lendigo-loan_credibility_predictor.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"cell_id":"f7aa4e23a04e46d9bf6204a3b60969b9","deepnote_cell_type":"text-cell-h1","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["# LOAN CREDIBILITY PREDICTOR"]},{"cell_type":"markdown","metadata":{"cell_id":"16a326ca9c704e208922e985da391ecb","deepnote_cell_type":"text-cell-h2","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["## PROBLEM STATEMENT"]},{"cell_type":"markdown","metadata":{"cell_id":"8ad4ceb9-d9cf-432b-858c-0b67280f511f","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["Conventional credit scoring methods like credit history, collateral, or any prior accounts might not give every person an equal opportunity to establish credit. As a result, a small number of people can access basic banking services like opening a bank account and acquiring a loan. Using the pre-existing datasets, we can understand the track record of banks and the reason why conventional credit scoring is needed. Come up with an AI based solution to perform credit scoring for people who don’t have any previous loans. This can be done by checking some other billing cycles they’ve been a part of and creating a scoring metrics on the basis of that."]},{"cell_type":"markdown","metadata":{"cell_id":"a0bba6172a26417d83d9911af410a60a","deepnote_cell_type":"text-cell-h2","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["## PROJECT DESCRIPTION"]},{"cell_type":"markdown","metadata":{"cell_id":"b9df1f3a-8e3e-49a1-a8b9-7acd46a97eca","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["This project is mainly focused on the population which is deprived of the basic banking services which include availing loans without having a credit score. "]},{"cell_type":"markdown","metadata":{"cell_id":"e61644e4-713a-4084-8a1b-d80f1e081790","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["We have come up with an AI solution where we are provided with data about historic customer behavior. Hence when we acquire new customers with no previous loan history, it can predict who is riskier and who is not."]},{"cell_type":"markdown","metadata":{"cell_id":"ef6775c2-2470-4571-b436-5f6cf484118f","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["We have used a basic Logistic Regression model to find the required solution to the given problem. We imported the dataset from kaggle.com and tried out various correlations, attribute combinations to analyze and understand the data. Once the raw data was completely cleaned and freed of redundancies we used it in the ML algorithm to build the solution - credit scorecard."]},{"cell_type":"markdown","metadata":{"cell_id":"6f1275fd30bc4cc0807915738fc4b021","deepnote_cell_type":"text-cell-h2","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["## DATASET USED:"]},{"cell_type":"markdown","metadata":{"cell_id":"ff4af58f-1de8-4b7f-a908-ec30f925e89c","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["To build our project, we decided to use dataset available on kaggle.com"]},{"cell_type":"markdown","metadata":{"cell_id":"68575fac12334b64b1bf555e64a532b6","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":64},"deepnote_cell_type":"text-cell-h2","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["## DATA FEATURES"]},{"cell_type":"markdown","metadata":{"cell_id":"00042d30-dbc8-451a-9463-bc6a59998b33","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":25},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- ID: Id of the user (all unique)"]},{"cell_type":"markdown","metadata":{"cell_id":"971f4735-0722-45e4-b7d6-c5853e524dfc","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":28},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- \n","Income: Income of the user\n"]},{"cell_type":"markdown","metadata":{"cell_id":"cd94c6bb-f839-4fd1-9bc4-3cfcb981cb86","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":31},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- Age: Age of the user"]},{"cell_type":"markdown","metadata":{"cell_id":"9fb67264-86cf-4914-9a06-80b48d2b2def","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":34},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- Experience: Professional experience of the user in years\n"]},{"cell_type":"markdown","metadata":{"cell_id":"371b15a2-75cb-4ffb-8c53-97ac475c81a6","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":37},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- Profession: Profession of the user\n"]},{"cell_type":"markdown","metadata":{"cell_id":"189c6948-ab83-4414-a302-23e0037fe9b3","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":40},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- Married/Single: Whether married or not\n"]},{"cell_type":"markdown","metadata":{"cell_id":"da952469-ce9c-474e-8ec7-e2d708dfef49","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":43},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- House Ownership: Owned or rented or neither\n"]},{"cell_type":"markdown","metadata":{"cell_id":"16719976-80af-41d6-b6fb-7a5591a1111c","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":46},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- Car Ownership: Does the person own a car\n"]},{"cell_type":"markdown","metadata":{"cell_id":"c84c7741-fbdf-4470-9bc5-38773d7d8019","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":49},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- STATE: State of residence\n"]},{"cell_type":"markdown","metadata":{"cell_id":"4872bb15-f83f-4ee0-bb34-6fb617d4d995","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":52},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- CITY: City of residence\n"]},{"cell_type":"markdown","metadata":{"cell_id":"dd6889ad-fe74-4d8b-b79a-05abd039db55","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":55},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- CURRENT_JOB_YRS: Years of experience in the current job\n"]},{"cell_type":"markdown","metadata":{"cell_id":"5d6c5eb4-9b9a-4ffb-9406-f63cf747858d","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":58},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- CURRENT_HOUSE_YRS: Number of years in the current residence\n"]},{"cell_type":"markdown","metadata":{"cell_id":"0bb166d7-8236-4b1b-97d6-314b42dcedd2","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":61},"deepnote_cell_type":"text-cell-bullet","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["- Risk_Flag: Risk of loan rejection(0 if approved, 1 if rejected)"]},{"cell_type":"markdown","metadata":{"cell_id":"9170ef85243d41dbaa2a65409660d6bc","deepnote_cell_type":"text-cell-h2","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["## INITIATING THE PROJECT"]},{"cell_type":"markdown","metadata":{"cell_id":"c01a06d246104911aaf54944a85a42d9","deepnote_cell_type":"text-cell-h3","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["### IMPORTING NECESSARY LIBRARIES"]},{"cell_type":"code","execution_count":1,"metadata":{"cell_id":"80850766359a4368a3b440357bd13434","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":1},"deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":2,"execution_start":1674972159232,"source_hash":"11c20b96","tags":[]},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import sklearn\n","from sklearn.linear_model import LogisticRegression\n","from imblearn.over_sampling import SMOTE\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import LabelEncoder\n","import math\n"]},{"cell_type":"markdown","metadata":{"cell_id":"ff984a67f11d41b39afe5d97c799c872","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":70},"deepnote_cell_type":"text-cell-h3","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["### IMPORTING DATA AND DATA CLEANING"]},{"cell_type":"code","execution_count":2,"metadata":{"cell_id":"f9482c4bff08426d9efe3476d61a8678","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":7},"deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":201,"execution_start":1674971631746,"source_hash":"563cb4e7","tags":[]},"outputs":[],"source":["df = pd.read_csv(\"data.csv\")"]},{"cell_type":"code","execution_count":3,"metadata":{"cell_id":"cef9403749554c63a3155b81a5a7c56f","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":13},"deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":35,"execution_start":1674971631989,"source_hash":"f88152d9","tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Id</th>\n","      <th>Income</th>\n","      <th>Age</th>\n","      <th>Experience</th>\n","      <th>CURRENT_JOB_YRS</th>\n","      <th>CURRENT_HOUSE_YRS</th>\n","      <th>Risk_Flag</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>252000.000000</td>\n","      <td>2.520000e+05</td>\n","      <td>252000.000000</td>\n","      <td>252000.000000</td>\n","      <td>252000.000000</td>\n","      <td>252000.000000</td>\n","      <td>252000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>126000.500000</td>\n","      <td>4.997117e+06</td>\n","      <td>49.954071</td>\n","      <td>10.084437</td>\n","      <td>6.333877</td>\n","      <td>11.997794</td>\n","      <td>0.123000</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>72746.278255</td>\n","      <td>2.878311e+06</td>\n","      <td>17.063855</td>\n","      <td>6.002590</td>\n","      <td>3.647053</td>\n","      <td>1.399037</td>\n","      <td>0.328438</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>1.031000e+04</td>\n","      <td>21.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>10.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>63000.750000</td>\n","      <td>2.503015e+06</td>\n","      <td>35.000000</td>\n","      <td>5.000000</td>\n","      <td>3.000000</td>\n","      <td>11.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>126000.500000</td>\n","      <td>5.000694e+06</td>\n","      <td>50.000000</td>\n","      <td>10.000000</td>\n","      <td>6.000000</td>\n","      <td>12.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>189000.250000</td>\n","      <td>7.477502e+06</td>\n","      <td>65.000000</td>\n","      <td>15.000000</td>\n","      <td>9.000000</td>\n","      <td>13.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>252000.000000</td>\n","      <td>9.999938e+06</td>\n","      <td>79.000000</td>\n","      <td>20.000000</td>\n","      <td>14.000000</td>\n","      <td>14.