGwyBShQvABXLYKbIQwYxTs1rGdB7x42XHiBPr/wBDcLeBUFKEAB5QUYMJQ3ZYkUoEAMBbhqETw+A0bJVuKxtiJkdO/C1YyStXgFBShAgXMCDBicCRSggCEEHNt/R96Hs3nWIoTRZMAIHqtpIwvEtqnaNbmaEbwar6QABcwqwIBh1pFnvylgIIH85R8h770ZcPPRsyGNKgNGSFwQ7864trsNXTtxNSM0OV5NAQqYTYABw2wjzv5SwEACrpPHPMGiYM2XBupV9LrCgBGedctmVvS73oqK5bmaEZ4g76IABYwuwIBh9BFm/yhgUIHC9WuQ9/4MOA/uNWgP1e8WA0b4xpUrWdD/OhuaNWbICF+Rd1KAAkYVYMAw6siyXxQwsEDeh28i75N5Bu5hdLrGgBG5843X2tCtM7dMRS7JEowoEOpjwu11G8PevLURKUzXJwYM0w05O0wB/Qo49/2NvHffQOFva/XbCQ21nAFDmcG4rJUV/a+3ISVZmfJYCgWMIpA18SE4flsXdHdSxr7IgBG0lrYvZMDQ9viwdRSgwL8CYktU7tyX4DpxlCYKCTBgKAQJoFYNC/pdZ0P9C7llSjlVlqR3gWgGjEXL1+DJKXO8ZM0a1sH0ySNRrkyq9/eka+a9PAatLmqAvLwCjHthLtq0bIw+Pdp7r5Nemjfq3r7Yf/CYp1zpHumiqbMWYs77y7y/v37TdtwxYvJ5QzbhkcHocWVrTz3LVq4tUo64Z+HSVRh1dz88+NTrCPSSPlGGvH16mBcMGHoYJbaRAiYXyF/2EXLffsXkCsp3nwFDWdM4O9Dvehs6teOWKWVlWZpeBaIdMNZu2IpnRg9CYmI8RJgQX96lkCHCxPNvLPBQJicnYdTdfT3/LMLE2Emz8fB9NyGjVlXP74nwULt6uudLvSjno6XfonG92p5rRNm79h7Cc69/4Pl14tghnrAiwsLUGQvPCzWiPCnIpFeqgMNHT3jbKAUMqc1Se4aOmQYRbkS5ev0wYOh15NhuCphEIPetachf8bFJehvdbjJgqOPdpYMVN/WxqVM4S6WAjgRiGTB8g4MIA+8tWukJDa+99SkmPjbEu7ohQoQUTjbv2FUkKIif7Tlw2KPevnVTz5d+EUBE4Jj34RfeIBBMwLimy+X4fOWP6NurozeUiBDEgKGjSc2mUoAC+hfInvwICjf8qP+OaLQHDBjqDUzzplYMv4shQz1hlqwHgVgGDBEoxk5+ExPH3OVZmRBBQXyk7UrSl3z5CoPYViW2Kcl/JgWM665u6wkodw3oiQ8++wY3974So56eHlLAEOWKjxQqRJhhwNDDTGYbKUABwwhkjroVzgN7DNMfLXaEAUPdUalRzYIR99hQtgzPZagrzdK1KhDLgCFWGaTtSMJHbI+6pU8Xb9iQb6cSP5fOUAwe0NO7fUr8vhQwxJYqUeaOnftw1y09cWHtapBvZQrmDIYIGE3rZ3jOY/iGDbH1Snzk5z+4RUqrM5vtogAF9CfgcuHMbV3gLijQX9t11mIGDPUHLCkReHCoHRm1GDLU12YNWhOIdsCQH/Lu2aVNwLMOvqsb8lUM+eqFb8CQn5nIzc8/L2CUdAbDd2uUtGWKW6S0NnPZHgpQwFAC7rwcnLmtq6H6pOXOMGBEb3TuvdOGS5rz8Hf0xFmTFgSiHTB8VyUkA+mJT74m8qczSQexiwsY8vt9VxqCOYMhlR3o0DdXMLQwa9kGClDAUALuM6dwZkgvQ/VJ651hwIjuCIl3ZVzVkSEjuuqsLZYCWggYgbYc+T7BKZoBQ4yJtKVKvtLCgBHL2cq6KUABwwm4jv6Ds8PPPTKQn+gJMGBEz1qq6fqeNvTsypARfXnWGAsBLQSMQCsLvk+ZUiJglPQeDH8Hy8W4cItULGYn66QABQwt4DywG5mjBhq6j1rtHANGbEaGKxmxcWet0ReIZsCIfu9YY3ECfA8G5wcFKBAzAeff25H52F0xq9/sFTNgxG4GDOxnQ4e2XMmI3Qiw5mgI5C2cG1I19rqNYW/eOqR7eLE2BRgwtDkubBUFDC/g+OM3ZD093PD91HIHGTBiOzqDb7XhslYMGbEdBdZOAQqoIcCAoYYqy6QABYoVcB0+gLMP3ESlGAswYMR4AADw6VKxHwO2gAIUUF6AAUN5U5ZIAQoUI+A6fgRnh95AIw0IMGBoYBAAPHCPHc0a8T0Z2hgNtoICFFBCgAFDCUWWQQEKBCXgPnMSZ4f3gzs/L6jreZG6AgwY6vqGUvro4XY0qMuQEYoZr6UABbQrwICh3bFhyyhgKAF3diaynrwPzgN7DNUvPXeGAUNbo/fihDiUKa2tNrE1FKAABcIRYMAIR433UIACIQmIFYucqU+gcOPakO7jxeoKMGCo6xtq6TYb8PqUONjtod7J6ylAAQpoS4ABQ1vjwdZQwHgCLieyX3wChevXGK9vOu8RA4b2BjCjlgVjRzFhaG9k2KJwBMYf+iWk21qnVEK30jVDuocXa1OAAUOb48JWUcAwAjmvjEfB918Zpj9G6ggDhjZHs2M7K27ta9Nm49gqCoQg0P2vZfji7L6g71hRtycDRtBa2r6QAUPb48PWUUDXAnkLZiFv0Xxd98HIjWfA0O7o9u5uQ69ufEeGdkeILQtGINoBIy+vAONemItlK//bjjt4QE+Muruvt7mnzmRi6JhpqFEtDc+MHoTExHjvz3btPYR7HnkRh46c8P6edL9UdpuWjdGnR3vvzxctX4O1G7aeV5Y/n/WbtuOOEZM9P6pauQJmTnkI5cqmetpz/ORpz79n1Krq+bkod8+Bw0XaHoy5Vq5hwNDKSLAdFDCYQMHXS5Ez8zmD9cpY3WHA0PZ43nKjDZ3aM2Roe5TYuuIEohkwpODQqkWDIl/KZ727FF3aX+z94i6+5C9cugqZWTl4+L6bvL8v3T/q3r5odVEDT7dEqJg+/zPc2b8bkhISPOElkoAhyhT1T52xENMnj0S5MqkQ9Y6dNBtVK1dEcnKSt+0MGPyzRQEKUMBHwLH5F2RNehhwFNJGwwIMGBoenH+bdvftNlzakiFD+yPFFvoTiGbACHYlYeqshWjfuinWrNuM2tXTvasRvl/8ffujxApGcQFj0M09MPeD5d7Qw4DBP1MUoAAFZAKuwweRNflhuA4Fv++WgLERYMCIjXsotYrH1o4aake1KnxHRihuvFYbAtEKGIG+/PsqiNWCSa++h8fuvwU79xz0rGRI26Sk7VHdO7fxuy1J7YAhVlN+2/q3d1sUA4Y25jBbQQEKaEHA5fKsXDg2rdNCa9iGEgQYMPQxRcRbvsXbvvmhgN4Eoh0w+vbq6N3e5M9KrFKIlQtxJsPflih/ZzDmvTzGU6a/8x1SHT27tAnqDEZxKxgiYIjzGGK7lG/Y0Nu4i/byDIYeR41tpoBGBXJmP4+Crz7TaOvYLF8BBgz9zIlrrrbiuh58spR+RowtFQLRDhi+5yPkoyAFBHkIEdulxEd+CFx+j1hFeOPtxZ7D1+KMhJpnMKTzINLKhdi+xUPe/HNEAQqYXiB/2YfIfftV0zvoCYABQ0+jBQwbbEOLZjyPoa9RM3c0aJoPAAAgAElEQVRroxUwhHJJZzD8rU6I+5o1rOM9cO07WvJVjqb1M6ISMAId+tbbTOIKht5GjO2lgAYFnDu3IeuZB+DOy9Vg69ikQAIMGPqaG5XTLBg11IYK5XkeQ18jZ97WRjNglPQUKXG+wfdxsvJVDTFK0vYpacTkB7/VfoqU/IlWIiw9OWUOfB+xq6eZxIChp9FiWymgUYGsZ0fBselnjbaOzWLAMM4caNXSintu51Yp44yosXsSzYAhJAO9B2Pobb39rj5IKx9iK5J4FK14H8Xv2/72Dop8dUOJQ96B3oMhnbuQ3oERKCzpabYwYOhptNhWCmhQIO/jt5D30RwNtoxNKkmAKxglCWnz5zdea0O3ztwqpc3RYavkAtEOGNTXjgADhnbGgi2hgO4EPO+7mDBSd+1mg88JMGDodyY8fL8d9S/kVin9jqA5Wj7+0C8hdbR1SiV0K10zpHu0crG06iBfAZG3Tc/bncIxZsAIR433UIACcBcWIHv8A3D8uYUaOhVgwNDpwAFoUM+C0cP46Fr9jiBbTgFjCzBgGHt82TsKqCaQ+87ryF/6gWrls2D1BRgw1DdWs4bre9rQsyu3SqlpzLIpQIHwBBgwwnPjXRQwtUDhL98je8oYUxsYofMMGPoeRbsNePgBO+rU5lYpfY8kW08B4wkwYBhvTNkjCqgrIN7WPW4ot0apqxyV0hkwosKsaiVNGlow8l5ulVIVmYVTgAIhCzBghEzGGyhgboG8RfORt2CWuREM0nsGDGMMJJ8qZYxxZC8oYCQBBgwjjSb7QgGVBZx7dyLryaFw5+WoXBOLj4YAA0Y0lNWvIyEBeOR+O2rV4FYp9bVZAwUoEIwAA0YwSryGAhTwCOS8/DQKflhJDYMIMGAYZCABNG9ixfAhfAGfcUbUGD35Y4kzpI6Uz7AivQmDckhoGr2YAUOjA8NmUUBrAgVr/g85rz6jtWaxPREIMGBEgKfBW2/tZ0PHtnyqlAaHxrRN+n6aA4e3uIPuf7uRdgaMoLW0fSEDhrbHh62jgCYE3DlZnq1Rzv27NNEeNkIZAQYMZRy1Ukq1KhY8/pAd8XFaaRHbYXaBaAWMqbMWYs77y/xy9+zSBqPu7ocHn3od8pfgid9/ZvQgJCbGe+/LyyvAuBfmYv/BY5g+eSTKlUn1/mzR8jV4csocyF+YJ16uN3bSbDx8303