Employee Retention.ipynb

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   "source": [
    "# Goal\n",
    "\n",
    "Employee turn-over is a very costly problem for companies. The cost of replacing an employee if often larger than 100K USD, taking into account the time spent to interview and find a replacement, placement fees, sign-on bonuses and the loss of productivity for several months.\n",
    "\n",
    "It is only natural then that data science has started being applied to this area. Understanding why and when employees are most likely to leave can lead to actions to improve employee retention as well as planning new hiring in advance. This application of DS is sometimes called people analytics or people data science.\n",
    "\n",
    "In this challenge, you have a data set with info about the employees and have to predict when employees are going to quit by understanding the main drivers of employee churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/yangzongkun/Desktop/sd/DS Take Home 1-3/3.Employee_rentation/Employee_Rentation.csv',parse_dates=['join_date', 'quit_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
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       "      <th>salary</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
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       "      <td>marketing</td>\n",
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       "      <td>2014-04-04</td>\n",
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       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
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       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
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       "      <th>3</th>\n",
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       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
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       "   Unnamed: 0  employee_id  company_id              dept  seniority    salary  \\\n",
       "0           1      13021.0           7  customer_service         28   89000.0   \n",
       "1           2     825355.0           7         marketing         20  183000.0   \n",
       "2           3     927315.0           4         marketing         14  101000.0   \n",
       "3           4     662910.0           7  customer_service         20  115000.0   \n",
       "4           5     256971.0           2      data_science         23  276000.0   \n",
       "\n",
       "   join_date  quit_date  \n",
       "0 2014-03-24 2015-10-30  \n",
       "1 2013-04-29 2014-04-04  \n",
       "2 2014-10-13        NaT  \n",
       "3 2012-05-14 2013-06-07  \n",
       "4 2011-10-17 2014-08-22  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0              int64\n",
       "employee_id           float64\n",
       "company_id              int64\n",
       "dept                   object\n",
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       "join_date      datetime64[ns]\n",
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   "source": [
    "df.dtypes"
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   "cell_type": "code",
   "execution_count": 217,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 24702 entries, 0 to 24701\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   Unnamed: 0   24702 non-null  int64         \n",
      " 1   employee_id  24702 non-null  float64       \n",
      " 2   company_id   24702 non-null  int64         \n",
      " 3   dept         24702 non-null  object        \n",
      " 4   seniority    24702 non-null  int64         \n",
      " 5   salary       24702 non-null  float64       \n",
      " 6   join_date    24702 non-null  datetime64[ns]\n",
      " 7   quit_date    13510 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(2), int64(3), object(1)\n",
      "memory usage: 1.5+ MB\n"
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    "df.info()"
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   "cell_type": "code",
   "execution_count": 218,
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       "      <td>24702.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>12351.500000</td>\n",
       "      <td>501604.403530</td>\n",
       "      <td>3.426969</td>\n",
       "      <td>14.127803</td>\n",
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       "      <th>std</th>\n",
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       "      <td>17000.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>6176.250000</td>\n",
       "      <td>250133.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
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       "      <th>50%</th>\n",
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       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>123000.000000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>18526.750000</td>\n",
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       "      <td>5.000000</td>\n",
       "      <td>21.000000</td>\n",
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       "         Unnamed: 0    employee_id    company_id     seniority         salary\n",
       "count  24702.000000   24702.000000  24702.000000  24702.000000   24702.000000\n",
       "mean   12351.500000  501604.403530      3.426969     14.127803  138183.345478\n",
       "std     7130.997511  288909.026101      2.700011      8.089520   76058.184573\n",
       "min        1.000000      36.000000      1.000000      1.000000   17000.000000\n",
       "25%     6176.250000  250133.750000      1.000000      7.000000   79000.000000\n",
       "50%    12351.500000  500793.000000      2.000000     14.000000  123000.000000\n",
       "75%    18526.750000  753137.250000      5.000000     21.000000  187000.000000\n",
       "max    24702.000000  999969.000000     12.000000     99.000000  408000.000000"
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   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0     24702\n",
       "employee_id    24702\n",
       "company_id        12\n",
       "dept               6\n",
       "seniority         31\n",
       "salary           367\n",
       "join_date        995\n",
       "quit_date        664\n",
       "dtype: int64"
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     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.nunique()"
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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Create a table with 3 columns: day, employee_headcount, company_id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define useful information\n",
    "unique_date = pd.date_range(start='2011-01-24', end='2015-12-13', freq='D')\n",
    "unique_company = sorted(df['company_id'].unique())\n",
    "\n",
    "day = []\n",
    "company = []\n",
    "headcount = []\n",
    "\n",
    "# Loop through date and company id\n",
    "for date in unique_date:\n",
    "    for idx in unique_company:\n",
    "        total_join = len(df[(df['join_date'] <= date) & (df['company_id'] == idx)])\n",
    "        total_quit = len(df[(df['quit_date'] <= date) & (df['company_id'] == idx)])\n",
    "        day.append(date)\n",
    "        company.append(idx)\n",
    "        headcount.append(total_join - total_quit)\n",
    "\n",
    "# Create table for day, employee_headcount, company_id\n",
    "table = pd.DataFrame({'day': day, 'company_id': company, 'employee_headcount': headcount}, \n",
    "                     columns=['day', 'company_id', 'employee_headcount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
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     "data": {
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       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>company_id</th>\n",
       "      <th>employee_headcount</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
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      "text/plain": [
       "         day  company_id  employee_headcount\n",
       "0 2011-01-24           1                  25\n",
       "1 2011-01-24           2                  17\n",
       "2 2011-01-24           3                   9\n",
       "3 2011-01-24           4                  12\n",
       "4 2011-01-24           5                   5"
      ]
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   "source": [
    "table.head()"
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  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Employee Churn Analysis\n",
    "What are the main factors that drive employee churn? "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1 Total working days for churn employee \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "quit_data=df[~df['quit_date'].isnull()]\n",
    "\n",
    "work_days = np.array(list(map(lambda x: x.days, quit_data['quit_date'] - quit_data['join_date'])))\n",
    "\n",
    "# list(map(lambda x: x.days, ...)): This uses the map function to apply the .days attribute to each element in the series of timedeltas. \n",
    "# This attribute returns the number of days in each timedelta as an integer. The map function returns a map object, which is then converted to a list.\n",
    "# np.array(...): This takes the list of days and converts it into a NumPy array, which is a more efficient and convenient data structure for numerical computations."
