{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[],"mount_file_id":"1JuvSx0912_tCxmgSlDua9vUy0XTn_f5V","authorship_tag":"ABX9TyOrI1dCpnPJ37LPdcCHNEiF"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"zv4MzMkCTplV","executionInfo":{"status":"ok","timestamp":1663869011988,"user_tz":-330,"elapsed":4755,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"6b5d417a-1b2e-4e6d-fc2b-f387597834c2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["#Mounting the drive to fetch the files\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","import pandas as pd\n","import numpy as np\n"]},{"cell_type":"code","source":["test = pd.read_csv(\"/content/drive/MyDrive/credit_test.csv\")\n","train = pd.read_csv(\"/content/drive/MyDrive/credit_train.csv\")"],"metadata":{"id":"7Q_STRL_UOsi","executionInfo":{"status":"ok","timestamp":1663869011994,"user_tz":-330,"elapsed":34,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["train.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CJm4TahQaJOT","executionInfo":{"status":"ok","timestamp":1663869011996,"user_tz":-330,"elapsed":34,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"7f55c2c0-843a-4b7e-98d9-087577afd8f7"},"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(100514, 19)"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Sn0q71fAaRJI","executionInfo":{"status":"ok","timestamp":1663869012887,"user_tz":-330,"elapsed":917,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"43cece49-596f-4a7e-bfd7-0e6b2affa1f7"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(10353, 18)"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["#Removing the Target Variable from Training DataFrame and Reassiging the Number of Rows in Training DataFrame\n","train=train.iloc[:100000]\n","print(train.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iqFm6O_e8pLx","executionInfo":{"status":"ok","timestamp":1663869012890,"user_tz":-330,"elapsed":90,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"0993aa5a-65df-4b21-b83d-69a3c53a35e3"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["(100000, 19)\n"]}]},{"cell_type":"code","source":["#Reassigning the Number of Rows in Testing DataFrame\n","test=test.iloc[:10000]\n","test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Kr_iIkZl8t4O","executionInfo":{"status":"ok","timestamp":1663869012896,"user_tz":-330,"elapsed":84,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"4ff7fc9b-d8cd-4b96-f1cf-958ad1e42617"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(10000, 18)"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["# concating both train and test datasets\n","df = pd.concat([train,test],axis=0)"],"metadata":{"id":"Ir8zwUxo7b6X","executionInfo":{"status":"ok","timestamp":1663869012899,"user_tz":-330,"elapsed":76,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BqzZEKbt7iBv","executionInfo":{"status":"ok","timestamp":1663869012902,"user_tz":-330,"elapsed":75,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"a435c4a5-b10e-4e58-81ee-63bca1629898"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(110000, 19)"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["# Dispalying the head of the data frame\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":582},"id":"ITTg7Paw7nx0","executionInfo":{"status":"ok","timestamp":1663869012905,"user_tz":-330,"elapsed":69,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"5d50eedb-b94e-4174-f18d-b207a2f50f90"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                Loan ID                           Customer ID  \\\n","0  14dd8831-6af5-400b-83ec-68e61888a048  981165ec-3274-42f5-a3b4-d104041a9ca9   \n","1  4771cc26-131a-45db-b5aa-537ea4ba5342  2de017a3-2e01-49cb-a581-08169e83be29   \n","2  4eed4e6a-aa2f-4c91-8651-ce984ee8fb26  5efb2b2b-bf11-4dfd-a572-3761a2694725   \n","3  77598f7b-32e7-4e3b-a6e5-06ba0d98fe8a  e777faab-98ae-45af-9a86-7ce5b33b1011   \n","4  d4062e70-befa-4995-8643-a0de73938182  81536ad9-5ccf-4eb8-befb-47a4d608658e   \n","\n","  Loan Status  Current Loan Amount        Term  Credit Score  Annual Income  \\\n","0  Fully Paid             445412.0  Short Term         709.0      1167493.0   \n","1  Fully Paid             262328.0  Short Term           NaN            NaN   \n","2  Fully Paid           99999999.0  Short Term         741.0      2231892.0   \n","3  Fully Paid             347666.0   Long Term         721.0       806949.0   \n","4  Fully Paid             176220.0  Short Term           NaN            NaN   \n","\n","  Years in current job Home Ownership             Purpose  Monthly Debt  \\\n","0              8 years  Home Mortgage   Home Improvements       5214.74   \n","1            10+ years  Home Mortgage  Debt Consolidation      33295.98   \n","2              8 years       Own Home  Debt Consolidation      29200.53   \n","3              3 years       Own Home  Debt Consolidation       8741.90   \n","4              5 years           Rent  Debt Consolidation      20639.70   \n","\n","   Years of Credit History  Months since last delinquent  \\\n","0                     17.2                           NaN   \n","1                     21.1                           8.0   \n","2                     14.9                          29.0   \n","3                     12.0                           NaN   \n","4                      6.1                           NaN   \n","\n","   Number of Open Accounts  Number of Credit Problems  Current Credit Balance  \\\n","0                      6.0                        1.0                228190.0   \n","1                     35.0                        0.0                229976.0   \n","2                     18.0                        1.0                297996.0   \n","3                      9.0                        0.0                256329.0   \n","4                     15.0                        0.0                253460.0   \n","\n","   Maximum Open Credit  Bankruptcies  Tax Liens  \n","0             416746.0           1.0        0.0  \n","1             850784.0           0.0        0.0  \n","2             750090.0           0.0        0.0  \n","3             386958.0           0.0        0.0  \n","4             427174.0           0.0        0.0  "],"text/html":["\n","  <div id=\"df-e7f714ff-841d-473c-806e-3f4385437734\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Loan ID</th>\n","      <th>Customer ID</th>\n","      <th>Loan Status</th>\n","      <th>Current Loan Amount</th>\n","      <th>Term</th>\n","      <th>Credit Score</th>\n","      <th>Annual Income</th>\n","      <th>Years in current job</th>\n","      <th>Home Ownership</th>\n","      <th>Purpose</th>\n","      <th>Monthly Debt</th>\n","      <th>Years of Credit History</th>\n","      <th>Months since last delinquent</th>\n","      <th>Number of Open Accounts</th>\n","      <th>Number of Credit Problems</th>\n","      <th>Current Credit Balance</th>\n","      <th>Maximum Open Credit</th>\n","      <th>Bankruptcies</th>\n","      <th>Tax Liens</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>14dd8831-6af5-400b-83ec-68e61888a048</td>\n","      <td>981165ec-3274-42f5-a3b4-d104041a9ca9</td>\n","      <td>Fully Paid</td>\n","      <td>445412.0</td>\n","      <td>Short Term</td>\n","      <td>709.0</td>\n","      <td>1167493.0</td>\n","      <td>8 years</td>\n","      <td>Home Mortgage</td>\n","      <td>Home Improvements</td>\n","      <td>5214.74</td>\n","      <td>17.2</td>\n","      <td>NaN</td>\n","      <td>6.0</td>\n","      <td>1.0</td>\n","      <td>228190.0</td>\n","      <td>416746.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4771cc26-131a-45db-b5aa-537ea4ba5342</td>\n","      <td>2de017a3-2e01-49cb-a581-08169e83be29</td>\n","      <td>Fully Paid</td>\n","      <td>262328.0</td>\n","      <td>Short Term</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>10+ years</td>\n","      <td>Home Mortgage</td>\n","      <td>Debt Consolidation</td>\n","      <td>33295.98</td>\n","      <td>21.1</td>\n","      <td>8.0</td>\n","      <td>35.0</td>\n","      <td>0.0</td>\n","      <td>229976.0</td>\n","      <td>850784.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4eed4e6a-aa2f-4c91-8651-ce984ee8fb26</td>\n","      <td>5efb2b2b-bf11-4dfd-a572-3761a2694725</td>\n","      <td>Fully Paid</td>\n","      <td>99999999.0</td>\n","      <td>Short Term</td>\n","      <td>741.0</td>\n","      <td>2231892.0</td>\n","      <td>8 years</td>\n","      <td>Own Home</td>\n","      <td>Debt Consolidation</td>\n","      <td>29200.53</td>\n","      <td>14.9</td>\n","      <td>29.0</td>\n","      <td>18.0</td>\n","      <td>1.0</td>\n","      <td>297996.0</td>\n","      <td>750090.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>77598f7b-32e7-4e3b-a6e5-06ba0d98fe8a</td>\n","      <td>e777faab-98ae-45af-9a86-7ce5b33b1011</td>\n","      <td>Fully Paid</td>\n","      <td>347666.0</td>\n","      <td>Long Term</td>\n","      <td>721.0</td>\n","      <td>806949.0</td>\n","      <td>3 years</td>\n","      <td>Own Home</td>\n","      <td>Debt Consolidation</td>\n","      <td>8741.90</td>\n","      <td>12.0</td>\n","      <td>NaN</td>\n","      <td>9.0</td>\n","      <td>0.0</td>\n","      <td>256329.0</td>\n","      <td>386958.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>d4062e70-befa-4995-8643-a0de73938182</td>\n","      <td>81536ad9-5ccf-4eb8-befb-47a4d608658e</td>\n","      <td>Fully Paid</td>\n","      <td>176220.0</td>\n","      <td>Short Term</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>5 years</td>\n","      <td>Rent</td>\n","      <td>Debt Consolidation</td>\n","      <td>20639.70</td>\n","      <td>6.1</td>\n","      <td>NaN</td>\n","      <td>15.0</td>\n","      <td>0.0</td>\n","      <td>253460.0</td>\n","      <td>427174.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7f714ff-841d-473c-806e-3f4385437734')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 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Reddy","userId":"07552141107752951949"}},"outputId":"8380fe68-8b9a-4385-e7a8-cc058390bea2"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                   Loan ID  \\\n","9995  c4ab66f9-833c-43b8-879c-4f8bcb64dd14   \n","9996  bbd3a392-01b4-4e0e-9c28-b2a4a39beac7   \n","9997  da9870de-4280-46a3-8fc6-91cfe5bfde9d   \n","9998  0cc8e0e0-1bc6-49d7-ad0f-0598b647458f   \n","9999  14f94b64-26c4-48fd-b916-1388d7adcc1d   \n","\n","                               Customer ID Loan Status  Current Loan Amount  \\\n","9995  8ee2002b-8fb6-4af0-ab74-25a1c23e7647         NaN             157806.0   \n","9996  6c306306-f5c2-4db5-b74a-af2895123ecb         NaN             132550.0   \n","9997  cc94e25e-1060-4465-b603-194e122f0239         NaN             223212.0   \n","9998  f90cf410-a34b-49e7-8af9-2b405e17b827         NaN           99999999.0   \n","9999  f1838fa9-7ad9-44d5-97a6-7a6d3f3529d7         NaN           99999999.0   \n","\n","            Term  Credit Score  Annual Income Years in current job  \\\n","9995  Short Term         731.0      1514376.0              6 years   \n","9996  Short Term         718.0       763192.0              4 years   \n","9997   Long Term           NaN            NaN                  NaN   \n","9998  Short Term         721.0       972097.0            10+ years   \n","9999  Short Term         748.0      1079960.0              6 years   \n","\n","     Home Ownership             Purpose  Monthly Debt  \\\n","9995           Rent  Debt Consolidation       4795.41   \n","9996  Home Mortgage  Debt Consolidation      12401.87   \n","9997           Rent  Debt Consolidation       4354.42   \n","9998  Home Mortgage  Debt Consolidation      12232.20   \n","9999  Home Mortgage  Debt Consolidation      12239.61   \n","\n","      Years of Credit History  Months since last delinquent  \\\n","9995                     12.5                           NaN   \n","9996                      9.9                          20.0   \n","9997                     27.2                           NaN   \n","9998                     16.8                          24.0   \n","9999                     19.7                           NaN   \n","\n","      Number of Open Accounts  Number of Credit Problems  \\\n","9995                      9.0                        0.0   \n","9996                      8.0                        0.0   \n","9997                      8.0                        1.0   \n","9998                      8.0                        1.0   \n","9999                     14.0                        0.0   \n","\n","      Current Credit Balance  Maximum Open Credit  Bankruptcies  Tax Liens  \n","9995                 87058.0             234410.0           0.0        0.0  \n","9996                 74309.0             329692.0           0.0        0.0  \n","9997                 99636.0             568370.0           1.0        0.0  \n","9998                184984.0             240658.0           0.0        0.0  \n","9999                179018.0             607882.0           0.0        0.0  "],"text/html":["\n","  <div id=\"df-b79ff858-f4a6-4b59-ac62-956789e14024\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Loan ID</th>\n","      <th>Customer ID</th>\n","      <th>Loan Status</th>\n","      <th>Current Loan Amount</th>\n","      <th>Term</th>\n","      <th>Credit Score</th>\n","      <th>Annual Income</th>\n","      <th>Years in current job</th>\n","      <th>Home Ownership</th>\n","      <th>Purpose</th>\n","      <th>Monthly Debt</th>\n","      <th>Years of 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lMsFwMXAqcHlEDK89NiFwALAhsB1wdkR8WB6rXsdRwdxmVP8ctAlM/wXYCXgf2CUibi7vmdWA+cjGwfcC/4qI/zVm9O2rzWtxETAPWbaoO3m8hwOntcrxjo6kVYB9yRURN5GrJu4H1oiIIY0cm9nYxsF5MzMzMzMzs+9BUi/g38BOEXF3o8fT2WoTFN2Aucis922AV0ug+WhgSzIIeUxpANqFzJjuDnxY9usGjKgawTbzJEdpcPogOUnxN+C6NgH6WYGrgfHJLPrDI+LT8lizH3u3arWApD5Ar4h4v/b4RsAOZIB+54i4pTEj7VySDgFWB9YFnisrSS4CViAnJy5v5r/7N5E0PnAWsCgwFHgMWD0i3m3kuMzGRq45b2ZmZmZmZvb9zAnMC8wGX68t3YrqdaNLYL43cBEwkAzGvkKWbyEitgaOIrPEt5T081LqZEhEfFALxn9RBSebMUhZ1VgvkxVvkRnSnwLHAku2qbX9TO2/AWRZH6Bpj717CcBSC8wfDdwGvCDp7FLOhYg4GTiErEF/YJsmsS3ZKFXSRMBCwInAnSUwPy3wO+BCcvKm6f7u30TSQrXGzh8BmwD9gPHI5r9Dy34t9/c2+zEcnDczMzMzMzP7HiLiv2Tm/LYla7hpy5F8F6WZ621VQ9OiPzk5sRCZDU8pT9Oj/LwtGaD/O7C7pEnqv7OZA5PVZExtYmFE+f+rwDLAEDJA/8daIHLWsv3PwCLVBEVnj709lLI0dwK7VAF6SecAq5ClS04i3xf7lf4DVYD+IPJ9c0q9QXAzvxe+QT/g18D7ETFM0q/J1+YmYJOI+EzS1pJmaugo20l5HxxArhSoTANcAfwX2ANYs5Svatr3vllHcENYMzNrefWltmZmZmbfR1UTvfbv7qVcyUnAGcCfgLOavTTJ6NQCaD8FboiIj6vHIuItSQsBFwCLS9ohIg4pgcgeETEsIrYtQbvfAC1RzqK6rpTUlww4zkAGnM8EbouIFyT9EbgSOAU4Q9JLwFrAR8CzzV7GJyI+l/QxWb7nY0k3ke+RtYD/RsRwSacDewLrSnopIk6IiFPL5M2GwFuNGn97a1Njv/p5CFniaIrSDPUO4Hpgo4j4tATlVwcGkz0YmlpEfCRpo4h4ppQ2miEi7pK0KtCTbBh9BHlauaDqL6FsitwnIt5u3OjNGss1583MrCWVjKZ5gIci4rOybT+yMZkv/szMzOw7KwHFfm3qaE9AZg8/EhFrNGxwHagWiO5Rgu69yTIdO0fEa2WfnwGXAJMBR0TEUWV7j4gYVn5uiea5teat45FNTIcCH5OJj/MClwODIuLuEqC8EJibbAb6ELBCCVx/ZcKnmbQJRP8TWAo4mzz+30fEB7WeBDMA55EB6KUioiprMn4pe9L02rwemwKfA9dGxBuS9gF2J//+ZwJbRsRQSROTqwh+CyxfSkI1JUm/APpGxKPl393Iicv1yBUit5ft4wHnkDXotyE/KwLOBy6JiP/r/NGbjR1c1sbMzFrV9MChwAkAki4DtgcmaOSgzMzMrDmUIFM14X87cLOkgyRNKKlvRHxIXmssK2mJRo61I0iaHThA0mQlMN8FmI9cKXCxpJ8ClCD9KsDbZJmfbcr2YdVrWALzXZo5MA9ZfqUc02lkFvyqwGIRMT+ZRf4HYGdJ00XEpxGxLLAgWWd82RKY79asgXkY1W+gKuuzCnAtsDEwNdC72q0ErZ8mPyOLANNUKzFaKDDfpRaYP49sejt1bZd/AlcBXwAvAuNJWho4GlgZWLdZA/NKEwFPAQdJmhlG9R84n+w9cL2khcv2j8mSTjeTE3xnAzcC05HBfLNxloPzZmbWqt4kl1mvKekFMjNl/oh4prHDMjMzs7FdCbp9UYJPWwE7AY8Ca5MlKI4tNbMfAF4BFqie16Ahd4S/kvXid5A0SQko300G4qcELq8F6F8lg41vAltL2q1sH1VWsJkD0m1MBMwMXB0Rz5USR0TE0cBuwApkFjll+3MR8WxtgqLpSy2WAH3P8vPKwFnA+MBO1Xul1oehJ5k5P6r5b6uo3tOS/kGeAzYEDo+IN8rjjwD7k1nzuwOPA8cAPyezyh9txLjbQ6T3gG3J1RN7SZqtPHYtsBdZY78eoB8CrAScDPQiV5NMX01aNeAwzMYKLmtjZmYtS1Iv4EGyFuhVwMrVDZSZmZnZ6NRKl/QE7iKzgecAAuhLZscuWP47kwxKfwjMGRHvNGbUHUPSSWQw7WyyXMtbpbTNADLI+CZZluONsv/PyIzZB4FVWyEY27YUj6RpyJI2J0fELmVbvbTJPcAHETGgmcvXtFU/xjE8/k/yvXIgWUbyTUmTk5nzcwILRURL9B2ok/RL4DJyNcXR1b1G/X1TziXTAL8CngferJfIakZlFUTXMom5Adlf4TyytNV9ZZ9Fgf3IUqN/iIhba88fVdpI7g9m47hWmtU3MzMDvtK8bAqy8dJRwJLAyVWWj5mZmVlbVemVkgG/OFmqZRVgWEQMjYj3ImJnMlN0DaAf8DqZSb52+R0a/W9vHrWyJRuTtaHXBnYpJW4+I0uZbEU2Ab28BGGrEjfzAquX17GpX4sSkA5J3UqdcMhGpq8AAyRNBplJXvbvRZYw+aBsb7nAvKQtJf1D0lWS1pP0axhV4uZSYGfgHkmXkgHbRYE1