classification-of-hand-postures-using-ml-models.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"#pip install numpy pandas matplotlib seaborn scikit-learn tensorflow","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:48.540969Z","iopub.execute_input":"2023-06-20T14:01:48.541427Z","iopub.status.idle":"2023-06-20T14:01:48.567995Z","shell.execute_reply.started":"2023-06-20T14:01:48.541394Z","shell.execute_reply":"2023-06-20T14:01:48.567128Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"# **importing some libraries required for classification**","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n","metadata":{"execution":{"iopub.status.busy":"2023-06-20T17:40:59.974768Z","iopub.execute_input":"2023-06-20T17:40:59.975163Z","iopub.status.idle":"2023-06-20T17:41:01.168696Z","shell.execute_reply.started":"2023-06-20T17:40:59.975135Z","shell.execute_reply":"2023-06-20T17:41:01.167557Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"# Preprocessing","metadata":{}},{"cell_type":"code","source":"data=pd.read_csv('/kaggle/input/mocap-allusers/allUsers.lcl.csv')\n","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:49.640329Z","iopub.execute_input":"2023-06-20T14:01:49.640640Z","iopub.status.idle":"2023-06-20T14:01:51.248737Z","shell.execute_reply.started":"2023-06-20T14:01:49.640613Z","shell.execute_reply":"2023-06-20T14:01:51.247497Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"data.describe()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:51.251665Z","iopub.execute_input":"2023-06-20T14:01:51.252011Z","iopub.status.idle":"2023-06-20T14:01:51.352384Z","shell.execute_reply.started":"2023-06-20T14:01:51.251979Z","shell.execute_reply":"2023-06-20T14:01:51.351308Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"              Class          User            X0            Y0            Z0  \\\ncount  78096.000000  78096.000000  78096.000000  78096.000000  78096.000000   \nmean       2.983738      7.959127     50.345664     85.812051    -29.984712   \nstd        1.421183      4.697810     32.696173     40.204363     34.361918   \nmin        0.000000      0.000000   -108.552738    -98.233756   -126.770872   \n25%        2.000000      5.000000     29.295062     63.494432    -56.356438   \n50%        3.000000      9.000000     54.619964     86.526246    -30.864125   \n75%        4.000000     12.000000     72.488686    113.107355     -1.418803   \nmax        5.000000     14.000000    190.017835    169.175464    113.345119   \n\n                 X1            Y1            Z1            X2            Y2  \\\ncount  78096.000000  78096.000000  78096.000000  78096.000000  78096.000000   \nmean      49.595209     86.192647    -29.509202     48.612121     83.771315   \nstd       32.478238     40.453214     34.764398     33.605390     41.023543   \nmin     -111.685241    -96.142589   -166.006838   -106.886524   -100.789312   \n25%       28.755137     64.154529    -57.360107     25.170006     58.052385   \n50%       54.215514     87.542751    -30.184005     53.814580     86.458324   \n75%       71.762039    116.219398     -0.366692     71.561951    106.660827   \nmax      188.691997    170.209350    104.697852    188.760168    168.186466   \n\n                 Z2  \ncount  78096.000000  \nmean     -30.560515  \nstd       35.120329  \nmin     -129.595296  \n25%      -58.654059  \n50%      -32.352414  \n75%       -0.944786  \nmax      104.590879  ","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>Class</th>\n      <th>User</th>\n      <th>X0</th>\n      <th>Y0</th>\n      <th>Z0</th>\n      <th>X1</th>\n      <th>Y1</th>\n      <th>Z1</th>\n      <th>X2</th>\n      <th>Y2</th>\n      <th>Z2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>78096.000000</td>\n      <td>78096.000000</td>\n      <td>78096.000000</td>\n      <td>78096.000000</td>\n      <td>78096.000000</td>\n      <td>78096.000000</td>\n      <td>78096.000000</td>\n      <td>78096.000000</td>\n      <td>78096.000000</td>\n      <td>78096.000000</td>\n      <td>78096.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2.983738</td>\n      <td>7.959127</td>\n      <td>50.345664</td>\n      <td>85.812051</td>\n      <td>-29.984712</td>\n      <td>49.595209</td>\n      <td>86.192647</td>\n      <td>-29.509202</td>\n      <td>48.612121</td>\n      <td>83.771315</td>\n      <td>-30.560515</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.421183</td>\n      <td>4.697810</td>\n      <td>32.696173</td>\n      <td>40.204363</td>\n      <td>34.361918</td>\n      <td>32.478238</td>\n      <td>40.453214</td>\n      <td>34.764398</td>\n      <td>33.605390</td>\n      <td>41.023543</td>\n      <td>35.120329</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-108.552738</td>\n      <td>-98.233756</td>\n      <td>-126.770872</td>\n      <td>-111.685241</td>\n      <td>-96.142589</td>\n      <td>-166.006838</td>\n      <td>-106.886524</td>\n      <td>-100.789312</td>\n      <td>-129.595296</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2.000000</td>\n      <td>5.000000</td>\n      <td>29.295062</td>\n      <td>63.494432</td>\n      <td>-56.356438</td>\n      <td>28.755137</td>\n      <td>64.154529</td>\n      <td>-57.360107</td>\n      <td>25.170006</td>\n      <td>58.052385</td>\n      <td>-58.654059</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>3.000000</td>\n      <td>9.000000</td>\n      <td>54.619964</td>\n      <td>86.526246</td>\n      <td>-30.864125</td>\n      <td>54.215514</td>\n      <td>87.542751</td>\n      <td>-30.184005</td>\n      <td>53.814580</td>\n      <td>86.458324</td>\n      <td>-32.352414</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>4.000000</td>\n      <td>12.000000</td>\n      <td>72.488686</td>\n      <td>113.107355</td>\n      <td>-1.418803</td>\n      <td>71.762039</td>\n      <td>116.219398</td>\n      <td>-0.366692</td>\n      <td>71.561951</td>\n      <td>106.660827</td>\n      <td>-0.944786</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>5.000000</td>\n      <td>14.000000</td>\n      <td>190.017835</td>\n      <td>169.175464</td>\n      <td>113.345119</td>\n      <td>188.691997</td>\n      <td>170.209350</td>\n      <td>104.697852</td>\n      <td>188.760168</td>\n      <td>168.186466</td>\n      <td>104.590879</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#dfs.to_excel(\"dataa.xlsx\")","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:51.354211Z","iopub.execute_input":"2023-06-20T14:01:51.354642Z","iopub.status.idle":"2023-06-20T14:01:51.359134Z","shell.execute_reply.started":"2023-06-20T14:01:51.354605Z","shell.execute_reply":"2023-06-20T14:01:51.358077Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"data.columns = list(range(len(data.columns)))\ncol=data.columns\ncol","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:51.360484Z","iopub.execute_input":"2023-06-20T14:01:51.360894Z","iopub.status.idle":"2023-06-20T14:01:51.376519Z","shell.execute_reply.started":"2023-06-20T14:01:51.360835Z","shell.execute_reply":"2023-06-20T14:01:51.375555Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n            34, 35, 36, 37],\n           dtype='int64')"},"metadata":{}}]},{"cell_type":"code","source":"columns_to_drop = [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37]  \ndata.drop(columns=columns_to_drop, inplace=True)\ndata","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:51.377876Z","iopub.execute_input":"2023-06-20T14:01:51.378195Z","iopub.status.idle":"2023-06-20T14:01:51.520220Z","shell.execute_reply.started":"2023-06-20T14:01:51.378169Z","shell.execute_reply":"2023-06-20T14:01:51.519063Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"       0   1          2           3          4          5           6   \\\n0       0   0   0.000000    0.000000   0.000000   0.000000    0.000000   \n1       1   0  54.263880   71.466776 -64.807709  76.895635   42.462500   \n2       1   0  56.527558   72.266609 -61.935252  39.135978   82.538530   \n3       1   0  55.849928   72.469064 -62.562788  37.988804   82.631347   \n4       1   0  55.329647   71.707275 -63.688956  36.561863   81.868749   \n...    ..  ..        ...         ...        ...        ...         ...   \n78091   5  14  54.251127  129.177414 -44.252511  27.720784  107.810661   \n78092   5  14  54.334883  129.253842 -44.016320  27.767911  107.914808   \n78093   5  14  54.151540  129.269502 -44.173273  27.725978  108.034006   \n78094   5  14  27.915311  108.007390  10.814957  -0.910435  122.464093   \n78095   5  14  27.898705  108.092877  11.107857 -30.031402   77.740235   \n\n              7          8           9   ...                 13  \\\n0       0.000000   0.000000    0.000000  ...                  0   \n1     -72.780545  36.621229   81.680557  ...   -73.684130041833   \n2     -49.596509  79.223743   43.254091  ...   -70.703990925959   \n3     -50.606259  78.451526   43.567403  ...  -71.1383441365739   \n4     -52.752784  86.320630   68.214645  ...  -68.9564252307431   \n...          ...        ...         ...  ...                ...   \n78091  11.099282  -1.270139  122.758679  ...   4.07501825540343   \n78092  11.069842 -30.334054   77.858214  ...  -53.0072437045207   \n78093  11.020347 -22.574718  104.222208  ...  -17.3548436885736   \n78094 -47.271248 -30.084588   77.705861  ...  -34.6757739677746   \n78095 -17.453099  -1.091566  122.827638  ...  -43.6796644772844   \n\n                      14                15                 16  \\\n0                      0                 0                  0   \n1       59.1885757027887  10.6789364098231  -71.2977813147725   \n2       61.5874515532753  11.7799190329758   -68.827417756239   \n3       61.6864271910576  11.7934398850428    -68.88931646056   \n4       77.3872254123912  42.7178334810919  -72.0151462991019   \n...                  ...               ...                ...   \n78091   26.0308784490985  136.368236497962  -32.7923925965895   \n78092   26.4469428817139   135.82349439399  -33.8514248055691   \n78093   26.5066400161928  135.605902859879  -34.4414628755671   \n78094  -22.3294952272238  104.026431451371  -54.3346226872107   \n78095  -1.55884578727193  98.0172007250434   3.42181376960772   \n\n                      17                18                 19  \\\n0                      0                 0                  0   \n1                      ?                 ?                  ?   \n2                      ?                 ?                  ?   \n3                      ?                 ?                  ?   \n4                      ?                 ?                  ?   \n...                  ...               ...                ...   \n78091   77.2937098066631  34.0708569530435  -43.2637900001125   \n78092  -1.09067420384509  122.724914373665  -46.5583729086904   \n78093   61.4753314934401  101.321181459117  -6.61698844268879   \n78094   61.6393864623392  101.224661857702   -6.8409418109905   \n78095   26.5869101964665  135.762921713685  -34.4459435415338   \n\n                      20                21                 22  \n0                      0                 0                  0  \n1                      ?                 ?                  ?  \n2                      ?                 ?                  ?  \n3                      ?                 ?                  ?  \n4                      ?                 ?                  ?  \n...                  ...               ...                ...  \n78091  -30.2822013321426  77.6223061447627    -17.15029172919  \n78092  -1.95850220442059  98.2889311555066   4.24833369793896  \n78093  -1.12573832398984    122.6140268058  -46.7960541509443  \n78094   54.0099616832869  129.477879104126  -43.9678201146984  \n78095  -22.3160291864615  104.124677433799  -54.3440786805432  \n\n[78096 rows x 23 columns]","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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>54.263880</td>\n      <td>71.466776</td>\n      <td>-64.807709</td>\n      <td>76.895635</td>\n      <td>42.462500</td>\n      <td>-72.780545</td>\n      <td>36.621229</td>\n      <td>81.680557</td>\n      <td>...</td>\n      <td>-73.684130041833</td>\n      <td>59.1885757027887</td>\n      <td>10.6789364098231</td>\n      <td>-71.2977813147725</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>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>56.527558</td>\n      <td>72.266609</td>\n      <td>-61.935252</td>\n      <td>39.135978</td>\n      <td>82.538530</td>\n      <td>-49.596509</td>\n      <td>79.223743</td>\n      <td>43.254091</td>\n      <td>...</td>\n      <td>-70.703990925959</td>\n      <td>61.5874515532753</td>\n      <td>11.7799190329758</td>\n      <td>-68.827417756239</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>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>55.849928</td>\n      <td>72.469064</td>\n      <td>-62.562788</td>\n      <td>37.988804</td>\n      <td>82.631347</td>\n      <td>-50.606259</td>\n      <td>78.451526</td>\n      <td>43.567403</td>\n      <td>...</td>\n      <td>-71.1383441365739</td>\n      <td>61.6864271910576</td>\n      <td>11.7934398850428</td>\n      <td>-68.88931646056</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>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>55.329647</td>\n      <td>71.707275</td>\n      <td>-63.688956</td>\n      <td>36.561863</td>\n      <td>81.868749</td>\n      <td>-52.752784</td>\n      <td>86.320630</td>\n      <td>68.214645</td>\n      <td>...</td>\n      <td>-68.9564252307431</td>\n      <td>77.3872254123912</td>\n      <td>42.7178334810919</td>\n      <td>-72.0151462991019</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>...</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      <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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>78091</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.251127</td>\n      <td>129.177414</td>\n      <td>-44.252511</td>\n      <td>27.720784</td>\n      <td>107.810661</td>\n      <td>11.099282</td>\n      <td>-1.270139</td>\n      <td>122.758679</td>\n      <td>...</td>\n      <td>4.07501825540343</td>\n      <td>26.0308784490985</td>\n      <td>136.368236497962</td>\n      <td>-32.7923925965895</td>\n      <td>77.2937098066631</td>\n      <td>34.0708569530435</td>\n      <td>-43.2637900001125</td>\n      <td>-30.2822013321426</td>\n      <td>77.6223061447627</td>\n      <td>-17.15029172919</td>\n    </tr>\n    <tr>\n      <th>78092</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.334883</td>\n      <td>129.253842</td>\n      <td>-44.016320</td>\n      <td>27.767911</td>\n      <td>107.914808</td>\n      <td>11.069842</td>\n      <td>-30.334054</td>\n      <td>77.858214</td>\n      <td>...</td>\n      <td>-53.0072437045207</td>\n      <td>26.4469428817139</td>\n      <td>135.82349439399</td>\n      <td>-33.8514248055691</td>\n      <td>-1.09067420384509</td>\n      <td>122.724914373665</td>\n      <td>-46.5583729086904</td>\n      <td>-1.95850220442059</td>\n      <td>98.2889311555066</td>\n      <td>4.24833369793896</td>\n    </tr>\n    <tr>\n      <th>78093</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.151540</td>\n      <td>129.269502</td>\n      <td>-44.173273</td>\n      <td>27.725978</td>\n      <td>108.034006</td>\n      <td>11.020347</td>\n      <td>-22.574718</td>\n      <td>104.222208</td>\n      <td>...</td>\n      <td>-17.3548436885736</td>\n      <td>26.5066400161928</td>\n      <td>135.605902859879</td>\n      <td>-34.4414628755671</td>\n      <td>61.4753314934401</td>\n      <td>101.321181459117</td>\n      <td>-6.61698844268879</td>\n      <td>-1.12573832398984</td>\n      <td>122.6140268058</td>\n      <td>-46.7960541509443</td>\n    </tr>\n    <tr>\n      <th>78094</th>\n      <td>5</td>\n      <td>14</td>\n      <td>27.915311</td>\n      <td>108.007390</td>\n      <td>10.814957</td>\n      <td>-0.910435</td>\n      <td>122.464093</td>\n      <td>-47.271248</td>\n      <td>-30.084588</td>\n      <td>77.705861</td>\n      <td>...</td>\n      <td>-34.6757739677746</td>\n      <td>-22.3294952272238</td>\n      <td>104.026431451371</td>\n      <td>-54.3346226872107</td>\n      <td>61.6393864623392</td>\n      <td>101.224661857702</td>\n      <td>-6.8409418109905</td>\n      <td>54.0099616832869</td>\n      <td>129.477879104126</td>\n      <td>-43.9678201146984</td>\n    </tr>\n    <tr>\n      <th>78095</th>\n      <td>5</td>\n      <td>14</td>\n      <td>27.898705</td>\n      <td>108.092877</td>\n      <td>11.107857</td>\n      <td>-30.031402</td>\n      <td>77.740235</td>\n      <td>-17.453099</td>\n      <td>-1.091566</td>\n      <td>122.827638</td>\n      <td>...