Logistic_Regression_Assumptions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "import math\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from scipy import stats\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.genmod.generalized_linear_model import GLM\n",
    "from statsmodels.genmod import families\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from IPython.display import Image\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load cleaned data\n",
    "listings_df = pd.read_csv(os.path.join('data', 'yvr_listing_data_cleaned.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   price  host_response_rate  review_scores_checkin  \\\n",
      "0  215.0               100.0                   4.83   \n",
      "1  150.0               100.0                   4.96   \n",
      "2  135.0               100.0                   4.85   \n",
      "3  143.0               100.0                   4.74   \n",
      "4  100.0               100.0                   4.87   \n",
      "5  300.0               100.0                   4.94   \n",
      "6  169.0               100.0                   4.88   \n",
      "7  127.0               100.0                   4.51   \n",
      "8  175.0               100.0                   4.97   \n",
      "9  125.0               100.0                   4.90   \n",
      "\n",
      "   review_scores_cleanliness  legal_listing  \n",
      "0                       4.81            1.0  \n",
      "1                       4.96            0.0  \n",
      "2                       4.93            1.0  \n",
      "3                       4.74            1.0  \n",
      "4                       4.87            1.0  \n",
      "5                       5.00            1.0  \n",
      "6                       4.74            0.0  \n",
      "7                       4.44            0.0  \n",
      "8                       4.99            1.0  \n",
      "9                       4.83            1.0  \n"
     ]
    }
   ],
   "source": [
    "## Assumption 1 — Linearity of independent variables and log-odds\n",
    "# This assumption means that relationships between the continuous predictors and the logit (log odds) is linear.\n",
    "# So the continuous variable should be filtered mannually\n",
    "# Box Tidwell only works for positive values. Hence, drop values where x = 0\n",
    "var = listings_df.columns.tolist()\n",
    "continuous_var = ['price','host_response_rate',\"review_scores_checkin\",\"review_scores_cleanliness\"]\n",
    "df_yvr_1 = listings_df.drop(listings_df[listings_df.price == 0].index)\n",
    "df_yvr_1 = df_yvr_1.drop(listings_df[listings_df.host_response_rate == 0].index)\n",
    "df_yvr_1 = df_yvr_1.drop(listings_df[listings_df.review_scores_checkin == 0].index)\n",
    "df_yvr_1 = df_yvr_1.drop(listings_df[listings_df.review_scores_cleanliness == 0].index)\n",
    "print(df_yvr_1[['price','host_response_rate','review_scores_checkin','review_scores_cleanliness','legal_listing']].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>has_availability</th>\n",
       "      <th>price</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_verifications</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>...</th>\n",
       "      <th>neighbourhood_cleansed_Strathcona</th>\n",
       "      <th>neighbourhood_cleansed_Sunset</th>\n",
       "      <th>neighbourhood_cleansed_Victoria-Fraserview</th>\n",
       "      <th>neighbourhood_cleansed_West End</th>\n",
       "      <th>neighbourhood_cleansed_West Point Grey</th>\n",
       "      <th>legal_listing</th>\n",
       "      <th>price:Log_price</th>\n",
       "      <th>host_response_rate:Log_host_response_rate</th>\n",
       "      <th>review_scores_checkin:Log_review_scores_checkin</th>\n",
       "      <th>review_scores_cleanliness:Log_review_scores_cleanliness</th>\n",
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       "      <td>135.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>662.212095</td>\n",
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       "      <td>7.658047</td>\n",
       "      <td>7.865021</td>\n",
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       "      <td>7.375616</td>\n",
       "      <td>7.375616</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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      "text/plain": [
       "   host_response_rate  number_of_reviews  availability_365  availability_30  \\\n",
       "0               100.0              476.0             108.0              4.0   \n",
       "1               100.0               99.0              40.0              9.0   \n",
       "2               100.0              269.0             124.0              5.0   \n",
       "3               100.0               51.0              37.0              4.0   \n",
       "4               100.0              230.0             155.0              6.0   \n",
       "\n",
       "   has_availability  price  host_response_time  host_verifications  \\\n",
       "0               1.0  215.0                1.00                 0.5   \n",
       "1               1.0  150.0                1.00                 0.5   \n",
       "2               1.0  135.0                0.75                 0.5   \n",
       "3               1.0  143.0                0.75                 0.5   \n",
       "4               1.0  100.0                1.00                 0.5   \n",
       "\n",
       "   host_total_listings_count  host_has_profile_pic  ...  \\\n",
       "0                        1.0                   1.0  ...   \n",
       "1                        4.0                   1.0  ...   \n",
       "2                        4.0                   1.0  ...   \n",
       "3                        5.0                   1.0  ...   \n",
       "4                        4.0                   1.0  ...   \n",
       "\n",
       "   neighbourhood_cleansed_Strathcona  neighbourhood_cleansed_Sunset  \\\n",
       "0                                0.0                            0.0   \n",
       "1                                0.0                            0.0   \n",
       "2                                0.0                            0.0   \n",
       "3                                0.0                            0.0   \n",
       "4                                0.0                            0.0   \n",
       "\n",
       "   neighbourhood_cleansed_Victoria-Fraserview  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
       "4                                         0.0   \n",
       "\n",
       "   neighbourhood_cleansed_West End  neighbourhood_cleansed_West Point Grey  \\\n",
       "0                              1.0                                     0.0   \n",
       "1                              0.0                                     0.0   \n",
       "2                              0.0                                     0.0   \n",
       "3                              0.0                                     0.0   \n",
       "4                              0.0                                     0.0   \n",
       "\n",
       "   legal_listing  price:Log_price  host_response_rate:Log_host_response_rate  \\\n",
       "0            1.0      1154.687176                                 460.517019   \n",
       "1            0.0       751.595294                                 460.517019   \n",
       "2            1.0       662.212095                                 460.517019   \n",
       "3            1.0       709.686782                                 460.517019   \n",
       "4            1.0       460.517019                                 460.517019   \n",
       "\n",
       "   review_scores_checkin:Log_review_scores_checkin  \\\n",
       "0                                         7.606508   \n",
       "1                                         7.942972   \n",
       "2                                         7.658047   \n",
       "3                                         7.375616   \n",
       "4                                         7.709667   \n",
       "\n",
       "   review_scores_cleanliness:Log_review_scores_cleanliness  \n",
       "0                                           7.555053        \n",
       "1                                           7.942972        \n",
       "2                                           7.865021        \n",
       "3                                           7.375616        \n",
       "4                                           7.709667        \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression with statsmodel \n",
    "# Inclusion of interaction term (logit transform) as part of Box-Tidwell test\n",
    "\n",
    "df_yvr_lt = df_yvr_1 .copy()\n",
    "\n",
    "# Add logit transform interaction terms (natural log) for continuous variables e.g. Age * Log(Age)\n",
    "for var in continuous_var:\n",
    "    df_yvr_lt[f'{var}:Log_{var}'] = df_yvr_lt[var].apply(lambda x: x * np.log(x)) #np.log = natural log\n",
    "\n",
    "df_yvr_lt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.474684\n",
      "         Iterations 7\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:          legal_listing   No. Observations:                 3573\n",
      "Model:                          Logit   Df Residuals:                     3564\n",
      "Method:                           MLE   Df Model:                            8\n",
      "Date:                Wed, 06 Dec 2023   Pseudo R-squ.:                 0.09786\n",
      "Time:                        12:40:55   Log-Likelihood:                -1696.0\n",
      "converged:                       True   LL-Null:                       -1880.0\n",
      "Covariance Type:            nonrobust   LLR p-value:                 1.317e-74\n",
      "===========================================================================================================================\n",
