04-fraud-detection-gradient-boosting.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20463,"status":"ok","timestamp":1698371880088,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"vsApOuqGbbS0","outputId":"368d134d-198c-4096-8be2-05714b107ca1"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"JFn_J9D7mV_W"},"source":["# Projeto Machine Learning 1 -- RUANN\n","\n","## Conjunto de dados de detecção de fraude em transações de cartão de crédito\n","\n","### Sobre o conjunto de dados\n","Este é um conjunto de dados simulados de transações de cartão de crédito contendo transações legítimas e fraudulentas entre 1º de janeiro de 2019 e 31 de dezembro de 2020. Abrange cartões de crédito de 1.000 clientes que fazem transações com um grupo de 800 comerciantes."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Zs4aeMF0SSk1"},"outputs":[],"source":["# Importando bibliotecas\n","import numpy as np\n","import pandas as pd\n","pd.set_option('display.max_columns', None)\n","import seaborn as sns\n","import warnings\n","warnings.filterwarnings(\"ignore\")\n","import plotly.express as px\n","import matplotlib.pyplot as plt\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, StratifiedKFold\n","from sklearn.svm import SVC\n","from sklearn.metrics import accuracy_score, classification_report, f1_score, roc_curve,auc\n","from sklearn import metrics\n","from scipy.stats import uniform\n","from sklearn.model_selection import train_test_split\n","from imblearn.under_sampling import RandomUnderSampler\n","from sklearn.ensemble import GradientBoostingClassifier\n","from lightgbm import LGBMClassifier"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d4FP0jpybGZS"},"outputs":[],"source":["df_train = pd.read_csv(\"/content/drive/MyDrive/Projeto ML1 - Fraude/fraudTrain.csv\")\n","df_test = pd.read_csv(\"/content/drive/MyDrive/Projeto ML1 - Fraude/fraudTest.csv\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":521},"executionInfo":{"elapsed":56,"status":"ok","timestamp":1698371895851,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"wGn0bFllpjo1","outputId":"d9907aaf-9050-4493-f3bb-ac25de61e953"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Unnamed: 0 trans_date_trans_time            cc_num  \\\n","0           0   2019-01-01 00:00:18  2703186189652095   \n","1           1   2019-01-01 00:00:44      630423337322   \n","2           2   2019-01-01 00:00:51    38859492057661   \n","3           3   2019-01-01 00:01:16  3534093764340240   \n","4           4   2019-01-01 00:03:06   375534208663984   \n","\n","                             merchant       category     amt      first  \\\n","0          fraud_Rippin, Kub and Mann       misc_net    4.97   Jennifer   \n","1     fraud_Heller, Gutmann and Zieme    grocery_pos  107.23  Stephanie   \n","2                fraud_Lind-Buckridge  entertainment  220.11     Edward   \n","3  fraud_Kutch, Hermiston and Farrell  gas_transport   45.00     Jeremy   \n","4                 fraud_Keeling-Crist       misc_pos   41.96      Tyler   \n","\n","      last gender                        street            city state    zip  \\\n","0    Banks      F                561 Perry Cove  Moravian Falls    NC  28654   \n","1     Gill      F  43039 Riley Greens Suite 393          Orient    WA  99160   \n","2  Sanchez      M      594 White Dale Suite 530      Malad City    ID  83252   \n","3    White      M   9443 Cynthia Court Apt. 038         Boulder    MT  59632   \n","4   Garcia      M              408 Bradley Rest        Doe Hill    VA  24433   \n","\n","       lat      long  city_pop                                job         dob  \\\n","0  36.0788  -81.1781      3495          Psychologist, counselling  1988-03-09   \n","1  48.8878 -118.2105       149  Special educational needs teacher  1978-06-21   \n","2  42.1808 -112.2620      4154        Nature conservation officer  1962-01-19   \n","3  46.2306 -112.1138      1939                    Patent attorney  1967-01-12   \n","4  38.4207  -79.4629        99     Dance movement psychotherapist  1986-03-28   \n","\n","                          trans_num   unix_time  merch_lat  merch_long  \\\n","0  0b242abb623afc578575680df30655b9  1325376018  36.011293  -82.048315   \n","1  1f76529f8574734946361c461b024d99  1325376044  49.159047 -118.186462   \n","2  a1a22d70485983eac12b5b88dad1cf95  1325376051  43.150704 -112.154481   \n","3  6b849c168bdad6f867558c3793159a81  1325376076  47.034331 -112.561071   \n","4  a41d7549acf90789359a9aa5346dcb46  1325376186  38.674999  -78.632459   \n","\n","   is_fraud  \n","0         0  \n","1         0  \n","2         0  \n","3         0  \n","4         0  "],"text/html":["\n","  <div id=\"df-44dfb7e1-68f6-460a-b67a-9937b59d68c1\" 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>Unnamed: 0</th>\n","      <th>trans_date_trans_time</th>\n","      <th>cc_num</th>\n","      <th>merchant</th>\n","      <th>category</th>\n","      <th>amt</th>\n","      <th>first</th>\n","      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<td>1988-03-09</td>\n","      <td>0b242abb623afc578575680df30655b9</td>\n","      <td>1325376018</td>\n","      <td>36.011293</td>\n","      <td>-82.048315</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2019-01-01 00:00:44</td>\n","      <td>630423337322</td>\n","      <td>fraud_Heller, Gutmann and Zieme</td>\n","      <td>grocery_pos</td>\n","      <td>107.23</td>\n","      <td>Stephanie</td>\n","      <td>Gill</td>\n","      <td>F</td>\n","      <td>43039 Riley Greens Suite 393</td>\n","      <td>Orient</td>\n","      <td>WA</td>\n","      <td>99160</td>\n","      <td>48.8878</td>\n","      <td>-118.2105</td>\n","      <td>149</td>\n","      <td>Special educational needs teacher</td>\n","      <td>1978-06-21</td>\n","      <td>1f76529f8574734946361c461b024d99</td>\n","      <td>1325376044</td>\n","      <td>49.159047</td>\n","      <td>-118.186462</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>2019-01-01 00:00:51</td>\n","      <td>38859492057661</td>\n","      <td>fraud_Lind-Buckridge</td>\n","      <td>entertainment</td>\n","      <td>220.11</td>\n","      <td>Edward</td>\n","      <td>Sanchez</td>\n","      <td>M</td>\n","      <td>594 White Dale Suite 530</td>\n","      <td>Malad City</td>\n","      <td>ID</td>\n","      <td>83252</td>\n","      <td>42.1808</td>\n","      <td>-112.2620</td>\n","      <td>4154</td>\n","      <td>Nature conservation officer</td>\n","      <td>1962-01-19</td>\n","      <td>a1a22d70485983eac12b5b88dad1cf95</td>\n","      <td>1325376051</td>\n","      <td>43.150704</td>\n","      <td>-112.154481</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>2019-01-01 00:01:16</td>\n","      <td>3534093764340240</td>\n","      <td>fraud_Kutch, Hermiston and Farrell</td>\n","      <td>gas_transport</td>\n","      <td>45.00</td>\n","      <td>Jeremy</td>\n","      <td>White</td>\n","      <td>M</td>\n","      <td>9443 Cynthia Court Apt. 038</td>\n","      <td>Boulder</td>\n","      <td>MT</td>\n","      <td>59632</td>\n","      <td>46.2306</td>\n","      <td>-112.1138</td>\n","      <td>1939</td>\n","      <td>Patent attorney</td>\n","      <td>1967-01-12</td>\n","      <td>6b849c168bdad6f867558c3793159a81</td>\n","      <td>1325376076</td>\n","      <td>47.034331</td>\n","      <td>-112.561071</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>2019-01-01 00:03:06</td>\n","      <td>375534208663984</td>\n","      <td>fraud_Keeling-Crist</td>\n","      <td>misc_pos</td>\n","      <td>41.96</td>\n","      <td>Tyler</td>\n","      <td>Garcia</td>\n","      <td>M</td>\n","      <td>408 Bradley Rest</td>\n","      <td>Doe Hill</td>\n","      <td>VA</td>\n","      <td>24433</td>\n","      <td>38.4207</td>\n","      <td>-79.4629</td>\n","      <td>99</td>\n","      <td>Dance movement psychotherapist</td>\n","      <td>1986-03-28</td>\n","      <td>a41d7549acf90789359a9aa5346dcb46</td>\n","      <td>1325376186</td>\n","      <td>38.674999</td>\n","      <td>-78.632459</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44dfb7e1-68f6-460a-b67a-9937b59d68c1')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    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\n","\n","                               merchant        category    amt   first  \\\n","0                 fraud_Kirlin and Sons   personal_care   2.86    Jeff   \n","1                  fraud_Sporer-Keebler   personal_care  29.84  Joanne   \n","2  fraud_Swaniawski, Nitzsche and Welch  health_fitness  41.28  Ashley   \n","3                     fraud_Haley Group        misc_pos  60.05   Brian   \n","4                 fraud_Johnston-Casper          travel   3.19  Nathan   \n","\n","       last gender                       street        city state    zip  \\\n","0   Elliott      M            351 Darlene Green    Columbia    SC  29209   \n","1  Williams      F             3638 Marsh Union     Altonah    UT  84002   \n","2     Lopez      F         9333 Valentine Point    Bellmore    NY  11710   \n","3  Williams      M  32941 Krystal Mill Apt. 552  Titusville    FL  32780   \n","4    Massey      M     5783 Evan Roads Apt. 465    Falmouth    MI  49632   \n","\n","       lat      long  city_pop                     job         dob  \\\n","0  33.9659  -80.9355    333497     Mechanical engineer  1968-03-19   \n","1  40.3207 -110.4360       302  Sales professional, IT  1990-01-17   \n","2  40.6729  -73.5365     34496       Librarian, public  1970-10-21   \n","3  28.5697  -80.8191     54767            Set designer  1987-07-25   \n","4  44.2529  -85.0170      1126      Furniture designer  1955-07-06   \n","\n","                          trans_num   unix_time  merch_lat  merch_long  \\\n","0  2da90c7d74bd46a0caf3777415b3ebd3  1371816865  33.986391  -81.200714   \n","1  324cc204407e99f51b0d6ca0055005e7  1371816873  39.450498 -109.960431   \n","2  c81755dbbbea9d5c77f094348a7579be  1371816893  40.495810  -74.196111   \n","3  2159175b9efe66dc301f149d3d5abf8c  1371816915  28.812398  -80.883061   \n","4  57ff021bd3f328f8738bb535c302a31b  1371816917  44.959148  -85.884734   \n","\n","   is_fraud  \n","0         0  \n","1         0  \n","2         0  \n","3         0  \n","4         0  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12:14:33</td>\n","      <td>3573030041201292</td>\n","      <td>fraud_Sporer-Keebler</td>\n","      <td>personal_care</td>\n","      <td>29.84</td>\n","      <td>Joanne</td>\n","      <td>Williams</td>\n","      <td>F</td>\n","      <td>3638 Marsh Union</td>\n","      <td>Altonah</td>\n","      <td>UT</td>\n","      <td>84002</td>\n","      <td>40.3207</td>\n","      <td>-110.4360</td>\n","      <td>302</td>\n","      <td>Sales professional, IT</td>\n","      <td>1990-01-17</td>\n","      <td>324cc204407e99f51b0d6ca0055005e7</td>\n","      <td>1371816873</td>\n","      <td>39.450498</td>\n","      <td>-109.960431</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>2020-06-21 12:14:53</td>\n","      <td>3598215285024754</td>\n","      <td>fraud_Swaniawski, Nitzsche and Welch</td>\n","      <td>health_fitness</td>\n","      <td>41.28</td>\n","      <td>Ashley</td>\n","      <td>Lopez</td>\n","      <td>F</td>\n","      <td>9333 Valentine Point</td>\n","      <td>Bellmore</td>\n","      <td>NY</td>\n","      <td>11710</td>\n","      <td>40.6729</td>\n","      <td>-73.5365</td>\n","      <td>34496</td>\n","      <td>Librarian, public</td>\n","      <td>1970-10-21</td>\n","      <td>c81755dbbbea9d5c77f094348a7579be</td>\n","      <td>1371816893</td>\n","      <td>40.495810</td>\n","      <td>-74.196111</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>2020-06-21 12:15:15</td>\n","      <td>3591919803438423</td>\n","      <td>fraud_Haley Group</td>\n","      <td>misc_pos</td>\n","      <td>60.05</td>\n","      <td>Brian</td>\n","      <td>Williams</td>\n","      <td>M</td>\n","      <td>32941 Krystal Mill Apt. 552</td>\n","      <td>Titusville</td>\n","      <td>FL</td>\n","      <td>32780</td>\n","      <td>28.5697</td>\n","      <td>-80.8191</td>\n","      <td>54767</td>\n","      <td>Set designer</td>\n","      <td>1987-07-25</td>\n","      <td>2159175b9efe66dc301f149d3d5abf8c</td>\n","      <td>1371816915</td>\n","      <td>28.812398</td>\n","      <td>-80.883061</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>2020-06-21 12:15:17</td>\n","      <td>3526826139003047</td>\n","      <td>fraud_Johnston-Casper</td>\n","      <td>travel</td>\n","      <td>3.19</td>\n","      <td>Nathan</td>\n","      <td>Massey</td>\n","      <td>M</td>\n","      <td>5783 Evan Roads Apt. 465</td>\n","      <td>Falmouth</td>\n","      <td>MI</td>\n","      <td>49632</td>\n","      <td>44.2529</td>\n","      <td>-85.0170</td>\n","      <td>1126</td>\n","      <td>Furniture designer</td>\n","      <td>1955-07-06</td>\n","      <td>57ff021bd3f328f8738bb535c302a31b</td>\n","      <td>1371816917</td>\n","      <td>44.959148</td>\n","      <td>-85.884734</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div 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merch_long  \\\n","2449  e8a81877ae9a0a7f883e15cb39dc4022  1325466397  36.430124  -81.179483   \n","2472  bc7d41c41103877b03232f03f1f8d3f5  1325468849  29.819364  -99.142791   \n","2523  b98f12f4168391b2203238813df5aa8c  1325473523  29.273085  -98.836360   \n","2546  397894a5c4c02e3c61c784001f0f14e4  1325475483  35.909292  -82.091010   \n","2553  7863235a750d73a244c07f1fb7f0185a  1325476547  29.786426  -98.683410   \n","\n","      is_fraud  \n","2449         1  \n","2472         1  \n","2523         1  \n","2546         1  \n","2553         1  "],"text/html":["\n","  <div id=\"df-e3de3d1e-e3e3-47a0-857c-64f623539fc9\" 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"," 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<td>Murphy</td>\n","      <td>M</td>\n","      <td>542 Steve Curve Suite 011</td>\n","      <td>Collettsville</td>\n","      <td>NC</td>\n","      <td>28611</td>\n","      <td>35.9946</td>\n","      <td>-81.7266</td>\n","      <td>885</td>\n","      <td>Soil scientist</td>\n","      <td>1988-09-15</td>\n","      <td>e8a81877ae9a0a7f883e15cb39dc4022</td>\n","      <td>1325466397</td>\n","      <td>36.430124</td>\n","      <td>-81.179483</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2472</th>\n","      <td>2472</td>\n","      <td>2019-01-02 01:47:29</td>\n","      <td>340187018810220</td>\n","      <td>fraud_Jenkins, Hauck and Friesen</td>\n","      <td>gas_transport</td>\n","      <td>11.52</td>\n","      <td>Misty</td>\n","      <td>Hart</td>\n","      <td>F</td>\n","      <td>27954 Hall Mill Suite 575</td>\n","      <td>San Antonio</td>\n","      <td>TX</td>\n","      <td>78208</td>\n","      <td>29.4400</td>\n","      <td>-98.4590</td>\n","      <td>1595797</td>\n","      <td>Horticultural consultant</td>\n","      <td>1960-10-28</td>\n","      <td>bc7d41c41103877b03232f03f1f8d3f5</td>\n","      <td>1325468849</td>\n","      <td>29.819364</td>\n","      <td>-99.142791</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2523</th>\n","      <td>2523</td>\n","      <td>2019-01-02 03:05:23</td>\n","      <td>340187018810220</td>\n","      <td>fraud_Goodwin-Nitzsche</td>\n","      <td>grocery_pos</td>\n","      <td>276.31</td>\n","      <td>Misty</td>\n","      <td>Hart</td>\n","      <td>F</td>\n","      <td>27954 Hall Mill Suite 575</td>\n","      <td>San Antonio</td>\n","      <td>TX</td>\n","      <td>78208</td>\n","      <td>29.4400</td>\n","      <td>-98.4590</td>\n","      <td>1595797</td>\n","      <td>Horticultural consultant</td>\n","      <td>1960-10-28</td>\n","      <td>b98f12f4168391b2203238813df5aa8c</td>\n","      <td>1325473523</td>\n","      <td>29.273085</td>\n","      <td>-98.836360</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2546</th>\n","      <td>2546</td>\n","      <td>2019-01-02 03:38:03</td>\n","      <td>4613314721966</td>\n","      <td>fraud_Erdman-Kertzmann</td>\n","      <td>gas_transport</td>\n","      <td>7.03</td>\n","      <td>Jason</td>\n","      <td>Murphy</td>\n","      <td>M</td>\n","      <td>542 Steve Curve Suite 011</td>\n","      <td>Collettsville</td>\n","      <td>NC</td>\n","      <td>28611</td>\n","      <td>35.9946</td>\n","      <td>-81.7266</td>\n","      <td>885</td>\n","      <td>Soil scientist</td>\n","      <td>1988-09-15</td>\n","      <td>397894a5c4c02e3c61c784001f0f14e4</td>\n","      <td>1325475483</td>\n","      <td>35.909292</td>\n","      <td>-82.091010</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2553</th>\n","      <td>2553</td>\n","      <td>2019-01-02 03:55:47</td>\n","      <td>340187018810220</td>\n","      <td>fraud_Koepp-Parker</td>\n","      <td>grocery_pos</td>\n","      <td>275.73</td>\n","      <td>Misty</td>\n","      <td>Hart</td>\n","      <td>F</td>\n","      <td>27954 Hall Mill Suite 575</td>\n","      <td>San Antonio</td>\n","      <td>TX</td>\n","      <td>78208</td>\n","      <td>29.4400</td>\n","      <td>-98.4590</td>\n","      <td>1595797</td>\n","      <td>Horticultural consultant</td>\n","      <td>1960-10-28</td>\n","      <td>7863235a750d73a244c07f1fb7f0185a</td>\n","      <td>1325476547</td>\n","      <td>29.786426</td>\n","      <td>-98.683410</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3de3d1e-e3e3-47a0-857c-64f623539fc9')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 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float64"]},"metadata":{},"execution_count":14}],"source":["df_fraudado[\"gender\"].value_counts(normalize=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-vvxK3uYV59-","outputId":"99e6def9-110c-4bc5-d731-3d056aff942c","colab":{"base_uri":"https://localhost:8080/","height":851},"executionInfo":{"status":"ok","timestamp":1698381777642,"user_tz":180,"elapsed":1013,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["plt.hist(df_fraudado[\"gender\"], color=\"blue\", edgecolor=\"black\", bins=3)\n","plt.xticks(rotation=45)\n","plt.show()\n","plt.hist(df_analise[\"gender\"], color=\"blue\", edgecolor=\"black\", bins=3)\n","plt.xticks(rotation=45)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"7GP4MklrV59-"},"source":["Verifica-se que a proporção entre gêneros que cometem fraude é muito parecida, o que torna esse fator provavelmente não predominante. Porém, observa-se que no dataset geral existe uma quantia relevantemente maior de mulheres, o que pode indicar que, proporcionalmente, os homens cometem mais fraudes."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7WVRYXvpV59-","outputId":"77243936-65fc-44cb-9b23-f35301f73a51","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1698371897325,"user_tz":180,"elapsed":10,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["531.3200919264589"]},"metadata":{},"execution_count":16}],"source":["df_fraudado[\"amt\"].mean()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WQRgbFJrV59-","outputId":"8beba1f3-c5f3-4b21-b07e-32d19c60b73b","colab":{"base_uri":"https://localhost:8080/","height":451},"executionInfo":{"status":"ok","timestamp":1698381790568,"user_tz":180,"elapsed":348,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.hist(df_fraudado[\"amt\"], color=\"blue\", edgecolor=\"black\", bins=50)\n","plt.xticks(rotation=45)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"i8GfmohGV59-"},"source":["A média das compras fraudadas é 531 dólares, o que indica que, provavelmente, um valor mais alto de compra está relacionado com a possibilidade da compra ser fraudada. Além disso, a grande quantidade observada no começo do gráfico pode indicar diversas fraudes menores como uma tentativa de não levantar suspeitas sobre a fraude."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C3UE_sy7V59-","outputId":"f0591465-2484-4b80-fb3a-cafa2c4490f5","colab":{"base_uri":"https://localhost:8080/","height":496},"executionInfo":{"status":"ok","timestamp":1698381794755,"user_tz":180,"elapsed":470,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.hist(df_fraudado[\"category\"], color=\"blue\", edgecolor=\"black\")\n","plt.xticks(rotation=45)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"dIlLvFdbV59_"},"source":["A porcentagem de compras fraudadas está muito concentrada nas categorias: grocery_pos, shopping_net, misc_net e shopping_pos, o que indica que essa feature categórica pode ter uma grande importância para o modelo."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LQJSadhxV59_","outputId":"6be8f81f-546d-4cde-9308-ef4a6352a7a4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1698371898101,"user_tz":180,"elapsed":8,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["NY    0.073941\n","TX    0.063816\n","PA    0.061018\n","CA    0.043432\n","OH    0.042766\n","FL    0.037437\n","IL    0.033040\n","MI    0.031708\n","AL    0.028644\n","MN    0.027578\n","VA    0.026379\n","SC    0.025713\n","MO    0.025446\n","NE    0.023981\n","WI    0.021716\n","AR    0.021450\n","MD    0.020917\n","KS    0.020783\n","KY    0.020650\n","OR    0.019851\n","NC    0.019851\n","GA    0.019451\n","WV    0.019451\n","OK    0.019051\n","IA    0.018918\n","IN    0.018918\n","TN    0.018652\n","ME    0.015854\n","NJ    0.015721\n","MS    0.015321\n","CO    0.015055\n","WY    0.014655\n","WA    0.012790\n","LA    0.012124\n","NM    0.010925\n","SD    0.009859\n","VT    0.009592\n","MA    0.009193\n","UT    0.008127\n","NH    0.007860\n","ND    0.007594\n","NV    0.006262\n","AZ    0.004929\n","AK    0.004796\n","MT    0.004263\n","DC    0.002798\n","CT    0.002132\n","RI    0.001998\n","ID    0.001465\n","DE    0.001199\n","HI    0.000933\n","Name: state, dtype: float64"]},"metadata":{},"execution_count":19}],"source":["df_fraudado[\"state\"].value_counts(normalize=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pkkIS2KXV59_","outputId":"955875e4-4840-4ec2-cf14-b19532d0f345","colab":{"base_uri":"https://localhost:8080/","height":676},"executionInfo":{"status":"ok","timestamp":1698381800426,"user_tz":180,"elapsed":704,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 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\n"},"metadata":{}}],"source":["plt.figure(figsize=(16, 8))\n","plt.hist(sorted(df_fraudado[\"state\"]), color=\"blue\", edgecolor=\"black\", bins=100)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"pYz5kMumV59_"},"source":["O número de fraudes por cidade parece estar diretamente relacionado com a população da mesma, então essa feature é muito abstrata e não relevante para o escopo do modelo"]},{"cell_type":"markdown","metadata":{"id":"tKc5fpLJV59_"},"source":["Além dessas features ponderadas, concluímos que algumas colunas categóricas não são tão relevantes para o modelo. Além disso, intuitivamente construiremos novas features: Idade do comprador, distância do comprador da loja"]},{"cell_type":"markdown","metadata":{"id":"OPTbU7BtmzsQ"},"source":["### Analisando Balanceamento dos dados"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1698371898893,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"9xthTFiAdIfl","outputId":"03c27d56-8200-4091-f89d-8a4ccca60a9c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    0.994211\n","1    0.005789\n","Name: is_fraud, dtype: float64"]},"metadata":{},"execution_count":21}],"source":["df_train1['is_fraud'].value_counts(normalize=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":435},"executionInfo":{"elapsed":437,"status":"ok","timestamp":1698382077300,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"z14-cJ7lnCbW","outputId":"ec045a2b-7095-4c99-e72f-2069f39dcb9a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 960x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["donut = df_train1[\"is_fraud\"].value_counts().reset_index()\n","\n","labels = [\"No\", \"Yes\"]\n","explode = (0, 0)\n","\n","fig, ax = plt.subplots(dpi=120, figsize=(8, 4))\n","plt.pie(donut[\"is_fraud\"],\n","        labels=donut[\"is_fraud\"],\n","        autopct=\"%1.1f%%\",\n","        pctdistance=0.8,\n","        explode=explode)\n","\n","centre_circle = plt.Circle((0.0, 0.0), 0.5, fc='white')\n","fig = plt.gcf()\n","fig.gca().add_artist(centre_circle)\n","\n","plt.title(\"Proporção de Fraudes em Transações\")\n","plt.legend(labels, loc=\"center\", frameon=False)\n","plt.show();"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1698371898893,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"Mui0yBb4dNve","outputId":"c8084b1f-e3d2-42f9-91fd-d578d0c3b41d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    0.99614\n","1    0.00386\n","Name: is_fraud, dtype: float64"]},"metadata":{},"execution_count":23}],"source":["df_test1['is_fraud'].value_counts(normalize=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":435},"executionInfo":{"elapsed":373,"status":"ok","timestamp":1698382087571,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"Zw2rIYbGneLm","outputId":"bf18a871-3457-4a43-fabf-78e91e57e832"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 960x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["donut = df_test1[\"is_fraud\"].value_counts().reset_index()\n","\n","labels = [\"No\", \"Yes\"]\n","explode = (0, 0)\n","\n","fig, ax = plt.subplots(dpi=120, figsize=(8, 4))\n","plt.pie(donut[\"is_fraud\"],\n","        labels=donut[\"is_fraud\"],\n","        autopct=\"%1.1f%%\",\n","        pctdistance=0.8,\n","        explode=explode)\n","\n","centre_circle = plt.Circle((0.0, 0.0), 0.5, fc='white')\n","fig = plt.gcf()\n","fig.gca().add_artist(centre_circle)\n","\n","plt.title(\"Proporção de Fraudes em Transações\")\n","plt.legend(labels, loc=\"center\", frameon=False)\n","plt.show();"]},{"cell_type":"markdown","source":["Podemos perceber que é mantida a proporção de classes tanto pra treino, como para teste. Contudo, podemos ver que há um grande desbalanceamento que será tratado nos próximos passos."],"metadata":{"id":"117RIqKBRDnR"}},{"cell_type":"markdown","metadata":{"id":"Ann4YYBlnp8s"},"source":["## Tratamento dos Dados"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5186,"status":"ok","timestamp":1698371904066,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"yXI4HGpOorOF","outputId":"66c6a83b-ea83-40cf-f669-7626e221bdfc"},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1296675 entries, 0 to 1296674\n","Data columns (total 23 columns):\n"," #   Column                 Non-Null Count    Dtype  \n","---  ------                 --------------    -----  \n"," 0   Unnamed: 0             1296675 non-null  int64  \n"," 1   trans_date_trans_time  1296675 non-null  object \n"," 2   cc_num                 1296675 non-null  int64  \n"," 3   merchant               1296675 non-null  object \n"," 4   category               1296675 non-null  object \n"," 5   amt                    1296675 non-null  float64\n"," 6   first                  1296675 non-null  object \n"," 7   last                   1296675 non-null  object \n"," 8   gender                 1296675 non-null  object \n"," 9   street                 1296675 non-null  object \n"," 10  city                   1296675 non-null  object \n"," 11  state                  1296675 non-null  object \n"," 12  zip                    1296675 non-null  int64  \n"," 13  lat                    1296675 non-null  float64\n"," 14  long                   1296675 non-null  float64\n"," 15  city_pop               1296675 non-null  int64  \n"," 16  job                    1296675 non-null  object \n"," 17  dob                    1296675 non-null  object \n"," 18  trans_num              1296675 non-null  object \n"," 19  unix_time              1296675 non-null  int64  \n"," 20  merch_lat              1296675 non-null  float64\n"," 21  merch_long             1296675 non-null  float64\n"," 22  is_fraud               1296675 non-null  int64  \n","dtypes: float64(5), int64(6), object(12)\n","memory usage: 227.5+ MB\n","<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 555719 entries, 0 to 555718\n","Data columns (total 23 columns):\n"," #   Column                 Non-Null Count   Dtype  \n","---  ------                 --------------   -----  \n"," 0   Unnamed: 0             555719 non-null  int64  \n"," 1   trans_date_trans_time  555719 non-null  object \n"," 2   cc_num                 555719 non-null  int64  \n"," 3   merchant               555719 non-null  object \n"," 4   category               555719 non-null  object \n"," 5   amt                    555719 non-null  float64\n"," 6   first                  555719 non-null  object \n"," 7   last                   555719 non-null  object \n"," 8   gender                 555719 non-null  object \n"," 9   street                 555719 non-null  object \n"," 10  city                   555719 non-null  object \n"," 11  state                  555719 non-null  object \n"," 12  zip                    555719 non-null  int64  \n"," 13  lat                    555719 non-null  float64\n"," 14  long                   555719 non-null  float64\n"," 15  city_pop               555719 non-null  int64  \n"," 16  job                    555719 non-null  object \n"," 17  dob                    555719 non-null  object \n"," 18  trans_num              555719 non-null  object \n"," 19  unix_time              555719 non-null  int64  \n"," 20  merch_lat              555719 non-null  float64\n"," 21  merch_long             555719 non-null  float64\n"," 22  is_fraud               555719 non-null  int64  \n","dtypes: float64(5), int64(6), object(12)\n","memory usage: 97.5+ MB\n"]},{"output_type":"execute_result","data":{"text/plain":["(None, None)"]},"metadata":{},"execution_count":25}],"source":["df_train1.info(), df_test1.info()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":790,"status":"ok","timestamp":1698371904851,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"72_9TK2eoyb9","outputId":"cd98c6da-ca04-4c85-d048-0f1ccfe87ee5"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(         Unnamed: 0        cc_num           amt           zip           lat  \\\n"," count  1.296675e+06  1.296675e+06  1.296675e+06  1.296675e+06  1.296675e+06   \n"," mean   6.483370e+05  4.171920e+17  7.035104e+01  4.880067e+04  3.853762e+01   \n"," std    3.743180e+05  1.308806e+18  1.603160e+02  2.689322e+04  5.075808e+00   \n"," min    0.000000e+00  6.041621e+10  1.000000e+00  1.257000e+03  2.002710e+01   \n"," 25%    3.241685e+05  1.800429e+14  9.650000e+00  2.623700e+04  3.462050e+01   \n"," 50%    6.483370e+05  3.521417e+15  4.752000e+01  4.817400e+04  3.935430e+01   \n"," 75%    9.725055e+05  4.642255e+15  8.314000e+01  7.204200e+04  4.194040e+01   \n"," max    1.296674e+06  4.992346e+18  2.894890e+04  9.978300e+04  6.669330e+01   \n"," \n","                long      city_pop     unix_time     merch_lat    merch_long  \\\n"," count  1.296675e+06  1.296675e+06  1.296675e+06  1.296675e+06  1.296675e+06   \n"," mean  -9.022634e+01  8.882444e+04  1.349244e+09  3.853734e+01 -9.022646e+01   \n"," std    1.375908e+01  3.019564e+05  1.284128e+07  5.109788e+00  1.377109e+01   \n"," min   -1.656723e+02  2.300000e+01  1.325376e+09  1.902779e+01 -1.666712e+02   \n"," 25%   -9.679800e+01  7.430000e+02  1.338751e+09  3.473357e+01 -9.689728e+01   \n"," 50%   -8.747690e+01  2.456000e+03  1.349250e+09  3.936568e+01 -8.743839e+01   \n"," 75%   -8.015800e+01  2.032800e+04  1.359385e+09  4.195716e+01 -8.023680e+01   \n"," max   -6.795030e+01  2.906700e+06  1.371817e+09  6.751027e+01 -6.695090e+01   \n"," \n","            is_fraud  \n"," count  1.296675e+06  \n"," mean   5.788652e-03  \n"," std    7.586269e-02  \n"," min    0.000000e+00  \n"," 25%    0.000000e+00  \n"," 50%    0.000000e+00  \n"," 75%    0.000000e+00  \n"," max    1.000000e+00  ,\n","           Unnamed: 0        cc_num            amt            zip  \\\n"," count  555719.000000  5.557190e+05  555719.000000  555719.000000   \n"," mean   277859.000000  4.178387e+17      69.392810   48842.628015   \n"," std    160422.401459  1.309837e+18     156.745941   26855.283328   \n"," min         0.000000  6.041621e+10       1.000000    1257.000000   \n"," 25%    138929.500000  1.800429e+14       9.630000   26292.000000   \n"," 50%    277859.000000  3.521417e+15      47.290000   48174.000000   \n"," 75%    416788.500000  4.635331e+15      83.010000   72011.000000   \n"," max    555718.000000  4.992346e+18   22768.110000   99921.000000   \n"," \n","                  lat           long      city_pop     unix_time  \\\n"," count  555719.000000  555719.000000  5.557190e+05  5.557190e+05   \n"," mean       38.543253     -90.231325  8.822189e+04  1.380679e+09   \n"," std         5.061336      13.721780  3.003909e+05  5.201104e+06   \n"," min        20.027100    -165.672300  2.300000e+01  1.371817e+09   \n"," 25%        34.668900     -96.798000  7.410000e+02  1.376029e+09   \n"," 50%        39.371600     -87.476900  2.408000e+03  1.380762e+09   \n"," 75%        41.894800     -80.175200  1.968500e+04  1.385867e+09   \n"," max        65.689900     -67.950300  2.906700e+06  1.388534e+09   \n"," \n","            merch_lat     merch_long       is_fraud  \n"," count  555719.000000  555719.000000  555719.000000  \n"," mean       38.542798     -90.231380       0.003860  \n"," std         5.095829      13.733071       0.062008  \n"," min        19.027422    -166.671575       0.000000  \n"," 25%        34.755302     -96.905129       0.000000  \n"," 50%        39.376593     -87.445204       0.000000  \n"," 75%        41.954163     -80.264637       0.000000  \n"," max        66.679297     -66.952026       1.000000  )"]},"metadata":{},"execution_count":26}],"source":["df_train1.describe(), df_test1.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1176,"status":"ok","timestamp":1698371906024,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"OGVu1w4QdXH_","outputId":"33b10956-5112-497b-f8dc-21522f28ff7a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Unnamed: 0               0\n","trans_date_trans_time    0\n","cc_num                   0\n","merchant                 0\n","category                 0\n","amt                      0\n","first                    0\n","last                     0\n","gender                   0\n","street                   0\n","city                     0\n","state                    0\n","zip                      0\n","lat                      0\n","long                     0\n","city_pop                 0\n","job                      0\n","dob                      0\n","trans_num                0\n","unix_time                0\n","merch_lat                0\n","merch_long               0\n","is_fraud                 0\n","dtype: int64"]},"metadata":{},"execution_count":27}],"source":["df_test1.isnull().sum()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2234,"status":"ok","timestamp":1698371908252,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"mHP72gYRb9Eq","outputId":"78fca35f-8333-449c-eb4f-2208b6f3841f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Unnamed: 0               0\n","trans_date_trans_time    0\n","cc_num                   0\n","merchant                 0\n","category                 0\n","amt                      0\n","first                    0\n","last                     0\n","gender                   0\n","street                   0\n","city                     0\n","state                    0\n","zip                      0\n","lat                      0\n","long                     0\n","city_pop                 0\n","job                      0\n","dob                      0\n","trans_num                0\n","unix_time                0\n","merch_lat                0\n","merch_long               0\n","is_fraud                 0\n","dtype: int64"]},"metadata":{},"execution_count":28}],"source":["df_train1.isnull().sum()"]},{"cell_type":"markdown","source":["Não há valores nulos na base de dados."],"metadata":{"id":"dNWo_L-nRdSL"}},{"cell_type":"markdown","metadata":{"id":"JoCc446ln35d"},"source":["### Excluindo Colunas"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hmVqxnuJdCsf"},"outputs":[],"source":["# Remover colunas de features de entrada que são categóricas com muitas categorias, sendo difícil aplicar uma transformação\n","excluir_colunas = ['Unnamed: 0','cc_num','merchant','trans_num','unix_time','first','last','street','zip']\n","df_train1.drop(columns=excluir_colunas,inplace=True)\n","df_test1.drop(columns=excluir_colunas,inplace=True)"]},{"cell_type":"markdown","metadata":{"id":"RzUBprr9pOJm"},"source":["## Pré Processamento dos Dados\n","- **Transformando coluna de data em datetime para conseguir uma nova informação que são as idades dos indivíduos**\n","- **Transformação e Construção de Novas Features a partir das Categóricas**\n","- **Tratando Desbalanceamento dos Dados com Reajuste de Proporção**\n","- **Fazendo o Split de Treino e Teste**\n","- **Função para Generalizar Métricas de Resultados**\n","- **Fazendo a Normalização dos Dados**\n"]},{"cell_type":"markdown","metadata":{"id":"hGmKYm5tcS5O"},"source":["#### Transformando coluna de data em datetime para conseguir uma nova informação que são as idades dos compradores.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HTb4V4g6ofcY"},"outputs":[],"source":["df_train1['trans_date_trans_time']=pd.to_datetime(df_train1['trans_date_trans_time'])\n","df_train1['trans_date']=df_train1['trans_date_trans_time'].dt.strftime('%Y-%m-%d')\n","df_train1['trans_date']=pd.to_datetime(df_train1['trans_date'])\n","df_train1['dob']=pd.to_datetime(df_train1['dob'])\n","\n","df_test1['trans_date_trans_time']=pd.to_datetime(df_test1['trans_date_trans_time'])\n","df_test1['trans_date']=df_test1['trans_date_trans_time'].dt.strftime('%Y-%m-%d')\n","df_test1['trans_date']=pd.to_datetime(df_test1['trans_date'])\n","df_test1['dob']=pd.to_datetime(df_test1['dob'])"]},{"cell_type":"markdown","metadata":{"id":"m2bPNI9jcaSG"},"source":["#### Transformação e Construção de Novas Features"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b919_-D0qAcM"},"outputs":[],"source":["#Calculando idades\n","df_train1[\"age\"] = df_train1[\"trans_date\"]-df_train1[\"dob\"]\n","df_train1[\"age\"]=df_train1[\"age\"].astype('timedelta64[Y]')\n","\n","df_test1[\"age\"] = df_test1[\"trans_date\"]-df_test1[\"dob\"]\n","df_test1[\"age\"]=df_test1[\"age\"].astype('timedelta64[Y]')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hh8qhlzzW6zI"},"outputs":[],"source":["# Construir uma feature que é a distância entre o comerciante e o local de residência\n","df_train1['latitudinal_distance'] = abs(round(df_train1['merch_lat']-df_train1['lat'],3))\n","df_train1['longitudinal_distance'] = abs(round(df_train1['merch_long']-df_train1['long'],3))\n","\n","df_test1['latitudinal_distance'] = abs(round(df_test1['merch_lat']-df_test1['lat'],3))\n","df_test1['longitudinal_distance'] = abs(round(df_test1['merch_long']-df_test1['long'],3))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N6-U1Q2GYO0L"},"outputs":[],"source":["# Excluindo outras features categóricas que não serão utilizadas para a construção da modelagem\n","# Um dos motivos é que essas features possuem muitas categorias, dificultando a transformação desses dados\n","drop_columns = ['trans_date_trans_time','city','lat','long','job','dob','merch_lat','merch_long','trans_date','state']\n","df_train1.drop(columns=drop_columns,inplace=True)\n","df_test1.drop(columns=drop_columns,inplace=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1698371911705,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"ob87Y9D_aQPY","outputId":"09cae7f8-8be4-4422-8f1a-58af408c252c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(F    709863\n"," M    586812\n"," Name: gender, dtype: int64,\n"," F    304886\n"," M    250833\n"," Name: gender, dtype: int64)"]},"metadata":{},"execution_count":34}],"source":["df_train1['gender'].value_counts(),df_test1['gender'].value_counts()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4pn0qHvnYkcN"},"outputs":[],"source":["# Convertendo a feature categórica de gênero 'F' e 'M' em 1 para 'M' e 0 para 'F'\n","df_train1.gender=df_train1.gender.apply(lambda x: 1 if x==\"M\" else 0)\n","df_test1.gender=df_test1.gender.apply(lambda x: 1 if x==\"M\" else 0)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8IQn9jANahNH"},"outputs":[],"source":["# Utilizando o One Hoting Encoding para construir features de entradas tendo como base as categorias\n","df_train1 = pd.get_dummies(df_train1, columns=['category'], prefix='category')\n","df_test1 = pd.get_dummies(df_test1, columns=['category'], prefix='category')\n","\n","df_test1 = df_test1.reindex(columns=df_train1.columns, fill_value=0)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"executionInfo":{"elapsed":316,"status":"ok","timestamp":1698382191385,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"HpUswBjAaz63","outputId":"1c214d14-15cf-426a-95f7-506d9cb41a93"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["      amt  gender  city_pop  is_fraud   age  latitudinal_distance  \\\n","0    4.97       0      3495         0  30.0                 0.068   \n","1  107.23       0       149         0  40.0                 0.271   \n","2  220.11       1      4154         0  56.0                 0.970   \n","3   45.00       1      1939         0  51.0                 0.804   \n","4   41.96       1        99         0  32.0                 0.254   \n","\n","   longitudinal_distance  category_entertainment  category_food_dining  \\\n","0                  0.870                       0                     0   \n","1                  0.024                       0                     0   \n","2                  0.108                       1                     0   \n","3                  0.447                       0                     0   \n","4                  0.830                       0                     0   \n","\n","   category_gas_transport  category_grocery_net  category_grocery_pos  \\\n","0                       0                     0                     0   \n","1                       0                     0                     1   \n","2                       0                     0                     0   \n","3                       1                     0                     0   \n","4                       0                     0                     0   \n","\n","   category_health_fitness  category_home  category_kids_pets  \\\n","0                        0              0                   0   \n","1                        0              0                   0   \n","2                        0              0                   0   \n","3                        0              0                   0   \n","4                        0              0                   0   \n","\n","   category_misc_net  category_misc_pos  category_personal_care  \\\n","0                  1                  0                       0   \n","1                  0                  0                       0   \n","2                  0                  0                       0   \n","3                  0                  0                       0   \n","4                  0                  1                       0   \n","\n","   category_shopping_net  category_shopping_pos  category_travel  \n","0                      0                      0                0  \n","1                      0                      0                0  \n","2                      0                      0                0  \n","3                      0                      0                0  \n","4                      0                      0                0  "],"text/html":["\n","  <div id=\"df-966b145f-451f-4689-95d5-518e546d514c\" 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>amt</th>\n","      <th>gender</th>\n","      <th>city_pop</th>\n","      <th>is_fraud</th>\n","      <th>age</th>\n","      <th>latitudinal_distance</th>\n","      <th>longitudinal_distance</th>\n","      <th>category_entertainment</th>\n","      <th>category_food_dining</th>\n","      <th>category_gas_transport</th>\n","      <th>category_grocery_net</th>\n","      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\n","\n","   longitudinal_distance  category_entertainment  category_food_dining  \\\n","0                  0.265                       0                     0   \n","1                  0.476                       0                     0   \n","2                  0.660                       0                     0   \n","3                  0.064                       0                     0   \n","4                  0.868                       0                     0   \n","\n","   category_gas_transport  category_grocery_net  category_grocery_pos  \\\n","0                       0                     0                     0   \n","1                       0                     0                     0   \n","2                       0                     0                     0   \n","3                       0                     0                     0   \n","4                       0                     0                     0   \n","\n","   category_health_fitness  category_home  category_kids_pets  \\\n","0                        0              0                   0   \n","1                        0              0                   0   \n","2                        1              0                   0   \n","3                        0              0                   0   \n","4                        0              0                   0   \n","\n","   category_misc_net  category_misc_pos  category_personal_care  \\\n","0                  0                  0                       1   \n","1                  0                  0                       1   \n","2                  0                  0                       0   \n","3                  0                  1                       0   \n","4                  0                  0                       0   \n","\n","   category_shopping_net  category_shopping_pos  category_travel  \n","0                      0                      0                0  \n","1                      0                      0   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Ela trata do problema de classes desproporcionalmente representadas, onde uma classe tem muito mais instâncias do que a outra. O Random Under-Sampling aborda isso reduzindo aleatoriamente o número de instâncias da classe majoritária para igualar o número de instâncias da classe minoritária.