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  Id        Income            Age     Experience  \\\n","count  252000.000000  2.520000e+05  252000.000000  252000.000000   \n","mean   126000.500000  4.997117e+06      49.954071      10.084437   \n","std     72746.278255  2.878311e+06      17.063855       6.002590   \n","min         1.000000  1.031000e+04      21.000000       0.000000   \n","25%     63000.750000  2.503015e+06      35.000000       5.000000   \n","50%    126000.500000  5.000694e+06      50.000000      10.000000   \n","75%    189000.250000  7.477502e+06      65.000000      15.000000   \n","max    252000.000000  9.999938e+06      79.000000      20.000000   \n","\n","       CURRENT_JOB_YRS  CURRENT_HOUSE_YRS      Risk_Flag  \n","count    252000.000000      252000.000000  252000.000000  \n","mean          6.333877          11.997794       0.123000  \n","std           3.647053           1.399037       0.328438  \n","min           0.000000          10.000000       0.000000  \n","25%           3.000000          11.000000       0.000000  \n","50%           6.000000          12.000000       0.000000  \n","75%           9.000000          13.000000       0.000000  \n","max          14.000000          14.000000       1.000000  "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]},{"cell_type":"code","execution_count":4,"metadata":{"cell_id":"fb78eaa34b964a518e49ba3216979f6b","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":76},"deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":3,"execution_start":1674971632087,"source_hash":"de1e323c","tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 252000 entries, 0 to 251999\n","Data columns (total 13 columns):\n"," #   Column             Non-Null Count   Dtype \n","---  ------             --------------   ----- \n"," 0   Id                 252000 non-null  int64 \n"," 1   Income             252000 non-null  int64 \n"," 2   Age                252000 non-null  int64 \n"," 3   Experience         252000 non-null  int64 \n"," 4   Married/Single     252000 non-null  object\n"," 5   House_Ownership    252000 non-null  object\n"," 6   Car_Ownership      252000 non-null  object\n"," 7   Profession         252000 non-null  object\n"," 8   CITY               252000 non-null  object\n"," 9   STATE              252000 non-null  object\n"," 10  CURRENT_JOB_YRS    252000 non-null  int64 \n"," 11  CURRENT_HOUSE_YRS  252000 non-null  int64 \n"," 12  Risk_Flag          252000 non-null  int64 \n","dtypes: int64(7), object(6)\n","memory usage: 25.0+ MB\n"]}],"source":["df.info()"]},{"cell_type":"markdown","metadata":{"cell_id":"50c769dbd68d40c2905585c4d3ff2b48","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":88},"deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["No missing values in the dataset."]},{"cell_type":"code","execution_count":5,"metadata":{"cell_id":"d9514c21e11048e78d18a00414d5d571","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":5,"execution_start":1674971632088,"source_hash":"c085b6ba","tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Id</th>\n","      <th>Income</th>\n","      <th>Age</th>\n","      <th>Experience</th>\n","      <th>Married/Single</th>\n","      <th>House_Ownership</th>\n","      <th>Car_Ownership</th>\n","      <th>Profession</th>\n","      <th>CITY</th>\n","      <th>STATE</th>\n","      <th>CURRENT_JOB_YRS</th>\n","      <th>CURRENT_HOUSE_YRS</th>\n","      <th>Risk_Flag</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1303834</td>\n","      <td>23</td>\n","      <td>3</td>\n","      <td>single</td>\n","      <td>rented</td>\n","      <td>no</td>\n","      <td>Mechanical_engineer</td>\n","      <td>Rewa</td>\n","      <td>Madhya_Pradesh</td>\n","      <td>3</td>\n","      <td>13</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>7574516</td>\n","      <td>40</td>\n","      <td>10</td>\n","      <td>single</td>\n","      <td>rented</td>\n","      <td>no</td>\n","      <td>Software_Developer</td>\n","      <td>Parbhani</td>\n","      <td>Maharashtra</td>\n","      <td>9</td>\n","      <td>13</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>3991815</td>\n","      <td>66</td>\n","      <td>4</td>\n","      <td>married</td>\n","      <td>rented</td>\n","      <td>no</td>\n","      <td>Technical_writer</td>\n","      <td>Alappuzha</td>\n","      <td>Kerala</td>\n","      <td>4</td>\n","      <td>10</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>6256451</td>\n","      <td>41</td>\n","      <td>2</td>\n","      <td>single</td>\n","      <td>rented</td>\n","      <td>yes</td>\n","      <td>Software_Developer</td>\n","      <td>Bhubaneswar</td>\n","      <td>Odisha</td>\n","      <td>2</td>\n","      <td>12</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>5768871</td>\n","      <td>47</td>\n","      <td>11</td>\n","      <td>single</td>\n","      <td>rented</td>\n","      <td>no</td>\n","      <td>Civil_servant</td>\n","      <td>Tiruchirappalli[10]</td>\n","      <td>Tamil_Nadu</td>\n","      <td>3</td>\n","      <td>14</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Id   Income  Age  Experience Married/Single House_Ownership Car_Ownership  \\\n","0   1  1303834   23           3         single          rented            no   \n","1   2  7574516   40          10         single          rented            no   \n","2   3  3991815   66           4        married          rented            no   \n","3   4  6256451   41           2         single          rented           yes   \n","4   5  5768871   47          11         single          rented            no   \n","\n","            Profession                 CITY           STATE  CURRENT_JOB_YRS  \\\n","0  Mechanical_engineer                 Rewa  Madhya_Pradesh                3   \n","1   Software_Developer             Parbhani     Maharashtra                9   \n","2     Technical_writer            Alappuzha          Kerala                4   \n","3   Software_Developer          Bhubaneswar          Odisha                2   \n","4        Civil_servant  Tiruchirappalli[10]      Tamil_Nadu                3   \n","\n","   CURRENT_HOUSE_YRS  Risk_Flag  \n","0                 13          0  \n","1                 13          0  \n","2                 10          0  \n","3                 12          1  \n","4                 14          1  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":6,"metadata":{"cell_id":"8cfea04a9ea54eff99e9408cbb579264","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":34,"execution_start":1674971632105,"source_hash":"b7e4c395","tags":[]},"outputs":[],"source":["df.drop([\"Id\"], axis = 1, inplace = True)\n","df.drop([\"Profession\"], axis=1, inplace = True)\n","df.drop([\"CITY\"], axis=1, inplace = True)\n","df.drop([\"STATE\"], axis=1, inplace = True)"]},{"cell_type":"markdown","metadata":{"cell_id":"50e2ef5e428f44c195eb962ef8990833","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["These attributes were dropped in our model, as we would be using only numerical values."]},{"cell_type":"code","execution_count":7,"metadata":{"cell_id":"38ea086c12b54bd1b61c53cd29317649","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":133,"execution_start":1674971632186,"source_hash":"67df20a6","tags":[]},"outputs":[],"source":["df.replace({'Married/Single': {'married': 1, 'single': 0}},inplace=True)\n","df.replace({'House_Ownership': {'owned': 1, 'rented': 0, \"norent_noown\":-1}},inplace=True)\n","df.replace({'Car_Ownership': {'yes': 1, 'no': 0}},inplace=True)"]},{"cell_type":"markdown","metadata":{"cell_id":"be0dcdc59e9949e6b5c801fc19e0c0eb","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["These attributes were replaced by numerical values so that we could use these attributes in our model."]},{"cell_type":"code","execution_count":8,"metadata":{"cell_id":"6189e4e42bbc48928f02adc688e7aa00","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":47,"execution_start":1674971632337,"source_hash":"c085b6ba","tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Income</th>\n","      <th>Age</th>\n","      <th>Experience</th>\n","      <th>Married/Single</th>\n","      <th>House_Ownership</th>\n","      <th>Car_Ownership</th>\n","      <th>CURRENT_JOB_YRS</th>\n","      <th>CURRENT_HOUSE_YRS</th>\n","      <th>Risk_Flag</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1303834</td>\n","      <td>23</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>13</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>7574516</td>\n","      <td>40</td>\n","      <td>10</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>9</td>\n","      <td>13</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3991815</td>\n","      <td>66</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>10</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>6256451</td>\n","      <td>41</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>12</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5768871</td>\n","      <td>47</td>\n","      <td>11</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>14</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    Income  Age  Experience  Married/Single  House_Ownership  Car_Ownership  \\\n","0  1303834   23           3               0                0              0   \n","1  7574516   40          10               0                0              0   \n","2  3991815   66           4               1                0              0   \n","3  6256451   41           2               0                0              1   \n","4  5768871   47          11               0                0              0   \n","\n","   CURRENT_JOB_YRS  CURRENT_HOUSE_YRS  Risk_Flag  \n","0                3                 13          0  \n","1                9                 13          0  \n","2                4                 10          0  \n","3                2                 12          1  \n","4                3                 14          1  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"markdown","metadata":{"cell_id":"9ad2a95c5c8840fbaf1b40fe56d69bb2","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":103},"deepnote_cell_type":"text-cell-h3","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["### PLOTTING HISTOGRAM"]},{"cell_type":"markdown","metadata":{"cell_id":"d3d3c560-8e8c-41a0-8db9-487444a7c9e2","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":108},"deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["We plotted histograms in order to visualise the data to conceptualise the relations among the various features of the dataset."]