      "                                                              coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------------------------------------\n",
      "price                                                       0.0221      0.002     13.234      0.000       0.019       0.025\n",
      "host_response_rate                                         -0.3835      0.229     -1.678      0.093      -0.831       0.064\n",
      "review_scores_checkin                                       5.6933      1.751      3.252      0.001       2.261       9.125\n",
      "review_scores_cleanliness                                   1.0826      1.828      0.592      0.554      -2.501       4.666\n",
      "price:Log_price                                            -0.0024      0.000    -12.542      0.000      -0.003      -0.002\n",
      "host_response_rate:Log_host_response_rate                   0.0768      0.043      1.799      0.072      -0.007       0.160\n",
      "review_scores_checkin:Log_review_scores_checkin            -2.7967      0.749     -3.733      0.000      -4.265      -1.328\n",
      "review_scores_cleanliness:Log_review_scores_cleanliness    -0.1293      0.761     -0.170      0.865      -1.621       1.363\n",
      "const                                                      -7.9604      4.316     -1.844      0.065     -16.419       0.499\n",
      "===========================================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# Keep columns related to continuous variables\n",
    "cols_to_keep = continuous_var + df_yvr_lt.columns.tolist()[-len(continuous_var):]\n",
    "cols_to_keep\n",
    "\n",
    "# Redefine independent variables to include interaction terms\n",
    "X_lt = df_yvr_lt[cols_to_keep]\n",
    "y_lt = df_yvr_lt['legal_listing']\n",
    "\n",
    "X_lt = sm.add_constant(X_lt, prepend=False)\n",
    "\n",
    "# # Build model and fit the data (using statsmodel's Logit)\n",
    "# logit_results = GLM(y_lt, X_lt, family=families.Binomial()).fit()\n",
    "\n",
    "# Build model and fit the data (using statsmodel's Logit)\n",
    "logit_model = sm.Logit(y_lt, X_lt)\n",
    "logit_results = logit_model.fit(maxiter=100000)\n",
    "\n",
    "# Display summary results\n",
    "print(logit_results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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2j7yxfeStMbWP7rOKIenE3qSkJAwYMEB/LTAwEOXl5fD29kZGRkaV+ysPMYkhCLB4g1ujTLIcto+8sX3kje0jb2wfQ5INJ2VlZeH1119HTk6O/trZs2fRvHlzhIeH49y5cygpKdE/l5iYiJCQECmqSkRERDIkWYgJDg5Gly5dMH36dKSlpeHIkSNYuHAh/vOf/yAyMhItWrTAtGnTkJqairVr1+KPP/7AyJEjpaouERERyYxkIcbe3h6fffYZnJ2d8cwzz+C9997DmDFjMHbsWP1zeXl5GDFiBPbs2YOVK1eiZcuWUlWXiIiIZKbBn52kVlt2Yq9K5WbRMsly2D7yxvaRN7aPvDWm9tF9VjFkscSaiIiIyFQMMURERGSTGGKIiIjIJjHEEBERkU2SxY69ZLs0WgFJ2YVQF5VB5eqIUD8P2NsppK4WERE1AgwxZLb4VDUWx6cht+ifM618XB0xNToQ0e1VEtaMiIgaAw4nkVniU9WI3ZNsEGAAILeoDLF7khGfqpaoZkRE1FgwxJDJNFoBi+PTjN6z5Kd0aLQNfDMDIiKSFEMMmSwpu7BKD0xlObdKkZRdWE81IiKixoghhkymriXAmHofERGRORhiyGQqV0eL3kdERGQOhhgyWaifB3xqCSi+bkqE+nnUU42IiKgxYoghk9nbKTA1OtDoPW9GBXC/GCIisiqGGDJLdHsV5scEVemR8XVTYn5MEPeJISIiq+Nmd2S26PYq9A/w4o69REQkCYYYqhN7OwXCW3lKXQ0iImqEGGLIJDwriYiI5IIhhkTjWUlERCQnnNhLovCsJCIikhuGGKoVz0oiIiI5YoihWvGsJCIikiOGGKoVz0oiIiI5YoihWvGsJCIikiOGGKoVz0oiIiI5YoihWvGsJCIikiOGGBKFZyUREZHccLM7Eo1nJRERkZwwxJBJeFYSERHJBYeTiIiIyCYxxBAREZFNYoghIiIim8QQQ0RERDaJIYaIiIhsEkMMERER2SSGGCIiIrJJDDFERERkk7jZXSOl0QrceZeIiGwaQ0wjFJ+qxuL4NOQWlemv+bg6Ymp0YI1nIDH0EBGR3DDENDLxqWrE7kmucj23qAyxe5KrPczRnNBDRERkbZLOicnJycGkSZMQGRmJfv36IS4uDqWlpQCAuXPnomPHjga/tm7dKmV1bZ5GK2BxfJrRe5b8lA6NVtA/1oWeewMM8E/oiU9VW6WuREREtZGsJ0YQBEyaNAnu7u748ssvUVhYiOnTp8POzg6xsbFIT0/H1KlT8cQTT+hf4+rqKlV1G4Sk7MIqYaSynFulSMouRHgrT9Ghp3+AF4eWiIio3knWE5ORkYGkpCTExcWhffv2iIiIwKRJk7Bv3z4AQHp6OoKCguDt7a3/5ezsLFV1GwR1LQGm8n2mhB4iIqL6JllPjLe3Nz7//HOoVIZzKoqKilBUVIScnBy0adNGmspZiLmTYa01iVbl6mjSfaaGHiIiovokWYhxd3dHv3799I+1Wi22bt2KXr16IT09HQqFAqtXr8bPP/8MT09PvPDCCwZDS2IpLDjKoStLTJnxF9VYVM1k2LeiAxHdoebJsOa+Towwfw+4OzngZklFjfd4ODkgzN8DCoVpoceSP2dzmdI+VP/YPvLG9pG3xtQ+pnxG2axOWrhwIZKTk7Fz506cO3cOCoUC7dq1w+jRo3Hy5EnMmDEDrq6ueOSRR0wq18vLzeJ1ra3M/569htg9yRAqXc/7v8mwq0Z3x6CuLSz2OrE0WgF2tfTo2NkpoFK5wd5OgUeau6LFgYv4u7CkSp0AQAHgPg8nPBLqL6s5MdZoc7Icto+8sX3kje1jSBYhZuHChdi8eTOWLl2KDh06oH379oiKioKnpycAoFOnTvjrr7+wbds2k0PM9eu3IFT3L7AZFIq7/wMZK1OjFTBz99lq/9HXXZv1/TmE+TQ1+Iff3NeZ4vcrBSi4U270nvw75TiYlIWI+z0BAFP6t8M71SzJ1tVrSv92yL9RZFZ9LE1M+5B02D7yxvaRt8bUPrrPKobkIWbOnDnYtm0bFi5ciIEDBwIAFAqFPsDotGvXDsePHze5fEGAxRvcWJmns8RNhj2ddXcFUF1fZwpT5rjoPl9UexXmxwRV2SfG102JN6MCENVeJbs/UNZoc7Icto+8sX3kje1jSNIQs2LFCmzfvh1LlizBoEGD9NeXLVuG06dPY9OmTfprKSkpaNeunQS1FEc3GffQhTxR9x9Ju24QRupjEq2pE3t1otur0D/Aizv2EhGRrEgWYtLT0/HZZ59h/PjxCA8PR17eP//4R0VFYe3atVi/fj0eeeQR/Prrr9i9eze++OILqapr1KGLeZh/MBUFRibMVvbf87l4o387fRAwNWCYs4Ip1M8DPq6ORnt8fN2UCPXzqHLd3k5hdg8QERGRNUgWYg4fPgyNRoNVq1Zh1apVBs9duHABy5Ytw6effoply5bBz88PixcvRlhYmES1rdmnRzKw5fcsk1+XX1yu31QOMC1gmHsMgL2dAlOjA6s9dkDnzagA9rAQEZFNUAhCwx5dU6stO7FXpXLTl3noQh6m7TtvdnlzB3fCwM4++sc1nWukMz8mCABqvae284yqC0G6OS62fBZS5fYheWH7yBvbR94aU/voPqsYkk/stVUarYD5h41vyV+b6uaeGJtE2z/ACzHrEoyWKeYYAM5xISKihoAhxkxJ2YUoKDa+XLk2iZkFVUKEsYCRmFlg0tlHxnCOCxER2TqGGDNZYqv9dceu6H9/75yWmgIGjwEgIiL6h2QHQNo6sauJxMr9v11541PVdX5PS9eNiIhIjhhizBTq5wFP5yYWL3fJT+nQaKuftaVbwWRMTUukiYiIGhqGGDPZ2ykQ+3CgxcvVzWmp6T2nRht/Ty6RJiKixoIhpg4GdPTGmAh/i5drbE6LbgVT5R4ZXzelqOXVREREDQUn9tbRhH5t8b9UNTILSyxWZm1zWrhEmoiIiCGmTuJT1Vh0OBV5t+u21PpeYue0cIk0ERE1dgwxZqptd11zcU4LERGROJwTYwaNVsDiePN36/137/s5p4WIiKiO2BNjhtNZhbXunFsTlyZ2aOXpjFmDOkIB4Madcs5pISIiMgNDjBnUt83fEfdOuRYz918A8M8uvZzbQkREZDoOJ5lB1dQyO+KK2aWXiIiIqscQY4Yw/9p3zr1XbYNExnbpJSIiouoxxJhBzM6596otnhjbpZeIiIiqxxBjppp2zr2Xr5sSz3b3E1UeT54mIiIyDSf21kHlnXObuTSpsuIoKbsQ205l11oWT54mIiIyDUNMHdW2c67u5GljS7J58jQREZHpOJxkZTx5moiIyDoYYuoBT54mIiKyPA4n1ROePE1ERGRZDDH1iCdPExERWQ6Hk4iIiMgmMcQQERGRTWKIISIiIpvEEENEREQ2iSGGiIiIbBJDDBEREdkkLrGuI41W4N4vREREEmCIqYP4VDUWx6cZnIvk4+qIqdGB3IWXiIjIyjicZKb4VDVi9yRXOdgxt6gMsXuSEZ+qlqhmREREjQNDjBk0WgGL49OM3rPkp3RotEI91YiIiKjxYYgxw+mswio9MJXl3CpFUnZhPdWIiIio8WGIMYP6tvEAo7+vlqBDRERE5mOIMYOqqaO4+1zF3UdERESmY4gxQ5i/BzycjC/s8nByQKifRz3ViIiIqPFhiDETp+wSERFJiyHGDKezCnGzpMLoPYUlFZzYS0REZEWShpicnBxMmjQJkZGR6NevH+Li4lBaWgoAyMzMxLhx4xAaGorBgwfj119/lbKqBjixl4iISHqShRhBEDBp0iQUFxfjyy+/xNKlS/HTTz/hk08+gSAImDBhAlQqFXbt2oVhw4bh9ddfx9WrV6WqrgFO7CUiIpKeZMcOZGRkICkpCb/99htUqrtb9E+aNAnz58/Hgw8+iMzMTGzfvh0uLi4ICAjAsWPHsGvXLkycOFGqKuuF+XvAx9XR6F4xvm5KTuwlIiKyIsl6Yry9vfH555/rA4xOUVERzpw5g6CgILi4uOivh4eHIykpqZ5rWT17OwWmRgcavefNqAAeBElERGRFkvXEuLu7o1+/fvrHWq0WW7duRa9evZCXlwcfHx+D+728vPD333+b/D4KC+YIXVkKBfBwBxUWxARhUaUDIH3dlJgaFYDoDjwAsr7d2z4kP2wfeWP7yFtjah9TPqNsTrFeuHAhkpOTsXPnTmzatAmOjobzSRwdHVFWZvpEWS8vN0tVsUqZT6vc8GSvNjhx6QZyb5XAx80JkW2bswdGYtZoc7Icto+8sX3kje1jSBYhZuHChdi8eTOWLl2KDh06QKlUoqCgwOCesrIyODk5mVz29eu3IFhoUxeF4u7/QJXLbO/hiPYed0NX/o0iy7wZmaym9iF5YPvIG9tH3hpT++g+qxiSh5g5c+Zg27ZtWLhwIQYOHAgA8PX1RVqa4SnRarW6yhCTGIIAize4Ncoky2H7yBvbR97YPvLG9jEk6T4xK1aswPbt27FkyRIMGTJEfz0kJATnzp1DSUmJ/lpiYiJCQkKkqCYRERHJkGQhJj09HZ999hn+/e9/Izw8HHl5efpfkZGRaNGiBaZNm4bU1FSsXbsWf/zxB0aOHClVdYmIiEhmJBtOOnz4MDQaDVatWoVVq1YZPHfhwgV89tlneO+99zBixAi0bt0aK1euRMuWLSWqLREREcmNQhAa9uiaWm3Zib0qlZtFyyTLYfvIG9tH3tg+8taY2kf3WcXgAZBERERkkxhiiIiIyCYxxBAREZFNYoghIiIim8QQQ0RERDaJIYaIiIhsEkMMERER2SSGGCIiIrJJDDFERERkkxhiiIiIyCYxxBAREZFNYoghIiIim8QQQ0RERDaJIYaIiIhsEkMMERER2SSGGCIiIrJJDDFERERkkxhiiIiIyCYxxBAREZFNYoghIiIim8QQQ0RERDaJIYaIiIhsEkMMERER2SSGGCIiIrJJDDFERERkkxhiiIiIyCYxxBAREZFNcpC6Ag2JRisgKbsQ6qIyqFwdEernAXs7hdTVIiIiapAYYiwkPlWNxfFpyC0q01/zcXXE1OhARLdXSVgzIiKihonDSRYQn6pG7J5kgwADALlFZYjdk4z4VLVENSMiImq4GGLqSKMVsDg+zeg9S35Kh0Yr1FONiIiIGgeGmDpKyi6s0gNTWc6tUiRlF9ZTjYiIiBoHhpg6OpJ2XdR96lqCDhEREZmGIaYO4lPV2HYqW9S9KldHK9eGiIiocWGIMZOYuTA6vm5KhPp5WLlGREREjQtDjJnEzIXReTMqgPvFEBERWRhDjJnEzoV5trsf94khIiKyAoYYM2i0AvYn54i6t3+gl5VrQ0RE1DgxxJjhdFYhCkoqar2vmXMTzoUhIiKyEoYYM6hvi5sLM6izD+fCEBERWYksQkxZWRmGDh2KhIQE/bW5c+eiY8eOBr+2bt0qYS3/oWoqbrk0h5KIiIisR/IDIEtLSzF16lSkpqYaXE9PT8fUqVPxxBNP6K+5urrWd/WqFebvAR9XR6Ork7ismoiIyLok7YlJS0vD008/jStXrlR5Lj09HUFBQfD29tb/cnZ2lqCWVdnbKTA1OtDoPVxWTUREZF2ShpgTJ06gZ8+e2LFjh8H1oqIi5OTkoE2bNtJUTITo9irMjwmCT6WdeH3dlJgfE8Rl1URERFYm6XDSqFGjqr2enp4OhUKB1atX4+eff4anpydeeOEFg6ElsRQW7AzRlaX778MdVHgo0Aunswqhvl0GVVNHhPl7sAdGIpXbh+SF7SNvbB95a0ztY8pnlHxOTHUyMjKgUCjQrl07jB49GidPnsSMGTPg6uqKRx55xKSyvLzcLF6/ymUO8nG3+HuQ+azR5mQ5bB95Y/vIG9vHkCxDzPDhwxEVFQVPT08AQKdOnfDXX39h27ZtJoeY69dvQRAsUy+F4u7/QJYskyyH7SNvbB95Y/vIW2NqH91nFUOWIUahUOgDjE67du1w/Phxk8sSBFi8wa1RJlkO20fe2D7yxvaRN7aPoTpN7NVqtQCA3Nxc7N+/HxkZGRap1LJlyzBu3DiDaykpKWjXrp1FyiciIiLbZ1aISUxMRL9+/XDixAnk5uZixIgRmDlzJmJiYrB///46VyoqKgonT57E+vXrceXKFXz11VfYvXs3XnzxxTqXTURERA2DWSEmLi4OgwcPRkhICL7++msolUr89ttvmDNnDj799NM6V6pbt25YtmwZvv/+ewwdOhRbtmzB4sWLERYWVueyiYiIqGEwa07MxYsX8emnn8LZ2Rnx8fF49NFH4ejoiMjISHzwwQdmVeTChQsGjwcMGIABAwaYVRYRERE1fGb1xKhUKqSlpSEtLQ3JycmIiooCABw9ehQtWrSwaAWJiIiIqmNWT8y4ceMwYcIE2NnZITg4GJGRkVi9ejVWrFiBuLg4S9eRiIiIqAqzQszYsWMRERGBq1evom/fvgCAXr164aGHHkKnTp0sWkEiIiKi6pi9T0xQUBCCgoL0j0NDQy1RHyIiIiJRRIeYTp06QSHyQIPz58+bXSEiIiIiMUSHmC+++EL/+z///BMbN27Ea6+9huDgYDRp0gTJyclYsWIFxo4da5WKEhEREd1LdIiJjIzU/37mzJmYP38+HnjgAf21Tp06wc/PD9OmTauy2y4RERGRpZm1xDo3NxdeXl5Vrjs7O+PmzZt1rhQRERFRbcwKMQ899BCmT5+OU6dO4c6dO7h9+zaOHz+O6dOn47HHHrN0HW2SRisgMbMAB87nIjGzABotT+wiIiKyJLNWJ3344YeYNWsWxowZoz8E0t7eHsOHD8f7779v0QraovhUNRbHpyG3qEx/zcfVEVOjAxHdXiVhzYiIiBoOhSCYf6h3UVERLl26BABo27YtXF1dLVYxS1Grb1ns2HKFAlCp3IyWGZ+qRuye5BrLmB8TxCBjJWLah6TD9pE3to+8Nab20X1WMUT3xJw8edLo8/cuq+7Ro4fYYm2eRisgKbsQ6qIyNHdpgsXxaUbvX/JTOvoHeMHeTtxydSIiIqqe6BAzZswYg8cKhQKCIMDZ2RlNmjTBzZs3YW9vD3d3dxw7dsziFZWj6oaNapNzqxRJ2YUIb+VpvYoRERE1AqJDTEpKiv73O3fuxM6dO/HRRx8hICAAAJCVlYX3339ffwxBQ1fbsJExahNCDxEREVXPrNVJixcvxgcffKAPMADg7++P6dOnY+3atRarnFxptEKtw0bGqFwdLVgbIiKixsmsEKNQKJCTk1Pl+l9//QWlUlnnSsnd6axCk4aQ7tXMuQlC/TwsXCMiIqLGx6wl1qNGjcI777yDF154AZ06dYIgCPjzzz/xxRdfYOLEiZauo+zkFpWa/dpBnX04qZeIiMgCzAoxr7/+Ory9vfHNN99gzZo1AID27dtj5syZiImJsWgF5Sj/TrnZr+0fWHWnYyIiIjKdWSEGAJ555hk888wzlqyLzWjm0sSs1/m6KTmUREREZCFmh5hDhw7h888/R0ZGBjQaDdq2bYvRo0dj+PDhFqyePPm4mjfv582oAA4lERERWYhZIWb79u2YP38+Ro8ejfHjx0Or1eLUqVOYPXs2ysvL8dRTT1m6nrIS5u8B76ZNkHdb3LCSr5sSb0YFcKdeIiIiCzIrxHz++eeYNWuWQa/LgAED0L59e6xevbrBhxh7OwXeeri9qH1iXunTGi/0vJ89MERERBZm1hLr69evIzQ0tMr1sLAwXLt2ra51sgnR7VV4Ltyv1vt2//l3rffwxGsiIiLTmdUT07lzZ+zevRuTJ082uP7dd98hMDDQEvWSvfhUNb5MzK71vtqOGeCJ10REROYxK8S8/fbbGDduHBISEhASEgIASEpKQkpKClavXm3RCsqRqTv2bky4ggu5RRgZ0hKODv90ftV0dEFuURli9yTzxGsiIiIjzAoxYWFh+Pbbb/HNN98gPT0dSqUSPXr0wNKlS9GiRQtL11F2TN2xN+FyARIuF+CT/2VgQAcV+geq0MylCRYdTjX6Op54TUREVDOzl1gHBATg3Xffxb59+xAVFYWmTZtasl6ypr5t3pEDAoCDF9U4eFEt6n6eeE1ERFQzsyb23mvmzJm4ceOGJepiM1RN6+8AR554TUREVL06hxhBaHwracL8PeBTTydR88RrIiKi6tU5xDRG9nYKTI22/iosHlNARERUszqHmA8//BBeXo3vUMPo9irMjwmyao8MjykgIiKqmVkTe0+ePKn//X333Ydz584BABQKBZo0aQJvb2+0bNnSMjWUsej2KvQP8MLGhCtYc/SyxcrlPjFERES1MyvEvPfee8jKyoJWq4WHhwcEQcDNmzehUCigUCggCAK6deuG5cuXw8fHx9J1lp1tp2rf9M4YOwXATXqJiIhMY9Zw0hNPPIHg4GDs378fCQkJOHHiBA4ePIiIiAi8/fbb+O233+Dr64u5c+daur6yszHhCm6WVNSpjMoBRrfZXXyquKXYREREjZFZIWbz5s2YPXs22rZtq7/WqlUrvPfee1izZg2aN2+ON954A8eOHbNYReVIoxVM6oUxdXbLkp/SeY4SERFRDcye2Jufn1/tNY1Go3+sUDTsSalJ2YUm9cKYGkd0m90RERFRVWbNiRk5ciRiY2MxZcoUdO3aFYIg4Ny5c1i2bBmeeOIJ5OfnY+HChYiMjLR0fWWlPjai42Z3RERE1TMrxEydOhVNmzbF0qVLkZubCwDw8fHB6NGj8dJLL+Ho0aNwcHDAzJkzLVpZuamPjei42R0REVH1FEIdt9zNz8+Hg4MD3NzcLFUni1Krb8FSmworFIBK5aYvU6MVELMuwaTDIE3h66bE9y9Hcq8YkSq3D8kL20fe2D7y1pjaR/dZxTB7TkxycjKmTp2KF198Ec899xzeeOMNnDhxwqyyysrKMHToUCQkJOivZWZmYty4cQgNDcXgwYPx66+/mltVq7G3U2DKQwFWK5+b3REREdXMrBBz8OBBPP300xAEASNGjMCIESOgUCjw4osv4tChQyaVVVpaijfffBOpqan6a4IgYMKECVCpVNi1axeGDRuG119/HVevXjWnulYTn6pG3MGLFi/X102J+TFB3OyOiIjICLPmxCxbtgxvvfUWxo0bZ3B906ZNWL58OQYMGCCqnLS0NEydOrXKIZLHjx9HZmYmtm/fDhcXFwQEBODYsWPYtWsXJk6caE6VLS4+VY3YPckWKy+6vQrR7VVQuToi1M+DPTBERES1MKsnJjMzE1FRUVWuR0VF4dKlS6LLOXHiBHr27IkdO3YYXD9z5gyCgoLg4uKivxYeHo6kpCRzqmtxGq2Aj360bA/M02EtMbCzD8JbeTLAEBERiWBWT0xAQAB+/vlnjBkzxuD6kSNH4OfnJ7qcUaNGVXs9Ly+vynEFXl5e+Pvvv02uqyW3qtGVdSqzoM679N7L102JMH8Pi9a1MdL9/PhzlCe2j7yxfeStMbWPKZ/RrBAzceJETJw4EWfOnEFISAgAICkpCQcOHMCCBQvMKdJAcXExHB0NlxY7OjqirMz0VUBeXpZfNXVOfcei5c0e1gW+Pu4WLbMxs0abk+WwfeSN7SNvbB9DZoWYqKgorFu3Dl999RW2bdsGpVKJtm3b4quvvkK3bt3qXCmlUomCggKDa2VlZXBycjK5rOvXLbvE2svLDcXFll1SffNmCdTqWxYtszHStY8l25wsh+0jb2wfeWtM7aP7rGKYFWIAoHfv3ujdu7fBtdLSUmRmZqJVq1bmFgsA8PX1RVpamsE1tVpt1onYggCLN3iYnweATIuVF3cwFQ+0bQ5HB7NXvNM9rNHmZDlsH3lj+8gb28eQRf/VPHHiBB599NE6lxMSEoJz586hpKREfy0xMVE/dCU1OwsPSuYXl2PImuM8tZqIiMgEsvzqHxkZiRYtWmDatGlITU3F2rVr8ccff2DkyJFSVw0AcKO43OJlFpRUIHZPMoMMERGRSLIMMfb29vjss8+Ql5eHESNGYM+ePVi5ciVatmwpddUAAKqm1jvPaMlP6dBo2VdIRERUG7PnxFjahQsXDB63bt0aW7dulag2xhVYoSdGJ+dWKZKyCxHeytNq70FERNQQiA4xJ0+erPWeykGkIdJoBSz5Kd2q76G20oGSREREDYnoEFN5Y7uaKBr4TjwnLt2w2qnVOipX6w1XERERNRSiQ0xKSoo162Ez/r5ZUvtNdeDrpkSon4dV34OIiKghkOXEXjm7UVRq1fLfjArg2UlEREQiMMSYqLkVVyb9K6wloturrFY+ERFRQ8IQY6L7PJytVvZDDDBERESiMcSYKNRKS585F4aIiMg0stknxlZ8lXDZKuWKnQuj0QpIyi6EuqgMKldHhPp5cA4NERE1SgwxJrp8447Fy3RxtIdWxC698alqLI5PM1ji7ePqiKnRgZxLQ0REjQ6Hk0zUqpmLxcu8U6bBtH3n8emRjBrviU9VI3ZPcpU9anKLynjmEhERNUoMMSbqdJ+b1cre8nsWDl3Mq3JdoxWwOD7N6Gt55hIRETU2DDEmunHHurv1LjiUViWMJGUX1rpLsO7MJSIiosaCIcZEPm5OVi0/v7i8ShgRe5YSz1wiIqLGhBN7TRTZtjlcmtjhTrnWau9ROYyIPUuJZy6RqbjajYhsGUOMGZrY2wFWDDGVw0ionwd8XB2NDilxnxkyFVe7EZGt43CSiU5cuoHCkgqrlV9TGBnerYXR19nKmUsarYDEzAIcOJ+LxMwCTkaWCFe7EVFDwJ4YE1n7FOvKYaS6b8v38nVT4s2oAJv45sxv/vIgdrVb/wAvmwjGRNR4sSfGRNY8xfqZSgdA1vRtWeeVPq3x/cuRJgcAKXpD+M1fPrjajYgaCvbEmMiap1hH3RNGxHxb3v3n33ih5/0mvYcUvSH85i8vXO1GRA0Fe2JM5ONuvSXW12//84+GNb4tS9Ubwm/+8sLVbkTUUDDEmEirsd7Qy6L4f3bdtfS3ZSl3/eU3f3nRrXYzhqvdiMgWMMSYKOGv61YrO7+4HImZBUjMLMClG7dFvUbst2Upe0P4zV9e7O0UmBodaPQeW1ntRkSNG+fEmMy6f7FP25uMm6UaUff6uikR3MIdiZkFtW5WJmVvCPe5kZ/o9irMjwmqMj/Klla7ERExxJjIwcp9V2IDDAA82tEbT6w/IWqSrpS9Ibpv/rF7kmu8h9/86190exX6B3hxx14islkcTjKBRitg49G/pK4GfN2UGBPhjy2/Z4mepCv1PAjdN//KdfB1U2J+TBC/+UvE3k6B8FaeGNjZB+GtPBlgiMimsCfGBImZBSgstt5uvbV5sWcrRLZuhuAW7nhi/Qmj91ZesiyH3hB+8yciIktiiDFBYmaBpO/fprkLQv08sON0tuhJuuGtPPXX6nMeRE0HC+q++RMREdUVQ4wJNIK05/ycuFKAFb9cqjXA6FQ3Sbc+ekN4vAAREdUHhhgTeCqbSPr++87lmHR/TZN0rdkbottQrzLdXB3OfyEiIkvhxF4TNLehfUykWLIs5YZ6RETU+DDEmMDHVSl1FUSTYskyjxcgIqL6xBBjgjB/D7g72UtdDaN8XZV4pU9rlFdo6+2Eah0eL0BERPWJc2JMYG+nwEt922HpoVSpq1KtoV18kfDXDaw5ell/rT4n1PJ4ASIiqk/siTHRqw8ZP3NGKh5ODth3Lgd5t8sNrlv7hOp7Sb2hHhERNS4MMSZKvJwvdRWqVdug0eL4NJy4nI8D53OtNszEgwWJiKg+cTjJRFn5d6SuggFfVyWGd7vPYAipOrlFZZiw80/9Y3cnBzzb3Q8v9LzfoqGCBwsSEVF9YYgx0Y/n/pa6CgZKKipw447pE2VvllRgzdHL2HYqG+892sGi4YLHCxARUX1giDFRcbn4U6brQ2GJBt8kXTP79TdLKkRtQlfTMQI14fECRERkbQwxJmqraopf065LXQ2LWxJveGDkvXiMABERyZGsJ/YePHgQHTt2NPg1adIkSesU2qqZpO9vLTlFpdiQcKXKdd0xApU3savPVU/WotEKSMwssOpkZyIish5Z98SkpaUhKioKc+bM0V9TKqXbNVejFTBrz1nJ3t/a1h69jABVU33viqhjBOLT4eZojxt3ym1q7gt7l4iIzGPq9AJrknWISU9PR4cOHeDt7S11VQAAp7MKUVQqrzkxlrbkp3+GlUQdI1BUitfuWfVkC0GAh1QSEZlHbl8AZT2clJ6ejjZt2khdDT317Ya/Xf69ZxuZczyA3IeZeEglEZF55Di9QLY9MYIg4NKlS/j111+xZs0aaDQaDBo0CJMmTYKjo/ht6xUW7OHybiTb5ecWlUKhqNvxAEt+SsdDgdVPFLYWXVsba3NTDqmMuN/TcpUjUe1D0mH7yJvU7SP2C6Al/t435TPKNsRcvXoVxcXFcHR0xCeffIKsrCzMnTsXJSUleP/990WX4+XlZrE6DWjuCuWuP1Fa0bC/pZcr7KBSueGR5q64778X8ffNEpPLyLlVioxb5egd4GWFGhpnrM1Ls26KKqPU7u7PgCzPkn8myfLYPvImVfscS78u6gtgff+9L9sQ4+fnh4SEBHh4eEChUKBz587QarV4++23MW3aNNjbiztN+vr1WxAslDkUCqCpowNKK8prv9mGNRG0UKtvIf6iGnfKKswuJ/1qAdp71F/vlUJx9w+4sTZXarWiylJq7/4MyHLEtA9Jh+0jb1K3T/rVAtH31fXvfd1nFUO2IQYAPD09DR4HBASgtLQUhYWFaN68uagyBAEWa/CyCi1u3GnYAQYAfFyVOHyx+smvprh0/TZ+v1Igaua6JWe7G2tz3SGVxr5R6A6p5F/k1mHJP5NkeWwfeZOqfVRNxQUTVVPHeq2fbEPML7/8grfeegv/+9//4OzsDAA4f/48PD09RQcYS/sm6aok71uffN2UCG7hjifWnzB6nwK1Hzq5/ngm1h/PrHXmen3OdtcdUmksoPGQSiIiQ6Z8AaxPsl2dFBYWBqVSiffffx8ZGRk4cuQIFixYgJdfflmyOmUVmD43xNa8GRWAP6/drHXs05SgbWzmuhSz3XWHVPpUmrjs66bk8moiomrovgAaI8UXQNn2xLi6umL9+vWYN28ennzySTRt2hT/+te/JA0x/p5Okr13ffBwckD/AC8cupAn6v5nu/vh8MW8WgOPzr170ADiZ7vXdBxCXfCQSiIi0+i+AFbuOfd1U+LNqABJvgDKNsQAQPv27bFx40apq6H3VGhLLP1fhtTVsJrCkgokZReKXlrdP9ALb/Rvh6TsQpy4nI8NCZlG79ctXdYdDGnKcmdrHCbJQyqJiEwjty+Asg4xcvNzesM7+LGy/6WqMfmhANFjn7ogIHZjvHvvM+c1REQkLTl9AZTtnBi50WgFxP14UepqWN2O01fxU5ra5LFPsb03995nzmuIiIh0GGJESsouRGEDPzcJuDthd9re8wBg0uRX3cx1YyrPXDfnNURERDocThKpsQ1pLPkpHd+/HCl67NOcpctc7kxERHXBnhiRGtuQhm5CrW7sc2BnH4S38jQaKMQuXdZoBSRmFuDA+Vx4ODkg7vHOXO5MREQmY0+MSKF+HnB2UKC4gZ+bdK/qep9q21m3tpnrNW1sN+WhADRzaSKL2e5ERGQbGGJEsrdTYHSPVlh37IrUVak3R9KvY2BnH/1jsTvr1jRzXbexXWW5RWWYtu885scEGbwfERGRMRxOMsELPe+Xugr16uCFPBy6eHfju7rurCt2YzuNtvH0dBERUd0wxJggKbtQ6irUuwUH01BWoa1zADFlYzsiIiIxGGJMkJhZIHUV6l1+STl2nrla5wDCje2IiMjSGGJM0FgHOsQefGksgHBjOyIisjSGGBNE+HtKXQVJiD34MuP6bSRmFlQ7rMSN7YiIyNIYYkwQfr8nGtuiXzvF3d6R2gIIAGxIyMR/vv4DMesSqkz0lesx7kREZLsYYkzkYIP/yNalxloBeG9fCgZ2Er/0uaYVS2I3wyMiIhKD+8SY4HRWIcptcAmwm5MDKrQC7pSZf/bTjxfyEDe0M5b+L73WSb46S35KR/8AL5M2wyMiIhKLIcYE6tu2uXLmZklFncvIuVWKZi5NsOffPZGUXYiTV/Kx/nhmra9Jyi6ssvGdpY5xr7x7cJg/59MQETUmDDEmyMwvlroKdeLh5ABHewXybpeb9frcW6X6ACL1kumadg/+cHhXRNznapX3JCIieeGcGJE0WgG7krKlrkadFJZUYOagjvB0bmLW65f8L0M/z0XKJdPGdg9+despxF80vnswERE1DAwxIiVlF0J9p+7DMlJb9etfKCg2ryemoLhcP2FXqiXTYo4vWMzjC4iIGgWGGJEayk6yyTlFdS5jyU/pACDJkunaji8QwOMLiIgaC4YYkbiT7D90IUGKJdNSz8UhIiL54MRekUL9PNDEDijXSl0TeTiSdh3hrTzrfck0jy8gIiIdhhiR7O0U6OjjhrN/35K6KrKw7VQ2Qv09EN1eZbEl02Lo5uLUNKSkAODD4wuIiBoFDieJpNEKyLh+W+pqWJWvmxJxj3cWdcQAcHduTH1PoBVzfMFUHl9ARNQosCdGpFOZBbjTQMeSHuvsg2HB9+mHgewUCsTuSa71dTVtZmdturk4lfeJ8XVTYvawLoi4zxUCFycRETV4DDEi/Z5ZIHUVrKZ3m2YGQSS6vQrPdvfDtlO174sj1QTa6ubihPl7wNfHHWo1h/yIiBoDhhiRGvI3ex83ZZVr/QO9RIUYKSfQVp6Lo+AIEhFRo8I5MSK5Ku2lroJVNHNuUu0kWKk2syMiIhKLIUakolLzT4CWs0GdfaqdBCtmAq01NrMjIiISiyFGLIV040kuTQybqZmZZx9Vp3+gV43PSbGZHRERkVicEyOSu9JywcFUd8q1eLyLL5ya2MPf0wlPBLfAyI0njW6/rwsexu4RMxxU35vZERERicUQI1IzF2l3gN17Lkf/+y9/z8LATj7Y8ntWjffrhoKMLZUWOxxUn5vZERERicXhJJEKS8w7+dkacovKsOX3LIyJ8Dc61FPjcJArh4OIiMj2sSdGJEvOQ7GUH1Py8N1Lkfjz2k2jQz1CpfXhWqFhbtpHRESNC3tiRKpuLxWp5RSVYvPJTIS38sTAzj4Ib+VpEGDiU9WI3ZOMvNuGvUh5t8sRuycZ8anq+q4yERGRxTDEiBTq5yHLvWLWHr1cbRjRaAUsjk8z+lopzj4iIiKyFIYYkeztFJj+SHupq1Gt6sJIUnah0ZVJwD9nHxEREdkihhgTPNrJBy6O8uuNqS6MiD3TSKqzj4iIiOqKIcZEnk7ynAtdOYyIPdNIyrOPiIiI6oIhxgTxF9W4erNU6mpUq3IY4dlHRETU0Mk6xJSWlmL69OmIiIhA3759sWHDBsnqotEKWFTLRFmpVBdGePYRERE1dPIcG/k/CxYswNmzZ7F582ZcvXoVsbGxaNmyJQYNGlTvdREzUVYqlcOIRisgKbsQ5RVavNKnNb7745pB3X3dlHgzKoCb3RERkU2TbYi5c+cOvvnmG6xbtw5dunRBly5dkJqaii+//FKSECPHCbDuSnu8N7CjPoxotAI2JFzB9sQs3Lzn1G3vpk0wvk9r3O/pzLOPiIiowZDtcFJKSgoqKioQFhamvxYeHo4zZ85Aq63/HWebuchrx94BHVT48bU++gATn6rGwFXHsPboZYMAA9zd3G7t0cto4mBXZUM8YzRaAYmZBThwPheJmQXcU4aIiGRFtj0xeXl5aNasGRwd/5mcqlKpUFpaioKCAjRv3lxUOQoLdTgIkM8/4KMj/DD5oQD94/iLaqMHPeos+SkdDwV6iQox8RfVWBSfZjAM5ePqiLeiAxHdQZ7DULq2tlSbk2WxfeSN7SNvjal9TPmMsg0xxcXFBgEGgP5xWZn4oR0vLzeL1Ccl8apFyrGEvp18oVLd/VwarYClR06Iel3OrVJk3CpH7wAvo/f99+w1xO5JrhLb8orKELsnGatGd8egri3MqXq9sFSbk3WwfeSN7SNvbB9Dsg0xSqWySljRPXZychJdzvXrtyBYoBPlTrF8llbP+v4cwnyawt5Ogd+vFOBaYYno16ZfLUB7j5qXXmu0AmbuPlttv5Pu2r3vLycKxd0/4JZqc7Isto+8sX3krTG1j+6ziiHbEOPr64v8/HxUVFTAweFuNfPy8uDk5AR3d3fR5QgCLNLgoS3Fv6e15dwqxemsQoS38jR5wrGqqaPRn8fpLHHHFejeX450ba5bpWXshG+qf5b6M0nWwfaRN7aPIdmGmM6dO8PBwQFJSUmIiIgAACQmJiI4OBh2dvU/Hzldfafe39MYXXgxZcddMZvbNZTjCuJT1VhczZyeqdGBXFpORNRAyHZ1krOzM4YPH44PPvgAf/zxBw4dOoQNGzZg7NixktRHbjv16sKLmJ15dcRsbtcQjivQTXSu3KOU+39zeqo79ZuIiGyPbEMMAEybNg1dunTB888/j9mzZ2PixIl49NFHJamLv6f4eTjW5u7koO9REbMzr4eTA+bHBInqgbD14wrE7Kxc3anfRERke2QdYpydnTF//nycPn0av/zyC8aNGydZXZ4Ils9qnAqtgCPp1/WPo9urMD8mqEr48HBywCt9WuPAq70NNsQztveLrR9XcOLSDVFzeiqf+k1ERLZHtnNi5OaPq/L5R+9OmQaxe5INelei26vQP8DL6ERWsfNEdKGo8r22cFxB7i1xK7XkPqeHiIhqxxAj0g/JOVJXoYp5P15E/4B/Nq+zt1PUuGIoPrX6DfF080QqDzeJCUVy5OMmbthPznN6iIhIHFkPJ8lJcbmm9pvqWWFJBRIzC2q9T6MVsNiMeSK6UDSws49JxxVIKbJtc5ue00NEROIxxIjUtYU8d0k8lVVQ6z1iTuBuKPNE7O0UeMuG5/QQEZF4DDEi2cn0wAoxmx41lL1fxIruUP1EZ183pehVWkREJH+cEyPS1UJ57ROjEyFi19yGsPeLqWx1Tg8REYnHEGPDFABCRMzt0O39YmxIqSHOEzE20ZmIiGwfh5NECrpPfnNiBAB/XrtZ6322vvcLERFRdRhiRMq/I8/5ImLnsdS0IR7niRARka3icJJIqeqienuvKQ+1Q2FxOTYkZNZ6rynzWDhPhIiIGhKGGJGKy7T19l5eLo54JswP+87lWHweC+eJEBFRQ8HhJJHC6nHSq8rVkfNYiIiIasEQI9KIkJb18j739q5wHgsREVHNOJwk0lkRq4AsoXLvCuexEBERVY8hRqTfRZxRVBfGTojmPBYiIqKqGGJEsvSpA//udT/C7/dk7woREZGZGGJE6u7vifWofcmzWK2bu7B3hYiIqA44sVekm6UVFi3vSkGxRcsjIiJqbBhiRNBoBcQdTLVomWuPXkZ8qtqiZRIRETUmDDEinMoswM0Sy/bEAMCSn9Kh0QoWL5eIiKgxYIgRwVork3JulWLdsb+QmFnAMENERGQiTuwVwdIrk+61/ngm1h/PhI+rI6ZGB3IDOyIiIpHYEyNCd39Pq79HblEZYvckc54MERGRSAwxIoS38oSHU/10WnGeDBERkTgMMSLY2ykw/dEO9fJeObdKkZRdWC/vRUREZMsYYkSKbq/Cs9396uW91EVl9fI+REREtowhxgT9A73q9PqmTcT9uFWVTq0mIiKiqrg6yQRh/h5o4eGEa4Ulou