linear-and-logistic-regression-using-python.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/rubensjrcosta/linear-and-logistic-regression-using-python?scriptVersionId=106330796\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"code","execution_count":1,"id":"494142d2","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2022-09-23T04:09:39.211775Z","iopub.status.busy":"2022-09-23T04:09:39.211285Z","iopub.status.idle":"2022-09-23T04:09:40.607541Z","shell.execute_reply":"2022-09-23T04:09:40.606436Z"},"papermill":{"duration":1.404916,"end_time":"2022-09-23T04:09:40.610277","exception":false,"start_time":"2022-09-23T04:09:39.205361","status":"completed"},"tags":[]},"outputs":[],"source":["import numpy as np \n","import pandas as pd \n","import seaborn as sns \n","from sklearn import datasets\n","import matplotlib.pyplot as plt\n","from sklearn.linear_model import LinearRegression\n","import os"]},{"cell_type":"code","execution_count":2,"id":"c19ec3a9","metadata":{"execution":{"iopub.execute_input":"2022-09-23T04:09:40.61871Z","iopub.status.busy":"2022-09-23T04:09:40.617892Z","iopub.status.idle":"2022-09-23T04:09:40.623446Z","shell.execute_reply":"2022-09-23T04:09:40.622278Z"},"papermill":{"duration":0.012349,"end_time":"2022-09-23T04:09:40.626027","exception":false,"start_time":"2022-09-23T04:09:40.613678","status":"completed"},"tags":[]},"outputs":[],"source":["# x from 0 to 30\n","x = 30 * np.random.random((20, 1))"]},{"cell_type":"code","execution_count":3,"id":"dc56be95","metadata":{"execution":{"iopub.execute_input":"2022-09-23T04:09:40.634451Z","iopub.status.busy":"2022-09-23T04:09:40.633404Z","iopub.status.idle":"2022-09-23T04:09:40.638722Z","shell.execute_reply":"2022-09-23T04:09:40.637948Z"},"papermill":{"duration":0.011671,"end_time":"2022-09-23T04:09:40.640796","exception":false,"start_time":"2022-09-23T04:09:40.629125","status":"completed"},"tags":[]},"outputs":[],"source":["# y = a*x + b with noise\n","y = 0.5 * x + 1.0 + np.random.normal(size=x.shape)"]},{"cell_type":"code","execution_count":4,"id":"b5fa3313","metadata":{"execution":{"iopub.execute_input":"2022-09-23T04:09:40.649046Z","iopub.status.busy":"2022-09-23T04:09:40.64813Z","iopub.status.idle":"2022-09-23T04:09:40.677426Z","shell.execute_reply":"2022-09-23T04:09:40.676278Z"},"papermill":{"duration":0.037037,"end_time":"2022-09-23T04:09:40.680896","exception":false,"start_time":"2022-09-23T04:09:40.643859","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["LinearRegression()"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# create a linear regression model\n","model = LinearRegression()\n","model.fit(x, y)"]},{"cell_type":"code","execution_count":5,"id":"705415a3","metadata":{"execution":{"iopub.execute_input":"2022-09-23T04:09:40.690194Z","iopub.status.busy":"2022-09-23T04:09:40.689759Z","iopub.status.idle":"2022-09-23T04:09:40.698544Z","shell.execute_reply":"2022-09-23T04:09:40.697684Z"},"papermill":{"duration":0.016637,"end_time":"2022-09-23T04:09:40.700883","exception":false,"start_time":"2022-09-23T04:09:40.684246","status":"completed"},"tags":[]},"outputs":[],"source":["# predict y from the data\n","x_new = np.linspace(0, 30, 100)\n","y_new = model.predict(x_new[:, np.newaxis])"]},{"cell_type":"code","execution_count":6,"id":"32449a27","metadata":{"execution":{"iopub.execute_input":"2022-09-23T04:09:40.709689Z","iopub.status.busy":"2022-09-23T04:09:40.708501Z","iopub.status.idle":"2022-09-23T04:09:40.946695Z","shell.execute_reply":"2022-09-23T04:09:40.945578Z"},"papermill":{"duration":0.245005,"end_time":"2022-09-23T04:09:40.949158","exception":false,"start_time":"2022-09-23T04:09:40.704153","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 288x216 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# plot the results\n","plt.figure(figsize=(4, 3))\n","\n","ax = plt.axes()\n","ax.scatter(x, y)\n","ax.plot(x_new, y_new)\n","ax.set_xlabel('x')\n","ax.set_ylabel('y')\n","ax.axis('tight')\n","\n","plt.show()"]},{"cell_type":"code","execution_count":7,"id":"b58706db","metadata":{"execution":{"iopub.execute_input":"2022-09-23T04:09:40.958624Z","iopub.status.busy":"2022-09-23T04:09:40.957452Z","iopub.status.idle":"2022-09-23T04:09:40.97194Z","shell.execute_reply":"2022-09-23T04:09:40.970644Z"},"papermill":{"duration":0.021526,"end_time":"2022-09-23T04:09:40.974263","exception":false,"start_time":"2022-09-23T04:09:40.952737","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["DataFrame diabetes successfully loaded!\n"]}],"source":["for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        filepath = os.path.join(dirname, filename)\n","        print(f'DataFrame {filename[:-4]} successfully loaded!')"]},{"cell_type":"code","execution_count":8,"id":"7ef28cb2","metadata":{"execution":{"iopub.execute_input":"2022-09-23T04:09:40.982725Z","iopub.status.busy":"2022-09-23T04:09:40.982344Z","iopub.status.idle":"2022-09-23T04:09:41.019018Z","shell.execute_reply":"2022-09-23T04:09:41.018008Z"},"papermill":{"duration":0.044145,"end_time":"2022-09-23T04:09:41.021963","exception":false,"start_time":"2022-09-23T04:09:40.977818","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Pregnancies</th>\n","      <th>Glucose</th>\n","      <th>BloodPressure</th>\n","      <th>SkinThickness</th>\n","      <th>Insulin</th>\n","      <th>BMI</th>\n","      <th>DiabetesPedigreeFunction</th>\n","      <th>Age</th>\n","      <th>Outcome</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>6</td>\n","      <td>148</td>\n","      <td>72</td>\n","      <td>35</td>\n","      <td>0</td>\n","      <td>33.6</td>\n","      <td>0.627</td>\n","      <td>50</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>85</td>\n","      <td>66</td>\n","      <td>29</td>\n","      <td>0</td>\n","      <td>26.6</td>\n","      <td>0.351</td>\n","      <td>31</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>8</td>\n","      <td>183</td>\n","      <td>64</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>23.3</td>\n","      <td>0.672</td>\n","      <td>32</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>89</td>\n","      <td>66</td>\n","      <td>23</td>\n","      <td>94</td>\n","      <td>28.1</td>\n","      <td>0.167</td>\n","      <td>21</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>137</td>\n","      <td>40</td>\n","      <td>35</td>\n","      <td>168</td>\n","      <td>43.1</td>\n","      <td>2.288</td>\n","      <td>33</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>763</th>\n","      <td>10</td>\n","      <td>101</td>\n","      <td>76</td>\n","      <td>48</td>\n","      <td>180</td>\n","      <td>32.9</td>\n","      <td>0.171</td>\n","      <td>63</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>764</th>\n","      <td>2</td>\n","      <td>122</td>\n","      <td>70</td>\n","      <td>27</td>\n","      <td>0</td>\n","      <td>36.8</td>\n","      <td>0.340</td>\n","      <td>27</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>765</th>\n","      <td>5</td>\n","      <td>121</td>\n","      <td>72</td>\n","      <td>23</td>\n","      <td>112</td>\n","      <td>26.2</td>\n","      <td>0.245</td>\n","      <td>30</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>766</th>\n","      <td>1</td>\n","      <td>126</td>\n","      <td>60</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>30.1</td>\n","      <td>0.349</td>\n","      <td>47</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>767</th>\n","      <td>1</td>\n","      <td>93</td>\n","      <td>70</td>\n","      <td>31</td>\n","      <td>0</td>\n","      <td>30.4</td>\n","      <td>0.315</td>\n","      <td>23</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>768 rows × 9 columns</p>\n","</div>"],"text/plain":["     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n","0              6      148             72             35        0  33.6   \n","1              1       85             66             29        0  26.6   \n","2              8      183             64              0        0  23.3   \n","3              1       89             66             23       94  28.1   \n","4              0      137             40             35      168  43.1   \n","..           ...      ...            ...            ...      ...   ...   \n","763           10      101             76             48      180  32.9   \n","764            2      122             70             27        0  36.8   \n","765            5      121             72             23      112  26.2   \n","766            1      126             60              0        0  30.1   \n","767            1       93             70             31        0  30.4   \n","\n","     DiabetesPedigreeFunction  Age  Outcome  \n","0                       0.627   50        1  \n","1                       0.351   31        0  \n","2                       0.672   32        1  \n","3                       0.167   21        0  \n","4                       2.288   33        1  \n","..                        ...  ...      ...  \n","763                     0.171   63        0  \n","764                     0.340   27        0  \n","765                     0.245   30        0  \n","766                     0.349   47        1  \n","767                     0.315   23        0  \n","\n","[768 rows x 9 columns]"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["diabetes= pd.read_csv(filepath) \n","diabetes"]},{"cell_type":"code","execution_count":9,"id":"4c11faef","metadata":{"execution":{"iopub.execute_input":"2022-09-23T04:09:41.031987Z","iopub.status.busy":"2022-09-23T04:09:41.030904Z","iopub.status.idle":"2022-09-23T04:09:41.476055Z","shell.execute_reply":"2022-09-23T04:09:41.474875Z"},"papermill":{"duration":0.45258,"end_time":"2022-09-23T04:09:41.478503","exception":false,"start_time":"2022-09-23T04:09:41.025923","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAA7z0lEQVR4nO3deXxc9X3o/c/3nJnRblu2JC+SjG3wijG2EdsDdRyWBLKwQ6BNetMmgfY2Tdp7k1fS3jxpH/p6bknTNk16eW6habbeNqyBOAlmj+OEYOINg22MbeSAJFubLctaZ/0+f5wz4xmtI0uj9fvmJaw553fO/HQ0+n1/2/kdUVWMMcYYAGeiM2CMMWbysKBgjDEmxYKCMcaYFAsKxhhjUiwoGGOMSQlMdAZGqqysTJcsWTLR2TDGmCll9+7drapaPly6KRcUlixZwq5duyY6G8YYM6WIyLvZpLPuI2OMMSkWFIwxxqRYUDDGGJNiQcEYY0yKBQVjjDEpU272US5tO9TMQ9trqWvrprq0kPs2LWPzqopxPf6BrW9x7GQ3AMvKivjSDasyzpFNmulqtNfXGDM8mWqrpNbU1Oi5TkkdqlDZdqiZLzyxj/buCLEEKCBASX6ACxfN5r5NywD42rOHONrcSUwVV2B5RQlfumEVAF/dcoCgKxQEXXqicaJx5f6bLuxXcA2UD4AvPLGP091RHPHSJRSK8lwWzsqntStCdzhOdzQOQMABR4SEQmlhkE9ccR6v1p5KnfPKZXMzXp9LAbrtUDNfe/YQta1dACydV8iXb1w9qoJ4uN/BQNfloe21HG46Q2c4ztyiIPOK8jKubzLNaIPFSPNmAclMJSKyW1Vrhk03U4LCtkPNAxbad2ys5NXaU7xWe5LEIMfOLQzS3hMlPsilml0QoCTk0todJZ5QQq5DYcilIxxDFTYuLuXKZXN5bFcd9ad7M44NucLsgiDzikL89lQ3sbiSUCX5a0kGp75vLUDQdRABVUVEqCotoCDo0toZpqUzQkVJ6JwL0G+9eJhvvnSk388swKoFZwPhcOdKL0xJJGjqjJBQJSCCArGEkhdwCDhCJK4ZhX7LmV56YglUIeFfkIDjMKcgQFckTjiW8K51nsvsguCwwXggyfwNF3SyDfjGTFYWFPq45+EdNHf0Uhg622PW2tlLS0cYVQYt8MdCyPUKvJEYKBD03S8CoYBDb9QLZ0FXcEWIxBMkBjhY8NKXFgYpK86jvq2b9t4YAhTnBfj01Uv53HUr2Haomc/8+y6ig+Q56MCcwhAKAxbG4AWLI80dtPdEQZUEQjyRLNiFWFoG/YYRjgOuOCyak48qvHvK6yLLDzj0xryf0fGvS8h1QJRwTAk4QuWcAmYVBAHojsSoKMnnh/deMeQ1Tq8o1J/qJhJX1H+/+bPyCbhCRUk+QL/PTrbvMVLWIjG5YkGhj6u/9jJzCoKISGrb2yfaiQzWPBgj4v8vV5fZEa+byWs5ZB98ksclJYPQrHyv4DvTGxvy+KADruswvySf1s4wkXgC1xHKCoM4rpsqaMMjCIbJoBXw+8+6InEEyA+6hGPxjPym5z/gQF7AZVl5MeC1nBrbe1g+f9aQhWuyohCLayoAJfMRcIVFs/NJqBcAeqMJInGvZVJWnMfp7gjt/jVyHeGmdQv4xt0bs/5ZBzJYa3amtUgsMOZGtkFhxsw+qi4tpMfvj0/KdUAAr6DNZdxNFoyueF0xMnTyfsclJV929MboigwdEACiCeiNJnjvVDfhWAJUCUcT1LeHaWzvIZ5QIvHs85PMgwhE4l4BDKSaEQEn86OqfiAUIJYg1ZIAaO0M0xGO09zRy5yCIM0dvXx1ywG2HWrOOEddW3equy15LhEvH16g6KG1M8yZ3pgX9ESIxZW6U92pgAAQTyhPvX6CP39kzwh+2v4e2l5L0BUKQwFEvH+DrvDQ9tpRnXcqSQbG4X53JndyFhRE5Dsi0iwi+wfZLyLyLRE5KiJviMjoqlnDuG/TMqJxpTsSQ9X7d7oIuoI43qDzaOOPAkEn+49Fclwgmjj73pG4cvx07znlpTeaQBOa6mpShZ5onFgikRqAB6/wDroOQdfx02nq99rWHWVuUXDYwjVZUYjEE6nWSfpYDngFfiLhjfEoIA4ZY08i3hfAljcaU9u3HWrmhm/8gpVf2crKr2zlxn/a3q9g23aomXse3sHVX3uZex7ewZHmDgqCbkaagqBLfVs3M4UFxomXy5bC94Abhth/I7Dc/7oX+N85zAubV1Vw/00XUlGST3tPlJDr4I6kGjsJuY5QGHRQhUWzCzIKzdGIxkffhFIUR84tSMWUfi2MhHpfQccLgkHHwREvnet43UntPVEqSvIpyQ8wrygv4/iBCtdkRcF1BBHveqZzxBtrch0h4H/F08dC+mQyuS85k+1oS1cqWB1p7uSLT+xLBYaBasQdvTFOdoUzztkTjVNVWjjCKzh1JVtv6cY7MPYN1jOtlZKz+xRUdbuILBkiyc3AD9Qb1NghInNEZKGqnshVnjavqkj1Td7z8A5K8gOc7pm6LQZVZXZhiNbOCE0dvcMfkKWxGHSP+l1HrozsfMkunL7HOAIVJXl0R+LezC6/SyfkOpTm57G0rDg16DvQpIKBCtfNqyq4H3hg61scaenMqCF5wcbrLhLHC0h9g0ayVZHcmtz/0PZaOsMxXBEcf5uo0tEb46HttWxeVZFRIwYoDAUoLQxyqitKYSiQMaaQnJo7E1SXFmb1u8uV9HGd9O6r++GcxzXGYoxkPMdZJnJMoRKoS3td72/rR0TuFZFdIrKrpaVlTN68rq170Nk1U4Wq1/ddEHBIJHTAGUcTbcX8Eqpmn621h1wh2KeJluzPD7kOytnxjuR2ABEhL+DQHYnR0hkmnlAWzc5nwex8QgE3o+AcqKtwsMJ186oKnv3z9/Fvv38pBX5BlJzuG3AcXMcbs4gllGg8wUATM5Jbblq3APA+W/GEZrQkVJXeWILf/PbUoF1FZcV5lOS5qdZsRUn+jBtkHsnvLhfGuvtqLMZIxnucZUoMNKvqw6pao6o15eXDPiMiKwMNPE81jiO0dUeIxBXHLzQnk7yA8KUbVlFSEEp90CJxxcG79yNd0HX61cSBVFRQVRpO9xJwHarmFIBA/eleQq7Tr+Ds21VYUZLPHRsreWh77aBdAptXVTCrIMh5cwsIuOLNxlLvuoIXzJIBKyD9u7fKioLcvL6Kex7eQUuHF7SS026Tg+7gTXcdqqsoP+hy4Hg7x9t7OXC8nTfqT2d5taeHgX534xkYx7r7aiyCzHiPs0zkMhcNQHXa6yp/27i4b9MyXq09OV5vN2ZCrhBXJZ6A8uIQTWfCxBLjMI3KN9z9E+l6Y8qnvr8TVW+KZ9xvzUQTyvxZ+VSU5NHeG6WlI0I0kSA9Rqe6nfw3S6jXhVSSF6C1M0I8oQRdQVUHLDDSuwq/9eJhHtz2DnH/RrlYPDFgl0Cy62LR7IKMabYBVVbML0FEONR4BleEgHiF/aoFs1JTYJPdDgtm5VF3qoeYKhAnOTHKEYjFvRlbqNLaGcnoKmo500tnJO6PYXhB4psvHwXgc9etGNHvaSpL/92Nt7Huvqpr62ZOQWYFaKRBZizOMRITWbXcAvy+PwvpCqA9l+MJfU3VJnkk7gWEOQUB4gkd9Wyjvl05wxlpYySu3mwdVQgFXEKuQyjgMKcwxJdvXE0srv5gbGawCfgtB0e8D6kIlBYGON0TJRZXXBESCeVIS+eQzehth5p5cNs73l3U/hjBya4IkVi8X00r2XURcIWlZUUsnltIRUk+F5QXp1qVIdcb2Ff1b6ADfwaTpmpzswpCVM8tJOicHRsJ+q0PRPyBbS8vIddJ1YhjieSgtoMjjv8vfPtXx0Z20c05G+vuq4F6JEYaZMbiHCORyympPwReBVaKSL2IfEpE/khE/shP8gxQCxwF/hX4r7nKy2CSN2pNJY54BXNVaSEnuyKjOldewOFP339BVt1OIt57RxP9Z91kI+73xYtfw65v62bzqgrmFYUIBhyCrlAUcqkoDhHwZwOtXlDC0rIiquYWsrKimI5wHAdv8FbE+wo6zpDN6Ie21xJLJFIFseMIDkJHb6xfTWuwrosv37g6VVCUFYf8lppSVhxKFRqhgJPR7TCrIMjKBbOomlPAlcvmea0L18ERIflfXtALjr/80jX88N4rCMcT/WaQOeLdxGfGx1h3X41FkBnvcZZczj66Z5j9CvxJrt4/G5++ein/9NKRcR2gPZclL8DrTgn6XR+u49ATjWeV7zzXAYFILEFe0CGe8KZgFucF+Ps7Lk592L/x0pEhb7Jz8Ja0ONUd9ZaakP43wKVLlm3JJKn5//4snmQtpzMS54Ly4ow7zQtCLo1nwrT3RKlKWxjvUz/YhStnbwhUhQWz84ZsRte1dZPnOsT1bDATgXAsMWBNa7Cui/vxAkx9WzcXlBchInSGvaUu7tu0jIe21w7a7XDfpmV86gc7cf31nlQhgbKoJD8j70UhrxspPTAk1Ntuxs9Ydl8lZ7glPztV5zBzaCzOMRJTr6o8hpL9tP+yvZbuHNfGXP9mq4TqoP3yhUGX/+/3vHv4PvfIXjr8u2aTg7BeF4jD0rIiuiLxfktV9CV4hc+8whBzCoKUFuUN+KFKXocHt71DLJ7ImA4qeP2XhXkuyytKqDvZSWt3lEgsgXB2Ln/f9w24klrILpnHeMJbk2lOXjBV0A/UhxtwHTYuLu23rtCKimKOtXalFh0sL8nDdc6uTzSQ6tJC4okEJzujJFB/uqsXGEdS08qmoPjqlgN0R2L9ppNuXlXB8vJifnuqO5X3suLMtZXAq6R88+WjqRv1kvdmfPrqpVnn00w+YxFkxnOcZcasfTSc5LSvSCxOW3fUW7oBr2afF/Bq5ueycJ4rMLcoxCeuOI+t+xs53NzpLUkRV1y/P39eUYhQwM1opiZvgOq7lHZpYZCv33ExAF956g3q28MDvi9AniuUDnDuoa7BcLWR9HncsXiCpo4w0biyoqKYG9cu4Jk3T3CkpZOg4zB/Vh6ReILWzgiqius4/ZbfHsl6P+eyNlDymGg8Tnt3lHA8QcBx+JPN54/54O1Q1y/bvH/rxcN8+1fH6IrEKQq5qUUKjRktWxDvHAz3R53cVxRyOd7em7Fo3Kz8AOFYgkTCnzIjgiPS7yE4yfMcae4gEksQcoXl82cNWgAP90CdZCHSGY5509WCDlWlhagqXZF4TpqawwWPbILLSM53rmlHc0wuTJZ8mJnJgoIxxpgUWyXVGGPMiFlQMMYYk2JBwRhjTIoFBWOMMSkWFIwxxqRYUDDGGJNiQcEYY0yKBQVjjDEpFhSMMcakWFAwxhiTYkHBGGNMigUFY4wxKRYUjDHGpFhQMMYYk2JBwRhjTIoFBWOMMSkWFIwxxqRYUDDGGJNiQcEYY0yKBQVjjDEpFhSMMcakWFAwxhiTYkHBGGNMSmCiM2CMMWZwZ3qj7H63jZ3HTrFwTgGfuOK8nL5fToOCiNwAfBNwgW+r6gN99i8Gvg/M8dN8WVWfyWWejDFmMmvtDLPz2CleO3aK3xw7xaHGMyTU23dx1eypGxRExAUeBK4H6oGdIrJFVQ+mJfsK8Jiq/m8RWQM8AyzJVZ6MMWayaTrTy47ak7x27BSv1Z7knZaufmlcR7hw0SyuWDYPVUVEcpafXLYULgOOqmotgIg8AtwMpAcFBWb5388GjucwP8YYM+GSQcD7OsWx1v5BIC/gsL56DpctnctlS+eyYXEpxXnj09ufy3epBOrSXtcDl/dJ89fA8yLyp0ARcN1AJxKRe4F7ARYvXjzmGTXGmFw52RlmR+0pfv1OK