comprison_of_ML_models.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Comparative Study of Several ML models on Rainfall Dataset "]},{"cell_type":"markdown","id":"1ff72243-51c5-43f8-a068-f51b26129f76","metadata":{},"source":["## Table of Contents\n","- Descrition\n","- About the Data\n","- Importing Data \n","- Data Preprocessing \n","- One Hot Encoding \n","- Train and Test Data Split \n","- Train Logistic Regression, KNN, Decision Tree, SVM, and Linear Regression models and return their appropriate accuracy scores.\n"]},{"cell_type":"markdown","id":"86eb3d6c-6c7b-4eea-a53b-4f02713b4bec","metadata":{},"source":["# Description\n"]},{"cell_type":"markdown","id":"ead7c400-ef26-4315-89dc-9c14d56b4453","metadata":{},"source":["In this notebook, we will see all the basic classification algorithms that I learnt and in the end we will see a comparative study of all of them:\n","\n","We will use the following algorithms:\n","\n","1. Linear Regression\n","2. KNN\n","3. Decision Trees\n","4. Logistic Regression\n","5. SVM\n","\n","We will evaluate our models using:\n","\n","1.  Accuracy Score\n","2.  Jaccard Index\n","3.  F1-Score\n","4.  LogLoss\n","5.  Mean Absolute Error\n","6.  Mean Squared Error\n","7.  R2-Score\n"]},{"cell_type":"markdown","id":"374126bb-a797-4d19-8763-3d080d584e8c","metadata":{},"source":["# About The Dataset\n"]},{"cell_type":"markdown","id":"c51d9782-82c3-416f-929b-6282f26bf8bf","metadata":{},"source":["The original source of the data is Australian Government's Bureau of Meteorology and the latest data can be gathered from [http://www.bom.gov.au/climate/dwo/](http://www.bom.gov.au/climate/dwo/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01).\n","\n","The dataset to be used has extra columns like 'RainToday' and our target is 'RainTomorrow', which was gathered from the Rattle at [https://bitbucket.org/kayontoga/rattle/src/master/data/weatherAUS.RData](https://bitbucket.org/kayontoga/rattle/src/master/data/weatherAUS.RData?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01)\n","\n","\n"]},{"cell_type":"markdown","id":"e75936cd-253a-4c07-8609-20c60ff0becf","metadata":{},"source":["This dataset contains observations of weather metrics for each day from 2008 to 2017. The **weatherAUS.csv** dataset includes the following fields:\n","\n","| Field         | Description                                           | Unit            | Type   |\n","| ------------- | ----------------------------------------------------- | --------------- | ------ |\n","| Date          | Date of the Observation in YYYY-MM-DD                 | Date            | object |\n","| Location      | Location of the Observation                           | Location        | object |\n","| MinTemp       | Minimum temperature                                   | Celsius         | float  |\n","| MaxTemp       | Maximum temperature                                   | Celsius         | float  |\n","| Rainfall      | Amount of rainfall                                    | Millimeters     | float  |\n","| Evaporation   | Amount of evaporation                                 | Millimeters     | float  |\n","| Sunshine      | Amount of bright sunshine                             | hours           | float  |\n","| WindGustDir   | Direction of the strongest gust                       | Compass Points  | object |\n","| WindGustSpeed | Speed of the strongest gust                           | Kilometers/Hour | object |\n","| WindDir9am    | Wind direction averaged of 10 minutes prior to 9am    | Compass Points  | object |\n","| WindDir3pm    | Wind direction averaged of 10 minutes prior to 3pm    | Compass Points  | object |\n","| WindSpeed9am  | Wind speed averaged of 10 minutes prior to 9am        | Kilometers/Hour | float  |\n","| WindSpeed3pm  | Wind speed averaged of 10 minutes prior to 3pm        | Kilometers/Hour | float  |\n","| Humidity9am   | Humidity at 9am                                       | Percent         | float  |\n","| Humidity3pm   | Humidity at 3pm                                       | Percent         | float  |\n","| Pressure9am   | Atmospheric pressure reduced to mean sea level at 9am | Hectopascal     | float  |\n","| Pressure3pm   | Atmospheric pressure reduced to mean sea level at 3pm | Hectopascal     | float  |\n","| Cloud9am      | Fraction of the sky obscured by cloud at 9am          | Eights          | float  |\n","| Cloud3pm      | Fraction of the sky obscured by cloud at 3pm          | Eights          | float  |\n","| Temp9am       | Temperature at 9am                                    | Celsius         | float  |\n","| Temp3pm       | Temperature at 3pm                                    | Celsius         | float  |\n","| RainToday     | If there was rain today                               | Yes/No          | object |\n","| RainTomorrow  | If there is rain tomorrow                             | Yes/No          | float  |\n","\n","Column definitions were gathered from [http://www.bom.gov.au/climate/dwo/IDCJDW0000.shtml](http://www.bom.gov.au/climate/dwo/IDCJDW0000.shtml?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01)\n","\n"]},{"cell_type":"markdown","id":"8cd5057f-8ab6-4df2-a3cb-997899f6d114","metadata":{},"source":["### Import the required libraries\n"]},{"cell_type":"code","execution_count":140,"id":"45ba0a15-6ae1-42e9-903c-7c8c902afb3e","metadata":{},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.linear_model import LinearRegression\n","from sklearn import svm\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.metrics import jaccard_score\n","from sklearn.metrics import f1_score\n","from sklearn.metrics import log_loss\n","from sklearn.metrics import accuracy_score\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.model_selection import train_test_split\n","import sklearn.metrics as metrics"]},{"cell_type":"markdown","id":"114dea97-d1e6-45f2-9955-aebd36d5dac1","metadata":{},"source":["### Importing the Dataset\n"]},{"cell_type":"code","execution_count":141,"id":"364fe315-082a-4255-b07e-f0cd75216a70","metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Date</th>\n","      <th>MinTemp</th>\n","      <th>MaxTemp</th>\n","      <th>Rainfall</th>\n","      <th>Evaporation</th>\n","      <th>Sunshine</th>\n","      <th>WindGustDir</th>\n","      <th>WindGustSpeed</th>\n","      <th>WindDir9am</th>\n","      <th>WindDir3pm</th>\n","      <th>...</th>\n","      <th>Humidity9am</th>\n","      <th>Humidity3pm</th>\n","      <th>Pressure9am</th>\n","      <th>Pressure3pm</th>\n","      <th>Cloud9am</th>\n","      <th>Cloud3pm</th>\n","      <th>Temp9am</th>\n","      <th>Temp3pm</th>\n","      <th>RainToday</th>\n","      <th>RainTomorrow</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2/1/2008</td>\n","      <td>19.5</td>\n","      <td>22.4</td>\n","      <td>15.6</td>\n","      <td>6.2</td>\n","      <td>0.0</td>\n","      <td>W</td>\n","      <td>41</td>\n","      <td>S</td>\n","      <td>SSW</td>\n","      <td>...</td>\n","      <td>92</td>\n","      <td>84</td>\n","      <td>1017.6</td>\n","      <td>1017.4</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>20.7</td>\n","      <td>20.9</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2/2/2008</td>\n","      <td>19.5</td>\n","      <td>25.6</td>\n","      <td>6.0</td>\n","      <td>3.4</td>\n","      <td>2.7</td>\n","      <td>W</td>\n","      <td>41</td>\n","      <td>W</td>\n","      <td>E</td>\n","      <td>...</td>\n","      <td>83</td>\n","      <td>73</td>\n","      <td>1017.9</td>\n","      <td>1016.4</td>\n","      <td>7</td>\n","      <td>7</td>\n","      <td>22.4</td>\n","      <td>24.8</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2/3/2008</td>\n","      <td>21.6</td>\n","      <td>24.5</td>\n","      <td>6.6</td>\n","      <td>2.4</td>\n","      <td>0.1</td>\n","      <td>W</td>\n","      <td>41</td>\n","      <td>ESE</td>\n","      <td>ESE</td>\n","      <td>...</td>\n","      <td>88</td>\n","      <td>86</td>\n","      <td>1016.7</td>\n","      <td>1015.6</td>\n","      <td>7</td>\n","      <td>8</td>\n","      <td>23.5</td>\n","      <td>23.0</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2/4/2008</td>\n","      <td>20.2</td>\n","      <td>22.8</td>\n","      <td>18.8</td>\n","      <td>2.2</td>\n","      <td>0.0</td>\n","      <td>W</td>\n","      <td>41</td>\n","      <td>NNE</td>\n","      <td>E</td>\n","      <td>...</td>\n","      <td>83</td>\n","      <td>90</td>\n","      <td>1014.2</td>\n","      <td>1011.8</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>21.4</td>\n","      <td>20.9</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2/5/2008</td>\n","      <td>19.7</td>\n","      <td>25.7</td>\n","      <td>77.4</td>\n","      <td>4.8</td>\n","      <td>0.0</td>\n","      <td>W</td>\n","      <td>41</td>\n","      <td>NNE</td>\n","      <td>W</td>\n","      <td>...</td>\n","      <td>88</td>\n","      <td>74</td>\n","      <td>1008.3</td>\n","      <td>1004.8</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>22.5</td>\n","      <td>25.5</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 22 columns</p>\n","</div>"],"text/plain":["       Date  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine WindGustDir  \\\n","0  2/1/2008     19.5     22.4      15.6          6.2       0.0           W   \n","1  2/2/2008     19.5     25.6       6.0          3.4       2.7           W   \n","2  2/3/2008     21.6     24.5       6.6          2.4       0.1           W   \n","3  2/4/2008     20.2     22.8      18.8          2.2       0.0           W   \n","4  2/5/2008     19.7     25.7      77.4          4.8       0.0           W   \n","\n","   WindGustSpeed WindDir9am WindDir3pm  ...  Humidity9am  Humidity3pm  \\\n","0             41          S        SSW  ...           92           84   \n","1             41          W          E  ...           83           73   \n","2             41        ESE        ESE  ...           88           86   \n","3             41        NNE          E  ...           83           90   \n","4             41        NNE          W  ...           88           74   \n","\n","   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n","0       1017.6       1017.4         8         8     20.7     20.9        Yes   \n","1       1017.9       1016.4         7         7     22.4     24.8        Yes   \n","2       1016.7       1015.6         7         8     23.5     23.0        Yes   \n","3       1014.2       1011.8         8         8     21.4     20.9        Yes   \n","4       1008.3       1004.8         8         8     22.5     25.5        Yes   \n","\n","   RainTomorrow  \n","0           Yes  \n","1           Yes  \n","2           Yes  \n","3           Yes  \n","4           Yes  \n","\n","[5 rows x 22 columns]"]},"execution_count":141,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv(\"Weather_Data.csv\")\n","df.head()"]},{"cell_type":"markdown","id":"49789b4d-0b96-433f-a9b2-e0e92dbdc3a4","metadata":{},"source":["### Data Preprocessing\n"]},{"cell_type":"markdown","id":"8a6f347b-73e9-428c-818d-2d4d943df0bd","metadata":{},"source":["#### One Hot Encoding\n"]},{"cell_type":"markdown","id":"b4438438-2335-42f5-a9e4-9c6f102ec0bf","metadata":{},"source":["First, we need to perform one hot encoding to convert categorical variables to binary variables.