   ]
  },
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   "cell_type": "code",
   "execution_count": 223,
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       " 725,\n",
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       " 851,\n",
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       " 676,\n",
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       " 753,\n",
       " 1033,\n",
       " 340,\n",
       " 620,\n",
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       " 697,\n",
       " 452,\n",
       " 724,\n",
       " 375,\n",
       " 340,\n",
       " 1019,\n",
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       " 1082,\n",
       " 367,\n",
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       " 696,\n",
       " 417,\n",
       " 305,\n",
       " 332,\n",
       " 347,\n",
       " 333,\n",
       " 361,\n",
       " 361,\n",
       " 259,\n",
       " 423,\n",
       " 1565,\n",
       " 675,\n",
       " 641,\n",
       " 865,\n",
       " 1295,\n",
       " 927,\n",
       " 340,\n",
       " 599,\n",
       " 416,\n",
       " 396,\n",
       " 354,\n",
       " 389,\n",
       " 417,\n",
       " 802,\n",
       " 1075,\n",
       " 416,\n",
       " 765,\n",
       " 255,\n",
       " 1096,\n",
       " 339,\n",
       " 403,\n",
       " ...]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(lambda x: x.days, quit_data['quit_date'] - quit_data['join_date']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 585,  340,  389, ...,  780, 1123,  864])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "work_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XdSPixY6ZiHhR0vo1isl6oTj6y8ysWVRbc3lJ0q4dM5J2A17pZn0zM1uDVVtzOQG4UtLSPD8c+JeaRGRmZgNetSdR3i1pB2B7QMDDEfFaTSMzM7MBqzcXrtwdeD+wC3C4pCP7elBJJ0qaL2mepCskrStpc0kzJT2SnzcrrD9Z0iJJCyXt19fjmplZfVR7EuWlwA+Bj5CSzO5AS18OKGkE8GWgJSJ2BgYB44FJwM0RMQa4Oc8jace8fCdgf+AcSYP6cmwzM6uPavtcWoAdIyJKPO56kl4jDWteCkwG9srLLwZuIV1qZhwwLSJWAIslLQLGArNLisXMzEpWbbPYPOBdZRwwIp4i1YKeBJYBf4+IG4EtImJZXmcZMCxvMgJYUthFWy57G0kTJbVKam1vby8jXDMz64Nqay5DgAX5asgrOgoj4pDeHjD3pYwDtgaeI41CO6K7TSqUVaxBRcRUYCpAS0tLWbUsMzPrpWqTy7dLPOYngMUR0Q4g6WrgQ8AzkoZHxDJJw4Hlef02YFRh+5GkZjQzM2tSVTWLRcStwOPAOnn6buDePh7zSWAPSetLErAv6WrLM4AJeZ0JwLV5egYwXtJgSVsDY4A5fTy2mZnVQVU1F0n/BkwENge2JfV5nEdKDL0SEXdJuoqUnFYC95GasjYEpks6hpSADsvrz5c0HViQ1z+u4wKaZmbWnKptFjuONELrLkg3DpM0rPtNuhYRpwKndipeQRfJKiKmAFP6ejwzM6uvakeLrYiIVztmJK1NF53qZmZm1SaXWyV9k3RuyieBK4Hf1S4sMzMbyKpNLpOAdmAu8L+B6wHfgdLMzCqq9sKVr5Nuc/yL2oZjZmarg2pHiy2mQh9LRGxTekRmZjbg9ebaYh3WJQ0T3rz8cMzMbHVQ7UmUfys8noqIHwP71DY0MzMbqKptFtu1MLsWqSazUU0iMjOzAa/aZrEfFaZXki4F88+lR2NmZquFakeL7V3rQMzMbPVRbbPYV7tbHhFnlBOOmZmtDnozWmx30hWKAQ4GZvHWm3iZmZkBvbtZ2K4R8QKApG8DV0bEF2oVmJmZDVzVXv5lK+DVwvyrwOjSozEzs9VCtTWXS4E5kn5LOlP/08AlNYvKzMwGtGpHi02RdAPw0Vx0dETcV7uwzMxsIKu2WQxgfeD5iDgLaMu3HDYzM3ubqpKLpFOBk4DJuWgd4LJaBWVmZgNbtTWXTwOHAC8BRMRSfPkXMzPrQrXJ5dWICPJl9yVtULuQzMxsoKs2uUyX9HNgU0n/BtxEP24cJmlTSVdJeljSQ5L2lLS5pJmSHsnPmxXWnyxpkaSFkvbr63HNzKw+ekwukgT8GrgK+A2wPXBKRPykH8c9C/hDROwAfAB4iHQr5ZsjYgxwc55H0o7AeGAnYH/gHEmD+nFsMzOrsR6HIkdESLomInYDZvb3gJI2Bj4GHJX3/yrwqqRxwF55tYuBW0iDCMYB0yJiBbBY0iJgLDC7v7GYmVltVNssdqek3Us65jZAO/BLSfdJOj/34WwREcsA8vOwvP4I3noNs7Zc9jaSJkpqldTa3t5eUrhmZtZb1SaXvUkJ5lFJD0qaK+nBPh5zbWBX4NyI2IU0Am1SN+urQllUWjEipkZES0S0DB06tI/hmZlZf3XbLCZpq4h4EvhUicdsA9oi4q48fxUpuTwjaXhELJM0HFheWH9UYfuRwNIS4zEzs5L11OdyDelqyE9I+k1EfKa/B4yIpyUtkbR9RCwE9gUW5McE4LT8fG3eZAbwK0lnAFsCY4A5/Y1joBo96feNDsHMrEc9JZdik9Q2JR73S8Dlkt4BPAYcTWqimy7pGOBJ4DCAiJgvaTop+awEjouIVSXGYmZmJespuUQX0/0SEfeTbkDW2b5drD8FmFLW8c3MrLZ6Si4fkPQ8qQazXp4mz0dEbFzT6MzMbEDqNrlEhE9WNDOzXuvNJffNzMyq4uRiZmalc3IxM7PSObmYmVnpnFzMzKx0Ti5mZlY6JxczMyudk4uZmZXOycXMzErn5GJmZqVzcjEzs9L1dOFKawK+h4uZDTSuuZiZWemcXMzMrHROLmZmVjonFzMzK52Ti5mZlc7JxczMStew5CJpkKT7JF2X5zeXNFPSI/l5s8K6kyUtkrRQ0n6NitnMzKrTyJrLV4CHCvOTgJsjYgxwc55H0o7AeGAnYH/gHEmD6hyrmZn1QkOSi6SRwIHA+YXiccDFefpi4NBC+bSIWBERi4FFwNg6hWpmZn3QqJrLj4FvAK8XyraIiGUA+XlYLh8BLCms15bL3kbSREmtklrb29tLD9rMzKpT98u/SDoIWB4R90jaq5pNKpRFpRUjYiowFaClpaXiOvam4mVlHj/twAZGYmarm0ZcW+zDwCGSDgDWBTaWdBnwjKThEbFM0nBgeV6/DRhV2H4ksLSuEZuZWa/UvVksIiZHxMiIGE3qqP9jRBwBzAAm5NUmANfm6RnAeEmDJW0NjAHm1DlsMzPrhWa6KvJpwHRJxwBPAocBRMR8SdOBBcBK4LiIWNW4MM3MrCcNTS4RcQtwS57+G7BvF+tNAabULTAzM+sXn6FvZmalc3IxM7PSNVOfi1lpPMzarLFcczEzs9I5uZiZWemcXMzMrHROLmZmVjp36Nsay53+ZrXjmouZmZXOycXMzErn5GJmZqVzcjEzs9I5uZiZWek8WsxWe8VRYWZWH04utlpwAjFrLm4WMzOz0jm5mJlZ6dwsZgOKz6o3GxhcczEzs9I5uZiZWenqnlwkjZL0J0kPSZov6Su5fHNJMyU9kp83K2wzWdIiSQsl7VfvmNcEoyf9/o2HmVl/NaLPZSXwtYi4V9JGwD2SZgJHATdHxGmSJgGTgJMk7QiMB3YCtgRukrRdRKxqQOy2mnJfjlm56p5cImIZsCxPvyDpIWAEMA7YK692MXALcFIunxYRK4DFkhYBY4HZ9Y3c1hRONGb919A+F0mjgV2Au4AtcuLpSEDD8mojgCWFzdpymZmZNamGDUWWtCHwG+CEiHheUperViiLLvY5EZgIsNVWW5URpjUx9w+ZNa+G1FwkrUNKLJdHxNW5+BlJw/Py4cDyXN4GjCpsPhJYWmm/ETE1IloiomXo0KG1Cd7MzHpU95qLUhXlAuChiDijsGgGMAE4LT9fWyj/laQzSB36Y4A59YvYGq2RNRT3v5j1TSOaxT4MfA6YK+n+XPZNUlKZLukY4EngMICImC9pOrCANNLsOI8UMzNrbo0YLfZnKvejAOzbxTZTgCk1C6oJuT/BzAYyn6FvZmalc3IxM7PSObmYmVnpnFzMzKx0vp+LNY1mH8TgYclm1XNysbfxl6iZ9ZebxczMrHSuuZj1QVdNeK7pmSVOLmarsc5J0MnP6sXJxczcz2alc3Kxhmr2EWLNrN4JwQnIesPJxWw109+E7YRvZXByaSL+p7a+6svfjv/erJacXMyaUK2aoGqdUNx0Zh2cXMzqbHX4Al4dXoPVlpOL1Z2bY97U2/eiGd+7ZozJGs/JxazJrQ5f3v056bSsWpJrW/Xl5NJgq8MXh1W2pn+2ta6VVbN+X5KIk1A5nFzMSrSmJ5Raa5ZmRCegnjm5WLf680/kL1rrSaP+Rvy3WXtOLmZmXajmx5VrMZUNmOQiaX/gLGAQcH5EnNbgkKwC/yK01VU1f9u+WvabFBGNjqFHkgYBfwE+CbQBdwOHR8SCrrZpaWmJ1tbWOkXYO/4CNrPuNDIZSbonIlr6u5+BUnMZCyyKiMcAJE0DxgFdJpdac4Iws1rp7fdLM9aMBkpyGQEsKcy3Af+j80qSJgIT8+yLkhbWIbbuDAH+2uAYKmnGuJoxJmjOuJoxJmjOuJoxJig5Lp1eym46Ynp3GTsbKMlFFcre1p4XEVOBqbUPpzqSWsuoXpatGeNqxpigOeNqxpigOeNqxpigOeMqO6a1ytpRjbUBowrzI4GlDYrFzMx6MFCSy93AGElbS3oHMB6Y0eCYzMysCwOiWSwiVko6Hvgv0lDkCyNifoPDqkbTNNF10oxxNWNM0JxxNWNM0JxxNWNM0JxxlRrTgBiKbGZmA8tAaRYzM7MBxMnFzMxK5+TSR5JGSfqTpIckzZf0lVy+uaSZkh7Jz5sVtpksaZGkhZL2q3F8gyTdJ+m6ZohL0qaSrpL0cH7P9mx0TPk4J+bPb56kKySt24i4JF0oabmkeYWyXschaTdJc/OysyVVGsbfn5h+kD/DByX9VtKm9Yypq7gKy74uKSQNqWdcXcUk6Uv5uPMlfb+eMXUVl6QPSrpT0v2SWiWNrUlcEeFHHx7AcGDXPL0R6fI0OwLfBybl8knA6Xl6R+ABYDCwNfAoMKiG8X0V+BVwXZ5vaFzAxcAX8vQ7gE2bIKYRwGJgvTw/HTiqEXEBHwN2BeYVynodBzAH2JN0btgNwKdKjul/Amvn6dPrHVNXceXyUaRBP08AQ5rgvdobuAkYnOeHNcN7BdzYsV/gAOCWWsTlmksfRcSyiLg3T78APET6shpH+iIlPx+ap8cB0yJiRUQsBhaRLmtTOkkjgQOB8wvFDYtL0sakP/ILACLi1Yh4rpExFawNrCdpbWB90vlTdY8rImYBz3Yq7lUckoYDG0fE7EjfCJcUtiklpoi4MSJW5tk7Seec1S2mruLKzgS+wVtPsG7YewUcC5wWESvyOsvrGVM3cQWwcZ7ehDfPGSw1LieXEkgaDewC3AVsERHLICUgYFherdIlbEbUKKQfk/7JXi+UNTKubYB24JdKTXXnS9qgwTEREU8BPwSeBJYBf4+IGxsdV0Fv4xiRp+sV3+dJv2IbHpOkQ4CnIuKBTosaGdd2wEcl3SXpVkm7N0FMACcAP5C0hPT3P7kWcTm59JOkDYHfACdExPPdrVqhrPRx4JIOApZHxD3VblKhrOy41iZVzc+NiF2Al0jNPI2MidyHMY7UBLAlsIGkIxodVxW6iqNu8Uk6GVgJXN7omCStD5wMnFJpcaPiIv3dbwbsAfwfYHruq2j053cscGJEjAJOJLcolB2Xk0s/SFqHlFguj4irc/EzuRpJfu6oCtfrEjYfBg6R9DgwDdhH0mUNjqsNaIuIu/L8VaRk0+j36hPA4ohoj4jXgKuBDzVBXB16G0cbbzZT1Sw+SROAg4DP5maSRse0LekHwgP5734kcK+kdzU4rjbg6kjmkFoShjQ4JoAJpL91gCt5s2m31LicXPoo/wK5AHgoIs4oLJpB+vDIz9cWysdLGixpa2AMqZOsVBExOSJGRsRo0mVy/hgRRzQyroh4GlgiaftctC/pdgkNfa9IzWF7SFo/f577kvrOGh1Xh17FkZvOXpC0R349Rxa2KYXSTftOAg6JiJc7xdqQmCJibkQMi4jR+e++jTTY5ulGxgVcA+wDIGk70kCWvzY4JkiJ4eN5eh/gkTxdblz9GYmwJj+Aj5Cqhg8C9+fHAcA7gZvzB3YzsHlhm5NJIzAW0s9RIFXGuBdvjhZraFzAB4HW/H5dQ2ouaPh7BXwHeBiYB1xKGilT97iAK0j9Pq+RvhyP6UscQEt+LY8CPyVfhaPEmBaR2uU7/ubPq2dMXcXVafnj5NFiDX6v3gFclo9xL7BPM7xXpO+ue0gjw+4CdqtFXL78i5mZlc7NYmZmVjonFzMzK52Ti5mZlc7JxczMSufkYmZmpXNysTWGpDMlnVCY/y9J5xfmfyTpq73Y3y2SWiqUX6/C1YL7Ee9oSa/kS+Y8JGlOPoHRrOk5udia5A7SGfhIWot0tvROheUfAm6vZkeSBnW1LCIOiHRhzjI8GhG7RMR7SSfFnijp6JL2bVYzTi62JrmdnFxISWUe6czjzSQNBt4L3Cdp31xbmJvvhzEYQNLjkk6R9GfgsI6dSlpL0sWSvldYb0iueTwk6RdK9/O4UdJ6eZ3dle6JMlvpHilvuzdJZxHxGOlWCl/O+xgr6Y4c6x0dV0CQdJukDxbiu13S+yV9XOkeHvfnbTbq7xtq1hUnF1tjRMRSYKWkrUhJZjbpDOU9SWcgP0j6n7gI+JeIeB/p4oPHFnbzj4j4SERMy/Nrky7e+JeI+FaFw44BfhYROwHPAZ/J5b8EvhgRewKrevEy7gV2yNMPAx+LdDHQU4D/zOXnk+5L03HZkcER8SDwdeC4iPgg8FHglV4c16xXnFxsTdNRe+lILrML83cA25MuZvmXvP7FpHvRdPh1p/39nHQjpildHG9xRNyfp+8BRuf+mI0i4o5c/qtexF+8Qu0mwJW51nMmbzbxXQkclC+s+nlSsoT02s+Q9GVg03jzvixmpXNysTVNR7/L+0jNYneSai4d/S093b71pQr721vSul2sv6IwvYpU0+nPrWt3IV1cE+C7wJ8iYmfgYGBdgEgXlJxJup3AP5OTV0ScBnwBWA+4U9IOmNWIk4utaW4nXS7+2YhYFRHPkm65vCepFvMwqXbxnrz+54Bbu9nfBcD1pBrE2tUEEBH/n3yV2Vw0vprtlG5K90PgJ7loE+CpPH1Up9XPB84G7s6vEUnbRrqC8Omki4g6uVjNOLnYmmYuaZTYnZ3K/h4Rf42IfwBHk5LFXNI9OM7rboeRbrlwL3BpHoVWjWOAqZJmk2oyf+9ivW07hiID04GfRMQv87LvA/9P0u3AW0avRbpZ3POkvp0OJ0iaJ+kBUn/LDZjViK+KbNYAkjaMiBfz9CRgeER8pcT9bwncAuwQEa/3sLpZ6VxzMWuMA/OQ4HmkkVvfK2vHko4kjYI72YnFGsU1FzMzK51rLmZmVjonFzMzK52Ti5mZlc7JxczMSufkYmZmpftvc4lGyT7QQJoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the histogram\n",
    "plt.hist(work_days, bins=100)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Working Days')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Working Days')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2 The number of quiting for each week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "quit_week = np.array(list(map(lambda x: x.week, quit_data['quit_date'])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([44, 14, 23, ..., 43,  2, 49])"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quit_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{44: 293,\n",
       " 14: 199,\n",
       " 23: 258,\n",
       " 34: 260,\n",
       " 35: 276,\n",
       " 43: 307,\n",
       " 9: 265,\n",
       " 46: 277,\n",
       " 39: 265,\n",
       " 28: 400,\n",
       " 47: 313,\n",
       " 22: 262,\n",
       " 33: 261,\n",
       " 31: 284,\n",
       " 6: 251,\n",
       " 40: 248,\n",
       " 15: 363,\n",
       " 20: 256,\n",
       " 3: 276,\n",
       " 41: 300,\n",
       " 30: 252,\n",
       " 10: 274,\n",
       " 5: 259,\n",
       " 18: 274,\n",
       " 51: 193,\n",
       " 13: 209,\n",
       " 42: 279,\n",
       " 36: 236,\n",
       " 8: 231,\n",
       " 2: 243,\n",
       " 25: 246,\n",
       " 1: 238,\n",
       " 26: 278,\n",
       " 7: 214,\n",
       " 4: 255,\n",
       " 52: 212,\n",
       " 49: 292,\n",
       " 45: 310,\n",
       " 17: 346,\n",
       " 32: 262,\n",
       " 48: 289,\n",
       " 24: 253,\n",
       " 21: 242,\n",
       " 37: 270,\n",
       " 38: 275,\n",
       " 12: 238,\n",
       " 29: 270,\n",
       " 50: 185,\n",
       " 16: 153,\n",
       " 19: 285,\n",
       " 11: 244,\n",
       " 27: 89}"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_count = {}\n",
    "for value in quit_week:\n",
    "    if value not in data_count:\n",
    "        data_count[value] = 1\n",
    "    else:\n",
    "        data_count[value] += 1\n",
    "\n",
    "data_count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the unique values and their counts from the dictionary\n",
    "values = list(data_count.keys())\n",
    "counts = list(data_count.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the countplot using Matplotlib\n",
    "plt.bar(values, counts)\n",
    "\n",
    "# Add labels and title to the plot\n",
    "plt.xlabel('Week')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Countplot of Quit Week')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.3 The number of quiting for each month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "quit_month = np.array(list(map(lambda x: x.month, quit_data['quit_date'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  4,  6, ..., 10,  1, 12])"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quit_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10: 1317,\n",
       " 4: 1066,\n",
       " 6: 1067,\n",
       " 8: 1234,\n",
       " 2: 930,\n",
       " 11: 1296,\n",
       " 9: 1047,\n",
       " 7: 1119,\n",
       " 5: 1278,\n",
       " 3: 1060,\n",
       " 1: 1209,\n",
       " 12: 887}"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary to store the count of each unique value in the array\n",
    "data_count = {}\n",
    "for value in quit_month:\n",
    "    if value not in data_count:\n",
    "        data_count[value] = 1\n",
    "    else:\n",
    "        data_count[value] += 1\n",
    "\n",
    "data_count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the unique values and their counts from the dictionary\n",
    "values = list(data_count.keys())\n",
    "counts = list(data_count.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the countplot using Matplotlib\n",
    "plt.bar(values, counts)\n",
    "\n",
    "# Add labels and title to the plot\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Countplot of Quit Month')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Model Building"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1 Feature preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "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>Unnamed: 0</th>\n",
       "      <th>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>825355.0</td>\n",
       "      <td>7</td>\n",
       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2014-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  employee_id  company_id              dept  seniority    salary  \\\n",
       "0           1      13021.0           7  customer_service         28   89000.0   \n",
       "1           2     825355.0           7         marketing         20  183000.0   \n",
       "2           3     927315.0           4         marketing         14  101000.0   \n",
       "3           4     662910.0           7  customer_service         20  115000.0   \n",
