nasa-machine-learning.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Load Libraries ","metadata":{}},{"cell_type":"code","source":"# Libraries needed for data Analysis \nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# # Libraries needed for model and visualization\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier , AdaBoostClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier \nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import KFold \nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import confusion_matrix, classification_report, make_scorer, accuracy_score\nimport matplotlib.pyplot as plt # plots \nimport seaborn as sns\n\n#ignore warnings\nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:16:10.197089Z","iopub.execute_input":"2022-06-27T21:16:10.197497Z","iopub.status.idle":"2022-06-27T21:16:10.755739Z","shell.execute_reply.started":"2022-06-27T21:16:10.197408Z","shell.execute_reply":"2022-06-27T21:16:10.754824Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"# Read data","metadata":{}},{"cell_type":"code","source":"# Load in the red wine data from kaggle.\ndf = pd.read_csv('../input/nasa-nearest-earth-objects/neo.csv',sep=',')\n\n# Review data\nprint(df.head(10))\n# Data dimensionality (rows, colums)\nprint(df.shape)\n# Data distributing\ndf.info()\n","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:16:10.757880Z","iopub.execute_input":"2022-06-27T21:16:10.758232Z","iopub.status.idle":"2022-06-27T21:16:11.040874Z","shell.execute_reply.started":"2022-06-27T21:16:10.758197Z","shell.execute_reply":"2022-06-27T21:16:11.039673Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"         id                 name  est_diameter_min  est_diameter_max  \\\n0   2162635  162635 (2000 SS164)          1.198271          2.679415   \n1   2277475    277475 (2005 WK4)          0.265800          0.594347   \n2   2512244   512244 (2015 YE18)          0.722030          1.614507   \n3   3596030          (2012 BV13)          0.096506          0.215794   \n4   3667127          (2014 GE35)          0.255009          0.570217   \n5  54138696          (2021 GY23)          0.036354          0.081291   \n6  54189957          (2021 PY40)          0.171615          0.383743   \n7  54230078           (2021 XD6)          0.005328          0.011914   \n8   2088213     88213 (2001 AF2)          0.350393          0.783502   \n9   3766065            (2016 YM)          0.105817          0.236614   \n\n   relative_velocity  miss_distance orbiting_body  sentry_object  \\\n0       13569.249224   5.483974e+07         Earth          False   \n1       73588.726663   6.143813e+07         Earth          False   \n2      114258.692129   4.979872e+07         Earth          False   \n3       24764.303138   2.543497e+07         Earth          False   \n4       42737.733765   4.627557e+07         Earth          False   \n5       34297.587778   4.058569e+07         Earth          False   \n6       27529.472307   2.906912e+07         Earth          False   \n7       57544.470083   5.511502e+07         Earth          False   \n8       56625.210122   6.903598e+07         Earth          False   \n9       48425.840329   3.835526e+07         Earth          False   \n\n   absolute_magnitude  hazardous  \n0               16.73      False  \n1               20.00       True  \n2               17.83      False  \n3               22.20      False  \n4               20.09       True  \n5               24.32      False  \n6               20.95      False  \n7               28.49      False  \n8               19.40      False  \n9               22.00      False  \n(90836, 10)\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 90836 entries, 0 to 90835\nData columns (total 10 columns):\n #   Column              Non-Null Count  Dtype  \n---  ------              --------------  -----  \n 0   id                  90836 non-null  int64  \n 1   name                90836 non-null  object \n 2   est_diameter_min    90836 non-null  float64\n 3   est_diameter_max    90836 non-null  float64\n 4   relative_velocity   90836 non-null  float64\n 5   miss_distance       90836 non-null  float64\n 6   orbiting_body       90836 non-null  object \n 7   sentry_object       90836 non-null  bool   \n 8   absolute_magnitude  90836 non-null  float64\n 9   hazardous           90836 non-null  bool   \ndtypes: bool(2), float64(5), int64(1), object(2)\nmemory usage: 5.7+ MB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Check on nulls ","metadata":{}},{"cell_type":"code","source":"print('Total Null values in the dataset : ',df.isna().sum().sum())","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:16:11.042561Z","iopub.execute_input":"2022-06-27T21:16:11.043238Z","iopub.status.idle":"2022-06-27T21:16:11.108036Z","shell.execute_reply.started":"2022-06-27T21:16:11.043194Z","shell.execute_reply":"2022-06-27T21:16:11.106747Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Total Null values in the dataset :  0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Check on dupes","metadata":{}},{"cell_type":"code","source":"print('Total duplicate values in the dataset : ',df.duplicated().sum())","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:16:11.111607Z","iopub.execute_input":"2022-06-27T21:16:11.111987Z","iopub.status.idle":"2022-06-27T21:16:11.207010Z","shell.execute_reply.started":"2022-06-27T21:16:11.111950Z","shell.execute_reply":"2022-06-27T21:16:11.205762Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Total duplicate values in the dataset :  0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Understand the stats for each column","metadata":{}},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:16:11.208516Z","iopub.execute_input":"2022-06-27T21:16:11.208954Z","iopub.status.idle":"2022-06-27T21:16:11.304728Z","shell.execute_reply.started":"2022-06-27T21:16:11.208913Z","shell.execute_reply":"2022-06-27T21:16:11.303692Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"                 id  est_diameter_min  est_diameter_max  relative_velocity  \\\ncount  9.083600e+04      90836.000000      90836.000000       90836.000000   \nmean   1.438288e+07          0.127432          0.284947       48066.918918   \nstd    2.087202e+07          0.298511          0.667491       25293.296961   \nmin    2.000433e+06          0.000609          0.001362         203.346433   \n25%    3.448110e+06          0.019256          0.043057       28619.020645   \n50%    3.748362e+06          0.048368          0.108153       44190.117890   \n75%    3.884023e+06          0.143402          0.320656       62923.604633   \nmax    5.427591e+07         37.892650         84.730541      236990.128088   \n\n       miss_distance  absolute_magnitude  \ncount   9.083600e+04        90836.000000  \nmean    3.706655e+07           23.527103  \nstd     2.235204e+07            2.894086  \nmin     6.745533e+03            9.230000  \n25%     1.721082e+07           21.340000  \n50%     3.784658e+07           23.700000  \n75%     5.654900e+07           25.700000  \nmax     7.479865e+07           33.200000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>est_diameter_min</th>\n      <th>est_diameter_max</th>\n      <th>relative_velocity</th>\n      <th>miss_distance</th>\n      <th>absolute_magnitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>9.083600e+04</td>\n      <td>90836.000000</td>\n      <td>90836.000000</td>\n      <td>90836.000000</td>\n      <td>9.083600e+04</td>\n      <td>90836.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.438288e+07</td>\n      <td>0.127432</td>\n      <td>0.284947</td>\n      <td>48066.918918</td>\n      <td>3.706655e+07</td>\n      <td>23.527103</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.087202e+07</td>\n      <td>0.298511</td>\n      <td>0.667491</td>\n      <td>25293.296961</td>\n      <td>2.235204e+07</td>\n      <td>2.894086</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>2.000433e+06</td>\n      <td>0.000609</td>\n      <td>0.001362</td>\n      <td>203.346433</td>\n      <td>6.745533e+03</td>\n      <td>9.230000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>3.448110e+06</td>\n      <td>0.019256</td>\n      <td>0.043057</td>\n      <td>28619.020645</td>\n      <td>1.721082e+07</td>\n      <td>21.340000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>3.748362e+06</td>\n      <td>0.048368</td>\n      <td>0.108153</td>\n      <td>44190.117890</td>\n      <td>3.784658e+07</td>\n      <td>23.700000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>3.884023e+06</td>\n      <td>0.143402</td>\n      <td>0.320656</td>\n      <td>62923.604633</td>\n      <td>5.654900e+07</td>\n      <td>25.700000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>5.427591e+07</td>\n      <td>37.892650</td>\n      <td>84.730541</td>\n      <td>236990.128088</td>\n      <td>7.479865e+07</td>\n      <td>33.200000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Histograms\ndf.hist(bins=10,figsize=(5, 4))\nplt.show()\n# Density\ndf.plot(kind='density', subplots=True, layout=(4,3), sharex=False)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:16:11.306239Z","iopub.execute_input":"2022-06-27T21:16:11.306800Z","iopub.status.idle":"2022-06-27T21:16:21.166356Z","shell.execute_reply.started":"2022-06-27T21:16:11.306747Z","shell.execute_reply":"2022-06-27T21:16:21.165384Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 360x288 with 6 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Split into X,y, removing unwanted columns and tarnsforming Hazardous data into int from bool\n\nX = df.drop(['id','name','est_diameter_max','orbiting_body','sentry_object','hazardous'],axis=1) \n\ny = df.hazardous.astype('int')\nprint(X.shape,y.shape)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:16:21.167832Z","iopub.execute_input":"2022-06-27T21:16:21.168940Z","iopub.status.idle":"2022-06-27T21:16:21.178886Z","shell.execute_reply.started":"2022-06-27T21:16:21.168900Z","shell.execute_reply":"2022-06-27T21:16:21.177709Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"(90836, 4) (90836,)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Split into Test and Train sets","metadata":{}},{"cell_type":"code","source":"# 90:10\n\nX_train, X_test, y_train, y_test = train_test_split(X,\n                                                    y, test_size = 0.1, random_state = 42)\nprint(X_train.shape), print(y_train.shape)\nprint(X_test.shape), print(y_test.shape)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:16:21.180386Z","iopub.execute_input":"2022-06-27T21:16:21.182367Z","iopub.status.idle":"2022-06-27T21:16:21.214960Z","shell.execute_reply.started":"2022-06-27T21:16:21.182329Z","shell.execute_reply":"2022-06-27T21:16:21.213822Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"(81752, 4)\n(81752,)\n(9084, 4)\n(9084,)\n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"(None, None)"},"metadata":{}}]},{"cell_type":"code","source":"# prepare configuration for cross validation test harness\nseed = 7\n# prepare models\nmodels = []\nmodels.append(('XGBClassifier', XGBClassifier())) \nmodels.append(('SupportVectorClassifier', SVC()))\nmodels.append(('RandomForestClassifier', RandomForestClassifier()))\nmodels.append(('DecisionTreeClassifier', DecisionTreeClassifier()))\nmodels.append(('AdaBoostClassifier', AdaBoostClassifier()))\nmodels.append(('KNeighborsClassifier', KNeighborsClassifier()))\nmodels.append(('GaussianNB', GaussianNB()))\nmodels.append(('LogisticRegression', LogisticRegression()))\n\n\n# evaluate