\n"]},{"cell_type":"code","execution_count":142,"id":"e9bb7083-34db-4eaf-878e-d9e62f7fe352","metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Date</th>\n","      <th>MinTemp</th>\n","      <th>MaxTemp</th>\n","      <th>Rainfall</th>\n","      <th>Evaporation</th>\n","      <th>Sunshine</th>\n","      <th>WindGustSpeed</th>\n","      <th>WindSpeed9am</th>\n","      <th>WindSpeed3pm</th>\n","      <th>Humidity9am</th>\n","      <th>...</th>\n","      <th>WindDir3pm_NNW</th>\n","      <th>WindDir3pm_NW</th>\n","      <th>WindDir3pm_S</th>\n","      <th>WindDir3pm_SE</th>\n","      <th>WindDir3pm_SSE</th>\n","      <th>WindDir3pm_SSW</th>\n","      <th>WindDir3pm_SW</th>\n","      <th>WindDir3pm_W</th>\n","      <th>WindDir3pm_WNW</th>\n","      <th>WindDir3pm_WSW</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2/1/2008</td>\n","      <td>19.5</td>\n","      <td>22.4</td>\n","      <td>15.6</td>\n","      <td>6.2</td>\n","      <td>0.0</td>\n","      <td>41</td>\n","      <td>17</td>\n","      <td>20</td>\n","      <td>92</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2/2/2008</td>\n","      <td>19.5</td>\n","      <td>25.6</td>\n","      <td>6.0</td>\n","      <td>3.4</td>\n","      <td>2.7</td>\n","      <td>41</td>\n","      <td>9</td>\n","      <td>13</td>\n","      <td>83</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2/3/2008</td>\n","      <td>21.6</td>\n","      <td>24.5</td>\n","      <td>6.6</td>\n","      <td>2.4</td>\n","      <td>0.1</td>\n","      <td>41</td>\n","      <td>17</td>\n","      <td>2</td>\n","      <td>88</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2/4/2008</td>\n","      <td>20.2</td>\n","      <td>22.8</td>\n","      <td>18.8</td>\n","      <td>2.2</td>\n","      <td>0.0</td>\n","      <td>41</td>\n","      <td>22</td>\n","      <td>20</td>\n","      <td>83</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2/5/2008</td>\n","      <td>19.7</td>\n","      <td>25.7</td>\n","      <td>77.4</td>\n","      <td>4.8</td>\n","      <td>0.0</td>\n","      <td>41</td>\n","      <td>11</td>\n","      <td>6</td>\n","      <td>88</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 68 columns</p>\n","</div>"],"text/plain":["       Date  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n","0  2/1/2008     19.5     22.4      15.6          6.2       0.0             41   \n","1  2/2/2008     19.5     25.6       6.0          3.4       2.7             41   \n","2  2/3/2008     21.6     24.5       6.6          2.4       0.1             41   \n","3  2/4/2008     20.2     22.8      18.8          2.2       0.0             41   \n","4  2/5/2008     19.7     25.7      77.4          4.8       0.0             41   \n","\n","   WindSpeed9am  WindSpeed3pm  Humidity9am  ...  WindDir3pm_NNW  \\\n","0            17            20           92  ...           False   \n","1             9            13           83  ...           False   \n","2            17             2           88  ...           False   \n","3            22            20           83  ...           False   \n","4            11             6           88  ...           False   \n","\n","   WindDir3pm_NW  WindDir3pm_S  WindDir3pm_SE  WindDir3pm_SSE  WindDir3pm_SSW  \\\n","0          False         False          False           False            True   \n","1          False         False          False           False           False   \n","2          False         False          False           False           False   \n","3          False         False          False           False           False   \n","4          False         False          False           False           False   \n","\n","   WindDir3pm_SW WindDir3pm_W  WindDir3pm_WNW  WindDir3pm_WSW  \n","0          False        False           False           False  \n","1          False        False           False           False  \n","2          False        False           False           False  \n","3          False        False           False           False  \n","4          False         True           False           False  \n","\n","[5 rows x 68 columns]"]},"execution_count":142,"metadata":{},"output_type":"execute_result"}],"source":["df_processed = pd.get_dummies(data=df, columns=['RainToday', 'WindGustDir', 'WindDir9am', 'WindDir3pm'])\n","df_processed.head()"]},{"cell_type":"markdown","id":"e2d8b302-b050-47ea-bb94-8010a697cdb3","metadata":{},"source":["Next, we replace the values of the 'RainTomorrow' column changing them from a categorical column to a binary column. We do not use the `get_dummies` method because we would end up with two columns for 'RainTomorrow' and we do not want, since 'RainTomorrow' is our target.\n"]},{"cell_type":"code","execution_count":143,"id":"2081b0cd-2c0c-459a-adf8-05ee85b6b8b3","metadata":{},"outputs":[],"source":["df_processed.replace(['No', 'Yes'], [0,1], inplace=True)"]},{"cell_type":"markdown","id":"7fa9af32-5b06-4d65-8716-a88f629771f3","metadata":{},"source":["### Training Data and Test Data\n"]},{"cell_type":"markdown","id":"4e795750-5a3e-4da5-b4d2-c34dc48de8d8","metadata":{},"source":["Now, we set our 'features' or x values and our Y or target variable.\n"]},{"cell_type":"code","execution_count":144,"id":"e122dd1b-e167-4736-a80d-6288fdceebe1","metadata":{},"outputs":[],"source":["df_processed.drop('Date',axis=1,inplace=True)\n","df_processed = df_processed.astype(float)"]},{"cell_type":"code","execution_count":145,"id":"760425a3-ef75-4bb7-be23-5fb572d08a32","metadata":{},"outputs":[],"source":["features = df_processed.drop(columns='RainTomorrow', axis=1).values\n","Y = df_processed['RainTomorrow'].values"]},{"cell_type":"markdown","metadata":{},"source":["### Function to calculate Metrics and return a pandas dataframe of it. "]},{"cell_type":"code","execution_count":146,"metadata":{},"outputs":[],"source":["def calc_metrics(model, y_true, y_pred):\n","    MAE = metrics.mean_absolute_error(y_true, y_pred).round(2)\n","    MSE = metrics.mean_squared_error(y_true, y_pred).round(2)\n","    R2 = metrics.r2_score(y_true, y_pred).round(2)\n","    \n","    Precision = metrics.precision_score(y_true, y_pred).round(2)\n","    Recall = metrics.recall_score(y_true, y_pred).round(2)\n","    F1_Score = f1_score(y_true, y_pred).round(2)\n","    \n","    Accuracy_Score = accuracy_score(y_true, y_pred).round(2)\n","    \n","    JaccardIndex = jaccard_score(y_true, y_pred).round(2)\n","\n","    _report = {\n","        'Model': [model],\n","        'MAE': [MAE],\n","        'MSE': [MSE], \n","        'R2': [R2],\n","        'Accuracy': [Accuracy_Score],\n","        'Jaccard Index': [JaccardIndex],\n","        'Precision': [Precision],\n","        'Recall': [Recall],\n","        'F1 Score': [F1_Score]\n","    }\n","    return pd.DataFrame(_report)\n","    "]},{"cell_type":"markdown","metadata":{},"source":["### Data Splitting"]},{"cell_type":"markdown","id":"ad13a058-92a9-4885-bf9d-da1ea61e772e","metadata":{},"source":["Use the `train_test_split` function to split the `features` and `Y` dataframes with a `test_size` of `0.2` and the `random_state` set to `10`.\n"]},{"cell_type":"code","execution_count":147,"id":"f1bc59ee-f73b-4463-8792-b434e4d6172a","metadata":{},"outputs":[],"source":["x_train, x_test, y_train, y_test = train_test_split(features, Y, random_state=10, test_size=0.2)"]},{"cell_type":"markdown","id":"b48559a0-48be-4e78-8268-827e17f9b2ab","metadata":{},"source":["### Linear Regression\n"]},{"cell_type":"markdown","id":"e05c7d19-5297-4027-8ca8-c202854dc54d","metadata":{},"source":["Create and train a Linear Regression model called LinearReg using the training data (`x_train`, `y_train`).\n"]},{"cell_type":"code","execution_count":232,"id":"efac07df-73b7-488d-822c-9bf7217c7919","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-37 {color: black;}#sk-container-id-37 pre{padding: 0;}#sk-container-id-37 div.sk-toggleable {background-color: white;}#sk-container-id-37 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-37 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-37 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-37 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-37 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-37 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-37 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-37 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-37 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-37 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-37 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-37 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-37 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-37 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-37 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-37 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-37 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-37 div.sk-item {position: relative;z-index: 1;}#sk-container-id-37 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-37 div.sk-item::before, #sk-container-id-37 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-37 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-37 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-37 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-37 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-37 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-37 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-37 div.sk-label-container {text-align: center;}#sk-container-id-37 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-37 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-37\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" checked><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"],"text/plain":["LinearRegression()"]},"execution_count":232,"metadata":{},"output_type":"execute_result"}],"source":["LinearReg = LinearRegression()\n","LinearReg.fit(x_train, y_train)"]},{"cell_type":"markdown","id":"253be0e9-43aa-4f39-9ede-bc99a99b6bde","metadata":{},"source":["Now use the `predict` method on the testing data (`x_test`) and save it to the array `predictions`.