WjEwX/wUmAl4uGSAj+493y8ino6IKyLiiWYPzBdVYL4PcF35bxXgb5LmAIiIm8kVA/cA1ykbSFcTFx/VfnZg3sZpDs6bmVnLqN1IRvn/CxGxFbmcdEdgTeCkcuNUPaenpP6NGK+ZmZmNXUrpkZ5kLeTNyVIcj5cyHqrVzP4kIi4CNiLrjN9N1h8fdR3SzNrUFf8LcAVjDtBPBlxasuaJiHeq5zfza1GyeUdI6gecDhwpaeZy7DuS/Y2OqEr7FNMC/YGnO33AHaQET6vA/MVkiad5ySSY04CjJQ2AUQH6s8jJqknJFRdzR8TDjRh7JxoOTFeb3Ktnza8E/LmVEoRqZb/GI899Z5JNb+8n68rvUnpW1AP0dwO3Spqtfl5o5nOEWXtxWRszM2sJbTJ6liMz2V4C7io32pOSF4sHkksutwa6kln1kwLLOWvDzMzMJPUgg9FLAM8BC0fEW2PYt2sJ4C4JXAkMiIibOm+07eubSpdIOgNYhq+XuFkSuBg4MyI27LzRdpxaaaN+ZAmbN8kA/UUR8WnZvj4wiHyPXAsMA5Yrv2LuVruulLQXsBm5YuShiBgsaR2yvNGDwN8j4oGy7zXAb4BZIhsnN71v+Ww8SJa9WiFKg9cykTcJ8H/Ae8A2ETG0s8bb0UqN+H+Rx7ge8AxZR35D4EjgIuDAiHiw7D8AWBHYqtU+G2Y/lhsumJlZS6gF5s8Dfk9eKL4FXCNp04h4W9I5ZfcDyNqH7wKzA0v4ItHMzGzcVQvGdo2IYZJWJoOO6wPrSjqxKsNQVwvWfQYMAZq2jEtV97msMPwd2bTyEeCNiHg5ItYrAfqqfE8VoP8PsBhwR8MG386qUjZkQP5d4C/Ai1XJo4gYIul0sjnwQWRw8h3gYWCD8jp+Y432sV29Xn55T8wP3FTVDS+fmbMkDSMTX5YiGyQTEX+UNFUrBuZLHfW+wKMR8XLZZScye/wCSfuRZVx+DWwMLAL8rpUC88UkwIzA2RHxJICkzyLi6DIxcQTwuaSjIuL+yCax15b9XGPerMbBeTMza2r1iztJOwJzAZuStUA3JzO8Lpa0akS8I+ks4MXy2MfA/BHxREMGb2ZmZg01mgBqV2BERHwiaXNgfGBvYIikc0cXoC/lKpYm76+f64Rht7vyOlRlKm4mA28/ITNhr5Z0ekRcUgL0pwJrASMlHRoRr5NNYL+1aWiTmQCYFTgVeLlWNnFkFaAHbpS0MDAxOUHzfhXYb+bgY5tgdO+I+KyU7/nsq7tJEXGBpBWB9SQdSZaCGlZlkLeC2mtxDnlv0R/4WNJuZBmf64FNgEPIFTSfk0lCI8gkoCcbMe4OFuRkZJ/6RmW/jlPJFcsrA5NI2joinh31xCb+bJh1BAfnzcysqdUC8/OSGUtHA1eUG6ftyWXIG/NlgP494DJJlwM9S91QMzMzG8fUMsX7AntKmhHoK+lc4NqIeFXSmsAFwKHlOedExOA2v6oHeW+9YC2TtqmU0jy9yCDjh2Qt+ceBBcis6GklPRoRz0bEXySdAvyNLCF4VP33dP7oO8zkwC+BJ8vrMyqTvFxnjg9MFRGPAa9WTyoB66YOPtaC0TcDH0j6E/A8ML+k6cv7oN7sdhgwNCI+7fzRdpw2kxR/A+YGNiCPd0WyfMskwJERcbmka8mJq/7AC8D9kU2Sm1q9hn7NW+Rk5HKSji6ra6qVFp+TTZHvBCYk3ztmNgZuCGtmZk1P0mbkxd+xwEflhql7yW47CDgJmA84X1J3yJsqB+bNzMzGTVUAtWSK3w8MIANqPcnrhuMkzRsRw4E/AdeQ1xSblmD+KBHxMbBzRDzaqQfRTkoJCoCFyNdgIHBvbZVAf+DEiHi2ampZasvvTl57NT2V5rcl67fyPvA6sJqkiaqM+bKfyKzg7SX9pP67mrnBZfU6lJ/XIlcPnFxKsuxBxpCOUWn+W/abhFw58JSknrX3U9OrBebnJSdqjo+ISyPiKnKl7hHk6/I3SZNHxOcRcWpEHBkRl7dIYL5bVc5JUjdJ/WrB+u3I4PtJkqauPW0GoDdZ4mm++mfHzL7OHw4zM2s6o7m4uw84jVyKPgdARAwvF5NDyJvp44EVyn5mZmY2DqvVFD+FzPBcIyIWJfvWrEM29txF0hQR8XnZdh9ZV/tr2cHNlDEuaXZJA6pAbC2Y/EtgWuCJch21FnAVsFtEHCOpP7BGFZiNiANKRnlTr8gv14sjyqTLsZK2BiiB1auBVflqgL4bMD3Z+PIL8v3TEmrB6OXIa+q7gOvKwy8AVQnJf0vaXdK2wAnAgsA+JTjdtJMToyNpEJkEtDjZ+LbKqB8ZETsAh5EB+o0kTd64kba/NuWuzgX+SzZ+/YekxcqE5DbAbMD1ko6WdCBwDlnu5o3ye9RmpYWZ1TT1l6iZmY2baksmZ4yIJyPiPklHk9lu20h6LiKOKxeT3SKbdh1OLkG9uJFjN7POUy9BYGY2Gv3JoNKFEfF02TYyIs4p2b9nApcBp5ea20uR9bRjDGUexnqSegCDgCWAZSVdVztPfkLWyu5VypicRQbmB5XXYzVy0uIrjV+buYRL+Z6ogo93kc1fB0vqExGfRsQmkqYky5f8TtIlZKPPFcikkM2a/P3Qlcx87g28ViYfVgAuJd8Lh5fVI0TEUEkXkb2bDgG2JWvQPwMsHK3bw+kAcmXN7MBKku4sr0WXKkAvaQSwDzBM0iGtcu1Rm7S6D3gPuIG83/oDOVG3dkScJ+lhsvTX0sBI4FFgzfL8VupDYdYh1ITfH2ZmZkg6gcxg2SQibizbfgPsAqwJbBURx5Xt3UsGWFPeOJnZd1OrH60SLPENoZmNUp0TamU3piYzYY+JiD1r55CuZOD1VrL++nJk0L7KKm7qiT9JM5DlaGYH1gOuK8f9E+BpspTLTMCetcD89GS5n+eBDVvpeqpMWFwNdAfWB16qMuTjy95G/yBXTUwNPAU8AqxdSwRpugkKSf2A48gJqknJY1o/It6UtB0ZbH2CXFXyeJvndiHfE0OAj+PrfRiaUtt7hdo9RB8ya3xastfCOWX7qHOBpH2B86KFmr+Wz/4RwMLk5Nz/yvXVIcDfycmpE2v7Tlye+m61OqkZPxtmnc3BeTMza0qSFgauJW+q94yIG8r2eoB+i4j4R+NGaWadRVKvksnWD9ifvIEeTDaIvqCxozOzRpE0ETAz8HhEvFcypPcADo6Id5UNLycF5oqIT2sB/C5kcP71iFi9YQfQzmqTl9ORQbffAhsBN5dz6DpkpvCnwErAO2R5k33J4PW8JSDd1BMUdZJ+C1wObBcRF5ZtahtclDQZ8BOyEeYHzRx8LCWK7iEnn24DpiQnoV4DFiiflb+TGfL/AA6K0uy4WY/52+irzV+7AaKslCnb+pKv2cTATowmQN8K2kw4dAH+AzwfEZuUbWuQTaJ3iohDyjl18oh4Zky/x8y+mWvOm5nZWE9tasyXm4LbyLqwvwX2l7Q4QKl9OIisdcuYbqMAACAASURBVHicpI06e7xm1jkkzSHp5nJOGCppAvLGeSmgCsidJ+l4Sb9o6GDNrFFmBE4ny979HHiczAKtHARMAVwiqW9ttc2MZLmPVsqCrRo79iYDsS+Tx34seU0F8C9gN/Icei2ZKX8Q8DEwX7WyoMWCbtOTr8O9MCpIG5Ale0rwkYh4q5RTfL+8jl2aMUhdjucBMhC/ekTsEBF/Ipt7Tg8cViYnDiMTXjYHdlRp+NmMx/xt2gTmdwcuBK4HjpDUCyAiPgHmJUsfHQT8SVKPVvosVJ9tSeNJmgLoQTYF7lEerwLzu5bAfE+yvNFSkrrXf1crvS5mHc3BeTMzG6uVm4Mqe2OSsrlaanw3sCi5HHe/NgH6w8kmb3d8/beaWbMry6fnBBYBbi5lKFYmb5qXj4gFgSWBLcimfds3aqxm1lCPAieTAecHyGDzShHxbnn8dmBvYD7gPkn7KRtAngUMBwZ2+og7QLme+qKsLnoIWJHsxXMGmRF/uqSlI2JwRJwJzADsAGwNbAYMKFnC3Zq9XFjbpA/yPQFZV7yqs9217NsN2FPSFm1/TzMGH0sA9SrgZ8A6EfFyrczTuWT9+AlrkxMHkQH6vwLbSpq2AcPuUOWzUQXmLwA2JuvqP0SuKrmhTGgREUPIAP2bwKlks+CWUL0OZYXA/8iyNb3ISbzZJe1ICcwDB5enzUwmRChKbwIz+/4cnDczs7FabSnpscBJkn5RbobqAfolyCXXe0haojzvQbKsTas2pzIbp5Vzw7nApsAswM3kTeItURo7RsQ7pbTV34DNJLXMTbSZfTcR8RE5WS+yJMmjZBPL6vGPy+OrA2+Q55RlyQz7uWo16JtayfQW2dh0GLBBRGwXERuQE5tPA6dJ+mMpE/ZeRJwfEadHxN0lm7ZZM8XnlzRI0mzl2EbWHhPwNtkMdnNJS0IG6Msu0wILAK2y+mpC8lhFfjcCdCl/2yHkZ+N9SV2roH0J0O9ETtRsXiYsWkbtXuMQckXumhGxHbmyoCswF3B3mwD9QuRKi/saMuh2VltV04VMaLgPOCUiPiQnNqcADgQOi4gDy/lgJnLVzefl/2b2A7nmvJmZNQVJ/0fWkb8U2D8iXigXkF1LJteB5HLcR4C/R8QtDRyumXWwWj3gPsC6ZCbXlMCBEbHraOoEX0/eSG8EzZnxaGbfXZsyFdMDm5AZ4luRJSkOiYgPRvO8KckSLoObuab4mEi6gVwRsEw9A17SXOQ11lBgS+DGVsiELeXO3ga6kc1N/0eulHiuTNxU+60CHEPW2D+B/M6Yiwxgd6GU9OnUwXeQ8p24BbA7cERE/L1s3xvYGfhNRDxbfc/WnrcN8J9mT3wpQfbeEfF+bduvyFUyl0bE+ZJ2AvYDNgD6k8Hnu4HfR8TQ8pyvvD7NrmTMbwYsSJbz2h1GTewtTzaEfhO4AehDriAYSX42htfPuWb2/bTUjKeZmbWG+sVu1UwoIv4qaTB5c91F0sCIeIG8KAQI4BYyc/blhgzczDpNlQUa2cDxLDK7bXtgFUmHRzZ67BYRX0TEW5I+A/o7KG/W+qrPfgnCDQBuIjN/+5ANMPco+40K0NdK571WuwZRCwVkRWZL9yCT9EaUbV0iYkRE3KdsjrsWcDWZGXxnwwbcTiLiQ0knk8c1kuwlcA9wjaQzyWDs8Ij4Z3k9tuHLLODXyZUWy8eXtfabPvhYvhOPK//cXdJH5HX0rmQN+mdHd6wRcVRnj7W9lVUw/wKmkzR/RLxdHnqdbIx7raQBZB31jSLi7PKcdclg9DOSZoiIz5o5MF8maKYCpo6If5bNa5MNgIcC19WPLyIul/QCOYExPznhdTkwsHw2WmoS06yzOThvZmZjldHcDPQDBgNExM7lAnmjsu/AkkE/CfBzYF/gjla4cTKz0aufI2o1cT8pAfqRwP7A1ZKWiYh3ynNmASYhS9+YWQsr54iqiee/yWaGAyPifGCIpMPJIPXuwBeSTiQDk1cAV0fE7tXvaubg2+iUUhQnk+VrNo2IE4D6NdNHwNHAp5TmqC3iLmAN4EqyZvbaZLB1aeB6SVcDx0TExZJuBKYBpiaz7B8pr1tLBR9rAXqRNeV7kuVcLiuPt+q1dBfgNLJEy+WSVohs9DtE0vHl3LEY8ALZELnqQfA6cB3wU7LEy/Nj+P1jPUnzkKuHpgUel/RyRNwbESeUCc3Dgb9Kuj0iHivP6RoRj5UVJl3rq2qqc24jjsWsVbisjZmZjTXaLEHfl8xQmQX4D5nBcW557CBgfTLL5Rbgl2XfBSLi2QYM3cw6QZts2D+SNYBfBR6IiGeUjQ7XBg4gG8NeTpZvWIzMFp27PL+llqKb2VeV8gz3kjWj9yHPEZ/WHu9NZgnvBjxGNj0cAczaCqVcvomkqYDDyMD09hFxvLJJ6LRk0PLsyF4dtFJAWtJNwGTA7BExTNK8wPTAocCkZI+Bk4FrIuKZNs/t0qqrriRNDvwF2BM4KiJ2bPCQOpyknuT7/1jgFWCFMlnRhZysuIzMKJ+17D8J+dk4k5zAG9KYkf94khYgJx3OA66KiH+NpnzRDmTw/jSyVOCzZXvLfg7MGs3BeTMzG+tIupgMtt9O1n1djrxY/r+I2Lfs81dgeTIw/yKwbUQ82pABm1mHq24KSzbsbcDE5b9u5Oqav0TEZSVAvw5Z4mZaMlD/LHCOl16btR5lg8+htX9XTU/nJpu8vl7OHTOQwdmPgcdLgHYdYBXyOmL7Zj9HlGMflcU6pmBaCUxvB6xGXmsNJ0tcfEppgtt5o+5YVeKHpNWAC4C9a9eS3chGuG+SE7hzlqetEhGXNmTA7ey7TEZL+inwV3I1yWHAjq0+gT2mAH15bHngYrJR9BPkPcni5AT/q40Z8Y9XzoHXkokLe0Y2e61/RkadLyTtRpawOQ0YFBHPNWrcZuMCB+fNzGysImkTsgHTKsA9EfG5sknZbsDCwG5lGTYlSNcTGBYRgxs1ZjNrf6MLKJSb6RvJeqh7knWDlwc2JrPjVytZYOORGfTHACdExBbl+S1RL9jMkqT5gU2BHaoyVmX7BWSZqz+T1wkbkJny/chA9CCyjMkX9eB+swbmJY0PfFwLrI0HTBLZm6e+X72nz5TA78hSgZ+RJVy2aaXa6nWlxvbtZM+B+YAJy7+HAGtHxFOS1gV+A+zSjO+DbyKpd0R89g2PV01idwROjYi/dtrgOln1OfiGDPoJgA3JcpmfkSvx1oiIhxs26B+hdry7ksf7l7arQ2r71lcx706+BieTjYOf7LRBm41jHJw3M7OxiqQjyOyU35YbxCpbdnbgbLIB0bL15elm1npqJWzqwaS5yZrBmwJX1G4gZyWbmM0JzBcRz5UM+j8AV7ZgkMVLy80ASfsAfSJih9q27sCFZNmrW8ja4QPIMg3XAgcDfYFFIuLj2vOastyVpB7AZsCSEbFs2TYYuAFY9duC7KMpadGUExTfpBacXIfMht6G/B4ZTtaef7rtObVZXwdlb6ZlgU8i4vqy7QRyMvu0b/ruKAH6HcnSkTPGl81Sm9o3TTbVyuQdQ5bJW6467jKB1Rd4NyLe66zxdhRJtwBvRsQa3+M5e5ClwXaLiEEdNjizcVyXRg/AzMysjd5k7c9RTctLIOoh8sJ5UbIZk5m1qFJy4TRJfdoEy6YhG7s+WpZg9wCIiEeAk4D+wKxl25CIuKwqU9G5R9C+qvGXkhVmVkTEXhGxg6Q+kgZKmrnUjN+YLHc1L7nSZrGy7x1kk9ghZLm8+u9qusB8MQJ4D/i9pAck/Q+4lSz3962B+bb/bsaA9Lep/W3vJUsYHUeWN1o7Ip4sSSBt3w/N+jr0AjYHTpX0R0mX8+Vq1G+c1C1lXQ4Eft2KgXlJW0o6XNI5kgZImrCsJvg3sBUwJXClpEkBIuLViHi6FQLzxXhk6SpKff2vkdRVUk9JSwNExEBy5dEhnTZKs3GQg/NmZtYQbS8KazdFT5DB+fXLEtyRQHVT1YOsC9q0jZjM7DtZDXh5NCtkHgGGkWUYKDWje5THribPFVO0/WVNHGSpAgtflJUAh0n6J3CKpBUaPTazRpA0p6SFy8/VtcR6ZPm7HSTNFBHvAksAiwAbRcQdJeg0PVkK68lWKYcXESMi4hxgJ2B2YAJgy4h4cUwBuNpzoz4p0cQTFN9JRDwFnFH+eWJEPF17rCWOPSI+IfuudAfOIUv4LBXfsS9TRLxTLxHVzEpyTxWYPw/4O9kEeHqylvrOkiYp1xpVgH5S4DZlE9iWoNSFDMzPVFuV/LXzQ3m9JgD2l7Rg2XZGKyQ6mI3NHJw3M7NOV4JNVV3UqSRNVN0URcTRwN3AXsCqpRZslIvkBciaqEPH9LvNrPlFxPYRsZuk3pIOqN0kfwhcD6whaf2y77CyjH8e4B0yK7JllBUC/ciMz2XJpo1zA5dKOlLS1A0doFknKp+FQ4FbJC1SXUtExD/I2sjLA7tI+nVEDI2IzyPiY0kTkiXzzgS6ks0vm341Spvg2s/ICUwBF0vqUQJwXRszurFL7W99BvA8WbqlJUjqJ+lKSRNL6l6C6y+RQdah5PdGte84EwOq3WscS66i+XNELEeeB35KZoTvLGniEqC/BtiZXI3StzGj7hjltTiBvH7Yuto2mmSprsCawBfk56T+O5o20cFsbDfOnJjNzGzsUJZMV1ksJ5KZKk9JOrDUk4a8uX6VbEB0naRTgYvJmpCbtUq2m5l9XZtg2V/J+reHl+y2t4C9gc+BPSUNkvQrYCXgAPK8cU0nD7lDtMlQW4+ceFg+IuYhM4L/DmwJ/K0BwzNriIgYAuwP3AZcL+l3tcf2Jhs7LgvsVrLkq8/SkcDxZPmXeePLpqdNmy1dJTpI6i6pPzCQvE7aHpgWuFNSzzLB16V6Tvl/y8QBRncso5t0qf2t3yAbi/9e0gIdPLzOMj8ZhB9ayjpBvt9XIVedHixpFRgVkP1aOaPOHGxnKtnfcwNbRMSdknYBjiAD0LcD25ITepOWEjf/AuaJiBcbNeb2Vnvv3wncDAyqJTi0LXX0C3L14oPAW500RLNxnhvCmplZQ0g6GliObPI6BbA22bxsUETcVvY5HJiDrJH4OHBgRDzZmBGbWWcrQZeD+PIm+m8R8ZakOYE9ySB1L/IG8gly6f5wfUPzt7GZ2jR6ldSXzBIeAgyJiH3K9qq54Xbl8RUi4oqGDNqsk1Tv+/LzouSE3NzAHyLiltp++5CZoVcCAyPiGUk/AxYCLirByaZs9lnRlw2z+wFnkUl3f4uI/0nqSZY1ORB4GZi7BOinIJuhbhERLzRs8O2o9jr0BBYkz5WvRcRrbc+nZf/q3DkHOcmzXDN+V7RVjn9EeS32AU6OiFfKY1ORzWA/AXaOiIvL9t5kUP++Vkh6KRMMPYHh9b+ppPHIAPwx5DXD8cA2EXGWpD7Aw+Tn50Zg11Yp6TMmkhYHDgNmIlcpHx8RHyhLBM5Pnlf7AnOV91NTNso2azYOzpuZWacYTdDpFOBS4Opyo7wReSP5ELBfRNxc9utFLtH+opYNZGYtrgqwlxvuQ4E1yAD9NiVAPwk5cTcz8ArwSDMH3Up231zACRExtGxbEzi37LJfROxZP74SbLuBbP64GXlt/40N/8yaWZsA/SzAecAMwOLVxH55bB9yZcmVwKH1etvNOnlX0Zf1oscjg66vkbXFLyilOarA65+Bg8nJy+PIJIhpgSmb8RzZVi3QPh5wLTAd0A94hqy5/9/RBehH83ua8v1QAstbAueUyQgBs5HviceAZSPi9bLv1MBd5OTFXmT29F5kucjFmj0gXSayDwBmIcuxVJ+Hz8vj3cvE/dlkWatNSrmrXuTrNSnZIHihskKv5bQ5d/6eXH23NDmB9yIwUdn1bZo80cGsGbXMcjYzMxt76as15n9ZylDMDbxTqxd7MrAD2cxsd5Vmb6Vm7GcOzJuNW0pgvio7sT1wAZn5emQpcfNORLwQEVdExENV7dQmDjrtBkxbBeaLa4FtyHI9K0saP2pN2Urg5TOgfyQH5q2lSPqFpBnbbOte/QhMTZbzuLFk0wMQEXsBR5MZ5CvVn9/swaZyrusB/JM8N2wEnBkRn0oar5wnPiMz6rcAhpPnl4+BqaIFGjtW3w3lOC4iS51tDOxBBmevl/T7GEPTy7omfj9sTya1bC9psvJd+TSwKjAhcGVZMUJEvETWXO9Bloy8vey3QQsE5vuTvaoWIsu/TUeWrRnVNL0EmnsAMwITRsTH5aHJyddsAVo4MA9Z2qYqXxQRN5ET+ssAdwDvk/189gOWLK9Xtyb+bJg1HWfOm5lZh2qTqXEqsBTZ1LE/sG5E3FhqolbZLesDg8gbzq0i4q7GjNzMOsO3ZbqPIYP+VmDriHj3u2RGNpOSDbkScE1EvC9pfGBd8qb5EbIMw4dl31mAK4DzI2KXRo3ZrCNI+ilZ9/ghYLuIeLJWxmQu4BayseNlZJmreYAlqpV35XdsCJzRxJN2oyVpGuBqYO+IuLBsW5FscDkdWTf7YDJTejxgGpp8dVFbpZTLhORxHh8Rd5TtvyObAy9ABhpvarXviYqkE8i68ueSpR9fL9ngS5JlXN4jvzNeK/tPDmwKBHBeRDzTmJG3j7Jq4hHgBWCjiPhf2f4qcHtErFnbtxd5nvgL2YPiXvK79Xdk6aemnqSAr95zjWnbdylT44x5s87n4LyZmXWY+s2QpEHAWuTS6hnJevODgflKiYoeETGs7LsZmRG0WES83JjRm1lHkfQTYCrg+YgYUjLfNgROjYiPRrN/PUB/GNms7GZyAu/DThx6h2hzrjwO2JyskXtGRHxYXp/1yAD9q2RAfjhZP7cnGVho+mCbWVuS9iczom8h68c/ouw5cStZumLLiBhWArIHkKWhloxaDfrye1oiIF2RNDPwKHnefBlYkcySv5JcTbMasGFEnNbmeS0RpFY2tr2BzAZ/DhhQlXApjy9E1pSfn3w/3NxKtbMl9Yovy58dTzZBvhTYPyLe/JYAfUu8Dsp+Cw+RDX5XjIj3qmQfZenMz4H/kN+Vt5QyNrOR36OLk5+Tj8meLQ835ijaT23isis5ITe09h4ZY7C9Vd4PZs3OZW3MzKzD1IJNvwJ+BuwUEQdFxPpks7YuwJ1lOe6wsuSUiDgemNOBebOWNT9ZimBlSZMBT5FL0EcbNGpT4mYHssTNosCGkrpUS7WbVT1YFhFbANeQK4g2kDRBZLO+M4DdyZvuHcnMxxOABVqhTIVZXVWKJCJ2I8vTLEGW71iVDNSfQ05gDS/73QLsSpa3uEnZ8HOUZg7M189vtdflcTL79xTgNOD3wPIRsTxZyucpshb/V7RCYL7oSh7308CUZC39+utzO1nK579kyaPftkoAsnwXVkHXdYCXgEnILPBdyjX1UOA6YCuylvilysawtMrrQDaK/wXwAFmuhxKYH48s17I0pfY88IikmUoQfktgALnKZMEWCcx3jS8bRJ9NJi9cImlL+PIaanTPbaH3g1lTc+a8mZl1KEnHAiuTdSD/XG4oq6ynVcnlyCPIDPq3qwx6Z3KYta5SruIAsmHhYHJZ+lrfVu+1nv0l6UHgpYhYsaPH21G+JZvt3+Ry+12B00oG/fjA+sDOwGMRsUTZt3dkjWmzltFmRcnu5KRUH+CSiFh9DPstSZaF2qqZA/KV2qqhbmQAcuJ64oKkxYCPgHcj4qUSyJ8ZuBA4LCJOacjA29kYynWMRwZgD6OsHijXkfX3w2Jk2ZdtWuH9UCfpQnKlyHlknf3FgAXJbPlBtQz6P5R97icbJ7dMuRJJ+5HfkQcD+0b2XXiMLOe0PdkceHkyW/4DYJ74st58S1E2gb6bfC/cT64q+SlZvmibso/L1ZiNpZxhY2Zm7WYMy6UvIkvY/AZYUNJTETGi3GxeXPY5AHhG0vRVzUcH5s1aVwka7EcGmicgM/y+Vs5mNM+rsr+6Aq8A3evL+5uJpO6RTdd6kSsJvgCejIh3ASJiKUnXkudHJFUB+tPJFQYHSLohIhZ3YN5aUXzZ5HlkROwnaRiwN9CtZME+UYK2I6vgbURcR55Pmr6UTS0w349cJTMLMKWkK4CDgKci4sba/n3IbPljyUDk6Z0/6vZXK9fRDZiYPP8PLmVKriBXER1LZgqvElkqsXrf3AjcWP89DTuQdiRpLbJszZ+Af5dr5r0knUk2+kRSFaC/AVgdeK7VArMRsXu5JtgJ6CJpGbKv1erAm+V4T5Y0XdlnNrIZbkuo3udlUm4qsuzdNhHxbFmVuA+wTnnvb1FbhdhS7wOzVuDgvJmZ/WjVDU8tU2lUkD4ibinL0P9F3jA8Ti4zrgJtF5M1k3cAxicz7M1ayhiy/sbJ1SG188Mk5PLrvmTzvg8lnfVtgeZy3piZDNDs2EyBeUkzkIGk50pgvj8ZSJyezAh+Q9JqEXE/QEQMKAH6/YCR5fV5X9Jp5IqjYyVdERHLNeaIzDpWmwD9wZK6k9cL+0jau1qNN7pzaTMHYssxV4H5e4E3geOB98ks6AmAA4G7yv7jk9nDc5O1tBdthUBcrVzHeGSJkunIJrBvSdoustnrZWX3Y4CLJK1aMui/8h3bzO+H0fg5OaF7Z0SESq31iFi3rEzbnPzOOCwiXiVLpbWkiNilVH7aibyHWDe+rK9fvf9fIVdXvNGwgXaAcn7sRd5XvQi8RfZgoExSDSSvOdYsH4cty3mhJXpPmLUS15w3M7MfRVJf4DFJJ0paXFLf+gVfuQC8l1xWPBVwqKQFq8fLRfM5ZN3k5zp7/GYdTV/WSkdSr5LdOM6tDinZbaNqHkfE3WQzwy3JmrBHA+uWpdnVc/qXoNNXRMQjwJ+qwFwzKIH4/5CBtV+WIOOZ5PL79cn62e8CN0j6Q/W8iBhA1tg+AliiBJyGAGcBm5KBSrOmV7I/q59H3adWAfry8/7AoWQN+r0kzdTpA+1gtdUAPcheE68Aq0XEP8iSPe+TNeYPkTRfedoEZPDxKmCRMvnXrZkD8zBqMrYvWa5jIuAfZK39V8hz5XoRMQy4nKyvPh1wq6QJW/E7tvYZeRfoDswEo2qt9yyPHUEG7jcCtpLUtf7ZakURsQu5qmYSspfN5GX7CEkTkyV/niVft1bzK/LvvSzQvUzWdCvXnq+Rk/sXAKtLOhtaqveEWctw5ryZmf1Yq5IXhr8ia38+r6wN+1xEvFELxN0paXnyBurQkvF0Z3lsBNCSNSBt3Kav1kg/gKwBOrGkF8jmns83U+b3D1UrS9CLvIEcCrwaEQ+R2eK7ll2PAr6QdAlZX/lf5OTdMbXfVZWvGNy5R/GjDSGDR/9HZsBuCwwj6+TeCiDpPjIb9hJJK0XEDQARsbSkw4F/lhtvlZIOJ7diAMrGHfqyz0xV5qnKAP5K8Ci+XuImyM/QRJI2jIgXG3IA7UTSjMBkEXFz9Rknm5x+QdaOf1fS+WRN8YWBaYBLgN0lDYyIuyUdWPu+6drsmeK1zPedgU+A9SPi2fLYZ+R3SdUIdKikq4De5HVps30/jFbbDOfa+f5+oBewnqRnI+KdiPi8PDYRWcrnJeDUZp+g+a4iYt+S/LAL+fY5BngP2J+czFokIr61fF6ziYhHJO1ITtT/SdLFEXGZpC7l/fOasozgBOT50lnzZmMhN4Q1M7MfRdJsZAbnELLW4VzAlMA9wMnARRHxSW3/BYB/kvWl142Iezp90GadTNJFZF3xi8lgwnzkSpKdgXNaOUBfBVhKWYKbgSnIusGvA/tFxEllvymAQcA6wA3A5GSg5dcRMbwRY29vJft3AFnOZ3D57w9Rem2UfeYADiHPpatUAfra4y1TN9nGbaVky1bACxFxQVkl8xqwbXVeGM1z6s0+DwJmB/7YrMGmEoTvTWaCvwtsFhE3lccmBH5Lnjc3Jst2rEvWzO4HXEt+lzwOrBERT3T2+DuDpEvJpMKVywTOGuQKpJ0jSx1NCPQoZTxG9SBpkZI+1WTLL4FJydUR70c2Pt2azJI/Bjg5Ih6TNBUwEBgSEVs2auztrZq8q/17jAFmSQeSzaMPJa8jVgEWjIgHO2WwHajte7o2gYWkRYC9gEWB5SLi6tqKo5FlBcH79YnOBhyCmY2By9qYmdmPEhEPAw+Q9S/3J28U9yMD9KcC10vaW1LfclF5B9nAqjvwdoOGbdZpJG1MBlj+DGwXEX8Ftiaz26Yga4e3pKqkTylpczrZpHAN8hzwMHCCpK0AIuJ14G9k/fkeZBmDGaryDI0Yf3up3yAD1wNrA8PJ5o3zVCV/yj4PkhlwdwP/kTRX/Xc5MG8tpB/wC+A8SZuR7/l7yaDzaLUpcbMTsFR9W7Mpq4A+BZb5f/bOOsqOKuvivxNPSIDgOrgF9+DuzuAwaICg8+Fug7u7Bg8eHIITGAbX4K7BYSAQIvv7Y99KVz+6SZjp7jdVXXutXulU1et1676qK/vssw8wJXBiRKyUzn0HPJrIuKWBN4An0mf+jf2zzwPeA96syw20DaYCxqS5YBNMzB+ciPlOOGhxZMq6GBvoLjgx3yFHzF+BPeOHAP8EBkbElJLOwgH+PYA7I+I54E5gPeCiujS8BZHseGYAyIj5iNg4/b9ZYlnSgbiQ+r7Axtg2swzEfCclm6eIOCoiBuK6MzsBpAy8w3Aw746IyIKWkZ6nrytivkKF/11UyvkKFSpUqPAfI1vgRcTseFN9h6St07mZMCF5Y7r8beyd+rBscdNd4yj8WKFCGRARZ2EV9NpyMc9Z8ftyH7CDpF9qVWFlQthDfmqsZLtJ0gPp+DyYWNgC+LukvHXNRFn6edGV4pnSLewx3zHZL3TFafaXYmKtn6Q3az63OA5i7Fvk+69QoRYRMZmkr9Pvs2L171rAUGDe8bFqqlHQF7q4dm6MWATXl3gNOCjLmknjxbU422pdSV9ExLzAhXh8eDL/d+pzF/89miMNI+IQYFds47Mbti05Oa0/F8YK6YckHd2mDW4DJGJ+ZMxTPwAAIABJREFUeeynPhTbGh0CjMDB65+SYnpDYAackXa2pDfq0d6WRESsgevSXC/ppogYhO9xdUlfjMfn9wHuk/RqKze11VGTgfgMtrv6ApgEB/nvwNnIv0XEEjh7Yhlcm+eWerW7QoUK44+KnK9QoUKFCv81Umr65ZhsWk/SE+n4NcBKOOV2WVzA7XNgTpxyW01CFUqLXPDqPrzmWjUFsp4C7seE7E8RcQAmXfYsm5opqVlvwMTBJ3hTPTR3fi5MNGwB7CHp3JrPF510y7z2e+Lg5HBsW/FzzuJmACbjdmmOUCl6gKJChQwRsSiuubCTpOfSsQdwAHNCYDtJA5LdS7spnN0MQX+gpIfS+R1wvz0AvIuzFH8DlikyIZ8hN1Z2xkr50cBXSS2/EK49MgdwraStUrbRXMDFuB9WTP1X6Dkjj4hYDLgeB7bvSoH8ubBt5J2YuP4lkbZZ/5VmrgjbYF4AdMVWmFPhrIAXy/Id/xmkLJGBmJDvnwX0k4J+Y2CVXEBvKZxV86OkZerU5AoVKvwJFDL9r0KFChUq/O8gbYR+Ak7FC8bl0vEbMPG0uaRjJa2GF9VLS/p3e1xYVyg3am0VckT73cBSiVwZgsmVnRIxPz0wH7Z56taW7W0jCNeeuB9vrOeBsT7LSHod22FdBZwdEX9t9OECjxMpODMqKd2eBnoBL+I+yZ6PwcC2uF/Oi4g5mvpbZSFb2jPC6DjuK0uPDjjL7rmcXdXR2Bf6NuDyiNi2uXe/qPY140IiljtKehavo+YGToiIVdL5S7F9y/TAysAHuMDl6KI/VzVj5R14nnwaeCQiFpf0PLAT8BawXBJ+3ARcib3oV871X5HnjI7p30iHpsNZZ08lYn5O3Dd34ay74cDGEdEjmyOKPldERI+UFULKCOkPTA4sCFwg6YUUjCjlODAOTIADUncA7wNExEZY/LC/pAcjYgKAJJLairQnq1Chwv8+KuV8hQoVKlT4r5E2Ep2xwmdJ7H06P1ZyPFg2NXCFCrWIxoXbJgJGqKEo3eJYwTQfMETSCun4VNgXdUWseHq7Lo1vQTRlS5AIuGWBk7Cn8jqSXswrHNNmfAPguKKTC3lERBccnBEmlz5KJNKEwM+5Z2ZNbHHzNbCWpI/q1eYKLYv0XY9KRFqWaTZnImHbDWoVzcnuagBwq6Tr0rGFsGfyesD2kq5Ix6fFdi7nt3nD2xjNKOgPlXR/Oj8V8AtWxI5VTNexyf8VcnYd3bDd26/A7ZiIXAvbmOwn6eJkYbMKsDrwIfAqcFrqr1L0Q/p9WxzQ7oOJ+Plx1tWLOKC7Qwrurwb0w4XVX6pLw1sQiXB/CXgI2zWNTAH7E7GNSwBHSLo+uz6/3ihT1kRTiIi5gVfwGuGeiNgSCxsOkXR8GlNPBu6RdFfuc4W2u6pQob2gIucrVKhQoUKLISK2wfY2HwNbYrVPYTdLFSqMD6Kx9/EZWKn0I/CAkgduRGyNi3xOBpwJ9Mb2TksBK5VkY523JZgRKxp/kvRxUgQujS2uJsZE24tNbaZLQLLkn4f5sMJzP0mD0rG1gG1woOJO4DJJ30TEBtimYL1qI10OhL3Ct8dk4l/T+/EJ8Dj2By5lnYlaJNJ9PxrUvllA7p+4yOkpOcJtYWx1tT6wF/ZXPgWYFZiqPQT7myDoX8UE3AM11xW6sGM0WL91xJlVV+D6I0PT+anxnLEyVsc3GdAqOvlYQ8wPxHPlqrgw+oP4GVgm/b5tUtFPhms1TAZsJembujS+hRERCwLvSvoxIqaS6yt0weuqE3AR6SNzAb3AtVwKu2YYF3IBrF7YFvEx4Dlc9PdQScel61YCDgbOkXRr3RpcoUKF/wjtMR2oQoUKFSq0MHIWFQOwSrQz8EYiIuIPP1yhQsGRI2LPx+nFTwMdgUMi4vp0zZWYnLoRK6iXw4XblioJMd8xZ0twLyadnwH+GRE7Ax0kPQrsAXwLDIqI+dOGs9EYUeRNduqHMRHROyImx2q/WYEOEbF8RJyGVaETYjX9idg7GuB2SWuXwaaigiFpBH4G+uJ34UPgZeCAdkTMdwAWADbFljXdACS9gmvSTAwcEBGbpePPYaur6zD5eAtWzE6b3q3CrynGdQ9NWNzMBWTK8fx1hSXmwe1PAayXgEH4e343d/5zYE8s+Dg1BX7H9l9u7VlkYn6sFU/YT74LtjobKukF4B84k+QHXAT3l6SgPhEH/fYpOjGfnoEMLyVi/hzgoYhYSNJvkgbjQsA/AUdGxCbp+i7AsRGxfxs3u9WQsg3zz3dmhfdvTMzvCJwPHC3puIjoGBGz4WdlOF5jVKhQoWColPMVKlSoUGG88Ucpo7nN5m7AGVjxdk6ZU0wrtG/UKKR7Y//bCyTdFRGTArtji4bbJG2U+1zPlJJeaIV4LSKiB7Yl+A5vHDviIo97AodjCx+wAvA0YF5g7jLY+eSR+uE9bOm1ZUTcglXAH2PLhv1zKvrPgHMlHVu3BldodYSLPh+PCbblJb1UdNXzn0FETIxt7k7D6t8tJf2czvXFxT7/DZyQU9BPh7OLpgWuLol1yZ9SeOcU9EtiX/5Vi0xEN4WImAUrolfBxPwqeA6J3Px6CrARsJikL+vV1tZEusc+OJi7jKRhuXP7YDL+DWAMMBIHtTYsenA/qeLvA76XtEHu+OrYKvNZ4CBJz6Tjq+KxdBIcuOsNbI6fjVfauPktjtw73xOvm2YGPgeezGUL3A6sjev5XIPXWRvjukWLJKFEKeaXslsVVaiQR6dxX1KhQoUKFdorajeSOXXP7xZLuXOXYBJuFeACvImoUKFUiMYe8xMAUwDD8EaSZFNyLjAaq7xulLRx+viI9G8pSJbceLArJg52kvRGOtc9XfZDjmh5Aqdeb45J7MKjhjTcB3gBe+wjacOI2BT4FPhM0ntJGTcftj96t6m/WaH4yD0XU2PF/BTARRGxjqQvi27HMT5IffB9RNyIx77LcPHnPST9LOmpsHfyNcCBEYGk6yV9AnyS+zuFtq6IBtuv7sBmuMjlz8C1pPGxdm2VU9A/ibMMCmvhEhGTYCuv2fG495akTyW9GxFHY0K+H7CnpCNJhbMTfsbzZeG5i5Q5sh6wOHC/pHtTdsliwKK47sgv6dqOkkZLOjUiXsEZFLPjorBPqBy1SSbAxU3XiojLJG0PkPplfVwk+sSIOEDSM5Luj4hRwP74PfoO6FsGYh7GvvM9ceZhV/w8LADsFhHLArtJWjciLsDPy/bY4uY1vPYaVfQgZoZcoKIz0FXST/VuU4UKrYlKOV+hQoUKFZpEDfm4HTAdXkA/JumjplQZuYXUtthv/o02b3iFCq2MPIGS2yBNi4npLSU9nLt2UmAX7KH8iKQ16tDkVkHtGBARVwCzYWXwyGRTcS1woKSTEjnzF9lrPp91UEiyqRYpSLMjJlA+knRsM+NkN6wIPhsTUCuU4f4rNKCJd2M6TLhtjbPKPse1Bb7IExAqmdVN7t564aD9NJhknhDb1uyoBg/6vri44Y/A2UrFYMuAaPBW7wU8ia04egITAR/gjILrJP1Sv1a2HiJiMVw3YEbsLd8J+AiT0zula/pg67dtsD3HOdiiY1b8XLwPbFBkFW36/u/Ez/9PWPl8o6ThKWB7BbAFcDGuU/JjWYjWppCtpdI66TBsfXWfpG1z16wA3Ao8j+3AMgX9VDg7b4Skr9u88S2Mmj1XP1y3a1dJr0fEDFgZfyxwqaRd03UT4LXnh8BvqS9L8bzkgpk98fv/Ba41MGwcH61QobCoPOcrVKhARHSIiD4R8ZeImCh3vPC+nhX+c+QWiQNxOu2e2Kri+ohYIG00OzT1GeDKipivUEYkkiUj5i8F1gRexAXbpgK2j4iZs+tlL9jzsXfyohExbdu3uuURDd7qE6VgHJhwmSAR8xtiYv7gRMx3BHYGDoqI3nniskTE9LqYZNsRB2oyT+Wxc2maY89PP51wMeDKY74kCHv/dkrfe+eImDkipseFkb/BxaDPwEr6QeldGB0R0wADw8UQS4N0bz2wCnROYDBWu14H/BW4Mp1H0lPAVsAcwPJ1aXArQQ3e6ncC32CibWGcSTENXl/NVL8Wth4iYglsZfQO8H/AQtgr/U1gh4gYkt6DoZjAH4DtPB7C/XU6zrjYOJGPheQvcmro0Tg7YBlJAxIxn2WFbIuJ6A1xFkmvRFCWdX7oCGPXSVdjP/VNU9Yh6dzDwAb4uTkxXCQZSV+kzIvCE/PQMFZGxIXY+u8JSa+ncx/iTOTDgP4pE4+UefSWpBHp3YiSEPMd1FDD6BkczHoD28Jl11QcRYXSoZCTW4UKFVoGEdE9Ig4GHsA+wS/jzeIu0GBTUqF9Ib8JiIiNsPXCFli9dADQC5MICzVF0EPxi5RVqNAU0sYnU3v/BVs2bSNpB6xy2if9e2ATBP0pwJySPm37lrcsUj9kpNuzwOqJXHwQmDoirgNuwmnnJ6WPzQOsCrwv6bt6tLsNcAcm5n/GKfozwe/m0gkwOTcE+wqPTGRuWQIU7RIRMX3OgiIjFe4HHgZeB+6KiM3T+HEGDQT9k+GCyTfjGgylsGaoQT/sC703cKGke3H2wH7AOtjmZwIASf/Cdh871KmtrYmF8Xd+IvCKpC9oKIh7laShZSOcUsbI+bgey16SbpH0qqT7cSDmEGzZMQhA0mvAyVhRPjOeYzfGtiXZWFm49WVSxV+C1b/bS3o8Hc/I6cy+aBRWjw/Bc8nBiaAfXdSgRHPICNj0+9XAkfjdHwnsErbIBBoR9PPi8WKBtm9x6yH33W6B7f42wTU4xhbLTZYuN2CV/GLpXKPxoiz79rS37ILnxU/xfHCOpF8jYsJ0WaPi0BUqlAGlGuQrVKgw/kgbx8G4oMwnmEQ5B5gBODcizspdW0187Qg1aZVdMcn2sKQfJJ2HCzGNwAr6Zgn6CuVAiRVbfxo5xfwpuIDZatiSAGC0pNOxMrAfVojPlPvst2VQeCUCQWlemAff/yGSPgPuxuqmTYE7JZ2SPrMgcCHeTB2ajhV6Xmnqvchtng/Em+dDImKKmms+w8Xt9ssUkWVQurVnpPf8MWxFkeFJXJzvTGxFAHBNRByYyMUzsc3LCFzo82egT0lVstNg+6aPMuWzXNRzIFYJbwGcmSPoXytpNsm02B4w64fNMSl9iOwpPjGwZ458KgPmwIGZgZJ+AI/9KcD7FXAeLga7dJpXSQr6M3Ex0FUwmZ2tM4saxJwemB9njHyYHVTjmk75gscbA09gG6xjw0XkCxeU+CPkhA7n40yZU7FifD48NmyUshOz6x/G4odJsM98YZEyrLpFxGQR0TX33V6Lg3c/AjulwMyIHEH/AfA9ztIsDRnfDGbF2UTnSvogvR9r4WyrR4HDImLKkvdBhXaGwhdVqVChwp9HWvg/j/0bD5L0bO7cjcDuwO5p8bxHNfG1P0TEmsBF6b9HpUVRF0m/Sbo28WoHAldFxPZJ7VahZIjGHphrA5PigM2N5Ap8tkN0BHpgv+Au6VgHTNCfld6PU4CeEXFQ2lCVAmks6I5JJeFiZe+lc8MiYnvgcmCRiBgCjMLkzHDsrT4qCu4xH40LO66PyYJfgAGS/p3Iho6YbFBEHJLISACUfMWzDIQ63EKFlsX3wEvAyhFxNlbIvg/sK+ktgIi4Ffg7cFxEfCDp+mRfcAu2NhmaCMhS+AXX4B1gMqyEfgE/+h3kgrjXkAhYPJYcmH2oyO9GM2PcD3i+6BwRq+ICuIdIOj6dXx6rZp8gFRYvAZYCukt6DH5XryVkT/WLcAbFShExURKCvBYRJ+Nx9KSI6CHpH3W7i/8eC2KycVAuQPW79VOaV7olhfDGOPtmDRzAK10xzHAdmqUwGf94Tkm/F14zbBcRv0jaHUAuBttHqU5FERER82OLv2Vw3YlfIuIW4HZJT0fEadgW70Dg3ohYQ9KP6bPz4bH0vfq0vk3RC6+tJoyIlfAYsSfOUARnYr2CBREVKpQCVUHYChXaGcKehy8Dr+JCM5+k451yi6IZgYNwSuVeks6sT2tbHzXkYxdJv9W7Tf8LiIjJsPp3T0wyLJsn6NM1m2ICchheXP9WBXLKg2hcsHMg0BcT0l2Br7C65wZJ39evla2PGjIh3ycHYxX4M9ja5oOa8/vjzdXckj6vU/NbDDlCugNWAF4PTAncLGmHcNp+Ri5MjS1sVgS+BYYCl9UoAwuJaFzYcQh+HybHpNv72KbhPhy42Al70A8AjijDc1ChMXLPQ2/gLEywfoytShYFhufGjzlx4Oo3YN1MSVz7t9qy/S2J5oJuETEXJt9+ATaX9F46HsAemKS6CHioyIR8htxY2QMHHT7GhFL39O/CmHjaVdIF6TNz4D74DBcVL+xzkEdE7IqDlH0lvdTE+ez92RZnVy0q6eXc+dlwhsnquJjst0VcZ0bEasA9eC09pJlrsuLJqwGdJN2VskemzvZqZUO4qOu7wImS/pHGhA6pH6YFnsPz662SNqpnW1sCEbEUcDvwNM4y/BFn2a2Ig5O7ShoULhq/N15DfoMLBffAY2U3YLEir6Nq0dzcFxF3A8vhTIkfsaBwUDr3GXBBwYN2FSo0QmVDUKFCO0Ja5J2HF7gX54j5RgVkksrzbLxw2CoiJm/71rY+aoj5vYEtoyTFGv8MImdJkxbGyPYbl+DU4oWx7x+Sfgv7ACJpILbw2ESpGFFbt71C6yFHMp8DLIE9H5fB6bQ/YiXXQnVrYBsgjRH55zpTySPpOOyNOwu2AptROYsnSScBs5SBkM3miERAPo6DM/vggn7bRcSaaQ7JyPfP5UJ320jaS9LFauypW1ioobDj3TjwsAX2TJ4XW1YcAUyX1PGXYm/tfsA29WlxhdZE9s7LdRT2xIWhZ8NjhZJKtnO69g1M0C2IiZbf/a22a3nLIr33o8O1jDaKiJ0jYpOI6CwXNTwL29tcHxELJwHAgti+42NJg7PgXT3v479FNsal4N0TWAk/LdBR0s/Yf/0d7Cc9NCJmiIj1cDHMXsDfVC6rwI9xALMvNF5vJmTz6w9YJd/o+5f0NiYoZ5f0TYHXmT9hNfTi0GQ/5DNFtgG2joYaFqUk5hO+BB4BNoiIOdL3mxU2/RTX63gZ6BOua1NYpCDljfhd3zatjY6QtAbQH78DN0fEOpJ+xUGtY/FccTAuinoOsFQaYwo9VmaIhgLqXdLcsExELAQgaU3gb3gcXSsFLrqk7IPvsCCiQoXSoBQvdYUKFcYPaePzENAHODsivpL0VDPXvhouxnMCVi181YZNbXWkzXRGzF+LNw6Zoq3doCZAMTFOIcwsKr6OBr/HoyPiNknrJ4K+ayLkb65T0yu0AcJeykviceCx9N1PC/wFuBP7KpcSNe/GoTgQMVdE3AMMlnSPpCPSJns7TNDvKunDTAWkEhQ+zSkbO2NP7V5AT0l3RsRo4BjgsojYRtJ9ETE6F+TL2xeoDKrYhMVxYcf+wIupfzbBNkc3ZMpg2ZpgAM4uGlS31lZoFeSe6zFpvPguIvYATsde0RdFxLYpUJNhJFZIdq5Hm1sDaYzICOnHsVVDb0wm/TMijpd0SRKI9MfZRt9hYvZjXPPod0KRIiKts7vh4tjfYgHD69l9SboxzRk74bpPP+GChx8CG6oEtl81uBdnEh0XEY9Jej03p4zNSsPe9C+Q82PPIOndNmxvq0DSExFxG3BoRNwv6ZVcP+Qz7vrg9dW1JXoGms2qSfd/F65ltWtEnJV93+EMvJ8xIf2oClqzJ/ecb4zJ5NMkDUvnOksaKenyiPgKE/JnRMTHkl6MiNMxX7cdMDtwgKRfIlkf1emWWgw1wcz7MN8wIzA8Iq4EDpN0S+76bsDcuKD699ijv0KF0qAi5ytUaAfIL4okXRERP+PU+4ERsZmkf+YXydFgO/AOLr40cd0a30rILYQvx5YsWwGvSfq2rg1rQ9SQj6cCK2Dy8RW8UB7cBEF/k6SNJI2oU7MrtCKa2ED1BOYCvkrE/OzAU5hU2CWRj9sB96oECvE8cu/GzZiMfQGruLYDNo6IYyRdKOmwiBiDybhrImJzSR/XreEtjEzRhC06JsRkU0Y+35MIt8OAKxJBf38N6VLGomUzYkufN1P/bIHVcAdLOimcYbCjpJOSWvZmaGwfV6HYiAbrkuxZz9Se34Uz8TpiK44BEbET3nNNC2yCM07KNkZ0xeTK9zhT5GvcB28Ch0fEy5IujIiHccB3BuAL4BKVwO6qBqtjX+iD8LpyTApYRgraDoyIO7AopBt+FrLrCtkP0YwtpKSR4doK8wGPRMQqSrY1uT3HzMAG2D+6FGvw2jkw4VJsYfJARKwgF77N70cmwRlpk2HxQylQs9f4P/wsdMZBq+MknRcRswC7AHNExHnp/AY4u2aHohLz0Gj9swIwTFK+IPDIXJD3zoiYDme4L40D/7+FCyWPwRZgt0TERiqJnaQaahg9hjMH9sQiuTlxBn/XiNhHruXTG/fNDOnjy6khI7M0gawK7RsVOV+hQsmRNkyPR8R5kq6AscodMEF/fZ6gT+ezjUFfHOV/te1b3vqIiL7YrmM3SY+nY1Nhm4LuwKtK3nZlQ1oMZovlgdi65lys9hoMHAVMGRFX5Qj6McCJEXG1pK3q1fYKrYOaZ+Ji7Cn+Lg7QdUjKloyY7ydpeEQsjAtbfYN9NEuFiNgTW/msD7yQFEur4DT7oyLiG0k3yQr67sBa9WxvayCpPB/F9j1fAv9KG8ZM8XVnmjoOxQr6XSWV7lmowRfABEDvcKr61biw4wnp/HLAJhHxoKTnsg8VkXSr8Hvk1H49scpxZqwUvz0ibpb0crigoXDgvy8eR1+nwW++2aKQRULuHpbHmXf9gecT0bwmtlC9QbaoQC6Q+1bN3yi83VUN+uDi6U8qpxBP42Sm/vxV0kP5D6W+LFw/RMSSQP+I2LMp0lDSbRExKbb8eiwiTsBZd+9ir+1dsPXNTqmfmiK2/+eR1khHA8cpVwA8QwpmH4XX149HxCHAv3Ax6fWAv+Kil8tK+qjtWt56qFlX3oT3XC/hLLN10xixg6R9IuJzbGFyG7ZO/AZYU0llXgJ0AQIaB2/yc4GkC1Iwd8MUpJCkEUlANRo/X1dFxLpFfEeaweY4SLkt8HLqj+nSuTcl/Tv9/jMm8IdgIcSoogYzK1RoDhU5X6FC+TEtntDODVe8HwjNE/TZhyJiVqxYGIyLeBUezSz4p8E+yd2xSuMsvCicCPg19dn9bdzUVkdOsXQIThHcStJT4ZT8SYDhuAiXIuKaRNBfgYmF0vVHe0c0Tqs+ExfzvBkr+m7Dlk8CrgP+LunnsGfwrulPPNv2rW4TzIMziP6Fg1NIGhwRv+ECXVtExJ2SfpW0f0ScVGSFV4b885DIpeOwpU0fYP2IuDYpvjJP3DsjQljp1I+SBGr+QJH1JvBP7Ds/DbCnpHPSZ+bEhdzewNkWFUqEjGxKxPzzmCx4DpOxW2Cv6M1z8+korKT+Ggf1smyLQpMKuXcj0qEZsNXTR+n+NsOWA4dIOj2pHtcHrpfUaE1ZFtVjbo05CvfLnBHxUg1B3wXYEQtfGqmjixioCXtDPwKc3hQxnyMdLw0XcNwery0zvI0V85sVmWyLiAkw6fw6Xj/Xns/3w9f4GTgP21z9gseR97GfeGkEUbm9xsHYGnBT4Kn0Xe+CRUGr4HHxlLCVSVb769OmghwFxvfYO79HErfUrrM6yxZobwAzA12TIKRDEkScifdgdxWRmI+IBYAekmrtMOfE4+XraYzcDO85DpJ0ajijZElJd+Lgb/b3yhbUrVChKghboULZIXvf7op9H6+MiE1z527ExWa+wwT9EgARMRFwAC5qdpYa+6UWEs0Q87/iTcEp2Cf1dKyAXAzYEEfyZ2/LdrYlErkwJXBdIhL+D/fF+jjt9EtMJmwZEb0kfQWcLRd3q1Ai5Ij5ibGN1UHY1mg0MACTjB1wMcMxEbE09nzcAKvoP6tLw1sQ0XQBvt7AZImAVtjCBUmP4s3DGsCE0VAEtjTEfCKRAJB0ByYef8Sbo6xY1+hcn9yFVcIbtH2rWx7RuMDl9hGxdUSsCCCnpV+JiZVPgJcjondErIXflwlwwbcyFXasQIPKEY9/3wEbS+ovaWPgYUxSz57eox+AfYEXcZDvDTV4TBeWVEjtH53mi+sjYgpsR9IN+HdErIGJ+YMlHZ8+tjouKj5zXRrdCsjGvgy5NebduC92ADqm/sqemz54LJ26TRvbwgijKy54/ZikAyJi4ohYMx0HGgomp9/vSe/J0sCawEbAuvgdKiwxn3AQ3lNsKemniFgyiX6A3/XDIHzfa2KS/nA8b/61LMR8JPVXDn1xtsRL6bueCavAr8V2PwBI+lLSC+mnFMR8ri8uwPvqI2DsMzF2DMnttScBPsiCmLk5Y4SkM+QiyYVCREyJxT1LNnH6N6B3CkCsS8PccWJ6ZzYFdsup6RtlZFSoUCZUyvkKFUqMjJCWCzAdlg5fGRGoeQX99phw2hwrOApfiAkaqTfOBiaXtJlcbOdEbFvxC7ZruCtd9x4u0vVjvdrc2kgbiIuAb8KV7/fFG60HZS/xa4EjgZOBERExoIjKrgrjh4g4C5Or3wHnqaFOxeCI6IHta27E78VwnJGzfBk2k9HYE3V6NXjGvwKsF/YUv0GNi/WNxIWyR5TpvcgR8/dHxBeSNkvHH0rB3ZuAEyLiQEnPKecXnSmi/kBxXhiooUjZEKyKnhL4LiIulXSQ7J/dAdgSeAArH78EPgDWV/kKO1ZoQBdMsj4s6QOAiNgIZ43sL+nKiJggIiaS9FlSAv6UEbRFHi9qgndZhsxk2PrqLRyImBrYQ9K56TNz4vnjPWBo27e65RENNQe6YVvAnjjTVJJeDduXHI0VoZdFxNuYmDocZ6FdVqemtwjSmnpEOItsnkS+vYgDEw/WXDum5v+/KyRf9IAV/p6nxsGp24EeOPgwNksk3w/p93vbupFzaHuiAAAgAElEQVRtgWbWUwtiIdC/03jwJH5fdpbV4UcAX2djRpmQC9o9D9wK7B0R30o6sXZ9EBHz4MyBwTV/o7BzBoCkYRGxXRKCdQPmkPRSOv1PYLuIuB9YGe9FT0/n+mBy/mW898j+XuEyBypUGB9U5HyFCiVGfvKSNHQ8CPoD8IJgBCbmX6r9m0VGygjoDawSERdK2lnS7RFxR76vUnT+cLy4frhOzW1R/AEh8FZSK6yFbTvukvRrOjcGq0Onwmmo1WKopIiITsAz2Ct7bqz+/Fc02JYMiohHgEWAv+BU9LfKoGyq2UieASwWEUfKdlanYfL1cByMGJTI6EkxIfM2tjAoGybElkarRsQFkvoDSLovkZA3Accngv75WlKlyIR0jnQLnHL/JVbAdge2AQ6IiJ6S9pB0fkTcjdVwE2NbghdUAtuSCn+IbsB0mGQlBa2uw2q/UxL5sB/wQdgC6sd0XVmI+c4kIhKPjZkdwbm4YHRH4L5wDZ/5MUndAdi+JAGKyAXvHgJmxVaILwL7R8RDwDl4DXUULhYO9tD+BFhBBS9kmMbHjtjebW0ceHkW2I3/YE4s6vOQ+iFwVuF2wDC8h1oevx/tDrn11FXAtxGxL85OXjkiVsAijwdw1uXPYRvV+XAGWmbtUjpI+jRsETgFXj9NDZycjnfE6+vTcUDnjDo2tUURDZZOT6X7vB4H9LZOgboHcHB3M/ycXJA+1xc4E8+ze6e5o5D1KCpUGF9U5HyFCu0I40HQd8fFeP4uqRTqpgxpQv8hIvbDyuAt0v3vXEPMb47TS5cDVs0paAuLGvJxUWByvHl4TNJvuUsnAuYCPgynqS8APCDpgrZuc4XWRe0CNxENt+JNwcnAgRExJKk+s4X1D9Qo4oqO1A/Zu3EjfubPxiRrll2yDt54D4iIR3ERu/mARYGlJf1cl8a3MLJnIn3fX0fE/lgNvnkaK2sJ+oHApeF6JW/Ws+0tifQu9MBj4XfATZKeBYiID9OxfVJ37Slb3HyY/xslUIG2e4Q9pHvhIJyw0vMXSSMw+fgJsGDYV/5M4GDgpPTx+bGP8mX5ObaIBGSOkO+YCOXOuDD4JNhH/83cXHIJJip3wUT1z5is/BxYWyXIJsn3Byamv8WkbGdsA3glsAdwm6TjI2IQfhY64cyCu5XLNqrLTbQA0nc+KiKG4nvvgLOLspoMpSVZ80j9IGBI2Et+TuAj4JMyfM9/BjV7jZ2BFbDqeQwWfq2E11L3S9okXTcZHjv7APuW/ZmR9FxE7InV4f2BrdK6olu65FtguaIH75pDuq+HsGXsKUng8VhE7IDnjqWB13DWeke83lqxDHNHhQrjg6iCTxUqlAt50q05dVJE9MFKprWBrTOCPp2bMFN5FRm19x4Nvsijk1rhEGzdc4OkXdI1KwH/wEV79lUJvNXz/RAutLQsVj6Pwh64O0kaEk6lvBEYjRdGPYHFgWXK0A8VGlCzgeqON9Vj5NTiCbCt1TlYCfdXSZ/Xr7Vtg4jYG9gTK6MfT+RLwFiP6d5Y0bQAJiKGAkdKeq1ebW4p5MimPOn0h2Nl+tx6mIRatYikY3NI3/sF2Av4W2AdSf/MBS+mw+rgvYEzJO1dx+ZWaAVExLyYPFmRhuKEn+GMkZNS0HKd9P/OwHGSDk2fnRN7KA8HVi8ymRARc2FybRngLEywdcLP/k6YlFwD239FbtycFBNzHbHN09NFzSbJvff5ebMrtqjZA9dleiQdnwq4Bfvq7wbcm5TBjYLhZSKZImJ1fK8P4+DEMKBvuu/Cfd//CXKq58vxmNCfdtYPNXuNvwIz4myzY3N70tPwvPov4BhgDjxOrA4sK+nlOjS9LoiIyXHG3TY4CDwMZ55cX6agTjRkInYB5pb0Qjq+Ha7T8A1woKRH07i6Eha+jMGFcW8pU39UqDAuVOR8hQolwp9Z8OcI+tWA3SQNaNXG1QkRcRlwp6Rbwv7Amapnauynvj0ucrp3un4e4DNJ39at0a2AiLgUq7cOweT7YtiqYQZcwGpwRKyGN1kzY6XbXiqBn3iFBtQQDEfjRfD02MLkH5KeTMrI9WhQkG8o6Yt6tbktEBEDseXVhpJ+yh3PbzgDF/rsgH3mR9Slsa2AsA3Hk8DRkm5tYqw8BqtDmySjmwsEFwW5DWRGxC0G/B0HJfaSdGbN9dPiYM5+wP9JOqvtW12hNRARS+G0+yewF+5gHND+O/bDfRKLGj6MiF1x0O4RTE5OhNdUAItLGllUIjb1w3WYeP8W29d8ku5pCpxleTQO6vdLx5u1HCjqGBERsyjVXkpzQGALuGmxB/ISsjVgNoZMDgzC5OTuwD1KhR3LgOae50SsbQmcgK3AFpM0vKykWlPPczjjahSu3VP6fkhBid5AR0nD0rHtcHByJHCApDMionv2DkTEgbgQ7mI4cPcGcEiZhA41x/60DUtR54xaREO2VS88TwRwkaSb0/k8QX+ApMf+6O+0VbsrVKgnKnK+QoUSIiJuAj6UtM84rpsLq6HmA2aVVHh/xIjoopRGHi5QNRiYB1hL0j01pNO0eAM+DbYu2KJuDW9FRMTcuEjXUcAVmYINq1YuxOTsPHKhpomAX4HOeZKyQrmQxoilcEGyHlj1NTUmGi9Kivq1sOd6Vvh1WL3a21pIZEs3XGzqSUnbNLPBWljSc3VpZCshv2mMiPmAAbjewPqS7q4ZK6fBqq7e2Hd/s7o1vJWQiJUTgP0kjQhbgB2JLc42kXR3zfV/AdYBLiwb6dJeERHL4DXDhcApqrG1i4iTsWL8aWALSV+F67UchC093sdk9gGJqC0kIZee/YewEvgMSe81cc1kWNxwbLpu1+xe/xNC6n8RETEjziDbXI0zTNcCbsaFgTeTdEM6nmUfTY4V9EthS4ZH2rjprYJcAKIrtnuaGXgJ+CZlk3QHtgCOp8TEdI3IYTZcl+l9bHv1TXvoh3CW5anAElghv6RshzcbzqDYFNtirp+u75oJGpKKekZsDRYqgTVg7t3I6nH8KOn7dK7dkcs5oUNPHMz8HNu+PZoPVuYI+q9xxvrvikVXqNCuIKn6qX6qn4L/YNVC9vvB2NdyZUywjuuzcwDT1fse/tv7B+arObYn0BWTjvfhFLk107lONAQn7wRewOqNqet9L63UP8um+181u/9cP6wH/IazJwA61Lu91U+rPw/9seJv6dyxPsDV2HN+jXSsB1bCvQHMWO92t9C9RzPHL8ZpxfOl/+fH1GVwEG/+ere/BfuhY/Zv9s7jLIr7a8fK9G8HvMEaAtxexnECK/nG4Jor2bEF0/zxEw7wNvfZTvVuf/XTIt//cOCU9Lxna4SoGQ/OxPZvR+Xej27YmqBD7rqObdX2Fu6HSTAxfwXQO3f8d2Mntq/ZH6tkLyzbe4BJtnXT711qzi2LVdJPYvu/7Hg2nk6J7bEK+Rw00RfZnNELZ5R8kcbLkbig41LpfDeclTkM1x2YoN5tb+F+yL/jl+J6I2PS2ul2nEmR74cv8R6jNP2QnoGhuIjnPmns7JY7P2PqmzHA6bnjnXO/N7kWK+JP7p3vhQUvH2Axwz9y15RiHPiT/RLA+akvZsn3V817tB3wOrZanbfe7a5+qp96/nSgQoUKhYcaFBzzYrLlEuBB/UFhnaQYRdKbkj5pk4a2HpYGjo+IowAi4jacgj65XMjvCFzI8s6IWFvSKElKdg2/YquXZVReb+0v8CZyQRhb8DBT8NyPNxWTpXOFSzuv8KcxBy70ObaWgFwA+nC8mT49IiaSNBz7py4q6YN6NLQlkdRLY+tx1Jx+JP17eET0yY2pU+AU9R54k114pHd/dFI0XQYcGRG9JT2Dn4FsrFxHDSq/PpiE+D9gPSWP+rrcQAshpeTn8TJwLrBrRCwJIPujHoCDM9dFxBpN/S2VRA3ZHhFGV2zj1Q14Vy6AnfmMK70vHQAk/R0/D5tgz3Uk/Srp38pZYKm4SskpgFmAOyR9lx3Mxs48JH2DA5uHYO/ka5t4rwoLSZ9Luj2pYZ+OiGty5x7DfvuLA8ekrAvUUDx3mKT+6dkpfJ+k++iGCdnhOGviLzi7aHrgqohYStKvONB/AM7KvaA+LW4dqHENp5WB43Cg5nSceXZNRCyT+uEaHLyaD2fkjN17FRUpK+ABrITeHjhN0tPpfrOx7wNcv+tyYLuw1zxKNl/p98Jn1mRI73wXLPbqhddV/wZ2iYjr0zWlGAf+JDrh4ugvKFmDwdh3SLn/X04DiT+0rRtZocL/Egq9sapQoUIDImI3nF56MPD9uBY+ZVoYAe/iib5fRAzF6bbrYlIaSU8Bh2EielBE7BMRW+F07EWAZyR9VZeWtyCaWviljcAwrOjZNyLWh7EEfQAzYdLx49z1FUqCZgjU6bHC8Zt0TScA2brgdpyiPUU6NkLlsLvKp6EfBAyMiLMiYkMASdfgoOZywE0RsXsK9l2M07O3KUPwLtkujAp7gD6Da078jIOU+bHyPuDWiDgh9dfF+Jl4LpGWhfSPzpDaPzoiukVEXzDBCtyGCdoNEhGFpBcx0TQEuCsilqhXuyu0PBL5PgKr914C9oqIfulcnpQfk5tjr8NBzrma+5ut3/JWw0J4DnhqXBdGxIR4/DgfOBmrxYt872NRsxbqjRXym0XEudlBSYNxMculgaMjYul0vFFgpsCBmlqsgr/jfwD3SfpE0rFYADMKB7enSu/Tddj2a9t6Nba1kGyfVsJBqUskDZF0cPr/r8CxETFDmlOuA7Ym9UPBxwYwId8BF/F8N60Hxr4r2f8lfYhrUtyM92YnpfNleRdq19eTA98Bu0v6B7aFvAxYJWwjWXqCPgUo8mPnBHiP+TvbovScTBgRm6T/n4XtwwrbR/l2Z/uqChX+LCpyvkKF8uBxTC51AebPJsmyIy0CP6FhAzAHcK+k19RQIT4jnQ7EJNPJODV9WawCLSwxHxE9IuKmiJirqUVNIh5+wCnnnwFnRsSOETEx3oTvDfTEaexl2DhUSEiEdKbymjUiJkmnbgBmSqRrFqjJ1gPfYsVPs1k3RUSOmL8O2AsTDJsD50TEoemaQzDJ8Cn22t8WZyItJenlOjS7xZEIxs6YMPgGE5InSfolp2h7Cqv9zsIp6zsBPwIrlYGYh0b98BjOEsiUfQ8C52BbtD6561/Ez8YZOKhRoURIY+VQPCaMAA6IiB1h7LOSjY/Zc1/mAtm/AZ2BCaHpAG+OfFkMWDsFcE/FtUlKkVWTZU6kTKMvsZjjJKyGbYqgXwK4KCLmr0+r2wQz4bnzmbTezNbX1wNXAcsDE6djIyTdVWSy7Q8wDbY8erqJfrgcPwvTpmMjJF0j6a26tbZlsSJeD4ytwdPcviEp6I/GBbb3jYhj2qKBbYE0LoyJiM7h+maZLdqrAHLm6fF4z7lCRNyYjo8u+viYR0T0joiVI2JKuTD2xMC5ETErzsp+DQco5mvi40sD24az/vOBncIFcGoEQIcC/cN17SpU+FMozeBQoUJ7QlMTeyKPzsaky05A6Qr2NYVsAwXMBnyE1Y1rJtUrabGQLZxflNQfp5gujwsYvVSflrcYVsNqpusjYrbajVC2iU4byKwewYWYgLwdp+WuoRLYllRoQM1C8TxMtK6ZnodncBbJbhGxF4wloCYFVsX2Jd/Wp+Uti5p3YTY8TmwkaVkcnBsC7J0bL84D1sa2DnPiQqCvtXnDWxcz4PT7KyW9n22s82OHpFck7Y37YWlgdTklvVPRifkcpgS+xxvITSPi7Yj4Ky7keAdwSUT0zi6W9IykvVMwq1JFlQgZYSLpDWBjrH7dr4agzwjbbtjO5HFMPJQNL2Pbs73DRRzH1KjIyfXDCXgNgaTvc+RKYceIaLD9mgAH4w6KiC6SPsXz6Ik0TdD/FQc8X61Hu9sImQVmlnE2dn2NrWw6kwtqZigi2TYOfIMDdSvC7/rhLOy13bdObWsVpEBVDyyAej4X4P4d0jjQKSImSXuLU7Bl3FVt1+LWQxrjsgzEh4B/4eyhRYH5cvuu7/EYeRGwbERkIqjCjo9NYAkcmD0wImbB88eMuEj0CMxLzIXnk7GBy4iYA2eajCA3jxZRIJbWDtl+63osepmOkomcKrQNqs1FhQoFQw3pNisuejpK9o5/JSKOxur5S71+UCkWQ7VIi6OxpBLwz4hYDqtZzsZplB0kHZYWzp1wQLKzpNJsniTdmgikA4HbImJ9SW9nz0lusyxJd0XEK5hw6wu8Bzwp6eN63kOFlkdujLgB2zydBDyc3pn3I+IIvGk4PiJWxmR8pv5ZRtKP9Wl5yyLXD+dgj9RXScpnSa+nfhgF7JFekyMljYiIz1VeH/EZMUH/Bowlo0bBWJKyJy5eN0zSR9mH0nha2D7J3yeApE/CvsEnY8X0Ojhj4GucZfQbsHVEnFNLLhW5Hyo0jUzxLemNiNgYuBET9Ei6OKd4nDv93FdEImE88CEu+Lkx8EREXJ2IqNqMmYXwOzIk/+Ei90laN2Wk2xN4XrwZZ1Ah6fM0l4CzK5C0Wzp3J/acbrROLxmewePjThHxhqRn0/q6Ixa9DCNZJJYc7+BAxbYR8VwK3Gb9MD/uo7fr2sIWRnqeh0fEJ1gJ3lPST3/wkWmAmyNiI0lvRcReZZg3s3Ew7SnvxkGaa/H6eWtgFyyE+hJM0EfECcBEwIxNjKOFhqS7I2IjGqybXsb1WIan84MiYk8ctFo8Ih7GNZwWweT1xrm5t5D9ooYM5Yvx3noLYGj67gt7XxXqg0o5X6FCgVATnb0UuBV4HngkIs4NF/V7EzgUq/8ui4gt69fi1kGmYEu/94qI6cHqFdnn8O845XL7iDgy99ETsa1LKca+aPDCvQyTr10wQd9IQV+zWR4JfCjpeEkDK2K+vIiI/sCSwM7YF/XTiOiQgjXPArthf/FpMdn0FbCEpFfq1uj/EhExQUTsEhHT5I7Ni/0/jwZ6yhYunVI/vI79c+/FasjjofTk69vAT1j9O7ZANIwdU/bE5EutRVahNxjpPrPnY5Z07GpMwp2CifmDMTm5Aw5UbU+qv1Ch/MgT9JicHgHsH8mDHvsKnw2MxjYnha3T0pTqNd175r//PXAUsE1SjmcERMdwFtIpuH8GtmGzWxVqKHqaFbzcGTg/zRkdU/98ip+BTEF/TVN/p00b3krIP9vp3j/GxNMiuHD838J1B5bH4+cb5OxOyoi0bvgCk5HzASeGM67AqvJ+OPPmxTo1sbXxEg5ArFa7RqjBkni8zFTkhV9Tpe8+K/66JA7i7yrXG9gL2A8Xxj46IsauG2Rr0f2BtVQC268MufvYFRPu3XHgqnsugIGkc4D1gfexCGImnHGwaLb+LPr6Mmzbswywr6QnEzE/NXBYRBwfEdvXuYkVCoIosMChQoV2i4i4ChcuPBKn5E+F1X93ANtL+i6ljB2OFYGbSbqhTs1tUdRkDpyA+2FmrIg9CHhV0vCImAn7yi8GvIDTtNcD+qr4VjZjkY/KJwLhAKxmyxT0Y9Wi4UyLE3HW1FYqQaHPCs0jIi7Bm8WVJP2WO95I1ZdlVtQqi4uIiNgbk0b/wKTKsHR8bWBfPB6sKmlI2jhk2SVz4NTcufGG4ev63EHrIpEtvXCK+Zp4I3F57vxswBV4HN25Lo1sRaQN0iXYkuR2SadGxAzAlbgI7vHp0nVxH32C7c8KvXGs0BjZmFf7e+58po6cEyvouwCX4jXExMACss1TIRXSEbEisCOwc22WVHZPETEPtr6bArgHuAAHJZbE70dXYLEi90NTiIhVcd2JrbC3ulJ/rYSzjs6S9K+ImA4H8xYAli7DGJGtAcJ2RiOaOJ+tFVYArgEmw1kFn+Og5vJleB7G1f5cP6yC/dQnwIT8N5ikXEOuU1Ia5O65F7ZxGYWDeC+ksbKzpJHp2slwAKsb8LdxKOwLhRTUvB+YBPhN0qK5c92wreypeL44XK5Xkf/87+abIiMR9HPivWcPPE5eDRwj6cuaPWindM3w/LGi7ztgrAjoAeD/0r+r42yBYXic/Amvt2+qWyMrFAIVOV+hQsGQNgkXA7sDD6ZUyvmAZ/FieRdJv6Zr58MTxclJIVpo1GyoM7uOS3D2wA3A65hcuT8pnWbAhP1iWOG1s0pS2DGPPyDoN5T0Zlo8zYSJ+XXwhro0AYr2jLQZ2AZ4XdJj6VjgDfM9QAdJK0UTqZVpLHlD0mfZ58qwaYiIk3Gh42OBC3L3t0Y6NguwnKQXazYOs+FNw6d1anqbISKWwEVv58DzyWBgdqwU74DHiFFFfSaSYmlmTJz9iufHdyX9FBF9Mam2BLZp+AewKX4u9pL0bvobM+Eso0KnXFdojBzx3hXo0lyQuoagvx6rZF/AAf6RUVBSIez7+xgwUNJOueNNzREZybY0FoF0xO/SC8BuOdVj4fqhOUTEFphcmhyYFCukD8ACkCmxuGFhSR+n/vkmkZaFHiNy5OuEeO1wlKT7/+C66fD4Oh3wATA4BXUK/TzU7DMuw0Hc25q7Ls0TKwKzYtXwgyppDafcPa+Na1d9jX3DH5ILoBLOSjsQ1yVYumx7z/T/0zEJPwYH71/JncsI+hNxcLO/pO/auMmtihqRXG3fXInrNl0NHCtpWNqDTgRMrlxh5AKvL5uaK2fAFmjd8Tw5IeZkjsZzyXOYizm2jZtboWCoyPkKFQqGiPgbLlK1iKT3E6H0L2zL0E9Wja8s6YF0fRflVLNlQNiqZhNgu6Rg2hv7Zw/Dyq6/443C8HBRrzHYa74UPtpNoRmCfgSwAe6XK3HhtqUqYr4cCHuD34xJxQ+wWmtk7vwZOMV6fknv1iibFsME9lmSnmzzxrcyIuJUnGZ8DI0J+tVxAG9mmiDo2xMSSd0fpxtPiJ+hl3AR3MKqH9N9nY/vaSqseh6Nifh+KWA5NbAUfj7Ac+jfgBPkFPX83ys06Vbh9wjbEgwGbpV0RnPfcY6g74Pn1B2KTEinwO2JOHtogRSg2FG2Hai9NlPQdwJ6Y/IRHAj+Pn9NW7W/LRC2o3gIBy7fwZkS+2A/+dmwDVY/2RIr+0whSaYMNd/1IKx43qs5McsfvC+Ffh5qSMfT8Pp5Z+CBPxof2riZdUcioFfFGdt/we/EvdjuZhr8nqyrEmQP5OaA2ozTA/H6YRBwsGwpm53rhtfXqwErlOkZiYbsmu74/ZgWeBr4IuuDcHb/mrgA8MlYLHY98Iqk/6tPy1sGNWPEVMCPOAN3REQshAuDf4fv9b503dQ4UHOppAuKPl9UaF1U5HyF/2lUA9jvkYjX83B0dlLs8TgYbxp/ioi1sGphZ0lD69fSlkMN8TwpXhC9Lem0RMwfj30wX8bpZD9gL+37Jf1Sp2a3OMa18WmGoB+JyflFcKHPwi+WK4wl5p/B3+3xwBBJPyfyhaRu6oMLVn2PyZgv02enwV7CSwGrlFUpPh4E/fTA6rL/finQ1JzZhLIprwzsiRVNM2MLlw/Ss1NU8nFx4EG8KbwBeAWnUe+NLd46YNLgn+n6jng+nRVYIf2ZFSU90rYtb100o/Rql8RShoh4HXhH0jrjuK6WlCnkuwFjyfmzsJ/+LtgC7D3gr02JF/5oDV7W9Xnqo1lwH32EbTuGpnMrYMVwP6VMtbIgEYpTYqHLRcAjZfx+xwcR0RuTio8C1xY54PCfYHyCLOk9mQyvJRfCJO3nmKQfoJSBVgakIOaTwMWSLsgdPwqLwe4Bjqwh6LsAI9N6qhRzbS5zohfuj+kwF9EFB2jOlXR9unYAztT+AhDOOJonLyAqGmr22KcDy+I15UPAKXLB8Nr1wgw4U3N1LAj6oO1bXqFIqMj5Cv8zSGlP3bBVyWhMOv/Q3hZFGf5AmbIwVsu+gIsw3Y2tbH6MiClxYdDewDYqcCpdWvh1VbLoScdmSQrgZfHzMSMuinsUcFmK5l+AUwqHYf/9e9q+9S2HRB7NDnyilH4f9k2+UzVehulcfvGwPbbx6AEsWynmy4Gw5+V1WNG3M/BeU5votDnYBG8yBVyLF9F98Di7XHPKuLLgDwj61TDJEvj9+q3oRERO0dQJPxvD1ZBq3uRmuxkyv5AbyaR4vRXbT+yXJxvT+mJjbGEzMbB4fpMUESvhd2VBXBS5NOuO3HPRBQekpsDrh5FJLVtKkrU55FTCf8N1af4m6a56t6utELZieQen3r+LA5RftLfn4M8gvTuz4jnjF9xnhRsjm0MaH+8BVsFB2hUlvVPfVtUHSTG/M7Zs2VLSkDo3qU3wZ/caNZ/tAPSW9E0Zx5GI+AvOxlsJ2EnSlblzGUF/N7aCerPms6Xoj9y82RH3xYzAEVgAsS6wJ15f7C1pYPrMYTiL4ldcOLfQWWc5UcsAnIl+I65TNS/wFq7v91kuiLErLhK7IhZIVXvwCuNEp3o3oEIFgIjogYubrow9DMFpQVdExICyE0i1qEmbWgwrwX+U9Dn2V78PF/N6EzgwEfOzYu+/LI2u6MT8RsAsEXFeur+bgO8joj/wRFokbA4MB+7KTfbfYRJyCrzxLDoWwkq3G4DTI+JuYFFckOh3UM4fWdJlETEaeFzSe23X5AqtjGkwwX5yClZFdiIiFsBZEj/jtMqrI+JtnE2zEX5fXsT2RoXNrBnfDY+kfVL3HJo+d4GkzyTdFxE7AO+ricJ3RUOaM0YlRdNl+Pn4KiLulnRStqmqJZ2b6sMCk05T4AyAk2qJ+XRPA1Pg4lzg7IjYBPhVxoPAg7lNVaHtGTLUPBeDsCJ4GuBj4JyIuFHSx3VtZCsjGteViNz3+i9MtC4H3FUWEmVckPR1RAgrHjthK4ov0nNf+j74s8HHiJgYB3hXBTpj4vp3NhcFRwdcwHJCvOacBwdw2hUS8fgUJtQWBmYC2gU5z5/ca0DDOiy9D4Xdc44Lkj6KiP/DZPRlEUFG0Es6Iq0xdwMmjYgdJX2U+2wpxtO0huyOOYYZsFXq02kMvDYiPsPiwF0j4jlJ70g6Ov83SkLMd8Dj5NZp3fsIpdEAACAASURBVEhE7IO//1siYgNZQT8nruH0GS6UXfjaCxXaBhU5X6HuSJvGh4F/Y3uWfrjy9yp4Qbx4RBwk6fH6tbJtkSPmr8SR+omBpyLibEm3RcQueGO1AjA4Ij5K10yBFT2FngTSJnEOnAo2dfp9fmCdRDRkZGQPPElOB3waEZPjZ2ewpPPq0fZWwDu4ANupEbEdtqBYTdInzX2ghqAf0FYNrdBmmB0/5y/D2PelG1aBrotT0wHei4jDJF0HbBAR0wLfpM/8+vs/WwykYO7hEXHt+ARuE0EvTNCPiYhLJX2cLayLjoxwTBunIdjK6gFc8HTfiJhL0nbNEfQlwkI4zT7bMGVj4JgciXBNRKwMrAdMIenD/MYrR1CWoo/Sd94Dp6B/hQukf4T99U8B5oqIvdVMUdSiIiJ6KGWNpDVDD+wh/ip+P5D0VkRcCOwXEZcXfd00LuQCT30xCTcEK/9OTO/KPWUn6KMhi6QbJmDfzBNpzWAZHNR6CdhdBVZ/ZqgNUKR7ugMrXE8BjomIF1VyC4baZz2Nl1k/nAQcHBFDJL1ft0a2Hf6TvUa+78bUHisiokEhXvtsvB1WyYMJ+sj2V4mg74VV1M32VwlwHd5jfAw8l/qps6SRkh6JiDOxpeBU1AT3Un8VcszMEfNX4cDEGCyOzHAa9tXfC7g1IjaS9EZErIczWEsbuKrQCpBU/VQ/dfsBemG/ywdwWlDnmvN7Y6XnE8CC9W5vG/fNEcDbwHaYVHoJ+/ltlc4HTsM/CbgCR21nrHe7W7gPzk3f/494kVh7fhHsp/0k9si8BfgSmK3ebW/hfuia7msULgbco95tqn7q+jxMgjcAg9I7sC1OLR2Dicl1sWLjO0zgT58+F/Vuewvdf790r9cAc/2Jz52YPncw0LHe99EC/RBAh9zvfyEVLUzHJgROx6TslbnPFf7em+mPNdL3u04z57O+WjyNpWvWu82t/Xykf3fD2TLz546tlfqqX73b2Qr3vQAWfCyVOzYg3e8dwO7YQrEDMF96P/ZM15Xu3WjqnnLvwnxpDfUSFnY0enbK9JP1A953PI59gtcf171itfzUuXen0M8I0Cn7F9tQzAhMko51BdYGPkxjxoz1bm9rPw/p94nTT6/0/27YLzvrhxnq3d426pNqr+F+6A7cBPRt4txsOLtgFLYxyZ/LxogO9b6HFuqHjjX/nxAXfx2D9+fd0/HO6d9JMEm9R73b3gp90Ru4GAuc3gYmT8e75K7ZA9vbvAVMU+82Vz/F/OlAhQp1QooyP4VVXBsDr0oaGREdUtoQkk7DVi1LAP0iolvewqFMSOmUeXTG0dgrJB0D7IN91s+MiC1l3CBpf0nbSjpXBVa5pO92p4hYOSlAwT7ZHdO/64U99cmeEbmQ46bYR3tFnDmwkqS363ALLY7csz4DTsEfhImFXcKFHCu0T/yMieal8UL5Mrwg3gVYT9Ltki7Dz8o8uFAXkgqtaMog6RI8L6yOFfRzjefnDsA1GG5VgZXRae4kzQFjkgr0ReyH/CPJzku2djkGpx+vEfbJRFY7lXH99y4O5q6aHcivF9SgFB2DidlSvA/NIfe+z43n0aGSFBFbAbcDh0q6JCImjIi+YcufMqALVjsfFy4QDC6OviUOYp2AA/rHYnHIs+kcRR4XmkJSeI9O66sNImKXsNd+x7SGehnXLpoBK+hXh4YMkjo2vUWRyy7qiefMX/BzcG/tvFg7Nsqq0M+zPinyMxKNba7uwJYlzwJPRsR6ssXbPTigNzFWgc5Qvxa3DqKxdehpeDx8Abg3IlaTMwvvpqEfBoV9x0uJaq/xO8yM95QXRsRC+RNpf3k+Jucvj4idc+eyMaKo1oBjkc/IDBfEzdaUK2DRz1bA3yKie8bd4P3GL7gIbKFRO//J6vfjgEvw83FUOv5buCYJks5O54fjQFeFCn8e9Y4OVD/t9wdPbmNwmnWPdKxD7nz+9wuxuqeUkUgaKzgWxaTb7eSUTOncClgR9jWwSc25wiqdgJ7Y0uhdrFboko7Pln4uAr5Nz8zUWZ/RoP6aAKuhJqr3vbRQf3So+X9gJc9kwNl4Ubgv0DN3TRdgwnq3vfpps2ekB/ZE3Q3YBi8Es/chUwjujhfJM9e7vS143/mx8pA0LlzHn1DQF/kHk473AzPljvXBGRO/4MBDNj5mCslJsVXBF8Ad9b6HVuqXTLF2alpXbFVzPns3OuDMi5fyfViWnybmjo5Y7fV8+v8WqX8OzJ0/HDivTPMHzo74CgtAFs09HxOn9+VKrH77ARMNY4Bt693uFu6DvFL8ORyIGAGMTs//WsAE6Zr5cabV88D69W57K/VHh/ScD8EkZNY/s+LM3UVy1xZ2PT0e/TAB8Frqh35pnXBNegd2Tdd0xgr6d7Fn8lT1bncr9cVAnIV4JM5CHpT6YS+87u6MFfRvYxX99PVucwvff7XXaKZf0rzxOrZCW7jmfFec0f8qzsIp5XiR1gcP4qz9brnjPYGhWAxyAc5W2yH1xTMUP7uoNmMgcr/PTMM68/Tc8byCvne976H6Ke5P3RtQ/bTvH+CRNLjvlk3+NYNgtqFeMw2E69a7za3cH9ek/vgK+6IeTUoby12zAiayxwAb1LvNLXDPPdNG4WFcZyAL1HSque5iGgj6KdKxCYD+tQunIv/QmHxcAQdq5s0dmwI4Jy2a98K+kF1S/5xY22/VT/v5qXl2JsN+wg9SkqBV7t7ygdt2RdDjYlznpN/zc+XiONV6LMmIN9p5gv4S4C5KknLdTP8sgkmnMcA2tc8N9o5+MvVVqTbUNJCNXXAhw67p/+uk/rg8rSsOzn1mbuBR4JR6t78V+qNvWks9ibMv8+9LpHdiLxzs+gW4tt5tboU+6IYDFI+ktcSs2MrleUxKrklD4GLe9JwMqHe7W7E/7gcuT7/3wOT0+9iq4Atgt3q3sRXvPfueD8fBmtlz53ZJ333/mmdn4/9n777DLCnKho3ftQmWJSNpQTIIqAgoIKBEcwRBEBUwI/oqYEAEA2LA8PqJARUDgqIIghkT6oKAYgRFMbwKS1hyTpJ26vvjqcPp6Z1ZBpg5Pd1z/66rLub0ObNU19Tp7noqAafT8mDbKOWxL9Fh9dTKtXOHUg7/j/4goSWBPYkOra4OdJiybY3K3z7VXs8AtiZmrP8V2KryO08hOnK2rPxep54nyjnNLNeGBaX+1wP0F5Tvy2XETJxPVJ47WnnNqH0v3kEsifcdYhZqL061FiMH6GcOMq+mbqbGM2CaWonobV66duw8YjPY/6E/imfYum3A3HIRfFHT5zDO5VG9CRxCbKDyivLz2aXB8Pz6QxAxbf8HwGOaPodHeP4ziQDiL4H1x/D5L5cyOb48TH6OCMx15oG5cq5fKw9E95UGxCGV91ahP6rlDGIa8n10qJPC9MDfOo308wifq15L1isNqFuAxzZ9DuNUDvURXlMuQF875znEiKWdK8e2oj/y76W9OkO/sblc/d7axURMRz+7lMNXiRFdTyCWhvsNsQRQr9OiE+VQOZ+lieUYvkwJSJe/+1eIpa96MytmER0655cy6f1+qwMM1AIlDA/QL7J+cPnMSsDrSvk8telzGOfyeDoRhH9a7fhaRGDlwtq9Y306EnSrne90Yh3prxKjO/8fMWr6fmJ5gpcRe1+dW71OdjGV8/4Z/XWi9y7XykPL6xUps4qoBJtoabBtMeXwfmLWTG+wz/rlOeLr9AcJrVH+u0jbtSuJKdzWqDwbLU08T51MzKDo7dM0kwjQX0x04r2tPE+cR8xu79TzFCO0N8qzwv7EXgR/YXiAfg7R0XsTsT9eLzC/RNPnMg7nfzoxY+C08ve+vHw/Ni3vr0kE6BcCX2w676bupMYzYJo6qVzgzyw3+OVq740UoK8GIg4C5tPdqZW7Ej3O/1M5tjERtL6ekQP0rd+oh2ggXgS8aoS/+ROIUU37UGkwEwH5m4nA4wJgy6bPY5zKotpAfj8xnXhvYnTPt4nG03sqn1mBWBLqb0RD63FNn4Np3OvErIf4+ZmlYXFGeYh8QtPnME7lUP1urEmMCl6j9pl3MQUC9JXz3a5cE37K8I0vn1QajfUA/YhLxnUp1RpWTyA2Vb+hlMUQMfrtBCobIjad53E6794ghmWIafi/IJavqTain0QEJofKc9gFRKP61/SDdK0Nvi0u7wwP0G8z0u8Qa+XeALyu6XMZ53LZnwgePHGEc96jvLfPCO+1+rsxWn0g9in5YXluOhXYofLep4gZBq39HoyxbM4Efll+fjHDl7maARxFdObNaTqvE1wOXwP+Vn5euzw/nEJ/dOxricBc12Ye2tYYXh5LEe3Qi4jR4dcSgdiNy/sziI6b3tKB1xEdeb37Zieep+gH46sdcr1ni16A/lpGHkH/T2Lm0X7V99qSqHXGAocSnTHbVcqgN7vonZVjaxHLLt8KrNr0eZi6kRrPgGnqpHKDOxG4AjgJWL72fjVA/8ASN8CjidHVJ3bxYZEIINxRHpB26p13+e8G9AP0z6XlDaYRzn3XcrPbpXJsKWL5hWvpB1WuAl5d+cwzgBcCazd9DhNQJquXB+ZXVx4ANiJGdQx7aC7vPaqL34upmoilN/ahMjKpNApe+iC/N4MIyJ1DjJrdsOlzGafyqDYkP0Vs5ncdsQbsC2qf7QXoT6IyPbtrqXJ/eAYxou0XLBqg742gf03T+W2ibCqv5xJrjG9frpW9suvavXQm0SlzFhFo6t07ViZGDPcCCS8ilir4GDFa+IEp/E2fwyM4915ny1LAYeWZ6lW1z4wWoO/VhxXKM9iRTZ/POJfNjuUa8YryelqlbmxMjIJ9RdP5HEB9qD4/rk6M+Fy+vJ5ZnrHOBz7ddP7HsRxGDBoCBxMjQr9Y7hGHVurEFsQSk0c1nf8BlMPLiJm47y3/PZkSiCfanScRs3S7OmJ+Src1Ktf+vYhOh7WJ5+i9iI6IGymjpCu/swWxFFyvvFp73xylTGYSnfbHVo5VA/QHlLrxayqj44kA/V/K/WSfps/jIZzviH+/ci04jbK3Av3OuxPpz6rpPTutSZl9YzKNR2o8A6apkSoX95lEgOUqYurgaAH6N5YH695mZldQWR+xS4lYguK35Yb3mRHe36A8OAxR2yC27YmYPruA6HzZhZgW9zdiNNeZwNPorwn6r67WgUp5vJt+Z8RzRqgnvYfmI5rOq2nC6sBqpUE4VBoC3yBGdG4zht9dpjQqO9GAYvhI6JOJ0UyvItZdv7hcJ15Z+513lrL7Eg9x5kGbUuWe+qxSDvUA/ROJJRp+3XRex/GcH9KIVkZZmoKOjHSrndNaxNq4+1WOvYjorLuYWI6gN1W/3oHRupHCRAfUOyqvexvU/Yf+zLqf1H7nyUSn/zkMn403ndgw+b9de8YgNsD9ATEAZMfaezuXZ6vnNZ3PAdWHn1ber842eQ7R9vgTLV7eicoSG5XzmFmeHdemPyt5Y/prRZ9ajk0HNiM6KH5FbR3uNiYqS7qV148ClqW/9MYG9Ntev658bi7xDHZZ164HlXOcsm2N+ncc2J3Kniul3jyXmGl3I6PMxOzKc0SlPJYEVgWOIZZ4+9BI50oMGBwiOvhmVa4Vy5brRyu+M0QH7c+BZ1fPs5TDP+nvTbIh/Vk1vWvoQcRgoNZeH02TN81AGoCc81BKaVrO+b6U0luJm98ewLEppTfmnG8pn9s+pXQe8GHiov94Yrrd9jnnfzWV//FSymCoeiznfElKaQ+il3aPlNI/c86frrz/75TSm4jlKi4ZbI4nTkppGtFoOoJY0meP8tYfiU1eT84531E+u5AYDbxSA1kdpF8RU66fRzQcHqgzpZ58lAjEvT+ldG/O+WMN5lXjKKU0Pee8MOd8TUrpy/RH8t1MBOmvfLB/I+d8O9G52Qk552gppXQYsUzJXjnn81NKbwHWIRrWX0op5ZzzCeV3jk4p3Qv8MOd8bzM5n3iVe+pPUkrPJZYyOiql9J6c83k55z+mlF5DdGq2XkppRs75/pTSbGJk26OIgOMpwK0555xKRej9TvXnqvo9uCMycW/YKqV0GxFwfA0x0us/xCy1N5fvUiJGU8cv5rxw8Nl9+FJKM4k68LZSLz4IHElcIw8iroEvB96RUjov57w9QLl2vIBYY/9lRJCenPPClNLPiI1QO/F96ck535JSOhZYA/hRSul/iKDTisRa61cRHTet9XDqQ7l+LgN8FngM0QG+Y7nGTG/bd6KYCywo17fe+X2HCMbPAq5KKb0t5/zzlNIriSW+tk8pfZ8Izs8lRr7uWr4TrSyHlNLMnPN95fq/sBz7FLHZ69LAFSmld+Wcz0spHUAMFFujPHfdRTx7bQE8o2vXg4op2dYodfr+lNLSwKdSSssTnVK/6n2m1JszUkoQsYizUkpPyzlfVP23uvAcUZ6Z7k8p9QLrbyH247gdOKK8/87aud5KPHdNAxaWa8WMnPNtKaVtR3vumoTmEve/c3oHynnenVI6m3iWeiHRUXcm8Nqc850ppQ2JTaT/SnR+dradoYY03TtgmlqJfg/rTGKTmdFG0J9F9MzeDGzRdL7H89zLzysTD4CzgNnl2LpEYPpS4E0j/H4ndwEvdWFjYrrcS0uZ9EY19erL64le+o2bzu84nvcioy6IoMn2pf7fCDyld7zymQ2Jh6dNB5FP00DqwtLEyPDqUjbfKdfAeygjomnhCNdxKJvlgQ/0ronEZtn3EGvlPo4Y7biQB1n2p42JMYzKYfgI+vuJWVY7j/SZtiaGj3L9CzGq6SrgTmL90/1p6QZkj6Q8asem0+/Av4YIQD+n93lipGyXluxYi1iaZ4gIwH6EyiyaUldeTwQSzqv97mPpwMjgB8t/7bnhWcRSV/eX6+UV5Tmj9fsNPJL6QGyYuw8tX96JmDF3L3BM7zzKtfE3RMDtXcTsgLuAt5TPbAK8mejY/UIpt7aXw6xyj/h05dg3iPbDJ4gR4ReWe8ch5f3HEevsn08s6fNxOrIsYDk/2xqVcyOWefs7MXDhPKIT72aiQ3t67XeeQyyj+KOm8z8B5dH7rk8HjiUGuzymHFuVeO4eKtfSmcRzxPrEhvO71v+dev2ZzIn+M2WvTnwAeGPl/X2ImXQLiYE+veMrEQMF/wms1/R5mLqZGs+Aaeolxh6g/zbd3NDwM8R02yFiRNuHgPXLe+sCfyAC9G9sIq+TIdXKayViVsEv63Wkral2fnOJNevmltcJ2IZoVF0/ykNzJztqpmoiptB+j8pm2MRmZK8m1na8m7IMQ73x0Pt80+cwjmUx0vntQiz38wQi+HhA5eG6t4zNEB1aO7nWcFpsg6dSFs8s5fDJpvM/AeWxBDG67SxipNtcYhr1LUQHTec3AS7l0JuCvgSxLMkelM6Yct3YnBj1uUY5NpMYGfwH4K1N538czn8a/Qb1GuUZshdw3rMc7313liICsjcD54xWlm1NY7lG1J4bliECcnsDO9GBdZMfQX04d7TybGMilq35Srn+f4CYGfETKpt3Es/SxxMB+p0X82+1uRzmEhu9DhFtq2lE2+GZlc/0lg78L7UlXcr7rQgwjrE8bGvkRa6VWwPfLeWxPLAl0Ra/mOisqwfot2vzd+JBymU28AJi9tRLqvcCIkD/XmI2zc/L9eVPwO+pBbfbnIi9F24of/9qh+7by3XkTCJY/0YiFnEzHYlNmSZnajwDpqmZGD1Av1zTeZvg8z6ZWMfwCGJ99W8TwYUzKOu0Ecs1nE+M8Hld03luqm6Unzcg9hy4BXhs03mbgPP7HBE0uYUYxXEQ/eDLNsSojuvpj5xu/YOQadR60RvB+A4qawMTgenfMDxAn8pD9VOazvcElsfOlI2XKsf2JkbAVWcYvJvo2PgIHQnQVq4Bc4CvAm/lQfYRqDSWtqHFwbbFnN9TiNFKz6QfjHteaTy9tbzu9PWx8ty0DNFpdzkRiBwi9m3Ztvb52aXcziU2Um5tgGGEa8GSxCauKxBBuHuJ0b/DRsQTAdneJnafb/o8xrE8xnyNWNz3oq11Ypzqw3FNn8c4l8naxOCfIaINcc4I5fSo8t4fRnivE9fPUg6fLuXw3fL8tGbtM8sT+7T8i7LpYwfLYUq3Nah1LJT74elEZ803GL6O+sbEwI8RA/T18mx7qlwPf1y+J5cC65Zj1QD9ssQs1YuJoPwplXrT+kFBlXLYlJhxczHwmsr7B5brxK3EfnjfoiMzSUyTNzWeAdPUTSwaoL8c+D4dDdAT0+OuBl5Yeyg4sjwUfQ5YoRxbtzxAbNB0vhsqqxnEdMofAv+mg73UREfNFeUh+WBiyu0Q8ElgyfKZbYCzy/EnN53nCSyL1jcEHsY5z6FML++VAbBV+VvPY/i00V2JYNxd5efVicDDEDEarlPlR3RcDhGjHJesHH8TEXTZtlwjliM6dY+kI5u/MnwJl78So8XfOZbzY/iIt04F6ImOmTt7DSNiJNMQ8M7yenngf4Blms7rBJfDbGL02tnEUiVPIUZ03UFMS+914C1frhHn0/LlS4hlv94IHFleTyMCTO8tr9cilqIYAt5V+b1ew3sOsFtXvhOP5BrRhWR9WGzZrEM8Q95MBJpWrdaZ8vNRRFtkbtP5neBy+BQRjF5AaUsxPGD9+vL+Zk3nd4LLYsq1NYgZQl+j0vFCbAg+nxglfVzleC8WsQnR3rwIeD4dCD6PUC7DAuvAKkRQeoiy11+1TCq/N5vK0oFtvXaO9PxTKYtNiQB8PUC/DNHmWoJKe8RkmqjUeAZM3Usj3dBGu8kxPED/FWIduE48MNbPuTQmbgNWKa9nVd47vjwwrFk51sqb3yOpD5W68Dwi0PBlOthBURqGlxCjontBkyeVB6TP9x6CiIDtDsT05I2azvc4nn/rAkQTUAa90XsfrR3fi5hJNA94WuX4rpXG09+Ba6mMIO9SIqbTfomYdn5gpQG5JBGYvJwYwfILIgjxmKbzPM7nvyQx6vHnwHqVBtUStc91rvG4mDJ5JjFK/HHAM6gE5sv7uxMjJLv6negFFl9KjHJ7cu397YjOi6+X1zOB9xMzcdq+jvRsYkTwTcQGnpcSU80fXfnMmvSDTtWAbP05rJVlMEKZTNlrhPVhWP4X6Zgn1gr/ZDn3T4zw/mHEDN61m87/eJcDwzuoNwSOKfeNz4zwOy+lQ/uajVIuU7KtAexH2X+hdnwXYs+Be4H9Ksd798iNq/fRLiaic/N4YK3yegXgH8Ta+i+rlMWIS9eMdM2ZzImYLXUS/aX+xhqgf/UI77fq3E3tTI1nwNStxPBRCY8GVhvpvdrvPLDuJWWUR1tTecCZVTvWuwG+htjIcOfKe71e6o3Lw9Je1X+r6fNpoj6U95YieqoXu5RDWxKLNgjfUhpHvYeFDYiG5tcpU42BzSuf70xvfa1OvBp4D7Hm4ZpN5amhcphbzn0hEUSoNir3IoLv9QD944kN3D5GRzYsW8x94VFEgP5eIkDf+16sR0xVvwD4KZU1dducan//pxFroG5XObYLcBxwAjGzoNVBpYdRH5YngpE3l3vlgZX3HkN0XA2bqt7FVO4dd9DfpyZVnqHeXK4nO4xQp1rdIVqeD79FBNrmM0JDm+EB2cObzvMElIHXCOvDsDIo/51GbcYQ8UzZW9rlGPp7dGxOBCd/SAfaGCP8zevtr2o5fJyybxWxP8E3iYEOKzd9DuNYFrY1KuVAtCU/RGmHl2M7A38kZtvsU69HxLJIrb5fVs5pkT0DiGV1/1p+7l1DVir3kyuITqthAfo2p3I+dxOzj1er/q1HqTO9AP1fgNc3nX/T1EszkMZJSinlnBeWnz9LjHSbmVK6EHhpzvmOkX4v5zyUUpqWc76fCEi1UkppGjHVfqmU0kk55/+mlE4lpskdTkwvT8B+KaWLc87X5pzvLb++LvHANL/37+Wc80BPYJw93PoAkHO+i1jCo/VK3R4qP+9JrI+9NNHxsCClNJdYD/hM4ICc810ppb2B16aUXpFzvjLnfHdjJzDOKnXim0SdWEg0Gn+WUnp3zvmCJvM3CCml6Tnnq1JKxxKBtv8FbkspHZVzXphzPjWlBNGoPCK+SvkXOeeLgIuqdartKvVhU+Bf5T5AzvmGlNI7y8c+CeSU0gk550uA3VJKSwFDXfhulPqwMKU0J+d8JzCLmEq7RErpCcR95W3EyPD1iVkU/yivO6NSDr2NG+8FLsk5/yjnfEtK6XPAoUTj+cKU0lrEJsHvIQJU+1WeJzrx/RjBf4lR048mGtPTiMATxHR8iOvpsGeI3vesjVJKM3LO96eUViBG980mNms7uNSXGTnn+3POV6aUesuZfCCltCDnfGKTeR8vXiP6rA8PPFfen1JamlgSc/2U0r+AU3LOP845/zul9Mny8TcDzyZm595BBKp2zznntl8rS/57zxDvAh6XUroK+E7O+ZxaORwC7JBSupEYHb09sVHs9Y1kfpzZ1uir1OmnETNFHp1SOiznvCDnPC+ldBixjMv7UkrknE8u145pOefLoH/NbegUHrGU0mbA51NK+/TOqViaiDcALCzneWNKaRtixvqHieftU9t8/hXfJDof3gZ8P6X0gpzzNSP8fTNAzvnilNIexMzcV6WUTs453zr4bGvKarp3wNSNxPARPccSPfXvIUbxXEf0Qq7XdD4HUA4fIRoChwI/Kuf+xF75EGviLiSm5G5ejq1DLN/ydyojy9ucrA8jlsO3iQ6aTYgHxutKedxEjPhcrnxuLrHB2yl0d/+F5xLLk+xCBBjeTky9PQ94UtP5m+Bzr47yeh0xsq83GvgDtTrTG0F/JtGIbDz/E1QmR5fvwdNYdK3L1YkNs+8AXkHH1hSv3BuWJta/fVu5BlwKXFP+exnwkvK5Vcs9ZL+m8jzB5bF0uT9cQywDNwR8sPL+y4g1tu8BbiRGN/2QFq+pPko5jDaDYLlSPhfWvwvEcnCX0ZGNomGR6fTbELMMTyaWScmqIwAAIABJREFU/vpk5ftT3cRuRSIQ14mR414jrA+jlMdsYgbZRcCp5R56KRGM75XDBsTI+evLNWNFKrOVmz6HcSyLk8r9oHdvuIRob1XL4ZPEc/cCogNrrSbzPM7nb1sjDxv93vu7L0EscXNnOfc1Kp99GrFJ7t+pLGHSlVSeB24nAu7VJb+OBr5Vfp5R++9KwP8RM5Ke3vQ5jEMZPLAcDbHnwuVEB9XiRtCvQQxwWJMpEKcwTb7UeAZM7U+1h4I1iN3QX1A59lzgn+UG2PkLXXkA+C/RiNq19t5somG1kNiQqdfIvoHK1MI2J+vDA+dZ3YBrrfJAsGN5vTSx+fEQ0bjqTS9dm1gLcAGwcdPnMI5lUQ+4vpDYrKvagD6QKRKgL+f77fI9+BQxzfQPpT78b+1zewL3lfqyVNP5nqCy2LiUxd+Ap49QX/YrZTPUpUYUw5cl+CDwM2DLcmwNYkr6iylr6hPriG9DrIf5rKbzP85l0WtMv4sItm8EbE3MKrkf+FTls8uUevJ8YpmGTgWbKvViNvDyUg+eTln6i+i0uwn4M7BTKaunlHvML+nGVPTqd2NO7b2VidFwV1FZV7h8Zz4ArFj/d9qavEZYH2rnWn2+fhIxOnrd8nrVcg24rNSL3jX1McCJRJCud6zV14haOaxPLAG4PRGEW414nqqXw0bEUnm3ASs0fQ7jWBa2NYaXxxzgO8Dq5fUsYH9iNnY9QL8rEbD9ZtP5noByWIJ4RrqcWMZn7XL8a8A3Kp+rd2isQiwZ1pWBDg8WoJ9Z+ez65dxPZ4pssG6afKn3RZQesZTSl4mHwGnA3jnnK8rxGcQI2c8QQenn5JwvbSyjE6Qy1farRIN6CDgSODbnfHPts9sTDaqVianHJ+ec/z3gLE+oqV4felJKnyI23Fkd2COX6XEppdWI0T6bEIHJa4ie+vWB5+acL2wmx+OrNtX2YCLgtCMwP+f8+urUwpTSgcQo+iuAd+Scz28q3xMppbQvUf93A87NOd+XUlqfWIP/MGIGzhGVctsNuDjn/K+m8jxeRptGn1LagJhttJAY+Xd2Lst+pZT2IzYBvQL4as757wPM8oRKKc0hlhzYixj1dywsuqxZSmkWsRbmp4ky2jV3YMpxfWpxSukIgJzzB8vr1YjNk99DbOp30Cj/TquXZ+gpy8HllNIyxOjH1YhlbGYTgfdjcs4/TCm9GHgvcY+9lxgdeQ2x3vx9bS6PXt7Lkh3HE4Gke4CPAr/OOd+UUlqFGAm7AxGwPoFYW3hpInjd+u9Gj9cI6wMMa2NMJ5ZgeDnReb975RlqLtHxvzpRHp8o15M1gKtzB5b9GuGesQ0xGvhFOedbyrHewKB6OawL/DfnfE0DWZ9QU72t0ZNS2p2YTbNJr22ZUppJrD3+OWLPorfnnBeU97YC/tSFa0RduSc8m7gnXE+Mpv8oMQNvD+K5Ykliqat7iM2Rz6/8fquX9ump3EMS0b54K/E92C3HEqPTiOWFP0LsgbZVzvnPzeVYU5nBeY2LEnA9BHh3OfTcnPM5tfd3IZZweBSwTc55/qDzORFGeFBcmxi9dDTwIiJA/9mc843l/WEPxr3G+GBzPbGsDw80lNYmluSYC1yQc961PBzMzDnfm1JaCdibGP04ixjtc3LO+T/N5H7ipJROJ0ap3ENs5nYHsVTLP2pldgDxgHQ+8MKc8z1N5XmipJTeDryDCBpcXjm+FjGS/gVEMPJDbW5E19X+zhtQgoq5rHOaUtqQ+L4sJJYGO5PY0OvjxNI/h+ayJn1XpJSOIkaL3wnsm3P+bv2ekFJ6FHEt3ZIYDbV9CcC2uuFUXUcbeC0xYusxxDXwtMrnViFm1rybCLS8vUvfi55KeUwHvkisK/8uYqTXk4nrIsAhOeczSgB/b6JOXEuss/zAmtsNnMIjVumcmE2M9vsvMYDhsUQw6SjgxJzzdaVefBjYnRgEcDEd6Jyom+LXCOsDw4JLyxAjX1cjAmrX55xfXIJL08u5ziUC06sCJ+Scj6r/O02cw3ioDfY4muiM+i+xXM9ziGeH3vN1r6PiUcBXeh2+XWFbY2Qp1lq/EHh2zvmnleO9AP1niXpxRO35u9XXytGklJYAnkV06t5CPEtvRswkWKZ8bIi4b/wKeEbXYhIwYoD+LUSA/oXEtfQEYqmj7Q3Mq1F5EgzfN3UjESNUXkM8KJ0ErFp7fwbRa/t7YP2m8ztO51xdP/odxBqH1V3hv0U8LL6bMo2SmHK3D7BpeZ0GlV/rw4Sfc3Wq7deJhsOuxIjHIeDllfdnDjp/Ay6L6ndjZ+DXRIBpfWLt8N5Uy0eP8PlXARs0fQ4TWDYHEUvV9M69Oq1yd2IZj2Frbbc91b4bx5W//7XAuQyfZrweEVS5pVwbziEaE5s0fQ4TVC4ziM6HIeA0YO4In9mA6Kj4LLU1Qtueyn3iYmK94OtLOZxX//4Tgft3l/ff3HS+J7A8Zpd7xhnE+vrV7812xAbzZ45UT8pnWjsVnf70+unAU4kRjnPpT0v/drluvhNYpRxbjljG5dmV3+/Ed6NSLlPyGmF9eKAcqutn/7Gk04hl4IaAN1Q+29t7Yy6xYfTpdKSNUbsWfq3cL84G5tfvC7Vy+AexN8mKg8zvAMtiyrY1Kte6anmsRDw/7l9eV5f9mUV/mcQjm87/BJTHaHvVzCZm6l5Uzv1NxKybPcq18oXl505cKxfz/khL3PwJ+CmxPn8nlhc2tTs1ngFT+9JoF//y3tLAG4kRkV9i0YDsdGrrRbY11W74JxPrZb+bCCJU3zu9NCA+QDSuP18eKh896DxbHwZWH44iRoY/vbzeiQg43QzsWf2d2kNlJxpRtXI5hNiQ7FRgiXJsyfIw2HswWiRA34XEKOu6EmusXwr8BFi6+llg33L8IDqyFmjtu3EM0Zh+GxFwuoRYrmaLymdmEcv+fJsyHbvpcxinchjx+13O93NEp8yhwEr1siM2qOoFaVr9PWF4R9yBpb5vRowU/yDReDwWWKf2e6sRHXetbkAurl6U+j5ErAXc2zR+RuVvv0d5f+em8z1BZTGbWBf4x8BpI9SX04jnqcOAlRdXt9qYvEZYH2r57/1tpwHPJNYP37Ac24J4rhoCXlf5nV5g+lHU1pNua6o9Q2xJBNN2LK+fWOrBaOWwWv1e0uaEbY16eSwNfIWYib038Hhi+Z4vMsK64eV++pwOPkf0OiqWItrd7yZGgj+qHJ9DDP75DzEoZsQYRFvLpfa9WPXBPkcE6N9E7N9jYN40aVLjGTC1K9Ueil9KbGT4duCpleP1gOwqTed7gsvkeGLjoacwyiZDlQfo64iNYJ/YdL6tDxNWJhuXv/crgSUrx3chRvncUHto7sxD8gh1Yg36G3meUPvcLPoB+t92qfE0QjlsBmwFbFZeTyNG+11DBKBXLMdXLdeT4+ng5q/EWqjfJNZD7R3biZhifRWVAH3l/U6M+qIyipMImqwJLFs9T+DL5Tr5doYH3zrXqCYakJ8gRvoeXHvvQ+Wa8dnRrgu0tAG5mHqxDLHm6ZpEIHKolM+y1fMl1tu+G3hj03mfoPLYhAgs3cnwTeuWqPz8rVIGH6x+h9qevEZYH0YpgyWAs4jA/Cm19x4PnMKigekZlZ9b3UFRO99PAV8o18ilKscfz2I6KrqYmOJtjcp57UlsdvvPcq24r9SDfxP7T3wMeD2xtMsTGT5TtRPPEZXzWQb4K3AlMWv9dmKWydrl/aWIdtdlxHP3uk3neQLK4ETg8PLzaJ3d1U7PA+nI7H1TN1LjGTC1JzG8V/Ik+kG1y4nplYdU3u8FZO8sD46LjGjpQio3+r8Tm4r0jq1OBN7eD7yicvwlxDT1TtwMrQ8jlsmxxIjHvwNPKMeqjaRdy0Pz1cDLms7vBJx/NUDwcWKq/ZblAfEuYvPf6udnAc8nRv6c1ZUH5dp344TSSLizlMGXgMcRAZgPEKPGryFGsvwFuA14XNPnMAFl8gViSu3vqSxZUh6On1IaCtXRwqn63zYn+kG3pYHvAX8mZk+dDjyl8rle8O0eYsOqTl4ny7m+gWg83gG8uByrBlt6AfpP09Elrip1fGmiMf3B8vNqxNqvN5Rnhl6APgHbEwGIvZrO/wSUR6/BvBmx1NHdwKsr71cDsr8sZdT660M5H68R1ofRymEJYnbRULlPPqr2/uOJTu8hKs/dXUul7p9ZzvMf1Ab6MLyjorNLn5VzndJtjcWUyxrAtsRsmxuJDq2Ly3PGwvI96tQ1ovZ3P5rokNiC/n5Nl5VyWKd8Zjaxr9W9wPFN538CyuNvwOfKzyPOXn6w90ymJlPjGTC1LxFBlkvpTyk8mphqezmxUVvvc3OIpQuuA1ZvOt/jdO7Taq83B24lpprPIZakuIkIxl1LBN1e3HS+rQ8Dqw87E8HWIWI5lwfWTK195s+ljizTlQdFhgekv0QsV7Jref1kIphwPrXlGIiG57MpU7W7lIjA/OXEVNvnECN8hoggwlyisbkrMRrsR8SSV51YyqZeN4BXl+vh3eXvPWykJxGgP7+8v1nTeR7Hc68GYC8i1tB/EXAAEWSdT5mSXj43k5iOPQS8tOn8T2C5LEtMyb8V+B39QFw14PaBUg5vbTq/E3D+Dyw3UerCz4nOzF59WY3o7L6B6KDYmhgheS4RoGv9aNjFnQMRXPgnEVjZt3K8Wj8emJ7e9Lk8wnLwGmF9WKQcavfIOcTSHUPAwcBytd95HBG4Pr/t57+4+lDK4UulHN4OLD9COfyYGOSwfIfKwrbGKHWiHO/NLOtdA94A3FJ+TsRys4/twn1zlPOfQwyA+ziwX+29I4l2SDVAP4fYx6PV5cHwdues8t+fA99rOm8m08NNjWfA1K5EBFV+Qz/odjgxhewtREPyFuAtlc/PYZSlXtqWRnqwITbhmVcaDRcRoxQ+TmxK9SQiEP32QefV+tBMfSjHtyZGtPyF4SPeqg/NO1CmGXYhMbwROZPosNmzds7bE4HX3wK7NJ3nAZTJdsSophfRX/t0RWK0ymfpyF4LD1YfKsdmAy8mgk0/qtf/0oDaiRgB2amOmvKd+GZpNKxcjn293B8uJkZN71z7/GF0ZybJaA3qZYD3EQH6MyqNq2rA7XVdKYcRzn8pYl3YM4hR88OCi8QyV2fRX4P+O8SIyV45tbZhzfD1cQ8jRoN/gZhJ1Vvm60nl2ervVAIODF+WoBOj37xGWB9q5TEb+CGwU+3YqcQMvDeyaIB+vfo1pAsJ+GitHJYiRsiPVg6bUNlkvu1ptL8lU6ytUblGzAb2B95BbYZE5d65GzEgaJGBYG2+by6mbI4qzwm3ATv0yqny/nuJEfTfpfZ83dbyGO1aTwzq+DMRh+nEnhumqZUaz4CpXYlY4+44YrTry4lAy8vLe1uVm+ElwAeazusElsGngB9XXj+t3BiPAJ5VOT6XWAfvdeV1524O1odF60M5ti0xJf3XwHaV4618CHoIZfF5+jNHtizHUuUBqRegPxd4ZtP5Hedzn157vRexnM8Ty+sNS9mcSlm+gwhGLzHIfA6yHIiRoNWRLUsB+xBTjL9dbzSWujJ7ovPYQJlsTGxYt2N5/U0i2LYRMauiF3zddYTfbXXwjX6DekngGcTar7tQ1vgkRtC/nwhCnkG/I2vJkf6dLiWiE3uo3Cv2L8fq15HViBGxtxIdfbPL8daupUw/gLgMEXj+B/11cm8lZhytXj7zxPL+RcDrm877BJbJVL5GWB8WLZOnESOj/8XwZ8jFBuir5dmFRHTIXElsYrntQymHriWmcFuD4Xuz/J7okLiQaHP+ANi09vk1iGXznjPovDZUPo8hOjSHgK9UjlcHOrybaJN/pOn8jvO5n1DuF98E3ky0LW4E1qH2HGkytSE1ngHT5E0MHw1bDbAsXf57RrkZ9NZDXYoIxM4ngtIrDSqvAyyTOUQw+jqGb06Vap9bi5h2fCndGbVgfVh8fTip9t52xJIEv6k2KrqaiPXjDyUazrdTNgVm0emm25YHyDPpyKante/D4aXu702sM780MQL2pvLw2Pu+PI9Y3uaxTed/gsrhA8Qo+O8DB1WOzyY2j+4F6NdqOt8DKpvnle/Iq8s1sbpp9rdKY+I+YJum8zqO51xtUP+RWNaot0H0v4CnlfeXIwL015b6MqvpvE9QeSwSRC3nPUQEGx9TjtWfJ1ajv2nynnRg1g0x8vvHwHlEAHo20cn/xVIPvkKZZUcsH3gzMZK8c4McKmUy5a4R1ofFlskewJ+I5+h6gP4UYpTsoV24HjxIOexZrn8jlcOpRAfO2+jgZsCVc53ybQ3iufp3xGyydcr14Zxy/zyXSoAeWKEcf0XT+Z6Acpg2ys/rEYHq+6kE4BkeoH8VHeq4AVYCjiFiDz8hOvKuLH/7q4nnzpOJPYxeD2zRdJ5NpgdLjWfANDlT/eLNoqPYVikXvo9Vjm1KNKx3BtZs+hwmoEx6wcUVgY8QoxVOHuFz+5UHxmsoG/W0PVkfxlwfvlH7zHalXP4ObN10nsf5/EdaumQZYhTTtcTolmHrYFbKbBtgk6bPYbzLATieGN24FRGQv7w0mG4EvgosUz63SnmY/Cm1zd26kIigyTVEAOHv5btxQuX9XoD+ZmIZh85cHx6s4UMsS3I2/U6aJYjG5reJkXGdaTiV81uSWMrq5+VeMAd4bTl2P/0A/TLE2qhD1ftI11I5/6fXjn2A6Kz6KrBeOVYP0K9KBCDuAV7Y9Hk8xHOeVfm5t+zAesQMq/8Z4fNfJpbEq85E3JCOTFGf6tcI68Mi5zej9nrJys8vph+gr48c/znwi7aff+WcRm1n1MqhHqD/CdFx2YklM0colynX1qDWQU/Mqnw7sYzso8ux04ilWg4gnjfnEXsOTCdm5H2w/t1qe6pc82YQeypsVHt/fSJAfyvDA/T1Nnur7yHlHBaZIVSek7Yu95LvAR8jZpVcSrRLO7VkpqmbqfEMmCZfYviyBIcTAdY/lgbD4+lPO+/thr4TMfXwC8RUs2WaPofxLofyOtEfBbwisQ7idcDXK5/ZnmhUfY/uBB+tDw+zPpT3diwPCus0fQ7jVA7TanViCcoot/J6WWIzptuIYFKvYTEsQN+FxPBRK6uXxsEzK9+JQ4lg/dX0R/1tDJxY6sqmg87zAMph+XIN3LG8ngN8gkVnG80mNri8kg4E52vXgyWBpxNLfW1NZURfuU4uqLxelwiyjLhmbJsS/UBbtcNqR6Lh/CwqDeVyn/gdMdNm3XJsGWLUcCvPf4x15H+JDog9au99lOis+mqlPOoB+tVLXWlNA5PYDPzzlFkBleObEh0Nb6gc631/lijXzGPL6+r1pbV1w2uE9aFaF2r/nQN8uvJ+dbTri4G/EUu7PKlyfBYdWWO+ds84eJRy2JNoU9TLYUm6tcb8lG5rjHSNKHV9L8pIeOBzxOCXJ5Trw9HlvvoDYLPav9eJAD39NtTSxMCXvxGdlt8DtqTf7tiQCNDfAhzddL7H6dyHtTtHeD9VrqWrELOWX1L7TCc770zdS41nwDS5Uu0B6TQicPKdcvG/rTwU7VsuhLsC55cb4rV0aKR4rUxOBF5TeV19SPoEMfrvS5X3NwKWbzrf1odG68OXa7/T+nXvGGFt9PJAfAaxJMPXKdPty8PjG4jRG+dWHppa2ZAeQ9l8pXwnzgFWqRxfkdiP4lpimYLfEgHJSzr83fgSsenU8pXjKxEjWIaN+CIC9K2eik5/dGt13eQ/EA3H28v14GTKqE/gqfQ3eTy+/PdPtHwUKLAFsXlrrzx63/n9yz3hUeV1ddTsAcBCKst3VN7r6rXi8cTMuruBF9fe+xix/NVXGCUA36ZyIToTessYnUiZFVDeW4sIuH4fWK32e0sTI0A/0/Q5jFM5eI2wPlTPp36tnEY8Rw8B3698rhqYPpj+cmA71f69Vg54IDok9uqVQzn2zHKePxylHN5Q3v8nZePLriamYFvjQa4RKxNB+o3K9WAf+s8ZO9NfzuQrTZ/HBJRL7zyXLveDXwKvqHxffk7s5dOrIxuUe8cQlQ7PtiVGbnceBHyaiEU8n7JkLsP3N/sD8Inyc6/TopX3TdPUS41nwDQ5U3kQXFBueL0GxfrlpnAlZVohsEl5uNqPlq8dTIy+2JtoIO9cHgLWJNaFHgJeWj5XHcWwQimT+6sPk11L1oeHVR++31TeJ6AsliWmBVYbCqcRU4k/CXyNWL5liBJwIhpeBxJrYf61Cw9Go9SJ5Ylp9/8t34WNy2d735OlgM2ItaU/TgQrW/3dGKVsHk00GO4lOmRmsuiIr4+VOtOJayUxQ+gUho+G/X0ph6cQAadnle/FWcQmZbOJtYTPJjpqvlH5/dYEXkcoi5+X83w9lTWQiVFw9wNvrxybVf67fPmdVzad/3Esh2qH9ogj9sp98nRipHA9QP+RUibvavpcxqEsViGW3fhnuT6eQZkVUN4/iOiceTeVgCwxivrfwCFNn8M4lIHXCOtDvRwWuVYSHdiH9Mql8tkly39XIwLz1wLfavocxqkcjh+lHN5KjHxdpBzKz38hnisvKN+VVj9bYlujWhaLvUaUz+xIbAS8e6VcXk8MENq0zdfIUcqk15aYRXTs/4z+YIdTiIFw1xIb5O5CPxi9MXAELZ05QL/d+brKsZPLsW+X++V1REdVb8PwXifGt4BfNX0OJtPDSY1nwDQ5EzG1+lz6jeheb+RqxOiW7zadx3E+32XKg9D5RCBxB8pmlcTmU6eXh6SXlWPTKjfM7xC9tH+jQ1MrrQ/Wh3I+yxIjvX8BzC3HDiBGgm9Hv7GweymT/1d5OFyaaGhdRvun2o5UJ3oNynWIWQT3Ax+u/E6rG41jKJP6shtPIhpIQ8ABlePVBuVngf/ryHfjrUSAtbdk0QvK9377yvVgl1Ieb6yXHZVptrS0AVU7n3nEjKo3VL4bc4kgyl+B51bPF3hGuX/s3HT+J6I8yn+XBTYY4f1qgP5Ftffe2Pb6UDmXjxCB1f2IAMIZwPrlvVnlfrGQGDH9ZmIgwB+JkeKtLwOvEdaHEcqgeq18I/29aFYo9eVuKoHpSr05CXg2HQk+lnI4i5hh+YYxlsM2xMbBrwTWbvocxqEMbGssWib1a8SPGN6J90Si8+azROf/lqUMj618ptXfEaLTaQ7DlwJcjxgI9bzy+pvAFUSHxHblezQPeBqlDVb53VZdOxne7ly5HPs0MVv/yeV1bxbNjcBngFUrv/9pYkZ3pzfLNnUzNZ4B0+RK5cY/neiZ/S3D1zTsBdzeVy6GG9DS6ZS1c16a6KX/BTGSaZFpgeUh6TvlRvDyyvFViJ7s59DNjR2tD1O4PpSyuIRoDK1ZOX4M0VmzfHm9LrEcw1fpNyx6Ixnm0PK1/sZYJ9Yq5TIEvLP2XnWN3NYG7Mvf8j0suqHlzMrPWxCjWoaA/SrHewH65aks/dPmBLyImCmwTnl9MLFueK/uv7SUw+GVc98TWK7277S2TtT+tqlcF+4kgk7LleNPIpbv+BuxqduqxHTk88rnW92QrpTDY4C3ELOJjiBmj5xKzKh53Aif35xoQN4BPH+0cm1jot+BP5vonP0YEWC8Bfhh5TszE3gTscTLf4nlCr5D//mi1XXDa4T1geGzaR5YYoFYBm+0AP1dxHP3tsTMirMZvnxm68qhViYPpRx+SszU3YIIyP6E8pzZ5oRtjfq5Lu4a8QOGB+h7gdk7iOUS/0DlOaTpc3mE5bAl8Qz9d2Jgw/OJ2RVLEZ03M4BXESPIdyjfoVn0Z+TMB7Zs+jwewfkv0u4k9mU5nzLAA3gncB9xf/1aOe/P0B9AtiMd2dPLNPVS4xkwTc5UHoqGgF3L62rv7ZHlxrlcE3kb5/OcQfQ+n0VlZC8jbLLE8FEMRwGvIdbEu5JKj20Xk/Vh6tWH8oB0AdHxsmGtHM4Azi8/r0ME5k+hP1r2NcSGTa3fDPgh1olHE4G5RQL0bU9EJ12vkThErC2/xyif3aLy2f165UTLAwojnOeqxPT6A8rrg4iG5BLEJn5DwGGV8z+ASiCqS6l2T/gzsdHrm+gH6J9INKDvKeVyFRFwam3QrXb+25dr/9/KNXOIGO37EmLk7wXA40f4vaMq36ldmz6PR1gGy9RezyrXzw8SDe1HE8s33F7/HhDLOGwErE1/1kFrOycq5zVlrxHWhwfOZYna62pg+lwWDUwvTzxDXVLqx03EsoGtXju5nm/6M3FHK4cVyvfhCvp7WV0LbN70uYxDWdjWyA/5GvEDyiyb8tlnELNrXkc/sN/Ka0TlnJ5a7g+nEx1R55T7xxa1+vElYrZAdWDMN4gOjW/Q0ucpRmh3luNLAYcTy2PuWa6J+5b3ZhJLPF1KDBBbvenzMJkeSWo8A6YG/ujRK73YkYvlwfgcYoTPkyvHVy03jZ/Vb6ptTOU8/0KsVzfizaz2kLQx8L/lIekGYtRDqzd2tD5YH0Y4x96UwvuJ9Qw/yPB1pA8lRrYdWB6Svkk/CLcOEaj/FDC76XMZRJ0Y4fOfJEZLvr/p/I9jOUwDjit1/dfl7z5ENKqfR2VmRfn8E4kA/ULgtU3nfwLKIwHLEdOvjy3HeoG4C6iss14+uzFl9CMtDa5Uzn0TatPo6TeONyRGzt9ITLOujqBfvdSLfYhp2L2GZtsb1E8p3/dPEAHFZYG3EcGmXxCj2/5NdE48juEzaT4MHEvMKGhtOZS/503lfrhX7b0nE6OgX15e713K5gdUNvyr/U4XZuFN5WuE9SHyvS0RNPoQsd/MLBZdcuLXxAjgamB6VrmO7AnsSsuDj0TQ7afEzKIXjvQ3LeVwe60cZhNLox1EbJq77iDzPYHlMeXbGhNwjWhlQLqS/+pzRG9W8vJEh9Qna5/9GrEM0nrl9aZEB97T2loePHi7szcz4kRi1kj1WvknYl+8/1DbUNxkaltqPAOmAf/BY/TjT4hpT4tdo47olf4dMdLti0Qv7g+JBvdElB9mAAAgAElEQVQiI8DamIgpc0OUTRwf5LMz6Y/gWa88LLV6OqH1wfowwnktS6wJ/mNiqZbvEJvufKjyMLRFeXgaAs6s/O6qxOaol1IZ9dDm9FDrRK0cbgJWavocxqEMevV8NaJT5kRiiZsPEY3EIWJ5jv2pNJyIEV9nlWvEcrQ44FTq/P5EsGU2/eWbPk+s8zmLmHr8ZmIm0d+JqecrE2tK/5YOTL0uf8f/o7JvAP3A0ZZEYP5TpX4sslzBCP9eqxqQo9SLISLAMJt+h8MSxBrRdxCdGU8t18U/ljq0fLlv/ILhG561LvhWzuXsynVgPhGIewH9tdY/Wur/mkRH317ECMHvMsKa/G1MXiOsDyOUwzmlHK4jlmiZT6yn/zzKaNjy2Z+Wa8X/AMuO8u+18lpJtDNOKuVwe0l/JZ6Rtqv+vYmBPncurhy6kJjibY1xuEZ0on1RKY9hzxGV47PKveOo8p14IdFZtR0RxD6HeB7/E7HsS1uvEaO1O4+m8uxIzKr4DTCvcmwd4LTy3Vi56XMxmR5pmoGmmmnACcQF77sppd1zzldWP5BSSjn8LKV0FfAKYA8iKHsxsF3O+W+DzfaEmUasWwZASmlaznmo+oHKsQ2A1VJKv8o5XzLgfE4U68NwU7o+pJSmE1Nl/0QEjK5IKe1JzI54TXwkfTjnfEFK6SXEKJbVU0pHE+X2RGLDrl1zzv/XzFmMu4dUJ1JKq+Scz04pvZdYR/jGAed33OWcc0opEUH2nxDXgE8D7yKm0b6G2BD4K8DvU0rzgA/mnC9MKb0CWJhzvrWJvI+HlNJSwBfob0R2M3BpSunHwGZEY2L5nPN1KaVvEXXmXUQgdhaxhMtNwLNyzvenlKbnnBc2cCrj4b/EeuofBr6XUtoz5zw/pbQl0VA8CXhXzvlO4KkppXOIEVBDKaWv5ZzvqP5jLS6H3vXy+eXlXTnn/5bjS+Sc70kp/YEIut5JLPPzUuJ++yMiODubCNgd3/s3c873D+wExs/dxDJmSxGzIw4iRoQeBcxIKR1KnG8mgipXppS+RwQjTgX+RczGai2vEcNM+fpQ3E3MiplFrJn+cmBnIrD2feCWlNKviGUCX0UEpg4GcrlW3lb9x1pcH6YT17wNgMcSs4R2IDosXwlcmVL6OVEmexMB+rcxSjl0xJRua+A14gEjPUdU/vZbEEvmrUlsHvwoolP32URHxvuAJxDPFy/POS9s273jQdqdryauAx8qz47TiE6cA1NK7yNm27yMWJP+zpzz9Y2chDSemu4dMA0+EQ+KuxHr+P2B2nIE5TPTa6+XJEaCtX4Tntp5bUQ0pP63cmzE0UrEw+WJTefZ+mB9mOAyeAL9EbEPbP5LjFbpjWToTbncjgg2/Y2YVXEc8Jimz6HhOvHVpvM8weXxVKKB9OHa8V8S028vIAKOdwLvH62s2pboNyJ3Kd+B04gpuJfSXwt22fLZJYjZEwcArwV2oiNLuJRzqN4zziMaireX739vtPD0yufPKmX0oqbzPgFlsTzwkXJ+76md9x+pzCwqx1YgNi77CvBx+qOkWznirXJeSxIddFcQswHWLteK44mG9/dLGZ1a+Z0Z5fvU+u9EOR+vEdaHxZXDL4FHl+NPJ/Zy+gsxu+gKItA0RGxwOeJ+Lm1NlXKYX8phLSJovwcxQrq3AfD/EYM+evuSvJaOPEPUysO2hteIallUnyPeV471ZiJ+EdikHDuofE9+XPm92dD6fTkerN1Znbm9CRG4v53o0L6Uli/xZDJVU+MZMDX0h19MQLb6gED02H8S2KbpPE9QOSxHbKqygLIOYjleXwtxE2In9Dc2nWfrg/VhwGXSW7Ki+qD0YfrrSM+iIwEm68SYy+QEIqCwbnl9CrHG+NbECJ/ty8PzY5vO6wSXw2pEg/LbjDAFd4TPt3Ld5FHOZYlyz+gFHk+qNwwr145ELIPWyobjGMpiWfrrAb+zHPshsf7po3plwejBl05cNyt14moi0LhmOf5M4DBipt3xjLAXSYfrhteIKV4fKuVwFdFht0blvaWJUfXvIdaRHiqf6cQ1YTHl8IdaOSxHzLr8JDGTYIiYnbt+U/md4LLwuTJ7jaidT/U54gvEM/ZxxBKB1U7/E4nlfer7/nSmE4uR251H0293ziX2JXg2MLfp/JpM45l6PW2aglJKs4DnEMsTXAvslitLmqSU1ieWLHgWsGXO+R+NZHSCpZSeQIz++ydwZM75B7X3VyBumE8BnpFzvmzwuZx41odgfVhUb5pkSmkm8C1ixPyXgQ/lnG/vTcHsLYHUbG7Hn3ViuJTSPkQg4X3EMg47AC8BfpFbNJ324ajW8d7Pte/FF4Gjc853tG168UOVUloCeC7wAWKa+gvyosuizciVpVrqr7sipbQs8F7gEKKTeyGwe875z9UlC7p6jewpdeLZxOyA64Hn5ZwXlPeWB4Zyzrd1uRy8RvRZH0KlHD5NBJpeWL9Wls+tC1yeW7g8xVjU2hkjlkNKaTYxEOiOnPOlg8/lYPhcGbxG9KWUlgHeTawv/29g65zz3eW9XjvrU0RH1tNzN5d8Ahbb7jy6y+ctGZyf4moPjNcCz885X10JxD4DeErO+cIGsznhUkrPIqYg30j0Sh9LrAf4VGBPYm23HXLOf24skwNgfQjWh0WN8KC0FVFGR+TaOtJdZJ0YrqwRuwsx/fjlwHldCySMRe17cSoxmud04O25rEHeZSmlJYkO2949Y/ec8xXN5qoZJUB/GBGgPzHn/PqGs9SIBwtEjrS+cpd5jbA+wIjP17vlWEt7kcENXQzM9zyUcug6nyuD14i+0iHxjpLel3N+X+W99YFvAn/KOR/QUBYHZqq3OzU1GZxXvdf6GmJNs4PLsc4HYntSSk8kppI9nphWlokphwuIKYV/bTB7A2N9CNaHRdUelH4KPJrYEHhKbMJjnRg2emdvoiF5+lRoJCxO7XvxC2I68vOnUIChOhryGiLYsqDZXDWjNKwPJzY0PDLnfFTDWWqEdWI4rxHWB7AcekYI0L9wKpYD+FzZ43ejr3T0vwd4CyVAn1Jai+jcXQLYKsfG4Z3vyJrq7U5NPQbnBQy7KX6MWP/wTuCpUyUQ25NSWglYB9icWCP2fODKnPNNTeZr0KwPwfqwqMqD0gxgtZGmZneZdSKklFYDziE2ZNol53xnw1lqVOV7MR3IU3AE4BLECPpPEmvMP2mqNp4qDetDiAD9+xvOUiOsE8N5jbA+gOXQU2lnfIJYBmyHnPNVzeaqGT5XBr8bfbXniE8So8ZXIjY/va/Ls2vqpnq7U1OLwXk9oNwUdwcOAN6cc76o4SypQdYHjWYqPRRqdCmllwDfAPbOOX+r6fw0LQ1fW3zKTMPuKcGWFwF7A3tO5WtEaVi/ixhB/5qc8/ENZ6kR1onhvEZYH8By6CnlsBsRhHx+l9eY19j43egrzxFHAG8H/kE/MN/JvXsWx3anpgqD8xqmrB87M+d8e9N5UfOsD5JGk1JaHfgusH/u6AbRemhSSjNzzveVn6d0YyqltBywL/D5qdaQrrJOqMr6ECyHUIKxs1xDWj1+N/pSbAy8B3BCWcpmygXmpanE4LwkSXpYUkpL5JzvaTof0mRmg1qSJD1cPkdI3WdwXpIkSZIkSZKkAZvWdAbaIqW0Z0rp0ymlc1JKt6WUckrppKbzJUmSJEmSJElqnxlNZ6BF3gU8AbgDuBLYuNnsSJIkSZIkSZLaypHzY3cIsBGwLHBgw3mRJEmSJEmSJLWYI+fHKOc8r/dzSqnJrEiSJEmSJEmSWs6R85IkSZIkSZIkDZjBeUmSJEmSJEmSBszgvCRJkiRJkiRJA+aa8wO000475abz0LRjjjkGgIMPPrjhnDTPsgiWQ7AcguUQLIdgOfRZFsFyCJZDsBzCMcccw+abb950NiaFCy+8ELBO+N0IlkOwHILl0GdZ9J111lld3FRyYLHHCy64gLe85S2D+t89ZPPmzXvwDz24Ca8jBuclSZIkqcUuvPBCgyz0A06SJGniXXTRRU1noRMMzkuSJpXNN9+cs846q+lsTAqWQ38UpCRJkiRp8thss82azkInGJyXJE0qjv5zqmmPIyAlSZIkaXJy5Pz4MDgvSZIkSS3mrLPgbCtJkgbn6quvbjoLnWBwfoxSSrsBu5WXq5X/bptSOqH8fEPO+W0Dz5gkdYwBhj7LwUCLJI2Fs86CM64kSRqc+fPnN52FTjA4P3abA/vXjq1XEsBlgMF5SXqEDDC4rE2PQRZJkiRJmpwOP/xw9t1336azMaKZM2c2nYUxMzg/RjnnI4EjG86GJHWeI+f7LAdHzkuSJEnSZHT22Wc3nYVR3XfffU1nYcwMzkuSJhVHzjtyvseR85I0NnZsBzt0JUkanDvvvLPpLHSCwXlJkiRJajE7toOdupIkDc5mm23GySef3HQ2Ws/gvCRJkiS1mCPngyPnJUkanNVXX73pLHSCwXlJkiRJajFHzgdHzkuSNDg556az0AkG5yVJkiSpxRw5Hxw5L0nS4KyzzjrMmzdvsZ+58cYb2XPPPQeUo3aa1nQGJEmSJEmSJEndsnDhwqazMOk5cl6SJEmSWsxlbYLL2kiSNDhnnnkmH/rQh5rORusZnJckSZKkFnNZm+CyNpIkDc4yyyzTdBY6weC8JEmSJLWYI+eDI+clSRqcjTbaiJVWWokbb7yx6ay0msF5SZIkSWoxR84HR85LkjQ4CxYsMDA/DtwQVpIkSZIkSZI0Ztddd13TWegER85LkiRJUou5rE1wWRtJkgbnnnvuaToLnWBwXpI0qTg1v89ycIkCSRoL753Be4YkSYMzd+7cprPQCQbnJUmTiqP/+iP/LAdHQEqSJEnSZHT77bc3nYVOMDgvSZpUHP3XZzk4ClKSxsKO7WCnriRJg7Phhhs2nYVOMDgvSZpUDDA4cr7HIIskjY0d28EOXUmSBucHP/hB01noBIPzkiRJktRidmwHO3UlSRqc3/zmN01noROmNZ0BSZIkSZIkSVJ7PP3pT286C53gyHlJkiRJajGXtQkuayNJ0uDsuOOOfOELX2g6G61ncF6SNKkYYOizHAy0SJIkSdJkdMcddzSdhU4wOC9JmlRcN9cNYXuOOeYYO2sKOykkSZIkTSYbbbQR8+bNW+xn5s+fzytf+coB5aidDM5LkqRJy86a4CaHkiRJktpm5syZTWdh0jM4L0maVBwp3Wc5OGJcksbCjsxgR6YkSYMzNDTE9773Pa677jpyziOm+fPnN53NSc/gvCRJkiS1mB3bwQ5dSZIG55RTTnFD2HFgcF6SNKk4+s8153scASlJY+O9M3jfkCRpcLbeemuD8+NgWtMZkCRJkiRJkiS1x4orrsjqq6/edDZaz5HzkiRp0nKphuBSDZIWx2tl8FopSdLgXHHFFVx99dVNZ6P1DM5LkqRJy6Uagks1SJIkSZpMNttsM7761a9y1113MTQ0NOKGsFdeeSUf+9jHms7qpGZwXpI0qTj6r89ycBSkJEmSJE1Gv/vd73jHO97RdDZaz+C8JGlScaS0G8L2OFpcksbGe2fwviFJktrG4LwkSZIktZizzoKzrSRJGpwNN9yQuXPnctVVVzWdlVab1nQGJEmSJEmSJEntcdlllxmYHwcG5yVJkiRJkiRJY3bTTTc1nYVOMDgvSZIkSZIkSRqze++9t+ksdIJrzkuSJhXXze2zHFw/WJIkSZImo1133ZUvfOEL3HzzzU1npdUMzkuSJpULL7yQgw8+uOlsNOqYY44BsBxKOUiSJEmSJpef/OQnBubHgcF5SZIkSWoxO7aDnbqSJA3OnDlzms5CJ7jmvCRJkiRJkiRpzFZeeeWms9AJjpyXJEmSJEmSJI3Z4x//eObNm7fYz1x33XXsvffeA8pROxmclyRJkqQWczP14CbikiRNLkNDQ01nYdIzOC9JkiRJLeaa88E15yVJmlzuvffeRv6/Sy65ZCP/34fDNeclSZIkSZIkSeNq+vTpjfx/77777kb+vw+HI+clSZOKU/P7LAeXKJCksfDeGbxnSJI0OL/5zW84/PDDm85G6xmclyRNKk7N70/LtxxcnkCSJEmSJqM77rij6Sx0gsF5SZIkSWoxO7aDnbqSJA3OtttuyxZbbMH8+fMfOJZSeiAB3HDDDQ3lrj0MzkuSJElSi7msTXBZG0mSBufiiy/mggsuaDobrWdwXpIkSZJazJHzwZHzkiQNzr/+9a+ms9AJ05rOgCRJkiRJkiSpPXbYYYems9AJjpyXJEmSpBZzWZvgsjaSJA3OXXfd1XQWOsHgvCRJkiS1mMvaBJe1kSRpcJZbbrmms9AJBuclSZIkqcUcOR8cOS9J0uD85z//aToLnWBwXpIkSZJazJHzwZHzkiQNzvbbb8+RRx7JTTfdBEBKaZHPXHnllZx++umDzlqrGJyXJEmSJEmSJI1ZSokdd9xxsZ9ZsGCBwfkHYXBekiRJkiRJkjRmd955J+9///u57LLLSCk9MHK++t+rrrqqySy2gsF5SZIkSWox15wPrjkvSdLgfO5zn+O3v/1t09lovWlNZ0CSJEmSJEmS1B69teb1yBiclyRJkiRJkiSN2Ytf/OKms9AJLmsjSZIkSS124YUXcvDBBzedjcYdc8wxTWdBkqQpY4sttuCkk07irrvuAiDnTM552GeuuuoqjjrqqCay1xoG5yVJkiSpxVxzPrjmvCRJg/P73/+eQw89tOlstJ7BeUmSJElqMUfOB0fOS5I0OJtuuinbbbcdl19+OQAppUX+e8UVVzA0NNRYHtvA4LwkSZIktZgj54Mj5yVJGpxZs2ax5pprMn369BHfzzkzc+ZM/v3vfw84Z+1icF6SJEmSJEmSNGa///3vOfXUU5vORusZnJckSZKkFnNZm+CyNpIkDc52223HJz7xCW677bZRP3PzzTd7f34QBuclSZIkSZIkSWO2YMECDjnkkKaz0XrTms6AJEmSJEmSJKk9ehvB6pFx5LwkSZIktZgbwgY3hJUkaXB+9KMfNZ2FTjA4L0mSJEkt5przwTVtJUkanA022IBzzz236Wy0nsF5SZIkSWoxR84HR85LkjQ4G2+8cdNZ6ASD85KkScUAQ5/lYKBFksbCkfPBkfOSJA3ON77xjaaz0AkG5yVJk4oBhn5wwXIwyCJJkiRJk9G2227LX/7yl6az0XoG5yVJkiSpxZx1FpxtJUnS4Oy0004cd9xxTWej9QzOS5IkSVKLOessOONKkqTBWW211Zg3b95iP7NgwQJe/vKXDyhHfUsttdTA/58Pl8F5SdKk4ui/PsvBUZCSNBbeO4P3DEmSJpeFCxc28v+96667Gvn/PhwG5yVJk4qj/1xzvscRkJIkSZI0OV1++eXsv//+TWej9QzOS5ImFUf/9VkOjoKUJEmSpMnod7/7XdNZ6ASD85KkScWR846c73HkvCRJkiRNTtdcc03TWeiEaU1nQJIkSZIkSZLUHrvttlvTWegER85LkiYVl7Xpsxxc1kaSJEmSJqM111yTefPmLfYzl156Ka961asGlKN2MjgvSZpUXNbGZW16XNZGkiRJktprxgxDzw/GEpIkSZOWMymCMwgkLY4d28FOXUmSBucf//gHBx54YNPZGFFKqeksjJnBeUmSJElqMTsygx2ZkiQNzg033NB0FkaVc246C2NmcF6SJE1ajgYNjgaVtDheK4PXSkmSBme99dZrOgudYHBekjSpOPqvz3JwFKQkjYX3zuA9Q5KkwbnkkkuazkInGJyXJE0qjv5zQ9geR0BKkiRJ0uQ0f/78prPQCdOazoAkSZIkSZIkqT0WLlzYdBY6wZHzkiRJkiRJkqQx23fffVlhhRUe2Bh2aGho2EasOWeuvfZafvnLXzaVxVYwOC9JkiRJkiRJGrNp06bxghe8YLGfueGGGwzOPwiD85IkSZIkSZKkMbv++uvZb7/9uPvuu5vOSqsZnJckTSqbb745Z511VtPZmBQsh9ggWJIkSZI0uVx33XUG5seBwXlJ0qRy4YUXcvDBBzedjUYdc8wxAJbDMcfYWVPYSSFJkiRpMnnsYx/LvHnzFvuZ66+/nr322mtAOWong/OSJGnSsrMm9DpsJGkkXiuD10pJkgbnrrvu4uijj+ayyy4DYgPY+oaw11xzTVPZaw2D85IkSZIkSZKkMfvrX//Kueee23Q2Ws/gvCRJkiRJkiRpzLbeemu++c1vcs899zxwLKVESumBn6+88koOO+ywprLYCgbnJUmTimuM91kOrrUuSZIkSZPRNddcwz777NN0NlrP4LwkaVJx3Vw3hO1x7WBJkiRJmpxuvfXWprPQCQbnJUmSJKnFnHUWnG0lSdLgPOYxj2HevHmL/czll1/O/vvvP6ActZPBeUmSJElqMWedBWdcSZKktjE4L0maVBz912c5BMvB0aCSFs97Z/BaKUnS4Ljm/PgwOC9JmlQc/eea8z2WQ5+jQSUtjvfO4LVSkqTBueGGG5rOQicYnJckTSqO/uuzHBwFKUmSJEmT0be//e2ms9AJBuclSZOKo/8cMd7jCEhJGhs7toMdupIkDc4znvGMB90QVg/O4LwkSZIktZgd28FOXUmSBueaa65pOgudMK3pDEiSJEmSJEmS2mPBggVNZ6ETHDkvSZIkSZKkTnMJsD6XAdN42HzzzTnttNOazkbrGZyXJEmSpBYz4BQMNklaHJcACy4BpvHy85//vOksdILL2kiSJEmSJEmSxuyggw5qOgud4Mh5SZI0aTkaNDgaVNLiOBo0OBpUkqTB2X333ZvOQicYnJckSZOWAadgwEnS4tiRGezIlCRpcI477jgOOOCAprPRei5rI0mSJEmSJEkas8svv7zpLHSCwXlJkiRJkiRJ0pjNmTOn6Sx0gsvaSJIkSVKLuQRYcAkwSZIGZ+7cuU1noRMMzkuSpEnLdZSD6yhLkiRJmkx+9atfNZ2FTjA4L0mSJi1HgwZHg0paHDsygx2ZkiQNziabbNJ0FjrB4LwkSZIktZgdmcGOTEmSBmeTTTZhm222eWBj2JzzsPdzzlx77bVNZK1VDM5LkiRJkiRJksZszpw5vOQlL+HWW28ddrwXpE8pce211/K5z32uiey1hsF5SZIkSWoxl7UJLmsjSdLg/PrXv+aII45oOhutZ3BekiRJklrMZW2Cy9pIkjQ4W221FXvttRdXX331A8dSSsM+c9lll3HZZZcNOmutYnBekiRJkiRJkvT/2bv7KL/q+l70nz2BJKDhmQJ65EGwSFpk6lUeRBSsvX1A1F4EHwgERL09oG1oD2C5Wqv06Cq1pwOCtz3KwYfVXqhQKUckBcrEcgHDhTJSJcQCSSYPZJLwjIQkJN/7x57JL6SZydYz7O/eO6/XWlkJk/1L3tnLNdv13p/92ZXttNNOccghh8SMGTNe9vWU0ubVNlOnTlXOb4dyHgAAoMWstSlZawMA9Zk7d25cdtlluWO0nnIegEZRMPQ4D4oWgCqstSlZawMA9Vm1alXuCJ2gnAegURQMvXLBeVCyAFThxnbJDV0AqM83vvGN3BE6oS93AAAAAAAA2uOiiy7KHaETTM4DAAAAAFDZb//2b8exxx4b69atG/eYRYsWxSWXXFJjqvZRzgMAALSYlXAl69AAoD733XdfXHjhhbljtJ5yHgAAoMXsnC/ZOQ8A9XnDG96QO0InKOcBAABazOR8yeQ8ANTnxz/+ce4InaCcB6BRTP/1OA+mIAEAAJro+OOPj3PPPTeWLl0afX19ERFRFMXLfixdujR+9KMfZU7abMp5ABrF9F9v8s95MAEJAADQRHfccUdcffXVuWO0nnIeAACgxTx1VvK0FQDUZ9q0abkjdIJyHoBGUTD0OA+KFoAqPHVW8sQVANTn+OOPj4suuihWrVr1slU2fX19URRFRESsWLEivve972VO2mzKeQAaRcFgrc0YJQsAAEAzXXXVVXH99dfnjtF6fbkDAAAAAADQHiMjI7kjdIJyHgAAAACAyk488cTcETrBWhsAAIAW876WkveUAEB9DjnkkNwROkE5DwAA0GLe11LyrhIAqM9NN92UO0InKOcBaBTTfz3OgylIAACAJjrhhBPixhtvzB2j9ZTzADSK6b/e5J/zYAISoAo3tktu6AJAfaZOnZo7Qico5wFoFAVDj/OgaAEAAGiiAw44IPbYY494+umnc0dpNeU8AAAAAACV7b333nHJJZdMWM6PjIzE1VdfXWOq9lHOA9Ao1tpYazPGWhuAalw7S64bAFCfu+66Kz7zmc/kjtF6ynkAAIAWsxKuZBUaANTnmGOOid/6rd+KxYsXj3vMkiVLYu3atfWFaiHlPAAAQIuZnC+ZnAeA+jzwwAMxd+7c3DFary93AAAAAAAA2uPxxx/PHaETTM4DAAC0mLU2JWttAKA+1tVMDuU8AI2iYOhxHhQtAAAATTR16tTcETpBOQ9Ao9ib29uZ6zzYHQwAANBEu+yyS+4InWDnPAAAAAAAlZ1wwgm5I3SCyXkAAIAW89RZyRNXAFCfv/mbv8kdoROU8wAAAC3mfS0l7ykBgPo8//zzuSN0gnIeAACgxUzOl0zOA0B9TjjhhBgcHMwdo/XsnAcAAAAAoLIZM2bkjjCuqVOn5o5Qmcl5AAAAAAAqO+KII+LYY4+N4eHhSClFSullv59SiieffDI2bNhQe7b169fX/nf+opTzAAAAAABU9qpXvSq+9KUvTXjMkiVL4uyzz64nUEsp5wEAAAAAqGzFihVxxhln5I7Resp5AACAFuvv74958+bljpHd0NBQ7ggAsMP49re/nTtCJyjnAWgUBUOP86BoAahiaGgo5syZkztGdgMDA7kjAMAO4z3veU/MnTs3d4zWU84D0CgKhl654DwMuFkzyk0KYCK+V5Z8rwSA+uyxxx65I3SCch4AaCw3a0qmQQEAgCZZu3Zt7gid0Jc7AAAAAAAA7fHggw/mjjriGzcAACAASURBVNAJJucBAABazFNGJU8ZAUB9Dj/88NwROkE5DwAA0GJ2zpfsnAeA+vT1WcgyGZTzAAAALWZyvmRyHgDqs9dee+WO0AnKeQAAgBYzOV8yOQ8A9dlvv/1icHBwwmOGh4dj9uzZNSVqJ+U8AAAAAACV/exnP4tLL700hoeHxz1mZGSkxkTtpJwHoFFM//U4D6YgAQAAmmjBggUxf/783DFaTzkPQKPYm9vbmes82B0MUIVrZ8l1AwDq89JLL+WO0AleqwsAAAAAQGVTp07NHaETlPMAAAAAAFT2ute9LnbZZZfcMVrPWhsAAIAW876WkveUAEB9nnrqqVi7dm3uGK1nch4AAAAAgMpmzJiRO0InmJwHoFFM//U4D6YgAQAAmuiAAw6IwcHBCY8ZHh6O2bNn15SonUzOAwAAAAAwqVJKuSM0nnIeAAAAAIBJtdNOlrZsjzMEQKMMDQ3FnDlzcsfIamBgICLCeRg9DwAAALTPxo0bc0doPOU8AABAi7mxXXJTFwDqMzg4GF/4whdyx2g9a20AAAAAAKjs+eefzx2hE0zOAwAAtFh/f3/Mmzcvd4zshoaGckcAgB3GM888kztCJyjnAWgUBUOP86BoAQAAaKLdd989d4ROUM4D0Cj25noh7Bi7gwGqce0suW4AQH1OOeWUOOWUUyY8Zvny5TFr1qyaErWTch6ARjE53+M8mJwHqMK1s+SaAQD1WbNmTZx22mm5Y7Sech6ARjH9Z3J+jAlIgGpcO0uuGwBQn5tvvjl3hE7oyx0AAAAAAID2+MAHPhCve93rcsfYpr6+9lTe7UkKAAAAAEB2P/zhD2Pp0qW5Y2zTpk2bckeozFobAACAFrNzvmTnPADU584778wdoROU8wAAAC1m53zJznkAqM/v/M7vxA9+8IPcMVpPOQ8AANBiJudLJucBoD5HH3103HDDDfHSSy9FURRRFMXmXe99fX1RFEUsW7YsPvnJT2ZO2mzKeQAAgBYzOV8yOQ8A9XnmmWfi4osvjkcffTQiIlJKmRO1k3IeAACgxUzOl0zOA0B9Fi5cGI888kjuGK2nnAcAAGgxk/Mlk/MAUJ+NGzfmjtAJynkAAAAAACo77rjj4qqrrornnntu3GNGRkbir/7qr2pM1T7KeQAaxaP5Pc6DFQUAVbh2llwzAKBeM2fOnPD3ly9fXlOS9lLOA9AoHs3vPZbvPFhPAFCFa2fJdQMA6vPEE0/Exz72sXj66adzR2k15TwAAECLmZwvmZwHgPosWLBAMT8JlPMAAAAtZnK+ZHIeAOrz2c9+NneETujLHQAAAAAAgPa44IILckfoBJPzAAAALWatTclaGwCoz3vf+95473vfO+ExS5cujbPOOqumRO1kch4AAAAAgEmVUsodofFMzgMAAAAAUNny5ctj1qxZuWO0nnIegEbxaH6P82BFAQAAQBOtXbs2d4ROUM4D0ChDQ0MxZ86c3DGyGhgYiIhwHkbPAwAAAM2y9957547QCXbOAwAAAABQ2bXXXps7QieYnAegUay16XEerLUBAABook984hMxY8aMWLlyZUREbNq06WUvgE0pxbJly+InP/lJroitoJwHoFGstbHWZoy1NgAAAM20aNGiuPrqq3PHaD3lPAAAQIu5sV1yUxcA6vPwww/njtAJynkAAIAWsxKuZBUaANRn6tSpuSN0gnIegEZRMPQ4D4oWgCpMzpdMzgPA5Ljtttvii1/8Yu4YOwTlPACNomCwc36MkgWgGje2S27oAsDkWL9+fe4IOwzlPAAAQIu5sV1yUxcAJsfJJ58cJ5988oTHLFu2LM4888yaEnWXch6ARjH91+M8mIIEAABooscffzx3hE5QzgPQKKb/rLUZYwISoBo3tktu6AJAfaZPn547Qif05Q4AAAAAAEB7fPe7380doROU8wAAAAAAVPaud70rd4ROsNYGgEbxaH6P82BFAUAVVsKVrEMDgPq89rWvzR2hE5TzADSKgsHO+TFKFoBq3NguuaELAPWZMmVK7gidoJwHAABoMTe2S27qAkB9RkZGckfoBOU8AABAi5mcL5mcB4D6zJw5M4477rgYHh4e95hVq1bFhg0bakzVPsp5ABpFwdDjPChaAKowOV8yOQ8A9RkZGYl77rknd4zWU84D0CgKBjvnxyhZAKpxY7vkhi4A1CellDtCJyjnAWgUBUOP86BoAajCje2Sm7oAUJ9bbrkld4ROUM4D0CgKBpPzY5QsANW4sV1yQxcA6vPSSy/ljtAJfbkDAAAAAADQHu9+97tzR+gEk/MANIrpvx7noeQ8mAYFJuaps5InrgCgPn19Zr4ng3IegEZRMFhrM8Z56FE4ARNxY7vkRiYA1GfmzJkxODg44THLly+PWbNm1ZSonZTzAAAAAABUtnDhwvi93/u93DFaTzkPAADQYp46K3nKCADq88ADD+SO0AnKeQAaxaP5Pc6DFQUAVbh2llwzAKA+IyMjuSN0gnIegEYx/WfX+hgTkAAAAM306KOP5o7QCcp5AACAFnNju+SmLgDU5+yzz44/+qM/yh2j9ZTzAAAALWatTclaGwCoz/Tp03NH6IS+3AEAAAAAAGiPKVOm5I7QCSbnAQAAWsxam5K1NgBQn/Xr1+eO0AnKeQAaxaP5Pc6DFQUAVbh2llwzAKA+d911V+4InaCcB6BRTP/1Jv+cBxOQAAAATXTKKafEddddlztG6ynnAWgU0389zoMpSIAq3NguuakLAPVZunRp7gidoJwHoFEUDCbnxyhZAAAAmunZZ5/NHaET+nIHAAAAAACgPTZs2JA7QieYnAcAAGgxK+FKVqEBQH0OOeSQ3BE6weQ8AAAAAACVTZs2LXeETjA5DwAA0GLe11LyrhIAqI8n1iaHch4AAKDFrLUpKQkAoD4vvvhi7gidoJwHAABoMZPzJZPzAFCfN7/5zbkjdIJyHgAAAACAyo444ogYHByc8Jgnn3wyTj311JoStZNyHgAAAACAyh5//PH4yEc+kjtG6ynnAWgUe3N7nAf7gwGqcO0suWYAQH2ee+653BE6QTkPQKPYm9vbmes82B0MAADQRPfee2/uCJ2gnAcAAGgxN7ZLbuoCQH2OP/74uPrqq3PHaD3lPAAAQItZa1Oy1gYA6rNixYrcETpBOQ8AANBiJudLJucBoD7HHHNMnHnmmbFy5cpxj1m5cmX827/9W42p2kc5DwAAAABAZTvttFN89KMfnfCY4eHhmD17dk2J2kk5DwAAAABAZatXr44zzzwz1q1blztKqynnAWgUe3N7nAf7gwGqcO0suWYAQH1Wr16tmJ8EynkAAIAWs3O+ZOc8ANRn5syZMTg4OOExIyMj8aEPfaimRO2knAegURQMvXLBeVCyAFRhcr5kch4A6vPss8/Gpz/96Vi0aNG4x7z44os1Jmon5TwAAAAAAJXdfffdsWDBgtwxtmnKlCm5I1SmnAcAAGgxT52VPHEFAPU5+OCDc0cY18aNG3NHqEw5DwAAAABAZW984xu3u3N++fLlMWvWrJoStZNyHoBGsTe3x3mwPxigCtfOkmsGANTn+eefj89//vOxePHiiIhIKW3+Meapp57KlK49lPMANIpH870Qdoz1BAAAAM20cOHCuO+++3LHaD3lPAAAAAAAlR111FFx8sknx/Dw8Mum5SNi838/8cQTsXLlyhzxWkM5DwAA0GKeOit54goA6jN//vy4+eabc8doPeU8AABAi9k5X7JzHgDqs88+++SO0AnKeQAAgBYzOV8yOQ8A9dl9991zR+gE5TwAAECLmZwvmZwHgPrsv//+cf3118e6dete9vWiKDb/eunSpXHxxRfXHa1VlPMAAAAAAFR23333xYUXXpg7Ruv15Q4AAAAAAEB7HHHEEbHrrrvmjtF6ynkAAAAAACq7/fbb44UXXsgdo/WU8wAAAAAAVPbcc8/ljtAJds4DAAC02NDQUMyZMyd3jOwGBgZyRwCAHcZ73vOeuPXWW2Pp0qW5o7SayXkAAAAAACpbunSpYn4SmJwHoFH6+/tj3rx5uWM0gvNQToMCAADQLOvXr88doROU8wAAAC3mxnbJDV0AqM++++6bO0InKOcBaBR7c3s7c50Hu4MBAACa6MADD4zBwcEJj1m9enWcfvrpNSVqJ+U8AABAi7mxXXJTFwCaZe3atbkjNJ4XwgIAAAAAMKl23nnn3BEaz+Q8AAAAAACV3XvvvXHxxRfnjtF6JucBAAAAAKjspptuyh2hE0zOA9Ao/f39MW/evNwxGsF5KPcoAzAx186SawYA1Of888+Pu+66K3eM1lPOA9AoXmrXe6Gd8+DFfgAAAE307LPP5o7QCcp5AAAAAAAqO/zww2NwcHDCY1asWBFnnHFGTYnaSTkPQKN4NL/HebCiAAAAoIleeumluOaaa2LlypUREVEUxeafx3499nuMTzkPQKNYa2OtzRhrbQAAAJpp/vz58Xd/93e5Y7Sech4AAAAAgMqOP/74+Iu/+It4+umnI6W0+esppc3/vXjx4rj22mtzRWwF5TwAAECLeeqs5IkrAKjXW97ylgl/f8WKFcr57VDOA9Aods73OA92zgNU4dpZcs0AANpGOQ9Ao5j+s3N+jAlIgGpcO0uuGwBQnzVr1sRpp52WO0br9eUOAAAAAABAe/zDP/xD7gidYHIeAAAAAIDKzj333Hj66adj0aJFsWnTpkgpxcaNGyOifCnspk2bYunSpbFp06bMSZtNOQ8AAAAAQGX3339/3HLLLbljbNPOO++cO0JlynkAGsVL7XqcBy/3AwAAaKLnnnsud4RxbdiwIXeEypTzAAAALebGdskNXQCozzve8Y744Ac/GCMjI1EURRRFEX19fZt/XRRFPP744/Hggw/mjtpoynkAGmVoaCjmzJmTO0ZWAwMDERHOw+h5AGBirp0l1w0AqM/ChQvjuuuuyx2j9ZTzADSK6b8e58EUJAAAQBO9+tWvzh2hE5TzADSK6T+T82NMQAJU48Z2yQ1dAKjPU089lTtCJyjnAWgUBUOP86BoAajCje2Sm7oAUJ9f+7Vfi1tuuSVSSjFlypRtHrNs2bI499xza07WLsp5ABpFwWByfoySBQAAoJkeeuihOP/883PHaD3lPACNYnK+x3kwOQ9QhWtnyTUDAOqzYMGC3BE6QTkPQKOYnDc5P8bkPAAAQDO97nWvyx2hE5TzADSK6b8e58EUJAAAQBPtueeeuSN0gnIegEYxOW9yfozJeYBqXDtLrhsAUJ+iKHJH6ATlPAAAQIt56qzkaSsAqM/rX//6+P3f//1Ys2ZNpJQioizsx34dEbFs2bK48847c0VsBeU8AABAi5mcL5mcB4D63H333XHFFVfkjtF6ynkAAIAWMzlfMjkPAPU54IADckfoBOU8AABAi5mcL5mcB4D6/OhHP8odoROU8wAAAC1mcr5kch4A6nPsscfGV77yldwxWq8vdwAAAAAAANpjcHAwd4ROMDkPAADQYtbalKy1AYD6LFu2LHeETlDOA9AoHs3vcR6sKAAAAGiic845J+bOnZs7Rusp5wFoFNN/vck/58EEJEAVbmyX3NAFgPo89thjuSN0gnIeAACgxdzYLrmpCwD1mTlzZu4InaCcBwAAaDGT8yWT8wBQn/POOy93hE5QzgMAALSYyfmSyXkAqM9VV10V73//+3PHaL2+3AEAAAAAAGiPe+65J3eETlDOAwAAAABQ2d///d/njtAJynkAAAAAACo75ZRTckfoBDvnAQAAAACo7Hd/93fjhBNOiHXr1kVKKTZt2rT591JKkVKKxx9/PC655JKMKZtPOQ9Ao/T398e8efNyx2gE56F8ySEAAADNs88++0z4+9OnT68pSXsp5wFolKGhoZgzZ07uGFkNDAxERDgPo+cBgIm5sV1yQxcA6vP000/HnDlzYsmSJbmjtJqd8wAAAAAAVLZ48WLF/CQwOQ8AANBinjoreeIKAOrT398ft99+e0REFEURRVFERLlvfuzn1atXx4c//OFsGdtAOQ8AANBi1tqUrLUBgPoMDQ3FBRdckDtG6ynnAQAAWszkfMnkPADUZ9myZbkjdIJyHgAAoMVMzpdMzgNAfWbMmJE7Qico5wEAAAAAqOyd73xnDA4OTnjMsmXL4swzz6wpUTsp5wGAxjINWjINCkzEWpuStTYAUJ+UUnz1q1+NRYsWRUT5UtiUUqSUNr8cdsmSJTkjtoJyHgBoLIVTSeEEAAA0yZe//OX4/ve/nzvGNu288865I1SmnAcAAGgxTxmVPGUEAPWZOXNmY8v5DRs25I5QmXIegEZRMPQ4D4oWgCo8ZVTylBEA0DbKeQAaRcHQKxecByULQBVubJfc0AWA+ixfvjx3hE5QzgPQKAqGHudB0QIAANBE55xzTqxZsyYWL14cRVFs88eqVati9erVuaM2mnIegEYxOW9yfozJeYBqXDtLrhsAUJ+bb745brvtttwxWq8vdwAAAAAAANrj0EMPzR2hE0zOAwAAAABQ2ZFHHhmDg4MTHvPII4/Exz/+8ZoStZNyHgAAAACAyp588sk477zzYmRkJHeUVrPWBgAAAACAyhYtWqSYnwTKeQAAAAAAKlu/fn3uCJ2gnAcAAAAAoLKHH344d4ROUM4DAAAAAFDZ9OnTc0foBC+EBQAaq7+/P+bNm5c7RnZDQ0O5IwAAAGy222675Y4wrhkzZuSOUJlyHgBorKGhoZgzZ07uGNkNDAzkjgAAALDZa17zmtwRxvXcc8/ljlCZch4AAKDFPGVU8pQRANRn06ZNuSN0gp3zAAAAAABU1uTJ+TYxOQ9Ao5j+63EeTEECVGEFWMkKMACoz/DwcO4InaCcB6BRFAy9csF5ULIAVOHGdskNXQCozx133JE7QidYawMAAAAAQGUm5yeHyXkAAIAW89RZyRNXAFCfY489Nh5++OHcMVpPOQ8AAAAAQGWzZ8+O2bNnT3jMk08+GaeeempNidpJOQ8ANJY9yiV7lIGJ+F5Z8r0SAOrz0EMPxfnnn587Rusp5wGAxrKqoWRVAzAR3ytLvlcCQH323Xff2GmnneKll17KHaXVlPMAAAAtZnK+ZHIeAOqz7777xm233TbhMcuXL49Zs2bVlKidlPMANIqCocd5ULQAAAC01aZNm3JHaDzlPACN4tH83mP5zoP1BAAAAG01bdq03BEaTzkPAADQYm5sl9zUBYBm6evryx2h8ZTzADSKtTY9zoO1NgAAAE20ZMmSOPvss3PHaD3lPACNYvrPWpsxJiABqnFju+SGLgDU56KLLsodoROU8wAAAC3mxnbJTV0AqM8f//EfxwUXXJA7Rusp5wEAAAAAqKy/vz8GBwcnPGb16tVx+umn15SonZTzAAAAAABUduONN8bll1+eO8Y2FUWRO0JlXpkLAAAAAEBlhx56aO4I40op5Y5Qmcl5AAAAAAAqO/LII7e71mbRokXx0Y9+tKZE7aScB6BR+vv7Y968ebljNILzUL7kEICJuXaWXDMAoD7Lly+PWbNm5Y7Resp5ABplaGgo5syZkztGVgMDAxERzsPoeQAAAKBZnnnmmdwROkE5D0CjmP7rcR5MQQIAADTR/Pnzc0foBOU8AI1ict7k/JiBgQE3a0a5SQEAADTJ4YcfnjtCJyjnAYDGcrOmZMUPMBHfK0u+VwJAfQ466KDcETpBOQ8AAAAAQGX7779/fOITn4iRkZFxj1m+fHncd999NaYqTZs2rfa/8xelnAegUawx6XEerHMBqMK1s+SaAQD1ufLKK+PGG2/MHWOb1q1blztCZcp5ABrFo/l2zo+xc75H4QQAADTJypUrc0foBOU8AI2ijO1xHtysGWOPMjAR3ytLvlcCQH2Gh4dzR+gE5TwAjaJgMDk/RskCUI0b2yVPGQFAfQYGBuL000/PHaP1+nIHAAAAAACgPdavX587QieYnAcAGss0aMk0KDART52VPHEFAPW54oorckfoBOU8ANBYCqeSwgmYiBuZJTcyAaA+u+66a+4InaCcBwAAaDE3MktuZAJAfYqiyB2hE5TzADSK6b8e58EUJEAVrp0l1wwAqM9hhx0Wg4ODuWNs0/Tp03NHqEw5D0CjmP7rTf45DwMKp1EKJ2Airp0lk/MAQES7pvqV8wA0ijK2x3lQOI1ROAETce0suZEJAPVJKeWOMK61a9fmjlCZch6ARlHGmpwfo5AGAABopl122SV3hE7oyx0AAAAAAID2eO6553JH6AST8wA0ikfze5wHKwoAqvDUWckTVwBQnwMPPDB3hE5QzgPQKAoGa23GKFkAqnFju+SGLgDU58QTT4y77747Fi5cGBs3boyUUmzatCk2bdoUKaXYuHFjPPXUU7ljNp5yHgAAoMXc2C65qQsA9ZkzZ048+OCDuWO0np3zAAAAAABUNmXKlNwROsHkPAAAQItZa1Oy1gYA6nPOOefEAw88kDtG6ynnAYDGUjiVFE7ARKy1KVlrAwD1+cpXvpI7Qico5wEAAFrMjcySG5kAUJ+PfexjcfHFF+eO0XrKeQCgsUyDlkyDAhPxvbLkeyUA1OfQQw+NPfbYI55++uncUVpNOQ8AANBiJudLJucBoD4PP/ywYn4SKOcBAABazOR8yeQ8ANRn4cKFuSN0gnIeAACgxUzOl0zOA0B9vv3tb+eO0AnKeQCgsRROJYUTAADQJAceeGAMDw/njtF6ynkAGkUZ2+M8WNUwxqoGAACgSb70pS/FGWeckTtG6ynnAWgUZWyviHUeFNIAVbh2llw3AKA+d9xxR+4InaCcBwAAaDFPnZWsAAOA+uy33365I3RCX+4AAAAAAAC0x2677ZY7Qico5wEAAAAAqOzZZ5/NHaETrLUBAAAAAKCyX//1X49169bFmjVrIqW0+etb/np4eDh+8IMf5IjXGsp5AACAFvNC2JIXwgJAfV566aVYsmRJrFixIoqiiIj4Dz8vXrw4V7zWUM4DAAAAAFDZTTfdFNdff33uGK2nnAcAAGix/v7+mDdvXu4Y2Q0NDeWOAAA7jP7+/twROkE5DwAA0GLW2pSstQGA+qxatSp3hE5QzgPQKKb/epwHU5AAAABN9OY3vzl3hE5QzgPQKKb/epN/zoMJSIAq3NguuaELAPW59tprc0foBOU8AABAi7mxXXJTFwDqs88+++SO0AnKeQAAgBYzOV8yOQ8A9XniiSdyR+iEvtwBAAAAAABoj7e+9a25I3SCyXkAAAAAACqbOXNmDA4OTnjMihUr4owzzqgpUTsp5wEAAFrMzvmSnfMAUK+hoaF45plnoiiKzT+2tGbNmkzJ2kM5DwAA0GJ2zpfsnAeA+sydOzf+/M//PHeM1lPOAwAAtJjJ+ZLJeQCoz7e+9a3cETrBC2EBAAAAAKjsC1/4Qu4InWByHoBG8Wh+j/NgRQFAFa6dJdcMAKjPI488kjtCJyjnAWgUj+b3Hst3HqwnAAAAaKIjjzwyd4ROUM4D0Cim/3qcB1OQAFW4sV1yUxcA6rN06dLcETpBOQ9AoygYTM6PUbIAAAA000EHHZQ7Qid4ISwAAAAAAJV95zvfyR2hE0zOA9Ao1tr0OA/W2gBU4dpZcs0AgPpMmTIld4ROUM4DAAC0mJVwJevQAKA+p59+elx//fW5Y7Sech6ARlEw2Dk/RskCUI3J+ZLJeQCoz7/8y7/kjtAJds4DAAAAAFDZUUcdlTtCJyjnAQAAAACo7MADD4zddtstd4zWU84DAAAAAFDZ5ZdfHs8++2zuGK2nnAcAAAAAoLLZs2fnjtAJynkAAAAAACpbtWpV7gidoJwHAAAAAKCyoaGh3BE6YafcAQBgS/39/TFv3rzcMRrBefB/+ACqGBoaijlz5uSOkd3AwEDuCACww1i8eHHuCJ2gnAegURQMvXLBeVCyAAAANNHb3/72+Od//ufcMVpPOQ8AANBinjoredoKAOrz+te/PneETlDOAwAAtJinzkqeuAKA+jz66KO5I3SCch4AAKDFTM6XTM4DQH3+8i//MneETlDOAwCNpXAqKZyAiZicL5mcB4D6nHjiiXHzzTfnjtF6ynkAoLEUTiWFEzARNzJLbmQCQH2OOuoo5fwkUM4D0CgKhh7nQdECAADQRFOmTMkdoROU8wA0iknp3pS082BaHAAAoIn6+vpyR+gE5TwAAECLubFdclMXAOrzzne+My688MJ48sknIyIipfQfjlmxYkXMnTu37mitopwHAABoMSvhSlahAUB9iqKIPfbYIzZs2BAppc3l/Ja/3nXXXXNGbAXlPAAAAAAAlV155ZVxww035I7Resp5ABrF9F+P82AKEgAAoInOOOMM5fwkUM4D0Cj25noh7Bi7gwGqce0suW4AQH323HPPGBwcnPCYpUuXxllnnVVTonZSzgMAALSYp85KnrYCgGbZ1ktieTnlPAAAAAAAP5cVK1bECy+8EEVRRET5ktixX0dErFq1Kle01lDOAwAAAABQ2TXXXBPf+ta3csdoPeU8ANBYVjWUrGoAJmLnfMnOeQCoz2/8xm8o5yeBch4AaCyFU0nhBEzEjcySG5kAQNv05Q4AAAAAAEB73HbbbbkjdILJeQAAgBbzlFHJU0YAUJ/jjjuusWttdt1119wRKlPOAwAAtJi1NiVrbQCgPitXrswdYVwvvPBC7giVKecBaBQFQ4/zoGgBqMLkfMnkPADUZ+bMmbkjdIJyHoBGUTD0ygXnQckCUIUb2yU3dAGgPq67k8MLYQEAAAAAqOyxxx7LHaETTM4DAAC0mKfOSp64AoD6nHrqqXHdddfljtF6ynkAAIAWs9am5PF6AKjPokWLckfoBGttAAAAAACobPfdd88doROU8wAAAAAAVHbTTTfljtAJ1toAAAC0mJ3zJTvnAaA+p59+enz/+9/PHaP1lPMAAAAtZud8yc55AKjPQQcdFIODgxMes3Tp0jjrrLNqStROynkAGkXBq0Ac3wAAETJJREFU0OM8KFoAqjA5XzI5DwD1Wb9+fVx11VWxYsWKzV8rimLzj4iIlStX5orXGsp5ABpFwdArF5wHJQtAFW5sl9zQBYD63H///fbOTwLlPAAAQIu5sV1yUxcA6rP//vvnjtAJynkAAIAWMzlfMjkPAPW56667ckfohL7cAQAAAAAAaI9f/dVfzR2hE0zOAwAAtJi1NiVrbQCgPoccckgcfPDBsXjx4txRWk05D0CjeDS/x3mwogCgCtfOkmsGANRn9913j2uuuWbCY0ZGRuJDH/pQTYnaSTkPQKOY/utN/jkPAwqnUQonAACgbTZt2pQ7QuMp5wFoFGVsj/PgZs0YqxqAifheWfK9EgCapSiK3BEaTzkPQKMoGEzOj1GyAAAANNPq1avjrLPOihdffDF3lFZTzgMAALSYp85KVoABQH3uv/9+xfwk6MsdAAAAAACA9jj88MNzR+gEk/MAAAAtZiVcyTo0AKjPd77zndwROkE5DwAA0GLW2pSstQGA+syZMyduv/322LBhQ+4oraacBwAAaDGT8yWT8wBQn6lTp8att9464THDw8Mxe/bsmhK1k3IeAACgxUzOl0zOA0B91qxZE7Nnz44XXnghd5RW80JYAAAAAAAqGxkZUcxPAuU8AAAAAACV7bbbbrkjdIJyHgAAAACAyrwIdnLYOQ8AANBiXghb8kJYAKjP4sWLc0foBOU8AABAi3khbMkLYQGgPtOmTcsdoROU8wAAAC1mcr5kch4A6rPffvvljtAJds4DAAAAAFDZunXrckfoBJPzAAAALWatTclaGwCoz/z583NH6AST8wAAAAAAVHbYYYfljtAJynkAAAAAACp785vfHG95y1tin3322fxj7733jr322iv22muv2HPPPXNHnDRFUbyjKIqbiqJYXhRFKori7K1+f7+iKL5RFMWKoiheKIpiblEUb6jyZ1trAwAAAABAZQsXLoz77rsvd4y6vDoifhwR3xr9sVlRFEVE3BgRmyLi/RHxTET8YUTcXhTFzJTSzyb6g5XzAAAALTY0NBRz5szJHSO7gYGB3BEAYIcxMjKSO0JtUkrfj4jvR0QURfGNrX77DRFxbET0p5R+NHrMf46IlRHx4Yj4+kR/tnIegEbxUrse58HL/QCqcO0suWYAQH1mzpyZO8KE7rnnnjj66KNjypQpr/RfNW305xfHvpBS2lQUxbqIeHso5wFoE9N/vck/58EEJAAAQBPddddduSNM6NJLL40jjjgiLrvssle6oH84IoYj4otFUXw8Ip6PiAsi4j9FxAHb+7ByHgAAoMXc2C65qQsA9bnhhhtyR5jQ2rVr46GHHop77703jjvuuFfs70kpbSiK4v+IiKsj4omI2BgRt0fELRFRbO/zynnIpL+/PyKsrfD4MQAAAEC7PPXUU7kjbNe6devikUceeUXL+YiIlNL9EdFfFMXuETE1pbS6KIr5EbHdN+Yq5wEAAAAAiIiI7373u3HFFVfkjrFNd9xxRxTFfxxIv+eee+LSSy+NtWvXbv7atGnT4rDDDqstW0rpmYiIoijeEBFviYjPbu8zynkAAIAW80LYkicyAWBy/PVf/3XuCD+3o48+Oo444oh46KGHYt26dTFt2rSYOXNmHH300f/Lf3ZRFK+OiLGWvy8iDiyKoj8inkwpDRdFcVpErImIJRFxZERcHhE3ppRu3d6frZwHAABoMTvnS3bOA8DkOOWUUxq/U35rU6ZMicsuuyzuvffeeOSRR+Kwww6Lo48+erJeBvuWiBjc4r8/P/rjmxFxdpQvfv1vEbFfRDweEd+KiEur/MHKeQAAgBYzOV8yOQ8Ak+Otb31r68r5iLKgP+644yZ9x3xKaV5M8HLXlNIVEfEL7QHq+wUzAQAAAADQMbvvvvu4v1cUxTZ3vvOLMTkPQKOY/utxHkxBAgAA1O2b3/zmuL+XUqoxSfcp5wFoFHtzeztznQe7gwEAAOq2YcOG3BF2GMp5AKCxPElR8gQBMBE3tktu6gLA5HjwwQdzRxhX11bqKOcBgMZSOJUUTgAAQF2aPDmfUupUQe+FsAAAAAAANF6XivkI5TwAAAAAAKPe9KY35Y4wrq69kNZaGwAAgBbzfo6S93MAwOS4/PLLt3vMSSedVEOS7lPOAwAAAAAQEREPPfRQnH/++blj7BCU8wAAAC3m5dklL88GgMlxyy235I4wrq7tnFfOAwAAtJi1NiVrbQBgcqxbty53hHGllDpV0HshLAAAAAAAERFx22235Y6wwzA5DwAA0GLW2pSstQEA2kY5D0CjeDS/x3mwogAAAKBu5557blx99dW5Y2xTl1baRFhrAwAAAADAqDe+8Y25I4wrpZQ7wqQyOQ9Ao3g0v/dYvvNgPQEAAEDdNmzYkDvCuEzOAwAAAADQSbfffnvuCOPq2uS8ch4AAAAAgIiIuOOOO3JH2GFYawMAANBiXqZe8hJxAJgcX/va1+JTn/pU9PX1RVEUm3+eMmVKFEURRVHEE088kTtmJyjnAQAAWsz7WkreVQIAk+Owww6LW265ZcJjTjrppJrSdJtyHoBGMf3X4zyYggSowrWz5JoBAJPjmWeeiW984xsvm5bv6+t72Y9cuvZCWOU8AI1i+q83+ec8mIAEAACo21e/+tW49dZbc8fYppRSpwp65TwAjWL6r8d5MAUJAABQt9NOO62x5XyXivkI5TwADWNy3uT8mIGBATdrRrlJAUzEtbPkiSsAmBw33XRT7gg7DOU8ANBYCqeSwgkAAKjLH/zBH8R73/veSClt/hERL/v5vPPOyxmxM5TzAAAALeYpo5KnjABgcnz961+Pa6+9NneMbbJzHgAAgMbwlFHJU0YAMDle9apX5Y6ww1DOAwAAtJjJ+ZLJeQCYHD/96U9zR9hhKOcBgMZSOJUUTgAAQF3uvPPO3BF2GMp5AKCxrGooWdUATMT3ypLvlQAwOQ488MAYHh7OHWOHoJwHoFFMSvc4DybGAapw7Sy5ZgDA5Nhll11yR9hhKOcBaBTTf73JP+fBBCQAAEDdFi5cmDvCuIqiyB1hUvXlDgAAAAAAQDMcc8wxuSOMK6WUO8KkMjkPAADQYp46K3niCgAmx/ve976YP39+7hjbZHIeAAAAAIBOuuSSS3JHGJfJeQAAABrDC2FLXggLALSNch4AAKDFrLUpWWsDAJPjV37lV+InP/lJ7hg7BOU8AI1i+q/HeTAFCQAAUDfFfH0qlfNFUXwgIt4ZEf0RcVREzIiIv00pzdrGsTtHxHmjx/5aRMyMiJ0j4uMppa9v5++ZHRHnj35mY0Q8EBFfTil9b5zjp0TE70fEORHxhohYGxE/jIg/SyndPc5ndomIT0fEhyLioIh4NiLmRcTnUkoLxvnMXhHxJxHx/og4ICKeiIi5EfEnKaVlE/2bAAAAXklubJfc0AWA7uvaC2GrTs5/JspS/vmIWBYRb5zg2FdFxNjzhCMRsTIiXre9v6Aoii9HxB+N/vlfi4ipURbo/7Moik+llK7c6vgiIq6NiA9ExMKIuDIi9oqID0bEvxRFcWpK6R+3+sy0iLgtIo6PiPsi4vLRbKdFxMlFUbwrpTR/q8/sHRF3R8QvR8Qdo3/nG6O8IXByURTHpZQe296/D4BqPJrfeyzfebCeAAAA2HEURfGOiPgvEfG/RcRrIuKclNI3xjn2byLiExFxYUrpy7WFzCyl1KmCvq/icRdEWU7vFhH/eTvHvhARvxMRr0kp7R8R/2N7f3hRFG+Lsph/NCLelFK6IKV0fpT/Q3wyIr5cFMXBW33sQ1EW83dHRH9K6cKU0rkRcVKUU/dfK4pixlaf+cMoi/nrI+KYlNLFKaWPjP45u0bE/yiKYutz8sXRf/t/Syn9ekrp0yml90fEH0TEL0XEV7f37wMAAAAA2I5XR8SPo+wd14530OiWk6MjYsUrEeJNb3rTK/HHsg2VyvmU0mBK6d9TSqnCsetTSreklB7/OXL83ujP/zWl9NQWf9biiLgqIqZFOam+pbGbBJ9JKb24xWf+v4i4LiL2jbJ0j4jNk/Zjf89FKaVNW3zmHyPizijX6bxzi8+8OiLOjIifRcSfbvX3XxkRSyLiN4uieH31fyoAAAAAwMullL6fUrokpXR9RGza1jFFURwU5TaQj0TEhsnOsHHjxjjxxBNj1113jd122y323Xff+KVf+qXNP/bbb7/Yb7/9Jvuv3WE15YWw7xr9ee42fu+WiPjs6DGfi4goimJ6RLwtyin9O8f5zJmjn7lm9GuHRsSBEfHTlNKicT5zwuhnBke/dmxE7BIRt6aUntvy4JTSpqIo/inKx0dOigirbQAAAACAV0RRFDtFxP8T5fs2F0z2epeNGzfGRRddFAsWLIgXX3wxpk+fHocddlhcdtllMWXKlJcde9JJJ03q372jyl7OF0Xxqoh4bUQ8P860/b+P/vzLW3zt0IiYEhGPpZReqviZw0d//uk4USbrMwAAAAAAk+3zEbEmpfR/vxJ/+L333hsLFiyItWvLjTpr166Nf/3Xf413v/vdr8RfR0QUFTbVvPwDRXFilJPlf5tSmlXh+D+NcuL94ymlr2/j918TEcsjYnlK6T9t4/d3joj1EbE+pTRt9Gtvi4i7IuKulNLbt/GZN0RZqP80pXT46Nc+EhF/O17uoih+IyJujXJK/jdHv3ZJRPzXKNftfGYbn/l4RPz3iPjvKaX/c3vnAgAAAABge4qieD4iPjn2QtjRTvZvo3z35urRry2OiCsn64WwJ5100mejXO295Sr0TRHxucHBwT+bjL+Dl8s+OQ8AAAAAwIROjIgDIuLxLdbZTImIPy+KYs62hp5/XoODg5dGxKX/q38O1TWhnH9m9Ofdx/n9sa8/3ZLPAAAAAABMpq9GxPVbfe2fotxB/7X64zAZspfzKaWfFUWxPCJeWxTFAdvYO/+G0Z+33Pv+aERsjIjXF0Wx0zb2zm/rMwtHfx5vP/xkfQYAAAAA4OdSFMWrI+Kw0f/si4gDi6Loj4gnU0rDEbFqq+M3RMTKlNLCoJX6tn9ILe4Y/fm3tvF7v73VMZFSejEi7o6IXSPihCqfibLQH46IXy6K4pCKn/lhRKyNiOOLopix5cFFUfRFxP8++p+D2/jzAAAAAACqektEPDD6Y5coXwD7QER8IWcoXjlNKef/evTn/6soij3HvlgUxcERcX5ErIuIa7b6zNhbif+sKIrpW3zmrRHxwYhYHRE3jH09lW++Hft7Lhst18c+874oS/6HIuIHW3zm+Yj4dkS8KsqXIWzpkxFxcET8U0rpsar/UAAAAACAraWU5qWUim38OHuc4w+erJfBkkdRdtbbOago3h8R7x/9z/0j4jcj4rGIuHP0a2tSSv9li+M/HRFvHP3P/og4KspJ938f/dr/m1L6+lZ/x19GxB9GxLIo9ydNjbJk3zsiPpVSunKr44uI+PuI+EBEPBwR/3P02A9GxPSIODWl9I9bfWZalJPxb4uI+yLinyPiwIg4LSLWR8S7Ukrzt/rM3qPZf3n0s/dGxBER8b4oHyV5W0rp0XFPHgAAAAAAbKVqOf+nEfG5CQ5ZklI6eIvj50XEOyc4/pvbuuNTFMXZUU7Kz4yITRHxrxHxFyml742Ta6eI+FREfDTKfUwvRsQ9EfFnKaW7x/nMrhHx6Yj4cJTF/LMRMS8iPpdSemicz+wV5b///VG+FfmJiLglIv4kpbRsgn/n/9/OHZsAAMMADPv/6x7QpUMxgUhneDAAAAAAAFye4jwAAAAAAPDPlOc8AAAAAACsIc4DAAAAAEBMnAcAAAAAgJg4DwAAAAAAMXEeAAAAAABi4jwAAAAAAMTEeQAAAAAAiInzAAAAAAAQE+cBAAAAACAmzgMAAAAAQOwAvimIZa8VtusAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# checking the null/NaN values\n","df.isna().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hcxARnKs8HYQ","executionInfo":{"status":"ok","timestamp":1663869015466,"user_tz":-330,"elapsed":103,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"c8d45d6a-16dd-4b5f-8901-c97f469253ca"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Loan ID                             0\n","Customer ID                         0\n","Loan Status                     10000\n","Current Loan Amount                 0\n","Term                                0\n","Credit Score                    21135\n","Annual Income                   21135\n","Years in current job             4649\n","Home Ownership                      0\n","Purpose                             0\n","Monthly Debt                        0\n","Years of Credit History             0\n","Months since last delinquent    58447\n","Number of Open Accounts             0\n","Number of Credit Problems           0\n","Current Credit Balance              0\n","Maximum Open Credit                 2\n","Bankruptcies                      226\n","Tax Liens                          11\n","dtype: int64"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["#Changing the empty spaces between Column names to underscore\n","df.columns = df.columns.str.replace(' ','_')"],"metadata":{"id":"FuPW7pHT9TAX","executionInfo":{"status":"ok","timestamp":1663869015471,"user_tz":-330,"elapsed":83,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["df.dtypes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ugIwonYk9UT9","executionInfo":{"status":"ok","timestamp":1663869015477,"user_tz":-330,"elapsed":88,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"94db5e40-456b-4e8a-bf11-dedaeba0e1e5"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Loan_ID                          object\n","Customer_ID                      object\n","Loan_Status                      object\n","Current_Loan_Amount             float64\n","Term                             object\n","Credit_Score                    float64\n","Annual_Income                   float64\n","Years_in_current_job             object\n","Home_Ownership                   object\n","Purpose                          object\n","Monthly_Debt                    float64\n","Years_of_Credit_History         float64\n","Months_since_last_delinquent    float64\n","Number_of_Open_Accounts         float64\n","Number_of_Credit_Problems       float64\n","Current_Credit_Balance          float64\n","Maximum_Open_Credit             float64\n","Bankruptcies                    float64\n","Tax_Liens                       float64\n","dtype: object"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lcgYjkVL-sSs","executionInfo":{"status":"ok","timestamp":1663869015479,"user_tz":-330,"elapsed":82,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"aaeaaf21-379d-4b23-d108-28b650991038"},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(110000, 19)"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["df.drop([\"Loan_ID\",\t\"Customer_ID\"],axis =1 , inplace = True)"],"metadata":{"id":"g831_lIL_uEI","executionInfo":{"status":"ok","timestamp":1663869015482,"user_tz":-330,"elapsed":77,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AFrqb-kM_qeF","executionInfo":{"status":"ok","timestamp":1663869015485,"user_tz":-330,"elapsed":79,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"35fb433a-a129-4ada-f2c5-e9ec78b351c7"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(110000, 17)"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["df[\"Years_in_current_job\"].fillna(df[\"Years_in_current_job\"].mode()[0], axis = 0, inplace = True)"],"metadata":{"id":"vUrgJb3ScDEC","executionInfo":{"status":"ok","timestamp":1663869015486,"user_tz":-330,"elapsed":74,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["df[\"Credit_Score\"].fillna(df[\"Credit_Score\"].mean(), axis = 0 , inplace = True)\n","df[\"Annual_Income\"].fillna(df[\"Annual_Income\"].mean(), axis = 0 , inplace = True)\n","df[\"Bankruptcies\"].fillna(df[\"Bankruptcies\"].mean(), axis = 0 , inplace = True)\n","df[\"Months_since_last_delinquent\"].fillna(df[\"Months_since_last_delinquent\"].mean(), axis = 0 , inplace = True)\n","df[\"Maximum_Open_Credit\"].fillna(df[\"Maximum_Open_Credit\"].mean(), axis = 0 , inplace = True)\n","df[\"Tax_Liens\"].fillna(df[\"Tax_Liens\"].mean(), axis = 0 , inplace = True)"],"metadata":{"id":"Az7yYu6GQ1s6","executionInfo":{"status":"ok","timestamp":1663869015493,"user_tz":-330,"elapsed":80,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["df.isna().mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wD0O6AwvBOHz","executionInfo":{"status":"ok","timestamp":1663869015494,"user_tz":-330,"elapsed":80,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"cfd8f0a5-950a-4ad5-f44b-83ec4aa2cda4"},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Loan_Status                     0.090909\n","Current_Loan_Amount             0.000000\n","Term                            0.000000\n","Credit_Score                    0.000000\n","Annual_Income                   0.000000\n","Years_in_current_job            0.000000\n","Home_Ownership                  0.000000\n","Purpose                         0.000000\n","Monthly_Debt                    0.000000\n","Years_of_Credit_History         0.000000\n","Months_since_last_delinquent    0.000000\n","Number_of_Open_Accounts         0.000000\n","Number_of_Credit_Problems       0.000000\n","Current_Credit_Balance          0.000000\n","Maximum_Open_Credit             0.000000\n","Bankruptcies                    0.000000\n","Tax_Liens                       0.000000\n","dtype: float64"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["df.dtypes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tTTf08nADJT-","executionInfo":{"status":"ok","timestamp":1663869015495,"user_tz":-330,"elapsed":74,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"c69b8900-5662-44f5-ef4e-f11ce41921fb"},"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Loan_Status                      object\n","Current_Loan_Amount             float64\n","Term                             object\n","Credit_Score                    float64\n","Annual_Income                   float64\n","Years_in_current_job             object\n","Home_Ownership                   object\n","Purpose                          object\n","Monthly_Debt                    float64\n","Years_of_Credit_History         float64\n","Months_since_last_delinquent    float64\n","Number_of_Open_Accounts         float64\n","Number_of_Credit_Problems       float64\n","Current_Credit_Balance          float64\n","Maximum_Open_Credit             float64\n","Bankruptcies                    float64\n","Tax_Liens                       float64\n","dtype: object"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["#removiong the full value and printing the extracted value as number of year only and converting the same into integer value\n","df['Years_in_current_job'] = df['Years_in_current_job'].str.extract(r\"(\\d+)\")\n","df['Years_in_current_job'] = df['Years_in_current_job'].astype(float)"],"metadata":{"id":"xS5B4mKDDuJU","executionInfo":{"status":"ok","timestamp":1663869015495,"user_tz":-330,"elapsed":68,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["df.dtypes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yHXZkaifDv-c","executionInfo":{"status":"ok","timestamp":1663869015496,"user_tz":-330,"elapsed":68,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"14960d5f-b0b7-4bfa-cbfd-dda3d5836751"},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Loan_Status                      object\n","Current_Loan_Amount             float64\n","Term                             object\n","Credit_Score                    float64\n","Annual_Income                   float64\n","Years_in_current_job            float64\n","Home_Ownership                   object\n","Purpose                          object\n","Monthly_Debt                    float64\n","Years_of_Credit_History         float64\n","Months_since_last_delinquent    float64\n","Number_of_Open_Accounts         float64\n","Number_of_Credit_Problems       float64\n","Current_Credit_Balance          float64\n","Maximum_Open_Credit             float64\n","Bankruptcies                    float64\n","Tax_Liens                       float64\n","dtype: object"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["df.groupby(\"Term\").size()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bUjhjvYNEKZV","executionInfo":{"status":"ok","timestamp":1663869015497,"user_tz":-330,"elapsed":63,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"15fe26ce-e032-4ae6-dd6c-4864ab7199eb"},"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Term\n","Long Term     30497\n","Short Term    79503\n","dtype: int64"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["#Label Encoder to handle categorical column\n","from sklearn.preprocessing import LabelEncoder\n","label_encoder = LabelEncoder()\n","df['Term']= label_encoder.fit_transform(df['Term'])\n","df['Home_Ownership']= label_encoder.fit_transform(df['Home_Ownership'])\n","df['Purpose']= label_encoder.fit_transform(df['Purpose'])"],"metadata":{"id":"6Ta3nXQ5DMzm","executionInfo":{"status":"ok","timestamp":1663869016220,"user_tz":-330,"elapsed":777,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g6xBVbbMEmhF","executionInfo":{"status":"ok","timestamp":1663869016223,"user_tz":-330,"elapsed":64,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"8561aab1-d348-4d37-ba32-2297c61694d7"},"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(110000, 17)"]},"metadata":{},"execution_count":26}]},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":270},"id":"L5lnr6fgEnsB","executionInfo":{"status":"ok","timestamp":1663869016228,"user_tz":-330,"elapsed":65,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"2429a91e-d40f-4ace-9af2-287f7ebabdd6"},"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  Loan_Status  Current_Loan_Amount  Term  Credit_Score  Annual_Income  \\\n","0  Fully Paid             445412.0     1    709.000000   1.167493e+06   \n","1  Fully Paid             262328.0     1   1076.594644   1.377449e+06   \n","2  Fully Paid           99999999.0     1    741.000000   2.231892e+06   \n","3  Fully Paid             347666.0     0    721.000000   8.069490e+05   \n","4  Fully Paid             176220.0     1   1076.594644   1.377449e+06   \n","\n","   Years_in_current_job  Home_Ownership  Purpose  Monthly_Debt  \\\n","0                   8.0               1        5       5214.74   \n","1                  10.0               1        3      33295.98   \n","2                   8.0               2        3      29200.53   \n","3                   3.0               2        3       8741.90   \n","4                   5.0               3        3      20639.70   \n","\n","   Years_of_Credit_History  Months_since_last_delinquent  \\\n","0                     17.2                     34.907086   \n","1                     21.1                      8.000000   \n","2                     14.9                     29.000000   \n","3                     12.0                     34.907086   \n","4                      6.1                     34.907086   \n","\n","   Number_of_Open_Accounts  Number_of_Credit_Problems  Current_Credit_Balance  \\\n","0                      6.0                        1.0                228190.0   \n","1                     35.0                        0.0                229976.0   \n","2                     18.0                        1.0                297996.0   \n","3                      9.0                        0.0                256329.0   \n","4                     15.0                        0.0                253460.0   \n","\n","   Maximum_Open_Credit  Bankruptcies  Tax_Liens  \n","0             416746.0           1.0        0.0  \n","1             850784.0           0.0        0.0  \n","2             750090.0           0.0        0.0  \n","3             386958.0           0.0        0.0  \n","4             427174.0           0.0        0.0  "],"text/html":["\n","  <div id=\"df-f984419d-aa2b-42e8-badd-08470cfe3aa5\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Loan_Status</th>\n","      <th>Current_Loan_Amount</th>\n","      <th>Term</th>\n","      <th>Credit_Score</th>\n","      <th>Annual_Income</th>\n","      <th>Years_in_current_job</th>\n","      <th>Home_Ownership</th>\n","      <th>Purpose</th>\n","      <th>Monthly_Debt</th>\n","      <th>Years_of_Credit_History</th>\n","      <th>Months_since_last_delinquent</th>\n","      <th>Number_of_Open_Accounts</th>\n","      <th>Number_of_Credit_Problems</th>\n","      <th>Current_Credit_Balance</th>\n","      <th>Maximum_Open_Credit</th>\n","      <th>Bankruptcies</th>\n","      <th>Tax_Liens</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Fully Paid</td>\n","      <td>445412.0</td>\n","      <td>1</td>\n","      <td>709.000000</td>\n","      <td>1.167493e+06</td>\n","      <td>8.0</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>5214.74</td>\n","      <td>17.2</td>\n","      <td>34.907086</td>\n","      <td>6.0</td>\n","      <td>1.0</td>\n","      <td>228190.0</td>\n","      <td>416746.