5v6miHjx8PQmFxBVSujghu4Y4n1p9ArpGeFl83JUL9PCxVZSIiogaLPTEmsLdTICakhej733+0I3q1aY6BnX0Q3soTjg52mBodaPQ1b0YF8CBIIiIiERhiTKDRCthz5pqoe8dE+GNAR+8q16PbqzA/Jgg+lYaMfN2UmB8TxH1iiIiIROJwkglOZxWKGkr6d+/7Mb5Pmxqfj26vQv8ALyRlF0JdVAaVqyNC/TzYA0NERGQChhgTqG+LWzXUuplLrffY2ykQ3sqzjjUiIiJqvDicZAJVU3Grhri6iIiIyPoYYkygW51kjIeTA1cXERER1QOGGBPY2ykw6/Ego/cUllTgSPr1eqoRERFR48UQY6JHgu6r9Rwlnn9ERERkfQwxJjpx6QYKSyqM3sPzj4iIiKyPIcZEubfE7dbL84+IiIisiyHGRD5uxif26nCFEhERkXXJdp+Y5ORkPPHEEwbXunTpgm+//VaiGt0V2bY5fFwdef4RERGRxGQbYtLS0tC5c2esW7dOf83BQfrq2tsp8FZ0IN7Zk1zjPTz/iIiIyPpkO5yUnp6OgIAAeHt76381a9ZM6moBAKI78PwjIiIiqUnftVGD9PR0dOzYUepq1IjnHxEREUlL1iFGq9Xi8ccfx61bt/Dggw/inXfegaurq0nlKCyYKXRl6f7rYK9AxP2elnsDqpPK7UPywvaRN7aPvDWm9jHlMyoEQZBkV7aSkhLk5ORU+1zz5s3Ru3dv9O7dG5MmTcLNmzcRFxeHVq1aYdWqVfVcUyIiIpIjyUJMQkICxo4dW+1zK1euRK9evaBUKtGkSRMAwNmzZ/Hkk0/i559/hq+vr+j3uX79Fiz1CRUKwMvLzaJlkuWwfeSN7SNvbB95a0zto/usYkg2nNSzZ09cuHBB9P0BAQEAgJycHJNCjCDA4g1ujTLJctg+8sb2kTe2j7yxfQzJcnVSWloawsLCkJmZqb92/vx5ODg4oHXr1hLWjIiIiORCliGmXbt2aN26NWbMmIGLFy/i999/x4wZM/DUU0/Bw4ObyBEREZFMQ4ydnR1WrVoFV1dXPPfcc5gwYQJ69+6N6dOnS101IiIikgnZLrFu0aIFVqxYIXU1iIiISKZk2RNDREREVBuGGCIiIrJJsh1OshRr7thL8sL2kTe2j7yxfeStMbWPTezYS0RERFQXHE4iIiIim8QQQ0RERDaJIYaIiIhsEkMMERER2SSGGCIiIrJJDDFERERkkxhiiIiIyCYxxBAREZFNYoghIiIim8QQI1JpaSmmT5+OiIgI9O3bFxs2bJC6Sg1aTk4OJk2ahMjISPTr1w9xcXEoLS0FAGRmZmLcuHEIDQ3F4MGD8euvvxq89ujRoxg6dChCQkIwduxYZGZmGjy/adMm9OvXD2FhYZg+fTqKi4vr7XM1ROPHj8e7776rf5ycnIynnnoKISEhePLJJ3H27FmD+/ft24cBAwYgJCQEEyZMwI0bN/TPCYKARYsWoVevXoiMjMSCBQug1Wrr7bM0FGVlZZg9ezZ69OiBPn36YMmSJdBtzs72kd61a9fwyiuvoHv37oiOjsamTZv0z7F9TCSQKB9++KHw+OOPC2fPnhV+/PFHISwsTNi/f7/U1WqQtFqt8PTTTwsvv/yycPHiReHkyZPCI488Inz88ceCVqsVHn/8cWHq1KlCWlqasHr1aiEkJETIzs4WBEEQsrOzhdDQUGH9+vXCxYsXhTfeeEMYOnSooNVqBUEQhP/+979CeHi4EB8fL5w5c0YYPHiwMHv2bCk/rk3bt2+f0KFDByE2NlYQBEG4ffu28MADDwgff/yxkJaWJsyZM0fo06ePcPv2bUEQBOHMmTNCt27dhO+++044f/68MHr0aGH8+PH68tavXy/0799fOHnypHDs2DGhb9++wueffy7JZ7NlM2bMEB599FHhzJkzwtGjR4WePXsK27ZtY/vIxNNPPy1MnjxZuHTpknDw4EEhJCRE+PHHH9k+ZmCIEeH27dtCcHCwcPz4cf21lStXCqNHj5awVg1XWlqa0KFDByEvL09/be/evULfvn2Fo0ePCqGhofo/1IIgCM8//7zw6aefCoIgCJ988olBu9y5c0cICwvTt92oUaP09wqCIJw8eVLo1q2bcOfOHWt/rAYnPz9fePDBB4Unn3xSH2K++eYbITo6Wh8atVqt8Mgjjwi7du0SBEEQ3n77bf29giAIV69eFTp27ChcuXJFEARB6N+/v/5eQRCE3bt3C1FRUfX1kRqE/Px8ISgoSEhISNBfW7NmjfDuu++yfWSgoKBA6NChg3DhwgX9tddff12YPXs228cMHE4SISUlBRUVFQgLC9NfCw8Px5kzZxp+V50EvL298fnnn0OlUhlcLyoqwpkzZxAUFAQXFxf99fDwcCQlJQEAzpw5g4iICP1zzs7O6NKlC5KSkqDRaPDnn38aPB8aGory8nKkpKRY90M1QPPnz8ewYcMQGBiov3bmzBmEh4dD8X/H0CoUCnTv3r3G9mnRogVatmyJM2fOICcnB9euXUOPHj30z4eHhyM7Oxu5ubn186EagMTERLi6uiIyMlJ/bfz48YiLi2P7yICTkxOcnZ3x7bffory8HBkZGTh16hQ6d+7M9jEDQ4wIeXl5aNasGRwdHfXXVCoVSktLUVBQIF3FGih3d3f069dP/1ir1WLr1q3o1asX8vLy4OPjY3C/l5cX/v77bwAw+vzNmzdRWlpq8LyDgwM8PT31rydxjh07ht9//x2vvfaawfXa2ic3N7fG5/Py8gDA4HldkGX7iJeZmQk/Pz/s3r0bgwYNwsMPP4yVK1dCq9WyfWRAqVRi5syZ2LFjB0JCQvDYY4/hwQcfxFNPPcX2MYOD1BWwBcXFxQYBBoD+cVlZmRRValQWLlyI5ORk7Ny5E5s2baq2LXTtUFNblZWVoaSkRP+4ptdT7UpLSzFr1izMnDkTTk5OBs8Z+/kDQElJiUntwz9nprtz5w4uX76M7du3Iy4uDnl5eZg5cyacnZ3ZPjKRnp6OqKgovPDCC0hNTcWcOXPQu3dvto8ZGGJEUCqVVf4n0D2u/Jc4WdbChQuxefNmLF26FB06dIBSqazS+1VWVqZvh5rayt3dHUqlUv+48vPOzs7W+xANzIoVK9C1a1eD3jKdmn7+tbWPs7OzwV+4lduK7SOeg4MDioqKsHjxYvj5+QEArl69im3btqF169ZsH4kdO3YMO3fuxJEjR+Dk5ITg4GDk5ORg1apVaNWqFdvHRBxOEsHX1xf5+fmoqKjQX8vLy4OTkxPc3d0lrFnDNmfOHGzcuBELFy7EwIEDAdxtC7VabXCfWq3Wd6HW9Ly3tzc8PT2hVCoNnq+oqEBBQQG8vb2t/Gkajh9++AGHDh1CWFgYwsLCsHfvXuzduxdhYWF1ah9fX18A0HeL3/t7to943t7eUCqV+gADAG3btsW1a9fYPjJw9uxZtG7d2uALcFBQEK5evcr2MQNDjAidO3eGg4ODfnIVcHfyXHBwMOzs+CO0hhUrVmD79u1YsmQJhgwZor8eEhKCc+fO6btOgbttERISon8+MTFR/1xxcTGSk5MREhICOzs7BAcHGzyflJQEBwcHdOrUqR4+VcOwZcsW7N27F7t378bu3bsRHR2N6Oho7N69GyEhITh9+rR+TxJBEHDq1Kka2+fatWu4du0aQkJC4Ovri5YtWxo8n5iYiJYtW1aZB0A1CwkJQWlpKS5duqS/lpGRAT8/P7aPDPj4+ODy5csGPSoZGRnw9/dn+5hDyqVRtmTGjBnCkCFDhDNnzggHDx4UunfvLhw4cEDqajVIaWlpQufOnYWlS5cKubm5Br8qKiqEwYMHC5MnTxYuXrworFmzRggNDdXvE5OZmSkEBwcLa9as0e8T8/jjj+uXLO7bt0/o3r27cPDgQeHMmTPCkCFDhDlz5kj5cW1ebGysftnnrVu3hF69eglz5swRUlNThTlz5ggPPPCAfkn8qVOnhC5dughff/21fp+LV155RV/WmjVrhL59+wrHjx8Xjh8/LvTt21fYsGGDJJ/Llo0fP1545plnhPPnzws///yz0KtXL2Hz5s1sHxm4efOm8MADDwhvv/22kJGRIRw+fFiIjIwUtm3bxvYxA0OMSHfu3BHeeecdITQ0VOjbt6+wceNGqavUYK1Zs0bo0KFDtb8EQRD++usv4bnnnhO6du0qDBkyRPjtt98MXv+///1PePTRR4Vu3boJzz//vH4PhXvL7927txAeHi5MmzZNKCkpqbfP1hDdG2IE4e6GXMOHDxeCg4OFkSNHCufOnTO4f9euXUL//v2F0NBQYcKECcKNGzf0z1VUVAjz5s0TIiIihJ49ewoLFy7UB1AS7+bNm8Lbb78thIaGCr179xaWL1+u/zmyfaSXmpoqjBs3TujevbswYMAAYePGjWwfMykE4f/6rYiIiIhsCCd0EBERkU1iiCEiIiKbxBBDRERENokhhoiIiGwSQwwRERHZJIYYIiIiskkMMURERGSTGGKIyKZER0fj22+/lboaRCQD3OyOiGzKjRs34OLiwhPkiYghhoiIiGwTh5OISFJZWVno2LEj9u7di379+iEiIgJz585FRUUFli9fjtdeew3PPfccIiMjceLECYPhpIqKCixZsgR9+/ZFeHg4Jk2ahPz8fABAWVkZ5s6di549e6Jnz5546623UFBQIOEnJSJLY4ghIllYsWIFli5dihUrVuDHH3/E8uXLAQCHDx/G0KFDsXnzZnTr1s3gNcuWLcN3332HefPmYceOHbh+/TpmzZoFAFiyZAnOnj2LdevW4YsvvkBRURHeeOONev9cRGQ9DlJXgIgIAN5++21EREQAAN544w0sWrQIzz77LFQqFZ599tkq9wuCgK+//hqxsbF48MEHAQCzZ8/G/v37UVxcjK1bt2LXrl3o2LEjAGDBggXo2bMnLly4oL9GRLaNIYaIZKF79+7633ft2hU3btxAfn4+/Pz8qr0/Pz8fBQUF6NKli/5aYGAgJk6ciIsXL6K8vBz/+te/DF6j1Wrx119/McQQNRAMMUQkC02aNNH/XqvVAgDs7OygVCqrvd/Boea/vjQaDQDgq6++gouLi8FzXl5eda0qEckE58QQkSycP39e//uzZ8/Cx8cHnp6eNd7v7u6OZs2aISUlxaCMBx98EK1atYK9vT0KCgrQunVrtG7dGq6uroiLi8P169et+TGIqB4xxBCRLHz00Uf4888/cfToUSxbtgzPPfdcra8ZM2YMli1bhuPHjyM1NRUfffQRQkND4erqiqeeegoffPABEhISkJaWhnfeeQeXL1+Gv79/PXwaIqoPHE4iIlkYPHgwXnnlFWi1Wjz77LMYP348Vq5cafQ148ePx61btzB58mRUVFTgoYcewowZMwAA7777LubPn49JkyahvLwcPXr0wNq1a2Fvb18fH4eI6gE3uyMiSWVlZeHhhx/G4cOH2UtCRCbhcBIRERHZJIYYIiIiskkcTiIiIiKbxJ4YIiIiskkMMURERGSTGGKIiIjIJjHEEBERkU1iiCEiIiKbxBBDRERENokhhoiIiGwSQwwRERHZJIYYIiIiskn/HyvCbpSu5YcIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual Check\n",
    "# Define dependent and independent variables\n",
    "X_cols = listings_df.columns.to_list()[:-1]\n",
    "X = listings_df[X_cols].drop(['room_type_Hotel room', 'room_type_Shared room'], axis=1)\n",
    "y = listings_df['legal_listing']\n",
    "\n",
    "# Add constant\n",
    "X = sm.add_constant(X, prepend=False)\n",
    "\n",
    "# Re-run logistic regression on original set of X and y variables\n",
    "logit_results = GLM(y, X, family=families.Binomial()).fit()\n",
    "predicted = logit_results.predict(X)\n",
    "\n",
    "# Get log odds values\n",
    "log_odds = np.log(predicted / (1 - predicted))\n",
    "\n",
    "# Visualize predictor continuous variable vs logit values (Age)\n",
    "plt.scatter(x = listings_df['price'].values, y = log_odds);\n",
    "plt.title(\"price vs logit values\")\n",
    "plt.xlabel(\"price\")\n",
    "plt.ylabel(\"Log-odds\")\n",
    "plt.show()\n",
    "# Confirming that there is no logit linearity for the 'price' variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize predictor variable vs logit values for 'host_response_rate'\n",
    "plt.scatter(x = listings_df['host_response_rate'].values, y = log_odds);\n",
    "plt.title(\"host_response_rate vs logit values\")\n",
    "plt.xlabel(\"host_response_rate\")\n",
    "plt.ylabel(\"Log-odds\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize predictor variable vs logit values for 'review_scores_checkin'\n",
    "plt.scatter(x = listings_df['review_scores_checkin'].values, y = log_odds);\n",
    "plt.title(\"review_scores_checkin vs logit values\")\n",
    "plt.xlabel(\"review_scores_checkin\")\n",
    "plt.ylabel(\"Log-odds\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize predictor variable vs logit values for 'review_scores_cleanliness'\n",
    "plt.scatter(x = listings_df['review_scores_cleanliness'].values, y = log_odds);\n",
    "plt.title(\"review_scores_cleanliness vs logit values\")\n",
    "plt.xlabel(\"review_scores_cleanliness\")\n",
    "plt.ylabel(\"Log-odds\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.258708\n",
      "         Iterations: 35\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:          legal_listing   No. Observations:                 3583\n",
      "Model:                          Logit   Df Residuals:                     3540\n",
      "Method:                           MLE   Df Model:                           42\n",
      "Date:                Wed, 06 Dec 2023   Pseudo R-squ.:                  0.5089\n",
      "Time:                        12:40:58   Log-Likelihood:                -926.95\n",
      "converged:                      False   LL-Null:                       -1887.6\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "===================================================================================================================\n",
      "                                                      coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "const                                               0.4831      1.582      0.305      0.760      -2.617       3.583\n",
      "host_response_rate                                 -0.0157      0.007     -2.406      0.016      -0.029      -0.003\n",
      "number_of_reviews                                   0.0187      0.002      8.710      0.000       0.015       0.023\n",
      "availability_365                                   -0.0013      0.001     -2.407      0.016      -0.002      -0.000\n",
      "availability_30                                     0.0033      0.008      0.434      0.664      -0.011       0.018\n",
      "has_availability                                   -0.3184      0.925     -0.344      0.731      -2.131       1.495\n",
      "host_response_time                                  1.0579      0.457      2.313      0.021       0.162       1.954\n",
      "host_verifications                                 -0.9667      0.532     -1.817      0.069      -2.009       0.076\n",
      "host_total_listings_count                           0.0003      0.001      0.175      0.861      -0.003       0.003\n",
      "host_has_profile_pic                               -0.4832      0.528     -0.914      0.360      -1.519       0.552\n",
      "host_acceptance_rate                                0.0037      0.004      0.923      0.356      -0.004       0.012\n",