\n"]},{"cell_type":"code","execution_count":233,"id":"838d1086-b315-40e1-b769-d16f298e03d7","metadata":{},"outputs":[],"source":["predictions = LinearReg.predict(x_test)\n","# predictions_in_binary = np.rint(predictions)"]},{"cell_type":"markdown","metadata":{},"source":["### Finding Threshold"]},{"cell_type":"code","execution_count":234,"metadata":{},"outputs":[],"source":["# This function will generate predictions with several thresholds\n","# Returns the matrix of predictictions and list thresolds\n","def getBinPredsAndThresh(thresh_lb, thres_ub, divs):\n","    thresholds = np.linspace(thresh_lb, thres_ub, divs)\n","    list_preds_bin = np.empty(shape = (thresholds.shape[0], predictions.shape[0]))\n","    i = 0\n","    for threshold in thresholds:\n","        list_preds_bin[i] = np.where(predictions < threshold, 0, 1)\n","        i += 1\n","    # display(list_predictions_binary)\n","    # display(thresholds.shape, predictions.shape, list_predictions_binary.shape)\n","    return (list_preds_bin, thresholds)\n","\n","# This function generates the dataframe of metrics called reports  \n","# corresponding to the predictions and adds thresolf column to it \n","def generate_reports(list_preds_bin, thresholds):\n","    Reports = calc_metrics('Linear Regression', y_test, list_preds_bin[0])\n","    for predictions_binary in list_preds_bin[1:]:\n","         Reports = pd.concat([Reports, calc_metrics('Linear Regression', y_test, predictions_binary)]) \n","    Reports['Threshold'] = thresholds\n","    # display(Reports)\n","    return Reports\n","\n","# Here we plot the metrics vs thresholds\n","from matplotlib import pyplot as plt\n","def getPlot(Reports):\n","    plt.clf()\n","    X = Reports['Threshold']\n","    Y = Reports[['MAE', 'MSE', 'R2', 'Accuracy', 'Jaccard Index', 'Precision', 'Recall', 'F1 Score']]\n","    Columns = Y.columns\n","    for col in Columns: \n","        plt.plot(X, Y[col], label=col)\n","    plt.legend(loc=3,bbox_to_anchor=(1,0))\n","    return plt\n","\n","# Here we call all the functions and show the plot at last\n","def displayPlotBetween(lb, ub, divs):\n","    list_predictions_binary, thresholds = getBinPredsAndThresh(lb, ub, divs)\n","    Reports = generate_reports(list_predictions_binary, thresholds)\n","    plot = getPlot(Reports)\n","    plot.show()"]},{"cell_type":"markdown","metadata":{},"source":["Now, we will find the appropriate threshold for classification as regression model outputs only continuous values. We will do this by observing the several metrics around 0.5 threshold value for classification.\n","Our interval of interest is [0, 1] as values close to 0 mean no rain and values close to 1 mean rain. We can guess by intuition (and also by graphs) that appropriate threshold may lie near 0.5.  "]},{"cell_type":"markdown","metadata":{},"source":["Below we can see a general trend. Here our most valuable metric is F1 and as we can see in the graph it is maximised at around 0.4. Around here we also see that R2 is large as well and MAE, MSE are small. Now let us zoom to that area in next iteration."]},{"cell_type":"code","execution_count":235,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure 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gepkw2kTtVP/2bX0QPFpzOR95vpQ4fcHmoHhx4+NRckIqJ6YVJs5Yr27IEuPx+CvT3kVcwKYg72UVGw7dix0fojagoc72oFTVaxSfMwi2U66JRqFJ/MYFJMRNQImBS3ENrcXOT+8APE4pJa1VP9b3VB96lT4P3cc+YIjYj+x8bPAT6zTJsjW5tbivSPjqMsqQD5fyQCJg6hkAc4wqGbd33CJCulKSvFiT82oqy4qMaybn6tEDnsnkaIiqjxMCluIbKXfIW8n36qc33nGP5xI2pKZO62sAlygjqlsNZLXtv4O0Duw5UUqXZO79iCQ7+a9j4SEtmdSTG1OEyKWwBRp4Ny504AgMv990Pm6Vmr+orwcNiGtzNHaERUD24PtkNxXCagN+1+6NLLedCkq1ByLptJMdVa/OEDAIC2vfrCzc+/2rKuNewnao6YFJtJ8anTSJ83F/qSUrP3Jep00GVnQ+LiAr+334JgU/3NPkQtkVarxa+//orMzEx0794dAwYMsHRI9Sb3tofL8BCTy8u87ZH3yxUU7rsB1alMk+o49PSF8+DAOkbY8um0Gvzx6QJkpyRbOhSzEkVAmZUBQSLB0JlPw97ZxdIhETU6JsVmkrN0KcquJjRqny5j7mNCTFYrISEB8fHxAIB9+/ahV69eVrcEvF1HDxTYy6Av1kKXa9oHcmXsdTj29YNEwbeDyiTFnULiiaOWDqPRhEX1ZkJMVot/BetJVKtRlpSM8mn5/7etrAyqv/8GAAR8+QXk3o1w04tMBtvwcPP3Q1QJURSRk5MDrbZxllWuTFxcnOFnrVaLkydPonXr1haLpzISiQSenp4VlrNvKFqJHm6zukBSVHEBEb1ej5yCXNw5Nb1y53U45kmhOpYORZgbAECn0yGnILf80uGdREBVkA+dpuyfbXZSCIry1+Lq7AyZrOW9pVzYuxsA0GngEEQOG2nhaMxLEAR4BnOaTLJeLe8vWCNLffJJqA4drnSfom0YnIcNa+SIiBrfoUOH8Oeff1o6DABA69atce3aNezatcvSoVSqZ8+eGDVqVIO3q9frsWzZMhQXF+Ppp5+GnZ3x4iAbNmzA2bNnK9Tzl7tj5FYJgCQAwC75GaRIs2vdv6REBfvkS2ipy4xEDL0Hfm154YGoJWNSXA/q1NTyhFgQIPX0MNonyOXwfOopC0VG1HhEUcSpU6cAAHZ2dpBKpRaLJSgoCMOHD8fPP/8MlUplsTgqI4oiVCoVzpw5g+HDh0Mulzdo+8nJycjMLB9HfPnyZXTr9s/Ub6Wlpbhw4QIAwMHhnxXyioqKkCbNhUqhgZNgj2KxFCna8oTYDv8aeiLq/7l4LADC/9JfraiBRtBDb+cAhacP5Fp1g76upiCgfSf4hfFmZKKWjkmxidQpKSj44w+jbaUXLwEA7HtHI3jlSkuERVSjtLQ0XL16FT4+Pmjfvr3RPqVSiTNnzkCnq3kxiaqo1Wrk5ORAKpXi+eefbxLjeJ9qgh9I9Xo9PvvsMyiVSmzbtg0uLg07bjMpKcnw85EjR1BQUGB4npubC51OB09PTzz99NOGpHjVqlVITk7G6bBM+Pr6lifVFwFXeztEtw66I3Ydjvy2FgDwxFer4OThCdWJdOStvwqpuy3+sruAqznJcO09CIGBTe+mPalUii5dusDV1dXSoRBRE8ak2ETqlFRkf7m40n2c45eaKr1ejzVr1qCoqHwy/lmzZsHzjin7tm3bhsuXLzdIX23btm0SCXFTJZFI0KlTJxw+fBinT582a18ZGRnIyMiosL1Tp06GhPj28+TkZFy+fNnoPCi5Fo9DJw9UqO8f3hFOHuXnj11HD+RJE6DLLUWgxBFXbYDExEQkJiaa4RXV3/Xr1/Hoo49aOgwiasKYFJtI7usD1wnjK2yXuXvA9f6xjR8QkQmuX79uSIgB4MKFCxg4cCCA8q/Ur169CgDo2rVrvW6SkkqliI6Orl+wVqB///4QBAFqtXmGGLi7u8PW1hZpaRUX+1AoFOjTp4/Rtm7duqGoqMgw1KRYqUTCob2wUxWg878WZpBIZYgYMuKf5/ZyeExsj9KreWh9TgplWQm07e0gc7M1wyurO51Oh9OnTyMxMREqlQoODpy/mYgqJ4jiv28xbt6USiVcXFxQUFAAZ2dnS4dDVC86nQ6///67YaxobalUKhQWFkIQBIiiCIVCATe38lkG1Go1cnNzK3ylTs3X32u/R9Lpk3WuX1KoRGFOFtpF98Po2XNMrpf/RyKKDqZB4iSH1KmJTQspCPhNcxCZymy4u7vD5l/TVrZu3RrDhw83qanc3Fxs3rwZpaUVp7sLCAjAqFGjzDaziDXg+zdZGq8UEzVhiYmJlc4YUFvjx4/Hxo0bUVZWhvT0dKN9kZGRTIhbgPyMdBzd+EuDtNVx4N21Km/fzRtFh9KgL9RAX6hpkBgaUht7T2QiG7m5uRX2paeno1u3bvDy8qqxnSNHjiA5ObnSfenp6ejatWuTHFNNRKZhUkxWSavVGg0X0Ol0KCwsrFUbEokETk5OFRLKurRVldsJcceOHY1mE6gNR0dH+Pn54amnnkJWVpbRPrlczjfxZkKn1UKVXzGpu+3C3vIp8fzahqPPg4/UuR87Ryf41nKmBZtWTvB5rjt0BWU1F25EIoC8dfHoUOyP0GEdIATZG+0/cOAAUlJSEBcXh549e9bY3sWLFwEAw4YNg/cd888fO3YMV69eRVxcHJycnIzqKBQKo+nx1Go1SkpK4OzszA+jRE0Mk2KyOufPn8f69etx3333oXv37tDr9fj222/rNEShV69eGDnynwn9dTodvvrqK+Tk5DRkyIiOjkZwcHC92nB1deXd982UXqfD9y/PQm7ajRrLdh40DKFdezRCVMbkvg6Q+za98bqlnTyhOp4O+z+VcB3tCcd+AYZ9RUVFSElJwcGDB3Hw4EGT2rO1tUV0dHSFD9VXr17FyZMncfKk8fAVQRAwefJktG7dGiqVCosXL0ZJSQk6duyIhx9+uGFeJBE1CA5+Iquzfv16AMDmzZsBACkpKYaEWCqVmvwAgNOnT0Oj+efr4uTkZENCXJu2qnu0bt2aV3OtXMqFs4aEWCqXV/nwaBWEdn36WzjapsWhj5/h56KjxkOHOnToAB8fH5N/F+VyOfr161fhptSwsDAEBgZWKH97LP/tebwvXryIkpISAMClS5eMboIlIsvjjXZkFa5du4bMzEzodDqjldfuvvtuXL9+HYmJiejatSvGjh1rUnuiKOKzzz5DQUEBevXqBXd3dwBAfHw8kpKSEBUVhXvvvdccL4VMpCktxeVD+6Epq3hTVHOTeOIoUs6fQcTQGAybOcvS4TQ7+lIt0t49AuhEOA8LhszLDnZdPM0+fOHGjRtYtmwZ5HI5hgwZgtOnTxtNlRcZGQk/Pz+jOl5eXmjTpo1Z42qq+P5NlsbhE9Ti5eTk4IcffkBln//++usvw8+dO3c2uU1BENCpUyccOnQIx44dq7C/U6dOdQuWGszhDWtx/Pf1lg6jQYX3GWDpEJolia0Mtu3cUHopF8o/rwMAPKZ0hF1Hjxpq1k9AQABcXV2Rn5+PHTt2GLb36tULx44dw5kzZ3DmzJkK9f7v//7PaMwyETUOJsXU4p0/fx6iKMLV1RUBAQGQSqVo06YNrl27Bq1WCwDw8PBA69ata9Vuv379UFZWVmF6Ji8vL4SEhDRU+FQHoiji8t/7AABBXbrCztGphhpNn3tAIAI7RVg6jGbL5Z5QSOxk0NxSQXNLheIzWWZPigVBwH333YdTp04ZPpSHhIQgIiICoiiiuLjYqHxaWhry8vJw4cIFJsVEFsDhE9Qo4uPjcfTo0Uqv1ppbeno6SkpKMGbMmDrP4ECWI4oi/v55NdITr5hcR6vRIi3+IuS2dnhq6Y+Q23ClPSpXlqJE1ldnAJkEimAnKMJc4Tw4qOaKjeDMmTPYuHEjbG1tKwyrqC9BENC7d2+0a1e7mUUaE9+/ydJ4pZjMThRFbNu2DQUFBRaLwcbGBu3bt7dY/1R3GdcScKyOwyDaRfdjQkxGbAKdIPO0gza7BGWJBShLLIB9hBdkHnY1Vzaz8PBwKBQKlJaWIikpqcHbz8vLQ9u2bTkVHFEVmBQ3MaIomrwErFwuN1o9SaPRQK/XV1vmNrVa3WhXbdPS0lBQUAAbGxuMHj26Ufr8Nx8fH6O5QskytGo19DptrepcPrgXABDUOQJd7h5RfeE7SGVyBEd0rVVf1PIJggDPmV2gTlai8MANaG4UoTguC479/RuidUgU0jrXtrW1xYwZMyossNMQNm/ejLy8PKSkpMDX17fe7QmCUGF1QKLmjklxEyKKIlatWoXr16+bVN7HxwdPPPEEpFIpLl68iPXr11dIij09PfGf//wHcrncsO3vv//G7t27GzR2U4SHh6NLly6N3i81DZcP7ce2LxdB/Nc5aqquw+9F2+i+DRwVWSOZiwKySC/oS7XIv5EA5Z/XDTfg1ZddF094TOpQ5/peXl4mra5XW5cvX8aFCxewcuXKBmmvTZs2mDx5coO0RdRUcJ7iJiQtLc3khBgAMjIyDF+xHTt2rEJCDADZ2dlISEgwPNfr9Thy5Ej9g60luVxu0opR1HKd3v5HnRNij1ZBCOnW+AtSUMtm38UTUreGHV5Tci4b2rymNw1gz549K8yvTETG+BvSQDIyMur9lVd8fDyA8iV977///mrL7ty5EydOnMDhw4dRUFCA5ORkAMAzzzxjuEEhNjYWR44cwdGjRw1DMgoKClBUVASFQoEXXnjBsAiFuUkkkkbriyxLmZ2FG5fOA3cMz9Gq1Ui7cgkQBMz4YikcXN1r1aZULuc4SGpwEns5fF/uCegaZihZ1orzUCcVQBmbAkVrl6oLCgJsW7tA6tJ4491DQkIwZ86cSi+e1AV/H6klYlLcAIqLi7Fs2TKjlc3qo3PnzkbDHaoqc+LECSQmJiIxMRFA+ZyYHh4eRmWOHDmC5ORkQ9J8W/v27WFra9sg8RLdJooifvtgLnJvpla6PyC8I1y86z+ekaihCBIBkDRMgmcf6Ql1UgGKT2Sg+ERGtWXlAY7weaZxZ8O5czVOIqqISXEDuHz5MjQaDezt7es9jY67uzvCw8NrLBcUFIS+ffsaVkeSSqXo3994edeAgAAMGDAAaWlpRtttbGxw11131StOospkJiUi92YqpHI5Ajsajx+XyGSIHvuwhSIjMj/77j5QpxZBV1j9zdJlCfnQ3CyCJkMFuY9DI0VHRDXhPMUmunXrFvbv31/pvtuzKwwePBgDBw5ssD6JmoKslGQc2/QrdCZ8E5KfcQtZ15PQrnd/jH7htUaIjqj5yV55HqXxeZAHOELmWj6EQuIoh8vIUEgU1nutivMUk6VZ729fLalUKly6dKnK/YIg1GqZYKLm4sBPK5EUd7JWddr354dDoqrYd/NGaXxe+dXim0WG7TJPOzgNaGXByIisG5NiE3l5eWHUqFHV7r9zPC9RS1BSVIjr5+IAAHdNegw2Jsz1bO/iirCo3maOjKj5sovwgrtUgF5VPme3+kYhik9koORsNpNiIgtiUmwiFxcXTilGLc7Z2J3Ys/Jb6LSVL6ghQgREEV7Boeh537hGjo6oZRIkAuy7/DMXsa7QA8UnM6BOLcSNOQdqrC/ztIP3/3WFxI5v4UQNifMUE1kpURRxfPN6aDVqiKK+0sftadW6jqj6WxIiqh+pkw3suniWPxFrfmizSlByLtsywRK1YPyYSdQMaUpLkZ9xC17BodWWuXH5QpXLKhfl5iI//RZkchtM++RryKpYslUqk8PW0bFB4iaiyrlPbA/96JpvZi06cguFsSlQnUiHxLH6qTuNSAQoQpwhseXbPlFV+NtB1Azt+u5LXD64D2NfmYs2PXpVWmb38q9wcf9fNbYV2j0KLt4+DR0iEdWCIAiQOlX+wfRODt29URibAnVKIXK+v1irPmw7esBzSse6hkjU4jEpJmpmSlVFuHxwHwDg759XV5oUa0pLceXoQQCAd2gbSKqYsF9uo0DvByaYL1gialAyDzs4DQlC6ZU80yuJIjQ3ilB6ORc6lQZSh1pcYSayIkyKiZo4vV6Hoxt+gTI7EwBQlJtj2JeXnoad33xeoU5xQT60ZWVw8fbBo/M/45KsRC2Iy7BguAwLrlWdjM9PQXNLhdILOXDoxVUliSrDpJioiUs8cRSHfv2p0n06jQbn9/xZZd3wvncxISYi2EV4QXNLheJzWUyKiarApJioiYs/VD5FU0hkd7TqUL5AjI2dHfzbdcD1c3EQ9fpK68lt7dB50JBGi5OImi77Lp5Q7kxGWWI+dEVqSB1rHr9MZG2YFBNZgF6nw6aP3sGthCs1li1TqQAA/R5+FL5h7Yz2+bQOM0t8RNSyyDztIA9whOZmEdIXnoAgreQbJAFwuisQTgO5gAhZJybFRBaQeuFcrZZO9goOhU+btmaMiIhaOodevsjfmACxTAexijLKPSlw7OcPQcZlDMj6mD0pXrJkCRYuXIj09HRERkbiyy+/RK9elU8hBQD5+fl44403sGHDBuTm5iI4OBifffYZRo4cae5QiWqkys9DXnoaBAjwDmkNua0tAECr0SAzKQH6KoYy/NvZ3dsBAB36D0L0/eNrLO/i48uxwURUL47RfrBt5wZRU8nfKVFE1vLz0CvVKDpyCzatqp+bXGIrg9zXwUyRElmGWZPidevWYfbs2fjmm28QHR2Nzz77DCNGjEB8fDy8vb0rlFer1Rg2bBi8vb2xfv16BAQE4Pr163B1dTVnmEQmUZcUY/VLT6OkUAkACOzYBQ/Pmw8A2Pn1Z4Zp0mqj8+Bh8GgV2KBxEhFVReZmW+U++y6eKDqYhoIt12psR9HODV6Pd27I0IgszqxJ8SeffIKZM2fiscceAwB888032Lp1K1asWIHXXnutQvkVK1YgNzcXhw4dglxePo9iSEiIOUMkMlniqeMoKVRCplBAq1Yj9eI5FGSmw9bRGVf/Nyewq68fBMG0rx19WoehVUe+qRBR0+DY1x9lKYUQSypfBfNOUmfeqEctj9mSYrVajZMnT2LOnDmGbRKJBEOHDsXhw4crrbN582b06dMHTz/9NH7//Xd4eXnhkUcewauvvgppFYsPlJWVoayszPBcqVQ27AuhFqesuBhnd29Hp0FDYe/sAgBIPHkMqRfPVVsv5expAEDUqLFIu3IJKefPYvfyr2GjsIVOq4V7QCCmffwVhzkQUbMk87CDz9NdLR0GkcWYLSnOzs6GTqeDj4/x8rE+Pj64fPlypXWuXbuGv/76C5MmTcK2bduQkJCA//u//4NGo8G8efMqrTN//ny8/fbbDR4/tVy7ly3B5YP7kHrxHB547S0UKwuw+eMPoNfVfHUEANr1GQAnTy+knD+L5DtulgvvM4AJMRERUTPVpGaf0Ov18Pb2xnfffQepVIoePXrg5s2bWLhwYZVJ8Zw5czB79mzDc6VSicBAjtGkyun1OsPY36TTJ6DVaJBw/DD0Oi2cvXwQ3qd/tfW9gkLgFRQCd/9WKCsuRomyAABgY2eP7veMNnv8REREZB5mS4o9PT0hlUqRkZFhtD0jIwO+vpWvpuPn5we5XG40VKJDhw5IT0+HWq2GjU3FMUwKhQIKhaJhg6cWRafVYstnHyI7JRl6vc5o34rnnkBZcfk8wBFDYxA99iGT2pTKZOg5+oEGj7WlEEUR+9bEQyIRMGBCOwiCgFsJ+dj3czw06qpn6JBKBfQa3RphPf65EVfUi/hz5UVkJDevoVEOLjYY+WQEbB3llg6l3gqySrBr2XloNXr0ezAMyuxSxO1OgbOHLWL+0wU2tk3q+goRUZ2Y7S+ZjY0NevTogdjYWIwdOxZA+ZXg2NhYzJo1q9I6/fr1w5o1a6DX6yGRlN+sdOXKFfj5+VWaEBOZ4vrZ00g4Xvk49sKcLACATG6D9n3vasywWrSMJCUuHEgDAHTo5w+vICec3HkdOTdVNdY99sc1tOnuZRiKcisxH1ePZ9RQq+lRZpXg8pFb6Do0yNKh1Nu5vTeQeb0QAHDsjyTkZxajTKVFQWYJrp3OQvs+fhaOkIio/sz68X727NmYOnUqoqKi0KtXL3z22WdQqVSG2SimTJmCgIAAzJ9fPq3VU089hcWLF+O5557DM888g6tXr+KDDz7As88+a84wqZkQRRGlRYWwc3KGTqtB3q00QKxqCvp/nN/7JwCgw4DB6Dp8JCRSGTyDQpCTeh06rQYA4OThBScPT7PG31yJooiCzBLotKbNwQwAFw+m/fPz32lo39cPqRdzAQAx/+kMB5eK3+7odXps/vwM8tKLcf1cDpw8bP/X1i0AQOtuXug2rHkkmMlns3Fyx3VcOZaBwA7ulg6n3hJPZRp+zkgyvmJ/+Ug6vIKcKtSxsZPByb3q6b+IiJoasybF48ePR1ZWFubOnYv09HR07doVO3bsMNx8l5KSYrgiDACBgYHYuXMnXnjhBURERCAgIADPPfccXn31VXOGSc3EqW2bsff7pRj57Ms4u3s7blw8X6v6kUPvgX+7DobnXCLZNCe3J+Po5qQ61z+//ybO778JAHD3d0CbbhXnKL8tqJM7ks5kY+tXZyvs69jfH76tXeocR2Ny8rDFyZ3XkZVSiLXvHrN0OA3CxlYKF297ZKWUXzEOCHfDzfg83IzPq/I13vOfLmjdzasxwyQiqjNBFE241NaMKJVKuLi4oKCgAM7OzpYOhxqIKIr4ZMK/bmQTBMOUajXxb9cB982eA0HCpUtrQxRFfP/GIRTllkHhIINEYvrsGq4+9hD1QEFWMQBAKpOg77gwtI3yqbLOrYR87F51EZoy47HfXkFOGPl/EZBKm8//39/rr+LK0XRLh9EgBEFA5NBAuPs5YN/P8ZDJpbjnyS6I252C5LPZFcrrNHqoS3UI7uyBe2dFWiBiao74/k2WxqSYLEav1+Fc7C7DDA7VKSspxok/NhhtC+naA+PmWOd0fIW5pUi7mo92PX0gVJOo5qWrkHg6y6RhJpUpVWlxJjYVcoUUjy/sD5lN5fOFE90pL12FNW8dhUQiIGpUCJrCTIVSmRTt+/jCzon3pzRVfP8mS+Mtw2Qxl//eh93LltS5fnjv6qdPa8m2LjmLnJtFKCvWImJwqyrL7Vp+AdmpRfXuLyTCkwkxmczN1wEeAY7IuVmEY3/UfehNQ8tLV+HuKR1qLkhEVolJMVnM7fmCA9p3gkdAzXNLS23kCO7SFclnTsHW0RkdBgw2d4hNUlFeGXJulie6Z/ekVpkU56WrkJ1aBIlEQPu+fkAdr9bJ5BJEDuHc31Q7d09pj0sHb0HfBL6MVJdokXAiE4mnszDwkXBIZc1nGA4RNR4OnyAAgKa0FLuXLUFgpwh0HjwM18/F4eAvP0KvNW2Vt7rITL4GUa/HtE++NikpbunSkwpw6LcEaKuZxxcAykq0UGaVAAAEAfAMrHjnPwCUqjQozCnluE6yenq9iNVzDqK4QA03PwfI5Hfc4N3BDX3ur/qmW71Ojz0/Xq5xOkGfEGfcNbEdV7WsB75/k6XxSjEBAM78uQ0XD+zBxQN7EN7vLhxc+wNuJcSbvV/fsHZMiP/nxNZk3EqoeXz1nUQRhtkAqhLeu/LFcoishUQioH1vX5zamYK8W8bJbVZKIdr38YObr0OldVMv5+Hy4ZpvmMxKKUR4b99mM0MKEVXEpNhKiaIIVX4eRH35VcnEU/9MqXR6+x/lCbEgYPTzr0JmphUDBQjwbRtulrbrSqvWoVSlMamsIBFg72xT4cqQTqdHiVJdq341ZTqkXiqfx3fItA6wdah+FTQbOxnc/RyQkayEqK/6yx5bBzl8QnnFhajX6NYI7OAOreafb2JO7biOW4kFuHw4HV0GBVRaL/5IeULcprtXlYuUnNt7EykXcnD5SDoc3Yz/XsptZVDY/fNWq9XoUFqkhYNrxb8dRGRZTIqt1O6lS3A2dkel+w6sWQUACOzQGe2s6GY2VUEZfn7nKMpUpg8Z6TK4Fe4a387wXK/TY927x5CXXlynGDwCHNC+t+mrgwV38qhTP0TWRiqToFV744VUSgrVuJVYgFM7r+PUzuvV1o8Y3Ar+bd0q3SfqRaRcyMGF/Tdx4X9zct8mCMB9z3dDq3A3lBSp8fPbR1FSqEFYD2+MmNm5fi+KiBoU7zawQmXFxbiwPxYAIJFKIZHKIJHK4BvWDo5u7pBIZVDYO6DHvWMtG2gjSziRWZ4QC4BEKlT/+N80aJcO3YJW/c+cujfj8w0JcY1t/OshV0jRbXiwRV47kTVq080bHgEONf5uBoS7wbeNa5XtBHX0gE+oc4V6glA+xOnigfJEOfFkJkoKy7+JSjyVieJafqNERObFK8XNSGFuNq6dPAa93vTldiuTk5oCnUYDN/9WeOyTr63iK7yb8XnIvVX9jTK3lyYe8HC7aqc5A8qHn/zwxmEU5pbi8MZEuPrYAwCuxWUBADrdFYBBjzStoSFEZMzGToYJb0bXux2pXIIHX42qsD0jSYn1H55A0rkcnN1zw2j5c1EEDm9KhPe/lsh29bVHYPvmvzQ4UXPEpLgZ2f7lx0i9eK7B2gvv3c8qEuL8jGL8/tlp09avEMrHDtZYTBAQ1sMbp/9Mwdk9NyrsDzOhDSJq2bxDnODkbovC3FIcWHfFsL3LoFY4t/cGLh+6hcuHbhlXEoCJb0bD3b/yG/+IyHyYFDcThbnZhoS4XXQ/1HeJKIWDA7qPHNMQoTV5CSczIIqAk4ctvIOrv+kssIMbHFxMu7Gw67AglBVrUFZivCSxu589AsIrH3tIRNZDEAQMntIeF/9Og/i/L/hahbuiXbQvRL2IkiLjm3qzUpRQZpfi6skMRPu3tkDERNaN8xSbSc6NVBz4eRU0ZWUN0l5JQT6yUpLhH94RE9/5qEHabA7UpVrs//kKVAV1P47ZN4pQWqTB3VPao0Nf/waMjoio4cQfTcfulRehcJDBq4r5x+tKIhHQZXArhHTxbNB2G1JTef8m68UrxWZy+LefkXjiaIO323HAoAZvsym7fDgd8UdrniO0JjKFFKGRHNJARE1XaIQn5LZSlKm0uHE5r8Hbz88sRnBnD6sYNkdUF0yKG4CmrBS443q7VqPGtZPl8/7e9ejjcHRrmJsmFPYOCOnavUHaupMoijWuomYpCSczAAAd+/sjoJ1rndvxaOVY49y/RESWZGMnw4OvRiE7tfoFeWpLFIG9P16GMrsU6deU8GzlWGk5QQBkNtIG7ZuoOWFSXE/bl3yCi/v/qnSfs5c3ou69v0l/Ktdp9fh1wQnk3CiydCjVihoZAid3W0uHQURkVu5+DnD3a/ib7JLOZCPxVCY2LDxZbbnIIYHo/1DbBu+fqDngPMX1oMrPw8UDeyrdJwgS9Bg5pkknxABw43Jek0+I20Z5MyEmIqqHLoMCIJXX/JZ/Yf9NqEtNX8CIqCXhlWITFeXlIvX8GaNtN+MvAaII37B2eHjuB0b7BIkUMnn51/WqgjLcvJJnuPu4Kbnyv/G6ne4KQL8HwywcTeXk/DqPiKheAtq5YeZnd0Gvq+LeehFY+94xKLNKcGJrMjyqGGJxm4OLTYUVAomaOybFJspOSca2xR9Xui+8zwDIFVVfydz21VlkXm/YMWINrV1PbyafREQtmFQqgbSaP/NhPbxxasd1nP4zpca2gjq6MymmFodJsYnsnJwRHNGtwnZ7Zxd0uXt4lfXy0lXIvF4IiURAQLirGSOsO49WTvCrZglTIiJq+SLvDkRBZgnUJZoay3oGNeyUcURNAecprqdrcVm4ciwDRtNP3KEwpxSZ1wsR1MkDo5+JNHs8REREzRHnKSZL45XietBp9fjr+0soK675poR2Pb0bISIiIiIiqgsmxfWQeikXZcVa2DnboOfIkCrL2TrKEdadSTERERFRU8Wk2ESpF3OxZbHx7BP6/408CevhjS6DWlkiLCIiIiJqAEyKTSRChF5fcdywRCagYz8/C0RERERERA2FSbGJ/Nu6YtqCfhW2y22lsLHlYSQiIiJqzpjNmUgml0Lmynl8iYiIiFoiLvNMRERERFaPSTERERERWT0mxURERERk9ZgUExEREZHVY1JMRERERFaPSTERERERWT0mxURERERk9ZgUExEREZHVY1JMRERERFaPSTERERERWT0mxURERERk9ZgUExEREZHVY1JMRERERFaPSTERERERWT0mxURERERk9ZgUExEREZHVY1JMRERERFavUZLiJUuWICQkBLa2toiOjsaxY8dMqrd27VoIgoCxY8eaN0AiIiIismpmT4rXrVuH2bNnY968eTh16hQiIyMxYsQIZGZmVlsvOTkZL730EgYMGGDuEImIiIjIypk9Kf7kk08wc+ZMPPbYY+jYsSO++eYb2NvbY8WKFVXW0el0mDRpEt5++220bt3a3CESERERkZUza1KsVqtx8uRJDB069J8OJRIMHToUhw8frrLeO++8A29vb0yfPr3GPsrKyqBUKo0eRERERES1YdakODs7GzqdDj4+PkbbfXx8kJ6eXmmdv//+G8uXL8fSpUtN6mP+/PlwcXExPAIDA+sdNxERERFZlyY1+0RhYSEmT56MpUuXwtPT06Q6c+bMQUFBgeGRmppq5iiJiIiIqKWRmbNxT09PSKVSZGRkGG3PyMiAr69vhfKJiYlITk7G6NGjDdv0en15oDIZ4uPj0aZNG6M6CoUCCoXCDNETERERkbUw65ViGxsb9OjRA7GxsYZter0esbGx6NOnT4Xy7du3x7lz5xAXF2d43HfffRg8eDDi4uI4NIKIiIiIzMKsV4oBYPbs2Zg6dSqioqLQq1cvfPbZZ1CpVHjssccAAFOmTEFAQADmz58PW1tbdO7c2ai+q6srAFTYTkRERETUUMyeFI8fPx5ZWVmYO3cu0tPT0bVrV+zYscNw811KSgokkiY1tJmIiIiIrIwgiqJo6SAaklKphIuLCwoKCuDs7GzpcIiIiMgEfP8mS+MlWiIiIiKyekyKiYiIiMjqMSkmIiIiIqvHpJiIiIiIrB6TYiIiIiKyekyKiYiIiMjqMSkmIiIiIqvHpJiIiIiIrB6TYiIiIiKyekyKiYiIiMjqMSkmIiIiIqvHpJiIiIiIrB6TYiIiIiKyekyKiYiIiMjqMSkmIiIiIqvHpJiIiIiIrB6TYiIiIiKyekyKiYiIiMjqMSkmIiIiIqvHpJiIiIiIrB6TYiIiIiKyekyKiYiIiMjqMSkmIiIiIqvHpJiIiIiIrB6TYiIiqrUL2Rew9dpWS4dBRNRgZJYOgIiImhetXosJWycAAFwVrugX0M/CERER1R+vFBMRUa0cTz9u+Hln8k4LRkJE1HB4pdhEl47uhMPOFypsL5K6wXv6z/D0DbJAVERE5nej8AZePfAqlGVKAEBBWYFh39ZrW3E68zRGhIzArG6zLBUiWQlRFKHVaqHT6SwdCjUTUqkUMpkMgiDUWJZJsYm0pSoE6W9W3KG/icPbl8DzsQ8bPygiokbw8+WfcTbrbKX71Ho1kpXJWHpuKSa0nwBPO89Gjo6shVqtxq1bt1BcXGzpUKiZsbe3h5+fH2xsbKotx6TYREFdBuCiYp3RtoKLseiT8h38UrfhevxEo30yuQL+IeEQJByhQkTNh06vQ2phKvTQG7btur4LAPBS1Evo7NkZAOBu6w53W3ck5Cfgw2Mf4lLuJfx25TcMCxkGAHCUO8Lb3rve8YiiiBuFN6ARNXWqL4EEgU6BkEqk9Y6FLEev1yMpKQlSqRT+/v6wsbEx6cofWTdRFKFWq5GVlYWkpCS0bdsWkmryMkEURbER4zM7pVIJFxcXFBQUwNnZ2ax9FeRmwe7zcNgIlX+NczhgGvrM/NysMRARNaTn9zyP2JTYCtsd5A7YN34fFFJFhX2rL6zGohOLKmz/fPDnuDvo7nrF88nJT7Dy/Mp6tTEydCQ+vIvf5jV11b1/l5aWIikpCcHBwbC3t7dQhNRcFRcX4/r16wgNDYWtrW2V5XgZsx5c3L1wMuBR5MEJ+XA0PJQo/4Vte3MTdFqthaMkIjJNdkk29qTuAQC4KFwMDzeFG2Z0mVFpQgwAo9uMRrhbuKG8ncwOAPDb1d/qFY9Wr8XGqxsBAE5yJ6OYTHk425QnVruu7zIaB03NV3VX+YiqYup5w+ET9dTniS8AfGG0TaMuQ8EHofBEPo589xREh/p/hVgTQSpHm7unwcs/xOx9EVHzk1qYit3Xd0MnVn2D0pW8K9CLekR4RuCnUT+Z3La7rTvW37fe8DwxPxFjfx+LQ2mHsOzcsjrHnFWchfyyfLgp3PDXw39BJqn9W9YDmx/A1byr2JO6B2PDxtY5FiJq+ZgUm4HcRoF4t0HolbcVvTN/abR+T6cdhdcr2xutPyJqPl478FqVN8v92/CQ4fXqq41rG7R1a4ureVfx+an6DyEbGjy0TgkxAIwIHoGreVexM3knk2IiqhaTYjMJHvcujm6yhaAtMXtfEr0aUcrd6KQ6ioK8bLi48e5vIvrHjcIbOJt1FhJBgtGtR1d7g5KrwhUPtXuo3n2+3edt/Hb1t2qvTJvCTmaHGV1m1Ln+8JDhWBy3GEfSjqCgrAAuCpd6xUNUW9OmTcPq1avxn//8B998843RvqeffhpfffUVpk6dilWrVhm2Hz58GP3790dMTAy2bjVeOTI5ORmhoaGV9nX48GH07t27wV+DtWBSbCY+rdrAZ9aKRusv+Z3OCNGn4taXg5Auqd1NCPnO4Yj6v5WQyng6UPOl1Wvx3pH3cDX/KsaGjUVXr6748PiHKNGUwEXhgrZubXEi/USjxeNl74X3+r0HRxtHs7SfkJeABccWoMSED975ZfkAgCifKLzX/z2zxPNvXby6oItXl0bpqzqhLqEIdwtHfF483j78NrJLsqHT/5OoR/tF49nuz1ZZX6fX4d0j7+Jq3lWT+hvZeiQmdZhU77ipZQkMDMTatWvx6aefws6ufMx9aWkp1qxZg6CgiuscLF++HM888wyWL1+OtLQ0+Pv7Vyize/dudOrUyWibh4eHeV6AlWAW1EKkh9yPkGtfIFifijtmUjJNbjzOH96KzgPGmCU2osZw5NYRw41dV/OuIiYkBkdvHTXsP3DzQKPHFO0XjYntJ9ZcsA6WnV+Go+lHay54h9FtRpsllqZuRMgIxOfF48/rf1bYdzb7LO5rcx9CXEIqrXvneWWKS7mXMKbNGLN9GKLmqXv37khMTMSGDRswaVL5h6YNGzYgKCiowlXfoqIirFu3DidOnEB6ejpWrVqF119/vUKbHh4e8PX1bZT4rQWT4hYiauKbOH8kCtpSVa3qiad/QLfiQyg5sQbZbbuZKbqKXD18IZNXP4k2UW3sSt5l+LlEW4Kt18q/cuzu3R2nMk8BAFq7tMbz3Z83eyyH0g5hbfxabE/ajmHBwwzbpYIUbrZuJrVxe7bM20MdtHqt4YqvVq/F3tS9AIDXer0Gf4eKV5H+zdHGEVE+Uaa/iBZkeMhwfHH6nxuiPx30KaSCFCvOr0BcVhw2J27GIx0eqbTulmtbAABDgoZgTJvqLxwsOrEIKYUp2Ja0zTAVnZ3MDg5yB0MZtU4NpVoJD1sPzrNbT6IookRjmZXt7OTSWv//Pf7441i5cqUhKV6xYgUee+wx7N2716jcL7/8gvbt2yM8PByPPvoonn/+ecyZM4fnSyNgUtxCyOQ2dbrSe05hB/x1CD0LdgDf7DBDZJW7LmkF/9dOQW5T+RRPRLWh0WkMc+t29uiM8znnodarAQCzus3C4zsfBwAMCBiAwUGDzR5Pe/f2WBu/FqczT2PwL8b9zegyA891f67GNtZcXoOPjn+Eb4Z+g56+PfHQHw8hIT/BqIyvgy8mtp8IicBpqqoT7Bxs+NnLzgtDg4cCKB9WEpcVh6XnlmLpuaXVtjGpwyT09O1ZbZmLuRfxzZlv8O6Rd/HukXcBADJBhu+Gf4eevj2RX5qPsb+PRU5pDudObgAlGh06zt1pkb4vvjMC9ja1S6EeffRRzJkzB9evXwcAHDx4EGvXrq2QFC9fvhyPPvooACAmJgYFBQXYt28fBg0aZFSub9++FaYaKyoqqt0LISP8S2rlOvQZhUvyjtCLQqM9ACBYfwOXDm2x8KunluLIrSNQqpXwtPPEa9GvGbYLENDZszNmdJmB1i6tMaXTlEaJx8/RD/e1uQ/CHf9u+yX+F2j01a/OJooiFhxbAL2ox38P/hcnMk4YEuLb7cklckzrNI0JsYk+HvgxvO28sXDgQsO2ocFD0dqltdH/U2X/evj0QHfv7jX2cX/Y/fC28zb6P9eKWqy/Uj5d3a7ru5BTmgMA2JG8A7mluWZ4pdRUeXl5YdSoUVi1ahVWrlyJUaNGwdPT+Mb4+Ph4HDt2DBMnlg+7kslkGD9+PJYvX16hvXXr1iEuLs7oQfXDK8VWTia3QYc3Djdqn0cXP4bo7A0QDn+Jo1lJ9W7Po10fhEX2a4DIqDnQ6DTYdX0XVJp/hgrdXoZ4WPAwRHhGIMAxADeLbqKVUyvYyezwXPfnTLo625De7/8+3u//vuG5Tq/D3b/ejdzSXCw+vRgBjgFV1r0zWcoszsSKc+U37Y5rOw5v9X3LbDG3ZMNDhleYas7Jxgm/j/29wfrwd/RH7MP/rAZ4JusMHt32KPam7sUv8b8YkmMA0It67L6+Gw+HP4yckhxczr2Mvv59+RV5LdjJpbj4zgiL9V0Xjz/+OGbNmgUAWLJkSYX9y5cvh1arNbqxThRFKBQKLF68GC4u/8yeEhgYiLCwsDrFQZVjUkyNzrH7Q8CuDehSdhq4cLre7RWfV0AZfAnOrrzr1hr8cOkHfHry00r3DQ8eDkEQMDxkOFaeX4m2rm0bObqqSSVSDAsehnXx67DifO1mpjl8q/yDa33nD6bGFeEZAT8HP9xS3TIMpwCA8eHjsS5+HXYl78LD4Q/j5f0v43j6cSy8ayFiQmMsGHHzIghCrYcwWFpMTAzUajUEQcCIEcYJvVarxffff4+PP/4Yw4cb/66PHTsWP//8M5588snGDNfqNK+ziVqEjr1jcOTidCjy4uvdVrDqLNwFJU7s/wVR9z3VANFRU7ft2jYAQFevrvCw++eDUFu3tujh0wNA+bhdjU6D+9veb5EYqzKjywyUaEuMrnJXxVZmi16+vXDw5kHoRB1au7RGbz/OP9qcCIKAt/q8hfVX10Mvlk8L1NO3JwYFDsK6+HU4nnEcl3Mv43j6cQDA8vPLmRS3cFKpFJcuXTL8fKctW7YgLy8P06dPN7oiDADjxo3D8uXLjZLinJwcpKenG5VzdXWFra2tmaJv+QTx9i3OZrRkyRIsXLgQ6enpiIyMxJdffolevXpVWnbp0qX4/vvvcf78eQBAjx498MEHH1RZ/t+USiVcXFxQUFAAZ2fnBnsN1DQdXv4S+qQuRTo8kWlb+WTmt4kANJ0eQtTo/zROcFTBuaxz+O7cd1WOqZULckzrPA16UY/VF1ZDK2qN9uv1ehy+dRgyQYa94/dyIQZqth7Z+gjOZZ+Dj70PMoozAAAyiQzRftE11g12CsbLPV+usMrfqYxTWHl+JTRixd8vmSDDpA6T0Me/T8O8ADOo7v27tLQUSUlJCA0NbXZJ37Rp05Cfn49NmzZVun/s2LFwdXVFTk4O9Hp9hcU6AODYsWOIjo7GmTNn4OzsXOXiHT///DMmTJjQkOG3CKaeP2a/Urxu3TrMnj0b33zzDaKjo/HZZ59hxIgRiI+Ph7e3d4Xye/fuxcSJE9G3b1/Y2triww8/xPDhw3HhwgUEBFQ9Bo+sk3//R6Fbswy+QjZ8S7NrLF904jxKhz4KWzuHGstSw/vk5Cc4kVH9AhpZJVnQ6rWIr+abhP6t+jMhpmZtVOtROJd9zpAQA+VT7R28ebDGugdxEH38+2BQ4CCj7YtOLMK57HNV1ssrzWvSSXFLdedKdZWpKlm+U69evXDnNcxGuJ5plcx+pTg6Oho9e/bE4sWLAZRf6QkMDMQzzzyD1157rYbagE6ng5ubGxYvXowpU2q+c5xXiq3P5eO7UXiz5qEYwXGL4I1cnOr9OToNergRIgMUtrVbXbAlyyrOwpBfh0CEiDd7vwlbmfGnda1ei7cPv234mlkmyDC3z1xIJcZfMUoFKfr594OrrWtjhU7U4LR6Lfbd2AeVRgVHuSPau7fHyYyTEFH9W3Ls9Vj8lfoXRoaOxLv9/hmnfEt1C/duvBcSQYK5vedCLpUb9uWV5mHRiUWwl9njyCNHmuzNfC31SjFZXpO4UqxWq3Hy5EnMmTPHsE0ikWDo0KE4fNi0GQ+Ki4uh0Wjg7u5e6f6ysjKUlZUZniuVyvoFTc1O+55DgZ5Dayx35NY5eGf8jO5HngOONM5MBMddhqPnC782Sl9N3Z/X/4QIEZFekXg4vPIPJduTtuPIrSMAgGj/6CY3JpioocgkMgwJGmK0zd+x5kVYgpyC8FfqX9iWtA3bkrZV2B/lE4Vx7cYZbdPoNfjs1Gco1hbjluqWSf0QWSOzTnCZnZ0NnU4HHx8fo+0+Pj4VBodX5dVXX4W/vz+GDq086Zk/fz5cXFwMj8DAwHrHTS2T3+AnUCTaNWqfPfL/RFZacqP22VTtTC6fZH9ESNVTKE1sPxEyQQaZRIaJ4eZZHpmoOYvwikCkV2Sl+2SCrNJlxeUSOUKcQwCgwgIwRPSPJj37xIIFCwyrvVR1uXvOnDmYPXu24blSqWRiTJUKbt8d6teTUVha3Cj93fxyJNprL+Hab/OQ0npAvdry6zQA/iHhDRRZ40hRpkAQBAQ6BSJDlYHTmeXT79257PG/3R10N45MKr9SrJBytUOif5MIEvxwzw+VzmAil8qr/L0Jcw1DQn4CEvMTcVeru5Bdkl0+vr+OAyg97DxqXOGPqLkxa1Ls6ekJqVSKjIwMo+0ZGRnw9fWttu6iRYuwYMEC7N69GxEREVWWUygUUCj45kmmsVHYwkbROOPR8luPAq5cQnTOJiBnU73ayjzuDu0b8ZDJbRokNnMrKCvA+C3jIZVIsXPcTsPQiW7e3eDrUP3vPpNhouoJggBHG8da1Wnj2gYAcCmnfDqwZ2Kfwfmc83WOoZ9/PybF1OKYNSm2sbFBjx49EBsbi7FjxwIov9EuNjbWsKJLZT766CO8//772LlzJ6KioswZIpHZdBr1NE7cOg27sppnxahOaNlleAu5OHd4K7rc1TzG2P6V8heKNEUAgL2pe00aOkFE5hPtF40lcUtw4OYBxOfG43zOecgEGbr71Lx8dWXaubdr4AiJLM/swydmz56NqVOnIioqCr169cJnn30GlUqFxx57DAAwZcoUBAQEYP78+QCADz/8EHPnzsWaNWsQEhJiGHvs6OgIR8fafTImsiQnF3dEvbih3u0c/XIKonN+h9PeuTh5/PsGiKxqavd26D11PgRJ7W43uK68jqVnl0KtUwMAzmafNexbErcEqYWpECBgaFDNN0QSUcOL9IqEt703MoszMXtv+ZDD3v698fXQry0cGVHTYfakePz48cjKysLcuXORnp6Orl27YseOHYab71JSUiC54w3466+/hlqtxoMPPmjUzrx58/DWW2+ZO1yiJscpaiKw83eE6FMQUphi3s4K/8LFo4PQsc89tar2+anP8ef1Pyvdl1qYCgCI8o2Cj4NPpWWIyLwkggQjQ0di1YVVSPnf35GRoSMtHBVR09IoK9o1Js5TTC3R6V0/oiznuln7cEraiU7qMzjqOQ7Rs1aYXK9YU4yB6waiVFeKpyKfMiyqEeYaBp1ehyRlEiSCBIMDB9c4npiIzKdYU4ztSdtRqiuFq8IV94TeA4lg1kmoaoXzFJO5mHr+MCkmIgDAmT2/InLfDOhFAepafIm0y8EWb/i4IVCjxW+pWcgXXCFO29rsZssgIstiUkzmYur503Q+IhKRRXXoOxqpgj8kgghbQWPyY49j+WwRMSoV7AQN/JCF639ynCIREQBMmzYNgiDgySefrLDv6aefhiAImDZtGgAgKysLTz31FIKCgqBQKODr64sRI0bg4MF/lv8OCQmBIAgVHgsWLGisl9RiNel5iomo8dgobOHz2mncSjd9mMaFgsvYffq/AIDuQ5fi6IUziL74PoLStuHs3vrNzVwX3m0i4RsYhoQzB1FalIuw7nfD1s4BhQW5KMhKQ6uwzo0WS352OlLOH6y5YB1JZPLy12fPG5BNodNqcf3ySYR27Gl0I2nq1TPIu1n9gha3zytTJJ47AlXOzXrF2hzYuXihbbe7LB1GsxEYGIi1a9fi008/hZ1d+SJSpaWlWLNmDYKCggzlxo0bB7