       "4           5     256971.0           2      data_science         23  276000.0   \n",
       "\n",
       "   join_date  quit_date  \n",
       "0 2014-03-24 2015-10-30  \n",
       "1 2013-04-29 2014-04-04  \n",
       "2 2014-10-13        NaT  \n",
       "3 2012-05-14 2013-06-07  \n",
       "4 2011-10-17 2014-08-22  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column in a pandas DataFrame that maps the NaN (null) values to 0 and the non-null values to 1\n",
    "\n",
    "df['quit'] = df['quit_date'].notna().astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
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       "      <th>quit</th>\n",
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       "      <td>2014-03-24</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>825355.0</td>\n",
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       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>24697</th>\n",
       "      <td>24698</td>\n",
       "      <td>549847.0</td>\n",
       "      <td>3</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>16</td>\n",
       "      <td>97000.0</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24698</th>\n",
       "      <td>24699</td>\n",
       "      <td>590087.0</td>\n",
       "      <td>4</td>\n",
       "      <td>engineer</td>\n",
       "      <td>10</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>2015-10-26</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24699</th>\n",
       "      <td>24700</td>\n",
       "      <td>777612.0</td>\n",
       "      <td>4</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>11</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>2013-09-03</td>\n",
       "      <td>2015-10-23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24700</th>\n",
       "      <td>24701</td>\n",
       "      <td>97289.0</td>\n",
       "      <td>10</td>\n",
       "      <td>engineer</td>\n",
       "      <td>98</td>\n",
       "      <td>266000.0</td>\n",
       "      <td>2011-12-13</td>\n",
       "      <td>2015-01-09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24701</th>\n",
       "      <td>24702</td>\n",
       "      <td>604052.0</td>\n",
       "      <td>1</td>\n",
       "      <td>marketing</td>\n",
       "      <td>99</td>\n",
       "      <td>185000.0</td>\n",
       "      <td>2011-07-26</td>\n",
       "      <td>2013-12-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24702 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  employee_id  company_id              dept  seniority  \\\n",
       "0               1      13021.0           7  customer_service         28   \n",
       "1               2     825355.0           7         marketing         20   \n",
       "2               3     927315.0           4         marketing         14   \n",
       "3               4     662910.0           7  customer_service         20   \n",
       "4               5     256971.0           2      data_science         23   \n",
       "...           ...          ...         ...               ...        ...   \n",
       "24697       24698     549847.0           3  customer_service         16   \n",
       "24698       24699     590087.0           4          engineer         10   \n",
       "24699       24700     777612.0           4  customer_service         11   \n",
       "24700       24701      97289.0          10          engineer         98   \n",
       "24701       24702     604052.0           1         marketing         99   \n",
       "\n",
       "         salary  join_date  quit_date  quit  \n",
       "0       89000.0 2014-03-24 2015-10-30     1  \n",
       "1      183000.0 2013-04-29 2014-04-04     1  \n",
       "2      101000.0 2014-10-13        NaT     0  \n",
       "3      115000.0 2012-05-14 2013-06-07     1  \n",
       "4      276000.0 2011-10-17 2014-08-22     1  \n",
       "...         ...        ...        ...   ...  \n",
       "24697   97000.0 2015-02-18        NaT     0  \n",
       "24698  160000.0 2015-10-26        NaT     0  \n",
       "24699   67000.0 2013-09-03 2015-10-23     1  \n",
       "24700  266000.0 2011-12-13 2015-01-09     1  \n",
       "24701  185000.0 2011-07-26 2013-12-06     1  \n",
       "\n",
       "[24702 rows x 9 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "#choose the columns we want"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_id</th>\n",
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       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>quit</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>24697</th>\n",
       "      <td>3</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>16</td>\n",
       "      <td>97000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24698</th>\n",
       "      <td>4</td>\n",
       "      <td>engineer</td>\n",
       "      <td>10</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24699</th>\n",
       "      <td>4</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>11</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24700</th>\n",
       "      <td>10</td>\n",
       "      <td>engineer</td>\n",
       "      <td>98</td>\n",
       "      <td>266000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24701</th>\n",
       "      <td>1</td>\n",
       "      <td>marketing</td>\n",
       "      <td>99</td>\n",
       "      <td>185000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24702 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       company_id              dept  seniority    salary  quit\n",
       "0               7  customer_service         28   89000.0     1\n",
       "1               7         marketing         20  183000.0     1\n",
       "2               4         marketing         14  101000.0     0\n",
       "3               7  customer_service         20  115000.0     1\n",
       "4               2      data_science         23  276000.0     1\n",
       "...           ...               ...        ...       ...   ...\n",
       "24697           3  customer_service         16   97000.0     0\n",
       "24698           4          engineer         10  160000.0     0\n",
       "24699           4  customer_service         11   67000.0     1\n",
       "24700          10          engineer         98  266000.0     1\n",
       "24701           1         marketing         99  185000.0     1\n",
       "\n",
       "[24702 rows x 5 columns]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.loc[:,['company_id','dept','seniority','salary','quit']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h2o\n",