},{"cell_type":"code","execution_count":9,"metadata":{"cell_id":"d57ee2d175aa4249a5f79f04967103a5","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":97},"deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1674971632382,"source_hash":"1ed3300c","tags":[]},"outputs":[],"source":["%matplotlib inline"]},{"cell_type":"code","execution_count":10,"metadata":{"cell_id":"001e22c99f3940fbb35472e99c52e745","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":91},"deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":189,"execution_start":1674971632383,"source_hash":"7293d7f2","tags":[]},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'Income'}>]], dtype=object)"]},"execution_count":10,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 300x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","df.hist(column=\"Income\",bins = 50, figsize = (3,3))"]},{"cell_type":"code","execution_count":11,"metadata":{"cell_id":"0976ea6565f04c55bb0d5f2608e7d5dd","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":129,"execution_start":1674971632578,"source_hash":"31c146bc","tags":[]},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'Age'}>]], dtype=object)"]},"execution_count":11,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 300x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["df.hist(column=\"Age\",bins =50 ,figsize = (3,3))"]},{"cell_type":"code","execution_count":12,"metadata":{"cell_id":"956d37f1afb9429986bb918728899796","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":72,"execution_start":1674971632757,"source_hash":"19a02c88","tags":[]},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'Experience'}>]], dtype=object)"]},"execution_count":12,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 300x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["df.hist(column=\"Experience\",bins =20 ,figsize = (3,3))"]},{"cell_type":"code","execution_count":13,"metadata":{"cell_id":"fb7e9b02c7374fd497ff3eb2d7d4a246","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":74,"execution_start":1674971632885,"source_hash":"338f38a7","tags":[]},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'Married/Single'}>]], dtype=object)"]},"execution_count":13,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 300x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["df.hist(column=\"Married/Single\",bins =20 ,figsize = (3,3))"]},{"cell_type":"code","execution_count":14,"metadata":{"cell_id":"1626087bc0ac46c9bbc1fe646ece0f63","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":104,"execution_start":1674971633005,"source_hash":"71e491b","tags":[]},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'Car_Ownership'}>]], dtype=object)"]},"execution_count":14,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 300x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["df.hist(column=\"Car_Ownership\",bins =20 ,figsize = (3,3))"]},{"cell_type":"code","execution_count":15,"metadata":{"cell_id":"40600b2dc8154d718d4059afe468d6bc","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":155,"execution_start":1674972220005,"source_hash":"96970da","tags":[]},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'House_Ownership'}>]], dtype=object)"]},"execution_count":15,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 300x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["df.hist(column=\"House_Ownership\",bins =20 ,figsize = (3,3))"]},{"cell_type":"code","execution_count":16,"metadata":{"cell_id":"32e24b8cdcf04f8dbb9416860133acb4","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":159,"execution_start":1674971633273,"source_hash":"b61b5d87","tags":[]},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'CURRENT_JOB_YRS'}>]], dtype=object)"]},"execution_count":16,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 300x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["df.hist(column=\"CURRENT_JOB_YRS\",bins =50 ,figsize = (3,3))"]},{"cell_type":"code","execution_count":17,"metadata":{"cell_id":"81b1aa133e784b80af6e30274d070415","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":142,"execution_start":1674971633431,"source_hash":"24cf5a09","tags":[]},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'CURRENT_HOUSE_YRS'}>]], dtype=object)"]},"execution_count":17,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 300x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["df.hist(column=\"CURRENT_HOUSE_YRS\",bins =50 ,figsize = (3,3))"]},{"cell_type":"code","execution_count":18,"metadata":{"cell_id":"42cb9c8aa5834d92a8d0e7a1aa2d2f3d","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":110,"execution_start":1674971633617,"source_hash":"379e47ce","tags":[]},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'Risk_Flag'}>]], dtype=object)"]},"execution_count":18,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 300x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["df.hist(column=\"Risk_Flag\",bins =50 ,figsize = (3,3))"]},{"cell_type":"markdown","metadata":{"cell_id":"d4db4e9cf1b84ceda0d1d4e16a26fe43","deepnote_cell_type":"text-cell-h3","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["### TRAIN- TEST SPLITTING"]},{"cell_type":"markdown","metadata":{"cell_id":"459d3794fb1e46adbe2dcedeea033982","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["We separated the features (independent variables) and label (dependent variable) in our dataframe.Then we split our dataframe into train and test data in the ratio 80:20."]},{"cell_type":"code","execution_count":19,"metadata":{"cell_id":"ece046fe4b7c41f597ff9896a9993206","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1674971633780,"source_hash":"b61f2e3f","tags":[]},"outputs":[],"source":["X = df.drop(['Risk_Flag'],axis=1)\n","Y = df.Risk_Flag"]},{"cell_type":"code","execution_count":20,"metadata":{"cell_id":"06eeddfbb4214d46aff42089d5b09c68","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1674971633781,"source_hash":"2926b7bc","tags":[]},"outputs":[],"source":["X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)"]},{"cell_type":"markdown","metadata":{"cell_id":"ca6b447509954bc7adfa91936a5a53cc","deepnote_cell_type":"text-cell-h3","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["### LOOKING FOR CORRELATIONS"]},{"cell_type":"markdown","metadata":{"cell_id":"939d15aa-ca9a-4150-836e-d367a5f042ce","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["We calculated Pearson Correlation Coefficient with respect to \"Risk_Flag\"  to analyze which features to use for our logistic regression model."]},{"cell_type":"code","execution_count":21,"metadata":{"cell_id":"4b1038d6a67f48f0b986a88d54d32c5b","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":49,"execution_start":1674971633782,"source_hash":"33704b9b","tags":[]},"outputs":[],"source":["corr_matrix = df.corr()"]},{"cell_type":"code","execution_count":22,"metadata":{"cell_id":"7f732216399e440892ba2f68de5c3ed6","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1674971633878,"source_hash":"24cd0f34","tags":[]},"outputs":[{"data":{"text/plain":["Risk_Flag            1.000000\n","Income              -0.003091\n","CURRENT_HOUSE_YRS   -0.004375\n","House_Ownership     -0.011171\n","CURRENT_JOB_YRS     -0.016942\n","Married/Single      -0.021092\n","Age                 -0.021809\n","Car_Ownership       -0.024036\n","Experience          -0.034523\n","Name: Risk_Flag, dtype: float64"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["corr_matrix[\"Risk_Flag\"].sort_values(ascending = False)"]},{"cell_type":"code","execution_count":23,"metadata":{"cell_id":"6098f9589e3940d6aeb2c0b3b87239fd","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":0},"deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":128,"execution_start":1674971633879,"source_hash":"345301ef","tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABO8AAANXCAYAAABt2Hu+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACStUlEQVR4nOzdeZzWZb0//tcMy6DSDKLAgOLuEdAUQ0XcFRTTUhNTjAqVRAv0KLToKSXbPKapueUxtyz9ndxOxz1BcCczzEoC0rJUFFCRGTfWuX9/dLi/jizO4Ix8aJ7Px+N+OPf1ua7r877u+dyTvvosFaVSqRQAAAAAoHAq13YBAAAAAMDKCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AgCTJ3//+91RUVOT666//wL7HHXdctthii0ZtFRUV+fa3v90qtbWG4447Lp07d25S3zVZ2/XXX5+Kior87ne/W4Pq/um+++5L//7906lTp1RUVGTBggVrPBfrrqVLl+brX/96evfuncrKyhxxxBFru6R/efvtt1/222+/NRq7xRZb5LjjjmvRegBo24R3AFAQy8Oe5a9OnTqlV69eGTp0aC655JK8+eaba7tEPkKvv/56jj766Ky33nq5/PLL8/Of/zwbbLBBi+/nz3/+c7797W/n73//e4vP/VG455571qnQeE1ce+21Of/883PUUUflZz/7WU4//fS1XRIA8BFqv7YLAAAa+853vpMtt9wyS5YsyZw5c/Lggw/mtNNOy4UXXpg77rgjO+64Y6vsd/PNN8+7776bDh06rNH4d999N+3b/2v+q8XaWNuTTz6ZN998M9/97nczZMiQVtvPn//855xzzjnZb7/9Vjibcl1wzz335PLLL/+XDvAmT56cTTbZJBdddNHaLgUAWAv+Nf8NGwDWYZ/85Cezyy67lN+feeaZmTx5cj71qU/lsMMOy4wZM7Leeuu1+H6Xn+23pj7M2KJbG2ubN29ekqRLly4f+b5bwttvv90qZwq2RfPmzWvR46ChoSGLFy9eq9/ZhQsXpmPHjqmsdCEQAHwQ/2sJAOuAAw44IGeddVb+8Y9/5Be/+EWjbTNnzsxRRx2Vrl27plOnTtlll11yxx13rDDHggULcvrpp2eLLbZIVVVVNt1003zxi1/Ma6+9lmTV97z71a9+lR122CGdOnXKDjvskP/5n/9ZaY3vvy/ct7/97VRUVOS5557Lcccdly5duqSmpibHH3983nnnnUZj33333Zx66qnZeOON87GPfSyHHXZYZs+e/YH3miuVStl4440zbty4cltDQ0O6dOmSdu3aNbpH3HnnnZf27dvnrbfeajTH7Nmzc8QRR6Rz587p1q1bvvrVr2bZsmWrXdvycaNGjUqvXr1SVVWVLbfcMl/+8pezePHiRv0WLVqUcePGpVu3btlggw3ymc98Jq+++uoq15T8835bI0eOTJLsuuuuqaioaHQPrSeeeCIHH3xwampqsv7662fffffNY4891miOf/zjH/nKV76S7bbbLuutt1422mijfPazn210eez111+fz372s0mS/fffv3zJ9oMPPrjKdScr3tNr+SXfDz30UL7yla+ke/fu2XTTTcvb77333uy9997ZYIMN8rGPfSyHHnpopk+f3mjOOXPm5Pjjj8+mm26aqqqq9OzZM4cffvhqL+c97rjjcvnll5drXf5a7u2338748ePTu3fvVFVVZbvttssFF1yQUqm0yjlXtcblVnYvtEsvvTTbb7991l9//Wy44YbZZZddctNNNzXqM3v27Jxwwgnp0aNHqqqqsv322+faa69dbQ3Lv5NTpkzJ9OnTV/j9NHV9FRUVGTt2bG688cZsv/32qaqqyn333bfafV9xxRXlvr169cqYMWNWuOdiUz+jBx98MBUVFfnv//7vfOtb38omm2yS9ddfP/X19atd9wUXXJDLL788W221VdZff/0cdNBBefHFF1MqlfLd7343m266adZbb70cfvjhmT9//hqtIUmuuuqqbL311llvvfWy22675ZFHHllpXYsWLcqECROyzTbbpKqqKr17987Xv/71LFq0aJWfIwC0BGfeAcA64gtf+EL+4z/+I/fff39OPPHEJMn06dOz5557ZpNNNskZZ5yRDTbYIDfffHOOOOKI3HbbbfnMZz6TJHnrrbey9957Z8aMGTnhhBPyiU98Iq+99lruuOOOvPTSS9l4441Xus/7778/w4YNS79+/XLuuefm9ddfLwcsTXX00Udnyy23zLnnnpunnnoqV199dbp3757zzjuv3Oe4447LzTffnC984QvZfffd89BDD+XQQw/9wLkrKiqy55575uGHHy63/fGPf0xdXV0qKyvz2GOPled55JFHsvPOOzd6SMWyZcsydOjQDBw4MBdccEEmTZqUH/3oR9l6663z5S9/eZX7ffnll7PbbrtlwYIFGT16dPr06ZPZs2fn1ltvzTvvvJOOHTuW+55yyinZcMMNM2HChPz973/PxRdfnLFjx+aXv/zlKuf/5je/me222y5XXXVV+TLqrbfeOsk/L6H85Cc/mQEDBmTChAmprKzMddddlwMOOCCPPPJIdttttyT/vOz28ccfz/Dhw7Ppppvm73//e37yk59kv/32y5///Oesv/762WeffXLqqafmkksuyX/8x3+kb9++SVL+Z3N95StfSbdu3XL22Wfn7bffTpL8/Oc/z8iRIzN06NCcd955eeedd/KTn/wke+21V37/+9+XL9UdNmxYpk+fnlNOOSVbbLFF5s2bl4kTJ+aFF15Y5eW8J510Ul5++eVMnDgxP//5zxttK5VKOeywwzJlypSMGjUq/fv3z69//et87Wtfy+zZs1vsEtSf/vSnOfXUU3PUUUfl3//937Nw4cL88Y9/zBNPPJHPfe5zSZK5c+dm9913L4do3bp1y7333ptRo0alvr4+p5122krn7tatW37+85/n+9//ft56662ce+65Sf75+2nu+iZPnpybb745Y8eOzcYbb7zaS6S//e1v55xzzsmQIUPy5S9/ObNmzcpPfvKTPPnkk3nsscfW+NL67373u+nYsWO++tWvZtGiRY2+Jytz4403ZvHixTnllFMyf/78/PCHP8zRRx+dAw44IA8++GC+8Y1v5Lnnnsull16ar371q43C0Kau4ZprrslJJ52UPfbYI6eddlr+9re/5bDDDkvXrl3Tu3fv8nwNDQ057LDD8uijj