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Fully Paid</td>\n","      <td>262328.0</td>\n","      <td>1</td>\n","      <td>1076.594644</td>\n","      <td>1.377449e+06</td>\n","      <td>10.0</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>33295.98</td>\n","      <td>21.1</td>\n","      <td>8.000000</td>\n","      <td>35.0</td>\n","      <td>0.0</td>\n","      <td>229976.0</td>\n","      <td>850784.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Fully Paid</td>\n","      <td>99999999.0</td>\n","      <td>1</td>\n","      <td>741.000000</td>\n","      <td>2.231892e+06</td>\n","      <td>8.0</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>29200.53</td>\n","      <td>14.9</td>\n","      <td>29.000000</td>\n","      <td>18.0</td>\n","      <td>1.0</td>\n","      <td>297996.0</td>\n","      <td>750090.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Fully Paid</td>\n","      <td>347666.0</td>\n","      <td>0</td>\n","      <td>721.000000</td>\n","      <td>8.069490e+05</td>\n","      <td>3.0</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>8741.90</td>\n","      <td>12.0</td>\n","      <td>34.907086</td>\n","      <td>9.0</td>\n","      <td>0.0</td>\n","      <td>256329.0</td>\n","      <td>386958.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Fully Paid</td>\n","      <td>176220.0</td>\n","      <td>1</td>\n","      <td>1076.594644</td>\n","      <td>1.377449e+06</td>\n","      <td>5.0</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>20639.70</td>\n","      <td>6.1</td>\n","      <td>34.907086</td>\n","      <td>15.0</td>\n","      <td>0.0</td>\n","      <td>253460.0</td>\n","      <td>427174.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f984419d-aa2b-42e8-badd-08470cfe3aa5')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 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Reddy","userId":"07552141107752951949"}},"outputId":"dbd13465-79ed-4b31-88c3-7634a6774aec"},"execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Loan_Status                      object\n","Current_Loan_Amount             float64\n","Term                              int64\n","Credit_Score                    float64\n","Annual_Income                   float64\n","Years_in_current_job            float64\n","Home_Ownership                    int64\n","Purpose                           int64\n","Monthly_Debt                    float64\n","Years_of_Credit_History         float64\n","Months_since_last_delinquent    float64\n","Number_of_Open_Accounts         float64\n","Number_of_Credit_Problems       float64\n","Current_Credit_Balance          float64\n","Maximum_Open_Credit             float64\n","Bankruptcies                    float64\n","Tax_Liens                       float64\n","dtype: object"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","source":["#creating train test dataset with and without target variables\n","x_train = df.drop(columns=['Loan_Status'])\n","y_train = df.iloc[:,0:1]\n","print(x_train.columns)\n","print(y_train.columns)"],"metadata":{"id":"aaQTaIF0Gt3T","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663869016236,"user_tz":-330,"elapsed":60,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"20a74d5a-30ae-4702-8499-711b48122b38"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["Index(['Current_Loan_Amount', 'Term', 'Credit_Score', 'Annual_Income',\n","       'Years_in_current_job', 'Home_Ownership', 'Purpose', 'Monthly_Debt',\n","       'Years_of_Credit_History', 'Months_since_last_delinquent',\n","       'Number_of_Open_Accounts', 'Number_of_Credit_Problems',\n","       'Current_Credit_Balance', 'Maximum_Open_Credit', 'Bankruptcies',\n","       'Tax_Liens'],\n","      dtype='object')\n","Index(['Loan_Status'], dtype='object')\n"]}]},{"cell_type":"code","source":["x_train.dtypes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-fSPl54484eR","executionInfo":{"status":"ok","timestamp":1663869016238,"user_tz":-330,"elapsed":55,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"1cf130c3-a0c7-4cfc-eeb8-973269802f86"},"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Current_Loan_Amount             float64\n","Term                              int64\n","Credit_Score                    float64\n","Annual_Income                   float64\n","Years_in_current_job            float64\n","Home_Ownership                    int64\n","Purpose                           int64\n","Monthly_Debt                    float64\n","Years_of_Credit_History         float64\n","Months_since_last_delinquent    float64\n","Number_of_Open_Accounts         float64\n","Number_of_Credit_Problems       float64\n","Current_Credit_Balance          float64\n","Maximum_Open_Credit             float64\n","Bankruptcies                    float64\n","Tax_Liens                       float64\n","dtype: object"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","source":["y_train.dtypes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m6TfWsqO89y5","executionInfo":{"status":"ok","timestamp":1663869016244,"user_tz":-330,"elapsed":55,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"0d07ac19-b93a-47ec-cdf4-76d0eae7ca8c"},"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Loan_Status    object\n","dtype: object"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["# since training data set contained only 100000 data-points , so it has been locked as-per \n","#( the remaining i.e after 100000 rows it doesnt have a loan_status value)\n","x_train = x_train.iloc[:100000]\n","y_train = y_train.iloc[:100000]"],"metadata":{"id":"Me0Rb9CmECIC","executionInfo":{"status":"ok","timestamp":1663869016246,"user_tz":-330,"elapsed":49,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":32,"outputs":[]},{"cell_type":"code","source":["#As the model above specifies that the Random Forest Classifier will be best suitable for the model creation\n","from sklearn.model_selection import train_test_split\n","from sklearn.ensemble import RandomForestClassifier\n","x_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size=0.2)"],"metadata":{"id":"IsY61Y2j3V4K","executionInfo":{"status":"ok","timestamp":1663869016247,"user_tz":-330,"elapsed":50,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":33,"outputs":[]},{"cell_type":"code","source":["print(x_train.shape)\n","print(y_train.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eF2IsoSz6SAO","executionInfo":{"status":"ok","timestamp":1663869016248,"user_tz":-330,"elapsed":49,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"2f9ebc06-4dd5-4578-c5d4-813675ecbad8"},"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["(80000, 16)\n","(80000, 1)\n"]}]},{"cell_type":"code","source":["#Create a RandomForestClassifier\n","rfc=RandomForestClassifier()\n","rfc.fit(x_train,y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jfmH3j0l3le-","executionInfo":{"status":"ok","timestamp":1663869051403,"user_tz":-330,"elapsed":35199,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"10f2b93b-d2ce-4b77-fcfb-4b46b45a09d4"},"execution_count":35,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n","  This is separate from the ipykernel package so we can avoid doing imports until\n"]},{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier()"]},"metadata":{},"execution_count":35}]},{"cell_type":"code","source":["# predicting with test data\n","y_test_pred=rfc.predict(x_test)"],"metadata":{"id":"xwzTBG2-T1F1","executionInfo":{"status":"ok","timestamp":1663869052184,"user_tz":-330,"elapsed":794,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":36,"outputs":[]},{"cell_type":"code","source":["# Model Accuracy, how often is the classifier correct?\n","from sklearn import metrics\n","print(\"Accuracy:\",metrics.accuracy_score(y_test, y_test_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3_mwTCPBB6go","executionInfo":{"status":"ok","timestamp":1663869052186,"user_tz":-330,"elapsed":11,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"1c58571e-76e2-4842-e2a2-f36484ba466c"},"execution_count":37,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.8224\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","\n","print(classification_report(y_test, y_test_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i_DkSCCjV0Ec","executionInfo":{"status":"ok","timestamp":1663869359787,"user_tz":-330,"elapsed":1393,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"d8f9864a-c4f7-4745-e28b-fb71870213d4"},"execution_count":52,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n"," Charged Off       0.45      0.44      0.45      4486\n","  Fully Paid       0.84      0.85      0.84     15514\n","\n","    accuracy                           0.76     20000\n","   macro avg       0.65      0.64      0.65     20000\n","weighted avg       0.75      0.76      0.75     20000\n","\n"]}]},{"cell_type":"code","source":["#predicting with train data\n","y_train_pred = rfc.predict(x_train)\n","print(y_train_pred)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V1F2EEcLH5cD","executionInfo":{"status":"ok","timestamp":1663869055663,"user_tz":-330,"elapsed":3483,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"fd11140f-431c-4316-f99f-ac7d3b895040"},"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["['Fully Paid' 'Charged Off' 'Fully Paid' ... 'Fully Paid' 'Fully Paid'\n"," 'Fully Paid']\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","\n","print(classification_report(y_train, y_train_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D_VVjV2VVkkh","executionInfo":{"status":"ok","timestamp":1663869316270,"user_tz":-330,"elapsed":4875,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"09e6a89d-0c81-4604-ee2d-b4978f1379fc"},"execution_count":51,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n"," Charged Off       1.00      1.00      1.00     18153\n","  Fully Paid       1.00      1.00      1.00     61847\n","\n","    accuracy                           1.00     80000\n","   macro avg       1.00      1.00      1.00     80000\n","weighted avg       1.00      1.00      1.00     80000\n","\n"]}]},{"cell_type":"code","source":["#printing accuracy predictions for train data\n","print(\"Accuracy:\",metrics.accuracy_score(y_train, y_train_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GGEjCzauUAPW","executionInfo":{"status":"ok","timestamp":1663869055669,"user_tz":-330,"elapsed":87,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"5584e1be-9fa4-4948-d33a-d6d7cf270dfd"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 1.0\n"]}]},{"cell_type":"code","source":["#save to .csv file\n","result = pd.DataFrame(y_test_pred,columns = ['predictions'])\n","result.to_csv('/content/drive/MyDrive/Loan_status.csv',index=False)"],"metadata":{"id":"xMC7GAojObNV","executionInfo":{"status":"ok","timestamp":1663869110502,"user_tz":-330,"elapsed":825,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":42,"outputs":[]},{"cell_type":"markdown","source":[" **using decision tree**"],"metadata":{"id":"cQHFVh-jS4R5"}},{"cell_type":"code","source":["from sklearn.tree import DecisionTreeClassifier\n","dt = DecisionTreeClassifier()"],"metadata":{"id":"HvUaxggWJQ2u","executionInfo":{"status":"ok","timestamp":1663869147727,"user_tz":-330,"elapsed":763,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":45,"outputs":[]},{"cell_type":"code","source":["dt.fit(x_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WzVvby9PSQKO","executionInfo":{"status":"ok","timestamp":1663869152358,"user_tz":-330,"elapsed":1927,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"cc6d9034-315a-4b4a-931b-6eb27d328310"},"execution_count":46,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier()"]},"metadata":{},"execution_count":46}]},{"cell_type":"code","source":["y_train_pred = dt.predict(x_train)"],"metadata":{"id":"sY6PAyljSYME","executionInfo":{"status":"ok","timestamp":1663869152361,"user_tz":-330,"elapsed":13,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":47,"outputs":[]},{"cell_type":"code","source":["y_test_pred = dt.predict(x_test)"],"metadata":{"id":"AI716TFPSdIN","executionInfo":{"status":"ok","timestamp":1663869161589,"user_tz":-330,"elapsed":13,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":49,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","\n","print(classification_report(y_train, y_train_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XqzmghLFSj5u","executionInfo":{"status":"ok","timestamp":1663869159344,"user_tz":-330,"elapsed":4207,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"beefceef-7c54-4ca1-c337-291cd957e70e"},"execution_count":48,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n"," Charged Off       1.00      1.00      1.00     18153\n","  Fully Paid       1.00      1.00      1.00     61847\n","\n","    accuracy                           1.00     80000\n","   macro avg       1.00      1.00      1.00     80000\n","weighted avg       1.00      1.00      1.00     80000\n","\n"]}]},{"cell_type":"code","source":["print(classification_report(y_test, y_test_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sWyLnqjCSowI","executionInfo":{"status":"ok","timestamp":1663869167186,"user_tz":-330,"elapsed":1414,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}},"outputId":"0e73e70d-0f46-4f46-ea11-96bc7c8456bd"},"execution_count":50,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n"," Charged Off       0.45      0.44      0.45      4486\n","  Fully Paid       0.84      0.85      0.84     15514\n","\n","    accuracy                           0.76     20000\n","   macro avg       0.65      0.64      0.65     20000\n","weighted avg       0.75      0.76      0.75     20000\n","\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"5bUXOWLkS21G","executionInfo":{"status":"aborted","timestamp":1663869055719,"user_tz":-330,"elapsed":105,"user":{"displayName":"Putturu kusumakar Reddy","userId":"07552141107752951949"}}},"execution_count":null,"outputs":[]}]}