each model in turn\nresults = []\nnames = []\nscoring = 'accuracy'\n\nfor name, model in models:\n   kfold = KFold(n_splits=10,shuffle=True, random_state=seed)\n   cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)\n   results.append(cv_results)\n   names.append(name)\n   msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n   print(msg)\n# boxplot algorithm comparison\nfig = plt.figure()\nfig.suptitle('Algorithm Comparison')\nax = fig.add_subplot(111)\nplt.boxplot(results)\nax.set_xticklabels(names)\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:16:21.216931Z","iopub.execute_input":"2022-06-27T21:16:21.217283Z","iopub.status.idle":"2022-06-27T21:26:19.879743Z","shell.execute_reply.started":"2022-06-27T21:16:21.217248Z","shell.execute_reply":"2022-06-27T21:26:19.878722Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"XGBClassifier: 0.918240 (0.003172)\nSupportVectorClassifier: 0.902290 (0.002680)\nRandomForestClassifier: 0.916137 (0.003452)\nDecisionTreeClassifier: 0.893678 (0.003950)\nAdaBoostClassifier: 0.912124 (0.002833)\nKNeighborsClassifier: 0.894290 (0.002782)\nGaussianNB: 0.896969 (0.003651)\nLogisticRegression: 0.902290 (0.002680)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAZoAAAEVCAYAAADZ4CNuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAsA0lEQVR4nO3debwcVZn/8c+XQALIlksiIgmLikrc4nAHGRWJKAjqj01HQWRxVNzQUYdRGB2JGZFRcdxgnEFlUxFwD4hEZXFB0NxIEhIygRgIJAEJkJgESCDJ8/vjPM0trnfpS7q42/f9evWrq2s5dc6p5alzqrpbEYGZmVldthjoDJiZ2fDmQGNmZrVyoDEzs1o50JiZWa0caMzMrFYONGZmVisHGmsJSRdK+kxNaR8n6Re9TJ8iaWkd6x7qJP2bpG8OdD5sZHOgsX6RdL2klZLGPFXrjIjvRsQhlTyEpOc8VetX8SFJ8yQ9JGmppO9LetFTlYcnKyI+GxHvGuh82MjmQGNNk7QncAAQwOFP0Tq3fCrW04evAP8MfAhoA54L/AR4wwDmqU+DpO7MHGisX04AbgIuBE7sbUZJH5N0j6Tlkt5VbYVI2lHSxZJWSFoi6ZOStshpJ0m6QdKXJD0ATM1xv8vpv8lVzJG0VtJbK+v8F0n35XrfURl/oaT/lvTzXOYGSc+Q9OVsnf2fpJf2UI69gQ8Ax0bEtRGxPiIezlbWf/azPKskLZb08hx/d+b3xC55/R9Jv5S0RtKvJe1Rmf6VXG61pFmSDqhMmyrpB5K+I2k1cFKO+05O3zqnPZB5mSlpl5z2TEnTJT0oaZGkd3dJ9/Is4xpJ8yW197b9zaocaKw/TgC+m6/XNU5SXUk6FPgo8FrgOcCULrN8DdgReBZwYKb7jsr0lwGLgV2AM6sLRsSrcvAlEbFdRFyWn5+Rae4GvBM4V9LYyqJvAT4JjAPWAzcCf8rPPwD+q4cyvwZYGhF/7GF6s+WZC+wMXAJcCvw9pW7eDpwjabvK/McB/5F5m02p74aZwGRKy+oS4PuStq5MPyLLs1OX5aBcHOwITMy8vBd4JKddCiwFngm8GfispIMqyx6e8+wETAfO6bk6zJ7IgcaaIumVwB7A5RExC/gz8LYeZn8LcEFEzI+Ih4GplXRGAccAp0fEmoi4E/gicHxl+eUR8bWI2BARj9Ccx4BpEfFYRFwFrAWeV5n+44iYFRHrgB8D6yLi4ojYCFwGdNuioZyQ7+lppU2W546IuKCyromZ1/UR8QvgUUrQafhZRPwmItYDnwD+QdJEgIj4TkQ8kHXzRWBMl3LeGBE/iYhN3dTdY1me50TExqyP1Zn2K4CPR8S6iJgNfJMSMBt+FxFXZRm+Dbykpzox68qBxpp1IvCLiLg/P19Cz91nzwTurnyuDo8DtgKWVMYtobREupu/WQ9ExIbK54eBaivhL5XhR7r5XJ33CekCu/ay3mbK03VdRERv63+8/BGxFniQUqdIOlXSAkl/lbSK0kIZ192y3fg2MAO4NLs0Py9pq0z7wYhY00sZ7q0MPwxs7XtA1iwHGuuTpG0orZQDJd0r6V7gI8BLJHV3ZXsPMKHyeWJl+H7KlfUelXG7A8sqnwfTT4pfA0zo5Z5EM+Xpr8frK7vU2oDleT/mY5RtMTYidgL+CqiybI91l629T0fEJODlwBsprZblQJuk7VtYBrPHOdBYM44ENgKTKPcHJgP7AL/lid0rDZcD75C0j6RtgX9vTMiul8uBMyVtnze6Pwp8px/5+QvlfkjtIuJ24L+B76l8X2d03lQ/RtJpLSpPV6+X9EpJoyn3am6KiLuB7YENwApgS0mfAnZoNlFJr5b0ouzuW00JkJsy7d8DZ2XZXky5z7U5ZTB7nAONNeNEyj2XuyLi3saLckP4uK5dKBHxc+CrwHXAIsqTalBuwgN8EHiIcsP/d5RuuPP7kZ+pwEX55NRbnmSZ+uNDlLKeC6yi3J86Crgip29uebq6BDiD0mW2L+WBASjdXlcDt1G6ttbRv27GZ1AeFFgNLAB+TelOAzgW2JPSuvkxcEZE/GozymD2OPmPz6xukvYB5gFjutxHsS4kXUh5yu2TA50Xs1Zxi8ZqIekoSWPyEePPAVc4yJiNTA40Vpf3APdRupk2Au8b2OyY2UBx15mZmdXKLRozM6uVA42ZmdXKgcbMzGrlQGNmZrVyoDEzs1o50JiZWa0caMzMrFYONGZmVisHGjMzq5UDjZmZ1cqBxszMatVUoJF0qKSFkhZJOq2b6XtIukbSXEnXS5qQ4ydLulHS/Jz21soyF0q6Q9LsfE1uWanMzGzQ6PNHNfPf+G4DDgaWAjOBYyPi1so83weujIiLJB0EvCMijpf0XCAi4nZJzwRmAftExKr8340rI+IHtZTMzMwGhWZaNPsBiyJicUQ8ClwKHNFlnknAtTl8XWN6RNyWf4VLRCyn/Gz8+FZk3MzMhoYt+56F3Xji38UuBV7WZZ45wNHAVyh/cbu9pJ0j4oHGDJL2A0ZT/p+k4cz83/NrgNMiYj1dSDoZOBngaU972r7Pf/7zm