2b06NHp27dv/vSnP+Wiiy7KX/7yl/zqV79ao88EAJqkBAAUwnXXXVdKUnryySdX2aempqa08847l98PHjy49PGPf7y0cOHCcltDQ0Npjz32KG277bbltrPPPruUpHT77bevMGdDQ0OpVCqVnn/++VKS0nXXXVfe1r9//1LPnj1LCxYsKLfdf//9pSSlzTffvNE8SUoTJkwov58wYUIpSemEE05o1O8zn/lMaaONNiq/nzZtWilJ6bTTTmvU77jjjlthzpU5//zzS+3atSvV19eXSqVS6ZJLLiltvvnmpd122630jW98o1QqlUrLli0rdenSpXT66aeXx40cObKUpPSd73yn0Xw777xzacCAAatd2xe/+MVSZWXlSn9Xyz/P5b/PIUOGlNtKpVLp9NNPL7Vr167RZ7oyKzseGhoaSttuu21p6NChjeZ85513SltuuWXpwAMPbNT2flOnTi0lKd1www3ltltuuaWUpDRlypQV+q/q8998881LI0eOXKHWvfbaq7R06dJy+5tvvlnq0qVL6cQTT2w0fs6cOaWamppy+xtvvFFKUjr//PNX/YGswpgxY0or+1faX/3qV6Ukpe9973uN2o866qhSRUVF6bnnnlvtvO9f43L77rtvad999y2/P/zww0vbb7/9aucaNWpUqWfPnqXXXnutUfvw4cNLNTU1K/1dvX+f799Hc9aXpFRZWVmaPn36avdTKpVK8+bNK3Xs2LF00EEHlZYtW1Zuv+yyy0pJStdee225ramf0ZQpU0pJSltttdUHrrVU+n9/i7p169boe3LmmWeWkpR22mmn0pIlS8rtxx57bKljx47lv4NNXcPixYtL3bt3L/Xv37+0aNGicr+rrrqqlKTRGn7+85+XKisrS4888kijWq+88spSktJjjz32gZ8LAKwpl80CwDqkc+fO5afOzp8/P5MnT87RRx+dN998M6+99lpee+21vP766xk6dGieffbZzJ49O0ly2223Zaeddiqfifde773M8L1eeeWVPP300xk5cmRqamrK7QceeGD69evX5JpPPvnkRu/33nvvvP766+VL5pZfvveVr3ylUb9TTjmlSfPvvffeWbZsWR5//PEk/zzDbu+9987ee+9dvvztmWeeyYIFC7L33ns3qb6//e1vq9xfQ0NDfvWrX+XTn/50o3sTLvf+z3P06NGN2pbX+49//KNJ63uvp59+Os8++2w+97nP5fXXXy//zt9+++0MHjw4Dz/8cBoaGpKk0X0RlyxZktdffz3bbLNNunTpkqeeeqrZ+26KE088Me3atSu/nzhxYhYsWJBjjz22XOtrr72Wdu3aZeDAgZkyZUq51o4dO+bBBx/MG2+80SK13HPPPWnXrl1OPfXURu3jx49PqVTKvffe2yL76dKlS1566aU8+eSTK91eKpVy22235dOf/nRKpVKjz2Ho0KGpq6tbo99Hc9e37777Nul7O2nSpCxevDinnXZao/vRnXjiiamurs7dd9/d7FqXGzlyZLPu1/nZz3620d+egQMHJkk+//nPN3qAzMCBA7N48eLy37umruF3v/td5s2bl5NPPrnRWYDHHXdco/0myS233JK+ffumT58+jX6HBxxwQJKUj2UAaA0umwWAdchbb72V7t27J0mee+65lEqlnHXWWTnrrLNW2n/evHnZZJNN8te//jXDhg1r1r6Wh0vbbrvtCtu22267JgcOm222WaP3G264YZLkjTfeSHV1df7xj3+ksrIyW265ZaN+22yzTZPm/8QnPpH1118/jzzySIYOHZpHHnkk55xzTmpra3PppZdm4cKF5RBvr732ajS2U6dO6dat2wr1rS5AevXVV1NfX58ddtihSfWtbv3N9eyzzyZJ+X54K1NXV5cNN9ww7777bs4999xcd911mT17dqP7oNXV1TV7303x/t/h8nqXBxzvV11dnSSpqqrKeeedl/Hjx6dHjx7Zfffd86lPfSpf/OIXU1tbu0a1/OMf/0ivXr3ysY99rFH78kuC1yQ8XZlvfOMbmTRpUnbbbbdss802Oeigg/K5z30ue+65Z5J/Hi8LFizIVVddlauuumqlcyx/OElzNHd97//drG7e5J/f8ffq2LFjttpqqw/1uTW1huXe/91ZHqi993LW97Yv/041dQ2r+hvXoUOHbLXVVo3ann322cyYMWOFvxfLrcnvEACaSngHAOuIl156KXV1deVQa/kZVl/96lczdOjQlY5pagDWmt57JtZ7lZrw0ICm6NChQwYOHJiHH344zz33XObMmZO99947PXr0yJIlS/LEE0/kkUceSZ8+fVb4D+9V1daSWnL9y3/n559/fvr377/SPsvv6XfKKafkuuuuy2mnnZZBgwalpqYmFRUVGT58eHmeNfX+B3os9/6zqpbv5+c///lKQ7j3nj112mmn5dOf/nR+9atf5de//nXOOuusnHvuuZk8eXJ23nnnD1XvmljVGanLli1r9Dvt27dvZs2albvuuiv33XdfbrvttlxxxRU5++yzc84555Q/g89//vOrDF133HHHll/A+7TWE6pX5v2f0ZrWsKrvTmv/TVmZhoaGfPzjH8+FF1640u3vDxQBoCUJ7wBgHbH8hvzLg7rlZ4Z06NAhQ4YMWe3YrbfeOs8880yz9rf55psn+X9nT73XrFmzmjXXB+2noaEhzz//fKMzYJ577rkmz7H33nvnvPPOy6RJk7LxxhunT58+qaioyPbbb59HHnkkjzzySD71qU+1SL3dunVLdXV1sz/PlrD8oRXV1dUf+Du/9dZbM3LkyPzoRz8qty1cuHCFp22uKoBJ/nmW4Pv7L168OK+88kqz6u3evfsH1ru8//jx4zN+/Pg8++yz6d+/f370ox+t8ITlptS/+eabZ9KkSXnzzTcbnZ02c+bM8vbVWdnak3+erfX+s7I22GCDHHPMMTnmmGOyePHiHHnkkfn+97+fM888M926dcvHPvaxLFu2rEmfQVN92PWtbt7kn9/x965z8eLFef755xutoTmf0UepqWt479+4954dumTJkjz//PPZaaedym1bb711/vCHP2Tw4MGr/c4AQGtwzzsAWAdMnjw53/3ud7PllltmxIgRSf4ZiOy33375r//6r5WGKa+++mr552HDhuUPf/hD/ud//meFfqs6W6Vnz57p379/fvaznzW6zHLixIn585///GGXVLY8jLziiisatV966aVNnmPvvffOokWLcvHFF2evvfYq/8f13nvvnZ///Od5+eWXV3q/uzVRWVmZI444InfeeWd+97vfrbC9Nc/+GTBgQLbeeutccMEFeeutt1bY/t7febt27Vao5dJLL13hrLkNNtggSVYawmy99daNnuSbJFddddUqz7x7v6FDh6a6ujo/+MEPsmTJklXW+84772ThwoUr7PtjH/tYFi1atNp9rKr+Qw45JMuWLctll13WqP2iiy5KRUVFPvnJT6523q233jq/+c1vsnjx4nLbXXfdlRdffLFRv9dff73R+44dO6Zfv34plUpZsmRJ2rVrl2HDhuW2225baeD73t9Zc3zY9a3KkCFD0rFjx1xyySWNjp9rrrkmdXV1jZ4C3dTP6KPW1DXssssu6datW6688spGa7j++utXOJ6OPvrozJ49Oz/96U9X2N+7775bfroyALQGZ94BQMHce++9mTlzZpYuXZq5c+dm8uTJmThxYjbffPPccccd6dSpU7nv5Zdfnr322isf//jHc+KJJ2arrbbK3LlzM3Xq1Lz00kv5wx/+kCT52te+lltvvTWf/exnc8IJJ2TAgAGZP39+7rjjjlx55ZWNzjB5r3PPPTeHHnpo9tprr5xwwgmZP39+Lr300my//fYrDY/WxIABAzJs2LBcfPHFef3117P77rvnoYceyl/+8pckqz8zbLlBgwalffv2mTVrVkaPHl1u32efffKTn/wkSVosvEuSH/zgB7n//vuz7777ZvTo0enbt29eeeWV3HLLLXn00UfTpUuXFtvXe1VWVubqq6/OJz/5yWy//fY5/vjjs8kmm2T27NmZMmVKqqurc+eddyZJPvWpT+XnP/95ampq0q9fv0ydOjWTJk3KRhtt1GjO/v37p127djnvvPNSV1eXqqqqHHDAAenevXu+9KUv5eSTT86wYcNy4IEH5g9/+EN+/etfZ+ONN25SvdXV1fnJT36SL3zhC/nEJz6R4cOHp1u3bnnhhRdy9913Z88998xll12Wv/zlLxk8eHCOPvro9OvXL+3bt8///M//ZO7cuRk+fPhq9zFgwIAkyamnnpqhQ4emXbt2GT58eD796U9n//33zze/+c38/e9/z0477ZT7778///u//5vTTjutfFbgqnzpS1/KrbfemoMPPjhHH310/vrXv+YXv/jFCuMOOuig1NbWZs8990yPHj0yY8aMXHbZZTn00EPLZ8T953/+Z6ZMmZKBAwfmxBNPTL9+/TJ//vw89dRTmTRpUubPn9+kz/O9Puz6VqVbt24588wzc8455+Tggw/OYYcdllmzZuWKK67Irrvums9//vPN/ow+ak1dQ4cOHfK9730vJ510Ug444IAcc8wxef7553PdddetcObgF77whdx88805+eSTM2XKlOy5555ZtmxZZs6cmZtvvjm//vWvV/oAGwBoEWvhCbcAwEpcd911pSTlV8eOHUu1tbWlAw88sPTjH/+4VF9fv9Jxf/3rX0tf/OIXS7W1taUOHTqUNtlkk9KnPvWp0q233tqo3+uvv14aO3ZsaZNNNil17NixtOmmm5ZGjhxZeu2110qlUqn0/PPPl5KUrrvuukbjbrvttlLfvn1LVVVVpX79+pVuv/320siRI0ubb755o35JShMmTCi/nzBhQilJ6dVXX13pOp9//vly29tvv10aM2ZMqWvXrqXOnTuXjjjiiNKsWbNKSUr/+Z//2aTPb9dddy0lKT3xxBPltpdeeqmUpNS7d+8V+o8cObK0wQYbrNC+vO7Vra1UKpX+8Y9/lL74xS+WunXrVqqqqipttdVWpTFjxpQWLVrUaJ1PPvlko3FTpkwpJSlNmTJltetZ1fhSqVT6/e9/XzryyCNLG220Uamqqqq0+eabl44++ujSAw88UO7zxhtvlI4//vjSxhtvXOrcuXNp6NChpZkzZ5Y233zz0siRIxvN99Of/rS01VZbldq1a9eotmXLlpW+8Y1vlDbeeOPS+uuvXxo6dGjpueeeW2GO1dW6fM1Dhw4t1dTUlDp16lTaeuutS8cdd1zpd7/7XalUKpVee+210pgxY0p9+vQpbbDBBqWamprSwIEDSzfffPNqP6NSqVRaunRp6ZRTTil169atVFFR0eh39+abb5ZOP/30Uq9evUodOnQobbvttqXzzz+/1NDQ8IHzlkql0o9+9KPSJptsUqqqqirtueeepd/97nelfffdt7TvvvuW+/zXf/1XaZ999in/LrbeeuvS1772tVJdXV2juebOnVsaM2ZMqXfv3qUOHTqUamtrS4MHDy5dddVVH1jHvvvuW9p+++1XaG/q+pKUxowZ06Q1L3fZZZeV+vTpU+rQoUOpR48epS9/+culN954Y4V+TfmMlh/zt9xyS5P2vfxv0fnnn9+ofVXzrOr4a+oarrjiitKWW25ZqqqqKu2yyy6lhx9+eIU1lEql0uLFi0vnnXdeafvtty9VVVWVNtxww9KAAQNK55xzTqPf98q+YwDwYVSUSq14bQcAwBp6+umns/POO+cXv/hF+VJhAABoa9zzDgBY6959990V2i6++OJUVlZmn332WQsVAQBAMbjnHQCw1v3whz/MtGnTsv/++6d9+/a59957c++992b06NHp3bv32i4PAADWGpfNAgBr3cSJE3POOefkz3/+c956661sttlm+cIXvpBvfvObad/e/9cIAEDbtc5dNnv55Zdniy22SKdOnTJw4MD89re/XW3/W265JX369EmnTp3y8Y9/PPfcc0+j7aVSKWeffXZ69uyZ9dZbL0OGDMmzzz7bqM/8+fMzYsSIVFdXp0uXLhk1alSLPWEPAEgOPPDAPProo5k/f34WL16c5557LhMmTBDcAQDQ5q1T4d0vf/nLjBs3LhMmTMhTTz2VnXbaKUOHDs28efNW2v/xxx/Psccem1GjRuX3v/99jjjiiBxxxBF55plnyn1++MMf5pJLLsmVV16ZJ554IhtssEGGDh2ahQsXlvuMGDEi06dPz8SJE3PXXXfl4YcfzujRo1t9vQAAAAC0bevUZbMDBw7MrrvumssuuyxJ0tDQkN69e+eUU07JGWecsUL/Y445Jm+//Xbuuuuuctvuu++e/v3758orr0ypVEqvXr0yfvz4fPWrX02S1NXVpUePHrn++uszfPjwzJgxI/369cuTTz6ZXXbZJUly33335ZBDDslLL72UXr16fQQrBwAAAKAtWmeuRVm8eHGmTZuWM888s9xWWVmZIUOGZOrUqSsdM3Xq1IwbN65R29ChQ/OrX/0qSfL8889nzpw5GTJkSHl7TU1NBg4cmKlTp2b48OGZOnVqunTpUg7ukmTIkCGprKzME088kc985jMr3feiRYuyaNGi8vuGhobMnz8/G220USoqKpq9fgAAAAD+NZRKpbz55pvp1atXKitXf2HsOhPevfbaa1m2bFl69OjRqL1Hjx6ZOXPmSsfMmTNnpf3nzJlT3r68bXV9unfv3mh7+/bt07Vr13KflTn33HNzzjnnNGFlAAAAALRFL774YjbddNPV9llnwrt1zZlnntnorL+6urpsttlmefHFF1NdXb0WK2s559bUrO0SPhJn1tWt7RL+5bSFY8dx0zocO6wpxw5ryrHDmmgLx03i2GkNbeHYcdy0DsfOuqe+vj69e/fOxz72sQ/su86EdxtvvHHatWuXuXPnNmqfO3duamtrVzqmtrZ2tf2X/3Pu3Lnp2bNnoz79+/cv93n/AzGWLl2a+fPnr3K/SVJVVZWqqqoV2qurq/9lwrtOa7uAj8i/yu+rSNrCseO4aR2OHdaUY4c15dhhTbSF4yZx7LSGtnDsOG5ah2Nn3dWUW6utM0+b7dixYwYMGJAHHnig3NbQ0JAHHngggwYNWumYQYMGNeqfJBMnTiz333LLLVNbW9uoT319fZ544olyn0GDBmXBggWZNm1auc/kyZPT0NCQgQMHttj6AAAAAOD91pkz75Jk3LhxGTlyZHbZZZfstttuufjii/P222/n+OOPT5J88YtfzCabbJJzzz03SfLv//7v2XffffOjH/0ohx56aP77v/87v/vd73LVVVcl+We6edppp+V73/tett1222y55ZY566yz0qtXrxxxxBFJkr59++bggw/OiSeemCuvvDJLlizJ2LFjM3z4cE+aBQAAAKBVrVPh3THHHJNXX301Z599dubMmZP+/fvnvvvuKz9w4oUXXmj0hI499tgjN910U771rW/lP/7jP7LtttvmV7/6VXbYYYdyn69//et5++23M3r06CxYsCB77bVX7rvvvnTq9P9OOr3xxhszduzYDB48OJWVlRk2bFguueSSj27hAAAAALRJFaVSqbS2i2gL6uvrU1NTk7q6un+Z67TPacJ12f8KJviKtLi2cOw4blqHY4c15dhhTTl2WBNt4bhJHDutoS0cO46b1uHYWfc0JydaZ+55BwAAAABtjfAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAAqq/douAAAAAIA1N6FUWtsl0IqceQcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEG1X9sFAAAAAMmEUmltlwAUkDPvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQa0z4d38+fMzYsSIVFdXp0uXLhk1alTeeuut1Y5ZuHBhxowZk4022iidO3fOsGHDMnfu3EZ9XnjhhRx66KFZf/31071793zta1/L0qVLy9sffPDBVFRUrPCaM2dOq6wTAAAAAJZbZ8K7ESNGZPr06Zk4cWLuuuuuPPzwwxk9evRqx5x++um58847c8stt+Shhx7Kyy+/nCOPPLK8fdmyZTn00EOzePHiPP744/nZz36W66+/PmefffYKc82aNSuvvPJK+dW9e/cWXyMAAAAAvFf7tV1AU8yYMSP33Xdfnnzyyeyyyy5JkksvvTSHHHJILrjggvTq1WuFMXV1dbnmmmty00035YADDkiSXHfddenbt29+85vfZPfdd8/999+fP//5z5k0aVJ69OiR/v3757vf/W6+8Y1v5Nvf/nY6duxYnq979+7p0qXLR7JeAAAAAEjWkTPvpk6dmi5dupSDuyQZMmRIKisr88QTT6x0zLRp07JkyZIMGTKk3NanT59sttlmmTp1annej3/84+nRo0e5z9ChQ1NfX5/p06c3mq9///7p2bNnDjzwwDz22GMfWPOiRYtSX1/f6AUAAAAAzbFOhHdz5sxZ4TLV9u3bp2vXrqu899ycOXPSsWPHFc6W69GjR3nMnDlzGgV3y7cv35YkPXv2zJVXXpnbbrstt912W3r37p399tsvTz311GprPvfcc1NTU1N+9e7du8nrBQAAAIBkLYd3Z5xxxkofBvHe18yZM9dmidluu+1y0kknZcCAAdljjz1y7bXXZo899shFF1202nFnnnlm6urqyq8XX3zxI6oYAAAAgH8Va/Wed+PHj89xxx232j5bbbVVamtrM2/evEbtS5cuzfz581NbW7vScbW1tVm8eHEWLFjQ6Oy7uXPnlsfU1tbmt7/9baNxy59Gu6p5k2S33XbLo48+utq6q6qqUlVVtdo+AAAAALA6azW869atW7p16/aB/QYNGpQFCxZk2rRpGTBgQJJk8uTJaWhoyMCBA1c6ZsCAAenQoUMeeOCBDBs2LMk/nxj7wgsvZNCgQeV5v//972fevHnly3InTpyY6urq9OvXb5X1PP300+nZs2ez1goAAAAAzbVOPG22b9++Ofjgg3PiiSfmyiuvzJIlSzJ27NgMHz68/KTZ2bNnZ/Dgwbnhhhuy2267paamJqNGjcq4cePStWvXVFdX55RTTsmgQYOy++67J0kOOuig9OvXL1/4whfywx/+MHPmzMm3vvWtjBkzpnzW3MUXX5wtt9wy22+/fRYuXJirr746kydPzv3337/WPg8AAAAA2oZ1IrxLkhtvvDFjx47N4MGDU1lZmWHDhuWSSy4pb1+yZElmzZqVd955p9x20UUXlfsuWrQoQ4cOzRVXXFHe3q5du9x111358pe/nEGDBmWDDTbIyJEj853vfKfcZ/HixRk/fnxmz56d9ddfPzvuuGMmTZqU/fff/6NZOAAAAABtVkWpVCqt7SLagvr6+tTU1KSuri7V1dVru5wWcU5Fxdou4SMxwVekxbWFY8dx0zocO6wpxw5ryrHDmmgLx03i2AH4MJqTE63Vp80CAAAAAKsmvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFNQ6E97Nnz8/I0aMSHV1dbp06ZJRo0blrbfeWu2YhQsXZsyYMdloo43SuXPnDBs2LHPnzm3U59RTT82AAQNSVVWV/v37r3SeP/7xj9l7773TqVOn9O7dOz/84Q9balkAAAAAsErrTHg3YsSITJ8+PRMnTsxdd92Vhx9+OKNHj17tmNNPPz133nlnbrnlljz00EN5+eWXc+SRR67Q74QTTsgxxxyz0jnq6+tz0EEHZfPNN8+0adNy/vnn59vf/nauuuqqFlkXAAAAAKxK+7VdQFPMmDEj9913X5588snssssuSZJLL700hxxySC644IL06tVrhTF1dXW55pprctNNN+WAAw5Iklx33XXp27dvfvOb32T33XdPklxyySVJkldffTV//OMfV5jnxhtvzOLFi3PttdemY8eO2X777fP000/nwgsv/MDwEAAAAAA+jHXizLupU6emS5cu5eAuSYYMGZLKyso88cQTKx0zbdq0LFmyJEOGDCm39enTJ5tttlmmTp3arH3vs88+6dixY7lt6NChmTVrVt54441Vjlu0aFHq6+sbvQAAAACgOdaJ8G7OnDnp3r17o7b27duna9eumTNnzirHdOzYMV26dGnU3qNHj1WOWdU8PXr0WGGO5dtW5dxzz01NTU351bt37ybvEwAAAACStRzenXHGGamoqFjta+bMmWuzxDV25plnpq6urvx68cUX13ZJAAAAAKxj1uo978aPH5/jjjtutX222mqr1NbWZt68eY3aly5dmvnz56e2tnal42pra7N48eIsWLCg0dl3c+fOXeWYVc3z/ifULn+/unmqqqpSVVXV5P0AAAAAwPut1fCuW7du6dat2wf2GzRoUBYsWJBp06ZlwIABSZLJkyenoaEhAwcOXOmYAQMGpEOHDnnggQcybNiwJMmsWbPywgsvZNCgQU2ucdCgQfnmN7+ZJUuWpEOHDkmSiRMnZrvttsuGG27Y5HkAAAAAoLnWiXve9e3bNwcffHBOPPHE/Pa3v81jjz2WsWPHZvjw4eUnzc6ePTt9+vTJb3/72yRJTU1NRo0alXHjxmXKlCmZNm1ajj/++AwaNKj8pNkkee655/L0009nzpw