Normalization_Standartization_qual_utilizar.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Porque-processar-os-dados.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1OwYDiGplHMnBRIY4P2fx2F-Q4RE0xEPz","authorship_tag":"ABX9TyMkUVdA4YOc7C4DcTvteBSq"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"mLIvUo2ZT5GD","colab_type":"code","colab":{}},"source":["# importa o pandas\n","import pandas as pd"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0Wkcq4uFJd6f","colab_type":"text"},"source":["Ler a base de dados"]},{"cell_type":"code","metadata":{"id":"u6esC5CvPYKg","colab_type":"code","colab":{}},"source":["# ler o dataset\n","df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\", sep=\",\")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3cNF68Owvl9_","colab_type":"code","colab":{}},"source":["# define o nome das colunas\n","df.columns = [ 'Class'\n","              ,'alcohol'\n","             \t,'malicAcid'\n","             \t,'ash'\n","            \t,'ashalcalinity'\n","             \t,'magnesium'\n","            \t,'totalPhenols'\n","             \t,'flavanoids'\n","             \t,'nonFlavanoidPhenols'\n","             \t,'proanthocyanins'\n","            \t,'colorIntensity'\n","             \t,'hue'\n","             \t,'od280_od315'\n","             \t,'proline'\n","             ]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-ZuiwaznPYD0","colab_type":"code","outputId":"99e13ad8-298a-46dc-8e46-74e5bb61f181","executionInfo":{"status":"ok","timestamp":1589482402773,"user_tz":180,"elapsed":1217,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":223}},"source":["# imprime as 5 primeiras linhas\n","df.head()"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Class</th>\n","      <th>alcohol</th>\n","      <th>malicAcid</th>\n","      <th>ash</th>\n","      <th>ashalcalinity</th>\n","      <th>magnesium</th>\n","      <th>totalPhenols</th>\n","      <th>flavanoids</th>\n","      <th>nonFlavanoidPhenols</th>\n","      <th>proanthocyanins</th>\n","      <th>colorIntensity</th>\n","      <th>hue</th>\n","      <th>od280_od315</th>\n","      <th>proline</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>13.20</td>\n","      <td>1.78</td>\n","      <td>2.14</td>\n","      <td>11.2</td>\n","      <td>100</td>\n","      <td>2.65</td>\n","      <td>2.76</td>\n","      <td>0.26</td>\n","      <td>1.28</td>\n","      <td>4.38</td>\n","      <td>1.05</td>\n","      <td>3.40</td>\n","      <td>1050</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>13.16</td>\n","      <td>2.36</td>\n","      <td>2.67</td>\n","      <td>18.6</td>\n","      <td>101</td>\n","      <td>2.80</td>\n","      <td>3.24</td>\n","      <td>0.30</td>\n","      <td>2.81</td>\n","      <td>5.68</td>\n","      <td>1.03</td>\n","      <td>3.17</td>\n","      <td>1185</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>14.37</td>\n","      <td>1.95</td>\n","      <td>2.50</td>\n","      <td>16.8</td>\n","      <td>113</td>\n","      <td>3.85</td>\n","      <td>3.49</td>\n","      <td>0.24</td>\n","      <td>2.18</td>\n","      <td>7.80</td>\n","      <td>0.86</td>\n","      <td>3.45</td>\n","      <td>1480</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>13.24</td>\n","      <td>2.59</td>\n","      <td>2.87</td>\n","      <td>21.0</td>\n","      <td>118</td>\n","      <td>2.80</td>\n","      <td>2.69</td>\n","      <td>0.39</td>\n","      <td>1.82</td>\n","      <td>4.32</td>\n","      <td>1.04</td>\n","      <td>2.93</td>\n","      <td>735</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>14.20</td>\n","      <td>1.76</td>\n","      <td>2.45</td>\n","      <td>15.2</td>\n","      <td>112</td>\n","      <td>3.27</td>\n","      <td>3.39</td>\n","      <td>0.34</td>\n","      <td>1.97</td>\n","      <td>6.75</td>\n","      <td>1.05</td>\n","      <td>2.85</td>\n","      <td>1450</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Class  alcohol  malicAcid   ash  ...  colorIntensity   hue  od280_od315  proline\n","0      1    13.20       1.78  2.14  ...            4.38  1.05         3.40     1050\n","1      1    13.16       2.36  2.67  ...            5.68  1.03         3.17     1185\n","2      1    14.37       1.95  2.50  ...            7.80  0.86         3.45     1480\n","3      1    13.24       2.59  2.87  ...            4.32  1.04         2.93      735\n","4      1    14.20       1.76  2.45  ...            6.75  1.05         2.85     1450\n","\n","[5 rows x 14 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"j-TUaDcUvx0K","colab_type":"code","outputId":"29376c23-dfee-4845-862b-444d6d016336","executionInfo":{"status":"ok","timestamp":1589482470196,"user_tz":180,"elapsed":876,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":315}},"source":["# estatística descritiva\n","df.describe()"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Class</th>\n","      <th>alcohol</th>\n","      <th>malicAcid</th>\n","      <th>ash</th>\n","      <th>ashalcalinity</th>\n","      <th>magnesium</th>\n","      <th>totalPhenols</th>\n","      <th>flavanoids</th>\n","      <th>nonFlavanoidPhenols</th>\n","      <th>proanthocyanins</th>\n","      <th>colorIntensity</th>\n","      <th>hue</th>\n","      <th>od280_od315</th>\n","      <th>proline</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>1.943503</td>\n","      <td>12.993672</td>\n","      <td>2.339887</td>\n","      <td>2.366158</td>\n","      <td>19.516949</td>\n","      <td>99.587571</td>\n","      <td>2.292260</td>\n","      <td>2.023446</td>\n","      <td>0.362316</td>\n","      <td>1.586949</td>\n","      <td>5.054802</td>\n","      <td>0.956983</td>\n","      <td>2.604294</td>\n","      <td>745.096045</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.773991</td>\n","      <td>0.808808</td>\n","      <td>1.119314</td>\n","      <td>0.275080</td>\n","      <td>3.336071</td>\n","      <td>14.174018</td>\n","      <td>0.626465</td>\n","      <td>0.998658</td>\n","      <td>0.124653</td>\n","      <td>0.571545</td>\n","      <td>2.324446</td>\n","      <td>0.229135</td>\n","      <td>0.705103</td>\n","      <td>314.884046</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>11.030000</td>\n","      <td>0.740000</td>\n","      <td>1.360000</td>\n","      <td>10.600000</td>\n","      <td>70.000000</td>\n","      <td>0.980000</td>\n","      <td>0.340000</td>\n","      <td>0.130000</td>\n","      <td>0.410000</td>\n","      <td>1.280000</td>\n","      <td>0.480000</td>\n","      <td>1.270000</td>\n","      <td>278.