IqFXVc61vW6pWroCZUx7y/Hz9pu24Y8TkIu2Ufv7b1r895ft+5r08Bk3rZ3jatWzlWu+PmzWsc14btTTfGDC0NBpsCwU0KpD3/gzkLX5Xo61js8IVYMAIV0679/XtbcPVV3IVQ7sjZK6WRStgyFXFF3zxGXV3X+9vS2/ZHnVvX7S6qAGkIJFeqUKR63btPYQZ7yxBZlYOBt3cw3Ot9BEBY+2GrZ6fSYHCN2BI18iDiwgVIrD06dHeEzCmzljoNxj4u1eqW2pvm5aNPeVIQebw0RPnhSStzDAGDK2MBNtBAY0KOHftQOaYwRptHZsViQADRiR62ry3QnkLnhhlR+p/f/GqzYayVaYQ0GrAEPj+vtCL35M+ew4cLhI+xM/E79Wunu75VQQYecCoWrmiZ5VBHgJ8B1nJgFFcWVqYXAwYWhgFtoECGhbImTUFBSuXaLiFbFq4AgwY4cpp+75e3azo3Z0HvrU9SuZonVYDhr8VAfF7z7+xALf06eIZHPHPEx8b4t0mJQWMO/t3826LKlc2tcgWKbF6suLrtd4tUWoGDH8rNVqaVQwYWhoNtoUCGhPg6oXGBkTh5jBgKAyqkeJSkoHHR9mRVpFP49HIkJi2GVoLGPIzGOJsg3wLlNge9d6ilZ7tT+LjuxohBQyxcuEvbAQ6gyE/6+HvDMaERwZ7tj1JZzx8J0ugMxjSfVqdXAwYWh0ZtosCGhDg6oUGBkHFJjBgqIgb46Kv6mhF/+u5ihHjYTB99VoLGNIZDH9/+y9+T2x/ks44+G6hkgcMaWuUOKcx94PlRQ55ywddOvvRqkUDz5YqpbZIFXdeQyuTjgFDKyPBdlBAYwJcvdDYgKjQHAYMFVA1UqTdfm4Vo0Y1rmJoZEhM2QytBgzfQ9/Sv8tXOMSAyZ8AJQ8Y4mf+Dn37G2R5GNi8Y5cih7z9bfHS2gRjwNDaiLA9FNCIAFcvNDIQKjaDAUNFXA0U3aGtFQP7cRVDA0Nh2iZoNWCIARGrCWMnzvaclzhx+qzfL/7yVQ3fgCGFkuMnT3vKEIe8pTMc0nYp3yCg1AqGaL/Y0nXPIy9i4tghRbZ6aWWyMWBoZSTYDgpoSICrFxoaDBWbwoChIq4GirbbgHGP2FE1nasYGhgOUzZBywFDWoV44+3FuPySJihTJqXIU6OkELJw6SrPo2CXf7PO+/QoaTBF6BD3S++58HeOQn5WoqSAUdJ7MHyfUCWd6fA9T6KFycaAoYVRYBsooDEBrl5obEBUag4DhkqwGipWvBNDvBuDHwrEQiAWASMW/WSd5wswYHBWUIACRQSc+3Yhc/RtVDGBAAOG8Qc5udS5VYwK5biKYfzR1l4P/1jiDKlR5TOsSG/CuRoSmkYvZsDQ6MCwWRSIlUDeBzOR9+k7saqe9UZRgAEjitgxrOra7lZc242rGDEcAlZNAdMJMGCYbsjZYQoEFnDn5iBz9O1wHfuHTCYQYMAwwSADntULsYohVjP4oQAFKBANAQaMaCizDgroREC8sVucv+DHHAIMGOYYZ9FLcQ5DnMfghwIUoEA0BBgwoqHMOiigE4Gs8ffDsXWjTlrLZgYSsMQnwFanAWx1GsJargIsyan//i+lyD/nWlORk+tGbi6Q4/mf2/PruX93Y+/+c/87fYbWehcQT5ISqxjiyVL8UIACFFBbgAFDbWGWTwGdCDg2/YysZ0fppLVsplzAllEf4n/2jAaeX8X/lPwcP+HGvgNu7D1w7lfxvzNnlayBZUVDQLwTQ7wbgx8KUIACagswYKgtzPIpoBOBnOnPomDVCp20ls2Ma9EG9uZtIH61plePOsimLS78tsUN8evZzKhXzwrDEGhYz4KHhtnDuJO3UIACFAhNgAEjNC9eTQFDCrgO7cdZ8WhaR6Eh+2eUTsU6VPhzFNupftviwqZ/w0ahwyjaxuzHEw/ZUbsmHwNqzNHVYK82fRJao9LqAlWbhXYPr9akAAOGJoeFjaJAdAXyFs6F+J9an+X5dryVE+ct/s5SheiRcO6b6BmXBU9lJeCg89yXnmo2N8an5KOM1R2wOfLyUi1ujEstQG2by3P9HqcVz2TGI9NtQcs4J0YlFyDh3+9Ts3PiPOVLdavVXyXLtaSWRcLV1yH+im4xWakIpS8nTooVDTe++8mFA4cCj18oZfJaZQV6drXi+p48iKGsKksLKLDyOeDQ78EDdXmUASN4LU1fyYCh6eFh4ygQHYHMMYPh3LVDlcry3cD83Dj0S3R4QoMUKO5IKkDzOBd+K7TikMvq/dIvwsOmQmuRYCBvmLh+Xm68N4T4/rsIEa3inJ6y5f8sgsdneXbcW+q/wKFKhxUq1JKYhPiu1yOh6/WwVqqiUKnRKUasYnzznQvfrHFBhA5+tCMgDns/8xi3SWlnRAzekigGjEXL1+DJKXOKgA4e0BOj7u7r/b31m7bjjhGTz0Of8Mhg9LiyNca9MBfLVq71/rxq5QqYOeUhZNSqiry8giI/F2XXrp5+Xp3Szc0a1sH0ySNRrkyqwQfZf/cYMEw57Ow0Bf4TcPyxEVlP3x81EhE4pmbH46I4l9+VBN/A4Nsw3wAigsNL2fF4MLkAVawuzMiJR+9Eh2dFQ1wrPmLFQh42otbZcCqy2TyhQoQLW7Va4ZSgmXvE2YxvvnPi6zUuz5Op+NGGwP1DbLioCQ97a2M0DN6KKAeMtRu24pnRg5CYGO8NBEJY+j0RMKbOWOj3i78UINq0bIw+Pdp7BkaEFn9lyq+RRtD3WoOPbIndY8AokYgXUMDYArnzX0P+5wui1klpC9MDyedWMHw/Iggcd1kCrmBIKyCVrS7PNV8X2D3bq4aUOnd+xN8KRlkrdLF6Ed+xOxJ69IOtdt2ojUc0Kjp81O1ZzRCrGvzEXqBdGyvuuJnbpGI/EiZoQQwDhtD1DQ2hBgzf6/2FEAYM//OYAcMEf77ZRQoUJ3B25M0Qh7zV/sjPWsjPYEj1SucqgjmDIUKECCp/OqwI5gyG2KIltk2Jz7NZCZ5fuyY4vKFE7b6XVL41rQoS+96J+I49SrpU1z/f/pcbn61w4q+/tbNtSjxVSTxdSfps+9ONF18/dz7oslZW3NLXhsRzU8bzcTiBL1a6sHj5ufkk/1zXw4ZuXazed03k5QPvLXTip/XngpX856fOuDHnXSe2/3nOQmxbEo/+lepWc6DFG73/93gcUlPUrIVlUwBAjAOGGAP5ysLmHbtCXsHYc+Cwd5sVA0bws5oBI3grXkkBwwkU/voDsp97NKr9UmKLlHzFQmypeiU7vshBb3mHpLMXdyQVYl5unGf7lNhKJbZpdU9w+F1FiSaIWLVI7DsY1rT0aFYbs7pcLuCz5U4s+yr2qxkN6lkw4AYbVqx0eUKAFCjWb3Th7Q+c5/17SWi332xDxfIWb0gQoSEpCd4gIf797z1uT9nyfxbBo/UlFry94L/AUVJdkf78tv42XHE5t0lF6sj7SxDQQMAQqxALl67ybJMSASOUMxiid/JzHAwYwc94BozgrXglBQwnkDNrCgpWLol6v8RqhTwkyBsgVjqez47HXaUKvU+Gkv/c90lQvofGfTsjbZm6wOYuUm6sz2SYZdUi0OTass3tCRq792lnNUMEjsG32nD4yLmVBN/AEeofFBE4WrWwelYxxIrF7TfZsO4Xt2f1Q3ofhahHHjZCrSPc65s2smDEPTzsHa4f7wtSQAMBI5IVDN9AwYAR5LgDYMAI3opXUsBQAu7cHGSOHADXqeOq9ksEgI/y7LgtqdDzuFgpEIgtSuLwtQgbVa0u70qC+Pf/y7d7nxLle2bD9+fFrWDInxwlOikdAI/1Ckbc5Vci6Zahplm1CDTB8gvOrWb837exX80QbZS2MInVDPkKhrRFqrjtUf76KEJEeuXiVzDOnEHUVy9EW202YNKTdpQvx3diqPofQLMXHuOAEekZDDF8IqBI26QYMIKf0AwYwVvxSgoYSqDg+6+Q88r4qPRJrBaI0CB95Gcw5O+tED/3PYPh71C4vDzfMxhSHWIrlvyJUuL35XXF6gxGfIfuKDXs8ai466WSXza6MGPe+WcaotV++VkL+RkM3/pFYLiglqXIuYpAbRRhpUtHK1au+u+8hr8zGGKLltg2JT7t25zbsnTosBvjJqn/xsI7B9jQtjW3SUVrnpmynhgGDCkMCPdwnyLFFYzwZy0DRvh2vJMCuhbIef1ZFKxeoes+6K3x8Vf1RqkhD+ut2VFpr1g1EIeeY/nx3SLl2xYRRPpfb8Oq7/0f8pau910JKS6EiLMXS1a4cG13q2f71PadLs82LbGFTKyiqPlpfYkVQwbyaVJqGpu+7CgHDKXfgyHGT7wjQzy21t97MOTv2OBjaovOdgYM0//pJ4ApBVxOnB3eD67jR0zZ/Vh0OuHaAUi6dWgsqtZNnd/96ML8D9X9Ul0ShlilKFMaflcQggkYwYYLz9+q/nvo+8+d7iLBJVpnMkqnApPGxSEhviQV/pwCYQpEMWCE2ULeppIAA4ZKsCyWAloWcPy+Hln/e1DLTTRU20rd9xjiO/U0VJ/U6sy6X12YPT86IUMEhu5drHj/k3NPb5K2Su3ee+5RteKQtvhIKwm+Zyp8w4S/bVGBnORPjhLXSAfAo7mCIeodfpcNzZtym5Ra89n05W76JDSCtLpA1Wah3cOrNSnAgKHJYWGjKKCuQN4HM5H36TvqVsLSPQIpT74Me9OLqRGCwG9bXHhtdnRChlgtqJru/z0Ywb7XQjoU7vtODanLa9aeOzQufcRWLPkTpcTvy