</td>\n      <td>-43.6796644772844</td>\n      <td>-1.55884578727193</td>\n      <td>98.0172007250434</td>\n      <td>3.42181376960772</td>\n      <td>26.5869101964665</td>\n      <td>135.762921713685</td>\n      <td>-34.4459435415338</td>\n      <td>-22.3160291864615</td>\n      <td>104.124677433799</td>\n      <td>-54.3440786805432</td>\n    </tr>\n  </tbody>\n</table>\n<p>78096 rows × 23 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.columns = list(range(len(data.columns)))\ncol=data.columns\ncol","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:51.521684Z","iopub.execute_input":"2023-06-20T14:01:51.522156Z","iopub.status.idle":"2023-06-20T14:01:51.531964Z","shell.execute_reply.started":"2023-06-20T14:01:51.522098Z","shell.execute_reply":"2023-06-20T14:01:51.530990Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n            17, 18, 19, 20, 21, 22],\n           dtype='int64')"},"metadata":{}}]},{"cell_type":"code","source":"data.drop(data[data[ 0 ] == 0].index, inplace=True)\ndata.drop(data[data[ 0 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 1 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 2 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 3 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 4 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 5 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 6 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 7 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 8 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 9 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 10 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 11 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 12 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 13 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 14 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 15 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 16 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 17 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 18 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 19 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 20 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 21 ] == '?'].index, inplace=True)\ndata.drop(data[data[ 22 ] == '?'].index, inplace=True)\n\n\ndata","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:51.538096Z","iopub.execute_input":"2023-06-20T14:01:51.538421Z","iopub.status.idle":"2023-06-20T14:01:52.291412Z","shell.execute_reply.started":"2023-06-20T14:01:51.538395Z","shell.execute_reply":"2023-06-20T14:01:52.290257Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"       0   1          2           3          4          5           6   \\\n1654    1   0 -11.632335   81.551968 -28.007209  73.564324   51.161458   \n1655    1   0  72.692270   50.023771 -72.963253 -11.593076   81.234142   \n1656    1   0  85.648015   73.561022 -71.838316  31.595034   90.217896   \n1657    1   0  52.895241   82.454113 -55.683891  32.285729   90.277753   \n1659    1   0  51.874242   82.509948 -56.138338  -9.177149   81.411469   \n...    ..  ..        ...         ...        ...        ...         ...   \n78091   5  14  54.251127  129.177414 -44.252511  27.720784  107.810661   \n78092   5  14  54.334883  129.253842 -44.016320  27.767911  107.914808   \n78093   5  14  54.151540  129.269502 -44.173273  27.725978  108.034006   \n78094   5  14  27.915311  108.007390  10.814957  -0.910435  122.464093   \n78095   5  14  27.898705  108.092877  11.107857 -30.031402   77.740235   \n\n              7          8           9   ...                 13  \\\n1654  -70.505689  86.213513   75.013944  ...  -56.2154522902372   \n1655  -28.450210  51.729995   82.844469  ...  -69.7342981118982   \n1656  -47.246688  52.230858   82.361548  ...  -70.1118284111086   \n1657  -46.444718  89.077013   74.655391  ...   -22.302190491041   \n1659  -25.398447  76.251197   50.602365  ...  -46.3213660327158   \n...          ...        ...         ...  ...                ...   \n78091  11.099282  -1.270139  122.758679  ...   4.07501825540343   \n78092  11.069842 -30.334054   77.858214  ...  -53.0072437045207   \n78093  11.020347 -22.574718  104.222208  ...  -17.3548436885736   \n78094 -47.271248 -30.084588   77.705861  ...  -34.6757739677746   \n78095 -17.453099  -1.091566  122.827638  ...  -43.6796644772844   \n\n                      14                15                 16  \\\n1654    32.2984892072433  91.5360396435441  -44.0743871801424   \n1655     30.760697382696  90.4491696415814  -47.9095106182335   \n1656   -8.90628847947269  81.5167927599509  -25.2439142696141   \n1657     5.7165380800747  24.4084028559536   7.77220505394253   \n1659    5.99293400228724  24.4486547593206   8.07013900151556   \n...                  ...               ...                ...   \n78091   26.0308784490985  136.368236497962  -32.7923925965895   \n78092   26.4469428817139   135.82349439399  -33.8514248055691   \n78093   26.5066400161928  135.605902859879  -34.4414628755671   \n78094  -22.3294952272238  104.026431451371  -54.3346226872107   \n78095  -1.55884578727193  98.0172007250434   3.42181376960772   \n\n                      17                18                 19  \\\n1654    9.92719635250188  89.3756053345133  -31.6501312425115   \n1655    10.0284391929149  88.9679786863632  -32.0451200624497   \n1656    10.2775163462491  88.6079577405045  -32.0375389006328   \n1657    76.3888808023934  50.6150565868792   -67.772758579423   \n1659    58.9863532908359  20.0609402463142  -65.2250536748152   \n...                  ...               ...                ...   \n78091   77.2937098066631  34.0708569530435  -43.2637900001125   \n78092  -1.09067420384509  122.724914373665  -46.5583729086904   \n78093   61.4753314934401  101.321181459117  -6.61698844268879   \n78094   61.6393864623392  101.224661857702   -6.8409418109905   \n78095   26.5869101964665  135.762921713685  -34.4459435415338   \n\n                      20                21                 22  \n1654    7.34923543195405  24.9862732677858   10.2428080013854  \n1655    7.73251054087557   25.135414843299   10.7233952361419  \n1656    56.3088290880038  18.7747679142722  -70.9965231015097  \n1657    56.5881764930507  18.5380486853742  -70.8212291698613  \n1659    87.3569292461206  73.7462524221128  -68.2950376409761  \n...                  ...               ...                ...  \n78091  -30.2822013321426  77.6223061447627    -17.15029172919  \n78092  -1.95850220442059  98.2889311555066   4.24833369793896  \n78093  -1.12573832398984    122.6140268058  -46.7960541509443  \n78094   54.0099616832869  129.477879104126  -43.9678201146984  \n78095  -22.3160291864615  104.124677433799  -54.3440786805432  \n\n[52247 rows x 23 columns]","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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1654</th>\n      <td>1</td>\n      <td>0</td>\n      <td>-11.632335</td>\n      <td>81.551968</td>\n      <td>-28.007209</td>\n      <td>73.564324</td>\n      <td>51.161458</td>\n      <td>-70.505689</td>\n      <td>86.213513</td>\n      <td>75.013944</td>\n      <td>...</td>\n      <td>-56.2154522902372</td>\n      <td>32.2984892072433</td>\n      <td>91.5360396435441</td>\n      <td>-44.0743871801424</td>\n      <td>9.92719635250188</td>\n      <td>89.3756053345133</td>\n      <td>-31.6501312425115</td>\n      <td>7.34923543195405</td>\n      <td>24.9862732677858</td>\n      <td>10.2428080013854</td>\n    </tr>\n    <tr>\n      <th>1655</th>\n      <td>1</td>\n      <td>0</td>\n      <td>72.692270</td>\n      <td>50.023771</td>\n      <td>-72.963253</td>\n      <td>-11.593076</td>\n      <td>81.234142</td>\n      <td>-28.450210</td>\n      <td>51.729995</td>\n      <td>82.844469</td>\n      <td>...</td>\n      <td>-69.7342981118982</td>\n      <td>30.760697382696</td>\n      <td>90.4491696415814</td>\n      <td>-47.9095106182335</td>\n      <td>10.0284391929149</td>\n      <td>88.9679786863632</td>\n      <td>-32.0451200624497</td>\n      <td>7.73251054087557</td>\n      <td>25.135414843299</td>\n      <td>10.7233952361419</td>\n    </tr>\n    <tr>\n      <th>1656</th>\n      <td>1</td>\n      <td>0</td>\n      <td>85.648015</td>\n      <td>73.561022</td>\n      <td>-71.838316</td>\n      <td>31.595034</td>\n      <td>90.217896</td>\n      <td>-47.246688</td>\n      <td>52.230858</td>\n      <td>82.361548</td>\n      <td>...</td>\n      <td>-70.1118284111086</td>\n      <td>-8.90628847947269</td>\n      <td>81.5167927599509</td>\n      <td>-25.2439142696141</td>\n      <td>10.2775163462491</td>\n      <td>88.6079577405045</td>\n      <td>-32.0375389006328</td>\n      <td>56.3088290880038</td>\n      <td>18.7747679142722</td>\n      <td>-70.9965231015097</td>\n    </tr>\n    <tr>\n      <th>1657</th>\n      <td>1</td>\n      <td>0</td>\n      <td>52.895241</td>\n      <td>82.454113</td>\n      <td>-55.683891</td>\n      <td>32.285729</td>\n      <td>90.277753</td>\n      <td>-46.444718</td>\n      <td>89.077013</td>\n      <td>74.655391</td>\n      <td>...</td>\n      <td>-22.302190491041</td>\n      <td>5.7165380800747</td>\n      <td>24.4084028559536</td>\n      <td>7.77220505394253</td>\n      <td>76.3888808023934</td>\n      <td>50.6150565868792</td>\n      <td>-67.772758579423</td>\n      <td>56.5881764930507</td>\n      <td>18.5380486853742</td>\n      <td>-70.8212291698613</td>\n    </tr>\n    <tr>\n      <th>1659</th>\n      <td>1</td>\n      <td>0</td>\n      <td>51.874242</td>\n      <td>82.509948</td>\n      <td>-56.138338</td>\n      <td>-9.177149</td>\n      <td>81.411469</td>\n      <td>-25.398447</td>\n      <td>76.251197</td>\n      <td>50.602365</td>\n      <td>...</td>\n      <td>-46.3213660327158</td>\n      <td>5.99293400228724</td>\n      <td>24.4486547593206</td>\n      <td>8.07013900151556</td>\n      <td>58.9863532908359</td>\n      <td>20.0609402463142</td>\n      <td>-65.2250536748152</td>\n      <td>87.3569292461206</td>\n      <td>73.7462524221128</td>\n      <td>-68.2950376409761</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      <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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>78091</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.251127</td>\n      <td>129.177414</td>\n      <td>-44.252511</td>\n      <td>27.720784</td>\n      <td>107.810661</td>\n      <td>11.099282</td>\n      <td>-1.270139</td>\n      <td>122.758679</td>\n      <td>...</td>\n      <td>4.07501825540343</td>\n      <td>26.0308784490985</td>\n      <td>136.368236497962</td>\n      <td>-32.7923925965895</td>\n      <td>77.2937098066631</td>\n      <td>34.0708569530435</td>\n      <td>-43.2637900001125</td>\n      <td>-30.2822013321426</td>\n      <td>77.6223061447627</td>\n      <td>-17.15029172919</td>\n    </tr>\n    <tr>\n      <th>78092</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.334883</td>\n      <td>129.253842</td>\n      <td>-44.016320</td>\n      <td>27.767911</td>\n      <td>107.914808</td>\n      <td>11.069842</td>\n      <td>-30.334054</td>\n      <td>77.858214</td>\n      <td>...</td>\n      <td>-53.0072437045207</td>\n      <td>26.4469428817139</td>\n      <td>135.82349439399</td>\n      <td>-33.8514248055691</td>\n      <td>-1.09067420384509</td>\n      <td>122.724914373665</td>\n      <td>-46.5583729086904</td>\n      <td>-1.95850220442059</td>\n      <td>98.2889311555066</td>\n      <td>4.24833369793896</td>\n    </tr>\n    <tr>\n      <th>78093</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.151540</td>\n      <td>129.269502</td>\n      <td>-44.173273</td>\n      <td>27.725978</td>\n      <td>108.034006</td>\n      <td>11.020347</td>\n      <td>-22.574718</td>\n      <td>104.222208</td>\n      <td>...</td>\n      <td>-17.3548436885736</td>\n      <td>26.5066400161928</td>\n      <td>135.605902859879</td>\n      <td>-34.4414628755671</td>\n      <td>61.4753314934401</td>\n      <td>101.321181459117</td>\n      <td>-6.61698844268879</td>\n      <td>-1.12573832398984</td>\n      <td>122.6140268058</td>\n      <td>-46.7960541509443</td>\n    </tr>\n    <tr>\n      <th>78094</th>\n      <td>5</td>\n      <td>14</td>\n      <td>27.915311</td>\n      <td>108.007390</td>\n      <td>10.814957</td>\n      <td>-0.910435</td>\n      <td>122.464093</td>\n      <td>-47.271248</td>\n      <td>-30.084588</td>\n      <td>77.705861</td>\n      <td>...</td>\n      <td>-34.6757739677746</td>\n      <td>-22.3294952272238</td>\n      <td>104.026431451371</td>\n      <td>-54.3346226872107</td>\n      <td>61.6393864623392</td>\n      <td>101.224661857702</td>\n      <td>-6.8409418109905</td>\n      <td>54.0099616832869</td>\n      <td>129.477879104126</td>\n      <td>-43.9678201146984</td>\n    </tr>\n    <tr>\n      <th>78095</th>\n      <td>5</td>\n      <td>14</td>\n      <td>27.898705</td>\n      <td>108.092877</td>\n      <td>11.107857</td>\n      <td>-30.031402</td>\n      <td>77.740235</td>\n      <td>-17.453099</td>\n      <td>-1.091566</td>\n      <td>122.827638</td>\n      <td>...</td>\n      <td>-43.6796644772844</td>\n      <td>-1.55884578727193</td>\n      <td>98.0172007250434</td>\n      <td>3.42181376960772</td>\n      <td>26.5869101964665</td>\n      <td>135.762921713685</td>\n      <td>-34.4459435415338</td>\n      <td>-22.3160291864615</td>\n      <td>104.124677433799</td>\n      <td>-54.3440786805432</td>\n    </tr>\n  </tbody>\n</table>\n<p>52247 rows × 23 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"print(\"Total missing values:\", data.isna().sum().sum())\ndata = data.dropna(how='any',axis=0)\n# I have cleaned the data thorugh excel sheets by:\n# removing rows and columns with very few values  \ndata\n#print(\"Total missing values:\", data.isna().sum().sum())","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:52.293123Z","iopub.execute_input":"2023-06-20T14:01:52.293800Z","iopub.status.idle":"2023-06-20T14:01:52.803626Z","shell.execute_reply.started":"2023-06-20T14:01:52.293760Z","shell.execute_reply":"2023-06-20T14:01:52.802779Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Total missing values: 0\n","output_type":"stream"},{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"       0   1          2           3          4          5           6   \\\n1654    1   0 -11.632335   81.551968 -28.007209  73.564324   51.161458   \n1655    1   0  72.692270   50.023771 -72.963253 -11.593076   81.234142   \n1656    1   0  85.648015   73.561022 -71.838316  31.595034   90.217896   \n1657    1   0  52.895241   82.454113 -55.683891  32.285729   90.277753   \n1659    1   0  51.874242   82.509948 -56.138338  -9.177149   81.411469   \n...    ..  ..        ...         ...        ...        ...         ...   \n78091   5  14  54.251127  129.177414 -44.252511  27.720784  107.810661   \n78092   5  14  54.334883  129.253842 -44.016320  27.767911  107.914808   \n78093   5  14  54.151540  129.269502 -44.173273  27.725978  108.034006   \n78094   5  14  27.915311  108.007390  10.814957  -0.910435  122.464093   \n78095   5  14  27.898705  108.092877  11.107857 -30.031402   77.740235   \n\n              7          8           9   ...                 13  \\\n1654  -70.505689  86.213513   75.013944  ...  -56.2154522902372   \n1655  -28.450210  51.729995   82.844469  ...  -69.7342981118982   \n1656  -47.246688  52.230858   82.361548  ...  -70.1118284111086   \n1657  -46.444718  89.077013   74.655391  ...   -22.302190491041   \n1659  -25.398447  76.251197   50.602365  ...  -46.3213660327158   \n...          ...        ...         ...  ...                ...   \n78091  11.099282  -1.270139  122.758679  ...   4.07501825540343   \n78092  11.069842 -30.334054   77.858214  ...  -53.0072437045207   \n78093  11.020347 -22.574718  104.222208  ...  -17.3548436885736   \n78094 -47.271248 -30.084588   77.705861  ...  -34.6757739677746   \n78095 -17.453099  -1.091566  122.827638  ...  -43.6796644772844   \n\n                      14                15                 16  \\\n1654    32.2984892072433  91.5360396435441  -44.0743871801424   \n1655     30.760697382696  90.4491696415814  -47.9095106182335   \n1656   -8.90628847947269  81.5167927599509  -25.2439142696141   \n1657     5.7165380800747  24.4084028559536   7.77220505394253   \n1659    5.99293400228724  24.4486547593206   8.07013900151556   \n...                  ...               ...                ...   \n78091   26.0308784490985  136.368236497962  -32.7923925965895   \n78092   26.4469428817139   135.82349439399  -33.8514248055691   \n78093   26.5066400161928  135.605902859879  -34.4414628755671   \n78094  -22.3294952272238  104.026431451371  -54.3346226872107   \n78095  -1.55884578727193  98.0172007250434   3.42181376960772   \n\n                      17                18                 19  \\\n1654    9.92719635250188  89.3756053345133  -31.6501312425115   \n1655    10.0284391929149  88.9679786863632  -32.0451200624497   \n1656    10.2775163462491  88.6079577405045  -32.0375389006328   \n1657    76.3888808023934  50.6150565868792   -67.772758579423   \n1659    58.9863532908359  20.0609402463142  -65.2250536748152   \n...                  ...               ...                ...   \n78091   77.2937098066631  34.0708569530435  -43.2637900001125   \n78092  -1.09067420384509  122.724914373665  -46.5583729086904   \n78093   61.4753314934401  101.321181459117  -6.61698844268879   \n78094   61.6393864623392  101.224661857702   -6.8409418109905   \n78095   26.5869101964665  135.762921713685  -34.4459435415338   \n\n                      20                21                 22  \n1654    7.34923543195405  24.9862732677858   10.2428080013854  \n1655    7.73251054087557   25.135414843299   10.7233952361419  \n1656    56.3088290880038  18.7747679142722  -70.9965231015097  \n1657    56.5881764930507  18.5380486853742  -70.8212291698613  \n1659    87.3569292461206  73.7462524221128  -68.2950376409761  \n...                  ...               ...                ...  \n78091  -30.2822013321426  77.6223061447627    -17.15029172919  \n78092  -1.95850220442059  98.2889311555066   4.24833369793896  \n78093  -1.12573832398984    122.6140268058  -46.7960541509443  \n78094   54.0099616832869  129.477879104126  -43.9678201146984  \n78095  -22.3160291864615  104.124677433799  -54.3440786805432  \n\n[52247 rows x 23 columns]","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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1654</th>\n      <td>1</td>\n      <td>0</td>\n      <td>-11.632335</td>\n      <td>81.551968</td>\n      <td>-28.007209</td>\n      <td>73.564324</td>\n      <td>51.161458</td>\n      <td>-70.505689</td>\n      <td>86.213513</td>\n      <td>75.013944</td>\n      <td>...</td>\n      <td>-56.2154522902372</td>\n      <td>32.2984892072433</td>\n      <td>91.5360396435441</td>\n      <td>-44.0743871801424</td>\n      <td>9.92719635250188</td>\n      <td>89.3756053345133</td>\n      <td>-31.6501312425115</td>\n      <td>7.34923543195405</td>\n      <td>24.9862732677858</td>\n      <td>10.2428080013854</td>\n    </tr>\n    <tr>\n      <th>1655</th>\n      <td>1</td>\n      <td>0</td>\n      <td>72.692270</td>\n      <td>50.023771</td>\n      <td>-72.963253</td>\n      <td>-11.593076</td>\n      <td>81.234142</td>\n      <td>-28.450210</td>\n      <td>51.729995</td>\n      <td>82.844469</td>\n      <td>...</td>\n      <td>-69.7342981118982</td>\n      <td>30.760697382696</td>\n      <td>90.4491696415814</td>\n      <td>-47.9095106182335</td>\n      <td>10.0284391929149</td>\n      <td>88.9679786863632</td>\n      <td>-32.0451200624497</td>\n      <td>7.73251054087557</td>\n      <td>25.135414843299</td>\n      <td>10.7233952361419</td>\n    </tr>\n    <tr>\n      <th>1656</th>\n      <td>1</td>\n      <td>0</td>\n      <td>85.648015</td>\n      <td>73.561022</td>\n      <td>-71.838316</td>\n      <td>31.595034</td>\n      <td>90.217896</td>\n      <td>-47.246688</td>\n      <td>52.230858</td>\n      <td>82.361548</td>\n      <td>...</td>\n      <td>-70.1118284111086</td>\n      <td>-8.90628847947269</td>\n      <td>81.5167927599509</td>\n      <td>-25.2439142696141</td>\n      <td>10.2775163462491</td>\n      <td>88.6079577405045</td>\n      <td>-32.0375389006328</td>\n      <td>56.3088290880038</td>\n      <td>18.7747679142722</td>\n      <td>-70.9965231015097</td>\n    </tr>\n    <tr>\n      <th>1657</th>\n      <td>1</td>\n      <td>0</td>\n      <td>52.895241</td>\n      <td>82.454113</td>\n      <td>-55.683891</td>\n      <td>32.285729</td>\n      <td>90.277753</td>\n      <td>-46.444718</td>\n      <td>89.077013</td>\n      <td>74.655391</td>\n      <td>...</td>\n      <td>-22.302190491041</td>\n      <td>5.7165380800747</td>\n      <td>24.4084028559536</td>\n      <td>7.77220505394253</td>\n      <td>76.3888808023934</td>\n      <td>50.6150565868792</td>\n      <td>-67.772758579423</td>\n      <td>56.5881764930507</td>\n      <td>18.5380486853742</td>\n      <td>-70.8212291698613</td>\n    </tr>\n    <tr>\n      <th>1659</th>\n      <td>1</td>\n      <td>0</td>\n      <td>51.874242</td>\n      <td>82.509948</td>\n      <td>-56.138338</td>\n      <td>-9.177149</td>\n      <td>81.411469</td>\n      <td>-25.398447</td>\n      <td>76.251197</td>\n      <td>50.602365</td>\n      <td>...</td>\n      <td>-46.3213660327158</td>\n      <td>5.99293400228724</td>\n      <td>24.4486547593206</td>\n      <td>8.07013900151556</td>\n      <td>58.9863532908359</td>\n      <td>20.0609402463142</td>\n      <td>-65.2250536748152</td>\n      <td>87.3569292461206</td>\n      <td>73.7462524221128</td>\n      <td>-68.2950376409761</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      <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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>78091</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.251127</td>\n      <td>129.177414</td>\n      <td>-44.252511</td>\n      <td>27.720784</td>\n      <td>107.810661</td>\n      <td>11.099282</td>\n      <td>-1.270139</td>\n      <td>122.758679</td>\n      <td>...</td>\n      <td>4.07501825540343</td>\n      <td>26.0308784490985</td>\n      <td>136.368236497962</td>\n      <td>-32.7923925965895</td>\n      <td>77.2937098066631</td>\n      <td>34.0708569530435</td>\n      <td>-43.2637900001125</td>\n      <td>-30.2822013321426</td>\n      <td>77.6223061447627</td>\n      <td>-17.15029172919</td>\n    </tr>\n    <tr>\n      <th>78092</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.334883</td>\n      <td>129.253842</td>\n      <td>-44.016320</td>\n      <td>27.767911</td>\n      <td>107.914808</td>\n      <td>11.069842</td>\n      <td>-30.334054</td>\n      <td>77.858214</td>\n      <td>...</td>\n      <td>-53.0072437045207</td>\n      <td>26.4469428817139</td>\n      <td>135.82349439399</td>\n      <td>-33.8514248055691</td>\n      <td>-1.09067420384509</td>\n      <td>122.724914373665</td>\n      <td>-46.5583729086904</td>\n      <td>-1.95850220442059</td>\n      <td>98.2889311555066</td>\n      <td>4.24833369793896</td>\n    </tr>\n    <tr>\n      <th>78093</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.151540</td>\n      <td>129.269502</td>\n      <td>-44.173273</td>\n      <td>27.725978</td>\n      <td>108.034006</td>\n      <td>11.020347</td>\n      <td>-22.574718</td>\n      <td>104.222208</td>\n      <td>...</td>\n      <td>-17.3548436885736</td>\n      <td>26.5066400161928</td>\n      <td>135.605902859879</td>\n      <td>-34.4414628755671</td>\n      <td>61.4753314934401</td>\n      <td>101.321181459117</td>\n      <td>-6.61698844268879</td>\n      <td>-1.12573832398984</td>\n      <td>122.6140268058</td>\n      <td>-46.7960541509443</td>\n    </tr>\n    <tr>\n      <th>78094</th>\n      <td>5</td>\n      <td>14</td>\n      <td>27.915311</td>\n      <td>108.007390</td>\n      <td>10.814957</td>\n      <td>-0.910435</td>\n      <td>122.464093</td>\n      <td>-47.271248</td>\n      <td>-30.084588</td>\n      <td>77.705861</td>\n      <td>...</td>\n      <td>-34.6757739677746</td>\n      <td>-22.3294952272238</td>\n      <td>104.026431451371</td>\n      <td>-54.3346226872107</td>\n      <td>61.6393864623392</td>\n      <td>101.224661857702</td>\n      <td>-6.8409418109905</td>\n      <td>54.0099616832869</td>\n      <td>129.477879104126</td>\n      <td>-43.9678201146984</td>\n    </tr>\n    <tr>\n      <th>78095</th>\n      <td>5</td>\n      <td>14</td>\n      <td>27.898705</td>\n      <td>108.092877</td>\n      <td>11.107857</td>\n      <td>-30.031402</td>\n      <td>77.740235</td>\n      <td>-17.453099</td>\n      <td>-1.091566</td>\n      <td>122.827638</td>\n      <td>...</td>\n      <td>-43.6796644772844</td>\n      <td>-1.55884578727193</td>\n      <td>98.0172007250434</td>\n      <td>3.42181376960772</td>\n      <td>26.5869101964665</td>\n      <td>135.762921713685</td>\n      <td>-34.4459435415338</td>\n      <td>-22.3160291864615</td>\n      <td>104.124677433799</td>\n      <td>-54.3440786805432</td>\n    </tr>\n  </tbody>\n</table>\n<p>52247 rows × 23 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data = data.drop_duplicates()\n\ndata\n","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:52.804788Z","iopub.execute_input":"2023-06-20T14:01:52.805286Z","iopub.status.idle":"2023-06-20T14:01:53.061488Z","shell.execute_reply.started":"2023-06-20T14:01:52.805258Z","shell.execute_reply":"2023-06-20T14:01:53.060801Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"       0   1          2           3          4          5           6   \\\n1654    1   0 -11.632335   81.551968 -28.007209  73.564324   51.161458   \n1655    1   0  72.692270   50.023771 -72.963253 -11.593076   81.234142   \n1656    1   0  85.648015   73.561022 -71.838316  31.595034   90.217896   \n1657    1   0  52.895241   82.454113 -55.683891  32.285729   90.277753   \n1659    1   0  51.874242   82.509948 -56.138338  -9.177149   81.411469   \n...    ..  ..        ...         ...        ...        ...         ...   \n78091   5  14  54.251127  129.177414 -44.252511  27.720784  107.810661   \n78092   5  14  54.334883  129.253842 -44.016320  27.767911  107.914808   \n78093   5  14  54.151540  129.269502 -44.173273  27.725978  108.034006   \n78094   5  14  27.915311  108.007390  10.814957  -0.910435  122.464093   \n78095   5  14  27.898705  108.092877  11.107857 -30.031402   77.740235   \n\n              7          8           9   ...                 13  \\\n1654  -70.505689  86.213513   75.013944  ...  -56.2154522902372   \n1655  -28.450210  51.729995   82.844469  ...  -69.7342981118982   \n1656  -47.246688  52.230858   82.361548  ...  -70.1118284111086   \n1657  -46.444718  89.077013   74.655391  ...   -22.302190491041   \n1659  -25.398447  76.251197   50.602365  ...  -46.3213660327158   \n...          ...        ...         ...  ...                ...   \n78091  11.099282  -1.270139  122.758679  ...   4.07501825540343   \n78092  11.069842 -30.334054   77.858214  ...  -53.0072437045207   \n78093  11.020347 -22.574718  104.222208  ...  -17.3548436885736   \n78094 -47.271248 -30.084588   77.705861  ...  -34.6757739677746   \n78095 -17.453099  -1.091566  122.827638  ...  -43.6796644772844   \n\n                      14                15                 16  \\\n1654    32.2984892072433  91.5360396435441  -44.0743871801424   \n1655     30.760697382696  90.4491696415814  -47.9095106182335   \n1656   -8.90628847947269  81.5167927599509  -25.2439142696141   \n1657     5.7165380800747  24.4084028559536   7.77220505394253   \n1659    5.99293400228724  24.4486547593206   8.07013900151556   \n...                  ...               ...                ...   \n78091   26.0308784490985  136.368236497962  -32.7923925965895   \n78092   26.4469428817139   135.82349439399  -33.8514248055691   \n78093   26.5066400161928  135.605902859879  -34.4414628755671   \n78094  -22.3294952272238  104.026431451371  -54.3346226872107   \n78095  -1.55884578727193  98.0172007250434   3.42181376960772   \n\n                      17                18                 19  \\\n1654    9.92719635250188  89.3756053345133  -31.6501312425115   \n1655    10.0284391929149  88.9679786863632  -32.0451200624497   \n1656    10.2775163462491  88.6079577405045  -32.0375389006328   \n1657    76.3888808023934  50.6150565868792   -67.772758579423   \n1659    58.9863532908359  20.0609402463142  -65.2250536748152   \n...                  ...               ...                ...   \n78091   77.2937098066631  34.0708569530435  -43.2637900001125   \n78092  -1.09067420384509  122.724914373665  -46.5583729086904   \n78093   61.4753314934401  101.321181459117  -6.61698844268879   \n78094   61.6393864623392  101.224661857702   -6.8409418109905   \n78095   26.5869101964665  135.762921713685  -34.4459435415338   \n\n                      20                21                 22  \n1654    7.34923543195405  24.9862732677858   10.2428080013854  \n1655    7.73251054087557   25.135414843299   10.7233952361419  \n1656    56.3088290880038  18.7747679142722  -70.9965231015097  \n1657    56.5881764930507  18.5380486853742  -70.8212291698613  \n1659    87.3569292461206  73.7462524221128  -68.2950376409761  \n...                  ...               ...                ...  \n78091  -30.2822013321426  77.6223061447627    -17.15029172919  \n78092  -1.95850220442059  98.2889311555066   4.24833369793896  \n78093  -1.12573832398984    122.6140268058  -46.7960541509443  \n78094   54.0099616832869  129.477879104126  -43.9678201146984  \n78095  -22.3160291864615  104.124677433799  -54.3440786805432  \n\n[52247 rows x 23 columns]","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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1654</th>\n      <td>1</td>\n      <td>0</td>\n      <td>-11.632335</td>\n      <td>81.551968</td>\n      <td>-28.007209</td>\n      <td>73.564324</td>\n      <td>51.161458</td>\n      <td>-70.505689</td>\n      <td>86.213513</td>\n      <td>75.013944</td>\n      <td>...</td>\n      <td>-56.2154522902372</td>\n      <td>32.2984892072433</td>\n      <td>91.5360396435441</td>\n      <td>-44.0743871801424</td>\n      <td>9.92719635250188</td>\n      <td>89.3756053345133</td>\n      <td>-31.6501312425115</td>\n      <td>7.34923543195405</td>\n      <td>24.9862732677858</td>\n      <td>10.2428080013854</td>\n    </tr>\n    <tr>\n      <th>1655</th>\n      <td>1</td>\n      <td>0</td>\n      <td>72.692270</td>\n      <td>50.023771</td>\n      <td>-72.963253</td>\n      <td>-11.593076</td>\n      <td>81.234142</td>\n      <td>-28.450210</td>\n      <td>51.729995</td>\n      <td>82.844469</td>\n      <td>...</td>\n      <td>-69.7342981118982</td>\n      <td>30.760697382696</td>\n      <td>90.4491696415814</td>\n      <td>-47.9095106182335</td>\n      <td>10.0284391929149</td>\n      <td>88.9679786863632</td>\n      <td>-32.0451200624497</td>\n      <td>7.73251054087557</td>\n      <td>25.135414843299</td>\n      <td>10.7233952361419</td>\n    </tr>\n    <tr>\n      <th>1656</th>\n      <td>1</td>\n      <td>0</td>\n      <td>85.648015</td>\n      <td>73.561022</td>\n      <td>-71.838316</td>\n      <td>31.595034</td>\n      <td>90.217896</td>\n      <td>-47.246688</td>\n      <td>52.230858</td>\n      <td>82.361548</td>\n      <td>...</td>\n      <td>-70.1118284111086</td>\n      <td>-8.90628847947269</td>\n      <td>81.5167927599509</td>\n      <td>-25.2439142696141</td>\n      <td>10.2775163462491</td>\n      <td>88.6079577405045</td>\n      <td>-32.0375389006328</td>\n      <td>56.3088290880038</td>\n      <td>18.7747679142722</td>\n      <td>-70.9965231015097</td>\n    </tr>\n    <tr>\n      <th>1657</th>\n      <td>1</td>\n      <td>0</td>\n      <td>52.895241</td>\n      <td>82.454113</td>\n      <td>-55.683891</td>\n      <td>32.285729</td>\n      <td>90.277753</td>\n      <td>-46.444718</td>\n      <td>89.077013</td>\n      <td>74.655391</td>\n      <td>...</td>\n      <td>-22.302190491041</td>\n      <td>5.7165380800747</td>\n      <td>24.4084028559536</td>\n      <td>7.77220505394253</td>\n      <td>76.3888808023934</td>\n      <td>50.6150565868792</td>\n      <td>-67.772758579423</td>\n      <td>56.5881764930507</td>\n      <td>18.5380486853742</td>\n      <td>-70.8212291698613</td>\n    </tr>\n    <tr>\n      <th>1659</th>\n      <td>1</td>\n      <td>0</td>\n      <td>51.874242</td>\n      <td>82.509948</td>\n      <td>-56.138338</td>\n      <td>-9.177149</td>\n      <td>81.411469</td>\n      <td>-25.398447</td>\n      <td>76.251197</td>\n      <td>50.602365</td>\n      <td>...</td>\n      <td>-46.3213660327158</td>\n      <td>5.99293400228724</td>\n      <td>24.4486547593206</td>\n      <td>8.07013900151556</td>\n      <td>58.9863532908359</td>\n      <td>20.0609402463142</td>\n      <td>-65.2250536748152</td>\n      <td>87.3569292461206</td>\n      <td>73.7462524221128</td>\n      <td>-68.2950376409761</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      <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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>78091</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.251127</td>\n      <td>129.177414</td>\n      <td>-44.252511</td>\n      <td>27.720784</td>\n      <td>107.810661</td>\n      <td>11.099282</td>\n      <td>-1.270139</td>\n      <td>122.758679</td>\n      <td>...</td>\n      <td>4.07501825540343</td>\n      <td>26.0308784490985</td>\n      <td>136.368236497962</td>\n      <td>-32.7923925965895</td>\n      <td>77.2937098066631</td>\n      <td>34.0708569530435</td>\n      <td>-43.2637900001125</td>\n      <td>-30.2822013321426</td>\n      <td>77.6223061447627</td>\n      <td>-17.15029172919</td>\n    </tr>\n    <tr>\n      <th>78092</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.334883</td>\n      <td>129.253842</td>\n      <td>-44.016320</td>\n      <td>27.767911</td>\n      <td>107.914808</td>\n      <td>11.069842</td>\n      <td>-30.334054</td>\n      <td>77.858214</td>\n      <td>...</td>\n      <td>-53.0072437045207</td>\n      <td>26.4469428817139</td>\n      <td>135.82349439399</td>\n      <td>-33.8514248055691</td>\n      <td>-1.09067420384509</td>\n      <td>122.724914373665</td>\n      <td>-46.5583729086904</td>\n      <td>-1.95850220442059</td>\n      <td>98.2889311555066</td>\n      <td>4.24833369793896</td>\n    </tr>\n    <tr>\n      <th>78093</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.151540</td>\n      <td>129.269502</td>\n      <td>-44.173273</td>\n      <td>27.725978</td>\n      <td>108.034006</td>\n      <td>11.020347</td>\n      <td>-22.574718</td>\n      <td>104.222208</td>\n      <td>...</td>\n      <td>-17.3548436885736</td>\n      <td>26.5066400161928</td>\n      <td>135.605902859879</td>\n      <td>-34.4414628755671</td>\n      <td>61.4753314934401</td>\n      <td>101.321181459117</td>\n      <td>-6.61698844268879</td>\n      <td>-1.12573832398984</td>\n      <td>122.6140268058</td>\n      <td>-46.7960541509443</td>\n    </tr>\n    <tr>\n      <th>78094</th>\n      <td>5</td>\n      <td>14</td>\n      <td>27.915311</td>\n      <td>108.007390</td>\n      <td>10.814957</td>\n      <td>-0.910435</td>\n      <td>122.464093</td>\n      <td>-47.271248</td>\n      <td>-30.084588</td>\n      <td>77.705861</td>\n      <td>...</td>\n      <td>-34.6757739677746</td>\n      <td>-22.3294952272238</td>\n      <td>104.026431451371</td>\n      <td>-54.3346226872107</td>\n      <td>61.6393864623392</td>\n      <td>101.224661857702</td>\n      <td>-6.8409418109905</td>\n      <td>54.0099616832869</td>\n      <td>129.477879104126</td>\n      <td>-43.9678201146984</td>\n    </tr>\n    <tr>\n      <th>78095</th>\n      <td>5</td>\n      <td>14</td>\n      <td>27.898705</td>\n      <td>108.092877</td>\n      <td>11.107857</td>\n      <td>-30.031402</td>\n      <td>77.740235</td>\n      <td>-17.453099</td>\n      <td>-1.091566</td>\n      <td>122.827638</td>\n      <td>...