5effdd/P000/n6aefzuLFi5Mkn/vc59KxY8eMGjUq06dPzy9/+cv8+Mc/zrhx4z76DwIAAACANmWtnnnXHDfeeGPGjh2bwYMHp7KyMsOGDcsll1xS3r5kyZLMmjUr77zzTrntoosuKvddtGhRhg4dmiuuuKLRvF/60pfy0EMPld/vvPPOSZLnn38+W2yxRWpqanL//fdnzJgxGTBgQDbeeOOcffbZGT16dCuvGAAAAIC2bp0J77p27Zqbbrppldu32GKLlEqlRm2dOnXK5Zdfnssvv3yV4x588MEP3PeOO+6YRx55pMm1AgAAAEBLWCcumwUAAACAtkh4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAF1X5tFwAAAPCvZEKptLZLAOBfiDPvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCg2je148MPP9zo/T777NPixQAAAAAA/0+Tw7uRI0eWf66oqMjf/va3VikIAAAAAPinJod3zz//fGvWAQAAAAC8j3veAQAAAEBBNfnMu+UuueSSlbZXVFSkU6dO2WabbbLPPvukXbt2H7o4AAAAAGjLmh3eXXTRRXn11VfzzjvvZMMNN0ySvPHGG1l//fXTuXPnzJs3L1tttVWmTJmS3r17t3jBAAAAANBWNPuy2R/84AfZdddd8+yzz+b111/P66+/nr/85S8ZOHBgfvzjH+eFF15IbW1tTj/99NaoFwAAAADajGafefetb30rt912W7beeuty2zbbbJMLLrggw4YNy9/+9rf88Ic/zLBhw1q0UAAAAABoa5p95t0rr7ySpUuXrtC+dOnSzJkzJ0nSq1evvPnmmx++OgAAAABow5od3u2///456aST8vvf/77c9vvf/z5f/vKXc8ABByRJ/vSnP2XLLbdsuSoBAAAAoA1qdnh3zTXXpGvXrhkwYECqqqpSVVWVXXbZJV27ds0111yTJOncuXN+9KMftXixAAAAANCWNPued7W1tZk4cWJmzpyZv/zlL0mS7bbbLtttt125z/77799yFQIAAABAG9Xs8G65Pn36pE+fPi1ZCwAAAADwHmsU3r300ku544478sILL2Tx4sWNtl144YUtUhgAAAAAtHXNDu8eeOCBHHbYYdlqq60yc+bM7LDDDvn73/+eUqmUT3ziE61RIwAAAAC0Sc1+YMWZZ56Zr371q/nTn/6UTp065bbbbsuLL76YfffdN5/97Gdbo0YAAAAAaJOaHd7NmDEjX/ziF5Mk7du3z7vvvpvOnTvnO9/5Ts4777wWLxAAAAAA2qpmh3cbbLBB+T53PXv2zF//+tfyttdee63lKgMAAACANq7Z97zbfffd8+ijj6Zv37455JBDMn78+PzpT3/K7bffnt133701agQAAACANqnZZ95deOGFGThwYJLknHPOyeDBg/PLX/4yW2yxRa655poWL3C5+fPnZ8SIEamurk6XLl0yatSovPXWW6sds3DhwowZMyYbbbRROnfunGHDhmXu3LmN+px66qkZMGBAqqqq0r9//xXm+Pvf/56KiooVXr/5zW9acnkAAAAAsIJmn3m31VZblX/eYIMNcuWVV7ZoQasyYsSIvPLKK5k4cWKWLFmS448/PqNHj85NN920yjGnn3567r777txyyy2pqanJ2LFjc+SRR+axxx5r1O+EE07IE088kT/+8Y+rnGvSpEnZfvvty+832mijD78oAAAAAFiNZod3a8OMGTNy33335cknn8wuu+ySJLn00ktzyCGH5IILLkivXr1WGFNXV5drrrkmN910Uw444IAkyXXXXZe+ffvmN7/5TfkS30suuSRJ8uqrr642vNtoo41SW1vb0ksDAAAAgFVq0mWzG264Ybp27dqkV2uYOnVqunTpUg7ukmTIkCGprKzME088sdIx06ZNy5IlSzJkyJByW58+fbLZZptl6tSpza7hsMMOS/fu3bPXXnvljjvu+MD+ixYtSn19faMXAAAAADRHk868u/jii1u5jNWbM2dOunfv3qitffv26dq1a+bMmbPKMR07dkyXLl0atffo0WOVY1amc+fO+dGPfpQ999wzlZWVue2223LEEUfkV7/6VQ477LBVjjv33HNzzjnnNHk/AAAAAPB+TQrvRo4c2So7P+OMM3Leeeetts+MGTNaZd9NtfHGG2fcuHHl97vuumtefvnlnH/++asN784888xG4+rr69O7d+9WrRUAAACAfy1Nvufd2WefnTPOOCPrr79+kuSNN97Ihhtu+KF2Pn78+Bx33HGr7bPVVlultrY28+bNa9S+dOnSzJ8/f5X3oautrc3ixYuzYMGCRmffzZ0790Pfu27gwIGZOHHiavtUVVWlqqrqQ+0HAAAAgLatyeHd97///YwdO7Yc3m2++eZ5+umnGz19trm6deuWbt26fWC/QYMGZcGCBZk2bVoGDBiQJJk8eXIaGhoycODAlY4ZMGBAOnTokAceeCDDhg1LksyaNSsvvPBCBg0atMY1J8nTTz+dnj17fqg5AAAAAOCDNDm8K5VKq33fmvr27ZuDDz44J554Yq688sosWbIkY8eOzfDhw8tPmp09e3YGDx6cG264IbvttltqamoyatSojBs3Ll27dk11dXVOOeWUDBo0qPyk2SR57rnn8tZbb2XOnDl599138/TTTydJ+vXrl44dO+ZnP/tZOnbsmJ133jlJcvvtt+faa6/N1Vdf/ZGtHwAAAIC2qcnh3dp24403ZuzYsRk8eHAqKyszbNiwXHLJJeXtS5YsyaxZs/LOO++U2y666KJy30WLFmXo0KG54oorGs37pS99KQ899FD5/fKQ7vnnn88WW2yRJPnud7+bf/zjH2nfvn369OmTX/7ylznqqKNacbUAAAAA0IzwrqKiIm+++WY6deqUUqmUioqKvPXWW6mvr2/Ur7q6usWLTJKuXbvmpptuWuX2LbbYYoWzATt16pTLL788l19++SrHPfjgg6vd78iRI1vtgR0AAAAAsDrNumz23/7t3xq9X36W2vL3FRUVWbZsWctWCAAAAABtVJPDuylTprRmHQAAAADA+zQ5vNt3332bNfF//ud/5uSTT06XLl2aWxMAAAAAkKSytSb+wQ9+kPnz57fW9AAAAADwL6/Vwrv3PzwCAAAAAGieVgvvAAAAAIAPR3gHAAAAAAUlvAMAAACAghLeAQAAAEBBtWh49+6775Z/3nvvvbPeeuu15PQAAAAA0KY0O7w79dRTV9r+9ttv55BDDim/v+eee9KzZ881rwwAAAAA2rhmh3d33313JkyY0Kjt7bffzsEHH5ylS5e2WGEAAAAA0Na1b+6A+++/P3vvvXc23HDDnHbaaXnzzTczdOjQtG/fPvfee29r1AgAAAAAbVKzw7utt9469913X/bff/9UVlbm//v//r9UVVXl7rvvzgYbbNAaNQIAAABAm9Ts8C5Jdtxxx9x111058MADM3DgwNx1110eTgEAAAAALaxJ4d3OO++cioqKFdqrqqry8ssvZ8899yy3PfXUUy1XHQAAAAC0YU0K74444ohWLgMAAAAAeL8mhXfvf7osAAAAAND6Kps74MUXX8xLL71Ufv/b3/42p512Wq666qoWLQwAAAAA2rpmh3ef+9znMmXKlCTJnDlzMmTIkPz2t7/NN7/5zXznO99p8QIBAAAAoK1qdnj3zDPPZLfddkuS3Hzzzfn4xz+exx9/PDfeeGOuv/76lq4PAAAAANqsZod3S5YsSVVVVZJk0qRJOeyww5Ikffr0ySuvvNKy1QEAAABAG9bs8G777bfPlVdemUceeSQTJ07MwQcfnCR5+eWXs9FGG7V4gQAAAADQVjU7vDvvvPPyX//1X9lvv/1y7LHHZqeddkqS3HHHHeXLaQEAAACAD699cwfst99+ee2111JfX58NN9yw3D569Oisv/76LVocAAAAALRlzQ7vkqRdu3aNgrsk2WKLLVqiHgAAAADg/zQpvPvEJz6RBx54IBtuuGF23nnnVFRUrLLvU0891WLFAQAAAEBb1qTw7vDDDy8/YfaII45ozXoAAAAAgP/TpPBuwoQJK/0ZAAAAAGg9zX7a7Kq88sorGTt2bEtNBwAAAABtXrMeWDF9+vRMmTIlHTt2zNFHH50uXbrktddey/e+973813/9V7baaqvWqhMAAAAA2pwmn3l3xx13ZOedd86pp56ak08+ObvsskumTJmSvn37ZubMmfmf//mfTJ8+vTVrBQAAAIA2pcnh3fe+972MGTMm9fX1ufDCC/O3v/0tp556au65557cd999Ofjgg1uzTgAAAABoc5oc3s2aNStjxoxJ586dc8opp6SysjIXXXRRdt1119asDwAAAADarCbf8+7NN99MdXV1kqRdu3ZZb7313OMOAIB/aRNKpbVdAgDQxjXrgRW//vWvU1NTkyRpaGjIAw88kGeeeaZRn8MOO6zlqgMAAACANqxZ4d3IkSMbvT/ppJMava+oqMiyZcs+fFUAAAAAQNPDu4aGhtasAwAAAAB4nyY/sAIAAAAA+Gg1+cy7hx9+uNH7ffbZp8WLAQAAAAD+nyaHd++9311FRUX+9re/tUpBAAAAAMA/NTm8e/7551uzDgAAAADgfdzzDgAAAAAKqsln3r3XAw88kAceeCDz5s1b4Sm01157bYsUBgAAAABtXbPDu3POOSff+c53sssuu6Rnz56pqKhojboAAAAAoM1rdnh35ZVX5vrrr88XvvCF1qgHAAAAAPg/zb7n3eLFi7PHHnu0Ri0AAAAAwHs0O7z70pe+lJtuuqk1agEAAAAA3qNJl82OGzeu/HNDQ0OuuuqqTJo0KTvuuGM6dOjQqO+FF17YshUCAAAAQBvVpPDu97//faP3/fv3T5I888wzLV4QAAAAAPBPTQrvpkyZ0tp1AAAAAADv0+x73p1wwgl58803V2h/++23c8IJJ7RIUQAAAADAGoR3P/vZz/Luu++u0P7uu+/mhhtuaJGiAAAAAIAmXjabJPX19SmVSimVSnnzzTfTqVOn8rZly5blnnvuSffu3VulSAAAAABoi5oc3nXp0iUVFRWpqKjIv/3bv62wvaKiIuecc06LFgcAAAAAbVmTw7spU6akVCrlgAMOyG233ZauXbuWt3Xs2DGbb755evXq1SpFAgAAAEBb1OTwbt99902SPP/889lss81SUVHRakUBAAAAAE0M7/74xz9mhx12SGVlZerq6vKnP/1plX133HHHFisOAAAAANqyJoV3/fv3z5w5c9K9e/f0798/FRUVKZVKK/SrqKjIsmXLWrxIAAAAAGiLmhTePf/88+nWrVv5ZwAAAACg9TUpvNt8881X+jMAAAAA0Hqa/MCK5TbbbLPst99+2XfffbPffvtl6623bo26AAAAAKDNq2zugB/84Afp1KlTzjvvvGy77bbp3bt3Pv/5z+enP/1pnn322daoEQAAAADapGafeff5z38+n//855Mkr7zySh566KHcdddd+cpXvpKGhgYPrAAAAACAFtLs8C5J3nnnnTz66KN58MEHM2XKlPz+97/PDjvskP3226+FywMAAACAtqvZ4d0ee+yR3//+9+nbt2/222+/nHHGGdlnn32y4YYbtkZ9AAAAANBmNfuedzNnzswGG2yQPn36pE+fPunbt6/gDgAAAABaQbPDu9dffz2TJ0/O7rvvnl//+tfZc889s8kmm+Rzn/tcfvrTn7ZGjQAAAADQJjU7vKuoqMiOO+6YU089NbfeemvuvffeHHjggbnlllty8sknt0aNAAAAANAmNfued0899VQefPDBPPjgg3n00Ufz5ptv5uMf/3hOOeWU7Lvvvq1RIwAAAAC0Sc0O73bbbbfsvPPO2XfffXPiiSdmn332SU1NTWvUBgAAAABtWrPDu/nz56e6uro1agEAAAAA3qPZ97wT3AEAAADAR6PZ4R0AAAAA8NEQ3gEAAABAQQnvAAAAAKCghHcAAAAAUFDNftrssmXLcv311+eBBx7IvHnz0tDQ0Gj75MmTW6w4AAAAAGjLmh3e/fu//3uuv/76HHroodlhhx1SUVHRGnUBAAAAQJvX7PDuv//7v3PzzTfnkEMOaY16AAAAAID/0+x73nXs2DHbbLNNa9QCAAAAALxHs8O78ePH58c//nFKpVJr1AMAAAAA/J9mXzb76KOPZsqUKbn33nuz/fbbp0OHDo2233777S1WHAAAAAC0Zc0O77p06ZLPfOYzrVELAAAAAPAezQ7vrrvuutaoAwAAAAB4n2aHd8u9+uqrmTVrVpJku+22S7du3VqsKAAAAABgDR5Y8fbbb+eEE05Iz549s88++2SfffZJr169MmrUqLzzzjutUSMAAAAAtEnNDu/GjRuXhx56KHfeeWcWLFiQBQsW5H//93/z0EMPZfz48a1RIwAAAAC0Sc2+bPa2227Lrbfemv3226/cdsghh2S99dbL0UcfnZ/85CctWR8AAAAAtFnNPvPunXfeSY8ePVZo7969u8tmAQAAAKAFNTu8GzRoUCZMmJCFCxeW2959992cc845GTRoUIsWBwAAAABtWbMvm/3xj3+coUOHZtNNN81OO+2UJPnDH/6QTp065de//nWLFwgAAAAAbVWzw7sddtghzz77bG688cbMnDkzSXLsscdmxIgRWW+99Vq8QAAAAABoq5od3iXJ+uuvnxNPPLGlawEAAAAA3qNJ4d0dd9yRT37yk+nQoUPuuOOO1fY97LDDWqQwAAAAAGjrmhTeHXHEEZkzZ066d++eI444YpX9KioqsmzZspaqDQAAAADatCY9bbahoSHdu3cv/7yqV2sGd/Pnz8+IESNSXV2dLl26ZNSoUXnrrbdWO2bhwoUZM2ZMNtpoo3Tu3DnDhg3L3Llzy9v/8Ic/5Nhjj03v3r2z3nrrpW/fvvnxj3+8wjwPPvhgPvGJT6SqqirbbLNNrr/++pZeHgAAAACsoEnh3XvdcMMNWbRo0Qrtixcvzg033NAiRa3MiBEjMn369EycODF33XVXHn744YwePXq1Y04//fTceeedueWWW/LQQw/l5ZdfzpFHHlnePm3atHTv3j2/+MUvMn369Hzzm9/MmWeemcsuu6zc5/nnn8+hhx6a/fffP08//XROO+20fOlLX/JkXQAAAABaXUWpVCo1Z0C7du3yyiuvlM/EW+71119P9+7dW+XsuxkzZqRfv3558skns8suuyRJ7rvvvhxyyCF56aWX0qtXrxXG1NXVpVu3brnpppty1FFHJUlmzpyZvn37ZurUqdl9991Xuq8xY8ZkxowZmTx5cpLkG9/4Ru6+++4888wz5T7Dhw/PggULct99962y5kWLFjUKOevr69O7d+/U1dWlurq6+R9CAZ1TUbG2S/hITGjeV4QmaAvHjuOmdTh2WFOOHQAAiqS+vj41NTVNyomafeZdqVRKxUr+Bfill15KTU1Nc6drkqlTp6ZLly7l4C5JhgwZksrKyjzxxBMrHTNt2rQsWbIkQ4YMKbf16dMnm222WaZOnbrKfdXV1aVr166N9v3eOZJk6NChq50jSc4999zU1NSUX717915tfwAAAAB4vyY9sCJJdt5551RUVKSioiKDBw9O+/b/b+iyZcvy/PPP5+CDD26VIpc/LOO92rdvn65du2bOnDmrHNOxY8d06dKlUXuPHj1WOebxxx/PL3/5y9x9992N5unRo8cKc9TX1+fdd9/Neuutt9K5zjzzzIwbN678fvmZdwAAAADQVE0O75Y/Zfbpp5/O0KFD07lz5/K2jh07ZosttsiwYcOatfMzzjgj55133mr7zJgxo1lzrqlnnnkmhx9+eCZMmJCDDjroQ89XVVWVqqqqFqgMAAAAgLaqyeHdhAkTkiRbbLFFjjnmmHTq1OlD73z8+PE57rjjVttnq622Sm1tbebNm9eofenSpZk/f35qa2tXOq62tjaLFy/OggULGp19N3fu3BXG/PnPf87gwYMzevTofOtb31phnvc+oXb5HNXV1as86w4AAAAAWkKTw7vlRo4c2WI779atW7p16/aB/QYNGpQFCxZk2rRpGTBgQJJk8uTJaWhoyMCBA1c6ZsCAAenQoUMeeOCB8hmBs2bNygsvvJBBgwaV+02fPj0HHHBARo4cme9///sr3fc999zTqG3ixImN5gAAAACA1tDsB1YsW7YsF1xwQXbbbbfU1tama9eujV6toW/fvjn44INz4okn5re//W0ee+yxjB07NsOHDy8/aXb27Nnp06dPfvvb3yZJampqMmrUqIwbNy5TpkzJtGnTcvzxx2fQoEHlJ80+88wz2X///XPQQQdl3LhxmTNnTubMmZNXX321vO+TTz45f/vb3/L1r389M2fOzBVXXJGbb745p59+equsFQAAAACWa3Z4d8455+TCCy/MMccck7q6uowbNy5HHnlkKisr8+1vf7sVSvynG2+8MX369MngwYNzyCGHZK+99spVV11V3r5kyZLMmjUr77zzTrntoosuyqc+9akMGzYs++yzT2pra3P77beXt99666159dVX84tf/CI9e/Ysv3bddddyny233DJ33313Jk6cmJ122ik/+tGPcvXVV2fo0KGttlYAAAAASJKKUqlUas6ArbfeOpdcckkOPfTQfOxjH8vTTz9dbvvNb36Tm266qbVqXafV19enpqYmdXV1qa6uXtvltIhzKirWdgkfiQnN+4rQBG3h2HHctA7HDmvKsQMAQJE0Jydq9pl3c+bMycc//vEkSefOnVNXV5ck+dSnPpW77757DcoFAAAAAFam2eHdpptumldeeSXJP8/Cu//++5MkTz75ZKqqqlq2OgAAAABow5od3n3mM5/JAw88kCQ55ZRTctZZZ2XbbbfNF7/4xZxwwgktXiAAAAAAtFXtmzvgP//zP8s/H3PMMdlss80yderUbLvttvn0pz/dosUBAAAAQFvW7PDu/QYNGpRBgwa1RC0AAAAAwHs0Kby74447mjzhYYcdtsbFAAAAAAD/T5PCuyOOOKJJk1VUVGTZsmUfph4AAAAA4P80KbxraGho7ToAAAAAgPdp9tNm32vhwoUtVQcAAAAA8D7NDu+WLVuW7373u9lkk03SuXPn/O1vf0uSnHXWWbnmmmtavEAAAAAAaKuaHd59//vfz/XXX58f/vCH6dixY7l9hx12yNVXX92ixQEAAABAW9bs8O6GG27IVVddlREjRqRdu3bl9p122ikzZ85s0eIAAAAAoC1rdng3e/bsbLPNNiu0NzQ0ZMmSJS1SFAAAAACwBuFdv3798sgjj6zQfuutt2bnnXdukaIAAAAAgKR9cwecffbZGTlyZGbPnp2GhobcfvvtmTVrVm644YbcddddrVEjAAAAALRJzT7z7vDDD8+dd96ZSZMmZYMNNsjZZ5+dGTNm5M4778yBBx7YGjUCAAAAQJvUrDPvli5dmh/84Ac54YQTMnHixNaqCQCgRU0oldZ2CQAAsEaadeZd+/bt88Mf/jBLly5trXoAAAAAgP/T7MtmBw8enIceeqg1agEAAAAA3qPZD6z45Cc/mTPOOCN/+tOfMmDAgGywwQaNth922GEtVhwAAAAAtGXNDu++8pWvJEkuvPDCFbZVVFRk2bJlH74qAAAAAKD54V1DQ0Nr1AEAAAAAvE+z7nm3ZMmStG/fPs8880xr1QMAAAAA/J9mhXcdOnTIZptt5tJYAAAAAPgINPtps9/85jfzH//xH5k/f35r1AMAAAAA/J9m3/Pusssuy3PPPZdevXpl8803X+Fps0899VSLFQcAAAAAbVmzw7sjjjiiFcoAAAAAAN6v2eHdhAkTWqMOAAAAAOB9mh3eLTdt2rTMmDEjSbL99ttn5513brGiAAAAAIA1CO/mzZuX4cOH58EHH0yXLl2SJAsWLMj++++f//7v/063bt1aukYAAAAAaJOa/bTZU045JW+++WamT5+e+fPnZ/78+XnmmWdSX1+fU089tTVqBAAAAIA2qdln3t13332ZNGlS+vbtW27r169fLr/88hx00EEtWhwAvNeEUmltlwAAAPCRavaZdw0NDenQocMK7R06dEhDQ0OLFAUAAAAArEF4d8ABB+Tf//3f8/LLL5fbZs+endNPPz2DBw9u0eIAAAAAoC1rdnh32WWXpb6+PltssUW23nrrbL311tlyyy1TX1+fSy+9tDVqBAAAAIA2qdn3vOvdu3eeeuqpTJo0KTNnzkyS9O3bN0OGDGnx4gAAAACgLWt2eJckFRUVOfDAA3PggQe2dD0AAAAAwP9p8mWzkydPTr9+/VJfX7/Ctrq6umy//fZ55JFHWrQ4AAAAAGjLmhzeXXzxxTnxxBNTXV29wraampqcdNJJufDCC1u0OAAAAABoy5oc3v3hD3/IwQcfvMrtBx10UKZNm9YiRQEAAAAAzQjv5s6dmw4dOqxye/v27fPqq6+2SFEAAAAAQDPCu0022STPPPPMKrf/8Y9/TM+ePVukKAAAAACgGeHdIYcckrPOOisLFy5cYdu7776bCRMm5FOf+lSLFgcAAAAAbVn7pnb81re+ldtvvz3/9m//lrFjx2a77bZLksycOTOXX355li1blm9+85utVigAAAAAtDVNDu969