    "from h2o.frame import H2OFrame\n",
    "from h2o.estimators.random_forest import H2ORandomForestEstimator\n",
    "from h2o.grid.grid_search import H2OGridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 . connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "\n",
       "#h2o-table-2.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-2 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-2 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-2 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-2 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-2 .h2o-table th,\n",
       "#h2o-table-2 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-2 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-2\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>4 mins 35 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>America/Los_Angeles</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.38.0.3</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>2 months and 20 days </td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_yangzongkun_lm1ws3</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>1.536 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.9.6 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  ----------------------------------\n",
       "H2O_cluster_uptime:         4 mins 35 secs\n",
       "H2O_cluster_timezone:       America/Los_Angeles\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.38.0.3\n",
       "H2O_cluster_version_age:    2 months and 20 days\n",
       "H2O_cluster_name:           H2O_from_python_yangzongkun_lm1ws3\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    1.536 Gb\n",
       "H2O_cluster_total_cores:    8\n",
       "H2O_cluster_allowed_cores:  8\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.9.6 final\n",
       "--------------------------  ----------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize H2O cluster\n",
    "import h2o\n",
    "h2o.init()\n",
    "h2o.remove_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kj/gjbr9xrs6ns8dc9jgs1f619h0000gn/T/ipykernel_53972/4280406866.py:6: H2ODeprecationWarning: ``summary()`` is deprecated, please use ``show_summary()`` or ``get_summary()`` instead\n",
      "  h2o_df.summary()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class='dataframe'>\n",
       "<thead>\n",
       "<tr><th>       </th><th>company_id        </th><th>dept            </th><th>seniority         </th><th>salary            </th><th>quit  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>int               </td><td>enum            </td><td>int               </td><td>int               </td><td>enum  </td></tr>\n",
       "<tr><td>mins   </td><td>1.0               </td><td>                </td><td>1.0               </td><td>17000.0           </td><td>      </td></tr>\n",
       "<tr><td>mean   </td><td>3.426969476155781 </td><td>                </td><td>14.127803416727366</td><td>138183.34547809843</td><td>      </td></tr>\n",
       "<tr><td>maxs   </td><td>12.0              </td><td>                </td><td>99.0              </td><td>408000.0          </td><td>      </td></tr>\n",
       "<tr><td>sigma  </td><td>2.7000109677072097</td><td>                </td><td>8.089519596306804 </td><td>76058.18457287704 </td><td>      </td></tr>\n",
       "<tr><td>zeros  </td><td>0                 </td><td>                </td><td>0                 </td><td>0                 </td><td>      </td></tr>\n",
       "<tr><td>missing</td><td>0                 </td><td>0               </td><td>0                 </td><td>0                 </td><td>0     </td></tr>\n",
       "<tr><td>0      </td><td>7.0               </td><td>customer_service</td><td>28.0              </td><td>89000.0           </td><td>1     </td></tr>\n",
       "<tr><td>1      </td><td>7.0               </td><td>marketing       </td><td>20.0              </td><td>183000.0          </td><td>1     </td></tr>\n",
       "<tr><td>2      </td><td>4.0               </td><td>marketing       </td><td>14.0              </td><td>101000.0          </td><td>0     </td></tr>\n",
       "<tr><td>3      </td><td>7.0               </td><td>customer_service</td><td>20.0              </td><td>115000.0          </td><td>1     </td></tr>\n",
       "<tr><td>4      </td><td>2.0               </td><td>data_science    </td><td>23.0              </td><td>276000.0          </td><td>1     </td></tr>\n",
       "<tr><td>5      </td><td>4.0               </td><td>data_science    </td><td>14.0              </td><td>165000.0          </td><td>1     </td></tr>\n",
       "<tr><td>6      </td><td>4.0               </td><td>customer_service</td><td>21.0              </td><td>107000.0          </td><td>0     </td></tr>\n",
       "<tr><td>7      </td><td>2.0               </td><td>customer_service</td><td>4.0               </td><td>30000.0           </td><td>0     </td></tr>\n",
       "<tr><td>8      </td><td>9.0               </td><td>engineer        </td><td>7.0               </td><td>160000.0          </td><td>1     </td></tr>\n",
       "<tr><td>9      </td><td>1.0               </td><td>sales           </td><td>7.0               </td><td>104000.0          </td><td>0     </td></tr>\n",
       "</tbody>\n",
       "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[24702 rows x 5 columns]</pre>"
      ],
      "text/plain": [
       "         company_id          dept              seniority           salary              quit\n",
       "-------  ------------------  ----------------  ------------------  ------------------  ------\n",
       "type     int                 enum              int                 int                 enum\n",
       "mins     1.0                                   1.0                 17000.0\n",
       "mean     3.426969476155781                     14.127803416727366  138183.34547809843\n",
       "maxs     12.0                                  99.0                408000.0\n",
       "sigma    2.7000109677072097                    8.089519596306804   76058.18457287704\n",
       "zeros    0                                     0                   0\n",
       "missing  0                   0                 0                   0                   0\n",
       "0        7.0                 customer_service  28.0                89000.0             1\n",
       "1        7.0                 marketing         20.0                183000.0            1\n",
       "2        4.0                 marketing         14.0                101000.0            0\n",
       "3        7.0                 customer_service  20.0                115000.0            1\n",