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"t54pwM2ba5nG"},"outputs":[],"source":["X = df_train1.drop(columns=[\"is_fraud\"])\n","y = df_train1[\"is_fraud\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"koI18UANn_Z2"},"outputs":[],"source":["# Peguei uma proporção de quase 90%/10%, trazendo assim mais generalização ao modelo\n","realocando = RandomUnderSampler(sampling_strategy=0.1, random_state=23)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uA8-tH_Bn_ci"},"outputs":[],"source":["X_undersampled, y_undersampled = realocando.fit_resample(X, y)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":29,"status":"ok","timestamp":1698371913692,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"Iz6iyVuJAroI","outputId":"defb111c-3147-4b12-8739-f2446f5257f9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    75060\n","1     7506\n","Name: is_fraud, dtype: int64"]},"metadata":{},"execution_count":42}],"source":["y_undersampled.value_counts()"]},{"cell_type":"markdown","metadata":{"id":"qhYFjCwtCUWb"},"source":["Abaixo apresento como ficou a proporção de balanceamento do modelo"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":435},"executionInfo":{"elapsed":449,"status":"ok","timestamp":1698382238542,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"MIn4KJ2yn_fM","outputId":"66acc1f7-ae16-4c3c-a4b4-99a9339f1a38"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 960x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["donut = y_undersampled.value_counts().reset_index()\n","\n","labels = [\"No\", \"Yes\"]\n","explode = (0, 0)\n","\n","fig, ax = plt.subplots(dpi=120, figsize=(8, 4))\n","plt.pie(donut[\"is_fraud\"],\n","        labels=donut[\"is_fraud\"],\n","        autopct=\"%1.1f%%\",\n","        pctdistance=0.8,\n","        explode=explode)\n","\n","centre_circle = plt.Circle((0.0, 0.0), 0.5, fc='white')\n","fig = plt.gcf()\n","fig.gca().add_artist(centre_circle)\n","\n","plt.title(\"Proporção Realocada com RandomUnderSampling\")\n","plt.legend(labels, loc=\"center\", frameon=False)\n","plt.show();"]},{"cell_type":"markdown","source":["#### Fazendo o split de treinamento e teste dos dados"],"metadata":{"id":"G3MT2l13ZJet"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"XjvrcFVGpWGC"},"outputs":[],"source":["X_train = X_undersampled\n","y_train = y_undersampled\n","X_test = df_test1.drop('is_fraud', axis=1)\n","y_test = df_test1['is_fraud']"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":25,"status":"ok","timestamp":1698371913694,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"Z5Sl7j-mA5R_","outputId":"c6f84599-6fb6-4297-a307-f54f1562dc06"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    75060\n","1     7506\n","Name: is_fraud, dtype: int64"]},"metadata":{},"execution_count":45}],"source":["y_train.value_counts()"]},{"cell_type":"markdown","metadata":{"id":"Sb2U94rQ37zj"},"source":["#### Função para Generalizar Métricas de Resultados\n","- Acurácia\n","- Precisão\n","- Sensibilidade\n","- F1-Score\n","- ROC-AUC"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-O-u5N-opWIT"},"outputs":[],"source":["def evaluate_model(target, predicted, y_score, normalize_matrix= None):\n","    accuracy = metrics.accuracy_score(target, predicted)\n","    precision = metrics.precision_score(target, predicted)\n","    recall = metrics.recall_score(target, predicted)\n","    f1 = f1_score(target, predicted)\n","    auc = metrics.roc_auc_score(target, y_score)\n","\n","    print(\"Accuracy\", accuracy.round(2))\n","    print(\"Precision:\", precision.round(2))\n","    print(\"Recall:\", recall.round(2))\n","    print(\"F1 Score\", f1.round(2))\n","    print(\"AUC:\", round(auc,4))\n","\n","    confusion_matrix = metrics.confusion_matrix(\n","        target, predicted, normalize=normalize_matrix)\n","    cm_display = metrics.ConfusionMatrixDisplay(\n","        confusion_matrix=confusion_matrix, display_labels=[False, True])\n","    cm_display.plot()\n","    plt.grid(False)\n","    plt.show()\n","\n","    fpr, tpr, threshold = roc_curve(target, y_score)\n","    plt.plot(fpr, tpr, label=\"Model\", c=\"blue\")\n","    plt.plot([0, 1], [0, 1], linestyle=\"--\", c=\"yellow\")\n","    plt.title(\"ROC Curve\")\n","    plt.legend()\n","    plt.show()\n","    return None"]},{"cell_type":"markdown","metadata":{"id":"AsEaPhqocf9H"},"source":["#### Fazendo a normalização dos dados\n","\n","A normalização dos dados é uma etapa crucial no pré-processamento de dados em muitos algoritmos de machine learning. Aqui estão algumas razões pelas quais a normalização é importante:\n","\n","- Escalas Diferentes:\n","\n","Diferentes características (atributos) podem ter escalas diferentes. Por exemplo, em um conjunto de dados em que uma característica representa a altura em centímetros e outra representa a renda em milhares de dólares, as escalas são muito diferentes. Algoritmos de machine learning, como aqueles baseados em distância (k-NN, SVM) ou que usam gradientes (regressão logística, redes neurais), podem ser influenciados pela escala dos dados.\n","\n","Convergência Mais Rápida:\n","\n","Algoritmos que envolvem otimização, como gradient descent, geralmente convergem mais rapidamente em dados normalizados. Isso é especialmente importante em modelos iterativos, onde a normalização pode acelerar o treinamento."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RsPfuucibG68"},"outputs":[],"source":["# Já fazendo o fit e transformação da normalização nos dados de treino\n","scaler = StandardScaler()\n","# Fazendo o fit na parte de treinamento\n","scaler.fit(X_train)\n","\n","# Transformando os dados de teste\n","X_test = scaler.transform(X_test)\n","X_train = scaler.transform(X_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1698371914052,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"EPlpR7c2BzdC","outputId":"0b69c275-66b1-45d2-ddb2-eb579cd3c6da"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.46981991,  1.08986986,  0.79768116, ..., -0.30903839,\n","        -0.31930012, -0.17421012],\n","       [-0.35094974, -0.91754074, -0.29427818, ..., -0.30903839,\n","        -0.31930012, -0.17421012],\n","       [-0.30054668, -0.91754074, -0.1822163 , ..., -0.30903839,\n","        -0.31930012, -0.17421012],\n","       ...,\n","       [-0.09963937, -0.91754074, -0.28319456, ..., -0.30903839,\n","        -0.31930012, -0.17421012],\n","       [-0.44721783,  1.08986986, -0.29484514, ..., -0.30903839,\n","        -0.31930012,  5.74019473],\n","       [-0.31442515,  1.08986986,  0.08489499, ..., -0.30903839,\n","        -0.31930012, -0.17421012]])"]},"metadata":{},"execution_count":48}],"source":["X_test"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1698371914052,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"toBk6wYr_upB","outputId":"ff523769-e94f-42f1-8cd3-8ae962450b87"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.27168828,  1.08986986, -0.20108336, ..., -0.30903839,\n","        -0.31930012, -0.17421012],\n","       [-0.02782382,  1.08986986, -0.29436011, ..., -0.30903839,\n","        -0.31930012, -0.17421012],\n","       [-0.43867046, -0.91754074, -0.29278048, ..., -0.30903839,\n","        -0.31930012,  5.74019473],\n","       ...,\n","       [-0.43730464,  1.08986986, -0.06148613, ..., -0.30903839,\n","        -0.31930012, -0.17421012],\n","       [-0.38685752, -0.91754074, -0.29519253, ..., -0.30903839,\n","        -0.31930012, -0.17421012],\n","       [-0.43748088,  1.08986986, -0.06148613, ..., -0.30903839,\n","        -0.31930012, -0.17421012]])"]},"metadata":{},"execution_count":49}],"source":["X_train"]},{"cell_type":"markdown","metadata":{"id":"-uhOWhaYd4AP"},"source":["## Construção dos Modelos"]},{"cell_type":"markdown","metadata":{"id":"ro4bBv-MggiA"},"source":["## Gradient Boosting Baseline\n","\n","O Gradient Boosting Classifier é um algoritmo de aprendizado de máquina que pertence à família de métodos de boosting. Essa abordagem constrói um modelo de previsão forte a partir de um conjunto de modelos de previsão fracos, geralmente árvores de decisão rasas. O processo de treinamento é iterativo, onde modelos fracos são ajustados para corrigir os erros dos modelos anteriores.\n","\n","Aqui estão alguns pontos chave sobre Gradient Boosting Classifier:\n","\n","- Modelos Fracos (Weak Learners):\n","\n","Os modelos fracos referem-se a algoritmos de aprendizado simples que têm desempenho ligeiramente melhor do que o acaso. Em geral, árvores de decisão rasas são frequentemente utilizadas como modelos fracos.\n","\n","- Treinamento Iterativo:\n","\n","O algoritmo treina modelos fracos de forma iterativa, enfatizando as instâncias que foram classificadas incorretamente pelos modelos anteriores.\n","\n","- Resíduos e Gradientes:\n","\n","No treinamento de cada modelo, o algoritmo se concentra nos resíduos ou gradientes dos modelos anteriores, ajustando os modelos subsequentes para corrigir esses erros.\n","\n","- Combinação Ponderada:\n","\n","Cada modelo fraco contribui para a previsão final do modelo combinando suas previsões ponderadas. A ponderação é determinada pelo desempenho do modelo fraco em relação aos erros anteriores.\n","\n","- Regularização:\n","\n","Gradient Boosting inclui técnicas de regularização para evitar overfitting, como a limitação da profundidade das árvores.\n","\n","O Gradient Boosting Classifier é conhecido por sua capacidade de criar modelos de alta precisão e é amplamente utilizado em competições de ciência de dados e em problemas do mundo real.\n","\n","- Diferenciações para outros modelos:\n","\n","Os modelos de gradient boosting podem ser utilizados para tarefas tanto de regressão quanto de classificação. Em contraste com a floresta aleatória, esses modelos buscam construir árvores de maneira sequencial, visando corrigir os erros das árvores anteriores (MÜLLER; GUIDO, 2017)."]},{"cell_type":"markdown","source":["![gradient_boosting.png](data:image/png;base64,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)"],"metadata":{"id":"W4KWJSEehnFJ"}},{"cell_type":"markdown","metadata":{"id":"1WQqGYAj505S"},"source":["### Gradient Boosting Baseline"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DYU4vsTOpWKR"},"outputs":[],"source":["gboost = GradientBoostingClassifier(random_state=23)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TJ0ufZbAqRBJ"},"outputs":[],"source":["gboost.fit(X_train, y_train)\n","\n","y_pred_train = gboost.predict(X_train)\n","y_score_train = gboost.predict_proba(X_train)[:,1]\n","\n","y_pred_test = gboost.predict(X_test)\n","y_score_test = gboost.predict_proba(X_test)[:,1]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":971},"executionInfo":{"elapsed":1070,"status":"ok","timestamp":1698379558758,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"ZcgZ174GqREB","outputId":"c97fbe35-2e05-44ce-8dcc-9210888af544"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy 0.98\n","Precision: 0.96\n","Recall: 0.85\n","F1 Score 0.9\n","AUC: 0.9899\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["# Métricas de Treino\n","evaluate_model(y_train, y_pred_train, y_score_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":971},"executionInfo":{"elapsed":3711,"status":"ok","timestamp":1698379562467,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"ditUAoU2qRHJ","outputId":"5a2788c9-297d-43fe-caf4-f4d825202094"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy 1.0\n","Precision: 0.46\n","Recall: 0.82\n","F1 Score 0.58\n","AUC: 0.9878\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Métricas de Teste\n","evaluate_model(y_test, y_pred_test, y_score_test)"]},{"cell_type":"markdown","metadata":{"id":"NaOdJxho-zvG"},"source":["## Gradient Boosting Otimizado\n","\n","#### Otimização com Grid Search CV\n","A otimização feita no modelo foi utilizando o Grid Search. A pesquisa de grade pode ser pensada como uma pesquisa exaustiva para selecionar um modelo. Na pesquisa de grade, configura-se uma grade de valores de hiperparâmetros e para cada combinação treina um modelo e pontua nos dados de teste. Nesta abordagem, todas as combinações de valores de hiperparâmetros são tentadas, o que pode ser muito ineficiente, pela questão do tempo e custo computacional. O Scikit Learn oferece a função 'GridSearchCV' para esse processo.\n","\n","Ressalta-se que através da 'GridSearchCV',a mesma já faz a divisão dos dados de treino em treino e validação através do seu fit, não necessitando assim, fazer a divisão dos dados de treino previamente.\n","\n","Os hiperparâmetros otimizados, foram:\n","   \n","  - Learning Rate: A taxa de aprendizagem reduz a contribuição de cada árvore em learning_rate. Existe uma compensação entre taxa de aprendizagem e n_estimadores.\n","  - max_depth: A profundidade máxima limita o número de nós na árvore. Ajuste este parâmetro para melhor desempenho; o melhor valor depende da interação das variáveis ​​de entrada.\n","  - n_estimators: O número de estágios de reforço a serem executados. O aumento de gradiente é bastante robusto ao ajuste excessivo, portanto, um número grande geralmente resulta em melhor desempenho."]