VajdWrV6N169bIyMhAbGwscnJyjNp75513MHPmTKNtTk5O5n8hLRyTYiIysFHYwi/YtGEPcZlxeGFveUIc4hyCfh2HQxXYC6UXPkIAMhCw93Fzhlqp/L2OON33Q3Q79DQA4OipsYh+ZjWSvxqHDqVxuDr290Z5Ixf1euR/NQwRevPeGHns5Cj0em6NWftoKY6vegW9byzH0U5vIvqhlwAAN69dgs+PdyNQ0FZbN3+vI1QvXoCDk2u15S4f3YX22x9qqJCbtLO2UUC3WEuH0Wx0794diYmJ2LBhAyZNmgQA2LBhA4KCghAaGgoAyM/Px4EDB7B3714MHDgQABAcHIxevXpVaM/JyanG9R6o9pgUE1Gd/JH4h+Hn57s/X76ggLMbjrR/AZ4Jv0Go61JZdeSrTYOrUITOB58FhPJtHXJ2IfXqGXQpOwUIQMGez4FGSIoTzvyNtvoUqEUpUmXBDd6+RNQhVH8dnXJjUVpcxKvFNdDrdOh9YzkAIPrCu8D/kuKU/asRIGiRByfkSr0qrXv7vDqx/1dEjZpZaZnbCo7+AADIghuUUrcGfAVNT4ljUM2FzE0UAU3jrFBagdweEIRaVXn88cexcuVKQ1K8YsUKPPbYY9i7dy+Af6ae3bRpE3r37s2FySyAN9oRUZWyS7Kx+sJqlGhLKuzbnrQdSrUS3w79Fn0D+logOmOHlz6HPjdXVdh+TRKC1vpkAEAenHDFY4jZY3FRXkF7zUWcdByEHi/93uDti3o9Mt5pB19k4bRDf6htPRu8j5ZEqilClHK34fkx99EQBSmCc/6GL7JxPOId9HzguUrrHl76PPrcXIlkSRAy3Kpf6KJDzp9whgrnh3yPzgPGNOhrsAa1vtFOrQI+8LdApABeTwNsHEwqOm3aNOTn52Pp0qUIDAxEfHw8AKB9+/ZITU3FjBkz4OrqilWrVuG3337DzJkzUVJSgu7du2PgwIGYMGGC0cq+ISEhuHXrFuRyuVE/27dvx4ABjT9srTkw9UY7Xikmoip9c+YbrItfV+V+d1t39PKr+NWeJfj0eQRYvwoAcAteuO49GL0zfzEkxADghsLyZbcbiSTCPF+lCxIJkvxGwPfWj+im+htQmaWbFqtX7j/fcqhFGdoNnFBlWd++E4FfV5bPE55T83CYbLiife/azfNN1sHLywujRo3CqlWrIIoiRo0aBU9P4w+048aNw6hRo3DgwAEcOXIE27dvx0cffYRly5YZbsYDgJdfftnoOQAEBAQ0wqto2ZgUE1GltHqtYUGOh9o9BC+7il8vD2g1ADJJ0/gz0rpzNE7d+BxlaRfhHTUWHQLb4chWf4jqYkjs3eES2gN5FypfYMQcpM5+6Dn0EbO133nCuzjyhxdENTNik8hs4dF5KHLP7oSo1xg2O7Xtj84eVS8qE9opGqdSP0dZ2gUTOhHg2e1eeMptGiBgqpHcvvyKraX6roPHH38cs2bNAgAsWbKk0jK2trYYNmwYhg0bhjfffBMzZszAvHnzjJJgT09PhIWZdvMnma5pvJsRkcWJoohX97+KExknAAA6UYfc0ly4KlwxJ3oO5BJ5DS1YXveYaUbPez/6tnGB6OGNF4yZObm4o/ejb1k6jOan+8DaV/nXeUVNhCCYPIShqYiJiYFarYYgCBgxYoRJdTp27IhNmzaZNzACwKSYiP7nfPZ5bE/eXmH7fW3uaxYJMRFRUyeVSnHp0iXDz3fKycnBQw89hMcffxwRERFwcnLCiRMn8NFHH2HMGOMx6oWFhUhPTzfaZm9vz3up6olJMZEV0+l1uJp/FRqdBr9e+RUAMChwEGZ1Lf96TyaRIdQl1JIhEhG1KFUlro6OjoiOjsann36KxMREaDQaBAYGYubMmXj99deNys6dOxdz58412vaf//wH33zzjdnitgacfYLIin184mOsurDKaNtngz/DkCDzz9BARHQnLvNM5sLZJ4ioWlq9Fr8nlE8X5mPvA5lEhjaubTAggFP6EBGR9WFSTNSCXcm7gh1JOyBWspBGbmku8sry4KZww45xO5rMLBJERESWwHdBohbs1f2vIiE/odoyQ4OHMiEmIiKrx3dCohYqIS8BCfkJkEvkGB8+vtIydjI7PNLBfHPpEhERNRdMiomaodiUWGy4ugFv930bnnaVL/G78/pOAEA//354tderjRkeERFRsyOxdABEVHvP73ke+2/sx7xD8yrdL4oidiaXJ8XDQ1rOghVERETmwivFRM3MnbMo7r+xH8kFyQAAf0d/FGuKkV+Wj5tFN5FUkAQbiQ0GBw62UKRERETNB5NiombmluqW0fPRm0YbfpYIEuhFveF5v4B+cLRxbLTYiIiImismxUTNzJ2zSbgoXKAX9VBpVNCLeuhFPWwkNlDIFLCX2WNKxykWjJSIiKj5YFJM1AzsTN6Jm0U3AQBxmXEAgBEhI7Bo4CIAwBt/v4HNiZsBAIsGLsLgIA6ZICIiqg3eaEfUxJ3PPo+X9r2ET09+ik9Pfoo9qXsAAO3c2hnK3Lksc7+Afo0eIxERVW7atGkQBAGCIEAulyM0NBSvvPIKSktLAQDJycmYPn06QkNDYWdnhzZt2mDevHlQq9UWjtz68EoxURN3IfsCACDAMQA9fHoAAJxsnPBQu4cMZQYHDsYrPV9BG5c2sJHaWCROIiKqXExMDFauXAmNRoOTJ09i6tSpEAQBH374IS5fvgy9Xo9vv/0WYWFhOH/+PGbOnAmVSoVFixZZOnSrwqSYqIm7PYZ4ePBwzI6aXWkZQRAwuePkxgyLiIhMpFAo4OvrCwAIDAzE0KFD8eeff+LDDz9ETEwMYmJiDGVbt26N+Ph4fP3110yKGxmTYqImLrEgEQDQxrWNhSMhImo6RFFEibbEIn3byewgCEKd6p4/fx6HDh1CcHBwlWUKCgrg7u5e1/CojpgUEzVxifnlSXGYa5iFIyEiajpKtCWIXhNtkb6PPnIU9nJ7k8tv2bIFjo6O0Gq1KCsrg0QiweLFiystm5CQgC+//JJXiS2ASTFRE5Zdko3c0lwAQKhLqIWjISKiuhg8eDC+/vprqFQqfPrpp5DJZBg3blyFcjdv3kRMTAweeughzJw50wKRWjcmxURN2IEbBwCUzzRRm6sSREQtnZ3MDkcfOWqxvmvDwcEBYWHl3/atWLECkZGRWL58OaZPn24ok5aWhsGDB6Nv37747rvvGjReMg2TYiILOp5+HNeV16vc/9uV3wCUz0lMRET/EAShWV4skEgkeP311zF79mw88sgjsLOzw82bNzF48GD06NEDK1euhETCGXMtgUkxkYVcybuC6TunQ4RYY9nhwcMbISIiImoMDz30EF5++WUsWbIEEydOxKBBgxAcHIxFixYhKyvLUO72jBXUOJgUE1nI9qTtECGilWMrtHVrW2W5Hj49EOIS0niBERGRWclkMsyaNQsfffQR7OzskJCQgISEBLRq1cqonCjWfNGEGo4gtrAjrlQq4eLigoKCAjg7O1s6HKJKiaKIURtHIbUwFQvvWoiY0JiaKxERtWDVvX+XlpYiKSkJoaGhsLW1tVCE1FyZev5w0AqRBVzKvYTUwlTYSm1xV6u7LB0OERGR1ePwCSIz0ot66ERdhe07knYAAAa0GtAsbxQhIiJqaZgUE5lJijIFk7ZNQn5ZfpVlOKsEERFR08DhE0RmsuHqhmoT4iCnIA6dICIiaiJ4pZioFq4VXMPVvKsQIKC7T3d42nlWKHNdeR2Xcy9jR3L5EIn3+7+PQYGDKpRzkDlAKpGaO2QiIiIyAZNiIhMVlBVg4paJKNYWAwA6eXTC2nvXGpUp1hRj4taJKFQXAihf9Who0FCOGyYiImrimBQTmeivlL9QrC2Gi8IFKrUKF3Iu4Fr+NbR2bW0os+/GPhSqC+Fk44T27u1xb+t7mRATERE1A0yKiapxKecSfrz0I7R6Lc5mnQUATOk4BXGZcThw8wB2Xt+Jp1yfQlxmHH6J/wXnss8BACaET8Cz3Z+1ZOhERERUC0yKiaox/9h8nM48bXguESQYETICPvY+OHDzAHYl78JTkU/h7cNvIyE/wVCOi3EQERE1L0yKiaqQrko3JMQv9HgBcokcYa5hCHYOhputG2SHZUjIT8Cu5F1IyE+ATCLD892fRxvXNmjn1s7C0RMREVFtNMqUbEuWLEFISAhsbW0RHR2NY8eOVVv+119/Rfv27WFra4suXbpg27ZtjREmWblCdSEe3Pwgev3UC71+6oWRG0YCALp7d8fjnR/H5I6T0ce/DwDA2cYZ/fz7AQBe3v8yAKCffz9M7TQV/QP6W+YFEBFRk3X48GFIpVKMGjXK0qFQFcyeFK9btw6zZ8/GvHnzcOrUKURGRmLEiBHIzMystPyhQ4cwceJETJ8+HadPn8bYsWMxduxYnD9/3tyhkpX78/qfiM+LR4m2BCXaEmj0GgDAg+0erLT8w+EPAyhfta66ckRERMuXL8czzzyD/fv3Iy0tzWJxqNVqi/Xd5Ilm1qtXL/Hpp582PNfpdKK/v784f/78Sss//PDD4qhRo4y2RUdHi//5z39M6q+goEAEIBYUFNQ9aLI6mapMse+avmLnVZ3FT098KqYqU8VUZaqYXZxdbb3cklwxVZkq5pbkNlKkREQtU3Xv3yUlJeLFixfFkpISC0RWf4WFhaKjo6N4+fJlcfz48eL7779vtH/z5s1iVFSUqFAoRA8PD3Hs2LGGfaWlpeIrr7witmrVSrSxsRHbtGkjLlu2TBRFUVy5cqXo4uJi1NbGjRvFO9O7efPmiZGRkeLSpUvFkJAQURAEURRFcfv27WK/fv1EFxcX0d3dXRw1apSYkJBg1FZqaqo4YcIE0c3NTbS3txd79OghHjlyRExKShIFQRCPHz9uVP7TTz8Vg4KCRJ1OV+9j1pBMPX/MOqZYrVbj5MmTmDNnjmGbRCLB0KFDcfjw4UrrHD58GLNnzzbaNmLECGzatKnS8mVlZSgrKzM8VyqV9Q+crIpap8bDWx6GUl1+7tzf9n60cmplUl03Wze42bqZMzwiIqqEKIoQS0os0rdgZwdBEEwu/8svv6B9+/YIDw/Ho48+iueffx5z5syBIAjYunUr7r//frzxxhv4/vvvoVarjYaNTpkyBYcPH8YXX3yByMhIJCUlITs7u1bxJiQk4LfffsOGDRsglZYvGqVSqTB79mxERESgqKgIc+fOxf3334+4uDhIJBIUFRVh4MCBCAgIwObNm+Hr64tTp05Br9cjJCQEQ4cOxcqVKxEVFWXoZ+XKlZg2bRokkua5YLJZk+Ls7GzodDr4+PgYbffx8cHly5crrZOenl5p+fT09ErLz58/H2+//XbDBExW6XDaYWSXlP+BeazTYwh2DrZwREREVBOxpATx3XtYpO/wUych2Js+B/3y5cvx6KOPAgBiYmJQUFCAffv2YdCgQXj//fcxYcIEo1wmMjISAHDlyhX88ssv+PPPPzF06FAAQOvWrSt2UAO1Wo3vv/8eXl5ehm3jxo0zKrNixQp4eXnh4sWL6Ny5M9asWYOsrCwcP34c7u7uAICwsDBD+RkzZuDJJ5/EJ598AoVCgVOnTuHcuXP4/fffax1fU9E8U/k7zJkzBwUFBYZHamqqpUOiJkqj0+CbM9/g/SPvGz2WxC0BADza4VHMjppdQytERESmi4+Px7FjxzBx4kQAgEwmw/jx47F8+XIAQFxcHIYMGVJp3bi4OEilUgwcOLBeMQQHBxslxABw9epVTJw4Ea1bt4azszNCQkIAACkpKYa+u3XrZkiI/23s2LGQSqXYuHEjAGDVqlUYPHiwoZ3myKxXij09PSGVSpGRkWG0PSMjA76+vpXW8fX1rVV5hUIBhULRMAFTi7Y5cbMhAa4M5xYmImo+BDs7hJ86abG+TbV8+XJotVr4+/sbtomiCIVCgcWLF8Oumraq2weUD0kVRdFom0ajqVDOwcGhwrbRo0cjODgYS5cuhb+/P/R6PTp37my4Ea+mvm1sbDBlyhSsXLkSDzzwANasWYPPP/+82jpNnVmTYhsbG/To0QOxsbEYO3YsAECv1yM2NhazZs2qtE6fPn0QGxuL559/3rDtzz//RJ8+fcwZKlmBHck7AACDWg1Ce4/2RvuCnYMR6RVpibCIiKgOBEGo1RAGS9Bqtfj+++/x8ccfY/jw4Ub7xo4di59//hkRERGIjY3FY489VqF+ly5doNfrsW/fPsPwiTt5eXmhsLAQKpXKkPjGxcXVGFdOTg7i4+OxdOlSDBgwAADw999/G5WJiIjAsmXLkJubW+XV4hkzZqBz58746quvoNVq8cADD9TYd5Nm7jv+1q5dKyoUCnHVqlXixYsXxSeeeEJ0dXUV09PTRVEUxcmTJ4uvvfaaofzBgwdFmUwmLlq0SLx06ZI4b948US6Xi+fOnTOpP84+QZXJKckRI1ZHiJ1XdRZTClIsHQ4REf1LS5x9YuPGjaKNjY2Yn59fYd8rr7wiRkVFiXv27BElEok4d+5c8eLFi+LZs2fFBQsWGMpNmzZNDAwMFDdu3Cheu3ZN3LNnj7hu3TpRFEUxJydHdHBwEJ999lkxISFB/Omnn0R/f/9KZ5+4k06nEz08PMRHH31UvHr1qhgbGyv27NlTBCBu3LhRFEVRLCsrE9u1aycOGDBA/Pvvv8XExERx/fr14qFDh4za6tu3r2hjYyM++eSTDXTUGp6p54/ZxxSPHz8eixYtwty5c9G1a1fExcVhx44dhpvpUlJScOvWLUP5vn37Ys2aNfjuu+8QGRmJ9evXY9OmTejcubO5Q6UWbPf13dCLenT06IhA50BLh0NERFZg+fLlGDp0KFxcXCrsGzduHE6cOAF3d3f8+uuv2Lx5M7p27Yq7777baJGzr7/+Gg8++CD+7//+D+3bt8fMmTOhUqkAAO7u7vjxxx+xbds2dOnSBT///DPeeuutGuOSSCRYu3YtTp48ic6dO+OFF17AwoULjcrY2Nhg165d8Pb2xsiRI9GlSxcsWLDAMHvFbdOnT4darcbjjz9ehyPUtAii+K/BKM2cUqmEi4sLCgoK4OzsbOlwqImYsXMGjqYfxQs9XsDjnZv/Ly4RUUtT3ft3aWkpkpKSEBoaCltbWwtFSJV599138euvv+Ls2bOWDqVKpp4/zX72CaKaFJQV4HjGcQDA8ODhNZQmIiKimhQVFeH8+fNYvHgxnnnmGUuH0yCYFFOLl6JMgV7Uw9ve2+RFOYiIiKhqs2bNQo8ePTBo0KAWMXQCMPPsE0RNQXpx+cIvvg6VT+tHREREtbNq1SqsWrXK0mE0KF4pphYvXfW/pNieSTERERFVjkkxtXgZqvLFYHilmIiIiKrCpJhavNvDJ3zsfSwcCRERETVVTIqpxTMMn+CVYiIiIqoCb7SjZk+lUSGnJKfK/beKyheHYVJMREREVWFSTM1adkk27tt0HwrVhTWW5fAJIiIiqgqHT1Cztit5FwrVhZAJMjjIHap8DAocBG97b0uHS0RE1CwMGjQIzz//fIvpxxS8UkzNhkqjwp7UPRgRPALJymQcSjuETQmbAACzo2ZjcsfJlg2QiIjoX6ZNm4b8/Hxs2rTJ0qE0qFWrVuH5559Hfn6+pUNpMEyKqdl478h72HJtCy7nXMbO6zsNN9ABwLDgYRaMjIiIqPkRRRE6nQ4yGdNBgMMnqJnQ6DTYcm0LAGD1xdVIV6XDXmaP0a1H452+7/AmOiIiavJ27NiB/v37w9XVFR4eHrj33nuRmJhoVObGjRuYOHEi3N3d4eDggKioKBw9etSw/48//kDPnj1ha2sLT09P3H///YZ9P/zwA6KiouDk5ARfX1888sgjyMzMNOzfu3cvBEHA9u3b0aNHDygUCvz9999QqVSYMmUKHB0d4efnh48//rjWr+2tt95C165d8cMPPyAkJAQuLi6YMGECCgv/uefHlH7Kysrw0ksvISAgAA4ODoiOjsbevXsBAKWlpejUqROeeOIJQ/nExEQ4OTlhxYoVtY7535gUU5OXVZyFmA0xFbYPCx6GDwZ8gPvb3l9JLSIiaslEUYSmTGeRhyiKdYpZpVJh9uzZOHHiBGJjYyGRSHD//fdDr9cDAIqKijBw4EDcvHkTmzdvxpkzZ/DKK68Y9m/duhX3338/Ro4cidOnTyM2Nha9evUytK/RaPDuu+/izJkz2LRpE5KTkzFt2rQKcbz22mtYsGABLl26hIiICLz88svYt28ffv/9d+zatQt79+7FqVOnav36EhMTsWnTJmzZsgVbtmzBvn37sGDBAsN+U/qZNWsWDh8+jLVr1+Ls2bN46KGHEBMTg6tXr8LW1hY//fQTVq9ejd9//x06nQ6PPvoohg0bhscff7zW8f4br5dTk/fTpZ+QWZxZYfvoNqMtEA0RETUFWrUe3z23zyJ9P/H5QMgV0lrXGzdunNHzFStWwMvLCxcvXkTnzp2xZs0aZGVl4fjx43B3dwcAhIWFGcq///77mDBhAt5++23DtsjISMPPdyaGrVu3xhdffIGePXuiqKgIjo6Ohn3vvPMOhg0rH3ZYVFSE5cuX48cff8SQIUMAAKtXr0arVq1q/fr0ej1WrVoFJycnAMDkyZMRGxuL999/36R+UlJSsHLlSqSkpMDf3x8A8NJLL2HHjh1YuXIlPvjgA3Tt2hXvvfceZsyYgQkTJuD69evYsmVLrWOtDJNiatJEUcTO5J0AgDFtxuDFqBdxJusMXBWu6Ord1bLBERER1cLVq1cxd+5cHD16FNnZ2YYrwCkpKejcuTPi4uLQrVs3Q0L8b3FxcZg5c2aV7Z88eRJvvfUWzpw5g7y8PKP2O3bsaCgXFRVl+DkxMRFqtRrR0dGGbe7u7ggPD6/16wsJCTEkxADg5+dnGL5hSj/nzp2DTqdDu3btjNotKyuDh4eH4fmLL76ITZs2YfHixdi+fbvRvvpgUkxN2sXci7hRdAO2Ulu8Hv067OX2GBQ4yNJhERGRhclsJHji84EW67suRo8ejeDgYCxduhT+/v7Q6/Xo3Lkz1Go1AMDOzq7a+tXtV6lUGDFiBEaMGIGffvoJXl5eSElJwYgRIwzt3+bg4FCn+Gsil8uNnguCYEjMTVFUVASpVIqTJ09CKjW+En/nle7MzExcuXIFUqkUV69eRUxMxSGWdcExxdSk3b5KfFeru2Avt7dwNERE1FQIggC5QmqRhyAItY43JycH8fHx+O9//4shQ4agQ4cOyMvLMyoTERGBuLg45ObmVtpGREQEYmNjK913+fJl5OTkYMGCBRgwYADat29vdJNdVdq0aQO5XG50M19eXh6uXLlSi1dXM1P66datG3Q6HTIzMxEWFmb08PX954b6xx9/HF26dMHq1avx6quv4tKlSw0SI68UU5MUnxuPCzkXsD1pOwAgJrRhPgUSERFZgpubGzw8PPDdd9/Bz88PKSkpeO2114zKTJw4ER988AHGjh2L+fPnw8/PD6dPn4a/vz/69OmDefPmYciQIWjTpg0mTJgArVaLbdu24dVXX0VQUBBsbGzw5Zdf4sknn8T58+fx7rvv1hiXo6Mjpk+fjpdffhkeHh7w9vbGG2+8AYmkYa+bmtJPu3btMGnSJEyZMgUff/wxunXrhqysLMTGxiIiIgKjRo3CkiVLcPjwYZw9exaBgYHYunUrJk2ahCNHjsDGxqZeMfJKMTU5Ko0KU3dMxbxD85CuSoedzA79A/pbOiwiIqJa0+v1kMlkkEgkWLt2LU6ePInOnTvjhRdewMKFC43K2tjYYNeuXfD29sbIkSPRpUsXLFiwwDCUYNCgQfj111+xefNmdO3aFXfffTeOHTsGAPDy8sKqVavw66+/omPHjliwYAEWLVpkUowLFy7EgAEDMHr0aAwdOhT9+/dHjx49GvZAmNjPypUrMWXKFLz44osIDw/H2LFjcfz4cQQFBeHy5ct4+eWX8dVXXyEwMBAA8NVXXyE7OxtvvvlmveMTxLrOK9JEKZVKuLi4oKCgAM7OzpYOh+pg67WteO3Aa3BTuCHSOxIxITEY1XqUpcMiIiIzqu79u7S0FElJSQgNDYWtra2FIqybmJgYhIWFYfHixZYOxWqZev5w+AQ1Or2oxxenvkBCfkKl+29vfzj8YczqNqsxQyMiImoQeXl5OHjwIPbu3Ysnn3zS0uGQCZgUU6M7mXESy88vr7aMAAH3hN7TSBERERE1rMcffxzHjx/Hiy++iDFjxlg6HDIBk2JqNDq9DgCwI2kHAKCvf1/EhFR+A12QcxDauLZptNiIiIga0saNGy0dAtUSk2JqFItPL8Z3Z7+DiH+GsE/tOBV9A/paMCoiIiKicpx9gsyuTFeGHy/9aJQQh7mGoadfTwtGRURERPQPXimmOjmXdQ5hbmGwk9khvzQfJzNOQo/KV61JyEuASqOCj70P1o9eD0EQ4GTjBInAz2RERETUNDApplrbdm0bXj3wKsa0GYP3+r+HF/a+gBMZJ2qsNzxkOFxtXc0fIBEREVEtMSmmWvvw+IcAgN8Tf8fjnR/HiYwTECCgm3e3Kus42ThhSscpjRUiERERUa0wKSYA5XMHrzy/EhFeEejp2xMJeQn4/uL30Og1RuVEiMgvyzc8f27PcwCAHj49sDJmZWOGTERERNRgmBQTAGBv6l58duozAMDpyafx0fGPcPjW4RrrJSuTAYBzChMRETURgiBg48aNGDt2bIOWbemYFBMA4FrBNcPPu6/vxrH08rXUn+76NOxkdkZlJYIEvf1643TmaZRoS+Bs44zRbUY3arxERETNwbRp07B69WoAgFwuR1BQEKZMmYLXX38dMpl50rBbt27Bzc2twcu2dEyKCQBwXXnd8PNrB16DTtShg3sHPBlZ9dKUbd3aNkZoREREzVpMTAxWrlyJsrIybNu2DU8//TTkcjnmzJljVE6tVsPGxqbe/fn6+pqlbEvHObEIAJCYn2j4WSeWrzz3UPhDlgqHiIioxVAoFPD19UVwcDCeeuopDB06FJs3b8a0adMwduxYvP/++/D390d4eDgAIDU1FQ8//DBcXV3h7u6OMWPGIDk52ajNFStWoFOnTlAoFPDz88OsWbMM+wRBwKZNmwCUJ9qzZs2Cn58fbG1tERwcjPnz51daFgDOnTuHu+++G3Z2dvDw8MATTzyBoqIiw/7bMS9atAh+fn7w8PDA008/DY3G+B6k5ohXignpqnScyz4HAPj+nu/haesJhUwBb3tvC0dGRERUOVEUoS0rs0jfMoUCgiDUub6dnR1ycnIAALGxsXB2dsaff/4JANBoNBgxYgT69OmDAwcOQCaT4b333kNMTAzOnj0LGxsbfP3115g9ezYWLFiAe+65BwUFBTh48GClfX3xxRfYvHkzfvnlFwQFBSE1NRWpqamVllWpVIa+jx8/jszMTMyYMQOzZs3CqlWrDOX27NkDPz8/7NmzBwkJCRg/fjy6du2KmTNn1vmYNAVMiq1ckboIIzeMBADIJDJ09uwMuURu4aiIiIiqpy0rwxdTH7RI38+uXg+5rW2t64miiNjYWOzcuRPPPPMMsrKy4ODggGXLlhmGTfz444/Q6/VYtmyZIfFeuXIlXF1dsXfvXgwfPhzvvfceXnzxRTz33HOGtnv2rHyV2JSUFLRt2xb9+/eHIAgIDg6uMr41a9agtLQU33//PRwcHAAAixcvxujRo/Hhhx/Cx8cHAODm5obFixdDKpWiffv2GDVqFGJjY5t9UszhE1ZuT+oew7RrT0U+xYSYiIiogW3ZsgWOjo6wtbXFPffcg/Hjx+Ott94CAHTp0sVoHPGZM2eQkJAAJycnODo6wtHREe7u7igtLUViYiIyMzORlpaGIUOGmNT3tGnTEBcXh/DwcDz77LPYtWtXlWUvXbqEyMhIQ0IMAP369YNer0d8fLxhW6dOnSCVSg3P/fz8kJmZaerhaLJ4pdhKXcq5hM2Jm3Hk1hEAwJORT+KJiCcsHBUREZFpZAoFnl293mJ918bgwYPx9ddfw8bGBv7+/kazTtyZgAJAUVERevTogZ9++qlCO15eXpBIanc9s3v37khKSsL27duxe/duPPzwwxg6dCjWr6/7sZPLjS+gCYIAvV5f5/aaCibFVur9o+/jTNYZw/MRwSMsGA0REVHtCIJQpyEMluDg4ICwsDCTynbv3h3r1q2Dt7c3nJ2dKy0TEhKC2NhYDB482KQ2nZ2dMX78eIwfPx4PPvggYmJikJubC3d3d6NyHTp0wKpVq6BSqQzJ+sGDByGRSAw3AbZkHD5hhfSiHlfyrgAAHmn/CBYNXIQwN9N+WYmIiMh8Jk2aBE9PT4wZMwYHDhxAUlIS9u7di2effRY3btwAALz11lv4+OOP8cUXX+Dq1as4deoUvvzyy0rb++STT/Dzzz/j8uXLuHLlCn799Vf4+vrC1dW10r5tbW0xdepUnD9/Hnv27MEzzzyDyZMnG8YTt2S8UmyF0orSUKItgVwix8s9X4ZMwtOAiIioKbC3t8f+/fvx6quv4oEHHkBhYSECAgIwZMgQw5XjqVOnorS0FJ9++ileeukleHp64sEHK7/p0MnJCR999BGuXr0KqVSKnj17Ytu2bZUOw7C3t8fOnTvx3HPPoWfPnrC3t8e4cePwySefmPU1NxWCKIqipYNoSEqlEi4uLigoKKjyawdrty91H2b9NQtt3dpiw30bLB0OERFRte/fpaWlSEpKQmhoKGybyZAJajpMPX/MNnwiNzcXkyZNgrOzM1xdXTF9+nSjyZ8rK//MM88gPDwcdnZ2CAoKwrPPPouCggJzhdjiFWuKodKoKmxPyE8AAIS5cMgEEREREWDGpHjSpEm4cOEC/vzzT2zZsgX79+/HE09UPbtBWloa0tLSsGjRIpw/fx6rVq3Cjh07MH36dHOF2KJp9BpM2DoB9226DyXaEqN9t8cTt3FtY4nQiIiIiJocswwmvXTpEnbs2IHjx48jKioKAPDll19i5MiRWLRoEfz9/SvU6dy5M3777TfD8zZt2uD999/Ho48+Cq1WazR9CdXs+K3jSCpIAgBczbuKCK8IAIBGp8HfN/8GAHT36W6x+IiIiIiaErNkmocPH4arq6shIQaAoUOHQiKR4OjRo7j//vtNauf2uCImxKbJKs7CxoSNUOvUOJZ+zLD981Ofo5t3t/IyJVlQqpXwtPNEd28mxURERESAmZLi9PR0eHt7G3ckk8Hd3R3p6ekmtZGdnY1333232iEXAFBWVoayO9Y+VyqVtQ+4hVh4YiG2J22vsP1Y+jGjJBkAhgUPg1QirVCWiIiIyBrVKil+7bXX8OGHH1Zb5tKlS/UKCChPbEeNGoWOHTsalkGsyvz58/H222/Xu8/mrkRbgr2pewEAY9qMgb3cHicyTuBq3lUAQAf3Dujq3RUAYCezw9ROUy0TKBEREVETVKuk+MUXX8S0adOqLdO6dWv4+vpWWANbq9UiNzcXvr6+1dYvLCxETEwMnJycsHHjxgpLCf7bnDlzMHv2bMNzpVKJwMDA6l9IC7T/xn6UaEsQ4BiAd/u9C0EQcCrjFKbuKE9+Z3Wbhbta3WXhKImIiIiaplolxV5eXvDy8qqxXJ8+fZCfn4+TJ0+iR48eAIC//voLer0e0dHRVdZTKpUYMWIEFAoFNm/ebNJchAqFAoparkHeEu1M3gkAGBEyAoIgAADC3cPhpnCDjdQGffz6WDI8IiIioibNLGOKO3TogJiYGMycORPffPMNNBoNZs2ahQkTJhhmnrh58yaGDBmC77//Hr169YJSqcTw4cNRXFyMH3/8EUql0jA+2MvLC1KpdY1/LdYUI6c0x6SyGr0GB24cAFCeFN/mIHfAhjEbIBWkkEurv+JOREREZM3MNq3DTz/9hFmzZmHIkCGQSCQYN24cvvjiC8N+jUaD+Ph4FBcXAwBOnTqFo0ePAgDCwowXlUhKSkJISIi5Qm1yckpycN+m+6BU1+6mwSCnIHRw72C0zdPOsyFDIyIiomZGEARs3LgRY8eORXJyMkJDQ3H69Gl07drV0qE1KWZLit3d3bFmzZoq94eEhODOFaYHDRqEFrbidJ3tTN4JpVoJqSCFjdTGpDpyiRwzuswwDJ0gIiIiy5s2bRpWr14NoHwmrlatWuGhhx7CO++8wyWrmxhOANzEnM8+j/nH5gMAXox6EZM7TrZwRERERFQfMTExWLlyJTQaDU6ePImpU6dCEIQaZ/SixmW2ZZ6p9oo1xZixa4bh+bDgYRaMhoiIiBqCQqGAr68vAgMDMXbsWAwdOhR//vknAECv12P+/PkIDQ2FnZ0dIiMjsX79eqP6Fy5cwL333gtnZ2c4OTlhwIABSExMBAAcP34cw4YNg6enJ1xcXDBw4ECcOnWq0V9jS8ArxU3I/pv7odKoAABv930bvg7VT19HRERkrURRhKjRW6RvQS6p83DF8+fP49ChQwgODgZQvt7Cjz/+iG+++QZt27bF/v378eijj8LLywsDBw7EzZs3cdddd2HQoEH466+/4OzsjIMHD0Kr1QIon8p26tSp+PLLLyGKIj7++GOMHDkSV69ehZOTU4O9ZmvApLiJ2Jm8Ey/vexkAML3zdDzQ9gELR0RERNR0iRo90uYeskjf/u/0hWBj+qxYW7ZsgaOjI7RaLcrKyiCRSLB48WKUlZXhgw8+wO7du9GnT/nUqa1bt8bff/+Nb7/9FgMHDsSSJUvg4uKCtWvXGtZuaNeunaHtu+++26iv7777Dq6urti3bx/uvffeBni11oNJcROg0+sw/+h8w/ORrUdaMBoiIiJqSIMHD8bXX38NlUqFTz/9FDKZDOPGjcOFCxdQXFyMYcOMh0uq1Wp069YNABAXF4cBAwZUuZhZRkYG/vvf/2Lv3r3IzMyETqdDcXExUlJSzP66WhomxY2kUF0IvVj51zxnss4Y5iT+4Z4f0M6tXaXliIiIqJwgl8D/nb4W67s2HBwcDNPNrlixApGRkVi+fDk6d+4MANi6dSsCAgKM6txemMzOzq7atqdOnYqcnBx8/vnnCA4OhkKhQJ8+faBWq2sVIzEpbhRzD87FxoSNNZa7P+x+dPXuav6AiIiImjlBEGo1hKGpkEgkeP311zF79mxcuXIFCoUCKSkpGDhwYKXlIyIisHr1amg0mkqvFh88eBBfffUVRo4s/5Y5NTUV2dnZZn0NLRVnnzCz/NJ8/JH4R43lHOQOGB8+vhEiIiIiIkt66KGHIJVK8e233+Kll17CCy+8gNWrVyMxMRGnTp3Cl19+aZjbeNasWVAqlZgwYQJOnDiBq1ev4ocffkB8fDwAoG3btvjhhx9w6dIlHD16FJMmTarx6jJVjleKzaRMV4Y9KXtwIuMEtKIW4W7hWHvv2irLSwQJJAI/oxAREbV0MpkMs2bNwkcffYSkpCR4eXlh/vz5uHbtGlxdXdG9e3e8/vrrAAAPDw/89ddfePnllzFw4EBIpVJ07doV/fr1AwAsX74cTzzxBLp3747AwEB88MEHeOmllyz58potQWxhy8gplUq4uLigoKAAzs7OFovjy9Nf4ruz3xmeP9vtWcyMmGmxeIiIiJqy6t6/S0tLkZSUhNDQUK4CR7Vm6vnDK8VmIIoitl7bCgDo6tUVQc5BGN+eQyOIiIiImiomxQ1MpVHhqd1P4WbRTdjJ7PDd8O9gJ+PYHiIiIqKmjINYG9j6K+txOvM0AODuoLuZEBMRERE1A7xS3EBuD83elbwLANDduzvm9JpjyZCIiIiIyERMihuAXtRj+s7pOJFxAgAgQMDHgz6Gi8LFwpERERERkSk4fKIB3Ci8YUiIASAmJAaedp4WjIiIiIiIaoNXihtAQn4CAKCdWzssH76cV4iJiIiImhkmxQ0gMT8RANDWrS1cbV0tGwwRERER1RqHTzSAq/lXAQBhrmEWjoSIiIiI6oJJcQO4faW4jUsbC0dCRERERHXBpLieCsoKcC3/GoDy4RNEREREt02bNg2CIFR4JCSU34+0f/9+jB49Gv7+/hAEAZs2baqxTZ1OhwULFqB9+/aws7ODu7s7oqOjsWzZMjO/mpaNY4rraU/qHmhFLcJcw9DKqZWlwyEiIqImJiYmBitXrjTa5uXlBQBQqVSIjIzE448/jgceeMCk9t5++218++23WLx4MaKioqBUKnHixAnk5eU1eOy3qdVq2NjYmK39poBXiuvp9mIdI0JGWDgSIiIiaooUCgV8fX2NHlKpFABwzz334L333sP9999vcnubN2/G//3f/+Ghhx5CaGgoIiMjMX36dLz00kuGMnq9Hh999BHCwsKgUCgQFBSE999/37D/3LlzuPvuu2FnZwcPDw888cQTKCoqMuyfNm0axo4di/fffx/+/v4IDw8HAKSmpuLhhx+Gq6sr3N3dMWbMGCQnJ9fzCDUNTIrrQRRFxGXGAQAGthpo2WCIiIisiCiKUKvVFnncXsXWUnx9ffHXX38hKyuryjJz5szBggUL8Oabb+LixYtYs2YNfHx8AJRfnR4xYgTc3Nxw/Phx/Prrr9i9ezdmzZpl1EZsbCzi4+Px559/YsuWLdBoNBgxYgScnJxw4MABHDx4EI6OjoiJiYFarTbra24MHD5RD5nFmSjUFEIqSNHGlTfZERERNRaNRoMPPvjAIn2//vrrtRpKsGXLFjg6Ohqe33PPPfj111/r3P8nn3yCBx98EL6+vujUqRP69u2LMWPG4J577gEAFBYW4vPPP8fixYsxdepUAECbNm3Qv39/AMCaNWtQWlqK77//Hg4ODgCAxYsXY/To0fjwww8NybODgwOWLVtmeK0//vgj9Ho9li1bBkEQAAArV66Eq6sr9u7di+HDh9f5NTUFTIrr4fasE4FOgbCRtuxxNkRERFQ3gwcPxtdff214fjsRrauOHTvi/PnzOHnyJA4ePGi4WW/atGlYtmwZLl26hLKyMgwZMqTS+pcuXUJkZKRRHP369YNer0d8fLwhKe7SpYtR8n/mzBkkJCTAycnJqL3S0lIkJibW6zU1BUyK6+H2Snacn5iIiKhxyeVyvP766xbruzYcHBwQFtawuYJEIkHPnj3Rs2dPPP/88/jxxx8xefJkvPHGG7Czs2uQPv6dvBcVFaFHjx746aefKpS9feNgc8ak2ETXCq7h13jjrzpOZJwAAA6dICIiamSCILT42RBqo2PHjgDKxwu3bdsWdnZ2iI2NxYwZMyqU7dChA1atWgWVSmVIfA8ePAiJRGK4oa4y3bt3x7p16+Dt7Q1nZ2fzvBALYlJsovSidPx46cdK93Vw79DI0RAREVFLUFRUZJizGACSkpIQFxcHd3d3BAUFVVrnwQcfRL9+/dC3b1/4+voiKSkJc+bMQbt27dC+fXvIZDK8+uqreOWVV2BjY4N+/fohKysLFy5cwPTp0zFp0iTMmzcPU6dOxVtvvYWsrCw888wzmDx5smHoRGUmTZqEhQsXYsyYMXjnnXfQqlUrXL9+HRs2bMArr7yCVq2a99S0TIpNFOAUgJldZlbY7mHngUGBgxo/ICIiImr2Tpw4gcGDBxuez549GwAwdepUrFq1qtI6I0aMwM8//4z58+ejoKAAvr6+uPvuu/HWW29BJitP7d58803IZDLMnTsXaWlp8PPzw5NPPgkAsLe3x86dO/Hcc8+hZ8+esLe3x7hx4/DJJ59UG6u9vT3279+PV199FQ888AAKCwsREBCAIUOGtIgrx4Jo6XlFGphSqYSLiwsKCgpaxH8QERGRNaju/bu0tBRJSUkIDQ2Fra2thSKk5srU84fzFBMRERGR1WNSTERERERWj0kxEREREVk9JsVEREREZPWYFBMRERGR1WNSTERERM2CXq+3dAjUDJl63nCeYiIiImrSbGxsIJFIkJaWBi8vL9jY2EAQBEuHRU2cKIpQq9XIysqCRCKpcQVEJsVERETUpEkkEoSGhuLWrVtIS0uzdDjUzNjb2yMoKAgSSfUDJJgUExERUZNnY2ODoKAgaLVa6HQ6S4dDzYRUKoVMJjPpmwUmxURERNQsCIIAuVwOuVxu6VCoBeKNdkRERERk9ZgUExEREZHVY1JMRERERFavxY0pFkURAKBUKi0cCREREZnq9vv27fdxosbW4pLiwsJCAEBgYKCFIyEiIqLaKiwshIuLi6XDICskiC3sI5ler0daWhqcnJwafGJvpVKJwMBApKamwtnZuUHbbml4rEzHY2U6HivT8VjVDo+X6cx1rERRRGFhIfz9/WucT5bIHFrclWKJRIJWrVqZtQ9nZ2f+0TQRj5XpeKxMx2NlOh6r2uHxMp05jhWvEJMl8aMYEREREVk9JsVEREREZPWYFNeCQqHAvHnzoFAoLB1Kk8djZToeK9PxWJmOx6p2eLxMx2NFLVWLu9GOiIiIiKi2eKWYiIiIiKwek2IiIiIisnpMiomIiIjI6jEpJiIiIiKrZ9VJ8ZIlSxASEgJbW1tER0fj2LFjVZZdunQpBgwYADc3N7i5uWHo0KEVyouiiLlz58LPzw92dnYYOnQorl69au6X0Sga+lhNmzYNgiAYPWJiYsz9MhpNbY7Xhg0bEBUVBVdXVzg4OKBr16744YcfjMrw3CpnyrFqyedWbY7VndauXQtBEDB27Fij7TyvKqrqWPG8Krdq1aoKx8HW1taoTEs+r6iFE63U2rVrRRsbG3HFihXihQsXxJkzZ4qurq5iRkZGpeUfeeQRccmSJeLp06fFS5cuidOmTRNdXFzEGzduGMosWLBAdHFxETdt2iSeOXNGvO+++8TQ0FCxpKSksV6WWZjjWE2dOlWMiYkRb926ZXjk5uY21ksyq9oerz179ogbNmwQL168KCYkJIifffaZKJVKxR07dhjK8NwqZ8qxaqnnVm2P1W1JSUliQECAOGDAAHHMmDFG+3heGavuWPG8Krdy5UrR2dnZ6Dikp6cblWmp5xW1fFabFPfq1Ut8+umnDc91Op3o7+8vzp8/36T6Wq1WdHJyElevXi2Koijq9XrR19dXXLhwoaFMfn6+qFAoxJ9//rlhg29kDX2sRLH8DebfbzotRX2PlyiKYrdu3cT//ve/oijy3KrJncdKFFvuuVWXY6XVasW+ffuKy5Ytq3BceF4Zq+5YiSLPq9tWrlwpuri4VNleSz6vqOWzyuETarUaJ0+exNChQw3bJBIJhg4disOHD5vURnFxMTQaDdzd3QEASUlJSE9PN2rTxcUF0dHRJrfZFJnjWN22d+9eeHt7Izw8HE899RRycnIaNHZLqO/xEkURsbGxiI+Px1133QWA51ZVKjtWt7W0c6uux+qdd96Bt7c3pk+fXmEfzytj1R2r23helSsqKkJwcDACAwMxZswYXLhwwbCvpZ5XZB1klg7AErKzs6HT6eDj42O03cfHB5cvXzapjVdffRX+/v6GX/z09HRDG/9u8/a+5sgcxwoAYmJi8MADDyA0NBSJiYl4/fXXcc899+Dw4cOQSqUN+hoaU12PV0FBAQICAlBWVgapVIqvvvoKw4YNA8Bz69+qO1ZAyzy36nKs/v77byxfvhxxcXGV7ud59Y+ajhXA8+q28PBwrFixAhERESgoKMCiRYvQt29fXLhwAa1atWqx5xVZB6tMiutrwYIFWLt2Lfbu3VvhBgMyVtWxmjBhguHnLl26ICIiAm3atMHevXsxZMgQS4RqUU5OToiLi0NRURFiY2Mxe/ZstG7dGoMGDbJ0aE1OTceK5xZQWFiIyZMnY+nSpfD09LR0OE2aqceK51W5Pn36oE+fPobnffv2RYcOHfDtt9/i3XfftWBkRPVnlUmxp6cnpFIpMjIyjLZnZGTA19e32rqLFi3CggULsHv3bkRERBi2366XkZEBPz8/oza7du3acME3MnMcq8q0bt0anp6eSEhIaNZvMHU9XhKJBGFhYQCArl274tKlS5g/fz4GDRrEc+tfqjtWlWkJ51Ztj1ViYiKSk5MxevRowza9Xg8AkMlkiI+P53n1P6YcqzZt2lSoZ43nVWXkcjm6deuGhIQEAC33vZCsg1WOKbaxsUGPHj0QGxtr2KbX6xEbG2v0CfjfPvroI7z77rvYsWMHoqKijPaFhobC19fXqE2lUomjR49W22ZTZ45jVZkbN24gJyfH6I9oc1TX4/Vver0eZWVlAHhu1eTOY1WZlnBu1fZYtW/fHufOnUNcXJzhcd9992Hw4MGIi4tDYGAgz6v/MeVYVcYaz6vK6HQ6nDt3znAcWup5RVbC0nf6WcratWtFhUIhrlq1Srx48aL4xBNPiK6uroapZSZPniy+9tprhvILFiwQbWxsxPXr1xtNRVNYWGhUxtXVVfz999/Fs2fPimPGjGkR09A09LEqLCwUX3rpJfHw4cNiUlKSuPv/27l7FIWhAIjjLviRNoWkM41ewEKwshAsPEG6VPb2ImJjZWfpBbyC4gFstFOwyDWClbOF8HaNAQm4627e/wcWwUcgwxSDSLZbNZtNNRoNXS6XtzzjK2XNazababPZKIoinU4nzedzFYtFLZdLc4Zu3TzLKs/dyppVUtrbE+hVumRW9Oorq+l0qvV6rSiKtN/vFQSBHMfR8Xg0Z/LaK+SftaNYkhaLhWq1msrlslqtlna7nfmu0+koDENz7fu+CoXCw2cymZgz1+tV4/FYnuepUqmo2+3qfD7/4hP9nFdmFcexer2eqtWqSqWSfN/XYDB4eNflf5Ylr9FopHq9Lsdx5Lqu2u22VqvV3f3o1s2zrPLerSxZJaWNYnqVLpkVvQrN9XA4NGc9z1O/39fhcLi7X557hXz7kKR3/UoNAAAA/AVW/qcYAAAA+I5RDAAAAOsxigEAAGA9RjEAAACsxygGAACA9RjFAAAAsB6jGAAAANZjFAMAAMB6jGIAAABYj1EMAAAA6zGKAQAAYD1GMQAAAKz3CYQaL70Jj4ufAAAAAElFTkSuQmCC","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["displayPlotBetween(0, 1, 20)\n","displayPlotBetween(0.2, 0.5, 500) #More division means more Resolution "]},{"cell_type":"markdown","metadata":{},"source":["In graph below we can more clearly see that we need a value of thresold slightly less than 0.42 as at that point we get max F1, Acc, Jacc and min MAE, MSE."]},{"cell_type":"code","execution_count":256,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["displayPlotBetween(0.4, 0.45, 100)"]},{"cell_type":"markdown","metadata":{},"source":["From the graph below we can choose a thresold value of 0.4155 as that is where we get good metrics. Also most of the time after we get rough estimation of parameters we then just automate the process of finding them using several techniques. Here I have used graphs as this was a simple case and I am comfortable with graphs as well. "]},{"cell_type":"code","execution_count":237,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["displayPlotBetween(0.414, 0.416, 100)"]},{"cell_type":"markdown","id":"aa6a63d3-ebbd-487d-8bb4-84842f9055b7","metadata":{},"source":["Using the `predictions` and the `y_test` dataframe to calculate the value for each metric using the appropriate function and show the report.