lung-cancer-prediction.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **Lung Cancer Prediction Using 10 models**","metadata":{}},{"cell_type":"markdown","source":"##### Lung cancer prediction using 10 machine learning classification models using Scikit-learn library in Python is a code implementation that aims to develop a predictive model for detecting lung cancer in patients. The code uses 10 different machine learning algorithms, including logistic regression, decision tree, k-nearest neighbor, Gaussian naive Bayes, multinomial naive Bayes, support vector classifier, random forest, XGBoost, multi-layer perceptron, and gradient boosting classifier, to predict the likelihood of lung cancer based on a range of variables. The dataset used in the code includes various columns such as gender, age, smoking, yellow fingers, anxiety, peer pressure, chronic disease, fatigue, allergy, wheezing, alcohol consuming, coughing, shortness of breath, swallowing difficulty, chest pain, and lung cancer. By analyzing these variables and using machine learning algorithms to identify patterns and correlations, the predictive models can provide accurate assessments of a patient's risk of developing lung cancer.####","metadata":{}},{"cell_type":"code","source":"#Importing Libraries\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n#For ignoring warning\nimport warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:46.790012Z","iopub.execute_input":"2023-03-23T10:38:46.790492Z","iopub.status.idle":"2023-03-23T10:38:47.413506Z","shell.execute_reply.started":"2023-03-23T10:38:46.790449Z","shell.execute_reply":"2023-03-23T10:38:47.412332Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"df=pd.read_csv('../input/lung-cancer/survey lung cancer.csv')\ndf","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.415623Z","iopub.execute_input":"2023-03-23T10:38:47.416349Z","iopub.status.idle":"2023-03-23T10:38:47.454820Z","shell.execute_reply.started":"2023-03-23T10:38:47.416302Z","shell.execute_reply":"2023-03-23T10:38:47.453110Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"    GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n0        M   69        1               2        2              1   \n1        M   74        2               1        1              1   \n2        F   59        1               1        1              2   \n3        M   63        2               2        2              1   \n4        F   63        1               2        1              1   \n..     ...  ...      ...             ...      ...            ...   \n304      F   56        1               1        1              2   \n305      M   70        2               1        1              1   \n306      M   58        2               1        1              1   \n307      M   67        2               1        2              1   \n308      M   62        1               1        1              2   \n\n     CHRONIC DISEASE  FATIGUE   ALLERGY   WHEEZING  ALCOHOL CONSUMING  \\\n0                  1         2         1         2                  2   \n1                  2         2         2         1                  1   \n2                  1         2         1         2                  1   \n3                  1         1         1         1                  2   \n4                  1         1         1         2                  1   \n..               ...       ...       ...       ...                ...   \n304                2         2         1         1                  2   \n305                1         2         2         2                  2   \n306                1         1         2         2                  2   \n307                1         2         2         1                  2   \n308                1         2         2         2                  2   \n\n     COUGHING  SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  \\\n0           2                    2                      2           2   \n1           1                    2                      2           2   \n2           2                    2                      1           2   \n3           1                    1                      2           2   \n4           2                    2                      1           1   \n..        ...                  ...                    ...         ...   \n304         2                    2                      2           1   \n305         2                    2                      1           2   \n306         2                    1                      1           2   \n307         2                    2                      1           2   \n308         1                    1                      2           1   \n\n    LUNG_CANCER  \n0           YES  \n1           YES  \n2            NO  \n3            NO  \n4            NO  \n..          ...  \n304         YES  \n305         YES  \n306         YES  \n307         YES  \n308         YES  \n\n[309 rows x 16 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>GENDER</th>\n      <th>AGE</th>\n      <th>SMOKING</th>\n      <th>YELLOW_FINGERS</th>\n      <th>ANXIETY</th>\n      <th>PEER_PRESSURE</th>\n      <th>CHRONIC DISEASE</th>\n      <th>FATIGUE</th>\n      <th>ALLERGY</th>\n      <th>WHEEZING</th>\n      <th>ALCOHOL CONSUMING</th>\n      <th>COUGHING</th>\n      <th>SHORTNESS OF BREATH</th>\n      <th>SWALLOWING DIFFICULTY</th>\n      <th>CHEST PAIN</th>\n      <th>LUNG_CANCER</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M</td>\n      <td>69</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>YES</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M</td>\n      <td>74</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>YES</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>F</td>\n      <td>59</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>NO</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M</td>\n      <td>63</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>NO</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>F</td>\n      <td>63</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NO</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>304</th>\n      <td>F</td>\n      <td>56</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>YES</td>\n    </tr>\n    <tr>\n      <th>305</th>\n      <td>M</td>\n      <td>70</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>YES</td>\n    </tr>\n    <tr>\n      <th>306</th>\n      <td>M</td>\n      <td>58</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>YES</td>\n    </tr>\n    <tr>\n      <th>307</th>\n      <td>M</td>\n      <td>67</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>YES</td>\n    </tr>\n    <tr>\n      <th>308</th>\n      <td>M</td>\n      <td>62</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>YES</td>\n    </tr>\n  </tbody>\n</table>\n<p>309 rows × 16 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"**Note: In this dataset, YES=2 & NO=1**","metadata":{}},{"cell_type":"code","source":"df.shape","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.457036Z","iopub.execute_input":"2023-03-23T10:38:47.457630Z","iopub.status.idle":"2023-03-23T10:38:47.468201Z","shell.execute_reply.started":"2023-03-23T10:38:47.457549Z","shell.execute_reply":"2023-03-23T10:38:47.466278Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"(309, 16)"},"metadata":{}}]},{"cell_type":"code","source":"#Checking for Duplicates\ndf.duplicated().sum()","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.471424Z","iopub.execute_input":"2023-03-23T10:38:47.472124Z","iopub.status.idle":"2023-03-23T10:38:47.487192Z","shell.execute_reply.started":"2023-03-23T10:38:47.472073Z","shell.execute_reply":"2023-03-23T10:38:47.485464Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"33"},"metadata":{}}]},{"cell_type":"code","source":"#Removing Duplicates\ndf=df.drop_duplicates()","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.488835Z","iopub.execute_input":"2023-03-23T10:38:47.489243Z","iopub.status.idle":"2023-03-23T10:38:47.501122Z","shell.execute_reply.started":"2023-03-23T10:38:47.489204Z","shell.execute_reply":"2023-03-23T10:38:47.499652Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"#Checking for null values\ndf.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.502848Z","iopub.execute_input":"2023-03-23T10:38:47.503284Z","iopub.status.idle":"2023-03-23T10:38:47.519890Z","shell.execute_reply.started":"2023-03-23T10:38:47.503241Z","shell.execute_reply":"2023-03-23T10:38:47.518307Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"GENDER                   0\nAGE                      0\nSMOKING                  0\nYELLOW_FINGERS           0\nANXIETY                  0\nPEER_PRESSURE            0\nCHRONIC DISEASE          0\nFATIGUE                  0\nALLERGY                  0\nWHEEZING                 0\nALCOHOL CONSUMING        0\nCOUGHING                 0\nSHORTNESS OF BREATH      0\nSWALLOWING DIFFICULTY    0\nCHEST PAIN               0\nLUNG_CANCER              0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.522567Z","iopub.execute_input":"2023-03-23T10:38:47.523163Z","iopub.status.idle":"2023-03-23T10:38:47.541929Z","shell.execute_reply.started":"2023-03-23T10:38:47.523083Z","shell.execute_reply":"2023-03-23T10:38:47.540678Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 276 entries, 0 to 283\nData columns (total 16 columns):\n #   Column                 Non-Null Count  Dtype \n---  ------                 --------------  ----- \n 0   GENDER                 276 non-null    object\n 1   AGE                    276 non-null    int64 \n 2   SMOKING                276 non-null    int64 \n 3   YELLOW_FINGERS         276 non-null    int64 \n 4   ANXIETY                276 non-null    int64 \n 5   PEER_PRESSURE          276 non-null    int64 \n 6   CHRONIC DISEASE        276 non-null    int64 \n 7   FATIGUE                276 non-null    int64 \n 8   ALLERGY                276 non-null    int64 \n 9   WHEEZING               276 non-null    int64 \n 10  ALCOHOL CONSUMING      276 non-null    int64 \n 11  COUGHING               276 non-null    int64 \n 12  SHORTNESS OF BREATH    276 non-null    int64 \n 13  SWALLOWING DIFFICULTY  276 non-null    int64 \n 14  CHEST PAIN             276 non-null    int64 \n 15  LUNG_CANCER            276 non-null    object\ndtypes: int64(14), object(2)\nmemory usage: 36.7+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.543675Z","iopub.execute_input":"2023-03-23T10:38:47.544392Z","iopub.status.idle":"2023-03-23T10:38:47.602982Z","shell.execute_reply.started":"2023-03-23T10:38:47.544344Z","shell.execute_reply":"2023-03-23T10:38:47.601531Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"              AGE     SMOKING  YELLOW_FINGERS     ANXIETY  PEER_PRESSURE  \\\ncount  276.000000  276.000000      276.000000  276.000000     276.000000   \nmean    62.909420    1.543478        1.576087    1.496377       1.507246   \nstd      8.379355    0.499011        0.495075    0.500895       0.500856   \nmin     21.000000    1.000000        1.000000    1.000000       1.000000   \n25%     57.750000    1.000000        1.000000    1.000000       1.000000   \n50%     62.500000    2.000000        2.000000    1.000000       2.000000   \n75%     69.000000    2.000000        2.000000    2.000000       2.000000   \nmax     87.000000    2.000000        2.000000    2.000000       2.000000   \n\n       CHRONIC DISEASE    FATIGUE     ALLERGY     WHEEZING  ALCOHOL CONSUMING  \\\ncount       276.000000  276.000000  276.000000  276.000000         276.000000   \nmean          1.521739    1.663043    1.547101    1.547101           1.550725   \nstd           0.500435    0.473529    0.498681    0.498681           0.498324   \nmin           1.000000    1.000000    1.000000    1.000000           1.000000   \n25%           1.000000    1.000000    1.000000    1.000000           1.000000   \n50%           2.000000    2.000000    2.000000    2.000000           2.000000   \n75%           2.000000    2.000000    2.000000    2.000000           2.000000   \nmax           2.000000    2.000000    2.000000    2.000000           2.000000   \n\n         COUGHING  SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  \ncount  276.000000           276.000000             276.000000  276.000000  \nmean     1.576087             1.630435               1.467391    1.557971  \nstd      0.495075             0.483564               0.499842    0.497530  \nmin      1.000000             1.000000               1.000000    1.000000  \n25%      1.000000             1.000000               1.000000    1.000000  \n50%      2.000000             2.000000               1.000000    2.000000  \n75%      2.000000             2.000000               2.000000    2.000000  \nmax      2.000000             2.000000               2.000000    2.000000  ","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>AGE</th>\n      <th>SMOKING</th>\n      <th>YELLOW_FINGERS</th>\n      <th>ANXIETY</th>\n      <th>PEER_PRESSURE</th>\n      <th>CHRONIC DISEASE</th>\n      <th>FATIGUE</th>\n      <th>ALLERGY</th>\n      <th>WHEEZING</th>\n      <th>ALCOHOL CONSUMING</th>\n      <th>COUGHING</th>\n      <th>SHORTNESS OF BREATH</th>\n      <th>SWALLOWING DIFFICULTY</th>\n      <th>CHEST PAIN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n      <td>276.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>62.909420</td>\n      <td>1.543478</td>\n      <td>1.576087</td>\n      <td>1.496377</td>\n      <td>1.507246</td>\n      <td>1.521739</td>\n      <td>1.663043</td>\n      <td>1.547101</td>\n      <td>1.547101</td>\n      <td>1.550725</td>\n      <td>1.576087</td>\n      <td>1.630435</td>\n      <td>1.467391</td>\n      <td>1.557971</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>8.379355</td>\n      <td>0.499011</td>\n      <td>0.495075</td>\n      <td>0.500895</td>\n      <td>0.500856</td>\n      <td>0.500435</td>\n      <td>0.473529</td>\n      <td>0.498681</td>\n      <td>0.498681</td>\n      <td>0.498324</td>\n      <td>0.495075</td>\n      <td>0.483564</td>\n      <td>0.499842</td>\n      <td>0.497530</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>21.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>57.750000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>62.500000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>69.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>87.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"**In this dataset, GENDER & LUNG_CANCER attributes are in object data type. So, let's convert them to numerical values using LabelEncoder from sklearn.\nLabelEncoder is a utility class to help normalize labels such that they contain only values between 0 and n_classes-1.\nIt can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. Also let's make every other attributes as YES=1 & NO=0.**","metadata":{}},{"cell_type":"code","source":"from sklearn import preprocessing\nle=preprocessing.LabelEncoder()\ndf['GENDER']=le.fit_transform(df['GENDER'])\ndf['LUNG_CANCER']=le.fit_transform(df['LUNG_CANCER'])\ndf['SMOKING']=le.fit_transform(df['SMOKING'])\ndf['YELLOW_FINGERS']=le.fit_transform(df['YELLOW_FINGERS'])\ndf['ANXIETY']=le.fit_transform(df['ANXIETY'])\ndf['PEER_PRESSURE']=le.fit_transform(df['PEER_PRESSURE'])\ndf['CHRONIC DISEASE']=le.fit_transform(df['CHRONIC DISEASE'])\ndf['FATIGUE ']=le.fit_transform(df['FATIGUE '])\ndf['ALLERGY ']=le.fit_transform(df['ALLERGY '])\ndf['WHEEZING']=le.fit_transform(df['WHEEZING'])\ndf['ALCOHOL CONSUMING']=le.fit_transform(df['ALCOHOL CONSUMING'])\ndf['COUGHING']=le.fit_transform(df['COUGHING'])\ndf['SHORTNESS OF BREATH']=le.fit_transform(df['SHORTNESS OF BREATH'])\ndf['SWALLOWING DIFFICULTY']=le.fit_transform(df['SWALLOWING DIFFICULTY'])\ndf['CHEST PAIN']=le.fit_transform(df['CHEST PAIN'])\ndf['LUNG_CANCER']=le.fit_transform(df['LUNG_CANCER'])","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.604802Z","iopub.execute_input":"2023-03-23T10:38:47.605213Z","iopub.status.idle":"2023-03-23T10:38:47.675051Z","shell.execute_reply.started":"2023-03-23T10:38:47.605171Z","shell.execute_reply":"2023-03-23T10:38:47.673440Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"#Let's check what's happened now\ndf","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.679574Z","iopub.execute_input":"2023-03-23T10:38:47.680059Z","iopub.status.idle":"2023-03-23T10:38:47.705148Z","shell.execute_reply.started":"2023-03-23T10:38:47.680012Z","shell.execute_reply":"2023-03-23T10:38:47.703643Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"     GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n0         1   69        0               1        1              0   \n1         1   74        1               0        0              0   \n2         0   59        0               0        0              1   \n3         1   63        1               1        1              0   \n4         0   63        0               1        0              0   \n..      ...  ...      ...             ...      ...            ...   \n279       0   59        0               1        1              1   \n280       0   59        1               0        0              0   \n281       1   55        1               0        0              0   \n282       1   46        0               1        1              0   \n283       1   60        0               1        1              0   \n\n     CHRONIC DISEASE  FATIGUE   ALLERGY   WHEEZING  ALCOHOL CONSUMING  \\\n0                  0         1         0         1                  1   \n1                  1         1         1         0                  0   \n2                  0         1         0         1                  0   \n3                  0         0         0         0                  1   \n4                  0         0         0         1                  0   \n..               ...       ...       ...       ...                ...   \n279                0         0         1         1                  0   \n280                1         1         1         0                  0   \n281                0         1         1         0                  0   \n282                0         0         0         0                  0   \n283                0         1         0         1                  1   \n\n     COUGHING  SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  \\\n0           1                    1                      1           1   \n1           0                    1                      1           1   \n2           1                    1                      0           1   \n3           0                    0                      1           1   \n4           1                    1                      0           0   \n..        ...                  ...                    ...         ...   \n279         1                    0                      1           0   \n280         0                    1                      0           0   \n281         0                    1                      0           1   \n282         0                    0                      1           1   \n283         1                    1                      1           1   \n\n     LUNG_CANCER  \n0              1  \n1              1  \n2              0  \n3              0  \n4              0  \n..           ...  \n279            1  \n280            0  \n281            0  \n282            0  \n283            1  \n\n[276 rows x 16 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>GENDER</th>\n      <th>AGE</th>\n      <th>SMOKING</th>\n      <th>YELLOW_FINGERS</th>\n      <th>ANXIETY</th>\n      <th>PEER_PRESSURE</th>\n      <th>CHRONIC DISEASE</th>\n      <th>FATIGUE</th>\n      <th>ALLERGY</th>\n      <th>WHEEZING</th>\n      <th>ALCOHOL CONSUMING</th>\n      <th>COUGHING</th>\n      <th>SHORTNESS OF BREATH</th>\n      <th>SWALLOWING DIFFICULTY</th>\n      <th>CHEST PAIN</th>\n      <th>LUNG_CANCER</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>69</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>74</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>59</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>63</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>63</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>279</th>\n      <td>0</td>\n      <td>59</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>280</th>\n      <td>0</td>\n      <td>59</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>281</th>\n      <td>1</td>\n      <td>55</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>282</th>\n      <td>1</td>\n      <td>46</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>283</th>\n      <td>1</td>\n      <td>60</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>276 rows × 16 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"**Note: Male=1 & Female=0. Also for other variables, YES=1 & NO=0**","metadata":{}},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.706767Z","iopub.execute_input":"2023-03-23T10:38:47.707154Z","iopub.status.idle":"2023-03-23T10:38:47.724570Z","shell.execute_reply.started":"2023-03-23T10:38:47.707116Z","shell.execute_reply":"2023-03-23T10:38:47.723058Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 276 entries, 0 to 283\nData columns (total 16 columns):\n #   Column                 Non-Null Count  Dtype\n---  ------                 --------------  -----\n 0   GENDER                 276 non-null    int64\n 1   AGE                    276 non-null    int64\n 2   SMOKING                276 non-null    int64\n 3   YELLOW_FINGERS         276 non-null    int64\n 4   ANXIETY                276 non-null    int64\n 5   PEER_PRESSURE          276 non-null    int64\n 6   CHRONIC DISEASE        276 non-null    int64\n 7   FATIGUE                276 non-null    int64\n 8   ALLERGY                276 non-null    int64\n 9   WHEEZING               276 non-null    int64\n 10  ALCOHOL CONSUMING      276 non-null    int64\n 11  COUGHING               276 non-null    int64\n 12  SHORTNESS OF BREATH    276 non-null    int64\n 13  SWALLOWING DIFFICULTY  276 non-null    int64\n 14  CHEST PAIN             276 non-null    int64\n 15  LUNG_CANCER            276 non-null    int64\ndtypes: int64(16)\nmemory usage: 36.7 KB\n","output_type":"stream"}]},{"cell_type":"code","source":"#Let's check the distributaion of Target variable.\nsns.countplot(x='LUNG_CANCER', data=df,)\nplt.title('Target Distribution');","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.726705Z","iopub.execute_input":"2023-03-23T10:38:47.727437Z","iopub.status.idle":"2023-03-23T10:38:47.965922Z","shell.execute_reply.started":"2023-03-23T10:38:47.727391Z","shell.execute_reply":"2023-03-23T10:38:47.964418Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"##### ***That is, Target Distribution is imbalanced.***","metadata":{}},{"cell_type":"code","source":"df['LUNG_CANCER'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.967644Z","iopub.execute_input":"2023-03-23T10:38:47.968114Z","iopub.status.idle":"2023-03-23T10:38:47.978959Z","shell.execute_reply.started":"2023-03-23T10:38:47.968068Z","shell.execute_reply":"2023-03-23T10:38:47.977468Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"1    238\n0     38\nName: LUNG_CANCER, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"***We will handle this imbalance before applyig algorithm.