},{"cell_type":"markdown","source":["### Gradient Boosting Otimizado"],"metadata":{"id":"UIc_9cId-WoL"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"8NlSSEfM-zMX"},"outputs":[],"source":["param_dist = {\n","    'learning_rate': [0.01, 0.1, 0.2],\n","    'n_estimators': [50, 100, 200],\n","    'max_depth': [3, 5, 7]\n","    }\n","\n","#stratified_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","\n","grid_search = GridSearchCV(gboost, param_dist, cv=5)\n","\n","\n","# Ajustando o modelo aos dados de treinamento\n","grid_search.fit(X_train, y_train)\n","\n","# Extraindo os melhores hiperparâmetros encontrados\n","best_params = grid_search.best_params_"]},{"cell_type":"code","source":["best_params"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L2CMZ9IT3PKs","executionInfo":{"status":"ok","timestamp":1698375336650,"user_tz":180,"elapsed":5,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"}},"outputId":"d5e8f2dd-4a14-436c-a20f-fa256e14077a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100}"]},"metadata":{},"execution_count":58}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"O7do0-OQ1SI_"},"outputs":[],"source":["y_pred_train = grid_search.predict(X_train)\n","y_score_train = grid_search.predict_proba(X_train)[:,1]\n","\n","y_pred_test = grid_search.predict(X_test)\n","y_score_test = grid_search.predict_proba(X_test)[:,1]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XSHp73X91SSI","colab":{"base_uri":"https://localhost:8080/","height":971},"executionInfo":{"status":"ok","timestamp":1698375330473,"user_tz":180,"elapsed":948,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"}},"outputId":"a26d778e-52a8-444a-9b95-0512a9ddbd36"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy 0.99\n","Precision: 0.97\n","Recall: 0.87\n","F1 Score 0.92\n","AUC: 1.0\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["# Métricas de Treino\n","evaluate_model(y_train, y_pred_train, y_score_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Q6b2oZP81Sfc","colab":{"base_uri":"https://localhost:8080/","height":971},"executionInfo":{"status":"ok","timestamp":1698379420433,"user_tz":180,"elapsed":1927,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"}},"outputId":"ddb82daa-6c65-42b4-a6c8-805200be48ae"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy 1.0\n","Precision: 0.45\n","Recall: 0.84\n","F1 Score 0.58\n","AUC: 0.9948\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["# Métricas de Teste\n","evaluate_model(y_test, y_pred_test, y_score_test)"]},{"cell_type":"markdown","source":["# Conclusão\n","\n","Conclui-se que o modelo baseline sem otimização e o modelo otimizado apresentam resultados bem relevantes e parecidos (Curva de Roc-Auc, por exemplo). Sendo que podemos destacar o aumento de 2% de sensibilidade no otimizado.\n","\n","Apesar de apresentar uma precisão 1% menor, quando comparamos as matrizes de confusão acima, ao fazer a predição dos valores de testes, o modelo otimizado apresenta uma maior quantidade de acerto na classe de Verdadeiros Positivos (Classe 1), classe com menos registros na base, em comparação com o baseline. Mas reforço que os modelos apresentam resultados bem parecidos.\n","\n","Sendo assim, para a nossa tabela final de comparação, escolho como representante, o modelo **Gradient Boosting Otimizado**, mas baseline seria um ótimo representante também.\n","\n","Abaixo segue as características e resultados do **Gradient Boosting Otimizado**:\n","- Dados normalizados com Standad Scaler\n","- Foi aplicado um balanceamento de RandomUnderSampler\n","- Otimizado com GridSearch e validação cruzada, sendo os hiperparâmetros otimizados:\n","  - {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100}\n","- Sendo seus resultados:\n","  - Accuracy 1.0\n","  - Precision: 0.45\n","  - Recall: 0.84\n","  - F1 Score 0.58\n","  - AUC: 0.9948"],"metadata":{"id":"1zTFtL8gTySx"}},{"cell_type":"markdown","source":["![Captura de tela 2023-10-27 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BlChahJtXMFoEEuyOQAACiEtmompkCIhFIAABR0WhHaLfWzBUQiUACAIiKwkXk6pmMlKZ5gEg8IwAAUUn5T3/I3ouj/SI1AgkAIMu0YiY5C0UPCVIjkAAAohKahkl7JGRPlaQkA0QgkAAAss4Fi117i2SugEj5OpDs2LHDVq5caUlJScE5yIxpf5S2bic2tfNatrALWrewyw5rZoMHVfGXPXFTPeu4byt/vuqha+rb2lVF/GVL5hW3R7vVs8va7G9XH7OfPy9s9bIi9tStdeyqo/ezLu32t/91aWCTx5S2ZPefKDkpwb58o4rdeHIT69J2f7vplCY25M3KlrjDfbpyl//wSUW767xGdqm735tPa2KfvVrVNq4rHNwzCqqTL1xlj388214f/bc9/9UsO7PrCitVJvQ3Xa/pFrv3lXk2YOR0e/Wn6db9yYVWqfoOf1mkA47cYB9NmWrn3bDcChcOvdWddulKe/Kz2Tbo17/t2S9n2XFnr7bCRXgbjL3Mj46Eir9t7C5PBZLExET77bffbMiQITvr22+/ta1btwbX2N20adPs7LPPtqVLlwbnILMquxf3xz6cbQNG/W39hs+wUy5eFVwSeqPQ+aoezy6w8pUS/fklSiVbuxPX20kXrd75xhFWvGSKnXzBanv47X/syU9mW7NDNrmQU9WFmaKWkJBizQ/dZD2eW2DPfDHLut6zxN55uoZN/q20b3ArUizFzu223F0221/254iy9ufIsu6y4M5RICUkmH3wQnW7q3Mje+vJGnbkaeusmXueqMWgkHvPGvdjOXvg8gY+FJevmGhX3OP+3iPaD6rV2u5CzEqbM7VEcE7Iju2F/PPrbhdyP3ypml3ba6ntd5C7X8SU/jx1cL3MF7C7PBVINm/ebH369LH+/fvbyJEjfY0ePdq2b98eXAOxtm1LIRs1pKIfnZg9peRu46gLZpWw4e9Xsj9+Kmub1u/6NFOp2g479uw1Vq/xVvdpNDgzULpckrU4bJPVqLvdhZ1Eq1Fnu3tTSbEU9+qT4J5tjZpvsfr7bbXKNRKtzr7brKQLN9u3FvKfXI8+c60deuwGq7rPDmvaerPVbrTN1q0qYsnJNL8VZMPeq2wTfi5ryxcXs6ljS9vyRcX9KIj+q8+dVtK++6iSLZlb3Ba65+PPQyv450X4GVHGPd8uv3upf47OmlwqODdk+IeVbNKvZfz9/ja8vHsuFbZaDXgtyQ7J7nUjs8UqG6SW56ZsypQpY507d7ZHHnnE1+23324fffSRde3a1S699FJ7/vnn0xwx0bTNzz//bN26dbPzzz/fbr75Zps7d66/TNM6/fr1swsuuMCuueYa++qrr/xoTLwrWyHRDj1unQsO230w+fjlajZicEU/fdL0gE1+GFyjFn+OKGfvPVfdNqwJTdlkxKsP7mMPXl3fvv+kkh1x8jorF4yuJLtgMnpYeXvwyvr25M113afhtdaizX8/ta5YUsyWLSpqNetts0KF3KsX4kLdJlutfOUdtmh2if+MjGlk7vhz19ik0WX8ZXpenNF1pa1wgUNBJb2mhFbtNlqZ8kk27Y/dQwtiI+0pmfSLfUiQWp58RhQtWtSKFy/uq0iRIj5sXHLJJXbRRRfZJ598Yk899VRwzV02bNhgDz74oO27774+jBx66KG2adMm27Jli7388ss2ceJEH0ZOPPFEe+mll/xUUGr6ObqN7kul2xZk+rR41lUr7ZSLV1sn9/WkC1bb79+V82FA5519zQo77dJVdt71y22x+4T6z9+7D4mn5/hzVvs5fE0JTR1X2gce0fB8Exd2Ol6x0g4+doP9OqyCrVxa1F8Wtm1Lgr39VA1r2GyrtWy7yd8GBZ9G3s65doXvOZoztWRwbkjRYsl+JET9IZp+kYOP2eBHS3R6x7Y9v5Q1aLbF3fZfG/DQPu55nPHnMDIu9ZbwGStgd3kukGg045ZbbrEGDRr4uu+++/zISKtWrezAAw+0a6+91t566y33CWn3p7PChKZ8DjjgAGvZsqUfDWnRooXNnj3bZs2a5Udd9t9/f2vbtq21a9fOTwelHiX5448/7NRTT7UmTZpY06ZN7YYbbgguKZg0lVKseIoVKeq+lkj20yx6VDdtKOxPFy0Wuqxm/W1WrmKSLVtYLHTDDGjUfKu1OW6D7wmZP6OkrVoWGl1RuKhSM9FaH7HRzr9huQscG+3bDyr5y2T9msL2yLX1rXyVRDv/xuX+UzEKvtJlk+yaB5bYyn+L2ldvVtkZYKVk6SQ7zz0Xqtfebr2uaGCb3fNT9nHPy+ZtNtnAn6f7htgzXci9sPsyu9rdj57beq5pevDS2/+1Hz6tYCMHV/RTh4gtvWb4npCUTFbe/DyMXJTnnhGVKlWyXr16+d4R1Y033mi9e/e2q6++2q666io/urF27Vrbtm1bcIsQTfWcddZZ9sILL9i9995rAwcOtOXLl/uRjvHjx9vDDz/sb3/dddfZmDFjrEaNGv/pTdGoikZO5syZ44OMRlYKsvVrivjRia2bC9m61YVt+vhSVqRIipUomWT/Lijm+0Y2byxkMyaU8r0cCiyiaZctmwr5Nw1N7yjA6D6UETesLWyr3JuKLtcbx1J3PzpfvSb6+q8LNVo5o+svW1TMVrifrz4AXbZ4bjF76Z7aVr5yonW5c6kfkk9KZHikoNO03G3PLPDPtY9frupXXYVXyqgn6YzLV/l+pTcer2nJ7jOEQrKot+SOsxtZ99Mb253nNrLvP65oQ96oYu8/X82v6Kq/3xbrfP0y31vy81cVrHCR5J33i1gKBYwk9zUzpVESIFKeCySFChWyChUqWM2aNX3NmDHDRo0aZT179rRBgwbZlVde6a+Xeqmvpnc0sqLpnOOPP97effdde/311/30j0ZaHnjgAXv77bd9vffee36kpWTJ3YeF9bNLlChhpUqV8qX7LMhmTy7pVze8+WQN3yOihsJjOq2xEqWT/fLct5+u7qdOvn63svskutE3pMrqZUXto77V3KfOSj5UDHy4pl8urICycHYJ+2JgFb9aQrcfObiC71NRk2KSe5P49v1K9s6z1e0td7964yhfKcmO7rjWdmxPsCGvV7Xxo8pa0eIp9sGL1e119wakVTZ6g0LBdWH35Xbo8Rv8iprO162wy+/61w48aoMf4dj/oE121pUrrVTZZD+lqMs6Xb3CheZkF2AK++lFNa3qqwKwwu7GtaHROE3/tG63yWq6IK3ROH+/7Tf4yxBbfgrGhZLMlBpbgUh5fsxMUzPab0RfFyxYYJ9//nlwye7Wr19vn332mfsEVNhP7yhQrF692urWrWu1atWyL7/80v79918/rfPnn3/a1KlT3QtefL/RNWy2xYeBA4/caG1PXG8X3/avn2bR9MyxZ62xQ47dYAcdvcHOvHyl7yfRp1UpVSbZWh++0c68YoXfF6LNCev9cl59cq3rPskefvI6O6j9Rj/Hr16R0y7T8uDQp9P2Z6y1Nu5+9YZz/Dlr7Or/LfF9ABpBOc6d7vH8Amtz/HrfhKiqWW+7C4r+x6KA0pTdo9fWt9++LW9Tfivja9mi4n4qYP7MkvbCXbX9NM7k0aHLZk4sZYmpRs62b0uw7z6uaL8MLb9zVdY371e2l+6tbaO/ibjfTEw7IuM0QpJW6Ei/+MPG7vL8M+LII4+0gw8+2K+cufPOO+2EE04ILtmdQotGRU4++WTfB6IpGV2/WrVqfqWORlQ0pXP00Udb3759ffNqvKtQNdH3cCgAHHjURhcmtvlQoWrcaosd4gKFluHud9BmK1shaWdzaamySXZAEGLCpaW+up1WMux/8GY79Lj1/vZNWm/x/QGi2zfYf6sPObpfNayq6VW07LdJ68273aeqzr5bfT8ACi6NzP32XbndauHs4r45YcWSov+5TKtsUo+aaYpGo3PatE/Tf/LX72ndL02t2SG8aiYzpRCTGZpKVy+gRsn1+q0PqVqs0KlTJ9/z16VLF/8hNGzVqlV+1aUWOuh9QSPt4d5DTflrBF3vLao33njDnx/2/fff2zHHHOP7CXv06OE/zIatWLHCrrjiCttvv/38Pljh1ZyIXkLbtm0z9Gqvvot4oifu4MGDbdQ3Xa3P/ZWtaJHM/fEAGaVGy5NrHxCcArJHUkqi/WxDbc2G1b7nLhbUo1ejaS3r8O75VqR4xrcFkB2bd9iEK7+z6dOn/2f6PDWNbL/yyit+yl2LFrSNg7Z/UDjRaQWDRx991J/3xRdf+Ndv9QuuWbPG9yQqYKg0pd+sWTP78ccffdBQK4BGyq+//nrfd3jKKaf4nkP1Lnbt2tUOOeQQe+yxx3youf/++/2U/sUXX+xvoxaB999/33799Vf75ZdffHvA3mhBBfaMMTMAQFSy0tSqlTmi0e3ExERfyeqYT8OIESPsn3/+sXPPPddvBSFaeKCpei14OOigg/yChsmTJ/v6+++/fdB58skn/RS+Vl2qN1Hn62e88847fiRdoysaOVf4UMhRkNF+Vgo52mZCKzsVbNTLOH/+fFu8eLEPM/fcc49fBKEFFNopXIEE0SOQAACiktWmVq2YfOihh/xIhSqt/aEUIjR6odBQvXr14NzQoUMqVqzoewRln3328ZdrakfBQQFEqzalcuXKfhpf0yuavldY0TYQYYcddpi/P03lLFmyxOrVq7dzxEO31YKHdevW+d7DYsWK+akcqVKlil80MWnSJH8a0SGQAACyzgWLrDW1Jvg39/AeUyqFikgKARqNuPDCC/3lkdRHotAQHjERTf1oQ0tt6aDLwgsXdB39LF2mURB91fRLWOnSpX0Y0e00YqPLwrfVQglJTEz0P1OndV9hWkBBT2JsEEgAAFmm0RE/ZZPJ0m30Zq7+DzWrqhROImmqRJtY3nTTTVa/fn2/UEGHElFDqUY/FCwi96TSiEv58uV9wNBl4SkgXUd9KBo10WiHRlY03ROmBlj11eh2+p10Wfi2CikKJwohGhHRad1XmG6r8xE9AgkAICoZWhmRSnhjNAUEveGHK5ICytixY+2nn36y4cOH22233WYnnXSS38ZBq2MUQDSNopCg7Rx0Wv0fDRs2tI0bN/pAoxEP9X8sXLjQr8bRCEebNm3s999/96FF9fXXX1v79u39ZWpg1e10X7pf9a5I1apVfa+KRlvUM6LLNPWj+z388MP9dRAdAgkAICpZGyEJbpwOTZ00btzYj4io1AeiERD1cKh3RIcC0e7dakjVYUbUZ6Iwor4ONcCqP0VHj9flmg5q3ry5Dz2XXXaZDxI6urxKAUTHQJNjjz3WB5MnnnjC79atlTQdOnTwP0+jKDqkiO5P20doF3E1xioEIXoEEgBAVBRIslKZpRGKjh07+umTsmXLWvfu3f1eVZpCUcjQlI4odOhgqgoomn7Rcc00uhLuUVGg0e7dmsJRwNFR5HUdUUPr//73Px9s1Bui+9V0UrjJVYFEq3a0xFgHa1XoQWywD8kesA8Jcgr7kCAnZNc+JNWb1LbD37zEChff+z4ckRI3b7fZ1w3N0D4kBQX7kKSPERIAQFS0YiatEZD0K7gxECCQAACyTLki1BOS2eLtB7vjGQEAiI4LGAommSkgNQIJACAqfsQjOfMFRCKQAACiEhrxUMDIeIX3IQHCCCQAgKj43VozXcDuCCQAgCzTChuaWhELPCMAANFxAQOIFoEEABAVP+KRRtPq3gqIRCABAEQltDFaoUyVbgNEIpAAAKKSlX1FWGWD1AgkAICoZGXreEZIkBqBBAAQlZSULFRwWyCMQAIAyDIFC02/hL5mroBIBBIAQFT8RmdpNK6mV0zZIDUCCQAgOlkY7qCpFakRSAAAUfhvw2pGC4hEIAEAZF2K+/8sFhCJQAIAiIpvak0jcKRXdLUiNQIJACDLfLZIUSBRo2pmiikb7I5AAgCIij82TURvSEZKRwgGIhFIAAC5gECC3RFIAABRSatHZK+VHNwYCBBIAABRCfWQZLKC2wJhBBIAQFQULtIMHekUiQSpEUgAAFHxIUNfM1Habh6IRCABAAC5jkACAMi6FPf/qaZjMlTJjJBgdwQSAEB0FEqSM1d+3gaIQCABAEQnRaMdWSlgFwIJACDLfJOqn7bJfAGRCCQAgKik2SOSgQIiEUgAAFHIYrBghASpEEgAAFHxIx7JmatQ3wmwC4EEABCdLIx2MECC1AgkAIDoKF1kpYAIMQkkiYmJtnXr1nRr+/btwbUBAAWGCxapm1UzWkCkhLZt22Yop44ZMyb47r9++ukn+/HHH4NTaWvatKldcsklwam8LyUlxQYPHmyjvulqfe6vbEWL8MeD7KFNok6ufUBwCsgeSSmJ9rMNtTUbVluZMmWCc6OzYcMGq9SwntV6/BZLKF40ODdjkrdsM3vodZs+fbqVLFkyOLdga9euXfAd0hKTEZJChQpZkSJF0q3ChQsH1wYAFCR+X5FUO7HurZiyQWoxGSFJS1JSkiUkJPiwkh8xQoKcohdnRkiQ3bJzhGSfPrdaQrFMjpBs3WYJjwxihAQ7xTwt6Anao0cPa968uX3wwQc+mDz99NM2cuTI4BoAAAC7i3kgeeCBB2zhwoV26KGH2pYtW/xIQ9WqVe3zzz8PrgEAKFg0gpyVAnaJeSAZPny49e7d25o1a+anbKRRo0a2ZMkS/z0AoIDRxH9WCogQ80CiEKJpmkgzZ8608uXLB6cAAAVKWmEjIwVEiHkgOf/88+3xxx+3adOm+Wal/v3728svv2wdO3YMrgEAKFB8wEjIZIVuCoTFPJDcdNNNvn/kr7/+sjfeeMPef/99u+eee+ykk04KrgEAKFhcwMi0rNwGBVnMA4mmZm644Qb77bffbP78+X7DtE6dOlnRoplbEgYAyB/8PiRZqIx46623/HJZ9SIedNBBfgR+06ZNvjVA7y/HH3+8NWjQwE4//XSbPXt2cCuzOXPm2HnnnWf16tWzI4880i+s2LFjh79s48aN/n7U66jq06fPzt3EExMT/QdpfbCuX7++XXHFFTZr1iz3+4Z+YX1/1llnWcOGDe24446zcePG+fMRvZgHEi37HTZsmD3xxBPWq1cve/HFF23s2LFsHQ8ABZXeq7NSGdCiRQvr16+f/fzzz/b888/bJ598YiNGjLDly5fbU089ZWeccYZ988031qRJE7v66qt92FDdf//9VrNmTfvhhx/sqquusoEDB/qRe9H19T6ldoJXXnnF3nnnHb9NhYwePdoGDBhg9957r1+kUapUKX+99evX+7Byyy23+DAydOhQO+WUU/x9r1u3zt8W0Yl5IHn11Vetb9++/vg1lSpV8kuA9R/2u+++C64BACgw0goaGSyNOmzbtm1n6Q0/NY2KHHjggbbPPvtY48aNrUKFCrZ582bfo6gPuhdccIE/NIkCiEblJ0+e7C9bvHix3X777bbvvvvaaaedZpUrV7a///7bkpOT7dNPP7UOHTrY0Ucfbe3bt/eHNXnzzTf976MP0NpHS20GCjnqi9T7mGru3Lk2fvx4Pwuw//7727XXXut/F4UlRC/mgUTJUglS+5Hcdddd9uijj/rhrY8++ii4BgCgQElJsIQs1MqVK/0bvt4jVAoFaZk4caKdc845dtlll/kgoCChqROFk2rVqvnr6AOwvlco+ffff337QLly5fxlup72w1qwYIEPPf/8848f5Qhr3bq1n+JRuFi2bJnVrl3bH/JEdFutHtXov1aMFitWzE/liO6/bt26PgAhejEPJKVLl/b/sfQfTTTcpZQZ/o8LAChYEtIY/chI6c1e0/uailGdeeaZ7oL/0ntK9+7dfZ/IH3/84UOHRlR0jLTIw5PofUd9IuoviTx+Wvh4a+EeEo2sRPY1Fi9e3AcV3U6ly8L7aIXvXyMr+pk6HXnf+pk6H9GLSSDRfJ0SqerKK6/0vSMTJkywpUuX+vm7hx9+2C688MLg2gAAmH/j1wfWcGkUIy0KLppaufnmm/3oiPo+1B+i5laVKFBoxEX3odERna/zRCMf6gHRZQoU1atXtxUrVvjLZNGiRX5KR8f40ajHqlWrdu6npR3HRcfbqVWrlg8fa9eu9efpOnrf0/mIXkwCyccff2ydO3f29d577/lhLTUXKe3eeeedvp9EzUEAgAIoJby3SCYq2VUGaGpm9erVPmAoOKg3RNMz6itR0Pjll19szZo1NmTIEN8D0qpVK99rIlqFo4ZT9ZVot3BN92ik5Nhjj/V9HzpP9dlnn/k+E4UV9Y+o10TTOgoeGpHRSL9CTMuWLa1KlSr+Z+ln6hht+nrUUUf5n4foxORov+HRkfQodUbO2eV1emJztF/kBI72i5yQbUf7bVDP9vnfHVk72u+zr+71aL/ax0ofajVNEh4RueOOO3yzqla//P777z4kKEDoQ7GW6er1W32L7777rl/2q9CgEZjrr7/eX3fevHl+JD88HaPQooPAampIq3eefPJJ30tStmxZP1py0UUX+cCi30H3qQ/harLVLICWJOuDd0ZwtN/0xSSQpEVDWZqn0xND9B9S83T5BYEEOYVAgpyQrYGkZxYDyXN7DyRaqqsPvOr/0EiFQkOdOnV8mNAIxtSpU/10jKZcDjjggJ39i5pa0W0VLPTv1ehIeEpI/SDqQwnvW6IRlXCjqug22m1cQUjBQ6t4SpQo4S/T/WrERdND+qCtn6neyYwgkKQv5oFE83JKtBrK0nBamDav0QY3+QWBBDmFQIKckK2B5P4sBpLn9x5IChICSfpi0kMSqUePHn5kRDvjXXzxxXb33Xf7hKnvAQAA0hLzQKJNZbRBjYbHNAzWrVs3H1K+/fbb4BoAgAIjrYbVjFQGm1oRP2IeSLSMS6VlV9qEJrxlvJp/AAAFj/YhyXQFtwXCYh5ITj75ZN/tfPDBB/stdrWtrzqWjznmmOAaAIACxQWMLBUQIeaBRFvGH3HEEX6N+DPPPGPnnnuu/9qlS5fgGgAAALuLSSDRtEx4fbhoya+WS+l4ADr+gEZLAAAFk6Zf0pyWSa9CNwV2ismyXzWs6lDO6WnWrJldc801wam8L7zs98P+19r159eywoX580H2SElJsF6XNwhOAdkj25b91q9vte/J2rLflL79WfaLnWISSLRTXnqX6yBFDRo02OOBk/KicCDp3ulOa5zQ0qX5mM9uAUCOydZAcvedlhBxsLqMSN7mAkm/Vwgk2CnbdmrN7wgkAAoSAknuI5Ckj3dZAEB09LE2KwVEIJAAAKKSZtNqBgqIRCABAEQnK+GCQIJUYh5ItORXR0LUIZo1N6hejClTpviD7gEAChgFC1VyJotAglRiHkh++OEHf7TfV155xcaNG+cP8zxq1Cj77LPPgmsAAADsLuaB5KmnnrIrr7zSjjvuONuxY4cfIdl3333jbpUOAMSLtPpDMlJApJgHkpkzZ/qlTSVKlAjOMb/ETMvDAAAFUOoj+WakONovUol5INlvv/1s9OjRfut4jZDoKL+ff/65tWjRIrgGAKBA0WhHZgtIJeaB5NZbb7UvvvjCvvvuO983cscdd9j8+fPtoosuCq4BAChQ0goceymmbJBazAPJ8ccfb7fddptdf/31dsopp9jZZ59tffr0saZNmwbXAAAUFJp4Sd0bkpHywQSIEPNAUqRIETvwwAP9iMgNN9xg5557rm9qBQAA2JOYB5IOHTrYSSed5EdHTjvtNDvhhBOsffv21rNnz+AaAIACJdXoR0bK70UCRIh5IDnvvPOsc+fOvjQ60rZtW9u+fbvVrVs3uAYAoMBQuMhKAanEPJBcddVVO+uaa66xRx991G688UabO3ducA0AQIGSVuDISAERYh5I0pKQkGALFiwITgEACpK0pmQyUkCkbB0h0Y6tHTt2tLvvvtuvvgEAAEhLzANJ8eLF/c6sZcuWtfLly9uhhx5qH330kXXt2jW4BgAg7jFCglRiGkh0ID3t1Nq7d2979tln7ZlnnrH777/fbyUPACigwkfvzUQxZYPUYhpIChUqZAMGDLCVK1cG5wAACrqsbI4GpBbzKZtLL73UXn/9dZszZ45t3LjRNm/e7Gvbtm3BNQAABYoCRmYLSCVmgWTw4MG2YsUKe+yxx/x0zeGHH+63i2/cuLHfqfXqq68OrgkAKFDSChwZKSBCzAKJRkV0ZN+vvvrKvv/+e3+AvU8++cTXp59+avfee29wTQBA3COQIJWYBZKUlNCzSw2saZWaXQEABU9aPSIZKSBSTHtINEry+OOPp1kaKQEAFEAKF5ktIJWYBpI1a9bYqlWr0qz169cH1wIAFBguXGRllY0KiJTQtm3bDD0txowZE3yXNu3Iet9991mLFi2Cc3ZXuHBhv2lafqEpKDXqdu90pzVOaOn+4GKa3QAgRyWlJNrPNtTWbFjtN6+MhQ0bNljlOvWt4TV3WaGiRYNzMyZp+zbb+tHLNn36dCtZsmRwbsGm9gXsWczeZXW8mhIlSlipUqXSrPwURgAA2YwREqQSs0CiLeLLlSsXnAIAxA2Fi0wWUzZILWaBRNM19evXD04BAOKF7wlJzlz5YAJEoDECABAdwgVigEACAIiOAklWCohAIAEARCVyKW9mCohEIAEA5DwCCVIhkAAAsk7BQqVG1UwUIyRIjUACAIiKn4LR10wUkBqBBAAQnfAoSWYLiEAgAQBEJ62wkZHKgE2bNtmKFSvs33//9V+3bt0aXBKi08uXL7elS5fa2rVrLTlZc0IhSUlJ/lhqukxfExMTg0vMX0/HWNP9Llu2zLZs2RJcErJ582Z/vi7XFvmR97tjxw5buXKlv9/Vq1fvdhmyjkACAMhxGZ22eeKJJ6xbt2525ZVX+nrhhRd8SBEFkH79+tnll19uXbp0seuuu87Gjx/vL9PxyL7++mu75JJL/GW67dChQ30o0WXTpk2zO+64wy677DK79NJLrU+fPrZu3Tp/W4WXBx54wJ+vy++55x6bM2eOv53CyKeffmpdu3b196vr/PTTT/52iA6BBACQZTv7QlIyVxkdIdEBWxUOXn/9dbvtttt8GFCYUDgYN26cjRw50m6//XZ74403bN9997Xnn3/ej3wsWbLE7rzzTjv//PNt0KBBdtJJJ1n//v196NBoyEcffWRFixa1l19+2YeRb7/91oYNG+Z/pn7G77//bk8//bS9+OKL/vqfffaZbd++3Y+aDBw40Dp37uzvt0OHDv7nKMQgOgQSAEBU0gocGSmFCk2NhEujD6npjb9ly5ZWrVo1O+CAA/yRgTWFoutOmjTJhxAdRXefffbxoxmLFy+2efPm2dixY/1R5i+66CKrXbu2nXHGGf52s2fP9j9r6tSpdsIJJ1ijRo3s4IMP9oHlhx9+8NMv+tqpUyf/c5s2bWrHHnus/1maHvrrr7/873X66af7+73qqqt8AJo4caI/H1lHIAEARMeFi6zQKIZGQBQKVI899lhwyX8pvGgUQxQUNPWi0YoqVar4I8qLDvCqo85rtELBRIGhWLFi/jIFGoWZ+fPn+9sqROi2YQ0bNvT3p/N1ewWdsKpVq/rpId1u7ty5VrZsWatYsaK/rHTp0v6+9fMQHQIJACA6CiRZKI1qaORh5syZvjT1kRaNWvzyyy9+WqZHjx5WuXLl4BIUJAQSAED00ggce6uEhAQ/2hCu4sWLuwv+a/To0b435MILL/Q9G4UKFbIiRYpY9erV/WoXTcGIRjc0raLAUqtWLVu0aJHv+xCtxFEvSL169fxtNZqi24Zp5EP3p/N1ezWxhml1T4UKFfztGjRo4Kd+1qxZ4y9Tg63uWz8P0SGQAACik0bY2FtldJXN8OHDrVevXnb00UfbKaec4gOG+kfUkNq6dWsfHMaMGeOnf95++20/6lK/fn1r06aNX/b7wQcf+OmUr776yoceTcVoiqd58+a+V+Sff/7xK3M0HXT88cf7sKOvn3/+uU2ZMsWP3GgVjX6WpoM0xaQgpRU7CjxqcNX9qr8F0Ulo27atnh57pf/g8UTzlYMHD7bune60xgkt3R8P2Q1A/pWUkmg/21Bbs2G1lSlTJjg3OhopqFKrvjW96C4rVKRocG7GJO3YZuu/edmmT5/uezv25KCDDvKhQI2n4RGUG264wTp27Oj7OrTS5fvvv/chpVKlSn4p