       "4        2.0                 data_science      23.0                276000.0            1\n",
       "5        4.0                 data_science      14.0                165000.0            1\n",
       "6        4.0                 customer_service  21.0                107000.0            0\n",
       "7        2.0                 customer_service  4.0                 30000.0             0\n",
       "8        9.0                 engineer          7.0                 160000.0            1\n",
       "9        1.0                 sales             7.0                 104000.0            0\n",
       "[24702 rows x 5 columns]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transform to H2O Frame, and make sure the target variable is categorical\n",
    "h2o_df = H2OFrame(df)\n",
    "\n",
    "h2o_df['quit'] = h2o_df['quit'].asfactor()\n",
    "\n",
    "h2o_df.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into 75% training and 25% test dataset\n",
    "strat_split = h2o_df['quit'].stratified_split(test_frac=0.25, seed=1)\n",
    "\n",
    "train = h2o_df[strat_split == 'train']\n",
    "test = h2o_df[strat_split == 'test']\n",
    "\n",
    "feature = ['company_id', 'dept', 'seniority', 'salary']\n",
    "target = 'quit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drf Model Build progress: |██████████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style='margin: 1em 0 1em 0;'>Model Details\n",
       "=============\n",
       "H2ORandomForestEstimator : Distributed Random Forest\n",
       "Model Key: DRF_model_python_1676249291128_1\n",
       "</pre>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-3.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-3 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-3 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-3 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-3 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-3 .h2o-table th,\n",
       "#h2o-table-3 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-3 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-3\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Model Summary: </caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>number_of_trees</th>\n",
       "<th>number_of_internal_trees</th>\n",
       "<th>model_size_in_bytes</th>\n",
       "<th>min_depth</th>\n",
       "<th>max_depth</th>\n",
       "<th>mean_depth</th>\n",
       "<th>min_leaves</th>\n",
       "<th>max_leaves</th>\n",
       "<th>mean_leaves</th></tr></thead>\n",
       "    <tbody><tr><td></td>\n",
       "<td>100.0</td>\n",
       "<td>100.0</td>\n",
       "<td>5414773.0</td>\n",
       "<td>20.0</td>\n",
       "<td>20.0</td>\n",
       "<td>20.0</td>\n",
       "<td>3559.0</td>\n",
       "<td>5220.0</td>\n",
       "<td>4309.58</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomial: drf\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.2621560047840295\n",
       "RMSE: 0.5120117232876895\n",
       "LogLoss: 0.7482543588963848\n",
       "Mean Per-Class Error: 0.4793378168889879\n",
       "AUC: 0.5951507723031288\n",
       "AUCPR: 0.5639439211481407\n",
       "Gini: 0.1903015446062577</pre>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-4.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-4 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-4 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-4 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-4 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-4 .h2o-table th,\n",
       "#h2o-table-4 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-4 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-4\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.13825023004727358</caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>0</th>\n",
       "<th>1</th>\n",
       "<th>Error</th>\n",
       "<th>Rate</th></tr></thead>\n",
       "    <tbody><tr><td>0</td>\n",
       "<td>707.0</td>\n",
       "<td>9415.0</td>\n",
       "<td>0.9302</td>\n",
       "<td> (9415.0/10122.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>289.0</td>\n",
       "<td>9843.0</td>\n",
       "<td>0.0285</td>\n",
       "<td> (289.0/10132.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>996.0</td>\n",
       "<td>19258.0</td>\n",
       "<td>0.4791</td>\n",
       "<td> (9704.0/20254.0)</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-5.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-5 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-5 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-5 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-5 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-5 .h2o-table th,\n",
       "#h2o-table-5 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-5 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-5\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
       "    <thead><tr><th>metric</th>\n",
       "<th>threshold</th>\n",
       "<th>value</th>\n",
       "<th>idx</th></tr></thead>\n",
       "    <tbody><tr><td>max f1</td>\n",
       "<td>0.1382502</td>\n",
       "<td>0.6698197</td>\n",
       "<td>362.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.0</td>\n",
       "<td>0.8334704</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.3716140</td>\n",
       "<td>0.5816484</td>\n",
       "<td>268.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.5405584</td>\n",
       "<td>0.5736645</td>\n",
       "<td>188.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.9339675</td>\n",
       "<td>0.5916230</td>\n",
       "<td>11.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.0</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.9985536</td>\n",
       "<td>0.9996048</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.3240541</td>\n",
       "<td>0.1477903</td>\n",
       "<td>289.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.5371251</td>\n",
       "<td>0.5719225</td>\n",
       "<td>190.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.5405584</td>\n",
       "<td>0.5736664</td>\n",
       "<td>188.0</td></tr>\n",
       "<tr><td>max tns</td>\n",
       "<td>0.9985536</td>\n",
       "<td>10118.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fns</td>\n",
       "<td>0.9985536</td>\n",
       "<td>10129.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fps</td>\n",
       "<td>0.0</td>\n",
       "<td>10122.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tps</td>\n",
       "<td>0.0</td>\n",
       "<td>10132.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tnr</td>\n",
       "<td>0.9985536</td>\n",