OiRxx9/PF/+8pdz5plnplQqJUkqKioydOjQXH755enRo0erFQoAAAAAbU2Tw7sk2XzzzXPPPffkjTfeyHPPPZdSqZRtt902G264YWvVBwAAAABtVrPCu+U23HDD7Lrrri1dCwAAAADwHk1+YAUAAAAA8NES3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACqr92i4AaHsmlEpruwQAAABYJzjzDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFNQ6E97Nnz8/I0aMSHV1dbp06ZJRo0blrbfeWu2YhQsXZsyYMdloo43SuXPnDBs2LHPnzi1v/8Mf/pBjjz02vXv3znrrrZe+ffvmxz/+caM5HnzwwVRUVKzwmjNnTqusEwAAAACWa7+2C2iqESNG5JVXXsnEiROzZMmSHH/88Rk9enRuuummVY45/fTTc/fdd+eWW25JTU1Nxo4dmyOPPDKPPfZYkmTatGnp3r17fvGLX6R37955/PHHM3r06LRr1y5jx45tNNesWbNSXV1dft+9e/fWWSgAAAAA/J+KUqlUWttFfJAZM2akX79+efLJJ7PLLrskSe67774ccsgheemll9KrV68VxtTV1aVbt2656aabctRRRyVJZs6cmb59+2bq1KnZfffdV7qvMWPGZMaMGZk8eXKSf555t//+++eNN95Ily5d1ngN9fX1qampSV1dXaMQcF12TkXF2i7hIzGh+F8RAAAAYB3SnJxonbhsdurUqenSpUs5uEuSIUOGpLKyMk888cRKx0ybNi1LlizJkCFDym19+vTJZpttlqlTp65yX3V1denatesK7f3790/Pnj1z4IEHls/cW51Fixalvr6+0QsAAAAAmmOdCO/mzJmzwmWq7du3T9euXVd577k5c+akY8eOK5wt16NHj1WOefzxx/PLX/4yo0ePLrf17NkzV155ZW677bbcdttt6d27d/bbb7889dRTq6353HPPTU1NTfnVu3fvJqwUAAAAAP6ftRrenXHGGSt9GMR7XzNnzvxIannmmWdy+OGHZ8KECTnooIPK7dttt11OOumkDBgwIHvssUeuvfba7LHHHrnoootWO9+ZZ56Zurq68uvFF19s7SUAAAAA8C9mrT6wYvz48TnuuONW22errbZKbW1t5s2b16h96dKlmT9/fmpra1c6rra2NosXL86CBQsanX03d+7cFcb8+c9/zuDBgzN69Oh861vf+sC6d9tttzz66KOr7VNVVZWqqqoPnAsAAAAAVmWthnfdunVLt27dPrDfoEGDsmDBgkybNi0DBgxIkkyePDkNDQ0ZOHDgSscMGDAgHTp0yAMPPJBhw4Yl+ecTY1944YUMGjSo3G/69Ok54IADMnLkyHz/+99vUt1PP/10evbs2aS+AAAAALCm1mp411R9+/bNwQcfnBNPPDFXXnlllixZkrFjx2b48OHlJ83Onj07gwcPzg033JDddtstNTU1GTVqVMaNG5euXbumuro6p5xySgYNGlR+0uwzzzyTAw44IEOHDs24cePK98Jr165dOVS8+OKLs+WWW2b77bfPwoULc/XVV2fy5Mm5//77186HAQAAAECbsU6Ed0ly4403ZuzYsRk8eHAqKyszbNiwXHLJJeXtS5YsyaxZs/LOO++U2y666KJy30WLFmXo0KG54ooryttvvfXWvPrqq/nFL36RX/ziF+X2zTffPH//+9+TJIsXL8748eMze/bsrL/++tlxxx0zadKk7L///q2/aAAAAADatIpSqVRa20W0BfX19ampqUldXV2qq6vXdjkt4pyKirVdwkdigq8IAAAA0IKakxOt1afNAgAAAACrJrwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABSU8A4AAAAACkp4BwAAAAAFJbwDAAAAgIIS3gEAAABAQQnvAAAAAKCghHcAAAAAUFDCOwAAAAAoKOEdAAAAABTUOhPezZ8/PyNGjEh1dXW6dOmSUaNG5a233lrtmIULF2bMmDHZaKON0rlz5wwbNixz584tb3/99ddz8MEHp1evXqmqqkrv3r0zduzY1NfXN5rnwQcfzCc+8YlUVVVlm222yfXXX98aSwQAAACARtaZ8G7EiBGZPn16Jk6cmLvuuisPP/xwRo8evdoxp59+eu68887ccssteeihh/Lyyy/nyCOPLG+vrKzM4YcfnjvuuCN/+ctfcv3112fSpEk5+eSTy32ef/75HHroodl///3z9NNP57TTTsuXvvSl/PrXv261tQIAAABAklSUSqXS2i7ig8yYMSP9+vXLk08+mV122SVJct999+WQQw7JSy+9lF69eq0wpq6uLt26dctNN92Uo446Kkkyc+bM9O3bN1OnTs3uu+++0n1dcsklOf/88/Piiy8mSb7xjW/k7rvvzjPPPFPuM3z48CxYsCD33Xdfk9dQX1+fmpqa1NXVpbq6usnjiuycioq1XcJHYkLxvyIAAADAOqQ5OdE6cebd1KlT06VLl3JwlyRDhgxJZWVlnnjiiZWOmTZtWpYsWZIhQ4aU2/r06ZPNNtssU6dOXemYl19+Obfffnv23XffRvt+7xxJMnTo0FXOsdyiRYtSX1/f6AUAAAAAzbFOhHdz5sxJ9+7dG7W1b98+Xbt2zZw5c1Y5pmPHjunSpUuj9h49eqww5thjj83666+fTTbZJNXV1bn66qsbzdOjR48V5qivr8+77767yprPPffc1NTUlF+9e/duylIBAAAAoGythndnnHFGKioqVvuaOXNmq9dx0UUX5amnnsr//u//5q9//WvGjRv3oec888wzU1dXV34tvwwXAAAAAJqq/drc+fjx43Pcccetts9WW22V2trazJs3r1H70qVLM3/+/NTW1q50XG1tbRYvXpwFCxY0Ovtu7ty5K4ypra1NbW1t+vTpk65du2bvvffOWWedlZ49e6a2trbRE2qXz1FdXZ311ltvlXVXVVWlqqpqtWsDAAAAgNVZq+Fdt27d0q1btw/sN2jQoCxYsCDTpk3LgAEDkiSTJ09OQ0NDBg4cuNIxAwYMSIcOHfLAAw9k2LBhSZJZs2blhRdeyKBBg1a5r4aGhiT/vGfd8n3fc889jfpMnDhxtXMAAAAAQEtYq+FdU/Xt2zcHH3xwTjzxxFx55ZVZsmRJxo4dm+HDh5efNDt79uwMHjw4N9xwQ3bbbbfU1NRk1KhRGTduXLp27Zrq6uqccsopGTRoUPlJs/fcc0/mzp2bXXfdNZ07d8706dPzta99LXvuuWe22GKLJMnJJ5+cyy67LF//+tdzwgknZPLkybn55ptz9913r62PAwAAAIA2Yp0I75LkxhtvzNixYzN48OBUVlZm2LBhueSSS8rblyxZklmzZuWdd94pt1100UXlvosWLcrQoUNzxRVXlLevt956+elPf5rTTz89ixYtSu/evXPkkUfmjDPOKPfZcsstc/fdd+f000/Pj3/842y66aa5+uqrM3To0I9m4QAAAAC0WRWlUqm0totoC+rr61NTU5O6urpUV1ev7XJaxDkVFWu7hI/EBF8RAAAAoAU1Jydaq0+bBQAAAABWTXgHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKSngHAAAAAAUlvAMAAACAghLeAQAAAEBBCe8AAAAAoKCEdwAAAABQUMI7AAAAACgo4R0AAAAAFJTwDgAAAAAKqv3aLoB114RSaW2XAAAAAPAvzZl3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABbXOhHfz58/PiBEjUl1dnS5dumTUqFF56623Vjtm4cKFGTNmTDbaaKN07tw5w4YNy9y5c8vbX3/99Rx88MHp1atXqqqq0rt374wdOzb19fXlPg8++GAqKipWeM2ZM6fV1goAAAAAyToU3o0YMSLTp0/PxIkTc9ddd+Xhhx/O6NGjVzvm9NNPz5133plbbrklDz30UF5++eUceeSR5e2VlZU5/PDDc8cdd+Qvf/lLrr/++kyaNCknn3zyCnPNmjUrr7zySvnVvXv3Fl8jAAAAALxXRalUKq3tIj7IjBkz0q9fvzz55JPZZZddkiT33XdfDjnkkLz00kvp1avXCmPq6urSrVu33HTTTTnqqKOSJDNnzkzfvn0zderU7L777ivd1yWXXJLzzz8/L774YpJ/nnm3//7754033kiXLl3WeA319fWpqalJXV1dqqur13geAAAAANZtzcmJ1okz76ZOnZouXbqUg7skGTJkSCorK/PEE0+sdMy0adOyZMmSDBkypNzWp0+fbLbZZpk6depKx7z88su5/fbbs++++66wrX///unZs2cOPPDAPPbYYx9Y86JFi1JfX9/oBQAAAADNsU6Ed3PmzFnhMtX27duna9euq7z33Jw5c9KxY8cVzpbr0aPHCmOOPfbYrL/++tlkk01SXV2dq6++urytZ8+eufLKK3PbbbfltttuS+/evbPffvvlqaeeWm3N5557bmpqasqv3r17N2PFAAAAALCWw7szzjhjpQ+DeO9r5syZrV7HRRddlKeeeir/+7//m7/+9a8ZN25cedt2222Xk046KQMGDMgee+yRa6+9NnvssUcuuuii1c555plnpq6urvxafhkuAAAAADRV+7W58/Hjx+e4445bbZ+tttoqtbW1mTdvXqP2pUuXZv78+amtrV3puNra2ixevDgLFixodPbd3LlzVxhTW1ub2tra9OnTJ127ds3ee++ds846Kz179lzp3LvttlseffTR1dZdVVWVqqqq1fYBAAAAgNVZq+Fdt27d0q1btw/sN2jQoCxYsCDTpk3LgAEDkiSTJ09OQ0NDBg4cuNIxAwYMSIcOHfLAAw9k2LBhSf75xNgXXnghgwYNWuW+GhoakvzznnWr8vTTT68y2AMAAACAlrJWw7um6tu3bw4++OCceOKJufLKK7NkyZKMHTs2w4cPLz9pdvbs2Rk8eHBuuOGG7LbbbqmpqcmoUaMybty4dO3aNdXV1TnllFMyaNCg8pNm77nnnsydOze77rprOnfunOnTp+drX/ta9txzz2yxxRZJkosvvjhbbrlltt9++yxcuDBXX311Jk+enPvvv39tfRwAAAAAtBHrRHiXJDfeeGPGjh2bwYMHp7KyMsOGDcsll1xS3r5kyZLMmjUr77zzTrntoosuKvddtGhRhg4dmiuuuKK8fb311stPf/rTnH766Vm0aFF69+6dI488MmeccUa5z+LFizN+/PjMnj0766+/fnbcccdMmjQp+++//0ezcAAAAADarIpSqVRa20W0BfX19ampqUldXV2qq6vXdjkAAAAArCXNyYnW6tNmAQAAAIBVE94BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoIR3AAAAAFBQwjsAAAAAKCjhHQAAAAAUlPAOAAAAAApKeAcAAAAABSW8AwAAAICCEt4BAAAAQEEJ7wAAAACgoNqv7QLailKplCSpr69fy5UAAAAAsDYtz4eW50WrI7z7iLz55ptJkt69e6/lSgAAAAAogjfffDM1NTWr7VNRakrEx4fW0NCQl19+OR/72MdSUVGxtstZJ9XX16d379558cUXU11dvbbLYR3i2GFNOXZYE44b1pRjhzXl2GFNOXZYU46dD69UKuXNN99Mr169Ulm5+rvaOfPuI1JZWZlNN910bZfxL6G6utofB9aIY4c19f+3d99hURxvHMC/d5Q7iqAgoShgQZqKFRUbGDVgb9hiASVWxB5JohEUFDUixt5BDfaGsXdFjR3QAIIFNCYajWDBSnl/f/jc/ljuDk4UweT9PM89eruzO3O7szPD7Ows5x1WHJxvWHFx3mHFxXmHFRfnHVZcnHc+TFEj7hT4hRWMMcYYY4wxxhhjjJVR3HnHGGOMMcYYY4wxxlgZxZ137LMhk8kQFBQEmUxW2klhnxnOO6y4OO+w4uB8w4qL8w4rLs47rLg477Di4rzzafELKxhjjDHGGGOMMcYYK6N45B1jjDHGGGOMMcYYY2UUd94xxhhjjDHGGGOMMVZGcecdY4wxxhhjjDHGGGNlFHfesc+Sr68vunbtWtrJYEwjJ06cgEQiwZMnT9SGCQ4ORt26dT9Zmhhj6lWpUgXz58//oH18jGtak7KjODw8PDB27NiPuk/GGCtKeno6JBIJ4uPj1YaJiopC+fLlP1maGGPsc8Gdd0wtX19fSCQSpY+Xl1dpJw0///wzoqKiSjsZ/wkPHjxAQEAAqlWrBplMBmtra3Tq1AlHjx4t0XgzMjIwduxY2NraQldXF1ZWVhg8eDDu3r1bovGWlokTJ5b4MWX/99tvv0FLSwsdOnQo7aQwNRR10PDhw5XW+fv7QyKRwNfXt0TivnjxIoYOHVoi+542bRr69+8PAEhISEDnzp3xxRdfQC6Xo0qVKujduzcePnwIAGjatCnu378PY2PjEklLWVZU3SORSLBr1y6l7Qre3PPw8BDaL3K5HPb29ggLC0P+97UpOhQUHxMTE7i7uyM2Nla07+DgYJXtIkdHR6X4Nm3aJNp2/vz5qFKlilKaVH08PDyKPD6qOpjPnj2L9u3bo0KFCpDL5ahduzbmzZuH3NxcUbj8cWlra8PGxgbjx4/Hmzdviow3JCQElpaWyMjIEC1PSEiATCbDnj17lOIwMjKCq6srYmJiRNvk5uZi1qxZcHR0hJ6eHkxMTNC4cWOsWrWqyHRoQt2N3pLqFC8pr169QlBQEOzt7SGTyVCxYkX07NkTiYmJpZ20EtG7d2+kpqZ+8H64DCmcuptUqm48adomV3djqGCH7MuXL/H999+jevXqkMvlMDMzg7u7u6iMUPcbVbUJ8ktNTYW+vj42bNggWp6Xl4emTZvC29sbgPjvXB0dHVStWhWTJk3C69evRdudPHkSX375JUxMTKCvr48aNWrAx8cHb9++LTQdZQkPevn34M47VigvLy/cv39f9Nm4cWOppSc3Nxd5eXkwNjbmu3KfQHp6Oho0aIBjx47hp59+wrVr13DgwAG0atUK/v7+xdqn4hwWJiMjA02aNMGRI0ewbNky3Lx5E5s2bcLNmzfh6uqK27dvFyvukpKdnf3B+zA0NISpqelHSA3TxOrVqxEQEIBTp07hr7/+Ku3kMDWsra2xadMmvHr1Slj2+vVrbNiwATY2Nh+0b1XXraIxbmZmBn19/Q/avzoxMTHo3LkzHj16hNatW8PExAQHDx5EcnIyIiMjYWVlhRcvXgAAdHV1YWFhAYlEUiJpKas+dt0zZMgQ3L9/HykpKfj+++8xdepULFu2TCnckSNHcP/+fZw6dQpWVlbo2LEj/v77b1GYmjVrKrWLTp8+LQojl8sxZcoUtXXDjh07hG0vXLggivv+/fvYsWPHe//GnTt3wt3dHZUrV8bx48dx/fp1jBkzBqGhoejTp4+oowEAIiMjcf/+faSlpWHJkiVYv349QkNDi4zn+++/h7W1teg8ZGdnw8fHB/3790fHjh2V4rh06RKaNWsGb29vXLt2TVg/bdo0REREICQkBElJSTh+/DiGDh362XSqfQpv3rxBmzZtsGbNGoSGhiI1NRX79u1DTk4OGjdujHPnzpV2EkU+RntIT08PX3zxxQftg8uQ9y9D1CmJNvnw4cOxY8cOLFy4ENevX8eBAwfg7e2Nx48fi8Ipjnv+z5w5cwrdt729PWbNmoWAgADcv39fWB4eHo7bt2+Lzpvi79zbt28jIiICy5cvR1BQkLA+KSkJXl5eaNiwIU6dOoVr165h4cKF0NXVVbopwtgnQYyp4ePjQ126dFG57vjx46Sjo0OnTp0Sls2ePZvMzMzowYMHRETk7u5O/v7+5O/vT0ZGRmRqakpTpkyhvLw8YZvXr1/ThAkTyMrKivT19alRo0Z0/PhxYX1kZCQZGxtTTEwMOTk5kZaWFqWlpSmlLTc3l2bOnElVqlQhuVxOLi4utHXrVlF6AdCRI0eoQYMGpKenR25ubnT9+nXR79q9ezc1bNiQZDIZmZqaUteuXTVO679Ru3btqFKlSpSVlaW0LjMzk4iIwsPDqVatWqSvr0+VK1emESNG0PPnz4Vw6s5hYYYPH04GBgZ0//590fKXL19SpUqVyMvLi4iIfv31VzI2NqacnBwiIoqLiyMAFBgYKGzj5+dH/fr1E6XlwIED5OjoSAYGBuTp6Ul//fWXKJ6VK1eSo6MjyWQycnBwoMWLFwvr0tLSCABt2rSJWrZsSTKZjCIjIyk9PZ06duxI5cuXJ319fXJ2dqa9e/cSkWb5LygoiOrUqSN8V+Tx4OBgqlixIpUrV46GDRtGb968KfTYsaI9f/6cDA0N6fr169S7d2+aMWOGaH1MTAzZ2dmRTCYjDw8PioqKIgBCniciio2NpebNm5NcLqfKlStTQECAyuuEFZ/iGqhVqxb98ssvwvLo6GhycXGhLl26kI+PDxER7d+/n5o1a0bGxsZkYmJCHTp0oJs3bwrbqLtuFXGEhoaSpaUlValShYiIbG1tKSIiQtg+MzOT/Pz8hGuxVatWFB8fL0pvWFgYffHFF2RoaEiDBw+mwMBA0TVNRHT37l3S1dWlp0+f0s6dO0lbW5uys7PVHgNF2aHIe5qUYdnZ2RQQECAci0mTJtHAgQNFdaa7uzuNGTNG+F7W6jdN6h4AtHPnTqX1BdsHBX8rEVH9+vWpW7duwndF/oiLixOWXb16lQBQTEyMsKxgOa2Ku7s7DRo0iExNTUV1R0REBNna2iqFVxW3JvLn0aysLDI1NaXu3bsrhdu9e7eQ9xVUHTs/Pz9q3769RnEnJyeTXC4X2llBQUFka2tLT58+VRvHs2fPCAD9/PPPwrI6depQcHCwRnEWh7p2bMHratu2beTs7Ey6urpka2tLc+fOFYVXdbyMjY0pMjKSiIjevHlD/v7+ZGFhQTKZjGxsbGjmzJlCWE3KD3VmzZpFEolEKXxubi41bNiQnJ2dKS8vj65du0YSiYQePnxIRESPHz8miURCvXv3FrYJCQmhZs2aiY5BUe3iXbt2Ub169Ugmk1HVqlUpODhYVGYBoCVLllCnTp1IX1+fgoKCKCMjg77++muqWLEiyeVysrOzozVr1hDR//P79u3bycPDg/T09MjFxYXOnj0r7FNRzikorrtly5ZR5cqVSU9Pj3r27ElPnjxRe9y4DClawXpOXRo1bZMr0l7wWBEpn1NjY2OKiooqNH3q9qWJvLw8atWqFXXo0IGI/l9m5T8XqsqH7t27U7169YTvERERQrvgc5b/t7q7u1NAQAB9++23VKFCBTI3N6egoCBR+MzMTBo6dCh98cUXJJPJqGbNmvTrr78K64sqM21tbSkkJIQGDBhABgYGZGNjQzExMfTw4UPq3LkzGRgYUO3atenixYui7bhtXTQeeceKRTEsesCAAXj69Cni4uLw448/YtWqVTA3NxfCrV27Ftra2rhw4QJ+/vlnzJs3T/Q4xKhRo/Dbb79h06ZNuHr1Knr27AkvLy/cuHFDCPPy5UvMnj0bq1atQmJiosq7cWFhYVi3bh2WLVuGxMREjBs3Dv3798fJkydF4SZPnozw8HBcunQJ2traGDx4sLBu79696NatG9q3b4+4uDgcPXoUjRo1eq+0/ptkZGTgwIED8Pf3h4GBgdJ6xchHqVSKBQsWIDExEWvXrsWxY8cwadIkUVhNzqFCXl4eNm3ahH79+sHCwkK0Tk9PDyNHjsTBgweRkZGBFi1a4Pnz54iLiwPwbmh7xYoVceLECWGbkydPih4fePnyJebOnYv169fj1KlTuHv3LiZOnCisj46OxtSpUzFjxgwkJydj5syZ+PHHH7F27VpRWr777juMGTMGycnJ8PT0hL+/P968eSPcmZs9ezYMDQ1F2xSW/1Q5evQokpOTceLECWzcuBE7duzAtGnTCt2GFW3Lli1wdHSEg4MD+vfvjzVr1gijUtLS0uDt7Y2uXbsiISEBw4YNw+TJk0Xb37p1C15eXujRoweuXr2KzZs34/Tp0xg1alRp/Jx/vcGDByMyMlL4vmbNGgwaNEgU5sWLFxg/fjwuXbqEo0ePQiqVolu3bkqjfAtet8C76ywlJQWHDx8WHvkrqGfPnnj48CH279+Py5cvo379+mjdurXw6OCWLVsQHByMmTNn4tKlS7C0tMSSJUuU9rN79254eHjAyMgIFhYWyMnJwc6dO5VGRRWmqDJs9uzZiI6ORmRkJM6cOYNnz56pfDQsv7JUv2la9xQHESE2NhbXr1+Hrq6u2nCvXr3CunXrAKDQcOoYGRlh8uTJmD59ujCKsiQdOnQIjx8/FuUDhU6dOsHe3r7QpyZSU1Nx7NgxNG7cWKP4HB0dERYWhhEjRuDgwYMICwtDZGQkjIyMVIbPycnB6tWrAYiPp4WFBY4dO4ZHjx5pFG9JuHz5Mnr16oU+ffrg2rVrCA4Oxo8//vheU7MsWLAAu3fvxpYtW5CSkoLo6Gjh8Uag6PKjMBs2bEDbtm1Rp04d0XKpVIpx48YhKSkJCQkJqFmzJkxNTYV2b2xsrOg7oNweAgpvl8