      "number_of_reviews_l30d                              0.0515      0.045      1.133      0.257      -0.038       0.141\n",
      "beds                                                0.1961      0.075      2.612      0.009       0.049       0.343\n",
      "calculated_host_listings_count_private_rooms       -0.0625      0.065     -0.969      0.333      -0.189       0.064\n",
      "instant_bookable                                   -0.1535      0.161     -0.952      0.341      -0.469       0.162\n",
      "review_scores_cleanliness                           0.5687      0.193      2.944      0.003       0.190       0.947\n",
      "host_identity_verified                             -0.3154      0.381     -0.828      0.408      -1.062       0.431\n",
      "host_is_superhost                                   0.1544      0.136      1.136      0.256      -0.112       0.421\n",
      "minimum_nights                                     -0.1122      0.007    -16.818      0.000      -0.125      -0.099\n",
      "room_type_Private room                             -1.6960      0.372     -4.556      0.000      -2.425      -0.966\n",
      "neighbourhood_cleansed_Downtown Eastside            0.1979      0.281      0.704      0.482      -0.353       0.749\n",
      "neighbourhood_cleansed_Dunbar Southlands            0.7068      0.395      1.791      0.073      -0.067       1.480\n",
      "neighbourhood_cleansed_Fairview                     0.3698      0.326      1.135      0.256      -0.269       1.008\n",
      "neighbourhood_cleansed_Grandview-Woodland           0.8273      0.365      2.268      0.023       0.112       1.542\n",
      "neighbourhood_cleansed_Hastings-Sunrise             1.4441      0.392      3.680      0.000       0.675       2.213\n",
      "neighbourhood_cleansed_Kensington-Cedar Cottage     1.4292      0.347      4.114      0.000       0.748       2.110\n",
      "neighbourhood_cleansed_Kerrisdale                  -0.4938      0.696     -0.710      0.478      -1.857       0.869\n",
      "neighbourhood_cleansed_Killarney                    0.5037      0.574      0.877      0.381      -0.622       1.630\n",
      "neighbourhood_cleansed_Kitsilano                    0.3955      0.217      1.825      0.068      -0.029       0.820\n",
      "neighbourhood_cleansed_Marpole                      0.3233      0.390      0.829      0.407      -0.442       1.088\n",
      "neighbourhood_cleansed_Mount Pleasant               0.5747      0.241      2.389      0.017       0.103       1.046\n",
      "neighbourhood_cleansed_Oakridge                    26.2408   3.54e+05    7.4e-05      1.000   -6.95e+05    6.95e+05\n",
      "neighbourhood_cleansed_Renfrew-Collingwood          0.8727      0.407      2.144      0.032       0.075       1.670\n",
      "neighbourhood_cleansed_Riley Park                   0.9529      0.287      3.323      0.001       0.391       1.515\n",
      "neighbourhood_cleansed_Shaughnessy                  1.4846      0.705      2.105      0.035       0.102       2.867\n",
      "neighbourhood_cleansed_South Cambie                 0.5974      0.618      0.966      0.334      -0.614       1.809\n",
      "neighbourhood_cleansed_Strathcona                   2.5556      1.214      2.104      0.035       0.175       4.936\n",
      "neighbourhood_cleansed_Sunset                       0.7796      0.454      1.716      0.086      -0.111       1.670\n",
      "neighbourhood_cleansed_Victoria-Fraserview          1.5370      0.545      2.821      0.005       0.469       2.605\n",
      "neighbourhood_cleansed_West End                    -0.4148      0.227     -1.831      0.067      -0.859       0.029\n",
      "neighbourhood_cleansed_West Point Grey              1.2438      0.574      2.165      0.030       0.118       2.370\n",
      "log_price                                           0.3871      0.160      2.421      0.015       0.074       0.700\n",
      "logmul_review_scores_checkin                       -0.2020      0.098     -2.063      0.039      -0.394      -0.010\n",
      "===================================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# the 'price' and 'review_scores_checkin' violate the assumption, \n",
    "# so we need to transform them so that find the transformation that could fit the relationship with logit-odds\n",
    "\n",
    "# replace price and review_scores_checkin value with their log tansformation\n",
    "listings_df_0 = listings_df.copy()\n",
    "listings_df_0['log_price'] = listings_df_0['price'].apply(lambda x: np.log(x) if x !=0 else 0)\n",
    "listings_df_0['logmul_review_scores_checkin'] = listings_df_0['review_scores_checkin'].apply(lambda x: x * np.log(x) if x !=0 else 0)\n",
    "listings_df_0 = listings_df_0.drop(['price','review_scores_checkin','room_type_Hotel room','room_type_Shared room'], axis=1)\n",
    "\n",
    "# fit the logistic regression model\n",
    "X_0 = listings_df_0.drop('legal_listing', axis=1)\n",
    "y_0 = listings_df_0['legal_listing']\n",
    "X_0 = sm.add_constant(X_0)\n",
    "\n",
    "logit_model_0 = sm.Logit(y_0, X_0)\n",
    "result_0 = logit_model_0.fit()\n",
    "print(result_0.summary())\n",
    "\n",
    "# The linearity relation of 'review_scores_checkin' can be built by using logarithmic multiplication transformation,  (the p-value of 'log_review_scores_checkin' is 0.039 less than 0.05) \n",
    "# The linearity relation of 'price' can be built by using log transformation,  (the p-value of 'log_price' is 0.039 less than 0.05) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>log_price</th>\n",
       "      <th>logmul_review_scores_checkin</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>has_availability</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_verifications</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>...</th>\n",
       "      <th>neighbourhood_cleansed_Renfrew-Collingwood</th>\n",
       "      <th>neighbourhood_cleansed_Riley Park</th>\n",
       "      <th>neighbourhood_cleansed_Shaughnessy</th>\n",
       "      <th>neighbourhood_cleansed_South Cambie</th>\n",
       "      <th>neighbourhood_cleansed_Strathcona</th>\n",
       "      <th>neighbourhood_cleansed_Sunset</th>\n",
       "      <th>neighbourhood_cleansed_Victoria-Fraserview</th>\n",
       "      <th>neighbourhood_cleansed_West End</th>\n",
       "      <th>neighbourhood_cleansed_West Point Grey</th>\n",
       "      <th>legal_listing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.370638</td>\n",
       "      <td>7.606508</td>\n",
       "      <td>100.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.010635</td>\n",
       "      <td>7.942972</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.905275</td>\n",
       "      <td>7.658047</td>\n",
       "      <td>100.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.962845</td>\n",
       "      <td>7.375616</td>\n",
       "      <td>100.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.605170</td>\n",
       "      <td>7.709667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3578</th>\n",
       "      <td>5.298317</td>\n",
       "      <td>8.047190</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3579</th>\n",
       "      <td>5.147494</td>\n",
       "      <td>8.047190</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3580</th>\n",
       "      <td>5.991465</td>\n",
       "      <td>8.047190</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3581</th>\n",
       "      <td>5.771441</td>\n",
       "      <td>8.047190</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3582</th>\n",
       "      <td>5.799093</td>\n",
       "      <td>8.047190</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3583 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      log_price  logmul_review_scores_checkin  host_response_rate  \\\n",
       "0      5.370638                      7.606508               100.0   \n",
       "1      5.010635                      7.942972               100.0   \n",
       "2      4.905275                      7.658047               100.0   \n",
       "3      4.962845                      7.375616               100.0   \n",
       "4      4.605170                      7.709667               100.0   \n",
       "...         ...                           ...                 ...   \n",
       "3578   5.298317                      8.047190               100.0   \n",
       "3579   5.147494                      8.047190               100.0   \n",
       "3580   5.991465                      8.047190                94.0   \n",
       "3581   5.771441                      8.047190               100.0   \n",
       "3582   5.799093                      8.047190               100.0   \n",
       "\n",
       "      number_of_reviews  availability_365  availability_30  has_availability  \\\n",
       "0                 476.0             108.0              4.0               1.0   \n",
       "1                  99.0              40.0              9.0               1.0   \n",
       "2                 269.0             124.0              5.0               1.0   \n",
       "3                  51.0              37.0              4.0               1.0   \n",
       "4                 230.0             155.0              6.0               1.0   \n",
       "...                 ...               ...              ...               ...   \n",
       "3578                1.0              74.0             15.0               1.0   \n",
       "3579                2.0             146.0              7.0               1.0   \n",
       "3580                1.0             361.0             26.0               1.0   \n",
       "3581                1.0              81.0             21.0               1.0   \n",
       "3582                1.0             263.0             24.0               1.0   \n",
       "\n",
       "      host_response_time  host_verifications  host_total_listings_count  ...  \\\n",