       "<td>0.9996048</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fnr</td>\n",
       "<td>0.9985536</td>\n",
       "<td>0.9997039</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fpr</td>\n",
       "<td>0.0</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tpr</td>\n",
       "<td>0.0</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-6.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-6 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-6 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-6 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-6 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-6 .h2o-table th,\n",
       "#h2o-table-6 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-6 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-6\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Gains/Lift Table: Avg response rate: 50.02 %, avg score: 52.29 %</caption>\n",
       "    <thead><tr><th>group</th>\n",
       "<th>cumulative_data_fraction</th>\n",
       "<th>lower_threshold</th>\n",
       "<th>lift</th>\n",
       "<th>cumulative_lift</th>\n",
       "<th>response_rate</th>\n",
       "<th>score</th>\n",
       "<th>cumulative_response_rate</th>\n",
       "<th>cumulative_score</th>\n",
       "<th>capture_rate</th>\n",
       "<th>cumulative_capture_rate</th>\n",
       "<th>gain</th>\n",
       "<th>cumulative_gain</th>\n",
       "<th>kolmogorov_smirnov</th></tr></thead>\n",
       "    <tbody><tr><td>1</td>\n",
       "<td>0.0100227</td>\n",
       "<td>0.9298958</td>\n",
       "<td>1.1422932</td>\n",
       "<td>1.1422932</td>\n",
       "<td>0.5714286</td>\n",
       "<td>0.9516096</td>\n",
       "<td>0.5714286</td>\n",
       "<td>0.9516096</td>\n",
       "<td>0.0114489</td>\n",
       "<td>0.0114489</td>\n",
       "<td>14.2293159</td>\n",
       "<td>14.2293159</td>\n",
       "<td>0.0028537</td></tr>\n",
       "<tr><td>2</td>\n",
       "<td>0.0200454</td>\n",
       "<td>0.9047525</td>\n",
       "<td>1.0930564</td>\n",
       "<td>1.1176748</td>\n",
       "<td>0.5467980</td>\n",
       "<td>0.9163793</td>\n",
       "<td>0.5591133</td>\n",
       "<td>0.9339945</td>\n",
       "<td>0.0109554</td>\n",
       "<td>0.0224043</td>\n",
       "<td>9.3056385</td>\n",
       "<td>11.7674772</td>\n",
       "<td>0.0047200</td></tr>\n",
       "<tr><td>3</td>\n",
       "<td>0.0300188</td>\n",
       "<td>0.8857990</td>\n",
       "<td>1.1479481</td>\n",
       "<td>1.1277327</td>\n",
       "<td>0.5742574</td>\n",
       "<td>0.8955007</td>\n",
       "<td>0.5641447</td>\n",
       "<td>0.9212054</td>\n",
       "<td>0.0114489</td>\n",
       "<td>0.0338531</td>\n",
       "<td>14.7948076</td>\n",
       "<td>12.7732679</td>\n",
       "<td>0.0076725</td></tr>\n",
       "<tr><td>4</td>\n",
       "<td>0.0400415</td>\n",
       "<td>0.8716779</td>\n",
       "<td>1.2604614</td>\n",
       "<td>1.1609558</td>\n",
       "<td>0.6305419</td>\n",
       "<td>0.8787607</td>\n",
       "<td>0.5807645</td>\n",
       "<td>0.9105811</td>\n",
       "<td>0.0126332</td>\n",
       "<td>0.0464864</td>\n",
       "<td>26.0461417</td>\n",
       "<td>16.0955778</td>\n",
       "<td>0.0128962</td></tr>\n",
       "<tr><td>5</td>\n",
       "<td>0.0500148</td>\n",
       "<td>0.8583452</td>\n",
       "<td>1.0291948</td>\n",
       "<td>1.1346816</td>\n",
       "<td>0.5148515</td>\n",
       "<td>0.8651690</td>\n",
       "<td>0.5676209</td>\n",
       "<td>0.9015256</td>\n",
       "<td>0.0102645</td>\n",
       "<td>0.0567509</td>\n",
       "<td>2.9194826</td>\n",
       "<td>13.4681630</td>\n",
       "<td>0.0134788</td></tr>\n",
       "<tr><td>6</td>\n",
       "<td>0.1000296</td>\n",
       "<td>0.8064512</td>\n",
       "<td>1.1504685</td>\n",
       "<td>1.1425751</td>\n",
       "<td>0.5755183</td>\n",
       "<td>0.8311485</td>\n",
       "<td>0.5715696</td>\n",
       "<td>0.8663371</td>\n",
       "<td>0.0575405</td>\n",
       "<td>0.1142914</td>\n",
       "<td>15.0468505</td>\n",
       "<td>14.2575067</td>\n",
       "<td>0.0285376</td></tr>\n",
       "<tr><td>7</td>\n",
       "<td>0.1500444</td>\n",
       "<td>0.7659752</td>\n",
       "<td>1.2234828</td>\n",
       "<td>1.1695443</td>\n",
       "<td>0.6120434</td>\n",
       "<td>0.7859353</td>\n",
       "<td>0.5850609</td>\n",
       "<td>0.8395365</td>\n",
       "<td>0.0611923</td>\n",
       "<td>0.1754836</td>\n",
       "<td>22.3482801</td>\n",
       "<td>16.9544312</td>\n",
       "<td>0.0509035</td></tr>\n",
       "<tr><td>8</td>\n",
       "<td>0.2000099</td>\n",
       "<td>0.7306598</td>\n",
       "<td>1.1772844</td>\n",
       "<td>1.1714779</td>\n",
       "<td>0.5889328</td>\n",
       "<td>0.7477110</td>\n",
       "<td>0.5860281</td>\n",
       "<td>0.8165971</td>\n",
       "<td>0.0588235</td>\n",
       "<td>0.2343071</td>\n",
       "<td>17.7284352</td>\n",
       "<td>17.1477889</td>\n",
       "<td>0.0686284</td></tr>\n",
       "<tr><td>9</td>\n",
       "<td>0.2999901</td>\n",
       "<td>0.6654458</td>\n",
       "<td>1.1776901</td>\n",
       "<td>1.1735483</td>\n",
       "<td>0.5891358</td>\n",
       "<td>0.6977029</td>\n",
       "<td>0.5870639</td>\n",
       "<td>0.7769722</td>\n",
       "<td>0.1177458</td>\n",
       "<td>0.3520529</td>\n",
       "<td>17.7690144</td>\n",
       "<td>17.3548300</td>\n",
       "<td>0.1041770</td></tr>\n",
       "<tr><td>10</td>\n",
       "<td>0.4000197</td>\n",
       "<td>0.5997518</td>\n",
       "<td>1.1406017</td>\n",
       "<td>1.1653096</td>\n",
       "<td>0.5705824</td>\n",
       "<td>0.6323513</td>\n",
       "<td>0.5829425</td>\n",
       "<td>0.7408081</td>\n",
       "<td>0.1140940</td>\n",
       "<td>0.4661469</td>\n",
       "<td>14.0601708</td>\n",
       "<td>16.5309619</td>\n",
       "<td>0.1323196</td></tr>\n",
       "<tr><td>11</td>\n",
       "<td>0.5</td>\n",
       "<td>0.5371471</td>\n",
       "<td>1.0710761</td>\n",
       "<td>1.1464666</td>\n",
       "<td>0.5358025</td>\n",
       "<td>0.5686177</td>\n",
       "<td>0.5735163</td>\n",
       "<td>0.7063768</td>\n",
       "<td>0.1070865</td>\n",
       "<td>0.5732333</td>\n",
       "<td>7.1076116</td>\n",
       "<td>14.6466640</td>\n",
       "<td>0.1465390</td></tr>\n",
       "<tr><td>12</td>\n",
       "<td>0.5999803</td>\n",
       "<td>0.4707300</td>\n",
       "<td>0.9723594</td>\n",
       "<td>1.1174535</td>\n",
       "<td>0.4864198</td>\n",
       "<td>0.5049215</td>\n",
       "<td>0.5590026</td>\n",
       "<td>0.6728064</td>\n",
       "<td>0.0972167</td>\n",
       "<td>0.6704501</td>\n",
       "<td>-2.7640576</td>\n",
