EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import os.path as osp\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_recall_curve\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier, SGDClassifier, LassoCV\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier, BaggingClassifier, RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import xgboost as xgb\n",
    "from sklearn.feature_selection import VarianceThreshold, SelectFromModel, SelectKBest, RFE, chi2\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, RobustScaler\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Simple EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('./train.csv')\n",
    "test_data =pd.read_csv('./test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199523, 41)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99762, 41)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate Train and Test Data\n",
    "X = pd.concat([train_data.iloc[:, :-1], test_data.iloc[:, :-1]])\n",
    "y = train_data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change ' ?' to nan value\n",
    "X[X == ' ?'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 299285 entries, 0 to 99761\n",
      "Data columns (total 40 columns):\n",
      " #   Column                Non-Null Count   Dtype \n",
      "---  ------                --------------   ----- \n",
      " 0   AGE                   299285 non-null  int64 \n",
      " 1   CLSWKR                299285 non-null  object\n",
      " 2   DTINRC                299285 non-null  int64 \n",
      " 3   DTOCRC                299285 non-null  int64 \n",
      " 4   EDU                   299285 non-null  object\n",
      " 5   WPH                   299285 non-null  int64 \n",
      " 6   EIEDUINLW             299285 non-null  object\n",
      " 7   MAST                  299285 non-null  object\n",
      " 8   MAINDC                299285 non-null  object\n",
      " 9   MAOCC                 299285 non-null  object\n",
      " 10  RACE                  299285 non-null  object\n",
      " 11  HISPOG                299285 non-null  object\n",
      " 12  SEX                   299285 non-null  object\n",
      " 13  MEMOFLAUNI            299285 non-null  object\n",
      " 14  REAFORUNEMP           299285 non-null  object\n",
      " 15  FUORPATIEMPST         299285 non-null  object\n",
      " 16  CAPGAIN               299285 non-null  int64 \n",
      " 17  CAPLOSS               299285 non-null  int64 \n",
      " 18  DIVFROMSTO            299285 non-null  int64 \n",
      " 19  TAXFILST              299285 non-null  object\n",
      " 20  REGOFPRERES           299285 non-null  object\n",
      " 21  STAOFPRERES           298247 non-null  object\n",
      " 22  DTHOUANDFAMST         299285 non-null  object\n",
      " 23  DTHOUSUMINHOU         299285 non-null  object\n",
      " 24  MIGCCINMSA            149643 non-null  object\n",
      " 25  MIGCCINREG            149643 non-null  object\n",
      " 26  MIGCMWIREG            149643 non-null  object\n",
      " 27  LIVINTHISHO1YR        299285 non-null  object\n",
      " 28  MIGPRERESINSUN        149643 non-null  object\n",
      " 29  NUMPERWOFOREMP        299285 non-null  int64 \n",
      " 30  FAMMEMUNDER18         299285 non-null  object\n",
      " 31  COUNOFBIRFA           289143 non-null  object\n",
      " 32  COUNOFBIRMA           290094 non-null  object\n",
      " 33  COUNOFBIRSE           294128 non-null  object\n",
      " 34  CITIZEN               299285 non-null  object\n",
      " 35  OWNBUSIORSEEMP        299285 non-null  int64 \n",
      " 36  FILLINQUEFORVETADMIN  299285 non-null  object\n",
      " 37  VETBENE               299285 non-null  int64 \n",
      " 38  WEEKWORKINYEAR        299285 non-null  int64 \n",
      " 39  YEAR                  299285 non-null  int64 \n",
      "dtypes: int64(12), object(28)\n",
      "memory usage: 93.6+ MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    187194\n",
       "1     12329\n",
       "Name: INCOME, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='INCOME', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_data['INCOME'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is imbalanced, so under/oversampling, threshold tuning, and other methods for handling imbalancedness could be helpful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of na value : 624096\n"
     ]
    }
   ],
   "source": [
    "# Check the total number of missing values\n",
    "print(f'Number of na value : {X.isna().sum().sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0,   1038, 149642,  10142,   9191,   5157])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I check that many missing values occurred in some columns\n",
    "X.isna().sum(0).unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         32\n",
       "149642     4\n",
       "1038       1\n",
       "10142      1\n",
       "9191       1\n",
       "5157       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isna().sum(0).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I checked which columns had missing values over 20000 and decided to delete those columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MIGCCINMSA', 'MIGCCINREG', 'MIGCMWIREG', 'MIGPRERESINSUN'], dtype='object')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns[(X.isna().sum(0) > 20000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COUNOFBIRFA\n",
      "COUNOFBIRMA\n",
      "COUNOFBIRSE\n",
      "STAOFPRERES\n"
     ]
    }
   ],
   "source": [
    "print(X.columns[(X.isna().sum(0) == 10142)][0])\n",
    "print(X.columns[(X.isna().sum(0) == 9191)][0])\n",
    "print(X.columns[(X.isna().sum(0) == 5157)][0])\n",
    "print(X.columns[(X.isna().sum(0) == 1038)][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_column = X.columns[(X.isna().sum(0) > 20000)]\n",
    "X.drop(drop_column, axis=1, inplace=True)\n",
    "col = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['AGE', 'CLSWKR', 'DTINRC', 'DTOCRC', 'EDU', 'WPH', 'EIEDUINLW', 'MAST',\n",
      "       'MAINDC', 'MAOCC', 'RACE', 'HISPOG', 'SEX', 'MEMOFLAUNI', 'REAFORUNEMP',\n",
      "       'FUORPATIEMPST', 'CAPGAIN', 'CAPLOSS', 'DIVFROMSTO', 'TAXFILST',\n",
      "       'REGOFPRERES', 'STAOFPRERES', 'DTHOUANDFAMST', 'DTHOUSUMINHOU',\n",
      "       'LIVINTHISHO1YR', 'NUMPERWOFOREMP', 'FAMMEMUNDER18', 'COUNOFBIRFA',\n",
      "       'COUNOFBIRMA', 'COUNOFBIRSE', 'CITIZEN', 'OWNBUSIORSEEMP',\n",
      "       'FILLINQUEFORVETADMIN', 'VETBENE', 'WEEKWORKINYEAR', 'YEAR'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since columns with many missing value were all categorical values, I impute that with mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null : 0\n"
     ]
    }
   ],
   "source": [
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "imputer.fit(X)\n",
    "imputed_value = imputer.transform(X)\n",
    "X = pd.DataFrame(imputed_value)\n",
    "X.columns = col\n",
    "\n",
    "print(f'Number of null : {X.isna().sum().sum()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Handle Categorical Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical Value must not have scale.  \n",
    "To do this, you need to one-hot encode(or other techniques) categorical features.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperate continuous and categorical features\n",
    "cont_col = ['AGE', 'WPH', 'CAPGAIN','CAPLOSS', 'DIVFROMSTO', 'NUMPERWOFOREMP','WEEKWORKINYEAR', 'YEAR']\n",
    "col = X.columns\n",
    "cat_col = list(set(col) - set(cont_col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "cont_X = X.loc[:, cont_col]\n",
    "cat_X = X.loc[:, cat_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_X = pd.get_dummies(cat_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MAINDC', 'STAOFPRERES', 'CLSWKR', 'EIEDUINLW', 'FILLINQUEFORVETADMIN', 'MEMOFLAUNI', 'DTHOUSUMINHOU', 'LIVINTHISHO1YR', 'COUNOFBIRFA', 'SEX', 'DTOCRC', 'RACE', 'MAOCC', 'COUNOFBIRSE', 'CITIZEN', 'FUORPATIEMPST', 'REGOFPRERES', 'DTINRC', 'OWNBUSIORSEEMP', 'EDU', 'HISPOG', 'REAFORUNEMP', 'COUNOFBIRMA', 'VETBENE', 'FAMMEMUNDER18', 'MAST', 'DTHOUANDFAMST', 'TAXFILST']\n"
     ]
    }
   ],
   "source": [
    "print(cat_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I printed out the values for all categorical columns to understand the data better. I also made box plots for each columns to see if there is any interesting pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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AYC+x0qvv/diC+3cm+Wxm54XaI939siQvWzT89cz2mlrq9eckOWeJ8W1Jjl5i/GsZmloAAAAA7H1WevW9n17riQAAAABw37HSq+9tqaq3V9XtVXVbVV1WVVvufUkAAAAAuKeVnuj8jzM7efgjkhyW5F3DGAAAAADstpU2pTZ39x93953D7cIkm9dwXgAAAADsw1balPpiVT2nqvYbbs9J8g9rOTEAAAAA9l0rbUo9L8mPJ/lCkluTnJrEyc8BAAAA2CMruvpekpcnOb27v5QkVfWQJL+VWbMKAAAAAHbLSveU+p5dDakk6e47knzv2kwJAAAAgH3dSptS96uqg3Y9GPaUWuleVgAAAABwNyttLP12kv+vqi5N0pmdX+qcNZsVAAAAAPu0FTWluvuNVbUtyZOTVJJndfeNazozAAAAAPZZKz4Eb2hCaUQBAAAAsGorPacUAAAAAIxGUwoAAACAyWlKAQAAADA5TSkAAAAAJqcpBQAAAMDkNKUAAAAAmJymFAAAAACT05QCAAAAYHKaUgAAAABMTlMKAAAAgMlpSgEAAAAwOU0pAAAAACanKQUAAADA5NalKVVV31pVl1bVJ6vqpqr6vqp6SFVdWVWfHr4etOD1Z1fV9qr6VFWdsGD88VV13fDca6uqhvH9q+qtw/g1VXXEOnxMAAAAAJaxXntKvSbJX3T3/5bk3yS5KclLklzV3UcluWp4nKp6dJKtSR6T5MQkr6uq/Yac85KcmeSo4XbiMH5Gki919yOTnJvklVN8KAAAAABWZvKmVFUdmOSJSc5Pku7+5+7+xyQnJbloeNlFSU4e7p+U5OLu/np3fybJ9iTHVtWhSQ7s7qu7u5O8cdEyu7IuTXL8rr2oAAAAAFh/67Gn1Hck2Znkj6vq41X1hqp6UJKHdfetSTJ8PWR4/WFJbl6w/I5h7LDh/uLxuy3T3Xcm+XKSh67NxwEAAABgd61HU2pTksclOa+7vzfJ/8hwqN4yltrDqeeMz1vm7sFVZ1bVtqratnPnzvmzBgAAAGA069GU2pFkR3dfMzy+NLMm1W3DIXkZvt6+4PWHL1h+S5JbhvEtS4zfbZmq2pTkwUnuWDyR7n59dx/T3cds3rx5hI8GAAAAwEpM3pTq7i8kubmqHjUMHZ/kxiSXJzl9GDs9yTuH+5cn2TpcUe/IzE5o/uHhEL+vVtVxw/minrtomV1ZpyZ5/3DeKQAAAAD2ApvW6X1/Icmbq+r+Sf4uyU9n1iC7pKrOSPK5JKclSXffUFWXZNa4ujPJ87v7riHnrCQXJjkgyRXDLZmdRP1NVbU9sz2ktk7xoQAAAABYmXVpSnX3tUmOWeKp45d5/TlJzllifFuSo5cY/1qGphYAAAAAe5/1OKcUAAAAAPdxmlIAAAAATE5TCgAAAIDJaUoBAAAAMDlNKQAAAAAmpykFAAAAwOQ0pQAAAACYnKYUAAAAAJPTlAIAAABgcppSAAAAAExOUwoAAACAyWlKAQAAADA5TSkAAAAAJqcpBQAAAMDkNKUAAAAAmJymFAAAAACT05QCAAAAYHKaUgAAAABMTlMKAAAAgMlpSgEAAAAwOU0pAAAAACanKQUAAADA5DSlAAAAAJicphQAAAAAk9OUAgAAAGBy69aUqqr9qurjVfXu4fFDqurKqvr08PWgBa89u6q2V9WnquqEBeOPr6rrhudeW1U1jO9fVW8dxq+pqiMm/4AAAAAALGs995R6QZKbFjx+SZKruvuoJFcNj1NVj06yNcljkpyY5HVVtd+wzHlJzkxy1HA7cRg/I8mXuvuRSc5N8sq1/SgAAAAA7I51aUpV1ZYkP5rkDQuGT0py0XD/oiQnLxi/uLu/3t2fSbI9ybFVdWiSA7v76u7uJG9ctMyurEuTHL9rLyoAAAAA1t967Sn1u0lenOQbC8Ye1t23Jsnw9ZBh/LAkNy943Y5h7LDh/uLxuy3T3Xcm+XKSh476CQAAAADYY5M3parq6Ulu7+6PrnSRJcZ6zvi8ZRbP5cyq2lZV23bu3LnC6QAAAACwWuuxp9QPJHlGVX02ycVJnlxVf5LktuGQvAxfbx9evyPJ4QuW35LklmF8yxLjd1umqjYleXCSOxZPpLtf393HdPcxmzdvHufTAQAAAHCvJm9KdffZ3b2lu4/I7ATm7+/u5yS5PMnpw8tOT/LO4f7lSbYOV9Q7MrMTmn94OMTvq1V13HC+qOcuWmZX1qnDe9xjTykAAAAA1sem9Z7AAq9IcklVnZHkc0lOS5LuvqGqLklyY5I7kzy/u+8aljkryYVJDkhyxXBLkvOTvKmqtme2h9TWqT4EAAAAAPduXZtS3f2BJB8Y7v9DkuOXed05Sc5ZYnxbkqOXGP9ahqYWAAAAAHuf9br6HgAAAAD3YZpSAAAAAExOUwoAAACAyWlKAQAAADA5TSkAAAAAJqcpBQAAAMDkNKUAAAAAmJymFAAAAACT05QCAAAAYHKaUgAAAABMTlMKAAAAgMlpSgEAAAAwOU0pAAAAACanKQUAAADA5DSlAAAAAJicphQAAAAAk9OUAgAAAGBymlIAAAAATE5TCgAAAIDJaUoBAAAAMDlNKQAAAAAmpykFAAAAwOQ0pQAAAACYnKYUAAAAAJPTlAIAAABgcppSAAAAAExu8qZUVR1eVX9ZVTdV1Q1V9YJh/CFVdWVVfXr4etCCZc6uqu1V9amqOmHB+OOr6rrhuddWVQ3j+1fVW4fxa6rqiKk/JwAAAADLW489pe5M8kvd/d1Jjkvy/Kp6dJKXJLmqu49KctXwOMNzW5M8JsmJSV5XVfsNWeclOTPJUcPtxGH8jCRf6u5HJjk3ySun+GAAAAAArMzkTanuvrW7Pzbc/2qSm5IcluSkJBcNL7soycnD/ZOSXNzdX+/uzyTZnuTYqjo0yYHdfXV3d5I3LlpmV9alSY7ftRcVAAAAAOtvXc8pNRxW971JrknysO6+NZk1rpIcMrzssCQ3L1hsxzB22HB/8fjdlunuO5N8OclD1+RDAAAAALDb1q0pVVXfnOSyJC/s7q/Me+kSYz1nfN4yi+dwZlVtq6ptO3fuvLcpAwAAADCSdWlKVdU3ZdaQenN3v20Yvm04JC/D19uH8R1JDl+w+JYktwzjW5YYv9syVbUpyYOT3LF4Ht39+u4+pruP2bx58xgfDQAAAIAVWI+r71WS85Pc1N2/s+Cpy5OcPtw/Pck7F4xvHa6od2RmJzT/8HCI31er6rgh87mLltmVdWqS9w/nnQIAAABgL7BpHd7zB5L8ZJLrquraYew/JXlFkkuq6owkn0tyWpJ09w1VdUmSGzO7ct/zu/uuYbmzklyY5IAkVwy3ZNb0elNVbc9sD6mta/yZAAAAANgNkzeluvv/zdLnfEqS45dZ5pwk5ywxvi3J0UuMfy1DUwsAAACAvc+6Xn0PAAAAgPsmTSkAAAAAJqcpBQAAAMDkNKUAAAAAmJymFAAAAACT05QCAAAAYHKb1nsCjOu28141WtbDznrxaFkAAAAAC9lTCgAAAIDJaUoBAAAAMDmH7wEAsCE849J3j5Z1+alPHy0LANgz9pQCAAAAYHKaUgAAAABMTlMKAAAAgMlpSgEAAAAwOU0pAAAAACbn6nsAwD087e3/92hZ73nmfxotC1jaKZd9ZLSsy075t6NlAcA89pQCAAAAYHKaUgAAAABMzuF7AAAAe6mLL/viaFlbTzl4tKyV+ss37xwt64d/YvNoWcDeQVMK2PDecuEJo2U9+6feO1oWAAAAy9OUYrfc8rpfGS3rET/36tGyYCM770/Ga6qd9RxNtd1x2jtPHC3rz076i9GyuG97+qVvHC3r3ac+d7QsYGnnve220bLOetbDRssC2Ag0pQAAAO6j3n3JeIcHPv3Hpz88cC1d919vHy3rf/+ZQ0bLgn2JphQAALCufuptnxst68JnfdtoWQCsLU0pIEnyF+c/bbSsE894z2hZAACr9Rtvv2W0rJc98xGjZbE611w43p5MT/gpezLBetCUAoA18NR3njla1hUnvX60LHj6pRePlvXuU7eOlsXe7bTLbhgt689OecxoWcC+6bZzrx0t62EveuxoWYxvn25KVdWJSV6TZL8kb+juV6zzlLgXf//ak0fL+vZffMdoWQAAq/XMyz44WtbbT3niaFkAu+sLv/03o2U9/Je+a7SslbrtNVePlvWwF3zfPcZu/72/HC3/kF/44dGy9kb7bFOqqvZL8gdJ/n2SHUk+UlWXd/eN6zszgGn99lvGu7rfLz377lf3+823jpf9a/+HKwfujqe94+zRst5z8n8ZLQsAWJm/e+0XRsv6jl98+GhZMKV9timV5Ngk27v775Kkqi5OclKSdW9K7fzD8Q7D2Pyz4x0ecl9w/eueMVrW0T93+WhZK/FXf/Sjo2X90P/556NlrdQ7LnjqaFknP++K0bJW4o8vespoWT99+vtGy7oveNFlJ46Wde4pfzFaFnu3H33b60bL+vNn/dxoWfcFP3bpZaNlvevUU0bLWqmTLx3v3+h3nDreugNgT9z6qs+PlnXoiw8bLQsW2pebUocluXnB4x1JnrBOc+E+4MP/9cdGyzr2Z941WhYA4/rRy84fLevPTzljtCwAYN93+++P9x/0h/z8eDsO7Knq7vWew5qoqtOSnNDd/2F4/JNJju3uX1jwmjOT7NrV6FFJPrUbb3Fwki+ONF35e0+2/PXLlr9+2Rs9fyPPfaPnb+S5b/T8jTz3jZ6/kee+0fM38tw3ev5GnvtGz9/Ic9/o+Rt57ntb/rd39+alntiX95TakeTwBY+3JLnbtWC7+/VJ9uhYuqra1t3H7Pn05O+N2fLXL1v++mVv9PyNPPeNnr+R577R8zfy3Dd6/kae+0bP38hz3+j5G3nuGz1/I899o+dv5LlvpPz7jTGZvdRHkhxVVUdW1f2TbE0y7UmAAAAAAFjSPrunVHffWVU/n+S9SfZLckF337DO0wIAAAAg+3BTKkm6+z1J3rNG8eNdQk/+3pQtf/2y5a9f9kbP38hz3+j5G3nuGz1/I899o+dv5Llv9PyNPPeNnr+R577R8zfy3Dd6/kae+4bJ32dPdA4AAADA3mtfPqcUAAAAAHspTak9UFUnVtWnqmp7Vb1k5OwLqur2qrp+zNwh+/Cq+suquqmqbqiqF4yc/4Cq+nBVfWLI/40x8xe8z35V9fGqevcaZH+2qq6rqmuratvI2d9aVZdW1SeHn8H3jZj9qGHOu25fqaoXjpU/vMeLhp/r9VX1lqp6wIjZLxhybxhr3kvVUlU9pKqurKpPD18PGjH7tGH+36iqVV2FYpn8Vw+/O39dVW+vqm8dOf/lQ/a1VfW+qnrEWNkLnvvlquqqOnjkuf96VX1+we//08bMH8Z/Yfh3/4aqetXI83/rgrl/tqquHTH7sVX1oV3/plXVsSPP/d9U1dXDv5vvqqoDV5G/5DpqjLqdkz1K3c7JH6Vu5+SPVbdztw9WU7tz5j5K3c6b+xh1O2f+Y9Xtcvmrrt052aPUbS2z3TdGzd5L/lh1u1z+qut2TvZYNTt3m3s1NXsv81913c6b+0g1u9zcx6rZ5fJHWd/OyR9zfXu3v6XGqtk5+aNtIy+TP+Y28uLsUWp2ufwF46veRl5m/qNtIy83/zHqNt3tthu3zE6a/rdJviPJ/ZN8IsmjR8x/YpLHJbl+DeZ+aJLHDfe/JcnfjDz3SvLNw/1vSnJNkuPW4HP8xyR/muTda5D92SQHr9HvzkVJ/sNw//5JvnWN3me/JF9I8u0jZh6W5DNJDhgeX5Lkp0bKPjrJ9UkemNl57v5bkqNGyL1HLSV5VZKXDPdfkuSVI2Z/d5JHJflAkmPWYO5PSbJpuP/KPZ37nPwDF9z/xSR/OFb2MH54Zhee+PvV1Ngyc//1JL880u/jUvk/PPxe7j88PmTM/EXP/3aSXxtx7u9L8tTh/tOSfGDk781HkvzQcP95SV6+ivwl11Fj1O2c7FHqdk7+KHU7J3+sul12+2C1tTtn7qPU7Zz8Uep23vdmwWtWU7fLzX/VtTsne5S6zTLbfWPU7L3kj1W3y+Wvum7nZI9Vs8tuc6+2Zu9l/quu2znZY9Xsvf49ssqaXW7+o6xv5+SPub69299SY9XsnPzRtpGXyR9zG3lx9ig1u1z+MDbKNvIy8191zd5L/ih1a0+p3Xdsku3d/Xfd/c9JLk5y0ljh3f3BJHeMlbco+9bu/thw/6tJbsqs2TBWfnf3fx8eftNwG/WkZVW1JcmPJnnDmLlrbfjfjCcmOT9Juvufu/sf1+jtjk/yt9399yPnbkpyQFVtyqyBdMtIud+d5EPd/U/dfWeSv0ryzNWGLlNLJ2XWHMzw9eSxsrv7pu7+1J7krTD/fcP3J0k+lGTLyPlfWfDwQdnD2p3zb9i5SV68p7kryB/FMvlnJXlFd399eM3tI+cnSaqqkvx4kreMmN1Jdv1v6oOzirpdJv9RST443L8yySmryF9uHbXqul0ue6y6nZM/St3OyR+rbudtH6yqdifY9lguf5S6vbf5j1C3y+WvunbnZI9St3O2+8Za1y6ZP2LdLpe/6rqdkz1Wzc7b5l71+nYtt+nnZI9Vs3PnPkLNLpc/yvp2Tv4odbvM31Kj1Oxy+WNuIy+TP8q6dpnsUWp2ufzBKNvIa/138jL5o9StptTuOyzJzQse78iIG1dTqaojknxvZt33MXP3G3aHvT3Jld09an6S382saL8xcu4uneR9VfXRqjpzxNzvSLIzyR8Puzy+oaoeNGL+Qluzhyva5XT355P8VpLPJbk1yZe7+30jxV+f5IlV9dCqemBm/7t0+EjZiz2su29NZhvrSQ5Zo/dZa89LcsXYoVV1TlXdnOQnkvzaiLnPSPL57v7EWJlL+Plh9+oLVrvb+RK+K8m/q6prquqvqurfjpy/y79Lclt3f3rEzBcmefXwc/2tJGePmJ3M6vcZw/3TMlLtLlpHjVq3a7X+W0H+KHW7OH/sul2YP3btLvG9GbVuF+WPXrfL/GxHq9tF+S/MiLW7KHu0ul1mu2+0ml3r7coV5O9x3S6XPVbNLpU/Zs3O+d6sum6XyR6tZu/l57rqml0m/4UZqWaXyR+rbn839/xbasz17FL5Y7q3/NWsa5fMHnE9e4/8kdez98gfjLWuXSp/lLrVlNp9tcTYqHsDrbWq+uYklyV54aLu76p1913d/djMOtTHVtXRY2VX1dOT3N7dHx0rcwk/0N2PS/LUJM+vqieOlLsps8Nezuvu703yPzLbPXZUVXX/zFZYfzZy7kGZ/S/KkUkekeRBVfWcMbK7+6bMdrW9MslfZHZI7J1zF7oPq6qXZvb9efPY2d390u4+fMj++TEyh0bjSzNik2sJ5yX5ziSPzaxp+tsj529KclBmu8//SpJLhv9pHduzM3JDObP/wXrR8HN9UYa9NUf0vMz+rfxoZocH/fNqA9dyHbWW2fPyx6rbpfLHrNuF+cN8R6vdJeY+at0ukT9q3c753RmlbpfIH612l8gerW7XcrtvvfNXW7fLZY9Vs0vkf09GrNll5j9K3S6TPVrN3svvzaprdpn80Wp2mfxV1+1a/y213vmrqdl52WPU7FL5Y24jz5n/KDU7J3+UutWU2n07cvfO9JaMdxjTmquqb8psw+TN3f22tXqfnh2a9oEkJ44Y+wNJnlFVn83ssMknV9WfjJif7r5l+Hp7krdndrjmGHYk2bHgf2ouzaxJNbanJvlYd982cu6PJPlMd+/s7n9J8rYk3z9WeHef392P6+4nZnZ40Jh7iix0W1UdmiTD1z0+DGs9VNXpSZ6e5Ce6ey2b4X+aVRyGtch3ZtbM/MRQu1uSfKyqHj5Sfrr7tmED7htJ/ijj1e0uO5K8bdil/sOZ/Q/Rqk5EuVjNDot9VpK3jpmb5PTM6jWZNatH/d509ye7+ynd/fjMNvL/djV5y6yjRqnbtV7/LZc/Vt2uYP6rqtsl8ker3aXmPmbdLvO9Ga1u5/xsR6nbZfJHqd1lvvej1u2Q+Y/51+2+0de1a7RduWz+mOvbOXMfZV27IH/Xfx6Our5dOP+x17eLvjejr2uX+LmOuq5dlD/6+nbR936Mul3ub6mxanat/1ZbNn+Eml3J3FdTs/fIT/KmjFezS85/xJpd7vszSt1qSu2+jyQ5qqqOHPZK2Zrk8nWe04oMXcvzk9zU3b+zBvmba7jaQVUdkFkj45Nj5Xf32d29pbuPyOz7/v7uHmVvnSSpqgdV1bfsup/ZSfNGuQpid38hyc1V9ahh6PgkN46Rvcha7GmRzA7bO66qHjj8Hh2f2bkpRlFVhwxfvy2zjYW1+AzJrFZPH+6fnuSda/Q+o6uqE5P8apJndPc/rUH+UQsePiMj1W53X9fdh3T3EUPt7sjsxLtfGCM/+V8bULs8MyPV7QLvyGzjIVX1XZldqOCLI7/HjyT5ZHfvGDn3liQ/NNx/ckZu+C6o3fsl+c9J/nAVWcuto1ZdtxOs/5bMH6tu5+SPUrdL5Y9Vu3PmPkrdzvnZviMj1O29/O6sum7n5K+6dud870ep2znbfaOsa9d6u3K5/DHqdk72WDW7VP7Hx1rfzpn/qut2zs/1HRmnZuf93oxRs8vlj7K+nfO9X3XdzvlbapSaXeu/1ZbLH6Nm52SPUrPL5J8yVs3Omf8o69o5P9t3ZIxt5B7pTOz3pVtm57z5m8w61C8dOfstme1a9y+Z/WKeMWL2D2Z2qOFfJ7l2uD1txPzvSfLxIf/67OFVLVb4Xk/KyFffy+y8T58Ybjeswc/2sUm2Dd+fdyQ5aOT8Byb5hyQPXqPv+W9k9g/x9Zl19vcfMfv/yaxJ94kkx4+UeY9aSvLQJFdltqFwVZKHjJj9zOH+15PcluS9I899e2bns9tVu3t89Y9l8i8bfrZ/neRdmZ1EeZTsRc9/Nqu7+t5Sc39TkuuGuV+e5NCR8++f5E+G78/Hkjx5zPxh/MIkP7sGv/M/mOSjQ21dk+TxI+e/ILP14d8keUWSWkX+kuuoMep2TvYodTsnf5S6nZM/Vt3e6/bBntbunLmPUrdz8kep23nfm4xTt8vNf9W1Oyd7lLrNMtt9GW9du1z+WHW7XP6q63ZO9lg1e6/b3FnF+nbO/Fddt3Oyx6rZZb83I9XscvMfZX07J3+09e2Q96T86xXURqnZOfmjbSMvkz/aNvIS2aPU7HL5i8b3uGbnzH+0beRl8kep2xrCAAAAAGAyDt8DAAAAYHKaUgAAAABMTlMKAAAAgMlpSgEAAAAwOU0pAAAAACanKQUAMIGququqrq2qG6rqE1X1H6vqflV1wjB+bVX996r61HD/jcNyP1hVH66qTw63MxflPreqrh9yb6yqXx7GL6yqzwxZn6iq4xcsc2xVfXB4r09W1Ruq6oHTfkcAgPu6Tes9AQCA+4j/2d2PTZKqOiTJnyZ5cHe/LMl7h/EPJPnl7t42PH748LqTu/tjVXVwkvdW1ee7+8+r6qlJXpjkKd19S1U9IMlPLnjPX+nuS6vqh5O8PslRVfWwJH+WZGt3X11VleSUJN+S5J/W+HsAAPC/2FMKAGBi3X17kjOT/PzQFFrO85Nc2N0fG5b7YpIXJ3nJ8PzZmTWxbhme/1p3/9ESOVcnOWxB5kXdffWwTHf3pd1922o/FwDA7tCUAgBYB939d5ltix0y52WPSfLRRWPbhvEkOXqJ55dyYpJ37OYyAABryuF7AADrZ95eUrue7yXGlxpbyqur6lWZNb6O252JAQCsNXtKAQCsg6r6jiR3Jbl9zstuSHLMorHHJ7lxwfOPn7P8ryR5ZJL/nOSiFS4DADAJTSkAgIlV1eYkf5jk97t73l5Pf5Dkp6rqscNyD03yyiSvGp7/L0leNZwQPVW1f1X94sKA7v5GktckuV9VnZDk95OcXlVPWDCf5+zKAACYisP3AACmcUBVXZvkm5LcmeRNSX5n3gLdfWtVPSfJH1XVt2R2ON/vdve7huffM1xN778NJ0zvJBcskdNV9X8leXF3H19VW5P81nAVwG8k+WCSt431QQEAVqLm/+ccAAAAAIzP4XsAAAAATE5TCgAAAIDJaUoBAAAAMDlNKQAAAAAmpykFAAAAwOQ0pQAAAACYnKYUAAAAAJPTlAIAAABgcv8/9piYGpEfpyYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAAAATE4oBQAAAMDkhFIAAAAATE4oBQAAAMDkhFIAAAAATE4oBQAAAMDkhFIAAAAATE4oBQAAAMDkhFIAAAAATE4oBQAAAMDk1iyUqqpzquqWqrp2rrZvVV1SVTeM3/vMLXtBVW2pqs9W1bFz9SOq6pqx7FVVVaN+r6q6YNQvr6qD5tqcMl7jhqo6Za32EQAAAIAds5Yjpc5Nctyi2vOTvK+7D0nyvvE8VXVokk1JDhtt/rCq7j7avDbJaUkOGT8L2zw1yW3d/YgkZyU5c2xr3yQvTvKDSY5M8uL58AsAAACA9bdmoVR3fzjJrYvKxyc5bzw+L8kJc/W3dPc3uvvGJFuSHFlVD06yd3df1t2d5PxFbRa2dVGSo8coqmOTXNLdt3b3bUkuybeGYwAAAACso6nnlHpQd9+cJOP3A0d9/yRfnFvvplHbfzxeXN+mTXffkeSrSR6wzLYAAAAA2EXsKhOd1xK1Xqa+o222fdGq06pqc1Vt3rp164o6CgAAAMB3bupQ6svjkryM37eM+k1JDpxb74AkXxr1A5aob9OmqvZKcv/MLhfc3ra+RXef3d0bu3vjhg0bvoPdAgAAAGA1pg6lLk6ycDe8U5K8fa6+adxR7+GZTWj+iXGJ3+1V9bgxX9TTF7VZ2NaJSd4/5p16d5JjqmqfMcH5MaMGAAAAwC5ir7XacFX9aZInJtmvqm7K7I54L0tyYVWdmuQLSU5Kku6+rqouTHJ9kjuSPLO77xybekZmd/K7T5J3jZ8keUOSN1bVlsxGSG0a27q1ql6a5Iqx3undvXjCdQAAAADW0ZqFUt198nYWHb2d9c9IcsYS9c1JHrlE/esZodYSy85Jcs6KOwsAAADApHaVic4BAAAA2IMIpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACYnFAKAAAAgMkJpQAAAACY3LqEUlX1+aq6pqquqqrNo7ZvVV1SVTeM3/vMrf+CqtpSVZ+tqmPn6keM7WypqldVVY36varqglG/vKoOmnwnAQAAANiu9Rwp9WPdfXh3bxzPn5/kfd19SJL3jeepqkOTbEpyWJLjkvxhVd19tHltktOSHDJ+jhv1U5Pc1t2PSHJWkjMn2B8AAAAAVmhXunzv+CTnjcfnJTlhrv6W7v5Gd9+YZEuSI6vqwUn27u7LuruTnL+ozcK2Lkpy9MIoKgAAAADW33qFUp3kPVV1ZVWdNmoP6u6bk2T8fuCo75/ki3Ntbxq1/cfjxfVt2nT3HUm+muQBa7AfAAAAAOyAvdbpdY/q7i9V1QOTXFJVf7XMukuNcOpl6su12XbDs0DstCR56EMfunyPAQAAANhp1mWkVHd/afy+JcnbkhyZ5MvjkryM37eM1W9KcuBc8wOSfGnUD1iivk2bqtoryf2T3LpEP87u7o3dvXHDhg07Z+cAAAAA+LYmD6Wq6n+rqvstPE5yTJJrk1yc5JSx2ilJ3j4eX5xk07ij3sMzm9D8E+MSv9ur6nFjvqinL2qzsK0Tk7x/zDsFAAAAwC5gPS7fe1CSt415x/dK8ifd/T+q6ookF1bVqUm+kOSkJOnu66rqwiTXJ7kjyTO7+86xrWckOTfJfZK8a/wkyRuSvLGqtmQ2QmrTFDsGAAAAwMpMHkp19+eSPHqJ+t8nOXo7bc5IcsYS9c1JHrlE/esZoRYAAAAAu571uvseAAAAAHswoRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADA5oRQAAAAAkxNKAQAAADC5vda7AwAArJ8P/ciPrncX2M386Ic/tN5dAOB/EUIpANiOo/7gqPXuAruZj/3Kx9a7CwAAuwyhFLBDvnD6v1vvLrCbeehvXbPeXQAAACa0W4dSVXVcklcmuXuS13f3y9a5SwAAALDTnfHUE9e7C+xmXvSmi9b8NXbbic6r6u5JXpPkJ5McmuTkqjp0fXsFAAAAQLIbh1JJjkyypbs/193/M8lbkhy/zn0CAAAAILv35Xv7J/ni3PObkvzgOvUFAABYJ6/+9XesdxfYzTzr95603l2A3UJ193r3YU1U1UlJju3uXxrPn5bkyO7+lbl1Tkty2nj6fUk+O3lH92z7JfnKencC1pjjnD2B45w9geOcPYHjnD2B43x6D+vuDUst2J1HSt2U5MC55wck+dL8Ct19dpKzp+wUd6mqzd29cb37AWvJcc6ewHHOnsBxzp7Acc6ewHG+a9md55S6IskhVfXwqrpnkk1JLl7nPgEAAACQ3XikVHffUVXPSvLuJHdPck53X7fO3QIAAAAgu3EolSTd/c4k71zvfrBdLp1kT+A4Z0/gOGdP4DhnT+A4Z0/gON+F7LYTnQMAAACw69qd55QCAAAAYBcllNrDVNXnq+qtc89PrKpzv02bJ1bV47ez7MlV9fyd3M3Fr7Gxql61lq8Bi42/lY8sql1VVdeOx9s9Lkfb/dagTydU1aE7e7uwPVX1q1X1f889f11VvXfu+a+s9v25qg6vqp/aid2EHVJVv1hV11TVp6vq2qo6fge3s80xXVUvqarn7ryewupV1WOqqqvq2LnaQQvnMWv82k+sqr9Y69dh97cjn12X2Mak79HO11dPKLVn2lhVh61i/ScmWTKU6u6Lu/tlO6VX29Hdm7v72d/pdqpqt55DjTVxv6o6MEmq6t/OL9hZx+UqnZDEf3JM6dJs+/5/eJL7V9Xdx/PHJ/nYKrd5eBKhFOuqqg5I8qIkT+juRyV5XJJP7+DmDo9jml3PyUk+On6vytx7POwKVvvZ9V+Nz3+HZ9r36BPifH1VhFJ7plckeeHiYlXtW1V/Pr4x/HhVPaqqDkryn5P82hgl8sOL2vx8Vb16PD63ql5VVZdW1eeq6sQlXmObb2iq6rlV9ZLx+INVdWZVfaKq/nrhtRa+bamqu420/Lvn2m+pqgdV1YaqemtVXTF+jhrLX1JVZ1fVe5KcX1WHje1fNfbzkLHeU+fqr/OfMcOFSZ4yHp+c5E8XFsx/C1hVD6iq91TVp6rqdUlq1P9bVT17PD6rqt4/Hh9dVW8aj4+pqsuq6pNV9d+r6r6j/rKqun4cp6+o2WjFJyd5+ThOD57mn4A93KeSfG9V3aeq7p/kn5JcleTfjeWPT3JpVR1cVf+jqq6sqo9U1fcnSVWdNEagXF1VH66qeyY5PclTxnH8lCVeE6bwwCS3J/mHJOnuf+juG5N//Vb94+P9921Vtc+of7CqNo7H+41zku0d04eO9T+38P8ATKWqKsmJSX4+yTFVde+5xXtV1Xnj+L6oqr5rtPl8Vf1WVX00yUlVdXLNRhJeW1Vnzm37tVW1uaquq6rfnqsfV1V/Ndr/zCQ7yp5ixZ9dR32bz39ZxXv09j4TLnPcO1/fCYRSe6YLk/xAVT1iUf23k3xqfGP4wiTnd/fnk/w/Sc7q7sO7+yNZ3oOTPCHJ/5FkR0ZQ7dXdRyZ5TpIXzy/o7m8meXuSn06SqvrBJJ/v7i8neeXo42OT/Ickr59rekSS47v7ZzML2F7Z3Ycn2ZjkppqNgHlKkqNG/c4kP7cDfWf3c1HuOrF6UpJ3bGe9Fyf5aHc/JsnFSR466h9OshDkbkxy36q6R2Z/Ix+p2SV+v5nkJ7r7B5JsTvJfq2rfzI7zw8bf4+9096Vj288bf4t/szN3FJbS3XdkFkI9NrORJJcn+XiSx1fVQzK7YcoXM7uLza909xFJnpvkD8cmfivJsd396CRP7u7/OWoXjOP4gkl3CO5ydZIvJ7mxqv64qp40t+z8JL8x3n+vyaLzkXnLHNPfn+TYJEcmefF474epHJXkxnGu8MFsO0rk+5KcPY7vryX5L3PLvt7dT8js/OXMJD+e2SiTx1bVCWOdF3X3xiSPSvKjNfsS+95J/iizc6UfTvJv1mi/2DOt+LPr3LL5z38reo/+Np8Jlzruna/vJEKpPdOdSV6e5AWL6k9I8sYk6e73J3nA+GZ8Nf68u7/Z3dcnedAO9O3Pxu8rkxy0xPILctfIlU3jeZL8RJJXV9VVmb0R7F1V9xvLLu7ufx6PL0vywqr6jSQPG/WjM3vjumK0PzrJ9+xA39n93JrktqralOQzmY0SWcqPJHlTknT3Xya5bdSvTHLEOBa/kdnxtzGzE7aPZPYh/9AkHxvH3ilJHpbZSeLXk7y+qn5mmdeFKXwssxFRj8/sGL5sPD4qs1FS9x3P//s4jl+X2RcUC23Prar/lMQIVHYZ3X1nkuMyG03y10nOGt+u3z/Jd3f3h8aq52X2Hr9af9nd3+juryS5JTt2TgQ76uQkbxmP35JtL+H7YncvXHb9pszO/xcsnFc/NskHu3vr+HLizbnr7+A/VtUnMxtJe1hm5zHfn1kIdkPPbu3+pp29Q+zRduSz6/znv6Us9R693GfCpY575+s7iTl29lxvzOwP+7q5Wi2xXq9yu9/4Ntu7I9uGofdetHyh/Z1Z+vi8LMkjqmpDZtfr/s6o3y3JDy1+85mNXs4/Ljzv7j+pqsuT/Psk766qXxr9PK+7F7/RQTI7QXtNZkPgl/Mtfyvd/S9V9fkkv5DZ3DyfTvJjSQ7OLOQ6OMkl3f0t8z1U1ZGZ/We4KcmzMvu2EtbDpUl+ObP369ck2ZrZydjWzEKnuyX5/8a3itvo7v88RrX++yRXVdW3rAPrZXx4/kSST1TVJUn+OMlZyzSZP4dZfP6y2Pz50PbOaWCnG5cb/YckT66qF2V2nvuAuS9rF5+vzD9fOGde6hw+VfXwzEbDPra7b6vZhNMLfwur/cwAq7Haz67/uMSyeUu9Ry/5mXB7x3133+F8fecwUmoP1d3/ktmJ13Pmyh/OGKJYVU9M8pXu/lpmcy7cLzvHl5M8sGZz8Nwrs8v8VmycQL4tye8n+Ux3//1Y9J7M3giSzOaDWKp9VX1Pks9196syG1H1qCTvS3JiVT1wrLNvVT1sVXvF7uxtSX43ybuXWWf+b+cnk+yzaNlzx++PZHYJ6VXjWP54kqMWhiNX1XdV1feOkSf37+53ZvY3evjY1s78W4SVujSzUX0buvuWcexuTXJ8kkvH/xM3VtVJyWwuk6p69Hh8cHdf3t2/leQrSQ6M45hdQFU9pKp+YK50eJK/7e6vZjZCduHS66clWRg19fnMvkVPZiOsFjim2ZX8RJKru/vA7j6oux+W5K2ZfZmbJA+tqh8ajxcmQ1/s8swuUdpvhFwnZ/Z3sHdmH/a/WlUPSvKTY/2/SvLwuflzVj25OixnlZ9dF1vpe/T2PhMuedw7X995hFJ7tjdk22/uXpLZ3Q0+ndl8UKeM+juS/HQtMdH5ao03lNMz+8/uLzL7T2y1Lkjy1Nw1xDhJnp3R96q6PrMP/kt5SpJrx5DM789s3qzrM5vX5z1j3y/JXZeesIfr7tu7+8wxb8j2/HaSHxnDeo9J8oW5ZR/J7Hi6bMx/9vVRS3dvzWwE1p+OY+/jmR2X90vyF6P2oSS/Nrb1liTPq9mE6iZOZBLdfVtmIdT8t5OXZTZR9NXj+c8lObWqrh7rHT/qL68xUW5mJ49XJ/lAZhOMmuic9XSPJK+o2cTMV2V2fvCrY9kpmR27n87sQ8bpo/6KJM+oqkuT7De3Lcc0u5KTM/tCbd5bk/zsePyZJKeM43vfJK9dvIHuvjmzUSkfyOx9+5Pd/fbuvjqzy5euS3JOxt1Xu/vrSU5L8pc1m+j8b3f2TkFW/tl1sRW9R2/vM+H2jvs4X99pavaFJwAAAABMx0gpAAAAACYnlAIAAABgckIpAAAAACYnlAIAAABgckIpAAAAACYnlAIAWIGqunPcUvraqnpHVX33qB9UVf88li38PH0su29Vvbaq/mbcHvrKqvpPc9s8rKreX1V/XVU3VNX/WVU1lv18VW2d2+b5o35uVd04ap+sqh9aon51VR099zofrKrPzm3rolF/SVX93ahdX1Unz7WZ395VVXXpqD+oqv5ivMb1VfXONf/HBwB2S3utdwcAAP4X8c/dfXiSVNV5SZ6Z5Iyx7G8Wli3y+iSfS3JId3+zqjYk+cWxjfskuTjJM7r7PVX1XUnemuS/JHnNaH9Bdz9rie0+r7svqqpjkrwuyaMW1X8sydlJDplr83PdvXmJbZ3V3a+oqkOSXFlVF3X3v8xvb9H6pye5pLtfOfbjUQEA2AFGSgEArN5lSfZfboWqOjjJkUl+s7u/mSTdvbW7zxyr/GySj3X3e8ayf0ryrCTPX0U/PpzkETvSv8W6+4Yk/5Rkn2+z6oOT3DTX7tOreR0AgAVCKQCAVaiquyc5OrNRTgsOXnT53g8nOSzJ1QuB1BIOS3LlfKG7/ybJfatq71F6ytw2f2GJbTwpyTVL1I9L8ueLam+e29bLl9ivH0hyQ3ffMld++VybN4/aa5K8oao+UFUvqqqHbGf/AACW5fI9AICVuU9VXZXkoMzCpEvmln3L5XtV9eRFz1+U5KQkD+zuhySpJL2d11qob+/yvZdX1W8m2Zrk1EX1303ywCSPW9Rme5fv/dqY5+p7Mguz5n3L5Xvd/e6qWlj3J5N8qqoe2d1bt7MvAABLMlIKAGBlFuaUeliSe2Y2p9Ryrk/y6Kq6W5J09xmj/cIoqOuSbJxvMMKef+ju27/Ntp/X3Yd39//e3dfO1zO7nO83k5z37XcpyWxOqe9L8pQk51fVvb9dg+6+tbv/pLufluSKJD+ywtcCAPhXQikAgFXo7q8meXaS51bVPZZZb0uSzUl+Z1zylxH41FjlzUmeUFU/MZbdJ8mrkvzud9i/byZ5ZZK7VdWxq2j3Z6O/pyy3XlX9+JiUPVV1vyQHJ/nCjvcYANhTCaUAAFapuz+V5Ookm0Zp8ZxSzx71X0rygCRbqurKJO9N8htjG/+c5Pgkv1lVn81sbqgrkrx6J/Svk/xOkv82V56fU+q922l6epL/ujC6K9vOKXVVVd0zyRFJNlfVpzObUP313X3Fd9pnAGDPU7NzFgAAAACYjpFSAAAAAExOKAUAAADA5IRSAAAAAExOKAUAAADA5IRSAAAAAExOKAUAAADA5IRSAAAAAExOKAUAAADA5P5/wJK8ij3jem4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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WHwL+GbgduA3YGfDl7JIkSZIkSRoTI/r1QODrwC6llPsAImJV4LtkMEuSJEmSJEkaVSO90+pVTcAKoJRyL/DqscmSJEmSJEmSnu1GGrRaIiJWaf6od1qN9C4tSZIkSZIkaZ6MNPD0PeAvEXE0UMj3W+0zZrmSJEmSJEnSs9qIglallMMj4mLgLUAA7yqlXDmmOZMkSZIkSdKz1ogf8atBKgNVkiRJkiRJGnMjfaeVJEmSJEmStMAYtJIkSZIkSVLnGLSSJEmSJElS5xi0kiRJkiRJUucYtJIkSZIkSVLnGLSSJEmSJElS5xi0kiRJkiRJUucYtJIkSZIkSVLnGLSSJEmSJElS5xi0kiRJkiRJUucYtJIkSZIkSVLnGLSSJEmSJElS54xZ0CoiDomIOyNiSmvaqhFxSkRcW/9fpfXdFyJiWkRcExHbtqZvEhGT63f7RUTU6ctExK/r9AsiYkJrmV3qOq6NiF3GqoySJEmSJEkaG2N5p9WhwHZ90z4PnFZK2QA4rf5NRGwITAReXpf5SUQsWZf5KbA7sEH916S5G3BfKeXFwA+AfWtaqwJfBl4HbAZ8uR0ckyRJkiRJUveNWdCqlHI2cG/f5B2Aw+rnw4AdW9N/VUp5opRyAzAN2Cwi1gJWKqWcV0opwOF9yzRpHQ1sXe/C2hY4pZRybynlPuAU5gyeSZIkSZIkqcMW9Dut1iil3AZQ/39enb42cHNrvlvqtLXr5/7psy1TSpkBPACsNkxakiRJkiRJWkR05UXsMWBaGWb6/C4z+0ojdo+IiyPi4rvuumtEGZUkSZIkSdLYW9BBqzvqI3/U/++s028B1m3Ntw5wa52+zoDpsy0TEeOAlcnHEYdKaw6llANLKZuWUjYdP378MyiWJEmSJEmSRtOCDlqdCDS/5rcLcEJr+sT6i4DrkS9cv7A+QvhQRGxe31f1gb5lmrR2Bk6v7706GdgmIlapL2Dfpk6TJEmSJEnSImLcWCUcEb8EtgRWj4hbyF/0+zZwVETsBtwEvBuglDI1Io4CrgRmAHuWUmbWpPYgf4lwOeCk+g/gYOCIiJhG3mE1saZ1b0R8Hbiozve1Ukr/C+ElSZIkSZLUYWMWtCqlvHeIr7YeYv59gH0GTL8YeMWA6Y9Tg14DvjsEOGTEmZUkSZIkSVKndOVF7JIkSZIkSdLTDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzDFpJkiRJkiSpcwxaSZIkSZIkqXMMWkmSJEmSJKlzFkrQKiKmR8TkiJgUERfXaatGxCkRcW39f5XW/F+IiGkRcU1EbNuavklNZ1pE7BcRUacvExG/rtMviIgJC7yQkiRJkiRJmm8L806rrUopG5dSNq1/fx44rZSyAXBa/ZuI2BCYCLwc2A74SUQsWZf5KbA7sEH9t12dvhtwXynlxcAPgH0XQHkkSZIkSZI0Srr0eOAOwGH182HAjq3pvyqlPFFKuQGYBmwWEWsBK5VSziulFODwvmWatI4Gtm7uwpIkSZIkSVL3LaygVQH+FBGXRMTuddoapZTbAOr/z6vT1wZubi17S522dv3cP322ZUopM4AHgNXGoBySJEmSJEkaA+MW0nrfWEq5NSKeB5wSEVcPM++gO6TKMNOHW2b2hDNgtjvAC17wguFzLEmSJEmSpAVmodxpVUq5tf5/J3AcsBlwR33kj/r/nXX2W4B1W4uvA9xap68zYPpsy0TEOGBl4N4B+TiwlLJpKWXT8ePHj07hJEmSJEmS9Iwt8KBVRDwnIlZsPgPbAFOAE4Fd6my7ACfUzycCE+svAq5HvnD9wvoI4UMRsXl9X9UH+pZp0toZOL2+90qSJEmSJEmLgIXxeOAawHH1vejjgF+UUv4YERcBR0XEbsBNwLsBSilTI+Io4EpgBrBnKWVmTWsP4FBgOeCk+g/gYOCIiJhG3mE1cUEUTJIkSZIkSaNjgQetSinXAxsNmH4PsPUQy+wD7DNg+sXAKwZMf5wa9JIkSZIkSdKiZ2H9eqAkSZIkSZI0JINWkiRJkiRJ6hyDVpIkSZIkSeocg1aSJEmSJEnqHINWkiRJkiRJ6hyDVpIkSZIkSeocg1aSJEmSJEnqHINWkiRJkiRJ6hyDVpIkSZIkSeocg1aSJEmSJEnqHINWkiRJkiRJ6hyDVpIkSZIkSeocg1aSJEmSJEnqHINWkiRJkiRJ6hyDVpIkSZIkSeocg1aSJEmSJEnqHINWkiRJkiRJ6hyDVpIkSZIkSeocg1aSJEmSJEnqHINWkiRJkiRJ6hyDVpIkSZIkSeocg1aSJEmSJEnqHINWkiRJkiRJ6hyDVpIkSZIkSeocg1aSJEmSJEnqHINWkiRJkiRJ6hyDVpIkSZIkSeocg1aSJEmSJEnqHINWkiRJkiRJ6hyDVpIkSZIkSeocg1aSJEmSJEnqnHELOwNaMO746TfHJN019vjimKQrSZIkSZKe3bzTSpIkSZIkSZ1j0EqSJEmSJEmdY9BKkiRJkiRJnWPQSpIkSZIkSZ1j0EqSJEmSJEmdY9BKkiRJkiRJnWPQSpIkSZIkSZ1j0EqSJEmSJEmdY9BKkiRJkiRJnWPQSpIkSZIkSZ1j0EqSJEmSJEmdM25hZ2AsRcR2wP8ASwIHlVK+vZCzJEmS+vzTsT8ck3R//669xiRdSZIkLRiLbdAqIpYEfgz8A3ALcFFEnFhKuXLh5kx69jn+kH8c9TR3/NBJo56mJEmSJKk7FtugFbAZMK2Ucj1ARPwK2AEwaKV5dukBbx/1NF/zsd+OeprPdr88dNsxSfe9u548JumOxP5Hjk2ZPv6+hVemBemDx2036mn+/J1/HDj9H0/YZdTXddIOh416ms92/3TMAaOe5u93+tiop6nFzzuPOXtM0j1upzePSbpd88njbh71NPd757qjnqYkaXQtzkGrtYH20e0W4HULKS/PKn/78Z6jnubae/541NPsqnN+9rZRT/NNH/ndHNNOPnj7UV/Ptrv9YdTT1IKz769GP0D27xMHB8e+8JvRDyZ9692Dg0mLm+2P//yYpPuHHed8gn774745+ut55xdHPc2uetsxPx/1NH+30wfnXM/RR47+enZ+36inOS/ecfSJY5LuiTu/Y45pOxw9+kH8E3YemwsOI7XTMReNeprH7PTagdP/+ZirR31dR+300lFPs4sOO/auMUl3l3eNn2PaCb+5e9TXs8O7V59j2mm/GJsybf0vc5bp/EPvHPX1bL7r8wZOn/K/d4z6ul7x0TXmmDb9h7eP+nom7LXmwOm3//eNo76uNT/3wjnX8/2xuWdkzc9sOCbpqnuilLKw8zAmIuLdwLallA/Xv/8V2KyU8onWPLsDu9c//w64Zj5WtTow+keBhbeeBbmuxbFMC3JdlmnRWNfiWKYFuS7LtGisa3Es04Jcl2VaNNa1OJZpQa7LMi0a61ocy7Qg12WZFo11LY5lWpDrmp/1vLCUMmf0eQQW5zutbgHa9/yuA9zanqGUciBw4DNZSURcXErZ9Jmk0aX1LMh1LY5lWpDrskyLxroWxzItyHVZpkVjXYtjmRbkuizTorGuxbFMC3JdlmnRWNfiWKYFuS7LtGisa3Es04Jc14IsE8ASC2pFC8FFwAYRsV5ELA1MBMbmXnNJkiRJkiSNqsX2TqtSyoyI+DhwMrAkcEgpZepCzpYkSZIkSZJGYLENWgGUUv4AjPWboZ/R44UdXM+CXNfiWKYFuS7LtGisa3Es04Jcl2VaNNa1OJZpQa7LMi0a61ocy7Qg12WZFo11LY5lWpDrskyLxroWxzItyHUtyDItvi9ilyRJkiRJ0qJrcX6nlSRJkiRJkhZRi3zQKiIe7vt714jYfy7LPD8ijq6fN46IqyNiUkTcFBF31c+TImJCa5lNI2K/1t9nRsQcb8yPiOUj4siImBwRUyLi3IhYISKeGxH/Nrc8tufrmz49Ilavn1ePiDMi4oqIuDAiVqjTX9nK+8yIeLx+PrV+PykipgxVd3Opszsj4pKIuC4ipkbE5RFx7lyWeXxu22KE694yIv4aEXsP+O4PEVEi4nutaXtHxFdGkOYbWn8fGhE7t/6eXrfh5RHxp4hYcy5p/a69jeahbLMt07SN4bbNSLZbLc9H+6btWOtr04jYLyImtNtD37wbR8T281KWkWjKO1Q7H2KZEbfTOv8KEfG/rbZ6dkS8bi55+nOzPzfbc17WOcJ8tdvUdRHx1Tr94Yg4JSK+3Df/xyLiA/Xz0+0zIg6KiA3nMw/viIjPD5O/eWq/Q6Qxuf67MiK+ERHL9H3X9FFvmFt6fWk/XR/DzDNbPz0Wat//14i4p/Zx182tjQ2T1goR8dOaxmW1j/1IDDi+1Pr7eNOnDqqP2n88/5mVcI78zbEvRcQtEXHVgPmnR8Qddf5h+815zMds/XVrXe32tF+d3t5XzoyIa+o+d1FE/CLyuDw5Iu6PiFfNZb0Dj/N9eWj33wP7jhjBuGS0jDRPfcu8qdbJdRFxQ63P19Xv5rrfDZHmhyLi5oi4u9b5DvORxlci4vsRsX1EfC0i3lqnzTEWGLBse8y0SS3Xq0e6zAjz9/QxtN33DNU258dQear1OzlyHDii+o05x8sTIuJfWn+P6NgXERfE3MfMzVhmyGNOX5oD+8K5LTeCdH8REXu0/n5drbMxfT3KgLoeUZuI1vnJM1j3kGO7Afm5fCR9dZ3/mNbfO0fEofXziLbxMzGv++Z8pD9HvY9Vm5yPvM1z2fv6v5Uj4uSIeKSW5fCIWHkEaewVEcv3pzlUH1zr8JF5aVd9y/f3RyM6v4+INSLPwS6PHHfO8VqguuxPI+KsiFiyTtugLndd3bZnRMSbR5rfvvTnZxttWdff7k8fj94x+EOR5/JNuT5alzmvL51xkeOutSLitIj4vzp92GNlzfONkTGQIbdVRIyPiL9F6xx5YVnkg1bzo5RyaymlqfyNgVNLKRsD/wX8upSycf03HbJBlFIuLqV8cgTJfwq4o5TyylLKK4DdgKeA5wIjOUkfyXx7AGeXUl4F7Ag8Wcs1uck78BhwG/D2UspbI+JlI1j3cJ4DnANsUEp5OfBtYOlnmOYzVkrZHngCeNc8dhhbAnM7ad6qlLIRcDHwxfnL4ULzS/IXM9smAr8cYVveGBj1oFXLcxnZ/jA/DgLupddWdwXGbLAzj5o2dSvwlshfNl0WuKSU8tX2jKWUA0oph/cnUEr5cCnlyvlZeSnlxFLKt+dn2XmwVSnllcBmwIuY/Zn3rVr961/aCzUDiaEMVR9984y0n36mbiDL9W/A9cx/GzsIuI9sq68GtgNWHcmCQ9THrsCoBa2Yv33p5aWU9RmlfrOeXG7J4P663Z6G2u7vq/vcJcDWwKtq+3wFcPMzzd9YmJ8T6rntP3OxJ/A7YAPgMPJdoKvDyPa7AXlZB/gP4JvAr4DNgSvmM2/PB7YvpfxXKeXUeV04MjB5NPCeUsplI1xmnutyQN8zkrY5X1r1u0UdB85v/U4A/mVuM/UrpbxuuDFz37wjPebMd18IEGnQOc2ngc/VE68lgP2BfyulzBhp2qNorm2i7/xkQeSn6RtH0ldvGhGv7J84WuOKZ9iHPSND1PuI2uTCyPcw7X2Qg8nzwTPqsfkGsmxzsxew/NxmapRSbgXuou/caSTHszrPBOajPwK+BpxSStmolLIhMFQAdUPg2FLKzIhYFvg9cGApZf1SyibAJ8gx6wLV15/eAHwOeC3wDeDxWpevBs4EzgbWidYFAuCtwJRSym3kmOb4eVj9X4D/ZJjxWinlLuBR4O/mId2xUUpZpP8BD/f9vSuwf/18KLAfuVGuB3au0ycAU8igy03kTjYJ+AlwAHAI8DfgbuAy4BfANnXaFXXZO8mO/nrgk631X0c2mqnA7q3pFwCzgIeBy4H/BU6r8z9a0zkVOI4MOE0Gptf1n08GZqYCXwX+nRxY/r6mNYUckM1WL8CRwN7Ae4E7gNuBu+r3S5LBrtvr+q4CxgOfAc4FLgKuAR4ANgFmAkfVvFxW6620yvJE/XsW2TnuBTxODhC2BC6sdXBZrZ9P1nzcX9N4oKbxHfIXH68DTqh5uKTW92TgyrqNlqjLT69l/UJr3tuB39bvxwPH1PJcBLyR3P631+05CXgTc7aVO4HVaxoH1XxOrvmfXLfFjWSbubZuy+nAP9Q07q/5uhbYnWyXxwIzyJPAO2td3kR2lDfVOn4MeKT++1zN38M1v1NqXh+u+VqhrvfSmqcdWu37KrJdXQP8iTzQ3l2Xn0YeiP+7luOQWjeXATsw537xHuArdb4zmbPNH1/rvb/NPwzsW787lQxiPF7r6exa1kk1HwPL0t7HgSP6ph8JvKOv3TcH5CUH9BUTyI69+XvvWq7pwJ+BH9ZtdwNwbp3nrcA9Na8PkUFg5lIfXwKuBk4hg4d7t9pq06b+r85zLLkf7kPuB5eSweErarrfqPNPI08k/1Lz8v9adTypTru71vPONV9NeaYAm42gf2y3+SlkG3yc3r70HLL93kmvjxpfv/sI2YaeBH4LLN/aRk+Q+/9TwEk17clku3qY7MseBE6vZWgOvM0+v34tz7lku/wr2Vb3reneS/YXVwD/Q558b1Lr8e663a4Edqp5+il5gJ4KfLW13aaT/WvTBl86xDHnfLLvWBJ4KXBlq34OIfetR2tem33/hFp395D78vnkvvYk8A6yb/xb3Q5r1PKeWOvsMbKdPEjuA/uT+/eT9fuzgRfX8s+s2+xKYLlaD2eR++DJwFo1r2cCP6jLXkUOkI4l+6tvtPalRxi8b99St8PPar4erOu8m2zzO5MD/EfJdti0l5eS+93PyHY3tdbXlLq9Xtpqm98HziD77/7++oU1zalkv/GCVt4OpXesPxPYtH7+NnBv3/Zu2vtpNa+PA5fXaS+sebuW3Fd/2jfO+FFN4w+tOvou8LtWv3hXrf+fkdt+AnkMeqT+/XDdBsvVfB9Zp91K9otTWn3XOWTbvBR4Q52+Za2jX9T6/3pNtynXPuQ+3uRps1rvl9X//47czreR+82EAXV9Nbm9R9K/n0m24b3q8h8k2/4fyWPRl8kLIufXMjbtZq9mW5HBmGnk9v113QZ3kfv598m+99K67JNk3x0D9tPpNf/Xk8GdZvp7ybY4Bdi3rzz31/LsXOug6etuBp5T5/sN2VYereue0toWTT3fSvZNT9dzqz02/dZfgTfV6cuT46srapkvoNdup7e25/E1f804c8lW3ptjyGVkm2rGhpeSffNfyP5hMnW8WLfDA3VbfbqvDHO0lVbbP7Zu09uBS+v0t9d8PV632f+RfdWu9f8l67YI8qLVLODNddmLyP7ydcOsczK9ceKf+8Y6P6nL/D3ZXg+qZT+SPIZfV5fdlzyB36Fuv2Y9+5LtalfyOPQQud0vrOnsRu5HP6/5uAk4qubh5XW+SXX7bdA3bmnGNk+Qbb0Zp+1LXvR4Mbn/3E7uZ28mxyDN/ngO2Q6nk4H7/6r1NYW8cNK8m3gTcpufx+x9x7KtfF9GBhQg++oTyOP1JLIfW7Iue1Ety0dbbfsx8rj5QM3XTcCh9ftfARfWz2uQ5zGX139v6Gu7g/qRr5FtfothyndrXfY8sk9u+v+f0xvTPEz2HdfUOj2mbssnyQu2MHRfOqFVZ0uSffaTtR4+Uae38/ZbZu97B9ZdXe5zrentMccH6rTLgSOGOWeZTl5EmMDs7f2FDD2emUGeT02t5diF3r69OnkMurrm/azWuPbKOv3umsbkWs7/IMcbZ9XyH9/q0w4GPkkGVkpN/6CaxhPkvja1bp9Hyf78b3U9N9RpD9HrVyeR/dHjzbat65oEHFc/70b2oWeS52Intfqh5lzzVGCNVh9yGzChtfxhg8Z4rbrYu/X3lNayx9PXlumdi7aPibvS67OuotefNfvk9fX7Zp/8Itmub6hpN2OEpm8/n9y/vk+OF5py3gp8pqZxbqsuni5DTfuBWr93k8fh22reHqh1/RjZhu+vn5+q5fp9zdPtZL9/LfCdVt0MN6b+JrnPXgy8ht75/cf6+sc5zv0GbpfhvlwU/jH3oNVvyDvKNgSmDeic2vPvWiv2/XVjT6ob5znAj4GbWhuoAK8nd857gKXqd28mDzAX1P9fC6xF7qBXkQGBP9f0VgJWqWlMAz5MdpRTyAPkf5HBspPq+saTB7IvkR3Oua1yr9xfL2QwpBkMTAZeWafvSJ5MFeB9wDI1f4fVuvoL8M5aF4fSC0o9WRvhfWQHM6OW7yF6g9qbyYZ+TV2mCVqdTO/gczrwx/r5frIBL1PrdUatk63r5+XJO35mkY1/STIY0JyUTK9lWqmWc2Uyyn4nsBp5QNmizvsC4KohOqRDmb2tPFW3yzZ1e+xLnmzdXLfx1rX+3lzL97ualwnkr3KuSg6WjqvL70l2UAV4F9nJ/pTsLG4hDwSzyI7pLLLDOBD4LBkF/11dz4r0BkPjgJVaB6Fp5IBwQq27X5N3/h1Vt8NvyAPkp2p6/00ePN5f03gueRB4Dq39olVff6nbqb/Nr1r/X66WdbX6dwH+sX4+jgyeTScHllcyewBpYFna+3hdrjlQrkx2ouP62v07qAe1AX3FBIYOWj1cyzSpbo8H6zwHUDvhOv/9w9UHedI1qdbFimT/MShodWHdxkfVemqCYdOAo+vnE+j1U9Pq+paoy97cquNJdfp3yANPE7T6WatPGtTfHcrQbf4wsi0tT54cvg3Yqa7vfXX5fVpprdYq4/foDfIOJffxzWsdzar5vYRe4PsrdZ61gDXJvuirZIDmQDLocWYt/95kf3BfXc8e5EH6tLq+t5MH2L+QfegPyeDYIcAqfe11yZruq1p5b/L9b8BBQ7SjyeQVS8iT7W/Wz98kjx2rkvvStWSfuCc5OCi1DqfU+j2u1u0qta73JgNV36v5uoHsR99c81boBa1eVOvt87XuppF99pnkic3yZHv8C73A4nuAQ+rnM6kn7GR/cGut/2XI9r8auS81g9z+ffsWso/ZuJb3KDJA8SQ54N655vMBMlAxpanTWs5vkfv8aeRg+hjyhPX0Vrv5Hb2T8q8we3/9W7Lvak4gHwA+3Vp2UNDqy7WuJtU6vpVs7y+v22p1sk2cC7yqruMqcp/+JK2xBnlM3qJulytrmpeTx7yTa102ZW+O+U3Q6lQyeLAx8CGyvb+/5vtBeserb9Pbb5cHlq2fNwAurp+3JMcC67X6uCdqvUyqn69vbceVqH0meXw6htzO57Tm6a/r5tg3kv59KWCjuu6Ta5kfJa+eN8tfSwa5J5Pt4MfkftKMvSYD65LHyGnkCfH+tX52rvm7kF7f+wSw44D9dDoZ6Nq+Ne35ZHsZT7a/05tla3nurGluTfZVTduZBhxQP08lj0VL1rSubW2Lpg5vbG2DafQCoWcC36uftyfv8qfW7//Wz68g961BQaum73oOvXHXz5n9GHIrcGT9/DtyXLkT2ZYfJk96biLb6NN5HlCGOdpK6xhyPXkM/gjZZtclA9JN3X6UbNdPB63qsn8k97e3kWPT/6jb8Q6yDQ21zv+u61mZbEeP1jJNqNtp81b7n0GOdZcgjzOH1M/XkPvKarWOp7TWcwW9oNWT5MXHZcl9+Opa33fTC9JcRvbpy5LB6+aYuDSwXN+4ZVwt13Syr3iCbBffJcd6F5B995Xkvv1SekGr5es69icvVl9MbQM17SPobfcrgL9v1VdTvs8CP6+fm220bC3PrWT/vT+5n+8O/Gedd5m6vvXIdjGLHO9fRbb/oYJWvwb2ah1jVx7BOPGfW2UaqnxP0utvfljTWI7cBw5qpXVBze9x5P69FBlMeZjs14fqSye06mwP8th5fF/e23n7EzneavreoepuG2rwjWyHvyOP6S8n22T/vj3HOQuzB62ebu9zGc/MqNv3enJ/eYDevv2jmu47yODQA62+/zayz1idPE6tSQZEJ5P92oSan1tafVozZugPWs0iz3P+vdb/y8gLCTPINr4bOe68utX//LFVtoFBK7Ifn0623aVq3p4gg2tfB55f5/8wvf52N+CRVlrfBz41aIw3xHGwHbSaoy3TG6O1j4mnkfvaRuRxoOnPPkv221vWPDf75Clkv7kz2Zc0fftZ5Ln6d8jzwdeS+3uQbe1Ben3sUEGrs+gF1A8hz/Wmk+3jL2QfcFCt853I89RLyQDd88k29BTZBy9LtoV159IGpwN71M8/qHlekTxG3NnuH+vn2c79Bv0bx+KptD4fX0qZBVwZEWuMYNkXkCcCq9d0nqrTNiIbE+QB9jbgqVLK3RFxJzkQuAV4S/1+DXLjnk7eBXQ+GWV9MiJ+TR48vkl2aGuS0cbPt9bxZrLR/Bu5o8+saa1AHpDfCpwZEceRO9+PI2KLUsqDrbI8TDbq6TVvD5Ed15trmSCvAHyO7Fy2KKXMinzW/mhywLcaedCm5uHFtey7kgPeN9AbHFxe1/f/yIFd+zbLZYGTI2ItYJ2an8bRpZQnIuIi8hG259QyPkLuuI+Sne+9JW/r/CV5wvD08+ellAcj4oaa10IOEjao9bRhRDSzrhQRKzJYu60sSXYma9ALnK1b87QBvStn9zP77cIrkYGAN5I7Z9T51qzLfIDszD5S67E5odmG7AQOJg9mr6/TtiK31SPAlqWUM1tlCeCb9RnsWcDaNb+QB4QfkYOX48kTw2/Wf5eTg7IjgI8Dn28997wsvbbR7/ellCeAJ/ra/Ccj4p11nnVr/dxDDjL+WKc3B5WXkIOzdciOem5lub2ZoZRyVkT8OCKeR+4bx5TRu8X/cuBLpZTTI2JL4KSIeC7Zzv4W+X6IAjyn9S6AQfWxBXBCKeUxgIj4bd96zoiImbW8Z5Db+SlyIAPwPHIfbfL01tayF9X981F6ba4AP6rTDyAHTo1fApRSzo6IlWp5+vW3ech2twO530G20deTgRaAjSLiJrKtHlunvSIivkEe3HYmT+obD9R8jgOeLHkbNBFxBHkyezbwxlLKbRGxE7nfvJ9si38gB0mQgyzIvmbZuu7PkHdXfat+9xDZR76CDApMJ09Abyul3Ffn+eeI2L3mZy0yYHdF/a4pzyVkGxvKphFxC3mQfk2r3t5BnoysXL9r9v1TyKDRseSx5AVk3wq5L/yarOPlyAHd+WS931C33xI1rca3yBOdqOsZV0o5LiI+RdbxoxHxiloPp9Q+Y0myz2mcWP+fDEwteWs5EXE9uR8DrB8Rl9fP7X0b4MZSyqTI9wduQQ5Gx5F9y2vIQMgDZB/w7ladXk0OtI4l+7c31HKsX6c3flNKmclgryePb1vVddxWSvnBEPMeGRHPqeXfiNzebyH31zeR9X8teRLSbhOvJ49jkAPv70bE5nXevyP7bch9d9v6+Sny+PU6su090jrmf7nO8xrg+lp3U8l+egK5vZ8spZxb5/sFeXIPOTDfPyI2Jo/DL2mV78JSyg0ApZTpETGLDLItQw7a9yeDIpDt8rCI2IDcJ5caos7aXlfX96/MpX8vpTwVEZNreV5NDprfTt5J8RIyoLw7eUw9juxTfkO2hYl1mePIgH4h2+igx12nkf3G8uT4YzMGPxJxKvDhiDi5tqXXAmeWfNyBiDiSHA8dT9Zr0+e9kTwOHVT3nZWBl9S+fy1yAD6O7GuePiC3LEEe315Yy9Ju1+0+ZkL9vAXZj1FKmRIRVzBY+1g7i7yw2RzzN4mIM+u6HqvTXk/2E98h29l3Sil3RMRZtS7a48V+w7WV00opD0TEU+QFhBeS7boJBC5N7zjWdg5Z3+uRfdhHyJOpa/vW+QZqAKtOfwVZz2fVvwsZTL6E7IfOb63jhlLKZIC6f51Wj48Hk3co3xMRLwFe0DquN3W4PNmOT6nLTyZPgB+JfL/O9RHxUrKPnka26fOA/6iPbB5bSrmW2QU57no+GaSYBWxXSrk9Iq4hj3fXA/eVUq7te+xnKXL/3Z4cA64ObBUR/6/mdVVgakScDTy3lNLUzxHAP9bPW5DbnlLK1RFxI73+Y1lye11BngwfBLyq9e6alcn9/Uny+HMDOaYc7tUObyHHudR9rmkHQ40TZ5LnEo1B5TuH3KcuqvMsQx4vzq/zNGOyJ8mLGZB90kZkEGtcTW9Dcpw9VF/aeCs5hnlNLce9EfEfZF+yNrkd1wT+1vS95PF/UN1tU/9dVqevUKdvRJ773N2so7Xu2c5ZyPbW6G/vw41ntqn1tFdNp7EFeQG+1Hwt3RrXTgdm1HPbmWSA4U1kv/wB8rh7LPC2yHcErgpcU/er5tzhDLK9P0Se//6cHPd/gwyAlZrXFajnihGxL72LYcMpZH9/VlNnEfEz8pzxDPJO2hsi4jrymN9snxWpr9IZpJ5LbwD8tZQy3NgPBrdlmPOYuCw5Dt+A7POasjX7ZCH362afvI6s6x3ICx0fjIj3kcenvcl9aVop5aK6vc6p61+CPDcfqmwrkBeOL6vj/CXoBTLXrnUzjbwb60Pk+PtNZABr+VLKrZHvMH1lKeWBmuaVZL9/M8O3wfY4c4VSykPAQ5Hv7npuLf+w535tSwxVyEXIY5HvhWmsSl5BaLQH+oMGF4PsRN5h8b1SygtKKVcNmGcWQG24E4A/Rb74+q3A60opE8iD2an0dtK2l5CdwW1kA7mJPGi0Bzel5vlb5OBiKzJifUYp5TzyqsGmZIewdF/AqnEuOej8Zd/0oHclcWPyykmz/A/Jgcip5B0Pj8KQAc7o+7+d98YMctCxf8l3ifyF2QdAj9T/Z9V/zbpK32ciYl1yIPwvEfGxpzORgYZlyE7jf8kGvyzZxl9feu8RWLvuNIM80ff3VuTB/7M132cC/1pKObh+P+iE6gvkTvxXcgB+O3lQWKqm/1TJkHITuHi6bK16aHyrlNJ0dv8O7BSzvxD3fWQb2qRuwztqmZuy/JnsQJ5Hdi6nMGc7hHxsqqmfodp7k2ZjJjCu1vtbyTreqJa1yUNTVsjt2ixfyINJ23BlaTuizvtBst33m0oGVQb1bTOYvc/rT7+/bgp54D2/5Pvp3k5us2a+OeqDufcxW9XyHUcOFvei19E32vtaO09PtT6PpC8bVJ5+/W0e8iB4N3kldDnywHhKKeWv5HacQvZJn2ileSgZAL2VHNQ2dTuOHGj/dYg8P1nTfKK+Y+An5DZ8D3nX6VL06mNm6/8mGBz06r5tKhkceUfJ9wtuAxAR65EH/61Lvgvm98zeDpr6eDrNyBeYToqIg+p3j5AH+xeRgYUf1+lBXq2eVutuafKkaqlaT/37w8P0rtQ3V7r/j17f2PT/jebzuuSJ6AH0+ptBdRtkMKrZt5+uh76ytvfN5u/mhHw98vGJ/n0b4MnW/v8jst99kjyx+Fwp5QM1rUfJ/a6p02XJE5tzyP38ZcDtNY8va6Xf3x8Opzk+nEwGDj8W+SLxTev3nyC31Q9KKSeVUj5HbsPtyeDh5gzdJhpPAP9Mjg+Oq9tyWfIkvOn/ptHr22Yxe3+zZF9a7bzPdpwb4NNkXW1Uy9Qe8/TX00NkAOiD5BXVtq+T44emP1uW3FfWH7TSun1fRAbGR9S/1yD4uPrdDeRgdSJZb08nPWB1s+j1r4PqvzGu5n3nely+j6Hfu/Lx+v9Phllv4/G+/N1Xeu8I3ZcMkr2QHOA3beWcIdJchbwTvl3PjTn6mLnkK2cYfKy9vpTyLTJItVMrnf5jyEjHvm2D2kp/GaDXX+xO3sn+SvJOq0EB0XPIk6HNyAsSzyXvNjiVbNvNOtcg+4Vmnwnyjphme5xB3skHc7b//ry1+7mmHj4NPNoqWzuv7X2wPU67kQxOzzb2KKX8guxzHiMvzL6lLz/N2OZWckzZHtscT7bd95B3K7XzsAS9/f44cty7NNmWm7b/s5pWe1zSb7htf2yt0w+UUu6v836idcxYr5Typ1aeIMdgmzF7HzRs8Hsu48THm4sTrTHAoPK103od2fY3IrdLs/6nWumuRAafm311Zv1uuL706dXUdNtjye+R2+qpmrffM/s5wFB1F+RYvpn+4noOMdQ2m+OcpW++p9v7CMYzT5L9+0uY/fgT5P51Fb1ASrOO/rHzUMemC8ibF55H3pUMvfPXrcg2e2OrXa1K9ot7kzdENE8nPEhetJtCBtffGhFfa+W/3ZctTx63B7XpJ+q+uCIZMPkS2Q819fFkXx1MpXfBkVLKO2t5mgvCA88X5vGcZyt6fejjrfma/N9T66RxPr1H4Zu+5MmSF+T+gWz37e1xExln+D7DHzOXqGV/Yx3Tf5R8kgPqU1Z1vPYgvQvq/0fGApZtpdFu78054EjH1EONM0d67vd0JhZ1Z5FBHyJiOXJQecY8LP8Q2cgbN5EDXGqar64fLycjgJA76VoApZTjyOjo9vQig8vUqzGbk4OcP5ONbeWIWIq86rw0eSv6SjWtF5KDjhk1P2eTG/NkspNtGvbtwNY1XzeSUdH1azkG2Z88abiY3N4rk3V2cv37PXW+TwDn1+jt6uQV3tXruh+q+Xqy5vE08q6wIINis8gAzTvJO3qWIO8WmlXTvpE8Abqzpv/mIfLa1lxFXZa8+2BtsjNpHrPco5RyQGv+lcmd/dfkDrlOnf4negNX6tUVmHO7D+Vk4EM1Un028JHIO33WIzvca/rmX4ms7/vIgfpSZDsYyizywHo5OYB7N3kVf2Zd78vIdvKHWrbXtJZdmbzF8qmI2IpsQ0+rnedRNR/XllLuIDv8V9ZZ3kd9jDTqZZ1Wex9p/axMDu4fbbX5kSh96Q9blpZDyUAPpZSp/V+WUq4j2/pXW2XaIPKXle4AnhcRq0X+ot3b+hZv9oVXkgefB8jt2FyJ+wowc4jgcONc4O0RsWxtM/80zLyUUo6hXp2pVx2uIdsWZFD6hiEWbQSwZx1YfYTZO/v3AETEFuTt34OufA9yJXkiv0Rrmz438lfplqj5/S45gG/uClmR3l08O9f1rkBe6b+o5F1Oj5MHuSUjYjzZDzR9BK28P0Xu8zszd38iBxqNFck2PZ4cBH08IpaKiJdHxCrk/vkI8EDknbf/yFyUUratg8cP10mPk33Ql8gLDZtHxHbk1fKJ9PaHd9LbH/6h1sly5OPZV5P9VSGvSt1NtseNW6t+GNi47g+z6A2ul63LPUgGVdcFHo2IHcn9dtXIX/y5BhgfEa+v614qIl4+t/K2PEIOnj7fagdrx+y/UrYyuY80j5YuM0cqWc7n1XmjlnMZsr5uIAeqzYtlNxoiL/390V/IO5og+7FzIbcVeWXvgFLKBWRf8L56nD4aeENEvKzuL0uTAb8ralpL1jaxfWsdz2ut4yxy272X7Iup5Xmg1f9tWKdfQG6Xzesxf1d6V7ovoXdnwPvo7eNPkhfhmjYzsVXelcm7yWaRdzz1B/3bHiXvLnwtvRP7djrN3dy7wtN95jXknURB1vUL63Zemfpui3ns3yMimmPVP5B3091M7m931XXsSI5f/kzv8ajH6+f3knX7dnKg2972zaD97trHtO8g6DerpvV39UToAuDvI38Ba8n63VkDlvszeVd2s/2Xo9d+Z5B3lq7B7HfCzlZ+ev3hrsPkr3EuOXYl8pdhXzlgnvax9k1k39pYgjxBfJA8EWrueDiPvDvibOo4qNX3Xsjwx/k52spcFPIOkdXq/OMHzHMB2RZmlVIeJx/3+Sh5gntxLVNTbx9uLTcZeFnd3pB94HNHkKehLA0sX/O6G72g56NkW2/aefsuvxPIsdm/kHX3AuCaiHgRGUDZj+x7XtW3rpXJMRzkWK89tjmc3Jd3AY6u45Jlyb5gQ3LMex+5P7+B3n7ftP2dAWpg4IF6rIfsVxrNuQRR7zBjznFr42Rgj9pnEREvibxL9WmllKfIAM548lxnGWY/bp1GPl5HPdavxMjHic0YoL9895H78iY1rRWBUtN6EfDi2qcHGVCD3kXs5ljf1N1I+tI/kYHIS4BvRP7I09Njq5q3vx9h3bXPIYiItWu/chp5h8pqdXoTLBnqnGWQuY5nSinTyO3dftLocfIVGNPI88/mTu5lmP2OnVnksfFsend2P4fsl6eQ761cnt4dTNsz2NnkmPJ+8hx2dXLfWoK8C2lGKeUIcjz1cCnlv+pyl5BBrCUi4sXktr6M3P/+PiJWiXyB+270jgur1PRvIverxqPkcanZjr8A3hgR72jN0774MZ16vlWPZc24fF7OeZal14e2A7vNPnktuT++iNxGG9T0/4u80699x9fG9O4ihDwub0FeAFyPYdTjQiGfEFiqrruJKcwgj2/NfJDn22eT+3FEPiH1UnJM2m+ex9R9RnruBwx998yi5FPA/0bEJ8kd6vBSytnzsPwZ5KB8EjlIvYhsXHuQO+yW5CD7BGDHyNu2C4N/deiP5G3wt5I78UyyIR5cP/8PudNeTe54m5Kd73+TDfBe8kDwZ3KgtxzZaG8jG/XZZNDhB+TtmWuSJwvXA8+PiJeUvBviaaWUaRGxJ9kRLg08Vko5IXq/ovKDyDsIHicPCD8gb9X9Jdm5vJDeuxDuJ2/nb67cBXk78ZHk1ad16nd3kHcBfLvm4eaI+H3N/0PM/ojKQKWUSyPiEfLupvtqXrYnB7pnk4Oc/rr/GLmtnkfv8cNPko9OXkG297PrfL8lBwk70ApSDsjHnyIDR+fV8j6PvFo4g7w1+Ino3cYLecfBj8hOczMyWHc+QyvkSdvnyIPHUmQQdCbZqf6JbBMzyUH9P9MbzB0J/DYiLiYHf1cPSP+XNe0mkPtB8oRrNXKQeGdd5xX1hGU6WYft/eJbDO2P5F0NV5Btfbiy9vtz5O35J5FXsudWFko+2nAVw/86xofJQdW0yEfp7iHv/HiqdeJyw4B13BcRfyG3Q3Nr6x7AsbUt3sfsd3EOyt9FEXEiGYS8kd6LS4czg9yfTySvYn6h1ucKZBsYziP0bjF+mDz5adbXlGcl8uRwJJ4g99v3kAfI5rGA5ckTqVnk1ZcVyfbYXBH7Elmva9Z8b0S2/4fJO3AgBw3PJeumkI8RN3ctUkq5P/I278+SQbGLmPsdAgeRJwnbRT7GdmZNe2eyf3sbub/fRb6/4NiIuIy8ynY9vUe85tU3yIslU2oef04GnHcBJkbE42R/3uwP55J9+SRym15PnpT8JxkMPZfeDzFcX5c5iWx/p5F9ZvPIz3XkhYrP12l3kMeuT5LHiRNrGpvUetgv8mLBOHLbzRHsHcIf6b2L6tNkO/0cGRxtz/Mx8jHN+xh8594Msp0cSg54T6zTmjuIX0MOYKeSdxtcPiCN/v76k+Rx6eaa1k0RcXi9WjiUlcnj5Z/JY3QBDi6l/C0iLiSPGU/WclDXcRnZh95E9p0/ATYspVxY53mMDHY1/d+VACUfdf0ieTy9j+xX76/LfJUMUl9Btsvmiic1/QNbx74HWtOPiYh3k33z3O5CO5e8e21m3/HpO+TjV58h7/Rs/Dd5zJ5G1ufa5KMle5AnFp8hL7zNS//+XfJq/rJk//AQOX44oa5rdXJb3kK+8+WP5EWWlcm2vTxZ9zeQJ5Drk/vUg2RfM5ms12a/GKgeo3cgg1N3kHcnnkHut38opZwwYLFpdd4/1bHSquS7Vi6PiFPJu5YfI9vFoKDPg+S4dH+yD1xhwDxtPyG3yxVkm7uCOY8b7WPtzWR/enhE3Evu25+q8+1HPq52RS3jx8m+5S4y6HI6+Zjc7RFxDzCj9p2H0nuECYZuK0P5T/LOmJvpvUB4NnVb3EyvHZ1D78X4Hybbxy8i4ufktm72xank3brn1fa8DvPwq2YD7EsGgW6hvuqj9d3p9PbBaJXjJ+TJ3vq1nLvW8rwHeH/ko5K30zsmNo4k+6+1yOPEE8AfIuLiUsoHIuJWclx5cs3HXuQ+eBTZf69e/16T3O9/Rq/tX9RazweBQ+qYpx2s/glwQOSjjjNa+R5ULweRT45cWseDd5Fj7n4/I99p+U/k49HNqy4g2+GBEbEb2e72YITjxNYYYFD57ifHdQ+TfdJ65J1555H1Npne6zYg99+/0TvWNxfIRtKXHkRerNyeDArsTo7nnqjrPp4cP67ft8wE+uqufQ5R6/xh8j2yUyNiH+CsyMfwLiODvYPOWQaq/dFIxjNfJ8/rtop8bO4S8lUXV5D9yC/ovSPp1tZy95L75E3kecNXyH7iHHLbnlHr42MR8f6a7iA/Icd1/0gGuqaSfdFHyXPPd0VeXJtBPoLb9EfvJ/v6R8i6nw5cUY/Z36zf3Vq/3ynyseKVyf72+2Q7awd0biYDPaeWUh6LiLcB34+IH5Lt5SFybEetrw/Uc6CLyP4Hhm/Ly0e+NqJZ15nAt2of2l8fB5DnB4/Vcl9A72LDWfQCVMvXPDxCjmmbC2OfJ/uIB8l+Z12G91Xy3PR/yHHOjXX6jcA7at3tTG6PT9ALnC5Nvh7hHgY8Tj4PbXAoIzmPfVrzomNpriKv3j9WSikRMRF4byllh7ktJ42W2gYnA6+ZhzuHFqiIWKGU8nDN69nkr4tcOkbrehhYs65vNfIK1BvJk/+9SykXjzCd8cCkeiv6sOsrpcztBEwtEbEr+Rj2x+c2r57dmr6jfv48+WuPn5rLYv1pLEG+QPXdZc7366iDIu/6WqqU8nhErE8Gql9SShnyHSwaG8PtgxHxO/IR49MWZh6fber45PJSyvPr3/PVN2r0PJPjTGuMPI68AeGQejf0mKYR+STJZ0op/zov6xJEvjdvh9J7N+xCsTjcaaUFZxPyBYZBXvUY6d0b0jMWEW8l39Hy/a4GrKoDIx/xWJZ8VnxMAlYtv4t8tHBp4Ov1CvqIF468Pfo75B0Ikhaef4qIL5BjsxsZ2WNZT6v9zu/Id24ZsFp0LE/+SMdS5N0OexiwWmjm2Afr8fVCMnBiwGoBao1Pjqt3nMxX36jRMwrHma/U8fyy5NMkxy+INEopl0XEGRGxZBn6R17UpwaNv7+wA1bgnVaSJEmSJEnqoMXhReySJEmSJElazBi0kiRJkiRJUucYtJIkSZIkSVLnGLSSJEmai4hYMyJ+FRHXRcSVEfGHiHhJRLw8Ik6PiL9GxLUR8aX6gyVExFciYu++dKZHxOr1c4mI77W+2zsivtL6e/eIuLr+uzAitmh9d2ZEXBMRk+q/nev0mfXvyyPi0vpz1kTEhIiYUj9vGREPRMRlNe3vttLdteZr69a0d9ZpO7emjY+IpyLio6NWyZIkSX0MWkmSJA2jBqGOA84spaxfStkQ+CKwBnAi8O1SykuAjYA3AP82wqSfAN7VBLH61vk24KPAFqWUlwIfA34REWu2ZntfKWXj+u/oOu2x+vdG5K+CfmuIdZ9TSnk18GrgbRHxxtZ3k4H3tv6eCFzet/y7gfP75pMkSRpVBq0kSZKGtxXwVCnlgGZCKWUS8BLgz6WUP9VpjwIfBz4/wnRnAAcCnx7w3b8Dnyul3F3TvhQ4DNhzHvK9EjDsT1WXUh4DJgFrtyafA2wWEUtFxArAi+s8be8FPgusExFrI0mSNAYMWkmSJA3vFcAlA6a/vH96KeU6YIWIWGmEaf8YeF9ErDy3tIGL6/TGka3HA1er05arf18NHAR8fbiVR8QqwAbA2e1iAKcC2wI7kHeTtZdZF1izlHIhcBTwnrmUUZIkab4YtJIkSZo/QQZ4Bilz+S4/lPIgcDjwyflYX/vxwHvqtObxwJcC2wGHN+/Y6vOmiLgCuB34XSnl9r7vf0U+FjgR+GXfdxPJYFUzn48ISpKkMWHQSpIkaXhTgU2GmL5pe0JEvAh4uJTyEHAPsErfMisC9/dN+yGwG/Cc1rQrB6zzNXX6iJRSzgNWB8YP+PqcUsqrgFcCe0TExn3LXkjeYbZ6KeWvfcu+F9g1IqaTd2FtFBEbjDRfkiRJI2XQSpIkaXinA8tExEeaCRHxWuBaYIuIeGudthywH/CdOtvZwDsiYsX6/buAy0spM9uJl1LuJe9c2q01+TvAvs1jfzWotCvwk5FmOiJeCixJBs8GqgGpb5Hv0Or3BfKF8+00/w54Till7VLKhFLKhLr8xJHmS5IkaaTGLewMSJIkdVkppUTEO4EfRsTngceB6cBe5DuffhQRPyYDREcA+9flroiI/YFzI6IAdwIfHmI13yNf4t6s88T6gvO/1GUfAt5fSrltLtldLiIm1c8B7FJKmTn4CcGnHQDsHRHr9ZX7pAHzvpf8JcW2Y8jHBId9f5YkSdK8ilKGet2CJEmSJEmStHD4eKAkSZIkSZI6x6CVJEmSJEmSOseglSRJkiRJkjrHoJUkSZIkSZI6x6CVJEmSJEmSOseglSRJkiRJkjrHoJUkSZIkSZI6x6CVJEmSJEmSOuf/AzZ1dMAG5maPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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AgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAYnVAKAAAAgNEJpQAAAAAY3arlbgDAUrnp+J9c7ibAFvHY37tquZsAAABbnJFSAAAAAIxOKAUAAADA6IRSAAAAAIxOKAUAAADA6IRSAAAAAIxuWUKpqrqxqq6qqiural0v272qzquq6/vf3SaWf11Vra+q66rquRPlT+vrWV9Vb62q6uU7VNWZvfySqloz+psEAAAAYEHLOVLqOa21/Vtra/v8a5Oc31rbN8n5fT5VtV+SI5M8KcmhSd5RVdv3Ou9McmySffvj0F5+TJI7WmtPSPLmJCeP8H4AAAAAmNHWdPreYUlO79OnJ3n+RPkZrbW7Wms3JFmf5ICqenSSnVtrF7fWWpL3zqszt66zkxw8N4oKAAAAgOW3XKFUS/LJqrq8qo7tZY9qrX0jSfrfR/byPZN8daLuzb1szz49v3yDOq21u5PcmeQRS/A+AAAAANgMq5bpdZ/ZWvt6VT0yyXlV9eVFlp02wqktUr5YnQ1XPARixybJYx/72MVbDAAAAMAWsywjpVprX+9/b01yTpIDktzST8lL/3trX/zmJHtPVN8rydd7+V5TyjeoU1WrkuyS5PYp7Tiltba2tbZ29erVW+bNAQAAALBRo4dSVfXQqnr43HSSQ5JcneTcJEf1xY5K8uE+fW6SI/sd9fbJcEHzS/spft+uqgP79aJeMq/O3LoOT/Kpft0pAAAAALYCy3H63qOSnNOvO74qyZ+31v66qi5LclZVHZPkpiRHJElr7ZqqOivJtUnuTvKy1to9fV0vTXJakp2SfLw/kuTdSd5XVeszjJA6cow3BgAAAMBsRg+lWmtfSfJTU8q/leTgBeqcmOTEKeXrkjx5Svn300MtAAAAALY+y3X3PQAAAAC2YUIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEYnlAIAAABgdEIpAAAAAEa3arkbAACsLM982zOXuwmwRXz2FZ9d7iYAwIpmpBQAAAAAoxNKAQAAADA6oRQAAAAAoxNKAQAAADA6oRQAAAAAoxNKAQAAADA6oRQAAAAAoxNKAQAAADA6oRQAAAAAoxNKAQAAADA6oRQAAAAAoxNKAQAAADA6oRQAAAAAoxNKAQAAADA6oRQAAAAAoxNKAQAAADA6oRQAAAAAoxNKAQAAADC6VcvdgKVUVYcmeUuS7ZO8q7V20jI3CQAAgBXm7b/1keVuAtxvL//DXxj9NVdsKFVV2yf54yT/JsnNSS6rqnNba9cub8sAAGDL+8yznr3cTYAt4tkXfma5mwCMZCWfvndAkvWtta+01v5PkjOSHLbMbQIAAAAgKzuU2jPJVyfmb+5lAAAAACyzaq0tdxuWRFUdkeS5rbVf7/O/muSA1torJpY5NsmxffbHk1w3ekPZUvZI8s3lbgRsg/Q9WB76Hiwf/Q+Wh773wPW41trqaU+s2GtKZRgZtffE/F5Jvj65QGvtlCSnjNkolkZVrWutrV3udsC2Rt+D5aHvwfLR/2B56Hsr00o+fe+yJPtW1T5V9eAkRyY5d5nbBAAAAEBW8Eip1trdVfXyJJ9Isn2SU1tr1yxzswAAAADICg6lkqS19rEkH1vudjAKp2HC8tD3YHnoe7B89D9YHvreCrRiL3QOAAAAwNZrJV9TCgAAAICtlFCKzVJVN1bVByfmD6+q0zZS56CqesYCz/1iVb12Czdz/musraq3LuVrwKx6H7qqqq7sfw/byPK7VtV/nJg/qKo+uvQt3XRV9V+Xuw2bo6qOrqrHLHc7GE/vhxfNK7uyqq7eSL39q+p5E/PHVdVrlqqdG7M1fx4sZrHjAh74NvNY8UfHavO3j6o6raoOX7IGL7H5+/Gt0QOhjWwZ845Dr1zss3jyuK6q1mxsH7nUetv3WM42bCp9a3FCKe6PtVX1pE1Y/qAkUz/wWmvnttZO2iKtWkBrbV1r7ZX3dz1VtaKvxcaontNa2z/J4Uk2FpjummTUndn92Na3eCg1Ur87OolQatvz8KraO0mq6idmrLN/kudtbKFZVdX2W2pdW8pIbTooCxwXsGJs0rHivGO1g7KFto8abLHvPZu5T9o193M/PkK/3DUjH2uwrJ7TWtu/Pz63yHJb7Lhua/0epW8tL6EU98cbM+VDqqp2r6q/rKovVtXnq+opVbUmyX9I8uqexv/MvDpHV9Xb+/RpVfXWqvpcVX1l2q9i81P6qnpNVR3Xpz9dVSdX1aVV9XdzrzX3S3JVbdcT9l0n6q+vqkdV1eqq+mBVXdYfz+zPH1dVp1TVJ5O8t6qe1Nd/ZX+f+/blXjxR/qdb4xcNtko7J7ljbqaq/lNVXd0fr+rFJyV5fN+23tDLHlZVZ1fVl6vq/VVV81fc+8Mf9f50dVUd0MsP6GVX9L8/3suPrqq/qKqPJPlkVT20qk7t/eGK6iO6+nIfqqq/rqrrq+oPevlJSXbq7Xx/r/9XVfW3/fVfuAltnN/vFuqfz657f+m7oqoe3sv/c1/ui1X133vZmqr6UlX9WVVdU1WfrKqd+ufM2iTv7+vZ6f79l/IAclaSue3yRUk+MPdEVe1YVe+p4dfkK6rqOVX14CTHJ3lh31bm6u7Xt+WvVNUrJ9Yxdb9QVf9cVcdX1SVJ/vVkg2oYifX5vu2eU1W79fKp+7eJetv1/rh6Yn59zftFufet91XVp/ry/76XH1RVF1TVnye5qqq2r6o3TPSj3+jLPbqqLuzv6eq6dz97SFVdXFVf6J8jD+vlN1bVf+/lV1XVE2sjxwWsGAsdKy60b5k7VluT6dvHs2rK8eFGPu/fkeQLSfae14YbJ/rTpVX1hF7+C1V1SW/X/6qqR/XyWfdJx/X3Nv/zYNp+fK4ta2rYl5/e38PZVfWQiXb+XlX9TZIjFulnJ1XVtb3+G3vZFmsjK18N398ur+H46NhetsFxXV90+5p3HNWXfXwNx4WXV9VFVfXEXn5aVb2pqi5IcvK817zPfraXTz3OnFf3hKr6zYn5Eye25bkyfeuBoLXm4bHJjyQ3JnlUki8leUKGkR6n9efeluT1ffpnk1zZp49L8poF1nd0krf36dOS/EWG0HS/JOunLL8mydUT869Jclyf/nSSP+zTz0vyv/r0QUk+2qffkuTf9emnTyzz50l+uk8/NsmXJtp+eZKdJt7jr/TpByfZKclPJPlIkgf18nckecly/195bJ2P3oeuSnJ1ku8m+fle/rRe/tAkD0tyTZKnTtnmD0pyZ5K9el+5eG7bnfc6n07yZ336WXPryBCErerTP5fkg3366CQ3J9m9z///SV7cp3dN8ne9bUcn+UqSXZLsmOTvk+zdl/vnidf/5bnX7/O7bEIb5/e7hfrnR5I8s08/LMOdZQ/JcIeW6v8+H+3rXpPk7iT79+XPmnh/n06ydrm3DY/xHr0f/liSz/X5KzLsd+a2wd9K8p4+/cQkN/Xt/ej0fVZ/7rgkn0uyQ5I9knwryYOyyH4hSUvybxdo1xeTPLtPH5/kj/r0p7Px/dvrk7yqTx8y17fnrf+4JH+bYd+1R5KvZhgleFCS7yTZpy93bJLf7dM7JFmXZJ/+7/I7vXz7JA/v67kwyUN7+W8n+b2Jf+dX9On/mORdE+2Yelzg8cB/ZPFjxYX2LZPb8gbbRxY4Pszin/c/THLgIu2b245fMvG6u+Xem0H9+kSfOy6z7ZOOy/TPgzWZ2I/Pa8uaDJ8Jc/uyU+fee2/nf+nTU/tZkt2TXDfR7l23dBs9VtYj9x6HXpnkkl42d+y3U4bj00f0+cnjujVZ+Djq/CT79umnJ/lUnz6t98vtp7Rjsf3sQseZN/btdk2SL/Sy7ZL877k2z2uvvrWVP7bK4XM8YNyT5A1JXpfk4xPlP53hi2haa5+qqkdU1S6buO6/bK39MMm1c79QbaIP9b+XZ/gQmO/MDB8070lyZJ9Phi/n+9W9A052rj7qIsm5rbXv9emLk/xOVe2V5EOtteur6uAMgcJlvf5OSW7djLaz7XhOa+2bVfX4JOdX1acz9J9zWmvfSZKq+lCSn0ly7pT6l7bWbu7LXZlhW/+bKct9IElaaxdW1c41jBJ8eJLTaxjl1zLsMOec11q7vU8fkuQX697r5eyYYeebJOe31u7sr39tksdl+HI76aokb6yqkzMc8F+U6aa1Mdmw3y3UPz+b5E39F7wPtdZurqpDetuv6Ms+LMm+GQ52bmitXdnLF/qMYNtxe5I7qurIDF+evzvx3E9n+BEirbUvV9XfZwixpvmr1tpdSe6qqlszfBlfbL9wT5IPzl9J31/u2lr7TC86PcMX8Tkb27+dmuTDSf4oya9l2M9N8+Het77Xf70+IMk/ZvhcuaEvc0iSp9S9I1J2ydCPLktyalU9KMP++sqqenaGoOCz/b0+OMO+clq7f2mBNrHyLHSsuNi+ZTHTjg8X+7z/+9ba5xdZ3wcm/r65T++V5MyqenSG7fiGieVn2Scl0z8PNuarrbXP9un/meSVGUaaJfcepx6Y6f3sn5J8P8m7quqvMgQAS9FGVpbntNa+OTH/yqp6QZ/eO0M/+taUevc5juqjip6R5C8mtrcdJur8RWvtninrWmw/u+hxZmvtxqr6VlU9NcP2e0VrbVp79a2tnFCK++t9GQ40rpkou88pRBm+9G6Kuzayvruz4emnOy5Q/55M384vTvKEGk5xeH6S/9HLt0vyrycOOIYGDB8435mbb639eQ2nXPx/ST5RVb/e23l6a+11C78tuK/W2v+uqlsy7Aynbe8LmewnC23ryX37X0tyQpILWmsvqOE0iU9PPP+dielK8suttesmV1BVT5/l9Vtrf1dVT8swquP3q+qTrbXjZ2zj/LZM7Z9JTuoHCs9L8vmq+rne7t9vrf3pvHavmdJup+pxZpI/zvDL7KT72x8X2y98f4ED9FlfZ6E+99WquqWqfjbDL9W/ssB6ZulzlWGE0yfmV66qZ2XYB76vn4pwR4ZA+0Wb025WtIWOFaftWzb25W3a8eFin/eT2/M0bcr025K8qbV2blUdlGHkw5yN7pP6MeOs++eF2jJ/fu51Kwv0sxpOfT84w4+tL89wtsKWbiMrVN/Wfy7D9vLd/kPp/O9Xc6YdR22X5B/bcK3UaRbqi4vtZ2fZRt+VYd/9LzL8KDONvrWVc00p7pfW2g8y/LL0qoniC9MPgvsH3Ddba/+U5NsZRmdsCbckeWQfhbVDkp/flMqttZbknCRvyjDcci5V/2SGD5skw3U9ptWvqv8nyVdaa2/NMILlKRmGrB5eVY/sy+xeVY/bpHfFNqlvM/tkGJp8YZLnV9VDquqhSV6Q5KLcv/7zwv46P53kzv6r0y5JvtafP3qRup9I8orqe9n+a9TG/KCPoEgNd7P7bmvtf2b4VepfbkIb55vaP6vq8a21q1prJ2c4veiJvd2/NnE9gD3n+uYituRnFA8s5yT5gwzbzaTJ/dmPZRjJcV1m31Y2eb/Qt/076t5r6Pxqks8sUmWad2X4NfisRYKvw2q4lscjMpwyddmUZT6R5KUT/fnHargW0OOS3Npa+7Mk787Qrz+f5Jl173V5HtL/zRajz20DFjhWnGXfMuv2sTmf93NeOPF3bmTf5P7xqEXqznTMOGFj7+exVTV3fbkXZfrI56n9rL/3XVprH8vw7zzXli3dRlauXZLc0QOpJ2YYOTTnR8d1C+nf9W6oqiOSH91c4KdmeN2F9rOzOifJoUn+Ve67D5+jb23lhFJsCe/OhgnwcRnutvLFDBd1m9uhfyTJC2oLXNC0H+Acn+SSDMMov7wZqzkzyYtz77DNZBjOubaGC9ldm+Eim9O8MMnVNZwy9cQk722tXZvkdzNcHPqLSc5L8ujNaBfbjgv6NnRBkte21m5prX0hw7n3l2bYvt/VWpsbjvzZGi4qvKkXSLyjqj6X5E+SHNPL/iDDyKXPZrgmzEJOyHBq3xdruLnACTO83il9+fcn+ckkl/b3+Tu5d1TiLG2cb6H++ar+7/K3Sb6X5OOttU9mON//4qq6KsnZ2fjBwGlJ/qRc6Hyb01r7dmvt5Nba/5n31DsyXND1qgz7iqP7kPwLMgzbn7zQ+bT1bu5+4agkb+h19s+wv9sU52Y4hWmhU/eS4TPmrzIciJ/QWvv6lGXeleTaJF/o/f9PM+zvD0pyZVVdkeF0/be01m7LEHB/oLf78xn2j4vZYscFbPXmHyvOsm+ZafvYzM/7OTvUMPL9N5O8upcdl+EUpIuSfHOhipn9mHGunRvbj38pyVG9/+ye5J1T1rFQP3t4ko/2ss9MvJct3UZWrr9OsqpvQydk2LbmTB7XLeZXkhzTj8euSXLYDK+70H52Jn2/fUEW/xFG39rKzV2wC4AVqIbh169pra1b7rYs5IHQRnggqaq1Sd7cWpv6Rb6Gu9X+c2vtjdOeh21BVd2Y4eYWiwVPY7VlTYbrLj55udsCDyRVtV2Gu2se0Vq7fsrza6JvbfWMlAIAWCGq6rUZLqDu+oYArFhVtV+S9RkuiH6fQIoHDiOlAAAAABidkVIAAAAAjE4oBQAAAMDohFIAAAAAjE4oBQBs06rqnn7b+bnHml7+6qr6flXtMrHsQVXVquqYibKn9rLX9PnTquq7VfXwiWXe0pfZY4HXfG0v/3RV3VRVNVH3L6vqnyde/6Pz2n9aVR0+UX/dxHNr+x0u5+reWVVXVNV1VXVhVf38xLLHVdXX5rVr13n1vlxVb5yo88Squriq7pp7/xPPvbqqrum3wP5AVe24Of8/AMDKJZQCALZ132ut7T/xuLGXvyjJZUleMG/5q5K8cGL+yCR/O2+Z9UkOS350y+rnJPnaIq950sRz/5jkmb3urkkevYnv55FV9f8u8NxFrbWnttZ+PMkrk7y9qg6eeP7N89r1j5P1kjw1yc9X1TN7+e19PW+cWEeqas9evrbfinv7DP9OAAA/IpQCAJinqh6f5GFJfjdDODXppiQ7VtWj+oimQ5N8fN4yH8i9wdVBST6b5O4ZX/6M3Bvg/FKSD21S45M3ZGj3olprVyY5PsnLZ11xa+17Sa5Msmefv7W1dlmSH0xZfFWSnapqVZKHJPn6rK8DAGwbhFIAwLZup4nT1c7pZS/KECxdlOTHq+qR8+qcneSIJM9I8oUkd817/vokq6tqt76uMxZ5zSuranLk1flJnlVVc6OLztzE93Nxkruq6jkzLPuFJE+cmH/1RJsumL9wfz/7JrlwsZW21r6WYfTUTUm+keTO1tonZ30DAMC2QSgFAGzrJk+lmztV78gkZ7TWfphhpNIR8+qc1cvmwqtpPtTX8/QM4dZCr7l/a20yeLonyd9kGGm108TphEnSFnit+eX/IzOMlkpS8+YnT9+bDLV+pqq+mOQfkny0tfYPi650CK8OS7JPksckeWhVvXiG9gAA2xChFADAhKp6SobRQOdV1Y0ZgqUNTuHrocwPkvybDCObpjkjyQlJzuvh1qY4I8nbMoRfk76VZLd5Zbsn+ea89n0qyY5JDtzI6zw1yZdmaM9FrbWnJPnJJC+tqv03svzPJbmhtXZba+0HGQK6Z8zwOgDANkQoBQCwoRclOa61tqY/HpNkz6p63Lzlfi/Jb7fW7pm2ktbaTUl+J8k7NqMNFyX5/dx3FNb1SR5TVT+RJL1NP5XhOk/znZjkvyz0Aj18+29J/njWRrXW/q6367c3suhNSQ6sqof0624dnNnCLwBgG7JquRsAALCVOTLJ/LvXndPLL5kraK19bmMraq396QJP7VRVV07M/3Vr7bUT9Vrm3dGul9/VT4N7T1XtmGG01q+31u6csuzHquq2ecU/U1VXZLjw+K1JXtlamxzp9ep5p9k9f0rb/yTJa6pqnyTfS7Iuyc5JflhVr0qyX2vtkqo6O8M1q+5OckWSU6asCwDYhtVwzAMAAAAA43H6HgAAAACjE0oBAAAAMDqhFAAAAACjE0oBAAAAMDqhFAAAAACjE0oBAAAAMDqhFAAAAACjE0oBAAAAMLr/C9w5bcS8enasAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in cat_col:\n",
    "    fig, ax = plt.subplots(figsize=(20, 5))\n",
    "    fig = sns.countplot(X[col])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Handle Continuous Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I check for the count, number of unique values, mode, and frequency for all continuous features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           AGE     WPH  CAPGAIN  CAPLOSS  DIVFROMSTO  NUMPERWOFOREMP  \\\n",
      "count   299285  299285   299285   299285      299285          299285   \n",
      "unique      92    1700      372      314        2467               7   \n",
      "top          0       0        0        0           0               0   \n",
      "freq      5792  282397   288184   293471      267844          143872   \n",
      "\n",
      "        WEEKWORKINYEAR    YEAR  \n",
      "count           299285  299285  \n",
      "unique              54       2  \n",
      "top                  0      94  \n",
      "freq            144108  149643  \n"
     ]
    }
   ],
   "source": [
    "print(cont_X.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data scaling is recommended for the continuous features. I experimented with various scalers here. However, changing the types of scaler had a minimal impact on the performace of the model. I also deleted the \"YEAR\" column, because this feature was not important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             AGE  WPH   CAPGAIN  CAPLOSS  DIVFROMSTO  NUMPERWOFOREMP  \\\n",
      "0       3.931826  0.0  0.000000      0.0    0.000000        0.693147   \n",
      "1       2.890372  0.0  0.000000      0.0    0.000000        0.000000   \n",
      "2       3.871201  0.0  0.000000      0.0    0.000000        1.945910   \n",
      "3       4.304065  0.0  0.000000      0.0    0.000000        0.000000   \n",
      "4       3.806662  0.0  0.000000      0.0    0.000000        0.693147   \n",
      "...          ...  ...       ...      ...         ...             ...   \n",
      "299280  3.951244  0.0  0.000000      0.0    7.393878        1.945910   \n",
      "299281  3.178054  0.0  0.000000      0.0    0.000000        0.000000   \n",
      "299282  3.091042  0.0  7.999343      0.0    0.000000        1.791759   \n",
      "299283  3.737670  0.0  0.000000      0.0    0.000000        0.693147   \n",
      "299284  4.077537  0.0  0.000000      0.0    8.412055        0.000000   \n",
      "\n",
      "        WEEKWORKINYEAR  \n",
      "0             3.135494  \n",
      "1             0.000000  \n",
      "2             3.988984  \n",
      "3             0.000000  \n",
      "4             3.988984  \n",
      "...                ...  \n",
      "299280        3.970292  \n",
      "299281        0.000000  \n",
      "299282        3.988984  \n",
      "299283        3.970292  \n",
      "299284        0.000000  \n",
      "\n",
      "[299285 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "cont_X= cont_X.astype(float)\n",
    "\n",
    "cont_X.drop(['YEAR'], axis=1, inplace=True)\n",
    "\n",
    "col = cont_X.columns\n",
    "\n",
    "#scaler = RobustScaler()\n",
    "#scaler = MinMaxScaler()\n",
    "#scaler = StandardScaler()\n",
    "\n",
    "#scaler.fit(cont_X)\n",
    "#cont_X = scaler.transform(cont_X)\n",
    "\n",
    "cont_X = np.log1p(cont_X)\n",
    "\n",
    "print(cont_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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OMQFlgSaM1LV0kZ0SH9L09v4QEa6eW0hLZy+bqppcvVZXbx8/f6uKUx093L6inOKsZFevF0orpuZyfnkOrx2oZ8fR4PUWjXGbBZowcqK1K6zvz/ialp/G9Pw0/rz/JN297txX6PH08/CGao63dnHrsilhky3BLQMz0cpzU3h+Ry3NNjnARAkLNGGiX5Wm9p6IWkb5qjkFdPT08bYL69Z4+vt57J3DHGns4JNLSplVmB7wa4Sj2Bjh5sWl9Ds53MIxv5wxY2WBJkyc7vLg6Vdy08I7X5ev0pwUzilM54336wOa3blflae21HDgRBs3LizmvJKsgJ07EuSkJnD1nEIOnGhjd4imkRsTSBZowkRjezcAuamR06MBuHJ2AV29/bxZ2RCQ86kqz20/xu5jLVw7r5DzyyMjh1mgrZiWS2FGEn/YXReUCRfGuMkCTZhoavOOx+emRk6PBqAoK5l5RRm8fbCB9gAk3PzT/pNsOXyKy2blc/GM8FzGIRhiRLju3Mmc6ujl7YOhWVLbmECxQBMmGtt7iBUhM8xynPnjI7ML6PH088b7Z7eQ3PYjp3hl30kWlmZx1eyCANUuck2flMY5hem8duBkWC88Z8xoLNCEica2brJT48Myx9loCjKSmF+axYZDjbR29o7rHFUN7Ty77RgVeancFGHZmN00MDT59qHADE0aEwoWaMJEY3tPxN2f8XXl7AL6FV4ax8OG9ae7+eXGw2SnJnDbsinExdg/ywFFWcnMLkznrcoG69WYiGX/o8OAqtLY3kNOBM04GywnNYELp+Wy7cgpdtU0+12uvdvDwxuqiRG444JykhPC+2HVULjiHG+vZnO1uw/HGuMWWyYgDLR1e+jx9EfcRIDBLps1ia1Hmln7y6186dLpxMZ8ePhr8IJoXb19/HLjYVo7e/n8RRXkRHj73VKcnUxFXiobDzXyiw2Hz/hzheAsNmfMeFmPJgw0tUfmjLPBkuJjWTW/iNrmLl7df3LEY/v7lX96ZheHmzq4eXGJrcUyigum5dLc2cveOnuuxkQeCzRhYCDQZEd4oAGYV5zJwtIs/rz/JAdOnB7ymP5+5Z9//S7P76jl6jkFE+6BzPGYPTmD7JR43j5okwJM5LFAEwZanJlaWcmRH2gAPja/iIKMJB7ddJiD9W0f2tfS0cuXH93GE5uP8jdXTOfSmRP3WZmxiBFhxdRcDjd2cKy5M9TVMWZMXA00IrJSRPaLSKWIfHWI/SIiP3T27xKRRT77HhKRkyKye1CZ/xSR95zjfy0iWc72chHpFJEdzmudm20LpJbOXpLjY0mIi464nxQfy50XVpCZnMBDb1bx9NYath0+xfdf2s/V//Maf9x3gq9fN5uvXD3LpjGPweIpOSTExvB2gLIwGBMsrv1mE5FY4F7gWmAOsEZE5gw67FpghvO6C7jPZ9/PgZVDnPplYJ6qngccAL7ms++gqi5wXmsD0pAgaOnsJTOKFvICSEuM48uXTePC6XnsPNrM09tq+PGrlUzLT+PZL1/AFy6ZGuoqRpzkhFgWTclm17EWTneN73klY0LBzVlnS4FKVT0EICJPAKuAvT7HrAIeUW+K2o0ikiUik1W1TlVfF5HywSdV1Zd8Pm4EbnatBUESjYEGvD2b686dzFVzCmjt7OWWZWURlZ06HF0wNZeNhxp5p7qJj5xj2RNMZHBzrKYYOOrzucbZNtZjRvJZ4Pc+nytEZLuIvCYiFw9VQETuEpEtIrKlvv7sUqYESktnb0SmnvFXfGwMuWmJFmQCIC89kRmT0thc1URfvy0hYCKDm4FmqMH3wf8z/Dlm6JOLfB3wAI86m+qAMlVdCHwFeExEMs44uer9qrpEVZfk54f+RnSPp5+Onj6yorBHY9yxfGourV0em+psIoabgaYGKPX5XALUjuOYM4jIZ4DrgVudYTdUtVtVG533W4GDwMxx1z5IBnKDRePQmXHHrMJ0slLi2ejCgnPGuMHNQLMZmCEiFSKSAKwG1g86Zj1wuzP7bDnQoqp1I51URFYC/wTcoKodPtvznQkIiMhUvBMMDgWuOe5otkBjxihGhGUVuVQ1tHOitSvU1TFmVK4FGlX1APcALwL7gKdUdY+IrBWRgRlhL+ANBpXAT4EvD5QXkceBDcAsEakRkc85u34MpAMvD5rGfAmwS0R2Ak8Da1U17JNDtVigMeOwZEo2cTFivRoTEVzNdaaqL+ANJr7b1vm8V+DuYcquGWb79GG2PwM8M+7KhkhLpzcrQIYFGjMGqYlxnFeSyfajzVwztzDU1TFmRNHxhGAEa+nsJTUhlvhY+6swY7N8ai49nn62HzkV6qoYMyL77RZi0T612binJDuFkuxkNlY14cyJMSYsWaAJMe/DmtGR48wE3/KKXOpPd7PhoN2rMeHLAk2ItXZ6yEiyZYHM+JxbkklKQiyPbDgc6qoYMywLNCHk6euns7ePdAs0ZpziY2M4vzyHl/Ye53Bje6irY8yQ7DdcCLV1ewBIT7R7NGb8VkzLZcOhRta9dojvfPzcD7Y/tunIGcfaSpwmFKxHE0Knu5xAYz0acxYykuL55JISntlaw/EWe4DThB8LNCE00KNJs0BjztIXL5lGnyoPvBH2yTDMBORXoBGRZ0TkoyJigSmA/tKjsaEzc3ZKc1JYNb+IRzcd4ZSzNLgx4cLfwHEfcAvwvoh8V0TOcbFOE8bA4lVpidajMWdv7WXT6Ozt42dvV4e6KsZ8iF+BRlX/qKq3AouAarx5xt4WkTtFxL6Oj9Ppbg8pCbHExthyxubszSxI5+o5Bfz8raoPcugZEw78HgoTkVzgDuDzwHbgB3gDz8uu1GwCaOvy2EQAE1B/e+UMWrs8rHvtYKirYswH/L1H8yzwBpACfExVb1DVJ1X1r4E0NysYzU539dr9GRNQc4syuXFBEQ+9ab0aEz787dE8oKpzVPU7A+vFiEgigKouca12Ue50t4d0uz9jAuwfrp5Fvyp/eu9EqKtiDOB/oPn3IbZtCGRFJhpVtaEz44rSnBRuWz6FLdWnOHnanqsxoTdioBGRQhFZDCSLyEIRWeS8LsM7jGbGqbXTg6dfSbOhM+OCey6fTkJcDC/tsV6NCb3Rvk5fg3cCQAnwfZ/tp4F/dqlOE0J9m/ebpg2dGTfkpiVy8Yx8/rjvBFUN7VTkpYa6SmYCG7FHo6oPq+rlwB2qernP6wZVfTZIdYxKJ093A5Z+xrjnoul5ZCbH89tdtfTbejUmhEb8LScit6nqL4FyEfnK4P2q+v0hihk/1DuBxtLPGLckxMVw7bxCnth8lK3Vpzi/ImfIRJtgyTaNu0abDDDQ304D0od4jUhEVorIfhGpFJGvDrFfROSHzv5dIrLIZ99DInJSRHYPKpMjIi+LyPvOz2yffV9zzrVfRK4ZrX6h1NDmTRNimZuNm84tzqQ8N4WX9h6ns6cv1NUxE9RoQ2f/6/z8t6FeI5UVkVjgXuBaYA6wRkTmDDrsWmCG87oLb6qbAT8HVg5x6q8Cr6jqDOAV5zPOuVcDc51yP3HqEJaa2ruJEUiKt/Rxxj0iwvXnFdHR02fTnU3I+PvA5n+ISIaIxIvIKyLSICK3jVJsKVCpqodUtQd4Alg16JhVwCPqtRHIEpHJAKr6OtA0xHlXAQ877x8GbvTZ/oSqdqtqFVDp1CEsNbb1kJoQh4ilnzHuKspKZkl5NhsONdp0ZxMS/n6dvlpVW4HrgRpgJvB/jVKmGDjq87nG2TbWYwYrGHho1Pk5aSznEpG7RGSLiGypr68f5VLuaWzvIdVmnJkguWpOIQlxMbzwbl2oq2ImIH8DzcCNhOuAx1V1qJ7GYEN9VR889cWfY/zl17lU9X5VXaKqS/Lz88d5qbPX1N5DamLYjuyZKJOWGMcV5xRw4EQb7x1vDXV1zATjb6D5jYi8BywBXhGRfGC0PngNUOrzuQSoHccxg50YGF5zfp48i3OFTJP1aEyQLZ+aQ15aIr/dVUePpz/U1TETiL/LBHwVWAEsUdVeoJ0z77cMthmYISIVIpKA90b9+kHHrAdud2afLQdaBobFRrAe+Izz/jPA8z7bV4tIoohU4J1g8I4fzQuJhrZuUhMs0JjgiYuJ4cYFRTS19/Dq/pOjFzAmQMbym2423udpfMs8MtzBquoRkXuAF4FY4CFV3SMia53964AX8A7HVQIdwJ0D5UXkceAyIE9EaoBvqOqDwHeBp0Tkc8AR4BPO+faIyFPAXsAD3K2qYTmfs8fTz+kujw2dmaCbmp/GorJs3ni/nvmlWRRmJIW6SmYC8CvQiMgvgGnADmDgl7cyQqABUNUX8AYT323rfN4rcPcwZdcMs70R+Mgw+74NfHukOoWDUx3eZ2hs6MyEwrXzCnnveCvPbT/GXZdMJUZkyAc57SFOEyj+/qZbAsxxAoM5S43Ow5o2dGZCITUxjuvOnczTW2vYXN3EsorcUFfJRDl/JwPsBgrdrMhE0tjuTT9jPRoTKgtLs5iWn8rv3z1OQ1t3qKtjopy/gSYP2CsiL4rI+oGXmxWLZk3tA0Nndo/GhIaIcPPiUmJjhCc3H8XTb7PQjHv8/Ur9TTcrMdEMDJ2l2dCZCaHM5HhuWljMY+8c4ZV9J7lmrg1aGHf4O735NaAaiHfebwa2uVivqNbU3kNsjJCUYD0aE1rzijM5vzyb1w/Uc7C+LdTVMVHK31lnX8Cb9DIH7+yzYmAdw8z+MiNrbO8mOyWBGMtzZsLAR88toqqhgyc2H+VLl04jJzUBwJYUMAHj7z2au4ELgVYAVX2fv+QYM2PU2NZDrvOf2ZhQS4iL4bZlZfT19/PwhmpbTsAEnL+BptvJwAyA89CmTXUep6b2ng++NRoTDiZlJHHbsik0tfXw6KbDNjnABJS/d6NfE5F/BpJF5Crgy8Bv3KtWdGtq72F2UUaoqxHVhhv2McObmp/GTYuKeXprDc9uO8bNi0tseNcEhL+B5qvA54B3gS/ifdr/AbcqFe0a23vIC3KPxn7xGn8sKsumtbOXl/aeQFU/mAJtzNnwK9Coar+IPAc8p6qhW8QlCvT29dPS2UtOamKoq2LMkC6bNQkR4cU9x/H0K586v5S4mL+Mslu6GjNWIwYa8S7/+A3gHrzrvYiI9AE/UtVvBaF+UeeU87BmTprdo4lmkd6DvHRmPvGxwm931fHLjYdZfX4ZSfFjn44/lj8HC1bRa7TJAH+Hd7bZ+aqaq6o5wDLgQhH5e7crF40anUBjs85MuLtgWh4fX1hM5ck27nvtII2WqsaM02iB5nZgjapWDWxQ1UPAbc4+M0YD6Wds1pmJBEvKc7jzwgraujz85M8HqTxpD3WasRst0MSrasPgjc59mvghjjejGEhgmGdDZyZCTMtP4+7Lp5ORHMfP3qrid7tqbYVOMyajBZqece4zw/hLj8YmA5jIkZOawNpLp7Fsag5vHWzkR396n+qG9lBXy0SI0WadzReR1iG2C2BL841DU3sPMQJZydYhNJElMS6WG+YXM7cok2e21XD/G4c4tziTK84JTJIQfycO2KSByDNioFFVy/oYYI3tPd48Z/ZsgolQ0/LT+NsrZvD6+w28WVnPu8da2Hr4FDcuLOayWfnkpVlv3XyYq3nqRWQl8AMgFnhAVb87aL84+68DOoA7VHXbSGVF5ElglnOKLKBZVReISDmwD9jv7Nuoqmvda934NLZ1k2v3Z0yES4yP5ao5BVwwLZd3qpvYW9vKP/5qJwClOcksLM1mVmE6R5s6yE1NJCc1gWTLVj5huRZoRCQWuBe4CqgBNovIelXd63PYtcAM57UMuA9YNlJZVf2UzzW+B7T4nO+gqi5wq02BYHnOTDRJTYzj8lmTuP/Ti9lZ08I7VY3sONrM5uom1u+s/dCxyfGx5KQmkJOaQG5qAuV5qZTnppIQ52/KRROp3OzRLAUqnenQiMgTwCrAN9CsAh5RVQU2ikiWiEwGykcr6/SGPglc4WIbAq6xvYfZhZbnzEQXEWFBaRYLSrM+2Nbe7WHdawc51d5DY3sPTc6rtrmTPbUt/PlAPXExQnleKnOLMji3OJMUWwwwKrn5t1oMHPX5XIO31zLaMcV+lr0YOOEsWTCgQkS2413O4F9U9Y3BlRKRu/CurUNZWfBvKlqPxkwUqYlxTM5MZnJm8hn7ejz9VDe28/6J0+w/cZrnd9Ty2511zCxIY35pFrMnZxAfaz2daOFmoBnqbvfgpQWGO8afsmuAx30+1wFlqtooIouB50Rkrqp+aNacqt4P3A+wZMmSoC510NvXT3NHrwUaM+ElxMUwsyCdmQXpXKdKbUsXO482s7OmmX3HT5MYF8Pcogzml2YxLT/NryzSloMtfLkZaGqAUp/PJUCtn8ckjFTWWQ/n48DigW2q2g10O++3ishBYCaw5WwbEiinOrzP0NjDmsb8hYhQnJVMcVYyK+cVUtXQzo6jzew+1sK2I82kJ8ZxXkkmC8uyKco6s3dkwp+bgWYzMENEKoBjwGrglkHHrAfuce7BLANaVLVOROpHKXsl8J6q1gxsEJF8oElV+0RkKt4JBodcatu42MOaxowsRoRp+WlMy0/jhvlF7D9+mh1Hm9lY1cRbBxspykzC09/PjQuLyUiyZ9EihWuBRlU9InIP8CLeKcoPqeoeEVnr7F+Hd12b64BKvNOb7xyprM/pV/PhYTOAS4BviYgH6APWqmqTW+0bj6Y2y3NmjL/iY2OYV5zJvOJMOnv62FHTzNbDTfzr83v4rxf3c9clU7njwgrSEm0CQbhz9W9IVV/AG0x8t63zea/A3f6W9dl3xxDbngGeOYvquq5hIHOzDZ0ZMybJCbGsmJrLiqm5zCvO4IevVPJfLx3ggTer+OsrZnDHBeWhrqIZgX0VCKImJ6HmRF0iINLXaDHh4bySLB74zBJ21TTzny/u5//97V6e33GMj5xTQH76h4elh/s3Z5MEgsvmDwZRU3sPIpCVMjEDjTGBdF5JFo98dik/WrOQo00d3PtqJTuPNoe6WmYIFmiCaCDPma3BbkxgiAgfm1/EC397MUVZSTy55Sgv7z1Ovwb1yQUzCgs0QWQPaxrjjsmZyXz2ogqWTMnm1f31PLf9mAWbMGL3aIKosc0CjTFuiYuJ4aaFxaQnxfPq/pN4+pWbF5f49bCncZcFmiBqbO9mVmF6qKthTNQSEa6aU0BsjPDHfSdIjIvhhvlFiAWbkLJAE0Q2dGZMcFw+K59uTx9vvN9AVnI8l84KzOJsZnws0ASJp6+f5s5eywpgTBCICCvnFtLS2ctLe09QmJnELMuaHjI2GSBITnX0ojpxn6ExJthEhI8vLGFypnc2Wv3p7lBXacKyQBMkTZYVwJigS4iL4bblU4gV4RcbD9PV2xfqKk1IFmiCpLHd+23K7tEYE1xZKQncsmwKTe3dPLfjGGrTnoPO7tEEyQc9GrtHY1xg6X1GVpGXypWzC3hp7wlmFqRz6/Ipoa7ShGKBJkgaLXOzMSF1ycx83j/ZxvqdtUzJSSE37cNf+iz/mXts6CxIGp0eTXaKraFhTCjEiPCJxSXECDy55Sh9/TaEFiwWaIKkqb2b7JR44mwddGNCJislgZsWllBzqpNX9p0IdXUmDPutFyT2sKYx4eHc4kyWTMnmtQP1HGpoC3V1JgQLNEHS0NZjEwGMCRMfPW8yOakJ/GpLDR09nlBXJ+pZoAkS69EYEz4S42JZfX4ZbV0efr3dpjy7zQJNkDS199jDmsaEkeLsZK6aU8Ce2la2VJ8KdXWimqvTm0VkJfADIBZ4QFW/O2i/OPuvAzqAO1R120hlReSbwBeAeuc0/6yqLzj7vgZ8DugD/kZVX3Szff7y9PVzqqPH0s8YE2YumpFHZX0bv323lim5KUzKSDrjGJv2fPZc69GISCxwL3AtMAdYIyJzBh12LTDDed0F3Odn2f9W1QXOayDIzAFWA3OBlcBPnPOEXFN7D6qcsZ65MSa0YkS4eXEJ8bExPLnlKL19/aGuUlRyc+hsKVCpqodUtQd4Alg16JhVwCPqtRHIEpHJfpYdbBXwhKp2q2oVUOmcJ+Tq27zpZyzQGBN+MpLiuXlRCXUtXby053ioqxOV3Aw0xcBRn881zjZ/jhmt7D0isktEHhKR7DFcDxG5S0S2iMiW+vr6wbtdMZA1Ni/NAo0x4eicyRmsmJrLWwcb2X+8NdTViTpuBpqhlrQbPLVjuGNGKnsfMA1YANQB3xvD9VDV+1V1iaouyc/PH6JI4DU46WesR2NM+Fo5r5DCjCR+tbWG5o6eUFcnqrgZaGqAUp/PJUCtn8cMW1ZVT6hqn6r2Az/lL8Nj/lwvJKxHY0z4i4+N4ZalZXj6lSc2W4qaQHIz0GwGZohIhYgk4L1Rv37QMeuB28VrOdCiqnUjlXXu4Qy4Cdjtc67VIpIoIhV4Jxi841bjxqL+dDcpCbGkJloOU2PCWV56Ih9fWMyRpg5etPs1AePabz5V9YjIPcCLeKcoP6Sqe0RkrbN/HfAC3qnNlXinN985Ulnn1P8hIgvwDotVA190yuwRkaeAvYAHuFtVw2KVo4a2bhs2MyZCnFeSRXVjO29WNlCemxrq6kQFV79iO1OPXxi0bZ3PewXu9ress/3TI1zv28C3x1tft9Sf7ibfhs2MiRjXzZvM0aZOnt52lLsumUpZbkqoqxTRLDNAENS3ddv9GWMiSFxsDGuWeh/UvPuxbXR7wmJwJGJZoAkCGzozJvLkpCZw86JS3j3Wwrd/ty/U1YlodnfaZT2efpo7ei3QGBOB5hRlcNH0PB7ZcJgeTz/nlWQBlpZmrCzQuKyx3aY2m+j32KYjoa6Ca66ZW8iRpg6e3X6Mosxk8uxL45jZ0JnLBp6hsR6NMZEpNkZYfX4pcTHCY+8cocdj+dDGygKNyyzQGBP5slIS+OSSUk60dvHcDlu/Zqxs6MxlDW0DQ2e2RIAxgRCqYbqZBelcOaeAl/ee4ME3q/j8xVNDUo9IZD0al1n6GWOix6Uz85kzOYPv/P493q5sCHV1IoYFGpcdb+0iOyWepPiwWBrHGHMWYkT4xOISKvJSufuxbdSc6gh1lSKCBRqX1TV3UZiZHOpqGGMCJDE+lvs/vRhPv/LFX2yls8ce5hyNBRqX1bV0UZR55vKwxpjINTU/jR+sXsDeula+9uwumxwwCgs0Lqtr6aTQAo0xUeeKcwr4ypUzeW5HLQ++WRXq6oQ1CzQu6urt41RHL0VZNnRmTDS6+/LpXD2ngO/8/j3esskBw7LpzS6qa+kCoDDDejTGRKOYGOH7n1rATfe+xd2PbePzF00lJ/XDjzJYuhrr0biqrqUTgMlZFmiMiVZpiXE88JklqMIjG6rp7rXJAYNZoHFRXbO3RzPZZp0ZE9Wm5KZy7y2LaGjr5qmtNfTb5IAPsUDjouOtNnRmzERx0Yw8rjt3MvvqWnll34lQVyes2D0aF9U2d5KdEk9ygj2sacxEsGJqLnUtXby6v57CzGTOLc4MdZXCggUaFx1vsYc1jZlIRIRV84uoP93N01uPkptqOQ7B5aEzEVkpIvtFpFJEvjrEfhGRHzr7d4nIotHKish/ish7zvG/FpEsZ3u5iHSKyA7ntc7Ntvmj1h7WNGbCiYuN4dZlZaQkxPGLjYc/SKw7kbkWaEQkFrgXuBaYA6wRkTmDDrsWmOG87gLu86Psy8A8VT0POAB8zed8B1V1gfNa607L/HfcHtY0ZkJKT4rntmVTaO/28KVfbp3wa9i4OXS2FKhU1UMAIvIEsArY63PMKuAR9eZv2CgiWSIyGSgfrqyqvuRTfiNws4ttGLeOHo89rGnMBFacncxfLSrhyS1HufWBTdy4oAgRASbeszVuDp0VA0d9Ptc42/w5xp+yAJ8Ffu/zuUJEtovIayJy8VCVEpG7RGSLiGypr6/3ryXjUN3gzeo6JTfFtWsYY8Lb/NIsLp2Zz+bqJjZVNYW6OiHjZqCRIbYNnlw+3DGjlhWRrwMe4FFnUx1QpqoLga8Aj4lIxhknUb1fVZeo6pL8/PxRmjB+1Y3tAJTnprp2DWNM+LtqTgHnFKbz2121HKxvC3V1QsLNQFMDlPp8LgFq/TxmxLIi8hngeuBWZ9gNVe1W1Ubn/VbgIDAzIC0Zh6oGb6CpyLNAY8xEFiPCJ5eUkpuWyOPvHKGpvSfUVQo6NwPNZmCGiFSISAKwGlg/6Jj1wO3O7LPlQIuq1o1UVkRWAv8E3KCqH6w6JCL5ziQCRGQq3gkGh1xs34iqGtqZlJ5IaqLNIDdmokuKj+X25VNQhV9srKa5Y2IFG9cCjap6gHuAF4F9wFOqukdE1orIwIywF/AGg0rgp8CXRyrrlPkxkA68PGga8yXALhHZCTwNrFXVkA2KVje0W2/GGPOB3LREbllWRkNbD7c9uImWjt5QVyloXP26raov4A0mvtvW+bxX4G5/yzrbpw9z/DPAM2dT30CqbmznytkFoa6GMSaMTMtP47ZlZTz+zlE++b8b+Nmd50+Imak2ruOC1q5eGtp6KLcejTFR6bFNR8ZddlZhBj+783zW/mIrN977Fv+zegEXTMsLYO3CjyXVdEG1TQQwxozgwul5/OpLK0hLjOPWBzbxzfV7onoozXo0LrAZZ8aYkQz0iG5fUc7vd9fx8NvVPLn5KBdMz2VFRS4pPpOIouHhTgs0LqhqaEcEynLsYU1jzPAS4mJYtaCYpRU5vLz3BK/sO8nrB+pZWp7DhdPzyEqJjqScFmhcsK+ulSk5KSTF2/IAxpjRTc5M5vYV5Rxv7eKNA/VsONTIhkONnF+ew0fPm0xmcnyoq3hWLNC4YFdNC+eX54S6GsaYCFOYkcQnlpRy5ZwCXj9QzztVTVz03T+xemnZGUPxkTSkZpMBAuxkaxd1LV3ML80KdVWMMREqOyWBVQuK+fJl00mMj+WhN6vYdvhUqKs1bhZoAmxnTQsA80tsZT1jzNkpzk7mS5dOoyIvlWe21bD9SGQGGws0AbarppnYGGFukQUaY8zZS06I5dMrplCRn8rTW2vYf/x0qKs0ZhZoAmxnTQszJqWRnGATAYwxgREfG8Onl0+hMDOJp7YcjbjEnBZoAkhV2VXTzAK7P2OMCbDEuFhuWVqGojy66TBdvX2hrpLfLNAE0P4Tp2nu6LVAY4xxRW5aIp9cUkpdSxdf//VunFVSwp4FmgD6w+7jiMAVsyeFuirGmCh1TmEGV5wziWe21fDoWeRcCyZ7jiaA/rD7OEumZDMpPSnUVTHGRLErzplEX7/yb7/Zw9yiDBaWZYe6SiOyHk2AVDe0897x06ycNznUVTHGRLkYEX6wegEFGUl8+dFtNLR1h7pKI7JAEyAv7K4D4Jq5tgaNMcZ9WSkJrLttMU3tPfz1Y9vx9PWHukrDskATAB09Hn7+VjVLy3MoybZEmsaY4JhXnMm/3ziPDYca+X+eD9/JAXaPJgAeerOKk6e7+cmti0JdFWPMBPOJJaVUN7Zz76sHSYyL5V+vn0NMjIS6Wh9igeYsVTW0s+61Q1w1p4AllkjTGBMC/3j1LLp6+3nwzSqONXfy/U/OJz0pfDI+uzp0JiIrRWS/iFSKyFeH2C8i8kNn/y4RWTRaWRHJEZGXReR952e2z76vOcfvF5Fr3GwbQOXJ06y+fwOJcTF8/brZbl/OGGOGJCL8y0dn882PzeFP753kI997jae31tDjCY/7Nq71aEQkFrgXuAqoATaLyHpV3etz2LXADOe1DLgPWDZK2a8Cr6jqd50A9FXgn0RkDrAamAsUAX8UkZmqGvDHZ4+3dPGDVw7wqy01ZCbH89gXllNuq2kaY0JIRLjjwgoWlGXzr8/v5h9/tZPvvLCPK2cXsHhKNiXZyRRmJpGfnkhKQhyxQRxec3PobClQqaqHAETkCWAV4BtoVgGPqPcO1kYRyRKRyUD5CGVXAZc55R8G/gz8k7P9CVXtBqpEpNKpw4ZAN+x0Vy+/3n6MW5eV8eXLp1OQYc/NGGPCw4LSLJ778oW8/n49T205yu931/HklqNnHPex+UX8aM3CoNTJzUBTDPi2rgZvr2W0Y4pHKVugqnUAqlonIgOP4RcDG4c414eIyF3AXc7HNhHZ72+DBvuW8zoLeUDD2Z0iYkyktoK1N9qFvL23nmX5HwM/vsXvwwe3d8pYruVmoBmqXzZ47t1wx/hTdjzXQ1XvB+4f5VxBISJbVHVJqOsRDBOprWDtjXbW3rFxczJADVDq87kEqPXzmJHKnnCG13B+nhzD9YwxxgSZm4FmMzBDRCpEJAHvjfr1g45ZD9zuzD5bDrQ4w2IjlV0PfMZ5/xngeZ/tq0UkUUQq8E4weMetxhljjPGPa0NnquoRkXuAF4FY4CFV3SMia53964AXgOuASqADuHOkss6pvws8JSKfA44An3DK7BGRp/BOGPAAd7sx4yzAwmIIL0gmUlvB2hvtrL1jIOGassAYY0x0sFxnxhhjXGWBxhhjjKss0ITAaKl5Ip2IlIrIqyKyT0T2iMjfOtuHTR8U6UQkVkS2i8hvnc/R3NYsEXlaRN5z/o5XRHl7/975d7xbRB4XkaRoaq+IPCQiJ0Vkt8+2gKb6skATZD7pda4F5gBrnPQ50cQD/IOqzgaWA3c7bRxIHzQDeMX5HC3+Ftjn8zma2/oD4A+qeg4wH2+7o7K9IlIM/A2wRFXn4Z2ctJroau/PgZWDtg3ZvkGpvlYCP3F+p43IAk3wfZCaR1V7gIH0OlFDVetUdZvz/jTeX0TFeNv5sHPYw8CNIalggIlICfBR4AGfzdHa1gzgEuBBAFXtUdVmorS9jjggWUTigBS8z+dFTXtV9XWgadDm4dr3QaovVa3CO2N46WjXsEATfMOl3YlKIlIOLAQ2MSh9EDBphKKR5H+A/xvwTZUbrW2dCtQDP3OGCh8QkVSitL2qegz4L7yPUtThfdbvJaK0vT6Ga9+4fn9ZoAm+8aTXiUgikgY8A/ydqraGuj5uEJHrgZOqujXUdQmSOGARcJ+qLgTaiexhoxE59yZWARV4s8Knishtoa1VSI3r95cFmuCbEKlyRCQeb5B5VFWfdTYPlz4okl0I3CAi1XiHQa8QkV8SnW0F77/fGlXd5Hx+Gm/gidb2XglUqWq9qvYCzwIXEL3tHRDQVF8WaILPn9Q8EU1EBO8Y/j5V/b7PruHSB0UsVf2aqpaoajnev8s/qeptRGFbAVT1OHBURGY5mz6CNxtHVLYX75DZchFJcf5dfwTvPcdobe+AgKb6sswAISAi1+Ed1x9Ir/Pt0NYosETkIuAN4F3+ct/in/Hep3kKKMNJH6Sqg29CRiwRuQz4R1W9XkRyidK2isgCvBMfEoBDeFNHxRC97f034FN4Z1NuBz4PpBEl7RWRx/Gu8ZUHnAC+ATzHMO0Tka8Dn8X75/F3qvr7Ua9hgcYYY4ybbOjMGGOMqyzQGGOMcZUFGmOMMa6yQGOMMcZVFmiMMca4ygKNMSEgIjeJiIrIOT7blorIn52MudtE5Hcicq6z75sickxEdvi8skLWAGPGwKY3GxMCzrLjk/FmyP2miBTgfc7oFlV92znmIiBPVZ8TkW8Cbar6XyGrtDHjZD0aY4LMyQF3IfA5vNkEAO4BHh4IMgCq+qaqPhf8GhoTWBZojAm+G/Gu53IAaBKRRXjX99g2Srm/9xk2e9XtShoTKBZojAm+NXgTcOL8XDP4ABHZ5Kxe+QOfzf+tqguc1+XBqKgxgRAX6goYM5E4OdCuAOaJiOLNd6d4F5dahJO8UFWXicjNwPWhqqsxgWI9GmOC62bgEVWdoqrlqloKVAEvAXeIyAU+x6aEpIbGBJj1aIwJrjXAdwdtewa4BW+G4P/fWaf+JNAAfMvnuL8ftOjWjapa7WJdjQkIm95sjDHGVTZ0ZowxxlUWaIwxxrjKAo0xxhhXWaAxxhjjKgs0xhhjXGWBxhhjjKss0BhjjHHV/wGAebg0VBRy0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in cont_col:\n",
    "    sns.distplot(X[col])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When I looked at the box plot for capital gain, I noticed that there is an outlier for capital gain data. I deleted the data point to eliminate outlier's effect on the model. I also combined CAPGAIN and CAPLOSS into a new column NETCAPGAIN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CAPGAIN'>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind=X[(X['CAPGAIN'] <1000)].index\n",
    "sns.boxplot(X.drop(ind)['CAPGAIN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = X[(X['CAPGAIN'] > 80000)].index\n",
    "X = X.drop(i)\n",
    "\n",
    "ind=X[(X['CAPGAIN'] <1000)].index\n",
    "sns.boxplot(X.drop(ind)['CAPGAIN'])\n",
    "\n",
    "cont_X['NETCAPGAIN'] = X.apply(lambda x: x['CAPGAIN'] + x['CAPLOSS'], axis=1)\n",
    "cont_X.drop(['CAPGAIN','CAPLOSS'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = pd.concat([cont_X, cat_X], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             AGE  WPH  DIVFROMSTO  NUMPERWOFOREMP  WEEKWORKINYEAR  NETCAPGAIN  \\\n",
      "0       3.931826  0.0    0.000000        0.693147        3.135494         0.0   \n",
      "1       2.890372  0.0    0.000000        0.000000        0.000000         0.0   \n",
      "2       3.871201  0.0    0.000000        1.945910        3.988984         0.0   \n",
      "3       4.304065  0.0    0.000000        0.000000        0.000000         0.0   \n",
      "4       3.806662  0.0    0.000000        0.693147        3.988984         0.0   \n",
      "...          ...  ...         ...             ...             ...         ...   \n",
      "299280  3.951244  0.0    7.393878        1.945910        3.970292         0.0   \n",
      "299281  3.178054  0.0    0.000000        0.000000        0.000000         0.0   \n",
      "299282  3.091042  0.0    0.000000        1.791759        3.988984      2978.0   \n",
      "299283  3.737670  0.0    0.000000        0.693147        3.970292         0.0   \n",
      "299284  4.077537  0.0    8.412055        0.000000        0.000000         0.0   \n",
      "\n",
      "        MAINDC_ Agriculture  MAINDC_ Armed Forces  \\\n",
      "0                         0                     0   \n",
      "1                         0                     0   \n",
      "2                         0                     0   \n",
      "3                         0                     0   \n",
      "4                         0                     0   \n",
      "...                     ...                   ...   \n",
      "299280                    0                     0   \n",
      "299281                    0                     0   \n",
      "299282                    0                     0   \n",
      "299283                    0                     0   \n",
      "299284                    0                     0   \n",
      "\n",
      "        MAINDC_ Business and repair services  MAINDC_ Communications  ...  \\\n",
      "0                                          0                       0  ...   \n",
      "1                                          0                       0  ...   \n",
      "2                                          0                       0  ...   \n",
      "3                                          0                       0  ...   \n",
      "4                                          1                       0  ...   \n",
      "...                                      ...                     ...  ...   \n",
      "299280                                     1                       0  ...   \n",
      "299281                                     0                       0  ...   \n",
      "299282                                     0                       0  ...   \n",
      "299283                                     0                       0  ...   \n",
      "299284                                     0                       0  ...   \n",
      "\n",
      "        DTHOUANDFAMST_ RP of unrelated subfamily  \\\n",
      "0                                              0   \n",
      "1                                              0   \n",
      "2                                              0   \n",
      "3                                              0   \n",
      "4                                              0   \n",
      "...                                          ...   \n",
      "299280                                         0   \n",
      "299281                                         0   \n",
      "299282                                         0   \n",
      "299283                                         0   \n",
      "299284                                         0   \n",
      "\n",
      "        DTHOUANDFAMST_ Secondary individual  \\\n",
      "0                                         0   \n",
      "1                                         0   \n",
      "2                                         0   \n",
      "3                                         0   \n",
      "4                                         0   \n",
      "...                                     ...   \n",
      "299280                                    0   \n",
      "299281                                    0   \n",
      "299282                                    0   \n",
      "299283                                    0   \n",
      "299284                                    0   \n",
      "\n",
      "        DTHOUANDFAMST_ Spouse of RP of unrelated subfamily  \\\n",
      "0                                                       0    \n",
      "1                                                       0    \n",
      "2                                                       0    \n",
      "3                                                       0    \n",
      "4                                                       0    \n",
      "...                                                   ...    \n",
      "299280                                                  0    \n",
      "299281                                                  0    \n",
      "299282                                                  0    \n",
      "299283                                                  0    \n",
      "299284                                                  0    \n",
      "\n",
      "        DTHOUANDFAMST_ Spouse of householder  TAXFILST_ Head of household  \\\n",
      "0                                          1                            0   \n",
      "1                                          0                            0   \n",
      "2                                          1                            0   \n",
      "3                                          0                            0   \n",
      "4                                          0                            0   \n",
      "...                                      ...                          ...   \n",
      "299280                                     0                            0   \n",
      "299281                                     0                            0   \n",
      "299282                                     0                            1   \n",
      "299283                                     1                            0   \n",
      "299284                                     1                            0   \n",
      "\n",
      "        TAXFILST_ Joint both 65+  TAXFILST_ Joint both under 65  \\\n",
      "0                              0                              1   \n",
      "1                              0                              1   \n",
      "2                              0                              1   \n",
      "3                              0                              0   \n",
      "4                              0                              0   \n",
      "...                          ...                            ...   \n",
      "299280                         0                              1   \n",
      "299281                         0                              0   \n",
      "299282                         0                              0   \n",
      "299283                         0                              1   \n",
      "299284                         0                              0   \n",
      "\n",
      "        TAXFILST_ Joint one under 65 & one 65+  TAXFILST_ Nonfiler  \\\n",
      "0                                            0                   0   \n",
      "1                                            0                   0   \n",
      "2                                            0                   0   \n",
      "3                                            0                   1   \n",
      "4                                            0                   0   \n",
      "...                                        ...                 ...   \n",
      "299280                                       0                   0   \n",
      "299281                                       0                   1   \n",
      "299282                                       0                   0   \n",
      "299283                                       0                   0   \n",
      "299284                                       1                   0   \n",
      "\n",
      "        TAXFILST_ Single  \n",
      "0                      0  \n",
      "1                      0  \n",
      "2                      0  \n",
      "3                      0  \n",
      "4                      1  \n",
      "...                  ...  \n",
      "299280                 0  \n",
      "299281                 0  \n",
      "299282                 0  \n",
      "299283                 0  \n",
      "299284                 0  \n",
      "\n",
      "[299285 rows x 470 columns]\n"
     ]
    }
   ],
   "source": [
    "print(train_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explore the relationships among various attributes, I made plotted a heatmap of numerical attributes excluding the class attribute (income). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cont_X.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NUMPERWOFOREMP (Number of persons worked for employer) and WEEKWORKINYEAR (weeks worked in a year) had high correlation. Other variables had little correlation with each other. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also tried Principal COmponent Analysis, a dimensionality reduction technique that transforms a set of features in a dataset into a smaller number of features called principal components while at the same time trying to retain as much information in the original dataset as possible. PCA can help remove correlated features and reduce overfitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.38385549 0.18988477 0.14990639 0.14369996 0.11402735 0.01862604]\n"
     ]
    }
   ],
   "source": [
    "imputer = SimpleImputer(strategy='mean')\n",
    "imputer.fit(cont_X)\n",
    "imputed_value = imputer.transform(cont_X)\n",
    "cont_X = pd.DataFrame(imputed_value)\n",
    "\n",
    "pca = PCA()\n",
    "pca.fit(cont_X)\n",
    "print(pca.explained_variance_ratio_)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ff7fec27faedcf80de72c9baf542c3260b20febdeb3ec356ac2d5b161b8aec3e"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}