       "0                   1.00                0.50                        1.0  ...   \n",
       "1                   1.00                0.50                        4.0  ...   \n",
       "2                   0.75                0.50                        4.0  ...   \n",
       "3                   0.75                0.50                        5.0  ...   \n",
       "4                   1.00                0.50                        4.0  ...   \n",
       "...                  ...                 ...                        ...  ...   \n",
       "3578                1.00                0.50                        1.0  ...   \n",
       "3579                1.00                0.75                        1.0  ...   \n",
       "3580                0.75                0.75                        3.0  ...   \n",
       "3581                1.00                0.25                        1.0  ...   \n",
       "3582                1.00                0.25                        3.0  ...   \n",
       "\n",
       "      neighbourhood_cleansed_Renfrew-Collingwood  \\\n",
       "0                                            0.0   \n",
       "1                                            0.0   \n",
       "2                                            0.0   \n",
       "3                                            0.0   \n",
       "4                                            1.0   \n",
       "...                                          ...   \n",
       "3578                                         0.0   \n",
       "3579                                         0.0   \n",
       "3580                                         0.0   \n",
       "3581                                         0.0   \n",
       "3582                                         1.0   \n",
       "\n",
       "      neighbourhood_cleansed_Riley Park  neighbourhood_cleansed_Shaughnessy  \\\n",
       "0                                   0.0                                 0.0   \n",
       "1                                   0.0                                 0.0   \n",
       "2                                   0.0                                 0.0   \n",
       "3                                   0.0                                 0.0   \n",
       "4                                   0.0                                 0.0   \n",
       "...                                 ...                                 ...   \n",
       "3578                                0.0                                 0.0   \n",
       "3579                                1.0                                 0.0   \n",
       "3580                                0.0                                 0.0   \n",
       "3581                                0.0                                 0.0   \n",
       "3582                                0.0                                 0.0   \n",
       "\n",
       "      neighbourhood_cleansed_South Cambie  neighbourhood_cleansed_Strathcona  \\\n",
       "0                                     0.0                                0.0   \n",
       "1                                     0.0                                0.0   \n",
       "2                                     0.0                                0.0   \n",
       "3                                     0.0                                0.0   \n",
       "4                                     0.0                                0.0   \n",
       "...                                   ...                                ...   \n",
       "3578                                  0.0                                0.0   \n",
       "3579                                  0.0                                0.0   \n",
       "3580                                  0.0                                0.0   \n",
       "3581                                  0.0                                0.0   \n",
       "3582                                  0.0                                0.0   \n",
       "\n",
       "      neighbourhood_cleansed_Sunset  \\\n",
       "0                               0.0   \n",
       "1                               0.0   \n",
       "2                               0.0   \n",
       "3                               0.0   \n",
       "4                               0.0   \n",
       "...                             ...   \n",
       "3578                            0.0   \n",
       "3579                            0.0   \n",
       "3580                            0.0   \n",
       "3581                            0.0   \n",
       "3582                            0.0   \n",
       "\n",
       "      neighbourhood_cleansed_Victoria-Fraserview  \\\n",
       "0                                            0.0   \n",
       "1                                            0.0   \n",
       "2                                            0.0   \n",
       "3                                            0.0   \n",
       "4                                            0.0   \n",
       "...                                          ...   \n",
       "3578                                         0.0   \n",
       "3579                                         0.0   \n",
       "3580                                         0.0   \n",
       "3581                                         0.0   \n",
       "3582                                         0.0   \n",
       "\n",
       "      neighbourhood_cleansed_West End  neighbourhood_cleansed_West Point Grey  \\\n",
       "0                                 1.0                                     0.0   \n",
       "1                                 0.0                                     0.0   \n",
       "2                                 0.0                                     0.0   \n",
       "3                                 0.0                                     0.0   \n",
       "4                                 0.0                                     0.0   \n",
       "...                               ...                                     ...   \n",
       "3578                              0.0                                     0.0   \n",
       "3579                              0.0                                     0.0   \n",
       "3580                              0.0                                     0.0   \n",
       "3581                              0.0                                     0.0   \n",
       "3582                              0.0                                     0.0   \n",
       "\n",
       "      legal_listing  \n",
       "0               1.0  \n",
       "1               0.0  \n",
       "2               1.0  \n",
       "3               1.0  \n",
       "4               1.0  \n",
       "...             ...  \n",
       "3578            1.0  \n",
       "3579            1.0  \n",
       "3580            0.0  \n",
       "3581            1.0  \n",
       "3582            1.0  \n",
       "\n",
       "[3583 rows x 43 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Move the transformed colunms to head\n",
    "listings_df_0 = listings_df_0[['log_price', 'logmul_review_scores_checkin'] + \n",
    "                               [col for col in listings_df_0.columns if col not in ['log_price', 'logmul_review_scores_checkin']]]\n",
    "listings_df_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.468916\n",
      "         Iterations 7\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:          legal_listing   No. Observations:                 3564\n",
      "Model:                          Logit   Df Residuals:                     3559\n",
      "Method:                           MLE   Df Model:                            4\n",
      "Date:                Wed, 06 Dec 2023   Pseudo R-squ.:                  0.1033\n",
      "Time:                        12:40:58   Log-Likelihood:                -1671.2\n",
      "converged:                       True   LL-Null:                       -1863.8\n",
      "Covariance Type:            nonrobust   LLR p-value:                 4.573e-82\n",
      "=================================================================================================================================\n",
      "                                                                    coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------------------------------------------\n",
      "const                                                           -30.9645      7.829     -3.955      0.000     -46.309     -15.620\n",
      "log_price                                                         7.9238      3.774      2.100      0.036       0.527      15.320\n",
      "logmul_review_scores_checkin                                      5.8413      1.179      4.953      0.000       3.530       8.153\n",
      "log_price:Log_log_price                                          -2.3876      1.416     -1.686      0.092      -5.163       0.388\n",
      "logmul_review_scores_checkin:Log_logmul_review_scores_checkin    -2.1292      0.417     -5.102      0.000      -2.947      -1.311\n",
      "=================================================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# Applying Box-Tidwell test to the fixed dataset\n",
    "var_1 = listings_df_0.columns.tolist()\n",
    "continuous_var_1 = ['log_price','logmul_review_scores_checkin']\n",
    "\n",
    "listings_df_1 = listings_df_0.copy()\n",
    "\n",
    "# Add logit transform interaction terms (natural log) for continuous variables e.g. Age * Log(Age)\n",
    "for var in continuous_var_1:\n",
    "    listings_df_1[f'{var}:Log_{var}'] = listings_df_1[var].apply(lambda x: x * np.log(x)) #np.log = natural log\n",
    "\n",
    "# Keep columns related to continuous variables\n",
    "cols_to_keep_1 = continuous_var_1 + listings_df_1.columns.tolist()[-len(continuous_var_1):]\n",
    "cols_to_keep_1\n",
    "\n",