6++c5LaAYJAQdClZkkply+dy+XL5rGuajZ5AXcCcjvxA833AN9T1X8QkSuBfxeRtaqaSE+kqg8DDwPU1NToBOTTGGOy0tEb5TfHTvHK0ZP8+p1WDjV29EtTGHK55LxSrjx/Hlcsm8dFlbMJupNjMmgug0IDUJ32usrflu5TwA0AqvqqiOQDZUBzDvNljDFjJhJLsPe9Nl452sor75zk9brTxBOZdde8gEPNklKuXDaPK88vY13V5AkCfeUyKOwElovIUrxgcDfwu33SvAdcC3xPRFYD+UBLDvNkjDGjoqocbe7kl0da+dXRVnbUnqQ7Es9IE3CEi6vncNX5XhDYeN6cCesOGqmcBQVVjYnIZ4Hn8KabfkdVD4jI/cAuVd0C/HfgX0Xkz/EGnT+pqtY9ZIyZVE51RfjV0VZ+ebiFXx5ppfFMb780K+eXcNUFZVy9fB6XLZ03bgPDYy2nufbvOXimz7avpn1/ELgql3kwxpiRisUTvF53ml8cbuEXh1t4s6GdvtXVipI8rl5exu8sL+OqC8qoKMmfmMyOsakZyowxZow1tvfyi8PN/MJvDXT0xjL25wcdLl86j99ZXsamFeUsryjO6f0CE8WCgjFmRorGE+x5t42fv93CtrebB5wltHJ+CZtWeEHg0iVzyQ9OjXGB0bCgYIyZMVo7w2x7u4Wfv93M9sMt/VoDJXkBrl5exuaV5WxaUc7C2QUTlNOJY0HBGDNtqSoHT5zh5beaeelQM/vqT/cbG1i1oIT3r6pg84pyNp5XOmmnio4XCwrGmGmlNxrn1++08uJbzbz8VnO/mUKFIZerLyjzAsHKmdkaGIoFBWPMlNfSEebnh5p54a0mfnWklZ5o5n0Di+cWcs2qCq5dXcFlS+dOmXsGJoIFBWPMlKOqvNPSyfMHm3jxYBN76zK7hRyBmvPmcu3qCq5dPZ/zy4um5UyhXLCgYIyZEhIJZW9dG88faOL5g039VhctzguwaUUZ16+Zz+YVFZQWhSYop1ObBQVjzKQViSX49TutPHegiRcONtHaGc7Yv3B2Ptetns/1a+Zz+TLrFhoLFhSMMZNKVzjGLw638Oz+Rn5+qJmOcOa00VULSrh+zXw+sGYBaytnWbfQGLOgYIyZcO3dUV58q4lnDzSy/XAL4djZ1fNF4JLFpXzwwgV84ML5nDevaAJzOv1ZUDDGTIiTnWGeP9jEM2+e4NV3ThJLW2466Ar/1/llfPDCBVy/Zj7lJXkTmNOZxYKCMWbcNHf08tyBJra+eYIdtSdJf+xAftBh84oKbli7gGtWVzArPzhxGZ3BLCgYY3Kq+UwvW/c38rM3T7Dzt6cypo4W5wW4ZlUFN65dwPtWllMYsiJpotlvwBgz5po7enlufyM/feMEv+kTCEryA1y/Zj43rl3I7ywvmxGLzE0lFhSMMWPiZGfYaxG8cYLXjmV2Dc3KD/CBCxfwoYsWcNUFZTZ1dBKzoGCMOWenuyM8d8BrEfz6nZMZzyYuyQ/wwQsX8OGLFnLVBWWEAjN7obmpwoKCMWZEOsMxXjzYxE/2HWf7kRai8bOBoDjP6xr6yLqFXL3cWgRTkQUFY8yweqNxtr3dzJZ9x3npreaM+wgKgi7X+YHgfSvKbYxgirOgYIwZUCye4NXak/z49eM8t78x487iUMBh84pyblq/iGtWVdisoWnEfpPGmBRVZW/daba8fpyfvnGc1s5Iap/rCFddUMZNFy/iAxfOt/sIpikLCsYYjjZ38uPXG/jx68d571R3xr6a80q5ef0iPnTRQuYV253F050FBWNmqOYzvWzZd5ynX29gf8OZjH2rFpRw8/pKPnrxQqpKCycoh2YiWFAwZgbpDMd4bn8jT7/ewCtHWzPuJaicU8BN6xdxy/pKVi4ombhMmgllQcGYaS4WT/DLo608vbeB5w400hs9O3NodkGQj6xbyC0bKrlkcSmOY8tQz3QWFIyZhlSVA8fP8KM9DWzZdzzj4TShgMP1q+dzy4ZK3rei3G4qMxksKBgzjZxo7+Hpvcf50Z56jjR3Zuy7Ytlcbt1QyQ1rFzK7wGYOmYFZUDBmiusKx3juQCM/2tPAK++0Ziw+d0FFMbduqOTm9YtswNhkJadBQURuAL4JuMC3VfWBAdLcBfw1oMA+Vf3dXObJmOkgkVB21J7kyT0NbN1/gu5IPLVvblGImy5exG0bK7mocrY9rtKMSM6Cgoi4wIPA9UA9sFNEtqjqwbQ0y4G/AK5S1TYRqchVfoyZDo61dvHk7nqe2ttAw+me1PaQ63Ddmgpu3VDF5pXlBF0bJzDnJpcthcuAo6paCyAijwA3AwfT0nwGeFBV2wBUtTmH+TFmSmrvifKzN07wxO469rx3OmPfxsVzuG1jFR9dt4jZhTZOYEYvq6AgIoXAfwcWq+pn/Br+SlX96RCHVQJ1aa/rgcv7pFnhn/8VvC6mv1bVZwd4/3uBewEWL16cTZaNmdLiCeVXR1t5cnc9zx1ozFiAbtHsfG7bWMVtGytZVl48gbk001G2LYXvAruBK/3XDcDjwFBBIdv3Xw5sBqqA7SJykaqeTk+kqg8DDwPU1NQoxkxTtS2dPLG7nh/taaDxTG9qe0HQ5ca1C7j9kiquXDbP7icwOZNtUDhfVT8mIvcAqGq3DD961QBUp72u8relqwdeU9UocExEDuMFiZ1Z5suYKa+j1+seenx3PbvfbcvYd9mSudxxSRUfWreQ4jybLGhyL9tPWURECvBmCCEi5wPhoQ9hJ7BcRJbiBYO7gb4zi54G7gG+KyJleN1JtVnmyZgpKzl76PHd9WzdfyLjLuPKOQXcvrGS2zZWsaSsaAJzaWaibIPCXwHPAtUi8h/AVcAnhzpAVWMi8lngObzxgu+o6gERuR/Ypapb/H0fEJGDQBz4oqqePLcfxZjJr+5UN0/uqeeJ3fXUt52dPZQfdLhx7ULusO4hM8FENbsuehGZB1wBCLBDVVtzmbHB1NTU6K5duybirY05J73ROM8daOSxXXW8cjSzzrNx8RzurKnmw+sW2vMJTE6JyG5VrRku3Ug6KSvxavwBYJOIoKo/OtcMGjOdqSpv1Lfz+O46fvz6cTp6zz61rLwkj9s2VnLnJdVcUGGzh8zkku2U1O8A64ADQLLzUwELCsakOdUV4am9DTy+q45DjR2p7QFHuHZ1BR+7tJpNy8sJ2M1lZpLKtqVwhaquyWlOjJmi4gll++EWHttVx4tvNRGNn+2SXTm/hLsureaW9YvsqWVmSsg2KLwqImvSl6gwZqZ792QXj+2q48ndmfcUlOQHuHn9Iu6qqba1h8yUk21Q+AFeYGjEm4oqgKrqupzlzJhJqCcS59kDJ3h0Zx07ak9l7Lty2Tw+dmk1N6xdQH7QnaAcGjM62QaFfwM+AbzJ2TEFY2YEVeXNhnYe3VnHln2Zg8YLZ+dzxyVV3HlJNYvn2dLUZurLNii0+PcVGDNjtHVFePr1Bh7dmTloHHSF69fM566aan5neTmu3VNgppFsg8JeEflP4Cek3clsU1LNdJNIKL9+5ySP7HyP5w80EYmfbRivmF/MXTXV3Lqh0gaNzbSVbVAowAsGH0jbZlNSzbRx/HQPj++q5/HddRl3GhfnBfjoxQu5q6aa9dVzbNDYTHtZBQVV/YNcZ8SY8RaJJXjxrSYe3VnH9iMtGY+xvHRJKXf5dxoXhmwhOjNzZHvzWhXwz3hrHgH8Evi8qtbnKmPG5MqRpg4e3VnHU3sbONkVSW0vK87j9ksquaummvPtOQVmhhrJ8xT+E7jTf/1xf9v1uciUMWOtKxzjZ2+c4NFddRnLUzsC719ZwV2XVnPNqgp7jKWZ8bINCuWq+t20198TkT/LQX6MGTOqyp73TvPYzjp++sZxutIebr94