TGxmLgwIEYM2YMkpKSsHz5ckRFRWHGjBmifQQHB6Nbt264du0aBg8ejB9//BFJSUnYv38/kpOTsXTpUlSsWFEp3okTJyI+Ph729vbo27cvcnJy1B6HmzdvYsuWLfj1119x4MABxMXFYeTIkSrDchny8bxPm/x9WFhYYN++fXj+/PnHTK5AIpEgMjISsbGxWLlyJXx9fdGnTx907txZ7Ta///47zp49q1RGKUZS/pusXbsWBgYGOH/+PObMmYPp06fj8OHDAN6d83bt2uHMmTP45ZdfkJSUhFmzZkFLSwuA5mVmREQEmjVrhri4OHTo0AEDBgzAwIED0b9/f1y5cgXVq1fHwIEDhfYPt601VKpdh6xM8/HxIS0tLTIwMBB9FKNU3rx5Q3Xr1qVevXqRs7MzDRkyRLS9u7s7OTk5iUbaBQYGkpOTExER3blzh7S0tOjPP/8Ubde6dWv6/vvviejdnRoASncc899BeP36Nenr64vu2hG9u4vct29fIhLfYVTYu3cvAaBXr14REZGbm5swQqsgTdL6b3P+/HkCQDt27Hiv7bZu3UqmpqbCd3XnUJ0HDx4QAJV3A4mIduzYQQDo/PnzRPTu7udPP/1ERERdu3alGTNmkK6uLj1//pzu3btHACg1NVWUlvwjchYvXkzm5ubC9+rVq9OGDRtEcYaEhJCbmxsR/f8O5/z580VhateurXYEgSb5T9XIOxMTE3rx4oWwbOnSpWRoaEi5ubkq42Gaadq0qXD+srOzqWLFisIoo8DAQKpVq5Yo/OTJk0WjNPz8/Gjo0KGiMLGxsSSVSoXzyT6copx/+PAhyWQySk9Pp/T0dJLL5fTo0SPRyLuCHj16RADo2rVrRKT+uvXx8SFzc3OlEa35RyTExsaSkZERvX79WhSmevXqtHz5ciJ6V3+MHDlStL5x48ZKIyzatm1LixYtEr7/8MMPpK2tTSYmJuTl5UVz5swRRq8TqR55V1QZZm5uLpSJREQ5OTlkY2OjdiRJWavfNK178B6jZnR0dMjAwIB0dHQIAMnlcjpz5owQRpE/9PT0yMDAgCQSCQGgBg0a0Nu3b4VwQUFBJJVKldpFw4YNE8U3ZswYev36Ndna2tL06dOJqGRHzcyaNUtpdHB+nTt3FtpeRCQcAwMDA5LJZASAOnbsKPqtRcnLyyMPDw+SSqUqR8fkj0MqlRIAqlKlCj1+/FgIk5iYSE5OTiSVSql27do0bNgw2rdvn8ZpKIq6dqxcLheO19dff01t27YVbfftt9+Ss7Oz6LcUNvIuICCAvvzyS1F7V0GT8qMwcrlc7eijK1euEADavHkzEb0bNeTv709ERGPHjhVG1iQnJ9Pbt29JX1+fDh06RESatUtat24tGkFIRLR+/XqytLQUvgOgsWPHisJ06tSJBg0apDLNivy+atUqYVliYiIBoOTkZCJSPfJOS0uL7t27Jyzbv38/SaVSpdFgRFyGaMrW1pZ0dXWV0qKjoyPUXe/bJtd05N3JkyepcuXKpKOjQw0bNqSxY8fS6dOnRdvkP+75P/lH4hdlzZo1JJVKycbGRjQymEhcPijKQalUStu2bRPC5OTkkK+vLwEgCwsL6tq1Ky1cuFBpX2VdwZF3zZs3F613dXUVnlo6ePAgSaVSSklJUbkvTcpMW1tb6t+/v/D9/v37BIB+/PFHYdlvv/1GAIRrmNvWmuGRd6xQrVq1Qnx8vOijmChUV1cX0dHR2L59O16/fo2IiAil7Zs0aSKaq8fNzQ03btxAbm4url27htzcXNjb28PQ0FD4nDx5Erdu3RK20dXVhYuLi9o03rx5Ey9fvkTbtm1F+1m3bp1oPwBE+7G0tAQAYWLw+Ph4tG7dWmUcmqb134Q0HAly5MgRtG7dGpUqVUK5cuUwYMAAPH78GC9fvhTCFHUOPyR+d3d3nDhxQrgb2r17dzg5OeH06dM4efIkrKysUKNGDSG8vr4+qlevLny3tLQU8sCLFy9w69Yt+Pn5ic5zaGio0nlu2LCh6Pvo0aMRGhqKZs2aISgoCFevXlVKa2H5T5U6deqI5t1yc3NDVlYW/vjjD00ODVMhJSUFFy5cQN++fQEA2tra6N27tzAqJCUlBa6urqJt8o/ABd5NzB4VFSXKI56ensjLy0NaWtqn+SH/IWZmZujQoQOioqIQGRmJDh06KI3iuHHjBvr27Ytq1arByMhIGPVScDLtgtctANSuXbvQkREJCQnIysqCqamp6JynpaUJ5UJycrLSqCU3NzfR92fPnuHkyZOiO/8zZszAgwcPsGzZMtSsWRPLli2Do6OjaF6wggorw54+fYq///5blGe1tLTQoEEDtfsra/WbpmX/++jXrx/i4+Nx5swZtGvXDpMnT0bTpk2Vwm3evBlxcXHYvn077OzsEBUVBR0dHVEYBwcHpXbR9OnTlfYlk8kwffp0zJ07F//8889H/02qvM+xi4iIQHx8PBISErBnzx6kpqZiwIABGm8vkUgwefJk5OXlYcqUKYXGsX//fjg7O2PVqlUwMTER1js7O+P333/HuXPnMHjwYDx8+BCdOnXCN998o3E6iqKqHZv/CZDk5GQ0a9ZMtE2zZs2EtqomfH19ER8fDwcHB4wePRqHDh0S1mlSfhTlfdtDwP8n2W/ZsiVOnDiBixcvIjs7W+m3FtYuSUhIwPTp00XpVsxBlr+NV7BcHTFiBDZt2oS6deti0qRJOHv2rFJa37c9ZGNjg0qVKgnf3dzckJeXh5SUFKWwXIZo7ttvv1X7d15+H/uYtmzZErdv38bRo0fh7e2NxMREtGjRAiEhIaJwiuOe/1PY6LmCBg0aBEtLSwQEBKgcGawoH86fPw8fHx8MGjQIPXr0ENZraWkhMjIS9+7dw5w5c1CpUiXMnDlTmLfwc1Xwb7L87Yj4+HhUrlwZ9vb2KrfVtMzMH4fiqbzatWsrLctf3nDbumjapZ0AVrYZGBjAzs5O7XpFhZyRkYGMjAyVw9PVycrKgpaWFi5fviwMxVXI/7ihnp5eoZN1Z2VlAXj32Gv+ih14V/Hll78CVexT8ViVnp7eB6f136RGjRqQSCS4fv262jDp6eno2LEjRowYgRkzZsDExASnT5+Gn58f3r59K3Q8FXUO8zMzM0P58uWRnJyscn1ycjIkEomQLz08PLBmzRokJCRAR0cHjo6O8PDwwIkTJ5CZmQl3d3fR9gUbURKJRGiUKPLSypUrlf4QL3jeC+b1b775Bp6enti7dy8OHTqEsLAwhIeHIyAgQGXcBfMf+zRWr16NnJwcWFlZCcuICDKZDIsWLdJoH1lZWRg2bBhGjx6ttO5DX6LAVBs8eLDw6MTixYuV1nfq1Am2trZYuXIlrKyskJeXh1q1aim9DU5VHVVUvZWVlQVLS0vR4/gK7/P4laIDw9raWrTc1NQUPXv2RM+ePTFz5kzUq1cPc+fOVXpUX6GwMqw4ylr9pkndAwDlypXD06dPlZY/efJE6e28xsbGQp2xZcsW2NnZoUmTJmjTpo0onLW1NWrUqIEaNWogJycH3bp1w++//y5qS+jq6hbaLsqvf//+mDt3LkJDQ0WPUX5sij+ykpOTVXYoJCcnw9nZWbTMwsJC+B0ODg54/vw5+vbti9DQUI1/n7a2tujfghRx2NnZITIyEu3bt0dSUpJo6gypVApXV1e4urpi7Nix+OWXXzBgwABMnjwZVatW1SgdhVHVjr1379577UPVNZb/RQL169dHWloa9u/fjyNHjqBXr15o06YNtm3b9sHlh729faHtIUUY4P9T2ty4cQNJSUlo3rw5rl+/LrSHGjZsqPQinsLaJVlZWZg2bRq6d++uFLdcLhf+X7AMbdeuHe7cuYN9+/bh8OHDaN26Nfz9/TF37lyN4v1QXIZormLFikppyd/B/r5tciMjI42PqY6ODlq0aIEWLVogMDAQoaGhmD59OgIDA4UbavmPe3Fpa2urLaPylw9r1qxBnTp1sHr1avj5+YnCVapUCQMGDMCAAQMQEhICe3t7LFu27LOdykZVO0KTv4eLG4fiGi+qvOG2ddF45B0rtlu3bmHcuHFCR4ePj49SxXv+/HnR93PnzqFGjRrQ0tJCvXr1kJubi4cPHwqNO8Wn4LwKhXF2doZMJsPdu3eV9lPwj6TCuLi4CK+PL+hjpfVzYmJiAk9PTyxevFjlfBtPnjzB5cuXkZeXh/DwcDRp0gT29vYf/OZOqVSKXr16YcOGDXjw4IFo3atXr7BkyRJ4enoKjQvFvHcRERFCR52i8+7EiRNK87sUxtzcHFZWVrh9+7bSedbkjwhra2vhDVoTJkzAypUrNf/hKiQkJIjesnnu3DkYGhq+V75m/5eTk4N169YhPDxcdBc3ISEBVlZW2LhxIxwcHHDp0iXRdhcvXhR9r1+/PpKSkpTyiJ2dXbHmtmFF8/Lywtu3b5GdnS3MVafw+PFjpKSkYMqUKWjdujWcnJyQmZn50eKuX78+Hjx4AG1tbaXzrRgB6OTkpLK+yy8mJgZdunQpNC5dXV1Ur1692HMcGRsbw9zcXJRnc3NzceXKFbXblLX6TZO6B3jX4XT58mXRutzcXCQkJKgdMQC865AcM2YMJk6cWGinp7e3N7S1tVXOXagpqVSKsLAwLF26FOnp6cXeT1G++uormJiYIDw8XGnd7t27hZGphVF03Oavcz6mRo0aoUGDBkrzpRWk6GT8VPN8OTk54cyZM6JlZ86cgb29vXBMzMzMRKNsbty4IRp5BrzrtOjduzdWrlyJzZs3Y/v27cjIyNCo/ChMnz59cOTIESQkJIiW5+XlISIiAs7OzsJ8eLVr10aFChUQGhqKunXrwtDQEB4eHjh58uR7t4eAd2VfSkqKyrpOKi38T0gzMzP4+Pjgl19+wfz587FixYr3irugu3fvitqX586dg1QqhYODg1JYLkM+nvdtkzs4OKisb65cuVLoMQXeXfs5OTl4/fr1x/sB70EqleKHH37AlClTCi0HK1SoAEtLy1Kdi7Akubi44N69e0hNTVW5XpMyszi4ba0Z7rxjhXrz5g0ePHgg+vzzzz/Izc1F//794enpiUGDBiEyMhJXr15VajjevXsX48ePR0pKCjZu3IiFCxdizJgxAN7dKezXrx8GDhyIHTt2IC0tDRcuXEBYWBj27t2rcRrLlSuHiRMnYty4cVi7di1u3bqFK1euYOHChWpHLqgSFBSEjRs3IigoCMnJycJLBz5mWj83ixcvRm5uLho1aoTt27fjxo0bSE5OxoIFC+Dm5gY7OztkZ2dj4cKFuH37NtavXy96BXtxzZw5ExYWFmjbti3279+PP/74A6dOnYKnpyeys7NFI28qVKgAFxcXREdHCw3Tli1b4sqVK0hNTVUaeVeUadOmISwsDAsWLEBqaiquXbuGyMhIzJs3r9Dtxo4di4MHDyItLQ1XrlzB8ePH4eTk9N6/Pb+3b9/Cz88PSUlJ2LdvH4KCgjBq1KgiG81MtT179iAzMxN+fn6oVauW6NOjRw+sXr0aw4YNw/Xr1xEYGIjU1FRs2bJFmIRXcZcwMDAQZ8+exahRoxAfH48bN24gJiaGJ9UtQVpaWkhOTkZSUpJS47BChQowNTXFihUrcPPmTRw7dgzjx4//aHG3adMGbm5u6Nq1Kw4dOoT09HScPXsWkydPFjp6x4wZgzVr1iAyMhKpqakICgpCYmKisI+cnBzs379f9LjPnj170L9/f+GRxZSUFMydOxf79u0rspOvMAEBAQgLC0NMTAxSUlIwZswYZGZmqh39XBbrt6LqHgAYP348Vq1ahSVLluDGjRuIj4/H0KFDkZmZWeRjl8OGDUNqaiq2b9+uNoxEIsHo0aMxa9YsUUdNTk6OUrvo77//VrufDh06oHHjxli+fPl7HgXNGRgYYPny5YiJicHQoUNx9epVpKenY/Xq1fD19YW3tzd69eol2ubJkyd48OAB/vrrL5w8eRLTp0+Hvb39B9dbhRk7diyWL1+OP//8E8C7zo2IiAicP38ed+7cwYkTJ+Dv7w97e3s4OjqWWDrymzBhAo4ePYqQkBCkpqZi7dq1WLRokejlH19++SUWLVqEuLg4XLp0CcOHDxeNIJk3bx42btyI69evIzU1FVu3boWFhQXKly+vUflRmHHjxqFRo0bo1KkTtm7dirt37+LixYvo0aMHkpOTsXr1auHalkgkaNmypag95OLigjdv3uDo0aPv3R6aOnUq1q1bh2nTpiExMRHJycnYtGmT2sek828XExODmzdvIjExEXv27PngfCWXy+Hj44OEhATExsZi9OjR6NWrl9obDFyGfDzv0yYfMWIEUlNTMXr0aFy9ehUpKSnC9TFhwgQhnIeHB5YvX47Lly8jPT0d+/btww8//IBWrVqJHm99+fKl0rH6mDfnCurZsye0tLSE37R8+XKMGDEChw4dwq1bt5CYmIjAwEAkJiaiU6dOJZaO0uTu7o6WLVuiR48eOHz4sDCq+MCBAwA0KzOLg9vWGiqNifbY58HHx4cAKH0cHBxo2rRpZGlpSf/8848Qfvv27aSrqyu8mMDd3Z1GjhxJw4cPJyMjI6pQoQL98MMPogl93759S1OnTqUqVaqQjo4OWVpaUrdu3ejq1atEpDzBaf605Z9MNi8vj+bPn08ODg6ko6NDZmZm5OnpSSdPniQi5Um/iYji4uIIAKWlpYl+Q926dUlXV5cqVqxI3bt31zit/1Z//fUX+fv7CxPbVqpUiTp37ixM8D9v3jyytLQkPT098vT0pHXr1ilNsK7qHBbl0aNHFBAQQNbW1qSjo0Pm5ubk6+tLd+7cUQo7ZswY0WTHRER16tQhCwsLUThVadm5cycVLAqjo6OFfFChQgVq2bKlMPGxuomBR40aRdWrVyeZTEZmZmY0YMAA4frQJP+pemFFly5daOrUqWRqakqGhoY0ZMgQpUmvmeY6duxI7du3V7lOMcF1QkICxcTEkJ2dHclkMvLw8KClS5eKJvEmIrpw4QK1bduWDA0NycDAgFxcXISX+bCPo2A5X1D+F1YcPnyYnJycSCaTkYuLC504cUI0Gbm661ZdHPlfBkBE9OzZMwoICCArKyvS0dEha2tr6tevH929e1cIM2PGDKpYsSIZGhqSj48PTZo0Sbimjxw5QpUrVxbFcevWLRoyZAjZ29uTnp4elS9fnlxdXYVJ8IlUv7CiqDIsOzubRo0aJdS7gYGB1LNnT+rTp48QpuCk4mWxfiuq7iF6V1Y3aNCAypUrR+bm5tS+fXtKSEgQ7UfdBOrDhg2jmjVrUm5urtr88eLFC6pQoQLNnj2biN6V06raRTKZrND4zp49SwA+6mTz1tbWtHDhQtGyU6dOkaenJxkZGZGuri7VrFmT5s6dSzk5OaJw+dMukUjI0tKSevfuTbdu3XqvNKiq2/LHUfBlAHl5eeTo6EgjRowgIqIVK1ZQq1atyMzMjHR1dcnGxoZ8fX0pPT39vdKhjrrru2C6t23bRs7OzqSjo0M2NjaiF74QEf3555/01VdfkYGBAdWoUYP27dsnemHFihUrqG7dumRgYEBGRkbUunVrunLlirC9JuVHYV68eEGTJ08mOzs70tHRIRMTE+rRo4fwQp78IiIiCADt379fWNalSxfS1tam58+fqz0GRKrbxQcOHKCmTZuSnp4eGRkZUaNGjWjFihXCelXnOSQkhJycnEhPT49MTEyoS5cudPv2bSJSnd8zMzMJgHBtq3phRZ06dWjJkiVkZWVFcrmcvL29KSMjo9DjxmVI4QrWcwoF26NE79cmV7SPzMzMyNjYmBo3bqyUR2bOnElubm5kYmJCcrmcqlWrRqNHjxb9Xenu7q7yWHl6en6U36mufAgLCyMzMzPKysqiK1euUP/+/alq1aokk8nI1NSUWrZsSbt3736vNJS2gi+sKJi/Cr4A7PHjxzRo0CAyNTUluVxOtWrVoj179gjriyozVR3zgmWFqnzLbeuiSYhKYFZPxvDurkrdunUxf/780k4KY58dX19fPHnyBLt27SrtpPznzZgxA8uWLeMXhbBiGz16NHJycj7o8aniysvLg5OTE3r16qU0GTj7POXm5sLIyAhr166Ft7d3aSeHsRIVHByMXbt2IT4+vrSTwhhjpYpfWMEYY4zls2TJEri6usLU1BRnzpzBTz/9xMP22QepVauW0ttnS8qdO3dw6NAhuLu7482bN1i0aBHS0tLw9ddff5L4Wcm6d+8e1q1bh9zcXDRv3ry0k8MYY4yxT4QnTmKMfXL5XwNe8BMbG1vayWP/cTdu3ECXLl3g7OyMkJAQTJgwAcHBwaWdLPYZGzp0KGrXrv1J4pJKpYiKioKrqyuaNWuGa9eu4ciRIyU6lxn7uKKjo9XWkba2tli7di3Wr19fYi8UqVmzptr4o6OjSyTO/yo+1qwkFFaG1KxZs7ST98Fmzpyp9ve1a9eutJPHWInhx2YZY5/czZs31a6rVKnSR3tNOWOMMfa5ef78udoJ7HV0dGBra1ui8d+5cwfZ2dkq15mbm6NcuXIlGv9/CR9rVhJKuwwpaRkZGcjIyFC5Tk9PD5UqVfrEKWLs0+DOO8YYY4wxxhhjjDHGyih+bJYxxhhjjDHGGGOMsTKKO+8YY4wxxhhjjDHGGCujuPOOMcYYY4wxxhhjjLEyijvvGGOMMcYYY4wxxhgro7jzjjHGGGOMMcYYY4yxMoo77xhjjDHG/sN8fX0hkUiUPjdv3vzgfUdFRaF8+fIfnkjGGGOMsf8w7dJOAGOMMcYYK11eXl6IjIwULTMzMyul1KiWnZ0NHR2d0k4GY4wxxtgnxyPvGGOMMcb+42QyGSwsLEQfLS0txMTEoH79+pDL5ahWrRqmTZuGnJwcYbt58+ahdu3aMDAwgLW1NUaOHImsrCwAwIkTJzBo0CA8ffpUGM0XHBwMAJBIJNi1a5coDeXLl0dUVBQAID09HRKJBJs3b4a7uzvkcjmio6MBAKtWrYKTkxPkcjkcHR2xZMkSYR9v377FqFGjYGlpCblcDltbW4SFhZXcgWOMMcYY+wR45B1jjDHGGFMSGxuLgQMHYsGCBWjRogVu3bqFoUOHAgCCgoIAAFKpFAsWLEDVqlVx+/ZtjBw5EpMmTcKSJUvQtGlTzJ8/H1OnTkVKSgoAwNDQ8L3S8N133yE8PBz16tUTOvCmTp2KRYsWoV69eoiLi8OQIUNgYGAAHx8fLFiwALt378aWLVtgY2ODP/74A3/88cfHPTCMMcYYY58Yd94xxhhjjP3H7dmzR9Sx1q5dO2RmZuK7776Dj48PAKBatWoICQnBpEmThM67sWPHCttUqVIFoaGhGD58OJYsWQJdXV0YGxtDIpHAwsKiWOkaO3YsunfvLnwPCgpCeHi4sKxq1apISkrC8uXL4ePjg7t376JGjRpo3rw5JBIJbG1tixUvY4wxxlhZwp13jDHGGGP/ca1atcLSpUuF7wYGBnBxccGZM2cwY8YMYXlubi5ev36Nly9fQl9fH0eOHEFYWBiuX7+OZ8+eIScnR7T+QzVs2FD4/4sXL3Dr1i34+flhyJAhwvKcnBwYGxsDePfyjbZt28LBwQFeXl7o2LEjvvrqqw9OB2OMMcZYaeLOO8YYY4yx/zgDAwPY2dmJlmVlZWHatGmikW8Kcrkc6enp6NixI0aMGIEZM2bAxMQEp0+fhp+fH96+fVto551EIgERiZZlZ2erTFf+9ADAypUr0bhxY1E4LS0tAED9+vWRlpaG/fv348iRI+jVqxfatGmDbdu2FXEEGGOMMcbKLu68Y4wxxhhjSurXr4+UlBSlTj2Fy5cvIy8vD+Hh4ZBK370DbcuWLaIwurq6yM3NVdrWzMwM9+/fF77fuHEDL1++LDQ95ubmsLKywu3bt9GvXz+14YyMjNC7d2/07t0b3t7e8PLyQkZGBkxMTArdP2OMMcZYWcWdd4wxxhhjTMnUqVPRsWNH2NjYwNvbG1KpFAkJCfj9998RGhoKOzs7ZGdnY+HChejUqRPOnDmDZcuWifZRpUoVZGVl4ejRo6hTpw709fWhr6+PL7/8EosWLYKbmxtyc3MRGBgIHR2dItM0bdo0jB49GsbGxvDy8sKbN29w6dIlZGZmYvz48Zg3bx4sLS1Rr149SKVSbN26FRYWFihfvnwJHSXGGGOMsZInLe0EMMYYY4yxssfT0xN79uzBoUOH4OrqiiZNmiAiIkJ4CUSdOnUwb948zJ49G7Vq1UJ0dDTCwsJE+2jatCmGDx+O3r17w8zMDHPmzAEAhIeHw9raGi1atMDXX3+NiRMnajRH3jfffINVq1YhMjIStWvXhru7O6KiolC1alUAQLly5TBnzhw0bNgQrq6uSE9Px759+4SRgYwxxhhjnyMJFZxwhDHGGGOMMcYYY4wxVibwbUjGGGOMMcYYY4wxxsoo7rxjjDHGGGOMMcYYY6yM4s47xhhjjDHGGGOMMcbKKO68Y4wxxhhjjDHGGGOsjOLOO8YYY4wxxhhjjDHGyijuvGOMMcYYY4wxxhhjrIzizjvGGGOMMcYYY4wxxsoo7rxjjDHGGGOMMcYYY6yM4s47xhhjjDHGGGOMMcbKKO68Y4wxxhhjjDHGGGOsjOLOO8YYY4wxxhhjjDHGyqj/Ad5texF0ckDiAAAAAElFTkSuQmCC","text/plain":["<Figure size 1500x1000 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["data=dict(corr_matrix[\"Risk_Flag\"].sort_values(ascending = True))\n","data.pop(\"Risk_Flag\")\n","features=list(data.keys())\n","values=list(data.values())\n","fig = plt.figure(figsize = (15,10))\n","plt.bar(features, values, color ='maroon',width = 0.4)\n","plt.xlabel(\"Features\")\n","plt.ylabel(\"Correlation with'Risk_Flag'\")\n","plt.title(\"Deciding which features to use for our model\")\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{"cell_id":"e886b33c4f4f448994e58c5b5b8ae3df","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["Since, \"Car_Ownership\", \"Experience\", \"Married/Single\" and \"Age\" showed highly negative correlation with respect to \"Risk_Flag\", we decided to use these features in our regression model."]