8iymZk1zJo16/6IGLCL/GYCTTNOBc6RdBLwG2AZ5c+uAJC0K+W/yU+MiE05+nTgXkrwOQ/4ODCta8IRcV5Op729PTo6OlqUZTOzkUHSkoFcfzOBZhkwsfJ5Qo57XHaLHQ0gaTvgTRGxKj/vAPwM+ERE3FRZ5p4cXC/pAkqwMjOzYaaZezQzgb0l7SVpNHAMML06g6RxkhppnQ6cn+NHAz8GLu560z9bOUgScCQwbzPKYWZmg1SfgSYiNgCnADOABcDlETFf0jRJh+dsU4CFkm4DdgHOzPFvAV4FnNTNY8zflXQLcAswDvhMi8pkZmaDSJ+PNw8mvkdjZtZ/kmZFRPtArd+/DGBmZrVyoDEzs1o50JiZWa0caMzMrFat+sLmkFCepG7OUHpIwsxsMBtRgaa74CHJQcXMrEbuOjMzs1o50JiZWa0caMzMrFYONGZmVisHGjMzq5UDjZmZ1cqBxszMajWivkdjrdOfL7+CvwBrNpI50NiT4i+/mlmz3HVmZma1cqAxM7NaOdCYmVmtmgo0kg6VtFDSIkmndTN9D0nXSJor6XpJE3L8ZEk3Spqf095aWWYvSX/INC+TNLpVhWpra0NSU6/MS5+vtra2VmXPzGxE6TPQSBoFnAscBkwCjpU0qctsZwMXR8SLgWnAWTn+YeCEiHgBcCjwZUk75bTPAV+KiOcAK4F3bmZZHrdy5UoioqWvlStXtip7ZmYjSjMtmv2ARRGxOCIeBS4FjugyzyTg2hy+rjE9Im6LiNtzeDlwHzBepSlxEPCDXOYi4MjNKIeZmQ1SzQSa3YC7K5+X5riqOcDROXwUsL2knaszSNoPGA38GdgZWBURG3pJ08zMhoFWPQxwKnCgpJuBA4FlwMbGREm7At8G3hERm/qTsKSTJXVI6lixYkWLsmtmZk+VZgLNMmBi5fOEHPe4iFgeEUdHxEuBT+S4VQCSdgB+BnwiIm7KRR4AdpK0ZU9pVtI+LyLaI6J9/PjxzZXKzMwGjWYCzUxg73xKbDRwDDC9OoOkcZIaaZ0OnJ/jRwM/pjwo0LgfQ5Svj18HvDlHnQj8dHMKYmZmg1OfgSbvo5wCzAAWAJdHxHxJ0yQdnrNNARZKug3YBTgzx78FeBVwkqTZ+Zqc0z4OfFTSIso9m2+1qExmZjaIaCj9NlV7e3t0dHT0OV8dv7nl3/Hqm+vIbHCSNCsi2gdq/f5lADMzq9Ww/PXmOGMHmLpj69M0M7N+G5aBRp9eXU/X2dSWJjlktLW1Nf3LCM3+T83YsWN58MEHNydbZjZEDMtAY63V+EmfVurvH6eZ2dDlezRmZlYrBxozM6uVA42ZmdXKgcbMzGrlQGNmZrVyoDEzs1o50JiZWa0caMzMrFYONGZmVisHGjMzq5UDjZmZ1cq/dWY2CPTnt9/8nz821DjQmA0C3QUP/5GcDRfuOjMzs1o1FWgkHSppoaRFkk7rZvoekq6RNFfS9ZImVKZdLWmVpCu7LHOhpDskzc7X5M0ujZmZDTp9dp1JGgWcCxwMLAVmSpoeEbdWZjsbuDgiLpJ0EHAWcHxO+wKwLfCebpL/14j4weYUwOrnfyw1s83RzD2a/YBFEbEYQNKlwBFANdBMAj6aw9cBP2lMiIhrJE1pQV5tgPgfS81sczTTdbYbcHfl89IcVzUHODqHjwK2l7RzE2mfmd1tX5I0pon5myappa+xY8e2MntmZiNGqx4GOBU4UNLNwIHAMmBjH8ucDjwf+HugDfh4dzNJOllSh6SOFStWNJWZiGj61ez8/n97M7Mnp5lAswyYWPk8Icc9LiKWR8TREfFS4BM5blVviUbEPVGsBy6gdNF1N995EdEeEe3jx49vIrtmZjaYNBNoZgJ7S9pL0mjgGGB6dQZJ4yQ10jodOL+vRCXtmu8CjgTm9SPfZmY2RPT5MEBEbJB0CjADGAWcHxHzJU0DOiJiOjAFOEtSAL8BPtBYXtJvKV1k20laCrwzImYA35U0HhAwG3hvS0tmLdWfb643w/e8zEYODaVvHre3t0dHR0dL0/S3r1vHddlark9rFUmzIqJ9oNbvXwYwM7NaOdCYmVmtHGjMzKxWIyrQdPdFzN7Gm9Whra2tqS8JQ/NfPG5raxvgUpn1bET9TYBvrNpgsHLlylp+0sdssBpRLRozM3vqOdCYmVmtRlTXmdlg4L9dsJHGgcbsKea/XbCRxl1nZmZWKwcaMzOrlQONmZnVyoHGzMxq5UBjZma1cqAxM7NaOdCYmVmtHGjMzKxW/sKmPSk9/YhjT+P9g6ZmI5cDjT0pDhxm1qymus4kHSppoaRFkk7rZvoekq6RNFfS9ZImVKZdLWmVpCu7LLOXpD9kmpdJGr35xTEzs8Gmz0AjaRRwLnAYMAk4VtKkLrOdDVwcES8GpgFnVaZ9ATi+m6Q/B3wpIp4DrATe2f/smw1Nzf6hWbOvsWPHDnSRzHrUTItmP2BRRCyOiEeBS4EjuswzCbg2h6+rTo+Ia4A11ZlVOvIPAn6Qoy4Cjuxv5s2Gooho6tWfeR988MEBLpVZz5oJNLsBd1c+L81xVXOAo3P4KGB7STv3kubOwKqI2NBLmgBIOllSh6SOFStWNJFdMzMbTFr1ePOpwIGSbgYOBJYBG1uRcEScFxHtEdE+fvz4ViRpZmZPoWaeOlsGTKx8npDjHhcRy8kWjaTtgDdFxKpe0nwA2EnSltmq+Zs0zcxseGimRTMT2DufEhsNHANMr84gaZykRlqnA+f3lmCUDujrgDfnqBOBn/Yn42ZmNjT0GWiyxXEKMANYAFweEfMlTZN0eM42BVgo6TZgF+DMxvKSfgt8H3iNpKWSXpeTPg58VNIiyj2bb7WoTGZmNohoKH3xrr29PTo6OgY6G2ZPCUn+Yqy1hKRZEdE+UOv3b52ZmVmtHGjMzKxWDjRmZlYrBxozM6uVA42ZmdXKgcbMzGrlQGNmZrVyoDEzs1o50JiZWa0caMzMrFYONGZmVqtm/ibAzGpW/nS2ufH+/TMbahxozAYBBw8bztx1ZmZmtXKgMTOzWjnQmJlZrXyPZpDp6aZwT9y3bzY0jaRj3YFmkOlpZ/K/LZoNLyPpWG+q60zSoZIWSlok6bRupu8h6RpJcyVdL2lCZdqJkm7P14mV8ddnmrPz9fTWFMnMzAaTPls0kkYB5wIHA0uBmZKmR8StldnOBi6OiIskHQScBRwvqQ04A2gHApiVy67M5Y6LiI4WlsfMzAaZZlo0+wGLImJxRDwKXAoc0WWeScC1OXxdZfrrgF9GxIMZXH4JHLr52R4e2trakNTUC2hqvra2tgEulZl1NdKP9Wbu0ewG3F35vBR4WZd55gBHA18BjgK2l7RzD8vuVvl8gaSNwA+Bz0Q3HZOSTgZOBth9992byO7QsXLlypb3xfb3BqOZ1W+kH+uterz5VOBASTcDBwLLgI19LHNcRLwIOCBfx3c3U0ScFxHtEdE+fvz4FmXXzMyeKs0EmmXAxMrnCTnucRGxPCKOjoiXAp/Icat6WzYiGu9rgEsoXXRmZjbMNBNoZgJ7S9pL0mjgGGB6dQZJ4yQ10jodOD+HZwCHSBoraSxwCDBD0paSxuWyWwFvBOZtfnHMzGyw6TPQRMQG4BRK0FgAXB4R8yVNk3R4zjYFWCjpNmAX4Mxc9kHgPyjBaiYwLceNoQScucBsSivnGy0sl5mZDRIaSl8Mam9vj46O4fM0dB1fzBqOX/YyG+oG+liXNCsi2luagX7wb52ZmVmtHGjMzKxW/q0zMxt2+vMdE3c118+BxsyGne6Ch+9fDhx3nZmZWa0caMzMrFYONGZmVisHGjMzq5UDjZmZ1cqBxszMauVAY2ZmtXKgMTOzWvkLmwMoztgBpu7Y+jTNbFAZ6ce6f715AA30L7qOBP39u1vX3fA1kMfGQB/rA/3rzW7R2LDmnyIxG3i+R2NmZrVyoDEzs1o50JiZWa2aCjSSDpW0UNIiSad1M30PSddImivpekkTKtNOlHR7vk6sjN9X0i2Z5lfV37u2ZmY2JPQZaCSNAs4FDgMmAcdKmtRltrOBiyPixcA04Kxctg04A3gZsB9whqSxuczXgXcDe+fr0M0ujZmZDTrNtGj2AxZFxOKIeBS4FDiiyzyTgGtz+LrK9NcBv4yIByNiJfBL4FBJuwI7RMRNUR7/uRg4cvOKYmYjTVtbG5KaegFNzdfW1jbApRp+mnm8eTfg7srnpZQWStUc4GjgK8BRwPaSdu5h2d3ytbSb8X9D0snAyQC77757E9kdWlrdYzh27Ni+ZzIbJlauXFnL91PqMJKP9VZ9j+ZU4BxJJwG/AZYBG1uRcEScB5wH5QubrUhzsOjPAeLvfpgNXSP9WG8m0CwDJlY+T8hxj4uI5ZQWDZK2A94UEaskLQOmdFn2+lx+QpfxT0jTzMyGh2bu0cwE9pa0l6TRwDHA9OoMksZJaqR1OnB+Ds8ADpE0Nh8COASYERH3AKsl7Z9Pm50A/LQF5TEzs0Gmz0ATERuAUyhBYwFweUTMlzRN0uE52xRgoaTbgF2AM3PZB4H/oASrmcC0HAfwfuCbwCLgz8DPW1UoMzMbPPyjmkPEcOy3HSiuy+FjoH+ssg41lWlAf1TTvwxgZma1cqAxM7Na+W8CBpnenrXvbpq7gMyGppF0rDvQDDJDeWcys+aNpGPdXWdmZlYrBxozM6uVu87MrGn9+b2ukdQ1ZL1zoDGzpnUXPAb6eyc2+LnrzMzMauVAY2ZmtXKgMTOzWjnQmJlZrRxozKxbzf5NMvgvkq13furMzLrV6r9Jrusvkm3wc4vGzMxq5UBjZma1cqCxYaPV9xR8X8GsNXyPxoaNVt9TAN9XMGuFplo0kg6VtFDSIkmndTN9d0nXSbpZ0lxJr8/xoyVdIOkWSXMkTaksc32mOTtfT29VoczMbPDos0UjaRRwLnAwsBSYKWl6RNxame2TwOUR8XVJk4CrgD2BdwNExIsykPxc0t9HxKZc7riI6GhdccxsJIkzdoCpO7Y+TWupZrrO9gMWRcRiAEmXAkcA1UATQGPr7Agsz+FJwLUAEXGfpFVAO/DHzc65mY14+vTqWrpLY2pLkxzxmuk62w24u/J5aY6rmgq8XdJSSmvmgzl+DnC4pC0l7QXsC0ysLHdBdpv9u9wZbmY2LLXqqbNjgQsjYgLweuDbkrYAzqcEpg7gy8DvgY25zHER8SLggHwd313Ckk6W1CGpY8WKFS3KrpmZPVWaCTTLeGIrZEKOq3oncDlARNwIbA2Mi4gNEfGRiJgcEUcAOwG35XzL8n0NcAmli+5vRMR5EdEeEe3jx49vumBmZjY4NBNoZgJ7S9pL0mjgGGB6l3nuAl4DIGkfSqBZIWlbSU/L8QcDGyLi1uxKG5fjtwLeCMxrSYnMzGxQ6fNhgIjYIOkUYAYwCjg/IuZLmgZ0RMR04F+Ab0j6COXBgJMiIvJJsxmSNlFaQY3usTE5fqtM81fAN1pdODMzG3gaSn/B2t7eHh0dfhraulfHXwqP5L8pbnXZh8r2GY7bXNKsiGgfqPX7lwHMrFut/o5KXd9PafUDq2PHjm1peuZAY2Y9aPV3VOr4fkp/8jccWypDhQONmfWola0FtxRGLgcaM+tWs1f/bilYXxxobNjw716ZDU4ONDZs+HevzAYn//GZmZnVyoHGzMxq5a4zMxt2enparrvxfpChfg40ZjbsOHgMLu46MzOzWjnQmJlZrRxozMysVg40ZmZWKwcaMzOrlZ86s2HFPxlvNvg40Niw4R+BNBuc3HVmZma1airQSDpU0kJJiySd1s303SVdJ+lmSXMlvT7Hj5Z0gaRbJM2RNKWyzL45fpGkr6rVfR5mZjYo9BloJI0CzgUOAyYBx0qa1GW2TwKXR8RLgWOA/87x7waIiBcBBwNflNRY59dz+t75OnTzimJmZoNRMy2a/YBFEbE4Ih4FLgWO6DJPAI0/7tgRWJ7Dk4BrASLiPmAV0C5pV2CHiLgpSmf5xcCRm1EOMzMbpJoJNLsBd1c+L81xVVOBt0taClwFfDDHzwEOl7SlpL2AfYGJufzSPtI0M7NhoFUPAxwLXBgRE4DXA9/OLrLzKUGkA/gy8HtgY38SlnSypA5JHStWrGhRds3M7KnSzOPNyyitkIYJOa7qneQ9loi4UdLWwLjsLvtIYyZJvwduA1ZmOr2lSaZ3HnAeQHt7u59JNTMbYppp0cwE9pa0l6TRlJv907vMcxfwGgBJ+wBbAyskbSvpaTn+YGBDRNwaEfcAqyXtn0+bnQD8tDVFMjOzwaTPFk1EbJB0CjADGAWcHxHzJU0DOiJiOvAvwDckfYTyYMBJERGSng7MkLSJ0mI5vpL0+4ELgW2An+fLzAYx/6GYPRkaSjtDe3t7dHR0DHQ2bIjzLwPYSCNpVkS0D9T6/csAZmZWKwcaMzOrlQONmZnVyoHGzMxq5UBjZma1cqAxM7NaOdCYmVmtHGjMzKxWDjRmZlYrBxozM6uVA42ZmdXKgcbMzGrVzP/RmA1Z/fm1YfAvDpvVwYHGhjUHDrOB564zMzOrlQONmZnVyoHGzMxq5UBjZma1cqAxM7NaOdCYmVmtHGjMzKxWDjRmZlYrDaUvtElaASxpcbLjgPtbnGYdhkI+h0IewflsNeezterI5x4RMb7FaTZtSAWaOkjqiIj2gc5HX4ZCPodCHsH5bDXns7WGSj77w11nZmZWKwcaMzOrlQMNnDfQGWjSUMjnUMgjOJ+t5ny21lDJZ9NG/D0aMzOrl1s0ZmZWqwELNJImSrpDUlt+Hpuf95S0t6QrJf1Z0ixJ10l6Vc53kqQVkmZLmi/pB5Km5vBcScsk3ZnTZ0o6IZe7XlJLnuSQ1C7pqzk8RtKvcn0XSrpA0qR8f09lmY1ZvjWSrpC0U5Pr2lPS23qZfpKkc3qY9lxJV0m6XdKfJF0uaRdJUyRd2c38d0q6JcsyW9LLM9+Nup4j6V8kbdFluW9KmpTD/yhpQW6zx+spp02T9FpJkyW9vksah0laktvvZklfzPFTJZ3aRx29rpLntZIW5vDFXeY7UlJIen5+/kKW6wuS3pv5bu+yzKmS/q+yPy3I9Nfm9jy5t7z1kue1leEPSVopaQ9J07PO50l6q6RvSXqssg1vlHRYLnenpHE5fFVf+1RPx0DuQ1dKOi0/j5f0h9wOBzSTdi/r7Fp/jePxBklXS1qscozfKOmoJ7mOno7HdV32zSfsj08i7Tsl/bAy7c0q55+3qfO8tFDSAzl+2x7SbBxncyX9WtIeT6bcdcjj4ISWJxwRA/YCPgacl8P/C5wObA3cBhxeme+FwEk5fBJwTmXaL4BFwBjgvcC1wHNz2g7AiTl8PdBeQxn2B34FjALuBMbl+EOA6yrzrQUuBU4ALgI+keNH9ZH+FODKXqY/oT5y3JZZj7cD/69LWi/sKc1q/qv5rgw/Pcv66a7rqwxfDbyyjzJ13YYvBP4MvKBRJ8D7cngqcGo/tke32znTvAz4bSP/wF+r9d912dyfZgA7VPanBUA78GvgIGAlMPpJ7Ddr8/01uf8+Oz/fA6wHPpef/xN4DBiTn3cB3tLL9upxf+qlbrpuj2OAbzZZDgFbdJeHHurvxFzmr8BnK8vsAXywv/XYTX72B37Vdd/tx/K91d+d+ZqUn9+c+/uV3dThJcA7ekmncZ74NPCNFpT7b7bDYHoN7MphK2Au8GFgfn5+J3BRL8s8vkEpJ9Q/AjPz813As7rZmO3AqnyfCiyknPjXA1fkPFNy+TXAOmA2pcX3j8DdwCPAQ8Dnct4r8/NKYCPlBPFopttBObE9kMN/AjZQvoS1Q+ZlDfAwJah2ADfnfL8H5mT+Z+S8G4G/AB+hBJDfZt4fynyck/XyS+DBzMOtwA+zbBcCXwduAhZnfd9FOWleCdyY618HvCyXeUHW7aZM7/+A64Absiwrc33LMy93Zh43Zrlm5roeBlYAt2QdLsp6Wpev+4HVmZc7yaACTM78/iWXHQu8O+e9L8t5O3BAbqN5WW+rKNv5BZmvv+R6P5br2pjrXZ7DKzO9h3L+Gyknq7uy7I/Qub9dmMvNo+wTbwCWAv+T2/DuXN88yn7yn7kdlmQdzAN+ku8bKfvHo7ncfOCfgMj1bgJeTgkyD+f6D8kyP0TnPvT0rLfHsq7vBn5K2cd/l9vgnqzDRZmvJVnWRZSLn5Mo+96ynLYq8zs76+3R3D4fBt6e49dR9t35lCDxWNbvI8BXux6PXY7h1zS2U37+etbffODLlP37T8C9wB2Uc8RllH21sa3vBxZWTuprcvmVlCB2B5377p9yvlMp+89DOf+DmfY64MeZ9/uzXGuyrr9HXpRlPS7N5W7OuliR67sr8z0F+Flug2OAC7Lu5wJv6ubcdChwVQ6PB35IOXZmAq+ojP9llu+buY3GAXtStvPFle3wr7nsXDovqJ6WeZqTdffWHN/YP+cCZ+e4qfztMTg362dsjr+esh/9MevqgEEdaDLTr6McXAfn5/8C/rmPQNM4CP5COfHNphw064ADmwg084BtKAfpOsqOfzhlx3wWJUhcR7liWUA5AMcDO1NaTJ+k7PSR6V1ZWecNuZ5xudy/5rTHcmcYQznJfKdSngtyeAFwYw6/m3KwvAG4Kneuibm+tZmfF1FOGF/PdB4B3p/LzwDmVk6Ql1Kueo6gHGi/pgTSm4F9c757KQfN7Bw+jhJU/inr69ws46pMbx7w75QdbyblgLoJODvTeR3lwH4B8FlKEH0z5WDYSDkATqKcyA7giTv5XODAHPdLyglo51zXF4HPZLl/levdLZf7Xdb/1yj7yceA0cAHs0xrMx9fzbJdkJ8vyDydTgkmr6KctP4h31+c9fgAnRcq64D3AG3AMyknmxuAlwK/oWz/xvhnUy6M1mZ9PpavF1NOTldXtsFi4FNZvsYFybis46soF2Qfz/W/P7fJypznlZT94JlZpw9X6vR3WXfLKfv7r4CdchusopzEtqGcTC8D9s26XUI5id1O2W+eQzlWfkhpoe9LORYaLa2dKBdUK3s4hj9ECYiNQNOW76Oy3tpzWy8GOnLaGynH3C3AbpSLq/fnfHfReQxW53uIsr9MybqZQ9kfD8z6/TVlv7o36/IfKnVxK+Wi7l46W0gPZ/oLcru9hSe2aFZQzkPrKUHn88CXK+Ue28256cvAyZWA+coc3h1YkMPnAKdXAlPQGWg2AftXLkTOI1s3ma9XAW+i0moCdsx6W0jnA2E7dRNo5tJ5Pp3WKEvW6Rdz+PWN+unttSUD7zDKlcILKSeUJ5D0Y2Bv4LaIODpHXxYRp0gS5eR3N2Un+iFwWaOvuRdLKCeERvkPA35O2ZEaQeQSykG7hLJTHQn8CPgu5QQK5cTUODC62p9yIp0q6bhc1+5Z1i2Al0qaTdmZx0q6lXIA/zmXf4xyIniIsjPdSjnYDwV+HxErgBWSllIObCg72DdyeAHwikp+roiIkHQL5aS0JiI2SfozcK6k7SgnzCURMVnlvtAnKAf/jRHxiKRzKd0hj1F29EeA4ykntW1z/CbguVmX63NdX6Zsw52AL2SaUK7EllNOpqsbGZW0I2XH/7WkV1Oudl9H2UcmUwLuFln3e2Y9XSjp8kp5b6QEgbFZb8dTTqLb5HZ4bqbxW8qB+CzK9nwXpXvqkszvtZQT+6RM975M62w6g93OlJb49pnu84BvU4LrxZQT5t0RsUFSB3AG5WSxIadfksNVX6VcmDTsD+yT5V9N2dajs/xkWa7P8irLOI8SxI+UNC/X8SNK4H931mdjvfOBdbmdZ1G26SspV7InUPbDRZRW1nTKtn4h5YR+RabxKkmrKd3Z29G8H0naL4cfplxsjgV2BXaXdDSd+8cNlIA/irKP/zXL+OJu5tsn0xiV9bdn5usrlP11W2C/nK6IuFHS84BrgOdFxDpJSyj7DJT9+dOU1tbplPNN1WXADygtp7uAtwGvbkyMiJWVea9TuT+9lnKxBvBaYFI5rQGwQx6XrwSOyjSullRNZ0lE3JTDh+Tr5vy8HeW4+y3wRUmfowTk30rakhJcv6Vyv/YJ92yrx2COugj4fmWWH+X7LDr3wR4N6FNnkiYDB1N2go9I2pWyw/9dY56IOIpytdDWdfkoIfUKStPtKkqzdxrlxLGBzvJtXVlsJ8qVy2si4sWUq7etKCeZP1F2lDcCH6WcDL5OucKfSKnUp1XSWkc5sXZbPErwWkHplw5KK6TRgvtOREymtADOBF6W+a/mdX1leCP0eWEQleFbu8zfSGsT5STRMBmYHxEvpJxExwBExCWUq16A6ZIOolzRrqbzJPMMSkvhZuDBiBgD/IFy1f2pXPYWSvAeTannT+e0/wW+Bbwky7xvH2WDcoK5HXhrpjOacn/ovbmOiZSgv2Pm/77M79VZzi0p2yUoJ7RN+Xk+pfvrPkpr4wHKtl1GOSl/h87tUt3eqyhXhR+inCyuoHRRbJ3zXUJpLU3IPEAJQDfk8P2Uk8H7KRczj4uIVZSLmsYNZVG6L8+OiG0iYmvKyezzub6tKRdMX6BchW+dJ7ZvUy5e3ksJgOspreRG62Um5Tip7jt087mRhz9QrmJvj4jnRcTUXM9DlF6A91Lu76wG1kp6VjfpzKcEZSTtlfXzIkr9L820XkIJ4KIcj5+j3INobOudKPvAjsD7KBdwb8z6IOd7lLI/f5vSwroZWJ/H3e8p+98CyjH6+Nk9l2vYROd55K+U7sN5lJZ5b/vsFY0y9uDVlAug2VkOcj37R8TkfO0WEWt7SiA9VBkWcFZl+edExLci4jbKOfUW4DOSPhURGyhBtnG+u/pvUu5d43zSzHlpQJ86E+Uk/uGIuItygJxNOThfIenwyuzdPr2RDqfzB+jOogSI5ZTm6SvyCYo3VeYfQznA1qk8fbQXpYtqG+DvKTveRyldHb+jdM+9gHKFuYJylTKnh7ysofNq/SZKi2IG5WpgI+Uqc04OfzivKnaiHEBrcnyjrFvmaw1P3GF/Drxc0s75NM1ulFYDlBPMMTkcwAZJb2gsqPLk3nO75HkrSp8zVK5C8wSxOPN0O+VE+DFKkPkvysG2gXKQbwKWSfoQZZ/6A/ABSl1vm8tfmNPGZRrPzouD72U5/41yYmnU4xaSDsjP+1K6ObannARGUbr1Gnl9dkT8ISI+ldN3yfxvoLTw/kw5oB8AHomIiZSW6ibKNmp0Q+5KCTAPZJ7OyuUb++KWlBNbwxhKwF1NaU28mnISFqX/fhZlHx8NTJY0ihLILst8rsjPW2W9kHXTOC4/T9mOYzKtZwLHSXqnpKflchNyfY37SUsp+9nofJrpMDqD/fY570RKsFuZ5dmaso+PkbQN5aR0D+VK+MhcZlvKMfH8xnaS1KbypNw4gIj4Ya6ncaF4FqW1vEPOv10ej9dmGd9ECQAP0XlhuDclgG9LaV1uQbk3+SzK1f4+lOA+Nuv9uZSLvxU537NzXc/OOrkzy3kPpYWzIferNsrx/utcfpOkl2W+96Z7W1K6s87KOn1Xbq/uAsorKfv9BxojJI2tzpAn+w8DJ2Tr5heUC7fG/JNz8AZKNx2SDsmyd2cG8E/ZCkLSbpKeLumZlPt836GcZ/8u59kxj8GPUAJ7NW9/BVZWjsHjKXX15PTVt1bXCziZ0gXW+DyK0qI4kLIzX0U50d1I2QCvjb+9RzOXEgz+SOdNrcZNz8WUk8ZiSgBbRec9mkWUg/LhXOdJQKPZvZoSrRsHw48oV46NG9fVhwHWUnmCi7KTPExnv/JBlBNyUHb6w3P8csrJ/a7M372Uq61zM19zKQfIxZSTybWZr3P424cBrqDzYYCLct65lOb/qylXKqsp3YuXZh3cUcnzzzI/N2cd3ZXjT6NceTa6eNZl3v6SdX8/5T7BZ7MellBO0GuzTL+g8yb1fXTeqF1COQA3ZHoPULq+3pj5+EtuywvpfBhgHuXgel8uM49yD+Z7uY4fUbb7PMqJtj3z/2iOf4ByBXoQJXDOpbRWNmSdNB5aeJQSlE7L6esrr6WZzlI679HcQQmQF1Juis6jXPUuzW3yx1zXXVn/83LZWyj7w31Zl3dm/R5OaRWsz/Hb5HJB2WfvyPnWZz08Rmkh35mfb6M8bHBnLvenrPO76DxeXkY5Zm7N8p5G58MAS3Pb/JjOByA+yhMfBnhrLruOEvz2p5ykNuY6ZgOH5bKiXJwszLLfDLw9p/2ecmK8g3I8P5z1MoPOhyMaD2zcQukV+HzW+5qs519QLg4WUPbxWyjB7UrKPtE47h7Osv0kl208+NE41lZRjsHGtpoD3JD5vJHO+6aNh1YaDycsp/OYuyvztijnu4rSpXQRnQ+qHB1d7tHk569RWsTjMt25Wcf/k9OfTjme51EufO6hXHzsCczrcl7956yHWzLvz6bsI3Nz28ykHB+70rl/3kLn07lT6f5hgJ/wxIcBGvfXxgF39nW+H3G/DCBpKuWxx7O7jJ9CqeA3DkC2zFpG0nYRsTa/x/Ebys3mPw10vgazRp3l8GnArhHxzwOcLaB8NwjYmPf4/gH4enb/DRmD4WEAM2ut87JbdWvKVwUcZPr2BkmnU86JSyitvMFid+BylS9KP0p5kGNIGXEtGjMze2oN6FNnZmY2/DnQmJlZrRxozMysVg40ZmZWKwcaMzOrlQONmZnV6v8DzUrGNzbhLj8AAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"XGBC = XGBClassifier()\nXGBC.fit(X_train, y_train)\npred_XGBC = XGBC.predict(X_test)\naccuracy_XGBC = round(accuracy_score(pred_XGBC, y_test) * 100, 2)\nprint('Accuracy score',accuracy_XGBC)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:26:19.883718Z","iopub.execute_input":"2022-06-27T21:26:19.884619Z","iopub.status.idle":"2022-06-27T21:26:24.192855Z","shell.execute_reply.started":"2022-06-27T21:26:19.884581Z","shell.execute_reply":"2022-06-27T21:26:24.191831Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Accuracy score 92.08\n","output_type":"stream"}]},{"cell_type":"code","source":"KNN = KNeighborsClassifier(n_neighbors = 10)\nKNN.fit(X_train, y_train)\npred_KNN = KNN.predict(X_test)\naccuracy_KNN = round(accuracy_score(pred_KNN, y_test) * 100, 2)\nprint('Accuracy score',accuracy_KNN)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:26:24.198238Z","iopub.execute_input":"2022-06-27T21:26:24.199088Z","iopub.status.idle":"2022-06-27T21:26:24.557675Z","shell.execute_reply.started":"2022-06-27T21:26:24.199051Z","shell.execute_reply":"2022-06-27T21:26:24.556642Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Accuracy score 90.6\n","output_type":"stream"}]},{"cell_type":"code","source":"ada_classifier = AdaBoostClassifier(n_estimators=25)\nada_classifier.fit(X_train, y_train)\npred_ada = ada_classifier.predict(X_test)\n\n# Cross-validation\nscores = cross_val_score(ada_classifier,X_test,y_test, cv=3)\nprint('Accuracy score',round(scores.mean() * 100,2))","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:26:24.559440Z","iopub.execute_input":"2022-06-27T21:26:24.561058Z","iopub.status.idle":"2022-06-27T21:26:26.260696Z","shell.execute_reply.started":"2022-06-27T21:26:24.561007Z","shell.execute_reply":"2022-06-27T21:26:26.259410Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Accuracy score 91.37\n","output_type":"stream"}]},{"cell_type":"code","source":"svm_rbf = SVC(kernel= 'rbf')\nsvm_rbf.fit(X_train, y_train)\npred_svm_rbf = svm_rbf.predict(X_test)\n\n# Cross-validation\nscores = cross_val_score(svm_rbf,X_test,y_test, cv=10)\nprint('Accuracy score',round(scores.mean() * 100,2))","metadata":{"execution":{"iopub.status.busy":"2022-06-27T21:26:26.262690Z","iopub.execute_input":"2022-06-27T21:26:26.263086Z","iopub.status.idle":"2022-06-27T21:27:25.442166Z","shell.execute_reply.started":"2022-06-27T21:26:26.263046Z","shell.execute_reply":"2022-06-27T21:27:25.441142Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Accuracy score 90.62\n","output_type":"stream"}]}]}