+uK1hkMUW/nK6y4+QZukwphevJSClAgCAEGjCCQeAkFjCaQ+fg9cP611Wjd0lx/St0/DvHtu2quXXpokHgp3wuvqX/QWQ8WaraxSroFEx7j42rVNGbZFDCjAAOGGUedfTa1gPPAbmSOGmhqg2h0PmngMCT0ujkaVRm2jh9/dmHue9FZyTAsYhAde3SEHXUz+LjaIKh4CQUoEKQAA0aQULyMAkYRyP/iE+TOfcko3dFkPxJ69EXSHSM02Ta9NUq8lO7zL7Xxngy92QXbXvEEq2u7cZtUsF68jgIUKFmAAaNkI15BAUMJZL/wOAp/Xm2oPmmpM3GXtEPyI5O11CTdt2XWfCd+/pUhQ62BbNbYggfu5jYptXxZLgXMKMCAYcZRZ59NKyDe3n126A1wZ2ea1kDNjttq1kHyY8/DWqGSmtWYruz8fHjOY+zed+6FdPwoKyAeVzv1f3HKFsrSKEABUwswYJh6+Nl5swk4Nv+CrAkjzdbtqPU3+eFJiGvVPmr1mamiPfvdeGm6A9k5Zup19Pr6xGg7atfgOYzoibMmChhbgAHD2OPL3lGgiIB4NK14RC0/ygsk9OiHpDseUL5glugV8H3cK2mUE7jlRhs6tef7MJQTZUkUMLcAA4a5x5+9N5lA9gtjUfjzdybrtfrdtdWui5SnXoUlOUX9ykxew7QZDmzZxq1SSk+Dyy+1YtAtPOittCvLo4BZBRgwzDry7LcpBc7eez1cJ4+Zsu9qdppbo9TULVr2HzvcmDqd78dQWly8bFC8dJAfClCAAkoIMGAoocgyKKADAb7/Qp1B4tYodVyLK/WdD51Y/SOfKqW0/MuT4pBcSulSWR4FKGBGAQYMM446+2xKgYJvPkfODD4+VcnB59YoJTWDL+vIUTcmTXMgKzv4e3hlyQIj77WjSUMe9C5ZildQgAIlCTBglCTEn1PAIAIiXIiQwY9yAkmDHkRCtxuUK5AlBS2w7CsXPv2cb/kOGiyIC/nCvSCQeAkFKBCUAANGUEy8iAL6F8gcNRBimxQ/yghY06sjdcpbsCQmKVMgSwlJoNABTHrJgX0HeOA7JLhiLm7Vwop77uBBb6U8WQ4FzCzAgGHm0WffTSPgLsjHmVs7m6a/0eho4k1DkNjn9mhUxToCCHy92oUPFnEVQ6kJUq2KBePH8KC3Up4shwJmFmDAMPPos++mEXDu24XM0beZpr9qd9SSWgapU+bBWiFN7apYfjEC4qV7459z4ORprmIoMVGsVmDWS3yjtxKWLIMCZhdgwDD7DGD/TSFQuH4Nsp9/zBR9jUYnE3rdjKSBw6JRFesoQWDR504s/4pPlFJqojz7uB2VK/Ggt1KeLIcCZhVgwDDryLPfphLIX7oAue+8Zqo+q9ZZm81z9sJWI0O1Klhw8AL/HHF7VjEc3CkVPFoxVw6/y4bmTflGb0UwWQgFTCzAgGHiwWfXzSOQ++YLyP+/xebpsIo9jW93FUo98JSKNbDoUAXmfeDE92u5ihGqm7/r+1xjQ4+rGDCUsGQZFDCzAAOGmUeffTeNQNb/HoTj9/Wm6a+aHS017AnEd+imZhUsO0SBHTvdeP5Vvt07RDa/l1/WyorBt/JJUkpYsgwKmFmAAcPMo8++m0bg7PB+cB09ZJr+qtVR8Uja0jMWw1IqWa0qWG6YAi+94cDW7TzsHSaf97baNSx4YjSfJBWpI++ngNkFGDDMPgPYf+MLuN043b+98fsZhR7Gte6A5IeejUJNrCJUgcXLnPj8/7hNKlQ33+vj44Hpz/NJUpE68n4KmF2AAcPsM4D9N7yA65/9ODviZsP3MxodLDX8CcRfwe1R0bAOtY7ftrjw2mye9A7Vzd/1r06OQxLfH6kEJcuggGkFGDBMO/TsuFkEHJt/QdaEkWbprnr9tNlQ9t2vARu3j6iHHH7JObnAiMcK4eYuqfAR/71z4hN2VErjo2ojhmQBFDCxAAOGiQefXTeHQOG61ch+8XFzdFbFXsa16YjkUf9TsQYWHanAxKkO7NrLhBGp42MP2lGnNgNGpI68nwJmFmDAMPPos++mEChYtRw50yeaoq9qdjLprtFI6HqdmlWw7AgFFixyYuVqnsOIkBH3D7HhoiZ8VG2kjryfAmYWYMAw8+iz76YQyF++ELnzXjZFX9XsZMqE6bDXb6ZmFSw7QoGNm114/U2ew4iQEXfcbEO7NgwYkTryfgqYWYABw8yjz76bQiDv47eQ99EcU/RVzU6WeftLWJL4eFo1jSMtW5zDeGBMYaTFmP7+G3rZ0L0LA4bpJwIBKBCBAANGBHi8lQJ6EMid/xryP1+gh6Zqto3WtCoo/fpCzbaPDftPYPS4Qpw+Q5FIBK6+0oq+vfmyvUgMeS8FzC7AgGH2GcD+G14gZ+ZzKPh6qeH7qWYH4y5ph+RHJqtZBctWSGDyyw7s3MWD3pFwXn6pFYNuYcCIxJD3UsDsAgwYZp8B7L/hBbJfGofCn74xfD/V7GBin9uQeNPdalbBshUSmPOuEz+t50HvSDibNrJgxD18HHMkhryXAmYXYMAw+wxg/w0vkPXsQ3BsWmf4fqrZweSR4xF3eWc1q2DZCgks+cKJJSsYMCLhrJthwaMjGDAiMeS9FDC7AAOG2WcA+294gaxxQ+HY/rvh+6lmB1OemQ57Az5BSk1jpcoWqxdiFYOf8AUuqGnB4w8xYIQvyDspQAEGDM4BChhcIPOxIXD+vc3gvVS3e6mT58CWUV/dSli6IgLi/IU4h8FP+ALVq1rw9KMMGOEL8k4KUIABg3OAAgYXyHz4djj3/m3wXqrbvdSp78JWvba6lbB0RQTEE6TEk6T4CV8gvbIF/xvLgBG+IO+kAAUYMDgHKGBwgbMjB8B1aJ/Be6lu90q/thDWSlXUrYSlKyLgdgNDRjJgRIJZsYIFk8cxYERiyHspYHYBBgyzzwD23/ACZ4f1hevYP4bvp5odLD3rM1jLVlCzCpatkEB+ATDsYQaMSDjLlrHghWcYMCIx5L0UMLsAA4bZZwD7b3iBs/f0huvUCcP3U80Olpn3BSylUtSsgmUrJJCVDYwcy4ARCWdKMjBtYlwkRfBeClDA5AIMGCafAOy+8QXODOoBd9ZZ43dUxR6Wff9bwM4vXCoSK1b0qdNuPPwUD3lHApqQALw+hfM9EkPeSwGzCzBgmH0GsP+GFzgzsAvc+XmG76eaHSz73rdAHL9wqWmsVNlHj7kx9n8MGJF42mzAzKmc75EY8l4KmF2AAcPsM4D9N7zA6Zs6AC6+FyCSgS796kewVq4aSRG8N0oCB/9x46nJDBiRcFutwKyXGDAiMeS9FDC7AAOG2WcA+294gdO3dAIKuSc9koFOGf867A0viqQI3hslgd373Hj2RQaMSLhLlQJemcSAEYkh76WA2QUYMMw+A9h/wwucuesauM+eNnw/1exgqRFPI75tFzWrYNkKCfz5txtTXmHAiISzfDkLpjzNp0hFYsh7KWB2AQYMs88A9t/wAmcf6A/X4YOG76eaHUwaOAwJvW5WswqWrZDAul9dmD2fWwIj4axWxYLxYxgwIjHkvRQwuwADhtlnAPtveIHMRwfBuftPw/dTzQ4m9OiHpDseULMKlq2QwNIvXfhsOQNGJJx1alvw2IMMGJEY8l4KmF2AAcPsM4D9N7xA1vj74di60fD9VLODcW06IXnUBDWrYNkKCcx9z4kff3YpVJo5i2ncwIIH72PAMOfos9cUUEaAAUMZR5ZCAc0KZE8Zg8Jfvtds+/TQMFtGfaROnqOHppq+jZNfdmDnLrfpHSIBuLi5FffdaYukCN5LAQqYXIABw+QTgN03vkDOqxNQsOZL43dU5R7yZXsqAytU/ENPFuIM3ysZkWa71lbcMYABIyJE3kwBkwswYJh8ArD7xhfInTMV+V8uMn5HVe5hyv9mwF6vicq1sPhIBPILgGEP85HMkRiKe7t0sOKmPgwYkTryfgqYWYABw8yjz76bQiDv/ZnIW/yOKfqqZieTBj2IhG43qFkFy45Q4MAhN55+jo+ojZAR11xtxXU9GDAideT9FDCzAAOGmUeffTeFQP6Kj5H71jRT9FXNTsZ36I5Swx5XswqWHaHAdz+6MP9DPkEqQkbc1t+GKy63RloM76cABUwswIBh4sFn180hULjhJ2RPftgcnVWxl7bqtZE69V0Va2DRkQq8OtuBTVt4wDtSx4eG2dGwniXSYng/BShgYgEGDBMPPrtuDgHXoX04O3KAOTqrci9Lv/4xrGnpKtfC4sMRyM8Hhj3C8xfh2PneM3mcHRUrMGAoYckyKGBWAQYMs448+20eAZcTp2/qYJ7+qtjTUsOfQPwV3VSsgUWHK7BhkwvT53J7VLh+0n0WCzB7WlykxfB+ClDA5AIMGCafAOy+OQTODusL17F/zNFZFXsZd2kHJI9+VsUaWHS4AuLshTiDwU9kApXSLJj4BF+yF5ki76YABRgwOAcoYAKBrAkj4dj8iwl6qnIXLVaUnvYerFVqqFwRiw9FwOUCHnm6EKfPhHIXr/UnwLd4c15QgAJKCDBgKKHIMiigcYGcWc+jYOVnGm+lPpqXdOtQJFzLMy1aGq3f/3DjlZl8PK0SY9KxnRW39uUjapWwZBkUMLMAA4aZR599N41A/pL3kfvudNP0V82O2us3QcqEGWpWwbJDFHjnQydWc3tUiGr+L+/b24arr+QjahXBZCEUMLEAA4aJB59dN49A4c+rkf0C3+Gg1IinjH8d9oYXKVUcy4lAQLxc75nnHRDbpPiJXGDoYBtaNmPAiFySJVDA3AIMGOYef/beJALOA3uQOepWk/RW/W4mXNMfSbfdr35FrKFEgQWLnFi5mumiRKggL/jf43akV+IjaoPk4mUUoEAAAQYMTg0KmETgdP/2gJsvIVNiuK3lKiJl8psQv/ITO4EjR8+tXuQXxK4NRqo5IR54/Xk+otZIY8q+UCBWAgwYsZJnvRSIskDm6Nvh3Pd3lGs1bqBthMsAACAASURBVHWJfQdB/I+f2Al8vMSJL77m6oVSI1A3w4JHR/ARtUp5shwKmFmAAcPMo8++m0og5+XxKPjhK1P1Wc3OchVDTd2Syz5xyo0JzzuQlV3ytbwiOIHOV1hx8w18glRwWryKAhQoToABg/ODAiYRyPt0PvI+mGWS3kanm1zFiI6zv1o+W+7E0i+5eqHkCNx5iw1tL+UBbyVNWRYFzCrAgGHWkWe/TSdQ+Mv3yJ4yxnT9VrPDXMVQUzdw2YcOu/Hcyw5k58SmfqPW+vSjdlSvygPeRh1f9osC0RRgwIimNuuiQAwFXEcO4uz9/WPYAmNWzVWM6I/rm+84sfYXrl4oKW+1ArNe4gFvJU1ZFgXMLMCAYebRZ99NJ3Dm1s5wF+Sbrt9qdvjrq67E5dffjSoVq6tZDcv+V+Cn9S7MeddJD4UFate04ImHeMBbYVYWRwHTCjBgmHbo2XEzCmQ+NgTOv7eZseuK93nvBbUxr0dnzCrtwCWl0rC6/nWK18ECiwocO+7Gq7OdEFuk+FFWoMPlVgzszwPeyqqyNAqYV4ABw7xjz56bUCB3/mvI/3yBCXuubJc/6Hc93qybjg2Os96C76rYELNrdVS2IpZWROCDT5z4+jtujVJjWgy6xYbLecBbDVqWSQFTCjBgmHLY2WmzChRuXIvsSaPN2v2I+72+dSu81f5ifGDL9FvW89Uvw+jKzSOuhwWcL7B+owsz53FrlFpz47mn7ahQjge81fJluRQwmwADhtlGnP01tYA7Nxtnbr/a1AbhdP5U2TJ4+4ZrMTs9AYccucUW8UvDG3FxqbRwquE9xQjsXvwhnl99DQpc8XRSWKBSmgUTn+D5C4VZWRwFTC3AgGHq4WfnzSiQ9dRwOLb9Zsauh9XnZT2vxpxmGfjW9d92qOIKSrTakNvi7rDq4k0BBNa/A2z7Ao6kdEz/exh+/6cGqRQUuOJyK27j+QsFRVkUBSjAgME5QAGTCeQtnAvxP36KF9jRoD7mdW2PGUnFr1j4K6VZUgVsatSPxAoIbPvchZSz36CGaz7gcgK2OPzoHoC56zspUDqLEAJ3DbShzSV8wR5nAwUooJwAA4ZyliyJAroQcGz6GVnPjtJFW2PRSKfFgvkDbsSbtcvjD0dW2E0YUakZptVoG/b9vBHY8YULmz8+d+6iXstdaFp+GiwF51aSTqRcjHFrhiDflUiqCAWefcKOymk8fxEhI2+nAAVkAgwYnA4UMJmAOIdxdsi1fB+Gn3H//oq2mNumKT61+D/EHepUebpKKzxV9ZJQb+P1AHaudOG3BUUPdafVPIPLG01DXM5uj5EzKQ1z9g/Hz3tr0SxMgWpVLBg/hucvwuTjbRSgQAABBgxODQqYUECsYIiVDH7OCfyTXhlv9+6GmRWsOO0sUJSFISN0zl2rXNgQ4GV6cQkOdO70JlJy1p4r2GrDr/b+eOOnrqFXxDvA8xecBBSggBoCDBhqqLJMCmhcgOcw/hugT667BnMa1cBPzuAOcYcztAwZwavt+d6FX4J4HO1lV36Fas4FgPvcKseZlKZ48vt7keNMDr4yXok7brahXRuev+BUoAAFlBVgwFDWk6VRQBcCzl07kDlmsC7aqlYjf7+oGd66sg3mxWerVUWRch+vcjH+V/XSqNSl10r2rXXh5zeDf9dFg0v+QuMyL8NSeO6sjCuxLN49+gC++ytDrwRRb7d4PK14TC0/FKAABZQUYMBQUpNlUUBHAlnj74dj60YdtViZpmbHx2H+zTdgZvXS2OuITriQWv5ApaZ4uUY7ZTpisFIO/OLC2hnBhwup+5UvOIXL6r4Ie96Bc79lsWFLYh+8/H0PuMEvzsVNk6YNLRhxL89fGOyPErtDAU0IMGBoYhjYCApEXyB/6QLkvvNa9CuOYY1fdemIuRc3xBdQbztUSd27s0IDzK3NR6xKTm43sHWRE9tXuEqiC/jzhFIFuPKKWUjO+cV7TVZqIzzz8304mZMadrlGv/HmG2zofAW3Rxl9nNk/CsRCgAEjFuqskwIaEHAd2o+zI2/WQEvUb8KeWjUxr2dnTC/tQKE7/C+ySrX0urIXYHzVVhDvyzDz58QutydcHN3uVoShbecVqOL4CBCpBYA7oQwWnhmO/9taV5HyjVYIt0cZbUTZHwpoR4ABQztjwZZQIOoC2c89isJff4h6vdGs8L2+vTG7XhVscijz6Fml2l7enojxVVpheKUmShWpq3LEY2i3fOqEI1/ZZje6dDsapr4CS2HOuYItFvyV3BtTVvfmlikZNbdHKTvvWBoFKFBUgAGDM4ICJhYoWLkEObOmGFLg50svxtz2l+BDe/gvy4sGTL9ydTC+6qVokFg2GtXFvI6c425s/tSF/evUW0lKzziBNhe+AHveP97+5pauj+d+uw8HTpnDuaSB5vaokoT4cwpQIBIBBoxI9HgvBXQu4Dp1HJkjB8Cd++/f9uq8P6L5J8ukYt6N12JGeiKOOfJ00aMqcaU8W6aGVGyki/aG28j9613YusiFrGPKbIkqrh2JKfm4su0MlMr970EG7vhkLMsbhsUbje0czPhwe1QwSryGAhQIV4ABI1w53kcBgwjkvDweBT98ZYjefN6jK2Y3vxDfOc/osj+3lq/nCRoZCaV12f5AjT61143dq13Y9Z16qxaB6m7fZSkqFywSJzL+vcSCvaV74NlVfeBy2wzlHGxnuD0qWCleRwEKhCvAgBGuHO+jgEEECn/6BtkvjdN1b7bXr4u3rr4Cs5L0sWJRHHat+FSMSW+BWyvUQ4o1TtfjEstgIYdr0norGiS/Bjhyvb+dn3IBpu14AH8dLqdr43Aaz+1R4ajxHgpQIBQBBoxQtHgtBYwo4HYjc9StcB7cq7veOawWvN3/BsysUwF//fuyNd11IkCDxSqGWNEQQaNuQhlddUsrwUKOVvXCY2id8QJseUe8v+2OK4WvXfdiwc/NdOUbSWNLlQKefsSO8uX4jpBIHHkvBShQvAADBmcIBSiAvA/fRN4n83Ql8V37y/Dm5c2xNIbvtIgGWJLV7gkaAyvUQ/uUKtGoMuw6tBgs5J0pVToXnS6bjqTczUX6eKhsV4z/pj+cJtgyJd57IVYw+KEABSigpgADhpq6LJsCOhFw7t2JzIfv0EVrD1VKw7zru2N6eQuyXQ5dtFmpRvYqU9uzoiGePKWlz8qzB+D4uCyyfkzQUrMCtMWNDl0+RVrBUtm5DKAgpSbe2D0Cm/cZ+90kj4+y44JaXL3QwURlEymgawEGDF0PHxtPAeUEsp9/DIXr1yhXoAolfdy7B2Y2qYVfHLF7E7cK3Qq5yEtKVULr5EpoWSoNlySnRf2FfVtyT2Lx6d34/MwerMs+6mn/2L1XoMXyeiH3JVY3NG3zO+onvQY4C/5rgj0Ra+z34O0fWsSqWarWe0lzK+69k6sXqiKzcApQwCPAgMGJQAEKeAQK1nyJnFcnaFJjU7PGeLNLO7wbp+13WsQKr2pcMlqWqohWyZXQNiUdlyenQ2ytUupz0pGHFWf3YcWZfZ5fT/p5O1757GS8u3wAso+r/whapfpVo/4htKo5Fdb840WKPFquE8Z9fQscbuUMlWpzJOUMv8uG5k2tkRTBeylAAQoEJcCAERQTL6KA8QXchQWew96uI4c009mseDvevukGTK9RGoccxnlXRzSAW5SqiJrxqUizJyLNnoSK//6aFpeIivYk7+873C4cLMzGgYJsHCzM8vx6oDAbBwuy/v01G8dkT18qru3vb+qLuB/1dSA9pWwOOrZ+DYm5fxTpWmFydcz7ZzjW/ZUejeFSvY66GRY8OsJYgUl1NFZAAQqELcCAETYdb6SA8QRy33sD+Z+9p4mO/V/nKzDr0ib42qXPd1poAjHKjbjhSGPctOiyKNcaeXUWiHMZC1GxYHnRwuwJ+DXhNrzxXdvIK4lxCQP72dChLVcvYjwMrJ4CphFgwDDNULOjFChZwLlrBzLHDC75QhWv2FWzBuZe2xWvJ+v/nRYqMmm26C+/GoSzO/X5RfaiyzegbvwbgKuwiO/JMq0x4afByMyN16x7cQ2rVNGCpx61I0GfzdelORtNAbMLMGCYfQaw/xTwEciZNQUFK5fExOWdG3tjRoMq+KMwMyb1s9LIBab+1Q01VlaPvKAYlVCrwX5cXOMlWPNPFmmBo1RlLDg5Aqu2Vo1Ry8Kv9truVlzbjYe7wxfknRSgQKgCDBihivF6ChhcIBarGOtatcSsTq2xyGLup0MZYWrVOVURLy65DoU6PjJTukIWOrR6FQk5O4oOiS0Om5NvxcvfdtDNUKWmAOMetqNcWT6aVjeDxoZSwAACDBgGGER2gQJKC0RrFeN4ajLeuqkPpqfZcVr+uFClO8TyoiqweN0tKNyQFNU6la7MYnGhU5cPUD7/q/OKPl3mYkzZMARHTycqXa3i5fW4yoo+13D1QnFYFkgBChQrwIDBCUIBCpwnEI1VjCXdr8KMlnXxk4OHuI02Be8/2BpXLGlqiG61bPsTMuLmAD4vdXQmpWFR9gh8uVG728FKJQFPPmxHWgWuXhhiMrITFNCRAAOGjgaLTaVANAXUWsXYVq8O3uzZGXPjs6PZHdYVRYHEQjsWr7gDmQejWKmKVV3QaC9aVnsJlvzTRWux2rEttT9e/OYqFWsPv+iunazodx1XL8IX5J0UoEC4AgwY4crxPgoYXEDpVYxCqxVvDbgR02uXxd5ChguDTx/M/eN6pK6uYJhulqmYhQ6XTEN8zs7z+nS2THO8svVu7DlSSjP9jY87t3pRpTJXLzQzKGwIBUwkwIBhosFmVykQqoBSqxir2rfBzHYX4wuXz98Ah9ogXq8bgSuP18F9Czvppr3BNNRicaJLl7kok//DeZe7EspiueMBLP45I5iiVL+m8xVW3HwDVy9Uh2YFFKCAXwEGDE4MClAgoIBz95/IevI+uAvyw1I6mFYBc/r1xmuphSh0u8IqgzfpV2DF6juQ9Yfx3h7dqt1q1LLPB1zOooNjseGv0jdgyjfd4UbsVg6sVuDJ0XbUqBa7Nuh31rLlFKCAEgIMGEoosgwKGFgg78PZyPvk7ZB7+GGfXnijUQ1sKuQh7pDxDHLDhD1XosEKbfyNvtKkdZrsQvP0abAUnP9o5ezSjfDG30OxfV+K0tUGVV6Hy60Y2J+rF0Fh8SIKUEAVAQYMVVhZKAWMI+DOOousccPgPLA7qE5taNYYs6++AgusfKdFUGAGvqhCVjLe/nwAck+5DdnLcpXO4IqW0xCXc/6fDVdCGfyf6358vPbCqPY9uRTw2Eg70nn2IqrurIwCFCgqwIDBGUEBCpQoUPDtMuS8ManY6zLj4zBnYH9MT0/AMUdeiWXyAnMILNx4E7A2Nn+THw1hq8WBq7q+idTctedXZ7Fgd5nrMOmba+FyR2e70g29bOjexRqNrrMOClCAAgEFGDA4OShAgaAEsp97FIW/nn+4Vdy8vGtnzGzVAN85eIg7KEwTXXTb4ebo9eklhu9xmw5foToWAG6fcxkAclPrY+6BYdi4s7SqDhm1LBgz0g5xBoMfClCAArEUYMCIpT7rpoCOBBy/r0fW/x4s0uKdNavjzT49MDOBj53V0VBGvakrvhiErN3G/9Zbt+mfaFb5FVgKss4zdsen4mvLcCz4sb5q/vcNsuHii4zvrBogC6YABRQTYMBQjJIFUcD4ArlzpiL/y0Wejs4dcCNmZFTEX4WZxu84exiRwOt/XoNKX6dHVIZebq5Q5RTaN3sR9twDfppswb6yPfHCj32Qk6tsEGhziRV3DeTBbr3ME7aTAkYXYMAw+gizfxRQUMB5cA+++OgVzOrYCkv4TgsFZY1dVLMz6Rj/yTVwhPe0Y93h2KwF6Np1FpJzfvHb9rzUC/HO4WFYt72cIn1LTIBna1T1qtE556FIo1kIBShgaAEGDEMPLztHAeUFXju6Gffv/175glmioQWW/DQQ+b8lGLqPvp27vNMKVHV9BLjPf4qWO64Uvrffi7e/bxaxSe/uNvTqpuyKSMSNYgEUoICpBRgwTD387DwFwhPotXM5Pj+zN7ybeZcpBR490A6XLG1gur7Xb74NTSu8ChTm+O37wXJX45Vf+uPE6fACQs3q5w52x8eZjpYdpgAFNCzAgKHhwWHTKKBVgZ35Z9Bw6wI4+HZurQ6R5toV57Rh8ZI7kXNYc01TvUFpVU+gXdMXYMv9x29dBSk18d7xEfhha4WQ2zJ0sA0tm4UXTkKujDdQgAIUCFKAASNIKF5GAQoUFZh1/A/cs3c1WSgQtMC7W25EwpqyQV9vpAvj7PnoetUMJGVv9N8teyJ+TLgHc79rEXS3r2xvxYAbebA7aDBeSAEKRE2AASNq1KyIAsYTuOHvL7Ho9C7jdYw9UkXgmhP1cftH7VUpWy+Ftu+yFJULxJPY/L/d/Ei5K/Haplvwz7Hig0OVdAueGWOHhee69TL0bCcFTCXAgGGq4WZnKaCswLa8U7hk28fIcTmULZilGVbg82/uQO4Ou2H7F0zHGl2yFY3KvAYU5vq9vDC5OhacHonVv1cMWNwDd9vRrDHTRTDevIYCFIi+AANG9M1ZIwUMJTD92BYM27fGUH1iZ9QTeGF3V9T6oqZ6Feik5MrVj6Ft4xdgzT3iv8X2ePycNASzVrU67+cd21lxa19ujdLJULOZFDClAAOGKYednaaAsgI37voSn5ziVillVY1ZWsXcFMxddBPyzxqzf6H0Kj4+F107T0di9uaAtx0v2x6vb70N+w+fe0xUWkULJj1p7hWgUIx5LQUoEBsBBozYuLNWChhK4PfcE+iw4zOcdprkTWqGGr3od+bjX26Ge31y9CvWZI1udOz6KSrmLQ14LsORVBmfZI/EVxur4PGH7LigJrdGaXIo2SgKUMArwIDByUABCigi8PLR3zFy/w+KlMVCjC1wz9FL0OWT5sbuZIi9a9ZmI+olTQechQHvXFnhRXTpmR5iybycAhSgQPQFGDCib84aKWBYgUF7vsVbJ7Ybtn/smHICy5YNQs4+vr9BLlot4xBa150Ka+7x86Ev7ABcfrdyA8CSKEABCqgowIChIi6LpoAZBS7bvghrswMcXDUjCPvsV2DOjt4o/U0adXwEEhJz0PXK15CQ9cd/PylXE+jxDGAr+rru9Zu2444Rkz3XNWtYB9Mnj0S5MqnFmk6dtRBz3l/muWbwgJ4YdXdfzz8vWr4GT06ZU+Re+c+lH4j7a1dPR58e/z1ueNfeQ7jnkRdx6MgJ9OzSBs+MHoTExHjPLf6u56BTgALGF2DAMP4Ys4cUiKqAOI9xxY7FOOMsiGq9rExfApdmVcOj73eHyxl8u7dkfoYfTs703JBoLY1rKk9EhfgMvwXkOk9jyZFHcbpwv+fnZeNq4NrKzyHJdu5Ff74/b5TaA+3LD/eWtebka/gjc7nn33tUmoAaSRd7/vlEwS6sOfk6rk570ltW8D0I7koL3OjUbSHK5ywHElKBXhOBUuWL3Cy+1I+d/CYmjrkLGbWqegLC2g1bi3y5961NfNkXHylUyH9e0v3yADLhkcFFAoYot33rpmh1UQNPoJD+WbTxvUUr8fB9N3kDR3ACvIoCFNC7AAOG3keQ7aeABgXeOfEnbtvztQZbxiZpSWDx9wNRuDkhqCbtz/0VP56a5Q0Jvv/uW4j4+RnHATRJ7e35kQgn+3M34Kq0xzz//tWxSaiR1NLzc4c7r8i/ixDx29mP0aHCAzhTeMj7z3ZLIkTwqJ10mTdwBNX4MC9q2XYdMq5JB9IuPK8E8YV/z4HD3rDgGzh8bxCrHQuXrgoYQEoKGFJ5visSeXkFeP6NBbilTxdv0BHXihUOedgIk4C3UYACOhVgwNDpwLHZFNC6wCMHfsLzR37TejPZvhgKPHWwA5osqRtUC+QBQXzRFyFg5fHJ6FJxTMBVDHnB8kAifl+sblxe7m5vUBDBQXzEKoa4dk/uT55/Fisdq068hI4VHkSO82SRsBFUwyO4qMUtNtTp5P+ciu9qxKkzmRg6ZhpG3dvXs5Lg+/G3BWrey2O81/r+3N/2KFGmvy1P/lYwKpQtzdWLCMaet1JA7wIMGHofQbafAhoWuHbnCiw9s0fDLVS2aRX+PIIWc/97ktbx+unYfMulcMbbYStwoOl7P6PijsPeSjcOaosT9Sp7/j0+Kx8Xz/wOyccykZ2Wil/vuQIFKef+dr/GD38j+ehZbL++hbINjnFpNpcViz+5E3nHS37sqrSlqbQ93bMKsT3rS5wq3F9kW1Nx3REBIstxzHOvCCjSdiux/alifB18eWwC2pcf5gkrgVYwfjr1ZtRWL+p3t6LpDYFfpuf7Rb+kgOF7vVjRGDtxNmZOeciz8iD/SGX17dWxyFaoQAHD3xmM6fM/82yVEh/pnEig0BLjacjqKUABFQQYMFRAZZEUoMB/AnW3vI+d+WdMQSKCQE5aiic0SIEir2ySJxiIAJHx1R/4q2dTT+AQYaTJh+ux4a72yKxSxhMixGd/2zpF/lnc12jhr/ij78XewGEkzPc29UX8j2WC6pIICeLL/5H87SWewZAKlIKE7xkMaQVEXHe68ABKOoNRylbOs3ohVj3Eisa+3PVBtyGozskuqtnaikuHFP+m7lBXMPxtbRr3wly0adn4vBAhmuK7BUtqXjCHtqWzF8MHXY/X5n7q2T5VtXJFiPpEaPG3whKqEa+nAAW0LcCAoe3xYesoYAgBy69vGKIfoXZChAYRJKRVDPn90orFn72aeQJJg0834ljjqp5/FvekbT3kCSby4BFq/Xq4vu+ZRuj3/uUlNlUEBfmKhdjG9M3x54s96C0v1HeLlHzFQjqDkWJPC7giIp29EGVK26dEmVsyl3hXRUrsRBAXVKxrQcdHS35Td6hnMHyvF2cnivvCH0nAkLZMXVi7GsZOmu055C1WSXgmI4gJwEsoYBABBgyDDCS7QQEtC4gnS130x0dabqIqbROhQXz8bW1K/ecMmr7/MzYPuDTgCsaRi6obevVCQl/y1R3I31n8l2rxBb9cXA3voW1py5T8HEVxgyiul0KFuM73SVC+ZzzkZcm3TMm3Zsl/X2y7ivRTqoIF3SfZYQni9SAlPUVKBARxqFt6dK20jWni2CGeFQSxRWrqjIWenyclJODjZatxY88Onqc9FbfdqqQVDPmTo4SHdACcKxiRzg7eTwF9CTBg6Gu82FoK6FZgVeYhdPrzM922P9SGi1WIekt/L3KWQipD2j4lVivElijx8XcGo/KmA56fnc6oiJZvrkFcdgHk5zpCbZNWr395d3dU/aJasc0TAWBr5vIiT5GSr2D4/lz8exl7