},{"cell_type":"code","execution_count":24,"metadata":{"cell_id":"0cecd4ceca7644c09059bee00e0807e8","deepnote_cell_type":"code","deepnote_table_loading":false,"deepnote_table_state":{"filters":[],"pageIndex":0,"pageSize":10,"sortBy":[]},"deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1674971634058,"source_hash":"b239dc30","tags":[]},"outputs":[],"source":["imp_features = [\"Car_Ownership\",\"Experience\",\"Married/Single\",\"Age\"]\n","\n","df_for_pca = df[imp_features]\n","label_encoder = LabelEncoder()\n","\n","for col in imp_features:\n","    df[col] = label_encoder.fit_transform(df[col])\n"]},{"cell_type":"markdown","metadata":{"cell_id":"528f55ea84184db8b11163049e615edf","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["We used label encoding to convert the categorical variables in the list \"imp_features\" to numerical values so that they can be used as input for our machine learning model."]},{"cell_type":"markdown","metadata":{"cell_id":"c96b0b13-246a-44a0-b34a-f74d4a1df6aa","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["Then we concatenated the selected features with the original dataframe."]},{"cell_type":"code","execution_count":25,"metadata":{"cell_id":"f343a7784e6343beb5e980918d6189af","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":22,"execution_start":1674971634059,"source_hash":"900ef282","tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Income</th>\n","      <th>Age</th>\n","      <th>Experience</th>\n","      <th>Married/Single</th>\n","      <th>House_Ownership</th>\n","      <th>Car_Ownership</th>\n","      <th>CURRENT_JOB_YRS</th>\n","      <th>CURRENT_HOUSE_YRS</th>\n","      <th>Risk_Flag</th>\n","      <th>Car_Ownership</th>\n","      <th>Experience</th>\n","      <th>Married/Single</th>\n","      <th>Age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1303834</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>23</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>7574516</td>\n","      <td>19</td>\n","      <td>10</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>9</td>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>10</td>\n","      <td>0</td>\n","      <td>40</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3991815</td>\n","      <td>45</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>10</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>66</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>6256451</td>\n","      <td>20</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>12</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>41</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5768871</td>\n","      <td>26</td>\n","      <td>11</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>14</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>11</td>\n","      <td>0</td>\n","      <td>47</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    Income  Age  Experience  Married/Single  House_Ownership  Car_Ownership  \\\n","0  1303834    2           3               0                0              0   \n","1  7574516   19          10               0                0              0   \n","2  3991815   45           4               1                0              0   \n","3  6256451   20           2               0                0              1   \n","4  5768871   26          11               0                0              0   \n","\n","   CURRENT_JOB_YRS  CURRENT_HOUSE_YRS  Risk_Flag  Car_Ownership  Experience  \\\n","0                3                 13          0              0           3   \n","1                9                 13          0              0          10   \n","2                4                 10          0              0           4   \n","3                2                 12          1              1           2   \n","4                3                 14          1              0          11   \n","\n","   Married/Single  Age  \n","0               0   23  \n","1               0   40  \n","2               1   66  \n","3               0   41  \n","4               0   47  "]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["df1 = pd.concat([df,df_for_pca],axis=1)\n","df1.head()"]},{"cell_type":"markdown","metadata":{"cell_id":"7b51c0ddaf7f410bb542ba51e080bc9d","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":0},"deepnote_cell_type":"text-cell-h2","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["## SCORECARD"]},{"cell_type":"markdown","metadata":{"cell_id":"fc68c5be-698b-449e-ae11-307d4741178f","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":0},"deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["In order to provide an equal opportunity for everyone to establish credit, we prepared a scorecard which evaluates the features of the dataframe and produces a score which can be used to assess their credibility."]},{"cell_type":"code","execution_count":26,"metadata":{"cell_id":"e2100463fca545818544c2dfc5ae318a","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":0},"deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1674972392678,"source_hash":"4c4f6ada","tags":[]},"outputs":[],"source":["df[\"Scorecard\"]=0\n","for i in data:\n","    df[\"Scorecard\"]+=abs(data[i])*df[i]"]},{"cell_type":"code","execution_count":27,"metadata":{"cell_id":"2d3cc91a32df45c88b3cb8e0f1cfffcc","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":0},"deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":81,"execution_start":1674972403781,"source_hash":"27f6f602","tags":[]},"outputs":[{"data":{"text/plain":["Scorecard            1.000000\n","Income               1.000000\n","House_Ownership      0.012282\n","CURRENT_JOB_YRS      0.007067\n","Experience           0.006449\n","Car_Ownership        0.004070\n","Age                 -0.000610\n","CURRENT_HOUSE_YRS   -0.002397\n","Married/Single      -0.002681\n","Risk_Flag           -0.003093\n","Name: Scorecard, dtype: float64"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["corr1_matrix = df.corr()\n","corr1_matrix[\"Scorecard\"].sort_values(ascending = False)"]},{"cell_type":"markdown","metadata":{"cell_id":"e0bc4139283942469767eab17d501e2b","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":0},"deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["After checking the correlation of \"Scorecard\" with \"Risk_Flag\", we came to the conclusion that having a high scorecard value means reduced risk for the loan rejection."]},{"cell_type":"markdown","metadata":{"cell_id":"e5a4a3e6-35d9-4b71-8bf0-fc04ddc9dcff","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":0},"deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["So, the people having no prior credit score can assess their credibility via this score."]},{"cell_type":"markdown","metadata":{"cell_id":"487fd28c79ee4282b7ceadd636f98c47","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":0},"deepnote_cell_type":"text-cell-h2","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["## APPLYING THE MODEL"]},{"cell_type":"code","execution_count":28,"metadata":{"cell_id":"a1cf9428380e4da296ca336a3eff9ed5","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":110,"execution_start":1674971634230,"source_hash":"92d4683a","tags":[]},"outputs":[],"source":["sm = SMOTE(random_state = 42)\n","X_resample, Y_resample = sm.fit_resample(X_train, Y_train)"]},{"cell_type":"markdown","metadata":{"cell_id":"36d29b391e4649dfb304fa436509b692","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["As there is a lot of imbalance between the amount of people who are eligible for availing loan and those who are not, therefore we decided to use SMOTE (Synthetic Minority Oversampling Technique) to eradicate this disparity."]},{"cell_type":"markdown","metadata":{"cell_id":"747952b2e15f4cd78b914b95b70cbd92","deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["After removing all the redundancies, we applied our model on test data to test its accuracy."]},{"cell_type":"code","execution_count":29,"metadata":{"cell_id":"eb74ca304ef5413bbacfab7e26b08375","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":630,"execution_start":1674971634386,"source_hash":"12adf02d","tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["The accuracy of the model is: 87.59325396825398 %\n"]}],"source":["model_used = LogisticRegression()\n","model_used.fit(X_resample, Y_resample)\n","Y_pred = model_used.predict(X_test)\n","model_accuracy = model_used.score(X_test, Y_test)\n","print(\"The accuracy of the model is:\",model_accuracy*100,\"%\")"]},{"cell_type":"markdown","metadata":{"cell_id":"a529898955944303bae23cd8021b3add","deepnote_app_coordinates":{"h":5,"w":12,"x":0,"y":0},"deepnote_cell_type":"text-cell-h2","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["## CONCLUSION"]},{"cell_type":"markdown","metadata":{"cell_id":"a6b7dbe54c7a4bbf8ccc0fec7ca4c236","deepnote_app_coordinates":{"h":2,"w":8,"x":0,"y":0},"deepnote_cell_type":"text-cell-p","formattedRanges":[],"is_collapsed":false,"tags":[]},"source":["The accuracy of our AI model is 87.6% .Thus our model allows us to efficiently predict the risk factor of the client's loan application."]},{"cell_type":"markdown","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown","tags":[]},"source":["<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=9ccfb84f-6e4f-4924-b293-4fcad2ff41b5' target=\"_blank\">\n","<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\n","Created in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>"]}],"metadata":{"deepnote":{},"deepnote_app_layout":"article","deepnote_execution_queue":[],"deepnote_notebook_id":"88ece83cb64940298638812062b6ffcf","kernelspec":{"display_name":"venv","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.3"},"orig_nbformat":2},"nbformat":4,"nbformat_minor":0}