    "# check the NAN value of X_1 and delete the rows\n",
    "np.where(np.isnan(listings_df_1[cols_to_keep_1]).any(axis=1))[0]\n",
    "listings_df_1 = listings_df_1.drop([2608, 2992, 3080, 3163, 3390])\n",
    "listings_df_1 = listings_df_1.drop([1724, 57, 844, 833, 499, 1459, 629, 1903, 1727, 1102, 1630, 1759, 1794, 882], axis=0)\n",
    "\n",
    "\n",
    "# Redefine independent variables to include interaction terms\n",
    "X_1 = listings_df_1[cols_to_keep_1]\n",
    "y_1 = listings_df_1['legal_listing']\n",
    "X_1 = sm.add_constant(X_1)\n",
    "\n",
    "# Build model and fit the data (using statsmodel's Logit)\n",
    "logit_model_1 = sm.Logit(y_1, X_1)\n",
    "logit_results_1 = logit_model_1.fit()\n",
    "\n",
    "# Display summary results\n",
    "print(logit_results_1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: 3.8781568423069723\n",
      "Intercept: -5.697922881986016\n",
      "R-squared: 0.044250983206094045\n",
      "P-value: 5.5396352686345895e-37\n",
      "Standard Error: 0.3017757111300134\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual Check\n",
    "# Define dependent and independent variables\n",
    "listings_df = listings_df.drop([1724, 57, 844, 833, 499, 1459, 629, 1903, 1727, 1102, 1630, 1759, 1794, 882], axis=0)\n",
    "\n",
    "X_cols = listings_df.columns.to_list()[:-1]\n",
    "X = listings_df[X_cols].drop(['room_type_Hotel room', 'room_type_Shared room'], axis=1)\n",
    "y = listings_df['legal_listing']\n",
    "\n",
    "\n",
    "\n",
    "# Add constant\n",
    "X = sm.add_constant(X, prepend=False)\n",
    "\n",
    "# Re-run logistic regression on original set of X and y variables\n",
    "logit_results = GLM(y, X, family=families.Binomial()).fit()\n",
    "predicted = logit_results.predict(X)\n",
    "\n",
    "\n",
    "# Get log odds values\n",
    "log_odds = np.log(predicted / (1 - predicted))\n",
    "\n",
    "# Calculate linear regression between log(price) and logit values\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(np.log10(listings_df['price'].values), log_odds)\n",
    "\n",
    "# Print the results\n",
    "print(\"Slope:\", slope)\n",
    "print(\"Intercept:\", intercept)\n",
    "print(\"R-squared:\", r_value**2)\n",
    "print(\"P-value:\", p_value)\n",
    "print(\"Standard Error:\", std_err)\n",
    "\n",
    "# Visualize predictor continuous variable vs logit values (Price)\n",
    "plt.scatter(x = np.log10(listings_df['price'].values), y = log_odds, alpha = 0.25);\n",
    "plt.title(\"log(price) vs logit values\")\n",
    "plt.xlabel(\"log(price)\")\n",
    "plt.ylabel(\"Log-odds\")\n",
    "\n",
    "# Add the slope line\n",
    "x = np.log10(listings_df['price'].values)\n",
    "y = slope * x + intercept\n",
    "plt.plot(x, y, color='red')\n",
    "\n",
    "plt.savefig('log_price_vs_logit.png', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:          legal_listing   No. Observations:                 3583\n",
      "Model:                            GLM   Df Residuals:                     3540\n",
      "Model Family:                Binomial   Df Model:                           42\n",
      "Link Function:                  Logit   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -928.63\n",
      "Date:                Wed, 06 Dec 2023   Deviance:                       1857.3\n",
      "Time:                        12:20:31   Pearson chi2:                 3.89e+04\n",
      "No. Iterations:                    22   Pseudo R-squ. (CS):             0.4145\n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================================================\n",
      "                                                      coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "host_response_rate                                 -0.0162      0.007     -2.467      0.014      -0.029      -0.003\n",
      "number_of_reviews                                   0.0185      0.002      8.661      0.000       0.014       0.023\n",
      "availability_365                                   -0.0013      0.001     -2.365      0.018      -0.002      -0.000\n",
      "availability_30                                     0.0053      0.007      0.709      0.478      -0.009       0.020\n",
      "has_availability                                   -0.3881      0.924     -0.420      0.674      -2.199       1.423\n",
      "price                                               0.0007      0.000      1.387      0.165      -0.000       0.002\n",
      "host_response_time                                  1.1096      0.456      2.434      0.015       0.216       2.003\n",
      "host_verifications                                 -0.9512      0.530     -1.794      0.073      -1.991       0.088\n",
      "host_total_listings_count                           0.0002      0.001      0.154      0.878      -0.003       0.003\n",
      "host_has_profile_pic                               -0.4877      0.527     -0.925      0.355      -1.521       0.546\n",
      "host_acceptance_rate                                0.0040      0.004      0.978      0.328      -0.004       0.012\n",
      "number_of_reviews_l30d                              0.0486      0.046      1.068      0.286      -0.041       0.138\n",
      "review_scores_checkin                              -0.4191      0.223     -1.878      0.060      -0.856       0.018\n",
      "beds                                                0.2407      0.071      3.370      0.001       0.101       0.381\n",
      "calculated_host_listings_count_private_rooms       -0.0605      0.065     -0.936      0.349      -0.187       0.066\n",
      "instant_bookable                                   -0.1512      0.161     -0.938      0.348      -0.467       0.165\n",
      "review_scores_cleanliness                           0.5727      0.192      2.980      0.003       0.196       0.949\n",
      "host_identity_verified                             -0.3326      0.381     -0.874      0.382      -1.079       0.414\n",
      "host_is_superhost                                   0.1582      0.136      1.164      0.244      -0.108       0.425\n",
      "minimum_nights                                     -0.1164      0.006    -18.153      0.000      -0.129      -0.104\n",
      "room_type_Private room                             -1.7703      0.372     -4.762      0.000      -2.499      -1.042\n",
      "neighbourhood_cleansed_Downtown Eastside            0.1852      0.281      0.659      0.510      -0.366       0.736\n",
      "neighbourhood_cleansed_Dunbar Southlands            0.6123      0.391      1.564      0.118      -0.155       1.380\n",
      "neighbourhood_cleansed_Fairview                     0.3006      0.323      0.932      0.351      -0.332       0.933\n",
      "neighbourhood_cleansed_Grandview-Woodland           0.7462      0.363      2.058      0.040       0.036       1.457\n",
      "neighbourhood_cleansed_Hastings-Sunrise             1.3205      0.386      3.420      0.001       0.564       2.077\n",
      "neighbourhood_cleansed_Kensington-Cedar Cottage     1.2948      0.339      3.819      0.000       0.630       1.959\n",
      "neighbourhood_cleansed_Kerrisdale                  -0.6240      0.705     -0.885      0.376      -2.005       0.757\n",
      "neighbourhood_cleansed_Killarney                    0.3501      0.568      0.616      0.538      -0.763       1.464\n",
      "neighbourhood_cleansed_Kitsilano                    0.3703      0.216      1.716      0.086      -0.053       0.793\n",
      "neighbourhood_cleansed_Marpole                      0.2349      0.387      0.606      0.544      -0.524       0.994\n",
      "neighbourhood_cleansed_Mount Pleasant               0.5321      0.242      2.201      0.028       0.058       1.006\n",
      "neighbourhood_cleansed_Oakridge                    20.7222   1.44e+04      0.001      0.999   -2.83e+04    2.83e+04\n",
      "neighbourhood_cleansed_Renfrew-Collingwood          0.7413      0.403      1.841      0.066      -0.048       1.531\n",
      "neighbourhood_cleansed_Riley Park                   0.8867      0.285      3.110      0.002       0.328       1.446\n",
      "neighbourhood_cleansed_Shaughnessy                  1.3892      0.710      1.957      0.050      -0.002       2.780\n",
      "neighbourhood_cleansed_South Cambie                 0.5520      0.622      0.887      0.375      -0.667       1.771\n",
      "neighbourhood_cleansed_Strathcona                   2.5565      1.213      2.108      0.035       0.180       4.933\n",
      "neighbourhood_cleansed_Sunset                       0.6721      0.452      1.488      0.137      -0.213       1.558\n",
      "neighbourhood_cleansed_Victoria-Fraserview          1.3693      0.538      2.545      0.011       0.315       2.424\n",
      "neighbourhood_cleansed_West End                    -0.4245      0.225     -1.885      0.059      -0.866       0.017\n",
      "neighbourhood_cleansed_West Point Grey              1.1977      0.577      2.076      0.038       0.067       2.329\n",
      "const                                               2.9203      1.472      1.984      0.047       0.036       5.805\n",
      "===================================================================================================================\n"
     ]
    }
   ],
   "source": [
    "## Assumption 2 — No strongly influential outliers\n",
    "\n",
    "# Use GLM method for logreg here so that we can retrieve the influence measures\n",
    "logit_model = GLM(y, X, family=families.Binomial())\n",
    "logit_results = logit_model.fit()\n",
    "print(logit_results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>cooks_d</th>\n",
       "      <th>std_resid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.448033e-11</td>\n",
       "      <td>0.029364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.693768e-03</td>\n",
       "      <td>0.796688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.840719e-06</td>\n",
