regression_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4fb148a6",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21fea5f",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e27d0436",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f254a49f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/clean_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1d42e5f",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>meals/day</th>\n",
       "      <th>physical_illness</th>\n",
       "      <th>screen_time</th>\n",
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       "      <th>smoke/drink</th>\n",
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       "      <th>no_exercise</th>\n",
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       "      <th>frequent_exercise</th>\n",
       "      <th>coffee</th>\n",
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       "  </tbody>\n",
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       "<p>92 rows × 20 columns</p>\n",
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      ],
      "text/plain": [
       "    age  meals/day  physical_illness  screen_time  bluelight_filter  \\\n",
       "0    22          2                 0         2.00                 1   \n",
       "1    22          3                 0         0.50                 0   \n",
       "2    23          3                 0         0.50                 0   \n",
       "3    23          2                 0         1.50                 0   \n",
       "4    22          3                 0         6.67                 1   \n",
       "..  ...        ...               ...          ...               ...   \n",
       "87   22          3                 0         0.50                 0   \n",
       "88   21          2                 0         6.67                 0   \n",
       "89   24          3                 0         6.67                 1   \n",
       "90   25          3                 1         6.67                 1   \n",
       "91   23          3                 0         6.67                 1   \n",
       "\n",
       "    smoke/drink  female   male  other_gender   east  north  south   west  \\\n",
       "0             0   False   True         False  False  False  False   True   \n",
       "1             0    True  False         False  False  False   True  False   \n",
       "2             0   False   True         False  False  False   True  False   \n",
       "3             0    True  False         False   True  False  False  False   \n",
       "4             1   False   True         False   True  False  False  False   \n",
       "..          ...     ...    ...           ...    ...    ...    ...    ...   \n",
       "87            0   False   True         False   True  False  False  False   \n",
       "88            0   False   True         False   True  False  False  False   \n",
       "89            0   False   True         False   True  False  False  False   \n",
       "90            1   False  False          True   True  False  False  False   \n",
       "91            0    True  False         False   True  False  False  False   \n",
       "\n",
       "    no_exercise  seldom_exercise  frequent_exercise  coffee    tea  \\\n",
       "0         False             True              False   False   True   \n",
       "1          True            False              False    True  False   \n",
       "2          True            False              False   False   True   \n",
       "3         False             True              False    True  False   \n",
       "4         False             True              False    True   True   \n",
       "..          ...              ...                ...     ...    ...   \n",
       "87        False             True              False   False   True   \n",
       "88        False             True              False   False   True   \n",
       "89        False             True              False   False  False   \n",
       "90        False            False               True    True   True   \n",
       "91        False             True              False   False   True   \n",
       "\n",
       "    no_beverage  sleep_time  \n",
       "0         False        6.76  \n",
       "1         False        8.00  \n",
       "2         False        8.00  \n",
       "3         False        6.50  \n",
       "4         False        6.00  \n",
       "..          ...         ...  \n",
       "87        False        6.00  \n",
       "88        False        7.00  \n",
       "89         True        4.00  \n",
       "90        False        8.00  \n",
       "91        False        8.00  \n",
       "\n",
       "[92 rows x 20 columns]"
      ]
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    "data"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92 entries, 0 to 91\n",
      "Data columns (total 20 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   age                92 non-null     int64  \n",
      " 1   meals/day          92 non-null     int64  \n",
      " 2   physical_illness   92 non-null     int64  \n",
      " 3   screen_time        92 non-null     float64\n",
      " 4   bluelight_filter   92 non-null     int64  \n",
      " 5   smoke/drink        92 non-null     int64  \n",
      " 6   female             92 non-null     bool   \n",
      " 7   male               92 non-null     bool   \n",
      " 8   other_gender       92 non-null     bool   \n",
      " 9   east               92 non-null     bool   \n",
      " 10  north              92 non-null     bool   \n",
      " 11  south              92 non-null     bool   \n",
      " 12  west               92 non-null     bool   \n",
      " 13  no_exercise        92 non-null     bool   \n",
      " 14  seldom_exercise    92 non-null     bool   \n",
      " 15  frequent_exercise  92 non-null     bool   \n",
      " 16  coffee             92 non-null     bool   \n",
      " 17  tea                92 non-null     bool   \n",
      " 18  no_beverage        92 non-null     bool   \n",
      " 19  sleep_time         92 non-null     float64\n",
      "dtypes: bool(13), float64(2), int64(5)\n",
      "memory usage: 6.3 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afeade38",
   "metadata": {},
   "source": [
    "All columns are ready for analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1798d25b",
   "metadata": {},
   "source": [
    "# Scikit-learn imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d552803",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_curve, auc, confusion_matrix, classification_report, accuracy_score, precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9abb698a",
   "metadata": {},
   "source": [