de8hbvnPRUXikHfj1B5FniIVaAVD/uQo+QFwJVcwLBag2yQ7kiuWfB5FgiruPRi+AUPcI7++auUKRc5fyN+PIa71fRSt7yFw3/vFPb5PlBK/Jz+fwTMYWv0TyHZRQHkBBgzlTVkiBSgQQMAsIcP3fIWcw/dsRqDJIj97Ic5uSNunxKpIdqXS3mBihMlWs6AMXvmgLwpziu+N/N0Uvu/B8A0YYnXh8yNjkec66ynU3xkM+c99z2BILZEHCun3pHaU9C6OUMam4yN2VKwXfLgIpWxeSwEKUCDaAgwY0RZnfRQwuYDRQ4YS4UJMEensxaFWtTxPnxIrHWLFw6hnMj5adzMsG5JN+afj0rtsqNkmiH1RptRhpylAAT0KMGDocdTYZgroXMCoISPUbVHBrF6IR9XKD4AbcQVDOIw4cSnafdRM5zM79OY36WNDgx4MF6HL8Q4KUEDLAgwYWh4dto0CBhYwYsgQX/6rr9tdZNQKk+M9j6IVH+kchfyCA60vOO8QuDxQiGvl5zOMeAZD8li6ZBDyDprny3bLW23I6Gie/hr4P2fsGgUo4CPAgMEpQQEKxEzAiCEjZpgGqHjW9t4o922aAXpSchfajbAjvSnPXJQsxSsoQAE9CjBg6HHU2GYKGEiAIcNAgxlhVzrk1cLwt66KsBTt397pMTsq1GG40P5IsYUUoEC4AgwY4crxPgpQQDEBhgzFKHVf0KJVA+HclqD7fvjrgHi/Recn7Shbg+HCkAPMTlGAAl4BBgxOBgpQQBMCG3OOo+W2hZpoCxsRO4Fn/umIhosvjF0DVKo5Oc2C9iNtSKnMcKESMYulAAU0JMCAoaHBYFMoYHaBY45c1N78LnJcDrNTmLb/NrcViz68EwWnjPNFPK2+BeJRtEnljNMn005QdpwCFAhKgAEjKCZeRAEKREugwO1Em+2LIFY0+DGnwPzfbkTST2UN0XlxkPvSu+yIN+crPgwxhuwEBSgQugADRuhmvIMCFIiCwM27v8KCkzujUBOr0JrAwKymuPad1lprVsjtqX6x1bNyYY0L+VbeQAEKUEDXAgwYuh4+Np4CxhYYc3Atnju80didZO/8Ciz+4nYU7tbvN/N6V1vRrK+No0sBClDAlAIMGKYcdnaaAvoRmHR4A8YeXKefBrOligi8uOdq1FxRQ5GyolmIeFJUiwF8gV40zVkXBSigPQEGDO2NCVtEAQr4CEw/tgXD9q2hi4kEGrgqYOK86+HM10+nUytb0HyADZUb8zC3fkaNLaUABdQQYMBQQ5VlUoACigu8d/JPDN23BmedBYqXzQK1KbBg7U2wbUzRZuN8WpXe5Fy4SKnEcKGLAWMjKUABVQUYMFTlZeEUoICSAuuzj2LsoXVYefaAksWyLI0KjDh5Kdp92EyjrfuvWXU6WT3bosBsofmxYgMpQIHoCDBgRMeZtVCAAgoKPHFoHZ79Z4OCJbIorQp8tmgQCo5Ytdo8NOtnQ72u2m2fZuHYMApQwNACDBiGHl52jgLGFVhyeo9nNWNr7knjdpI9wxs7eqHiN5U1JyFemtdigBVVWzBcaG5w2CAKUCDmAgwYMR8CNoACFAhX4HBhDh4/tA5zj28Ptwjep3GBboUZGPzmlZpqOdJ9OQAAC1hJREFUpQgVjXtbUaY690RpamDYGApQQDMCDBiaGQo2hAIUCFdg5rE/PEHjhCMv3CJ4n4YFPl51K9zbEmPewrhSQKNrbajbhasWMR8MNoACFNC0AAOGpoeHjaMABYIV+C3nuGfL1Ioz+4K9hdfpRODJw1eg2af1YtpasWrR6ForytbgqkVMB4KVU4ACuhBgwNDFMLGRFKBAsALjD/2CKUc2IsflCPYWXqdxgTK2eLw9fyAKz0b/yz1XLTQ+Odg8ClBAkwIMGJocFjaKAhSIRGBjznFPyFhwcmckxfBeDQm8takPUn4sH9UWcdUiqtysjAIUMJAAA4aBBpNdoQAFigqIgCGChggc/OhbYEB2Y1w//7KodIKrFlFhZiUUoICBBRgwDDy47BoFKADPVqnnD//GbVMGmAyfrrgdjj1xqvaEqxaq8rJwClDAJAIMGCYZaHaTAmYX4LYp/c+A5/ZehYzltVTpSIULLbiwsxU1WvEJUaoAs1AKUMBUAgwYphpudpYCFOC2Kf3OgaaoiKdnXwclz++npp8LFnU6MVjod2aw5RSggNYEGDC0NiJsDwUooLqAtG1q1vGtOFSYo3p9rEA5gffX9UfchtSIC0xI+f/27qY3qgIKA/DpJx+tlKJSsFRQKxp0YYKiJhg/0GCCGzcsdOVSf4E/xJ0uWJpICIkbNmiMiUtdaNRoQKKtUUEqtAVpoTW3tKgxMba17TnTp8lkMtC5897nnc2bCzMRw4c6Yvj59ujatOzDOQABAgQI/EXAwPB2IEBg3Qr8OD0Zb5//KgyNOm+BN8YejWfffWTpgdsi7j/UPnfVoufO1f/Y26UH90wCBAjUETAw6nQlKQECKyRgaKwQ7Aod9uSJ12L6545FH/3uJ24Oi233GBaLxvMEAgQILELAwFgEll8lQKC1BQyNGv2+9c2RGDi98z+H3bW/PfY81R47HjYs/jOaXyRAgMAyBAyMZeB5KgECrSlgaOTu9bnp3fH6Oy/8a8ieO9pi12NtMXSgPbYOGRa5G5WOAIFWEzAwWq1R50OAwP8msDA0jv36dZybGv/fjutAyxd476NXI7785//O3r6vbe6jZpurFs0X5vkhQIAAgdUXMDBW39wrEiBQTKD51KnjY2fi+NjZeP/SuWLpWzPumz8fjP0nHpw7ue7NEbsea4/B/e0xsM/VitZs3FkRIFBJwMCo1JasBAisucCnVy7Mj40z8e21S2ueZ70GuL1zYxw7+UoMP9oVg/vbovk+Cz8ECBAgkEPAwMjRgxQECBQTmJ6duXVV48RvZ4ulrxt3e+emeLn/3jjaf188vXEwOrrqnovkBAgQaFUBA6NVm3VeBAismsDnVy/euqrx5e9jq/a66+WFBrt64nDfUBztH47DW4bWy2k7TwIECJQVMDDKVic4AQIZBT4YH40Px0ejuf9k4qeMEUtk2tN9WxzuuztemrvtKZFZSAIECBC4KWBgeCcQIEBghQS+u3Y5Tl3+IU6Pj8yNjovXr63QK7XGYR/atC2evW0wXtwyFEf6drfGSTkLAgQIrEMBA2Mdlu6UCRBYG4HT46Nx6tL3cery9/HF1YtrEyLRqz6y+Y442LsjDmweiMd7tsfejVsTpROFAAECBJYqYGAsVc7zCBAgsAyBkanJ+Hjix2g+leqzK+fn7sdutPYVjmZQPNN7Vxzs3Tk3KHZ19y5D0FMJECBAIKuAgZG1GbkIEFh3As0/qWqGxqfzg6O5/+X61XIObdEWD2zceuv2ZM9APNW7M5qPlvVDgAABAq0vYGC0fsfOkACBwgLNlY5maHx25UKMTE9E87i5H52aXPMrHju6NsfeDX8OiYVRcf+GvsLiohMgQIDAcgUMjOUKej4BAgTWSGByZnp+cEzG6NREjExPxsjURIxOT8bY9WsxNTsTzfd1TM3emL9vHt+IqZmFP59/PDsTWzs2RH9n99z9wu3vj7ujv/Pm3zXfRdGMib6O7jU6cy9LgAABApkFDIzM7chGgAABAgQIECBAoJiAgVGsMHEJECBAgAABAgQIZBYwMDK3IxsBAgQIECBAgACBYgIGRrHCxCVAgAABAgQIECCQWcDAyNyObAQIECB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Count the number of occurrences of each value in the 'isFraud' column\nfraud_counts = df['isFraud'].value_counts()\n\n# Calculate the percentage of fraud and non-fraud cases\nfraud_percentage = (fraud_counts[1] / len(df)) * 100\nnon_fraud_percentage = (fraud_counts[0] / len(df)) * 100\n\n# Print the fraud and non-fraud percentages\nprint(\"Fraud percentage:\", fraud_percentage)\nprint(\"Non-fraud percentage:\", non_fraud_percentage)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:18.907548Z","iopub.execute_input":"2024-01-01T02:21:18.908302Z","iopub.status.idle":"2024-01-01T02:21:18.975591Z","shell.execute_reply.started":"2024-01-01T02:21:18.908269Z","shell.execute_reply":"2024-01-01T02:21:18.974067Z"},"trusted":true},"execution_count":132,"outputs":[{"name":"stdout","text":"Fraud percentage: 0.12908204481801522\nNon-fraud percentage: 99.87091795518198\n","output_type":"stream"}]},{"cell_type":"code","source":"print('\\n The types of fraudulent transactions are {}'.format(\\\nlist(df.loc[df.isFraud == 1].type.drop_duplicates().values)))\n\ndfFraudTransfer = df.loc[(df.isFraud == 1) & (df.type == 'TRANSFER')]\ndfFraudCashout = df.loc[(df.isFraud == 1) & (df.type == 'CASH_OUT')]\n\nprint ('\\n The number of fraudulent TRANSFERs = {}'.