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>1.000000</td>\n","      <td>12.360000</td>\n","      <td>1.600000</td>\n","      <td>2.210000</td>\n","      <td>17.200000</td>\n","      <td>88.000000</td>\n","      <td>1.740000</td>\n","      <td>1.200000</td>\n","      <td>0.270000</td>\n","      <td>1.250000</td>\n","      <td>3.210000</td>\n","      <td>0.780000</td>\n","      <td>1.930000</td>\n","      <td>500.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>2.000000</td>\n","      <td>13.050000</td>\n","      <td>1.870000</td>\n","      <td>2.360000</td>\n","      <td>19.500000</td>\n","      <td>98.000000</td>\n","      <td>2.350000</td>\n","      <td>2.130000</td>\n","      <td>0.340000</td>\n","      <td>1.550000</td>\n","      <td>4.680000</td>\n","      <td>0.960000</td>\n","      <td>2.780000</td>\n","      <td>672.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>3.000000</td>\n","      <td>13.670000</td>\n","      <td>3.100000</td>\n","      <td>2.560000</td>\n","      <td>21.500000</td>\n","      <td>107.000000</td>\n","      <td>2.800000</td>\n","      <td>2.860000</td>\n","      <td>0.440000</td>\n","      <td>1.950000</td>\n","      <td>6.200000</td>\n","      <td>1.120000</td>\n","      <td>3.170000</td>\n","      <td>985.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>3.000000</td>\n","      <td>14.830000</td>\n","      <td>5.800000</td>\n","      <td>3.230000</td>\n","      <td>30.000000</td>\n","      <td>162.000000</td>\n","      <td>3.880000</td>\n","      <td>5.080000</td>\n","      <td>0.660000</td>\n","      <td>3.580000</td>\n","      <td>13.000000</td>\n","      <td>1.710000</td>\n","      <td>4.000000</td>\n","      <td>1680.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            Class     alcohol   malicAcid  ...         hue  od280_od315      proline\n","count  177.000000  177.000000  177.000000  ...  177.000000   177.000000   177.000000\n","mean     1.943503   12.993672    2.339887  ...    0.956983     2.604294   745.096045\n","std      0.773991    0.808808    1.119314  ...    0.229135     0.705103   314.884046\n","min      1.000000   11.030000    0.740000  ...    0.480000     1.270000   278.000000\n","25%      1.000000   12.360000    1.600000  ...    0.780000     1.930000   500.000000\n","50%      2.000000   13.050000    1.870000  ...    0.960000     2.780000   672.000000\n","75%      3.000000   13.670000    3.100000  ...    1.120000     3.170000   985.000000\n","max      3.000000   14.830000    5.800000  ...    1.710000     4.000000  1680.000000\n","\n","[8 rows x 14 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"cp02d7LlVA-G","colab_type":"code","colab":{}},"source":["# selecionando algumas colunas\n","df_sub = df[['proline','alcohol','malicAcid','magnesium','flavanoids']]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ee9OpEYlaVkc","colab_type":"code","outputId":"5924e44e-eecc-491d-89c6-626ea98b0a99","executionInfo":{"status":"ok","timestamp":1589482540795,"user_tz":180,"elapsed":1058,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":322}},"source":["# plota boxplot de algumas colunas\n","import seaborn as sns\n","ax = sns.boxplot(data=df_sub)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n","  import pandas.util.testing as tm\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"pBtAKDKUVOaD","colab_type":"code","outputId":"eb77af38-cf30-4c42-9ba1-38125ac920a9","executionInfo":{"status":"ok","timestamp":1589482586927,"user_tz":180,"elapsed":835,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":295}},"source":["# estatística descritiva\n","df_sub.describe()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>proline</th>\n","      <th>alcohol</th>\n","      <th>malicAcid</th>\n","      <th>magnesium</th>\n","      <th>flavanoids</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>745.096045</td>\n","      <td>12.993672</td>\n","      <td>2.339887</td>\n","      <td>99.587571</td>\n","      <td>2.023446</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>314.884046</td>\n","      <td>0.808808</td>\n","      <td>1.119314</td>\n","      <td>14.174018</td>\n","      <td>0.998658</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>278.000000</td>\n","      <td>11.030000</td>\n","      <td>0.740000</td>\n","      <td>70.000000</td>\n","      <td>0.340000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>500.000000</td>\n","      <td>12.360000</td>\n","      <td>1.600000</td>\n","      <td>88.000000</td>\n","      <td>1.200000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>672.000000</td>\n","      <td>13.050000</td>\n","      <td>1.870000</td>\n","      <td>98.000000</td>\n","      <td>2.130000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>985.000000</td>\n","      <td>13.670000</td>\n","      <td>3.100000</td>\n","      <td>107.000000</td>\n","      <td>2.860000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1680.000000</td>\n","      <td>14.830000</td>\n","      <td>5.800000</td>\n","      <td>162.000000</td>\n","      <td>5.080000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           proline     alcohol   malicAcid   magnesium  flavanoids\n","count   177.000000  177.000000  177.000000  177.000000  177.000000\n","mean    745.096045   12.993672    2.339887   99.587571    2.023446\n","std     314.884046    0.808808    1.119314   14.174018    0.998658\n","min     278.000000   11.030000    0.740000   70.000000    0.340000\n","25%     500.000000   12.360000    1.600000   88.000000    1.200000\n","50%     672.000000   13.050000    1.870000   98.000000    2.130000\n","75%     985.000000   13.670000    3.100000  107.000000    2.860000\n","max    1680.000000   14.830000    5.800000  162.000000    5.080000"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"HYpM5ZB_wIFc","colab_type":"code","outputId":"13d55fb0-c806-43a1-be62-e05fb59b7b65","executionInfo":{"status":"ok","timestamp":1589482609124,"user_tz":180,"elapsed":1106,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":279}},"source":["# distribuição da coluna alcohol\n","ax = sns.distplot(df_sub.alcohol)"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"9eW0rnEhwjH_","colab_type":"code","outputId":"f54a1c40-cfd0-4fab-dc8f-e0e6de76409a","executionInfo":{"status":"ok","timestamp":1589482615354,"user_tz":180,"elapsed":798,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":279}},"source":["# distribuição da coluna malicAcid\n","ax = sns.distplot(df_sub.malicAcid, color='y')"],"execution_count":10,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"qGJSu0ddwjFO","colab_type":"code","outputId":"d05544ad-0983-4a1a-cbd6-79e490a01d3d","executionInfo":{"status":"ok","timestamp":1589482629549,"user_tz":180,"elapsed":750,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":279}},"source":["# distribuição