    "## Preparing data for train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc0ecd24",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop([\"sleep_time\"],axis = 1)\n",
    "y = data['sleep_time'].apply(lambda x: 1 if x >= 7 else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f1c908",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8be7784b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "365d2a00",
   "metadata": {},
   "source": [
    "# Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "94243352",
   "metadata": {},
   "outputs": [
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       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=200)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=200)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(max_iter=200)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d23753",
   "metadata": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea92c4f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = model.predict(X_train)\n",
    "y_prob_train = model.predict_proba(X_train)[:, 1]\n",
    "y_pred_test = model.predict(X_test)\n",
    "y_prob_test = model.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b67bdbcf",
   "metadata": {},
   "source": [
    "## Evaluation metrics for training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "330daa94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Results:\n",
      "Accuracy: 0.875\n",
      "Precision: 0.8863636363636364\n",
      "Recall: 0.9285714285714286\n",
      "F1 Score: 0.9069767441860465\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.77      0.81        22\n",
      "           1       0.89      0.93      0.91        42\n",
      "\n",
      "    accuracy                           0.88        64\n",
      "   macro avg       0.87      0.85      0.86        64\n",
      "weighted avg       0.87      0.88      0.87        64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Results:\")\n",
    "print(\"Accuracy:\", accuracy_score(y_train, y_pred_train))\n",
    "print(\"Precision:\", precision_score(y_train, y_pred_train))\n",
    "print(\"Recall:\", recall_score(y_train, y_pred_train))\n",
    "print(\"F1 Score:\", f1_score(y_train, y_pred_train))\n",
    "print(\"Classification Report:\\n\", classification_report(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504a8ffb",
   "metadata": {},
   "source": [
    "## Evaluation metrics for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d9072b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results:\n",
      "Accuracy: 0.8571428571428571\n",
      "Precision: 0.8333333333333334\n",
      "Recall: 0.9375\n",
      "F1 Score: 0.8823529411764706\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.75      0.82        12\n",
      "           1       0.83      0.94      0.88        16\n",
      "\n",
      "    accuracy                           0.86        28\n",
      "   macro avg       0.87      0.84      0.85        28\n",
      "weighted avg       0.86      0.86      0.85        28\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results:\")\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred_test))\n",
    "print(\"Precision:\", precision_score(y_test, y_pred_test))\n",
    "print(\"Recall:\", recall_score(y_test, y_pred_test))\n",
    "print(\"F1 Score:\", f1_score(y_test, y_pred_test))\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e61d597",
   "metadata": {},
   "source": [
    "## ROC Curve train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b1de2f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr_train, tpr_train, thresholds_train = roc_curve(y_train, y_prob_train)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_train, tpr_train, color='green', label='Train ROC Curve (AUC = %0.2f)' % auc(fpr_train, tpr_train))\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve for Training Data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772a1523",
   "metadata": {},
   "source": [
    "## ROC Curve test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "48e6c895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4cOFCsW1ZWFhACKHeVkn09fUxevRoREdHq78aXbNmDZydnREQEKCxj23bthXbR9OmTTXqLVL0derzFH21XPSVZ0lu374NAHBzc9Nod3JyKrZsUdujR48qrO6oqCgMGzYMdevWxW+//YYTJ07g9OnTmDBhAvLy8sp1nKUxNTWFsbGxRpuRkZHGdh89eqQR2ouU1FaSevXqlfn+lsTW1rZYm5GREXJzc9XPtX1fSnpvT506BX9/fwDAL7/8gmPHjuH06dP4+OOPAUC9v4cPHwLAc38WSnP//n2kpaXB0NCw2GchOTn5hT+/s2fPxjfffIOTJ08iMDAQtra26N27d6lTXj3Pw4cP4eLiojHkp7xyc3OLfZae9uWXX+L06dM4dOgQPv74Y9y/fx+DBw9Gfn6+epny/DxmZ2cjJSVF/fNYnnVKUlTr05+pkrzI51cbJZ1rGxsbDBw4EGvXroVSqQTw5Pdi+/bt1b87Hj16BIVCgR9//LHYZ6poGEJZv3upZuKYWaoxfHx80LZtWwBAz549oVQqsWLFCkRERCAsLAx2dnYAnvxDGBISUuI2GjduDODJuNaiXsbS2NnZwcTEBKtWrSr19bKMHz8eX3/9tXrM7tatWzFjxgzI5XKNbbRo0QL//ve/S9yGi4uLxvPy9tr07dsXH330ETZv3lys57FI0XyUffv21WhPTk4utmxRW1EYq4i6f/vtN3h6emLjxo0arz8dAiqTra0tTp06Vay9pOMvSUBAAH788UecPHmyQscdavu+lPTebtiwAQYGBti+fbtGEHt2DlJ7e3sAQGJiYrE/asrDzs4Otra22L17d4mvW1hYPLfWkujr62PWrFmYNWsW0tLS8Mcff+Cjjz5CQEAA7t69C1NTU63qtLe3x9GjR6FSqbQOtHZ2dkhNTS319fr166t/L3Xr1g0mJib45JNP8OOPP+Ldd98FAPj6+sLa2hpbt27FggULSnwftm7dCpVKpf55bNu2LWxsbLBly5ZS1ylJUa3P+/2k7efX2Ni4xM9gSkpKifsqrd7x48dj06ZNiImJQb169XD69GksWbJE/bq1tTXkcjlGjx5d6jcGnp6ez62XahiJhzkQvbTSZjNITU1VXyFcNBa2UaNGIigo6LnbLBozW9aY0s8//1yYmpqKW7duPXd7pY0n69Chg2jfvr346aefShzDOmnSJOHi4vLcMWBFY0+//vrr59ZSpGhqrmfnrhTi/6fm6tevn0Y7yhgz26BBgwqtOyQkRGMMqxBPZkgwNzcXz/7qsrGxEcOGDSu2jbJmM3hW0RXwRYrGzD499lmI8o+ZLc/URlFRUernpU3NNXbsWI3xqNq8L/jfbAbPmjVrljA3N9cYp5yTkyPq1aunMc40Pj5eyOVyMXr06DKPNSQkRDg4OBRr/+2339Tj2Z+naDaD0l573tRrCxcu1BiPXTT+sqRx76WNmV25cuVz63zWhAkThI2NTbH20mYzKCgoEA0bNhS2trYiIyND3V40NdeXX35ZbFv3799XT8319GfpeVNz3b9/v9jPd3h4uAAgzp8/X+Zxafv5DQgIEE2aNNFY5u+//xb6+voljpl99n0polAoRN26dcWwYcPEu+++K4yNjYvtv0+fPqJly5blni+Xaj72zFKNZW1tjdmzZ+P999/HunXr8Oqrr2LZsmUIDAxEQEAAxo0bh7p16yI1NRVXrlzBX3/9hU2bNgEAPv30U+zatQvdunXDRx99hObNmyMtLQ27d+/GrFmz4O3tjRkzZiAyMhLdunXDzJkz0aJFC6hUKiQkJGDv3r1455130KFDhzJrnDBhAqZMmYJ79+7Bz89P3TNc5NNPP0VMTAz8/Pwwffp0NG7cGHl5ebh9+zZ27tyJpUuXvvBXwGvXrkWfPn3g7++P6dOno3fv3gCejKX84Ycf4O3trTF2rYidnR169eqFf/3rX+rZDK5evYoNGzZUaN0DBgxAVFQUpk2bhrCwMNy9exefffYZnJ2di93ZrXnz5jh48CC2bdsGZ2dnWFhYFHsvtTV27Fh8//33ePXVV/H555+jYcOG2LVrF/bs2QMAz+3B8/T0VPe6t2rVCm+++SZat24N4MnV2KtWrYIQAkOGDNGqLm3el9L0798f3333HUaOHInJkyfj0aNH+Oabb2BkZKSxnIeHBz766CN89tlnyM3NxSuvvAIrKyvExcUhJSUF8+fPB/Dk/Y+KisKSJUvg6+sLPT09tG3bFiNGjEB4eDiCgoLw9ttvo3379jAwMEBiYiIOHDiAQYMGaX38wJMr+5s1a4a2bdvC3t4ed+7cwcKFC+Hu7q6ewaN58+YAgB9++AFjx46FgYEBGjduXKw3GHgyDnr16tWYOnUq/v77b/Ts2RMqlQp//vknfHx8MGLEiFJr6dGjB1atWoVr166Va7yvgYEBvvjiCwwbNgw//PADPvnkEwDABx98gPPnz6v/O3z4cFhZWeHChQv4+uuvkZmZie3bt8PKykq9rffeew9XrlzB3LlzcerUKYwcORJubm5IT0/H4cOHsXz5csyfPx+dO3dWr3Py5EnY2tqq35/SaPv5HT16NF599VVMmzYNoaGhuHPnDr766it17355yeVyjBkzBt999x0sLS0REhKicczAk3PapUsXdO3aFa+//jo8PDyQmZmJGzduYNu2bdi/f79W+6QaQOo0TfSySuuZFeLJnJz16tUTjRo1EgqFQgghxPnz58WwYcOEg