\n"]},{"cell_type":"code","execution_count":238,"id":"71cb1320-34c6-4359-922d-3f0dc5344fd2","metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Model</th>\n","      <th>MAE</th>\n","      <th>MSE</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","      <th>Jaccard Index</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1 Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Linear Regression</td>\n","      <td>0.15</td>\n","      <td>0.15</td>\n","      <td>0.28</td>\n","      <td>0.85</td>\n","      <td>0.59</td>\n","      <td>0.74</td>\n","      <td>0.74</td>\n","      <td>0.74</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               Model   MAE   MSE    R2  Accuracy  Jaccard Index  Precision  \\\n","0  Linear Regression  0.15  0.15  0.28      0.85           0.59       0.74   \n","\n","   Recall  F1 Score  \n","0    0.74      0.74  "]},"execution_count":238,"metadata":{},"output_type":"execute_result"}],"source":["predictions_in_binary = np.where(predictions < 0.4155, 0, 1)\n","Report = calc_metrics('Linear Regression', y_test, predictions_in_binary)\n","Report"]},{"cell_type":"markdown","id":"86cc3e76-f489-4a59-b435-f527c208610d","metadata":{},"source":["### KNN\n"]},{"cell_type":"markdown","id":"9912cd81-1de8-4e82-9c70-f0ed9b4d2fb0","metadata":{},"source":["Create and train a KNN model called KNN using the training data (`x_train`, `y_train`) with the `n_neighbors` parameter set to `5`.\n"]},{"cell_type":"code","execution_count":239,"id":"480b5e80-3c4e-4a93-804e-5051b1dcfda2","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-38 {color: black;}#sk-container-id-38 pre{padding: 0;}#sk-container-id-38 div.sk-toggleable {background-color: white;}#sk-container-id-38 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-38 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-38 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-38 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-38 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-38 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-38 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-38 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-38 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-38 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-38 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-38 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-38 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-38 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-38 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-38 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-38 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-38 div.sk-item {position: relative;z-index: 1;}#sk-container-id-38 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-38 div.sk-item::before, #sk-container-id-38 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-38 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-38 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-38 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-38 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-38 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-38 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-38 div.sk-label-container {text-align: center;}#sk-container-id-38 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-38 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-38\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" checked><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"],"text/plain":["KNeighborsClassifier()"]},"execution_count":239,"metadata":{},"output_type":"execute_result"}],"source":["KNN = KNeighborsClassifier(n_neighbors=5)\n","KNN.fit(x_train, y_train)"]},{"cell_type":"markdown","id":"5b0374cd-b8ee-4768-8408-c08d67dfb0d9","metadata":{},"source":["Now using the `predict` method on the testing data (`x_test`) and save it to the array `predictions`.\n"]},{"cell_type":"code","execution_count":240,"id":"c0f89ed3-5afd-4326-9304-05ecf5b1cc29","metadata":{},"outputs":[],"source":["predictions2 = KNN.predict(x_test)"]},{"cell_type":"markdown","id":"478aea0d-8374-4e7c-bf8b-9bacfbac7a7a","metadata":{},"source":["Using the `predictions` and the `y_test` dataframe calculate the value for each metric using the appropriate function and show the report.\n"]},{"cell_type":"code","execution_count":241,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Model</th>\n","      <th>MAE</th>\n","      <th>MSE</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","      <th>Jaccard Index</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1 Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>KNN</td>\n","      <td>0.18</td>\n","      <td>0.18</td>\n","      <td>0.11</td>\n","      <td>0.82</td>\n","      <td>0.47</td>\n","      <td>0.74</td>\n","      <td>0.56</td>\n","      <td>0.64</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Model   MAE   MSE    R2  Accuracy  Jaccard Index  Precision  Recall  \\\n","0   KNN  0.18  0.18  0.11      0.82           0.47       0.74    0.56   \n","\n","   F1 Score  \n","0      0.64  "]},"execution_count":241,"metadata":{},"output_type":"execute_result"}],"source":["Report2 = calc_metrics('KNN', y_test, predictions2)\n","Report2"]},{"cell_type":"markdown","id":"254250b6-7c79-4edd-bc79-a19d5e1feac7","metadata":{},"source":["### Decision Tree\n"]},{"cell_type":"markdown","id":"b30f6e9d-5346-44c5-8613-e37db294175c","metadata":{},"source":["Create and train a Decision Tree model called Tree using the training data (`x_train`, `y_train`).\n"]},{"cell_type":"code","execution_count":242,"id":"334c059a-1bbb-4aec-9a42-ea46d9238300","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-39 {color: black;}#sk-container-id-39 pre{padding: 0;}#sk-container-id-39 div.sk-toggleable {background-color: white;}#sk-container-id-39 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-39 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-39 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-39 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-39 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-39 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-39 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-39 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-39 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-39 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-39 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-39 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-39 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-39 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-39 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-39 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-39 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-39 div.sk-item {position: relative;z-index: 1;}#sk-container-id-39 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-39 div.sk-item::before, #sk-container-id-39 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-39 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-39 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-39 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-39 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-39 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-39 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-39 div.sk-label-container {text-align: center;}#sk-container-id-39 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-39 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-39\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" checked><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"],"text/plain":["DecisionTreeClassifier()"]},"execution_count":242,"metadata":{},"output_type":"execute_result"}],"source":["Tree = DecisionTreeClassifier()\n","Tree.fit(x_train, y_train)"]},{"cell_type":"markdown","id":"fa602ff2-4de4-46ad-a546-9cbb28c89dc7","metadata":{},"source":["Now use the `predict` method on the testing data (`x_test`) and save it to the array `predictions`.\n"]},{"cell_type":"code","execution_count":243,"id":"f2979c54-0dc7-4645-b01d-09d6b4ca1869","metadata":{},"outputs":[],"source":["predictions3 = Tree.predict(x_test)"]},{"cell_type":"markdown","id":"a10cfd05-1e28-4dd7-9264-40b0f04f5d5d","metadata":{},"source":["Using the `predictions` and the `y_test` dataframe calculate the value for each metric using the appropriate function and show the report.\n"]},{"cell_type":"code","execution_count":244,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Model</th>\n","      <th>MAE</th>\n","      <th>MSE</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","      <th>Jaccard Index</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1 Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Decision Tree</td>\n","      <td>0.23</td>\n","      <td>0.23</td>\n","      <td>-0.14</td>\n","      <td>0.77</td>\n","      <td>0.41</td>\n","      <td>0.59</td>\n","      <td>0.58</td>\n","      <td>0.59</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           Model   MAE   MSE    R2  Accuracy  Jaccard Index  Precision  \\\n","0  Decision Tree  0.23  0.23 -0.14      0.77           0.41       0.59   \n","\n","   Recall  F1 Score  \n","0    0.58      0.59  "]},"execution_count":244,"metadata":{},"output_type":"execute_result"}],"source":["Report3 = calc_metrics('Decision Tree', y_test, predictions3)\n","Report3"]},{"cell_type":"markdown","id":"b003ddfe-0e3b-4223-8070-dfe43a43151b","metadata":{},"source":["### Logistic Regression\n"]},{"cell_type":"markdown","id":"82a2c86c-25b4-4891-9690-f2252356a67b","metadata":{},"source":["Create and train a LogisticRegression model called LR using the training data (`x_train`, `y_train`) with the `solver` parameter set to `liblinear`.\n"]},{"cell_type":"code","execution_count":245,"id":"743428f1-7022-4e74-822a-8c1bd1279aaf","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-40 {color: black;}#sk-container-id-40 pre{padding: 0;}#sk-container-id-40 div.sk-toggleable {background-color: white;}#sk-container-id-40 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-40 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-40 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-40 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-40 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-40 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-40 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-40 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-40 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-40 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-40 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-40 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-40 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-40 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-40 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-40 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-40 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-40 div.sk-item {position: relative;z-index: 1;}#sk-container-id-40 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-40 div.sk-item::before, #sk-container-id-40 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-40 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-40 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-40 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-40 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-40 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-40 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-40 div.sk-label-container {text-align: center;}#sk-container-id-40 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-40 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-40\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" checked><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"],"text/plain":["LogisticRegression(solver='liblinear')"]},"execution_count":245,"metadata":{},"output_type":"execute_result"}],"source":["LR = LogisticRegression(solver='liblinear')\n","LR.fit(x_train, y_train)"]},{"cell_type":"markdown","id":"edf1789d-873e-4243-a34d-6c40b6a92bc0","metadata":{},"source":["Now, use the `predict` and `predict_proba` methods on the testing data (`x_test`) and save it as 2 arrays `predictions` and `predict_proba`.