biF31VRxxyXVLJidP4G5NGZyyTYonBSRjwM/9F/fA9gS12ZSOtkZ5qm93lTSI82dqe2hgMONaxfwsUuruWKpLU9tzECyDQp/iDem8A28WUe/Bmzw2UwayWcaP7rzPV44mLn+0JqFs7j7smpuvrjSHm5vzDCynX30LnBTjvNizIjVt3Xz+C7voTUNp89OJU2uP3T3pYtZWzl7AnNozNSS7eyj7+PNNjrtvy4F/kFV/zCHeTNmQOFYnOcPNPHYrjp+dbQ1Yyrp5Uvn8rFLq7lx7UIKQrb+kDEjlW330bpkQABQ1TYR2ZCbLBkzsEONZ3h0Zx1P722grTua2l5ekscdl1RxV001S+2ZxsaMSrZBwRGRUlVtAxCRuSM41phzdqY3yk/2HeexnXXsq29PbXcd4f0rvYfWvH+lPbTGmLGSbcH+D3hLZz/uv74T+J+5yZKZ6VSV146d4rGddTyz/wS90bPrDy0tK+Kummpu31hJxSybSmrMWMt2oPkHIrILuMbfdJs9cMeMtcb2Xp7cU8/ju+r47cnu1Pb8oMOHLlrI3Zcu5tIlpbb+kDE5lO1A87+r6ieAgwNsM+acRWIJXj7krT/0i8MtJNIGjS+unsPHaqr56MULKcm3qaTGjIdsu48uTH8hIi5wydhnx8wUbzd28Piu/usPzS0KcesGb/2hlQtKJjCHxsxMQwYFEfkL4C+BAhE5g/cYToAI8HCO82ammdSg8a569tWdTm13BDatKOdjNdVcu3o+oYANGhszUYYMCqr6t8DfisjfqupfjFOezDSSSCg7ak/y+O56nnnzBOHY2UHj8+YVcldNNbdtrGTh7IIJzKUxJinb7qOtIrKp70ZV3T7G+THTRH1bN0/ubuj30JqCoMuHLlrInTVVXL50rg0aGzPJZBsUvpj2fT5wGbCbs7ORjKEnEufZAyd4fFc9v34nc73EjYvncFdNNR+5eBHFeXaLizGTVbZTUj+a/lpEqoF/ykWGzNSiquytO83ju+r56b7jdIRjqX3lJXnctrGSOy+p5oIKe2iNMVPBuVbZ6oHVY5kRM7U0nenlR3saeGJ3He+0dKW2B13hutXzubOmik3L7U5jY6aabO9T+Ge8JbMBHGADsCeL424Avgm4wLdV9YFB0t0OPAFcqqq7ssmTGX+90TgvHGziid31/PJI5j0FqxfO4s5LqrhlQyVzi0ITl0ljzKhk21I4iFewA5wGfqiqrwx1gH8vw4N4j+ysB3aKyJa+d0KLSAnweeC1EeTbjJPk08ue3FPPT/Ydp6P3bPfQ3KIQN69fxO0bq2x5amOmieHuUwjgrXH0h8B7/ubFwHdE5DeqGh30YG8w+qiq1vrnegS4mbS7on1/A3yNzMFsM8GOn+7hqb0NPLm7ntrWs91DAUd4/6oKbt9YxTWrKuyeAmOmmeFaCl8HSoClqtoBICKzgL/3vz4/xLGVQF3a63rg8vQEIrIRqFbVn4nIoEFBRO4F7gVYvHjxMFk256orHOO5A408ucebPaR9uodu31jJLRsqKSvOm7hMGmNyarig8BFgherZ4kFVz4jIHwOHGDooDElEHOAfgU8Ol1ZVH8a/g7qmpkaHSW5GIHlz2ZN7Gti6/wTdaQ+3LysOcfP6Sm7fWMWaRbMmMJfGmPEyXFDQ9ICQtjEuIsMVzg1AddrrKn9bUgmwFtjm38C0ANgiIjfZYHPuHW3u4Ed7Gnh6bwPH23tT20Ouw3VrvO6hTSvKCdrsIWNmlOGCwkER+X1V/UH6RhH5OF5LYSg7geUishQvGNwN/G5yp6q2A2Vp59wGfMECQu60dobZ8vpxntrbwJsN7Rn7Ni6ew+2XVPGRixbZw+2NmcGGCwp/AvxIRP4Q7w5mgBqgALh1qANVNSYinwWew5u59B1VPSAi9wO7VHXL6LJustETifPCW008taee7UdaiafNI60qLeC2jVXcuqHSHmNpjAFABugd6p9I5BrOLp99UFVfymmuhlBTU6O7dlljYihxf5zgqb0NPLu/kc60u4xL8gN8ZN1Cbt1QRc15pTiOrT1kzEwgIrtVtWa4dNkuc/Ey8PKoc2VyRlU5cPwMP369gS37jtN0JpzaF3CEzSsruG1jJdesqiA/6A5xJmPMTGYrk01xdae6+fHrDfz49eMcae7M2HfJeaXcsn4RH163yO4yNsZkxYLCFNTaGeaZN0/w9N4G9rx3OmPfsvIibl1fyc3rK1k8r3BiMmiMmbIsKEwRHb1Rnj/QxJZ9x/nV0cwB44qSPD568SJuWV/J2spZ9owCY8w5s6AwifVG4/z8UDNb9h3npUPNRNKeWlaSF+CGtQu4ZUMlVyybh2sDxsaYMWBBYZKJxBK8crSVn+w7zvMHmzJmDoUCDteuquDm9ZVsXlluA8bGmDFnQWESiMUTvHbsFD994zhb9zdyuvvsOoOuI1x9QRk3XbyID1w4n5J8u7HMGJM7FhQmSDyh7PytFwie3d9Ia2cktU8ELl0yl5suXsSHLlpoM4eMMePGgsI4SiSU3e+18bM3TvDMmydo7ghn7N+weA4fXbeID69byPxZ+ROUS2PMTGZBIcfSA8HW/ScybioDuKhyNh9Zt5APr1tIValNITXGTCwLCjmQ7Bp65s0TPLu/sV+L4MJFs/jwuoV8+KKFnDfP1hwyxkweFhTGSDSeYEftSbbub+T5A5ljBHA2EHxo7UKW2OJzxphJyoLCKPRG47xytJWt+xt58a2mjFlDAOuqZvOhixZy49oF1iIwxkwJFhRGqKM3yra3W3j2QCPbDjXTlfakMvAGiz980UI+eOECqufaGIExZmqxoJCF1s4wLx5s4rkDjbxy9CSR+Nk7ix2By5bO5ca1XiBYMNtmDRljpi4LCoP4bWsXLxxs4vmDjex6ty3jIfYh1+GqC+Zx49qFXLu6gnn2IHtjzDRhQcGXSCj76k/zwsEmXjjY1G8Z6uK8AJtXlvPBCxeweWW53VlsjJmWZnRQ6Il4A8UvvtXEi28109qZOXW0vCSP61bP54MXzufK8+eRF7C1howx09uMCwpNZ3p5+VAzL73VxC+PtBJOW3kU4IKKYq5fM5/r18xnfdUce1ylMWZGmRFBIRJL8ODPj/LyoWbebGjP2Oc6Qs15pVy/Zj7XrZ5v9xAYY2a0GREUgq7w1N4G3jvVDXgPr3/finKuXzOf960oZ06hLThnjDEwQ4KCiHDPZYs52Rnm2tXzqVlSStB1Jjpbxhgz6cyIoADwx5vPn+gsGGPMpGfVZWOMMSkWFIwxxqRYUDDGGJNiQcEYY0yKBQVjjDEpOQ0KInKDiLwtIkdF5MsD7P9vInJQRN4QkZdE5Lxc5scYY8zQchYURMQFHgRuBNYA94jImj7J9gI1qroOeAL4u1zlxxhjzPBy2VK4DDiqqrWqGgEeAW5OT6CqP1fVbv/lDqAqh/kxxhgzjFwGhUqgLu11vb9tMJ8Ctg60Q0TuFZFdIrKrpaVlDLNojDEm3aQYaBaRjwM1wNcH2q+qD6tqjarWlJeXj2/mjDFmBsnlMhcNQHXa6yp/WwYRuQ74H8D7VDXcd78xxpjxk8uWwk5guYgsFZEQcDewJT2BiGwAHgJuUtXmHObFGGNMFnIWFFQ1BnwWeA54C3hMVQ+IyP0icpOf7OtAMfC4iLwuIlsGOZ0xxphxkNNVUlX1GeCZPtu+mvb9dbl8f2OMMSMzKQaajTHGTA4WFIwxxqRYUDDGGJNiQcEYY0yKBQVjjDEpFhSMMcakWFAwxhiTYkHBGGNMigUFY4wxKRYUjDHGpFhQMMYYk2JBwRhjTIoFBWOMMSkWFIwxxqRYUDDGGJNiQcEYY0yKBQVjjDEpFhSMMcakWFAwxhiTYkHBGGNMigUFY4wxKRYUjDHGpFhQMMYYkxKY6AxMpG2Hmvm/f7yfuraejO0CBF0hrpBIKPlBh6KQS0c4TiKhBF2HgpDDivmzuG/TMgC+9uwhalu7AFg6r5Av37gagK88/SYN7b2oQsgR5s/OR4Hq0kLu27SMzasqAPjWi4f59q+O0RWJUxRy+fTVS/ncdSvYdqiZh7bXcqS5g0gsQdCV1PtuXlXBnz+yhy1vNBJPKI5AaUGAgrwgqHKqK0o4nkidb13VHB7aXktdW3e/90++T/o+YETpk/uGut5jeb5cmmz5MWa8iKpOdB5GpKamRnft2jWqc2w71MwDW9/i7aZORvPTO0BiFMe7Dnz+muUAfOPFIyPOS54L4fjIjgm5QjyhJBREYEVFMR+6aCFP7Gkg6AoFQZeeaJz2nijReIJwNEEklvAiJbByfgk3rl3QL300rtx/04VsXlUxaID56pYDAx4D8MUn9tHRGyOWSBBwHEryA3z9jov7FcTjUVhvO9Q8aF5HE/iMmUgisltVa4ZNN9OCQvIPvrmjl97oaIr06cEVL7A5QH7Qpaw4j1kFQd5uPEMkrgQciKVdJgECrjCvKER5SX5qe3ckRkVJPvdtWjZggaqqtHSEiasSch3KS/JwHaGiJJ/T3RGONHfiOoIIqEI8oSyvKGbrn21KvcdoCut0wxXc9zy8g+aOXgpDZxvSyZ/vh/deMeR5xyJ/w+W5OOQiInSEYxZ4TNayDQozrvvooe21RGJxwhYQAIj7dYI40B2J8+6pbvICDhF/R6zPZVIgGlfauqPkBVwa23tSad872U1bV5igK6kCtTAUoKWjl8YzYUKu4DpCLKEcP93Lwtl51Ld109wRxhFwxGuOiICKprrjwCsUP/fIXrojcfICTip4dUdiPLS9NutCMb3gnlMQpLmjl69uOcD9kDpHXVs3cwqCGccVBF3q27qHPPdD22v7/ewjzd9weXYFjrZ416VyTv6A+TdmNGZcUNjfcJrOcHxU3UbTVfKahPtGggGEYwnq2rpJpF3IaEJ5u6mTxXMLMtJ29MYAkOR/AgmUpjNhNiwu5UR7L/GEEo3HEYGAkzn/IVkodkViBBwhFleOt3vjQCX5gWEL63TZFNzVpYX9Wgo90ThVpYVDnvtcg8lI8lzb0okrAgKtnRGWlRePSeDJ1mBjXwOxrrSpKaezj0TkBhF5W0SOisiXB9ifJyKP+vtfE5EluczPt148TIcFhDGTGOBCKvDeqR46eqN09EapbemkK+INfETjCXqjcXqicSKxBOFYgiuXzSWhivrHJhQi8QTxhLJ0nlcIJ1t3qhCOKdFEAk0orZ3hrArrdHVt3RQEXc70eHk71HiGxvZejjSdYduhZu55eAeHm85Q39ZDa2cvqkp3JEY0rqmxkcFUlxbSE80c5Blp/obKM3jXRsRrTUXiXvAei8CTjW+9eJhvvnyUnmicgOP9bN98+SjfevFwv7Tp3bTpLbJth5pznk8zOjkLCiLiAg8CNwJrgHtEZE2fZJ8C2lT1AuAbwNdykZdth5q58Z+2848vHsnF6U0fCtSd6qbuVLdXiOGNRSQ42xpRvLGJx3bVJcew+50jOYPrcNMZTnZFUt1LCYWYQlckTn1bD1cum5tVvrYdauZMT5QDx8/w3qluwrEErgiReIL23hhfeGIfzR29LJxdQGlhkFNdURrP9FJRkp/VuMB9m5YRjXtBZCTBZDjpwSbkOqh64y4h1/vzPdfAkwyCV3/tZe55eMewBfa3f3UMx2/JOeL4/3rb+0pv3Yh4/wZd4aHttSPOpxlfuWwpXAYcVdVaVY0AjwA390lzM/B9//sngGtFZKAy4pwlayyHmzrG8rRmGHH1vkKuQ1lxKLXdEW+6L3hjE/Wne0moN+Cd/otPKHzmB7tY+ZWtnOqKEvenAgeczI9HaWGQJ/Y0pAq0wQq65OegMOTVuBVvMDuu6ndqQWc4lirE8gIuIme7vrKxeVUF9990IRUl+bT3RLMOJsNJDzZlxSHiqsQTSllx6JwDz7nU5Lsicfpcfhwh1RJMl966SRqvFo0ZnVyOKVQCdWmv64HLB0ujqjERaQfmAa3piUTkXuBegMWLF48oE8muh7j1GU2IopCb6hoCr7BP9PllKAz4+4kmFDfZtaRejbhvjaGjN0ZJPqka6Bee2EdnOEbc7176whP7+Ps7Lk7VXGcX5NPSGSGR0FRgWDy3gPq2btQfSjnTE+V4e4/XulEd0WDu5lUVY95vvnlVBffj/Yz1bd1cUF6EiNAZPjvja6TveS6D4kUhb0ZVemBIqLe9r3Mdl5kpJvN4y5QYaFbVh4GHwZuSOpJj69q6R1TbM2OruTMyquP7Bov0lwLE4srJrgix+Bke2PoWp7ujuCK4ImgCTndHeWDrW3RG4qlB4PyAQziWIKHe/RotHWEcERy/tGvtDOPgNV3yXGfMZhGNxlgHm3MZFP/01Uv55stHiSUSOOIHePW295WcmtwdiWVMzx1tV9p0kM0MuImUy+6jBqA67XWVv23ANCISAGYDJ8cyEwMN/pnJL5s+RNfxCvJEQmnriXGoqZO43wIQv5B3BI6d7M74HBSFXGLJG/jwBmxjCSXkCN2RGJF4AkVRvxZc29LJe6e62fNe26QaKB3pmEC6cxkU/9x1K/j8NRdQEHSJJbwg8vlrLhhw9lGuutKmg8k+3pLLlsJOYLmILMUr/O8GfrdPmi3AfwFeBe4AXtYxvpvuvk3LeLV2TOOMyYLAqGZ5ZXNsQpVoPEFcwUVT7xmNe7fjuWn9HOk1185wDMe/Sc5xhJDrUFIUYE5BkNKiPOrbvK6jWfkB2nqiOP6Yg8CgNbqx7A7I5lyjrW2ea03+c9etyAgCycA0UF5z0ZU2HeRq6vJYyVlLQVVjwGeB54C3gMdU9YCI3C8iN/nJ/g2YJyJHgf8G9Ju2Olr2oRx/5zJVQPAGm90sjz3bfaG4jnc3dihw9uMcS5ztHlpWVpRRcw3HlYKgy+K5haxZOItl5cWUFefRFYnzw3uv4KGPX0LFrHzOhGNei8X/34LZ+QPW6MZy+mW25xptbXMsavI27fTc5Grq8ljJ6ZiCqj4DPNNn21fTvu8F7sxlHsCr8Z2xcYVxo0qqJp4NEVgwK4+8gEtLh3fvgWr/1kJ668N1hHxHCMcTOCKUFecBUH+6h0Ra11BpYZAv3bAKOFtzHWgZi/Q/yuTA7n3/ZzcJVfL8ZTlK8oOoar8a3VjeyZztucaitjnamnyu7uCe7ib7eMuMWDp7oIGw6SrZzZFtjXuszS0MUhh0EH/q6aw8l9LCQL+pjIK3OF9ZcYh5RSHmFXmF7rLyYi5cNJsLF80iL+CwcHYeeWk/TMCBoOMtiVGYF6AoFGBeUYhZBUFmFQSpmlNAXtAhL+CwYXHpgIvqZXM/weZVFWxcXMp584pYVl5MSb5XAA9UoxvL6ZfZnmsy1DZt2um5mezjLVNi9tFofe66FRxr7eSp10/02+f6pWh8GiyF5IpXkxa8gdag490LkDTafv6BJM8ZdIWPXLSAxjMR6tu6qeqz/HZy25XL5vJq7amMNA9trx2w5r6srIiuSJyquYXE4gmaOsJE495CeV+6YVVqRdb0WlfAlWH/yPpO8awapN8+2xrdWE6/zPZck6G2adNOz91kHm+ZUaukJpfMPnaym3giQSjgUhhyKS/Oo6UzTMARTnZFSKhmBIlk7RvOfalsr/Ys9ETiWZ3DWyCu/4J0Z8/nMrcoSG80nlqQDpK17zxEhJaOXjrC3ho1vdE44bi3LPWfbD4fgH/++dFU0Ag4XnePCBQEHUQcVJVIXJlbFCTkOpxo7yUSVxwg4AjqL2K3rKwoVUifi6FWF4XhC+/kwOxQac5VNucey9VRR3KuXP7cY51XM/Fs6ewRSj3MpslbMjoUcFheUZL6Qxts/5XL5rJ1f2O/B+yk/1EM9McLmQ/mKS8KUlIQojMcG9M/8KEKjm2Hmgd8ONBwec/FH/xEF3CjNZb5n0rXYirldaazoGCMMSYl26AwIwaajTHGZMeCgjHGmBQLCsYYY1IsKBhjjEmxoGCMMSZlys0+EpEW4N0+m8vo8wyGGciugV0DsGsAdg1g4GtwnqqWD3fglAsKAxGRXdlMtZrO7BrYNQC7BmDXAEZ3Daz7yBhjTIoFBWOMMSnTJSg8PNEZmATsGtg1ALsGYNcARnENpsWYgjHGmLExXVoKxhhjxoAFBWOMMSlTKiiIyA0i8raIHBWRfs9zFpE8EXnU3/+aiCyZgGzmVBbX4JMi0iIir/tfn56IfOaKiHxHRJpFZP8g+0VEvuVfnzdEZON45zHXsrgGm0WkPe0z8NWB0k1lIlItIj8XkYMickBEPj9Ammn9WcjyGoz8s6CqU+ILcIF3gGVACNgHrOmT5r8C/+J/fzfw6ETnewKuwSeB/zXRec3hNdgEbAT2D7L/Q8BWvOciXQG8NtF5noBrsBn46UTnM8fXYCGw0f++BDg8wN/CtP4sZHkNRvxZmEothcuAo6paq6oR4BHg5j5pbga+73//BHCtiEzQ04pzIptrMK2p6nbg1BBJbgZ+oJ4dwBwRWTg+uRsfWVyDaU9VT6jqHv/7DuAtoLJPsmn9WcjyGozYVAoKlUBd2ut6+l+AVBpVjQHtwLxxyd34yOYaANzuN5efEJHq8cnapJHtNZrurhSRfSKyVUQunOjM5JLfTbwBeK3PrhnzWRjiGsAIPwtTKSiY7PwEWKKq64AXONtyMjPHHrx1bi4G/hl4emKzkzsiUgw8CfyZqp6Z6PxMhGGuwYg/C1MpKDQA6bXeKn/bgGlEJADMBk6OS+7Gx7DXQFVPqmrYf/lt4JJxyttkkc3nZFpT1TOq2ul//wwQFJGyCc7WmBORIF5h+B+q+qMBkkz7z8Jw1+BcPgtTKSjsBJaLyFIRCeENJG/pk2YL8F/87+8AXlZ/tGWaGPYa9OkzvQmvn3Em2QL8vj/z5AqgXVVPTHSmxpOILEiOpYnIZXh/59OpcoT/8/0b8Jaq/uMgyab1ZyGba3Aun4XAWGc0V1Q1JiKfBZ7Dm4XzHVU9ICL3A7tUdQveBfp3ETmKNxB398TleOxleQ0+JyI3ATG8a/DJCctwDojID/FmVJSJSD3wV0AQQFX/BXgGb9bJUaAb+IOJyWnuZHEN7gD+WERiQA9w9zSrHAFcBXwCeFNEXve3/SWwGGbMZyGbazDiz4Itc2GMMSZlKnUfGWOMyTELCsYYY1IsKBhjjEmxoGCMMSbFgoIxxpgUCwpmzIhI3F+J8YB/W/1/FxHH31cjIt8a5vhPisj/GuF7/uUo8vs9ETnm53mPiFw5gmNTeRWRPxKR3z/XfGT5fktEpCdttcvX/XtVxur8nxSRRWmvvy0ia8bq/GbqmDL3KZgpoUdV1wOISAXwn8As4K9UdRewKwfv+ZfA/xzF8V9U1SdE5APAQ8C6kZ7Anw+eNREJ+GtzjdQ7yeubA58E9gPHAVR1Wi25brJnLQWTE6raDNwLfNa/o3SziPwUvDsrReRVEdkrIr8WkZVph1aLyDYROSIif5XcKCIfF5Hf+DXkh0TEFZEHgAJ/238Mkc71WwX7ReRNEfnzAbK8HbhgsHP42/9ARA6LyG/wbhxK5u2vReQL/veXircY4esi8nXxn3ng18S3iMjLwEsiUiTecxF+41+Hm/10rn/cTv889w11nUWkM+37O0Tke/733xPvWQK/FpFaEbkjLd2X/OuwT0Qe8PfVAP/h57vA/x3U+Onv8dPvF5Gvpb+3iPy//nl2iMj8ofJqpgYLCiZnVLUW787rij67DgG/o6obgK+SWdO/DLgdr8Z+p9/ttBr4GHCVX1OOA7+nql/Gb52o6u8Nlg5YD1Sq6lpVvQj47gDZ/SjenaEDnkO85UP+H7xgcDUwWNfKd4H70o5NtxG4Q1XfB/wPvGVYLgPeD3xdRIqAT+Etx3ApcCnwGRFZ6h9/flrX0YODvH+6hX5ePwI8ACAiN+ItKX25v0ja36nqE3ituN/zr2VP8gR+l9LXgGvwruOlInKLv7sI2OGfZzvwmSzyZCY56z4yE2E28H0RWQ4o/hINvhdU9SSAiPwIr1CL4S3st1O8ZVwKgOYBznvtIOl+AiwTkX8GfgY8n3bM10XkK0ALXoE82DkuB7apaouft0eBFelvLiJzgBJVfdXf9J94BXL6z5Z8DsIHgJuSLQwgH295gg8A69Jq9rOB5XgPUBlp99HTqpoADqbV4q8Dvquq3QBp+RnMpWT+3P+B95Cfp4EI8FM/3W7g+hHkzUxSFhRMzojIMrzacjOwOm3X3wA/V9VbxVsHflvavr7rrijek7O+r6p/MdxbDpZORC4GPgj8EXAX8If+ri/6NeVkuvcPdI602vFodPXJ6+2q+naf9xHgT1X1uT7blwxyzvTrld9nXzjt+1w8bCqato5OHCtPpgXrPjI5ISLlwL/gPRq0b0E/m7NLGH+yz77rRWSuiBQAtwCvAC8Bd4g3eI2//zw/fVS85YMZLJ14SwU7qvok8BW8bpzBDPZerwHvE5F5/vvd2fdAVT0NdIjI5f6moRZkfA74Uz8IICIb0rb/cfJnEpEVfrfSYJpEZLV4s7xuHSJd0gvAH4hIYfLn87d34D3Ssa/f4P3cZf7Yyj3AL7J4HzNFWWQ3Y6lAvNUag3hdPv8ODLSk79/hdR99Ba87J91v8NaHrwL+jz9rCT/t837hFwX+BHgXeBh4Q0T2+OMKA6XrAb7rbwMYtMWhqgcHOoeq7hCRvwZeBU4Drw9yik8B/yoiCbzCs32QdH8D/JOfdwc4htfV9G1gCbDHDxgteMFxMF/G68JpwRsXKB4iLar6rIisB3aJSARvJdG/BL4H/IuI9ABXpqU/ISJfBn6O19r4mar+eKj3MFObrZJqzBgSkeLkQ038wnShqn5+grNlTNaspWDM2PqwiPwF3t/Wu0yz51mY6c9aCsYYY1JsoNkYY0yKBQVjjDEpFhSMMcakWFAwxhiTYkHBGGNMyv8P4m2c/Zoj36AAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns_plot=sns.regplot(\n","    x=\"DiabetesPedigreeFunction\", \n","    y = \"Outcome\", \n","    y_jitter = 0.03,\n","    data = diabetes,\n","    logistic =True,\n","    ci = None\n",")\n","\n","fig = sns_plot.get_figure()\n","fig.savefig(\n","    \"output.png\",bbox_inches =\"tight\", \n","    pad_inches = 1, \n","    transparent = True, \n","    facecolor =\"w\", \n","    edgecolor ='w', \n","    orientation ='landscape'\n",")"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"},"papermill":{"default_parameters":{},"duration":11.996012,"end_time":"2022-09-23T04:09:42.305487","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2022-09-23T04:09:30.309475","version":"2.3.4"}},"nbformat":4,"nbformat_minor":5}