4ODMDAwEE5OTqJXr17Frgq+e/eumDBhgnByclLPITts2DBx//599TJZWVnik08+Uc+hWTTf5cyZMzV6L0vrWUpPTxcmJiZlXkn98OFDMX36dOHp6SkMDAyEjY2N8PX1FR9//LF6PtsX6Zktqv+LL74QrVq1EqampsLU1FS0aNFCfP7558XmyhXi/3v6Fi9eLBo0aCAMDAyEt7d3iZOwV0Td//nPf4SHh4cwMjISPj4+4pdffinWgyqEELGxsaJz587C1NS03PPMPquk7SYkJIiQkBBhbm4uLCwsRGhoaIlzXpbl5s2bYtq0aaJhw4bCyMhImJiYiCZNmohZs2ZpXGlf3p5Zbd4XlNIzK4QQq1atEo0bNxZGRkaifv36YsGCBWLlypUlzgCwdu1a0a5dO2FsbCzMzc1F69atNXqmU1NTRVhYmKhTp46QyWQadRQWFopvvvlGtGzZUr2+t7e3mDJlirh+/bp6OW16Zr/99lvh5+cn7OzshKGhoahXr56YOHGiuH37tsZ6s2fPFi4uLkJPT++588zm5uaKOXPmqOdPtrW1Fb169RLHjx8vsaYi6enpwtzcXHz11Vca7c/rgezQoYOwtrbW6HVUqVQiPDxc9OjRQ9SpU0cYGhoKT09P8frrrxebGeRpW7ZsEf379xf29vZCX19fWFtbi549e4qlS5dq9F6qVCrh7u4u3nrrrTKP6Wnl/fyqVCrx1Vdfifr16wtjY2PRtm1bsX///lJnMyjtfRHiyRyy+N/cwDExMSUuEx8fLyZMmKCex9re3l74+fmJzz//vNzHRjWHTIj/XbJMRPQcMpkMb7zxBn766SepS5HMF198gU8++QQJCQkv3CtONctbb72Fffv24fLlyy8920Bl2rdvH/z9/XH58mV4e3tLXQ5RheEwAyKiUhSFdm9vbxQWFmL//v1YtGgRXn31VQZZUvvkk0+wdu1aREZGqm8cUh19/vnnmDBhAoMs1TgMs0REpTA1NcX333+P27dvIz8/H/Xq1cMHH3ygHudIBDyZri08PByPHz+WupRSPX78GN27d1dPQ0hUk3CYARERERHpLN40gYiIiIh0FsMsEREREekshlkiIiIi0lm17gIwlUqFe/fuwcLColpPoUJERERUWwkhkJmZCRcXl+fepKbWhdl79+690D3GiYiIiKhq3b1797lTIda6MFt0K8O7d+/C0tJS4mqIiIiI6FkZGRlwc3Mr8RbUz6p1YbZoaIGlpSXDLBEREVE1Vp4hobwAjIiIiIh0FsMsEREREekshlkiIiIi0lm1bsxseQghoFAooFQqpS6FqMYzMDCAXC6XugwiItJRDLPPKCgoQFJSEnJycqQuhahWkMlkcHV1hbm5udSlEBGRDmKYfYpKpUJ8fDzkcjlcXFxgaGjIGysQVSIhBB4+fIjExEQ0atSIPbRERKQ1htmnFBQUQKVSwc3NDaamplKXQ1Qr2Nvb4/bt2ygsLGSYJSIirfECsBI877ZpRFRx+O0HERG9DKY2IiIiItJZDLNEREREpLMYZon+p6CgAA0bNsSxY8ekLqXGePDgAezt7fHPP/9IXQoREdVQDLM1xLhx4yCTySCTyaCvr4969erh9ddfx+PHj4ste/z4cQQFBcHa2hrGxsZo3rw5vv322xLn1T1w4ACCgoJga2sLU1NTNGnSBO+8885zw8m5c+cwdOhQODo6wtjYGF5eXnjttddw7dq1CjvmirZ8+XK4u7ujc+fOxV6bPHky5HI5NmzYUOy1cePGYfDgwcXaY2NjIZPJcPv2bXWbEALLly9Hhw4dYG5ujjp16qBt27ZYuHBhpU4H9/jxY4wePRpWVlawsrLC6NGjkZaWVuY6WVlZePPNN+Hq6goTExP4+PhgyZIlGstMmTIFDRo0gImJCezt7TFo0CBcvXpV/bqDgwNGjx6NuXPnVsZhERERMczWJP369UNSUhJu376NFStWYNu2bZg2bZrGMtHR0ejevTtcXV1x4MABXL16FW+//Tb+/e9/Y8SIERBCqJddtmwZ+vTpAycnJ0RGRiIuLg5Lly5Feno6vv3221Lr2L59Ozp27Ij8/HyEh4fjypUr+PXXX2FlZYV//etfL3x8hYWFL7xuefz444+YNGlSsfacnBxs3LgR7733HlauXPlS+xg9ejRmzJiBQYMG4cCBA4iNjcW//vUvbNmyBXv37n2pbZdl5MiRiI2Nxe7du7F7927ExsZi9OjRZa4zc+ZM7N69G7/99huuXLmCmTNn4q233sKWLVvUy/j6+mL16tW4cuUK9uzZAyEE/P39Nf4wGj9+PMLDw0v8w4qIiOiliVomPT1dABDp6enFXsvNzRVxcXEiNzdX3aZSCZGVJc1DpSr/cY0dO1YMGjRIo23WrFnCxsZG/TwrK0vY2tqKkJCQYutv3bpVABAbNmwQQghx9+5dYWhoKGbMmFHi/h4/flxie3Z2trCzsxODBw8uc73Vq1cLKysrjdeio6PF0x/JuXPnipYtW4qVK1cKT09PIZPJxNKlS4WLi4tQKpUa6wYHB4sxY8ZoHE+bNm2EkZGR8PT0FPPmzROFhYUl1iSEEGfPnhV6enolfi7WrFkjOnbsKNLS0oSJiYmIj4/XeL2k914IIc6dOycAqJffuHGjACA2b95cbFmVSiXS0tJKre9lxMXFCQDi5MmT6rYTJ04IAOLq1aulrte0aVPx6aefarS1adNGfPLJJ6Wuc/78eQFA3LhxQ6Pdw8NDrFy5ssR1Svq5IyKi2q2svPYsSXtmDx8+jODgYLi4uEAmk2Hz5s3PXefQoUPw9fWFsbEx6tevj6VLl1ZqjTk5gLm5NI+X+db51q1b2L17NwwMDNRte/fuxaNHj/Duu+8WWz44OBheXl5Yv349AGDTpk0oKCjA+++/X+L269SpU2L7nj17kJKSovV6pblx4wZ+//13REZGIjY2FmFhYUhJScGBAwfUyzx+/Bh79uzBqFGj1DW8+uqrmD59OuLi4rBs2TKsWbMG//73v0vdz+HDh+Hl5QVLS8tir61cuRKvvvoqrKysEBQUhNWrV2t1DEXCw8PRuHFjDBo0qNhrMpkMVlZWpa5rbm5e5iMwMLDUdU+cOAErKyt06NBB3daxY0dYWVnh+PHjpa7XpUsXbN26Ff/88w+EEDhw4ACuXbuGgICAEpfPzs7G6tWr4enpCTc3N43X2rdvjyNHjpS6LyIiohcl6U0TsrOz0bJlS4wfPx6hoaHPXT4+Ph5BQUF47bXX8Ntvv+HYsWOYNm0a7O3ty7V+Tbd9+3aYm5tDqVQiLy8PAPDdd9+pXy8ar+rj41Pi+t7e3uplrl+/DktLSzg7O2tVw/Xr19XbqggFBQX49ddfYW9vr27r168f1q1bh969ewN4ErxtbGzUz//973/jww8/xNixYwEA9evXx2effYb333+/1LGbt2/fhouLS4nHc/LkSURFRQGAOiTPnTtX6/mIr1+/jsaNG2u1TpHY2NgyXzcxMSn1teTkZDg4OBRrd3BwQHJycqnrLVq0CK+99hpcXV2hr68PPT09rFixAl26dNFYbvHixXj//feRnZ0Nb29vxMTEwNDQUGOZunXr4ty5c2UeAxER0YuQNMwGBgaW2aP0rKVLl6JevXpYuHAhgCeh7MyZM/jmm28qLcyamgJZWZWy6XLtWxs9e/bEkiVLkJOTgxUrVuDatWt46623ii0nnhoX+2x70QT2T/+/Nkrb9otyd3fXCLIAMGrUKEyePBmLFy+GkZERwsPDMWLECPXdo86ePYvTp09r9MQWBfycnJwS7+6Wm5sLY2PjYu0rV65EQEAA7OzsAABBQUGYOHEi/vjjD/j7+2t1LC/6ngJAw4YNX2i9IiXt93n1LFq0CCdPnsTWrVvh7u6Ow4cPY9q0aXB2dkafPn3Uy40aNQp9+/ZFUlISvvnmGwwbNgzHjh3TeD9NTEwq9QK3iiDEy30bQkRUk6lUKujp6cHUFKhu97rRqdvZnjhxoliACAgIwMqVK1FYWKjxlXqR/Px85Ofnq59nZGRotU+ZDDAze7F6q5qZmZk69CxatAg9e/bE/Pnz8dlnnwEAvLy8AABXrlyBn59fsfWvXr2KJk2aqJdNT09HUlKSVr2zRfu4evUqOnXqVOpyenp6xYJvSRd4mZXw5gcHB0OlUmHHjh1o164djhw5otEDrVKpMH/+fISEhBRbt6TACgB2dna4ePGiRptSqcTatWuRnJwMfX19jfaVK1eqP4uWlpa4c+dOsW0WzRZQNHzAy8sLV65cKXH/z2Nubl7m6127dsWuXbtKfM3JyQn3798v1v7w4UM4OjqWuE5ubi4++ugjREdHo3///gCAFi1aIDY2Ft98841GmC2aIaFRo0bo2LEjrK2tER0djVdeeUW9TGpqarE/SqoTIYAuXYAyRl0QEdVajRv/jYCAPVi7dgwSE+tUu1ykU7MZJCcnF/vH19HREQqFAikpKSWus2DBAvU/tlZWVsXG8tVkc+fOxTfffIN79+4BAPz9/WFjY1PiTARbt27F9evX1QEkLCwMhoaG+Oqrr0rcdmnTOvn7+8POzu6569nb2yMzMxPZ2dnq1573VXoRExMThISEIDw8HOvXr4eXlxd8fX3Vr7dp0wZ///03GjZsWOxR2tCA1q1b4+rVqxoBe+fOncjMzMS5c+cQGxurfmzatAmbN2/Go0ePADwZUnHp0iX10I4ip0+fhr29PaytrQE8mVHg2rVrGrMBFBFCID09vdRjfnr/JT1WrFhR6rqdOnVCeno6Tp06pW77888/kZ6eXuIfNcCTPywKCwuLvV9yuRwqlarUfRUdy9N/QALApUuX0Lp16zLXk1JODoMsEdGz5HIlAgL24JVXNsDG5jG6dKme1z7oVM8sUPzr0qLwUdrXpbNnz8asWbPUzzMyMmpNoO3RoweaNm2KL774Aj/99BPMzMywbNkyjBgxApMnT8abb74JS0tL7Nu3D++99x7CwsIwbNgwAICbmxu+//57vPnmm8jIyMCYMWPg4eGBxMRErF27Fubm5iWGYjMzM6xYsQJDhw7FwIEDMX36dDRs2BApKSn4/fffkZCQgA0bNqBDhw4wNTXFRx99hLfeegunTp3CmjVryn1so0aNQnBwMC5fvoxXX31V47U5c+ZgwIABcHNzw9ChQ6Gnp4cLFy7g4sWL+Pzzz0vcXs+ePZGdnY3Lly+jWbNmAJ4MMejfvz9atmypsWzTpk0xY8YM/Pbbb3j77bcxatQofPbZZxg9ejQ++OADWFtb48SJE1iwYAFmz56tXm/YsGHqHst//etf6Nu3L+zt7XHx4kV8//33eOutt0qcrxZ4uWEGPj4+6NevH1577TUsW7YMwJN5cwcMGKAxhtfb2xsLFizAkCFDYGlpie7du+O9996DiYkJ3N3dcejQIaxdu1bdC37r1i1s3LgR/v7+6hsjfPnllzAxMUFQUJB6uzk5OTh79iy++OKLFz6GqnT/vu58G0NEVFnS0h5j+/ZIJCc/mVfe17cDZs7sq/UQyCpRSTMqaA2AiI6OLnOZrl27iunTp2u0RUVFCX19fVFQUFCu/Wg7NZeuKG16qPDwcGFoaCgSEhLUbYcPHxb9+vUTVlZWwtDQUDRp0kR88803QqFQFFs/JiZGBAQECGtra2FsbCy8vb3Fu+++K+7du1dmPadPnxYhISHC3t5eGBkZiYYNG4rJkyeL69evq5eJjo4WDRs2FMbGxmLAgAFi+fLlJU7NVRKFQiGcnZ0FAHHz5s1ir+/evVv4+fkJExMTYWlpKdq3by+WL19eZs0jRowQH374oRBCiOTkZKGvry9+//33Epd96623RPPmzdXPr1+/LkJDQ0XdunWFmZmZaN68ufjpp5+KTSGmVCrFkiVLRLt27YSpqamwtLQUvr6+4ocffhA5OTll1vcyHj16JEaNGiUsLCyEhYWFGDVqVLHp1QCI1atXq58nJSWJcePGCRcXF2FsbCwaN24svv32W6H635xx//zzjwgMDBQODg7CwMBAuLq6ipEjRxab7mvdunWicePGpdZWHX7usrKEeDLY4Mn/ExHVZnFxcWLBggVi3rx54j//+U+Z0zhWFm2m5pIJUcFX7LwgmUyG6OjoUnumAOCDDz7Atm3bEBcXp257/fXXERsbixMnTpRrPxkZGbCyskJ6enqxaZjy8vIQHx8PT0/PUsdWUs118eJF9OnTBzdu3ICFhYXU5dQY7du3x4wZMzBy5MgSX68OP3fZ2U+mwwOeXPDJnlkiqq1yc3OxaNEi5OXlwdXVFaGhoVpPq1kRysprz5J0zGxWVpZ6zB/wZOqt2NhYJCQkAHgyRGDMmDHq5adOnYo7d+5g1qxZuHLlClatWoWVK1eWOG8qkbaaN2+Or776SuP2s/RyHjx4gLCwMI2LwYiIqPoyMTHBoEGD4Ofnh3HjxkkSZLUlac/swYMH0bNnz2LtY8eOxZo1azBu3Djcvn0bBw8eVL926NAhzJw5E5cvX4aLiws++OADTJ06tdz7ZM8sUfVSHX7u2DNLRLXZ5cuXYWRk9NLTQFYkbXpmJb0ArEePHmXOS1rSBUHdu3fHX3/9VYlVEREREdV8hYWF2LNnD86ePQsTExO8/vrrOjnMTudmMyAiIiKil5OSkoKIiAj1PORt27YtcW53XcAwW4Jqck0cUa3Anzcioqp14cIFbN++HYWFhTAzM8OQIUPQoEEDqct6YQyzTym6g1hOTk6Z97onoopTUFAAAOrbERMRUeVQqVTYvn07zp07BwDw8PBASEiITg4teBrD7FPkcjnq1KmDBw8eAABMTU3LvHc9Eb0clUqFhw8fwtTUVOOWwUREVPGevqtj9+7d0a1bt1LvjKlL+K/HM5ycnABAHWiJqHLp6emhXr16/MORiKiSKBQKdYdBYGAgWrVqhXr16klcVcVhmH2GTCaDs7MzHBwcUFhYKHU5RDWeoaFhjegZICKqbgoKCrBz505kZWVh1KhRkMlkMDAwqFFBFmCYLZVcLucYPiIiItJJ9+/fR0REBFJSUiCTyZCYmAg3Nzepy6oUDLNERERENYQQAn/99Rd2794NhUIBCwsLhIaG1tggCzDMEhEREdUI+fn52L59Oy5dugQAaNiwIYYMGQJTU1OJK6tcDLNERERENUBERARu3LgBmUyG3r17w8/Pr1ZcXMswS0RERFQD9OrVC6mpqRg8eHCNHlbwLF5CTERERKSD8vLycP36dfVzZ2dnvPHGG7UqyAIMs0REREQ65969e1i+fDk2bNiAe/fuqdtr41SHHGZAREREpCOEEPjzzz8RExMDlUqFOnXqSF2S5BhmiYiIiHRAbm4utm7diqtXrwIAfHx8MHDgQBgbG0tcmbQYZomIiIiqucTERERERCA9PR1yuRz+/v5o165drZit4HkYZomIiIiquTt37iA9PR3W1tYYOnQonJ2dpS6p2mCYJapmhABycqSuonbJzpa6AiKisvn5+QEA2rZtCyMjI4mrqV4YZomqESGALl2A48elroSIiKSUkJCAw4cPY9iwYTA0NIRMJkPnzp2lLqtaqn3zNxBVYzk5DLJS6twZqOF3fSSiak4IgSNHjmDNmjW4efMmjhw5InVJ1R57Zomqqfv3ATMzqauoXUxNAV5LQURSyc7ORnR0NG7evAkAaNGiBbp27SpxVdUfwyxRNWVmxjBLRFRb3L59G5GRkcjKyoK+vj6CgoLQqlUrzlZQDgyzRERERBK6cOECNm/eDCEE7O3tERYWBgcHB6nL0hkMs0REREQS8vT0hImJCby8vBAYGAhDQ0OpS9IpDLNEREREVezRo0ewtbUFAFhYWGDq1KmwsLCQuCrdxNkMiIiIiKqISqXCgQMH8PPPPyMuLk7dziD74tgzS0RERFQFMjIyEBUVhTt37gB4covaJk2aSFyV7mOYJSIiIqpkN27cQHR0NHJycmBoaIjg4GA0a9ZM6rJqBIZZIiIiokqiVCpx4MABHDt2DADg5OSEsLAw9XhZenkMs0RERESV5M6dO+og265dO/j7+0Nfn/GrIvHdJCIiIqok9evXR5cuXeDs7MzxsZWEsxkQERERVRClUon9+/cjIyND3da7d28G2UrEnlkiIiKiCpCWloaIiAj8888/SEhIwNixY3k72irAMEtERET0kq5cuYKtW7ciLy8PxsbG6NixI4NsFWGYJSIiInpBCoUCMTExOHXqFADA1dUVoaGhqFOnjrSF1SIMs0REREQvICMjAxs2bEBSUhIAwM/PD7169YJcLpe4stqFYZaIiIjoBRgbG0OhUMDExASDBw+Gl5eX1CXVSgyzREREROWkUCggl8shk8lgaGiI4cOHw8DAAJaWllKXVmtxai4iIiKickhJScGKFSvUN0EAAFtbWwZZibFnloiIiOg5Lly4gO3bt6OwsBDZ2dlo3749DA0NpS6LwDBLREREVKrCwkLs2rUL586dAwB4eHggJCSEQbYaYZglIiIiKsHDhw8RERGBBw8eAAC6d++Obt26QU+PozSrE4ZZIiIiomfk5+dj1apVyMvLg7m5OUJCQuDp6Sl1WVQChlkiIiKiZxgZGaFnz574+++/MWTIEJibm0tdEpWCYZaIiIgIwP379yGEgJOTEwCgXbt2aNeuHW9LW81x0AcRERHVakIInD17FitWrMDvv/+O/Px8AIBMJmOQ1QHsmSUiIqJaKz8/H9u3b8elS5cAPJk3VqlUSlwVaYNhloiIiGql5ORkbNq0CampqZDJZOjVqxc6d+7M3lgdwzBLREREtYoQAmfOnMGePXugVCphaWmJsLAwuLm5SV0avQCGWSIiIqp1rl27BqVSCS8vLwwaNAimpqZSl0QviGGWiIiIahWZTIbBgwcjLi4Obdu25bACHcfZDIiIiKhGE0Lg5MmT2L59u7rNzMyM027VEOyZJSIiohorNzcXW7duxdWrVwEATZs25Z28ahiGWSIiIqqREhMTERERgfT0dMjlcvj7+8PDw0PqsqiCMcwSERFRjSKEwIkTJ7Bv3z6oVCpYW1sjLCwMLi4uUpdGlYBhloiIiGqUrVu3IjY2FsCTYQUDBgyAsbGxtEVRpWGYJSIiohqladOmuHTpEgICAuDr68uLvGo4hlkiIiLSaUIIPHr0CHZ2dgCAhg0b4u2334a5ubnElVFV4NRcREREpLOys7MRHh6OFStW4PHjx+p2Btnagz2zREREpJNu376NyMhIZGVlQV9fHw8ePIC1tbXUZVEVY5glIiIinaJSqXDkyBEcOnQIQgjY2dlh6NChcHBwkLo0kgDDLBEREemMrKwsREVFIT4+HgDQqlUrBAYGwtDQUOLKSCoMs0RERKQzTp48ifj4eBgYGKB///5o2bKl1CWRxBhmiYiISGf06NEDmZmZ6Nq1q3r2AqrdOJsBERERVVsZGRnYu3cvVCoVAEBfXx9DhgxhkCU19swSERFRtXTjxg1ER0cjJycHRkZG6N69u9QlUTXEMEtERETVilKpxIEDB3Ds2DEAgJOTE5o1ayZxVVRdMcwSERFRtZGeno7IyEjcvXsXANC2bVsEBARAX5+RhUrGTwYRERFVC7du3UJERARyc3NhZGSE4OBgNG3aVOqyqJpjmCUiIqJqwdzcHIWFhXB2dkZYWBhsbGykLol0AMMsERERSaagoEB9wwMHBweMGTMGzs7OHFZA5capuYiIiEgSV69exQ8//KAeHwsAbm5uDLKkFX5aiIiIqEopFArExMTg1KlTAJ7c1cvNzU3iqkhXSd4zu3jxYnh6esLY2Bi+vr44cuRImcuHh4ejZcuWMDU1hbOzM8aPH49Hjx5VUbVERET0MlJTU7Fq1Sp1kO3UqRNCQkIkrop0maRhduPGjZgxYwY+/vhjnDt3Dl27dkVgYCASEhJKXP7o0aMYM2YMJk6ciMuXL2PTpk04ffo0Jk2aVMWVExERkbYuX76MZcuWISkpCSYmJnjllVfg7+8PuVwudWmkwyQNs9999x0mTpyISZMmwcfHBwsXLoSbmxuWLFlS4vInT56Eh4cHpk+fDk9PT3Tp0gVTpkzBmTNnqrhyIiIi0kZ8fDwiIiJQUFAANzc3TJkyBV5eXlKXRTWAZGG2oKAAZ8+ehb+/v0a7v78/jh8/XuI6fn5+SExMxM6dOyGEwP379xEREYH+/fuXup/8/HxkZGRoPIiIiKhqeXh4wMfHB126dMG4ceNgZWUldUlUQ0gWZlNSUqBUKuHo6KjR7ujoiOTk5BLX8fPzQ3h4OIYPHw5DQ0M4OTmhTp06+PHHH0vdz4IFC2BlZaV+cIA5ERFR1YiLi0N+fj4AQCaTYejQoejduzf09CS/ZIdqEMk/TTKZTOO5EKJYW5G4uDhMnz4dc+bMwdmzZ7F7927Ex8dj6tSppW5/9uzZSE9PVz+env6DiIiIKl5hYSG2bt2KTZs2Ydu2bRBCACj+bz5RRZBsai47OzvI5fJivbAPHjwo1ltbZMGCBejcuTPee+89AECLFi1gZmaGrl274vPPP4ezs3OxdYyMjGBkZFTxB0BERETFPHz4EBEREXjw4AEAwNbWVuKKqKaTrGfW0NAQvr6+iImJ0WiPiYmBn59fievk5OQU+2qi6ArIor/6iIiISBqxsbH45Zdf8ODBA5iZmWH06NHo2bMne2SpUkl604RZs2Zh9OjRaNu2LTp16oTly5cjISFBPWxg9uzZ+Oeff7B27VoAQHBwMF577TUsWbIEAQEBSEpKwowZM9C+fXu4uLhIeShERES1VkFBAXbu3Inz588DADw9PRESEgJzc3OJK6PaQNIwO3z4cDx69AiffvopkpKS0KxZM+zcuRPu7u4AgKSkJI05Z8eNG4fMzEz89NNPeOedd1CnTh306tULX375pVSHQEREVOsVFhbi5s2bkMlk6NGjB7p06cKLvKjKyEQt+34+IyMDVlZWSE9Ph6WlpdTlEGnIzgaKOjKysgAzM2nrISIqrzt37kAIAQ8PD6lLoRpAm7wmac8sERER6Z78/Hzs2LEDDRs2RIsWLQBA/a0qUVVjmCUiIqJyS05OxqZNm5Camorr16+jcePGnDWIJMUwS0RERM8lhMCZM2ewZ88eKJVKWFpaIjQ0lEGWJMcwS0RERGXKy8vDtm3bEBcXBwDw8vLCoEGDYGpqKnFlRAyzREREVIaCggIsX74cjx8/hp6eHvr06YOOHTty7liqNhhmiYiIqFSGhobw8fHB5cuXERYWBldXV6lLItLAMEtEREQacnNzUVhYqJ4SqVevXujSpQtMTEwkroyoOM5oTERERGqJiYlYtmwZfv/9dyiVSgBPbh3PIEvVFXtmiYiICEIInDhxAvv27YNKpYKenh4yMzNRp04dqUsjKhPDLBERUS2Xk5ODLVu24Nq1awCAJk2aIDg4GMbGxhJXRvR8DLNERES1WEJCAiIjI5GRkQG5XI5+/frB19eXsxWQzmCYJSIiqqWEENizZw8yMjJgY2ODoUOHwsnJSeqyiLTCMEtERFRLyWQyhISE4NixYwgICODdvEgncTYDIiKiWuT27ds4efKk+rmtrS0GDhzIIEs6iz2zREREtYBKpcKRI0dw6NAhCCHg7OwMd3d3qcsiemkMs0RERDVcVlYWoqKiEB8fDwBo2bIlnJ2dJa6KqGIwzBIREdVgt27dQlRUFLKzs2FgYICgoCC0atVK6rKIKgzDLBERUQ115MgR7N+/HwDg4OCAsLAw2NvbS1wVUcVimCUiIqqhzMzMAACtW7dGYGAgDAwMJK6IqOIxzBIREdUgBQUFMDQ0BPAkxNrZ2aFevXoSV0VUeTg1FxERUQ2gUqnwxx9/YPHixcjNzQXwZB5ZBlmq6dgzS0REpOPS09MRGRmJu3fvAgDi4uLg6+srcVVEVYNhloiISIddu3YNmzdvRm5uLoyMjBAcHIymTZtKXRZRlWGYJSIi0kFKpRL79u3DiRMnAADOzs4ICwuDjY2NxJURVS2GWSIiIh108OBBdZBt3749+vbtC319/rNOtQ8/9URERDrIz88P169fR/fu3eHj4yN1OUSS4WwGREREOkChUOD8+fMQQgAATExMMGXKFAZZqvXYM0tERFTNPX78GJs2bUJSUhIUCoV6pgKZTCZxZUTSY5glIiKqxuLi4rB161bk5+fDxMQEFhYWUpdEVK0wzBIREVVDCoUCe/bswZkzZwAAbm5uCA0NhZWVlcSVEVUvDLNERETVzKNHjxAREYHk5GQAQOfOndGzZ0/I5XKJKyOqfhhmiYiIqpmMjAwkJyfD1NQUQ4YMQcOGDaUuiajaYpglIiKqBoQQ6gu6PD09MXjwYHh6esLS0lLiyoiqN07NRUREJLGHDx9i9erVePTokbqtZcuWDLJE5cAwS0REJKHY2Fj88ssvuHv3Lnbt2iV1OUQ6h8MMiIiIJFBQUICdO3fi/PnzAP5/aAERaYdhloiIqIo9ePAAmzZtQkpKCmQyGbp3746uXbtCT49fmBJpi2GWiIioCiUmJuK///0vFAoFzM3NERoaCg8PD6nLItJZDLNERERVyNnZGU5OTjAyMsKQIUNgZmYmdUlEOo1hloiIqJI9fPgQNjY2kMvlkMvlGDlyJIyNjdVTcRHRi+PgHCIiokoihMCZM2ewbNky7N+/X91uYmLCIEtUQdgzS0REVAny8/Oxbds2XL58GQCQkpIClUrFi7yIKhjDLBERUQW7d+8eIiIi8PjxY+jp6aF3797o1KkTe2OJKgHDLBERUQURQuDUqVOIiYmBUqmElZUVwsLC4OrqKnVpRDUWwywREVEFyczMxP79+6FUKuHt7Y2BAwfCxMRE6rKIajSGWSIiogpiaWmJ4OBgZGdno3379hxWQFQFGGaJiIhekBACJ0+ehJOTEzw9PQEAzZo1k7gqotqFYZaIiOgF5ObmYvPmzbh27RrMzc0xbdo0DikgkgDDLBERkZbu3r2LiIgIZGRkQC6Xo1u3bjA2Npa6LKJaiWGWiIionIQQOHbsGPbv3w8hBGxsbDB06FA4OTlJXRpRrcUwS0REVA6FhYX4/fffcePGDQBPxsYOGDAARkZGEldGVLsxzBIREZWDvr4+jI2Noa+vj379+qFNmzacrYCoGmCYJSIiKoVKpYJCoYChoSFkMhkGDBiArl27wsHBQerSiOh/eINoIiKiEmRlZSE8PBzR0dEQQgAAjIyMGGSJqhn2zBIRET0jPj4eUVFRyMrKgoGBAVJSUmBvby91WURUAoZZIiKi/1GpVDh06BAOHz4MALC3t8fQoUMZZImqMYZZIiIiAJmZmYiKisLt27cBAK1bt0ZgYCAMDAykLYyIysQwS0REtZ4QAhs2bMC9e/dgYGCAAQMGoEWLFlKXRUTl8EIXgCkUCvzxxx9YtmwZMjMzAQD37t1DVlZWhRZHRERUFWQyGfr16wdnZ2dMmTKFQZZIh2jdM3vnzh3069cPCQkJyM/PR9++fWFhYYGvvvoKeXl5WLp0aWXUSUREVKEyMjKQnJwMLy8vAICbmxtee+01zh1LpGO07pl9++230bZtWzx+/BgmJibq9iFDhmDfvn0VWhwREVFluH79OpYuXYpNmzbhwYMH6nYGWSLdo3XP7NGjR3Hs2DEYGhpqtLu7u+Off/6psMKIiIgqmlKpxP79+3H8+HEAgLOzM/T1efkIkS7T+idYpVJBqVQWa09MTISFhUWFFEVERFTR0tLSEBkZicTERABA+/bt0bdvX4ZZIh2n9TCDvn37YuHChernMpkMWVlZmDt3LoKCgiqyNiIiogpx9epVLFu2DImJiTAyMsKwYcMQGBjIIEtUA2j9U/z999+jZ8+eaNKkCfLy8jBy5Ehcv34ddnZ2WL9+fWXUSERE9FKSkpKQl5eHunXrIjQ0FNbW1lKXREQVROsw6+LigtjYWGzYsAFnz56FSqXCxIkTMWrUKI0LwoiIiKQkhFBf0NW9e3eYmZnB19cXcrlc4sqIqCLJhBBCmxUOHz4MPz+/Yl/NKBQKHD9+HN26davQAitaRkYGrKyskJ6eDktLS6nLIdKQnQ2Ymz/5/6wswMxM2nqIdFVcXBxOnz6NUaNGcSgBkQ7SJq9pPWa2Z8+eSE1NLdaenp6Onj17ars5IiKiCqNQKLBz505s2rQJt2/fxqlTp6QuiYgqmdZ/rj79tc3THj16BDN2IxERkUQePXqEiIgIJCcnAwA6d+6MDh06SFwVEVW2cofZkJAQAE9mLxg3bhyMjIzUrymVSly4cAF+fn4VXyEREdFzXLp0Cdu2bUNBQQFMTU0xePBgNGrUSOqyiKgKlDvMWllZAXjSM2thYaFxsZehoSE6duyI1157reIrJCIiKsPx48cRExMDAKhXrx5CQ0N5TQRRLVLuMLt69WoAgIeHB959910OKSAiomqhSZMmOHr0KNq2bYsePXpAT0/ry0GISIdpPZuBruNsBlSdcTYDovJJSkqCs7Oz+nlubi6nhySqQSp1NgMAiIiIwLBhw9CxY0e0adNG46GtxYsXw9PTE8bGxvD19cWRI0fKXD4/Px8ff/wx3N3dYWRkhAYNGmDVqlUvchhERKRjCgoKsGXLFixfvhzXr19XtzPIEtVeWofZRYsWYfz48XBwcMC5c+fQvn172Nra4tatWwgMDNRqWxs3bsSMGTPw8ccf49y5c+jatSsCAwORkJBQ6jrDhg3Dvn37sHLlSvz9999Yv349vL29tT0MIiLSMQ8ePMCKFSsQGxsLmUyGlJQUqUsiompA62EG3t7emDt3Ll555RVYWFjg/PnzqF+/PubMmYPU1FT89NNP5d5Whw4d0KZNGyxZskTd5uPjg8GDB2PBggXFlt+9ezdGjBiBW7duwcbGRpuy1TjMgKozDjMgKk4IgdjYWOzcuRMKhQLm5uYIDQ2Fh4eH1KURUSWp1GEGCQkJ6im4TExMkJmZCQAYPXo01q9fX+7tFBQU4OzZs/D399do9/f3x/Hjx0tcZ+vWrWjbti2++uor1K1bF15eXnj33XeRm5tb6n7y8/ORkZGh8SAiIt1QUFCAzZs3Y+vWrVAoFGjQoAGmTp3KIEtEalrfNMHJyQmPHj2Cu7s73N3dcfLkSbRs2RLx8fHQppM3JSUFSqUSjo6OGu2Ojo7qCa+fdevWLRw9ehTGxsaIjo5GSkoKpk2bhtTU1FLHzS5YsADz588v/wESEVG1cfPmTVy4cAEymQw9e/ZEly5dSrxxDxHVXlr3zPbq1Qvbtm0DAEycOBEzZ85E3759MXz4cAwZMkTrAp79pVTaHcYAQKVSQSaTITw8HO3bt0dQUBC+++47rFmzptTe2dmzZyM9PV39uHv3rtY1EhGRNHx8fNClSxeMGzcOXbt2ZZAlomK07pldvnw5VCoVAGDq1KmwsbHB0aNHERwcjKlTp5Z7O3Z2dpDL5cV6YR88eFCst7aIs7Mz6tatq76BA/DkF50QAomJiSXe7cXIyEjjbmVERFR95efn448//kCPHj3U85n37t1b4qqIqDrTumdWT08P+vr/n4GHDRuGRYsWYfr06Xj48GG5t2NoaAhfX1/1XVuKxMTElHpb3M6dO+PevXvIyspSt127dg16enpwdXXV8kiIiKg6SUpKwrJly3DmzBls3bpV6nKISEdUyG1SkpOT8dZbb6Fhw4ZarTdr1iysWLECq1atwpUrVzBz5kwkJCSoe3hnz56NMWPGqJcfOXIkbG1tMX78eMTFxeHw4cN47733MGHCBM4xSESko4QQOHXqFFauXInHjx/DysoKXbp0kbosItIR5Q6zaWlpGDVqFOzt7eHi4oJFixZBpVJhzpw5qF+/Pk6ePKn1zQuGDx+OhQsX4tNPP0WrVq1w+PBh7Ny5E+7u7gCe/JX+9Jyz5ubmiImJQVpaGtq2bYtRo0YhODgYixYt0mq/RERUPeTl5WHTpk3YtWsXlEolGjdujClTpsDNzU3q0ohIR5R7ntlp06Zh27ZtGD58OHbv3o0rV64gICAAeXl5mDt3Lrp3717ZtVYIzjNL1RnnmaXaJCUlBeHh4UhLS4Oenh769u2LDh068CIvItIqr5X7ArAdO3Zg9erV6NOnD6ZNm4aGDRvCy8sLCxcufNl6iYioFrKwsICenh7q1KmDsLAw1K1bV+qSiEgHlTvM3rt3D02aNAEA1K9fH8bGxpg0aVKlFUZERDVPfn4+DA0NIZPJYGRkhFdeeQXm5uYwNjaWujQi0lHlHjOrUqlgYGCgfi6Xy9XTphARET3P3bt3sXjxYpw6dUrdZmdnxyBLRC+l3D2zQgiMGzdOPWdrXl4epk6dWizQRkVFVWyFRESk04QQOH78OPbt2wchBM6ePYu2bdtCLpdLXRoR1QDlDrNjx47VeP7qq69WeDFERFSzZGdnY/Pmzbhx4wYAoFmzZhgwYACDLBFVmHKH2dWrV1dmHUREVMPcuXMHkZGRyMzMhL6+Pvr164c2bdpwtgIiqlBa386WSFcIAeTkSF2FdrKzpa6AqGJkZmbi119/hVKphK2tLYYOHVrqrcqJiF4GwyzVSEIAXboAx49LXQlR7WRhYYEePXrg4cOH6N+/PwwNDaUuiYhqKIZZqpFycnQ7yHbuDJiaSl0FkXbi4+NhZmYGBwcHAEDnzp0BgMMKiKhSMcxSjXf/vu7dScvUFOC//6QrVCoVDh8+jEOHDsHe3h6TJk1SzyVLRFTZGGapxjMz070wS6QrMjMzERUVhdu3bwMA6tatyxBLRFWq3DdNeNqvv/6Kzp07w8XFBXfu3AEALFy4EFu2bKnQ4oiIqPq6efMmli1bhtu3b8PAwABDhgzBoEGDNG6wQ0RU2bQOs0uWLMGsWbMQFBSEtLQ0KJVKAECdOnWwcOHCiq6PiIiqGZVKhf379+O3335DdnY2HB0dMXnyZLRo0ULq0oioFtI6zP7444/45Zdf8PHHH2tMet22bVtcvHixQosjIqLqKSEhAQDg6+uLiRMnws7OTuKKiKi20nrMbHx8PFq3bl2s3cjICNmcJJOIqMYSQkAmk0FPTw+hoaFISEhA06ZNpS6LiGo5rXtmPT09ERsbW6x9165daNKkSUXURERE1YhSqURMTAx2796tbrOwsGCQJaJqQeue2ffeew9vvPEG8vLyIITAqVOnsH79eixYsAArVqyojBqJiEgi6enpiIiIQGJiIgCgdevWcHJykrgqIqL/p3WYHT9+PBQKBd5//33k5ORg5MiRqFu3Ln744QeMGDGiMmokIiIJ/P3339i8eTPy8vJgZGSEgQMHMsgSUbUjE0KIF105JSUFKpVKfbcXXZCRkQErKyukp6fD0tJS6nKokmRnA+bmT/4/K4vzzBJpo2hYwZ9//gkAcHFxQVhYGKytrSWujIhqC23ymtZjZufPn4+bN28CAOzs7HQqyBIRUdmEEFi/fr06yHbs2BETJkxgkCWiakvrMBsZGQkvLy907NgRP/30Ex4+fFgZdRERkQRkMhl8fX1hbGyMESNGICAgQGMaRiKi6kbrMHvhwgVcuHABvXr1wnfffYe6desiKCgI69atQ05OTmXUSERElUihUOD+/fvq5z4+Pnj77bfRuHFjCasiIiqflxozCwDHjh3DunXrsGnTJuTl5SEjI6OiaqsUHDNbO3DMLFH5pKamYtOmTcjIyMCUKVP4e5GIqgVt8prWsxk8y8zMDCYmJjA0NERmZubLbo6IiKrIpUuXsG3bNhQUFMDExARpaWkMs0Skc14ozMbHx2PdunUIDw/HtWvX0K1bN8ybNw9Dhw6t6PqIiKiCFRYWYs+ePTh79iwAoF69eggNDWWQJSKdpHWY7dSpE06dOoXmzZtj/Pjx6nlmiYio+ktJSUFERIR6jGzXrl3Ro0cP6OlpfQkFEVG1oHWY7dmzJ1asWMHbGBIR6aA///wT9+/fh5mZGYYMGYIGDRpIXRIR0UvROsx+8cUXlVEHERFVgb59+0KlUqFHjx6wsLCQuhwiopdWrjA7a9YsfPbZZzAzM8OsWbPKXPa7776rkMKIiOjlPXjwAGfPnkW/fv0gk8lgaGiI4OBgqcsiIqow5Qqz586dQ2Fhofr/iYioehNCIDY2Fjt37oRCoYC1tTU6duwodVlERBWuXGH2wIEDJf4/ERFVPwUFBdixYwcuXLgAAGjQoAGaN28ucVVERJVD68tXJ0yYUOJ8stnZ2ZgwYUKFFEVERC/m/v37WL58OS5cuACZTIZevXph1KhRMOOdQ4iohtL6DmByuRxJSUlwcHDQaE9JSYGTkxMUCkWFFljReAew2oF3AKPa6NKlS9iyZQsUCgUsLCwQGhoKd3d3qcsiItJapdwBLCMjA0IICCGQmZkJY2Nj9WtKpRI7d+4sFnCJiKjq2NjYQAiBhg0bYsiQITA1NZW6JCKiSlfuMFunTh3IZDLIZDJ4eXkVe10mk2H+/PkVWhwREZUtLy9P3bng4uKCiRMnwsnJCTKZTOLKiIiqRrnD7IEDByCEQK9evRAZGQkbGxv1a4aGhnB3d4eLi0ulFElERJqEEDh9+jT279+PsWPHwtnZGQDU/yUiqi3KHWa7d+8OAIiPj0e9evX4Vz8RkUTy8vKwbds2xMXFAQBiY2MZYomo1ipXmL1w4QKaNWsGPT09pKen4+LFi6Uu26JFiworjoiINP3zzz+IiIhAWloa9PT00LdvX3To0EHqsoiIJFOuMNuqVSskJyfDwcEBrVq1gkwmQ0mTIMhkMiiVygovkoiothNC4M8//0RMTAxUKhXq1KmDsLAw1K1bV+rSiIgkVa4wGx8fD3t7e/X/ExFR1bpy5Qr27NkDAPDx8cHAgQM1ZpUhIqqtyhVmn56nkHMWEhFVPR8fHzRu3Bj169dHu3bteN0CEdH/aH0HsP/+97/YsWOH+vn777+POnXqwM/PD3fu3KnQ4oiIaishBM6ePYvCwkIAT4ZxDR8+HO3bt2eQJSJ6itZh9osvvoCJiQkA4MSJE/jpp5/w1Vdfwc7ODjNnzqzwAomIapucnBysX78e27dvx86dO9XtDLFERMWVe2quInfv3kXDhg0BAJs3b0ZYWBgmT56Mzp07o0ePHhVdHxFRrXLnzh1ERkYiMzMT+vr6cHV1hRCCQZaIqBRah1lzc3M8evQI9erVw969e9W9scbGxsjNza3wAomIagMhBI4ePaq+QY2trS2GDh0KR0dHqUsjIqrWtA6zffv2xaRJk9C6dWtcu3YN/fv3BwBcvnwZHh4eFV0fEVGNl52djejoaNy8eRPAk/m6+/fvD0NDQ4krIyKq/rQeM/vzzz+jU6dOePjwISIjI2FrawsAOHv2LF555ZUKL5CIqKZTKpVISkqCvr4+Bg4ciMGDBzPIEhGVk0yUdPeDGiwjIwNWVlZIT0+HpaWl1OVQJcnOBszNn/x/VhZgZiZtPUTPenYc7O3bt2FqagoHBwcJqyIiqh60yWtaDzMAgLS0NKxcuRJXrlyBTCaDj48PJk6cCCsrqxcqmIioNsnKykJUVBTatWsHHx8fAOAwLSKiF6T1MIMzZ86gQYMG+P7775GamoqUlBR8//33aNCgAf7666/KqJGIqMa4desWli5divj4eOzevZu3ACcieklaDzPo2rUrGjZsiF9++QX6+k86dhUKBSZNmoRbt27h8OHDlVJoReEwg9qBwwyoulGpVDh48CCOHDkCAHB0dERYWBjs7OwkroyIqPqp1GEGZ86c0QiyAKCvr4/3338fbdu21b5aIqIaLiMjA5GRkUhISAAA+Pr6IiAgAAYGBhJXRkSk+7QOs5aWlkhISIC3t7dG+927d2FhYVFhhRER1QTZ2dlYtmwZcnJyYGhoiODgYDRr1kzqsoiIagytw+zw4cMxceJEfPPNN/Dz84NMJsPRo0fx3nvvcWouIqJnmJmZoWnTprh79y7CwsLU0xkSEVHF0DrMfvPNN5DJZBgzZgwUCgUAwMDAAK+//jr+85//VHiBRES6Jj09HXp6eupvq/z9/QFAY3gWERFVjBeeZzYnJwc3b96EEAINGzaEqalpRddWKXgBWO3AC8BIKn///Tc2b94MR0dHjBkzBnp6Wk8aQ0RU62mT18r9WzYnJwdvvPEG6tatCwcHB0yaNAnOzs5o0aKFzgRZIqLKolQqsWfPHmzYsAF5eXkoLCxEbm6u1GUREdV45f7Oa+7cuVizZg1GjRoFY2NjrF+/Hq+//jo2bdpUmfUREVV7jx8/RmRkJP755x8AQMeOHdGnTx/I5XKJKyMiqvnKHWajoqKwcuVKjBgxAgDw6quvonPnzlAqlfyFTUS11pUrV7Blyxbk5+fD2NgYgwcPRuPGjaUui4io1ih3mL179y66du2qft6+fXvo6+vj3r17cHNzq5TiiIiqM6VSiQMHDiA/Px+urq4IDQ1FnTp1pC6LiKhWKXeYVSqVMDQ01FxZX189owERUW0jl8sRFhaGixcvokePHvyWiohIAuUOs0IIjBs3DkZGRuq2vLw8TJ06FWZPXSoeFRVVsRUSEVUjly9fRnZ2Ntq3bw8AcHBwQO/evSWuioio9ip3mB07dmyxtldffbVCiyEiqq4KCwuxZ88enD17FjKZDG5ubnB2dpa6LCKiWq/cYXb16tWVWQcRUbWVkpKCiIgI3L9/HwDQpUsXODo6SlwVEREBL3AHMCKi2uTChQvYvn07CgsLYWZmhiFDhqBBgwZSl0VERP/DMEtEVIodO3bgzJkzAAAPDw+EhISob1FLRETVA8MsEVEp7OzsAADdu3dHt27deGtaIqJqiGGWiOgpubm5MDExAfBkPm13d3c4OTlJXBUREZWG3QxERAAKCgqwefNmrFixAvn5+QAAmUzGIEtEVM29UJj99ddf0blzZ7i4uODOnTsAgIULF2LLli0VWhwRUVW4f/8+fvnlF5w/fx6PHz9GfHy81CUREVE5aR1mlyxZglmzZiEoKAhpaWlQKpUAgDp16mDhwoUVXR8RUaURQuDs2bNYsWIFUlJSYGFhgbFjx8Lb21vq0oiIqJy0DrM//vgjfvnlF3z88ccat25s27YtLl68WKHFERFVlvz8fERFRWH79u1QKBRo2LAhpk6dCnd3d6lLIyIiLWh9AVh8fDxat25drN3IyAjZ2dkVUhQRUWXbu3cvLl26BJlMht69e8PPzw8ymUzqsoiISEta98x6enoiNja2WPuuXbvQpEkTrQtYvHgxPD09YWxsDF9fXxw5cqRc6x07dgz6+vpo1aqV1vskIurVqxdcXV0xfvx4dO7cmUGWiEhHad0z+9577+GNN95AXl4ehBA4deoU1q9fjwULFmDFihVabWvjxo2YMWMGFi9ejM6dO2PZsmUIDAxEXFwc6tWrV+p66enpGDNmDHr37q2+vSQRUVny8vJw+fJl+Pr6AgDMzMwwYcIEhlgiIh0nE0IIbVf65Zdf8Pnnn+Pu3bsAgLp162LevHmYOHGiVtvp0KED2rRpgyVLlqjbfHx8MHjwYCxYsKDU9UaMGIFGjRpBLpdj8+bNJfYUlyYjIwNWVlZIT0+HpaWlVvWS7sjOBszNn/x/VhZgZiZtPSSte/fuYdOmTUhLS0NISAiaN28udUlERFQGbfLaC9004bXXXsNrr72GlJQUqFQqODg4aL2NgoICnD17Fh9++KFGu7+/P44fP17qeqtXr8bNmzfx22+/4fPPP3/ufvLz89VzRgJP3hwiqh2EEPjzzz8RExMDlUqFOnXqwMbGRuqyiIioAr3UHcCKbvX4IlJSUqBUKuHo6KjR7ujoiOTk5BLXuX79Oj788EMcOXIE+vrlK33BggWYP3/+C9dJRLopNzcXW7duxdWrVwE8+dZn4MCBMDY2lrgyIiKqSFqHWU9PzzLHmN26dUur7T27LSFEidtXKpUYOXIk5s+fDy8vr3Jvf/bs2Zg1a5b6eUZGBtzc3LSqkYh0S2JiIiIiIpCeng65XA5/f3+0a9eO42OJiGogrcPsjBkzNJ4XFhbi3Llz2L17N957771yb8fOzg5yubxYL+yDBw+K9dYCQGZmJs6cOYNz587hzTffBACoVCoIIaCvr4+9e/eiV69exdYzMjKCkZFRuesiIt2Xm5uL9PR0WFtbY+jQoXB2dpa6JCIiqiRah9m33367xPaff/4ZZ86cKfd2DA0N4evri5iYGAwZMkTdHhMTg0GDBhVb3tLSsthNGRYvXoz9+/cjIiICnp6e5d43EdU8T3+r06hRI4SEhMDLy4t/zBIR1XBazzNbmsDAQERGRmq1zqxZs7BixQqsWrUKV65cwcyZM5GQkICpU6cCeDJEYMyYMU8K1dNDs2bNNB4ODg4wNjZGs2bNYMbL1YlqrYSEBCxduhRpaWnqtubNmzPIEhHVAi91AdjTIiIitL5KePjw4Xj06BE+/fRTJCUloVmzZti5c6f6dpJJSUlISEioqBKJqIYRQuDo0aM4cOAAhBA4cOCAxjc9RERU82k9z2zr1q01LqIQQiA5ORkPHz7E4sWLMXny5AovsiJxntnagfPM1nzZ2dmIjo7GzZs3AQAtWrRA//79YWhoKHFlRET0sip1ntnBgwdrPNfT04O9vT169OgBb29vbTdHRKS127dvIzIyEllZWdDX10dQUBBatWrF2QqIiGohrcKsQqGAh4cHAgIC4OTkVFk1ERGV6vr161i/fj2EELC3t0dYWNgL3biFiIhqBq3CrL6+Pl5//XVcuXKlsuohIiqTp6cnHB0d4eTkhMDAQA4rICKq5bQeZtChQwecO3dOfZEWEVFlS0xMhIuLC/T09KCvr49x48ZxpgIiIgLwAmF22rRpeOedd5CYmAhfX99iU2K1aNGiwoojotpNpVLh4MGDOHLkCLp3744ePXoAAIMsERGplTvMTpgwAQsXLsTw4cMBANOnT1e/JpPJ1BOWK5XKiq+SiGqdjIwMREVF4c6dOwCArKysUm93TUREtVe5p+aSy+VISkpCbm5umctV9+EHnJqrduDUXLrtxo0biI6ORk5ODgwNDREcHIxmzZpJXRYREVWRSpmaqyjzVvewSkS6S6lU4sCBAzh27BgAwMnJCWFhYbC1tZW4MiIiqq60GjPLr/eIqDI9fvwYf/75JwCgXbt28Pf3h75+hd2okIiIaiCt/pXw8vJ6bqBNTU19qYKIqPays7PDgAEDYGBggCZNmkhdDhER6QCtwuz8+fNhZWVVWbUQUS2jVCqxf/9+eHt7w83NDQDQsmVLiasiIiJdolWYHTFiBO+0Q0QVIi0tDREREfjnn39w+fJlvPnmmxxSQEREWiv3vxwcL0tEFeXKlSvYunUr8vLyYGxsjH79+jHIEhHRC9F6NgMiohelUCgQExODU6dOAQBcXV0RGhqKOnXqSFsYERHprHKHWZVKVZl1EFENl5ubi19//RVJSUkAAD8/P/Tq1QtyuVziyoiISJfxez0iqhLGxsawtLREWloaBg8eDC8vL6lLIiKiGoBhlogqjUKhgEqlgqGhIWQyGQYNGoTCwkLefY+IiCqMntQFEFHN9OjRI6xYsQLbtm1Tj7k3MTFhkCUiogrFnlkiqnAXL17E9u3bUVBQgMzMTGRmZjLEEhFRpWCYJaIKU1hYiF27duHcuXMAAA8PD4SEhMDCwkLiyoiIqKZimCWiCvHw4UNERETgwYMHAIDu3bujW7du0NPjaCYiIqo8DLNE9NJUKhXWr1+Px48fw9zcHCEhIfD09JS6LCIiqgUYZonopenp6SE4OBjHjh3D4MGDYW5uLnVJRERUSzDMEtELuX//PtLT09XzxXp6esLDw4O3viYioirFMEtEWhFC4Ny5c9i1axf09PQwefJk2NraAgCDLBERVTmGWSIqt/z8fOzYsQMXL14EADRs2BDGxsYSV0VERLUZwywRlUtycjI2bdqE1NRUyGQy9O7dG35+fuyNJSIiSTHMEtFznTlzBrt374ZSqYSlpSXCwsLg5uYmdVlEREQMs0T0fKmpqVAqlfDy8sKgQYNgamoqdUlEREQAGGaJqBRCCPUQgt69e8PJyQnNmzfnsAIiIqpWeGseItIghMDJkyfx3//+F0qlEgAgl8vRokULBlkiIqp22DNLRGq5ubnYunUrrl69CgC4dOkSWrZsKXFVREREpWOYJSIAQGJiIiIiIpCeng65XA5/f3+0aNFC6rKIiIjKxDBLVMsJIXDixAns27cPKpUK1tbWCAsLg4uLi9SlERERPRfDLFEtFxMTgxMnTgAAmjZtiuDgYBgZGUlcFRERUfkwzBLVcm3atMH58+fRs2dP+Pr68iIvIiLSKQyzRLWMEAJ3795FvXr1AAB2dnZ4++23YWhoKHFlRERE2uPUXES1SHZ2NsLDw7FmzRrcvn1b3c4gS0REuoo9s0S1xO3btxEZGYmsrCzo6+sjMzNT6pKIiIheGsMsUQ2nUqlw5MgRHDp0CEII2NnZYejQoXBwcJC6NCIiopfGMEtUg2VlZSEqKgrx8fEAgFatWiEwMJDDCoiIqMZgmCWqwa5fv474+HgYGBigf//+vJsXERHVOAyzRDVYq1at8PjxYzRv3hz29vZSl0NERFThOJsBUQ2SmZmJqKgo5ObmAgBkMhl69erFIEtERDUWe2aJaogbN24gOjoaOTk5AICQkBCJKyIiIqp8DLNEOk6lUmH//v04duwYAMDJyQndu3eXuCoiIqKqwTBLpMPS09MRGRmJu3fvAgDatm2LgIAA6OvzR5uIiGoH/otHpKMSExOxbt065ObmwsjICAMHDkSTJk2kLouIiKhKMcwS6ShbW1sYGBjA2toaYWFhsLa2lrokIiKiKscwS6RDsrOzYWpqCplMBhMTE4wZMwZWVlYcVkBERLUWp+Yi0hFXrlzBTz/9hHPnzqnbbG1tGWSJiKhWY5glquYUCgV27dqF33//HXl5ebh48SKEEFKXRUREVC2wS4eoGktNTUVERASSkpIAAJ06dULv3r0hk8kkroyIiKh6YJglqqYuX76Mbdu2IT8/HyYmJhg8eDC8vLykLouIiKhaYZitAkIA/7spE1WR7GypK3g5jx49QmRkJIQQcHNzQ2hoKKysrKQui4iIqNphmK1kQgBdugDHj0tdCekSW1tbdOvWDUqlEj179oSeHoe3ExERlYRhtpLl5DDISqlzZ8DUVOoqyufixYtwcXGBra0tAKBHjx7SFkRERKQDGGar0P37gJmZ1FXULqamQHW/VqqwsBC7du3CuXPn4OTkhIkTJ3K6LSIionLiv5hVyMyMYZY0PXz4EBEREXjw4AEAwMvLi0MKiIiItMAwSySR2NhY7Ny5E4WFhTAzM0NISAjq168vdVlEREQ6hWGWqIoVFhZix44dOH/+PADA09MTISEhMDc3l7gyIiIi3cMwS1TF9PT0kJKSAplMhh49eqBLly4cWkBERPSCGGaJqkDR7WdlMhnkcjnCwsKQlpYGDw8PaQsjIiLScQyzRJUsPz8fO3bsgIWFBfr27QsAqFOnDurUqSNtYURERDUAwyxRJUpOTsamTZuQmpoKPT09tGvXjiGWiIioAjHMElUCIQTOnDmDPXv2QKlUwtLSEqGhoQyyREREFYxhlqiC5eXlYdu2bYiLiwPwZO7YQYMGwVRXbkVGRESkQxhmiSqQEAJr1qzB/fv3oaenhz59+qBjx46QVffbkBEREekozgdEVIFkMhn8/PxgZWWF8ePHo1OnTgyyRERElYg9s0QvKTc3F+np6XBycgIAtGjRAj4+PjAwMJC4MiIiopqPYZboJSQmJiIiIgJKpRJTp06FmZkZADDIEhERVRGGWaIXIITAiRMnsG/fPqhUKlhbWyM7O1sdZomIiKhqMMwSaSknJwdbtmzBtWvXAABNmjRBcHAwjI2NJa6MiIio9pH8ArDFixfD09MTxsbG8PX1xZEjR0pdNioqCn379oW9vT0sLS3RqVMn7NmzpwqrpdouISEBy5Ytw7Vr1yCXyxEUFISwsDAGWSIiIolIGmY3btyIGTNm4OOPP8a5c+fQtWtXBAYGIiEhocTlDx8+jL59+2Lnzp04e/YsevbsieDgYJw7d66KK6fa6syZM8jIyICNjQ0mTZqEdu3acbYCIiIiCcmEEEKqnXfo0AFt2rTBkiVL1G0+Pj4YPHgwFixYUK5tNG3aFMOHD8ecOXPKtXxGRgasrKyQnp4OS0vLF6pbG9nZgLn5k//PygI4pFK35efn4+DBg+jRoweMjIykLoeIiKhG0iavSdYzW1BQgLNnz8Lf31+j3d/fH8ePHy/XNlQqFTIzM2FjY1PqMvn5+cjIyNB4EJXX7du3sWPHDhT9zWdkZISAgAAGWSIiompCsgvAUlJSoFQq4ejoqNHu6OiI5OTkcm3j22+/RXZ2NoYNG1bqMgsWLMD8+fNfqlaqfVQqFY4cOYJDhw5BCIG6deuiVatWUpdFREREz5D8ArBnxxsKIco1BnH9+vWYN28eNm7cCAcHh1KXmz17NtLT09WPu3fvvnTNVLNlZWXht99+w8GDByGEQMuWLdGkSROpyyIiIqISSNYza2dnB7lcXqwX9sGDB8V6a5+1ceNGTJw4EZs2bUKfPn3KXNbIyIhfCVO53bp1C1FRUcjOzoaBgQGCgoLYI0tERFSNSdYza2hoCF9fX8TExGi0x8TEwM/Pr9T11q9fj3HjxmHdunXo379/ZZdJtcjJkyfx66+/Ijs7Gw4ODnjttdcYZImIiKo5SW+aMGvWLIwePRpt27ZFp06dsHz5ciQkJGDq1KkAngwR+Oeff7B27VoAT4LsmDFj8MMPP6Bjx47qXl0TExNYWVlJdhxUM9StWxcymQytWrVCYGAgb0lLRESkAyQNs8OHD8ejR4/w6aefIikpCc2aNcPOnTvh7u4OAEhKStKYc3bZsmVQKBR444038MYbb6jbx44dizVr1lR1+VQDZGVlwfx/c6e5ublh2rRpsLOzk7gqIiIiKi9J55mVAueZJeDJbAX79+/HqVOnMGnSpDIvIiQiIqKqpU1ek7RnlkgK6enpiIyMVM9sce3aNYZZIiIiHcUwS7XKtWvXsHnzZuTm5sLIyAjBwcFo2rSp1GURERHRC2KYpVpBqVRi3759OHHiBADA2dkZYWFhZd49joiIiKo/hlmqFc6dO6cOsu3bt0ffvn2hr8+PPxERka7jv+ZUK7Rp0wY3b95EixYt4OPjI3U5REREVEEkv50tUWVQKpU4duwYFAoFAEBPTw/Dhw9nkCUiIqph2DNLNc7jx48RERGBe/fuIT09HUFBQVKXRERERJWEYZZqlLi4OGzduhX5+fkwMTFBw4YNpS6JiIiIKhHDLNUICoUCe/bswZkzZwA8uZtXaGgob3NMRERUwzHMks5LTU3Fpk2bkJycDADo3LkzevbsCblcLnFlREREVNkYZknnyWQyPH78GKamphgyZAiHFhAREdUiDLOkk1QqFfT0nkzGYW1tjeHDh8PW1va5928mIiKimoVTc5HOefjwIZYvX44bN26o2zw9PRlkiYiIaiH2zJJOOX/+PHbs2IHCwkLExMSgQYMGkMlkUpdFREREEmGYJZ1QUFCAXbt2ITY2FsCTntiQkBAGWSIiolqOYZaqvQcPHmDTpk1ISUmBTCZD9+7d0bVrV/WYWSIiIqq9GGapWnv8+DF++eUXKBQKmJubIzQ0FB4eHlKXRURERNUEwyxVa9bW1mjWrBkyMzMxZMgQmJmZSV0SERERVSMMs1TtJCcnw8LCQh1c+/fvD7lczvGxREREVAwHHVK1IYTAmTNnsGLFCmzevBlCCACAvr4+gywRERGViD2zVC3k5eVh+/btuHz5MoAnd/UqLCyEoaGhxJURERFRdcYwS5K7d+8eIiIi8PjxY+jp6aF3797o1KkTe2OJiIjouRhmSTJCCJw6dQoxMTFQKpWwsrJCWFgYXF1dpS6NiIiIdATDLEmmsLAQf/75J5RKJRo3boxBgwbBxMRE6rKIiIhIhzDMkmQMDQ0RFhaGhIQEdOjQgcMKiIiISGsMs1RlhBA4efIkDAwM0LZtWwCAi4sLXFxcJK6MiIiIdBXDLFWJ3NxcbN68GdeuXYNcLkf9+vVhY2MjdVlERESk4xhmqdLdvXsXERERyMjIgFwuR0BAAKytraUui4iIiGoAhlmqNEIIHDt2DPv374cQAjY2Nhg6dCicnJykLo2IiIhqCIZZqhRCCGzYsAHXrl0DADRr1gwDBgyAkZGRxJURERFRTcIwS5VCJpPB1dUVt27dQmBgIFq3bs3ZCoiIiKjCMcxShVGpVMjJyYG5uTkAoEuXLmjatCkv9CIiIqJKoyd1AVQzZGVlITw8HGvXrkVhYSGAJ72zDLJERERUmdgzSy8tPj4ekZGRyM7OhoGBAZKSklCvXj2pyyIiIqJagGGWXphKpcKhQ4dw+PBhAIC9vT2GDh0Ke3t7iSsjIiKi2oJhll5IZmYmoqKicPv2bQBA69atERgYCAMDA2kLIyIiolqFYZZeyK5du3D79m0YGBhgwIABaNGihdQlERERUS3EMEsvpF+/fsjLy0NQUBDs7OykLoeIiIhqKc5mQOWSkZGBU6dOqZ9bWlpizJgxDLJEREQkKfbM0nNdv34d0dHRyM3NhaWlJby9vaUuiYiIiAgAwyyVQalUYv/+/Th+/DgAwNnZGQ4ODhJXRURERPT/GGapRGlpaYiMjERiYiIAoH379ujbty/09fmRISIiouqDyYSKuXbtGqKjo5GXlwcjIyMMGjQIPj4+UpdFREREVAzDLBWjUCiQl5eHunXrIjQ0FNbW1lKXRERERFQihlkC8ORuXnp6Tya3aNKkCYYNGwYvLy/I5XKJKyMiIiIqHafmIsTFxeHnn39GZmamus3Hx4dBloiIiKo9htlaTKFQYMeOHdi0aRNSU1Nx7NgxqUsiIiIi0gqHGdRSjx49QkREBJKTkwEAnTt3Rs+ePSWuioiIiEg7DLO10KVLl7Bt2zYUFBTA1NQUgwcPRqNGjaQui4iIiEhrDLO1zPnz57F582YAQL169RAaGgpLS0tpiyIiIiJ6QQyztYyPjw+OHTsGb29v9OjRQz2DAREREZEuYpitBW7evIn69etDJpPB0NAQkydP5p28iIiIqEZgt1wNVlBQgC1btuC3337D8ePH1e0MskRERFRTMNXUUA8ePEBERAQePnwImUwGlUoldUlEREREFY5htoYRQiA2NhY7d+6EQqGAubk5QkND4eHhIXVpRERERBWOYbYGKSgowPbt23Hx4kUAQIMGDTBkyBCYmZlJXBkRERFR5WCYrUEePXqEy5cvQyaToWfPnujSpQtkMpnUZRERERFVGobZGsTZ2RkDBgyAra0t6tWrJ3U5RERERJWOsxnosPz8fERHRyMpKUnd1rp1awZZIiIiqjUYZnVUUlISli1bhgsXLiAqKoqzFRAREVGtxGEGOkYIgdOnT2Pv3r1QKpWwsrLCwIEDeScvIiIiqpUYZnVIXl4etm7diitXrgAAGjdujEGDBsHExETiyoiIiIikwTCrIzIyMrB69WqkpaVBT08Pffv2RYcOHThbAREREdVqDLM6wsLCAjY2NgCAsLAw1K1bV+KKiIiIiKTHMFuN5ebmQl9fHwYGBpDJZAgNDYWenh6MjY2lLo2IiIioWuBVQ9XU3bt3sXTpUuzatUvdZmpqyiBLRERE9BT2zFYzQggcP34c+/btgxACd+7cQV5eHkMsERERUQkYZquR7OxsbN68GTdu3AAANGvWDAMGDICRkZHElRERERFVTwyz1cSdO3cQGRmJzMxM6Ovro1+/fmjTpg1nKyAiIiIqA8NsNVBYWIhNmzYhOzsbtra2GDp0KBwdHaUui4iIiKjaY5itBgwMDDBo0CBcunQJ/fv3h6GhodQlEREREekEhlmJxMfHQ6FQoFGjRgCARo0aqf+fiIiIiMqHYbaKqVQqHD58GIcOHYKxsTGmTp0KKysrqcsiIiIi0kmSzzO7ePFieHp6wtjYGL6+vjhy5EiZyx86dAi+vr4wNjZG/fr1sXTp0iqq9OVlZWXi119/xaFDhwAA3t7eMDU1lbgqIiIiIt0laZjduHEjZsyYgY8//hjnzp1D165dERgYiISEhBKXj4+PR1BQELp27Ypz587ho48+wvTp0xEZGVnFlWuvQYOb+O9/l+L27dswMDDAkCFDMGjQIBgYGEhdGhEREZHOkgkhhFQ779ChA9q0aYMlS5ao23x8fDB48GAsWLCg2PIffPABtm7diitXrqjbpk6divPnz+PEiRPl2mdGRgasrKyQnp4OS0vLlz+I58jKEhg0aD+6dj0KmQxwdHREWFgY7OzsKn3fRERERLpIm7wmWc9sQUEBzp49C39/f412f39/HD9+vMR1Tpw4UWz5gIAAnDlzBoWFhSWuk5+fj4yMDI1HVZLJZDAxyYNMBrRs6YuJEycyyBIRERFVEMnCbEpKCpRKZbH5VB0dHZGcnFziOsnJySUur1AokJKSUuI6CxYsgJWVlfrh5uZWMQeghT17ArBu3Svo23cAhxUQERERVSDJLwB79g5XQogy73pV0vIltReZPXs20tPT1Y+7d+++ZMXaMTUF0tL08ddfXuC1XkREREQVS7Kpuezs7CCXy4v1wj548KDUu185OTmVuLy+vj5sbW1LXMfIyAhGRkYVU/QLkMkAMzPJdk9ERERUo0nWM2toaAhfX1/ExMRotMfExMDPz6/EdTp16lRs+b1796Jt27b8+p6IiIioFpJ0mMGsWbOwYsUKrFq1CleuXMHMmTORkJCAqVOnAngyRGDMmDHq5adOnYo7d+5g1qxZuHLlClatWoWVK1fi3XffleoQiIiIiEhCkt4BbPjw4Xj06BE+/fRTJCUloVmzZti5cyfc3d0BAElJSRpzznp6emLnzp2YOXMmfv75Z7i4uGDRokUIDQ2V6hCIiIiISEKSzjMrhaqeZ5aIiIiItKMT88wSEREREb0shlkiIiIi0lkMs0RERESksxhmiYiIiEhnMcwSERERkc5imCUiIiIincUwS0REREQ6i2GWiIiIiHQWwywRERER6SyGWSIiIiLSWQyzRERERKSzGGaJiIiISGcxzBIRERGRztKXuoCqJoQAAGRkZEhcCRERERGVpCinFeW2stS6MJuZmQkAcHNzk7gSIiIiIipLZmYmrKysylxGJsoTeWsQlUqFe/fuwcLCAjKZrEr2mZGRATc3N9y9exeWlpZVsk+qODx/uo/nUPfxHOo2nj/dV9XnUAiBzMxMuLi4QE+v7FGxta5nVk9PD66urpLs29LSkj/EOoznT/fxHOo+nkPdxvOn+6ryHD6vR7YILwAjIiIiIp3FMEtEREREOothtgoYGRlh7ty5MDIykroUegE8f7qP51D38RzqNp4/3Vedz2GtuwCMiIiIiGoO9swSERERkc5imCUiIiIincUwS0REREQ6i2GWiIiIiHQWw2wFWLx4MTw9PWFsbAxfX18cOXKkzOUPHToEX19fGBsbo379+li6dGkVVUql0eYcRkVFoW/fvrC3t4elpSU6deqEPXv2VGG1VBJtfw6LHDt2DPr6+mjVqlXlFkjPpe05zM/Px8cffwx3d3cYGRmhQYMGWLVqVRVVS8/S9vyFh4ejZcuWMDU1hbOzM8aPH49Hjx5VUbX0rMOHDyM4OBguLi6QyWTYvHnzc9epNnlG0EvZsGGDMDAwEL/88ouIi4sTb7/9tjAzMxN37twpcflbt24JU1NT8fbbb4u4uDjxyy+/CAMDAxEREVHFlVMRbc/h22+/Lb788ktx6tQpce3aNTF79mxhYGAg/vrrryqunIpoew6LpKWlifr16wt/f3/RsmXLqimWSvQi53DgwIGiQ4cOIiYmRsTHx4s///xTHDt2rAqrpiLanr8jR44IPT098cMPP4hbt26JI0eOiKZNm4rBgwdXceVUZOfOneLjjz8WkZGRAoCIjo4uc/nqlGcYZl9S+/btxdSpUzXavL29xYcfflji8u+//77w9vbWaJsyZYro2LFjpdVIZdP2HJakSZMmYv78+RVdGpXTi57D4cOHi08++UTMnTuXYVZi2p7DXbt2CSsrK/Ho0aOqKI+eQ9vz9/XXX4v69etrtC1atEi4urpWWo1UfuUJs9Upz3CYwUsoKCjA2bNn4e/vr9Hu7++P48ePl7jOiRMnii0fEBCAM2fOoLCwsNJqpZK9yDl8lkqlQmZmJmxsbCqjRHqOFz2Hq1evxs2bNzF37tzKLpGe40XO4datW9G2bVt89dVXqFu3Lry8vPDuu+8iNze3Kkqmp7zI+fPz80NiYiJ27twJIQTu37+PiIgI9O/fvypKpgpQnfKMfpXurYZJSUmBUqmEo6OjRrujoyOSk5NLXCc5ObnE5RUKBVJSUuDs7Fxp9VJxL3IOn/Xtt98iOzsbw4YNq4wS6Tle5Bxev34dH374IY4cOQJ9ff4alNqLnMNbt27h6NGjMDY2RnR0NFJSUjBt2jSkpqZy3GwVe5Hz5+fnh/DwcAwfPhx5eXlQKBQYOHAgfvzxx6oomSpAdcoz7JmtADKZTOO5EKJY2/OWL6mdqo6257DI+vXrMW/ePGzcuBEODg6VVR6VQ3nPoVKpxMiRIzF//nx4eXlVVXlUDtr8HKpUKshkMoSHh6N9+/YICgrCd999hzVr1rB3ViLanL+4uDhMnz4dc+bMwdmzZ7F7927Ex8dj6tSpVVEqVZDqkmfYJfES7OzsIJfLi/3l+eDBg2J/rRRxcnIqcXl9fX3Y2tpWWq1Ushc5h0U2btyIiRMnYtOmTejTp09llkll0PYcZmZm4syZMzh37hzefPNNAE+CkRAC+vr62Lt3L3r16lUltdMTL/Jz6OzsjLp168LKykrd5uPjAyEEEhMT0ahRo0qtmf7fi5y/BQsWoHPnznjvvfcAAC1atICZmRm6du2Kzz//nN9S6oDqlGfYM/sSDA0N4evri5iYGI32mJgY+Pn5lbhOp06dii2/d+9etG3bFgYGBpVWK5XsRc4h8KRHdty4cVi3bh3HeElM23NoaWmJixcvIjY2Vv2YOnUqGjdujNjYWHTo0KGqSqf/eZGfw86dO+PevXvIyspSt127dg16enpwdXWt1HpJ04ucv5ycHOjpaUYQuVwO4P9796h6q1Z5psovOathiqYjWblypYiLixMzZswQZmZm4vbt20IIIT788EMxevRo9fJFU1nMnDlTxMXFiZUrV3JqLolpew7XrVsn9PX1xc8//yySkpLUj7S0NKkOodbT9hw+i7MZSE/bc5iZmSlcXV1FWFiYuHz5sjh06JBo1KiRmDRpklSHUKtpe/5Wr14t9PX1xeLFi8XNmzfF0aNHRdu2bUX79u2lOoRaLzMzU5w7d06cO3dOABDfffedOHfunHp6teqcZxhmK8DPP/8s3N3dhaGhoWjTpo04dOiQ+rWxY8eK7t27ayx/8OBB0bp1a2FoaCg8PDzEkiVLqrhiepY257B79+4CQLHH2LFjq75wUtP25/BpDLPVg7bn8MqVK6JPnz7CxMREuLq6ilmzZomcnJwqrpqKaHv+Fi1aJJo0aSJMTEyEs7OzGDVqlEhMTKziqqnIgQMHyvy3rTrnGZkQ7M8nIiIiIt3EMbNEREREpLMYZomIiIhIZzHMEhEREZHOYpglIiIiIp3FMEtEREREOothloiIiIh0FsMsEREREekshlkiIiIi0lkMs0REANasWYM6depIXcYL8/DwwMKFC8tcZt68eWjVqlWV1ENEVFUYZomoxhg3bhxkMlmxx40bN6QuDWvWrNGoydnZGcOGDUN8fHyFbP/06dOYPHmy+rlMJsPmzZs1lnn33Xexb9++CtlfaZ49TkdHRwQHB+Py5ctab0eX/7ggoqrDMEtENUq/fv2QlJSk8fD09JS6LACApaUlkpKScO/ePaxbtw6xsbEYOHAglErlS2/b3t4epqamZS5jbm4OW1vbl97X8zx9nDt27EB2djb69++PgoKCSt83EdU+DLNEVKMYGRnByclJ4yGXy/Hdd9+hefPmMDMzg5ubG6ZNm4asrKxSt3P+/Hn07NkTFhYWsLS0hK+vL86cOaN+/fjx4+jWrRtMTEzg5uaG6dOnIzs7u8zaZDIZnJyc4OzsjJ49e2Lu3Lm4