da coluna magnesium\n","ax = sns.distplot(df_sub.magnesium, color='g')"],"execution_count":12,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Mc-mqtVywi8E","colab_type":"code","outputId":"536b48b1-b22e-4b1b-ced5-e00f9937cb41","executionInfo":{"status":"ok","timestamp":1589482626428,"user_tz":180,"elapsed":768,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":280}},"source":["# distribuição da coluna magnesium\n","ax = sns.distplot(df_sub.flavanoids, color='r')"],"execution_count":11,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXoAAAEHCAYAAACgHI2PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deZiU1ZXH8e+hWWVxbVQWBdlaFJXYQtxQEQU1QhQdwWQ00QQTRRNNnrgkYzJkkhidMclEkkiMmdGJIm4jiQiioIZNuxGQpQUaVGjMSMtiRJH1zB/37VC23XR1d1W9tfw+z1NPVb1L1WnoPnXr3vuea+6OiIjkrxZxByAiIumlRC8ikueU6EVE8pwSvYhInlOiFxHJc0r0IiJ5rmUyB5nZCOBXQBHwgLvfVc9xo4EngFPcvTzadjtwLbAHuMndZ+zvvQ477DDv0aNH0j+AiIjAwoUL33f34rr2NZjozawImAicB1QBZWY21d1X1DquI/At4NWEbf2BMcBxQBfgBTPr6+576nu/Hj16UF5e3vBPJSIi/2Bm79S3L5mum0FApbuvdfedwGRgVB3H/Rj4OfBJwrZRwGR33+HubwGV0euJiEiGJJPouwLrE55XRdv+wcw+B3R392cbe250/jgzKzez8urq6qQCFxGR5DR7MNbMWgD3At9p6mu4+yR3L3X30uLiOruYRESkiZIZjN0AdE943i3aVqMjcDzwkpkBHAFMNbORSZwrIiJplkyLvgzoY2Y9zaw1YXB1as1Od//A3Q9z9x7u3gNYAIyMZt1MBcaYWRsz6wn0AV5L+U8hIiL1arBF7+67zWw8MIMwvfJBd19uZhOAcnefup9zl5vZFGAFsBu4YX8zbkREJPUs28oUl5aWuqZXiog0jpktdPfSuvbpylgRkTynRC8ikueSKoEgGTZpUvpee9y49L22iGQltehFRPKcEr2ISJ5TohcRyXNK9CIieU6DsVLYNPAtBUAtehGRPKdELyKS55ToRUTynBK9iEieU6IXEclzSvQiInlOiV5EJM8p0YuI5DklehGRPKdELyKS55JK9GY2wsxWmlmlmd1Wx/5vmNlSM1tsZnPMrH+0vYeZbY+2Lzaz36X6BxARkf1rsNaNmRUBE4HzgCqgzMymuvuKhMMecfffRcePBO4FRkT71rj7SakNW0REkpVMi34QUOnua919JzAZGJV4gLv/PeFpeyC7VhwXESlgyST6rsD6hOdV0bZPMbMbzGwNcDdwU8Kunma2yMxeNrMz63oDMxtnZuVmVl5dXd2I8EVEpCEpG4x194nu3gu4FfhBtPlvwFHuPhC4BXjEzDrVce4kdy9199Li4uJUhSSSGtu3w5Il8PrrsGwZvP9+3BGJNEoy9eg3AN0TnneLttVnMvBbAHffAeyIHi+MWvx9gfImRSuSSW++CTNnhvvdu/dtN4Pjj4dhw6CkJL74RJKUTKIvA/qYWU9Cgh8DXJl4gJn1cffV0dOLgNXR9mJgs7vvMbNjgD7A2lQFL5IWH34ITzwBCxbAwQfD2WfDiSfCAQfAjh2wfDm88gr84hch2Y8eDS00U1myV4OJ3t13m9l4YAZQBDzo7svNbAJQ7u5TgfFmNgzYBWwBro5OHwJMMLNdwF7gG+6+OR0/iEhKrFsHv/41fPQRXHABXHghtG796WN69Qr7nnwSXngBqqvh2muhTZt4YhZpQFJLCbr7NGBarW13Jjz+Vj3nPQk82ZwARTJm+XK4/35o3x7uuAO6dav/2FatYMwY6NwZpkwJ540fr5a9ZCX9VooALF4M990HxcVw6637T/KJhg6FsWPDh8S0aQ0fLxIDJXqRVavg97+Ho4+G734XDjqocecPGQKDB8Nf/gIrVjR8vEiGKdFLYVu/HiZODC358eOhXbvGv4YZfOlLcOSR8OCDoX9fJIso0Uvheu+9kOTbtYObboIOHZr+Wm3awDXXwLZt8OyzqYtRJAWU6KUw7doFl18eEvP118MhhzT/Nbt3hzPOgNmz4f/+r/mvJ5IiSvRSmG6+Gf76V7jqKjjqqNS97siRYTrm44+n7jVFmkmJXgrPE0+ELptbboFBg1L72p06wRe+EEolPP98al9bpImU6KWwrF8PX/96SPB33ZWe9zjnnHBF7c9+lp7XF2kkJXopHHv2hK6a3bvhT38KFz2lQ8uWcO658NJL8Npr6XkPkUZQopfC8e//HpLvr38NvXun973OPBMOPBDuuSe97yOSBCV6KQzl5fCDH4SZNldf3fDxzdW2LXzzm/DUU1BZmf73E9kPJXrJfx99BFdeCUccEWrSmGXmfW+6KXTj3HtvZt5PpB5K9JL/vv3t0Kp++OEwSJopRx4ZPmAefjjM1xeJiRK95LennoIHHgiFys4+O/Pv/7WvhSQ/ZUrm31skokQv+WvDhjCVsrQU/vVf44nhtNPCKlR/+EM87y+CEr3kq717w1TKTz4JUylrLx6SKWZhUZJ586CiIp4YpOAp0Ut+uvdemDUL/vM/oW/feGO56qowKKtWvcREiV7yz+uvhxWiLr00VJSMW+fOoQbOQw/Bzp1xRyMFKKlEb2YjzGylmVWa2W117P+GmS01s8VmNsfM+ifsuz06b6WZDU9l8CKf8fHHYaZL585hMZFMTaVsyDXXhLVlVf9GYtBgojezImAicAHQHxibmMgjj7j7AHc/CbgbuDc6tz8wBjgOGAH8Jno9kfS4+eawYtRDD6Wm9HCqnHdemNr52GNxRyIFKJkW/SCg0t3XuvtOYDIwKvEAd/97wtP2gEePRwGT3X2Hu78FVEavJ5J6jz4KkyaFqZRDh8Ydzae1bh26kp55JgwQi2RQMom+K7A+4XlVtO1TzOwGM1tDaNHf1Mhzx5lZuZmVV1dXJxu7yD6rVsG4cXD66fDjH8cdTd3+6Z/gww/huefijkQKTMoGY919orv3Am4FftDIcye5e6m7lxYXF6cqJCkU27eHJNqmTWjVt2wZd0R1GzoUDjtM3TeScckk+g1A94Tn3aJt9ZkMfLGJ54o03s03w5IloV++e/eGj49Ly5YwejT8+c9aQFwyKplEXwb0MbOeZtaaMLg6NfEAM+uT8PQiYHX0eCowxszamFlPoA+gAt2SOpMnh0Jlt94KF14YdzQNu+KKMDNo2rS4I5EC0mCid/fdwHhgBlABTHH35WY2wcxGRoeNN7PlZrYYuAW4Ojp3OTAFWAFMB25w9z1p+DmkEK1aFUocnHZa9vbL1zZkCBx+eFjOUCRDkurMdPdpwLRa2+5MePyt/Zz7E+AnTQ1QhEmTPrtt+/awFKA7XHwx/PGPmY+rIXXFDdCnD0ydCr/5TdPHE8aNa3pcUnB0Zazknr17Q0XKjRvhuuuya758Mk44IUyxXLUq7kikQCjRS+556ilYtgzGjoV+/eKOpvGOPTasV7tkSdyRSIFQopfcMmsWzJwZassPGRJ3NE3TujX07x8SvXvDx4s0kxK95I7y8rCAx0knhdkruezEE2HLFli/vuFjRZpJiV5yw4oVYcD1mGNCffcWOf6rO2BAKLim7hvJgBz/a5GCUFERZqgccQTccEN8i4ikUqdO4UNLiV4yQIlestsLL8DEiaHs8M03Q/v2cUeUOiecELputm6NOxLJc0r0hcA9FNN6913YsSPuaJI3ZQpcdBEUF4ck36FD3BGl1nHHhfvly+ONQ/JellZ/kpR4/314+unQPbBrV9h2111wyilhxaPrrgtdCNnovvvgpptCNcpLL82vlnyNbt3gwANDoj/99LijkTymFn0+2rs3XHn5wx/CG2+EEgFXXBFWObrxxtCq/9734OijwzHZVGBrx47wAXTjjeHD6Pnn8zPJQxiMPe64MAaxR5VBJH3Uos837uGCopkzYdCg0Bo++OB9+2sunS8rg5/9DCZMCFUf778fzj8/nphrVFXB5ZfDggVw++2hfk1Rni9IdvzxMG8evPUW9O4ddzSSp9SizzczZuy7oOiaaz6d5BOdckr4QHj55TCLZfhw+Od/Dt09cXj00TDlcOlSePxx+OlP8z/JQ7hKtkWLcKWvSJoo0eeTxYtDn/wpp4SummQWxh4yJPTh/+AHoeRvSQk8/HDmrtisqgqLhlx5ZUh6ixfDZZdl5r2zwQEHQM+eGpCVtFKizxc7d4ZE3a0bfOUrjbugqG3b0E2yaBH07QtXXRW6cSor0xYun3wCd98dPlimToWf/AReeaUwuy+OPx7WrYO//73hY0WaQIk+X0ybFi6pHzu26aVvjz8e/vrXMG/9tdfC89tvhw8+SF2cO3aE1+/VKywWcu654arXO+7I3iUA0+3448P9ihXxxiF5S4k+H2zcGPrlBw9ufou4qAiuvz7MBLnssjAd85hjwsBtc/rv33orfGh07w7jx4dEP2sWPPNMeP1C1q1bmFn05ptxRyJ5Sok+Hzz55L71SFOlSxf4n/+B118Pff533BES0lVXhdWRGmrl79wZZvb8/Odh9s8xx4SumtNPhxdfDIPA55yTunhzWYsWoQurokLVLCUtCvS7ch55770wgHnRReHim1QbOBCmTw+DhRMnwp/+FAZri4rCt4c+faBr1/B8717429/CZf0rVoR+eAgfFD/9KXz5y9m9eHecSkpg4cLw/3nEEXFHI3kmqURvZiOAXwFFwAPuflet/bcAXwN2A9XANe7+TrRvD7A0OnSdu49EUmfWrNCaP+us9L7PcceFwmK/+lWY5/788yH5V1aG/vy9e8NxRxwRWv5nnQWnnhpa8F26pDe2fHDsseG+okKJXlKuwURvZkXAROA8oAooM7Op7p44crQIKHX3j83sm8DdQE3B8O3uflKK4xaAjz+G+fNDizkdrfm6tGoFZ54ZbpI6xcVw6KGhn15dWpJiyfTRDwIq3X2tu+8EJgOjEg9w99nu/nH0dAHQLbVhSp3mzg2zWIYOjTsSSYVjj4WVK/d9OxJJkWQSfVcgcRmcqmhbfa4Fnkt43tbMys1sgZl9sa4TzGxcdEx5dXV1EiEJe/bA7Nmhj/yoo+KORlKhpAS2b4d33ok7EskzKZ11Y2ZfBkqBexI2H+3upcCVwC/NrFft89x9kruXuntpcXFxKkPKXxUVsGmTWvP5pGah84qKeOOQvJNMot8AJE6V6BZt+xQzGwZ8Hxjp7v8oeu7uG6L7tcBLwMBmxCs1ysqgXbtQH0byQ6dOYSBb8+klxZJJ9GVAHzPraWatgTHA1MQDzGwgcD8hyW9M2H6wmbWJHh8GnA7o8r/m2rkzTKkcODAMjkr+KCmBNWvC/7FIijSY6N19NzAemAFUAFPcfbmZTTCzmqmS9wAdgMfNbLGZ1XwQHAuUm9kSYDZwV63ZOtIUy5aFOeqnnBJ3JJJqxx4Lu3eHZC+SIknNo3f3acC0WtvuTHg8rJ7z5gHqW0i1sjLo2HFfn67kj969w5WyFRX75taLNJNKIOSa7dtDzfaTTy6Meu2Fpm3bUC5CA7KSQiqBkGtq1n/Nxm6bSZPijiA/lJTAs8+GJR7zdRlFySi16HPNokVh1ahCr/iYz449NhQ3W7ky7kgkTyjR55Jdu8JX+gEDGrewiOSWnj2hTRtNs5SUUbbIJZWVoeRBzUIVkp+KisJKX+qnlxRRos8lS5eGSpUlJXFHIulWUhIWlNm8Oe5IJA8o0eeSZcvClMo2beKORNKtZmqlum8kBTTrJlds3BgWpTj77Oa9jmbG5IYjjwzXSqxcCaedFnc0kuPUos8VS6O1W1TbpjC0aBH66d98U8sLSrMp0eeKpUvDykOq7lk4Skpg69bwbU6kGZToc8HOnbB6tWbbFJqaEhfqp5dmUqLPBWvWhEJXqn1SWDp3DhfH6cIpaSYl+lzw5puhz7Z377gjkUwyC616LS8ozaREnwtWrgxXS7ZtG3ckkmklJbBtG7z7btyRSA5Tos9227fD22/rIqlCpX56SQEl+my3enWYXqfa84XpkENCX7366aUZlOiz3ZtvhuUCVa2ycPXrB6tWwZ49cUciOSqpRG9mI8xspZlVmtltdey/xcxWmNkbZvaimR2dsO9qM1sd3a5OZfAFYeVK6NVLa8MWsn79wtKR69bFHYnkqAYTvZkVAROBC4D+wFgz61/rsEVAqbufADwB3B2dewjwQ2AwMAj4oZkdnLrw89y2bVBVpW6bQlfz/6/uG2miZFr0g4BKd1/r7juBycCoxAPcfba7fxw9XQB0ix4PB2a6+2Z33wLMBEakJvQCUPOHrURf2Dp1gi5dNCArTZZMou8KrE94XhVtq8+1wHNNPFcSrV4NrVtDjx5xRyJxKykJ6xHs2hV3JJKDUjoYa2ZfBkqBexp53jgzKzez8urq6lSGlNsqK8P8eS0CLv36hST/1ltxRyI5KJlEvwHonvC8W7TtU8xsGPB9YKS772jMue4+yd1L3b20WEW7gu3bQ/98nz5xRyLZoG/fcKWs+umlCZJJ9GVAHzPraWatgTHA1MQDzGwgcD8hySeW2psBnG9mB0eDsOdH26Qha9aE+fMqeyAABxwARx2lfnppkgYTvbvvBsYTEnQFMMXdl5vZBDMbGR12D9ABeNzMFpvZ1OjczcCPCR8WZcCEaJs0pLIy1Lfp2TPuSCRb9OsXum527Gj4WJEESa0w5e7TgGm1tt2Z8HjYfs59EHiwqQEWrNWrQwtO9W2kRkkJPP98+LYn0gi6MjYb7doV6tuo20YS9e4dvuWp+0YaSYk+G73zTqg/r4FYSdSmTSiFoQFZaSQl+mxUWRnue/WKNw7JPv36hYbA1q1xRyI5RIk+G1VWhvVhO3aMOxLJNiUlYTbWK6/EHYnkECX6bLN3L6xdq/55qVvPnqHA3axZcUciOUSJPtusWgUffaSyxFK3Vq1CI0CJXhpBiT7bzJsX7tU/L/Xp1w+WLoWNGxs+VgQl+uwzf364CrJz57gjkWxVs6zkSy/FGobkDiX6bDNvXui2aaH/GqnHUUeFgXp130iSlE2yydatsGKFum1k/4qK4KyzlOglaUr02WTBgnCvgVhpyNChoUzG+vUNHysFT4k+m8yfH7pstNCINGTo0HA/e3a8cUhOUKLPJvPmwQknqJCZNGzAADj0UCV6SYoSfbbYswdefRVOOy3uSCQXtGgB55wT+und445GspwSfbZYvhw+/BBOPTXuSCRXDB0K69aFK6lF9kOJPlvUXCilFr0kq6afXrNvpAFK9Nli/nw4/HCtKCXJ69sXunSBF1+MOxLJckr02WLevNBtYxZ3JJIrzGDYMHjhhTDGI1KPpBK9mY0ws5VmVmlmt9Wxf4iZvW5mu83sslr79kTryP5jLVmppbo6lCZWt4001vDhsGkTLFwYdySSxRpM9GZWBEwELgD6A2PNrH+tw9YBXwEeqeMltrv7SdFtZB37Zf78cK+BWGms884LLfsZM+KORLJYMi36QUClu691953AZGBU4gHu/ra7vwHsTUOM+W/+/FB+9uST445Eck1xcfi9mT497kgkiyWT6LsCiddZV0XbktXWzMrNbIGZfbGuA8xsXHRMeXV1dSNeOk/MmwcDB0K7dnFHIrloxIhQPmPLlrgjkSyVicHYo929FLgS+KWZfaZil7tPcvdSdy8tLi7OQEhZZNcuKCtT/7w03YgRYWUyzb6ReiST6DcA3ROed4u2JcXdN0T3a4GXgIGNiC//LVkC27cr0UvTDR4MBx6o7hupVzKJvgzoY2Y9zaw1MAZIavaMmR1sZm2ix4cBpwMrmhpsXqq5UEoDsdJULVuGaZYzZqgcgtSpwUTv7ruB8cAMoAKY4u7LzWyCmY0EMLNTzKwKuBy438yWR6cfC5Sb2RJgNnCXuyvRJ5o3D7p3h27d4o5EctmIEVBVFdYzEKmlZTIHufs0YFqtbXcmPC4jdOnUPm8eMKCZMeYvd5gzB848M+5IJNcNHx7up0+H446LNxbJOroyNk7r1sGGDXDGGXFHIrmue3fo31/z6aVOSvRxmjMn3J9+erxxSH4YMQJefhk++ijuSCTLKNHHae7csMjzAPVuSQoMHw47d4ZkL5JAiT5Oc+eG2TZFRXFHIvlgyJBw0Z26b6QWJfq4bN0KS5eq20ZSp21bOPtszaeXz1Cij8uCBWHWjQZiJZWGD4dVq+Ctt+KORLKIEn1c5swJXTaDB8cdieSTESPCvVr1kkCJPi5z54ZCZu3bxx2J5JO+fcMqZc8+G3ckkkWU6OOwaxe8+qr65yX1zODii0OBM02zlIgSfRwWLQqFzJToJR1GjoRPPglLDIqgRB+PuXPDvRK9pMOZZ0KnTvDnP8cdiWQJJfo4zJkT+lG7dIk7EslHrVuHQdm//CXUqZeCp0Sfae6hRa9plZJOI0fCe++FRW2k4CnRZ9qaNeEPUN02kk4XXBCm76r7RlCizzz1z0smHHJI+Nb4zDNxRyJZQIk+0+bOhYMOCiVlRdJp1ChYtgwqK+OORGKmRJ9pc+aE9WFb6J9e0uySS8L9U0/FG4fETtkmkzZtgooKDcRKZvToASefDE8+GXckErOkEr2ZjTCzlWZWaWa31bF/iJm9bma7zeyyWvuuNrPV0e3qVAWek2oWAlf/vGTK6NHw2muwfn3ckUiMGkz0ZlYETAQuAPoDY82sdgfzOuArwCO1zj0E+CEwGBgE/NDMDm5+2DnqpZegTRsYNCjuSKRQjB4d7p9+Ot44JFbJtOgHAZXuvtbddwKTgVGJB7j72+7+BlD76ozhwEx33+zuW4CZwIgUxJ2bZs0K/fNt28YdiRSKvn3DYuHqviloyST6rkDi976qaFsykjrXzMaZWbmZlVdXVyf50jlm82ZYsgTOOSfuSKTQjB4Nf/1ruH5DClJWDMa6+yR3L3X30uLi4rjDSY+XXw5XxSrRS6aNHh1+99R9U7CSSfQbgO4Jz7tF25LRnHPzy6xZcMAB6p+XzBswAI49Fh59NO5IJCbJJPoyoI+Z9TSz1sAYYGqSrz8DON/MDo4GYc+PthWe2bPDtMrWreOORAqNGYwdG7pvqqrijkZi0GCid/fdwHhCgq4Aprj7cjObYGYjAczsFDOrAi4H7jez5dG5m4EfEz4syoAJ0bbCsnEjLF+ubhuJz9ixofvmscfijkRi0DKZg9x9GjCt1rY7Ex6XEbpl6jr3QeDBZsSY+156Kdwr0UtceveG0tLQffOd78QdjWRYVgzG5r1Zs6Bjx3CVokhcxo6FhQth1aq4I5EMU6LPhBdegLPOgpZJfYESSY8rrgj99RqULTjKPOlWWRlq0N98c9yRSD6ZNKlp5/XpA7/5DRxxREj6dRk3rulxSVZSiz7dpk8P9yMK94JgySKnnRYmB6xZE3ckkkFK9Ok2fXoYCOvVK+5IROBznwv1lmoK7ElBUKJPp08+CfPn1ZqXbNGmTZgUUF4OO3bEHY1kiBJ9Os2ZAx9/rEQv2eW000KSf/31uCORDFGiT6fp08OVsGefHXckIvv07g2dO6v7poAo0afT9OkwZAi0bx93JCL7mMGpp4b59Bs3xh2NZIASfbqsWxfKHgwfHnckIp9Vs27xK6/EHYlkgBJ9uvzv/4b7kSPjjUOkLgcdBAMHwty5sHNn3NFIminRp8tTT4WVffr2jTsSkbqddVaYLFBeHnckkmZK9OlQXR1Kwl56adyRiNSvb1848sh9RfckbynRp8PUqbB3L1xySdyRiNTPLLTq33kH3n477mgkjZTo0+Hpp6FHDzjppLgjEdm/z38+XET14otxRyJppESfan//O8ycGVrz9RWNEskW7dqFlc/Ky2HTprijkTRRok+1554LsxjUPy+5YtiwcK9Wfd5Sok+1Rx8NJWBPPTXuSESSc8ghYdH6OXPgo4/ijkbSIKlEb2YjzGylmVWa2W117G9jZo9F+181sx7R9h5mtt3MFke336U2/Czz/vvw7LPwpS9BUVHc0Ygk7/zzQ/2bl1+OOxJJgwYXHjGzImAicB5QBZSZ2VR3X5Fw2LXAFnfvbWZjgJ8DV0T71rh7YYxKPvoo7N4NV18ddyQijdO1a7juY9Ys2LYNOnSIOyJJoWRa9IOASndf6+47gcnAqFrHjAL+O3r8BHCuWQGORD70ULjacMCAuCMRabyLLoIPP4T77os7EkmxZBJ9V2B9wvOqaFudx7j7buAD4NBoX08zW2RmL5vZmXW9gZmNM7NyMyuvrq5u1A+QNVasCDMXrroq7khEmqZXLzj+eLj7bvjgg7ijkRRK92Ds34Cj3H0gcAvwiJl1qn2Qu09y91J3Ly0uLk5zSGny0EOhX/7KK+OORKTpRo2CLVvgF7+IOxJJoWQS/Qage8LzbtG2Oo8xs5bAgcAmd9/h7psA3H0hsAbIv+Ivu3bBww/DBReEOt8iueqoo2D0aLj33jC5QPJCMom+DOhjZj3NrDUwBpha65ipQM0I5GXALHd3MyuOBnMxs2OAPsDa1ISeRZ54At59F77xjbgjEWm+CRPCNMsf/SjuSCRFGkz0UZ/7eGAGUAFMcfflZjbBzGpq8P4BONTMKgldNDVTMIcAb5jZYsIg7TfcfXOqf4hYuYevuX37hha9SK7r3x+++U347W/hjTfijkZSoMHplQDuPg2YVmvbnQmPPwEur+O8J4Enmxljdps/H8rKYOLEsJCDSD6YMAEmT4YbbwzVLQtwEl0+UWZqrl/+MizioNk2kk8OOQR+8pOwAtVjj8UdjTSTEn1zvPMOPPkkfP3rusBE8s/Xvgaf+xx8+9sqeJbjlOib49/+LUypvPHGuCMRSb2iInjwQdi8GW64Ie5opBmU6JvqzTfDH8E3vwnduzd8vEguOvFEuPPO0H3z+ONxRyNNpETfVP/yL3DAAfD978cdiUh63XYblJaGRk1VVdzRSBMo0TdFWVmYO//d7+oCKcl/LVuGCwJ37AgXU+3YEXdE0khK9I21dy/ccgsUF4d7kUJQUgL//d/w2mswfnzc0UgjKdE31n33hQUa7r4bOnaMOxqRzLn0Urj9dnjgAfj1r+OORhohqQumJFJZGforL7xQNeelMP34x7B8Odx0E3TqpL+DHKEWfbL27IFrr4XWrWHSJF0pKIWpqCjMwDn3XLjmmnAdiWQ9Jfpk3XpruErwl78Mq/GIFKq2beGZZ+DznwPtWd4AAAnCSURBVIcrroD77487ImmAEn0yfv97+I//CINQX/lK3NGIxK99e5g+HYYPD1Vbv/e9MFFBspISfUOmT4frr4cRI7QYg0iijh1Dy/766+Gee+Ccc+Dtt+OOSuqgRL8/kyfDyJFh0eTJk8N8YhHZp2XLMBPtj3+ERYvghBNCeeNdu+KOTBIo0dfFPfTFX3ll6Id86SU48MC4oxLJTmahS/ONN8IVtNdfv69xpISfFdREre3dd0PVvueegy9+ER55BNq1izsqkezXowe8+CL85S9wxx0wdiwceSR89ath0HbAgDDelS7jxqXvtXOcEn2Njz+G3/0uVKT85JNwQcj112sxEZHGMIOLLw7Xmjz7bJiKfNdd8NOfQpcu0K1bKALYrVuoeX/QQWEWTzLcwzTnPXtg9+59j2tuy5aFv9cOHcKtffswHVpToZXoefvtUMfjvvtg40YYNiysFtU3/9YwF8mYoqIwvjVyJPztb2FSw3PPwcyZoYxCohYtwvEtW4ZbUdG+pJ6Y0Bua1XPnnZ/d1rJl+EDp3Hnf7fDDwzeNLl3CVOkuXcKtU6fU/fxZJqlEb2YjgF8BRcAD7n5Xrf1tgIeAk4FNwBXu/na073bgWmAPcJO7z0hZ9E2xbRssXAizZ4dfunnzwvbzzgu/KGecEWt4Inmnpvvmq18NLfxt22DDBtiyBbZuDd+gaxJ6zb1ZSPg1t5oPgPq2tWwZpnru3RsWNt+2Ldw+/DC8z3vvhYZcWVl4vG3bZ+Ps0GFf0q/5AEj8IOjaNfwsbdpk/t+wmRpM9GZWBEwEzgOqgDIzm+ruKxIOuxbY4u69zWwM8HPgCjPrD4wBjgO6AC+YWV9335PqH4Tdu2H16vCLs3UrfPBBuN+8OZRWfecdqKiAt96q+cFg4MCwXNqXvgRHH53ykESkDh06QL9+qX/dyz+zbHX9PvwwfNPYsCGMy7377qcfz50b7nfu/Oy5hx766Q+AQw8NP1PHjuGW+LhdO2jVKtxattz3uFWr8AG1d2/49uIeHrdqlZaKuMm06AcBle6+FsDMJgOjgMREPwr4UfT4CeA+M7No+2R33wG8ZWaV0evNT034CTZtCqvX1+XQQ0O/4CmnhMu2TzwRzjwz9A+KSOGpScT766J1Dw3FxA+A2h8IS5aEBuX27amJa/BgWLAgNa+VIJlE3xVYn/C8Chhc3zHuvtvMPgAOjbYvqHXuZ+oHmNk4oGbIfJuZrawjjsOA95OI97M2bQq3xYthypQmvUQTND3ezMulWCG34s2lWAEO47rrcivemn/f666LN5KGNfy78OqrzRk8rrdbIisGY919EjBpf8eYWbm7l2YopGbLpXhzKVbIrXhzKVZQvOkUZ6zJzB3cACQuitot2lbnMWbWEjiQMCibzLkiIpJGyST6MqCPmfU0s9aEwdWptY6ZCtQUpr4MmOXuHm0fY2ZtzKwn0AeoNbdKRETSqcGum6jPfTwwgzC98kF3X25mE4Byd58K/AF4OBps3Uz4MCA6bgph4HY3cEMzZtzst2snC+VSvLkUK+RWvLkUKyjedIotVgsNbxERyVe6vl9EJM8p0YuI5LmcSvRmdrmZLTezvWaWlVOqzGyEma00s0ozuy3uePbHzB40s41mtizuWJJhZt3NbLaZrYh+D74Vd0z1MbO2ZvaamS2JYv3XuGNqiJkVmdkiM/tL3LE0xMzeNrOlZrbYzMrjjqchZnaQmT1hZm+aWYWZnZrJ98+pRA8sAy4FXok7kLoklIu4AOgPjI3KQGSr/wJGxB1EI+wGvuPu/YHPAzdk8b/vDmCou58InASMMLPPxxxTQ74FVMQdRCOc4+4n5cg8+l8B0929BDiRDP8751Sid/cKd6/rqtls8Y9yEe6+E6gpF5GV3P0VwiypnODuf3P316PHHxL+WLJypXYPaipntYpuWTvzwcy6ARcBD8QdS74xswOBIYTZibj7TnffmskYcirR54C6ykVkZSLKdWbWAxgIvBpvJPWLukIWAxuBme6etbECvwS+B+TKCt8OPG9mC6MSKtmsJ1AN/DHqGnvAzNpnMoCsS/Rm9oKZLavjlrUtY8ksM+sAPAl8293/Hnc89XH3Pe5+EuGK8EFmdnzcMdXFzL4AbHT3hXHH0ghnuPvnCN2kN5jZkLgD2o+WwOeA37r7QOAjIKPjd1lR6yaRuw+LO4ZmUMmHNDOzVoQk/yd3fyrueJLh7lvNbDZhPCQbB75PB0aa2YVAW6CTmf2Pu3855rjq5e4bovuNZvY0ods0K8fuCN/sqxK+0T1BhhN91rXoc1wy5SKkiaLS138AKtz93rjj2R8zKzazg6LH7QjrObwZb1R1c/fb3b2bu/cg/M7OyuYkb2btzaxjzWPgfLLzAxQAd/8/YL2Z1RThP5dPl3lPu5xK9GZ2iZlVAacCz5pZvKtV1eLuu4GachEVwBR3Xx5vVPUzs0cJawP0M7MqM7s27pgacDrwz8DQaFrd4qgVmo2OBGab2RuEBsBMd8/6aYs54nBgjpktIdTOetbdp8ccU0NuBP4U/T6cBPw0k2+uEggiInkup1r0IiLSeEr0IiJ5ToleRCTPKdGLiOQ5JXoRkTynRC95w8xuiioDbjCz+7Ignnn1bP8vM7ss0/FI4cq6K2NFmuF6YFh0i72iobufFncMIqAWveQJM/sdcAzwHHBwwvaLzezVqJjUC2Z2uJm1iOqZH5Rw3Opo32eOj/b/KKrf/5KZrTWzmxLOvSWhJtO3E7Zvi+7NzO6L1il4AeiccMxdUX39N8zs39P5bySFS4le8oK7fwN4FzgH2JKwaw7w+aiY1GTge+6+F3gGuATAzAYD77j7e3Udn/BaJcBwQl2VH5pZKzM7GfgqMJhQI//rZjawVniXAP0IaxRcBZwWve+h0b7j3P0E4N9S8W8hUpu6biTfdQMeM7MjgdbAW9H2x4A7gT8S6rs81sDxEC613wHsMLONhEvxzwCedvePAMzsKeBMYFHCeUOAR919D/Cumc2Ktn8AfAL8IVrVSSUSJC3Uopd892vgPncfAFxHqM4IocZPbzMrBr4IPNXA8RBWjaqxh2Y2lKLaSIMI1Qy/AGR7vRbJUUr0ku8OZF+p6KtrNnoo8vQ0cC+hGuam/R2/H38FvmhmB0SVFC+JtiV6BbgiWojkSEL3Uk1d/QPdfRpwM2GJOZGUU9eN5LsfAY+b2RZgFmG1nxqPESpLfiXJ4z/D3V83s/8iVFEEeMDdF9U67GlgKKE07TrCtwmAjsAzZtYWMOCWRvxcIklT9UoRkTynrhsRkTynRC8ikueU6EVE8pwSvYhInlOiFxHJc0r0IiJ5ToleRCTP/T/SuqGqL3HU3wAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"u4s0XAfwbQxf","colab_type":"text"},"source":["Aplicando o Standartization"]},{"cell_type":"code","metadata":{"id":"gT0SDv6KWelg","colab_type":"code","colab":{}},"source":["# importa o Standscaler da scikit-learn\n","from sklearn.preprocessing import StandardScaler"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"xN9HtcKdWzrJ","colab_type":"code","colab":{}},"source":["# cria o objeto obj_std\n","obj_std = StandardScaler().fit(df)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZIeWdegzXEbd","colab_type":"code","colab":{}},"source":["# aplica o padronizador nos dados\n","df_std = obj_std.transform(df)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"WLkUoEjgXZbG","colab_type":"code","outputId":"6bb2b776-ce92-440c-c4b6-85e821934e58","executionInfo":{"status":"ok","timestamp":1589482772140,"user_tz":180,"elapsed":805,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":240}},"source":["# imprime os dados\n","df_std"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.22246766,  0.2558245 , -0.50162433, ...,  0.40709978,\n","         1.13169801,  0.97105248],\n","       [-1.22246766,  0.20622873,  0.01802001, ...,  0.3195674 ,\n","         0.80457911,  1.40099798],\n","       [-1.22246766,  1.70650069, -0.34931478, ..., -0.4244579 ,\n","         1.20281081,  2.34050852],\n","       ...,\n","       [ 1.36887097,  0.34261709,  1.73822194, ..., -1.60614514,\n","        -1.48525319,  0.28632445],\n","       [ 1.36887097,  0.21862767,  0.22408586, ..., -1.56237895,\n","        -1.39991783,  0.30224836],\n","       [ 1.36887097,  1.40892609,  1.57695301, ..., -1.51861275,\n","        -1.42836295, -0.58949046]])"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"r5YuFJksYxVI","colab_type":"code","outputId":"3e0bc973-1573-44a6-f99e-e36a3ab48f1e","executionInfo":{"status":"ok","timestamp":1589482815210,"user_tz":180,"elapsed":1127,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":374}},"source":["# visualizando o boxplot dos dados\n","import seaborn as sns\n","from matplotlib import pyplot as plt\n","plt.figure(figsize=(10,6))\n","\n","ax = sns.boxplot(data=df_std)"],"execution_count":17,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"B0483kKDN93A","colab_type":"code","colab":{}},"source":["# transforma em dataframe\n","df_std = pd.DataFrame(df_std)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"YuxRpxJcOEaQ","colab_type":"code","outputId":"0b2cacd4-dffc-4e88-9256-2e78041770c7","executionInfo":{"status":"ok","timestamp":1589482901485,"user_tz":180,"elapsed":747,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":315}},"source":["# imprime as estatísticas descritivas do conjunto\n","df_std.describe()"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","      <td>1.770000e+02</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>1.327250e-15</td>\n","      <td>-2.609338e-16</td>\n","      <td>4.252719e-16</td>\n","      <td>-4.378168e-16</td>\n","      <td>-6.410440e-16</td>\n","      <td>-1.028681e-16</td>\n","      <td>-1.279579e-16</td>\n","      <td>1.505387e-16</td>\n","      <td>-5.595022e-16</td>\n","      <td>5.645202e-17</td>\n","      <td>1.568112e-16</td>\n","      <td>6.310081e-16</td>\n","      <td>6.335171e-16</td>\n","      <td>2.389802e-16</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","      <td>1.002837e+00</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-1.222468e+00</td>\n","      <td>-2.434746e+00</td>\n","      <td>-1.433400e+00</td>\n","      <td>-3.668064e+00</td>\n","      <td>-2.680472e+00</td>\n","      <td>-2.093373e+00</td>\n","      <td>-2.100648e+00</td>\n","      <td>-1.690491e+00</td>\n","      <td>-1.868993e+00</td>\n","      <td>-2.065084e+00</td>\n","      <td>-1.628565e+00</td>\n","      <td>-2.087573e+00</td>\n","      <td>-1.897707e+00</td>\n","      <td>-1.487599e+00</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>-1.222468e+00</td>\n","      <td>-7.856866e-01</td>\n","      <td>-6.628933e-01</td>\n","      <td>-5.692924e-01</td>\n","      <td>-6.964846e-01</td>\n","      <td>-8.198411e-01</td>\n","      <td>-8.840502e-01</td>\n","      <td>-8.268924e-01</td>\n","      <td>-7.426883e-01</td>\n","      <td>-5.912136e-01</td>\n","      <td>-7.959038e-01</td>\n","      <td>-7.745875e-01</td>\n","      <td>-9.590184e-01</td>\n","      <td>-7.805774e-01</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>7.320166e-02</td>\n","      <td>6.984037e-02</td>\n","      <td>-4.209899e-01</td>\n","      <td>-2.245039e-02</td>\n","      <td>-5.094986e-03</td>\n","      <td>-1.123234e-01</td>\n","      <td>9.242960e-02</td>\n","      <td>1.069996e-01</td>\n","      <td>-1.795360e-01</td>\n","      <td>-6.483128e-02</td>\n","      <td>-1.617011e-01</td>\n","      <td>1.320404e-02</td>\n","      <td>2.498992e-01</td>\n","      <td>-2.327949e-01</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>1.368871e+00</td>\n","      <td>8.385748e-01</td>\n","      <td>6.810145e-01</td>\n","      <td>7.066723e-01</td>\n","      <td>5.961134e-01</td>\n","      <td>5.244425e-01</td>\n","      <td>8.127835e-01</td>\n","      <td>8.400546e-01</td>\n","      <td>6.249672e-01</td>\n","      <td>6.370119e-01</td>\n","      <td>4.940731e-01</td>\n","      <td>7.134631e-01</td>\n","      <td>8.045791e-01</td>\n","      <td>7.640417e-01</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1.368871e+00</td>\n","      <td>2.276852e+00</td>\n","      <td>3.100048e+00</td>\n","      <td>3.149233e+00</td>\n","      <td>3.151249e+00</td>\n","      <td>4.415790e+00</td>\n","      <td>2.541633e+00</td>\n","      <td>3.069345e+00</td>\n","      <td>2.394874e+00</td>\n","      <td>3.497023e+00</td>\n","      <td>3.427800e+00</td>\n","      <td>3.295669e+00</td>\n","      <td>1.985052e+00</td>\n","      <td>2.977465e+00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 0             1   ...            12            13\n","count  1.770000e+02  1.770000e+02  ...  1.770000e+02  1.770000e+02\n","mean   1.327250e-15 -2.609338e-16  ...  6.335171e-16  2.389802e-16\n","std    1.002837e+00  1.002837e+00  ...  1.002837e+00  1.002837e+00\n","min   -1.222468e+00 -2.434746e+00  ... -1.897707e+00 -1.487599e+00\n","25%   -1.222468e+00 -7.856866e-01  ... -9.590184e-01 -7.805774e-01\n","50%    7.320166e-02  6.984037e-02  ...  2.498992e-01 -2.327949e-01\n","75%    1.368871e+00  8.385748e-01  ...  8.045791e-01  7.640417e-01\n","max    1.368871e+00  2.276852e+00  ...  1.985052e+00  2.977465e+00\n","\n","[8 rows x 14 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"markdown","metadata":{"id":"wLlmK2TzbsaB","colab_type":"text"},"source":["Aplicando Normalização usando Scikit-learn"]},{"cell_type":"code","metadata":{"id":"F6NO49iwbxFR","colab_type":"code","colab":{}},"source":["# importa o MinMaxScaler\n","from sklearn.preprocessing import MinMaxScaler"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"1LkH2oTecLrD","colab_type":"code","outputId":"d3a271bb-5288-4122-ce43-132cfc256256","executionInfo":{"status":"ok","timestamp":1589483595962,"user_tz":180,"elapsed":1423,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":223}},"source":["# imprime as 5 primeiras linhas\n","df.head()"],"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Class</th>\n","      <th>alcohol</th>\n","      <th>malicAcid</th>\n","      <th>ash</th>\n","      <th>ashalcalinity</th>\n","      <th>magnesium</th>\n","      <th>totalPhenols</th>\n","      <th>flavanoids</th>\n","      <th>nonFlavanoidPhenols</th>\n","      <th>proanthocyanins</th>\n","      <th>colorIntensity</th>\n","      <th>hue</th>\n","      <th>od280_od315</th>\n","      <th>proline</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>13.20</td>\n","      <td>1.78</td>\n","      <td>2.14</td>\n","      <td>11.2</td>\n","      <td>100</td>\n","      <td>2.65</td>\n","      <td>2.76</td>\n","      <td>0.26</td>\n","      <td>1.28</td>\n","      <td>4.38</td>\n","      <td>1.05</td>\n","      <td>3.40</td>\n","      <td>1050</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>13.16</td>\n","      <td>2.36</td>\n","      <td>2.67</td>\n","      <td>18.6</td>\n","      <td>101</td>\n","      <td>2.80</td>\n","      <td>3.24</td>\n","      <td>0.30</td>\n","      <td>2.81</td>\n","      <td>5.68</td>\n","      <td>1.03</td>\n","      <td>3.17</td>\n","      <td>1185</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>14.37</td>\n","      <td>1.95</td>\n","      <td>2.50</td>\n","      <td>16.8</td>\n","      <td>113</td>\n","      <td>3.85</td>\n","      <td>3.49</td>\n","      <td>0.24</td>\n","      <td>2.18</td>\n","      <td>7.80</td>\n","      <td>0.86</td>\n","      <td>3.45</td>\n","      <td>1480</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>13.24</td>\n","      <td>2.59</td>\n","      <td>2.87</td>\n","      <td>21.0</td>\n","      <td>118</td>\n","      <td>2.80</td>\n","      <td>2.69</td>\n","      <td>0.39</td>\n","      <td>1.82</td>\n","      <td>4.32</td>\n","      <td>1.04</td>\n","      <td>2.93</td>\n","      <td>735</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>14.20</td>\n","      <td>1.76</td>\n","      <td>2.45</td>\n","      <td>15.2</td>\n","      <td>112</td>\n","      <td>3.27</td>\n","      <td>3.39</td>\n","      <td>0.34</td>\n","      <td>1.97</td>\n","      <td>6.75</td>\n","      <td>1.05</td>\n","      <td>2.85</td>\n","      <td>1450</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Class  alcohol  malicAcid   ash  ...  colorIntensity   hue  od280_od315  proline\n","0      1    13.20       1.78  2.14  ...            4.38  1.05         3.40     1050\n","1      1    13.16       2.36  2.67  ...            5.68  1.03         3.17     1185\n","2      1    14.37       1.95  2.50  ...            7.80  0.86         3.45     1480\n","3      1    13.24       2.59  2.87  ...            4.32  1.04         2.93      735\n","4      1    14.20       1.76  2.45  ...            6.75  1.05         2.85     1450\n","\n","[5 rows x 14 columns]"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"id":"Q9V4CyPLbxd7","colab_type":"code","colab":{}},"source":["# treina o algoritmo e cria o objeto obj_norm\n","obj_norm = MinMaxScaler().fit(df)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"yGExpk8_b4rC","colab_type":"code","colab":{}},"source":["# aplica o normalizador\n","norm_df = obj_norm.transform(df)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"P-FnFkEnbxbH","colab_type":"code","outputId":"b0d470c5-8d08-4244-dc0f-2290d25f4c3d","executionInfo":{"status":"ok","timestamp":1589483652234,"user_tz":180,"elapsed":660,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":240}},"source":["# imprime os dados normalizados\n","norm_df"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.        , 0.57105263, 0.2055336 , ..., 0.46341463, 0.78021978,\n","        0.55064194],\n","       [0.        , 0.56052632, 0.3201581 , ..., 0.44715447, 0.6959707 ,\n","        0.64693295],\n","       [0.        , 0.87894737, 0.23913043, ..., 0.30894309, 0.7985348 ,\n","        0.85734665],\n","       ...,\n","       [1.        , 0.58947368, 0.69960474, ..., 0.08943089, 0.10622711,\n","        0.39728959],\n","       [1.        , 0.56315789, 0.36561265, ..., 0.09756098, 0.12820513,\n","        0.40085592],\n","       [1.        , 0.81578947, 0.66403162, ..., 0.10569106, 0.12087912,\n","        0.20114123]])"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"CndoH_LhcWuG","colab_type":"code","outputId":"a25d108d-299b-48d9-b698-a5acf9e1c5ac","executionInfo":{"status":"ok","timestamp":1589483684835,"user_tz":180,"elapsed":1072,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":374}},"source":["# visualiza os boxplots\n","import seaborn as sns\n","from matplotlib import pyplot as plt\n","plt.figure(figsize=(10,6))\n","ax = sns.boxplot(data=norm_df)"],"execution_count":25,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x432 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"RHLs_q9yOllU","colab_type":"code","colab":{}},"source":["# transforma o numpy array para um dataframe\n","norm_df = pd.DataFrame(norm_df)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"5f0fKcWKOlbs","colab_type":"code","outputId":"b811829e-e743-4178-d1fd-88b6d64861a6","executionInfo":{"status":"ok","timestamp":1589483739211,"user_tz":180,"elapsed":849,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":315}},"source":["# estatística descritiva\n","norm_df.describe()"],"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","      <td>177.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.471751</td>\n","      <td>0.516756</td>\n","      <td>0.316183</td>\n","      <td>0.538053</td>\n","      <td>0.459637</td>\n","      <td>0.321604</td>\n","      <td>0.452503</td>\n","      <td>0.355157</td>\n","      <td>0.438333</td>\n","      <td>0.371277</td>\n","      <td>0.322082</td>\n","      <td>0.387791</td>\n","      <td>0.488752</td>\n","      <td>0.333164</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.386996</td>\n","      <td>0.212844</td>\n","      <td>0.221208</td>\n","      <td>0.147102</td>\n","      <td>0.171962</td>\n","      <td>0.154065</td>\n","      <td>0.216022</td>\n","      <td>0.210687</td>\n","      <td>0.235194</td>\n","      <td>0.180298</td>\n","      <td>0.198332</td>\n","      <td>0.186289</td>\n","      <td>0.258279</td>\n","      <td>0.224596</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\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>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","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.000000</td>\n","      <td>0.350000</td>\n","      <td>0.169960</td>\n","      <td>0.454545</td>\n","      <td>0.340206</td>\n","      <td>0.195652</td>\n","      <td>0.262069</td>\n","      <td>0.181435</td>\n","      <td>0.264151</td>\n","      <td>0.264984</td>\n","      <td>0.164676</td>\n","      <td>0.243902</td>\n","      <td>0.241758</td>\n","      <td>0.158345</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.500000</td>\n","      <td>0.531579</td>\n","      <td>0.223320</td>\n","      <td>0.534759</td>\n","      <td>0.458763</td>\n","      <td>0.304348</td>\n","      <td>0.472414</td>\n","      <td>0.377637</td>\n","      <td>0.396226</td>\n","      <td>0.359621</td>\n","      <td>0.290102</td>\n","      <td>0.390244</td>\n","      <td>0.553114</td>\n","      <td>0.281027</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>1.000000</td>\n","      <td>0.694737</td>\n","      <td>0.466403</td>\n","      <td>0.641711</td>\n","      <td>0.561856</td>\n","      <td>0.402174</td>\n","      <td>0.627586</td>\n","      <td>0.531646</td>\n","      <td>0.584906</td>\n","      <td>0.485804</td>\n","      <td>0.419795</td>\n","      <td>0.520325</td>\n","      <td>0.695971</td>\n","      <td>0.504280</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               0           1           2   ...          11          12          13\n","count  177.000000  177.000000  177.000000  ...  177.000000  177.000000  177.000000\n","mean     0.471751    0.516756    0.316183  ...    0.387791    0.488752    0.333164\n","std      0.386996    0.212844    0.221208  ...    0.186289    0.258279    0.224596\n","min      0.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n","25%      0.000000    0.350000    0.169960  ...    0.243902    0.241758    0.158345\n","50%      0.500000    0.531579    0.223320  ...    0.390244    0.553114    0.281027\n","75%      1.000000    0.694737    0.466403  ...    0.520325    0.695971    0.504280\n","max      1.000000    1.000000    1.000000  ...    1.000000    1.000000    1.000000\n","\n","[8 rows x 14 columns]"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"markdown","metadata":{"id":"Fujflz2GduUj","colab_type":"text"},"source":["## Algoritmos de Machine Learning"]},{"cell_type":"markdown","metadata":{"id":"eaQKLcM9KR7O","colab_type":"text"},"source":["Separa os dados e suas classes"]},{"cell_type":"code","metadata":{"id":"hj8gFppo0ZEk","colab_type":"code","colab":{}},"source":["# cria o dataframe X com todos os dados removendo a classe\n","X = df.drop('Class', axis=1)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"_6ASkdrV0m3j","colab_type":"code","colab":{}},"source":["# a variável y recebe os dados de classes\n","y = df['Class']"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"qme9kuipWtSJ","colab_type":"code","colab":{}},"source":["# Split into training and test\n","from sklearn.model_selection import train_test_split\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"XuD5a10BXgCh","colab_type":"text"},"source":["Aplicando Machine Learning"]},{"cell_type":"code","metadata":{"id":"EslEZNNsdzmo","colab_type":"code","colab":{}},"source":["# treinando um modelo KNN\n","from sklearn.neighbors import KNeighborsClassifier"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"whL2WP-Md2in","colab_type":"code","colab":{}},"source":["# cria o objeto knn\n","knn = KNeighborsClassifier()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"CnFOJMprd2Yp","colab_type":"code","outputId":"6f00aecf-52b7-4ecc-9e93-23e97828b658","executionInfo":{"status":"ok","timestamp":1589483942602,"user_tz":180,"elapsed":675,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["# treina os dados\n","knn.fit(X_train, y_train)"],"execution_count":33,"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n","                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n","                     weights='uniform')"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"id":"K1SNNfzpd2KJ","colab_type":"code","colab":{}},"source":["# atribui as classificações a variável resultados\n","resultados = knn.predict(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"6h682gEyXPw-","colab_type":"code","colab":{}},"source":["# carrega o pacote accuracy_score\n","from sklearn.metrics import accuracy_score"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"a5DcxGEhXGMK","colab_type":"code","colab":{}},"source":["# cria a variável score\n","score = accuracy_score(y_test, resultados)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BbyFR3vnXXzb","colab_type":"code","outputId":"b9542b92-09e1-4304-ae9c-2ff43781acba","executionInfo":{"status":"ok","timestamp":1589483997384,"user_tz":180,"elapsed":852,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# imprime score (acurácia)\n","score"],"execution_count":37,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.75"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"markdown","metadata":{"id":"j59vl0m4XnPB","colab_type":"text"},"source":["Machine Learning com Pipelines"]},{"cell_type":"code","metadata":{"id":"IarS6NPZX0Jk","colab_type":"code","colab":{}},"source":["# importa o pacote do pipeline\n","from sklearn.pipeline import Pipeline"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"KvKeb6c8X0_y","colab_type":"code","colab":{}},"source":["# carrega os pacotes\n","from sklearn.preprocessing import StandardScaler, MinMaxScaler"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3zhvmRSY7Hum","colab_type":"code","colab":{}},"source":["from sklearn import svm"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"YUQyTuuIX7Bb","colab_type":"code","colab":{}},"source":["# cria os pipelines\n","pip_std = Pipeline([('scaler', StandardScaler()),\n","                    ('KNN', KNeighborsClassifier())\n","                   ])\n","\n","pip_minmax = Pipeline([('minmax', MinMaxScaler()),\n","                       ('KNN', KNeighborsClassifier())\n","                      ])\n","\n","pip_std_svm = Pipeline([('scaler', StandardScaler()),\n","                       ('KNN', svm.SVC())\n","                      ])\n","pip_minmax_svm = Pipeline([('minmax', MinMaxScaler()),\n","                       ('KNN', svm.SVC())\n","                      ])"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0DnBgTVfaXiW","colab_type":"text"},"source":["Aplicando o MixMaxScaler"]},{"cell_type":"code","metadata":{"id":"_6sPlzmyaVVt","colab_type":"code","outputId":"05d75c87-4e06-4c61-e0ce-ee097862677a","executionInfo":{"status":"ok","timestamp":1589484204953,"user_tz":180,"elapsed":827,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":154}},"source":["# executa o pipeline\n","pip_minmax.fit(X_train, y_train)"],"execution_count":42,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Pipeline(memory=None,\n","         steps=[('minmax', MinMaxScaler(copy=True, feature_range=(0, 1))),\n","                ('KNN',\n","                 KNeighborsClassifier(algorithm='auto', leaf_size=30,\n","                                      metric='minkowski', metric_params=None,\n","                                      n_jobs=None, n_neighbors=5, p=2,\n","                                      weights='uniform'))],\n","         verbose=False)"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"code","metadata":{"id":"g0ZygJmwaWLh","colab_type":"code","outputId":"fa612e0b-620b-4f6e-f9eb-b76c14f430e0","executionInfo":{"status":"ok","timestamp":1589484212953,"user_tz":180,"elapsed":617,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# imprime o score\n","pip_minmax.score(X_test, y_test)"],"execution_count":43,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9722222222222222"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"markdown","metadata":{"id":"GhrnZlTm1587","colab_type":"text"},"source":["Aplicando StandardScaler"]},{"cell_type":"code","metadata":{"id":"9xzNWn1PZWIl","colab_type":"code","outputId":"f7e01ae2-5d48-4890-8551-bc4853749d8b","executionInfo":{"status":"ok","timestamp":1589484253680,"user_tz":180,"elapsed":763,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":171}},"source":["# executa o pipeline pip_std\n","pip_std.fit(X_train, y_train)"],"execution_count":44,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Pipeline(memory=None,\n","         steps=[('scaler',\n","                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n","                ('KNN',\n","                 KNeighborsClassifier(algorithm='auto', leaf_size=30,\n","                                      metric='minkowski', metric_params=None,\n","                                      n_jobs=None, n_neighbors=5, p=2,\n","                                      weights='uniform'))],\n","         verbose=False)"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"code","metadata":{"id":"XORzbYnRZe-M","colab_type":"code","outputId":"29d0e48e-97bc-44c4-ff42-fb920261bab5","executionInfo":{"status":"ok","timestamp":1589484258281,"user_tz":180,"elapsed":715,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# imprime o score\n","pip_std.score(X_test, y_test)"],"execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9722222222222222"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"markdown","metadata":{"id":"VUgd-ptj73AX","colab_type":"text"},"source":["Aplica SVM com os pré-processadores"]},{"cell_type":"code","metadata":{"id":"AducOf71713Z","colab_type":"code","outputId":"9bd46a61-2df9-4d73-d846-bbebe4e54238","executionInfo":{"status":"ok","timestamp":1589484289397,"user_tz":180,"elapsed":647,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":188}},"source":["pip_std_svm.fit(X_train, y_train)"],"execution_count":46,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Pipeline(memory=None,\n","         steps=[('scaler',\n","                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n","                ('KNN',\n","                 SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None,\n","                     coef0=0.0, decision_function_shape='ovr', degree=3,\n","                     gamma='scale', kernel='rbf', max_iter=-1,\n","                     probability=False, random_state=None, shrinking=True,\n","                     tol=0.001, verbose=False))],\n","         verbose=False)"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"code","metadata":{"id":"aONKqeFN8FBy","colab_type":"code","outputId":"70e37bc7-6516-40b2-df82-08f5d81f8765","executionInfo":{"status":"ok","timestamp":1589484294917,"user_tz":180,"elapsed":740,"user":{"displayName":"Rodrigo Santana Ferreira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh4OVUnkCrXudTIiz5YNwndI2zAhcS1gWXUSvoqJQ=s64","userId":"14710330936720624047"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["pip_std_svm.score(X_test, y_test)"],"execution_count":47,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{"tags":[]},"execution_count":47}]},{"cell_type":"markdown","metadata":{"id":"0PzBtK2y8x7-","colab_type":"text"},"source":["## **Quando usar Padronização ou Normalização?**\n","\n","*   Algoritmos que utilizam cálculos de distância. (Knn, K-means, SVM).\n","*   Algoritmos que usam Gradient Decendent.\n","*   Algoritmos baseados em árvores.\n","*   Redes Neurais.\n"]}]}