\n"]},{"cell_type":"code","execution_count":246,"id":"80fb13fc-ad37-43a6-8abc-18d5245c1a92","metadata":{},"outputs":[],"source":["predictions4 = LR.predict(x_test)"]},{"cell_type":"code","execution_count":247,"id":"8d5a88b1-37c1-440e-a53a-058ad355cf65","metadata":{},"outputs":[],"source":["predict_proba = LR.predict_proba(x_test)"]},{"cell_type":"markdown","id":"1aeddde4-ecbe-47b2-970c-04353199e89a","metadata":{},"source":["Using the `predictions`, `predict_proba` and the `y_test` dataframe calculate the value for each metric using the appropriate function and show the report.\n"]},{"cell_type":"code","execution_count":248,"id":"78e4bcbe-b39a-4df0-a700-69c9345d60e4","metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Model</th>\n","      <th>MAE</th>\n","      <th>MSE</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","      <th>Jaccard Index</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1 Score</th>\n","      <th>Log_Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Logistic Regression</td>\n","      <td>0.16</td>\n","      <td>0.16</td>\n","      <td>0.19</td>\n","      <td>0.84</td>\n","      <td>0.52</td>\n","      <td>0.75</td>\n","      <td>0.62</td>\n","      <td>0.68</td>\n","      <td>0.356993</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 Model   MAE   MSE    R2  Accuracy  Jaccard Index  Precision  \\\n","0  Logistic Regression  0.16  0.16  0.19      0.84           0.52       0.75   \n","\n","   Recall  F1 Score  Log_Loss  \n","0    0.62      0.68  0.356993  "]},"execution_count":248,"metadata":{},"output_type":"execute_result"}],"source":["LR_Log_Loss = log_loss(y_test, predict_proba)\n","Report4 = calc_metrics('Logistic Regression', y_test, predictions4)\n","Report4['Log_Loss'] = [LR_Log_Loss]\n","Report4"]},{"cell_type":"markdown","id":"8d58b0b2-b31e-41e5-b52f-262f71f3e259","metadata":{},"source":["### SVM\n"]},{"cell_type":"markdown","id":"838f35c6-c940-4c8d-bbe4-5b9dd2f17083","metadata":{},"source":["Create and train a SVM model called SVM using the training data (`x_train`, `y_train`).\n"]},{"cell_type":"code","execution_count":249,"id":"1e605611-dfcd-48ef-9190-5e7c12404b16","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-41 {color: black;}#sk-container-id-41 pre{padding: 0;}#sk-container-id-41 div.sk-toggleable {background-color: white;}#sk-container-id-41 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-41 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-41 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-41 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-41 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-41 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-41 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-41 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-41 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-41 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-41 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-41 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-41 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-41 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-41 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-41 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-41 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-41 div.sk-item {position: relative;z-index: 1;}#sk-container-id-41 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-41 div.sk-item::before, #sk-container-id-41 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-41 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-41 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-41 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-41 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-41 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-41 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-41 div.sk-label-container {text-align: center;}#sk-container-id-41 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-41 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-41\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" checked><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"],"text/plain":["SVC(kernel='linear')"]},"execution_count":249,"metadata":{},"output_type":"execute_result"}],"source":["SVM = svm.SVC(kernel='linear')\n","SVM.fit(x_train, y_train)"]},{"cell_type":"markdown","id":"a77423b3-cee7-4ad9-9548-fda634e5b035","metadata":{},"source":["Now use the `predict` method on the testing data (`x_test`) and save it to the array `predictions`.\n"]},{"cell_type":"code","execution_count":250,"id":"89ac8f54-9caa-4e88-9189-7369aa575f8a","metadata":{},"outputs":[],"source":["predictions5 = SVM.predict(x_test)"]},{"cell_type":"markdown","id":"072c9838-83cc-4e6e-b70f-80f3cd7bf1ab","metadata":{},"source":["Using the `predictions` and the `y_test` dataframe calculate the value for each metric using the appropriate function and show the report.\n"]},{"cell_type":"code","execution_count":251,"id":"4cf0dc36-b215-4061-9cda-f38b9db1739c","metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Model</th>\n","      <th>MAE</th>\n","      <th>MSE</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","      <th>Jaccard Index</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1 Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>SVM</td>\n","      <td>0.16</td>\n","      <td>0.16</td>\n","      <td>0.2</td>\n","      <td>0.84</td>\n","      <td>0.52</td>\n","      <td>0.76</td>\n","      <td>0.61</td>\n","      <td>0.68</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Model   MAE   MSE   R2  Accuracy  Jaccard Index  Precision  Recall  F1 Score\n","0   SVM  0.16  0.16  0.2      0.84           0.52       0.76    0.61      0.68"]},"execution_count":251,"metadata":{},"output_type":"execute_result"}],"source":["Report5 = calc_metrics('SVM', y_test, predictions5)\n","Report5"]},{"cell_type":"markdown","id":"506f03ee-5914-40fa-b6c3-787afc728957","metadata":{},"source":["### Report\n"]},{"cell_type":"markdown","id":"1730801e-e027-4429-b0e6-40590fb5ab23","metadata":{},"source":["Show the Accuracy, Jaccard Index, F1-Score and LogLoss in a tabular format using data frame for all of the above models.\n","\n","\\*LogLoss is only for Logistic Regression Model\n"]},{"cell_type":"code","execution_count":252,"id":"b6b2cee7-4b35-4677-b3ae-c75b1cd29413","metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Model</th>\n","      <th>MAE</th>\n","      <th>MSE</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","      <th>Jaccard Index</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1 Score</th>\n","      <th>Log_Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Linear Regression</td>\n","      <td>0.15</td>\n","      <td>0.15</td>\n","      <td>0.28</td>\n","      <td>0.85</td>\n","      <td>0.59</td>\n","      <td>0.74</td>\n","      <td>0.74</td>\n","      <td>0.74</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>KNN</td>\n","      <td>0.18</td>\n","      <td>0.18</td>\n","      <td>0.11</td>\n","      <td>0.82</td>\n","      <td>0.47</td>\n","      <td>0.74</td>\n","      <td>0.56</td>\n","      <td>0.64</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Decision Tree</td>\n","      <td>0.23</td>\n","      <td>0.23</td>\n","      <td>-0.14</td>\n","      <td>0.77</td>\n","      <td>0.41</td>\n","      <td>0.59</td>\n","      <td>0.58</td>\n","      <td>0.59</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Logistic Regression</td>\n","      <td>0.16</td>\n","      <td>0.16</td>\n","      <td>0.19</td>\n","      <td>0.84</td>\n","      <td>0.52</td>\n","      <td>0.75</td>\n","      <td>0.62</td>\n","      <td>0.68</td>\n","      <td>0.356993</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>SVM</td>\n","      <td>0.16</td>\n","      <td>0.16</td>\n","      <td>0.20</td>\n","      <td>0.84</td>\n","      <td>0.52</td>\n","      <td>0.76</td>\n","      <td>0.61</td>\n","      <td>0.68</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 Model   MAE   MSE    R2  Accuracy  Jaccard Index  Precision  \\\n","0    Linear Regression  0.15  0.15  0.28      0.85           0.59       0.74   \n","1                  KNN  0.18  0.18  0.11      0.82           0.47       0.74   \n","2        Decision Tree  0.23  0.23 -0.14      0.77           0.41       0.59   \n","3  Logistic Regression  0.16  0.16  0.19      0.84           0.52       0.75   \n","4                  SVM  0.16  0.16  0.20      0.84           0.52       0.76   \n","\n","   Recall  F1 Score  Log_Loss  \n","0    0.74      0.74       NaN  \n","1    0.56      0.64       NaN  \n","2    0.58      0.59       NaN  \n","3    0.62      0.68  0.356993  \n","4    0.61      0.68       NaN  "]},"execution_count":252,"metadata":{},"output_type":"execute_result"}],"source":["FinalReport = pd.concat([Report, Report2, Report3, Report4, Report5], ignore_index=True)\n","FinalReport"]},{"cell_type":"markdown","metadata":{},"source":["__Conclusions:__ \n","- Here, although I have used R2 score but it is really not a good metric for classification tasks. It is much better used for Regression Tasks with continuous values. Hence, we will not take R2 into account here but still you can take look if you want.\n","- We can see that the worst performer is `Decision Tree` as not only MAE, MSE are higher than other models but Acc., Jacc. and F1 are less then other models. There `Decision Tree` is not fit for our job.\n","- We see models `SVM`, `Logistic Reg.` and `Lin. Reg.` show similar performance. Out of these three: `Linear Reg.` has least `MAE` and `MSE`and have highest `acc.`\n","- Also `Lin. Reg.` has higher `Jacc.`, `precision`, `recall` and `F1`.\n","- Although KNN has comparable `acc.` of 0.82 but it shows much less `Recall` and hence, `F1` is also less. Also `MAE` and `MSE` are large as well.  \n","- Therefore, in this condition we can choose `Lin. Reg.` for our classification and optimize it further for the task. \n","- However, since the `Logistic Regression` and `SVM` also performed quite well and close to top we can also experiment with them to look for improvements as well. Here I only experimented with `Lin. Reg.` in depth get the task done\n","\n","**Results of `Linear Regression` might change on rerun as I calculated the threshold for classification manually using graphs. So you have to recalculate that part.**"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.6"}},"nbformat":4,"nbformat_minor":4}