dOmSuud4yZIlaNCgAQwNDdG4cWP8+uuvGuvPmzcP9erVg5GREVxcXDB9+nT1a08PM/Dw8AAADBkyBDKZTP386WEGe/bsgbGxMdLS0jT2MX36dHTv3r3CjrNt27aYOXMm7ty5g7///lu9TFnn4+DBgxg/fjzS09PVPbzz5s0DABQUFOD9999H3bp1YWZmhg4dOuDgwYNl1kNENRvDLBHVCnp6eli0aBEuXbqE//73v9i/fz/ef//9UpcfNWoUXF1dcfr0aZw9exYffvghDAwMAAAXL15EQEAAQkJCcOHCBWzcuBFHjx7Fm2++qVVNJiYmAIDCwkJER0fj7bffxjvvvINLly5hypQpGD9+PA4cOAAAiIiIwPfff49ly5bh+vXr2Lx5M5o3b17idk+fPg0AWL16NZKSktTPn9anTx/UqVMHkZGR6jalUonff/8do0aNqrDjTEtLw7p16wBA/f4BZZ8PPz8/LFy4UN3Dm5SUhHfffRcAMH78eBw7dgwbNmzAhQsXMHToUPTr1w/Xr18vd01EVMMIIqIaYuzYsUIulwszMzP1IywsrMRlf//9d2Fra6t+vnr1amFlZaV+bmFhIdasWVPiuqNHjxaTJ0/WaDty5IjQ09MTubm5Ja7z7Pbv3r0rOnbsKFxdXUV+fr7w8/MTr732msY6Q4cOFUFBQUIIIb799lvh5eUlCgoKSty+u7u7+P7779XPAYjo6GiNZebOnStatmypfj59+nTRq1cv9fM9e/YIQ0NDkZqa+lLHCUCYmZkJU1NTAUAAEAMHDixx+SLPOx9CCHHjxg0hk8nEP//8o9Heu3dvMXv27DK3T0Q1l760UZqIqGL17NkTS5YsUT83MzMDABw4cABffPEF4uLikJGRAYVCgby8PGRnZ6uXedqsWbMwadIk/Prrr+jTpw+GDh2KBg0aAADOnj2LGzduIDw8XL28EAIqlQrx8fHw8fEpsbb09HSYm5tDCIGcnBy0adMGUVFRMDQ0xJUrVzQu4AKAzp0744cffgAADB06FAsXLkT9+vXRr18/BAUFITg4GPr6L/5rfNSoUejUqRPu3bsHFxcXhIeHIygoCNbW1i91nBYWFvjrr7+gUChw6NAhfP3111i6dKnGMtqeDwD466+/IISAl5eXRnt+fn6VjAUmouqJYZaIahQzMzM0bNhQo+3OnTsICgrC1KlT8dlnn8HGxgZHjx7FxIkTUVhYWOJ25s2bh5EjR2LHjh3YtWsX5s6diw0bNmDIkCFQqVSYMmWKxpjVIvXq1Su1tqKQp6enB0dHx2KhTSaTaTwXQqjb3Nzc8PfffyMmJgZ//PEHpk2bhq+//hqHDh3S+PpeG+3bt0eDBg2wYcMGvP7664iOjsbq1avVr7/ocerp6anPgbe3N5KTkzF8+HAcPnwYwIudj6J65HI5zp49C7lcrvGaubm5VsdORDUHwywR1XhnzpyBQqHAt99+Cz29J5cK/P77789dz8vLC15eXpg5cyZeeeUVrF69GkOGDEGbNm1w+fLlYqH5eZ4Oec/y8fHB0aNHMWbMGHXb8ePHNXo/TUxMMHDgQAwcOBBvvPEGvL29cfHiRbRp06bY9gwMDMo1S8LIkSMRHh4OV1dX6OnpoX///urXXvQ4nzVz5kx89913iI6OxpAhQ8p1PgwNDYvV37p1ayiVSjx48ABdu3Z9qZqIqObgBWBEVOM1aNAACoUCP/74I27duoVff/212NfeT8vNzcWbb76JgwcP4s6dOzh27BhOnz6tDpYffPABTpw4gTfeeAOxsbG4fv06tm7dirfeeuuFa3zvvfewZs0aLF26FNevX8d3332HqKgo9YVPa9aswcqVK3Hp0iX1MZiYmMDd3b3E7Xl4eGDfvn1ITk7G48ePS93vqFGj8Ndff+Hf//43wsLCYGxsrH6too7T0tISkyZNwty5cyGEKNf58PDwQFZWFvbt24eUlBTk5OTAy8sLo0aNwpgxYxAVFYX4+HicPn0aX375JXbu3KlVTURUg0g5YJeIqCKNHTtWDBo0qMTXvvvuO+Hs7CxMTExEQECAWLt2rQAgHj9+LITQvOAoPz9fjBgxQri5uQlDQ0Ph4uIi3nzzTY2Lnk6dOiX69u0rzM3NhZmZmWjRooX497//XWptJV3Q9KzFixeL+vXrCwMDA+Hl5SXWrl2rfi06Olp06NBBWFpaCjMzM9GxY0fxxx9/qF9/9gKwrVu3ioYNGwp9fX3h7u4uhCh+AViRdu3aCQBi//79xV6rqOO8c+eO0NfXFxs3bhRCPP98CCHE1KlTha2trQAg5s6dK4QQoqCgQMyZM0d4eHgIAwMD4eTkJIYMGSIuXLhQak1EVLPJhBBC2jhNRERERPRiOMyAiIiIiHQWwywRERER6SyGWSIiIiLSWQyzRERERKSzGGaJiIiISGcxzBIRERGRzmKYJSIiIiKdxTBLRERERDqLYZaIiIiIdBbDLBERERHpLIZZIiIiItJZ/we/5iBMlLek8QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob_test)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='blue', label='ROC Curve (AUC = %0.2f)' % auc(fpr, tpr))\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435cd380",
   "metadata": {},
   "source": [
    "## Confusion Matrix Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6ec30f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_train = confusion_matrix(y_train, y_pred_train)\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.heatmap(cm_train, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix for Training Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df3011c6",
   "metadata": {},
   "source": [
    "## Confusion Matrix Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "83b46c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_test)\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ffcff85",
   "metadata": {},
   "source": [
    "## Coefficient table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a8a24d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no_beverage</td>\n",
       "      <td>0.907254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>north</td>\n",
       "      <td>0.797334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>south</td>\n",
       "      <td>0.702015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>smoke/drink</td>\n",
       "      <td>0.463489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>no_exercise</td>\n",
       "      <td>0.400117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>screen_time</td>\n",
       "      <td>0.253920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>frequent_exercise</td>\n",
       "      <td>0.215719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>male</td>\n",
       "      <td>0.162992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.023314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>female</td>\n",
       "      <td>-0.023345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>other_gender</td>\n",
       "      <td>-0.137953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bluelight_filter</td>\n",
       "      <td>-0.171111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>tea</td>\n",
       "      <td>-0.187962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>physical_illness</td>\n",
       "      <td>-0.244721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>meals/day</td>\n",
       "      <td>-0.353681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>east</td>\n",
       "      <td>-0.576348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>seldom_exercise</td>\n",
       "      <td>-0.614142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>west</td>\n",
       "      <td>-0.921307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>coffee</td>\n",
       "      <td>-1.280775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Feature  Coefficient\n",
       "0         no_beverage     0.907254\n",
       "1               north     0.797334\n",
       "2               south     0.702015\n",
       "3         smoke/drink     0.463489\n",
       "4         no_exercise     0.400117\n",
       "5         screen_time     0.253920\n",
       "6   frequent_exercise     0.215719\n",
       "7                male     0.162992\n",
       "8                 age    -0.023314\n",
       "9              female    -0.023345\n",
       "10       other_gender    -0.137953\n",
       "11   bluelight_filter    -0.171111\n",
       "12                tea    -0.187962\n",
       "13   physical_illness    -0.244721\n",
       "14          meals/day    -0.353681\n",
       "15               east    -0.576348\n",
       "16    seldom_exercise    -0.614142\n",
       "17               west    -0.921307\n",
       "18             coffee    -1.280775"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coefficient table\n",
    "coefficients = pd.DataFrame({'Feature': X.columns, 'Coefficient': model.coef_[0]})\n",
    "coefficients = coefficients.sort_values(by='Coefficient', ascending=False)\n",
    "# Resetting the index and dropping the 'index' column\n",
    "coefficients = coefficients.reset_index().drop(['index'], axis=1)\n",
    "coefficients"
   ]
  }
 ],
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