***","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"**Now let's do some Data Visualizations for the better understanding of how the independent features are related to the target variable..**","metadata":{}},{"cell_type":"code","source":"# function for plotting\ndef plot(col, df=df):\n    return df.groupby(col)['LUNG_CANCER'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(8,5))","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.980840Z","iopub.execute_input":"2023-03-23T10:38:47.981428Z","iopub.status.idle":"2023-03-23T10:38:47.990365Z","shell.execute_reply.started":"2023-03-23T10:38:47.981376Z","shell.execute_reply":"2023-03-23T10:38:47.989017Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"plot('GENDER')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:47.992070Z","iopub.execute_input":"2023-03-23T10:38:47.992455Z","iopub.status.idle":"2023-03-23T10:38:48.259400Z","shell.execute_reply.started":"2023-03-23T10:38:47.992418Z","shell.execute_reply":"2023-03-23T10:38:48.258059Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='GENDER'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAp8AAAG7CAYAAACFPf61AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAl8UlEQVR4nO3de3SU9Z348c8QJBExYQEJqFFQxBvV2uAFlOMdi5eVLl2obsEbu7JSLKAWqa0iW4u9iHgLagFZ92AXt15qW9YaXaworhUEj11xvQtqUgquCQVFhPn9YcmvYwJkAn5DyOt1zvyR7zzPPJ+EOufdZ2aeyWSz2WwAAEACbZp7AAAAWg/xCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkEzb5h6gMTZt2hTvv/9+7LnnnpHJZJp7HAAAPiebzcaaNWti7733jjZttnx+s0XE5/vvvx9lZWXNPQYAANuwYsWK2Hfffbd4f4uIzz333DMiPvtliouLm3kaAAA+r7a2NsrKyuq6bUtaRHxufqm9uLhYfAIA7MS29RZJHzgCACAZ8QkAQDLiEwCAZFrEez4ba+PGjbFhw4bmHoM87bbbblFQUNDcYwAACewS8ZnNZqO6ujo+/PDD5h6FJurYsWN069bNdVwBYBe3S8Tn5vDs2rVrtG/fXsC0INlsNtatWxcrV66MiIju3bs380QAwBepxcfnxo0b68Kzc+fOzT0OTbD77rtHRMTKlSuja9euXoIHgF1Yi//A0eb3eLZv376ZJ2F7bP73855dANi1tfj43MxL7S2bfz8AaB12mfgEAGDnJz4BAEhml4/PCy+8MAYPHlxv/aSTToqxY8fWW3/44YdzXgKePXt2ZDKZ+OpXv5qz3YcffhiZTCaefPLJnPX58+fH2WefHXvttVcUFRXFgQceGMOGDYunnnqq0TNns9m4++6749hjj40OHTpEx44do2/fvjFt2rRYt25dzrbvvvtutGvXLg455JAGHyuTyURRUVG88847OeuDBw+OCy+8MGeturo6xowZEwcccEAUFhZGWVlZnHPOOfHEE0/UbdOjR4/IZDL1bjfeeGNERLz99ts56yUlJXHcccfFr371q0b//gDArmuXj88doW3btvHEE0/E/Pnzt7pdRUVFnHrqqdG5c+eYO3duLFu2LP7t3/4t+vfvH+PGjWv08YYPHx5jx46Nc889N+bPnx9Lly6N73//+/HLX/4yHnvssZxtZ8+eHUOHDo1169bFM8880+DjZTKZuPbaa7d6zLfffjvKy8vjv/7rv+LHP/5xvPTSS/Hoo4/GySefHKNHj87ZdvLkyVFVVZVzGzNmTM42jz/+eFRVVcVzzz0XxxxzTAwZMiT+8Ic/NPpvAADsmlr8pZZS2GOPPWLo0KFx9dVXx3PPPdfgNsuXL4+xY8fG2LFjY+rUqXXrPXv2jP79+8fll1/eqGPdf//9MWfOnHj44Yfj3HPPrVvv0aNH/O3f/m3U1tbWrWWz2bjnnnuioqIi9t1335g5c2Ycf/zx9R5zzJgxcdNNN8WVV14ZX/rSlxo87mWXXRaZTCZ+//vfxx577FG3fvjhh8fFF1+cs+2ee+4Z3bp12+rv0blz5+jWrVt069Ytbrjhhrjtttti/vz50adPn0b9HQCAXZMzn400adKkeOmll+IXv/hFg/c/8MADsWHDhvjOd77T4P2N/TT3nDlz4uCDD84Jz79+jJKSkrqf58+fH+vWrYvTTjsthg8fHvfff3+sWbOm3n79+/ePs88+OyZOnNjgMT/44IN49NFHY/To0TnhuVnHjh0bNXtDNmzYED/72c8i4rOv0QQAWjdnPhtp7733jm9/+9txzTXXNPge0ldffTWKi4tzzgg+8MADccEFF9T9/Oyzz27xzONmr732Whx88MGNmmnmzJnxjW98IwoKCuLwww+PXr16xdy5c2PkyJH1tp0yZUocccQRsWDBghgwYEDOfa+//npks9ktvm/08yZMmBDf+973ctZ+/etfx0knnVT3c//+/aNNmzbx0UcfxaZNm6JHjx4xdOjQRj0+ADvYpJJtb0OuSTXNPcEuy5nPPEyYMCH+9Kc/xaxZsxq8//NnN88444xYunRp/OY3v4m1a9fGxo0bt3mMbDbbqLOkH374YTz44IPxzW9+s27tm9/85hZnO+yww2LEiBExYcKEBo/Z0PxbctVVV8XSpUtzbscee2zONnPnzo0lS5bEI488Er169YoZM2ZEp06dGvX4AMCuq9We+SwuLo6amvr/r+bDDz+M4uLiBvfp2LFjTJw4Ma6//vo4++yzc+476KCDoqamJqqrq+vOfnbo0CF69eoVbds2/s/cu3fvWLZs2Ta3u+++++Ljjz/Oib5sNhubNm2Kl19+OQ477LB6+1x//fXRu3fvePjhh+vNnslkYtmyZQ2e1f28Ll26RK9evba6TVlZWRx00EFx0EEHRYcOHWLIkCHx8ssvR9euXbf5+ADArqvVnvk85JBDYtGiRfXWn3/++a2+7D1mzJho06ZN3HLLLTnrX//612O33XaLH/3oR9s11/nnnx+vvvpq/PKXv6x3XzabrQvmmTNnxhVXXJFz9vHFF1+Mk08+eYtnP8vKyuJb3/pWfPe73805C9upU6c444wz4o477oi1a9fW2+/DDz/crt/pxBNPjD59+sQNN9ywXY8DALR8rSI+a2pq6r1MfPbZZ8cbb7wRo0ePjhdffDFeffXVuOOOO2LmzJlx1VVXbfGxioqK4vrrr49bb701Z32//faLm266KW655Za44IILYv78+fH222/HCy+8ULdtQUHBNmcdOnRoDBs2LM4777yYMmVKLFq0KN5555349a9/HaeddlrdpZdeeOGFGDlyZPTp0yfndt5558W99967xe9InzhxYrz//vvx+OOP56xXVFTExo0b45hjjokHHnggXnvttVi2bFnceuut0a9fv5xt16xZE9XV1Tm3v/4UfkOuuOKKuOuuu+K9997b5t8AANh1tYr4fPLJJ+Ooo47Kuc2aNSsWLFgQb7zxRgwcODCOPvromD17dsyePTv+/u//fquPd8EFF8QBBxxQb33MmDHx2GOPxZ/+9Kf4+te/HgcddFCceeaZ8dZbb8Wjjz66zQ8bRXz2vsv77rsvpk6dGg899FCceOKJccQRR8SkSZPi3HPPjTPOOCNmzpwZhx12WIMfEBo8eHB88MEHW7yoe6dOnWLChAnx8ccf56z37NkzXnjhhTj55JPjiiuuiD59+sTpp58eTzzxREyfPj1n22uvvTa6d++ec9vSp/w3O/vss6NHjx7OfgJAK5fJbv60yU6strY2SkpKoqampt77MT/++ON46623omfPnlFUVNRME7K9/DsCfIF82j1/Pu2et6312l9rFWc+AQDYOYjPxAYNGhQdOnRo8PbDH/6wuccDAPhCtdpLLTWXGTNmxEcffdTgfa6DCQDs6sRnYvvss09zjwAA0Gy87A4AQDLiEwCAZMQnAADJiE8AAJIRnwAAJOPT7vB5vgkkf74JBIBGEp87SI+rf5P0eG/feFaT9quoqIif/OQnUVVVFYcffnhMmzYtBgwYsIOnAwBomJfdW5G5c+fG2LFj45prroklS5bEgAEDYtCgQbF8+fLmHg0AaCXEZysyderUuOSSS2LkyJFx6KGHxrRp06KsrCymT5/e3KMBAK2E+GwlPvnkk1i8eHEMHDgwZ33gwIGxcOHCZpoKAGhtxGcrsWrVqti4cWOUlpbmrJeWlkZ1dXUzTQUAtDbis5XJZDI5P2ez2XprAABfFPHZSnTp0iUKCgrqneVcuXJlvbOhAABfFPHZSrRr1y7Ky8ujsrIyZ72ysjL69+/fTFMBAK2N63y2IuPHj4/hw4dH3759o1+/fnH33XfH8uXLY9SoUc09GgDQSojPHaSpF31PadiwYbF69eqYPHlyVFVVRZ8+fWLevHmx//77N/doAEArIT5bmcsuuywuu+yy5h4DAGilvOcTAIBkmhSfFRUV0bNnzygqKory8vJYsGDBVrefM2dOHHnkkdG+ffvo3r17XHTRRbF69eomDQwAQMuVd3zm+/3gTz/9dIwYMSIuueSS+J//+Z/4j//4j3j++edj5MiR2z08AAAtS97xme/3g//3f/939OjRIy6//PLo2bNnnHDCCXHppZfGokWLtnt4AABalrzisynfD96/f/949913Y968eZHNZuOPf/xj/OIXv4izztr5Px0OAMCOlVd8NuX7wfv37x9z5syJYcOGRbt27aJbt27RsWPHuO2227Z4nPXr10dtbW3ODQCAlq9JHzjK5/vBX3755bj88svj2muvjcWLF8ejjz4ab7311lYvbD5lypQoKSmpu5WVlTVlTAAAdjJ5xWdTvh98ypQpcfzxx8dVV10VRxxxRJxxxhlRUVERs2bNiqqqqgb3mThxYtTU1NTdVqxYkc+YAADspPKKz6Z8P/i6deuiTZvcwxQUFETEZ2dMG1JYWBjFxcU5NwAAWr68X3YfP358zJgxI2bNmhXLli2LcePG5Xw/+MSJE2PEiBF1259zzjnx4IMPxvTp0+PNN9+MZ555Ji6//PI45phjYu+9995xvwkAADu9vL9ec1vfD15VVZVzzc8LL7ww1qxZE7fffntcccUV0bFjxzjllFPiRz/60Y77LXYGk0oSH68m712eeuqp+MlPfhKLFy+OqqqqeOihh2Lw4ME7fjYAgC1o0ne7b+37wWfPnl1vbcyYMTFmzJimHIodaO3atXHkkUfGRRddFEOGDGnucQCAVqhJ8UnLNGjQoBg0aFBzjwEAtGJNutQSAAA0hfgEACAZ8QkAQDLiEwCAZMQnAADJ+LR7K/LnP/85Xn/99bqf33rrrVi6dGl06tQp9ttvv2acDABoLcTnjtKEi76ntmjRojj55JPrfh4/fnxERFxwwQUNXp8VAGBHE5+tyEknnRTZbLa5xwAAWjHv+QQAIBnxCQBAMuITAIBkxCcAAMnsMvHpgzQtm38/AGgdWnx87rbbbhERsW7dumaehO2x+d9v878nALBravGXWiooKIiOHTvGypUrIyKiffv2kclkmnkqGiubzca6deti5cqV0bFjxygoKGjukQCAL1CLj8+IiG7dukVE1AUoLU/Hjh3r/h0BgF3XLhGfmUwmunfvHl27do0NGzY09zjkabfddnPGEwBaiV0iPjcrKCgQMQAAO7EW/4EjAABaDvEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACTTpPisqKiInj17RlFRUZSXl8eCBQu2uv369evjmmuuif333z8KCwvjwAMPjFmzZjVpYAAAWq62+e4wd+7cGDt2bFRUVMTxxx8fd911VwwaNChefvnl2G+//RrcZ+jQofHHP/4xZs6cGb169YqVK1fGp59+ut3DAwDQsuQdn1OnTo1LLrkkRo4cGRER06ZNi9/+9rcxffr0mDJlSr3tH3300fjd734Xb775ZnTq1CkiInr06LF9UwMA0CLl9bL7J598EosXL46BAwfmrA8cODAWLlzY4D6PPPJI9O3bN3784x/HPvvsE717944rr7wyPvrooy0eZ/369VFbW5tzAwCg5cvrzOeqVati48aNUVpamrNeWloa1dXVDe7z5ptvxtNPPx1FRUXx0EMPxapVq+Kyyy6LDz74YIvv+5wyZUpcf/31+YwGAEAL0KQPHGUymZyfs9lsvbXNNm3aFJlMJubMmRPHHHNMnHnmmTF16tSYPXv2Fs9+Tpw4MWpqaupuK1asaMqYAADsZPI689mlS5coKCiod5Zz5cqV9c6Gbta9e/fYZ599oqSkpG7t0EMPjWw2G++++24cdNBB9fYpLCyMwsLCfEYDAKAFyOvMZ7t27aK8vDwqKytz1isrK6N///4N7nP88cfH+++/H3/+85/r1l599dVo06ZN7Lvvvk0YGQCAlirvl93Hjx8fM2bMiFmzZsWyZcti3LhxsXz58hg1alREfPaS+YgRI+q2P//886Nz585x0UUXxcsvvxxPPfVUXHXVVXHxxRfH7rvvvuN+EwAAdnp5X2pp2LBhsXr16pg8eXJUVVVFnz59Yt68ebH//vtHRERVVVUsX768bvsOHTpEZWVljBkzJvr27RudO3eOoUOHxg9+8IMd91sAANAiZLLZbLa5h9iW2traKCkpiZqamiguLm7ucdjVTSrZ9jbkmlTT3BMAW+N5LX+e1/LW2F7z3e4AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSaVJ8VlRURM+ePaOoqCjKy8tjwYIFjdrvmWeeibZt28aXv/zlphwWAIAWLu/4nDt3bowdOzauueaaWLJkSQwYMCAGDRoUy5cv3+p+NTU1MWLEiDj11FObPCwAAC1b3vE5derUuOSSS2LkyJFx6KGHxrRp06KsrCymT5++1f0uvfTSOP/886Nfv35NHhYAgJYtr/j85JNPYvHixTFw4MCc9YEDB8bChQu3uN8999wTb7zxRlx33XWNOs769eujtrY25wYAQMuXV3yuWrUqNm7cGKWlpTnrpaWlUV1d3eA+r732Wlx99dUxZ86caNu2baOOM2XKlCgpKam7lZWV5TMmAAA7qSZ94CiTyeT8nM1m661FRGzcuDHOP//8uP7666N3796NfvyJEydGTU1N3W3FihVNGRMAgJ1M405F/kWXLl2ioKCg3lnOlStX1jsbGhGxZs2aWLRoUSxZsiS+9a1vRUTEpk2bIpvNRtu2beOxxx6LU045pd5+hYWFUVhYmM9oAAC0AHmd+WzXrl2Ul5dHZWVlznplZWX079+/3vbFxcXx0ksvxdKlS+tuo0aNioMPPjiWLl0axx577PZNDwBAi5LXmc+IiPHjx8fw4cOjb9++0a9fv7j77rtj+fLlMWrUqIj47CXz9957L+69995o06ZN9OnTJ2f/rl27RlFRUb11AAB2fXnH57Bhw2L16tUxefLkqKqqij59+sS8efNi//33j4iIqqqqbV7zEwCA1imTzWazzT3EttTW1kZJSUnU1NREcXFxc4/Drm5SSXNP0PJMqmnuCYCt8byWP89reWtsr/ludwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMk0KT4rKiqiZ8+eUVRUFOXl5bFgwYItbvvggw/G6aefHnvttVcUFxdHv3794re//W2TBwYAoOXKOz7nzp0bY8eOjWuuuSaWLFkSAwYMiEGDBsXy5csb3P6pp56K008/PebNmxeLFy+Ok08+Oc4555xYsmTJdg8PAEDLkslms9l8djj22GPjK1/5SkyfPr1u7dBDD43BgwfHlClTGvUYhx9+eAwbNiyuvfbaRm1fW1sbJSUlUVNTE8XFxfmMC/mbVNLcE7Q8k2qaewJgazyv5c/zWt4a22t5nfn85JNPYvHixTFw4MCc9YEDB8bChQsb9RibNm2KNWvWRKdOnfI5NAAAu4C2+Wy8atWq2LhxY5SWluasl5aWRnV1daMe46abboq1a9fG0KFDt7jN+vXrY/369XU/19bW5jMmAAA7qSZ94CiTyeT8nM1m66015Oc//3lMmjQp5s6dG127dt3idlOmTImSkpK6W1lZWVPGBABgJ5NXfHbp0iUKCgrqneVcuXJlvbOhnzd37ty45JJL4v7774/TTjttq9tOnDgxampq6m4rVqzIZ0wAAHZSecVnu3btory8PCorK3PWKysro3///lvc7+c//3lceOGFcd9998VZZ521zeMUFhZGcXFxzg0AgJYvr/d8RkSMHz8+hg8fHn379o1+/frF3XffHcuXL49Ro0ZFxGdnLd9777249957I+Kz8BwxYkTccsstcdxxx9WdNd19992jpMSn7wAAWpO843PYsGGxevXqmDx5clRVVUWfPn1i3rx5sf/++0dERFVVVc41P++666749NNPY/To0TF69Oi69QsuuCBmz569/b8BAAAtRt7X+WwOrvNJUq6Hlz/Xw4Odm+e1/Hley9sXcp1PAADYHuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSadvcA/DF6XH1b5p7hBbp7aLmngAAdl3OfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJJp29wDAEBj9bj6N809Qov0dlFzTwD/nzOfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMk2Kz4qKiujZs2cUFRVFeXl5LFiwYKvb/+53v4vy8vIoKiqKAw44IO68884mDQsAQMuWd3zOnTs3xo4dG9dcc00sWbIkBgwYEIMGDYrly5c3uP1bb70VZ555ZgwYMCCWLFkS3/3ud+Pyyy+PBx54YLuHBwCgZck7PqdOnRqXXHJJjBw5Mg499NCYNm1alJWVxfTp0xvc/s4774z99tsvpk2bFoceemiMHDkyLr744vjpT3+63cMDANCytM1n408++SQWL14cV199dc76wIEDY+HChQ3u8+yzz8bAgQNz1s4444yYOXNmbNiwIXbbbbd6+6xfvz7Wr19f93NNTU1ERNTW1uYzbqu3af265h6hRarNZJt7hJbHf5sk4nmtaTyvNYHntbxt7rRsduv/e8srPletWhUbN26M0tLSnPXS0tKorq5ucJ/q6uoGt//0009j1apV0b1793r7TJkyJa6//vp662VlZfmMC01S0twDtEQ3+qvBzsx/oU3gea3J1qxZEyUlW/775RWfm2UymZyfs9lsvbVtbd/Q+mYTJ06M8ePH1/28adOm+OCDD6Jz585bPQ5sr9ra2igrK4sVK1ZEcXFxc48DsN08r5FKNpuNNWvWxN57773V7fKKzy5dukRBQUG9s5wrV66sd3Zzs27dujW4fdu2baNz584N7lNYWBiFhYU5ax07dsxnVNguxcXFnqSBXYrnNVLY2hnPzfL6wFG7du2ivLw8Kisrc9YrKyujf//+De7Tr1+/ets/9thj0bdv3wbf7wkAwK4r70+7jx8/PmbMmBGzZs2KZcuWxbhx42L58uUxatSoiPjsJfMRI0bUbT9q1Kh45513Yvz48bFs2bKYNWtWzJw5M6688sod91sAANAi5P2ez2HDhsXq1atj8uTJUVVVFX369Il58+bF/vvvHxERVVVVOdf87NmzZ8ybNy/GjRsXd9xxR+y9995x6623xpAhQ3bcbwE7SGFhYVx33XX13vYB0FJ5XmNnk8lu6/PwAACwg/hudwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgmSZ9vSbsKt59992YPn16LFy4MKqrqyOTyURpaWn0798/Ro0aFWVlZc09IgDsUlxqiVbr6aefjkGDBkVZWVkMHDgwSktLI5vNxsqVK6OysjJWrFgR//mf/xnHH398c48KsEOsWLEirrvuupg1a1Zzj0IrJj5ptY4++ug44YQT4uabb27w/nHjxsXTTz8dzz//fOLJAL4YL774YnzlK1+JjRs3NvcotGLik1Zr9913j6VLl8bBBx/c4P2vvPJKHHXUUfHRRx8lngygaR555JGt3v/mm2/GFVdcIT5pVt7zSavVvXv3WLhw4Rbj89lnn43u3bsnngqg6QYPHhyZTCa2dl4pk8kknAjqE5+0WldeeWWMGjUqFi9eHKeffnqUlpZGJpOJ6urqqKysjBkzZsS0adOae0yARuvevXvccccdMXjw4AbvX7p0aZSXl6cdCj5HfNJqXXbZZdG5c+e4+eab46677qp7GaqgoCDKy8vj3nvvjaFDhzbzlACNV15eHi+88MIW43NbZ0UhBe/5hIjYsGFDrFq1KiIiunTpErvttlszTwSQvwULFsTatWvjq1/9aoP3r127NhYtWhQnnnhi4sng/xOfAAAk4xuOAABIRnwCAJCM+AQAIBnxCQBAMuIT4K9UV1fHt7/97ejVq1cUFRVFaWlpnHDCCXHnnXfGunXrIiKiR48ekclk6t1uvPHGiIh4++23I5PJRNeuXWPNmjU5j//lL385Jk2aVPfzSSedVLd/YWFh7LPPPnHOOefEgw8+WG+2ho6ZyWTi3//93yMi4sknn8xZ79y5c5xyyinxzDPPfEF/LYD8iU+Av3jzzTfjqKOOisceeyx++MMfxpIlS+Lxxx+PcePGxa9+9at4/PHH67adPHlyVFVV5dzGjBmT83hr1qyJn/70p9s87j/+4z9GVVVVvP766/HAAw/EYYcdFt/4xjfin/7pn+pte88999Q77uev6fi///u/UVVVFU8++WTstddecdZZZ8XKlSub9kcB2MFcZB7gLy677LJo27ZtLFq0KPbYY4+69S996UsxZMiQnItz77nnntGtW7etPt6YMWNi6tSpMXr06OjatesWt2vfvn3dY5WVlcVxxx0XhxxySFx88cUxdOjQOO200+q27dix4zaP27Vr17rtvve978X9998fzz33XJxzzjlb3Q8gBWc+ASJi9erV8dhjj8Xo0aNzwvOv5fud2Oedd1706tUrJk+enPc8F1xwQfzN3/xNgy+/N9a6devinnvuiYjwxQnATkN8AkTE66+/HtlsNg4++OCc9S5dukSHDh2iQ4cOMWHChLr1CRMm1K1vvj355JM5+25+H+jdd98db7zxRl7ztGnTJnr37h1vv/12zvp5551X77hvvvlmzjb77rtv3X0333xzlJeXx6mnnprX8QG+KF52B/grnz+7+fvf/z42bdoU//AP/xDr16+vW7/qqqviwgsvzNl2n332qfd4Z5xxRpxwwgnx/e9/P+677768Zslms/Xmufnmm3Neho/47KX6v7ZgwYLYY489YsmSJTFhwoSYPXu2M5/ATkN8AkREr169IpPJxCuvvJKzfsABB0RExO67756z3qVLl+jVq1ejHvvGG2+Mfv36xVVXXdXoeTZu3BivvfZaHH300Tnr3bp12+Zxe/bsGR07dozevXvHxx9/HF/72tfiD3/4QxQWFjb6+ABfFC+7A0RE586d4/TTT4/bb7891q5du0Mf+5hjjom/+7u/i6uvvrrR+/zrv/5r/N///V8MGTJku449fPjw2LRpU1RUVGzX4wDsKM58AvxFRUVFHH/88dG3b9+YNGlSHHHEEdGmTZt4/vnn45VXXony8vK6bdesWRPV1dU5+7dv3z6Ki4sbfOwbbrghDj/88Gjbtv7T7rp166K6ujo+/fTTeO+99+LBBx+Mm2++Of75n/85Tj755JxtP/zww3rH3XPPPbf4Iak2bdrE2LFj4wc/+EFceuml0b59+0b9LQC+KM58AvzFgQceGEuWLInTTjstJk6cGEceeWT07ds3brvttrjyyivjX/7lX+q2vfbaa6N79+45t+985ztbfOzevXvHxRdfHB9//HG9+372s59F9+7d48ADD4yvfe1r8fLLL8fcuXMbPFt50UUX1TvubbfdttXf6+KLL44NGzbE7bffnsdfA+CLkcn+9YXrAADgC+TMJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBI5v8B+0frakfjq1cAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('AGE')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:48.261669Z","iopub.execute_input":"2023-03-23T10:38:48.262722Z","iopub.status.idle":"2023-03-23T10:38:48.895570Z","shell.execute_reply.started":"2023-03-23T10:38:48.262659Z","shell.execute_reply":"2023-03-23T10:38:48.894028Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='AGE'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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AAAAsIbwCQAAAGsInwAAALCG8AkAAABruOAIQIPV4C+m4bfRPWrw//8A1AuOfAJocPz9/SVJZWVl9dwJaqOkpESSFBgYWM+dAGhIOPIJoMEJCAjQZZddpoMHDyowMFB+fg30c/Lp847snTxpt762LlL/xhiVlJSosLBQ4eHhzg8TACARPgE0QA6HQ61atdKuXbu0Z8+e+m6naj8edJ0v3mW3vrYucv/h4eGKjIysQWMAfsoInwAapKCgIMXGxjbsr95f/pXr/MPZdutr6yL2HxgYyBFPAG4RPgE0WH5+fgoJCanvNqp2fK/rvLe91ra+tny9fwA+qYGeSAUAAICfIsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKypUficP3++YmJiFBISori4OG3YsMHj9osXL9Z1112nyy67TK1atdL999+vw4cP16hhAKgzM8JcJwDARed1+MzMzNSkSZM0bdo05ebmqlevXkpMTFReXp7b7T/55BMlJSVpzJgx+uabb/TXv/5VX331lZKTk2vdPAAAAHyL1+Fz3rx5GjNmjJKTk9WlSxelpqYqOjpaaWlpbrf//PPP1b59e02cOFExMTG65ZZb9NBDDyk7O7vWzQMAAMC3eBU+y8rKlJOTo4SEBJflCQkJ2rhxo9ua+Ph47du3T6tXr5YxRgcOHNDbb7+t2267reZdAwAAwCd5FT4PHTqk8vJyRUREuCyPiIhQQUGB25r4+HgtXrxYI0aMUFBQkCIjIxUeHq6XXnqpyv2Ulpbq6NGjLhMAAAB8X40uOHI4HC7zxphKy87aunWrJk6cqKeeeko5OTlas2aNdu3apbFjx1b5+LNnz1ZYWJhzio6OrkmbAAAAaGC8Cp8tW7aUv79/paOchYWFlY6GnjV79mzdfPPNeuyxx3TttddqwIABmj9/vhYuXKj8/Hy3NSkpKSoqKnJOe/fu9aZNAAAANFBehc+goCDFxcUpKyvLZXlWVpbi4+Pd1pSUlMjPz3U3/v7+ks4cMXUnODhYoaGhLhMAAAB8n9dfu0+ZMkWvv/66Fi5cqG3btmny5MnKy8tzfo2ekpKipKQk5/ZDhgzRsmXLlJaWpp07d+rTTz/VxIkTdcMNNygqKqrungkAAAAavABvC0aMGKHDhw9r1qxZys/PV9euXbV69Wq1a9dOkpSfn+9yz8/Ro0fr2LFjevnll/XII48oPDxct956q5577rm6exYAAADwCV6HT0kaN26cxo0b53ZdRkZGpWUTJkzQhAkTarIrAAAA/ITw2+4AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrAuq7AQCosRlh580X1U8f9eVSf/4AfBJHPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA2/7Q74On7fG7CP9x1QYxz5BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWBNQ3w2gAZgRdt58Uf30Ad/Dawe+itcuUG848gkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrahQ+58+fr5iYGIWEhCguLk4bNmzwuH1paammTZumdu3aKTg4WD/72c+0cOHCGjUMAAAA3xXgbUFmZqYmTZqk+fPn6+abb9af/vQnJSYmauvWrWrbtq3bmuHDh+vAgQNKT09Xhw4dVFhYqNOnT9e6eQAAAPgWr8PnvHnzNGbMGCUnJ0uSUlNT9Y9//ENpaWmaPXt2pe3XrFmjjz/+WDt37lTz5s0lSe3bt69d1wAAAPBJXn3tXlZWppycHCUkJLgsT0hI0MaNG93W/P3vf1f37t31hz/8Qa1bt1bHjh316KOP6sSJE1Xup7S0VEePHnWZAAAA4Pu8OvJ56NAhlZeXKyIiwmV5RESECgoK3Nbs3LlTn3zyiUJCQrR8+XIdOnRI48aN05EjR6o873P27NmaOXOmN60BAADAB9TogiOHw+Eyb4yptOysiooKORwOLV68WDfccIMGDRqkefPmKSMjo8qjnykpKSoqKnJOe/furUmbAAAAaGC8OvLZsmVL+fv7VzrKWVhYWOlo6FmtWrVS69atFRYW5lzWpUsXGWO0b98+xcbGVqoJDg5WcHCwN60BAADAB3h15DMoKEhxcXHKyspyWZ6VlaX4+Hi3NTfffLP279+v48ePO5dt375dfn5+atOmTQ1aBgAAgK/y+mv3KVOm6PXXX9fChQu1bds2TZ48WXl5eRo7dqykM1+ZJyUlObe/99571aJFC91///3aunWr1q9fr8cee0wPPPCAGjVqVHfPBAAAAA2e17daGjFihA4fPqxZs2YpPz9fXbt21erVq9WuXTtJUn5+vvLy8pzbN2nSRFlZWZowYYK6d++uFi1aaPjw4fr9739fd88CAAAAPsHr8ClJ48aN07hx49yuy8jIqLSsc+fOlb6qBwAAwKWH33YHAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWFOj8Dl//nzFxMQoJCREcXFx2rBhQ7XqPv30UwUEBOj666+vyW4BAADg47wOn5mZmZo0aZKmTZum3Nxc9erVS4mJicrLy/NYV1RUpKSkJP3iF7+ocbMAAADwbV6Hz3nz5mnMmDFKTk5Wly5dlJqaqujoaKWlpXmse+ihh3TvvfeqZ8+eNW4WAAAAvs2r8FlWVqacnBwlJCS4LE9ISNDGjRurrFu0aJG+++47TZ8+vVr7KS0t1dGjR10mAAAA+D6vwuehQ4dUXl6uiIgIl+UREREqKChwW7Njxw49+eSTWrx4sQICAqq1n9mzZyssLMw5RUdHe9MmAAAAGqgaXXDkcDhc5o0xlZZJUnl5ue69917NnDlTHTt2rPbjp6SkqKioyDnt3bu3Jm0CAACgganeocj/T8uWLeXv71/pKGdhYWGlo6GSdOzYMWVnZys3N1cPP/ywJKmiokLGGAUEBOj999/XrbfeWqkuODhYwcHB3rQGAAAAH+DVkc+goCDFxcUpKyvLZXlWVpbi4+MrbR8aGqotW7Zo8+bNzmns2LHq1KmTNm/erBtvvLF23QMAAMCneHXkU5KmTJmi++67T927d1fPnj21YMEC5eXlaezYsZLOfGX+/fff64033pCfn5+6du3qUn/FFVcoJCSk0nIAAAD89HkdPkeMGKHDhw9r1qxZys/PV9euXbV69Wq1a9dOkpSfn3/Be34CAADg0uR1+JSkcePGady4cW7XZWRkeKydMWOGZsyYUZPdAgAAwMfx2+4AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrAuq7AdjX/slVLvO7Q+qpEfgcXjvwVbx2gYaDI58AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG33YHfAy/UQ3Yx/sOqDsc+QQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDX8tjsAn3Gp/772pf78Afw0cOQTAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1tQofM6fP18xMTEKCQlRXFycNmzYUOW2y5YtU//+/XX55ZcrNDRUPXv21D/+8Y8aNwwAAADf5XX4zMzM1KRJkzRt2jTl5uaqV69eSkxMVF5entvt169fr/79+2v16tXKyclR3759NWTIEOXm5ta6eQAAAPgWr8PnvHnzNGbMGCUnJ6tLly5KTU1VdHS00tLS3G6fmpqqxx9/XD169FBsbKyeffZZxcbGauXKlbVuHgAAAL7Fq/BZVlamnJwcJSQkuCxPSEjQxo0bq/UYFRUVOnbsmJo3b+7NrgEAAPATEODNxocOHVJ5ebkiIiJclkdERKigoKBaj/H888+ruLhYw4cPr3Kb0tJSlZaWOuePHj3qTZsAAABooGp0wZHD4XCZN8ZUWubOkiVLNGPGDGVmZuqKK66ocrvZs2crLCzMOUVHR9ekTQAAADQwXoXPli1byt/fv9JRzsLCwkpHQ8+XmZmpMWPGaOnSperXr5/HbVNSUlRUVOSc9u7d602bAAAAaKC8Cp9BQUGKi4tTVlaWy/KsrCzFx8dXWbdkyRKNHj1af/nLX3TbbbddcD/BwcEKDQ11mQAAAOD7vDrnU5KmTJmi++67T927d1fPnj21YMEC5eXlaezYsZLOHLX8/vvv9cYbb0g6EzyTkpL0wgsv6KabbnIeNW3UqJHCwsLq8KkAAACgofM6fI4YMUKHDx/WrFmzlJ+fr65du2r16tVq166dJCk/P9/lnp9/+tOfdPr0aY0fP17jx493Lh81apQyMjJq/wwAoJraP7nKZX53SD01AgCXMK/DpySNGzdO48aNc7vu/ED50Ucf1WQXAAAA+Anit90BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYE1AfTcAAL6i/ZOrXOZ3h9RTIwDgwzjyCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAa7jaHbiE1PfV2vW9fwBA/ePIJwAAAKwhfAIAAMAawicAAACsIXwCAADAGi44AoBLBBd8+S7+3+GnhCOfAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIb7fAIAGjzucwn8dHDkEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA23WgIA4CLjVlHA/48jnwAAALCG8AkAAABr+NodACzhq1cA4MgnAAAALCJ8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAmhqFz/nz5ysmJkYhISGKi4vThg0bPG7/8ccfKy4uTiEhIbryyiv16quv1qhZAAAA+Davw2dmZqYmTZqkadOmKTc3V7169VJiYqLy8vLcbr9r1y4NGjRIvXr1Um5urqZOnaqJEyfqnXfeqXXzAAAA8C1eh8958+ZpzJgxSk5OVpcuXZSamqro6GilpaW53f7VV19V27ZtlZqaqi5duig5OVkPPPCA/vjHP9a6eQAAAPiWAG82LisrU05Ojp588kmX5QkJCdq4caPbms8++0wJCQkuywYMGKD09HSdOnVKgYGBlWpKS0tVWlrqnC8qKpIkHT161LmsorTEpebcdTVSalznvX282tR7WVvb516p3lGPz5362v//9+L/X23/31N/addXUp/jno/97eq7vhIfG/c81vty7z5Q703mOLvOGFPlNmc3qLbvv//eSDKffvqpy/JnnnnGdOzY0W1NbGyseeaZZ1yWffrpp0aS2b9/v9ua6dOnG0lMTExMTExMTEw+Nu3du9djnvTqyOdZDofDZd4YU2nZhbZ3t/yslJQUTZkyxTlfUVGhI0eOqEWLFm5rjh49qujoaO3du1ehoaHVfh4Nod6Xe6fet+t9uXfqfbvel3un3rfrfbl3X6g3xujYsWOKiory+Dhehc+WLVvK399fBQUFLssLCwsVERHhtiYyMtLt9gEBAWrRooXbmuDgYAUHB7ssCw8Pv2B/oaGhNfpjNoR6X+6det+u9+Xeqfftel/unXrfrvfl3ht6fVhY2AXrvbrgKCgoSHFxccrKynJZnpWVpfj4eLc1PXv2rLT9+++/r+7du7s93xMAAAA/XV5f7T5lyhS9/vrrWrhwobZt26bJkycrLy9PY8eOlXTmK/OkpCTn9mPHjtWePXs0ZcoUbdu2TQsXLlR6eroeffTRunsWAAAA8Alen/M5YsQIHT58WLNmzVJ+fr66du2q1atXq127dpKk/Px8l3t+xsTEaPXq1Zo8ebJeeeUVRUVF6cUXX9Sdd95ZZ08iODhY06dPr/RVvS/U+3Lv1Pt2vS/3Tr1v1/ty79T7dr0v9/5TqD/LYcyFrocHAAAA6ga/7Q4AAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGt+cuHzwIEDmjVrVn234ZUrr7xSO3bsuOB2+/bt06FDh5zzGzZs0K9//Wv16tVLI0eO1Geffeax/vnnn9eePXtq1evKlSs1ffp0574+/PBDDRo0SAMHDtSCBQsuWH/ixAktXLhQDzzwgBITEzV48GBNmDBBa9eurVVfwIUUFxfrtdde0/3336/ExEQNGjRI999/v15//XUVFxfX6rGrO+7s27dPx48fr7T81KlTWr9+vcfaw4cPa926dTpy5Igk6dChQ3ruuec0a9Ysbdu2rUZ9V3fscdfvihUrNHfuXL311lse/36MW0DDdKFxqy7ee1Xy+MvvPmjz5s3Gz8/P4zZ///vfzVNPPWU2btxojDFm7dq1JjEx0QwYMMD86U9/uuA+SkpKTHp6urn//vvNwIEDzW233WYefvhh88EHH3ise+GFF9xO/v7+JiUlxTlflZ49e5rVq1cbY4xZsWKF8fPzM7fffrt54oknzB133GECAwPNypUrq6x3OBzG39/f9OvXz/zf//2fKS0tveBzPVdaWpoJCAgwcXFxJjQ01Lz11lumadOmJjk52Tz00EOmUaNGJjU1tcr6HTt2mHbt2pkWLVqYVq1aGYfDYW677TZz4403Gn9/f/OrX/3KnDp1yquezlVQUGBmzpx5we327t1rjh07Vml5WVmZ+fjjjz3WHjp0yHz44Yfm8OHDxhhjDh48aObMmWNmzpxptm7dWqO+Y2JizPbt272uKysrM8uXLzd/+MMfzJtvvmmOHz/ucfu9e/eagwcPOufXr19v7r33XnPLLbeYX//61873gzt//OMfze7du73u8Vz19b4zxphvvvnGREVFmfDwcDN06FDzm9/8xjz44INm6NChJjw83LRu3dp88803NX5uFxp39u/fb3r06GH8/PyMv7+/SUpKcnkNFhQUeKz/4osvTFhYmHE4HKZZs2YmOzvbxMTEmNjYWNOhQwfTqFEjk5OTU2V9XYw9P/zwgzHGmMLCQnPNNdeYoKAgExsba0JCQkzbtm3Nvn37qqy91Met48ePmwULFpjRo0ebgQMHmsTERDN69Gjz2muvXfB9eyGMe4x7NXWhcau27z1PfC58fv311x6nzMxMj3/M+hyIHA6HadOmjWnfvr3L5HA4TOvWrU379u1NTExMlftu2rSp2bVrlzHGmBtvvNHMmTPHZf1LL71kunXrVmW9w+EwixYtMkOHDjWBgYGmRYsW5re//a3ZsmVLlTXn6tKli1mwYIExxpgPP/zQhISEmFdeecW5ftGiRaZLly5V1icmJpqHHnrIlJeXG2OMmT17tklMTDTGGLN9+3bTvn17M3369Gr14g4BoOoAcLa+piHA1wNAnz59zN133+2279LSUnPPPfeYPn36VFlf23EnKSnJ3HTTTearr74yWVlZpnv37iYuLs4cOXLEGHPmtedwOKqs79evn0lOTjZHjx41c+fONW3atDHJycnO9WPGjDHDhg2rsr62Y4/D4TAHDhwwxhjz4IMPmuuvv97k5+cbY84Ek/j4ePPAAw+4rb3Uxy0++DDu1de4V9txq7bvPU98Lnw6HA7j5+dnHA5Hpensck9/zPociH7zm9+Y66+/vtInxYCAgGoNPmFhYebrr782xhhzxRVXOP/7rG+//dZcdtllVdaf+w/IgQMHzHPPPWc6d+5s/Pz8TI8ePcyCBQvM0aNHq6xv1KiR2bNnj3M+MDDQ5UW4a9cuj/u/7LLLXD7plpaWmsDAQHPo0CFjzJmBoX379lXWEwBqHgCMqV0I8PUA0KhRI4/vsS1btphGjRpVub62405UVJT54osvnPMnT540Q4cONddff705fPjwBQNAs2bNnONGWVmZ8fPzc3m8TZs2mdatW1dZX9ux59zXXseOHc27777rsn7dunVVvncv9XGLDz6Me8bUz7hX23Grtu89T3wufLZs2dKkp6eb3bt3u51WrVrl8Y9Z3wPR8uXLTXR0tHnppZecy6r7D8Dtt99unnzySWOMMQMGDKj0afG1114zsbGxVdaf+0I61/r1682oUaNM48aNTePGjausb9OmjVm/fr0xxpjvv//eOBwOs2rVKuf6jz76yLRp06bK+qioKJdPyD/88INxOBzOF+/OnTtNcHCwx/4JADULAMbULgT4egCIiooyK1asqHL98uXLTVRUVJXrazvuNG7cuNJXjKdOnTLDhg0z1157rfnnP/95wfqz/4AaY0yTJk3Md99955zfs2ePCQkJqbL+7HOs6djjcDhMYWGhMebMa+f8mt27d1f53r3Uxy0++DDunWV73KvtuFXb954nPhc+BwwYYJ5++ukq12/evNnjp7j6HoiMMWbfvn3m1ltvNQMHDjT5+fnVfhNu3brVtGjRwiQlJZmnn37aNGnSxIwcOdI888wzJikpyQQHB5tFixZVWe/n5+f2hXRWUVGR81OaO+PHjzexsbHm97//vbnhhhvMqFGjTOfOnc17771n1qxZY6655hqPn0BHjRplevfubbZt22Z27txpRowY4fKJ86OPPjLR0dFV1hMAah4AjKldCPD1ADB9+nQTFhZm5s6dazZv3mzy8/NNQUGB2bx5s5k7d65p1qyZx/PmajvuXHPNNebtt9+utPzs669t27YeX3udO3c2a9eudc6/++67pqSkxDn/+eefe/z7nVXTscfhcJhBgwaZO+64wzRr1sz5NeZZn332mYmIiHBbe6mPW3zwYdwzpn7GvdqOW7V973nic+Fz2bJl5s0336xy/ZEjR0xGRkaV6+t7IDqroqLCPPvssyYyMtL4+/tX+5yfb7/91tx9992madOmzk+/gYGBJj4+3ixfvtxjbVVvpOo6fvy4SU5ONl27djVjx441ZWVlZu7cuSYoKMg4HA7Tp08fj49/4MABc9NNNzk/qbdv395s2rTJuf6vf/2refHFF6usJwDUPAAYU7sQ4OsBwBhj5syZ4zxvys/Pz3nUqFWrVua5557zWFvbcefxxx83CQkJbtedOnXK3H777R5fezNmzDBLliypcv3UqVPNL3/5yyrXn6smY8/o0aNdpqVLl7qsf/TRR82AAQOqrL+Uxy0++JzBuGd/3KvtuFXb954nPhc+a6u+B6LzZWdnm3nz5jnPv6muiooKU1BQYPbv31/rK9AqKipqVV9cXOzVeR/bt283W7ZscZ4oXd39EwBqFwCMqXkIaIgB4NwjAt6873bu3Gk2btxoNm7caHbu3Fnj5+SNU6dOmaKioirXnz59ulZX1RYXF5uTJ096VZOdnW1SU1O9HnvcOX78uDlx4sQFtzt33CorK6v1fmvjxIkTtRq3vMEHnzNqMu6NGjWKca+O8kZD4jDGmItzEyffcvLkSZ06dUpNmzat1vY7duxQaWmpOnfurICAgFrtOygoSF9//bW6dOlC/UV0+vRplZSUKDQ01O368vJy7du3T+3atavR45eUlMjf31/BwcHVrtm0aZM2bNigpKQkNWvWrEb7Pau4uFj+/v4KCQm54LbGGBUWFqqiokItW7ZUYGBgrfZdU/X5vgNs27VrlwoKCiRJkZGRiomJuej7bIjjXk5Ojj755BPGvWqOe9u3b1dZWdlPatzzyWdx4sQJ5eTkqHnz5rrqqqtc1p08eVJLly5VUlJSlfXbtm3T559/rvj4eHXq1En//ve/9cILL6i0tFQjR47Urbfe6nH/59YHBARUu37KlClul5eXl2vOnDlq0aKFJGnevHnUV8MPP/ygP//5z9qxY4datWqlUaNGKTo6usrtAwICXAbg8+tHjx7t1QDs7f7d1a9fv147duzQK6+8UqP6mu7f4XAoKCjIWR8VFaWkpKRq19fFcz9336NGjVKbNm0uWBcbG1uj/efm5io8PNz5j/1bb72ltLQ05eXlqV27dnr44Yd19913U38R6n2597qonzBhgoYPH65evXopJibG68B5bn1NTJ482WO9v7+/x3HvQvu/7LLLPO7fXX1cXJzi4uKq0f2F99+4ceNq1zscDkVERFRrv9XZd03rQ0JCqhWWa7t/SXrppZeUnZ2t2267TcOHD9ebb76p2bNnq6KiQr/85S81a9asKgOtMUYvv/xyjes9qtfjrjXwn//8x7Rr1855GLp3795m//79zvUXunLvvffeM0FBQaZ58+YmJCTEvPfee+byyy83/fr1M7/4xS9MQECAy/ktdVnvcDjM9ddfb/r06eMyORwO06NHD9OnTx/Tt2/fKvd9qde3atXKeZXfzp07TWRkpImMjDT9+/c3bdq0MWFhYWbbtm3UX4R6X+7dGGO6detmPvzwQ2PMmQsMGjVqZCZOnGjS0tLMpEmTTJMmTUx6ejr1F6Hel3uvi/qz/1bFxsaaOXPmOG8TVF3U11+9L/dujDGzZs0yTZs2NXfeeaeJjIw0c+bMMS1atDC///3vzbPPPmsuv/xy89RTT120ek98LnwOGzbMDB482Bw8eNDs2LHDDBkyxMTExDhvZ3Ch8NmzZ08zbdo0Y4wxS5YsMc2aNTNTp051rp86darp37//Ral/9tlnTUxMTKVwWt0Try/1+nPPv7n77rtNnz59THFxsTHmzO1DBg8ebO666y7qL0K9L/duzJlblpwdI7p161bpl0UWL15srrrqKuovQr0v914X9Q6Hw3zwwQfmt7/9rWnZsqUJDAw0t99+u1m5cqXz/o2eUF9/9b7cuzHGXHnlleadd94xxpy5MM3f39+89dZbzvXLli0zHTp0uGj1nvhc+LziiivMP//5T5dl48aNM23btjXffffdBcNnaGio2bFjhzHGmPLychMQEOBy4cKWLVs8XjlX2/ovv/zSdOzY0TzyyCPOE+6rG74u9fpzA4i7EHuhqy6pr3m9L/dujDEtWrQw2dnZxpgzY8jmzZtd1n/77bce77VIfc3rfbn3uqg/97VbVlZmMjMzzYABA4y/v7+JiooyU6dOdf6bQn3Dqvfl3o1xf5/Rf/3rX8753bt3e7zPaG3rPfGr8YkE9eTEiROVzi945ZVXdPvtt6t3797avn17tR/Lz89PISEhCg8Pdy5r2rSpioqKLlp9jx49lJOTo4MHD6p79+7asmWLHA5HtXu+1OvPbltaWlrp3J2IiAgdPHiQ+otU78u9JyYmKi0tTZLUu3dvvf322y7rly5dqg4dOlB/Eep9ufe6qD9XYGCghg8frjVr1mjnzp168MEHtXjxYnXq1In6Bl7vi71HRkZq69atks5crFleXu6cl6RvvvlGV1xxxUWr96hGkbUe9ejRw7zxxhtu140fP96Eh4d7PPJ57bXXmvfee885f/6tMzZs2ODxp75qW3+uJUuWmIiICOPn51ej3/a91OodDoe55pprTLdu3UyTJk3MsmXLXNZ//PHHHn9pg/qa1/ty78acucFz+/btzc9//nMzZcoU06hRI3PLLbeYBx980Pz85z83QUFBLjd/pr7u6n2597qov9DteioqKsz7779PfQOs9+XejTFm2rRp5vLLLzfJyckmJibGpKSkmLZt25q0tDTz6quvmujoaDN58uSLVu+Jz13tfscdd2jJkiW67777Kq17+eWXVVFRoVdffbXK+v/+7/9WeXm5c75r164u69977z2PV7vXtv5cd999t2655Rbl5OTU6DYXl1r99OnTXebPv8py5cqVHq8KpL7m9b7cuyRFRUUpNzdXc+bM0cqVK2WM0Zdffqm9e/fq5ptv1qeffqru3btTfxHqfbn3uqhv166d/P39q1zvcDjUv39/6htgvS/3LkkzZ85Uo0aN9Pnnn+uhhx7SE088oWuvvVaPP/64SkpKNGTIED399NMXrd4T7vMJAAAAa3zunE8AAAD4LsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicA1KGNGzfK399fAwcOrLSurKxMc+fO1X/913+pcePGCgsL03XXXaff/e532r9/v3O70aNHy+FwVJrcPSYA+BputQQAdSg5OVlNmjTR66+/rq1bt6pt27aSzvwyU0JCgv75z39q5syZuvnmmxUWFqbvvvtOK1asUHh4uGbPni3pTPg8cOCAFi1a5PLYwcHBatasmfXnBAB1yeduMg8ADVVxcbGWLl2qr776SgUFBcrIyNBTTz0lSfrf//1fffLJJ8rOzla3bt2cNR06dNCAAQN0/nGA4OBgRUZGWu0fAGzga3cAqCOZmZnq1KmTOnXqpJEjR2rRokXOULlkyRL179/fJXie6+xv1wPATx3hEwDqSHp6ukaOHClJGjhwoI4fP661a9dKkrZv365OnTq5bH/HHXeoSZMmatKkieLj413Wvfvuu851Z6ea/pQdADQkfO0OAHXgP//5j7788kstW7ZMkhQQEKARI0Zo4cKF6tevn6TKRzfnz5+v4uJivfjii1q/fr3Lur59+yotLc1lWfPmzS/iMwAAOwifAFAH0tPTdfr0abVu3dq5zBijwMBA/fDDD4qNjdW///1vl5pWrVpJch8qGzdurA4dOlzcpgGgHvC1OwDU0unTp/XGG2/o+eef1+bNm53T119/rXbt2mnx4sW65557lJWVpdzc3PpuFwDqFUc+AaCW3n33Xf3www8aM2aMwsLCXNbdddddSk9P12effaZVq1bp1ltv1YwZM9SrVy81a9ZM27dv13vvvSd/f3+XutLSUhUUFLgsCwgIUMuWLS/68wGAi4n7fAJALQ0ZMkQVFRVatWpVpXWbNm1SXFyccnJydPXVVys1NVVLlizR9u3bVVFRoZiYGCUmJmry5MmKjo6WdOY+n3/+858rPVanTp0qfXUPAL6G8AkAAABrOOcTAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgzf8DObk+Kia2W/IAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('SMOKING')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:48.897720Z","iopub.execute_input":"2023-03-23T10:38:48.898178Z","iopub.status.idle":"2023-03-23T10:38:49.152943Z","shell.execute_reply.started":"2023-03-23T10:38:48.898126Z","shell.execute_reply":"2023-03-23T10:38:49.151111Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='SMOKING'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('YELLOW_FINGERS')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:49.155217Z","iopub.execute_input":"2023-03-23T10:38:49.155725Z","iopub.status.idle":"2023-03-23T10:38:49.406840Z","shell.execute_reply.started":"2023-03-23T10:38:49.155677Z","shell.execute_reply":"2023-03-23T10:38:49.405338Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='YELLOW_FINGERS'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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vHnzFBgYqDvvvFNPP/301XsUAAAA8AgWqwe89l1bW6uAgADV1NTI39/f7tipU6d08OBBRUREqHPnzm5aIa5UQ0ODDh06ZPvlLLdaEuDe+/dEHvBtD0C7xvOa63hec9mleu37PP633Tt16iTpwntL4bma/v2a/j0BAEDb5PLL7tcbLy8vBQYGqrq6WpLUpUuXZr/2Cdcfq9WqkydPqrq6WoGBgbYPowEAgLbJ4+NTkkJDQyXJFqDwPIGBgbZ/RwAA0Ha1ifi0WCwKCwtTz549dfbsWXcvBy7q1KkTO54AALQTbSI+m3h5eRExAAAA1zGP/8ARAAAAPAfxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjGlVfObk5CgyMlK+vr6KiYlRcXHxJeefPn1aixYtUt++feXj46Mbb7xR69ata9WCAQAA4Lk6unpCXl6e0tPTlZOTo5EjR2rVqlVKSkrSvn371KdPH6fnJCcn6+uvv9batWsVFRWl6upqnTt37ooXDwAAAM/icnwuW7ZM06dPV2pqqiRp+fLl2rJli3Jzc5Wdne0wf/Pmzdq+fbs+//xzde/eXZIUERFxZasGAACAR3LpZfczZ86otLRUiYmJduOJiYkqKSlxes67776r2NhYPfPMM7rhhhs0YMAAzZ8/Xw0NDc3ez+nTp1VbW2t3AQAAgOdzaefz6NGjamxsVEhIiN14SEiIqqqqnJ7z+eefa8eOHfL19dXbb7+to0ePaubMmTp+/Hiz7/vMzs7W0qVLXVkaAAAAPECrPnBksVjsrlutVoexJufPn5fFYtHGjRs1bNgw3XPPPVq2bJnWr1/f7O5nZmamampqbJfDhw+3ZpkAAAC4zri08xkcHCwvLy+HXc7q6mqH3dAmYWFhuuGGGxQQEGAbGzRokKxWq7788kv179/f4RwfHx/5+Pi4sjQAAAB4AJd2Pr29vRUTE6OCggK78YKCAsXFxTk9Z+TIkTpy5Ii+++4729iBAwfUoUMH9e7duxVLBgAAgKdy+WX3jIwMrVmzRuvWrdP+/fs1d+5cVVRUKC0tTdKFl8wnT55sm//AAw8oKChI06ZN0759+/Tee+9pwYIFeuihh9S5c+er90gAAABw3XP5q5ZSUlJ07NgxZWVlqbKyUtHR0crPz1ffvn0lSZWVlaqoqLDN9/PzU0FBgWbPnq3Y2FgFBQUpOTlZTz755NV7FAAAAPAIFqvVanX3Ii6ntrZWAQEBqqmpkb+/v7uXg7ZuScDl58Dekhp3rwDApfC85jqe11zW0l7jt90BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMa0Kj5zcnIUGRkpX19fxcTEqLi4uEXn7dy5Ux07dtQPfvCD1twtAAAAPJzL8ZmXl6f09HQtWrRIZWVlio+PV1JSkioqKi55Xk1NjSZPnqy77rqr1YsFAACAZ3M5PpctW6bp06crNTVVgwYN0vLlyxUeHq7c3NxLnjdjxgw98MADGjFiRKsXCwAAAM/mUnyeOXNGpaWlSkxMtBtPTExUSUlJs+e9+uqr+uyzz/TEE0+06H5Onz6t2tpauwsAAAA8n0vxefToUTU2NiokJMRuPCQkRFVVVU7P+eSTT/TII49o48aN6tixY4vuJzs7WwEBAbZLeHi4K8sEAADAdapVHziyWCx2161Wq8OYJDU2NuqBBx7Q0qVLNWDAgBbffmZmpmpqamyXw4cPt2aZAAAAuM60bCvy/wQHB8vLy8thl7O6utphN1SS6urqtHfvXpWVlenhhx+WJJ0/f15Wq1UdO3bU1q1bdeeddzqc5+PjIx8fH1eWBgAAAA/g0s6nt7e3YmJiVFBQYDdeUFCguLg4h/n+/v768MMPVV5ebrukpaVp4MCBKi8v1/Dhw69s9QAAAPAoLu18SlJGRoYmTZqk2NhYjRgxQqtXr1ZFRYXS0tIkXXjJ/KuvvtKGDRvUoUMHRUdH253fs2dP+fr6OowDAACg7XM5PlNSUnTs2DFlZWWpsrJS0dHRys/PV9++fSVJlZWVl/3OTwAAALRPFqvVanX3Ii6ntrZWAQEBqqmpkb+/v7uXg7ZuSYC7V+B5ltS4ewUALoXnNdfxvOaylvYav+0OAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMZ0dPcCcO1EPPJHdy/BIx3ydfcKAABou1q185mTk6PIyEj5+voqJiZGxcXFzc596623NHbsWPXo0UP+/v4aMWKEtmzZ0uoFAwAAwHO5HJ95eXlKT0/XokWLVFZWpvj4eCUlJamiosLp/Pfee09jx45Vfn6+SktLlZCQoHvvvVdlZWVXvHgAAAB4FovVarW6csLw4cP1wx/+ULm5ubaxQYMGacKECcrOzm7RbQwePFgpKSlavHhxi+bX1tYqICBANTU18vf3d2W57Rovu7fOId8H3L0Ez7Okxt0rAHApSwLcvQLPw/Oay1raay7tfJ45c0alpaVKTEy0G09MTFRJSUmLbuP8+fOqq6tT9+7dm51z+vRp1dbW2l0AAADg+VyKz6NHj6qxsVEhISF24yEhIaqqqmrRbTz33HOqr69XcnJys3Oys7MVEBBgu4SHh7uyTAAAAFynWvWBI4vFYnfdarU6jDnz2muvacmSJcrLy1PPnj2bnZeZmamamhrb5fDhw61ZJgAAAK4zLn3VUnBwsLy8vBx2Oaurqx12Qy+Wl5en6dOn64033tCYMWMuOdfHx0c+Pj6uLA0AAAAewKWdT29vb8XExKigoMBuvKCgQHFxcc2e99prr2nq1KnatGmTfvSjH7VupQAAAPB4Ln/JfEZGhiZNmqTY2FiNGDFCq1evVkVFhdLS0iRdeMn8q6++0oYNGyRdCM/JkyfrhRde0O23327bNe3cubMCAvj0HQAAQHvicnympKTo2LFjysrKUmVlpaKjo5Wfn6++fftKkiorK+2+83PVqlU6d+6cZs2apVmzZtnGp0yZovXr11/5IwAAAIDHaNXPa86cOVMzZ850euzioCwqKmrNXQAAAKANatWn3QEAAIDWID4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxHd29AAAAWirikT+6ewke6ZCvu1cA/D92PgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMIb4BAAAgDHEJwAAAIwhPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMIb4BAAAgDHEJwAAAIwhPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMIb4BAAAgDHEJwAAAIwhPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAmFbFZ05OjiIjI+Xr66uYmBgVFxdfcv727dsVExMjX19f9evXTy+//HKrFgsAAADP5nJ85uXlKT09XYsWLVJZWZni4+OVlJSkiooKp/MPHjyoe+65R/Hx8SorK9Ojjz6qOXPm6M0337zixQMAAMCzuByfy5Yt0/Tp05WamqpBgwZp+fLlCg8PV25urtP5L7/8svr06aPly5dr0KBBSk1N1UMPPaR/+7d/u+LFAwAAwLN0dGXymTNnVFpaqkceecRuPDExUSUlJU7P2bVrlxITE+3Gxo0bp7Vr1+rs2bPq1KmTwzmnT5/W6dOnbddramokSbW1ta4st907f/qku5fgkWotVncvwfPw/00YwvNa6/C81go8r7msqdOs1kv/782l+Dx69KgaGxsVEhJiNx4SEqKqqiqn51RVVTmdf+7cOR09elRhYWEO52RnZ2vp0qUO4+Hh4a4sF2iVAHcvwBP9hr8acD3j/6GtwPNaq9XV1SkgoPm/n0vx2cRisdhdt1qtDmOXm+9svElmZqYyMjJs18+fP6/jx48rKCjokvcDXKna2lqFh4fr8OHD8vf3d/dyAOCK8bwGU6xWq+rq6tSrV69LznMpPoODg+Xl5eWwy1ldXe2wu9kkNDTU6fyOHTsqKCjI6Tk+Pj7y8fGxGwsMDHRlqcAV8ff350kaQJvC8xpMuNSOZxOXPnDk7e2tmJgYFRQU2I0XFBQoLi7O6TkjRoxwmL9161bFxsY6fb8nAAAA2i6XP+2ekZGhNWvWaN26ddq/f7/mzp2riooKpaWlSbrwkvnkyZNt89PS0vTFF18oIyND+/fv17p167R27VrNnz//6j0KAAAAeASX3/OZkpKiY8eOKSsrS5WVlYqOjlZ+fr769u0rSaqsrLT7zs/IyEjl5+dr7ty5WrlypXr16qUVK1Zo4sSJV+9RAFeJj4+PnnjiCYe3fQCAp+J5Ddcbi/Vyn4cHAAAArhJ+2x0AAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYEyrfl4TaCu+/PJL5ebmqqSkRFVVVbJYLAoJCVFcXJzS0tIUHh7u7iUCANCm8FVLaLd27NihpKQkhYeHKzExUSEhIbJaraqurlZBQYEOHz6sP/3pTxo5cqS7lwoAV8Xhw4f1xBNPaN26de5eCtox4hPt1m233aY77rhDzz//vNPjc+fO1Y4dO7Rnzx7DKwOAa+ODDz7QD3/4QzU2Nrp7KWjHiE+0W507d1Z5ebkGDhzo9PjHH3+soUOHqqGhwfDKAKB13n333Use//zzzzVv3jziE27Fez7RboWFhamkpKTZ+Ny1a5fCwsIMrwoAWm/ChAmyWCy61L6SxWIxuCLAEfGJdmv+/PlKS0tTaWmpxo4dq5CQEFksFlVVVamgoEBr1qzR8uXL3b1MAGixsLAwrVy5UhMmTHB6vLy8XDExMWYXBVyE+ES7NXPmTAUFBen555/XqlWrbC9DeXl5KSYmRhs2bFBycrKbVwkALRcTE6P333+/2fi83K4oYALv+QQknT17VkePHpUkBQcHq1OnTm5eEQC4rri4WPX19br77rudHq+vr9fevXs1atQowysD/h/xCQAAAGP4hSMAAAAYQ3wCAADAGOITAAAAxhCfAAAAMIb4BOB2VqtVY8aM0bhx4xyO5eTkKCAgQBs2bJDFYnF6qaqqkiQtWbJEP/jBD5q9n9GjRys9Pb3Z48ePH1d6eroiIiLk7e2tsLAwTZs2TRUVFbY5L7/8srp166Zz587Zxr777jt16tRJ8fHxdrdXXFwsi8WiAwcOXPZvEBER4fC4evfubXf8+9872zR/9+7ddreTnp6u0aNH243V1tbq8ccf1+DBg9W5c2cFBQXptttu0zPPPKNvv/3W7u/j7O+blpZmm/P9cT8/Pw0ZMkTr1693eDyrVq3SkCFD1LVrVwUGBmro0KF6+umnL/t3AND28T2fANzOYrHo1Vdf1S233KJVq1ZpxowZkqSDBw9q4cKFevHFF9WnTx9J0t///nf5+/vbnd+zZ88rXsPx48d1++23y9vbWzk5OYqOjtahQ4f02GOP6bbbbtOuXbvUr18/JSQk6LvvvtPevXt1++23S7oQmaGhodqzZ49OnjypLl26SJKKiorUq1cvDRgwoEVryMrK0s9//nPbdS8vr0vO9/X11cKFC7V9+/ZLPq477rhDtbW1+tWvfqWYmBh5e3vr008/1aZNm7Rp0ybNmjXLNv/nP/+5srKy7G6j6fE0efXVV3X33Xervr5eeXl5mjZtmsLCwmz/8bB27VplZGRoxYoVGjVqlE6fPq2//e1v2rdvX4v+DgDaNuITwHUhPDxcL7zwgh5++GElJiYqIiJC06dP11133aWpU6eqqKhI0oXQDAwMvOr3v2jRIh05ckSffvqpQkNDJUl9+vTRli1b1L9/f82aNUt/+tOfNHDgQPXq1UtFRUW2+CwqKtJ9992nwsJClZSUaMyYMbbxhISEFq+hW7dutvtuiRkzZig3N1f5+fm65557nM559NFHVVFRob///e+64YYbbOM33XSTxo8f7/CF4126dLnsGgIDA21zHn30UT333HPaunWrLT5///vfKzk5WdOnT7edM3jw4BY/LgBtGy+7A7huTJkyRXfddZemTZuml156SR999JFWr159ze/3/Pnz+q//+i/97Gc/cwivzp07a+bMmdqyZYuOHz8u6cLL04WFhbY5hYWFGj16tEaNGmUbP3PmjHbt2uVSfLoqIiJCaWlpyszM1Pnz550+rry8PD344IN24fl9V/I7342NjXr99dd1/Phxux9mCA0N1e7du/XFF1+0+rYBtF3EJ4DryurVq7Vv3z6lp6dr1apVDi+p9+7dW35+frbLwIEDr/g+v/nmG504cUKDBg1yenzQoEGyWq369NNPJV2Iz507d+rcuXOqq6tTWVmZ/vEf/1GjRo2y7dDu3r1bDQ0NLsXnwoUL7R7bihUrLnvOY489poMHD2rjxo3NPq6L/0YxMTG2+7j//vvtjuXk5Nitwc/PT//+7/9uN+f++++Xn5+ffHx8lJKSou7duys1NdV2/IknnlBgYKAiIiI0cOBATZ06Va+//rrTQAbQ/vCyO4DrSs+ePfWLX/xC77zzjv7pn/7J4XhxcbG6detmu96x47V/Gmt6abpplzAhIUH19fXas2ePvv32Ww0YMEA9e/bUqFGjNGnSJNXX16uoqEh9+vRRv379Wnw/CxYs0NSpU23Xg4ODL3tOjx49NH/+fC1evFgpKSlO51y8u/n222/rzJkzWrhwoRoaGuyO/exnP9OiRYvsxi7+D4Dnn39eY8aM0eHDh5WRkaG5c+cqKirKdjwsLEy7du3SRx99pO3bt6ukpERTpkzRmjVrtHnzZnXowL4H0J4RnwCuOx07dmw2KiMjI6/6ez579OihwMDAZj8Q8/HHH8tisejGG2+UJEVFRal3794qLCzUt99+a/ud7NDQUEVGRmrnzp0qLCzUnXfe6dI6goOD7SKupTIyMpSTk6OcnBynj+vjjz+2G2/68Fa3bt104sQJu2MBAQGXXUNoaKiioqIUFRWlN954Q0OHDlVsbKxuvvlmu3nR0dGKjo7WrFmztGPHDsXHx2v79u3X9K0IAK5//OcngHavQ4cOSk5O1qZNm2xf29SkoaFBOTk5GjdunLp3724bT0hIUFFRkYqKiuy+2mjUqFHasmWLdu/ebSyy/Pz89Pjjj+upp55SbW2tbbzpcf3nf/6nvvrqq2ty31FRUZo4caIyMzMvOa8pTOvr66/JOgB4DnY+AXiU6upqnTp1ym4sKCjI9oGXhoYGlZeX2x338/Oz7eZ98803DsdDQ0P11FNPadu2bRo7dqyeeeYZRUdH6+DBg3rsscd09uxZrVy50u6chIQEzZo1S2fPnrXtfEoX4vOXv/ylTp06ZXSH7xe/+IWef/55vfbaaxo+fLht/Ne//rWKioo0fPhwZWVlKTY2Vl27dtXf/vY37dq1S9HR0Xa3c/LkSYcA9/Hx0T/8wz80e9/z5s3TkCFDtHfvXsXGxuqXv/ylevXqpTvvvFO9e/dWZWWlnnzySfXo0UMjRoy4ug8cgMdh5xOARxk4cKDCwsLsLqWlpbbjBw4c0NChQ+0u3/8wzKZNmxyOv/zyywoODrbtVs6YMUP9+vVTcnKy+vXrpz179ji8dzMhIUENDQ2KiopSSEiIbXzUqFGqq6vTjTfeqPDw8Gv/B/k/nTp10q9+9SunYf7Xv/5VkydP1rPPPqthw4bplltu0ZIlS5SSkqJXXnnFbv4rr7zi8Pe9+ENJF7vllls0ZswYLV68WJI0ZswY7d69Wz/96U81YMAATZw4Ub6+vtq2bZuCgoKu7gMH4HEs1ou/5A0AAAC4Rtj5BAAAgDHEJwBcYxs3bnT47symC7/8A6C94WV3ALjG6urq9PXXXzs91qlTJ/Xt29fwigDAfYhPAAAAGMPL7gAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMb8Lwx8Zexhqa3zAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('ANXIETY')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:49.409208Z","iopub.execute_input":"2023-03-23T10:38:49.410106Z","iopub.status.idle":"2023-03-23T10:38:49.666497Z","shell.execute_reply.started":"2023-03-23T10:38:49.410040Z","shell.execute_reply":"2023-03-23T10:38:49.664751Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='ANXIETY'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('PEER_PRESSURE')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:49.668185Z","iopub.execute_input":"2023-03-23T10:38:49.668664Z","iopub.status.idle":"2023-03-23T10:38:49.925076Z","shell.execute_reply.started":"2023-03-23T10:38:49.668614Z","shell.execute_reply":"2023-03-23T10:38:49.923409Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='PEER_PRESSURE'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('CHRONIC DISEASE')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:49.927152Z","iopub.execute_input":"2023-03-23T10:38:49.928472Z","iopub.status.idle":"2023-03-23T10:38:50.186246Z","shell.execute_reply.started":"2023-03-23T10:38:49.928385Z","shell.execute_reply":"2023-03-23T10:38:50.184995Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='CHRONIC DISEASE'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('FATIGUE ')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:50.187712Z","iopub.execute_input":"2023-03-23T10:38:50.188857Z","iopub.status.idle":"2023-03-23T10:38:50.453498Z","shell.execute_reply.started":"2023-03-23T10:38:50.188805Z","shell.execute_reply":"2023-03-23T10:38:50.451766Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='FATIGUE '>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAp8AAAG7CAYAAACFPf61AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAmCElEQVR4nO3de3SU9Z348c8QLlEx4QASUCOgokVZbRuqEou3ahTFlV1bWF1FKuzKwcIiXpGuIqcttV0RbYXqEmTdaovHey1rTS0oittVBNeubL0b1KQUPCQICgrz+8Mf2Y4Jl4n4DSGv1zlzTuc7zzPzGdLOefeZeWYy2Ww2GwAAkEC7lh4AAIC2Q3wCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAk076lB9gZW7Zsiffeey/23XffyGQyLT0OAACfkc1mY926dbH//vtHu3bbPr7ZKuLzvffei9LS0pYeAwCAHVi5cmUceOCB27y9VcTnvvvuGxGfPpmioqIWngYAgM+qr6+P0tLShm7bllYRn1vfai8qKhKfAAC7sR19RNIJRwAAJCM+AQBIRnwCAJBMq/jM587avHlzfPzxxy09Bnnq0KFDFBQUtPQYAEACe0R8ZrPZqK2tjbVr17b0KDRTly5domfPnr7HFQD2cHtEfG4Nzx49esTee+8tYFqRbDYbGzZsiFWrVkVERK9evVp4IgDgi9Tq43Pz5s0N4dmtW7eWHodm2GuvvSIiYtWqVdGjRw9vwQPAHqzVn3C09TOee++9dwtPwuex9e/nM7sAsGdr9fG5lbfaWzd/PwBoG/aY+AQAYPcnPgEASGaPj89Ro0bFsGHDGq2fdNJJMXHixEbrDz30UM5bwPPmzYtMJhNnnHFGznZr166NTCYTixYtyllfuHBhDB06NPbbb78oLCyMQw45JEaMGBFPPfXUTs+czWbjjjvuiGOPPTY6d+4cXbp0iYEDB8bMmTNjw4YNOdu+88470bFjx/jSl77U5H1lMpkoLCyMt99+O2d92LBhMWrUqJy12traGD9+fBx88MHRqVOnKC0tjbPPPjueeOKJhm369OkTmUym0eWHP/xhRES89dZbOevFxcVx3HHHxa9+9audfv4AwJ5rj4/PXaF9+/bxxBNPxMKFC7e73axZs+Ib3/hGdOvWLebPnx8rVqyIf//3f4/y8vK47LLLdvrxLrzwwpg4cWKcc845sXDhwli+fHn88z//czz88MPx+OOP52w7b968GD58eGzYsCGeeeaZJu8vk8nEddddt93HfOutt6KsrCx+97vfxY9+9KN46aWX4rHHHouTTz45Lr300pxtp02bFjU1NTmX8ePH52zz29/+NmpqauL3v/99HHPMMXHuuefGH/7wh53+NwAA9kyt/quWUthnn31i+PDhcc0118Tvf//7Jreprq6OiRMnxsSJE2PGjBkN63379o3y8vKYMGHCTj3WvffeG3fffXc89NBDcc455zSs9+nTJ/76r/866uvrG9ay2WzceeedMWvWrDjwwAOjsrIyjj/++Eb3OX78+LjpppviiiuuiL/6q79q8nHHjRsXmUwm/uu//iv22WefhvUjjzwyLr744pxt99133+jZs+d2n0e3bt2iZ8+e0bNnz/j+978fP/nJT2LhwoUxYMCAnfp3AAD2TI587qSpU6fGSy+9FPfdd1+Tt99///3x8ccfx1VXXdXk7Tt7Nvfdd98dhx9+eE54/uV9FBcXN1xfuHBhbNiwIU499dS48MIL4957741169Y12q+8vDyGDh0akydPbvIx33///Xjsscfi0ksvzQnPrbp06bJTszfl448/jn/913+NiE9/RhMAaNsc+dxJ+++/f/zTP/1TTJkypcnPkL7yyitRVFSUc0Tw/vvvj4suuqjh+rPPPrvNI49bvfrqq3H44Yfv1EyVlZXxd3/3d1FQUBBHHnlkHHrooTF//vwYM2ZMo22nT58eRx11VCxevDgGDx6cc9trr70W2Wx2m58b/ayrr746vvvd7+asPfroo3HSSSc1XC8vL4927drFhx9+GFu2bIk+ffrE8OHDd+r+AdjFphbveBtyTa1r6Qn2WI585uHqq6+OP//5zzF37twmb//s0c3TTz89li9fHr/+9a9j/fr1sXnz5h0+Rjab3amjpGvXro0HHnggLrjggoa1Cy64YJuzHXHEETFy5Mi4+uqrm3zMpubfliuvvDKWL1+eczn22GNztpk/f34sW7YsHnnkkTj00ENjzpw50bVr1526fwBgz9Vmj3wWFRVFXV3j/1ezdu3aKCoqanKfLl26xOTJk+OGG26IoUOH5tzWr1+/qKuri9ra2oajn507d45DDz002rff+X/mww47LFasWLHD7e6555746KOPcqIvm83Gli1b4uWXX44jjjii0T433HBDHHbYYfHQQw81mj2TycSKFSuaPKr7Wd27d49DDz10u9uUlpZGv379ol+/ftG5c+c499xz4+WXX44ePXrs8P4BgD1Xmz3y+aUvfSmef/75RuvPPffcdt/2Hj9+fLRr1y5uueWWnPVvfvOb0aFDh7jxxhs/11znn39+vPLKK/Hwww83ui2bzTYEc2VlZVx++eU5Rx9ffPHFOPnkk7d59LO0tDS+853vxLXXXptzFLZr165x+umnx2233Rbr169vtN/atWs/13M68cQTY8CAAfH973//c90PAND6tYn4rKura/Q28dChQ+P111+PSy+9NF588cV45ZVX4rbbbovKysq48sort3lfhYWFccMNN8Stt96as37QQQfFTTfdFLfccktcdNFFsXDhwnjrrbfihRdeaNi2oKBgh7MOHz48RowYEeedd15Mnz49nn/++Xj77bfj0UcfjVNPPbXhq5deeOGFGDNmTAwYMCDnct5558Vdd921zd9Inzx5crz33nvx29/+Nmd91qxZsXnz5jjmmGPi/vvvj1dffTVWrFgRt956awwaNChn23Xr1kVtbW3O5S/Pwm/K5ZdfHrfffnu8++67O/w3AAD2XG0iPhctWhRf+cpXci5z586NxYsXx+uvvx4VFRXxta99LebNmxfz5s2Lb33rW9u9v4suuigOPvjgRuvjx4+Pxx9/PP785z/HN7/5zejXr1+ceeaZ8eabb8Zjjz22w5ONIj793OU999wTM2bMiAcffDBOPPHEOOqoo2Lq1KlxzjnnxOmnnx6VlZVxxBFHNHmC0LBhw+L999/f5pe6d+3aNa6++ur46KOPctb79u0bL7zwQpx88slx+eWXx4ABA+K0006LJ554ImbPnp2z7XXXXRe9evXKuWzrLP+thg4dGn369HH0EwDauEx269kmu7H6+vooLi6Ourq6Rp/H/Oijj+LNN9+Mvn37RmFhYQtNyOfl7wjwBXK2e/6c7Z637fXaX2oTRz4BANg9iM/EhgwZEp07d27y8oMf/KClxwMA+EK12a9aailz5syJDz/8sMnbfA8mALCnE5+JHXDAAS09AgBAi/G2OwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZX7W0i/S55tdJH++tH57VrP1mzZoVP/7xj6OmpiaOPPLImDlzZgwePHgXTwcA0DRHPtuQ+fPnx8SJE2PKlCmxbNmyGDx4cAwZMiSqq6tbejQAoI0Qn23IjBkzYvTo0TFmzJjo379/zJw5M0pLS2P27NktPRoA0EaIzzZi06ZNsXTp0qioqMhZr6ioiCVLlrTQVABAWyM+24jVq1fH5s2bo6SkJGe9pKQkamtrW2gqAKCtEZ9tTCaTybmezWYbrQEAfFHEZxvRvXv3KCgoaHSUc9WqVY2OhgIAfFHEZxvRsWPHKCsri6qqqpz1qqqqKC8vb6GpAIC2xvd8tiGTJk2KCy+8MAYOHBiDBg2KO+64I6qrq2Ps2LEtPRoA0EaIz12kuV/6ntKIESNizZo1MW3atKipqYkBAwbEggULonfv3i09GgDQRojPNmbcuHExbty4lh4DAGijfOYTAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGSaFZ+zZs2Kvn37RmFhYZSVlcXixYu3u/3dd98dRx99dOy9997Rq1ev+Pa3vx1r1qxp1sAAALReef+85vz582PixIkxa9asOP744+P222+PIUOGxMsvvxwHHXRQo+2ffvrpGDlyZNx8881x9tlnx7vvvhtjx46NMWPGxIMPPrhLnsRuYWpx4sery3uXp556Kn784x/H0qVLo6amJh588MEYNmzYrp8NAGAb8j7yOWPGjBg9enSMGTMm+vfvHzNnzozS0tKYPXt2k9v/53/+Z/Tp0ycmTJgQffv2ja9//etxySWXxPPPP/+5hyc/69evj6OPPjp++tOftvQoAEAblVd8btq0KZYuXRoVFRU56xUVFbFkyZIm9ykvL4933nknFixYENlsNv70pz/FfffdF2eddVbzp6ZZhgwZEt/73vfib//2b1t6FACgjcorPlevXh2bN2+OkpKSnPWSkpKora1tcp/y8vK4++67Y8SIEdGxY8fo2bNndOnSJX7yk59s83E2btwY9fX1ORcAAFq/Zp1wlMlkcq5ns9lGa1u9/PLLMWHChLjuuuti6dKl8dhjj8Wbb74ZY8eO3eb9T58+PYqLixsupaWlzRkTAIDdTF7x2b179ygoKGh0lHPVqlWNjoZuNX369Dj++OPjyiuvjKOOOipOP/30mDVrVsydOzdqamqa3Gfy5MlRV1fXcFm5cmU+YwIAsJvKKz47duwYZWVlUVVVlbNeVVUV5eXlTe6zYcOGaNcu92EKCgoi4tMjpk3p1KlTFBUV5VwAAGj98n7bfdKkSTFnzpyYO3durFixIi677LKorq5ueBt98uTJMXLkyIbtzz777HjggQdi9uzZ8cYbb8QzzzwTEyZMiGOOOSb233//XfdMAADY7eX9PZ8jRoyINWvWxLRp06KmpiYGDBgQCxYsiN69e0dERE1NTVRXVzdsP2rUqFi3bl389Kc/jcsvvzy6dOkSp5xyStx444277lmwUz744IN47bXXGq6/+eabsXz58ujatWuT39EKALCrZbLbeu97N1JfXx/FxcVRV1fX6C34jz76KN58882GX1xi2xYtWhQnn3xyo/WLLroo5s2bl36gv+DvCPAFSv1DKHuCZvyYS1u3vV77S3kf+aT1Oumkk7b5OVv+ghfp/HmRBmAnNeurlgAAoDnEJwAAyYhPAACSEZ8AACSzx8SnE2laN38/AGgbWn18dujQISI+/SUlWq+tf7+tf08AYM/U6r9qqaCgILp06RKrVq2KiIi99947MplMC0/Fzspms7Fhw4ZYtWpVdOnSpeGnVwGAPVOrj8+IiJ49e0ZENAQorU+XLl0a/o4AwJ5rj4jPTCYTvXr1ih49esTHH3/c0uOQpw4dOjjiCQBtxB4Rn1sVFBSIGACA3VirP+EIAIDWQ3wCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgmWbF56xZs6Jv375RWFgYZWVlsXjx4u1uv3HjxpgyZUr07t07OnXqFIccckjMnTu3WQMDANB6tc93h/nz58fEiRNj1qxZcfzxx8ftt98eQ4YMiZdffjkOOuigJvcZPnx4/OlPf4rKyso49NBDY9WqVfHJJ5987uEBAGhd8o7PGTNmxOjRo2PMmDERETFz5sz4zW9+E7Nnz47p06c32v6xxx6LJ598Mt54443o2rVrRET06dPn800NAECrlNfb7ps2bYqlS5dGRUVFznpFRUUsWbKkyX0eeeSRGDhwYPzoRz+KAw44IA477LC44oor4sMPP2z+1AAAtEp5HflcvXp1bN68OUpKSnLWS0pKora2tsl93njjjXj66aejsLAwHnzwwVi9enWMGzcu3n///W1+7nPjxo2xcePGhuv19fX5jAkAwG6qWSccZTKZnOvZbLbR2lZbtmyJTCYTd999dxxzzDFx5plnxowZM2LevHnbPPo5ffr0KC4ubriUlpY2Z0wAAHYzecVn9+7do6CgoNFRzlWrVjU6GrpVr1694oADDoji4uKGtf79+0c2m4133nmnyX0mT54cdXV1DZeVK1fmMyYAALupvOKzY8eOUVZWFlVVVTnrVVVVUV5e3uQ+xx9/fLz33nvxwQcfNKy98sor0a5duzjwwAOb3KdTp05RVFSUcwEAoPXL+233SZMmxZw5c2Lu3LmxYsWKuOyyy6K6ujrGjh0bEZ8etRw5cmTD9ueff35069Ytvv3tb8fLL78cTz31VFx55ZVx8cUXx1577bXrngkAALu9vL9qacSIEbFmzZqYNm1a1NTUxIABA2LBggXRu3fviIioqamJ6urqhu07d+4cVVVVMX78+Bg4cGB069Ythg8fHt/73vd23bMAAKBVyGSz2WxLD7Ej9fX1UVxcHHV1dd6C54s3tXjH25Bral1LTwBsj9e1/Hldy9vO9prfdgcAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIplnxOWvWrOjbt28UFhZGWVlZLF68eKf2e+aZZ6J9+/bx5S9/uTkPCwBAK5d3fM6fPz8mTpwYU6ZMiWXLlsXgwYNjyJAhUV1dvd396urqYuTIkfGNb3yj2cMCANC65R2fM2bMiNGjR8eYMWOif//+MXPmzCgtLY3Zs2dvd79LLrkkzj///Bg0aFCzhwUAoHXLKz43bdoUS5cujYqKipz1ioqKWLJkyTb3u/POO+P111+P66+/fqceZ+PGjVFfX59zAQCg9csrPlevXh2bN2+OkpKSnPWSkpKora1tcp9XX301rrnmmrj77rujffv2O/U406dPj+Li4oZLaWlpPmMCALCbatYJR5lMJud6NptttBYRsXnz5jj//PPjhhtuiMMOO2yn73/y5MlRV1fXcFm5cmVzxgQAYDezc4ci/7/u3btHQUFBo6Ocq1atanQ0NCJi3bp18fzzz8eyZcviO9/5TkREbNmyJbLZbLRv3z4ef/zxOOWUUxrt16lTp+jUqVM+owEA0ArkdeSzY8eOUVZWFlVVVTnrVVVVUV5e3mj7oqKieOmll2L58uUNl7Fjx8bhhx8ey5cvj2OPPfbzTQ8AQKuS15HPiIhJkybFhRdeGAMHDoxBgwbFHXfcEdXV1TF27NiI+PQt83fffTfuuuuuaNeuXQwYMCBn/x49ekRhYWGjdQAA9nx5x+eIESNizZo1MW3atKipqYkBAwbEggULonfv3hERUVNTs8Pv/AQAoG3KZLPZbEsPsSP19fVRXFwcdXV1UVRU1NLjsKebWtzSE7Q+U+taegJge7yu5c/rWt52ttf8tjsAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIpn1LD8AXp881v27pEVqltwpbegIA2HM58gkAQDLNis9Zs2ZF3759o7CwMMrKymLx4sXb3PaBBx6I0047Lfbbb78oKiqKQYMGxW9+85tmDwwAQOuVd3zOnz8/Jk6cGFOmTIlly5bF4MGDY8iQIVFdXd3k9k899VScdtppsWDBgli6dGmcfPLJcfbZZ8eyZcs+9/AAALQumWw2m81nh2OPPTa++tWvxuzZsxvW+vfvH8OGDYvp06fv1H0ceeSRMWLEiLjuuut2avv6+vooLi6Ourq6KCoqymfcNs1nPpvnrcLzW3qE1mdqXUtPAGzP1OKWnqD18bqWt53ttbyOfG7atCmWLl0aFRUVOesVFRWxZMmSnbqPLVu2xLp166Jr1675PDQAAHuAvM52X716dWzevDlKSkpy1ktKSqK2tnan7uOmm26K9evXx/Dhw7e5zcaNG2Pjxo0N1+vr6/MZEwCA3VSzTjjKZDI517PZbKO1pvziF7+IqVOnxvz586NHjx7b3G769OlRXFzccCktLW3OmAAA7Gbyis/u3btHQUFBo6Ocq1atanQ09LPmz58fo0ePjnvvvTdOPfXU7W47efLkqKura7isXLkynzEBANhN5RWfHTt2jLKysqiqqspZr6qqivLy8m3u94tf/CJGjRoV99xzT5x11lk7fJxOnTpFUVFRzgUAgNYv7184mjRpUlx44YUxcODAGDRoUNxxxx1RXV0dY8eOjYhPj1q+++67cdddd0XEp+E5cuTIuOWWW+K4445rOGq61157RXGxs+8AANqSvONzxIgRsWbNmpg2bVrU1NTEgAEDYsGCBdG7d++IiKipqcn5zs/bb789Pvnkk7j00kvj0ksvbVi/6KKLYt68eZ//GQAA0Go067fdx40bF+PGjWvyts8G5aJFi5rzEAAA7IH8tjsAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACTTvqUHAICd1eeaX7f0CK3SW4UtPQH8H0c+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgGfEJAEAy4hMAgGTEJwAAyYhPAACSEZ8AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLiEwCAZMQnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEhGfAIAkIz4BAAgmWbF56xZs6Jv375RWFgYZWVlsXjx4u1u/+STT0ZZWVkUFhbGwQcfHD/72c+aNSwAAK1b3vE5f/78mDhxYkyZMiWWLVsWgwcPjiFDhkR1dXWT27/55ptx5plnxuDBg2PZsmVx7bXXxoQJE+L+++//3MMDANC65B2fM2bMiNGjR8eYMWOif//+MXPmzCgtLY3Zs2c3uf3PfvazOOigg2LmzJnRv3//GDNmTFx88cXxL//yL597eAAAWpf2+Wy8adOmWLp0aVxzzTU56xUVFbFkyZIm93n22WejoqIiZ+3000+PysrK+Pjjj6NDhw6N9tm4cWNs3Lix4XpdXV1ERNTX1+czbpu3ZeOGlh6hVarPZFt6hNbH/zZJxOta83hdawava3nb2mnZ7Pb/+5ZXfK5evTo2b94cJSUlOeslJSVRW1vb5D61tbVNbv/JJ5/E6tWro1evXo32mT59etxwww2N1ktLS/MZF5qluKUHaI1+6F8Ndmf+F9oMXteabd26dVFcvO1/v7zic6tMJpNzPZvNNlrb0fZNrW81efLkmDRpUsP1LVu2xPvvvx/dunXb7uPA51VfXx+lpaWxcuXKKCoqaulxAD43r2ukks1mY926dbH//vtvd7u84rN79+5RUFDQ6CjnqlWrGh3d3Kpnz55Nbt++ffvo1q1bk/t06tQpOnXqlLPWpUuXfEaFz6WoqMiLNLBH8bpGCts74rlVXiccdezYMcrKyqKqqipnvaqqKsrLy5vcZ9CgQY22f/zxx2PgwIFNft4TAIA9V95nu0+aNCnmzJkTc+fOjRUrVsRll10W1dXVMXbs2Ij49C3zkSNHNmw/duzYePvtt2PSpEmxYsWKmDt3blRWVsYVV1yx654FAACtQt6f+RwxYkSsWbMmpk2bFjU1NTFgwIBYsGBB9O7dOyIiampqcr7zs2/fvrFgwYK47LLL4rbbbov9998/br311jj33HN33bOAXaRTp05x/fXXN/rYB0Br5XWN3U0mu6Pz4QEAYBfx2+4AACQjPgEASEZ8AgCQjPgEACAZ8QkAQDLN+nlN2FO88847MXv27FiyZEnU1tZGJpOJkpKSKC8vj7Fjx0ZpaWlLjwgAexRftUSb9fTTT8eQIUOitLQ0KioqoqSkJLLZbKxatSqqqqpi5cqV8R//8R9x/PHHt/SoALvEypUr4/rrr4+5c+e29Ci0YeKTNutrX/tafP3rX4+bb765ydsvu+yyePrpp+O5555LPBnAF+PFF1+Mr371q7F58+aWHoU2THzSZu21116xfPnyOPzww5u8/X//93/jK1/5Snz44YeJJwNonkceeWS7t7/xxhtx+eWXi09alM980mb16tUrlixZss34fPbZZ6NXr16JpwJovmHDhkUmk4ntHVfKZDIJJ4LGxCdt1hVXXBFjx46NpUuXxmmnnRYlJSWRyWSitrY2qqqqYs6cOTFz5syWHhNgp/Xq1Stuu+22GDZsWJO3L1++PMrKytIOBZ8hPmmzxo0bF926dYubb745br/99oa3oQoKCqKsrCzuuuuuGD58eAtPCbDzysrK4oUXXthmfO7oqCik4DOfEBEff/xxrF69OiIiunfvHh06dGjhiQDyt3jx4li/fn2cccYZTd6+fv36eP755+PEE09MPBn8H/EJAEAyfuEIAIBkxCcAAMmITwAAkhGfAAAkIz6BNm/UqFGRyWQaXV577bWGbX7wgx9EQUFB/PCHP2xY69OnT5P7bb2cdNJJDdt99jtjly1bFiNGjIhevXpFp06donfv3jF06ND41a9+1fBVOIsWLYpMJhNr165tNPOXv/zlmDp16g5n+ct5AXYHvucTICLOOOOMuPPOO3PW9ttvv4b/fOedd8ZVV10Vc+fOjWuuuSYiIp577rmG74ddsmRJnHvuufHHP/4xioqKIiKiY8eOTT7Www8/HMOHD49TTz01/u3f/i0OOeSQWLNmTfz3f/93fPe7343BgwdHly5d8n4O06ZNi3/4h3/IWdt3333zvh+AL5L4BIiITp06Rc+ePZu87cknn4wPP/wwpk2bFnfddVc89dRTccIJJ+TEadeuXSMiokePHtsNx/Xr18fo0aPjrLPOigceeKBh/ZBDDoljjjkmxowZ0+wvAd933323+RwAdhfedgfYgcrKyjjvvPOiQ4cOcd5550VlZWWz7+vxxx+PNWvWxFVXXbXNbfz2NrAnE58AEfHoo49G586dGy7f+ta3IiKivr4+7r///rjgggsiIuKCCy6I++67L+rr65v1OK+88kpERBx++OENa88991zOYz/66KPNuu+rr7465346d+4cixYtatZ9AXxRvO0OEBEnn3xyzJ49u+H6PvvsExER99xzTxx88MFx9NFHR8SnJ/ocfPDB8ctf/jL+8R//cZc89lFHHRXLly+PiIh+/frFJ5980qz7ufLKK2PUqFE5awcccMDnnA5g1xKfAPFpbB566KGN1ufOnRv/8z//E+3b/9/L5ZYtW6KysrJZ8dmvX7+IiPjjH/8Yxx13XER8+nnTph5764lLdXV1jT5Hunbt2iguLs5Z6969e5P3A7A7EZ8A2/DSSy/F888/H4sWLWo4oSji0/A74YQT4g9/+EMMGDAgr/usqKiIrl27xo033hgPPvjgdrft169ftGvXLp577rno3bt3w3pNTU28++67OW/dA7QW4hNgGyorK+OYY46JE044odFtgwYNisrKyrj55pvzus/OnTvHnDlzYsSIEXHWWWfFhAkTol+/fvHBBx/EY489FhERBQUFEfHp2euXXHJJXH755dG+ffs4+uij47333ospU6ZE//79o6KiIue+161bF7W1tTlre++9d8MRVIDdgROOAJqwadOm+PnPfx7nnntuk7efe+658fOf/zw2bdqU933/zd/8TSxZsiT23nvvGDlyZBx++OFxyimnxO9+97v45S9/GUOHDm3Y9uabb44xY8bEtddeG0ceeWT8/d//ffTt2zcef/zxnI8CRERcd9110atXr5zL9s6qB2gJmWxzv1AOAADy5MgnAADJiE8AAJIRnwAAJCM+AQBIRnwCAJCM+AQAIBnxCQBAMuITAIBkxCcAAMmITwAAkhGfAAAkIz4BAEjm/wFkAK5YmK3DawAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('ALLERGY ')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:50.455080Z","iopub.execute_input":"2023-03-23T10:38:50.456373Z","iopub.status.idle":"2023-03-23T10:38:50.680510Z","shell.execute_reply.started":"2023-03-23T10:38:50.456317Z","shell.execute_reply":"2023-03-23T10:38:50.677959Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='ALLERGY '>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('WHEEZING')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:50.682516Z","iopub.execute_input":"2023-03-23T10:38:50.683093Z","iopub.status.idle":"2023-03-23T10:38:50.926690Z","shell.execute_reply.started":"2023-03-23T10:38:50.683041Z","shell.execute_reply":"2023-03-23T10:38:50.925007Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='WHEEZING'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('ALCOHOL CONSUMING')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:50.928288Z","iopub.execute_input":"2023-03-23T10:38:50.928828Z","iopub.status.idle":"2023-03-23T10:38:51.383822Z","shell.execute_reply.started":"2023-03-23T10:38:50.928777Z","shell.execute_reply":"2023-03-23T10:38:51.382144Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='ALCOHOL CONSUMING'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('COUGHING')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:51.385998Z","iopub.execute_input":"2023-03-23T10:38:51.386884Z","iopub.status.idle":"2023-03-23T10:38:51.638470Z","shell.execute_reply.started":"2023-03-23T10:38:51.386825Z","shell.execute_reply":"2023-03-23T10:38:51.636894Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='COUGHING'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('SHORTNESS OF BREATH')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:51.640749Z","iopub.execute_input":"2023-03-23T10:38:51.641187Z","iopub.status.idle":"2023-03-23T10:38:51.902671Z","shell.execute_reply.started":"2023-03-23T10:38:51.641141Z","shell.execute_reply":"2023-03-23T10:38:51.901135Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='SHORTNESS OF BREATH'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 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cnNzc3ZJAADgN+Ty4VOSAgMDJckWQOF6/Pz8bN8jAACou+pE+LRYLAoKClKLFi106dIlZ5cDBzVs2JARTwAA6ok6ET7Lubm5EWIAAABuYC5/wxEAAABcB+ETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGBMrcJnamqqQkND5eXlpYiICG3btq3a/iUlJUpOTlbr1q3l6emptm3bKj09vVYFAwAAwHW5O7pAZmamEhISlJqaqt69e+vNN99UdHS0Dhw4oFatWlW6zLBhw/TTTz8pLS1N7dq1U35+vi5fvnzNxQMAAMC1OBw+582bp/Hjxys2NlaStGDBAm3YsEGLFy9WSkpKhf7r16/XZ599pqNHj6pZs2aSpJCQkGurGgAAAC7JodPuFy9e1J49exQVFWXXHhUVpR07dlS6zJo1a9StWze9/PLLuvnmmxUWFqann35aFy5cqH3VAAAAcEkOjXyePn1apaWlCggIsGsPCAhQXl5epcscPXpU27dvl5eXlz744AOdPn1aEyZM0NmzZ6u87rOkpEQlJSW2z0VFRY6UCQAAgBtUrW44slgsdp+tVmuFtnJlZWWyWCxasWKFevTooQcffFDz5s1TRkZGlaOfKSkp8vX1tU3BwcG1KRMAAAA3GIfCp7+/v9zc3CqMcubn51cYDS0XFBSkm2++Wb6+vra2Tp06yWq16ocffqh0maSkJBUWFtqmEydOOFImAAAAblAOhU8PDw9FREQoKyvLrj0rK0uRkZGVLtO7d2+dPHlS//73v21thw4dUoMGDXTLLbdUuoynp6d8fHzsJgAAALg+h0+7JyYmaunSpUpPT9fBgwc1efJk5eTkKC4uTtIvo5ajRo2y9X/sscfUvHlzjR07VgcOHNDWrVs1depUjRs3To0aNbp+ewIAAIAbnsOPWoqJidGZM2c0Z84c5ebmKjw8XOvWrVPr1q0lSbm5ucrJybH19/b2VlZWluLj49WtWzc1b95cw4YN0/PPP3/99gIAAAAuwWK1Wq3OLuJqioqK5Ovrq8LCQk7B47c3y/fqfWBvVqGzKwBQHX7XHMfvmsNqmtd4tzsAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMqVX4TE1NVWhoqLy8vBQREaFt27bVaLnPP/9c7u7uuv3222uzWQAAALg4h8NnZmamEhISlJycrH379qlPnz6Kjo5WTk5OtcsVFhZq1KhRuu+++2pdLAAAAFybw+Fz3rx5Gj9+vGJjY9WpUyctWLBAwcHBWrx4cbXLPfHEE3rsscfUq1evWhcLAAAA1+ZQ+Lx48aL27NmjqKgou/aoqCjt2LGjyuWWLVum7777TjNnzqzRdkpKSlRUVGQ3AQAAwPU5FD5Pnz6t0tJSBQQE2LUHBAQoLy+v0mUOHz6s6dOna8WKFXJ3d6/RdlJSUuTr62ubgoODHSkTAAAAN6ha3XBksVjsPlut1gptklRaWqrHHntMs2fPVlhYWI3Xn5SUpMLCQtt04sSJ2pQJAACAG0zNhiL/P39/f7m5uVUY5czPz68wGipJxcXF2r17t/bt26cnn3xSklRWViar1Sp3d3dt3LhR9957b4XlPD095enp6UhpAAAAcAEOjXx6eHgoIiJCWVlZdu1ZWVmKjIys0N/Hx0dfffWVsrOzbVNcXJw6dOig7Oxs9ezZ89qqBwAAgEtxaORTkhITEzVy5Eh169ZNvXr10pIlS5STk6O4uDhJv5wy//HHH7V8+XI1aNBA4eHhdsu3aNFCXl5eFdoBAABQ9zkcPmNiYnTmzBnNmTNHubm5Cg8P17p169S6dWtJUm5u7lWf+QkAAID6yWK1Wq3OLuJqioqK5Ovrq8LCQvn4+Di7HNR1s3ydXYHrmVXo7AoAVIffNcfxu+awmuY13u0OAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwplbhMzU1VaGhofLy8lJERIS2bdtWZd/3339f/fv310033SQfHx/16tVLGzZsqHXBAAAAcF0Oh8/MzEwlJCQoOTlZ+/btU58+fRQdHa2cnJxK+2/dulX9+/fXunXrtGfPHvXr10+DBg3Svn37rrl4AAAAuBaL1Wq1OrJAz549dccdd2jx4sW2tk6dOmnIkCFKSUmp0Tq6dOmimJgYzZgxo0b9i4qK5Ovrq8LCQvn4+DhSLuC4Wb7OrsD1zCp0dgUAqsPvmuP4XXNYTfOaQyOfFy9e1J49exQVFWXXHhUVpR07dtRoHWVlZSouLlazZs2q7FNSUqKioiK7CQAAAK7PofB5+vRplZaWKiAgwK49ICBAeXl5NVrHq6++qnPnzmnYsGFV9klJSZGvr69tCg4OdqRMAAAA3KBqdcORxWKx+2y1Wiu0Veavf/2rZs2apczMTLVo0aLKfklJSSosLLRNJ06cqE2ZAAAAuMG4O9LZ399fbm5uFUY58/PzK4yG/lpmZqbGjx+vVatW6f7776+2r6enpzw9PR0pDQAAAC7AoZFPDw8PRUREKCsry649KytLkZGRVS7317/+VWPGjNHKlSv10EMP1a5SAAAAuDyHRj4lKTExUSNHjlS3bt3Uq1cvLVmyRDk5OYqLi5P0yynzH3/8UcuXL5f0S/AcNWqUXnvtNd155522UdNGjRrJ15e77wAAAOoTh8NnTEyMzpw5ozlz5ig3N1fh4eFat26dWrduLUnKzc21e+bnm2++qcuXL2vixImaOHGirX306NHKyMi49j0AAACAy3D4OZ/OwHM+YRTPw3Mcz8MDbmz8rjmO3zWH/SbP+QQAAACuBeETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgjMMPmYfrCJn+sbNLcEnHvZxdAQAAdRcjnwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADDG3dkFAABQUyHTP3Z2CS7puJezKwD+DyOfAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCmVuEzNTVVoaGh8vLyUkREhLZt21Zt/88++0wRERHy8vJSmzZt9MYbb9SqWAAAALg2h8NnZmamEhISlJycrH379qlPnz6Kjo5WTk5Opf2PHTumBx98UH369NG+ffv07LPPatKkSVq9evU1Fw8AAADX4nD4nDdvnsaPH6/Y2Fh16tRJCxYsUHBwsBYvXlxp/zfeeEOtWrXSggUL1KlTJ8XGxmrcuHH605/+dM3FAwAAwLW4O9L54sWL2rNnj6ZPn27XHhUVpR07dlS6zM6dOxUVFWXXNmDAAKWlpenSpUtq2LBhhWVKSkpUUlJi+1xYWChJKioqcqTceq+s5LyzS3BJRRars0twPfy3CUP4Xasdftdqgd81h5XnNKu1+r83h8Ln6dOnVVpaqoCAALv2gIAA5eXlVbpMXl5epf0vX76s06dPKygoqMIyKSkpmj17doX24OBgR8oFasXX2QW4ohc5asCNjP9Ca4HftVorLi6Wr2/Vx8+h8FnOYrHYfbZarRXarta/svZySUlJSkxMtH0uKyvT2bNn1bx582q3A1yroqIiBQcH68SJE/Lx8XF2OQBwzfhdgylWq1XFxcVq2bJltf0cCp/+/v5yc3OrMMqZn59fYXSzXGBgYKX93d3d1bx580qX8fT0lKenp12bn5+fI6UC18THx4cfaQB1Cr9rMKG6Ec9yDt1w5OHhoYiICGVlZdm1Z2VlKTIystJlevXqVaH/xo0b1a1bt0qv9wQAAEDd5fDd7omJiVq6dKnS09N18OBBTZ48WTk5OYqLi5P0yynzUaNG2frHxcXp+++/V2Jiog4ePKj09HSlpaXp6aefvn57AQAAAJfg8DWfMTExOnPmjObMmaPc3FyFh4dr3bp1at26tSQpNzfX7pmfoaGhWrdunSZPnqxFixapZcuWWrhwoYYOHXr99gK4Tjw9PTVz5swKl30AgKvidw03Gov1avfDAwAAANcJ73YHAACAMYRPAAAAGEP4BAAAgDGETwAAABhD+AQAAIAxtXq9JlBX/PDDD1q8eLF27NihvLw8WSwWBQQEKDIyUnFxcQoODnZ2iQAA1Ck8agn11vbt2xUdHa3g4GBFRUUpICBAVqtV+fn5ysrK0okTJ/TJJ5+od+/ezi4VAK6LEydOaObMmUpPT3d2KajHCJ+ot7p376677rpL8+fPr3T+5MmTtX37du3atctwZQDw29i/f7/uuOMOlZaWOrsU1GOET9RbjRo1UnZ2tjp06FDp/H/+85/63e9+pwsXLhiuDABqZ82aNdXOP3r0qKZMmUL4hFNxzSfqraCgIO3YsaPK8Llz504FBQUZrgoAam/IkCGyWCyqblzJYrEYrAioiPCJeuvpp59WXFyc9uzZo/79+ysgIEAWi0V5eXnKysrS0qVLtWDBAmeXCQA1FhQUpEWLFmnIkCGVzs/OzlZERITZooBfIXyi3powYYKaN2+u+fPn680337SdhnJzc1NERISWL1+uYcOGOblKAKi5iIgI7d27t8rwebVRUcAErvkEJF26dEmnT5+WJPn7+6thw4ZOrggAHLdt2zadO3dODzzwQKXzz507p927d+uee+4xXBnwfwifAAAAMIY3HAEAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AnBIfn6+nnjiCbVq1Uqenp4KDAzUgAEDtHPnTlufkJCQSp+ROmvWLN1+++12bWfPnlVCQoJCQkLk4eGhoKAgjR07Vjk5OXb9xowZI4vFIovFInd3d7Vq1Ur/9V//pX/961/asmWLbV5VU0ZGhq1feHh4hTe8+Pn5KSMjw24fKlvPiy++aOuzevVq9ezZU76+vmratKm6dOmiKVOm2OaXlpYqJSVFHTt2VKNGjdSsWTPdeeedWrZsWbXHuLS0VPPnz1fXrl3l5eUlPz8/RUdH6/PPP7frl5GRUWmNS5curXLdV/YrP46JiYkqKSmpcr0BAQEaNGiQvvnmmyq/kyunyu60njt3rtzc3OyOX1XHuHzq27evrV9N/54A3Ph4zicAhwwdOlSXLl3SW2+9pTZt2uinn37Spk2bdPbsWYfXdfbsWd15553y8PBQamqqwsPDdfz4cT333HPq3r27du7cqTZt2tj6P/DAA1q2bJkuX76sAwcOaNy4cSooKNBbb72l3NxcW7+nnnpKRUVFdiHP19dXX375pSTpu+++0/LlyzV27Nhq65szZ47+8Ic/2LU1bdpUkvTpp59q+PDhmjt3rgYPHiyLxaIDBw5o06ZNtr6zZs3SkiVL9Prrr6tbt24qKirS7t279a9//avKbVqtVg0fPlyffvqpXnnlFd13330qKirSokWL1LdvX61atcruGY4+Pj769ttv7dbh6+tb7X4tW7ZMDzzwgC5duqT9+/dr7NixatKkif77v/+7wnqtVqt+/PFHPfPMM3rooYd06NAheXh42PqVfydX8vT0rHSbzzzzjNLT0zV9+nRJ0q5du2z/CNixY4eGDh2qb7/9Vj4+PpJktx0AdQfhE0CNFRQUaPv27dqyZYvtOYGtW7dWjx49arW+5ORknTx5UkeOHFFgYKAkqVWrVtqwYYPat2+viRMn6pNPPrH1Lx9plaRbbrlFMTExysjIkIeHh61dkho1aqSSkhK7tivFx8dr5syZGjFihLy8vKqsr2nTplWuY+3atbrrrrs0depUW1tYWJhdMPzoo480YcIEPfroo7a22267rZojIr377rt67733tGbNGg0aNMjWvmTJEp05c0axsbHq37+/mjRpIumXkcyqaqyKn5+fbZng4GANHjxYe/futetz5XqDgoI0efJkDR48WN9++61uvfVWW78rv5OqfPbZZ7pw4YLmzJmj5cuXa+vWrbr77rt100032fo0a9ZMktSiRQv5+fk5tD8AXAun3QHUmLe3t7y9vfXhhx/anaatjbKyMr3zzjv6/e9/XyG8NGrUSBMmTNCGDRuqHFE9evSo1q9fX6sXAiQkJOjy5ct6/fXXa1W7JAUGBuqbb77R119/XW2fv//97zp16lSN17ty5UqFhYXZBc9yU6ZM0ZkzZ5SVlVWrmitz6NAhbd68WT179qyyT0FBgVauXClJtTreaWlpGjFihBo2bKgRI0YoLS2t1vUCcH2ETwA15u7uroyMDL311lvy8/NT79699eyzz+p///d/K/SdNm2aLayWT3PnzrXNP3XqlAoKCtSpU6dKt9WpUydZrVYdOXLE1rZ27Vp5e3urUaNGatu2rQ4cOKBp06Y5vB+NGzfWzJkzlZKSosLCwir7VbYPW7ZskfTL6Gn37t116623KiQkRMOHD1d6erpdKJ83b55OnTqlwMBAde3aVXFxcXYjuZU5dOhQtcekvE+5wsJCu/pqMgo6YsQIeXt7y8vLSx06dFCXLl2UlJRk16d8vU2aNNF//Md/6J133tHgwYPVsWNHu37l38mV05Wn74uKirR69Wo9/vjjkqTHH39c7733noqKiq5a55Wu9vcEwHUQPgE4ZOjQoTp58qTWrFmjAQMGaMuWLbrjjjvsbtaRpKlTpyo7O9tuiouLq/F2yl++ZrFYbG39+vVTdna2vvzyS8XHx2vAgAGKj4+v1X6MHz9e/v7+eumll6rsU9k+lI8QNmnSRB9//LGOHDmi5557Tt7e3poyZYp69Oih8+fPS5I6d+6sr7/+Wl988YXGjh2rn376SYMGDVJsbGytai535TFp2rSpXX07duy46vLz589Xdna29u/fr7Vr1+rQoUMaOXKkXZ/y9e7Zs0dvvPGG2rZtqzfeeKPCusq/kyuniRMn2uavXLlSbdq0sV1ucPvtt6tNmzZ65513HNrna/17AnDj4JpPAA7z8vJS//791b9/f82YMUOxsbGaOXOmxowZY+vj7++vdu3a2S1Xfl2fJN10003y8/PTgQMHKt3GP//5T1ksFrVt29bW1qRJE9s6Fy5cqH79+mn27Nl2I2015e7urueff15jxozRk08+WWmfyvbh19q2bau2bdsqNjZWycnJCgsLU2Zmpu1mpgYNGqh79+7q3r27Jk+erLffflsjR45UcnKyQkNDK6wvLCysymNy8OBBSVL79u1tbQ0aNLhqjb8WGBhoW6ZDhw4qLi7WiBEj9Pzzz9var1xvx44dlZeXp5iYGG3dutVuXVd+J5VJT0/XN998I3f3//vfTVlZmdLS0vSf//mfNa75an9PAFwHI58Arlnnzp117tw5h5Zp0KCBhg0bppUrVyovL89u3oULF5SamqoBAwZUGzBmzpypP/3pTzp58mSt6n700UfVpUsXzZ49u1bL/1pISIgaN25c7bHo3LmzJFXZZ/jw4Tp8+LA++uijCvNeffVVNW/eXP37978u9ZZzc3OT9Mtxr8rkyZO1f/9+ffDBBzVe71dffaXdu3dry5YtdiOWW7du1a5du6q9XhZA3cXIJ4AaO3PmjB599FGNGzdOXbt2VdOmTbV79269/PLLevjhhx1e3wsvvKBNmzapf//+evnllxUeHq5jx47pueee06VLl7Ro0aJql+/bt6+6dOmiuXPn1vrmoRdffFEDBgyodF5xcXGFYNy4cWP5+Pho1qxZOn/+vB588EG1bt1aBQUFWrhwoS5dumQLh4888oh69+6tyMhIBQYG6tixY0pKSlJYWFiFayfLDR8+XKtWrdLo0aMrPGppzZo1WrVqle1O99oqKChQXl6eysrKdPjwYc2ZM0dhYWFVXmsq/fLopfIR7iFDhthO/ZeUlFQ4Ru7u7vL391daWpp69Oihu+++u8L6evXqpbS0NM2fP/+a9gWA62HkE0CNeXt7q2fPnpo/f77uvvtuhYeH649//KP+8Ic/1Cr8+fv764svvlC/fv30xBNPqE2bNho2bJjatGmjXbt22T3jsyqJiYn6y1/+ohMnTtRml3Tvvffq3nvv1eXLlyvMmzFjhoKCguymZ555RpJ0zz336OjRoxo1apQ6duyo6Oho5eXlaePGjerQoYMkacCAAfroo480aNAghYWFafTo0erYsaM2btxodxr6ShaLRe+++66Sk5M1f/58dezYUX369NH333+vzZs32z3KqbbGjh2roKAg3XLLLRoxYoS6dOmiTz75pMqayj311FM6ePCgVq1aZWtbv359hWN011136eLFi3r77bc1dOjQStc1dOhQvf3227p48eI17w8A12Kxll/VDwAAAPzGGPkEAACAMYRPAAAAGEP4BAAAgDGETwAAABhD+AQAAIAxhE8AAAAYQ/gEAACAMYRPAAAAGEP4BAAAgDGETwAAABhD+AQAAIAxhE8AAAAY8/8AGyRQzCH6djcAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('SWALLOWING DIFFICULTY')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:51.911397Z","iopub.execute_input":"2023-03-23T10:38:51.912853Z","iopub.status.idle":"2023-03-23T10:38:52.136214Z","shell.execute_reply.started":"2023-03-23T10:38:51.912768Z","shell.execute_reply":"2023-03-23T10:38:52.134784Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='SWALLOWING DIFFICULTY'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":"plot('CHEST PAIN')","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:52.138106Z","iopub.execute_input":"2023-03-23T10:38:52.138678Z","iopub.status.idle":"2023-03-23T10:38:52.393786Z","shell.execute_reply.started":"2023-03-23T10:38:52.138610Z","shell.execute_reply":"2023-03-23T10:38:52.392657Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='CHEST PAIN'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"**From the visualizations, it is clear that in the given dataset, the features GENDER, AGE, SMOKING and SHORTNESS OF BREATH don't have that much relationship with LUNG CANCER. So let's drop those features to make this dataset more clean.**","metadata":{}},{"cell_type":"code","source":"df_new=df.drop(columns=['GENDER','AGE', 'SMOKING', 'SHORTNESS OF BREATH'])\ndf_new","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:52.395423Z","iopub.execute_input":"2023-03-23T10:38:52.396256Z","iopub.status.idle":"2023-03-23T10:38:52.417818Z","shell.execute_reply.started":"2023-03-23T10:38:52.396206Z","shell.execute_reply":"2023-03-23T10:38:52.416138Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"     YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  CHRONIC DISEASE  FATIGUE   \\\n0                 1        1              0                0         1   \n1                 0        0              0                1         1   \n2                 0        0              1                0         1   \n3                 1        1              0                0         0   \n4                 1        0              0                0         0   \n..              ...      ...            ...              ...       ...   \n279               1        1              1                0         0   \n280               0        0              0                1         1   \n281               0        0              0                0         1   \n282               1        1              0                0         0   \n283               1        1              0                0         1   \n\n     ALLERGY   WHEEZING  ALCOHOL CONSUMING  COUGHING  SWALLOWING DIFFICULTY  \\\n0           0         1                  1         1                      1   \n1           1         0                  0         0                      1   \n2           0         1                  0         1                      0   \n3           0         0                  1         0                      1   \n4           0         1                  0         1                      0   \n..        ...       ...                ...       ...                    ...   \n279         1         1                  0         1                      1   \n280         1         0                  0         0                      0   \n281         1         0                  0         0                      0   \n282         0         0                  0         0                      1   \n283         0         1                  1         1                      1   \n\n     CHEST PAIN  LUNG_CANCER  \n0             1            1  \n1             1            1  \n2             1            0  \n3             1            0  \n4             0            0  \n..          ...          ...  \n279           0            1  \n280           0            0  \n281           1            0  \n282           1            0  \n283           1            1  \n\n[276 rows x 12 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>YELLOW_FINGERS</th>\n      <th>ANXIETY</th>\n      <th>PEER_PRESSURE</th>\n      <th>CHRONIC DISEASE</th>\n      <th>FATIGUE</th>\n      <th>ALLERGY</th>\n      <th>WHEEZING</th>\n      <th>ALCOHOL CONSUMING</th>\n      <th>COUGHING</th>\n      <th>SWALLOWING DIFFICULTY</th>\n      <th>CHEST PAIN</th>\n      <th>LUNG_CANCER</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>279</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>280</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>281</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>282</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>283</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>276 rows × 12 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"**CORRELATION**","metadata":{}},{"cell_type":"code","source":"#Finding Correlation\ncn=df_new.corr()\ncn","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:52.419881Z","iopub.execute_input":"2023-03-23T10:38:52.420286Z","iopub.status.idle":"2023-03-23T10:38:52.454216Z","shell.execute_reply.started":"2023-03-23T10:38:52.420246Z","shell.execute_reply":"2023-03-23T10:38:52.452814Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"                       YELLOW_FINGERS   ANXIETY  PEER_PRESSURE  \\\nYELLOW_FINGERS               1.000000  0.558344       0.313067   \nANXIETY                      0.558344  1.000000       0.210278   \nPEER_PRESSURE                0.313067  0.210278       1.000000   \nCHRONIC DISEASE              0.015316 -0.006938       0.042893   \nFATIGUE                     -0.099644 -0.181474       0.094661   \nALLERGY                     -0.147130 -0.159451      -0.066887   \nWHEEZING                    -0.058756 -0.174009      -0.037769   \nALCOHOL CONSUMING           -0.273643 -0.152228      -0.132603   \nCOUGHING                     0.020803 -0.218843      -0.068224   \nSWALLOWING DIFFICULTY        0.333349  0.478820       0.327764   \nCHEST PAIN                  -0.099169 -0.123182      -0.074655   \nLUNG_CANCER                  0.189192  0.144322       0.195086   \n\n                       CHRONIC DISEASE  FATIGUE   ALLERGY   WHEEZING  \\\nYELLOW_FINGERS                0.015316 -0.099644 -0.147130 -0.058756   \nANXIETY                      -0.006938 -0.181474 -0.159451 -0.174009   \nPEER_PRESSURE                 0.042893  0.094661 -0.066887 -0.037769   \nCHRONIC DISEASE               1.000000 -0.099411  0.134309 -0.040546   \nFATIGUE                      -0.099411  1.000000 -0.001841  0.152151   \nALLERGY                       0.134309 -0.001841  1.000000  0.166517   \nWHEEZING                     -0.040546  0.152151  0.166517  1.000000   \nALCOHOL CONSUMING             0.010144 -0.181573  0.378125  0.261061   \nCOUGHING                     -0.160813  0.148538  0.206367  0.353657   \nSWALLOWING DIFFICULTY         0.068263 -0.115727 -0.037581  0.108304   \nCHEST PAIN                   -0.048895  0.013757  0.245440  0.142846   \nLUNG_CANCER                   0.143692  0.160078  0.333552  0.249054   \n\n                       ALCOHOL CONSUMING  COUGHING  SWALLOWING DIFFICULTY  \\\nYELLOW_FINGERS                 -0.273643  0.020803               0.333349   \nANXIETY                        -0.152228 -0.218843               0.478820   \nPEER_PRESSURE                  -0.132603 -0.068224               0.327764   \nCHRONIC DISEASE                 0.010144 -0.160813               0.068263   \nFATIGUE                        -0.181573  0.148538              -0.115727   \nALLERGY                         0.378125  0.206367              -0.037581   \nWHEEZING                        0.261061  0.353657               0.108304   \nALCOHOL CONSUMING               1.000000  0.198023              -0.000635   \nCOUGHING                        0.198023  1.000000              -0.136885   \nSWALLOWING DIFFICULTY          -0.000635 -0.136885               1.000000   \nCHEST PAIN                      0.310767  0.077988               0.102674   \nLUNG_CANCER                     0.294422  0.253027               0.268940   \n\n                       CHEST PAIN  LUNG_CANCER  \nYELLOW_FINGERS          -0.099169     0.189192  \nANXIETY                 -0.123182     0.144322  \nPEER_PRESSURE           -0.074655     0.195086  \nCHRONIC DISEASE         -0.048895     0.143692  \nFATIGUE                  0.013757     0.160078  \nALLERGY                  0.245440     0.333552  \nWHEEZING                 0.142846     0.249054  \nALCOHOL CONSUMING        0.310767     0.294422  \nCOUGHING                 0.077988     0.253027  \nSWALLOWING DIFFICULTY    0.102674     0.268940  \nCHEST PAIN               1.000000     0.194856  \nLUNG_CANCER              0.194856     1.000000  ","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>YELLOW_FINGERS</th>\n      <th>ANXIETY</th>\n      <th>PEER_PRESSURE</th>\n      <th>CHRONIC DISEASE</th>\n      <th>FATIGUE</th>\n      <th>ALLERGY</th>\n      <th>WHEEZING</th>\n      <th>ALCOHOL CONSUMING</th>\n      <th>COUGHING</th>\n      <th>SWALLOWING DIFFICULTY</th>\n      <th>CHEST PAIN</th>\n      <th>LUNG_CANCER</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>YELLOW_FINGERS</th>\n      <td>1.000000</td>\n      <td>0.558344</td>\n      <td>0.313067</td>\n      <td>0.015316</td>\n      <td>-0.099644</td>\n      <td>-0.147130</td>\n      <td>-0.058756</td>\n      <td>-0.273643</td>\n      <td>0.020803</td>\n      <td>0.333349</td>\n      <td>-0.099169</td>\n      <td>0.189192</td>\n    </tr>\n    <tr>\n      <th>ANXIETY</th>\n      <td>0.558344</td>\n      <td>1.000000</td>\n      <td>0.210278</td>\n      <td>-0.006938</td>\n      <td>-0.181474</td>\n      <td>-0.159451</td>\n      <td>-0.174009</td>\n      <td>-0.152228</td>\n      <td>-0.218843</td>\n      <td>0.478820</td>\n      <td>-0.123182</td>\n      <td>0.144322</td>\n    </tr>\n    <tr>\n      <th>PEER_PRESSURE</th>\n      <td>0.313067</td>\n      <td>0.210278</td>\n      <td>1.000000</td>\n      <td>0.042893</td>\n      <td>0.094661</td>\n      <td>-0.066887</td>\n      <td>-0.037769</td>\n      <td>-0.132603</td>\n      <td>-0.068224</td>\n      <td>0.327764</td>\n      <td>-0.074655</td>\n      <td>0.195086</td>\n    </tr>\n    <tr>\n      <th>CHRONIC DISEASE</th>\n      <td>0.015316</td>\n      <td>-0.006938</td>\n      <td>0.042893</td>\n      <td>1.000000</td>\n      <td>-0.099411</td>\n      <td>0.134309</td>\n      <td>-0.040546</td>\n      <td>0.010144</td>\n      <td>-0.160813</td>\n      <td>0.068263</td>\n      <td>-0.048895</td>\n      <td>0.143692</td>\n    </tr>\n    <tr>\n      <th>FATIGUE</th>\n      <td>-0.099644</td>\n      <td>-0.181474</td>\n      <td>0.094661</td>\n      <td>-0.099411</td>\n      <td>1.000000</td>\n      <td>-0.001841</td>\n      <td>0.152151</td>\n      <td>-0.181573</td>\n      <td>0.148538</td>\n      <td>-0.115727</td>\n      <td>0.013757</td>\n      <td>0.160078</td>\n    </tr>\n    <tr>\n      <th>ALLERGY</th>\n      <td>-0.147130</td>\n      <td>-0.159451</td>\n      <td>-0.066887</td>\n      <td>0.134309</td>\n      <td>-0.001841</td>\n      <td>1.000000</td>\n      <td>0.166517</td>\n      <td>0.378125</td>\n      <td>0.206367</td>\n      <td>-0.037581</td>\n      <td>0.245440</td>\n      <td>0.333552</td>\n    </tr>\n    <tr>\n      <th>WHEEZING</th>\n      <td>-0.058756</td>\n      <td>-0.174009</td>\n      <td>-0.037769</td>\n      <td>-0.040546</td>\n      <td>0.152151</td>\n      <td>0.166517</td>\n      <td>1.000000</td>\n      <td>0.261061</td>\n      <td>0.353657</td>\n      <td>0.108304</td>\n      <td>0.142846</td>\n      <td>0.249054</td>\n    </tr>\n    <tr>\n      <th>ALCOHOL CONSUMING</th>\n      <td>-0.273643</td>\n      <td>-0.152228</td>\n      <td>-0.132603</td>\n      <td>0.010144</td>\n      <td>-0.181573</td>\n      <td>0.378125</td>\n      <td>0.261061</td>\n      <td>1.000000</td>\n      <td>0.198023</td>\n      <td>-0.000635</td>\n      <td>0.310767</td>\n      <td>0.294422</td>\n    </tr>\n    <tr>\n      <th>COUGHING</th>\n      <td>0.020803</td>\n      <td>-0.218843</td>\n      <td>-0.068224</td>\n      <td>-0.160813</td>\n      <td>0.148538</td>\n      <td>0.206367</td>\n      <td>0.353657</td>\n      <td>0.198023</td>\n      <td>1.000000</td>\n      <td>-0.136885</td>\n      <td>0.077988</td>\n      <td>0.253027</td>\n    </tr>\n    <tr>\n      <th>SWALLOWING DIFFICULTY</th>\n      <td>0.333349</td>\n      <td>0.478820</td>\n      <td>0.327764</td>\n      <td>0.068263</td>\n      <td>-0.115727</td>\n      <td>-0.037581</td>\n      <td>0.108304</td>\n      <td>-0.000635</td>\n      <td>-0.136885</td>\n      <td>1.000000</td>\n      <td>0.102674</td>\n      <td>0.268940</td>\n    </tr>\n    <tr>\n      <th>CHEST PAIN</th>\n      <td>-0.099169</td>\n      <td>-0.123182</td>\n      <td>-0.074655</td>\n      <td>-0.048895</td>\n      <td>0.013757</td>\n      <td>0.245440</td>\n      <td>0.142846</td>\n      <td>0.310767</td>\n      <td>0.077988</td>\n      <td>0.102674</td>\n      <td>1.000000</td>\n      <td>0.194856</td>\n    </tr>\n    <tr>\n      <th>LUNG_CANCER</th>\n      <td>0.189192</td>\n      <td>0.144322</td>\n      <td>0.195086</td>\n      <td>0.143692</td>\n      <td>0.160078</td>\n      <td>0.333552</td>\n      <td>0.249054</td>\n      <td>0.294422</td>\n      <td>0.253027</td>\n      <td>0.268940</td>\n      <td>0.194856</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#Correlation \ncmap=sns.diverging_palette(260,-10,s=50, l=75, n=6,\nas_cmap=True)\nplt.subplots(figsize=(18,18))\nsns.heatmap(cn,cmap=cmap,annot=True, square=True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:52.456711Z","iopub.execute_input":"2023-03-23T10:38:52.458004Z","iopub.status.idle":"2023-03-23T10:38:53.595322Z","shell.execute_reply.started":"2023-03-23T10:38:52.457927Z","shell.execute_reply":"2023-03-23T10:38:53.593566Z"},"trusted":true},"execution_count":32,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1800x1800 with 2 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ExhUUO0+7KzKVarLyir9sXtmYDAYtGb1fL1Vt4VKl0kaK7br8Kni42/rr63pX1PXrJ6vQoVL6c2678jNPb/erPuOCoaU0JpVyf8XipesoKLFyylfPk/ly+epxk3byMYmu04eP2RMc+L4Qb1SqaaCCxZT3ryuqlz1TeXPX0CnT2Wu6+W8ebNUu/abeqNOXXl5+ahT50/k7OyspUvSbuedOn+it99+V0FBBeXhkV9t23aQu4entm7dkiKlhRydnEw2JJk1a6befKuu6t7rzz/p1l3OLi5auGB+mukDAwNVo2ZN+d7rz19/vbbKpujPS5YsqddeqywfHx95eHioabO3VcDPT3sfSAPg2SMQn8nMmTNHgYGBCgwMVIsWLTRp0iQZDIYMK4/BYNCmTZt0+PBhk4HnrFmzlCtXLnXs2DHN4+4vYWNjY6OpU6fq119/1eLFi/XBBx/os88+U6lSpZ5J+c0pLOyKYqIjVDAk+VysrLLJP7CITp449JAj/53Qq5f0afem6vvpuxr/yyBdu3b5ifN80YXfq/PgkOSAlpVVNvkFFNHpk09W5/v3/qX83v4a//NX6t2tsYYM7KA/N6980iK/UK5du6zo6AgVKmTapgODiurE8YPpHnfyxCGFFDINMhYqXMrkmD27/5KPT4DGjPpSH3dsoP592+n335anm2d0dIT27d2mSv/LXD/NTEhI0IkTx1S8RGmT/cWLl9bhwweeynsYDAbt3bNTFy+cU0ihYk8lz+dZaOhlRUVFqEiR5Dq1ssqm4ILFdOxY+u36+LGDKlzE9HMoUrSMjt87JjExQadPHTXJV5IKFy390HzTEhhUWNv+2qjr12N09+5dbf1zgxIS4lWwYLHHygdIT0JCgo4fP6oSJcuY7C9RsowOHXo6fYskrV2zXP/7XzXZ2GR/anm+qBISEnTkyBGVLWv6q6OyZcpq3/5/9yXo0aNHtW/fPpUokbz0QbGiRXXkyBEdPJjUz1y4eFFbt25VxYqZZzZwehISEnTs2FGVLGXazkuWLKuDB/f/qzzu3r2rm7duKretrTmK+MK7FnpZ0VERKlQkxVgxuKjx+piWE8cPqVBh0/u/wkVK63g648u7d+9o29aNiou7LT//EOP+gMDC+mf3VkVEXJPBYNChg//oypULqa7XL7OEhAQdO3pUpUqb9i2lSpfVgcdo57du3pRtbtN2fuvWLTVrWk+NG72pPp921/HjR59auV9kCQkJOppOf77/Mfrz/fv2qXiJEmn+3WAwaMeO7Tp39qyKFU87DYBng6VpMpkJEyaoRYsWkpKWkLl+/bo2bNigatWqPbX3WL58uXLlMp3Z1Lt3b33xxRcmr/v27av4+HglJCTIxsZGH3/8sfHvx44dk6+vr7JmTW6iI0eOVL9+/YyvL168KDs7O5UqVUp9+vRRw4YNVbx4cfXt2/epnUtGiomOkCTltrU32W9r66Dw8KtPlLe3b5Batu4lFxcPxcREatXyGfp2SBd9MXC8cuXKvDcGyXVu+gsOW1t7RYSHPlHeYdcu649Ny1WlekPVrP22zpw+onmzxyprViuVrVD9ifJ+UURHJdWvrZ1p/drZOijsIW06OipCtnaOJvts7RwVHR1pfB167ZI2blyqmrUaq85b7+jUqSOaMW20slplU8VXaqTK888/1srGJodKpVhn/mUXExOtu3fvyN7etD7tHRwVFRn+RHnfuHFdrd6tr4SEeFlaZlGHj7qlCvi/jO63a7sU7drWzkFh19Jv11FREamOsbNzUNS9/GJjonX37t0009x/z3/r4679Ner7AWrX+i1lyZJF2bLZqFuPr+Xi6v7og4F/ISYmSnfv3pFDyr7F3kGRT9i33Hf0yCGdOXNKXT/p81Tye9FFRUXpzp07cnQ0nVHq5OSo8G0Pr/M36tRRZFSk7ty5o7Zt2qpe3XrGv9WoUUORUZFq066tDAaD7ty5o4YNG6pVy5bmOI0XSnT0vXbuYNrOHRwcFBnx7/rl+fNm6vatW/rf/6qao4gvvOjotK+pdrYOCgt71Fgx9XU45fXy/LlTGtj/IyUkxMvGJru6fDJQ7h7exr+/27KzJoz7Vl07NVGWLFlkYWGp1m17KDCo8BOe2Ysj/XbuqMiIf9efz507U7dv39JrlZPbef783vr0077y8fXTzZs3tGD+HHXu1E7jJ0yTh8fTXSr3RZPcn5vWuaOT0yP78zfrvKGoe/15mzZtVfeB/lySrl+/rjfr1FZ8fLyyZMminj17pwr44/lg8egkeEkQiM9Ejh49qu3bt2vhwqSflGXNmlVNmzbVxIkTn2ogvnLlyvrpp59M9qW8qPTs2VOtWrXStWvX9Pnnn6tKlSqqUKGCSZqUD2794IMP9NZbb+nvv/9WixYtTGby9+3bVwMHDtSnn35qErz/N+Li4hQXF2eyLz4+TtmyPdv16rZv26CZ0743vu74cdLDbCxSdMkGgyHVvsdVqHDyTB53+ci3QLD69WmpbVvXqlqNRk+U94tk+7YNmjU9eS3Ojp3v17kpgyRZPFmdGwwG5fcOUN0GH0iSPPP76fKls/rj9+UvbSD+rz/Xa8qkkcbXXbsnPbch5f9tgx7dplNVv8Fg8kEZ7hrk7ROgRk3aSJK8vP116cIZ/bZhaZqB+D82r1K5ClVllS3bY5zRyyPVZ2AwPHEbz549h34YPUm3b93S3r07NWHcaLm6uqlwkZdr1s2WP9ZpwrgRxte9Ph2a9I+U9WcwPLJK0/ocUh2TRr6PewmYO3uCbty4rs/6jlDu3HbauWOLfviuv/oN+FH58/s+XmbAQ6Ru06nHMf/VmjXL5e3tq8Cggk8lv5dF6i7CkOpzSOnXX3/RrZu3tP/AAY0ZM1qeHh6qWbOmJGnXrl2aOGmSevfqpUIhhXT+wnmNGDlS453yqE3r1uY6jRdKqrG5/t0ldOPGtZo2dYIGDByWKsiZWW3dsk6TJiSPFbv3ujdWTLOOHzVWfPR1OJ+bp74eMl43bl7Xzu2b9evPQ/XZF98bg/FrVy/UyROH9Un3QXLK66Kjh/dpyqTvZWfv9NAl4F5GadXnvxmAbNiwVlMmj9fXXw83aecFQwqpYEgh4+tChYqoXduWWrhwnj7+uPvTKvYLLe1x4cPr/Jdff9XNm7d04MB+jR0zRh4enqpxrz+XpBw5cmjqtBm6deumduzYoR9++E5u7u4qWTJztWfgeUIgPhOZMGGCEhMT5e6ePAPOYDDIyspKkZGRqdZx/69y5swpPz+/h6bJkyeP/Pz85OfnpwULFsjPz0/lypUzfiHg7++vLVu2KCEhwbhkjb29vezt7XXhwoVU+d1P87hBeEkaMmSIBgwYYLLvvVZd1fKDTx47rydRpFh5efsEGV8n3ns4WUxMpOzsk2c7xcZGpZqx/aSsrbPLzd1HoVcvPtV8n3dFipWXt+8DdZ6QTp3HRMk2xS8THpetnaPy5TOd7eGaL7/27E65duLLo1iJCvK99zBVSUpMiJeUNGvJ/oH6jYmJSjWL6UF29o6pZjTFxETK7oH/B/b2jnJz9zZJk88tv3bu3Jwqv2NH9+nK5fPq8FG/VH972dna2snSMkuqGarRUZGpZsk/LktLS7m5eUiSfAv46/y5s5o3d/pLF4gvWaqi/PwfbNdJ/UZ0VIQcHEzbtZ1d+nVqb+9onP2e1jG5be1kaWmZqu1HPyLflK5euai1axZp+LeT5OGZ9DA6L28/HTmyT+vWLFLrttz84snZ2trL0jKLIlL2LdGRsn8KAcfbt2/r903r9e57bZ44r5eFvb29smTJovBw0zqPiIhMNQEmJXe3pHsBPz8/RUSE69fx44yB+J9/+Vm1X69tnCXv5+enW7dua/CQwfrg/fdlaZl5Vza1s0u7nUdFPrqdb/ptvUZ+O1hf9BuUagmnzKx4yYrGh6lKUkJi0lgxKjpC9ibX1Mj/MFaMSvWLyqxZrYy/BvP1DdSpk0e0dvUCvd+mu+Lj4zRvznh16TZQxYonPcgyf/4COnf2hFatmJNpAvHGdp5i9ntkVKQcHtG3bNy4Tt8MH6T+Xw5OtYRTSpaWlgoKCtbFC+efuMwvuvT688iIiEf2524m/XmExo//1SQQb2lpKU9PT0lSQECgzpw5o6lTJhOIBzJQ5h1JZTKJiYmaOnWqRowYoT179hi3vXv3ysvLSzNmzMiwsjk4OKhz587q0aOHcZb722+/revXr2vs2LFmf/8+ffooOjraZHu7Rdpr05uTjU0OObu4G7d8bl6ytXPU4YO7jGkSExN0/Og+kwHr05CQEK8rV87J7gkDcS8aG5sccnZ2N2736/zIod3GNImJCTpxbJ98CjxZnRfwC9HVK6ZfIoVevSBHJ5cnyvd5lj17Drm4uBs3N3dv2dk56uAB0zZ99Mhek/U5UyrgV9DkGEk6eGCXyTF+AYV05bLpQP7qlQtySqN+N29aJW+fAOX3KvBfT+2FZWVlJT+/AP3zzw6T/Xv+2ang4ELpHPVfGZRw78uXl0n27Dnk6uph3Nw9vGVv76j9+3Ya0yQmJujwoT0KCEi/XfsHhJgcI0n79+2Q/71jsma1ko9vYKo0B/btfGi+KcXFJ/3iy8LCdMhnaZlFdzPwGTF4uVhZWcnfP1D/7DbtW3bv3qGCBZ+8b/lj8wYlJCSoStWaj06cSVhZWSkoKEh/b99usn/79u0qUrjIv87HYEhan/i+27fjZGlpOgMzy73ge0Y+V+p5YGVlpYCAQO3elaKd79qukJD0ly7ZuHGtvhn+lfp8NkBly1U0dzFfKNmz55CLq7txc3f3lp29ow7uN72mHj2813h9TIuff0Ed2G86Vjywf6f8HzK+TGJQwr3JT3cSE3XnTmIa10vLTNX2raysFBAYqJ07TfuWXTu3q9BD2vmGDWs1bOjX6tt3oMqXf3Q7NxgMOnHiuByd8jxxmV90VlZWCgwK0vbtf5vs3759uwo/Rn8ug0HxD/Tn6ad5+cbnwIuEGfGZxPLlyxUZGanWrVvLzs7O5G+NGjXShAkT1KlTJ+O+o0dTPzilYMGkQOSdO3dMnsYtSdmyZTP+PS4uTleuXDH5e9asWZUnT/oX2Y8++kjDhg3TggUL1KhRI5UvX17du3dX9+7ddfbsWTVo0ECenp66fPmyJkyYIAsLi6c2I8fa2lrW1qbL0GTLFvVU8n4SFhYWqlKtvlavnCVnF3fldXHX6hWzlC2btUqXrWJMN3nCMNnb51G9hkk/F05MTNDlS2clSXcSExQVFabz507I2jq7nF2SvjFfMPcXFS5aTo6OzoqNjdKq5TN1+9ZNlauQegmPzMTCwkKVq9bXmpWzlNfZTc4u7lqzcnaqOp8yYbjsHZxUt8GDdX5O0r06jwzT+XMnZW1jI2fnpDqvUq2Bvh3WVatXzFKJ0q/q7Omj+nPzSr39btdnfp4ZxcLCQtVrNdTyZTOSbrhcPLR82QxZZ7NRufLJa0iO+3mI7B3yqHHTtpKk6jUaaOigrlqxfJZKlKio3bv/1KGDu9Sn7yjjMTVqNdLggZ21fOkMlS77mk6dPKJNv61Qqw+6mZTh1q0b2rH9dzVr3v7ZnPRzqF79Zho54iv5+wcpKKiQVq9eqmvXrur12vUkSVP+z95dh0WV9XEA/9Jg0CWghHSJifG6dqxrd7euioGt69oK6qrrrh0oJqKiYit2ro2tgJ3EkEoNMO8fo4NDKCrDCPP97HOfZ+fOuXfOORzPPfObc8/dsAoCQTTGjMt+rseTx+EAgNSUFCQkxOPJ43CoqqmiQgXxDOudgZtha+eIcuXMIMzIwPWrl3DyxBEM9RpX5OUrakpKSmjeoiOC926BaTkLmJqaI3jvVqhraKL2/7KXfVuxzAf6+obo2n0wAKD5rx0wa8ZI7AvehqrV6uD6tQu4e+c6ps9cKjmmxW+dsGKZD2wqOsDOzgUnT+xHTEwkGjVpLUnz/n0iYmIiJXc5vH0j/kFKV1cfuroGMDOrABNTc/itXYTuvYaibBltXLt6HnfvXMO4ib5FUUU/tZS0VLwRREtev4sT4PGblyhbqjSMFezH6R/Vrn0XLPxrNuzsHeHk5IrDh4IRHRWJFr+1AwBsWL8SgpgYjJuQ3bc8fhwGAEhNSUZCQjwePw6DqqoaLC2tpc599MgB1KpdF9ra0mNYRde9W3dMnzEdzo5OcHNzw569e/Au8h06tG8PAFi2fDmio6Mwc4b4zs8dO3fC1NQUVpaWAIDQW7ewZesWdOncWXLOunX/h23bAuBg7wAXVxe8evkKq9asRt26daGiolL0hfzJdOjYDfPnzYS9vSOcnN1w6OBeREVFomUrcTv3W7cCMTHRmDhpOgBxEH7BvFkY5jUaTs6uklnGGuoaKP3xmVpCoRDPnz8V/39GBmJiohEREQYtLS2Ym5eXQynlR0lJCc2ad8T+4K0w+fiD977gLVBX10St2tnX1NUrfKCnb4TOXcVjxWbNO2DurFE4sC8AVarWwY3rF3Dv7nX8OT17rLhz+1q4e3hC38AYqSnJ+O/SSTy4fwvjJ80HAGiVKg1Hp0rYvm0V1NU1YGhogocPbuH8uWPoLodJWvLUqVM3+PrMhIODE1xcXHFgfzAiIyPRqrW4na9dswLRMdH44w9xOz9x4hh8fWZi+IjRcHZ2RezHmd3qGhqSZ8dt9F8HJ2dXWFiUR/KHDwjavQMREWEY5V3yx4oF0a1bd8ycMR1Ojs5wdXND8N49iIx8h3btOwAAVixfhujoaEz/2J/v2rkDJqamsLS0AgDcvhWKrVu3oFPnLpJzbvTfAEcnZ1hYmEMozMDFixdw6NBBTJg4qcjLR1RUVqxYgb/++gtv376Fi4sLlixZgrp1v/5cuAsXLqBevXpwdXXNFe8sbAzEKwg/Pz80btw4VxAeADp06AAfHx/cuHFDcutT165dc6V7+lQ8QHz//j0qV64s9Z6lpSWePXsGADhy5AjKlSsn9b6DgwMePnyYb/6MjIzQq1cvzJgxA+3bt4eysjIWLlyIGjVqYOXKlVi/fj2Sk5NhYmKCX375BZcuXYK2dsl/qGjT5l0gTE9HwNalSP6QBGsbR4wYMw+amqUkaWIFUVJrxyXEC+Aza6jk9fGjO3H86E7Y2btjzATxusZxcTFYv8YH798nokxZHVjbOGHCH//mOXtY0TRp3hlCYRoCty1D8ockWNk4YvhoX6k6j4vNXefzZmfX+Ylju3Di2C7Y2bvDe/xCAICltQMGD52OfXvW4/CBLTAwNEXHLkNRo6ZiPayrxW9dIUxPw2b/f/AhOQkVbZwwdsICaGll169AECU1G8nO3hVDvKZi96712LNrA4xNzDDEayoqfrbsjY2NI4aPmoVdO9YheO8mGBmVQ/eew1CrjvTzLy5fOgVABM9aDaGo6tZrhMSkBGzf5o/YWAEsrawxfeZfMDYxBQDExgkQneMho6NG9JP8f0TEI5w5HQJjY1P4+e8CAKSmpmDlikUQxERBXV0DFuUtMXbcNNRVkIfRtWrdDenpadjg9zc+fEhCRVtnTP7jrxztOlJqlqm9gytGjJqGHYF+2Bm4HiYmZhgxajps7bLvvqlVuyHeJyVid9BGb4+iLgABAABJREFUxMfFwqK8NSZMmg8jI1NJmuvXLmD1yvmS10v/mQUAaN+xDzp26gdVVVVMmDQf27etwcIFfyAtNQUmJuYYMmwyKleuKctqKRbCXr/ABL/sZ4WsPhQEAGhS2RPjOvaWV7aKpXr1GyMpKRHbtm5AbKwAVpY2mDVnIUw+9S2xAkTl6FuGD8vuW8LDH+H0qRAYm5hi46Ygyf5Xr17g3r3bmOvzd9EUpBhp2qQJEhISsG69H2JiYlDRpiKW/P23ZBweI4jBu8jsOheJsrB8xXK8efMGKioqsLCwwHAvL7Rv116Spn+//lBSUsLK1asQHR0NXV1d1P1fXQwbOjTX5yui+g0aIzExAVs2rxe3cysbzPVdBBMTcZ0LBAJERWXX+cEDe5GZmYml/y7E0n8XSvY3adoCEyZO/XhMDIb+nv0w3J07tmHnjm1wr1QZixbL/u7gn81vrboiPT0NGzcsQfKHJNhUdMKEyTmvqVFQUpYeKw4bMQ1BO/wQtHM9jE3MMGzENKm7iBMS47B6hQ/i42OhVao0ype3wfhJ8+HqVk2SZtiIadi5fS1WLZ+L9+8TYWhogo6dB6Bh4+wfwBVBw4ZNkJiYgE0b/cTt3NoG8+Yvhqnpp3Yeg6jI7Il3+/ftQWZmJv5ZshD/LMlu582atcCkyeKlIN+/f4/Fi+YhNlaA0qXLwNbOHv/8uwpOTgW/y68ka9KkKRISEuC3fh0EMTGwsamIxX8vydGfZ9d5lkiElZ/15+YWFhjmNRztPuvPU1JT8deC+YiOjoKGhgYsLS0xY+YsNGmi2JPvflo/+LwuAgIDA+Ht7Y0VK1agTp06WL16NX799Vfcv38fFSrk/1DohIQE9O7dG40aNUJkZP4PBi8sSiJFus+KqIBOnnsh7ywoHC6RUPRKafK32KJmaKCYD4eVp6SkDHlnQeEYRNyTdxYUTlY1D3lnQeEY6qrJOwsKJ/59pryzoHDeRqbKOwsKp7yZlryzoHC0tHi3T1HT0y35kyq/VWJsnLyz8FPS1i/48xE9PT1RpUoVrFy5UrLPyckJbdu2ha9v/ncCd+3aFXZ2dlBRUcHevXtlPiOea8QTERERERERERER0U8jLS0NiYmJUltaWlqudOnp6bh+/TqaNpW+46Np06a4ePFivuffsGEDHj9+jOnTpxd63vPDQDwRERERERERERER/TR8fX2ho6MjteU1uz0mJgaZmZkwMZFebtnExCTXMyw/CQ8Px6RJk7B161aoqhbdagFcl4CIiIiIiIiIiIiIfhqTJ0/GmDFjpPZpaGjkm14px1r7IpEo1z4AyMzMRPfu3TFz5kzY29sXTmYLiIF4IiIiIiIiIiIiIvppaGhofDHw/omhoSFUVFRyzX6PiorKNUseAJKSknDt2jXcvHkTw4cPBwBkZWVBJBJBVVUVx44dQ8OGDQunEDlwaRoiIiIiIiIiIiIiKnbU1dVRtWpVhISESO0PCQlB7dq1c6XX1tbGnTt3EBoaKtmGDBkCBwcHhIaGwtPTU2Z55Yx4IiIiIiIiIiIiIiqWxowZg169eqFatWqoVasW1qxZgxcvXmDIkCEAxMvcvH79Gps2bYKysjJcXV2ljjc2Noampmau/YWNgXgiIiIiIiIiIiIiKpa6dOkCgUCAWbNm4e3bt3B1dcWhQ4dgaWkJAHj79i1evHgh51wCSiKRSCTvTBD9bE6ek/8/TkWTxa6oyJXS5G+xRc3QQF3eWVA4SUkZ8s6CwjGIuCfvLCicrGoe8s6CwjHUVZN3FhRO/PtMeWdB4byNTJV3FhROeTMteWdB4Whpqcg7CwpHT1db3ln46STGxsk7Cz8lbX09eWeh0HGNeCIiIiIiIiIiIiIiGeJ0SCIiIiIiIiIiIiJ5UFKSdw6oiHBGPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEOq8s4AERERERERERERkUJSUpJ3DqiIcEY8EREREREREREREZEMMRBPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyRAD8UREREREREREREREMsRAPBERERERERERERGRDKnKOwNEREREREREREREikhJSd45oKLCGfFERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyRAD8UREREREREREREREMqQq7wwQERERERERERERKSQlJXnngIoIZ8QTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEN8WCtRHrQ0VeSdBYXzPjlD3llQOCoqfCBMUYuNT5d3FhSOob6GvLOgcLKqecg7CwpH+VqovLOgcJLrVpV3FhSOTlk1eWdB4SSncHxe1D6kZMo7CwqHz8ikn4EIbIiKgjPiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGRIVd4ZICIiIiIiIiIiIlJEIpG8c0BFhTPiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhPqyViIiIiIiIiIiISC74tFZFwRnxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDKkKu8MEBERERERERERESkikUjeOaCiwhnxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyRAD8UREREREREREREREMqQq7wwQERERERERERERKSKRvDNARYYz4omIiIiIiIiIiIiIZIiBeCIiIiIiIiIiIiIiGWIgnoiIiIiIiIiIiIhIhhiIJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkSFXeGSAiIiIiIiIiIiJSSCJ5Z4CKCmfEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDfFgrERERERERERERkRzwWa2KgzPiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZ4tI0xdyqVaswfvx4xMXFQVVV/Od8//499PT0ULNmTZw7d06S9ty5c/jll1/w6NEjNG3aFN7e3vD29pY634wZM7B3716EhoZKXs+cOTPX5zo4OODhw4cAgPr16+PMmTO50vz+++9YtWoV+vbti40bN+ZbBn9/f/Tp0yffz/50nk9CQ0NRuXJlPH36FFZWVpL9QUFBWL58OW7evIm0tDSUL18ederUwYgRI1C5cuUv1uPPQCQSYe/ujThz6iA+fEiCTUUn9O47EuYW1l887uqVs9izawOiot7A2NgMHTr1R9XqdSXv7wnyR/CeTVLHaOvo4d/lQZLXCQmx2LF9Le7duYbk5Pewd3BHzz4jYGpqUbiFLAZEIhEO7d+MC+cOISX5PSytHdGl+3CUM7PK95i3b57hQPAmvHwRjlhBJDp0HoIGjdtLpZk2uRdiBZG5jq1bvxW6dB9R2MX4aYlEIuwJ2ojTJw/gw4ckVLR1Qu9+o2Dx1XZ+BkE7NyAq8g2MTczQsfMAVPusnZ8ICcbJ4/sQHfMOAGBuboW27XujkoenJE1qagp2BKzB9evn8T4pEYZGpmjarD0aNWkjm8L+JEQiEfYGbcTpkwcldd6r38gC1PlZ7P6szjt07i9V55/bH7wNuwLXoWnz9ujRe7hk/7UrZ3HqxAE8exqG9+8TMctnDSytbAu1fMXFgf27sXvXNsTGClDB0hqDh4yEq6tHnmljBTFYt3YZIsIf4s2bV2jdpiMGD/GWShNy7CCWLPbJdeyefSehrq4hgxIUPwf278auneI6t7S0xu9DRsLVzSPPtLGCGKxdswzhEQ/x5rW4zocM9ZZKM2H8cNy5fTPXsdVr1MKs2QtlUIKS6c7TcOw8dxzhb14iNikB03sMRm3nSvLOVrG1Z88ubA/YithYAaysrDF8xGhUquSRZ9qzZ05hb/BuRISHQyhMh5W1Dfr1G4gaNWpKpUtKSsK6tatw9uxpvH+fBFPTcvDyGoWatWoXQYl+frt27cTmzZshEMTAxsYGo0ePzfe7RmhoKJYt+xfPnj1HWloqTE1N0a5de3Tv3kOS5sCB/Zg1K/f3rXPnLkBDg/05ABw6uAd7dgcgLlaAChWsMGDQSLi45t1vxMbGYIPfckREPMLbN6/QslVHDBw8UirNi+dPsW2rHx5HPEJU1DsMGDQCrdt0LoqiFBsHD0iPWwb9/oVxS2wM/D4bt7RqnXvc8rkzp4/jr/nTUbNWXfw5bZ5sClAM7d2zC9u3b4UgVgBrK2sMHz4a7vn152dPIXjvbkREfOzPrWzQN0d/fvjwAcyfNyfXsUePnWHfQiRHDMQXcw0aNMD79+9x7do11Kwp7nTPnTsHU1NTXL16FcnJyShVqhQA4PTp0zAzM4O9vf03fYaLiwuOHz8ute9T0P+TQYMGYdasWVL7Pn3uP//8g3nzcl9ge/XqhfDwcPz222/5frampib8/PwwZsyYL+Z74sSJWLRoEUaOHImZM2fCwsICL168wPnz5/HHH3/g8OHDXy2nvB06sB1HD+/CwN8nwNS0PPYFb8Ff8ybA96+N0NIqlecxEeH3sHLZLLTv2B9Vqv0PN66dx4pls/DH1H9R0dZJks7cwgrjJ2UHCZSVs2+GEYlE+PfvaVBRUcHI0bOhpVUKRw/vwl++4+AzfwM0NLVkV+if0PGjO3Dq+G707DsOxibmOHJwG5b+PQnTZq+Hpmbef4f09DQYGpmictW62L1jdZ5pxv+xFKKsLMnrN6+fYdmSSahc9ReZlONndXD/dhw5vBODfp+IcuXKI3jPZizwGY/5izbl287Dw+5h+b+zxD8yVauL69fOYfm/M/Hn9H9R0dYZAKCvb4TOXQfB2NQcAHD+7FEsWfQnZvuukQSct25ejgf3b2LIsCkwNDLF3dtXsXHDEujqGaBqtf8VTQXIwaH923Hk8C4M+n0CTMuVx749W/CXzwTMW/SFviXsHlb8OwvtO/VH1Wr/w/Vr57Hi31mYMl26bwGAJ48f4vTJAyhfwSbXedLSUmHn4IrqNethw9pFMilfcXD2zHGsXf0PhnmNhZOLO44c2ovpf47DyjVbYGxsmiu9UCiEjo4uunTrg717AvM9b6lSpbF6XYDUPgbhxc6cPo7Vq/6B1/CxcHZxx6GDezH1z3FYvfYLda6ri65d+2BPPnU+daoPhBlCyeukxAQMG9oXdes2kFk5SqLU9HTYlLNA06q1MHvbWnlnp1g7eSIEy5Yuwegx4+Hq6o79+/Zi4oTR2LgpACYmudv5rVuhqFatBgYNGoqyZcrg0OGDmDxpHFau8oO9vQMA8b+FsWNHQk9XD7Nm+8DIyBhRUZEoVap0URfvpxQScgyLFy/ChAmTUKlSJezZsxve3iMRGLgTpqa561xLSxOdOnWGra0dtLS0cOtWKHx9faClpYV27bInbZQuXRo7dwZJHctAmdi5syfgt/Zf/D50DJyc3XD08D7MmjEey1ZshpGxSa70QqEQ2tq66NS5N/YF78jznGlpqTAxLYfadepj/bqlsi5CsfNp3DLUayycnd1x+NBezJg6DitW538N1dbRReeufRD8hXELAERFvsP6dcvy/SFFUZ08GYJly5bAe/R4uLm6Y9/+vZgwcTQ2bvx6f16mbBkcPnQQf0weh5Ur/WD3sT8HxH3Lps3S/w7YtxDJF5emKeYcHBxgZmaG06dPS/adPn0abdq0QcWKFXHx4kWp/Q0afPuXRVVVVZiamkpthoaGUmlKlSqVK422tjYAQEdHJ9d7fn5+uHjxIoKDg3OdK2f5GjRogD///DPfNP/99x8WLFiAxYsXY/Hixahbty6sra1Rr149TJkyBYcOHfrmMhc1kUiEY0eC0KpND1Sr/gssyltj0O8TkZaeiv8unsj3uGNHguDiWg0tW3eHmVkFtGzdHU7OVXDsyC6pdMrKKtDV1Zds2tq6kvci373C44j76NPPGzYVHVHOrAJ69xuF1LRU/HfppKyK/FMSiUQ4dXwPmrXoBo8q/4OZuTV69RsPYXoarl3Ovy4srRzQruNgVKvRAKpqanmmKVtWF9o6+pLt7p3LMDQyg529u6yK89MRiUQ4emQXWrfpieo1xO188NBJSE9PxaWLx/M97uiRXXB1q4ZWbXrAzLwCWrXpAWeXKjh6OPsLa+WqtVGpck2UK1ce5cqVR6cuA6GpqYXH4fclaSLC7+F/dZvBydkDRkamaNCoFSpUqIinT8JkWm55Etd5EFq36YFqH+t80NCJSP9K33L0SBBc3KqhVZvuH+u8+8c6l+5bUlNTsGq5D/oPHIvSpcvmOk+duk3Rtn1vuLhWLfSyFSd7dgeiabOWaPZra1SoYIXBQ7xhaGSMQwf25JnexLQcfh/qjUaNf0XpUmXyPa+SkhL09Q2kNhL7VOfNP9b5kKHeMDIyxsEv1PmQod5o3ORXlC6dd52X1daWqusbN65CQ1MDdX9pKMuilDjVHVzQt0kr/M/FQ95ZKfZ27AhAi99aoWXLNrCyssaIkaNhZGSM4L2780w/YuRodO/eC05OzrAoXwGDBw+FhUV5XLx4XpLm0KH9SEpMxFyfBXBzqwRT03Jwd/eAra1dURXrp7Zt21a0bt0Gbdu2hbW1NcaMGQsTExMEBe3KM72DgyOaNWuOihUrwszMDL/+2gI1a9ZCaKj03TVKSkowNDSU2kgseG8gGjf5DU2btUL58lYYOHgkDA2NcfhQPv25STkM+n0UGjZqjtL5/IBkZ++Efv298Eu9xlBTU5dl9oulvXsC0aRpSzRr3hrlPx+3HMy/zn8fIh63lMrnGgoAmZmZWLhgJnr0GgBTUzNZZb9Y2rkjAC1aiPtzSytrjBgxGsZGxggOzqc/HzEa3br3gqOTMywsKmBQHv05AEBJCQYGBlIbEckXA/ElQP369XHq1CnJ61OnTqF+/fqoV6+eZH96ejouXbr0XYH4wnbgwAFMmzYN/v7+qFTp67+Ez5s3D0FBQbh69Wqe7wcEBKBMmTIYNmxYnu8rKSn9UH6LQnT0WyQkxMLVrZpkn5qaOhwdKyEi/F6+x0VE3Ierm3SAy829Wq5jIiNfw3t4J4wb3R0rls1GVNQbyXufZvd9PghVVlaBqooqwsLu/lC5ihtBzDskJsbC0Tm7TtXU1GFr744nT+5/4chvk5EhxNX/TqBWnWbFon0Wluiot0iIj4Wru3Q7d3CqhPCwL7Tz8PtS/zYAwM29OsLz+beRlZWJ/y6eRFpaKmztXCT77R3ccPPGRcTGRkMkEuH+vZt49+4V3Nyr/2DJfl4/VufSfYtrHn3Lpg3/oFJlT7i4KXag/UuEQiEiwh+hcpUaUvurVKmBBw9+rI9NSUlB397t0btnW8yYNh6PI0ruj0rfQigUIjz8EapUzVHnVWvg/v3Cu64dO3oA9eo1hqaC3TlGPwehUIiwsEeoXt1Tan/16p64e/dOgc6RlZWF5ORkaJfVluy7cP4cXFxc8ffff6Ftm1/Rt093bN7sj8zMzELNf3EkFArx8OFDeHpKL+Xj6VkTt2/fLtA5Hj16iNu3b6NyZenrZkpKClq3bomWLVtg9GhvPHr0sNDyXZwJhUI8jgiDR2Xp/tyjcnU8fKhY31OKSn7jlspVauDhD15Dt2/bAG0dXTRt1uqHzlPSCIVCPMqnP7/3jf15WW1tqf0pKSno0rktOnZshUmTxiI87FGh5ZsKmUjELa+tBGIgvgSoX78+Lly4gIyMDCQlJeHmzZv45ZdfUK9ePclM+f/++w8pKSlSgfiJEyeiTJkyUpuPT+71Zu/cuZMr3cCBA6XSrFixIleavNaFf/jwIXr06IHJkyejU6dOBSpflSpV0LlzZ0yaNCnP98PCwmBjYyO1XM7ixYul8pKQkFCgz5KXhPhYAOK12z+nraOHhITYLx6nraOf4xh9JCTESV5XtHXCoN8nYezE+eg3YCwS4mMxZ+YIvE8S10m5chVgYGiCnYHr8OFDEjIyhDiwbxsSEmKREC8orCIWC4mJ4rouqy39dyirrYvEz+r0R90OvYiUlPfwrN200M5ZHHxqyzo52rmOtp7k30Cex8XH5v1vI8cxL188waB+v6J/76bwX78Yo0bPgrmFleT9Xn1GwMzcEt7DO6N/7yZYOH8i+vTzhoOj2w+W7Of1qc5z1V8B6lwnR9+io6OPhPjsfwf/XTyJ58/C0anLoELMccmTmBiPrKxM6OpJ16eunh7iYr+/jy1f3hKjx07BtBnzMWHSDKirq2P82CF4/frlj2a52PtU53q6OepcVw9xcYVzXXv08D6ePXuC5s0ZTCD5SEiIR2ZmJvRz9C16+vqILWDfEhi4DampKWjQsJFk39u3b3DmzClkZWZh/oK/0at3P+wI3IbNm/0LM/vFUny8uM4NDKTrXF9fHwJBzBePbdmyBerUqYU+fXqjU6dOaNu2reQ9S0srTJs2HQsXLsbs2XOhoaGOgQMH4MWLF7IoRrGSmJjw8RoqPY7R1dNDXFz+4xj6fpJraM6+5Qevoffv3caxowcwYtTEH81iiZOQEI+szEzo6eeoc72C9+c7PvXnDbL78woVrDBp0p+Y6/MXpk2dDXV1dQwfPhivXrFvIZInrhFfAjRo0AAfPnzA1atXERcXB3t7exgbG6NevXro1asXPnz4gNOnT6NChQqwsclew3f8+PHo27ev1Ln+/fdfnD17Vmqfg4MD9u3bJ7WvbFnpJQh69OiBKVOmSO0zNjaWep2QkIC2bduiXr16mD179jeVcc6cOXBycsKxY8dynRfIPeu9f//+aN26NS5fvoyePXtC9IVf0tLS0pCWlia1Lz09Tabr7F68cBwb1y+WvB49zhcAoATpcojz/eUZ0znfFYlEUjvdK332y3p5wNbWGePH9sT5c8fQvEUnqKqqYsSomfBb+xe8fm8DZWVlOLtUhXulGijprl4+gYAt/0heDx0ufphNrhoXFe6dFRfPH4Gza3Xo6pbsWwMvng/BBr/sdj52Qj7tHF+v31zvi0TIuaucWXnM8V2HD8nvce3KWaxZNQ9/TF0iCcYfO7IbjyMeYPTYuTAwMsGjB7exccMS6Oga5Jr9XVxdPH8c/p/V+Zh86hwQfb1N56ry7DoXCKKwddNyjJ+8AOrqvKW7IHL37z/Wrzg6ucLRyVXy2tnZHSOH98P+4F0YMmz0d5+3JMlZv+LLY+H05UePHoCVlQ0cHJ0L5XxE3y3P6+PX2/nx48fgv2Ed5voskAq4ZWVlQVdXD+PGT4KKigocHBwRExON7QFb0bfvgMLOfTGVe7z+tTpfvXotUlJScPfuHSxbtgwWFhZo1qw5AMDNzQ1ubtmTAipVqoRevXpix45AjBs3vvCzXwwV9jWUCiCva+h31nly8gcs+msWRoyaCB0d3ULIXMmU+zuSKHcfn4cTx4/B338d5syV7s9dXFzh4pI9VnR1c8egQX2wO2gnRo4aW3gZJ6JvwkB8CWBrawsLCwucOnUKcXFxqFevHgDA1NQU1tbWuHDhAk6dOoWGDaXXMDU0NIStra3UPv0cv8ICgLq6eq50Oeno6HwxTVZWFnr06AFlZWVs2bLlmy/iFStWxKBBgzBp0iT4+flJvWdnZ4fz589DKBRC7eP63Lq6utDV1cWrV6++em5fX1/MnDlTal//gaMxcLDsLk6Vq9RGxYrZDzzMyEgHIJ69qquXHZxNSozPNXv4czq6+rlmzCclxkFHO/9jNDS1UL68DSIjs+vGytoes33WIjn5PTIyMqCtrYtZ04fBytoh3/OUBG6VasHK2lHyOuPjMj2JiXHQ+SxInpQUj7Kfrav/I2IFkXj04CYGDZ1WKOf7mVWuWkfyMFUAEH5s5/E52nliYlyuGduf09HVzzV7OzExPtfdIKqqajD5+LBWGxsHPHn8EMeOBKHfwLFIT0/DzsB1GDVmFjwq1wIAVKhQES+eR+DwwcASE4ivXLW21MNUhfn0LeL6+9Y6z/47PXsShsTEOEyf8rvk/aysLDx6eBvHj+2F36ajUFZWKZQyFXfa2rpQVlbJNYssIT4u1yz5H6GsrAx7eye8efP1615J96nOY3PWeULh1HlqairOnD6OXr0Hfj0xkYzo6OhCRUUl12zJuLi4XDNZczp5IgQL5s/FzFk+qFZNeuKFgYEhVFVVoKKS3YdbWlohNlYgNdZWRLq64joXCHLX+dee0WFuLh6f2NraQiAQYO3aNZJAfE7KyspwdnbGy5e8w0lbW+fjNVR6TJIQHwdd3fzHMfT9JOOWHH1LfEIcdHW/7xr67u1rREa+xawZ2bPhRaIsAEDr337B6rXbUM7M4vszXczp6OhCOY/+PD4uLtddTzmdPBmCBQvmYsbM3P15TsrKynB0cMKrV+xbiOSJS9OUEA0aNMDp06dx+vRp1K9fX7K/Xr16OHr0KP777z+5rg//559/4sKFCwgODpY8xPVbTZs2DWFhYdi+fbvU/m7duuH9+/dYsWLFd5138uTJSEhIkNp69x3+XecqKC2tUjAxNZdsZuZW0NHRx7271yVpMjKEePjwltQa1znZ2jpLHQMAd+9c/+IxQmE63rx+nuds7FKlykBbWxfv3r3C0ydhqFK19neUrvjQ1CwFI2NzyWZazhLa2vp4eP+GJE1GhhARYbdhY1M4sx4vXTiKsmV14eLm+fXExVzOdm5ubgUdXX3cu3NNkiYjQ4hHD27Bzv4L7dzOGXfv5Gzn12D3hXYuJpI8AyEzIwOZmRlQUpK+7CkrK3/xjpniJr86/7z+Clrn93LVeXbf4uxaBXPn+2G271rJZm3jgFp1GmG271oG4T+jpqYGWzsH3Lwp/ZyTmzevwumzGe0/SiQS4cnjcD6wFeI6t7NzwM0b0nV+48ZVODv/eJ2fO3sCQqEQDRs1++FzEX0vNTU12Ns74Nq1K1L7r127AlfX/JdcO378GHx952DqtFmoVatOrvdd3dzx+vUrZGVlSfa9evkSBgaGCh2EB8R17ujoiCtXLkvtv3LlMtzd3b/pXEKhMN/3RCIRwsLCYGjI/lxNTQ0Vbe1xK1S6Pw8NvQpHx8K7hlK2T+OW0BzjltAbV+H4nddQi/KWWLZyM/5d7i/ZPGv+D27uVfDvcn8YGpkURtaLLTU1NTjk05+7fKE/P3H8GOb5zsGfU/Puz3MSiUSIiAiHgQEfBk0kT5wRX0I0aNAAXl5eEAqFkhnxgDgQP3ToUKSmpn53ID4jIwPv3r2T2qekpAQTk+wLZnJycq40Ghoa0NPTw44dOzBv3jxs2LABZcuWzZXu0zruX2NiYoIxY8bgr7/+ktpfq1YtjB07FmPHjsXz58/Rvn17lC9fHm/fvoWfnx+UlJSgrJz/b04aGhrQ0JBehkZdPemr+SlMSkpKaNq8A/bv2woTE3OYmFrgwL6t0FDXRM3a2eu8rVnlCz09Q8mazE2atYfvHG8c3B+AylXr4Ob1C7h/7zr+mPqv5Jjt21bCo3JtGBgYIzExHvuCNyMlJRl16mavT37l8mmULasLA0NjvHr5FFs3L0OVanXg6lZyH2KZFyUlJTRo3A7HDgfA2MQMRsbmOHp4O9TUNVDNM/uOkk3rF0BH1wBt2otv0c7IEOLd2xeS/4+Pj8Grl4+hoaEJI2NzyXFZWVn47+IxeNZuIjXTTFEoKSmhWfOO2B+8FSamFjA1tcC+4C1QV9dErdqNJelWr/CBnr4ROncVt/NmzTtg7qxROLAvAFWq1sGN6xdw7+51/Dk9u53v3L4W7h6e0DcwRmpKMv67dBIP7t/C+EnzAQBapUrD0akStm9bBXV1DRgamuDhg1s4f+4YuvfM+0HPJYG4zjvgQPBWmJiaw9TUAvuDt0I9R9+yeoUv9PQNJXXetHl7+MzyxsF9n/Utd69jysc619IqBYvy1lKfpaGhiTJltKX2v3+fCEFMFOLjxGvnvnsrnoGjo6v/3bOqiqN27btg0V+zYWfnCEcnVxw5HIzoqEi0+K0dAMB//UoIBDEYO36q5JjHj8UPXk1JTUZCQjwePw6DmqoaKliK63fblvVwcHSBmbkFkpM/YH/wLjx5Eo6hw3mrMSCu84V/zYadvSOcnFxx+JB0nW9YvxKCmBiMm5C7zlNTsutcVVUNlpbSbf3okQOoVbsutLV1iq5AJUhKWireCKIlr9/FCfD4zUuULVUaxgrULxSGzp27Ye7cmXBwcIKLiysO7A9GVFQkWrcRt/M1q1cgOiYaU6ZMByAOwvvMnYkRI0fD2dlVMrNbQ0NDMhZv26Y9dgftxL//LkaHDp3x6tVLbNnijw4dOsunkD+Z7t17YPr0aXBycoKbmzv27NmNd+/eoX37DgCA5cuXISoqCjNnzgIA7Ny5A6amprC0tAIA3LoVii1bNqNz5y6Sc65duwaurm6oUKE8Pnz4gMDA7QgLe4QJEyYUefl+Rm3adsGSxXNga+sIBycXHD2yDzHRUWjeoi0AYJP/KggEMRg99k/JMU+ehAMAUlJTkJAQjydPwqGqqooKFcT9uVAoxMuXz8T/nyGEQBCNJ0/CoaWppdAzsz9p264LFi+cDVs78TX0yOFgREdHokWLj+OWDR/HLeOyr6FPPl1DP45bnny8hlawtIa6ugasrGykPqN0aXGfk3O/ourUuRt8PuvP9x8IRmRUJFq3/tifr1mBmOho/PGxPz9x/Bh8fGZixIj8+3N//3VwdnaFhYW4b9kdtAMREWHwHj1OPoWkLyo5U8PoaxiILyEaNGiAlJQUODo6SgXI69Wrh6SkJFSsWBHly5f/rnPfu3cP5cqVk9qnoaGB1NRUyeu1a9di7dq1UmmaNWuGI0eOYOXKlRCJRLnWo/9k+vTpmDFjRoHyMn78eKxcuVLqswFg4cKFqFGjBlauXIn169cjOTkZJiYm+OWXX3Dp0qXvnoVflFq07Ir09DRs8v8HH5KTULGiE8ZNXAAtrVKSNIKYKKkZvXb2rhg6fCqCdq7H7l0bYGxihqHDp0otTREbG4NVy+cgKSkBZbV1UNHWGVNnLoOhoakkTUJ8LLZvXSm+bV9XH7X/1xRt2vUqmoL/ZBo364z09DQEbl2G5OQkWFk7Yri3LzQ1s/8OsbFRUssrJcQLMG/2UMnrE8d24cSxXbC1d4f3uIWS/Y8e3EBcbBRq1lHcWZS/tRK3840bliD5QxJsKjphwuS/pNu5IApKytLtfNiIaQja4YegnethbGKGYSOmSS17k5AYh9UrfBAfHwutUqVRvrwNxk+aD1e3apI0w0ZMw87ta7Fq+Vy8f58IQ0MTdOw8AA0bty6awstJi491vmnDP5I6Hz9Zum+JFURJ/WAprvOpCNqxHkE7N3ysc+m+pSBuXr+IdasXSF6vWCp+Pkjb9r3RrmPfHytYMfJLvcZITExEwNYNiI0TwNLSBjNnL4Sxibgfjo0VIDoqUuqYkV79JP8fEf4Ip0+FwNjYFBs2BQEA3n9IwtJ/5yMuLhalS5VGxYr2mP/XCjg4cM1yAKhXvzGSkhKxbesGxMYKYGVpg1lzFsLkszqPipau8+HDsus8/FOdm5hi48c6B4BXr17g3r3bmOvzd9EUpAQKe/0CE/yyn8+y+pC4fptU9sS4jr3lla1iqWGjJkhITMCmjX4QCASwtrbB/PmLYWoqHrcLBDGIisyeALN/3x5kZmZiyd8LseTv7PFJ8+YtMPkP8ZJ5xiYmWLjoHyxftgT9+/WEoaEROnTsgu7dFXNcmFOTJk2RkJAAP791iImJQcWKFfH33/9IvivFxMQg8rM6z8rKwvLly/DmzRuoqKjAwsICXl4j0L59e0mapKQk+PrOhUAgQJkyZWBv74DVq9dKre2syOr+0ghJSYkI3O6P2FgBLC2tMW3GAhgbi/vzuDgBYnL056NH9pf8/+OIRzh7RnwNXbt+JwDx96PP0+zdvR17d2+Hq6sH5s5bWgSl+rn9Uk98Dd2+TXwNtbSywYxZ2eOWuLzGLcOlxy1nTovrfP3GINDXNWzYBIkJCdi4yQ+x+fTnkVHZfcu+/R/78yULsWRJdn/erHkLTJ4s7s/fv3+PRQvnITZWgNKly8DOzh7//rsKTk5fu6uYiGRJSVSS7sknKiSXrr6WdxYUzvvkDHlnQeFol1HsW8zlQcS5DkXOUF92D96mvCnz+XlFTvlaqLyzoHA065aMZ4sUJ1qanENW1N5Gpcg7CwpHRYWrBxe1MqUU705leStnymc85CSITZB3Fn5KBvol7w5U9vJERERERERERERERDLEQDwRERERERERERERkQzx/j4iIiIiIiIiIiIieeAKpgqDM+KJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhliIJ6IiIiIiIiIiIiISIYYiCciIiIiIiIiIiIikiEG4omIiIiIiIiIiIiIZEhV3hkgIiIiIiIiIiIiUkQieWeAigxnxBMRERERERERERERyRAD8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQ3xYKxEREREREREREZE88GmtCoMz4omIiIiIiIiIiIiIZIiBeCIiIiIiIiIiIiIiGWIgnoiIiIiIiIiIiIhIhhiIJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkSFXeGSAiIiIiIiIiIiJSRCJ5Z4CKDGfEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDfFgrERERERERERERkTyI+LhWRcEZ8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEB/WSkRERERERERERCQHfFSr4uCMeCIiIiIiIiIiIiIiGWIgnoiIiIiIiIiIiIhIhhiIJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhliIJ6IiIiIiIiIiIiISIZU5Z0BIiIiIiIiIiIiIkUkEsk7B1RUGIgnykOUIE3eWVA4ZUqzOypqyakZ8s6CwtEpqybvLCicJ88/yDsLCqemh668s6BwkutWlXcWFE7quevyzoLCSfOsLO8sKJwPHzLlnQWFY1VeQ95ZUDhZDIASURHi0jRERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyRAD8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQypCrvDBAREREREREREREpIpFI3jmgosIZ8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQypCrvDBAREREREREREREpJpG8M0BFhDPiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhPqyViIiIiIiIiIiISA5EfFarwuCMeCIiIiIiIiIiIiIiGWIgnoiIiIiIiIiIiIhIhhiIJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhliIJ6IiIiIiIiIiIiISIZU5Z0BIiIiIiIiIiIiIkUkEsk7B1RUOCOeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhniw1qJiIiIiIiIiIiI5IDPalUcnBFPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDL0U6wRf/HiRdStWxdNmjTBkSNHpN579uwZrK2tcfPmTXh4eOR5fHp6OpYsWYKtW7ciPDwcpUqVgoODAwYOHIiePXtCTU0NAPDy5UvMmDEDhw8fRkxMDMqVK4e2bdti2rRpMDAwkJyvfv368PDwwJIlS6Q+x9/fH97e3oiPj5fsS0lJwbx587B9+3Y8e/YMZcuWRf369TFz5ky4uLhI0s2YMQN79+5FaGhogetFFuU6c+YMAgIC0LVrV8n+JUuWYMmSJXj27BkAIDMzEwsWLMDGjRvx/PlzaGlpwd7eHr///jv69ev3xTrau3cv2rVrB5FIJKmzfv36wdHREQ8ePJBKu2PHDnTp0gWWlpaSz85Zx5+Ob9asmVTbiI+Ph56eHk6dOoX69etL9p86dQqLFi3C5cuXkZSUBHNzc1SrVg1eXl745ZdfClz3PwuRSISQI1tx+eJhpKS8RwVLB7Tt6AXTcpb5HnP54mFcv3oCkW+fAwDMy9uiecu+qGDpIEnjO7MP4mKjch1b638t0a6TV+EXpBgRiUQ4uG8zLpw9iOTk97CydkSXHiNgZm6V7zFvXj/DgeCNePE8HLGCSHTsMhQNm7SXSnMgeBMO7d8stU9bWw/zFu+QRTGKFZFIhAPBm3H+zMc6t3FEt55fr/P9ezfi+TNxnXfqOhSNmkrX+ZlT+3H21H4IYiIBAOXMLfFbq55wda8hy+IUC8eO7sWB4O2IjxfAwsIavfsNh6OTe77p798LxZaNK/Dq1VPo6RmiZZuuaNK0jeT9ly+fYlfgBjx58ggx0ZHo1dcLLX7rVBRFKTZEIhEOH9iMC+cOISX5PSytHdG523CUM7PK95i3b57h4L5NePlC3M7bdxqCBo2l23lmZiYO79+Eq1dOIikxDto6+vCs1RTNWnSHsrJiz7fYuWsXtmzZjBiBADbWNhgzejQqV66cZ9rQ0FAsXb4Mz589Q2paGkxNTdG+XTt079ZdKt22gAAE7Q5CZGQkdHR00KhhQ3gN84KGhkZRFOmnt2fPLmwP2IrYWAGsrKwxfMRoVKrkkWfas2dOYW/wbkSEh0MoTIeVtQ369RuIGjVqSqVLSkrCurWrcPbsabx/nwRT03Lw8hqFmrVqF0GJSoY7T8Ox89xxhL95idikBEzvMRi1nSvJO1slxr7gIOzcuQ2xAgEsrawxdNgouLl55Jn2/LnT2L9/D548Frd7S0tr9Oo9ANWq18wzfUkkEokQtMsfJ08cwIf3SbC1c0K//t6wKG/9xeOuXD6DnYHrERn5BiYmZujcdSCq16grlSbk6F4c2C8e35hbWKN3H+nxTUE/OyzsHnZsX4fHEQ+goqICSytbTJy8AOrq4r5+4YI/8PxZBBIT41C6dFm4ulVFt+6/Q0/fsJBqSf6CgnZh27bNEAgEsLa2wahRo+Hhkfc19NatUKxYsQzPnz9Daqr4Gtq2bTt07Zp9DX3y5DHWrVuDhw8f4t27txg1ajS6dOlWVMUpFnbv3oWAj3VuZW2DUSNHo1I+dX7m9Cns2ROEiIgwpKcLYW1tjf4DBsHTs5YkTUZGBjZv8sfhwwcRExON8hUqYOjQEahZs1ae5ySiovFTfENbv349RowYgfPnz+PFixffdGx6ejqaNWuGefPmYfDgwbh48SKuXLkCLy8vLF26FPfu3QMAPHnyBNWqVUNYWBgCAgIQERGBVatW4cSJE6hVqxZiY2O/Od9paWlo3Lgx1q9fj9mzZyMsLAyHDh1CZmYmPD098d9//33zOWVdLk1NTfz5558QCoX5fvaMGTOwZMkSzJ49G/fv38epU6cwaNAgxMXFfVdZSpcujaioKFy6dElq//r161GhQoWvHq+qqooTJ07g1KlTX0y3YsUKNGrUCAYGBggMDMSDBw+wefNm1K5dG6NHj/6uvMvb6RM7ce7UbrTtOAwjx/yDsmX1sHbFH0hNTc73mMcRt+FRpT5+Hz4PXqMXQ1fPCOtWTkFCfIwkzYix/2Dq7K2SbdAwHwCAu0fd/E6rMEKOBOJkSBA6dx+OiX8ug7aOPpYunvjFOk9PT4OhUTm07TAA2jr6+aYrZ2YF30WBkm3KzDWyKEKxc+xwIE4cC0LXnsMxaeoy6Ojo45+FE5Ga8vU6b9cx/zrX0zNE244DMHnackyethwOjh5YuXQ63rx+JqOSFA+XLpzEpg3L0LZDT/guWAcHJzfMmzsBMdGReaaPinyLBb6T4ODkBt8F69CmfQ9sXL8Ul/87I0mTnpYGY+Ny6NZjMHR18/83oMiOH92BU8d3o1PX4Rg3eSm0tfWwbMmkr/cthqZo3a4/tLXzrtfjRwNx/uxBdOo2HFNmrEOb9gNx4thOnD0VLKuiFAvHQkKw+O/F6NevH7Zs2gwPDw+MGu2Nd+/e5ZleS0sLnTt2wurVq7FjeyD69+uPlatWYfeePZI0h48cwfIVyzFo4EDs2B6IqVP+RMjx41i+YnlRFeundvJECJYtXYJevfti7bqNcHf3wMQJoxEZmXed37oVimrVamD+gsVYu9YflStXxeRJ4xAW9kiSRigUYuzYkXj37i1mzfbB5i2BGD9hMgyNjIqqWCVCano6bMpZwKtVZ3lnpcQ5feo4Vq38B92798HKVf5wc6uEKZPHIiqfdn/nTiiqVq2OOXMXYvmKDajkURXTpk5ARPijPNOXRPv3BeDwwZ3o228U5visgo6OPnzmjkPKF8Z9YWH38O+Smfhf3abwXbAO/6vbFP8umYGI8PuSNJcunsSmjcvQtl1P+MxbB0dHN8z3nYCYmOzxTUE+OyzsHub7TIC7ezXMnrsSs31Wo2mzdlBSUpKkcXapjJHe07Hw783wHjMLkZFvsOTv6YVcU/Jz/HgI/vlnMfr06Qd//82oVMkDY8fmfw3V1NRChw6dsGLFagQEBKJv3/5Ys2YV9u7NvoampqbBzMwcQ4d6SU0WJLETx0Pw7z+L0bt3P6zfsBmV3D0wblz+dR4aehPVa9TAXwuXwG/9RlSpUg0TJ4yVuoauWbMSwcF7MHr0OGzeEoi2bdvjj8kTpNIQUdGTeyD+w4cP2LFjB4YOHYqWLVvC39//m45fsmQJzp49ixMnTsDLywseHh6wsbFB9+7dcfnyZdjZ2QEAvLy8oK6ujmPHjqFevXqoUKECfv31Vxw/fhyvX7/GlClTvjnvS5YswaVLl3DgwAF07twZlpaWqFGjBoKCguDk5IQBAwZIZoZ/z7llUa5u3bohISEBa9euzfez9+/fj2HDhqFTp06wtrZGpUqVMGDAAIwZM+a7yqKqqoru3btj/fr1kn2vXr3C6dOn0b179y8cKVa6dGn069cPkyZNyjfNixcv4O3tDW9vb2zcuBENGzaEtbU1ateujVGjRuHatWvflXd5EolEOH9mLxo27Qq3SnVgamaFLj3HQihMQ+j10/ke1733RNSu2xJmFhVhbFIeHbuOgigrCxFhoZI0Zcrooqy2vmR7cO8yDAzLwcbWTfYF+4mJRCKcPL4HzX/rhspV68LM3Bq9+49Henoarl4+me9xVtYOaN9pMKrVaABVVbV806moKENHR1+ylS2rK4NSFC8ikQgnQvbg15biOje3sEafAeI6v/KVOu/QeTCqe+Zf5+4eteDm7gkTUwuYmFqgbYf+0NDUwtPHD/JMrygOHtiJBg1boGGjljC3sESffiNgYGiMkGN5B26Ph+yDgaEx+vQbAXMLSzRs1BL1G/6Kg/sCJWkq2jqiR++hqF2nEVTV8v83oKhEIhFOn9iDpr92g0eV/8HM3Bo9+46HMD0N167k384trRzQtuNgVK3eIN96ffrkAdw8asHVzRMGhqaoXPUXODpXxYvnYbIqTrGwLWAb2rRujbZt2sLa2hpjx4yBiYkJdgUF5ZnewcEBzZo1Q0WbijAzM0OLX39FzZo1pe5kvHPnDtzd3dG8WXOYmZmhZs2aaNq0aa47/hTVjh0BaPFbK7Rs2QZWVtYYMXI0jIyMEbx3d57pR4wcje7de8HJyRkW5Stg8OChsLAoj4sXz0vSHDq0H0mJiZjrswBubpVgaloO7u4esLW1K6pilQjVHVzQt0kr/M/FQ95ZKXGCgrajefNW+LVFa1SwtMLQYd4wMjbG/v178kw/dJg3OnfpCQdHZ5hblEf/AUNgbl4e//13oYhzLh8ikQhHDu1Cm3Y9UcPzF5SvYIOhXpORnpaKi+eP53vckUO74OZeDW3a9YC5uSXatOsBF9cqOHxolyTNoYM7Ub9hCzT4OL7p3XcEDAyMcfzj+Kagn71l4zI0+7U9WrftAYvy1ihXzgKeNetDTU1dkqbFb51gZ+8CIyNT2Du4onWb7ogIv4+MjAwZ1FrR2759G1q1ao3WrdvCysoa3t5jYGxsgj178r+GNm3aDDY2FVGunBmaN/8Vnp41cetWqCSNs7Mzhg8fiSZNmkrVJYltD9yGli1bo9XHOh/1sc735lPno7zHoEeP3nByckb58hXw+5BhsLAojwvnz0nSHD1yGL1690Wt2nVgbm6Odu06wtPTE9sDthZVsehbiLjluZVAcg/EBwYGwsHBAQ4ODujZsyc2bNjwTcHrrVu3onHjxnneaqympobSpUsjNjYWR48exbBhw6ClpSWVxtTUFD169EBgYOA3B823bduGJk2aoFIl6Vs7lZWVMXr0aNy/fx+3bt36pnN+IqtyaWtr448//sCsWbPw4cOHPD/b1NQUJ0+eRHR09HflPS8DBgxAYGAgkpPFsw38/f3RvHlzmJiYFOj4GTNm4M6dO9i1a1ee7wcFBUEoFGLChAl5vv/5DIbiIlbwDkmJcbB3rCLZp6qqDpuKbnj+9P4XjpSWnp6GzKxMaJUqm+f7GRlC3Lh2CtU9mxbLeipMgph3SEyIhZNLNck+NTV12Dm440lEwes8P1GRbzB5bBdMndQLfqvnIib67Q+fs7iLiZZtnX+SlZWJq5dPIT0tFdYVnQvtvMVNhlCIp08ewb1Sdan97u7VEfboXp7HhIfdg7u7dPpKlWrgyZNHJeYLp6wJYt4hMTEWjs5VJfvU1NRha++Op49/rJ3b2Log7GEooiJfAQBevXyMJxF34eyquEswCYVCPHz4EJ6enlL7PWt44vad2wU6x6NHj3D79m1UqZI9DvOoVAkPHz6U3JX46vVrXLx4EXXq/K/wMl9MCYVChIU9QvXq0nVevbon7t69U6BzZGVlITk5GdpltSX7Lpw/BxcXV/z9919o2+ZX9O3THZs3+yMzM7NQ80/0PYRCIcLDHqFKNen+tmrVGrh//9vafdnP2n1JFhX1FvHxsVLjCjU1dTg5eyAsLO9xCCAei7jlGIu4V6qB8I/HZGR8HN/kSONWqbrkvAX57ISEOEREPICOth6mT/XCkMHtMGvGKDx8mP+14/37RFw4fxx29i5QVf0pVv79IUKhEI8ePUSNGtL9eY0anrjzDdfQO3du57scHEkTCoUIe/QQ1XPUefUanrh7t2B1npWVheSUZGhrZ/clQmE6NNSlf/RQ19DE7dvfF6MiosIh90C8n58fevbsCQBo3rw53r9/jxMnThT4+PDwcDg6On41jUgkgpOTU57vOzk5IS4uTirwvGLFCpQpU0ZqGzJkiNRxYWFhXzznpzTfQ1blAoBhw4ZBU1MTixcvzvO4xYsXIzo6GqampnB3d8eQIUNw+PDh7yrHJx4eHqhYsSJ27doFkUgEf39/9O/fv8DHm5mZYdSoUZgyZUqegZ+wsDBoa2vD1NRUsi8oKEjq73fnTsEGxD+LpCTxUkBlyupJ7S9TVlfyXkEc3r8BOjoGsHPIeyB0784lpKa8R1XPJt+f2RIiIUG8lFNZbV2p/WW19ZCY+O3LV33O2sYRfQZMwPDR89Cj92gkJsRioe8ovH+f+EPnLe4+1at2jjrX1tZDYsKP1TkAvH71FKOGtsLwwS2wbdM/+H34dJiZ5/+MhZIuMSkBWVlZ0NGV7ld0dPWQEJ93fcfHx+aZPjMzE0lJCTLLa0mS3c6l67FsWV0kJn7fsm+fNGnWBVWr18ec6QMwauivWDB3GOo3aodqNRr80HmLs/j4eGRmZkJfX/rWdwMDfQgEgi8e+1vLlqj9vzro3bcPOnXshLZt2krea9q0KYb8/jsGDh6EmrVroV37dqhatSr69ukji2IUKwkJH+tcT3oJJT19fcTGfrnOPwkM3IbU1BQ0aNhIsu/t2zc4c+YUsjKzMH/B3+jVux92BG7D5s3+hZl9ou+SmBCPrKxM6OVs93r6iCvgsqe7dgYgNTUFv9RrKIss/nQ+jTV0dKSvh9o6eojPZxwCfByL5DhG57NjkhI/jm/ySPPpMwvy2VGRbwAAQbv80aBhS0yavADW1nbwmT0Wb9++kjouYOtq9OvdHIMHtEZMTCTGjp/79QooBvK7huoXoD9v06Yl6tWrgwED+qBDh05o3bqtDHNaciTkV+d6Xx+3fLI9YCtSU1LQsFFjyb4anjWxffs2vHz5AllZWbh65TLOnzsDgSDmC2ciIlmT60+2jx49wpUrV7B7t/iWVVVVVXTp0gXr169H48aNv3K0mEgk+uFZvJ9mjH9+nh49euRa1mX37t3w8fH57nN+a55kUS4A0NDQwKxZszB8+HAMHTo013HOzs64e/curl+/jvPnz+Ps2bNo1aoV+vbti3Xr1n13fvr3748NGzagQoUKeP/+PVq0aIFly5YV+PiJEydi9erVWL9+PTp3zr3GZc5yNmvWDKGhoXj9+jXq16+f7+yptLQ0pKWlSe0TpqdBTb1oH7x249pJ7A5cKnnd7/eZAAAl5NUOCtY2Tp/YidAbpzFk+IJ8bwG8+t9RODhVg46O4q3Vd+W/EwjYvETyeujIOQDyqHORKJ+/Q8G5uH0+W8oa1hWdMH1yH1y+eAyNmnb8oXMXJ5cvncC2TUskr728P9Z5jn+/IoiAQrhDw8TUAlNmrEJK8nvcuH4eG9f9hTETFyl0MF4sR32LRF+u7px/n0/Xl8LOVglx9fIJbN/6j+T1kOHidp6ra0F+fXzB3bh2Glcvn0CfAZNQzswKr14+RtCOldDRNYBnraY/dO7iLmebLsjYas2a1UhJTsGdu3exfPkylLewQLNmzQAA169fx/oNGzBxwgS4urji5auXWLR4MdYZGGLggAGyKkbxkrvSCzSePX78GPw3rMNcnwVSQc2srCzo6uph3PhJUFFRgYODI2JiorE9YCv69mWd088hr76mIF37qZPHsHmzH2bOnJ8rmF9SnD8XAr+1iySvJ0yaJ/6fPPuKL58r11gxr2PyOG+uv8UXPvvT+KZh41ao3+BXAICVtR3u3r2BM6cOoWv3wZLDfmvVBfUbtEBMTCSCdvlj5XJfjJ/oW2LvMBbXzZfLtnLlaqSkpODu3btYuXIZzM0t0LRps6LJYAmQq2miYNfQkJCjWL9+LXznLZTqS0aNGosF8+eiR/fOUFJSgpmZOVr81gqHDu4v7KwT0TeQayDez88PGRkZMDc3l+wTiURQU1NDXFwc9PT0vnC0mL29/VfX5rS1tYWSkhLu37+Ptm3b5nr/4cOH0NPTg6Fh9lPOdXR0YGtrK5XO2Ng412ffv5/3LeUPHz4EAMla7t9KVuX6pGfPnli4cCHmzJkDKyurXO8rKyujevXqqF69OkaPHo0tW7agV69emDJlCqytraGtrY2EhNwzIePj46Vuh/pcjx49MGHCBMyYMQO9e/f+5lv3dHV1MXnyZMycORMtW7aUes/Ozg4JCQl49+6dZFZ8mTJlYGtr+9XP8fX1xcyZM6X2dekxEt16jvqm/P0oZ9eaqGCZfRdERob4gbpJSbFSD6N8nxRfoLXFz5zchZMhgRg0zAflzK3zTBMXG4nwR6HoPeDPH8t8MeXuUQtW1rnrPDExDjq62T9MJCXFo6z21/ujb6GhoQUzc2tERb4u1PP+7Cp51IK1Te46T0jIUeeJ8blmD38PVVU1GJuIrzGW1g54/vQRTh3fgx59vH/43MWRdlkdKCsr55r9npgQn+9Db3V19ZEQlzu9iooKypTVkVleizO3Svn0LQlxUj96vk+Kz3UHzrfaG7QWTZp1RdXq4hnwZubWiBVE4tjh7QobiNfV1YWKikquWWSxsXHQ1/9ysMvcTNxf2NraIjZWgDXr1koC8atWr0KLX1tIZsnb2toiJSUVPr4+6N+vH5SV5X6jqdzo6IjrPOdsSfF4/st1fvJECBbMn4uZs3xQLccSHwYGhlBVVYGKiopkn6WlFWJjBRAKhVDjMylIjrR1dKGsrILYHLPf4+O/3u5PnzqOxYt88efUOahStfoX0xZnVavVga1d9t3bGcKP4774WOjpZV8PExPjoZPPOAQQj0Vyzpj//Jiy2nmPbxI+S6Pz8WHyX/ps3Y/7LSykJ2yYm1siJiZKap+2ti60tXVRzqw8zMwrYMSwzggPvw97e5d8y1EcfLqG5tWff+0aavbxGlqxovgaun79WgbiC0Ann3FLQer8xPEQzPOdg9lzfFG9uvQ1VE9PD77zFiItLQ2JiQkwNDTCypXLUK6cWaGXgYgKTm7fGDIyMrBp0yYsWrQIoaGhku3WrVuwtLTE1q0Fe4BE9+7dcfz4cdy8eTPPz/jw4QMMDAzQpEkTrFixAikpKVJp3r17h61bt6JLly7f/Ot1165dcfz48VzrwGdlZeHvv/+Gs7NzrvXjC0rW5VJWVoavry9WrlyJZ8+efTU/zs7iNZU/rSvv6OiY5wNQr169CgcHhzzPoa+vj9atW+PMmTPftCzN50aMGAFlZWX8888/Uvs7duwINTU1zJ8//5vPOXnyZCQkJEhtHTsP+fqBhUxTsxQMjcwkm4lpBZTV1kP4o+w2kJEhxJPHd2Bp/eU1rk+f2IUTRwMwYMhslK9gn2+6q5dDUKasDhydFXMtYU3NUjA2MZds5cwsoa2jjwf3rkvSZGQIEf7oNmxsC3ddcaEwHe/evZB8KVAUmlr51Pl92dc5IJ5ZIsxIL/TzFheqamqwtnHA7dvS/fed29dg75D3F0c7exfcyZH+9q2rsLFxKBFrocqCpmYpGBmbSzbTcpbQ1tbHowc3JGkyMoSICLv9w88sSE9Pg5Ky9HVeWVn5ux8WXxKoqanB0dERl69ckdp/5coVuLu5F/g8IpF43dZPUlPToJyjrlU+Bt8Vub4BcZ3b2zvg2jXpOr927QpcXfN/EPzx48fg6zsHU6fNQq1adXK97+rmjtevXyErK0uy79XLlzAwMGQQnuROTU0NdvYOuHFdut3fuH4Vzs75t/tTJ49h4V9zMOmPGfCsmbvdlyRaWqVgamoh2cwtrKCrqy81rsjIEOLB/dAvBrDzGovcuX0Vdh+PUVUVj29yprl7+5rkvMbG5b762UZGptDTM8SbNy+lzvP27UsYGn3h2WYfLwEZwuI/xlRTU4ODgyOu5LiGXr16BW7fcA0FgPR04dcTkfga6uCIq1dzXEOvXoGra/51HhJyFHPnzsL0GbNRu3b+z6vR0NCAkZExMjMzceb0KdStW6/Q8k6FR8T/8vyvJJLbN+gDBw4gLi4OAwYMgI6O9Iy6jh07ws/PD8OHD5fse/ToUa5zODs7w9vbGwcPHkSjRo0we/Zs/O9//0PZsmVx7do1zJ8/H35+fvDw8MCyZctQu3ZtNGvWDHPmzIG1tTXu3buH8ePHw9zcHHPnfvuabqNHj0ZwcDBatWqFRYsWwdPTE5GRkfDx8cGDBw9w/PhxqSB4SkoKQkNDpc7xadZ2TkVRrt9++w2enp5YvXq11ENTO3bsiDp16qB27dowNTXF06dPMXnyZNjb20vWrR82bBiWLVsGLy8vDB48GFpaWggJCYGfnx82b96c72f6+/tjxYoVMDD4vmVQNDU1MXPmTHh5eUntr1ChAhYtWoRRo0YhNjYWffv2hbW1NWJjY7FlyxYAkJpN9TkNDQ1oaEgvQ6OmLv9105SUlPC/em1xMiQQhoZmMDQyx8mQQKipacCjan1Juu1bFkJHxwC/tuoHQLwczdGDm9C990To65sg6ePaxOoaWtDQyH6ob1ZWFq5dDkHV6o3zrRtFo6SkhIaN2+HooQBJoPjIwQCoq2ugumf22p3+fvOhq2uIth3Et8VnZAjx9s1zAEBmhhDx8TF4+SICGhpaktnYQTtWw61STejrGyMpKR6HD2xDakoyPGsr5ozVT5SUlNCoSTscORAAY2PpOq/xWZ1vWDsfunqGaNex4HW+N8gPLm41oKdvhLTUFFy9fAphD29jxJiCLTFWUv3WshOWL/WBTUUH2Nu74MTx/YiJiUTjpq0BAAFb1yAuNgbDRvwBAGjcpDWOHdmDzf7L0bBxS4SF3cOpk4cwwnuq5JwZQiFevXom/v+MDMQJYvDsaTg0NbVgWs6iyMv4s1FSUkL9Ru1w7HAAjIzNYGRsjmOHt0NNXQPVamS3800bFkBX1wCt22W383dvX0j+PyE+Bq9ePoaGhiaMjMXt3NW9Jo4dCoCevjHKlbPEq5cROHV8N2rWVuwZaN27dcf0GdPh7OgENzc37Nm7B+8i36FD+/YAgGXLlyM6OgozZ4jviNuxcydMTU1hZSmeBRl66xa2bN2CLp8thVe37v+wbVsAHOwd4OLqglcvX2HVmtWoW7cur6MAOnfuhrlzZ8LBwQkuLq44sD8YUVGRaN2mHQBgzeoViI6JxpQp0wGIg/A+c2dixMjRcHZ2lcwE1NDQQJkyZQAAbdu0x+6gnfj338Xo0KEzXr16iS1b/NGhQ+4lCil/KWmpeCPIfmbUuzgBHr95ibKlSsNYwSYEFLYOHbpiwfxZsLd3grOzKw4eFLf7lq3aAgD81q2EICYaEyZNAyAOwi+YPxtDh3nDyclVMutYQ10DpT+2+5JMSUkJzVt0RPDeLTAtZwFTU3ME790KdQ1N1P5f9tK0K5b5QF/fULIUTPNfO2DWjJHYF7wNVavVwfVrF3D3znVMn5m9rGeL3zphxTLx+MbOzgUnT4jHN42atC7wZyspKaFlqy7YtdMflpYVYWlli7NnjuLN6xfwHi2+XkREPMDjiAdwcHRD6dJlERX5Frt2roeJiZnkh4HirmvX7pg1azqcnJzg6uqG4OA9iIx8h7ZtxdfQlSvF19Bp08R1EhS0EyYmprD8eA29desWtm3bgo4ds/tqoVCIp0+fAhCPaaKjoxEWFoZSpbRgYVG+iEv48+napTtmz54OR0dxne/7VOftxHW+auVyRMdEYepUcZ2HhBzFnNkzMMp7LFxcXCXrvmtoaEquoffu3UVMdDRs7ewREx2F9evXIkuUhe49esmnkEQEQI6BeD8/PzRu3DhXEB4AOnToAB8fH9y4cUNyK07Xrl1zpXv69CmsrKwQEhKCv//+G6tXr8a4ceNQqlQpODk5YeTIkXB1dQUgXrrk2rVrmDFjBrp06QKBQABTU1O0bdsW06dP/+otP3nR1NTEyZMn4evriz/++APPnz9H2bJl0aBBA/z333+Sz/4kLCws15PD69Wrh9OnT+c6t4aGRpGUa/78+ahdu7bUvmbNmiEgIAC+vr5ISEiAqakpGjZsiBkzZkhmP1pZWeHcuXOYMmUKmjZtitTUVNjb28Pf3x+dOnXK9/O0tLSgpaWV7/sF0adPHyxatCjXskAjRoyAk5MTFi9ejI4dOyIxMREGBgaoVasWjhw5Aje3/Gem/KzqN+oEoTAde3YtR0rye5S3dMCgoXOhqVlKkiY+LkrqB59L5w8gMzMDmzdI/wjTuHkPNP21p+R1RNhNxMdFoXpNxQ4E59SkeRekp6dj+9alSP6QBCsbR4wYM0+qzuMEUVD+rM4T4gXwnZX9vIXjR3fi+NGdsLN3x+gJ4nUx4+NisGGND96/T0SZsjqwtnHC+D/+hYHBF2bXKIimv4rrPGCLuM6tbRwxcuw8aGpl13lsbJTUrN/4eAHmzsiu85AjOxFyZCfsHNwxdqK4zhMT4rFh7XwkJsRCS6s0zC2sMWKMD5xdqhZd4X5Cteo0RNL7ROzetRHxcbEoX94aE/+YDyMj8bJe8XECxMREStIbm5TDhMnzsHnjchw7uhd6egbo038EPGtmz6aJi4vB5AmDJK8P7A/Egf2BcHKuhGkzpe9gUlSNm3WGUJiGHduWITk5CVbWjvAa5Svdt8RK9+cJ8QLMn5Pdzk+E7MKJkF2wtXfHqLELAQCdunrhYPBG7Ni2FO+T4qGjY4A6dVugecvs/l4RNW3SBAkJCVi33g8xMTGoaFMRS/7+G+XKlQMAxAhi8C4yu52LRFlYvmI53rx5AxUVFVhYWGC4lxfaf/wCDAD9+/WHkpISVq5ehejoaOjq6qLu/+piWB7P21FEDRs1QUJiAjZt9INAIIC1tQ3mz18MU1NxnQsEMYiKfCdJv3/fHmRmZmLJ3wux5O+Fkv3Nm7fA5D/EQUtjExMsXPQPli9bgv79esLQ0AgdOnZB9+4MInyLsNcvMMEvuy9efSgIANCksifGdewtr2yVCPUbNEZiYgK2blmP2FgBLK1sMMdnIUxMxO0+NlaAqKjsvubggWBkZmZi2dJFWLY0e+30Jk1bYPwExVgqslXrbkhPT8MGv7/x4UMSKto6Y/Iff0Hrs3GfQBApdQeSvYMrRoyahh2BftgZKA56jxg1HbZ22XeV1ardEO+TErE7SDy+sShvjQmTssc3Bf3sX38Tf//avGk5PrxPQgXLipj850KYmIp/AFdX18DVK+cQtNMfaWkp0NU1gLtHDYwYNS3fZ3IVN40bi6+h69f7QSCIgY1NRSxcmH0NFQhiEPnZNTQrKwsrVy7H27fia6i5uQWGDvWSBO4BICYmGn37Zo9Ntm3bgm3btqBy5SpYvnxV0RXuJ9Wosfga6r9BXOfWNhXx18K/pa6hn9d5cLD4Grp40QIsXrRAsv/XX3/DlD/FP3inp6dj7dpVePPmNbS0tFCzVm1MnToTZcuWLdrCEZEUJZGi30tLlIfgI0/knQWFU6Y0l7goagq8nLHc6JTlUgpFLSa2+N8mXtzU9NCVdxYUTnJa1tcTUaFKPXf964moUCl5Vv56IipUMQJeQ4uaVflSX09EhSqLEbEiZ2TI50zl9OpN7NcTKSALs5J35x7DMEREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDqvLOABEREREREREREZEiEonknQMqKpwRT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyZCqvDNAREREREREREREpIhEInnngIoKZ8QTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyRAD8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQyxEA8ERERERERERERkRyIRCJueWzfasWKFbC2toampiaqVq2Kc+fO5Zt29+7daNKkCYyMjKCtrY1atWrh6NGjP/JnLBAG4omIiIiIiIiIiIioWAoMDIS3tzemTJmCmzdvom7duvj111/x4sWLPNOfPXsWTZo0waFDh3D9+nU0aNAArVq1ws2bN2WaTyXR9/zEQFTCBR95Iu8sKJwypVXlnQWFo8yfYoucTlk1eWdB4cTEpss7CwqnpoeuvLOgcJLTsuSdBYWTeu66vLOgcJQ8K8s7CwonRsBraFGzKl9K3llQOFmMiBU5I0MdeWfhp/P8pUDeWfgpWZY3KHBaT09PVKlSBStXrpTsc3JyQtu2beHr61ugc7i4uKBLly6YNm3aN+e1oBiGISIiIiIiIiIiIqKfRlpaGhITE6W2tLS0XOnS09Nx/fp1NG3aVGp/06ZNcfHixQJ9VlZWFpKSkqCvr18oec8PA/FERERERERERERE9NPw9fWFjo6O1JbX7PaYmBhkZmbCxMREar+JiQnevXtXoM9atGgRPnz4gM6dOxdK3vPDtSCIiIiIiIiIiIiI6KcxefJkjBkzRmqfhoZGvumVlJSkXotEolz78hIQEIAZM2YgODgYxsbG35fZAmIgnoiIiIiIiIiIiIh+GhoaGl8MvH9iaGgIFRWVXLPfo6Kics2SzykwMBADBgzAzp070bhx4x/Kb0FwaRoiIiIiIiIiIiIiKnbU1dVRtWpVhISESO0PCQlB7dq18z0uICAAffv2xbZt2/Dbb7/JOpsAOCOeiIiIiIiIiIiIiIqpMWPGoFevXqhWrRpq1aqFNWvW4MWLFxgyZAgA8TI3r1+/xqZNmwCIg/C9e/fGP//8g5o1a0pm02tpaUFHR0dm+WQgnoiIiIiIiIiIiIiKpS5dukAgEGDWrFl4+/YtXF1dcejQIVhaWgIA3r59ixcvXkjSr169GhkZGfDy8oKXl5dkf58+feDv7y+zfCqJRCKRzM5OVEwFH3ki7ywonDKl+btgUVPm4mRFTqesmryzoHBiYtPlnQWFU9NDV95ZUDjJaVnyzoLCST13Xd5ZUDhKnpXlnQWFEyPgNbSoWZUvJe8sKJwsRsSKnJGh7GYbF1fPXgjknYWfklUFA3lnodAxDENEREREREREREREJEOcgkqUhwrmnIlQ1N5Fpco7CwqHs7OLXmw8Z5YVNZsKpeWdBYUT/z5T3llQOOzPi14aZ2cXOdHlm/LOgsJJNLWXdxYUzrtoficqavq66vLOAhEpEM6IJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhliIJ6IiIiIiIiIiIiISIZU5Z0BIiIiIiIiIiIiIkUkEsk7B1RUOCOeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhliIJ6IiIiIiIiIiIiISIYYiCciIiIiIiIiIiIikiEG4omIiIiIiIiIiIiIZIiBeCIiIiIiIiIiIiIiGWIgnoiIiIiIiIiIiIhIhhiIJyIiIiIiIiIiIiKSIVV5Z4CIiIiIiIiIiIhIEYnknQEqMpwRT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEM8WGtRERERERERERERPIg4uNaFQVnxBMRERERERERERERyRAD8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJkKq8M0BERERERERERESkiETyzgAVGc6IJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiG+LBWIiIiIiIiIiIiInng01oVBmfEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDfFhrCfHu3TvMnTsXBw8exOvXr2FsbAwPDw94e3ujUaNGAICLFy9izpw5uHTpElJSUmBnZ4e+ffvC29sbKioqAIBnz57B2toaN2/ehIeHh9RntG3bFrq6uvD395fsi4iIgI+PD44fP47IyEgYGhrC0dER/fv3R5cuXaCqKm5iSkpK2LNnD9q2bSt1zr59+yI+Ph579+7N9/XGjRvh6+uLSZMmSY7bu3cv2rVrB5Eo+4kWIpEI69atw/r163Hv3j1kZWXB0tISjRs3xogRI2Bra1sINS07x47swf592xEfFwuL8lbo3Xc4nJwr5Zv+/r1QbN64HK9ePoOengFatemGJs3aSN4/EbIfZ88cxauXTwEA1jYO6Np9EGztnCRpHty/hf3BAXj6JAxxcQKMnTAH1WvUlV0hiwGRSIQjB7fg0oVDSEl+jwpWjujYxQvlzKzyPebS+UO4evk43r55DgAoX8EWv7XpB0srR0makCPbcTv0AqIiX0JNTR1WNs5o1W4ATEzKy7pIPxWRSITgPRtx5tRBfPiQBJuKTujVZyTMLay/eNy1q2exZ9cGREW9gbGxGdp36o+q1aTbalxsNHYErsWd21cgTE+DiakF+g8cDytrewBAamoKdgauwc3rF/D+fSIMjUzRuEk7NGzcJq+PLNFEIhEO7d+MC2cPITn5PaysHdG5+3CYmVvle8yb189wcN8mvHgejlhBJDp0GYKGjdvnShcfF4O9Qetw/+5VpAvTYWxsjp59x6CCpb0MS/TzO3BgN3bv2obYWAEqWFpj8O8j4erqkWfa2NgYrFu7DBHhD/HmzSu0bt0Rg4d4S6UJCTmIJYt9ch27J/gk1NU1ZFCC4mdfcBB27tgKgUAAKytrDB3mDTd3jzzTnjt3Ggf27cbjx+EQCtNhaWmDXn0GoHr1mpI0z549wUb/tQgPe4jIyHcYOmwU2nfoWiRlKS527dqJzZs3QyCIgY2NDUaPHovKlSvnmTY0NBTLlv2LZ8+eIy0tFaampmjXrj26d+8hSXPgwH7MmjUz17Hnzl2AhgbbeV72BQdh585tiBUIYGlljaHDRsHNzSPPtOfPncb+/XvwRNLurdGr9wBU+6zd0/e58zQcO88dR/ibl4hNSsD0HoNR+wvjevoykUiEA/s24/yZg+Jxi40juvUY8dVxy/69G/H847ilU9ehaNQk97jlkyMHA7B393o0bNwOnbsNk0Epfl6HD+1B8J4AxMXFonwFK/QfMALOLvm313t3Q7Fh/TK8fPEM+voGaNuuO5r9mj2enjplJO7dDc11XJWqNfHntAUAgMzMDAQGbMDZMyGIj4+Fnp4BGjT8FR0794aysmLOF927Zxe2b98KQawA1lbWGD58NNwreeSZ9uzZUwjeuxsREeL+28rKBn37DUSNGtn99+HDBzB/3pxcxx49dobXUCI5UsweroR59uwZqlatipMnT2LBggW4c+cOjhw5ggYNGsDLywsAsGfPHtSrVw8WFhY4deoUHj58iFGjRmHu3Lno2rWrVEC7oK5cuYIqVargwYMHWL58Oe7evYsDBw6gf//+WLVqFe7du1co5dPU1MT8+fMRFxeXbxqRSITu3btj5MiRaNGiBY4dO4bbt2/j33//hZaWFubMyX0B+plcvHASG/2XoV37Xpj311o4Orljns9ExERH5pk+KvIt5vtMFKf7ay3atu8J/w3/4vJ/ZyRp7t8LRZ3/NcLUGUswy2cFDA2N4TN7HGIF0ZI0qakpsLSyRb8B3rIuYrFxImQHTp/cjQ6dvTBm4lJoa+th5dLJSE1NzveYiPDbqFKtAby8F8B7/N/Q1TfGyqV/ID4+RpLmccRt/K9eK3iPX4KhI32RlZWJVUv/QFpaalEU66dx6OB2HD28Cz16j8C0mSuho6OPhfMnICXlS/V7DyuXzUKtOk0wa+5a1KrTBCuXzcLjiAeSNB8+JGHu7JFQVVHBmHG+mDtvA7p2H4pSpUpL0gRsXY67t69i8NA/4DPfH02bdcTWzUtx4/oFmZb5ZxRyZAdOhuxG5+7DMWHKUmjr6GHZ35O+2M6F6WkwMDRFm/b9oa2jn2ea5A9JWDR/NFRUVDFs1FxMnbkW7Tv/Di2tMrIqSrFw9sxxrF39D7p07Y1/l22Aq4s7pk8dh6iod3mmFwqF0NHRRZeufWBtnf+PyKVKlcbmrfukNgbhxU6fOo6VK5agW/e+WLl6I1zdKuGPyWMQFZl3nd+5fRNVqtbAXJ9FWL7SH5U8qmDan+MREf5IkiYtNRXlyplhwMBh0Nc3KKqiFBshIcewePEi9OvXH5s3b4WHR2V4e4/Eu3d517mWliY6deqM1avXIDBwJ/r3H4BVq1Ziz57dUulKly6NQ4eOSG0MIOTt9KnjWLXyH3Tv3gcrV/nDza0Spkwem3+7vxOKqlWrY87chVi+YgMqeVTFtKkTpNo9fZ/U9HTYlLOAV6vO8s5KiXDscCBOHAtC1x7DMenPZdDR1sc/iyYi9Qvjx/T0NBgalUO7DgPyHbd88uzpI5w7ewjmFjaFnfWf3vlzJ7DBbyk6dOqNRX+vg5OzO+bMmoDofL6HRka+wZxZE+Dk7I5Ff69D+4694LfuH1y6eFqSZsKkOfDz3yPZlizdCGVlFdSu00CSZk/QNhw9sg8Dfx+Nf5dtRq8+Q7F3TwAOHQySdZF/SidPhmDZsiXo2asv1q3dCDd3D0yYOBqR+fTft26Folq1Gpg/fzHWrPVH5cpV8cfkcQgPk+6/S5cujaDdB6U2XkN/TiJueW4lEWfElwDDhg2DkpISrly5gtKls4NOLi4u6N+/Pz58+IBBgwahdevWWLNmjeT9gQMHwsTEBK1bt8aOHTvQpUuXAn+mSCRC3759YW9vjwsXLkj9al25cmX06NHju4L7eWncuDEiIiLg6+uLBQsW5JkmMDAQ27dvR3BwMFq3bi3Zb2Njg0aNGhVaXmTl4P4daNCwBRo2bgkA6NNvBG6FXkHIsWB06zE4V/qQY8EwMDRGn34jAADmFlZ48vgRDuzbDs+a9QAAI7ynSh0zeMh4XP7vDO7euY5f6jcHAFSuUhOVq3DW0ycikQhnT+5Fk+ZdUany/wAAPXqPw5+TuuL61VOoU/e3PI/r1W+S1OuuPbxx6+Z5hD28iRo1mwAAhgyXnrnavddY/DmxC169CEdFOzcZlObnIxKJEHIkCC3b9EC16r8AAAb+PhGjhnfAf5dOoEHDVnked+xoEFxcq6Fl6+4AgJatu+PRw1sIOboLFW3F7fzQgQDo6xtjwOCJkuMMjUylzvM4/D7q1G0GRycPAED9hi1x+tR+PHv6CFWq1ins4v60RCIRTp3Yg2YtusGjirid9+o3HpPHdsHVyydRt17LPI+ztHaApbUDACB49/o80xw7sgN6ekbo1W+cZJ+BoWmeaRXJnj2BaNq0JZo1F1+fBg/xxvUbV3Do4B707Tc0V3oTk3L4/eMM+JBjB/M9r5KSEgPC+QjaFYDmv7ZCi9/EdT7MazSuXbuM/ft3Y8DA3DMdh3mNlno9YOBQXLp4DpcunYetnbjdOzg6w8HRGQDgt26FjEtQ/GzbthWtW7eR3P04ZsxY/PffJQQF7YKX1/Bc6R0cHOHgkH3nmJmZGU6dOoXQ0Jto1y571qqSkhIMDQ1lnv+SIChoO5o3b4VfW4jb/dBh3h/b/R4MGJi7rxk6zFvqdf8BQ3Dp4jn8998FSbun71PdwQXVHVzknY0SQSQS4cTxPfj1t26oXFV8N2SfAeMxYXRnXLl8Er/Uz3vcYmXtAKuP45Y9QX75nj81NQXr1/qiZ5/ROHRga+EX4Ce3P3gHGjX+DU2aiutxwMCRCL15BUcP70XP3r/nSn/0SDAMjYwxYOBIAIBFeSs8jniI4L2BqFW7PgCgbFltqWPOnzsBDQ0N1K5TX7Lv0aN7qOFZB9Wq1QIAGJuUw/lzx/E4QjF/CNy5IwAtWrRCy5biOwtGjBiNq1f+Q3DwbgwenHvcMmKE9Lhl0OChuHDhLC5ePA87+8/6byUlGBhwrEj0M+GM+GIuNjYWR44cgZeXl1QQ/hNdXV0cO3YMAoEA48aNy/V+q1atYG9vj4CAgG/63NDQUDx48ADjxo3L99YxJSWlbzpnflRUVODj44OlS5fi1atXeaYJCAiAg4ODVBBeFnmRhQyhEE+fhMG9UnWp/e6VqiPs0d08jwkPu5c7vUd1PHn8CBkZGXkek5aehozMDJQuo53n+wQIBO+QmBgLR6eqkn2qauqwtXPDsyf3C3ye9PQ0ZGVmoHTpsvmmSUn5AAAo9YU0JU109FskJMTC1bWaZJ+amjocHCshIjz/O2geR9yHi2tVqX2ubtWkjgm9cQnW1vZY/u8MjBzWHtP/HIwzpw5IHWPn4IabNy4iLjYaIpEID+7fROS7V3B1k/63VNIJYt4hMSEWTi7Zdaqmpg5be3c8fVzwdp6XO7cuoYKVHdatmo2JYzrBd9ZQXDh76EezXKwJhUJEhD9C5So1pPZXqVIDD+7n3ccXVEpKCvr2aY/ePdtixvTxeBwR9kPnKymEQiHCwh6hajXpOq9a1RP37t0p0DmysrKQnJKMstq8ZhaEUCjEw4cP4ekp/eO+p2dN3L59u0DnePToIW7fvo3KlaX7+5SUFLRu3RItW7bA6NHeePToYaHluyQRCoUID3uEKrnafQ3cv/8N7T45OVcQjUieYiTjFunxo52DO5784LgFALZvXQpXd084OVf54XMVN0KhEI8fh6GSh/RY2MOjOh4+zHuMEvbwHjxypq9cA48jHub7PfTE8YP4X91G0NTUkuxzcnLD7ds38Ob1SwDA06cReHD/DqpUVbxJYkKhEI/CHqF6dU+p/dWre+Le3W/sv3OMW1JSUtClc1t07NgKkyaNzTVjnoiKHmfEF3MREREQiURwdHTMN01YmPiLuZOTU57vOzo6StIU1Kf0Dg7Zv7ZGRUXBxib7dr4FCxZg2LDsX2+7desmWYv+k7S0NPz2W96zjD/Xrl07eHh4YPr06fDzyz2jISwsTCovAODt7Y1169YBEP8gkV8QX94SkxKQlZUJnRy3TOro6CE+PjbPY+LjY6Gjo5cjvT4yMzORlJQAPb3cv3oHbFkNfX0juLlXzfUeiSUliOu7bFnpui1bVg+xsVEFPs+Bveuho2sAe8e8B/QikQh7g9bApqLLF9eeL2kSPrZn7ZxtV1sPMYK8b3/9dFzOW4q1dfSRkJC9XFVU9BucPLkPzZp3QsvWPfDkyUNs3bwMqmrqqPO/pgCAHr2GY4PfIowZ1QUqKipQUlJGvwFjYe+gGHckfJL4qZ1rS/8dtLV1ESsoeDvPS0z0W5w7fQANm3RAsxbd8OzpQ+zcvgKqqmrwrN3kh85dXCUmxiMrKxO6etJtWFdXD3Fxgu8+b3kLS4weOwVWVjZITv6AfXt3Yvy4IVi6fCPMzRXr2RM5JSSI61wvR53r6ekhLjbv62pOu3ZuQ2pKCurVaySLLJY48fHxyMzMhIGBdJ3r6+tDIIjJ5yixli1bIC4uDpmZmRg0aLDU84QsLa0wbdp0VKxoiw8fPiAwMAADBw7A1q0BqFChgiyKUmwl5tvu9b+h3QcgNTUFv9RrKIssEn2XT+MWbW1dqf3a2nqI/cL4sSCuXj6FF8/DMXnq8h86T3GVlCj+Hqqrm2NsrquP+Li8+424+Fh46OYe02RmZiIxMR76+tJ3MIWH3ceL50/hNXyi1P52HXogOfkDRnj1hLKyMrKystC95yDU/aVxIZSseElIiEdWZib09HP337GxBRsr7gjchtTUFDRokD1uqVDBCpMm/QkbG1skf/iAXUGBGD58MPzWb4aFBa+hRPLCQHwx92nJlYLM+M5veRaRSPTdM8Y/P87AwAChoaEAgPr16yM9PV0q7d9//43GjaUvrBMnTkRmZmaBPmv+/Plo2LAhxo4d+9W8AMCUKVMwfPhw7N69Gz4+uR9o90laWhrS0tKk9qWnpxX5Ort5/QmUkP/fJfff7GNbyCPtvr3bcOHCCUyb8Q/XD/7MtSsnsSPgH8nrwUNni/8nRyWKIMrz75OXE8d24Ma1Uxju/RfU1NTzTBMUuBxvXj/FqLGLvifbxcalC8exccNiyWvvsb4AcrddEURfbOviY3LsEImk/k6iLBGsrO3RsfNAAICllR3evHqGUyf2SQLxIUd340nEfYwaPQcGhiZ49Og2Nm/8Bzq6Brlm3JckV/47gYAt2e182AjxMzPy7EF+8O4hkUiEClb2aNO+PwDxg4vfvnmOc2cOKGwg/pNc7V70Y3drOTq5wtHJVfLa2dkdI0f0w/59uzBk6OgvHKk4cvYrIhSsiZ88eQybN/lh5qz5uYKa9DU52/nXx5irV69FSkoK7t69g2XLlsHCwgLNmomX0HNzc4ObW/aPpZUqVUKvXj2xY0cgxo0bX/jZLwFyVrcox/UyP6dOHsPmzX6YOZPtnuTr8n8nsG3TEslrr1Efxy25rqOiHxq3xMZGYcf2FRg1Zl6+Y3ZFkauf/krfnXssn895ABw/fhAVLK1hZ+8stf/CuZM4c/oYRo+ZhvIVrPD0aQTW+y2Fvr74oa2KKPe4pWBt/MTxY/D3X4c5cxdI9d8uLq5wcckeK7q6uWPQoD7YHbQTI0flHVMhItljIL6Ys7Ozg5KSEh48eCA1g+hz9vb2AIAHDx6gdu3aud5/+PAhnJ3FF0YdHR0AQEJCQq508fHxsLS0lHzup2M9PDwAiJeQsbUVP1ROVTV30zI1NZW8/0nZsmURHx//lVKK/fLLL2jWrBn++OMP9O3bV+o9Ozs7PHwofauykZERjIyMYGxs/MXz+vr6YubMmVL7Bg8ZiyHDci/lIwvaZXWgrKySa/Z7QkIcdHLMTvhEV1c/z/QqKiooU1ZHav/+4O3Yu3srpkxbBEurioWb+WLO1b0mLK2y76TIyBACAJIS46Cjk31Xwfuk+Fyz5PNyMmQnQo5ux7CR82CWz8OeggKX4+7tSxgxZhF09Yx+sAQ/N48qtWFjm30nToZQ/ONcQnwsdHWz6zcxMT7XLPnP6ejqS2bTZx8TB53PZnTr6urDzNxKKk05swq4du0sAPGPa0E7/TDCexYqeYhveS1foSJePH+MI4d2lOhAvLtHLVjZZN81lSEUt/PExDjofPZ3SEqMzzXb7Ftp6+ijXDnpGTam5Sog9Mb5HzpvcaatrQtlZRXE5ZjRlJAQB13dwgt2KSsrw97eCW/e/Jx3fxUlHR1xncfmuOMgPi4u150JOZ0+dRyLF/pg6rS5qFK1xhfTUjZdXV2oqKhAIJCu87i4uK8+x8Dc3BwAYGtrC4FAgLVr10gC8TkpKyvD2dkZL1++LJyMlyDan9p9jtnv8fFxXw2snz51HIsX+eLPqXNQpapiLddGP59KlWrBevpn45aP43Pxd6PPxi1J8dDW/vr4PD8vnoUjKTEePrOy7+DOyspCRNgdnD4ZjGWrD0FZWeULZyj+ymqLv4fGxRX8e6ierj7ic1xfE+LF30PL5vgempaWigvnTqJr9/65zrPRfwXad+iB//0insFtaVUR0dHvsHvXVoULxOvo6EJZRSXX7Pf4uDjof6X/PnkyBAsWzMWMmT6oVu3L4xZlZWU4Ojjh1SteQ39KP/djDakQcY34Yk5fXx/NmjXD8uXL8eHDh1zvx8fHo2nTptDX18eiRbln3+7btw/h4eHo1q0bAPFt20ZGRrh69apUupSUFNy7d0+y/EvlypXh6OiIhQsXIisrSwYly9u8efOwf/9+XLx4UWp/t27d8OjRIwQHB3/zOSdPnoyEhASprf/AEYWV5a9SVVODtY097ty+JrX/zu1rsHdwzfMYO3uXXOlv37oKm4oOUj+C7A8OwO6gTZj85wJUtM1/+SJFpalZCkbG5pLNtJwltLX18ejBDUmajAwhIsLvwMrG+QtnEgfhjx3ehiHD56KCpX2u90UiEXYFLsPt0Avw8l6gEA+w1NIqBRMTc8lmZm4FHR193Lt7XZImI0OIRw9vwdYu/weaVbR1ljoGAO7dvS51jK29K969lR5URr57BQMDEwBAZmYGMjMzcs3UUVZWhkhUdH2YPGhqloKxsblkK2dmCW0dfTy8n6Odh92GdcUvt/OvqWjrgsh30oHgqMhX0P/4d1BEampqsLVzwM2b0tfVmzeuwsk57z7+e4hEIjx5HA79PJYmUzRqamqwt3fAjevSdX7j+hW4uOS/FNXJk8fw14LZmPzHTHjWVJwHOBcGNTU1ODo64sqVy1L7r1y5DHd39286l/Djj4V5EYlECAsLg6Eh23lOampqsLN3wI3rV6T237h+Fc7O+bf7UyePYeFfczDpjxls9/RT0NQqBWMTc8n2adzy4L70+DH80W3Y/MC4xdGpMqbOXIMp01dJNksre9TwbIgp01eV+CA8IO43Kla0x61b0t8rb4Veg6Nj3mMUe0cX3ArNmf4qKto65pqMd+H8KQiFQtSr1zTXedLS06CU41lzysoqyCrh4/K8qKmpwcHeAdeuSfff165dgYtr/v33iePHMM93Dv6cOgu1an29/xaJRIiICIeBAR+ATiRPnBFfAqxYsQK1a9dGjRo1MGvWLLi7uyMjIwMhISFYuXIlHjx4gNWrV6Nr164YPHgwhg8fDm1tbZw4cQLjx49Hx44d0blzZ8n5xo0bBx8fH5iYmKB27dqIi4vD/Pnzoaqqip49ewIQ33a2YcMGNGnSBHXq1MHkyZPh5OQEoVCIs2fPIjo6Otd68IXBzc0NPXr0wNKlS6X2d+3aFbt370bXrl0xefJkNGvWDCYmJnj+/DkCAwO/mBcNDQ1oaEgv16Kunlzoef+S31p1xvKlc2Fj4wB7BxccDzmAmJgoNG4qfvhswNY1iBVEw2vkFABAk6ZtcOzIHmzyX4ZGjVsi7NE9nDp5CCO9p0nOuW/vNuzYvh4jvKfCyMhUMnNBU1MLmlqlAACpKcl49+615JioyLd49jQcZcpow9BI8QJnSkpK+KVhW4Qc3S4JzoccCYC6ugaqVm8gSbfFfwF0dA3Rqq14dseJYztw6MAm9O43Efr6JpK1LDU0tKDx8aFEu7Yvw/VrpzDw9xnQ0NCSpNHUKq0wywUpKSmhSfMOOLB/K0xMzWFiYoED+7dCQ10TNWtlr2e4dpUvdPUM0anLIABAk6btMW+uNw4eCECVKnVw48YF3L93HZP//FdyTNPmHeEzawQO7NuK6p718eTxQ5w+dRB9+48BAGhplYaDYyXsCFgNdXUNGBiY4NHDW7h4/hi6dh9atBUhZ0pKSmjQqB2OHgqAkbEZjE3McfTQdqira6C6Z/a6wBv9FkBXzwBt2g8AIP7S+/bNCwBAZoYQ8XExePniMTQ0NWFsLJ7R2rBxeyyc740jBwNQpfoveP70ES6cPYRuvbyLvJw/k3btumDRwtmws3OEo5MrjhwORnR0JFq0aAcA8N+wEgJBDMaOmyo55vFj8bNYUlKTkZAQj8ePw6CmqoYKltYAgG1b18PB0QVmZhZITv6A/cG78ORJOIZ68VZjAOjQsRvmz5sJe3tHODm74dDBvYiKikTLVuI691u3AjEx0Zg4aToAcRB+wbxZGOY1Gk7OrpJZaRrqGihdpgwAcYD4+fOn/2fvvqOiOto4jv9AEStNBSwoYKHaWyxJbGjsGruxxvLGjhp7NPYWW+wNe41do7FrYhITTbF3TbNLNwqCsO8f6OoKWKILIt/POXsO3J17d2b2Ybj77Ny5cT8/eKDAwNu6ePG8MmTIkOrX5ZekFi0+0uefD5WXl5cKFSqsjRs36MaNG/rww4aSpJkzZ+jWrVsaPnyEJGnt2q/k7OysvHldJUnHjh3V8uXL1KRJU+Mx58+fJ1/fQsqTx+XhGvGrdf78OfXr1y/J25cSNGzYTBPGj1DBgl7y9vbVtm2bH8Z9fUlSwILZCgq8rX4D4s4X9+/bpQnjR6pzF395eSUc9/hvIu5H6lrQbePvN0KCdOnaP8qSMZMcX+PVUKmBhYWFqlRtoB3bVsUl5x1zacf2uPPz0k+ctyxaMF529tnUoOGT5y1/SXryvOWirK0zyNEpl9JnyKhcud1MXiuddXplymwTb/vbrE69Jpo2dbTy5/eQh4ePdu3cqsDAW6r2QT1J0vKlcxUU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\n"},"metadata":{}}]},{"cell_type":"code","source":"kot = cn[cn>=.40]\nplt.figure(figsize=(12,8))\nsns.heatmap(kot, cmap=\"Blues\")","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:53.597334Z","iopub.execute_input":"2023-03-23T10:38:53.597775Z","iopub.status.idle":"2023-03-23T10:38:54.137007Z","shell.execute_reply.started":"2023-03-23T10:38:53.597732Z","shell.execute_reply":"2023-03-23T10:38:54.135425Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x800 with 2 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"# ***Feature Engineering***","metadata":{}},{"cell_type":"markdown","source":"Feature Engineering is the process of creating new features using existing features.","metadata":{}},{"cell_type":"markdown","source":"***The correlation matrix shows that ANXIETY and YELLOW_FINGERS are correlated more than 50%. So, lets create a new feature combining them.***","metadata":{}},{"cell_type":"code","source":"df_new['ANXYELFIN']=df_new['ANXIETY']*df_new['YELLOW_FINGERS']\ndf_new","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.138985Z","iopub.execute_input":"2023-03-23T10:38:54.139393Z","iopub.status.idle":"2023-03-23T10:38:54.161599Z","shell.execute_reply.started":"2023-03-23T10:38:54.139352Z","shell.execute_reply":"2023-03-23T10:38:54.160154Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"     YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  CHRONIC DISEASE  FATIGUE   \\\n0                 1        1              0                0         1   \n1                 0        0              0                1         1   \n2                 0        0              1                0         1   \n3                 1        1              0                0         0   \n4                 1        0              0                0         0   \n..              ...      ...            ...              ...       ...   \n279               1        1              1                0         0   \n280               0        0              0                1         1   \n281               0        0              0                0         1   \n282               1        1              0                0         0   \n283               1        1              0                0         1   \n\n     ALLERGY   WHEEZING  ALCOHOL CONSUMING  COUGHING  SWALLOWING DIFFICULTY  \\\n0           0         1                  1         1                      1   \n1           1         0                  0         0                      1   \n2           0         1                  0         1                      0   \n3           0         0                  1         0                      1   \n4           0         1                  0         1                      0   \n..        ...       ...                ...       ...                    ...   \n279         1         1                  0         1                      1   \n280         1         0                  0         0                      0   \n281         1         0                  0         0                      0   \n282         0         0                  0         0                      1   \n283         0         1                  1         1                      1   \n\n     CHEST PAIN  LUNG_CANCER  ANXYELFIN  \n0             1            1          1  \n1             1            1          0  \n2             1            0          0  \n3             1            0          1  \n4             0            0          0  \n..          ...          ...        ...  \n279           0            1          1  \n280           0            0          0  \n281           1            0          0  \n282           1            0          1  \n283           1            1          1  \n\n[276 rows x 13 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>YELLOW_FINGERS</th>\n      <th>ANXIETY</th>\n      <th>PEER_PRESSURE</th>\n      <th>CHRONIC DISEASE</th>\n      <th>FATIGUE</th>\n      <th>ALLERGY</th>\n      <th>WHEEZING</th>\n      <th>ALCOHOL CONSUMING</th>\n      <th>COUGHING</th>\n      <th>SWALLOWING DIFFICULTY</th>\n      <th>CHEST PAIN</th>\n      <th>LUNG_CANCER</th>\n      <th>ANXYELFIN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>279</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>280</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>281</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>282</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>283</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>276 rows × 13 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#Splitting independent and dependent variables\nX = df_new.drop('LUNG_CANCER', axis = 1)\ny = df_new['LUNG_CANCER']","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.163352Z","iopub.execute_input":"2023-03-23T10:38:54.163963Z","iopub.status.idle":"2023-03-23T10:38:54.175734Z","shell.execute_reply.started":"2023-03-23T10:38:54.163918Z","shell.execute_reply":"2023-03-23T10:38:54.174626Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"markdown","source":"# ***Target Distribution Imbalance Handling***","metadata":{}},{"cell_type":"code","source":"from imblearn.over_sampling import ADASYN\nadasyn = ADASYN(random_state=42)\nX, y = adasyn.fit_resample(X, y)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.176991Z","iopub.execute_input":"2023-03-23T10:38:54.177960Z","iopub.status.idle":"2023-03-23T10:38:54.322622Z","shell.execute_reply.started":"2023-03-23T10:38:54.177914Z","shell.execute_reply":"2023-03-23T10:38:54.321100Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"len(X)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.324112Z","iopub.execute_input":"2023-03-23T10:38:54.324551Z","iopub.status.idle":"2023-03-23T10:38:54.334768Z","shell.execute_reply.started":"2023-03-23T10:38:54.324508Z","shell.execute_reply":"2023-03-23T10:38:54.333149Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"477"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Logistic Regression**","metadata":{}},{"cell_type":"code","source":"#Splitting data for training and testing\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test= train_test_split(X, y, test_size= 0.25, random_state=0)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.336852Z","iopub.execute_input":"2023-03-23T10:38:54.337463Z","iopub.status.idle":"2023-03-23T10:38:54.349868Z","shell.execute_reply.started":"2023-03-23T10:38:54.337399Z","shell.execute_reply":"2023-03-23T10:38:54.347876Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"#Fitting training data to the model\nfrom sklearn.linear_model import LogisticRegression\nlr_model=LogisticRegression(random_state=0)\nlr_model.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.353903Z","iopub.execute_input":"2023-03-23T10:38:54.354427Z","iopub.status.idle":"2023-03-23T10:38:54.376833Z","shell.execute_reply.started":"2023-03-23T10:38:54.354376Z","shell.execute_reply":"2023-03-23T10:38:54.374923Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"LogisticRegression(random_state=0)"},"metadata":{}}]},{"cell_type":"code","source":"#Predicting result using testing data\ny_lr_pred= lr_model.predict(X_test)\ny_lr_pred","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.381725Z","iopub.execute_input":"2023-03-23T10:38:54.382225Z","iopub.status.idle":"2023-03-23T10:38:54.395930Z","shell.execute_reply.started":"2023-03-23T10:38:54.382177Z","shell.execute_reply":"2023-03-23T10:38:54.394166Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"array([1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n       1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n       0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n       1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"},"metadata":{}}]},{"cell_type":"code","source":"#Model accuracy\nfrom sklearn.metrics import classification_report, accuracy_score, f1_score\nlr_cr=classification_report(y_test, y_lr_pred)\nprint(lr_cr)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.398106Z","iopub.execute_input":"2023-03-23T10:38:54.398743Z","iopub.status.idle":"2023-03-23T10:38:54.415639Z","shell.execute_reply.started":"2023-03-23T10:38:54.398629Z","shell.execute_reply":"2023-03-23T10:38:54.413990Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.96      1.00      0.98        64\n           1       1.00      0.95      0.97        56\n\n    accuracy                           0.97       120\n   macro avg       0.98      0.97      0.97       120\nweighted avg       0.98      0.97      0.97       120\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**This model is almost 97% accurate.**","metadata":{}},{"cell_type":"markdown","source":"# **Decision Tree**","metadata":{}},{"cell_type":"code","source":"#Fitting training data to the model\nfrom sklearn.tree import DecisionTreeClassifier\ndt_model= DecisionTreeClassifier(criterion='entropy', random_state=0)  \ndt_model.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.421218Z","iopub.execute_input":"2023-03-23T10:38:54.421754Z","iopub.status.idle":"2023-03-23T10:38:54.437026Z","shell.execute_reply.started":"2023-03-23T10:38:54.421699Z","shell.execute_reply":"2023-03-23T10:38:54.435235Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"DecisionTreeClassifier(criterion='entropy', random_state=0)"},"metadata":{}}]},{"cell_type":"code","source":"#Predicting result using testing data\ny_dt_pred= dt_model.predict(X_test)\ny_dt_pred","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.438890Z","iopub.execute_input":"2023-03-23T10:38:54.440057Z","iopub.status.idle":"2023-03-23T10:38:54.453513Z","shell.execute_reply.started":"2023-03-23T10:38:54.439979Z","shell.execute_reply":"2023-03-23T10:38:54.451995Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"array([1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n       1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n       1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n       1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n       1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n       1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"},"metadata":{}}]},{"cell_type":"code","source":"#Model accuracy\ndt_cr=classification_report(y_test, y_dt_pred)\nprint(dt_cr)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.455177Z","iopub.execute_input":"2023-03-23T10:38:54.456013Z","iopub.status.idle":"2023-03-23T10:38:54.470225Z","shell.execute_reply.started":"2023-03-23T10:38:54.455937Z","shell.execute_reply":"2023-03-23T10:38:54.468811Z"},"trusted":true},"execution_count":44,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.93      0.97      0.95        64\n           1       0.96      0.91      0.94        56\n\n    accuracy                           0.94       120\n   macro avg       0.94      0.94      0.94       120\nweighted avg       0.94      0.94      0.94       120\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**This model is 94% accurate.**","metadata":{}},{"cell_type":"markdown","source":"# **K Nearest Neighbor**","metadata":{}},{"cell_type":"code","source":"#Fitting K-NN classifier to the training set  \nfrom sklearn.neighbors import KNeighborsClassifier  \nknn_model= KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2 )  \nknn_model.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.471851Z","iopub.execute_input":"2023-03-23T10:38:54.472276Z","iopub.status.idle":"2023-03-23T10:38:54.485832Z","shell.execute_reply.started":"2023-03-23T10:38:54.472233Z","shell.execute_reply":"2023-03-23T10:38:54.483815Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"KNeighborsClassifier()"},"metadata":{}}]},{"cell_type":"code","source":"#Predicting result using testing data\ny_knn_pred= knn_model.predict(X_test)\ny_knn_pred","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.488423Z","iopub.execute_input":"2023-03-23T10:38:54.489834Z","iopub.status.idle":"2023-03-23T10:38:54.507062Z","shell.execute_reply.started":"2023-03-23T10:38:54.489756Z","shell.execute_reply":"2023-03-23T10:38:54.505730Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"array([1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n       1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n       1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n       1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n       1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"},"metadata":{}}]},{"cell_type":"code","source":"#Model accuracy\nknn_cr=classification_report(y_test, y_knn_pred)\nprint(knn_cr)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.508273Z","iopub.execute_input":"2023-03-23T10:38:54.508738Z","iopub.status.idle":"2023-03-23T10:38:54.523263Z","shell.execute_reply.started":"2023-03-23T10:38:54.508693Z","shell.execute_reply":"2023-03-23T10:38:54.521889Z"},"trusted":true},"execution_count":47,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.93      1.00      0.96        64\n           1       1.00      0.91      0.95        56\n\n    accuracy                           0.96       120\n   macro avg       0.96      0.96      0.96       120\nweighted avg       0.96      0.96      0.96       120\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**This model is 96% accurate.**","metadata":{}},{"cell_type":"markdown","source":"# **Gaussian Naive Bayes**","metadata":{}},{"cell_type":"code","source":"#Fitting Gaussian Naive Bayes classifier to the training set  \nfrom sklearn.naive_bayes import GaussianNB\ngnb_model = GaussianNB()\ngnb_model.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.525219Z","iopub.execute_input":"2023-03-23T10:38:54.525698Z","iopub.status.idle":"2023-03-23T10:38:54.541394Z","shell.execute_reply.started":"2023-03-23T10:38:54.525649Z","shell.execute_reply":"2023-03-23T10:38:54.540213Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"GaussianNB()"},"metadata":{}}]},{"cell_type":"code","source":"#Predicting result using testing data\ny_gnb_pred= gnb_model.predict(X_test)\ny_gnb_pred","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.542836Z","iopub.execute_input":"2023-03-23T10:38:54.543406Z","iopub.status.idle":"2023-03-23T10:38:54.561204Z","shell.execute_reply.started":"2023-03-23T10:38:54.543357Z","shell.execute_reply":"2023-03-23T10:38:54.559772Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"array([1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n       1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1,\n       1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n       0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n       1, 1, 1, 0, 0, 1, 0, 1, 1, 0])"},"metadata":{}}]},{"cell_type":"code","source":"#Model accuracy\ngnb_cr=classification_report(y_test, y_gnb_pred)\nprint(gnb_cr)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.562719Z","iopub.execute_input":"2023-03-23T10:38:54.563189Z","iopub.status.idle":"2023-03-23T10:38:54.580652Z","shell.execute_reply.started":"2023-03-23T10:38:54.563105Z","shell.execute_reply":"2023-03-23T10:38:54.578996Z"},"trusted":true},"execution_count":50,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.95      0.89      0.92        64\n           1       0.88      0.95      0.91        56\n\n    accuracy                           0.92       120\n   macro avg       0.92      0.92      0.92       120\nweighted avg       0.92      0.92      0.92       120\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**This model is 92% accurate.**","metadata":{}},{"cell_type":"markdown","source":"# **Multinomial Naive Bayes**","metadata":{}},{"cell_type":"code","source":"#Fitting Multinomial Naive Bayes classifier to the training set  \nfrom sklearn.naive_bayes import MultinomialNB\nmnb_model = MultinomialNB()\nmnb_model.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.582742Z","iopub.execute_input":"2023-03-23T10:38:54.583451Z","iopub.status.idle":"2023-03-23T10:38:54.599156Z","shell.execute_reply.started":"2023-03-23T10:38:54.583397Z","shell.execute_reply":"2023-03-23T10:38:54.597682Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"MultinomialNB()"},"metadata":{}}]},{"cell_type":"code","source":"#Predicting result using testing data\ny_mnb_pred= mnb_model.predict(X_test)\ny_mnb_pred","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.601735Z","iopub.execute_input":"2023-03-23T10:38:54.603383Z","iopub.status.idle":"2023-03-23T10:38:54.616113Z","shell.execute_reply.started":"2023-03-23T10:38:54.603318Z","shell.execute_reply":"2023-03-23T10:38:54.614687Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"array([1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0,\n       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1,\n       1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n       0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n       1, 1, 1, 1, 1, 1, 0, 0, 1, 0])"},"metadata":{}}]},{"cell_type":"code","source":"#Model accuracy\nmnb_cr=classification_report(y_test, y_mnb_pred)\nprint(mnb_cr)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.617810Z","iopub.execute_input":"2023-03-23T10:38:54.618353Z","iopub.status.idle":"2023-03-23T10:38:54.634941Z","shell.execute_reply.started":"2023-03-23T10:38:54.618292Z","shell.execute_reply":"2023-03-23T10:38:54.632377Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.89      0.73      0.80        64\n           1       0.75      0.89      0.81        56\n\n    accuracy                           0.81       120\n   macro avg       0.82      0.81      0.81       120\nweighted avg       0.82      0.81      0.81       120\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**This model is 81% accurate.