</td>\n      <td>-43.6796644772844</td>\n      <td>-1.55884578727193</td>\n      <td>98.0172007250434</td>\n      <td>3.42181376960772</td>\n      <td>26.5869101964665</td>\n      <td>135.762921713685</td>\n      <td>-34.4459435415338</td>\n      <td>-22.3160291864615</td>\n      <td>104.124677433799</td>\n      <td>-54.3440786805432</td>\n    </tr>\n  </tbody>\n</table>\n<p>52247 rows × 23 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.dtypes\n","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:53.062633Z","iopub.execute_input":"2023-06-20T14:01:53.063150Z","iopub.status.idle":"2023-06-20T14:01:53.072905Z","shell.execute_reply.started":"2023-06-20T14:01:53.063113Z","shell.execute_reply":"2023-06-20T14:01:53.071737Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"0       int64\n1       int64\n2     float64\n3     float64\n4     float64\n5     float64\n6     float64\n7     float64\n8     float64\n9     float64\n10    float64\n11     object\n12     object\n13     object\n14     object\n15     object\n16     object\n17     object\n18     object\n19     object\n20     object\n21     object\n22     object\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nchart=data[0].value_counts()\nprint(chart)\ncount = np.array([15406, 14761, 13018, 6423,2639])\nmylabels = [5, 4, 3, 2,1]\n\nplt.pie(count, labels = mylabels)\nplt.show() ","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:53.074315Z","iopub.execute_input":"2023-06-20T14:01:53.074656Z","iopub.status.idle":"2023-06-20T14:01:53.242854Z","shell.execute_reply.started":"2023-06-20T14:01:53.074618Z","shell.execute_reply":"2023-06-20T14:01:53.241353Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"5    15406\n2    14761\n4    13018\n3     6423\n1     2639\nName: 0, dtype: int64\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"## **splitting**\n","metadata":{}},{"cell_type":"code","source":"# the target variable is in the first column\nX = data.iloc[:, 1:]  # Features (excluding the first column)\ny = data.iloc[:, 0]  # Target variable (first column)\n#y2= data.iloc[:, 1]\nprint(\"X\\n\",X,\"\\n\\n\")\nprint(\"y\\n\",y,\"\\n\\n\")","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:53.244847Z","iopub.execute_input":"2023-06-20T14:01:53.246154Z","iopub.status.idle":"2023-06-20T14:01:53.330000Z","shell.execute_reply.started":"2023-06-20T14:01:53.246101Z","shell.execute_reply":"2023-06-20T14:01:53.329220Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"X\n        1          2           3          4          5           6          7   \\\n1654    0 -11.632335   81.551968 -28.007209  73.564324   51.161458 -70.505689   \n1655    0  72.692270   50.023771 -72.963253 -11.593076   81.234142 -28.450210   \n1656    0  85.648015   73.561022 -71.838316  31.595034   90.217896 -47.246688   \n1657    0  52.895241   82.454113 -55.683891  32.285729   90.277753 -46.444718   \n1659    0  51.874242   82.509948 -56.138338  -9.177149   81.411469 -25.398447   \n...    ..        ...         ...        ...        ...         ...        ...   \n78091  14  54.251127  129.177414 -44.252511  27.720784  107.810661  11.099282   \n78092  14  54.334883  129.253842 -44.016320  27.767911  107.914808  11.069842   \n78093  14  54.151540  129.269502 -44.173273  27.725978  108.034006  11.020347   \n78094  14  27.915311  108.007390  10.814957  -0.910435  122.464093 -47.271248   \n78095  14  27.898705  108.092877  11.107857 -30.031402   77.740235 -17.453099   \n\n              8           9          10  ...                 13  \\\n1654   86.213513   75.013944 -69.068351  ...  -56.2154522902372   \n1655   51.729995   82.844469 -55.674024  ...  -69.7342981118982   \n1656   52.230858   82.361548 -55.975665  ...  -70.1118284111086   \n1657   89.077013   74.655391 -66.253441  ...   -22.302190491041   \n1659   76.251197   50.602365 -66.696980  ...  -46.3213660327158   \n...          ...         ...        ...  ...                ...   \n78091  -1.270139  122.758679 -46.460186  ...   4.07501825540343   \n78092 -30.334054   77.858214 -17.002723  ...  -53.0072437045207   \n78093 -22.574718  104.222208 -53.939140  ...  -17.3548436885736   \n78094 -30.084588   77.705861 -17.460853  ...  -34.6757739677746   \n78095  -1.091566  122.827638 -46.760985  ...  -43.6796644772844   \n\n                      14                15                 16  \\\n1654    32.2984892072433  91.5360396435441  -44.0743871801424   \n1655     30.760697382696  90.4491696415814  -47.9095106182335   \n1656   -8.90628847947269  81.5167927599509  -25.2439142696141   \n1657     5.7165380800747  24.4084028559536   7.77220505394253   \n1659    5.99293400228724  24.4486547593206   8.07013900151556   \n...                  ...               ...                ...   \n78091   26.0308784490985  136.368236497962  -32.7923925965895   \n78092   26.4469428817139   135.82349439399  -33.8514248055691   \n78093   26.5066400161928  135.605902859879  -34.4414628755671   \n78094  -22.3294952272238  104.026431451371  -54.3346226872107   \n78095  -1.55884578727193  98.0172007250434   3.42181376960772   \n\n                      17                18                 19  \\\n1654    9.92719635250188  89.3756053345133  -31.6501312425115   \n1655    10.0284391929149  88.9679786863632  -32.0451200624497   \n1656    10.2775163462491  88.6079577405045  -32.0375389006328   \n1657    76.3888808023934  50.6150565868792   -67.772758579423   \n1659    58.9863532908359  20.0609402463142  -65.2250536748152   \n...                  ...               ...                ...   \n78091   77.2937098066631  34.0708569530435  -43.2637900001125   \n78092  -1.09067420384509  122.724914373665  -46.5583729086904   \n78093   61.4753314934401  101.321181459117  -6.61698844268879   \n78094   61.6393864623392  101.224661857702   -6.8409418109905   \n78095   26.5869101964665  135.762921713685  -34.4459435415338   \n\n                      20                21                 22  \n1654    7.34923543195405  24.9862732677858   10.2428080013854  \n1655    7.73251054087557   25.135414843299   10.7233952361419  \n1656    56.3088290880038  18.7747679142722  -70.9965231015097  \n1657    56.5881764930507  18.5380486853742  -70.8212291698613  \n1659    87.3569292461206  73.7462524221128  -68.2950376409761  \n...                  ...               ...                ...  \n78091  -30.2822013321426  77.6223061447627    -17.15029172919  \n78092  -1.95850220442059  98.2889311555066   4.24833369793896  \n78093  -1.12573832398984    122.6140268058  -46.7960541509443  \n78094   54.0099616832869  129.477879104126  -43.9678201146984  \n78095  -22.3160291864615  104.124677433799  -54.3440786805432  \n\n[52247 rows x 22 columns] \n\n\ny\n 1654     1\n1655     1\n1656     1\n1657     1\n1659     1\n        ..\n78091    5\n78092    5\n78093    5\n78094    5\n78095    5\nName: 0, Length: 52247, dtype: int64 \n\n\n","output_type":"stream"}]},{"cell_type":"code","source":"# Split data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n\nprint(len(X))      # 52247\nprint(len(X_train)) # 41797\nprint(len(X_test))  # 10450","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:53.331208Z","iopub.execute_input":"2023-06-20T14:01:53.334099Z","iopub.status.idle":"2023-06-20T14:01:53.384764Z","shell.execute_reply.started":"2023-06-20T14:01:53.334052Z","shell.execute_reply":"2023-06-20T14:01:53.383581Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"52247\n41797\n10450\n","output_type":"stream"}]},{"cell_type":"code","source":"X_train.shape\n","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:53.386761Z","iopub.execute_input":"2023-06-20T14:01:53.387092Z","iopub.status.idle":"2023-06-20T14:01:53.394226Z","shell.execute_reply.started":"2023-06-20T14:01:53.387065Z","shell.execute_reply":"2023-06-20T14:01:53.393150Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"(41797, 22)"},"metadata":{}}]},{"cell_type":"code","source":"print(X.shape)  # Check the shape of X\nprint(y.shape)  # Check the shape of y\n# print(y2.shape)","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:53.395655Z","iopub.execute_input":"2023-06-20T14:01:53.396031Z","iopub.status.idle":"2023-06-20T14:01:53.404600Z","shell.execute_reply.started":"2023-06-20T14:01:53.395972Z","shell.execute_reply":"2023-06-20T14:01:53.403536Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"(52247, 22)\n(52247,)\n","output_type":"stream"}]},{"cell_type":"code","source":"accuracy_arr = []#poly svm, random forest, knn, mlp,gaussian NB","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:53.406244Z","iopub.execute_input":"2023-06-20T14:01:53.406645Z","iopub.status.idle":"2023-06-20T14:01:53.416156Z","shell.execute_reply.started":"2023-06-20T14:01:53.406609Z","shell.execute_reply":"2023-06-20T14:01:53.414913Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":"# **poly svm**","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n\n\n# Create an SVM classifier with polynomial kernel\nsvm = SVC(kernel='poly', degree=9)#trying with different values of degree\n\n# Train the classifier\nsvm.fit(X_train, y_train)\n\n# Predict the classes for the test set\ny_pred = svm.predict(X_test)\n\n# Calculate accuracy\naccuracy = accuracy_score(y_test, y_pred)\naccuracy_arr.append(accuracy)\n\nprint(f\"Accuracy: {accuracy}\\n\\n\")\n\nprint(classification_report(y_test, y_pred))\n\n\n\ncm = confusion_matrix(y_test, y_pred)\nsns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\nplt.xlabel(\"Predicted\")\nplt.ylabel(\"Actual\")\nplt.title(\"Confusion Matrix\")\nplt.xticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\nplt.yticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:01:53.417899Z","iopub.execute_input":"2023-06-20T14:01:53.418338Z","iopub.status.idle":"2023-06-20T14:02:44.834937Z","shell.execute_reply.started":"2023-06-20T14:01:53.418299Z","shell.execute_reply":"2023-06-20T14:02:44.833751Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Accuracy: 0.9555023923444976\n\n\n              precision    recall  f1-score   support\n\n           1       0.91      0.99      0.95       536\n           2       0.96      0.93      0.95      2873\n           3       0.92      0.97      0.94      1283\n           4       0.96      0.95      0.96      2618\n           5       0.97      0.97      0.97      3140\n\n    accuracy                           0.96     10450\n   macro avg       0.94      0.96      0.95     10450\nweighted avg       0.96      0.96      0.96     10450\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAiQAAAHFCAYAAADCA+LKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABlG0lEQVR4nO3dd1gU1xoG8HdpSxFW6aDYFUWwoSLEgmIjKqImGk2wl9iJNWhUrKgxGhV7VOzYjRpD1NhiFAuR2LBjQUGKdHFBmPuH101W0AWzyyz4/u4zz+OeOTP7zdyFfHznnFmJIAgCiIiIiESkI3YARERERExIiIiISHRMSIiIiEh0TEiIiIhIdExIiIiISHRMSIiIiEh0TEiIiIhIdExIiIiISHRMSIiIiEh0TEioVLty5Qr69++PKlWqwNDQEGXKlEHDhg2xYMECPH/+XKPvffnyZbRs2RIymQwSiQQ//vij2t9DIpEgMDBQ7edVJSQkBBKJBBKJBCdPnsy3XxAEVK9eHRKJBJ6enh/0HitWrEBISEiRjjl58uQ7YyIi7aYndgBEmrJ27VoMHz4cjo6OmDBhApycnJCTk4NLly5h1apVOHfuHPbt26ex9x8wYAAyMzMRGhqKcuXKoXLlymp/j3PnzqFChQpqP29hmZqaYt26dfmSjlOnTuHevXswNTX94HOvWLEClpaW6NevX6GPadiwIc6dOwcnJ6cPfl8iEgcTEiqVzp07h2HDhqFt27bYv38/pFKpYl/btm0xbtw4hIWFaTSGa9euYfDgwfD29tbYezRt2lRj5y6Mnj17YuvWrVi+fDnMzMwU7evWrYO7uzvS0tKKJY6cnBxIJBKYmZmJfk+I6MNwyIZKpblz50IikWDNmjVKycgbBgYG8PHxUbzOy8vDggULUKtWLUilUlhbW6NPnz6IiYlROs7T0xPOzs64ePEimjdvDmNjY1StWhXz5s1DXl4egH+GM169eoWVK1cqhjYAIDAwUPHvf3tzzIMHDxRtx48fh6enJywsLGBkZISKFSuie/fuePHihaJPQUM2165dQ5cuXVCuXDkYGhqifv362Lhxo1KfN0Mb27dvx5QpU2Bvbw8zMzO0adMGt27dKtxNBtCrVy8AwPbt2xVtqamp2LNnDwYMGFDgMTNmzICbmxvMzc1hZmaGhg0bYt26dfj393xWrlwZ169fx6lTpxT3702F6U3smzdvxrhx41C+fHlIpVLcvXs335BNYmIiHBwc4OHhgZycHMX5b9y4ARMTE/j5+RX6WolIs5iQUKmTm5uL48ePw9XVFQ4ODoU6ZtiwYZg0aRLatm2LAwcOYNasWQgLC4OHhwcSExOV+sbFxeHLL7/EV199hQMHDsDb2xsBAQHYsmULAKBjx444d+4cAOCzzz7DuXPnFK8L68GDB+jYsSMMDAywfv16hIWFYd68eTAxMUF2dvY7j7t16xY8PDxw/fp1LF26FHv37oWTkxP69euHBQsW5Os/efJkPHz4ED/99BPWrFmDO3fuoHPnzsjNzS1UnGZmZvjss8+wfv16Rdv27duho6ODnj17vvPahg4dip07d2Lv3r3o1q0bRo0ahVmzZin67Nu3D1WrVkWDBg0U9+/t4bWAgAA8evQIq1atwsGDB2FtbZ3vvSwtLREaGoqLFy9i0qRJAIAXL17g888/R8WKFbFq1apCXScRFQOBqJSJi4sTAAhffPFFofpHRUUJAIThw4crtZ8/f14AIEyePFnR1rJlSwGAcP78eaW+Tk5OQvv27ZXaAAgjRoxQaps+fbpQ0I/dhg0bBABCdHS0IAiCsHv3bgGAEBkZ+d7YAQjTp09XvP7iiy8EqVQqPHr0SKmft7e3YGxsLKSkpAiCIAgnTpwQAAiffvqpUr+dO3cKAIRz5869933fxHvx4kXFua5duyYIgiA0btxY6NevnyAIglCnTh2hZcuW7zxPbm6ukJOTI8ycOVOwsLAQ8vLyFPvedeyb92vRosU79504cUKpff78+QIAYd++fULfvn0FIyMj4cqVK++9RiIqXqyQ0EfvxIkTAJBv8mSTJk1Qu3Zt/P7770rttra2aNKkiVJb3bp18fDhQ7XFVL9+fRgYGGDIkCHYuHEj7t+/X6jjjh8/Di8vr3yVoX79+uHFixf5KjX/HrYCXl8HgCJdS8uWLVGtWjWsX78eV69excWLF985XPMmxjZt2kAmk0FXVxf6+vqYNm0akpKSEB8fX+j37d69e6H7TpgwAR07dkSvXr2wceNGLFu2DC4uLoU+nog0jwkJlTqWlpYwNjZGdHR0ofonJSUBAOzs7PLts7e3V+x/w8LCIl8/qVSKrKysD4i2YNWqVcOxY8dgbW2NESNGoFq1aqhWrRqWLFny3uOSkpLeeR1v9v/b29fyZr5NUa5FIpGgf//+2LJlC1atWoWaNWuiefPmBfa9cOEC2rVrB+D1Kqg///wTFy9exJQpU4r8vgVd5/ti7NevH16+fAlbW1vOHSHSQkxIqNTR1dWFl5cXIiIi8k1KLcib/yjHxsbm2/f06VNYWlqqLTZDQ0MAgFwuV2p/e54KADRv3hwHDx5EamoqwsPD4e7uDn9/f4SGhr7z/BYWFu+8DgBqvZZ/69evHxITE7Fq1Sr079//nf1CQ0Ohr6+PQ4cOoUePHvDw8ECjRo0+6D0Lmhz8LrGxsRgxYgTq16+PpKQkjB8//oPek4g0hwkJlUoBAQEQBAGDBw8ucBJoTk4ODh48CABo3bo1ACgmpb5x8eJFREVFwcvLS21xvVkpcuXKFaX2N7EURFdXF25ubli+fDkA4K+//npnXy8vLxw/flyRgLyxadMmGBsba2xJbPny5TFhwgR07twZffv2fWc/iUQCPT096OrqKtqysrKwefPmfH3VVXXKzc1Fr169IJFI8OuvvyIoKAjLli3D3r17//O5iUh9+BwSKpXc3d2xcuVKDB8+HK6urhg2bBjq1KmDnJwcXL58GWvWrIGzszM6d+4MR0dHDBkyBMuWLYOOjg68vb3x4MEDTJ06FQ4ODvjmm2/UFtenn34Kc3NzDBw4EDNnzoSenh5CQkLw+PFjpX6rVq3C8ePH0bFjR1SsWBEvX75UrGRp06bNO88/ffp0HDp0CK1atcK0adNgbm6OrVu34pdffsGCBQsgk8nUdi1vmzdvnso+HTt2xKJFi9C7d28MGTIESUlJWLhwYYFLs11cXBAaGoodO3agatWqMDQ0/KB5H9OnT8cff/yBI0eOwNbWFuPGjcOpU6cwcOBANGjQAFWqVCnyOYlI/ZiQUKk1ePBgNGnSBIsXL8b8+fMRFxcHfX191KxZE71798bIkSMVfVeuXIlq1aph3bp1WL58OWQyGTp06ICgoKAC54x8KDMzM4SFhcHf3x9fffUVypYti0GDBsHb2xuDBg1S9Ktfvz6OHDmC6dOnIy4uDmXKlIGzszMOHDigmINREEdHR5w9exaTJ0/GiBEjkJWVhdq1a2PDhg1FeuKpprRu3Rrr16/H/Pnz0blzZ5QvXx6DBw+GtbU1Bg4cqNR3xowZiI2NxeDBg5Geno5KlSopPaelMI4ePYqgoCBMnTpVqdIVEhKCBg0aoGfPnjhz5gwMDAzUcXlE9B9IBOFfTyMiIiIiEgHnkBAREZHomJAQERGR6JiQEBERkeiYkBAREZHomJAQERGR6JiQEBERkeiYkBAREZHoSuWD0W48zRQ7hBKtqrWJ2CEQERUbw2L4L6FRg5GqOxVC1uVgtZxHG7FCQkRERKJjQkJERKRpEh31bEWwcuVK1K1bF2ZmZjAzM4O7uzt+/fVXxX5BEBAYGAh7e3sYGRnB09MT169fVzqHXC7HqFGjYGlpCRMTE/j4+OT7FvXk5GT4+flBJpNBJpPBz88PKSkpRb5FTEiIiIg0TSJRz1YEFSpUwLx583Dp0iVcunQJrVu3RpcuXRRJx4IFC7Bo0SIEBwfj4sWLsLW1Rdu2bZGenq44h7+/P/bt24fQ0FCcOXMGGRkZ6NSpE3JzcxV9evfujcjISISFhSEsLAyRkZHw8/Mr+i0qjd9lwzkk/w3nkBDRx6RY5pA0Us+3hmddWvyfjjc3N8f333+PAQMGwN7eHv7+/pg0aRKA19UQGxsbzJ8/H0OHDkVqaiqsrKywefNm9OzZEwDw9OlTODg44PDhw2jfvj2ioqLg5OSE8PBwuLm5AQDCw8Ph7u6OmzdvwtHRsdCxsUJCRERUQsjlcqSlpSltcrlc5XG5ubkIDQ1FZmYm3N3dER0djbi4OKVvD5dKpWjZsiXOnj0LAIiIiEBOTo5SH3t7ezg7Oyv6nDt3DjKZTJGMAEDTpk0hk8kUfQqLCQkREZGmqWnIJigoSDFX480WFBT0zre9evUqypQpA6lUiq+//hr79u2Dk5MT4uLiAAA2NjZK/W1sbBT74uLiYGBggHLlyr23j7W1db73tba2VvQprFK57JeIiEirFHFC6rsEBARg7NixSm1SqfSd/R0dHREZGYmUlBTs2bMHffv2xalTp/4J6615KYIg5Gt729t9CupfmPO8jRUSIiKiEkIqlSpWzbzZ3peQGBgYoHr16mjUqBGCgoJQr149LFmyBLa2tgCQr4oRHx+vqJrY2toiOzsbycnJ7+3z7NmzfO+bkJCQr/qiChMSIiIiTRNhlU1BBEGAXC5HlSpVYGtri6NHjyr2ZWdn49SpU/Dw8AAAuLq6Ql9fX6lPbGwsrl27pujj7u6O1NRUXLhwQdHn/PnzSE1NVfQpLA7ZEBERaZqahmyKYvLkyfD29oaDgwPS09MRGhqKkydPIiwsDBKJBP7+/pg7dy5q1KiBGjVqYO7cuTA2Nkbv3r0BADKZDAMHDsS4ceNgYWEBc3NzjB8/Hi4uLmjTpg0AoHbt2ujQoQMGDx6M1atXAwCGDBmCTp06FWmFDcCEhIiIqFR69uwZ/Pz8EBsbC5lMhrp16yIsLAxt27YFAEycOBFZWVkYPnw4kpOT4ebmhiNHjsDU1FRxjsWLF0NPTw89evRAVlYWvLy8EBISAl1dXUWfrVu3YvTo0YrVOD4+PggOLvoj7vkcEsqHzyEhoo9JsTyHxP1btZwn69w8tZxHG7FCQkREpGkiDNmUNLxDREREJDpWSIiIiDRNDStkSjsmJERERJrGIRuVmJAQERFpGiskKjFlIyIiItGxQkJERKRpHLJRiQkJERGRpjEhUYl3iIiIiETHCgkREZGm6XBSqypMSIiIiDSNQzYq8Q4RERGR6FghISIi0jQ+h0QlJiRERESaxiEblXiHiIiISHSskBAREWkah2xUYkJCRESkaRyyUYkJCRERkaaxQqISExI1CA1ZhR0b1yi1lS1ngQ17j+LVqxxsW7cCEef/xLPYGBiblEG9hm7wGzIa5pZWiv7JzxOxcdWP+PvSeWRlZaK8Q2V0/3IAPFq2Ke7L0TrebVvj6dMn+dp7ftEbk6dOFyGikmfH9q0I2bAOiQkJqFa9BiZ+OxkNXRuJHZbWi7h0ESHr1yHqxjUkJCRg8dLlaO3Fn8mi4GePCosJiZo4VK6GGT+sVLzW0dEFAMhfvsT9OzfRw28QKleriYyMNKwPXoi5U/yxcPVWRf8lc6ciMzMDAXMWw0xWFn/8HoYfZn4L21VbULVGrWK/Hm2ydcdu5OXmKl7fvXsHQwf1R9v2HUSMquQI+/UwFswLwpSp01G/QUPs3hmK4UMHY9+BX2Bnby92eFotK+sFHB0d0aVrN4zzHyV2OCUOP3v/wiEblXiH1ERXVxflzC0Vm6xsOQCASRlTBC5ciU9atUP5ipXh6FQXg0ZPwr3bUUh4Fqs4/tb1K+jYtSdq1naGrX0FfO43CMZlTHH/9k2xLklrmJubw9LKSrGdPnkCDg4V0ahxE7FDKxE2b9yArt27o9tnn6NqtWqYGDAFtna22Llju9ihab1mzVti5Jhv0KZtO7FDKZH42fsXiUQ9WynGhERNYp88woDP2mFor074Yea3iHsa886+LzIzIJFIYFLGVNFW26U+zpw4gvS0VOTl5eGP47/hVXY26tR3LY7wS4yc7Gz8cugAfLt1h6SU/3CqQ052NqJuXIe7RzOldnePT/B35GWRoqKPAT97VFQcslGDGrVdMObbWbB3qIiU5OfYtfknBIzsjyUbdsFMVlapb3a2HJvXLEVzrw4wNimjaB83bR5+mPkt+nRpBV1dPUgNDTFp1g+wK+9QzFej3Y4fP4b09HT4+HYVO5QSITklGbm5ubCwsFBqt7CwRGJigkhR0ceAn723cMhGJa2+Q48fP8aAAQPe20culyMtLU1py5bLiynC11zdPoF7Sy9UqloD9Vzd8F3QUgDAid8OKfV79SoHP8wMgCAIGOofoLRv2/oVyEhPx4yFK/H9qi3w+fxLfB84EQ/v3ym26ygJ9u3Zg0+atYC1tY3YoZQob1eTBEFghYmKBT97/8chG5W0OiF5/vw5Nm7c+N4+QUFBkMlkStva4IXFFGHBDI2MUKlqdcQ+eaRoe/UqBwtnfIv42CeY/v0KpepI7JPHOLxvB0ZOnI66rm6oUr0mevYdiuqOTji8f6cYl6CVnj59gvPhZ9Hts8/EDqXEKFe2HHR1dZGYmKjU/vx5EiwsLEWKij4G/OxRUYk6ZHPgwIH37r9//77KcwQEBGDs2LHKxyW9+k9x/Vc52dmIeRiN2i4NAPyTjDyNeYRZi9fkH8aRvwQASHSUs18dHR0IeXnFEnNJ8PO+vTA3t0DzFp5ih1Ji6BsYoLZTHYSf/RNebdoq2sPPnoVnay8RI6PSjp+9t3DIRiVRExJfX19IJBIIgvDOPqpKe1KpFFKpVKnNICNTLfEVVsjKxWjk3gJWNrZITX6OXVt+wosXmWjVvhNyc19hwfSJuH/nJqbMXYK8vFwkP3/9F0MZUxn09fVRvmJl2JV3wKpFc9D3629gaibDhT9P4u+I85gyd0mxXou2ysvLw8/79qJzF1/o6XHqU1H49e2PKd9OhJOzM+rVa4A9u3YgNjYWn/f8QuzQtN6LzEw8evRPpfNJTAxuRkVBJpN9fMtWPwA/e//ChEQlUX+z29nZYfny5fD19S1wf2RkJFxdtX+VSVLCMyyaHYD01BSYlS2HmrVdMH/5Rljb2iM+7ikunj0FABg7WPmHcNbiNXCu3wh6evr4bt4ybF6zFHOn+ONl1gvY2Ttg9Lcz4Nq0WUFv+dEJP3cWsbFP4dutu9ihlDgdvD9Fakoy1qxcgYSEeFSvURPLV62BvX15sUPTetevX8Og/n0UrxcuCAIA+HTpillz54kVVonBzx4VhUR4X3lCw3x8fFC/fn3MnDmzwP1///03GjRogLwiDlvceFq8FZLSpqq1idghEBEVG8Ni+NPcyGel6k6FkHVgmFrOo41ErZBMmDABmZnvTh6qV6+OEydOFGNEREREGsAhG5VETUiaN2/+3v0mJiZo2bJlMUVDRESkIaV8ya46MGUjIiIi0XG5AhERkaZxyEYlJiRERESaxiEblZiyERERkehYISEiItKwj/L7e4qICQkREZGGMSFRjUM2REREJDpWSIiIiDSNBRKVmJAQERFpGIdsVOOQDREREYmOFRIiIiINY4VENSYkREREGsaERDUmJERERBrGhEQ1ziEhIiIi0bFCQkREpGkskKjEhISIiEjDOGSjGodsiIiISHSskBAREWkYKySqsUJCRESkYRKJRC1bUQQFBaFx48YwNTWFtbU1fH19cevWLaU+/fr1y/ceTZs2Veojl8sxatQoWFpawsTEBD4+PoiJiVHqk5ycDD8/P8hkMshkMvj5+SElJaVI8TIhISIiKoVOnTqFESNGIDw8HEePHsWrV6/Qrl07ZGZmKvXr0KEDYmNjFdvhw4eV9vv7+2Pfvn0IDQ3FmTNnkJGRgU6dOiE3N1fRp3fv3oiMjERYWBjCwsIQGRkJPz+/IsXLIRsiIiINE2PIJiwsTOn1hg0bYG1tjYiICLRo0ULRLpVKYWtrW+A5UlNTsW7dOmzevBlt2rQBAGzZsgUODg44duwY2rdvj6ioKISFhSE8PBxubm4AgLVr18Ld3R23bt2Co6NjoeJlhYSIiEjTJOrZ5HI50tLSlDa5XF6oEFJTUwEA5ubmSu0nT56EtbU1atasicGDByM+Pl6xLyIiAjk5OWjXrp2izd7eHs7Ozjh79iwA4Ny5c5DJZIpkBACaNm0KmUym6FMYTEiIiIhKiKCgIMU8jTdbUFCQyuMEQcDYsWPRrFkzODs7K9q9vb2xdetWHD9+HD/88AMuXryI1q1bK5KcuLg4GBgYoFy5ckrns7GxQVxcnKKPtbV1vve0trZW9CkMDtkQERFpmLqGbAICAjB27FilNqlUqvK4kSNH4sqVKzhz5oxSe8+ePRX/dnZ2RqNGjVCpUiX88ssv6Nat2zvPJwiC0jUVdH1v91GFCQkREZGGqSshkUqlhUpA/m3UqFE4cOAATp8+jQoVKry3r52dHSpVqoQ7d+4AAGxtbZGdnY3k5GSlKkl8fDw8PDwUfZ49e5bvXAkJCbCxsSl0nByyISIi0jAxlv0KgoCRI0di7969OH78OKpUqaLymKSkJDx+/Bh2dnYAAFdXV+jr6+Po0aOKPrGxsbh27ZoiIXF3d0dqaiouXLig6HP+/HmkpqYq+hQGKyRERESl0IgRI7Bt2zb8/PPPMDU1VcznkMlkMDIyQkZGBgIDA9G9e3fY2dnhwYMHmDx5MiwtLdG1a1dF34EDB2LcuHGwsLCAubk5xo8fDxcXF8Wqm9q1a6NDhw4YPHgwVq9eDQAYMmQIOnXqVOgVNgATEiIiIs0T4UGtK1euBAB4enoqtW/YsAH9+vWDrq4url69ik2bNiElJQV2dnZo1aoVduzYAVNTU0X/xYsXQ09PDz169EBWVha8vLwQEhICXV1dRZ+tW7di9OjRitU4Pj4+CA4OLlK8EkEQhA+8Vq1142mm6k70TlWtTcQOgYio2BgWw5/mNoN2qeU8z376XC3n0UacQ0JERESiK5VDNpWtjMUOoUQr99kasUMosZJ2DRY7hBIrL0/sCOijpaf58RR+uZ5qpTIhISIi0iZMSFTjkA0RERGJjhUSIiIiDWOFRDUmJERERJrGfEQlDtkQERGR6FghISIi0jAO2ajGhISIiEjDmJCoxoSEiIhIw5iQqMY5JERERCQ6VkiIiIg0jQUSlZiQEBERaRiHbFTjkA0RERGJjhUSIiIiDWOFRDUmJERERBrGhEQ1DtkQERGR6FghISIi0jBWSFRjQkJERKRpzEdU4pANERERiY4VEiIiIg3jkI1qTEiIiIg0jAmJakxIiIiINIz5iGqcQ0JERESiY4WEiIhIwzhkoxoTEiIiIg1jPqIah2yIiIhIdKyQEBERaRiHbFRjQkJERKRhzEdU45ANERERiY4VEiIiIg3T0WGJRBUmJERERBrGIRvVmJBowLq1q3H82FE8iL4PqaEh6tVvgDHfjEPlKlUVfRo41yrwWP+xE9B3wMDiCrXYje9eH75NK6NmhbLIkufi/K1nmLLxPO48TVXq51ihLGb3cUPzOnbQ0QGiHiXjq++P4XFiJgDApqwR5vZritb1ysPUSB+3n6Ti+92Xse9ctOIcN9f0QiVrU6XzLtwTiambL2j+QkVSmM9eUmIilixeiHNn/0RGejoaujbCxMnfoVKlyuIFriXinz3D0h8X4uyZ03gpl6NSpcqYNmM2ajs5AwCSkhKxdPFChJ/7E+np6WjYsBEmBnyHirx3AFTfv9UrluG3sMN4FhcHfX191Haqg+Gj/OFSt57IkZM2YEKiAX9duoievXqjjrMLXr3KxfKlizFsyCDs/fkQjIyNAQBHT/6hdMyff5zGjGnfwattOzFCLjbN69hh1a83EHEnAXq6EgR+2RiHAj9Fg1G78EL+CgBQxdYUv8/1wcbfb2H29ktIfZGNWhXK4mVOruI86/xbQWZsgM/n/obEtJfo2aI6No/3wifj9+Hv6CRFvxnbLmLDkZuK1xkvc4rvYkWg6rMnCAK+GTMCenr6+HHpCpiUMcGWTSH4etAApc/nxygtLRUD+vZCo8ZuWLpiLczNzRHz+DHKmJoBAARBwLj/37tFS1bAxMQEWzeHYNiQAdi97+O+d4Dq+wcAFStVxqTJU1G+ggPkL19i6+aNGPH1QPx86AjKmZuLGL3mcZWNahJBEASxg1C3FznadUnPnz+HVwsP/BSyGa6NGhfY55vRI/AiMxOr14UUb3AFsPh8bbG9l6WZIR5v6oM2kw/gzxtxAIBN47yQk5uHgT+eeOdxCdv7Y/TqM9h+8o6iLWZTH0zZdB4bj90C8LpCEnzwKoIPXtPsRfxL0q7BxfZehfH2Z+/hg2j4dvLG7v0HUa16DQBAbm4uvFp4YPQ349Hts89FizUvT7S3BgAs/fEH/H35L6zbuLXA/Q8fRKObjzd27lW+d209PTDKfzy6dhfv3mkDVfevIBkZGWjp0Qgr12xAk6buGozu/cpINZ8suEw9qpbzXJ3VVi3n0UZcZVMMMjLSAQAymazA/UmJiThz+hR8u3UvzrC0gpmxAQAgOUMO4PU4a4dGDrjzNAUHpnvjYYgfTi/wRWe3SkrHnY2Kw2efVEW5MlJIJMDnzapBqq+L09eeKvUb27U+Yjb1Qfjibpj4WQPo631cH/m3P3vZ2dkAAAMDqaKPrq4u9PUNEHk5ovgD1CKnTx6HUx1nTBw3Bm1aeqB3j67Yu3unYr/i3kmV750e7x0A1ffvbTk52di7ewfKmJqihmPBQ9iliUQiUctWmn1cv51FIAgCflgwDw0auqJ6jZoF9jl4YD+MjU3Quk3pHq4pyPwB7vjzRixuPEoGAFjLjGBqZIDx3erj6F8x6DzjMA6ERyN0Ujs0q2OnOM5v4THo6erg6Za+SN01CMuGNUfPeUcQHZeu6LP84DX0+eF3dJh6CKt+uY6RnZ2xZGizYr9GsRT02atcpSrs7O2xbMkipKWmIicnG+t/WoPExAQkJiSIHLG4nsQ8xu6d21GxYiUEr/oJ3T/viYXz5+DQgf0A/rl3wUsWIS3t9b3bsG4NkhITkJj4cd87QPX9e+P0qRNo5tYQ7o3qYduWjVixej3KlSsnTtCkVUSfQ5KVlYWIiAiYm5vDyclJad/Lly+xc+dO9OnT553Hy+VyyOVypbZcHQNI//VXjJjmzZmFO7dvYcOmbe/s8/O+PfDu1ElrYi4ui4d8ApfK5vAKOKBo0/n/XwCHLjzEsoNXAQBXopPgVssWg9vXxpnrsQCAwC8bo1wZKbynHUJS2kt0dquMrRPboM3kA7j+8HVy8+Z4ALj28DlSMrOxfVJbfLfpPJ6nK39mSqOCPnv6+vpYuHgpZkz7Di0/cYOuri7cmrrjk+YtRIxUO+TlCXCqUwcjx4wFANSq7YR79+5i987t6OTjC319fXy/aClmTv8OrZq9vndN3NzxSTPeO0D1/XujcWM3bN+1DynJydi3dxe+He+PjVt3wtzCQqTIi0dpr26og6gVktu3b6N27dpo0aIFXFxc4OnpidjYWMX+1NRU9O/f/73nCAoKgkwmU9oWzg/SdOiFMm/uLJw6cRxr12+Cja1tgX3+iriEB9HR6Nrt4xp/XjTYA52aVEL77w7hSVKmoj0x/SVyXuUh6nGyUv9bMclwsCoD4PWk12EdnTF02SmcvPIUVx88x9wdf+GvuwkY6l3nne954dYzAEA1W7N39ikt3vfZc6rjjB179uP0uYs4cuIPLF/9E1JTUlC+fAWRotUOllZWqFK1ulJblSrVEBf3z++k2k7O2L5rP07+eRG//f4Hglf9hJSUFNh/5PcOKNz9AwAjY2M4VKwEl3r1MW3GHOjq6WH/vt3FGaooJBL1bKWZqAnJpEmT4OLigvj4eNy6dQtmZmb45JNP8OjRo0KfIyAgAKmpqUrb+EkBGoxaNUEQMG/OTBw/dhSr14egfIV3/7Lav3c3ajvVgWOt0j+G+sbiwZ+gS9Mq6DD1EB7Gpyvty3mVh4i78ahZvqxSew17GR4lZAAAjKWvC3t5b83Hzs0T3vvwoXpVLQEAcckv/uslaK2ifPZMTU1hbm6Ohw8f4Mb1a/Bs1boYI9U+9eo3wMMH0Uptjx4+gJ2dfb6+pqamKGdujkcPHyDqxjW0/MjvHVC0+/dvgiAg5//zc+jjJuqQzdmzZ3Hs2DFYWlrC0tISBw4cwIgRI9C8eXOcOHECJiYmKs8hlUrzDXWIvcomaPZM/Hr4EBYvXQ4TExPF+HKZMqYwNDRU9MvIyMDRI79h7PhJYoVa7H4c+gl6tqiOz+ceQUZWDmzKGgEAUl9k42X262W9i/ddwebxXjhzPRanrj5Fu4YO+LRxJbT/7iAA4FZMCu4+TUXwsOYICAlHUvpL+LhVhle9Cug2JwwA4OZojSY1bXDq2lOkZmajUQ0rLBjgjoPnHyieZVIaFeazd/S3MJQrVw62dva4c+c2vp83B56tveD+ycczv6YgX/r1Q/8+vbB+7Sq0be+Na1evYO/unZgyfaaiz9Ej/9y7u3duY+H8OfBs5QV3j4/73gGq71/WixdYt3YVWnq2hqWVFVJSUrBrx3bEP4tDm3YdRI5e8zhko5qoy37NzMxw/vx51K5dW6l91KhR2L9/P7Zt2wZPT0/k5ua+4wwFEzsheddDz2bMngsf326K13t27cDC+UE4cuIPmJqaFniMGDS57Ddr/5AC2wcvPYktx28rXvfxcsSE7vVR3sIEt5+mYPb2CBy68FCxv5qdGWb3cYN7bRuUMdTHvdg0/PjzFcUy4PpVLbBkaDPUrFAWUj1dPErIwK4z97BobySysov2eSoKsZf9Fuazt23LJmzasB5JSUmwtLJCJ58uGPL1MOjrGxRnqPmIvewXeD3hMnjJIjx+9BD25SvgS79+6PZZD8X+7Vs3YXPIP/euY+cuGDxU/HunLd53/+RyOaZ8Ox7Xrv6NlORkyMqWRZ06Lhg4ZBjqOLuIGndxLPttOPO4Ws7z17TSW40TNSFp0qQJRo0aBT8/v3z7Ro4cia1btyItLa3EJSQlXXE+h6S0ETshKcm0ISGhjxMTEu0g6hySrl27Yvv27QXuCw4ORq9evVAKn9tGREQfGT6HRDU+qZXyYYXkw7FC8uFYISGxFEeFpNHsdz95uigufddKLefRRnwwGhEREYlO9AejERERlXalfbhFHZiQEBERaRjzEdWYkBAREWkYKySqcQ4JERERiY4JCRERkYaJ8V02QUFBaNy4MUxNTWFtbQ1fX1/cunVLqY8gCAgMDIS9vT2MjIzg6emJ69evK/WRy+UYNWoULC0tYWJiAh8fH8TExCj1SU5Ohp+fn+I75fz8/JCSklKkeJmQEBERaZgYzyE5deoURowYgfDwcBw9ehSvXr1Cu3btkJn5z9dnLFiwAIsWLUJwcDAuXrwIW1tbtG3bFunp/3zPmL+/P/bt24fQ0FCcOXMGGRkZ6NSpk9JDS3v37o3IyEiEhYUhLCwMkZGRBT709L33iM8hobfxOSQfjs8h+XB8DgmJpTieQ+I+/7RaznNuUosPPjYhIQHW1tY4deoUWrRoAUEQYG9vD39/f0ya9Po71eRyOWxsbDB//nwMHToUqampsLKywubNm9GzZ08AwNOnT+Hg4IDDhw+jffv2iIqKgpOTE8LDw+Hm5gYACA8Ph7u7O27evAlHR8dCxccKCRERkYapa8hGLpcjLS1NaZPL5YWKITU1FQBgbm4OAIiOjkZcXBzatWun6COVStGyZUucPXsWABAREYGcnBylPvb29nB2dlb0OXfuHGQymSIZAYCmTZtCJpMp+hQGExIiIiINU9eQTVBQkGKexpstKChI5fsLgoCxY8eiWbNmcHZ2BgDExcUBAGxsbJT62tjYKPbFxcXBwMAA5cqVe28fa2vrfO9pbW2t6FMYXPZLRERUQgQEBGDs2LFKbVKpVOVxI0eOxJUrV3DmzJl8+96emyIIgsr5Km/3Kah/Yc7zb6yQEBERaZi6hmykUinMzMyUNlUJyahRo3DgwAGcOHECFSpUULTb2toCQL4qRnx8vKJqYmtri+zsbCQnJ7+3z7Nnz/K9b0JCQr7qy/swISEiItIwMVbZCIKAkSNHYu/evTh+/DiqVKmitL9KlSqwtbXF0aNHFW3Z2dk4deoUPDw8AACurq7Q19dX6hMbG4tr164p+ri7uyM1NRUXLlxQ9Dl//jxSU1MVfQqDQzZERESl0IgRI7Bt2zb8/PPPMDU1VVRCZDIZjIyMIJFI4O/vj7lz56JGjRqoUaMG5s6dC2NjY/Tu3VvRd+DAgRg3bhwsLCxgbm6O8ePHw8XFBW3atAEA1K5dGx06dMDgwYOxevVqAMCQIUPQqVOnQq+wAZiQEBERaZwYj45fuXIlAMDT01OpfcOGDejXrx8AYOLEicjKysLw4cORnJwMNzc3HDlyBKampor+ixcvhp6eHnr06IGsrCx4eXkhJCQEurq6ij5bt27F6NGjFatxfHx8EBwcXKR4+RwSyofPIflwfA7Jh+NzSEgsxfEckpaL/1TLeU5984lazqONWCEhIiLSMH65nmqc1EpERESiY4WEiIhIw1ggUY0JCRERkYZxyEY1DtkQERGR6FghISIi0jAWSFRjQkJERKRhOsxIVOKQDREREYmOFRIiIiINY4FENSYkREREGsZVNqoxISEiItIwHeYjKnEOCREREYmOFRIiIiIN45CNakxIiIiINIz5iGqlMiGRgP/P/xfJu4eIHUKJtSY8WuwQSqwhTauIHQIRiahUJiRERETahH8oq8aEhIiISMO4ykY1rrIhIiIi0bFCQkREpGFcZaMaExIiIiINYz6iGodsiIiISHSskBAREWmYDkskKjEhISIi0jDmI6oxISEiItIwTmpVjXNIiIiISHSskBAREWkYCySqMSEhIiLSME5qVY1DNkRERCQ6VkiIiIg0jPUR1ZiQEBERaRhX2ajGIRsiIiISHSskREREGqbDAolKhUpIDhw4UOgT+vj4fHAwREREpRGHbFQrVELi6+tbqJNJJBLk5ub+l3iIiIjoI1SohCQvL0/TcRAREZVaLJCoxjkkREREGsYhG9U+KCHJzMzEqVOn8OjRI2RnZyvtGz16tFoCIyIiKi04qVW1Iickly9fxqeffooXL14gMzMT5ubmSExMhLGxMaytrZmQEBERUZEV+Tkk33zzDTp37oznz5/DyMgI4eHhePjwIVxdXbFw4UJNxEhERFSiSSQStWylWZETksjISIwbNw66urrQ1dWFXC6Hg4MDFixYgMmTJ2siRiIiohJNoqatNCtyQqKvr6/I0mxsbPDo0SMAgEwmU/ybiIiIqCiKPIekQYMGuHTpEmrWrIlWrVph2rRpSExMxObNm+Hi4qKJGImIiEo0nVI+3KIORa6QzJ07F3Z2dgCAWbNmwcLCAsOGDUN8fDzWrFmj9gCJiIhKOolEPVtpVuQKSaNGjRT/trKywuHDh9UaEBEREX18+GA0IiIiDSvtK2TUocgJSZUqVd57Y+/fv/+fAiotIi5dxMYN6xB14xoSEhKwaMlytPZqo9Tn/r17WLL4e0Rcuoi8vDxUq14DC374EXZ29iJFrZ1evXqFVcuX4ZdfDiIpMRGWVlbw6dIVQ74eDh2dIo86llhPb13F5d92I/7BHbxIfQ7vEdNQtaEHACD31Suc37cRD69eRFpCLAyMTODg1ADu3QfApJyF4hwnNi1BzI1IZKYkQV9qBNvqteHx2UCUs3NQ9El4eAdnd69HfPRtSHR0UM21GT7pOQQGhkbFfs3FLeLSRYSs/+fndvFS5Z9bQRCwakUw9uzagbS0NLjUrYeA76ahevUaIkatHXjv3o/5iGpFTkj8/f2VXufk5ODy5csICwvDhAkT1BVXiZeV9QI1HR3Rxbcbxn0zKt/+x48eoX+f3vDt1h3DRoxGmTKmuH//HqQGUhGi1W4b1q3Frp2hmDV3PqpVr44b165h2ncBMDU1xZd+fcUOr9jkZL+ERYUqqPVJW4StmK2071W2HAmP7qJR596wdKgCeWYGzoSuxi/LAtFj2jJFP+tKNeDo1hplLKwgz0zHhZ+34MCiyfCbHwIdHV1kJifh54UBqN6kJVp8ORzZWS9wJnQ1jq//AR2Gf1fcl1zssrJewNHREV26dsM4//w/txvWrcXmjRswc848VKpcGWtXr8TXg/rj51/CYGJSRoSItQfvHf1XRU5IxowZU2D78uXLcenSpf8cUGnRrHlLNGve8p37g5cuRrPmLfDNuImKtgoODu/s/zH7++9IeLb2QouWngCA8uUr4NfDv+D69WviBlbMKrk0RiWXxgXukxqboMu4IKW25r2HYffsMUhPioephTUAoE7LT//pYGkLt659sSNwONITn0FmbY8HV85DR08PLb8cAcn/q08tvhyBnTNGIOXZU5S1Kd3Vu/f93AqCgK2bN2HQkK/Rpm07AMDsufPRuoUHDv9yCJ/3+KI4Q9U6vHfvx1U2qqmt3u3t7Y09e/ao63SlWl5eHv44fRKVKlfGsCED0aqFO77q9TmO/35M7NC0UoMGrrgQHo4HD6IBALdu3sTlyxFo/p6Ej4DsrExAIoHU2KTA/Tnyl7j551GYWdqijLkVACD3VQ50dfUUyQgA6BkYAABi73xcCeDbnsTEIDExAe6fNFO0GRgYwLVRY/x9+bKIkWk/3juusikMtSUku3fvhrm5ubpOV6o9f56EFy9eYP26tfBo1hwr16xHa6+2GOc/EpcuXhA7PK0zYNBgdPi0I3w7ecO1Xh30/MwXX/n1hXfHTmKHprVe5WTj3O4NqOnmCQMj5YTk6vGDWD3cF2uG++LRtUvwGTcXunr6AIDyterhRVoy/grbhdxXOXiZmY7wPSEAgBepz4v7MrRKYmICAMDCwkKp3cLCEomJiWKEVGLw3on36PjTp0+jc+fOsLe3h0Qiwf79+5X29+vXL997NG3aVKmPXC7HqFGjYGlpCRMTE/j4+CAmJkapT3JyMvz8/CCTySCTyeDn54eUlJQixfpBD0b7900RBAFxcXFISEjAihUrino6REVFITw8HO7u7qhVqxZu3ryJJUuWQC6X46uvvkLr1q3fe7xcLodcLldqy9ORQirV3rkYeXl5AADPVl7w69MPAFCrVm38HfkXdu8MRaPGTUSMTvuE/XoYvxw6gKAFP6B69eq4eTMK388LgpWVNXx8u4odntbJffUKR1YFQRDy0PKrkfn212zaGg51GuJFynNc/m03fls1F90CFkFP3wAW5SvDa8B4nNmxBuF7NkCio4u6Xj4wMisHiY6uCFejfd7+j4IgCKX+L1d14b0rfpmZmahXrx769++P7t27F9inQ4cO2LBhg+K1wf+rom/4+/vj4MGDCA0NhYWFBcaNG4dOnTohIiICurqvfy/07t0bMTExCAsLAwAMGTIEfn5+OHjwYKFjLXJC0qVLF6UPlY6ODqysrODp6YlatWoV6VxhYWHo0qULypQpgxcvXmDfvn3o06cP6tWrB0EQ0L59e/z222/vTUqCgoIwY8YMpbbJ303Hd9MCixRLcSpXrhz09PRQrVo1pfYqVavh8l8RIkWlvRb/sAADBg6B96cdAQA1ajoi9ulTrPtpNROSt+S+eoXfVs1FWmIcfCfMz1cdAV7PN5Eam6CsTXnYVKuFn0Z9hvt//Ymabq0AADWbtkLNpq3wIjUZelJDSCQS/H1kH8wsbYr7crSKpeXrYa3ExERYWVkr2p8/T4KFhaVYYZUIvHdqHI4oIm9vb3h7e7+3j1Qqha2tbYH7UlNTsW7dOmzevBlt2rxeNbVlyxY4ODjg2LFjaN++PaKiohAWFobw8HC4ubkBANauXQt3d3fcunULjo6OhYq1yAlJYGBgUQ95p5kzZ2LChAmYPXs2QkND0bt3bwwbNgxz5swBAEyZMgXz5s17b0ISEBCAsWPHKrXl6WhvdQQA9PUN4FTHBQ+io5XaHz54ADv78iJFpb1eZr2Ejo7yn1G6urrIyxNEikg7vUlGUp89ge/E+TAsY1b4Y3Ny8rUZy8oBAG788Rt09fXhUKeh2mIticpXqABLSyuEn/0TtWs7AQBysrMRcekixowdL3J02o33TrufQ3Ly5ElYW1ujbNmyaNmyJebMmQNr69eJY0REBHJyctCuXTtFf3t7ezg7O+Ps2bNo3749zp07B5lMpkhGAKBp06aQyWQ4e/as5hISXV1dxMbGKoJ9IykpCdbW1sjNzS30ua5fv45NmzYBAHr06AE/Pz+lklKvXr2wbt26955DKs0/PJOV/3drsXvxIlPpywafPInBzZtRkMlksLOzR7/+AzFx/Ddo2KgxGjdxw9kzf+D0qRP4acMmEaPWTi09W2HtmlWwtbNHterVcTMqCps3bkCXrgWXH0ur7JdZSI1/qnidlhiHhEf3YGhiCpOyFghbORuJD++i45iZyMvLQ+b/53wYmphCV08fqQmxuHvhFBzquMLIVIbM5ET89esu6OoboFLdf4YJr/x+AHbVa0NfaoTHN/7C2V3r0LR7f0iNS//SzBeZb/3cxsTgZtT/f27t7fGlXx+sW7saFStVRsVKlbBuzWoYGhriU85n4r0rJgVNUyjov4OF5e3tjc8//xyVKlVCdHQ0pk6ditatWyMiIgJSqRRxcXEwMDBAuXLllI6zsbFBXFwcACAuLi5fTgAA1tbWij6FUeSERBAK/qtULpfnG3cqCh0dHRgaGqJs2bKKNlNTU6Smpn7wOcV0/do1DB7QR/H6hwWvl2R27tIVs+bMQ+s2bfHdtECs+2kNFgTNRqXKVbBw8VI0aNjoXaf8aH075TssX7oEc2fNwPPnSbCytsZnn/fE0GEjxA6tWCU8uI39309SvP5zx+vvjqrl0QaNu3yFB5HhAIAdgcOVjvOdMB/la9WDnp4Bnt65jr+P7Yc8MwPGZmVhV9MF3ScvgrFZWUX/+OhbuPDzZuTIX6KcbQV4+o2Co4fyQ/1Kq+vXr2FQ/39+bhf+/+fWp0tXzJo7D/0HDoZcLsfcWTOQlpYKl7r1sHLtej5HA7x3quioqUBS0DSF6dOnf/DoRc+ePRX/dnZ2RqNGjVCpUiX88ssv6Nat2zuPez3/55+LKqgC9HYfVSTCuzKMtyxduhQA8M0332DWrFkoU+afD1Fubi5Onz6NBw8e4HIRlnDVq1cP8+fPR4cOHQAA165dQ61ataCn9zpPOnPmDPr06VPkp79qQ4WkJNPiyqLWWxMerboTFWhI0ypih0AfKcNi+BKVsQduquU8Qe2rfHCFRCKRYN++ffD19X1vvxo1amDQoEGYNGkSjh8/Di8vLzx//lypSlKvXj34+vpixowZWL9+PcaOHZtvVU3ZsmWxePFi9O/fv1DXVuj/GxYvXgzg/4//XbVKMbMWeD0jt3Llyli1alVhTwcAGDZsmNIQj7Ozs9L+X3/9VeUqGyIioo/FfxmeKYykpCQ8fvwYdnZ2AABXV1fo6+vj6NGj6NGjBwAgNjYW165dw4IFCwAA7u7uSE1NxYULF9Ckyevh3/PnzyM1NRUeHh6Ffu9CJyTR/5+A2apVK+zduzffeNKH+Prrr9+7/83kViIiopJMrEmtGRkZuHv3ruJ1dHQ0IiMjYW5uDnNzcwQGBqJ79+6ws7PDgwcPMHnyZFhaWqJr19crGGUyGQYOHIhx48bBwsIC5ubmGD9+PFxcXBSrbmrXro0OHTpg8ODBWL16NYDXy347depU6AmtwAfMITlx4kRRDyEiIvqoqWsOSVFdunQJrVq1Urx+syq1b9++WLlyJa5evYpNmzYhJSUFdnZ2aNWqFXbs2AFTU1PFMYsXL4aenh569OiBrKwseHl5ISQkRGmkZOvWrRg9erRiNY6Pjw+Cg4OLFGuh55C88dlnn6FRo0b49ttvldq///57XLhwAbt27SpSAJrAOST/DeeQfDjOIflwnENCYimOOSQTDt1Sy3m+71T4ikNJU+RntZw6dQodO3bM196hQwecPn1aLUERERGVJvwuG9WKnBdmZGQUuLxXX18faWlpagmKiIioNOG3/apW5AqJs7MzduzYka89NDQUTk5OagmKiIioNNFR01aaFblCMnXqVHTv3h337t1TLMn9/fffsW3bNuzevVvtARIREVHpV+SExMfHB/v378fcuXOxe/duGBkZoV69ejh+/DjMzAr/3RlEREQfC47YqPZBc4s7duyomNiakpKCrVu3wt/fH3///XeRvsuGiIjoY8A5JKp98JDU8ePH8dVXX8He3h7BwcH49NNPcenSJXXGRkRERB+JIlVIYmJiEBISgvXr1yMzMxM9evRATk4O9uzZwwmtRERE78ACiWqFrpB8+umncHJywo0bN7Bs2TI8ffoUy5Yt02RsREREpYKORD1baVboCsmRI0cwevRoDBs2DDVq1NBkTERERPSRKXSF5I8//kB6ejoaNWoENzc3BAcHIyEhQZOxERERlQo6EolattKs0AmJu7s71q5di9jYWAwdOhShoaEoX7488vLycPToUaSnp2syTiIiohKLj45XrcirbIyNjTFgwACcOXMGV69exbhx4zBv3jxYW1vDx8dHEzESERFRKfefnkTr6OiIBQsWICYmBtu3b1dXTERERKUKJ7WqppYvXdbV1YWvry98fX3VcToiIqJSRYJSnk2ogVoSEiIiInq30l7dUIfS/uWBREREVAKwQkJERKRhrJCoxoSEiIhIwySlfc2uGnDIhoiIiETHCgkREZGGcchGNSYkREREGsYRG9U4ZENERESiY4WEiIhIw0r7F+OpAxMSIiIiDeMcEtU4ZENERESiY4WEiIhIwzhioxoTEiIiIg3T4ZfrqVQqExJmov9NXp4gdgglVv/GlcQOocRyGLxD7BBKtMdre4odAr0H/7ukGueQEBERkehKZYWEiIhIm3CVjWpMSIiIiDSMzyFRjUM2REREJDpWSIiIiDSMBRLVmJAQERFpGIdsVOOQDREREYmOFRIiIiINY4FENSYkREREGsbhCNV4j4iIiEh0rJAQERFpmIRjNioxISEiItIwpiOqMSEhIiLSMC77VY1zSIiIiEh0rJAQERFpGOsjqjEhISIi0jCO2KjGIRsiIiISHSskREREGsZlv6oxISEiItIwDkeoxntEREREomOFhIiISMM4ZKMaKyREREQaJlHTVlSnT59G586dYW9vD4lEgv379yvtFwQBgYGBsLe3h5GRETw9PXH9+nWlPnK5HKNGjYKlpSVMTEzg4+ODmJgYpT7Jycnw8/ODTCaDTCaDn58fUlJSihQrExIiIqJSKjMzE/Xq1UNwcHCB+xcsWIBFixYhODgYFy9ehK2tLdq2bYv09HRFH39/f+zbtw+hoaE4c+YMMjIy0KlTJ+Tm5ir69O7dG5GRkQgLC0NYWBgiIyPh5+dXpFglgiAIH3aZ2uvlK7EjKNny8krdR6LY5Ja+H6diU/XrXWKHUKI9XttT7BBKLMNimLyw++9YtZzns3p2H3ysRCLBvn374OvrC+B1dcTe3h7+/v6YNGkSgNfVEBsbG8yfPx9Dhw5FamoqrKyssHnzZvTs+foz9vTpUzg4OODw4cNo3749oqKi4OTkhPDwcLi5uQEAwsPD4e7ujps3b8LR0bFQ8bFCQkREpGE6atrkcjnS0tKUNrlc/kExRUdHIy4uDu3atVO0SaVStGzZEmfPngUAREREICcnR6mPvb09nJ2dFX3OnTsHmUymSEYAoGnTppDJZIo+hcGEhIiISMMkEolatqCgIMU8jTdbUFDQB8UUFxcHALCxsVFqt7GxUeyLi4uDgYEBypUr994+1tbW+c5vbW2t6FMYXGVDRERUQgQEBGDs2LFKbVKp9D+d8+0VQIIgqFwV9HafgvoX5jz/xgoJERGRhqlrlY1UKoWZmZnS9qEJia2tLQDkq2LEx8crqia2trbIzs5GcnLye/s8e/Ys3/kTEhLyVV/ehwkJERGRhkkk6tnUqUqVKrC1tcXRo0cVbdnZ2Th16hQ8PDwAAK6urtDX11fqExsbi2vXrin6uLu7IzU1FRcuXFD0OX/+PFJTUxV9CoNDNkRERKVURkYG7t69q3gdHR2NyMhImJubo2LFivD398fcuXNRo0YN1KhRA3PnzoWxsTF69+4NAJDJZBg4cCDGjRsHCwsLmJubY/z48XBxcUGbNm0AALVr10aHDh0wePBgrF69GgAwZMgQdOrUqdArbAAmJMVm5fJlWLVCeR24hYUljp/+U6SISoZ1P61G8JLF6P1VH0yYNBkAMG3Ktzh4YL9SP5e69bBp6w4RItQunTt4Ifbp03ztn/fshUlTpiHwuwAceuveObvURUgpv3djOtZGR9cKqGFriqycXFy8m4iZu67gXlx6gf0X9m2Evp7V8N22y1h99Lai3drMENN71oNnHRuYGOrjXlw6fjx0AwcvKT8kqm1dO4zzqQMnBxleyHNx7nYC+gd/XD/r3m1b4+nTJ/nae37RG5OnThchInHpfNBjzf67S5cuoVWrVorXb+af9O3bFyEhIZg4cSKysrIwfPhwJCcnw83NDUeOHIGpqanimMWLF0NPTw89evRAVlYWvLy8EBISAl1dXUWfrVu3YvTo0YrVOD4+Pu989sm7MCEpRtWq18CanzYoXuv86/9Myu/6tavYu3snatTMn2F7fNIcM2bPVbzW19cvztC01qZtu5Cb98/Diu7dvYMRQwbCq10HRZvHJ80xbdYcxeuP4d55OFph/e93cDn6OfR0dTC5mwt2jWuJZlN+xYvsXKW+3g3Kw7WqOWKTX+Q7z/IhbjAz0sdXS87geYYc3ZtWwtph7mg74yiuPkoBAHRyrYBF/Rphzp6r+CPqGSQSCZwqyIrjMrXK1h27kfevB2fdvXsHQwf1R9v2Hd5zVOkl1pPjPT098b7HjUkkEgQGBiIwMPCdfQwNDbFs2TIsW7bsnX3Mzc2xZcuW/xKq9iUkRZ2VW5Lo6erC0spK7DBKhBcvMjH52/GYOn0WflqzMt9+AwMDWFryXr6tnLm50uuN69aigkNFuDZqrGjT/wjvXc9Fp5Vej15/ATeX+qJeZXOcu52gaLcta4R5XzVEjx9OYds3LfKdp3E1C0zYFIHL0c8BAIsO3sDQdjVRt1I5XH2UAl0dCeb0boAZO//G1j+iFce9qxJTmpm/9Vlc/9MaODhURKPGTUSKiLSd1k1qlUqliIqKEjsMjXj46CHaeDaDd7vWmDj+G8Q8fix2SForaM5MNG/uiabuBU+IunTpAlq39ECXTu0xM3AqniclFXOE2i8nJxuHfzkIH99uSkl+xKULaNvyE3Tr3AGzP9J7Z2b0uiqUnJmtaJNIgBVD3LA87CZuPU0r8LjzdxLh26QiypoYQCIBfJs4QKqngz9vxgMA6lYqB3tzY+QJwPHAdri22Aeh37SAo72Z5i9Ki+VkZ+OXQwfg2617qf2DUxWJmv5XmolWIXl7HfUbubm5mDdvHiwsLAAAixYtKs6wNMalbl3MmTsflSpXRlJSEtauXok+X36BvQcOoWzZcqpP8BEJ+/UX3LxxA1tCdxe4/5PmLdC2fQfY2dnjyZMYrAheiiGD+mHbjj0wMDAo5mi118njvyMjPR2du3RVtHk0a4427drD1s4eT588warlS/H1oH7Y8pHdu5lf1Ef47QTcfJKqaBv9aW28yhWw5uiddx43aOU5/DTMHXeCuyLnVR6ysl+h77I/8SAhEwBQycoEADChSx1MC43Eo8RMDO/giJ+/bY2mAYeR8q8E6GNy/PgxpKenw8e3q+rOpdRHmocViWgJyY8//oh69eqhbNmySu2CICAqKgomJiaFyqTlcnm+x+YKutL//KAYdWvWvKXi3zUA1K1XH506tMWB/fvRp19/8QLTMnFxsfh+3lysWLPunf8ftu/wqeLf1WvUhFMdZ3zazgt/nD4JrzbtCjzmY/Tzvj3w+KQ5rP71BMV2+e5dHXRq3wZnTp9E64/k3s3/qiGcHMqi09zfFW11K5XDkLY10DrwyHuPndzNBTJjA3RbcALPM7Lh3bA81o3wQOeg44iKSYWOzuvfWYsP3cChiNcTXUevu4C/F3WGT2MHbDp5T3MXpsX27dmDT5q1gLV14Z9JQR8f0RKSOXPmYO3atfjhhx/QunVrRbu+vj5CQkLg5ORUqPMEBQVhxowZSm1Tpk7Hd9MC1Rmu2hkbG6NGzZp49OiB2KFolajr1/H8eRK+7Nld0Zabm4u/Ii5hx/atOB9xRWlmNwBYWVnDzt4ejx4+LO5wtVbs0ye4EH4OCxYvfW8/Sytr2Nnb4dGjj+PeBX3ZEO0blIdP0HHEJmcp2t1rWsHS1BCRCzsr2vR0dTDji3oY0q4mXCccQmUrEwxqUwPNpvyqGNK5/jgFTWtYYkDr6piwKQLPUl4CAG7/a8gn+1UeHsZnooK5cTFdpXZ5+vQJzoefxaIl754Q+TEQa5VNSSJaQhIQEIA2bdrgq6++QufOnREUFPRBs/0LeoyuoKtd1ZGCZGdn4/79e2jQ0FXsULRKk6ZNsWvvAaW26VMno0qVqug3YFC+ZAQAUlKS8SwulhOG/+XA/n0oZ26uVJkryOt7F/dRTHKd91VDfNqwPHznn8CjxEylfTvPPsCpG8pPmtw5rgV2nX2IbWdeT041kr7+dZn31oqFPEGAzv+ruX8/eI6XObmobmuK83cSAQB6uhI4WJrgcZLye34sft63F+bmFmjewlPsUETFIRvVRF1l07hxY0RERGDEiBFo1KgRtmzZUuQJT1Jp/uGZl6/UGaV6/PD9fLT0bAVbOzs8f/4ca1etRGZGxkc9ploQE5MyqF6jplKbkZERZGXLonqNmnjxIhOrVgTDq007WFlZ4enTJ1i2ZDHKli2H1l5tRIpau+Tl5eHgz3vRyccXenr//Ii/eJGJNSuWo3XbtrC0tMbTp0+wYunre9fKq62IEWvefD9XdG9aEX2WnkFG1itYmxkCANKycvAyJxfJmdlKE1wBICdXQHzqS8UKmTuxabj/LB0/9G2E6Tv+RnKGHN4NK6Clky2+XPIHACDj5StsPHEPE32d8eT5CzxOeoGR3rUAAAcufnyT2PPy8vDzvr3o3EX5s/gxYkKimuifkDJlymDjxo0IDQ1F27ZtkZubq/qgEujZszh8O2EskpNTUM68HOrWrY/N23bC3r682KGVKDo6urh75zYOHfwZ6WnpsLSyQuPGTTB/4WKYmJQROzytcCH8HOJiY+Hj202pXUdHF3fv3sYvB39Geno6LK0s0aixG+Z+vwgmJiYiRVs8BrSuDgD4+dvWSu2jfjqP0D8fFOocr3IF9Fp8GlM/q4stY5rDxFAP0c8yMPKn8zh2JVbRL3BnJF7l5WH54KYwMtBFxP0kdFtwAqkvctR2PSVF+LmziI19Ct9u3VV3po+eRHjfE1OKWUxMDCIiItCmTZv/9AtSGyskJUlentZ8JEqcXO35cSpxqn69S+wQSrTHa3uKHUKJZVgMf5ofjUpUy3na1rZUy3m0kegVkn+rUKECKlSoIHYYREREaqXDIRuVtO7BaERERPTx0aoKCRERUWlU2p+yqg5MSIiIiDSMq2xU45ANERERiY4VEiIiIg3jkI1qTEiIiIg0jKtsVOOQDREREYmOFRIiIiIN45CNakxIiIiINIyrbFRjQkJERKRhzEdU4xwSIiIiEh0rJERERBqmwzEblZiQEBERaRjTEdU4ZENERESiY4WEiIhI01giUYkJCRERkYbxOSSqcciGiIiIRMcKCRERkYZxkY1qTEiIiIg0jPmIahyyISIiItGxQkJERKRpLJGoxISEiIhIw7jKRjUmJERERBrGSa2qcQ4JERERiY4VEiIiIg1jgUQ1JiRERESaxoxEJQ7ZEBERkehYISEiItIwrrJRjQkJERGRhnGVjWocsiEiIiLRsUJCRESkYSyQqCYRBEEQOwh1e/lK7AhKtle5pe4jUWz0dPlr50OVvt9Excu8yUixQyixsi4Ha/w9/n6crpbz1HMwVct5tBGHbIiIiEh0HLIhIiLSMK6yUY0JCRERkYZxlY1qTEiIiIg0jPmIapxDQkRERKJjhYSIiEjTWCJRiQkJERGRhnFSq2ocsiEiIiLRMSEhIiLSMIlEPVtRBAYGQiKRKG22traK/YIgIDAwEPb29jAyMoKnpyeuX7+udA65XI5Ro0bB0tISJiYm8PHxQUxMjDpuST5MSIiIiDRMoqatqOrUqYPY2FjFdvXqVcW+BQsWYNGiRQgODsbFixdha2uLtm3bIj39n6fK+vv7Y9++fQgNDcWZM2eQkZGBTp06ITc39wOieT/OISEiIiql9PT0lKoibwiCgB9//BFTpkxBt27dAAAbN26EjY0Ntm3bhqFDhyI1NRXr1q3D5s2b0aZNGwDAli1b4ODggGPHjqF9+/ZqjZUVEiIiIk1TU4lELpcjLS1NaZPL5e982zt37sDe3h5VqlTBF198gfv37wMAoqOjERcXh3bt2in6SqVStGzZEmfPngUAREREICcnR6mPvb09nJ2dFX3UiQkJERGRhknU9L+goCDIZDKlLSgoqMD3dHNzw6ZNm/Dbb79h7dq1iIuLg4eHB5KSkhAXFwcAsLGxUTrGxsZGsS8uLg4GBgYoV67cO/uoE4dsiIiISoiAgACMHTtWqU0qlRbY19vbW/FvFxcXuLu7o1q1ati4cSOaNm0KAJC8NVNWEIR8bW8rTJ8PwQoJERGRhqlrlY1UKoWZmZnS9q6E5G0mJiZwcXHBnTt3FPNK3q50xMfHK6omtra2yM7ORnJy8jv7qBMTEiIiIg0Ta5XNv8nlckRFRcHOzg5VqlSBra0tjh49qtifnZ2NU6dOwcPDAwDg6uoKfX19pT6xsbG4du2aoo86cciGiIhI00R4UOv48ePRuXNnVKxYEfHx8Zg9ezbS0tLQt29fSCQS+Pv7Y+7cuahRowZq1KiBuXPnwtjYGL179wYAyGQyDBw4EOPGjYOFhQXMzc0xfvx4uLi4KFbdqBMTEiIiolIoJiYGvXr1QmJiIqysrNC0aVOEh4ejUqVKAICJEyciKysLw4cPR3JyMtzc3HDkyBGYmpoqzrF48WLo6emhR48eyMrKgpeXF0JCQqCrq6v2eCWCIAhqP6vIXr4SO4KS7VVuqftIFBs9XX5fxYcqfb+Jipd5k5Fih1BiZV0O1vh73HmWpZbz1LAxUst5tBErJERERBqmgUUppQ4ntRIREZHoWCEhIiLSMBZIVGNCQkREpGnMSFTikA0RERGJjhUSIiIiDZOwRKISExIiIiIN4yob1ThkQ0RERKJjhYSIiEjDWCBRjQkJERGRpjEjUYkJCRERkYZxUqtqnENSTHaGbsNnXTvDo0lDeDRpCL/ePXHmj1Nih6WV4p89w3cBE9C6uRs8mtRHr899EXXjWoF958ycBte6tbBt88ZijlI7RVy6iFHDv0Ybz2aoV8cRx38/prT/2NEj+HrwQLT8xA316jjiZlSUSJFqp4hLFzF6xNdo26oZ6jvnv39Tp3yL+s6OSptf7x4iRVt8Bn/eDBd2BODZH9/j2R/f4+TGcWj3iZNSnylDP8X9I3Pw/Nwi/LZ2DGpXtX3n+fYHD0PW5WB09qyraGvuWgNZl4ML3FydKmrs2kh7sEJSTKxtbDHmm/FwqPj6B+vgz/sxZuQI7NizD9Wr1xA5Ou2RlpaKAX17oVFjNyxdsRbm5uaIefwYZUzN8vU9cfwYrl29AitraxEi1U5ZWS/g6OiILl27YZz/qAL312/QAO3ad8CM6d+JEKF2y8p6gZqOjuji2w3jvsl//wDgk2bNMWN2kOK1vr5+cYUnmifPUjB12c+49ygRAPBVZzfsWjwETb+Yh6j7cRjXrw1Gf9UKQ6ZvwZ2H8fh2cAf8smoU6vrORMYLudK5Rn3ZqsAvUgz/+z4qtwlQaps2vBNauzki4sYjjV1bceEqG9WYkBQTz1atlV6PGvMNdoZux5W/I5mQ/EvI+p9gY2OHwFn//MK3L18hX7/4Z8+wYO4sBK/6CWNGDi3OELVas+Yt0ax5y3fu7+zjCwB48iSmmCIqWVTdPwDQNzCApaVVMUWkHQ6fVq5QBi4/iMGfN0OTulUQdT8OI3q3woJ1v+Hn438DAAZN3YyHv89FT+9GWLfnT8VxLjXLY/RXrdHsqwV4cCxI6Zw5r3LxLCld8VpPTwcdW7pg1Y7TGryy4sN8RDUO2YggNzcXvx7+BVlZL1CvXgOxw9Eqp08eh1MdZ0wcNwZtWnqgd4+u2Lt7p1KfvLw8TJ08EX79BqIakzkqZpcuXkCrFu7w6dgeM6Z/h+dJSWKHVKx0dCT4vL0rTIwMcP5KNCqXt4CdlQzHzt1U9MnOeYU/Iu6iab2qijYjQ31sDOqHb+bvVEo83qVTy7qwLFsGWw6Ea+Q6SPtoVYUkOTkZGzduxJ07d2BnZ4e+ffvCwcFB7LDU5s7tW/Dr/QWys+UwNjbG4qXLUa16dbHD0ipPYh5j987t+NKvHwYMGorr165g4fw5MDAwQKf//3Ufsn4tdPV00etLP3GDpY9Os2Yt0LZdB9jb2+PJkxgsX7YEgwf2xfade2FgYCB2eBpVp7o9Tm4cB0MDPWRkydFz3FrcvB+HpvWqAADinysnGfFJ6ahoZ654vWBcd4T/HY1DJ68W6v36+rrj6LkoxDxLUds1iIlDNqqJmpDY29vj6tWrsLCwQHR0NDw8PAAALi4uOHDgABYuXIjw8HDUqlXrneeQy+WQy5XHKAVdKaRSqUZj/xCVK1fBzj37kZ6ehmNHj2Dq5ElYF7KFScm/5OUJcKpTByPHjAUA1KrthHv37mL3zu3o5PN6cmvo1s3YumMPJPwJp2LW3vtTxb+r16gJpzrO8G7bGn+cOgmvtu1EjEzzbj94BrcvglDW1Bi+XvWxdqYf2g1aotgvvDUxRCL5p61jSxd4NqmJpl/MK9R7lbcui7butfHVpPXquwDR8feVKqIO2cTFxSE3NxcAMHnyZNSqVQv37t3DkSNHcPfuXTRv3hxTp0597zmCgoIgk8mUtu/nB733GLHoGxigYqVKqOPsgjHfjENNx1rYumWT2GFpFUsrK1SpqpygValSDXFxsQCAyxEReP48CR3bt0aTBnXQpEEdxD59isU/zEenDq0LOiWRxlhZWcPO3h6PHj0QOxSNy3mVi/uPE/HXjUeYtuwArt5+ghG9PBGXmAYAsLFQnnhuZW6qqJp4Nq6JqhUsEXf6e6RfXIL0i68Tme0LB+G3tWPyvZdfl6ZISs3EoVNXNHxVpE20Zsjm/Pnz+Omnn2BsbAwAkEql+O677/DZZ5+997iAgACMHTtWqU3Q1b7qSEEEQUBOdrbYYWiVevUb4OGDaKW2Rw8fwM7OHgDwaWcfNGnqrrR/5LBB+LRTF/h06VpscRIBQEpKMp7FxcLS8uNb6SWBBFIDPTx4koTYhFR4Na2Fv2+9niytr6eL5q7V8d2SnwEACzccwYZ9Z5WOj9g9BRN/2INfTuVf0t/Hpym2HbqAV6/yNH8hxYQFXdVET0jelN3lcjlsbGyU9tnY2CAhIeG9x0ul+YdnXr5Sb4zqsPTHRWjWvAVsbG3xIjMTYb8exqWLF7Bi9U9ih6ZVvvTrh/59emH92lVo294b165ewd7dOzFl+kwAQNmy5VC2bDmlY/T09GBpYYnKVaoWdMqPyovMTDx69M8SyScxMbgZFQWZTAY7e3ukpqQgNjYWCQnxAIAH/0/+LC0tYWn1ca0cKciLF2/dvycxuHkzSlF9XbU8GF5t28HSygpPnzzBsiWLUbZcObRu00bEqDVvxsjOOPLnDTyOS4apiSE+b++KFo1qwGfECgDA8m0nMGFgO9x9FI+7jxIwcWB7ZL3MwY5fLwEAniWlFziR9XFsMh4+VZ4U7NmkJqpUsETI/rP5+pdkzEdUEz0h8fLygp6eHtLS0nD79m3UqVNHse/Ro0ewtLQUMTr1SUpKxJRvJyIhIR5lTE1Rs6YjVqz+Ce4en4gdmlap4+yChYuXIXjJIqxdvQL25Stg3MQAfNqxs9ihlQjXr1/DoP59FK8XLng9fOnTpStmzZ2HkyeOY9p3/zzrYdL4bwAAXw8fiWEjCn7uxsfk+rVrGDzgn/v3w//vX+cuXTFlaiDu3LmNgwf3Iz0tHVZWVmjUxA0LFi6GiUkZsUIuFtYWplg3uw9sLc2QmvES1+48gc+IFTh+/vXKmh9CjsFQaoAfA3qinJkxLl57gE7DgvM9g6Qw+vl64FzkPdyKfqbuyyAtJxHenolUjGbMmKH0umnTpmjfvr3i9YQJExATE4Pt27cX6bzaWCEpSV7livaRKPH0dPl30IcS7zdR6WDeZKTYIZRYWZeDNf4esanqGZ63k5Xe1VyiJiSawoTkv2FC8uGYkHy40vebqHgxIflwxZGQxKXmqOU8trLS+2Rg0YdsiIiISj3+raISn9RKREREomOFhIiISMNYIFGNCQkREZGG8TkkqnHIhoiIiETHCgkREZGGSThooxITEiIiIk1jPqISh2yIiIhIdKyQEBERaRgLJKoxISEiItIwrrJRjUM2REREJDpWSIiIiDSMq2xUY0JCRESkYRyyUY1DNkRERCQ6JiREREQkOg7ZEBERaRiHbFRjQkJERKRhnNSqGodsiIiISHSskBAREWkYh2xUY0JCRESkYcxHVOOQDREREYmOFRIiIiJNY4lEJSYkREREGsZVNqpxyIaIiIhExwoJERGRhnGVjWpMSIiIiDSM+YhqHLIhIiLSNImatg+wYsUKVKlSBYaGhnB1dcUff/zxny5FU5iQEBERlVI7duyAv78/pkyZgsuXL6N58+bw9vbGo0ePxA4tH4kgCILYQajby1diR1CyvcotdR+JYqOny8Lshyp9v4mKl3mTkWKHUGJlXQ7W/HvkqOc8RvpF6+/m5oaGDRti5cqVirbatWvD19cXQUFB6glKTVghISIi0jCJRD1bUWRnZyMiIgLt2rVTam/Xrh3Onj2rxqtTD05qJSIiKiHkcjnkcrlSm1QqhVQqzdc3MTERubm5sLGxUWq3sbFBXFycRuP8EKUyITHU4quSy+UICgpCQEBAgR8graCnncMOJeLeaTHevw9XEu5dcQw7fIiScO+Kg7r+uxQ4OwgzZsxQaps+fToCAwPfeYzkrdKKIAj52rRBqZxDos3S0tIgk8mQmpoKMzMzscMpUXjv/hvevw/He/fheO/UqygVkuzsbBgbG2PXrl3o2rWron3MmDGIjIzEqVOnNB5vUXAOCRERUQkhlUphZmamtL2r8mRgYABXV1ccPXpUqf3o0aPw8PAojnCLRIsHN4iIiOi/GDt2LPz8/NCoUSO4u7tjzZo1ePToEb7++muxQ8uHCQkREVEp1bNnTyQlJWHmzJmIjY2Fs7MzDh8+jEqVKokdWj5MSIqZVCrF9OnTP+rJXR+K9+6/4f37cLx3H473TnzDhw/H8OHDxQ5DJU5qJSIiItFxUisRERGJjgkJERERiY4JCREREYmOCQkRERGJjglJMTl9+jQ6d+4Me3t7SCQS7N+/X+yQSoygoCA0btwYpqamsLa2hq+vL27duiV2WCXCypUrUbduXcUDlNzd3fHrr7+KHVaJFBQUBIlEAn9/f7FDKRECAwMhkUiUNltbW7HDIi3GhKSYZGZmol69eggO1s7vm9Bmp06dwogRIxAeHo6jR4/i1atXaNeuHTIzM8UOTetVqFAB8+bNw6VLl3Dp0iW0bt0aXbp0wfXr18UOrUS5ePEi1qxZg7p164odSolSp04dxMbGKrarV6+KHRJpMT6HpJh4e3vD29tb7DBKpLCwMKXXGzZsgLW1NSIiItCiRQuRoioZOnfurPR6zpw5WLlyJcLDw1GnTh2RoipZMjIy8OWXX2Lt2rWYPXu22OGUKHp6eqyKUKGxQkIlTmpqKgDA3Nxc5EhKltzcXISGhiIzMxPu7u5ih1NijBgxAh07dkSbNm3EDqXEuXPnDuzt7VGlShV88cUXuH//vtghkRZjhYRKFEEQMHbsWDRr1gzOzs5ih1MiXL16Fe7u7nj58iXKlCmDffv2wcnJSeywSoTQ0FD89ddfuHjxotihlDhubm7YtGkTatasiWfPnmH27Nnw8PDA9evXYWFhIXZ4pIWYkFCJMnLkSFy5cgVnzpwRO5QSw9HREZGRkUhJScGePXvQt29fnDp1ikmJCo8fP8aYMWNw5MgRGBoaih1OifPvIWoXFxe4u7ujWrVq2LhxI8aOHStiZKStmJBQiTFq1CgcOHAAp0+fRoUKFcQOp8QwMDBA9erVAQCNGjXCxYsXsWTJEqxevVrkyLRbREQE4uPj4erqqmjLzc3F6dOnERwcDLlcDl1dXREjLFlMTEzg4uKCO3fuiB0KaSkmJKT1BEHAqFGjsG/fPpw8eRJVqlQRO6QSTRAEyOVyscPQel5eXvlWhfTv3x+1atXCpEmTmIwUkVwuR1RUFJo3by52KKSlmJAUk4yMDNy9e1fxOjo6GpGRkTA3N0fFihVFjEz7jRgxAtu2bcPPP/8MU1NTxMXFAQBkMhmMjIxEjk67TZ48Gd7e3nBwcEB6ejpCQ0Nx8uTJfCuXKD9TU9N885RMTExgYWHB+UuFMH78eHTu3BkVK1ZEfHw8Zs+ejbS0NPTt21fs0EhLMSEpJpcuXUKrVq0Ur9+Mofbt2xchISEiRVUyrFy5EgDg6emp1L5hwwb069ev+AMqQZ49ewY/Pz/ExsZCJpOhbt26CAsLQ9u2bcUOjUq5mJgY9OrVC4mJibCyskLTpk0RHh6OSpUqiR0aaSmJIAiC2EEQERHRx43PISEiIiLRMSEhIiIi0TEhISIiItExISEiIiLRMSEhIiIi0TEhISIiItExISEiIiLRMSEhKoUCAwNRv359xet+/frB19e32ON48OABJBIJIiMji/29iahkYUJCVIz69esHiUQCiUQCfX19VK1aFePHj0dmZqZG33fJkiWFfiIwkwgiEgMfHU9UzDp06IANGzYgJycHf/zxBwYNGoTMzEzFI/LfyMnJgb6+vlreUyaTqeU8RESawgoJUTGTSqWwtbWFg4MDevfujS+//BL79+9XDLOsX78eVatWhVQqhSAISE1NxZAhQ2BtbQ0zMzO0bt0af//9t9I5582bBxsbG5iammLgwIF4+fKl0v63h2zy8vIwf/58VK9eHVKpFBUrVsScOXMAQPFtyg0aNIBEIlH6DqENGzagdu3aMDQ0RK1atbBixQql97lw4QIaNGgAQ0NDNGrUCJcvX1bjnSOi0owVEiKRGRkZIScnBwBw9+5d7Ny5E3v27FF8vX3Hjh1hbm6Ow4cPQyaTYfXq1fDy8sLt27dhbm6OnTt3Yvr06Vi+fDmaN2+OzZs3Y+nSpahateo73zMgIABr167F4sWL0axZM8TGxuLmzZsAXicVTZo0wbFjx1CnTh0YGBgAANauXYvp06cjODgYDRo0wOXLlzF48GCYmJigb9++yMzMRKdOndC6dWts2bIF0dHRGDNmjIbvHhGVGgIRFZu+ffsKXbp0Ubw+f/68YGFhIfTo0UOYPn26oK+vL8THxyv2//7774KZmZnw8uVLpfNUq1ZNWL16tSAIguDu7i58/fXXSvvd3NyEevXqFfi+aWlpglQqFdauXVtgjNHR0QIA4fLly0rtDg4OwrZt25TaZs2aJbi7uwuCIAirV68WzM3NhczMTMX+lStXFnguIqK3cciGqJgdOnQIZcqUgaGhIdzd3dGiRQssW7YMAFCpUiVYWVkp+kZERCAjIwMWFhYoU6aMYouOjsa9e/cAAFFRUXB3d1d6j7df/1tUVBTkcjm8vLwKHXNCQgIeP36MgQMHKsUxe/ZspTjq1asHY2PjQsVBRPRvHLIhKmatWrXCypUroa+vD3t7e6WJqyYmJkp98/LyYGdnh5MnT+Y7T9myZT/o/Y2MjIp8TF5eHoDXwzZubm5K+94MLQmC8EHxEBEBTEiIip2JiQmqV69eqL4NGzZEXFwc9PT0ULly5QL71K5dG+Hh4ejTp4+iLTw8/J3nrFGjBoyMjPD7779j0KBB+fa/mTOSm5uraLOxsUH58uVx//59fPnllwWe18nJCZs3b0ZWVpYi6XlfHERE/8YhGyIt1qZNG7i7u8PX1xe//fYbHjx4gLNnz+K7777DpUuXAABjxozB+vXrsX79ety+fRvTp0/H9evX33lOQ0NDTJo0CRMnTsSmTZtw7949hIeHY926dQAAa2trGBkZISwsDM+ePUNqaiqA1w9bCwoKwpIlS3D79m1cvXoVGzZswKJFiwAAvXv3ho6ODgYOHIgbN27g8OHDWLhwoYbvEBGVFkxIiLSYRCLB4cOH0aJFCwwYMAA1a9bEF198gQcPHsDGxgYA0LNnT0ybNg2TJk2Cq6srHj58iGHDhr33vFOnTsW4ceMwbdo01K5dGz179kR8fDwAQE9PD0uXLsXq1athb2+PLl26AAAGDRqEn376CSEhIXBxcUHLli0REhKiWCZcpkwZHDx4EDdu3ECDBg0wZcoUzJ8/X4N3h4hKE4nAgV8iIiISGSskREREJDomJERERCQ6JiREREQkOiYkREREJDomJERERCQ6JiREREQkOiYkREREJDomJERERCQ6JiREREQkOiYkREREJDomJERERCQ6JiREREQkuv8BlsVYvjdUQJcAAAAASUVORK5CYII="},"metadata":{}}]},{"cell_type":"markdown","source":"# **random forest**","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n\n# Create a Random Forest Classifier\nclf = RandomForestClassifier(n_estimators=100, random_state=42)\n\n# Train the model using the training data\nclf.fit(X_train, y_train)\n\n# Make predictions on the test data\ny_pred = clf.predict(X_test)\n\n# Calculate the accuracy of the model\naccuracy = accuracy_score(y_test, y_pred)\nprint(\"Accuracy:\", accuracy,\"\\n\\n\")\naccuracy_arr.append(accuracy)\n\n\nprint(classification_report(y_test, y_pred))\n\n\ncm = confusion_matrix(y_test, y_pred)\nsns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\nplt.xlabel(\"Predicted\")\nplt.ylabel(\"Actual\")\nplt.title(\"Confusion Matrix\")\nplt.xticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\nplt.yticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:02:44.836498Z","iopub.execute_input":"2023-06-20T14:02:44.836940Z","iopub.status.idle":"2023-06-20T14:03:10.547509Z","shell.execute_reply.started":"2023-06-20T14:02:44.836909Z","shell.execute_reply":"2023-06-20T14:03:10.546341Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Accuracy: 0.9845933014354067 \n\n\n              precision    recall  f1-score   support\n\n           1       1.00      0.98      0.99       536\n           2       0.98      0.98      0.98      2873\n           3       0.98      0.97      0.98      1283\n           4       0.98      0.98      0.98      2618\n           5       0.98      1.00      0.99      3140\n\n    accuracy                           0.98     10450\n   macro avg       0.99      0.98      0.98     10450\nweighted avg       0.98      0.98      0.98     10450\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# from sklearn.ensemble import RandomForestClassifier\n# from sklearn.model_selection import train_test_split\n# from sklearn.metrics import accuracy_score,mean_squared_error\n\n\n# # Create a Random Forest Classifier\n# clf = RandomForestClassifier(n_estimators=100, random_state=42)\n\n# from sklearn.model_selection import cross_val_score\n\n# # Train the model using the training data\n# clf.fit(X_train, y_train)\n\n# from sklearn.metrics import mean_absolute_error,make_scorer\n# mae_scorer=make_scorer(mean_absolute_error)\n\n# n= X.shape[0]\n\n# cv_results=cross_val_score(\n#     estimator=clf,\n#     X=X,\n#     y=y,\n#     cv=n,\n#     scoring=mae_scorer\n# )\n# print(cv_results)\n# print(format(cv_results.mean()))\n# print(format(cv_results.std()))","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:03:10.548714Z","iopub.execute_input":"2023-06-20T14:03:10.549060Z","iopub.status.idle":"2023-06-20T14:03:10.555276Z","shell.execute_reply.started":"2023-06-20T14:03:10.549006Z","shell.execute_reply":"2023-06-20T14:03:10.553929Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"markdown","source":"# **KNN**","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n\n# Create a KNN classifier\nknn = KNeighborsClassifier(n_neighbors=7)  # You can adjust the number of neighbors as needed\n\n# Train the classifier\nknn.fit(X_train, y_train)\n\n# Predict the classes for the test set\ny_pred = knn.predict(X_test)\n\n# Calculate accuracy\naccuracy = accuracy_score(y_test, y_pred)\naccuracy_arr.append(accuracy)\n\nprint(f\"Accuracy: {accuracy}\\n\\n\")\nprint(classification_report(y_test, y_pred))\n\n\ncm = confusion_matrix(y_test, y_pred)\nsns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\nplt.xlabel(\"Predicted\")\nplt.ylabel(\"Actual\")\nplt.title(\"Confusion Matrix\")\nplt.xticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\nplt.yticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:03:10.556794Z","iopub.execute_input":"2023-06-20T14:03:10.557774Z","iopub.status.idle":"2023-06-20T14:03:12.788101Z","shell.execute_reply.started":"2023-06-20T14:03:10.557734Z","shell.execute_reply":"2023-06-20T14:03:12.787302Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"Accuracy: 0.8725358851674642\n\n\n              precision    recall  f1-score   support\n\n           1       0.91      0.98      0.95       536\n           2       0.85      0.87      0.86      2873\n           3       0.81      0.87      0.84      1283\n           4       0.83      0.88      0.85      2618\n           5       0.97      0.85      0.90      3140\n\n    accuracy                           0.87     10450\n   macro avg       0.87      0.89      0.88     10450\nweighted avg       0.88      0.87      0.87     10450\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# multilayer perceptron","metadata":{}},{"cell_type":"code","source":"from sklearn.neural_network import MLPClassifier\n\n\nmlp = MLPClassifier()\n\n\n\n# Train the classifier\nmlp.fit(X_train, y_train)\n\n# Predict the classes for the test set\ny_pred = mlp.predict(X_test)\n\n# Calculate accuracy\naccuracy = accuracy_score(y_test, y_pred)\naccuracy_arr.append(accuracy)\n\nprint(f\"Accuracy: {accuracy}\\n\\n\")\nprint(classification_report(y_test, y_pred))\n\n\ncm = confusion_matrix(y_test, y_pred)\nsns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\nplt.xlabel(\"Predicted\")\nplt.ylabel(\"Actual\")\nplt.title(\"Confusion Matrix\")\nplt.xticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\nplt.yticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:03:12.789451Z","iopub.execute_input":"2023-06-20T14:03:12.790003Z","iopub.status.idle":"2023-06-20T14:04:42.243082Z","shell.execute_reply.started":"2023-06-20T14:03:12.789974Z","shell.execute_reply":"2023-06-20T14:04:42.242120Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Accuracy: 0.9577033492822966\n\n\n              precision    recall  f1-score   support\n\n           1       0.99      0.98      0.98       536\n           2       0.96      0.93      0.95      2873\n           3       0.93      0.97      0.95      1283\n           4       0.96      0.95      0.95      2618\n           5       0.96      0.98      0.97      3140\n\n    accuracy                           0.96     10450\n   macro avg       0.96      0.96      0.96     10450\nweighted avg       0.96      0.96      0.96     10450\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# naive Bayes Classifier","metadata":{}},{"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\ngnb = GaussianNB()\n\n\n\n\n# Train the classifier\ngnb.fit(X_train, y_train)\n\n# Predict the classes for the test set\ny_pred = gnb.predict(X_test)\n\n# Calculate accuracy\naccuracy = accuracy_score(y_test, y_pred)\naccuracy_arr.append(accuracy)\n\nprint(f\"Accuracy: {accuracy}\\n\\n\")\nprint(classification_report(y_test, y_pred))\n\n\ncm = confusion_matrix(y_test, y_pred)\nsns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\nplt.xlabel(\"Predicted\")\nplt.ylabel(\"Actual\")\nplt.title(\"Confusion Matrix\")\nplt.xticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\nplt.yticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:04:42.249290Z","iopub.execute_input":"2023-06-20T14:04:42.250024Z","iopub.status.idle":"2023-06-20T14:04:43.129111Z","shell.execute_reply.started":"2023-06-20T14:04:42.249980Z","shell.execute_reply":"2023-06-20T14:04:43.127981Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"Accuracy: 0.700287081339713\n\n\n              precision    recall  f1-score   support\n\n           1       0.98      0.95      0.96       536\n           2       0.71      0.80      0.75      2873\n           3       0.63      0.38      0.48      1283\n           4       0.61      0.68      0.64      2618\n           5       0.75      0.72      0.73      3140\n\n    accuracy                           0.70     10450\n   macro avg       0.73      0.70      0.71     10450\nweighted avg       0.70      0.70      0.70     10450\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# evaluate","metadata":{}},{"cell_type":"code","source":"accuracy_arr\n","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:04:57.896386Z","iopub.execute_input":"2023-06-20T14:04:57.896754Z","iopub.status.idle":"2023-06-20T14:04:57.903482Z","shell.execute_reply.started":"2023-06-20T14:04:57.896726Z","shell.execute_reply":"2023-06-20T14:04:57.902353Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"[0.9555023923444976,\n 0.9845933014354067,\n 0.8725358851674642,\n 0.9577033492822966,\n 0.700287081339713,\n 0.5735885167464115,\n 0.5932057416267943]"},"metadata":{}}]},{"cell_type":"markdown","source":"#  evaluation using leave one group out strategy","metadata":{}},{"cell_type":"code","source":"data2=pd.read_csv('/kaggle/input/molap-data-preprocessed/preprocessed data.csv')\ndata3=pd.read_csv('/kaggle/input/molap-data-preprocessed/preprocessed data.csv')\ndata4=pd.read_csv('/kaggle/input/molap-data-preprocessed/preprocessed data.csv')","metadata":{"execution":{"iopub.status.busy":"2023-06-20T17:50:15.917582Z","iopub.execute_input":"2023-06-20T17:50:15.917993Z","iopub.status.idle":"2023-06-20T17:50:16.939736Z","shell.execute_reply.started":"2023-06-20T17:50:15.917958Z","shell.execute_reply":"2023-06-20T17:50:16.938554Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"data2","metadata":{"execution":{"iopub.status.busy":"2023-06-20T17:41:16.878295Z","iopub.execute_input":"2023-06-20T17:41:16.878830Z","iopub.status.idle":"2023-06-20T17:41:16.971102Z","shell.execute_reply.started":"2023-06-20T17:41:16.878786Z","shell.execute_reply":"2023-06-20T17:41:16.969828Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"       Class  User         X0          Y0         Z0         X1          Y1  \\\n0          1     0 -11.632335   81.551968 -28.007209  73.564324   51.161458   \n1          1     0  72.692270   50.023771 -72.963253 -11.593076   81.234142   \n2          1     0  85.648015   73.561022 -71.838316  31.595034   90.217896   \n3          1     0  52.895241   82.454113 -55.683891  32.285729   90.277753   \n4          1     0  51.874242   82.509948 -56.138338  -9.177149   81.411469   \n...      ...   ...        ...         ...        ...        ...         ...   \n52242      5    14  54.251127  129.177414 -44.252511  27.720784  107.810661   \n52243      5    14  54.334883  129.253842 -44.016320  27.767911  107.914808   \n52244      5    14  54.151540  129.269502 -44.173273  27.725978  108.034006   \n52245      5    14  27.915311  108.007390  10.814957  -0.910435  122.464093   \n52246      5    14  27.898705  108.092877  11.107857 -30.031402   77.740235   \n\n              Z1         X2          Y2  ...         Z3         X4  \\\n0     -70.505689  86.213513   75.013944  ... -56.215452  32.298489   \n1     -28.450210  51.729995   82.844469  ... -69.734298  30.760697   \n2     -47.246688  52.230858   82.361548  ... -70.111828  -8.906288   \n3     -46.444718  89.077013   74.655391  ... -22.302190   5.716538   \n4     -25.398447  76.251197   50.602365  ... -46.321366   5.992934   \n...          ...        ...         ...  ...        ...        ...   \n52242  11.099282  -1.270139  122.758679  ...   4.075018  26.030878   \n52243  11.069842 -30.334054   77.858214  ... -53.007244  26.446943   \n52244  11.020347 -22.574718  104.222208  ... -17.354844  26.506640   \n52245 -47.271248 -30.084588   77.705861  ... -34.675774 -22.329495   \n52246 -17.453099  -1.091566  122.827638  ... -43.679664  -1.558846   \n\n               Y4         Z4         X5          Y5         Z5         X6  \\\n0       91.536040 -44.074387   9.927196   89.375605 -31.650131   7.349235   \n1       90.449170 -47.909511  10.028439   88.967979 -32.045120   7.732511   \n2       81.516793 -25.243914  10.277516   88.607958 -32.037539  56.308829   \n3       24.408403   7.772205  76.388881   50.615057 -67.772759  56.588176   \n4       24.448655   8.070139  58.986353   20.060940 -65.225054  87.356929   \n...           ...        ...        ...         ...        ...        ...   \n52242  136.368236 -32.792393  77.293710   34.070857 -43.263790 -30.282201   \n52243  135.823494 -33.851425  -1.090674  122.724914 -46.558373  -1.958502   \n52244  135.605903 -34.441463  61.475331  101.321181  -6.616988  -1.125738   \n52245  104.026432 -54.334623  61.639386  101.224662  -6.840942  54.009962   \n52246   98.017201   3.421814  26.586910  135.762922 -34.445944 -22.316029   \n\n               Y6         Z6  \n0       24.986273  10.242808  \n1       25.135415  10.723395  \n2       18.774768 -70.996523  \n3       18.538049 -70.821229  \n4       73.746252 -68.295038  \n...           ...        ...  \n52242   77.622306 -17.150292  \n52243   98.288931   4.248334  \n52244  122.614027 -46.796054  \n52245  129.477879 -43.967820  \n52246  104.124677 -54.344079  \n\n[52247 rows x 23 columns]","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>Class</th>\n      <th>User</th>\n      <th>X0</th>\n      <th>Y0</th>\n      <th>Z0</th>\n      <th>X1</th>\n      <th>Y1</th>\n      <th>Z1</th>\n      <th>X2</th>\n      <th>Y2</th>\n      <th>...</th>\n      <th>Z3</th>\n      <th>X4</th>\n      <th>Y4</th>\n      <th>Z4</th>\n      <th>X5</th>\n      <th>Y5</th>\n      <th>Z5</th>\n      <th>X6</th>\n      <th>Y6</th>\n      <th>Z6</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>-11.632335</td>\n      <td>81.551968</td>\n      <td>-28.007209</td>\n      <td>73.564324</td>\n      <td>51.161458</td>\n      <td>-70.505689</td>\n      <td>86.213513</td>\n      <td>75.013944</td>\n      <td>...</td>\n      <td>-56.215452</td>\n      <td>32.298489</td>\n      <td>91.536040</td>\n      <td>-44.074387</td>\n      <td>9.927196</td>\n      <td>89.375605</td>\n      <td>-31.650131</td>\n      <td>7.349235</td>\n      <td>24.986273</td>\n      <td>10.242808</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>72.692270</td>\n      <td>50.023771</td>\n      <td>-72.963253</td>\n      <td>-11.593076</td>\n      <td>81.234142</td>\n      <td>-28.450210</td>\n      <td>51.729995</td>\n      <td>82.844469</td>\n      <td>...</td>\n      <td>-69.734298</td>\n      <td>30.760697</td>\n      <td>90.449170</td>\n      <td>-47.909511</td>\n      <td>10.028439</td>\n      <td>88.967979</td>\n      <td>-32.045120</td>\n      <td>7.732511</td>\n      <td>25.135415</td>\n      <td>10.723395</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>85.648015</td>\n      <td>73.561022</td>\n      <td>-71.838316</td>\n      <td>31.595034</td>\n      <td>90.217896</td>\n      <td>-47.246688</td>\n      <td>52.230858</td>\n      <td>82.361548</td>\n      <td>...</td>\n      <td>-70.111828</td>\n      <td>-8.906288</td>\n      <td>81.516793</td>\n      <td>-25.243914</td>\n      <td>10.277516</td>\n      <td>88.607958</td>\n      <td>-32.037539</td>\n      <td>56.308829</td>\n      <td>18.774768</td>\n      <td>-70.996523</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>52.895241</td>\n      <td>82.454113</td>\n      <td>-55.683891</td>\n      <td>32.285729</td>\n      <td>90.277753</td>\n      <td>-46.444718</td>\n      <td>89.077013</td>\n      <td>74.655391</td>\n      <td>...</td>\n      <td>-22.302190</td>\n      <td>5.716538</td>\n      <td>24.408403</td>\n      <td>7.772205</td>\n      <td>76.388881</td>\n      <td>50.615057</td>\n      <td>-67.772759</td>\n      <td>56.588176</td>\n      <td>18.538049</td>\n      <td>-70.821229</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>51.874242</td>\n      <td>82.509948</td>\n      <td>-56.138338</td>\n      <td>-9.177149</td>\n      <td>81.411469</td>\n      <td>-25.398447</td>\n      <td>76.251197</td>\n      <td>50.602365</td>\n      <td>...</td>\n      <td>-46.321366</td>\n      <td>5.992934</td>\n      <td>24.448655</td>\n      <td>8.070139</td>\n      <td>58.986353</td>\n      <td>20.060940</td>\n      <td>-65.225054</td>\n      <td>87.356929</td>\n      <td>73.746252</td>\n      <td>-68.295038</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      <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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>52242</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.251127</td>\n      <td>129.177414</td>\n      <td>-44.252511</td>\n      <td>27.720784</td>\n      <td>107.810661</td>\n      <td>11.099282</td>\n      <td>-1.270139</td>\n      <td>122.758679</td>\n      <td>...</td>\n      <td>4.075018</td>\n      <td>26.030878</td>\n      <td>136.368236</td>\n      <td>-32.792393</td>\n      <td>77.293710</td>\n      <td>34.070857</td>\n      <td>-43.263790</td>\n      <td>-30.282201</td>\n      <td>77.622306</td>\n      <td>-17.150292</td>\n    </tr>\n    <tr>\n      <th>52243</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.334883</td>\n      <td>129.253842</td>\n      <td>-44.016320</td>\n      <td>27.767911</td>\n      <td>107.914808</td>\n      <td>11.069842</td>\n      <td>-30.334054</td>\n      <td>77.858214</td>\n      <td>...</td>\n      <td>-53.007244</td>\n      <td>26.446943</td>\n      <td>135.823494</td>\n      <td>-33.851425</td>\n      <td>-1.090674</td>\n      <td>122.724914</td>\n      <td>-46.558373</td>\n      <td>-1.958502</td>\n      <td>98.288931</td>\n      <td>4.248334</td>\n    </tr>\n    <tr>\n      <th>52244</th>\n      <td>5</td>\n      <td>14</td>\n      <td>54.151540</td>\n      <td>129.269502</td>\n      <td>-44.173273</td>\n      <td>27.725978</td>\n      <td>108.034006</td>\n      <td>11.020347</td>\n      <td>-22.574718</td>\n      <td>104.222208</td>\n      <td>...</td>\n      <td>-17.354844</td>\n      <td>26.506640</td>\n      <td>135.605903</td>\n      <td>-34.441463</td>\n      <td>61.475331</td>\n      <td>101.321181</td>\n      <td>-6.616988</td>\n      <td>-1.125738</td>\n      <td>122.614027</td>\n      <td>-46.796054</td>\n    </tr>\n    <tr>\n      <th>52245</th>\n      <td>5</td>\n      <td>14</td>\n      <td>27.915311</td>\n      <td>108.007390</td>\n      <td>10.814957</td>\n      <td>-0.910435</td>\n      <td>122.464093</td>\n      <td>-47.271248</td>\n      <td>-30.084588</td>\n      <td>77.705861</td>\n      <td>...</td>\n      <td>-34.675774</td>\n      <td>-22.329495</td>\n      <td>104.026432</td>\n      <td>-54.334623</td>\n      <td>61.639386</td>\n      <td>101.224662</td>\n      <td>-6.840942</td>\n      <td>54.009962</td>\n      <td>129.477879</td>\n      <td>-43.967820</td>\n    </tr>\n    <tr>\n      <th>52246</th>\n      <td>5</td>\n      <td>14</td>\n      <td>27.898705</td>\n      <td>108.092877</td>\n      <td>11.107857</td>\n      <td>-30.031402</td>\n      <td>77.740235</td>\n      <td>-17.453099</td>\n      <td>-1.091566</td>\n      <td>122.827638</td>\n      <td>...</td>\n      <td>-43.679664</td>\n      <td>-1.558846</td>\n      <td>98.017201</td>\n      <td>3.421814</td>\n      <td>26.586910</td>\n      <td>135.762922</td>\n      <td>-34.445944</td>\n      <td>-22.316029</td>\n      <td>104.124677</td>\n      <td>-54.344079</td>\n    </tr>\n  </tbody>\n</table>\n<p>52247 rows × 23 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#data3.columns = list(range(len(data.columns)))\n# group_criteria\n# unique_groups[1:]","metadata":{"execution":{"iopub.status.busy":"2023-06-20T18:02:21.093843Z","iopub.execute_input":"2023-06-20T18:02:21.094239Z","iopub.status.idle":"2023-06-20T18:02:21.103040Z","shell.execute_reply.started":"2023-06-20T18:02:21.094208Z","shell.execute_reply":"2023-06-20T18:02:21.101606Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"array([ 1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"},"metadata":{}}]},{"cell_type":"code","source":"data2.columns\ndata2.columns = list(range(len(data2.columns)))\n\nX2 = data2.iloc[:, 1:]  # Features (excluding the first column)\ny2 = data2.iloc[:, 0]\n# columns_to_drop =  [1]\n# data2.drop(columns=columns_to_drop, inplace=True)\n# data2\n\ngroup_criteria = data2[1]  # grouping by users\n\n# Get unique groups\nunique_groups = group_criteria.unique()\n\n\n# group_counts = data2[1].value_counts()\n# print(\"items   :\",group_counts.items())\n\n\nfor group in unique_groups[1:]:\n    group_data = data2[group_criteria == group]\n    X2_test = group_data.iloc[:, 1:]  # Features (excluding the first column)\n    print(\"X2_test\",X2_test)\n    y2_test = group_data.iloc[:, 0]\n    print(\"y2_test\",y2_test)\n    X2_train = X2[~X2.isin(X2_test)].dropna()\n    print(\"X2_train\",X2_train)\n    y2_train = y2[~y2.isin(y2_test)].dropna()\n    print(\"y2_train\",y2_train)\n    print(\"group \",group)\n#     print(\"X2\",X2_train)\n#     print(\"y2\",y2_train)\n    from sklearn.ensemble import RandomForestClassifier\n    from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n\n    # Create a Random Forest Classifier\n    clf = RandomForestClassifier(n_estimators=100, random_state=42)\n\n    # Train the model using the training data\n    clf.fit(X2_train, y2_train)\n\n    # Make predictions on the test data\n    y2_pred = clf.predict(X2_test)\n\n    # Calculate the accuracy of the model\n    accuracy = accuracy_score(y2_test, y2_pred)\n    print(\"Accuracy:\", accuracy,\"\\n\\n\")\n    accuracy_arr2.append(accuracy)\n\n\n    print(classification_report(y2_test, y2_pred))\n\n\n    cm = confusion_matrix(y2_test, y2_pred)\n    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n    plt.xlabel(\"Predicted\")\n    plt.ylabel(\"Actual\")\n    plt.title(\"Confusion Matrix\")\n    plt.xticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\n    plt.yticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\n    plt.show()\n    \n    \n\n\n\n\n\n# columns_to_drop =  [1]\n# data2.drop(columns=columns_to_drop, inplace=True)\n# X2=data2\n# print(X2)","metadata":{"execution":{"iopub.status.busy":"2023-06-20T18:02:59.920198Z","iopub.execute_input":"2023-06-20T18:02:59.920742Z","iopub.status.idle":"2023-06-20T18:03:00.175580Z","shell.execute_reply.started":"2023-06-20T18:02:59.920708Z","shell.execute_reply":"2023-06-20T18:03:00.172924Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"X2_test       1           2           3          4           5           6   \\\n4956   1   91.954207   30.520454 -74.350182   74.664648  145.586475   \n4957   1   74.202397  145.441010 -12.137054  106.829768   48.974694   \n4958   1   75.062998  144.977795 -10.904926   61.928850   94.226740   \n4959   1  105.554495   48.812583 -84.585550   61.154185   94.384083   \n4960   1   35.251571  156.954916  14.021846  106.752287   47.172923   \n...   ..         ...         ...        ...         ...         ...   \n7522   1   65.805808   95.862657 -22.934007   44.003841  102.581872   \n7523   1   66.396721   95.970294 -21.222178   13.107724  102.737399   \n7524   1   44.059559  102.415351  -1.092215   66.492754   95.910419   \n7525   1   44.026991  102.451826  -1.278688   66.463999   95.927675   \n7526   1   43.747502  102.575826  -2.235376   66.032496   96.012128   \n\n             7           8           9          10  ...         13         14  \\\n4956 -10.645704   63.824820  157.774846   5.834552  ... -82.154939  35.774019   \n4957 -83.124660   63.960178  157.426064   4.686021  ... -64.616623  61.497569   \n4958 -16.586337  107.200367   49.021410 -82.730842  ...  16.439011  91.527192   \n4959 -18.051808   74.263173  145.081261 -12.879987  ...   2.842849  34.471817   \n4960 -84.049299   91.580987   28.194562 -75.267039  ... -18.644042  66.341850   \n...         ...         ...         ...        ...  ...        ...        ...   \n7522  -1.821793   12.598201  102.886442   5.535568  ... -93.505257  53.658696   \n7523   7.157727   44.459848  102.265183  -0.351244  ... -91.909613  54.808652   \n7524 -21.213886   13.226157  102.785258   7.329587  ... -53.905702 -15.374031   \n7525 -21.319648   13.172992  102.702014   7.181130  ...  -9.482159  54.533340   \n7526 -22.354489   12.756220  102.833559   6.024041  ... -92.995293  51.701563   \n\n              15         16         17          18         19         20  \\\n4956  156.145866  18.603676  42.471219  100.489977  -6.307221  17.972480   \n4957   94.368989 -17.110208  40.664801  101.243463  -8.758549  17.665172   \n4958   30.061042 -74.923948  17.496576   97.854266   3.900636  68.183622   \n4959  156.945072  15.416046  63.493751  157.597872   3.362281  65.366624   \n4960    5.378806 -65.276608  64.839842  157.023948   2.423755  17.988520   \n...          ...        ...        ...         ...        ...        ...   \n7522  126.492524 -55.983064 -16.233236   95.268128 -10.834326  62.660937   \n7523  127.008336 -53.475576 -15.245208   95.239555  -8.806240  52.379837   \n7524   95.220582  -9.102143  52.320072   23.997639 -72.507134  28.370810   \n7525  127.036878 -53.973566  52.597618   23.799876 -72.190701  60.020240   \n7526   23.697590 -73.527430  54.388004  126.828373 -54.798112 -15.510020   \n\n              21         22  \n4956   97.813680   4.954045  \n4957   97.746559   3.921897  \n4958    6.143974 -63.626404  \n4959    6.693201 -66.091197  \n4960   97.859565   2.419050  \n...          ...        ...  \n7522   54.298298 -95.129800  \n7523   24.073025 -72.510675  \n7524  137.146043 -42.591622  \n7525   80.530971 -91.836955  \n7526   95.201809 -10.067796  \n\n[2571 rows x 22 columns]\ny2_test 4956    2\n4957    2\n4958    2\n4959    2\n4960    2\n       ..