       "      <td>0.081388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.034099e-03</td>\n",
       "      <td>0.395302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.064458e-06</td>\n",
       "      <td>0.077876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cooks_d  std_resid\n",
       "0  1.448033e-11   0.029364\n",
       "1  2.693768e-03   0.796688\n",
       "2  7.840719e-06   0.081388\n",
       "3  1.034099e-03   0.395302\n",
       "4  6.064458e-06   0.077876"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get influence measures\n",
    "influence = logit_results.get_influence()\n",
    "\n",
    "# Obtain summary df of influence measures\n",
    "summ_df = influence.summary_frame()\n",
    "\n",
    "# Filter summary df to Cook distance\n",
    "diagnosis_df = summ_df.loc[:,['cooks_d']]\n",
    "\n",
    "# Append absolute standardized residual values\n",
    "diagnosis_df['std_resid'] = stats.zscore(logit_results.resid_pearson)\n",
    "diagnosis_df['std_resid'] = diagnosis_df.loc[:,'std_resid'].apply(lambda x: np.abs(x))\n",
    "\n",
    "# Sort by Cook's Distance\n",
    "diagnosis_df.sort_values(\"cooks_d\", ascending=False)\n",
    "diagnosis_df.head()\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold for Cook Distance = 0.0011163829193413341\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set Cook's distance threshold\n",
    "cook_threshold = 4 / len(listings_df)\n",
    "print(f\"Threshold for Cook Distance = {cook_threshold}\")\n",
    "\n",
    "# Plot influence measures (Cook's distance)\n",
    "fig, ax = plt.subplots()\n",
    "influence.plot_index(y_var=\"cooks\", threshold=cook_threshold, ax=ax, alpha=0.2)  # Set alpha here\n",
    "axhline = ax.axhline(y=cook_threshold, ls=\"--\", color='red')\n",
    "\n",
    "# Remove existing labels\n",
    "for text in ax.texts:\n",
    "    text.set_visible(False)\n",
    "\n",
    "# Highlight points with std_resid > 3\n",
    "highlight_indices = diagnosis_df[diagnosis_df['std_resid'] > 2.5].index\n",
    "scatter = ax.scatter(\n",
    "    highlight_indices,\n",
    "    diagnosis_df.loc[highlight_indices, 'cooks_d'],\n",
    "    color='red',  # Set a color for the highlighted points\n",
    "    marker='o',   # Set a marker style for the highlighted points\n",
    "    alpha=0.75,    # Set transparency for the highlighted points\n",
    "    s=50          # Set marker size\n",
    ")\n",
    "\n",
    "\n",
    "fig.tight_layout(pad=2)\n",
    "\n",
    "plt.legend([axhline, scatter], ['Cook\\'s Distance Threshold', 'Highly-influential Outlier Points'], loc='center right')\n",
    "\n",
    "plt.title(\"Influential Outliers in Data\")\n",
    "\n",
    "fig.tight_layout(pad=2)\n",
    "plt.savefig('outliers.png', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of data points that are highly influential = 7.2%\n"
     ]
    }
   ],
   "source": [
    "# Find number of observations that exceed Cook's distance threshold\n",
    "outliers = diagnosis_df[diagnosis_df['cooks_d'] > cook_threshold]\n",
    "prop_outliers = round(100*(len(outliers) / len(listings_df)),1)\n",
    "print(f'Proportion of data points that are highly influential = {prop_outliers}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>cooks_d</th>\n",
       "      <th>std_resid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1724</th>\n",
       "      <td>0.036355</td>\n",
       "      <td>16.050794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.030566</td>\n",
       "      <td>52.380381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>0.009038</td>\n",
       "      <td>11.854802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>0.008019</td>\n",
       "      <td>2.638577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>0.007459</td>\n",
       "      <td>6.412854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>0.006941</td>\n",
       "      <td>3.977317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>0.005187</td>\n",
       "      <td>4.854631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>0.005130</td>\n",
       "      <td>2.919283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>0.003544</td>\n",
       "      <td>6.396491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>0.003014</td>\n",
       "      <td>3.156169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>0.002603</td>\n",
       "      <td>2.942862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>0.002547</td>\n",
       "      <td>4.402524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1794</th>\n",
       "      <td>0.002070</td>\n",
       "      <td>3.660081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>0.001867</td>\n",
       "      <td>3.806502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cooks_d  std_resid\n",
       "1724  0.036355  16.050794\n",
       "57    0.030566  52.380381\n",
       "844   0.009038  11.854802\n",
       "883   0.008019   2.638577\n",
       "499   0.007459   6.412854\n",
       "1459  0.006941   3.977317\n",
       "629   0.005187   4.854631\n",
       "1903  0.005130   2.919283\n",
       "1727  0.003544   6.396491\n",
       "1102  0.003014   3.156169\n",
       "1630  0.002603   2.942862\n",
       "1759  0.002547   4.402524\n",
       "1794  0.002070   3.660081\n",
       "882   0.001867   3.806502"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find number of observations which are BOTH outlier (std dev > 3) and highly influential\n",
    "extreme = diagnosis_df[(diagnosis_df['cooks_d'] > cook_threshold) & \n",
    "                       (diagnosis_df['std_resid'] > 2.5)]\n",
    "prop_extreme = round(100*(len(extreme) / len(listings_df)),1)\n",
    "# Display top most influential outliers\n",
    "extreme.sort_values(\"cooks_d\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "host_response_rate                                 100.00\n",
      "number_of_reviews                                  134.00\n",
      "availability_365                                    73.00\n",
      "availability_30                                     13.00\n",
      "has_availability                                     1.00\n",
      "price                                              129.00\n",
      "host_response_time                                   1.00\n",
      "host_verifications                                   0.50\n",
      "host_total_listings_count                            6.00\n",
      "host_has_profile_pic                                 1.00\n",
      "host_acceptance_rate                                99.00\n",
      "number_of_reviews_l30d                               6.00\n",
      "review_scores_checkin                                4.80\n",
      "beds                                                 3.00\n",
      "calculated_host_listings_count_private_rooms         0.00\n",
      "instant_bookable                                     0.00\n",
      "review_scores_cleanliness                            4.88\n",
      "host_identity_verified                               1.00\n",
      "host_is_superhost                                    1.00\n",
      "minimum_nights                                       2.00\n",
      "room_type_Hotel room                                 0.00\n",
      "room_type_Private room                               0.00\n",
      "room_type_Shared room                                0.00\n",
      "neighbourhood_cleansed_Downtown Eastside             0.00\n",
      "neighbourhood_cleansed_Dunbar Southlands             0.00\n",
      "neighbourhood_cleansed_Fairview                      0.00\n",
      "neighbourhood_cleansed_Grandview-Woodland            0.00\n",
      "neighbourhood_cleansed_Hastings-Sunrise              0.00\n",
      "neighbourhood_cleansed_Kensington-Cedar Cottage      0.00\n",
      "neighbourhood_cleansed_Kerrisdale                    0.00\n",
      "neighbourhood_cleansed_Killarney                     0.00\n",
      "neighbourhood_cleansed_Kitsilano                     0.00\n",
      "neighbourhood_cleansed_Marpole                       0.00\n",
      "neighbourhood_cleansed_Mount Pleasant                0.00\n",
      "neighbourhood_cleansed_Oakridge                      0.00\n",
      "neighbourhood_cleansed_Renfrew-Collingwood           0.00\n",
      "neighbourhood_cleansed_Riley Park                    0.00\n",
      "neighbourhood_cleansed_Shaughnessy                   0.00\n",
      "neighbourhood_cleansed_South Cambie                  0.00\n",
      "neighbourhood_cleansed_Strathcona                    1.00\n",
      "neighbourhood_cleansed_Sunset                        0.00\n",
      "neighbourhood_cleansed_Victoria-Fraserview           0.00\n",
      "neighbourhood_cleansed_West End                      0.00\n",
      "neighbourhood_cleansed_West Point Grey               0.00\n",
      "legal_listing                                        0.00\n",
      "Name: 1724, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Deep dive into index 1724 (extreme outlier)\n",
    "print(listings_df.iloc[1724])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the fixed row is 3572; the origin row is 3583.\n",
      "There are 11 rows have been deleted.\n"
     ]
    }
   ],
   "source": [
    "# delete all the high influential outliers row: 1724, 57, 844, 499, 1459, 629, 1727, 1102, 1759, 1794, 882\n",
    "listings_df_fixed = listings_df_0.drop([1724, 57, 844, 499, 1459, 629, 1727, 1102, 1759, 1794, 882], axis=0)\n",
    "\n",
    "# check if they are deleted\n",
    "origin_row = listings_df_0.shape[0]\n",
    "fixed_row = listings_df_fixed.shape[0]\n",
    "print(f'the fixed row is {fixed_row}; the origin row is {origin_row}.\\nThere are {origin_row - fixed_row} rows have been deleted.')\n",
    "\n",
    "# save the fixed data into csv\n",
    "listings_df_fixed.to_csv('data/yvr_listing_data_cleaned_fixed.csv', index=False)"
   ]
  }
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