\\\n       format(len(dfFraudTransfer)))\n\nprint ('\\n The number of fraudulent CASH_OUTs = {}'.\\\n       format(len(dfFraudCashout)))\n","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:18.977507Z","iopub.execute_input":"2024-01-01T02:21:18.978016Z","iopub.status.idle":"2024-01-01T02:21:20.054668Z","shell.execute_reply.started":"2024-01-01T02:21:18.977971Z","shell.execute_reply":"2024-01-01T02:21:20.053345Z"},"trusted":true},"execution_count":133,"outputs":[{"name":"stdout","text":"\n The types of fraudulent transactions are ['TRANSFER', 'CASH_OUT']\n\n The number of fraudulent TRANSFERs = 4097\n\n The number of fraudulent CASH_OUTs = 4116\n","output_type":"stream"}]},{"cell_type":"code","source":"#Checking for balance in target\nfig = go.Figure(data=[go.Pie(labels=['Not Fraud','Fraud'], values=df['isFraud'].value_counts())])\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:20.056604Z","iopub.execute_input":"2024-01-01T02:21:20.057554Z","iopub.status.idle":"2024-01-01T02:21:20.130223Z","shell.execute_reply.started":"2024-01-01T02:21:20.057516Z","shell.execute_reply":"2024-01-01T02:21:20.128992Z"},"trusted":true},"execution_count":134,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"data":[{"labels":["Not 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"},"metadata":{}}]},{"cell_type":"code","source":"# replacing string to integer values bassed the max occurance in the data\ndf.replace(to_replace = ['PAYMENT', 'TRANSFER', 'CASH_OUT', 'DEBIT', 'CASH_IN'], value = [2,4,1,5,3],inplace = True)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:20.131576Z","iopub.execute_input":"2024-01-01T02:21:20.131928Z","iopub.status.idle":"2024-01-01T02:21:28.226508Z","shell.execute_reply.started":"2024-01-01T02:21:20.131898Z","shell.execute_reply":"2024-01-01T02:21:28.225393Z"},"trusted":true},"execution_count":135,"outputs":[]},{"cell_type":"code","source":"# taking the required columns for further analysis\nx = df[['type','amount','oldbalanceOrg','newbalanceOrig']]\ny = df[['isFraud']]","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:28.227860Z","iopub.execute_input":"2024-01-01T02:21:28.228196Z","iopub.status.idle":"2024-01-01T02:21:28.343834Z","shell.execute_reply.started":"2024-01-01T02:21:28.228167Z","shell.execute_reply":"2024-01-01T02:21:28.342627Z"},"trusted":true},"execution_count":136,"outputs":[]},{"cell_type":"code","source":"from imblearn.under_sampling import RandomUnderSampler\n\n# Undersampling:\nundersampler = RandomUnderSampler(random_state=42)\nX_res, y_res = undersampler.fit_resample(x, y)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:28.345246Z","iopub.execute_input":"2024-01-01T02:21:28.345631Z","iopub.status.idle":"2024-01-01T02:21:29.830331Z","shell.execute_reply.started":"2024-01-01T02:21:28.345599Z","shell.execute_reply":"2024-01-01T02:21:29.829221Z"},"trusted":true},"execution_count":137,"outputs":[]},{"cell_type":"code","source":"#Checking for balance in target\nfig = go.Figure(data=[go.Pie(labels=['Not Fraud','Fraud'], values=y_res['isFraud'].value_counts())])\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:29.834580Z","iopub.execute_input":"2024-01-01T02:21:29.834920Z","iopub.status.idle":"2024-01-01T02:21:29.848028Z","shell.execute_reply.started":"2024-01-01T02:21:29.834891Z","shell.execute_reply":"2024-01-01T02:21:29.846705Z"},"trusted":true},"execution_count":138,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"data":[{"labels":["Not 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"},"metadata":{}}]},{"cell_type":"code","source":"# Train-test split\nX_train, X_test, y_train, y_test = train_test_split(X_res, y_res, train_size=0.7, random_state=111)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:29.849445Z","iopub.execute_input":"2024-01-01T02:21:29.851188Z","iopub.status.idle":"2024-01-01T02:21:29.861523Z","shell.execute_reply.started":"2024-01-01T02:21:29.851146Z","shell.execute_reply":"2024-01-01T02:21:29.860495Z"},"trusted":true},"execution_count":139,"outputs":[]},{"cell_type":"code","source":"#Standardizing the numerical columns\ncol_names=['amount','oldbalanceOrg','newbalanceOrig']\nfeatures_train = X_train[col_names]\nfeatures_test = X_test[col_names]\nscaler = StandardScaler().fit(features_train.values)\nfeatures_train = scaler.transform(features_train.values)\nfeatures_test = scaler.transform(features_test.values)\nX_train[col_names] = features_train\nX_test[col_names] =features_test","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:29.862859Z","iopub.execute_input":"2024-01-01T02:21:29.863513Z","iopub.status.idle":"2024-01-01T02:21:29.873828Z","shell.execute_reply.started":"2024-01-01T02:21:29.863482Z","shell.execute_reply":"2024-01-01T02:21:29.872726Z"},"trusted":true},"execution_count":140,"outputs":[]},{"cell_type":"code","source":"#creating the objects\nlogreg_cv = LogisticRegression(solver='liblinear',random_state=123)\ndt_cv=DecisionTreeClassifier(random_state=123)\nknn_cv=KNeighborsClassifier()\nsvc_cv=SVC(kernel='linear',random_state=123)\nnb_cv=GaussianNB()\nrf_cv=RandomForestClassifier(random_state=123)\ncv_dict = {0: 'Logistic Regression', 1: 'Decision Tree',2:'KNN',3:'SVC',4:'Naive Bayes',5:'Random Forest'}\ncv_models=[logreg_cv,dt_cv,knn_cv,svc_cv,nb_cv,rf_cv]\n\n\nfor i,model in enumerate(cv_models):\n    print(\"{} Test Accuracy: {}\".format(cv_dict[i],cross_val_score(model, X_train, y_train, cv=10, scoring ='accuracy').mean()))","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:21:29.878551Z","iopub.execute_input":"2024-01-01T02:21:29.878907Z","iopub.status.idle":"2024-01-01T02:22:12.897448Z","shell.execute_reply.started":"2024-01-01T02:21:29.878873Z","shell.execute_reply":"2024-01-01T02:22:12.896307Z"},"trusted":true},"execution_count":141,"outputs":[{"name":"stdout","text":"Logistic Regression Test Accuracy: 0.8843279978809552\nDecision Tree Test Accuracy: 0.9920858969992811\nKNN Test Accuracy: 0.9906939115298747\nSVC Test Accuracy: 0.8763267869981458\nNaive Bayes Test Accuracy: 0.670637378438718\nRandom Forest Test Accuracy: 0.992781397812843\n","output_type":"stream"}]},{"cell_type":"code","source":"# hyperparameter tuning\nparam_grid = {\n    'n_estimators': [100, 200, 500],\n    'max_depth': [2, 4, 8],\n    'min_samples_split': [2, 5, 10],\n}\n# Create the Random Forest classifier\nrfc = RandomForestClassifier()\n\n# Create the GridSearchCV object\ngrid_search = GridSearchCV(estimator=rfc, param_grid=param_grid, scoring='accuracy')\n\n# Fit the GridSearchCV object to the training data\ngrid_search.fit(X_train, y_train)\n\n# Get the best estimator and its parameters\nbest_estimator = grid_search.best_estimator_\nbest_params = grid_search.best_params_\n\n# Print the best parameters\nprint(\"Best parameters:\", best_params)\n\n# Evaluate the best model on the test data\ny_pred = best_estimator.predict(X_test)\ntest_accuracy = accuracy_score(y_test, y_pred)\nprint(\"Test accuracy:\", test_accuracy)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:22:12.899035Z","iopub.execute_input":"2024-01-01T02:22:12.899839Z","iopub.status.idle":"2024-01-01T02:26:16.678572Z","shell.execute_reply.started":"2024-01-01T02:22:12.899793Z","shell.execute_reply":"2024-01-01T02:26:16.677382Z"},"trusted":true},"execution_count":142,"outputs":[{"name":"stdout","text":"Best parameters: {'max_depth': 8, 'min_samples_split': 2, 'n_estimators': 100}\nTest accuracy: 0.9902597402597403\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report\n\n# Assuming y_test and y_pred are already defined\n\n# Print the classification report\nprint(classification_report(y_test, y_pred, target_names=['Not Fraud', 'Fraud']))","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:26:16.680174Z","iopub.execute_input":"2024-01-01T02:26:16.680756Z","iopub.status.idle":"2024-01-01T02:26:16.706072Z","shell.execute_reply.started":"2024-01-01T02:26:16.680712Z","shell.execute_reply":"2024-01-01T02:26:16.704831Z"},"trusted":true},"execution_count":143,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n   Not Fraud       1.00      0.98      0.99      2432\n       Fraud       0.98      1.00      0.99      2496\n\n    accuracy                           0.99      4928\n   macro avg       0.99      0.99      0.99      4928\nweighted avg       0.99      0.99      0.99      4928\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report\n\n# Assuming y_test and y_pred are already defined\n\n# Print the classification report\nprint(classification_report(y_test, y_pred, target_names=['Not Fraud', 'Fraud']))\n","metadata":{"execution":{"iopub.status.busy":"2024-01-01T02:27:14.842165Z","iopub.execute_input":"2024-01-01T02:27:14.842600Z","iopub.status.idle":"2024-01-01T02:27:14.868827Z","shell.execute_reply.started":"2024-01-01T02:27:14.842565Z","shell.execute_reply":"2024-01-01T02:27:14.867692Z"},"trusted":true},"execution_count":146,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n   Not Fraud       1.00      0.98      0.99      2432\n       Fraud       0.98      1.00      0.99      2496\n\n    accuracy                           0.99      4928\n   macro avg       0.99      0.99      0.99      4928\nweighted avg       0.99      0.99      0.99      4928\n\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}