\n7522    5\n7523    5\n7524    5\n7525    5\n7526    5\nName: 0, Length: 2571, dtype: int64\nX2_train          1          2           3          4          5           6   \\\n0       0.0 -11.632335   81.551968 -28.007209  73.564324   51.161458   \n1       0.0  72.692270   50.023771 -72.963253 -11.593076   81.234142   \n2       0.0  85.648015   73.561022 -71.838316  31.595034   90.217896   \n3       0.0  52.895241   82.454113 -55.683891  32.285729   90.277753   \n4       0.0  51.874242   82.509948 -56.138338  -9.177149   81.411469   \n...     ...        ...         ...        ...        ...         ...   \n52242  14.0  54.251127  129.177414 -44.252511  27.720784  107.810661   \n52243  14.0  54.334883  129.253842 -44.016320  27.767911  107.914808   \n52244  14.0  54.151540  129.269502 -44.173273  27.725978  108.034006   \n52245  14.0  27.915311  108.007390  10.814957  -0.910435  122.464093   \n52246  14.0  27.898705  108.092877  11.107857 -30.031402   77.740235   \n\n              7          8           9          10  ...         13         14  \\\n0     -70.505689  86.213513   75.013944 -69.068351  ... -56.215452  32.298489   \n1     -28.450210  51.729995   82.844469 -55.674024  ... -69.734298  30.760697   \n2     -47.246688  52.230858   82.361548 -55.975665  ... -70.111828  -8.906288   \n3     -46.444718  89.077013   74.655391 -66.253441  ... -22.302190   5.716538   \n4     -25.398447  76.251197   50.602365 -66.696980  ... -46.321366   5.992934   \n...          ...        ...         ...        ...  ...        ...        ...   \n52242  11.099282  -1.270139  122.758679 -46.460186  ...   4.075018  26.030878   \n52243  11.069842 -30.334054   77.858214 -17.002723  ... -53.007244  26.446943   \n52244  11.020347 -22.574718  104.222208 -53.939140  ... -17.354844  26.506640   \n52245 -47.271248 -30.084588   77.705861 -17.460853  ... -34.675774 -22.329495   \n52246 -17.453099  -1.091566  122.827638 -46.760985  ... -43.679664  -1.558846   \n\n               15         16         17          18         19         20  \\\n0       91.536040 -44.074387   9.927196   89.375605 -31.650131   7.349235   \n1       90.449170 -47.909511  10.028439   88.967979 -32.045120   7.732511   \n2       81.516793 -25.243914  10.277516   88.607958 -32.037539  56.308829   \n3       24.408403   7.772205  76.388881   50.615057 -67.772759  56.588176   \n4       24.448655   8.070139  58.986353   20.060940 -65.225054  87.356929   \n...           ...        ...        ...         ...        ...        ...   \n52242  136.368236 -32.792393  77.293710   34.070857 -43.263790 -30.282201   \n52243  135.823494 -33.851425  -1.090674  122.724914 -46.558373  -1.958502   \n52244  135.605903 -34.441463  61.475331  101.321181  -6.616988  -1.125738   \n52245  104.026432 -54.334623  61.639386  101.224662  -6.840942  54.009962   \n52246   98.017201   3.421814  26.586910  135.762922 -34.445944 -22.316029   \n\n               21         22  \n0       24.986273  10.242808  \n1       25.135415  10.723395  \n2       18.774768 -70.996523  \n3       18.538049 -70.821229  \n4       73.746252 -68.295038  \n...           ...        ...  \n52242   77.622306 -17.150292  \n52243   98.288931   4.248334  \n52244  122.614027 -46.796054  \n52245  129.477879 -43.967820  \n52246  104.124677 -54.344079  \n\n[49676 rows x 22 columns]\ny2_train 0        1\n1        1\n2        1\n3        1\n4        1\n        ..\n46478    1\n46479    1\n46480    1\n46481    1\n46482    1\nName: 0, Length: 2639, dtype: int64\ngroup  1\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[0;32mIn[16], line 40\u001b[0m\n\u001b[1;32m     37\u001b[0m clf \u001b[38;5;241m=\u001b[39m RandomForestClassifier(n_estimators\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# Train the model using the training data\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m \u001b[43mclf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX2_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my2_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;66;03m# Make predictions on the test data\u001b[39;00m\n\u001b[1;32m     43\u001b[0m y2_pred \u001b[38;5;241m=\u001b[39m clf\u001b[38;5;241m.\u001b[39mpredict(X2_test)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:345\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(y):\n\u001b[1;32m    344\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msparse multilabel-indicator for y is not supported.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 345\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    346\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\n\u001b[1;32m    347\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sample_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    349\u001b[0m     sample_weight \u001b[38;5;241m=\u001b[39m _check_sample_weight(sample_weight, X)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/base.py:584\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    582\u001b[0m         y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[1;32m    583\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 584\u001b[0m         X, y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_X_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    585\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m    587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:1124\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1106\u001b[0m X \u001b[38;5;241m=\u001b[39m check_array(\n\u001b[1;32m   1107\u001b[0m     X,\n\u001b[1;32m   1108\u001b[0m     accept_sparse\u001b[38;5;241m=\u001b[39maccept_sparse,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1119\u001b[0m     input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1120\u001b[0m )\n\u001b[1;32m   1122\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[0;32m-> 1124\u001b[0m \u001b[43mcheck_consistent_length\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m X, y\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:397\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    395\u001b[0m uniques \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(lengths)\n\u001b[1;32m    396\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(uniques) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 397\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    398\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    399\u001b[0m         \u001b[38;5;241m%\u001b[39m [\u001b[38;5;28mint\u001b[39m(l) \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m lengths]\n\u001b[1;32m    400\u001b[0m     )\n","\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [49676, 2639]"],"ename":"ValueError","evalue":"Found input variables with inconsistent numbers of samples: [49676, 2639]","output_type":"error"}]},{"cell_type":"code","source":"from sklearn.model_selection import LeaveOneGroupOut\nlogo = LeaveOneGroupOut()\n\nusers=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14]\naccuracy_arr4=[]\n\nfor train_indices, test_indices in logo.split(X2, y2, groups=users):\n    # Split the data into training and testing sets for the current user\n    X4_train, X4_test = X2[train_indices], X2[test_indices]\n    y4_train, y4_test = y2[train_indices], y2[test_indices]\n    \n    \n    print(\"train set:\",len(X2_train)) \n    print(\"test test:\",len(X2_test))  \n    \n    \n    from sklearn.ensemble import RandomForestClassifier\n    from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n\n    # Create a Random Forest Classifier\n    clf = RandomForestClassifier(n_estimators=100, random_state=42)\n\n    # Train the model using the training data\n    clf.fit(X2_train, y2_train)\n\n    # Make predictions on the test data\n    y2_pred = clf.predict(X2_test)\n\n    # Calculate the accuracy of the model\n    accuracy = accuracy_score(y2_test, y2_pred)\n    print(\"Accuracy:\", accuracy,\"\\n\\n\")\n    accuracy_arr2.append(accuracy)\n\n\n    print(classification_report(y2_test, y2_pred))\n\n\n    cm = confusion_matrix(y2_test, y2_pred)\n    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n    plt.xlabel(\"Predicted\")\n    plt.ylabel(\"Actual\")\n    plt.title(\"Confusion Matrix\")\n    plt.xticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\n    plt.yticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\n    plt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-06-20T18:00:56.282644Z","iopub.execute_input":"2023-06-20T18:00:56.283040Z","iopub.status.idle":"2023-06-20T18:00:56.537639Z","shell.execute_reply.started":"2023-06-20T18:00:56.283010Z","shell.execute_reply":"2023-06-20T18:00:56.535082Z"},"trusted":true},"execution_count":14,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[0;32mIn[14], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m users\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m11\u001b[39m,\u001b[38;5;241m12\u001b[39m,\u001b[38;5;241m13\u001b[39m,\u001b[38;5;241m14\u001b[39m]\n\u001b[1;32m      5\u001b[0m accuracy_arr4\u001b[38;5;241m=\u001b[39m[]\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train_indices, test_indices \u001b[38;5;129;01min\u001b[39;00m logo\u001b[38;5;241m.\u001b[39msplit(X2, y2, groups\u001b[38;5;241m=\u001b[39musers):\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;66;03m# Split the data into training and testing sets for the current user\u001b[39;00m\n\u001b[1;32m      9\u001b[0m     X4_train, X4_test \u001b[38;5;241m=\u001b[39m X2[train_indices], X2[test_indices]\n\u001b[1;32m     10\u001b[0m     y4_train, y4_test \u001b[38;5;241m=\u001b[39m y2[train_indices], y2[test_indices]\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_split.py:83\u001b[0m, in \u001b[0;36mBaseCrossValidator.split\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msplit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, groups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m     60\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Generate indices to split data into training and test set.\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \n\u001b[1;32m     62\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[38;5;124;03m        The testing set indices for that split.\u001b[39;00m\n\u001b[1;32m     82\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 83\u001b[0m     X, y, groups \u001b[38;5;241m=\u001b[39m \u001b[43mindexable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     84\u001b[0m     indices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(_num_samples(X))\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m test_index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iter_test_masks(X, y, groups):\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:443\u001b[0m, in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m    424\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Make arrays indexable for cross-validation.\u001b[39;00m\n\u001b[1;32m    425\u001b[0m \n\u001b[1;32m    426\u001b[0m \u001b[38;5;124;03mChecks consistent length, passes through None, and ensures that everything\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    439\u001b[0m \u001b[38;5;124;03m    sparse matrix, or dataframe) or `None`.\u001b[39;00m\n\u001b[1;32m    440\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    442\u001b[0m result \u001b[38;5;241m=\u001b[39m [_make_indexable(X) \u001b[38;5;28;01mfor\u001b[39;00m X \u001b[38;5;129;01min\u001b[39;00m iterables]\n\u001b[0;32m--> 443\u001b[0m \u001b[43mcheck_consistent_length\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    444\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:397\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    395\u001b[0m uniques \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(lengths)\n\u001b[1;32m    396\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(uniques) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 397\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    398\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    399\u001b[0m         \u001b[38;5;241m%\u001b[39m [\u001b[38;5;28mint\u001b[39m(l) \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m lengths]\n\u001b[1;32m    400\u001b[0m     )\n","\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [52247, 52247, 15]"],"ename":"ValueError","evalue":"Found input variables with inconsistent numbers of samples: [52247, 52247, 15]","output_type":"error"}]},{"cell_type":"code","source":"from sklearn.model_selection import LeaveOneGroupOut\nlogo = LeaveOneGroupOut()\n\n\naccuracy_arr2=[]\n# Iterate over unique values and split the data\nfor group_value, count in group_counts.items():\n    group_data = data3[data3[1] == group_value]\n    print(f\"Group: {group_value}\")\n    #print(group_data)\n    # the target variable is in the first column\n    X2_test = group_data.iloc[:, 1:]  #test dataset x2\n    y2_test = group_data.iloc[:, 0]   #test dataset y2\n    \n    \n    \n    X2_train = X2.drop(X2_test) #train dataset\n    y2_train = y2.drop(y2_test) #test dataset\n\n\n    #print(len(X2))      \n    print(\"train set:\",len(X2_train)) \n    print(\"test test:\",len(X2_test))  \n    \n    \n    from sklearn.ensemble import RandomForestClassifier\n    from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n\n    # Create a Random Forest Classifier\n    clf = RandomForestClassifier(n_estimators=100, random_state=42)\n\n    # Train the model using the training data\n    clf.fit(X2_train, y2_train)\n\n    # Make predictions on the test data\n    y2_pred = clf.predict(X2_test)\n\n    # Calculate the accuracy of the model\n    accuracy = accuracy_score(y2_test, y2_pred)\n    print(\"Accuracy:\", accuracy,\"\\n\\n\")\n    accuracy_arr2.append(accuracy)\n\n\n    print(classification_report(y2_test, y2_pred))\n\n\n    cm = confusion_matrix(y2_test, y2_pred)\n    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n    plt.xlabel(\"Predicted\")\n    plt.ylabel(\"Actual\")\n    plt.title(\"Confusion Matrix\")\n    plt.xticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\n    plt.yticks([0.5, 1.5, 2.5, 3.5, 4.5], [\"1\", \"2\",\"3\",\"4\",\"5\"])\n    plt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:04:59.569896Z","iopub.status.idle":"2023-06-20T14:04:59.570307Z","shell.execute_reply.started":"2023-06-20T14:04:59.570111Z","shell.execute_reply":"2023-06-20T14:04:59.570135Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X2_train\n#y2_test","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:04:59.572399Z","iopub.status.idle":"2023-06-20T14:04:59.573254Z","shell.execute_reply.started":"2023-06-20T14:04:59.573044Z","shell.execute_reply":"2023-06-20T14:04:59.573064Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# from sklearn.model_selection import LeaveOneGroupOut\n# from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n# from sklearn.ensemble import RandomForestClassifier\n\n\n# # we have features X and corresponding labels y and user information users\n\n# # Step 2: Iterative Training and Testing\n# logo = LeaveOneGroupOut()\n# users = [1,2,3,4,5] # List of unique user identifiers\n# performance_metrics = {'accuracy': [], 'precision': [], 'recall': [], 'f1_score': []}\n\n# for train_index, test_index in logo.split(X, y, X[1]==users):\n#     X_train, X_test = X[train_index], X[test_index]\n#     y_train, y_test = y[train_index], y[test_index]\n\n#     # Step 3: Model Training\n#     linear_svm = LinearSVC()\n#     linear_svm.fit(X_train, y_train)\n\n# #     rbf_svm = SVC(kernel='rbf')\n# #     rbf_svm.fit(X_train, y_train)\n\n#     random_forest = RandomForestClassifier()\n#     random_forest.fit(X_train, y_train)\n\n# #     knn = KNeighborsClassifier()\n# #     knn.fit(X_train, y_train)\n\n# #     logistic_regression = LogisticRegression()\n# #     logistic_regression.fit(X_train, y_train)\n\n#     # Step 4: Model Testing\n#     linear_svm_predictions = linear_svm.predict(X_test)\n# #     rbf_svm_predictions = rbf_svm.predict(X_test)\n#     random_forest_predictions = random_forest.predict(X_test)\n# #     knn_predictions = knn.predict(X_test)\n# #     logistic_regression_predictions = logistic_regression.predict(X_test)\n\n#     # Step 5: Performance Metrics\n#     accuracy = accuracy_score(y_test, linear_svm_predictions)\n#     precision = precision_score(y_test, linear_svm_predictions, average='weighted')\n#     recall = recall_score(y_test, linear_svm_predictions, average='weighted')\n#     f1 = f1_score(y_test, linear_svm_predictions, average='weighted')\n\n#     performance_metrics['accuracy'].append(accuracy)\n#     performance_metrics['precision'].append(precision)\n#     performance_metrics['recall'].append(recall)\n#     performance_metrics['f1_score'].append(f1)\n\n# # Step 6: Statistical Analysis (if desired)\n# # Perform statistical tests to compare the performance of different models\n\n# # Print the average performance metrics across all users\n# print(\"Average Performance Metrics:\")\n# for metric, values in performance_metrics.items():\n#     average_metric = sum(values) / len(values)\n#     print(metric + \": \", average_metric)\n","metadata":{"execution":{"iopub.status.busy":"2023-06-20T14:04:59.574622Z","iopub.status.idle":"2023-06-20T14:04:59.574988Z","shell.execute_reply.started":"2023-06-20T14:04:59.574807Z","shell.execute_reply":"2023-06-20T14:04:59.574824Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Results","metadata":{}}]}