**","metadata":{}},{"cell_type":"markdown","source":"# **Support Vector Classifier**","metadata":{}},{"cell_type":"code","source":"#Fitting SVC to the training set  \nfrom sklearn.svm import SVC\nsvc_model = SVC()\nsvc_model.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.637055Z","iopub.execute_input":"2023-03-23T10:38:54.637553Z","iopub.status.idle":"2023-03-23T10:38:54.654698Z","shell.execute_reply.started":"2023-03-23T10:38:54.637501Z","shell.execute_reply":"2023-03-23T10:38:54.653256Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"SVC()"},"metadata":{}}]},{"cell_type":"code","source":"#Predicting result using testing data\ny_svc_pred= svc_model.predict(X_test)\ny_svc_pred","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.657713Z","iopub.execute_input":"2023-03-23T10:38:54.658347Z","iopub.status.idle":"2023-03-23T10:38:54.673998Z","shell.execute_reply.started":"2023-03-23T10:38:54.658281Z","shell.execute_reply":"2023-03-23T10:38:54.672371Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"array([1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n       1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n       1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n       1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"},"metadata":{}}]},{"cell_type":"code","source":"#Model accuracy\nsvc_cr=classification_report(y_test, y_svc_pred)\nprint(svc_cr)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.678447Z","iopub.execute_input":"2023-03-23T10:38:54.678969Z","iopub.status.idle":"2023-03-23T10:38:54.694174Z","shell.execute_reply.started":"2023-03-23T10:38:54.678921Z","shell.execute_reply":"2023-03-23T10:38:54.692697Z"},"trusted":true},"execution_count":56,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.98      0.98      0.98        64\n           1       0.98      0.98      0.98        56\n\n    accuracy                           0.98       120\n   macro avg       0.98      0.98      0.98       120\nweighted avg       0.98      0.98      0.98       120\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**This model is 98% accurate.**","metadata":{}},{"cell_type":"markdown","source":"# **Random Forest**","metadata":{}},{"cell_type":"code","source":"#Training\nfrom sklearn.ensemble import RandomForestClassifier\nrf_model = RandomForestClassifier()\nrf_model.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.696113Z","iopub.execute_input":"2023-03-23T10:38:54.697767Z","iopub.status.idle":"2023-03-23T10:38:54.871519Z","shell.execute_reply.started":"2023-03-23T10:38:54.697705Z","shell.execute_reply":"2023-03-23T10:38:54.869838Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier()"},"metadata":{}}]},{"cell_type":"code","source":"#Predicting result using testing data\ny_rf_pred= rf_model.predict(X_test)\ny_rf_pred","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.873182Z","iopub.execute_input":"2023-03-23T10:38:54.873728Z","iopub.status.idle":"2023-03-23T10:38:54.905178Z","shell.execute_reply.started":"2023-03-23T10:38:54.873667Z","shell.execute_reply":"2023-03-23T10:38:54.903686Z"},"trusted":true},"execution_count":58,"outputs":[{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"array([1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n       1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n       1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n       1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"},"metadata":{}}]},{"cell_type":"code","source":"#Model accuracy\nrf_cr=classification_report(y_test, y_rf_pred)\nprint(rf_cr)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.907096Z","iopub.execute_input":"2023-03-23T10:38:54.907706Z","iopub.status.idle":"2023-03-23T10:38:54.921730Z","shell.execute_reply.started":"2023-03-23T10:38:54.907644Z","shell.execute_reply":"2023-03-23T10:38:54.920038Z"},"trusted":true},"execution_count":59,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.98      0.98      0.98        64\n           1       0.98      0.98      0.98        56\n\n    accuracy                           0.98       120\n   macro avg       0.98      0.98      0.98       120\nweighted avg       0.98      0.98      0.98       120\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**This model is also 98% accurate.**","metadata":{}},{"cell_type":"markdown","source":"# **XGBoost**","metadata":{}},{"cell_type":"code","source":"from xgboost import XGBClassifier\nxgb_model = XGBClassifier()\nxgb_model.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:54.923861Z","iopub.execute_input":"2023-03-23T10:38:54.926009Z","iopub.status.idle":"2023-03-23T10:38:55.230181Z","shell.execute_reply.started":"2023-03-23T10:38:54.925931Z","shell.execute_reply":"2023-03-23T10:38:55.228721Z"},"trusted":true},"execution_count":60,"outputs":[{"execution_count":60,"output_type":"execute_result","data":{"text/plain":"XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n              early_stopping_rounds=None, enable_categorical=False,\n              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n              importance_type=None, interaction_constraints='',\n              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n              missing=nan, monotone_constraints='()', n_estimators=100,\n              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,\n              reg_alpha=0, reg_lambda=1, ...)"},"metadata":{}}]},{"cell_type":"code","source":"#Predicting result using testing data\ny_xgb_pred= xgb_model.predict(X_test)\ny_xgb_pred","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:55.232204Z","iopub.execute_input":"2023-03-23T10:38:55.233078Z","iopub.status.idle":"2023-03-23T10:38:55.250336Z","shell.execute_reply.started":"2023-03-23T10:38:55.233019Z","shell.execute_reply":"2023-03-23T10:38:55.248717Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"array([1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n       1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n       1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n       1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n       1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"},"metadata":{}}]},{"cell_type":"code","source":"#Model accuracy\nxgb_cr=classification_report(y_test, y_xgb_pred)\nprint(xgb_cr)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:55.252154Z","iopub.execute_input":"2023-03-23T10:38:55.252913Z","iopub.status.idle":"2023-03-23T10:38:55.269909Z","shell.execute_reply.started":"2023-03-23T10:38:55.252865Z","shell.execute_reply":"2023-03-23T10:38:55.268811Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.98      0.97      0.98        64\n           1       0.96      0.98      0.97        56\n\n    accuracy                           0.97       120\n   macro avg       0.97      0.98      0.97       120\nweighted avg       0.98      0.97      0.98       120\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**This model is also 97% accurate.**","metadata":{}},{"cell_type":"markdown","source":"# **Multi-layer Perceptron classifier**","metadata":{"execution":{"iopub.status.busy":"2023-02-24T06:18:38.849101Z","iopub.execute_input":"2023-02-24T06:18:38.849499Z","iopub.status.idle":"2023-02-24T06:18:38.854549Z","shell.execute_reply.started":"2023-02-24T06:18:38.849467Z","shell.execute_reply":"2023-02-24T06:18:38.853244Z"}}},{"cell_type":"code","source":"#Training a neural network model\nfrom sklearn.neural_network import MLPClassifier\nmlp_model = MLPClassifier()\nmlp_model.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:55.271790Z","iopub.execute_input":"2023-03-23T10:38:55.272695Z","iopub.status.idle":"2023-03-23T10:38:56.100179Z","shell.execute_reply.started":"2023-03-23T10:38:55.272625Z","shell.execute_reply":"2023-03-23T10:38:56.098710Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"MLPClassifier()"},"metadata":{}}]},{"cell_type":"code","source":"#Predicting result using testing data\ny_mlp_pred= mlp_model.predict(X_test)\ny_mlp_pred","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:56.102267Z","iopub.execute_input":"2023-03-23T10:38:56.103088Z","iopub.status.idle":"2023-03-23T10:38:56.118922Z","shell.execute_reply.started":"2023-03-23T10:38:56.103025Z","shell.execute_reply":"2023-03-23T10:38:56.116983Z"},"trusted":true},"execution_count":64,"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"array([1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n       1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n       1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n       1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"},"metadata":{}}]},{"cell_type":"code","source":"#Model accuracy\nmlp_cr=classification_report(y_test, y_mlp_pred)\nprint(mlp_cr)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:56.121358Z","iopub.execute_input":"2023-03-23T10:38:56.122230Z","iopub.status.idle":"2023-03-23T10:38:56.142214Z","shell.execute_reply.started":"2023-03-23T10:38:56.122168Z","shell.execute_reply":"2023-03-23T10:38:56.140700Z"},"trusted":true},"execution_count":65,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.98      0.98      0.98        64\n           1       0.98      0.98      0.98        56\n\n    accuracy                           0.98       120\n   macro avg       0.98      0.98      0.98       120\nweighted avg       0.98      0.98      0.98       120\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**This model is also 98% accurate.**","metadata":{}},{"cell_type":"markdown","source":"# **Gradient Boosting**","metadata":{"execution":{"iopub.status.busy":"2023-02-24T06:21:03.647154Z","iopub.execute_input":"2023-02-24T06:21:03.647586Z","iopub.status.idle":"2023-02-24T06:21:03.653955Z","shell.execute_reply.started":"2023-02-24T06:21:03.647552Z","shell.execute_reply":"2023-02-24T06:21:03.652238Z"}}},{"cell_type":"code","source":"#Training\nfrom sklearn.ensemble import GradientBoostingClassifier\ngb_model = GradientBoostingClassifier()\ngb_model.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:56.144452Z","iopub.execute_input":"2023-03-23T10:38:56.145312Z","iopub.status.idle":"2023-03-23T10:38:56.275787Z","shell.execute_reply.started":"2023-03-23T10:38:56.145255Z","shell.execute_reply":"2023-03-23T10:38:56.274115Z"},"trusted":true},"execution_count":66,"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"GradientBoostingClassifier()"},"metadata":{}}]},{"cell_type":"code","source":"#Predicting result using testing data\ny_gb_pred= gb_model.predict(X_test)\ny_gb_pred","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:56.277471Z","iopub.execute_input":"2023-03-23T10:38:56.277918Z","iopub.status.idle":"2023-03-23T10:38:56.289213Z","shell.execute_reply.started":"2023-03-23T10:38:56.277875Z","shell.execute_reply":"2023-03-23T10:38:56.288198Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"array([1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n       1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n       1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n       1, 0, 0, 0, 0, 1, 0, 1, 1, 0])"},"metadata":{}}]},{"cell_type":"code","source":"#Model accuracy\ngb_cr=classification_report(y_test, y_gb_pred)\nprint(gb_cr)","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:56.290617Z","iopub.execute_input":"2023-03-23T10:38:56.291202Z","iopub.status.idle":"2023-03-23T10:38:56.308122Z","shell.execute_reply.started":"2023-03-23T10:38:56.291164Z","shell.execute_reply":"2023-03-23T10:38:56.306906Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.98      0.98      0.98        64\n           1       0.98      0.98      0.98        56\n\n    accuracy                           0.98       120\n   macro avg       0.98      0.98      0.98       120\nweighted avg       0.98      0.98      0.98       120\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**This model is also 98% accurate.**","metadata":{}},{"cell_type":"markdown","source":"From the above calculated accuracies, it is clear that the SVC, Random Forest, Multi-layer Perceptron and Gradient Boost models performed atmost level while the worst performed one is Multinomial Naive Bayes. \nHowever, I'm interested in a more efficient way of evaluating these models. Let's go for the Cross Validation methods using both K-Fold and Stratified K-Fold","metadata":{}},{"cell_type":"markdown","source":"# **Cross Validation**","metadata":{}},{"cell_type":"markdown","source":"K-Fold cross validation is a popular technique used in machine learning for model evaluation and selection. It involves dividing a dataset into K subsets of equal size, called folds. The algorithm then trains and evaluates the model K times, each time using a different fold as the validation set and the remaining K-1 folds as the training set.\n\nDuring each iteration of K-Fold cross validation, the model is trained on K-1 folds and evaluated on the remaining fold. The performance metrics are then averaged over all K iterations to obtain an estimate of the model's overall performance.\n\nK-Fold cross validation is a robust method for model evaluation because it uses all the available data for training and testing. It also helps to reduce the risk of overfitting and provides a more accurate estimate of the model's performance than using a single training-test split.\n\nTypically, values of K between 5 and 10 are used for K-Fold cross validation, but the optimal value of K may vary depending on the size and complexity of the dataset, as well as the type of model being evaluated.\n\nHere are some general guidelines that can help you choose an appropriate value of K:\n\n1. For small datasets, it is recommended to use a larger value of K, such as 10. This is because the larger value of K allows for more robust estimates of model performance, given the limited amount of data.\n\n2. For larger datasets, a smaller value of K can be used, such as 5. This is because a larger value of K will result in K smaller training sets, which may not be representative of the full dataset. Using a smaller value of K ensures that each fold has a sufficient amount of data for both training and testing.\n\n3. For models that are computationally expensive or time-consuming to train, a smaller value of K is preferred to reduce the overall training time.\n\n4. It's also essential to note that the choice of K should not be based solely on the accuracy of the model. Other metrics, such as precision, recall, and F1 score, should also be considered, as they can provide valuable insights into the performance of the model.","metadata":{}},{"cell_type":"code","source":"# K-Fold Cross Validation\n\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\n\nk = 10\nkf = KFold(n_splits=k, shuffle=True, random_state=42)\n\n\n# Logistic regerssion model\nlr_model_scores = cross_val_score(lr_model,X, y, cv=kf)\n\n# Decision tree model\ndt_model_scores = cross_val_score(dt_model,X, y, cv=kf)\n\n# KNN model\nknn_model_scores = cross_val_score(knn_model,X, y, cv=kf)\n\n# Gaussian naive bayes model\ngnb_model_scores = cross_val_score(gnb_model,X, y, cv=kf)\n\n# Multinomial naive bayes model\nmnb_model_scores = cross_val_score(mnb_model,X, y, cv=kf)\n\n# Support Vector Classifier model\nsvc_model_scores = cross_val_score(svc_model,X, y, cv=kf)\n\n# Random forest model\nrf_model_scores = cross_val_score(rf_model,X, y, cv=kf)\n\n# XGBoost model\nxgb_model_scores = cross_val_score(xgb_model,X, y, cv=kf)\n\n# Multi-layer perceptron model\nmlp_model_scores = cross_val_score(mlp_model,X, y, cv=kf)\n\n# Gradient boost model\ngb_model_scores = cross_val_score(gb_model,X, y, cv=kf)\n\n\nprint(\"Logistic regression models' average accuracy:\", np.mean(lr_model_scores))\nprint(\"Decision tree models' average accuracy:\", np.mean(dt_model_scores))\nprint(\"KNN models' average accuracy:\", np.mean(knn_model_scores))\nprint(\"Gaussian naive bayes models' average accuracy:\", np.mean(gnb_model_scores))\nprint(\"Multinomial naive bayes models' average accuracy:\", np.mean(mnb_model_scores))\nprint(\"Support Vector Classifier models' average accuracy:\", np.mean(svc_model_scores))\nprint(\"Random forest models' average accuracy:\", np.mean(rf_model_scores))\nprint(\"XGBoost models' average accuracy:\", np.mean(xgb_model_scores))\nprint(\"Multi-layer perceptron models' average accuracy:\", np.mean(mlp_model_scores))\nprint(\"Gradient boost models' average accuracy:\", np.mean(gb_model_scores))","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:38:56.309686Z","iopub.execute_input":"2023-03-23T10:38:56.310275Z","iopub.status.idle":"2023-03-23T10:39:13.267312Z","shell.execute_reply.started":"2023-03-23T10:38:56.310237Z","shell.execute_reply":"2023-03-23T10:39:13.265861Z"},"trusted":true},"execution_count":69,"outputs":[{"name":"stdout","text":"Logistic regression models' average accuracy: 0.930895390070922\nDecision tree models' average accuracy: 0.9539893617021278\nKNN models' average accuracy: 0.9330673758865249\nGaussian naive bayes models' average accuracy: 0.8846187943262411\nMultinomial naive bayes models' average accuracy: 0.7421542553191489\nSupport Vector Classifier models' average accuracy: 0.9497340425531917\nRandom forest models' average accuracy: 0.949822695035461\nXGBoost models' average accuracy: 0.9497783687943263\nMulti-layer perceptron models' average accuracy: 0.9372340425531915\nGradient boost models' average accuracy: 0.9560726950354612\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**So the K-Fold cross validation is showing Gradient Boost model gives the most accuracy of 95.6%, and Decision Tree model also gives almost same accuracy, while Multinomial Naive Bayes model gives the least accuarcy of 74.2%.**","metadata":{}},{"cell_type":"markdown","source":"Stratified K-Fold cross-validation is a modification of the standard K-Fold cross-validation technique that is commonly used in machine learning when working with imbalanced datasets. The goal of Stratified K-Fold cross-validation is to ensure that each fold is representative of the overall dataset in terms of the class distribution.\n\nIn standard K-Fold cross-validation, the data is split into K folds, and each fold is used as the validation set in turn. However, if the dataset has an imbalanced class distribution, this can lead to some of the folds having significantly fewer samples from the minority class, which can result in biased performance estimates.\n\nTo address this issue, Stratified K-Fold cross-validation ensures that each fold has a similar proportion of samples from each class. It works by first dividing the dataset into K folds, as in standard K-Fold cross-validation. Then, for each fold, the algorithm ensures that the proportion of samples from each class is roughly the same as the proportion in the full dataset. This ensures that the model is evaluated on a representative sample of the data, regardless of the class distribution.\n\nStratified K-Fold cross-validation is a powerful tool for evaluating the performance of machine learning models on imbalanced datasets. It can help to ensure that the model's performance is accurately estimated and that the model is robust to class imbalances in the dataset.","metadata":{}},{"cell_type":"code","source":"# K-Fold Cross Validation\n\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.model_selection import cross_val_score\n\nk = 10\nkf = StratifiedKFold(n_splits=k)\n\n\n# Logistic regerssion model\nlr_model_scores = cross_val_score(lr_model,X, y, cv=kf)\n\n# Decision tree model\ndt_model_scores = cross_val_score(dt_model,X, y, cv=kf)\n\n# KNN model\nknn_model_scores = cross_val_score(knn_model,X, y, cv=kf)\n\n# Gaussian naive bayes model\ngnb_model_scores = cross_val_score(gnb_model,X, y, cv=kf)\n\n# Multinomial naive bayes model\nmnb_model_scores = cross_val_score(mnb_model,X, y, cv=kf)\n\n# Support Vector Classifier model\nsvc_model_scores = cross_val_score(svc_model,X, y, cv=kf)\n\n# Random forest model\nrf_model_scores = cross_val_score(rf_model,X, y, cv=kf)\n\n# XGBoost model\nxgb_model_scores = cross_val_score(xgb_model,X, y, cv=kf)\n\n# Multi-layer perceptron model\nmlp_model_scores = cross_val_score(mlp_model,X, y, cv=kf)\n\n# Gradient boost model\ngb_model_scores = cross_val_score(gb_model,X, y, cv=kf)\n\n\nprint(\"Logistic regression models' average accuracy:\", np.mean(lr_model_scores))\nprint(\"Decision tree models' average accuracy:\", np.mean(dt_model_scores))\nprint(\"KNN models' average accuracy:\", np.mean(knn_model_scores))\nprint(\"Gaussian naive bayes models' average accuracy:\", np.mean(gnb_model_scores))\nprint(\"Multinomial naive bayes models' average accuracy:\", np.mean(mnb_model_scores))\nprint(\"Support Vector Classifier models' average accuracy:\", np.mean(svc_model_scores))\nprint(\"Random forest models' average accuracy:\", np.mean(rf_model_scores))\nprint(\"XGBoost models' average accuracy:\", np.mean(xgb_model_scores))\nprint(\"Multi-layer perceptron models' average accuracy:\", np.mean(mlp_model_scores))\nprint(\"Gradient boost models' average accuracy:\", np.mean(gb_model_scores))","metadata":{"execution":{"iopub.status.busy":"2023-03-23T10:39:13.269304Z","iopub.execute_input":"2023-03-23T10:39:13.269950Z","iopub.status.idle":"2023-03-23T10:39:31.507033Z","shell.execute_reply.started":"2023-03-23T10:39:13.269893Z","shell.execute_reply":"2023-03-23T10:39:31.505291Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"Logistic regression models' average accuracy: 0.9288120567375886\nDecision tree models' average accuracy: 0.9227393617021278\nKNN models' average accuracy: 0.9184397163120567\nGaussian naive bayes models' average accuracy: 0.8870124113475178\nMultinomial naive bayes models' average accuracy: 0.7572251773049644\nSupport Vector Classifier models' average accuracy: 0.9476063829787235\nRandom forest models' average accuracy: 0.9456560283687944\nXGBoost models' average accuracy: 0.9457446808510639\nMulti-layer perceptron models' average accuracy: 0.93927304964539\nGradient boost models' average accuracy: 0.947695035460993\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**So the Stratified K-Fold cross validation is showing Random Forest model gives the most accuracy of 94.6%, and also other models like Gradient Boost, Support Vector Classifier, XGBoost gives almost same accuracies, while Multinomial Naive Bayes model gives the least accuarcy of 75.7%.**","metadata":{}}]}