7yGHHOJfw4cMGeJX0SiY6GdcccUVdtppp/lmVzW1agWNRkbEvRf61Trly5f3PSTqZ9F0kjRp0sS6d+/uw4z6SLQK591337Vt27b5UROt/lGD7N6o+RZ7RiDZAwIJgIIkzwWS7S6QfLv3QKKNyVJvPKbQoGZS0RSNpmrCoUNTLhrlEJ2n0KJRFTW36nYKEKL73Lhxo5/u0YiHbhf5e+h8/fv0XqCfpQrfr8KPlg/rq0KSpnPCl6WHQJI+3mUBAFFJa0lvRiojatSo4adhIiscRkTTKFrlUrNmzf8EA42EqB9El+lrOIyIrqcQovtX70jqUKRpHZ2vyzUlE3m/mi7SCh3dr0ZlMhJGsHc8igCA6ChcZKWACAQSAEB00gobeymO+IvUCCQAgKikNR2zt/LBBIhAIAEARCccMDJbQAQCCQAg5xFIkAqBBAAQlbSmZDJSQCQCCQAg6xQsslpABAIJACAqCSkpmS8SCVIhkAAAopN65COjBUQgkAAAch6BBKkQSAAAWaYNztJqWM1IAZEIJACA6ChcZKWACAQSAEBU0hr92GsFtwXCCCQAgOi4gJGlAiIQSAAAUUlzBGRvlRzcGAgQSAAAQK4jkAAAsi4lKI14ZKZ0GyACgQQAEDW//DcTBaRGIAEARCclJWsFRCCQAACikmbTagYKiEQgAQBEh3CBGCCQAACi4kc70mpcTadY9ovUCCQAgOhETMNktBhVQWoEEmRIizYbredr8+y1n6dbv+Ez7JLb/7WKVRODS0MOOXa99f12pnU4f3Vwzi6lyibZS9/MtN5vzLVixflohF3qNd1qtz270F75YYbd13++Vau1PbjErHrtbXb7swtswIjp9uyXs+zE83Y9twoXSbHTuqz0t+v33Uw7/bKVVqzErufW1f9b4m/zwtcz/c9ANgoHjMwWECFPB5Jly5bZkiVL/lNLly61LVu2BNdCTti4vrANeaOy3XdxQ3v+rjrWqPkW/+ZQtHjoVaVG3e128NEbrEyFJCteavfAUbRYil1571JbtrCYla/sQgxr/hChqAuoC2cWt6/erGxlKyZaocLBBc6V9y21HdsT7MGr6tuHL1a3Gx5ebAe23+AvO6bjWjux8xrre38tG/BQTTvqjLV2xCnrLCF4fm1yz9kx35b3X4u48IJs4h7atEZA9l7uf4AIeTqQ3HbbbXb55ZfbhRdeaAcffLCdfvrp/vS1115ro0ePDq7l/h54Yme7edNL2sRfytqyBcXsn6klbfqEUlbOvXnohV6B46jT19q/C4rbzImlgluEFCmaYsectca2b0uwYe9VCs4Fdpk9uZR9/HI1mzquTHDOLgq4f/9Z2hbPLW5Tfi9tyxYVs4pVEq2EC72tDt9o339c0f76vYxNHl3GRg+rYPsfvMlKl0vyt33vuer2x4iytnkjA8HZy73+Ri7lzXAFNwcCefov9dVXX7XPPvvMBgwYYEcddZTde++9/vT//vc/mzFjhn3//ff2xBNP2F9//WWff/65jRw5Mril2YgRI+yDDz4ITpn9/fff9vLLL9ujjz5qH374oS1fvjy4ZJfk5GTbvn27bdu2zdeOHTuCSyD69HrBzcvs0h5LrW7jrTbh57K2bWuCtTtpnVWvvd1GflnBEt2n2TB9Um3carM1ab3ZvnqrinsNYmgEmTNycAU/8nZpj3+t691Lbd6MEjbx1zJWtkKiH22bP7OEpSSbJSUm2PLFRX0YKVmaKcGcphGPndMwGSxeDZBang4kpUuXtjJlyvivRYoUsRIlSvjTixYtsocffti++eYbK1++vBUtWtS++OILH0LCFE7CgWTatGn2zDPP2MqVK61q1ao2atQo69+/v61fv95fHqbr3XTTTXbWWWdZp06d7LHHHgsugehFf82KIrZ1cyH/oq++kIbNttgZl6+0L1+vYmtXFgmuGVKh6g4fVib9Wtb+XVAsOBfIOI2waVpw8/rCLvwWsqr7bLdSZZKtsHuqFSqc4gLwrpcwPT8LuZOFCukdDzlKASOT5YMJECHfjmUqWGgq54orrrAmTZoE5/6XRj2GDRtm9erVs5tvvtm6du3qp31mz57tK1KjRo3snnvuseeff96ee+45f9/YZfOGwvb9R5Xs437V7devK1ib49f7efz9D95sj77/j70zbpodfso663LHv3bXS/OtUtVEO+r0dXZjn0X25pi/7c4X59u+Lbf4xsW6TWgyRPrUW3LG5avs0/5V7fPXqtpbT9awJfOK22mXrPJTgEk7EqxE6dD0jBRzwSUpKcESXTBBPkAgQSr5NpBUrlzZBxGNjhTSx6JUwn0lmzdvtgULFviQccABB1jjxo2tc+fONnnyZH9ZpJIlS1r9+vX9/apq1aoVXAKtXtAn1fBrSIJ7yNU7MvzDSnbd8U3s7vMb2t0XNLSJv5SxL9+obAMfqWkLZpewey9saD3ObuQve72PO29mCXu1d01bOo8RE4Roak8rZlTh7zX6of6k4iWT/ZSMbzlwX5Nd/ihcNNnWrSriR91attlkxd1zs1SZJKu//xZbvayob2KVwu4+VLrPQsH9I3ukNQKy93L/A0TIt4GkcOHCPoyEFS9e3Pd/hK1duzb4zj3x3StSt27d/JSOmmHHjBnjp3sOPfTQ4BrYmyNPXWdnXr7CTjh3jZ+iaXviOps0uowtXVDcFs0psbM0irJmRVFbubSY7XCfYpfO33X5iiXF/Cdbfd0RMdSO+FauUqKddtkqa3/mWqtSc4cdd/YaP/q2dXNhGz+yrJ3ZdaWdfOEqO+e6Fdaw2VYb9WUFPz0zelh527fVZut45UrreMVKa7j/Vhv3Y1k/pShHnrbW2p+xzmrW2+6/nt5llZUotWtEBTG0s1E1MxXcFggUmHeF/fbbzze3zp0718aPH+9Dh5QqVcoOPPBAmzp1qs2fP98Hl61bt/o+FH1FxiyYVcKFiUJWucYO/0l16NuVbcQXFX3oiPSLe5NIvdImTKMi37xX2b+ZAGEaxShbPsm2b02wnz6v4N+rfGOqe5q880x1++Oncla6XLJt2VTI+t1fy/4aG1qN89e40vbBi9X981EB95P+Vf2KnLBSZZN90/Wv7jm5bUuCb4QtVIjnXqzpEdWIhw8YmSj+SyC1hLZt2+rpsVfhN/jcsHjxYrvjjjvsoosu8kt/1cDat29fGzJkiG90FQWRHj162MaNG61hw4a+gVWrZHTdVatW2ZtvvumbWbV/iZpkDzvsMN9LUr16dX/71DTlM3jwYOve6U5rnNDS/fHwiR5A/pWUkmg/21Bbs2G1XxwQCxs2bLBqNerZQcf0cMFy14h1RiQmbrPFUwfY9OnT/XR5PGjXrl3wHdKSLwJJUlKSXxGjJ60CiEY2Nm3aZJUqVfLTMaIAsW7dOn9ZsWLF/JSObqfriM7XH09iYqK/TPelYJJW/4kQSAAUJHkukOxwgWQagQS75It3WQWIihUr7hwN0Vc1tYbDiOj7ChUqWI0aNXwI0XLgcBgR3UYrc2rWrGnVqlWzsmXL7jGMAAAySNMvmmHT10yV+x8gAu/IAIDoJLtwkekKbgsECCQAgOi5jJHpAiIQSAAAUUl7SmYvFdwWCCOQAACio36QrBQQgUACAAByHYEEABCVNKdkMlBAJAIJACA6ChdZKSACgQQAEJUEly60r0imikSCVAgkAICsU67QniKZLY5ziFQIJACA6GRpsIMREuyOQAIAiEqaUzJ7reDGe7FmzRr77bff7JtvvrEJEybYtm3bgktCx9L5/fffbdiwYTZ69GjbvHlzcIkO3pdokydP9peNGDHCli5dajpGmSQnJ/sjvv/www82fPhwmzlzpj8/bPXq1fbzzz/72/7555/+2Glhut+JEyf630cHbNUx1BAbBBIAQHT0Rp/m9vDpVBAO9mbKlCn22muv2dNPP+1r7dq1/nwFkw8//NCeeeYZ++qrr+zRRx+1l19+2R9UVb7//nu77777/GWDBg2y5557zv79919/mY4G//DDD/ujwH/yySd22223+Z8jChgvvPCC9evXz77++mt78skn/VHjt2/f7i//7rvvrFevXv7gq7re448/7kMKokcgAQBEJ2PZYncZvE2zZs3szjvvtAsvvDA4J0QjJ59++qmdc845PlzcfffdPpD8/fffPlT079/fTjzxRH/ZrbfearNmzbJx48b50REFjcWLF1vv3r3toYce8gdlVfCQsWPH+lGXO+64w9/21FNP9aFm4cKFfoTlscces/bt2/vLFHjee+89f7+IHoEEABCV1PuLZLT0Bq9pl3BFTseEValSxZo0aeKP5h5p7ty5ftTiyCOP9EeDP+KII6x27dp+ikWXLVu2zM4991x/WatWraxp06Z+qkWjGSNHjrRDDz3UGjRoYNWrV7eLLrrIn6epmalTp1qjRo389XXU+EMOOcSPumjKRyFmxowZdvrpp/sjzh9wwAHWokULPxqD6BFIAABR8MkiS7VkyRJr3bq1DxyqJ554IrjPvdP0S4kSJaxcuXL+dEJCglWrVs1WrFjhR0h0WfHixf1lhQsX9tfTdI/ChcJK1apV/WWiIKMwpFEX3bZMmTL+NlKyZEn/devWrT6U6OeEb6vv99lnH//vQPQIJACArHPZwh8oz33NTOk2ejP/66+/bM6cOb407ZJRxYoV8+Ei3DMiO3bssKJFi1qRIkV2O190WucrROg6um6Ywkj4fF1H0zqRDbBSqFChnQEn8rYapdHvgugRSAAA0UlOsYRMlr+NCwGlSpXaWQoEGVWvXj0fJMKNqgoGmqrR+ZqGUQAJj1xs3LjRX08jIRr50JSMrhsOHVqNo6khTe/othpBCTeqasREoaRs2bJWt25d/zsqPImCyfTp023//ff3pxEdAgkAIDqh9/XMyeBtNFXyzz//2PLly32wWLBggf9eoysKDx999JFftvvss8/6gHP44Yf74KC+EU0B6bJvv/3Wh4iDDjrIj4CceeaZvnlVS37/+OMP69u3r+8j0UhHmzZt/JLgzz//3N9Gq3AqVarkg476WE444QS/6ke9Jm+99ZZfsXPaaacFvy2iQSABAERH4SIrlQEavWjbtq3dddddfu+PU045xe6//34fDrRcVytiOnTo4EPHwIEDfX+HgkXPnj39aIYu69Onj1+lowZVhZbDDjvMLrnkEr+SRkFEDbG33HKL/3nNmze322+/3a/SOe6443woueGGG3b2jWh1jUZmzjjjDB9MtLJHIy+IXoL7D52hp8WYMWOC7+KDhvK0zrx7pzutcUJLSyC7AcjHklIS7Wcbams2rPZNm7GglTHVq9a1dq1ussKFMz7dIomJ22zO8rf9lEe4cbSga9euXfAd0sK7LAAgShkc7thNVm6DgoxAAgCIjrJF0Kia4XL/D0QikAAAopLWKpq9VrDCBQgjkAAAch55BKkQSAAAWadgodGOrBQQgUACAIhOWmFjrxXcFggQSAAA0VG4yEoBEQgkAICoJCRnobFVoyRABAIJACBKWQgX5BGkQiABAETnP/0hGanQUXSBMAIJACAKaYWNjFRwcyBAIAEAREfhIs3QkU6RSJAKgQQAkHU+WyhkhL/PYDFjg1QIJACAqKS5iiYDBUQikAAAcgGBBLsjkAAAoqPRjsyW7yMBdiGQAACiE9msmtGihwSpEEgAANFx+SLN0JFeMWWDVAgkAIAoJf83cGSkgAgEEgBALiCQYHcEEgBA1mmkI62m1b0VIyRIhUACAIhO5DRMRoumVqRCIAEARCetwLHXIpFgdwQSAEB0FC4yXS6UABEIJACA6KTVI5KRAiIQSAAA0clStiCQYHcEEgBA1ilXaPolOTlzxZQNUiGQAAByHnkEqRBIAADR2blyJjMV3BYIEEgAAFFwySKtKZm9VlJweyCEQAIAiA6jHYgBAgkAIDraVyTNUZB0StM2QAQCCQAg5xFIkAqBBACQdcoVaW16ttcK3RwII5AAAKKSkpKchaKpFbsjkAAAopDW6EcGiikbpEIgAQBEKQgYmSkgFQIJACA64RGPzJRW2gARCCQAgOikFTgyUkAEAgkAIDpphY2MFBCBQAIAyDpli+TkLBUQiUACAIhSqpGPjBSQCoEEABAFFy6y1NTqCohAIAEAREfZItOl/wF2IZAAAKLjRz10wLzMFIEEuyOQAACyzmeRtJtW0y8CCXZHIAEARCfcF5KpSg5uDIQQSAAA0XEBIyWT5ftIgAgEEgBAlilXaArGbwWfifK3ASIktG3bNkM5dcyYMcF38UEJ/vPPP7cbz7nN6llTS3D/BwD5VbIl2R82wlavW2XlypULzo3Ohg0brHqFmtYquZ37dFs4ODdjkizRltefY9OmTbOSJUsG5xZs7dq1C75DWggke5Ds0vuIESPs8ccft0KFGEjKKD1uv/76q//DK1KkSHAuEFsLFy60xMREa9CgQXAOMiIpKck+/PBDq1ixYnBOdHbs2GFdunSxNWvWBOdkTtOmTe2pp56Km9cKAkn6CCTp0B/b9u3beWPNhC1btlibNm1s9OjRVrZs2eBcIHYUel977TXbuHGjde/e3QoXztwn83i2bds2/3eZkBC7EV8FQwWdrNB/u3h6fSWQpI9AgphSIDnwwANt3LhxBBJkCwWSV1991U8X3HbbbQQS5BsEkvQxFwEAAHIdgQQxVbRoUbvzzjutePHiwTlAbGm6QZ80jznmGPq7gAKEKRsAAHIAUzbp4+MFAADIdQQSAACQ6wgkiIkVK1bYrbfeakOGDAnOAQAg4wgk2KNLLrnEypQpYzVq1NhZ8+fPDy7dnZZianMkLfsFsiL8HKtcubJvjg6fPuGEE2zChAnBtQAUVDS1Yo+0A2OVKlXs/vvvD84JjYQsWrTIbxpXqVIla968uZUqVcqWLVtmd911l5166ql23nnn2dq1a23KlCl+8yqtuNH1qlev7jdRmjdvns2dO9ffX61atfxujewlgfBun3/88YffX+SHH37wq2j++ecfH1D03NHzTPvc6KueO3pOyeTJk/3zcN999/Wn58yZ459jCsp16tSxRo0aWbFixfxlQG6hqTV9jJAgXSVKlPDbTIfrq6++sm+++ca+/fZbe/LJJ/021Gl5+umn7aWXXrLvv//eT+PoeBU6PpDeOHQ7nTds2DDr2bOnjRo1KrgV4ln4OaYN9RRQK1So4I9x8tlnn9ntt99uH3/8sf34448+bGi7cQWXsH79+vnLZeLEifa///3PvvzySxs6dKg//IM+UPkjzALIswgk2CN9utSbQefOnX316tXLOnXqZPfcc4///txzz/VbeKfl9ddftzPPPNN69+5t9913n/9Uq0+4n3zyiTVu3NgeeOAB/6Zx7LHH+uCS1a2nER90MLjLL7/cHnroIT/isSd6Hj377LN2+OGH++eYnqcanfv00095jgF5HIEEe6QNqA477DA/fK5SKNEw+GWXXWZHHHGE9ejRw28Rn1bfSDjAKHTMmDHDSpcu7d8Qvv76a3vmmWesbdu2duihh/pPr+pLWb16dXBL4L8UaDXtoueRpm/2ZNWqVfbXX3/555WeYzqukp5vP//8sw/YAPIuAgn2SIFE8/Sa91SpwbVjx4524okn2gcffGB9+/b1DYg6AGFqehPQdI5uc/XVV9sLL7zgz9dpfcr97rvvfOmNQkPr6kcB9kRTN+E+Iz0vVZFTMFu3bvVfdfA4BY8BAwbsfI5pukZThOkFGQC5j0CCDFuwYIF/I9C0Tc2aNf1oSfiNIJLeEKZOneoPDX/HHXfYxRdf7EdS9IaiT7qzZ8/2ja5qmFWPiu6DplZklJpT1cCq59+6dets5syZNmnSJH+ZVuXsv//+viE2/BzTV0ZHgLyPQIIM0wu9pmruvfde69Onjw8daTUKqldE8/jqH9E8vhpZ1U+iw4xrumf58uV+OkeXPfbYY/5TLJBR5cuXt6OOOso3uOo5NGjQoJ1HltYoiBpgFVbCU4Z6jo0YMYKmViCPY9kv9kirFTSCsd9++wXnmB/d0DJMhYvatWv7/g8d5EyfQP/++2+rVq2a/5SqfSM0n6/zNa3TokULP+yusLJw4UJ/H5rqUU9Aw4YN/X0BouW/CrEKHQoRGgHRqIieJxqhE42MaOWWvuq5o/4kPZfCy361tFyrccLPMfWf7LPPPjtvD+QGlv2mj0ACAEAOIJCkjykbAACQ6wgkAAAg1xFIAABAriOQAACAXEcgAQAAuY5AAmSBlqZu3rzZf68jG69fv95/n110dOUlS5akuZeGLtMS670dq0WX63dNa6v/vdHmddrenw3GAGQXAgkKLO0sqyMTh48sPHbsWL9vRbT0xn7zzTfb22+/7U/rQILvv/++/35PNm7c6DfyUnjICu1Eqv02dD+pac+Oiy66yO/vkh5tSHfLLbf4IzZnlnY+veqqqzjmEIBsQyBBgTV8+HC76667bOTIkT6YaMfON9980zZt2hRcI0Sf+qPZxfP666+3rl27BqfStnjxYrvuuuv+87MBACEEEhRo2t3zkUce8Qf000EBNVqycuVKe+655+zpp5+2J554wq688kofGDSCoK3GL7zwQn8kY+08G6Zt8nXEY41EKNREBphXX31152iJwo0OGHjTTTfZ2WefbXfffbeNHz/e3nvvPT/lovu+5JJL/GiJ7kPb5mvkQec///zztmzZMn8/ohDVpUsXu+GGG/wRbDPqjTfe8PepYwg9+eSTtmLFiuCSEO1wqu3Vzz33XL/teuTBEfX76GCIum2/fv38FA8A5AQCCQq0QoUK+YOrlStXzurWreuPBqtpE4WEV155xV9Hoxs6ForeoNUroZEMXb9bt27+ugoJOnqx3rivuOIKv2tx5M7Fmk5RYBGFkZ49e/rplTvvvNMOOeQQv13+cccd57fQv++++3xIOeCAA3woUSDq0KGDDwG6n/79+/tRFAUkBSBdT1vzDxw40N9/Rmjq5rzzzrPLL7/c/vjjD3vxxReDS0JTRzq6sn4vhQ79vI8//thf9tNPP/kgdfzxx/tjDum0fm40o0cAkFEEEhRoCiBLly616dOn+94JHc9EYUN0sECNFLRv394ffE0jJLfeeqs1adLEBw+NDqh3Qm/wug8FlBNOOMFeeuklf1ye1NQs+v3331vbtm39qIa+KuS0adPGH99HwUjH9FHpd/jwww/9QQd1zBb9LqeffrofvdDvodESHRlZv0/nzp39yExGaXRHt23ZsqUfpVFICocKhSONxuj30lGb9e9Uf42Cig6IqN6Yo48+2lq1amVnnXWWv4xAAiAnEEhQoCmIaPpCow06sNo999xjVatW9d/rgGuFCxf219MB3DSVo+uqrr32Wn89jZAomOgggxrhEB1YsHHjxv77SAokChO6Xx3QLT1qrtXP01RP+GcOGDDA37dq1qxZ1rRp0+DaofCUEVrto6PcXnPNNX7URVNTegzC9HsplOmouKL71e+i31ujMm+99dbO3+edd97xI0cckA5ATiCQoEBr1qyZf9P/6KOPrG/fvta6dWv/BqvSEWTDNGJRpUoV31Oh66s0gqG+E42GKJiEl/mqT0RhIjXdX4UKFfwS3Mi+DNHUkYRHGxQINGKiaZPwz3v33Xf96IsCg36XyN4PBYaM+P333+21117zwUtfb7zxxt3CkX4vhZbwEmHdr8KW/o26XurfR48HgQRATiCQoEDTG3+lSpWsTJky6b6xnnzyyf7NWiMEGhHR8tavv/7a93MoIChsqCF27ty5vtkzrSW2pUqV8tMzo0aN8g2p6j2ZOHGiPwx++fLl/e+iwLBhwwYfRtQbovvUdTS68s8///ilyQo+l156qb8fNZlOmDBhr8uKwxSWwkuLFTY05RS5R4qmbNRD8ueff/q+FwWPgw8+2KpVq+ancRRi9PP0O+j3/uWXX5iyAZAjCCSAo4bXL774wn799Vc/KtKxY0f79ttv/fSJLtMUhkZZdNm8efPs0EMPDW65i0ZBTjnlFN+b8fDDD/vGUTWxKhhoxENTKN27d/eHIB88eLBvHFV/iJpfdZ4aSmfMmOHv64gjjvCrcTTCoSbbgw46yJ+/N8cee6wPM+oT0bSTwljFihWDS0MjQXXq1PHLoU866ST/vf5tmrrSNI9+lppujzzySP+7Llq0KLglAGSvhLZt22bo40/kqgIAAJA5+uCBPWOEBAAA5DoCCQAAyHUEEgAAkOsIJAAAINcRSAAAQK4jkAAAgFxHIAEAALmOQAIAAHIdgQQAAOQ6dmoFACAK7MAaG4yQAACAXEcgAQAAuY5AAgAAch2BBAAA5DoCCQAAyHUEEgAAkOsIJAAAINcRSAAAQK4jkADZ5KOPPrJ69er5ql+/vh100EHWo0cPW7VqVXCNXdasWWNPPPGEHXnkkbbffvtZhw4d7N1337Xt27cH19hl0aJF9uCDD/rNmBo1amTHHHOMPfPMM7Zs2bLgGmlbunSp/e9//7PDDz/cmjRpYkcffbQ999xztnLlyuAaAJB7CteuXbtX8H26rrrqquA7ABnx+++/24gRI+yee+6x008/3cqXL28vv/yyTZgwwTp16mRFixb119uwYYM/reueeuqpdtZZZ9mWLVvs2Wef9SFDAaJ48eL+utOmTbNLL73Uxo8fbx07drRzzz3XypYtax988IGtXbvW2rRpY8WKFfPXjTR37lw76aSTbMaMGXbmmWf6n1GxYkV78803bcGCBf7nAsiagQMHBt8hGmwdD2STQYMG+ZGLoUOH+lESeeyxx+zVV1/1ox8a4UhOTrbrrrvOfv75ZxswYIAfIQl77733rHfv3vbAAw/Y+eef78+76KKL/AjLCy+8YM2aNfPnyeLFi3140XklSpQIzg3ZuHGjnXDCCT7U6IVz3333DS4xf5uZM2faUUcdZVOnTrVNmzb5UBM2e/Zsf98aTUlKSvLXUZAqWbKkzZs3z0qXLm2FCxf2962fnZCQ4G+nkZ0//vjDatWq5f/t+nfqZ/3zzz/+ZyhE6feoWrWqvz6Qn7F1fGwwZQPkoNq1a1tiYqJt3rzZn9b0yzvvvONHUI444gh/Xtjxxx9vrVu3th9++MHWrVtnc+bMsVGjRvlRkf333z+4Voje+DUllDqMiMLO2LFjrVu3bruFEalevboPI/Liiy/anXfe6b8PUyi6/PLL/fcatXnyySft9ttvt/vvv9+HoiFDhvjgddddd/nQEfb333/bFVdcYdOnT7eUlBQ/sqPpoocfftj/nPvuu8+HM43OAIAQSIBspDdpjQioR0RTNRoF0VRJOBhMnDjRtm7d6kcgwqMLYRUqVLADDjjAv7mvX7/efv31Vx84FFJSXzc9CiMKBfoZsaCpKAUoBQqN7mj0ZdasWbuFC4WgMmXK+BEfhSmFFj0WGu15/vnnfS+NHo9hw4b5gAYABBIgG2nK48QTT/Qh4pRTTvFv2n369LE6der4y8MNpWlNXagXRKFEb+g7duzwTamaLtF5mbFixQr/NVbTIxqJOeecc3xj7D777GPt27f3UzDvv/++v1y/77hx4/zIi6Z29G/UaTXfNm7c2P/+rVq18o+Jztf1AYBAAmQjTaWoOVUjI2effbZ/8y1XrtzOEQ71YIhGUVJTz4amSfSmrj4NNcWGz8sMjVSIeknSk9FRF63s0e8TVqlSJTv22GPt9ddf97+felI0VXPaaadZoUKF/AiQgodGUxo2bGgNGjSw5s2b+1GTbdu2+bAFAAQSIBspcBx22GF+hctLL73k36Tvvvtu+/fff/3lLVq0sCJFivhpldQ0TaPpGgUAhYpDDz3UhxG92WeGRiMUNhQK0qPfI/X0SVrhJ61VPFolpCbX0aNH+34RjZgoeIh+tnpnHn/8cb+SKLK0fLly5cr+egDiG4EEyCEaLejVq5ctX77cBg8e7Ps6mjZtascdd5x98cUXfklupD///NMHFfV+aJrjwAMP9NMkaiTVypdI6lHR0t60Rht0/xqZeO211/6zV4lGbMIBRw2umlLSiIZoOfKUKVP877k3Bx98sP+3aBWPVuTpd61WrZq/TGFKgUQrb7QSR5epFJQ0dRVe/gwgvrEPCZBN1LSpN+eLL754Z9+HGloVPHSZeizCQePzzz/3TasKFOr5ULOnmj/1Jn/99df7aRFNk9SsWdM+/fRTH1Q03aElwBqV6Nu3rw8luq/UIxgKBGqi1Z4jChga9VAo0u+mlTJa6aMpF42Q6H7CjbhamqwGVv1Ot9xyiw8UWsKsKaSTTz55tyke3VZTQpqG0Qqizp07+99F19GSYIUbrdjR76jAM2nSJPvwww/9bdRXorAG5FfsQxIbvAoAOUhv0JdccomfigmvflE/xRtvvOH369BurVouq34MbWCmfUzCDbCi0Q4tm1UfyiOPPOL3JXnqqaf8aIQ2N1NYSIumjN5++20fVnQ7LeXV7WrUqOEDk2j/Ef18BQetgtHvqobVyOCRni5duvggo99BU1Hh2+n0lVdeaddcc419/fXX1rVrV79sWMFM0zUZvX8ABRsbowEAEAU2RosNRkgAAECuI5AAAIBcRyABAAC5jkACAAByXb5satWyRDrzAQA5aU/vOzS1xka+CyTaw+DLL7/0SwkJJQCA7KY9eHQMJy3NT+t9h0ASG/kukOhAXXXr1vX7KGgzJgAAsotG5H/77Te79dZb7cILL0xzEz8CSWzku0CiHR+1DbaOolqqVKngXAAAYk/Hd3rggQfskEMO8TsQM0KSffJdINFW0wokOpAXgQQAkJ0USHr27OkPhUAgyV6ssgEAALmOQAIAAHIdgQQAAOQ6AgkAAMh1BBIAAJDrCCQAACDXEUgAAECui3kg0f4gH3/8sfXr189Gjx7t13CnJSkpySZPnmwDBw601157zSZOnBhcAgAA4k3MA8mUKVPsl19+sffff9+GDh26x0Ci4KLd76ZNm2YzZsyw2267zf7+++/gUgAAEE9ivlOrtnbXgYieeeYZf1o73JUoUcJ/H0lhRKHkhRde8DvfXX/99Va2bFl7+eWXg2uEpKSk2LZt23YGG+3U2qpVK3ZqzUNWr9bBp3ScIbPNm4Mzsyg52Wzp0tD9AYg9/Y3pb9a9VBd4nTubNWoUnMgidmrNOdm2dXzv3r19MNlTIDnuuOPsggsusGuuucaf/uijj+zee+/1x6iJtGnTJrvxxhvtgw8+8KcVUMqVK1fgA4n7Z/oXDn1V7Un4Ml030ty5ZhpwSkoKzoig86ZMMStZMnRa9zF1qlnRoqHTYd9/b1arllkax5KyHTtCP2PduuCMQBp/q5kWvo9Y3BeAtBXEv6+EhGRXKe71sLA//eWXZqec4r/NMgJJzsm1QNK6dWu777777LzzzvOnf/zxR38kxYULF1qxYsX8eWGpR0hatmyZ7wOJQoH+SSq9qbvc5f6du0qjBL/8YrZkidmqVaEAkBadr5EJhYPgIdpN2bLBN6kowOjvKvIhLOz+hosXD0444bCT3sOsEFO6dOi2UrmyWZkyoe+zSv/5q1T5b0ACED393et1oUKFghZKVrvXn6+sWrXZ7sPYbe50BTv5ZLO6dUOXZhWBJOfkWiA57LDD/MjHpZde6k+r3+S6665zb6xzrUiRIv68tOSXg+vpzVwhY9Ys9cuERiO2bNkVOFasCF2u0qDQokWhYKKhVPdPtK1bQ/dTu7ZZzZqhN/20RipEYUCX6wUm/NDpPL2xN2gQOp2apkR0Xd13mMKI7iMsHEhq1Ah9TU2X64VNoyiEBwC5Q8PDk131d/WWq/au1DKwv6voEUhyTq4Fki5dulidOnXs4Ycf9qcfeughGzVqlH333Xf+9J7kh0CiN/sBA8xGjDCbPz8UNvQQKJDoMo1qKHToDV2hQOHhoINCc53lyoXe5OvXD91GgaFatVCgSS+Q6KHQ7cKBRNdVSChYn4AAIJI+uQ1z9Zyr0a46u7rF1YGuYvMpiUCSc7KlqXXx4sXWt29f98a7w7p16+Y+YdewzZs323vvvWdnn3227b///vb999/bPffcY7169fIjImpqffLJJ/3l6cnLgUQB48MPzR591GzBgtCIhwKCAsXRR5v7vUNTGvq1q1c323ffUGBQKYgodOj6qvQCCABgmyuNijzuapOrXq4ucVXFVewQSHJOzN/yfvjhB98f8uqrr9rrr79ubdq0sccff9wHlQkTJtiaNWv89dq3b29XXXWV3XTTTXbttdf6Zb97CyN5kUKI+jG0uqRTJ7MLLww1jGoExP2zfP+Hy2cujJlpMOjWW0Pnn3WWWYsWZs2bmzVrFpqaUVgpXz796RkAgOhFUv2GFV195OpmV7ENI8hZ2TZlk13ywgiJGlLV96F+DwUO7ek2ZIjZ2LGhQNGqldnll2taalezJwAgWhoJmepqX1eVXLlPfn7axn2as+yZn2aEJOfwOTyTtKLlrbdC69sPP9zsjDO0p0oooBx7rHpnzEaONLviCsIIAMSGPjcvddXXlVZmvuFKtKRPoyLZE0aQswgkmaCVL088YXbbbaFpGTWcPvig2aBBZq+9ZvbKK2Y33hjqCQEAxMp0V3e4Ur9IbVfNXaGgIZBkkKZn2rQx69fPbO3aUOPqDz+4PxH3N6KpGfWPNGnCqAgAxI5GRoa4Un/hYFenuVIj64muUNAQSPZCTauffx5aEaNRETWvahSke3ez/fcPLc0FAGSHCa4uc7XQ1e2uXnGl0RHeugoi/qumQ6tnFEYUPrQ4qGVLs7vvNnv++fR3LwUAZJWOd6FjUmjDM+0ncpert1094IoX3oKMQJIO7bDav7/L5i6cd+wYOi6CpmpYkgsA2UFH1dTUTB9XOq6ZGvIUSDoF36Mg4611D7SjqvYOGT48NDLy1FOh3VMBANlhmaverrTTqvv0Z4tdCUEkXhBI9uDff82efTb0vXpGCCMAkF0mubralV50y7l6wdVhrhBPCCR7oFERrazREt+uXXcdIwYAECvqE/nelfYW+cqVVs/omDQnuKJfJN4QSNKgHVfXrw8dxVabn3EkWwDILjtc6TDj6hv50JVGSBCPCCSpaGWNRkXUuNq+fWh/EQBArCS6+s3V3670FuReaH0Q6e6KfRTiGYEklQkTzGbNCh3gTgfBY0UNAMSKjj3zvqubXLlPfn5VjXuxNTXpEUbiHW+3qWiZrw6Yp11XjzwyOBMAEKX1rh5x1cPVIlcXuKI5D7sQSCJs2RJaXaOj+fZwfzNsAw8AsbDS1emuHnNVy5X2GtEOrLwFYReeDRGmT3e53QV3NbGef35wJgAgCltdqRlPS3svdqV+kTau2F8EuyOQRJgzx2zZstAxajhiLwBklQ6Kt9mVekTUG6LdVu9z9aKrxq6A/yKQBLS6ZsGC0N4jnTsHZwIAsmCcKwWQX10pnJzqSk2sFV0BaSOQBFasMPvjD7NixczOOSc4EwCQCdro7HVXF7l6y9VkV9pnRG81NLAifQSSgDZCW7LErEIFs/LlgzMBABmkVTQ6Ds11rpa7etDV9a7cpzwgAwgkAfWO/PWX2QknmJUpE5wJAMiAea6ucDXIVXNX2gb+Bldsc42MI5AEEhPNtm0LHURP0zYAgIzS6Ih2YL3E1ReutPsqkDkEEiclxWzdOrPNmwkkAJAxc139EPrW9nf1pKtHXdXVGUCmEUgcbYSmFTbFi4d6SNguHgD2RI2rP7u62tWVrnRMGk3NaDlvJVdA1vDW62jJr3Zo1VeNlgAA0qJpmY9daRWNRkdOdtXIFRA9AomjEZKFC92flfu7qlEjOBMAEGGZK62cucqV5rUHuHop+B6IHoHE2bHDbNKk0HSNjvILAEjtPVePuzrK1SuuurhibxHEDoHEUTOrRkhq1TKrxBQoAASSXGljM9Fuq/e7esbVCa5Y0ovYIpA4v/5qtmaNWb16ZlWrBmcCQFzT5mZ3u9LB8NQ70tRVD1f7ueJgX4g9Aokzf757INwjUbKkWRFGIAHEvdmujnel0ZCvXa10Je5FEsgmBBJnxgyzunXNDj88OAMA4o6WGK5z9aarw1wtdnWNK+0vQrc/sh+BxPntt9B28dWrB2cAQNxZ5eppV7e62sfVQ66eclXLFZD9CCTO5Mmh1TX0jwCIX2pgXeHqJFf9XOnAeCw7RM6J+0CiJb+i/hEt+wWA+LHNlQ6Ep+maaq56unrO1ZGuaFxFzor7QDJ1auhr0aLsQQIgnmijMx2h91xX2n1VAURTNZq7Jowg58V9IPnnH/en5/72NEKirwBQsG13pWPRnO9qsKuDXTVzBeSuuA8kOqieju7btm1wBgAUWGtdDXKl7d+1tPc6VwolLVwBuSvuA8m4cWaFC5s11oEqAaBAm+XqXlean37U1f9cVXEF5L64DyR//hkKJNXUzwUABZp2W73H1UBXF7gq5wrIG+I6kKSosdxRING28QBQsGgVjY7K+6yrza4UQG5zdYArjtKLvCWuA4kOqrdlSyiQ1GAjQgAFRrIr7bSqTc5ucvWFqwWuxL3gsYoGeVBcBxIdUC8pKXT8muLFgzMBIF/b5Gq4q/NcveHqdFePuaJRDnlbXAeS9etDgUQjJACQ/2109barG1zNcKWj9Wr793aueKFD3hbXgWTlSrPExNBxbAAg/1NfyGpX2uDsHVe3u6rvCsj74jqQqH9Eja0VKwZnAEC+NNmVjoOhQHKzq69d6Zg0bD+N/COuA8myZaFj2bDCBkD+o2WCa1zp2DPa2fFxV6Ih37KuaFxF/hLXgUSNrIXcI6CmVgDIP3RkXo2K3OHqAVfNXTVwFexlAORDMQ8kO3bssCFDhtgDDzxgTzzxhM2cOTO4ZHfbt2+3L7/80nr37m29evWyTz/91BLV0JGDtm0LTdk0bBicAQB5npb0ahmvpmY+d9XJVX9XF7tiVAT5V8wDybBhw+z555+3ChUq2JIlS6xbt262bt264NJdvv76a3vxxRetatWqVrNmTXvmmWfsrbfeCi7NGYsXh6ZsSpQIzgCAPG+lK+22qm3g73KlJb0HugLyt5gHkmeffdbOPPNMu+mmm+yxxx6z1atX2/vvvx9cust3331nBx54oF1zzTV21VVX2XHHHedHTHJS6dKhKRst/wWA/EHHnrnPlfYY0cZn2tWRkRHkfzENJFu3brUpU6ZY27ZtrUiRIlaiRAkfNH799dfgGrscccQRNnXqVPv9999t3Lhx9ssvv1inThp63F1ycrLNnz/fJk6c6GvSpEnBJdHbsCG0DwkH1gOQd2119a2rJ10tdaWX7S6uOrgq6gooGGIaSMJTM+XK7TpgU6VKlWzVqlXBqV3atWvnp3V69uxp99xzj5UpU8YHmdTUk/LFF1/YQw895Et9KbGiX0uBRKMkAJD3LHPV19VVrl53tcgVjasomGL6VlyyZEn/dYs2+Ahs2rTJSmtuJJWXX37ZqlWrZq+99pq9/vrr1rhxY7v33nstJXzEu0Dx4sXthhtusPfee8/XoEGDgkuiF56ySePXA4BcpNfB313pQHi9XGlvgodcaTUN0zMomGIaSMqWLWt16tSxadOm+dMKF2PHjvW9IqlpGqdly5bWsGFDq1+/vrVv397+/PPP/wQS0fSPgkm4YmX79tAqGzZGA5B3aEnvp660/fsHrs5xpVU0HV2VcgUUTDENJAkJCXbllVdav379bMSIEfbCCy/Y9OnTrUuXLvbXX3/ZHXfcYTNm6PgKZocddpi9/fbbNmbMGB9ann76ad9vUigH50/mzg0t/Q0GdgAgD9BroHth8lvAv+zqFVcaGWHDJBRsMX/3VyA59dRT7fbbb7evvvrKr7CpVauW7wVZv379zr1GtP/I4Ycfbt27d7cbb7zRf6/lwjmpShU2RQOQF2hUZH5QmpLp7Gq0K/WOsC8B4kNC27ZtM9QhpZGMvGDjxo1+mmfevHlWqlR0w5fXXWc2cKDZuHFmBxwQnAkAOUqLAb539T9XzVzpaL2EkLxCH6K1+EKtB507d/YzAalpkQail3PzI3mQVtioh4RREgA5T58FF7p62pV2XdWGSIe70mgJEH/iPpAIgQRAzpvq6kZXOjheE1caGbneFcv+EJ8YIWGEBECO0+hIV1ffuTrf1ZuujnYVu1WEQH4T14Fk0ybtBEsgAZATdFA87bqqoVn1ITzu6kFXGiGpG5wHxK+4DiTh3iR2agWQvbS6cJyrZ11pnyaNkBznqocrpmgAIZA4xYqFvgJA7O1w9Y6rm1xpa4PZrhRIAESK60Ci6RopXDj0FQBiS8eiucXVPa5WutI28O1dMSwLpBa3fxVqZg3vUk8gARB7E12d52qAq6qu3nN1pavKrgCkFreBRKMj4UBCUyuA2NO+IhtddXI10pWOZl7UFYC0MELiMEICIDYWuBrrSvPBR7l6zdW7rjiCJ7A3jJA4rLIBEB2tovnRVXdXal5V46q65nWkc4ZggYyI60ASbmplygZA1mlaRiMh2v59iCs1rTIiAmQWIyQOUzYAsmatq/td3eFquasPXWmzMzWxAsgMekgcpmwAZE0fV6+40uHCv3Z1tquSrgBkVtyPkDA6AiBzNrnSZmdymavbXL3u6hBXbP8OZFXcBxL6RwBkjIZUZ7l60tUoV2pCa+ZKUzT7ugIQjbifsmGEBEDGjHZ1vatnXI1wpUCiERE+1QCxENcjJCoCCYD0aWREu62e5ep3V9e5usEVQQSIJXpICCQA9ki7rd7oqpsrNatquuZhVzVcAYiluA0kSUmMkADYm1WutPuq9hbREXuvdcXhwYHsEPcjJFu2BGcAgLfZ1XBX21w1cPWQK4URhRIA2SVuA0lYiRLBNwBgc12pR0RLebXrqmiPkX1C3wLINnEbSMKrbEqyhxEA37iqlTOdXb3vqrKrVq4A5JS4DyTs0grEO210po3NLnE13ZX6RL511cQVgJwS14FECCRAvBvj6h5XlVz1dfW8K+ZygZzGCAmBBIhD210lhb61/V11d9XP1UWuWHoH5AYCCYEEiDOrXWkU5A1XCiU1XamJ9QhXRV0ByA1xHUiEQALEk2WubnXV25VGRDRSohcBTdFwYDwgNzFCQiAB4oCOzjvF1YmuPnClPUW0moZldkBeQSAhkAAFnMLI266OdrXC1c2uXnPFKhogLyGQEEiAAi7R1SRX+7p63NUDrtjoDMhr4jqQCIEEKKjGu1rpStMyPVzpiL3aa6SMKwB5DSMkBBKggNEUzSOuOrrSviI6XcdVa1f8wQN5FYGE1yeggEh2Nd+V9hLRtEwRV81dsa8IkB/EdSCRBFb6AQXAVlfDXF3s6jtXHVwNdnWuKz51APlB3P6lJrsPU4yQAAXFv64edLXElfYZeccVB8cD8pO4HiFRFWY0F8jHgqFOfxyaa1w95UoNrDoNID+J+/EBRkiA/Ehbvn/u6n+utKy3rKtLXZ3pqrQrAPlN3I+QEEiA/GadKx2L5mpXz7qa5krNYMVcqZEVQH4Ut2/H9JAA+Y1W0Sh83O5KIyMNXL3rqoUrAPkdIyQEEiCf0Cqarq50LJpzXL3s6nRX/BEDBUHcBxKW/QL5xRpX/7h6yNWjrg5xRVc6UFDEdSARRkiAvEwhZFHoWz8q8rOr613V0hkACpC4HyEhkAB5kbZ7H+3qYFfqF9noSsek2c9VcVcAChoCCYEEyEM0dLnYlfYTOd6VlvfqKL1a2gugIIvrQCJsjAbkFQodY13d4Uo9Im1cPeNKG51VcAWgIIt5INm2bZu98847ds0111iPHj1sypQpwSW7S3GJYPbs2fbII49Y165d7ZZbbrG//voruDT7JbrXviT34YumViCvWOvqRVffuOriSqto1Dei/UUAFHQxDySfffaZ9e/f34488kgrVqyYdenSxdasUWPa7latWuUDy9atW+3CCy/010/W5iA5xOUm27HDbNOm4AwAuay8q/NcabMzraRp5gpAvIh5IHnxxRft0ksvtcsuu8yPfhQpUsTeeuut4NJdBg0aZBUrVrS77rrLjjjiCDvjjDOsVas9HwxLIyrhioUSJdznLvfBi0AC5BZ9AJnuSlMy+kMs6kpbv2t0pKIrAPEkpoFk8+bNNnPmTDvwwAP96YSEBDv66KPt999/96cjffPNN37kRNM15557rt100002f/784NJdkpKSbNKkSf76quHDhweXREe5RlWL1YNALtCqmc9cne9qkKvBrgDEs5gGkg0bNvivZcvqQFchFSpUsLVrNTe8u4ULF/ppG/WOPP3007Z+/Xrr3bt3cOkuCiQTJ060oUOH+oplIBF6SICctsTVk65ucbXd1b2uznIFIJ7FNJCULh06yuamiHkQBY1y5coFp3bRdM3BBx9shx9+uDVr1swuuugi++GHH3wAiaQ+lEsuucSeffZZX4899lhwSfQUSggkQE4a7yp8ULymwdcbXJVyBSCexTyQNGzY0CZPnuxPq99j9OjRPnikdsghh9jGjRq2DdEoSpkyZaxQGhuDFC5c2IoWLbqzYoEREiA3aMXMCFfhqZoTXWnDMwDxLqaBRD0j1113nb3yyit+euXRRx+1RYsW+T4RTbtcf/31Nm2ajtZp/npjx471za3ffvutPfHEE/5y3UdOUShhYzQgu212tcKVPgXoyLwTXL3qqp4rNgICEBLzt2NNvWiVzcsvv2x///23ffjhh1alShU/sqF+Eq26EU3T9OnTx4cRBZju3btbt27d/GU5gRESILtpozONll7m6nJXC11JE1f84QHYXULbtm2Dt+b0jRkzJvgud2maR9NC8+bNs1Klsj7vPHq02TXXmDVvbi40BWcCiBHtPaSVM4+7+teVAkl3V/VdAflHYmKi9ezZ068e7dy5c5qj+O3atQu+QzTifsKCKRsg1ua6etDVXa7UJ/aEK62kIYwA2LO4fTvWlI2KKRsglta7esVVf1fNXX3tShudVXUFAHsW14FECCRALISX62uJ/ymuHnD1sauWrjgWDYC9Y4SEQAJEQUFEOyw/4mqSznCOcaXpmsr+FABkBD0k9JAAWaRdVke6utGVdl791BUAZA0jJIyQAFmgkZHXXGnlzFhX2m21qysAyBp6SAgkQCatcqVG1ftcbXH1tCutomngCgCyJu4nLAgkQFZoqkYBRI2rl7j67/GqACAz4n7Khh4SICPWuZrtaqsrNatqOa+OvH2gKwCIXty+HW/frh34GCEB9m6OK210dqWrP3SGo+W8VULfAkAMxG0gURhJSjLbsCE4A0Aqalwd6uoaV9roTCMjFV0BQOzFbSApVsysaFGzwhxsFEiDtnx/wdVNrn4Mvr7oan9XABB7cRtIwlM15ejFA1LZ4ep1V/e40lbw6hfp46qWK5quAGQPXl0ABLa50mZnRV1pt1XtMfKdK20FDwDZK24DCfuQAJGWudJGZ++60h9HC1ePumIVDYCcEdcjJOzUCih8THPV09X9rt50paW9+sOgwQpAzmGEhECCuPazKy3nfdvV8a5ecVXcFQDkrLjvISGQID4pkQ901cHVFFc9XKmRdT9XtJYByHm88gBxSXuMfOVqX1cvudJ0TVlXAJA74nrKhh4SxBcdCO8vVzo4XhFX/VwNcKUD5TFNAyB3MUICxAU1qj7v6jpXX7rS8t6artq5IpUDyH30kPBajAJvnqvzXGlzMx0rob4rjZAAQN4R11M2QiBBwfaTqxNcDXF1kKvPXR3risFRAHlLXL8qhUMJUDBpSe+NrnRcmttcqYm1oSsAyHuYsmGEBAXO5uBrI1cXuFLvyFOuSrsCgLyJKRsCCQoMNap+6Eq7ruq4NPu40v4i57riiQ4gb4v7KRsCCQqG1a507JnbXb3q6m9XUtIVW8ADyPuYsiGQIN9b6upaVw+50p/0SFcHuAKA/CPup2yA/G2iq1NdfevqbFc/utJqGgDIXxghYYQE+dpoV2pi1VTNC660FTwA5D+MkAD5io5B84+rJf6UWVdXL7tS86p2XgWA/CmuR0hoakX+o71ELnTV19U6V2paPc4VS3oB5G9M2RBIkC8ku+rt6hxXs1xVd1XMFQAUDEzZAHnaDlc6Qq+CyIOuWrsa5OoGVxodAYCCgSkbRkiQpy1z9YArLeU939Ubrs5yxd4iAAqWuB8hIZAgb9IUjVRwdaKrR1z1c9XSFQAUPPSQEEiQ50xypdGQla7KuLrU1VWuFE4AoGAikBBIkGdoVOQTV6e5+tSVtoAXraApGvoWAAqouJ6yobEVece/rnQsmm6utNeIvr/DFQDEB0ZIGCFBrpvvSjutPumquSutornbFaMiAOJH3AcSIPdtcTXW1SWuXnLVwRUAxJe4nrIRRkiQO1a4Wu5KT8Qmrj5ypY3PNELCkl4A8YcpGwIJctxsVxe7utaVekf0Z3igqyqueEICiE9xPUJCUyty1kZX37jSlMzvriq5YtYUACRuXw23bjXbsYMREuQEJd85rh531clVEVfafVVNrDomDQAgbgNJcnKo1q8PzgCyzWZXr7l6xtXRrl5wpWPRaIQEACAxDyRbt261l156yU4//XS75JJLbNy4ccElaZs7d65dffXVdvvtWvaYc0qWNCtWLPQVyF7FXR3mSgfH6+/qpOA8AEBYzAPJe++9Z2+99ZZde+21tt9++9l5551nq1atCi7d3ZYtW+zdd9+1CRMm2KxZOqR6ztJ0TXHeF5AttJT3JldTXGmK5kxXt7iq54p5QgBILaaBJCUlxV555RXr1q2bnXHGGXbfffdZrVq1bNAgbfS0u+TkZBs5cqStXbvWTjjhBCtSRC/aadP96vqqpCTtYgnkVdtd/eHqFFevuHrWlZ6z+lNjOS8A7ElMA8mmTZvsn3/+sVatWvnTCQkJdsQRR9iff/7pT0eaN2+eDR482Dp27Gg1a9YMzv2vxMREH1wUalRvv/12cEn0WGWD2Frr6h1Xl7uaFnzt6YogAgB7E/NAIqVL62BgIeXKlbP1qTpHt2/fbk899ZS1bt3a2rZtG5ybNo2O6PYrVqzwtXKljoAK5DUbXPVydb8r/Vk97Op5Vw1cAQD2JqaBpGzZsv7rhg16cQ7RlEyFCrsfNn3hwoW+z2TgwIF20kknWd++fe2XX36xTp06/affpGjRor5B9s477/R1yy2ah48dlv0iNnTcGY0EtnbV15VGR0q5AgBkREwDSalSpaxJkya+SVU0ujFq1Cg77DCtMNhln332sR9++MGvxunTp4/vN2nevLn17NnTj6ikVqhQIStcuLCv9HpNgJylfhGF72RXJVxp+3dN2RzpqpgrAEBGxTSQyM0332zvvPOO7/W4//77bceOHXbppZf6kKKVN1OnTrWSJUv6kBKuunXrWsWKFe2ggw7yIyI5gf4RRGeBq/tcqXlVm56JeqEqu2LYDQAyK+aBRNMu2ldEIyPak+SNN96wSpUqWZkyZfzoSWR/SVjTpk3tyCP1qRLI63a4GuVKG5u96kojeomuAADRiHkgKV68uB8RGTBggD399NM7V9w0btzYb35Wv359fzrSySefbLfddltwKmfRQ4KMW+2qn6urXI1xdZ0r7bra1BUAIBoxDyRAwaQ+ES05f8SVVpO95OpuV/u64s8IAKLFKymQIfpTOc5VR1c6Uu95riq6AgDEAoEE2CPtn/Oxq09dabfVlq4GuKrtij8dAIglXlWB/1D40E6r2uTsQldvulrnCgCQXQgkwG62ufrWVXdXA111ctXD1X/3xwEAxE5cBxL2IsHu1LiqXVZ1lN6Jru519Zyr9q7YkA8AshMjJMBOSqiTXOnPQr0iGhmp5QoAkN3iPpCwD0m80xTNUlfaBl5H5X3W1RBXZ7kq7goAkBMYIUEcW+FKu62e7eoXneFUcrVf6FsAQI6J20BC/0i8m+FKPSIP+lNkcwDIXbwKI86ocfULVxe5+sCVNjt70dURrgAAuYUeEnpI4swnrsKraG51pSmbg13lzFGmAQBpY4QEcUDzc2peFY2ItHM12JWma8q7IpUCQG4jkKCA2+rqe1eDXG1wVcXVe65OdwUAyCsIJCjAtJxXy3i7uurvaqErYZMzAMhr6CFhtL4A0hTNZFe3uerjStu+P+yqjisAQF7ECAkKGIWRn1xd7EpH6j3Z1QhXp7oq6woAkBfFbSDRPiTsRVIQ6T/qWFdrXPV2pd6Raq7I3gCQl/EqjQJghyttdLbOlZ7SV7nS6Mjtrsq4AgDkdXEfSOghye82u/rc1fWuBuoMRytptLS3hD8FAMj7GCFBPrbFlZpWdVTeWa6quwIA5EcEEuRTWsKrhtUnXVV29bqrzq4AAPkRgQT5kEZDTnOl5tWTXGk7+ONdFXMFAMiPCCTIR8Lbv9d1pSByh6t3XDVyBQDIz2hqpak1n/jD1fOuFrgq7up/rrSsl71FAKAgiNtAwh4k+YWW9L7h6kpXz7ka7UoUREiTAFBQMGWDPGy1q3tdaT+Rv4Pvz3AFAChomLLhQ3YeNd/V1a5edKWdVn91daOr0q4AAAUNIyTIgzSfNs2VRkUucDXU1aGuAAAFVVwHEvpI8pq5rv51pWGrI1z1d/W4q4auAAAFGSMkyAOSXKlZ9VZX6hdRI2s5V0e5YvdVAIgH9JDQQ5LLkl194eoyV8NcqV+ksCsAQDxhhAS5RPNl611pL5ELXW11pWW9OjYNT0sAiDe88iOXbHelg+I94kr9Iq+60qoajtALAPEobgMJDa25RQ+8qqirdq6uc9XX1amuirgCAMQjRkiQg7TRmY7Oq4Pi6amnqRqNkDRzBQCIZwQS5JAprk509ZCrD1xtcaXpmfKuAADxjkCCbKZm1XddneBKG511cnWXq5KuAAAIIZAgG61ypRGRW1xVcKXpmQGuargCAGCXuA8k7EOSnVa6GuOqrauXXSmYFHcFAMDuGCFBjG12pS3gpb6rZ1xpf5FjXJH+AABpi9tAomW/LP2NtSWuNAqiPhFN12g05ABXjVyRfQEAe8a7BGJAx6KZ5OpiV2+40h4jG10BAJAx9JAwixClNa4+cnWOq2mutNHZV67quQIAIGMYIUGUdGA8bfmuZbwPu9KxaThCLwAgcwgkiFILVx1d9XelI/ZWdAUAQObEPJBs2bLFHnnkETv44IPtxBNPtF9++SW4ZHdvvvmmnX766XbggQdax44d93g95DXqF/nE1RB/yuwQVzoWjY5Lw5JeAEDWxDyQDBw40IYOHWpPPfWUnXbaaXbBBRfYkiVafbG7sWPHWteuXe29996zo48+2s477zxbuHBhcCnyHi1JWuuqm6vzXb3mar4rNeFo0zOacQAAWRfTQJKSkuIDyfXXX2/HHnusde/e3Zo2bWpvvKGVF7vr27evnXPOObb//vvbzTffbGXLlrU//vgjuHR3ycnJlpiYuLNiiabWjNDeIiNdneHqTVcdXN3hqo4rAACiF9NAsmHDBj/K0bx5c386wb3bt2nTxiZOnOhP78mECRN8mNlvv/2Cc3bZsWOHDR482Pr06eNLIy/ISetdveXqWlezXHV3pV1Xj3RFCxIAIDZi+o6i/hEpWXLXgdPKlCljGzfueU+KGTNm2NNPP21XX321H01JTaGmcuXKVqdOHV+1a9cOLokOm6Jl1A5XGrkq6+p5V71caQdWAABiJ6aBpHz50KHk16/Xp+qQ1atX+0CRFo2maNSjZcuWdu2111qhQv/9dYoUKWJHHXWUXXbZZb4uuuii4BJkr+XBV62aud9VeK+RUq4AAIitmAaSEiVKWKtWrey3337zvR5bt261n376yY444ghLSkryIyjqBxEFld69e/sQ061bN99DommbtGiURGElXLFED0kkPf7qF3nRVUtX2n1Vj7dGRBq6KuIKAIDYi+27u3PLLbfYkCFD7KWXXrK7777bKlasaBdeeKFNmjTJbrrpJps2Tbt5mt11113266+/+mmaH3/80T799FObNUs9CsgdCiMzXKlZ9X+uqrhi1RMAIGfEPJCcfPLJPnhoBGSfffaxfv36+VEQTdsceeSRPqBIvXr1/D4kc+fOtd9//91XWsuDsxN9JGFaufS1q5tdvefqRFevuzrdFQAA2S+hbdu2GXpbHjNmTPBd7lKDbMOGDW3evHlWqlTW+xk+/NCse3ezO+4wu/324My4pSW9XV2p+fgmV11caUkv81kA4pvaD3r27Ok38ezcubNvIUitXTttDIloxXyEJL+hh0RauzrAlUZFbnNV1xUPDAAg58RtIInv6Rpt//6nq89daVmvdlr9zNVprkq7AgAgZ8X9CEn8WeFqkCv1hzzrarorAAByF4EkbmhUZLyre13d6ErTMtp9tYErAAByF4EkLmxz9aUrraLRNvAXuOrnSgfJK+MKAIDcRVNrXPRuqmFmqqvZrnQsoOdcHeyKjc4AAHkDIyQFlkLIBleaqinh6kpXv7u63lVoLxgAAPIKAkmBpP1EhrnSEXm1lFdquqrnqrA/BQBAXkIgKXCWutKxaLq5WhcUAAB5W1wHEu1FUrB6SHScoFtdqU9Eq2decKWdVwEAyNviNpAUvI3RFrk6w9VXrrTHyCuutNFZMVcAAORtTNnke+FkVdtVB1d3uXrV1X6u6BcBAOQPBJJ8K9nVJFefutLuq6LekftdFfenAADILwgk+dJWV5+4UuOqdl7VHiMaKdG+IhwUDwCQ/xBI8p0lrnq5usWV+kYUSvZ3RRABAORfBJJ8QyMgE1xd40qrZyq5esfVda6quwIAIP+K+0CSf5b9qmfkN1e/ujrT1VhX7V2VdAUAQP7GCEmeplERbXS2zJVWzOigeDpI3iBXpVwBAFAwxG0gyfv7kOxwNdqVekXudrXZlY5Bc5QrwggAoGBhhCRP0rFo3nWlXVa10Zn6RWhaBQAUXASSPEcjIVpFc4cr7S/Sz9X/XNErAgAouOI6kOS9Y9mocfUkV8+4qu9Ke41c5qq8KwAACi5GSPKERFcKI/rPcbmrS1297+owV0zVAAAKPgJJrlJn7VxX6heZFZzu6qqvq31dAQAQHwgkuUr7itzsSgfEG+lKIyX6T1LGFQAA8SPuA0nu9JAoePR3daGrUa7Oc6XeER2LBgCA+BO3gST39iFZ40p7i3R3tdzVk66edVXPFf0iAID4xJRNjhvvStu/H+jqR1c6No12YQUAIH4RSHLEeld/hb61I1094upjV211BgAAcY8ekmyfJZnt6l5Xal7V98VdneqqtisAACCMkGQb7SuihlVNyQxwleSK3VYBAEgLgSRbKHxol9XzXY1wdZ0r9YvUcgUAAFIjkGSLN11px9VyrjQ68oQrGlcBANgTAknMaB1xeC3x/q7OcaUD4+lYNOobAQAAexK3gUT7kMTu4HraW0SjItp5Vb0jOgaNDpB3nKuirgAAQHoYIYnaKld3BPWaqw2u9LBWdcVGZwAAZASBJCp/u9JeIoNctXR1u6vyrgAgb0hJSbHt27fb1q1bqXQqKUmLEZCbEtq2bZuhTdTHjBkTfJe7Nm7caA0bNrR58+ZZqVKlgnMz7513zG691axXL7MbbgjOzLDNrj5ydacrTdFoNc39rmq6AoC8YcuWLbZ8+XLbsWOHJeTOgbvyDQW38uXLW6VKlaxw4V2LEBITE61nz5524IEHWufOndN8HNu1axd8h2jEfSDp3dvs+uuDMzNsviulmKWutM+IGlfZYwRA3qE32AULFlixYsWsYsWKu73J4r8U3lavXm0VKlTwFUYgyTlM2WSYctuU4GsdV3e70hF7r3VFGAGQt2hURFM1enMtUaKEFS1alEqnypYt6z/kbtu2zZKTNfKNnEYgyZAtrvq4usTVd670sB3h6hBXAJD36E1Vn+aLFCkSnIO90WOlkSXkDgLJXi1xdaWr3q4WuNrqSpiPBQAgVuI+kOy5z2u7q5GuLnQ1xJVGRLTPyJmuAABZtWLFCpsyZYotXao+vF10WuevXLkyOCdjJk+e7Ptk1q/XkdXTd8ghh9hrr2mLBuQ1jJDs0SxXV7ua60oNrB+4auoKABCNN99804455hi78847d/Zr6Outt95qxx9/vL3//vv+PMQXAskelXF1gqvHXN3jqporAEAsNG/e3KZPn26zZ8/2p2fOnOlPN2vWzJ9WQBk1apRf4dKjRw/78MMPbd26df4y+eOPP/xlDz74oP3111+79X5otcx7773nb6fLf/nlF/YZyQfiNpD8t29ph6uhrr4Jvq/r6hFX57piszMA+Zte87Zu1fLW7KvMvOdXrlzZj5IMHarXXbPBgwfbySef7PcBEW010adPHytXrpwPLwoYH3zwgV+Gqymfbt26+e/32Wcf++ijj2zzZu0Ppd9jiw0cONB+/vlnPz1TpUoVe+655+zXX3/1lyPviusRkl2hRAFER+W91JU2JVnmSs0lFV0VcwUA+du2be5jlvucpS02squGDUvrw96eXXjhhTZixAi/X8qPP/5ol1+uo6Qr2CT5oNKqVSu78cYbrUuXLnbllVfaDz/8YAsXLvSBQ8t0H374YbviiivsjDPO8Et3ZcaMGTZr1iy77rrr7Pzzz/eXH3zwwf7nsJw3b4t5IFmyZIl/ImgTGW0W8/rrr6c5VLZq1So/f3jQQQdZmzZt7JlnnvHrv3NS4cJJVqqUNjnr5upWV7VdPemquisAKDjUwN+wodm++2ZflS4d/LAMqlevntWsWdO//jdo0MBq1arlz9+0aZMfBdHIiDZ2K1SokFWvXt0vy9XmmJra0XuHNnvTZbpeOJCsXbvWh48LLrjAvw+1bdvW3n33XX89bRGPvCvmgeT555/383xvv/223XfffT7Bjh8/Prh0l3feecemTp3qu50ff/xxGzBggA0fPjy4dHcKNNrkJ7zRTyzs2LHd2rf/zo455gJ36l1XZ7l61VUnVxyhF0DBUry42ciR5l6Ps6+OPjq9lYv/pVGO1q1b2+eff26HHXaYlSwZ2mRSwUObuem9JNwbojChEQ5dpmkc9YmE6Xrh0Q+FlKZNm9orr7xi3377rS+NrHTv3n3n/SNvimkgUXD47LPP7KKLLrIWLVrYaaed5ofKPv744+Aau+i8Tp06+ZR77LHH2imnnOJTbGoaNVFH9s033+zrjjt0VN3obdmS4gLJcKtUaZE71dPVU650oLy4nsUCUIAplGRnFcrky6dGPzp06GB33XWXHXfccTs3cVNw0HSNpnEmTpzoDxWiKZwaNWr4OvPMM2306NG+T0TTM+o/CY+wa9RFfSi6TH0lCjT//POPLVq0KM1t35F3xPTdV0Nla9as8UNvov/46pjWnF5qc+bM2Xk90ZBbWtfTE/TQQw/14UWlJ2IstGhR3CVpNUUpLGm6RtvBAwByko5Ndu2111pdNbgE9N6h1/uWLVv6pcDqNdEUjnpMtBV++/bt7eKLL/YfUG+66Sa/5Xt4ykbTPtdff73vS7n00kvtvPPOs379+vnbI2+L6cH1li1b5oOFlljtt99+/rxHHnnEdzd//fXX/nSYOp8//fRTO1pjfM5bb71lTzzxhF++lZ4NGzZYo0aNoj64nkb3NBLI8aYAFESa4tCogEYMwm/WeYVWx2jkIq3fS1Pz6vfQ1ItG3cPX1Wl9QA2PcoQvE12m7zXNI7q+TmsaR9/r/nRbfdW0v66viqTr6QO1Lq9WrZq/ruh+OLhezojpCImWcRUvXtz+/fff4Byz+fPn75Z8w2rXrp2h66UWqyE3PddSPR8BADlA4WBPIUnnh8OCvuo9JXxwwMjX//BlKt1fOIyIrqfrh28bbowVfZ86jCBviGkg0ZNCu+x99dVXfgtgjXZotES9JEqeY8eO3bmxjdabq9lICV5zgMOGDbOOHTv6ywAAQHyJaSCRW265xa8T15LeBx54wE499VQ74YQTfM/ICy+84EdCRGvKNRSmZiatxlETrIbDAABA/IlpD4lozk79HcuXL/dDZmpY0kGP1PuhoKJpmTJlyvjrLV682O9boqG0+vXrW9WqVYN72TOtQdd9RttDAgAFWV7uIcmL6CHJfTEfIdF/RAUGbUajJb8KI6L15lpxozAiul6dOnX82nOtoslIGAEAZJzeZMP7c2Dv9Hgh98R8hCS7MUICAHsXHq1Wb582EqORc88URLSPyfr16/2HaFV4JIQRkpxDIAGAAkrTD+FpCKRPo/Yawddofni6RggkOYdAAgAFWHjahumI9CmERAaRMAJJzol5DwkAIO/QG6imazR1Q+250gojyFn8FwAAALku303ZhLeO18GSmLIBAGQnTdn873//8weCZcome+W7QKIGLa2r13FvtAUwAADZRcfM+fzzz/2B+s4///w0p3YIJLGRL5taw2EkraSaUUq9H3zwgZ144olWvXr14FykRY/VJ598YkcddZQ/kibSp4OD9e/f36666qrdjq+B/9JjNXDgQP9iX7p06eBcpEV/h2+88Yb/lF6+fPngXKRFj9U777xjZ5xxhj/GWrR0fzrS/AEHHMAISTbKd4FEtF482jX1WgZ3wQUX2EMPPeSPUIw902PVpUsX69Gjh9/sDunTDpmHH364jRw50i8hxJ7psTruuONsyJAhMXnjKMj0uqdjgOmNlg8G6dNj1alTJ3vxxRf9FH+0tEpJ7zl7et8hkMRGvgwksaAn7HnnnWePPPKIP44O9kyBRJ9g77jjDjvkkEOCc7EnW7Zs8TsV//zzz35DKuyZAsnRRx9tQ4cOtSpVqgTnIi16rDp06GDvv/8+gWQv9PquEY2+ffvavvvuG5ybfQgksRG3q2w07KZPr+xemDHaMEhL47B3em5pSD2aKcV4otDGksu90/OJxyrjeH3PfwrXrl27V/B9ujQfXpDoj7tGjRp+OI95/vRFPlYlS5YMzsWe6PHSQST1yYwXxPTpsXKvQda4cWMeq70IP1Z6XnGwvPQptO2zzz7+NSsnFj+oDwrRi9spGwAAYoEpm9hg7A8AAOQ6AgkAAMh1BBIAAJDrCnwPyebNm+2vv/6yJUuW+I2XtGy1YsWKwaW7aEnd33//bQsWLLDixYv7IzvG24ZpWq6qx2rx4sV7fKx0xNDp06f7x0mPmbr+tVlQWo9pQbdu3Tr/eC1fvtyvqtHjld4y33///dd++eUXa9KkibVq1So4Nz7okA9TpkyxZcuW+cdIj1Vam3tpv4eFCxf655g2QdTeJPpbjKeNwPTvDj9WWt126KGHpvnv12M6depUfz01BKuBs2nTpnGzCkfPlfnz59ucOXNs/fr1fmv3+vXrB5fuTsuAZ86c6Q85ooZg/f2pQThW6CGJjQK9yka762nDpQEDBtjKlSvtp59+ssmTJ/t9DyKXsOqJrcu0iY7+uH///XcbNWqU35k0XlaV6DEYNmyYvfzyy/6x0r//jz/+sGOOOWa3jn7trPnMM8/YjBkz/Bvsjz/+6B8vPabxtJW/gq72g/joo49s1apV9u233/og16ZNmzSXR+v6Tz75pD377LN+XxftJxEvFFz1OL333nu2evVqGz58uH8j0V4tkY+Vwq7eYPv16+ffPPQ81N+j3mTiZdM0vXF++umn9vbbb/t///fff2+zZ8/2G+1FPla6nnZP1mO6du1amzVrln3xxRd+BY5Wl8QDPQZff/21/9vTfiMNGzb0H45S02vbb7/95v/29MF00qRJ9t133/nnX6w2LmSVTWwU6CitT/wffvih3wmyd+/e9vDDD/s/2l9//TW4RoheMD/++GP/SSx8vT///NO++eab4BoFn94ktQNk+/bt/WOgHWz1WGlzr0h6Uezatavde++9dv/999sDDzzgt+DXH3k80Qub3ljPOecc/3g9+OCD/ngXEydODK6xi95o9SajgKxPu/FGoUIbn3Xs2NF69erl/770WOlvLJI+8etNtmrVqnbPPff4A5rdcMMNVqdOneAaBZ8C2+DBg+3UU0/d+Vqkx2rcuHHBNUI0iqKdgBVUevbs6UsfHBRg4oU+ACnY63Uovc3P9GHgs88+8yOT+jvVa5s+TOn1DXlLgQ4k+qPVJ7EjjzzSKlSoYPvtt5//BDt69OjgGiF6M9YnsiOOOMIqVapkDRo0sOOPP94Pr8cLhTd9ylIg0fSLhn71uKV+DDQcvP/++1u1atX8cLL229Coid5044mmafSYaSt9TUFot1+9KKYVzBRSFOyuuOKKuNzHRW+yGlIPTz3oUA36W5wwYUJwjRAdOFNTYPqbPfvss+2EE06wL7/8Mq5G3vQYaMTtsMMO849Vs2bNrGXLln60MpLCh553ixYt8qc3bdrkp1HTe2MuaPRaVLNmTR9YNc2+J/o71VSNHlONtGmqRkEm9fsAcl+BDiT6w9bcauS8vjb40lBoJB3NUZ/OIq+nJ3rq6xVk+rfqDzxyrlqPgd5490SfPPSJV8OkCnrxRG8c2lAv8oBwCr06P5LejPWpX0FPm3/FIz0GChUKsGEK/jo/kobgx44d699YdUgHjcJpmF3TPfFCj4nCRuRUgrbU12tZJF2u40sp7Cq0KOxptEQjK9idXt8V2CJf39UfqB4wjVoi7yjQgUQvgpo/1BMyTKMhkT0Roh0QNRUReT29OKa+XkGmx0qjHJF/oHqs9vTpVPPW6jfRqMpbb70VV4+V6N+r50vkc0aPXerHYfz48f6TmKYF9Wlfb7bqCdAn3nh5MdRjkvrvUKNqqR8r/R0q1F1yySV+mvW0007zIyXqZ4qXEbi0XrPSeqw04vTuu+/6DwLqh1CvnBqBBw0aFFwDYXpe6YNp6tc2vebrMuQdBTqQ6FOYhvLUbCjhpjlNyUTSk1VDeeHriYaO1SQVL/RYaTohPASsx0rTD2kdKVOfLPTCp8dS87Gatok3er7oBS78yVWPl6YaUh/0TG+w6ujX80lz/pq71mM8bdq0uAkkeqz0JrtixQp/Wo/V3Llz/7PKQSNOamCN3EI+/AYdL/R3qDdJ9d2IHiuF/tR/YwokCiAKbuGpaE1xqcFct8EuCnP6O9TfXphWVGqqJ/K5htxXoAOJhtNbt27tG1v1BvDaa6/5JYUnnXSS/8Pt37+/7zPRC6HmF9V4qFU4GmLXMufTTz89uKeCT8FNL2pqUFXQeP311/286ymnnOI/zb/yyis7hzi1akk9ERdffLGfAtM0hUaU4omms/RGq+eM3jC0QkvDwlr+p65/PUai6SxNP6g5UaXGafUq6UjT6c17FyTqN1JQ09+VerVeeuklP8KmVWw//PCDf6wUOvR46jmo1V5a9qpGTjWWazVEvHySDfewqcleK9nCq9602k2B9tVXX/UjJnru6HVLTaz6O9XUjR4rTQvGy2Ol4KXXHi371fSxppcVdPU6pZHJ559/3p+v9wFNaalZWH1Lepz0HNMIHPKWAh1I9Ad74403+nnZk08+2QcSlT7160mspb4aStcfd7jhUIes1hvH008/7RsW44U+id50003+D1ghRC+Eb775pn+Bmzdvnn+sdJleDPXmqz/ozp07+09nmsPWG3M8URDTChCtFNGbhd5s9ZzRpy69merNQzQsrN6JcOnTmkrPzXh541APhB4rfSjQJ3otl9abhT71K/yOGDHCB5JSpUrZ5Zdf7oOJ/g6vvPJK69Spk11wwQXBPRV8aijXa5ZChkY8tPxXS1o1cqQRET1W+lCgx/T222/3zzU9phdddJH/8KXHOV5o2kXLbRU2NC2qVUkKufpQpdFKTWXpg5Je3/X46G9WU4B63Hr06OEXLiBv4eB6AABEgY3RYqNAj5AAAID8gUACAAByHYEEAADkOgIJAADIdQQSAACQ6wgkAAAg1xFIAABAriOQAACAXEcgAQAAuS7DO7UCAABkF0ZIAABAriOQAACAXEcgAQAAuY5AAgAAch2BBAAA5DoCCQAAyHUEEgAAkOsIJAAAINcRSAAAQK4jkAAAgFxHIAEAALmOQAIAAHIdgQQAAOQ6AgkAAMh1BBIAAJDrCCQAACDXEUgAAEAuM/s/SEg5Z3kMELkAAAAASUVORK5CYII=)"],"metadata":{"id":"XqMFAepSXxiV"}},{"cell_type":"markdown","metadata":{"id":"xsJenjnv4jCk"},"source":["# Apêndice"]},{"cell_type":"markdown","metadata":{"id":"Zu-zhyvtgX_1"},"source":["## Light GBM"]},{"cell_type":"markdown","metadata":{"id":"Fh36P5Qa6RWN"},"source":["### LightGBM Baseline"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dk8C5EZKq6Md"},"outputs":[],"source":["lgbm = LGBMClassifier(random_state=23)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9235,"status":"ok","timestamp":1698241098381,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"iv3ZhWfa6Vu1","outputId":"d3603380-3a31-40df-85ab-92d7423e78cf"},"outputs":[{"name":"stdout","output_type":"stream","text":["[LightGBM] [Info] Number of positive: 7506, number of negative: 75060\n","[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.019336 seconds.\n","You can set `force_row_wise=true` to remove the overhead.\n","And if memory is not enough, you can set `force_col_wise=true`.\n","[LightGBM] [Info] Total Bins 1147\n","[LightGBM] [Info] Number of data points in the train set: 82566, number of used features: 20\n","[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.090909 -> initscore=-2.302585\n","[LightGBM] [Info] Start training from score -2.302585\n"]}],"source":["#LightGBM\n","\n","lgbm.fit(X_train, y_train)\n","\n","y_pred_train = lgbm.predict(X_train)\n","y_score_train = lgbm.predict_proba(X_train)[:,1]\n","\n","y_pred_test = lgbm.predict(X_test)\n","y_score_test = lgbm.predict_proba(X_test)[:,1]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":621,"status":"ok","timestamp":1698241098989,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"ZzIVvW0-6f2K","outputId":"1b4a4492-fbdb-44fa-96e9-7a01e1218717"},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy 0.99\n","Precision: 0.96\n","Recall: 0.89\n","F1 Score 0.93\n","AUC: 1.0\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Métricas de Treinamento\n","evaluate_model(y_train, y_pred_train, y_score_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2772,"status":"ok","timestamp":1698241101757,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"z-vKXU6Q6f4p","outputId":"882b102d-2a5e-47d5-ac7b-320d22dd3ef4"},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy 0.99\n","Precision: 0.39\n","Recall: 0.85\n","F1 Score 0.53\n","AUC: 1.0\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 2 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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Métricas de Testes\n","evaluate_model(y_test, y_pred_test, y_score_test)"]},{"cell_type":"markdown","metadata":{"id":"jkAXXrZ7_SEH"},"source":["### Light GBM Otimizado"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4fwUu4Ub6f67"},"outputs":[],"source":["# grid search  hyperparameter tuning\n","\n","# parameters = {\n","#     'task' : ['predict'],\n","#     'boosting': ['gbdt' ],\n","#     'objective': ['root_mean_squared_error'],\n","#     'num_iterations': [  1500, 2000,5000  ],\n","#     'learning_rate':[  0.05, 0.005 ],\n","#    'num_leaves':[ 7, 15, 31  ],\n","#    'max_depth' :[ 10,15,25],\n","#    'min_data_in_leaf':[15,25 ],\n","#   'feature_fraction': [ 0.6, 0.8,  0.9],\n","#     'bagging_fraction': [  0.6, 0.8 ],\n","#     'bagging_freq': [   100, 200, 400  ],\n","\n","# }\n","\n","# gsearch_lgb = GridSearchCV(lgb_model, param_grid = parameters, n_jobs=6)\n","# gsearch_lgb.fit(X_train,y_train)"]},{"cell_type":"markdown","metadata":{"id":"WX5pgCuugVWn"},"source":["## Regressão Logística\n","A regressão logística é um modelo estatístico usado para análise de dados em que a variável de resposta é binária, ou seja, assume dois valores possíveis, geralmente 0 e 1. Seu objetivo principal é modelar a relação entre uma variável dependente binária e uma ou mais variáveis independentes, expressando essa relação em termos de probabilidade.\n","\n","Aqui estão os principais pontos organizados sobre regressão logística:\n","\n","- Objetivo:\n","\n","A regressão logística é usada quando a variável de resposta é categórica e binária, representando, por exemplo, eventos como \"sucesso\" ou \"fracasso\", \"sim\" ou \"não\", \"positivo\" ou \"negativo\".\n","\n","- Função Logística:\n","\n","Utiliza a função logística, também conhecida como função sigmoid, para modelar a probabilidade de ocorrência do evento de interesse. A função logística transforma valores reais para o intervalo (0, 1).\n","\n","- Coeficientes e Interpretação:\n","\n","Os coeficientes da regressão logística indicam como a mudança em uma unidade nas variáveis independentes afeta o log-odds (logaritmo da razão de probabilidade). A interpretação é realizada usando a função exponencial para obter as chances (odds) reais.\n","\n","- Treinamento do Modelo:\n","\n","Os coeficientes do modelo são estimados por métodos como Máxima Verossimilhança Condicional. O ajuste do modelo é realizado para maximizar a probabilidade condicional dos dados observados."]},{"cell_type":"markdown","metadata":{"id":"3YSyxP7nRqrx"},"source":["#### Modelo Baseline RL"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27,"status":"ok","timestamp":1698176302602,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"11jqsG7lRqry","outputId":"ed95dcd1-fe02-42dc-efce-bac4e359a168"},"outputs":[{"data":{"text/plain":["dict_keys(['C', 'class_weight', 'dual', 'fit_intercept', 'intercept_scaling', 'l1_ratio', 'max_iter', 'multi_class', 'n_jobs', 'penalty', 'random_state', 'solver', 'tol', 'verbose', 'warm_start'])"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["# Balanceamento dos dados: Definindo pesos das classes\n","weights = {0: 1, 1: 400, 2: 1}  # Peso 250 vezes maior para a classe com índice 1 (classe minoritária)\n","model_LR_baseline = LogisticRegression(class_weight=weights)\n","model_LR_baseline.get_params().keys()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"executionInfo":{"elapsed":15791,"status":"ok","timestamp":1698176318368,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"vc5R_rNoRqry","outputId":"28f3130f-d109-4142-b5e5-832c82ad0035"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"],"text/plain":["LogisticRegression()"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["model_LR_baseline.fit(X_train,y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qwtxIlN4Rqrz"},"outputs":[],"source":["y_pred_LR_baseline = model_LR_baseline.predict(X_test);"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1698176318369,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"j9sKNgwYRqrz","outputId":"959e6c2b-6bb5-425b-bb9a-c6f6dbbf631b"},"outputs":[{"name":"stdout","output_type":"stream","text":["A acurácia do LR baseline foi: 48.72%\n"]}],"source":["print(f'A acurácia do LR baseline foi: {round(accuracy_score(y_test,y_pred_LR_baseline)*100,2)}%')\n","acuracia_baseline_LR = round(accuracy_score(y_test,y_pred_LR_baseline)*100,2)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1295,"status":"ok","timestamp":1698176319657,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"iWDFQ6zDRqrz","outputId":"4f32609a-874a-416b-ed6c-17fb41084a8c"},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       1.00      0.49      0.65    553574\n","           1       0.01      0.69      0.01      2145\n","\n","    accuracy                           0.49    555719\n","   macro avg       0.50      0.59      0.33    555719\n","weighted avg       0.99      0.49      0.65    555719\n","\n"]}],"source":["print(classification_report(y_test,y_pred_LR_baseline))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":538,"status":"ok","timestamp":1698176320191,"user":{"displayName":"Ruann Campos","userId":"08282121699602487065"},"user_tz":180},"id":"LDCXpOF6Rqrz","outputId":"2a2d8101-6d79-4454-8c74-c9deaede21db"},"outputs":[{"data":{"text/plain":["<Axes: >"]},"execution_count":36,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["cm_base_LR = metrics.confusion_matrix(y_test,y_pred_LR_baseline)\n","sns.heatmap(cm_base_LR, annot=True, fmt = 'd')"]},{"cell_type":"markdown","metadata":{"id":"tYIkvApKSJh8"},"source":["#### Modelo LR com Otimização de Hiperparâmetros e Validação Cruzada\n","##### Otimização --- GridSearch CV\n","A otimização feita no modelo LR foi utilizando o Grid Search. A pesquisa de grade pode ser pensada como uma pesquisa exaustiva para selecionar um modelo. Na pesquisa de grade, o cientista de dados configura uma grade de valores de hiperparâmetros e, para cada combinação, treina um modelo e pontua nos dados de teste. Nesta abordagem, todas as combinações de valores de hiperparâmetros são tentadas, o que pode ser muito ineficiente, pela questão do tempo e custo computacional. O Scikit Learn oferece a função 'GridSearchCV' para esse processo.\n","\n","Ressalta-se que através da 'GridSearchCV',a mesma já faz a divisão dos dados de treino em treino e validação através do seu fit, não necessitando assim, fazer a divisão dos dados de treino previamente.\n","\n","Os hiperparâmetros otimizados, foram:\n","   \n","  - C: Inverso da força de regularização, quanto menor o seu valor, maior a força de regularização.\n","  - Penalty: Tecnica de regularização que será utilizada.\n","  - Max_iter: Número máximo de iterações tomadas para os solucionadores convergirem.\n","  - Tol: Tolerância para critérios de parada."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pyHXUYNytWcv"},"outputs":[],"source":["weights = {0: 1, 1: 250, 2: 1}\n","from sklearn.model_selection import GridSearchCV, StratifiedKFold\n","model_LR_otimizado = LogisticRegression()\n","# Definindo os parâmetros que serão utilizados no GridSearchCV\n","param_dist = {\n","    'C': [0.1, 1, 10],\n","    'penalty': ['l1', 'l2', 'elasticnet'],\n","    'max_iter': [50, 100],\n","    'tol':[0.01,0.001,0.0001]\n","}\n","stratified_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n","\n","grid_search = GridSearchCV(model_LR_otimizado, param_dist, cv=stratified_cv)\n","\n","\n","# Ajustando o modelo aos dados de treinamento\n","grid_search.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7W4EMWCUTi0C"},"outputs":[],"source":["grid_search.best_estimator_"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aqXL3KVpTmVA"},"outputs":[],"source":["y_pred_LR_otimizado = grid_search.predict(X_test);"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3nzYZyECTmtp"},"outputs":[],"source":["print(f'A acurácia do Modelo LR otimizado foi: {round(accuracy_score(y_test,y_pred_LR_otimizado)*100,2)}%')\n","acuracia_otimizado_LR = round(accuracy_score(y_test,y_pred_LR_otimizado)*100,2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hP9u5p6nTmwJ"},"outputs":[],"source":["print(classification_report(y_test,y_pred_LR_otimizado))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4KDOC3OSTmyY"},"outputs":[],"source":["cm_otimizado_LR = metrics.confusion_matrix(y_test,y_pred_LR_otimizado)\n","sns.heatmap(cm_otimizado_LR, annot=True, fmt = 'd')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3eI8gdqnsCRc"},"outputs":[],"source":["from imblearn.over_sampling import SMOTE\n","\n","smote = SMOTE(random_state=42)\n","\n","# Aplicando o SMOTE nos dados de treino\n","X_train, y_train = smote.fit_resample(X_train, y_train)"]}],"metadata":{"colab":{"collapsed_sections":["OPTbU7BtmzsQ","Ann4YYBlnp8s","RzUBprr9pOJm","ro4bBv-MggiA","NaOdJxho-zvG","1zTFtL8gTySx","xsJenjnv4jCk","Zu-zhyvtgX_1","WX5pgCuugVWn"],"provenance":[],"authorship_tag":"ABX9TyMh/cWIHRF/BCXGT7LqK5kX"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}