       "<td>11.7453547</td>\n",
       "<td>0.1410092</td></tr>\n",
       "<tr><td>13</td>\n",
       "<td>0.7000099</td>\n",
       "<td>0.3983771</td>\n",
       "<td>0.9649727</td>\n",
       "<td>1.0956644</td>\n",
       "<td>0.4827246</td>\n",
       "<td>0.4348580</td>\n",
       "<td>0.5481027</td>\n",
       "<td>0.6388042</td>\n",
       "<td>0.0965259</td>\n",
       "<td>0.7669759</td>\n",
       "<td>-3.5027275</td>\n",
       "<td>9.5664427</td>\n",
       "<td>0.1339982</td></tr>\n",
       "<tr><td>14</td>\n",
       "<td>0.7999901</td>\n",
       "<td>0.3167360</td>\n",
       "<td>0.9141166</td>\n",
       "<td>1.0729751</td>\n",
       "<td>0.4572840</td>\n",
       "<td>0.3595694</td>\n",
       "<td>0.5367525</td>\n",
       "<td>0.6039063</td>\n",
       "<td>0.0913936</td>\n",
       "<td>0.8583695</td>\n",
       "<td>-8.5883425</td>\n",
       "<td>7.2975147</td>\n",
       "<td>0.1168165</td></tr>\n",
       "<tr><td>15</td>\n",
       "<td>0.8999704</td>\n",
       "<td>0.2120798</td>\n",
       "<td>0.7670287</td>\n",
       "<td>1.0389867</td>\n",
       "<td>0.3837037</td>\n",
       "<td>0.2673187</td>\n",
       "<td>0.5197498</td>\n",
       "<td>0.5665139</td>\n",
       "<td>0.0766877</td>\n",
       "<td>0.9350572</td>\n",
       "<td>-23.2971297</td>\n",
       "<td>3.8986692</td>\n",
       "<td>0.0702084</td></tr>\n",
       "<tr><td>16</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.6492352</td>\n",
       "<td>1.0</td>\n",
       "<td>0.3247779</td>\n",
       "<td>0.1304497</td>\n",
       "<td>0.5002469</td>\n",
       "<td>0.5228945</td>\n",
       "<td>0.0649428</td>\n",
       "<td>1.0</td>\n",
       "<td>-35.0764772</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div></div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-7.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-7 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-7 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-7 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-7 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-7 .h2o-table th,\n",
       "#h2o-table-7 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-7 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-7\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Scoring History: </caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>timestamp</th>\n",
       "<th>duration</th>\n",
       "<th>number_of_trees</th>\n",
       "<th>training_rmse</th>\n",
       "<th>training_logloss</th>\n",
       "<th>training_auc</th>\n",
       "<th>training_pr_auc</th>\n",
       "<th>training_lift</th>\n",
       "<th>training_classification_error</th></tr></thead>\n",
       "    <tbody><tr><td></td>\n",
       "<td>2023-02-12 16:54:42</td>\n",
       "<td> 0.228 sec</td>\n",
       "<td>0.0</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:43</td>\n",
       "<td> 1.402 sec</td>\n",
       "<td>1.0</td>\n",
       "<td>0.6169864</td>\n",
       "<td>9.7201866</td>\n",
       "<td>0.5413398</td>\n",
       "<td>0.5306769</td>\n",
       "<td>1.0887226</td>\n",
       "<td>0.4995280</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:43</td>\n",
       "<td> 1.948 sec</td>\n",
       "<td>2.0</td>\n",
       "<td>0.6040382</td>\n",
       "<td>8.5218919</td>\n",
       "<td>0.5482369</td>\n",
       "<td>0.5368070</td>\n",
       "<td>1.1021149</td>\n",
       "<td>0.4981422</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:44</td>\n",
       "<td> 2.237 sec</td>\n",
       "<td>3.0</td>\n",
       "<td>0.5947136</td>\n",
       "<td>7.5843564</td>\n",
       "<td>0.5495067</td>\n",
       "<td>0.5393546</td>\n",
       "<td>1.1073625</td>\n",
       "<td>0.4965017</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:44</td>\n",
       "<td> 2.433 sec</td>\n",
       "<td>4.0</td>\n",
       "<td>0.5851715</td>\n",
       "<td>6.7276339</td>\n",
       "<td>0.5553452</td>\n",
       "<td>0.5442666</td>\n",
       "<td>1.1257282</td>\n",
       "<td>0.4962516</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:44</td>\n",
       "<td> 2.609 sec</td>\n",
       "<td>5.0</td>\n",
       "<td>0.5791222</td>\n",
       "<td>6.1272189</td>\n",
       "<td>0.5580673</td>\n",
       "<td>0.5470899</td>\n",
       "<td>1.1349639</td>\n",
       "<td>0.4958511</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:44</td>\n",
       "<td> 2.770 sec</td>\n",
       "<td>6.0</td>\n",
       "<td>0.5697736</td>\n",
       "<td>5.2779577</td>\n",
       "<td>0.5629567</td>\n",
       "<td>0.5495888</td>\n",
       "<td>1.1434043</td>\n",
       "<td>0.4969796</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:44</td>\n",
       "<td> 2.929 sec</td>\n",
       "<td>7.0</td>\n",
       "<td>0.5626915</td>\n",
       "<td>4.6338884</td>\n",
       "<td>0.5661425</td>\n",
       "<td>0.5503092</td>\n",
       "<td>1.1450270</td>\n",
       "<td>0.4983314</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:44</td>\n",
       "<td> 3.077 sec</td>\n",
       "<td>8.0</td>\n",
       "<td>0.5571729</td>\n",
       "<td>4.1236515</td>\n",
       "<td>0.5688952</td>\n",
       "<td>0.5512667</td>\n",
       "<td>1.1392816</td>\n",
       "<td>0.4988358</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:45</td>\n",
       "<td> 3.217 sec</td>\n",
       "<td>9.0</td>\n",
       "<td>0.5518210</td>\n",
       "<td>3.6088963</td>\n",
       "<td>0.5701070</td>\n",
       "<td>0.5505822</td>\n",
       "<td>1.1255219</td>\n",
       "<td>0.4989474</td></tr>\n",
       "<tr><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></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:51</td>\n",
       "<td> 9.674 sec</td>\n",
       "<td>91.0</td>\n",
       "<td>0.5121359</td>\n",
       "<td>0.7533704</td>\n",
       "<td>0.5952258</td>\n",
       "<td>0.5636861</td>\n",
       "<td>1.1127511</td>\n",
       "<td>0.4775353</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:51</td>\n",
       "<td> 9.720 sec</td>\n",
       "<td>92.0</td>\n",
       "<td>0.5121067</td>\n",
       "<td>0.7518642</td>\n",
       "<td>0.5953347</td>\n",
       "<td>0.5638121</td>\n",
       "<td>1.0930564</td>\n",
       "<td>0.4763997</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:51</td>\n",
       "<td> 9.764 sec</td>\n",
       "<td>93.0</td>\n",
       "<td>0.5120795</td>\n",
       "<td>0.7516756</td>\n",
       "<td>0.5952206</td>\n",
       "<td>0.5638486</td>\n",
       "<td>1.1127511</td>\n",
       "<td>0.4763997</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:51</td>\n",
       "<td> 9.817 sec</td>\n",
       "<td>94.0</td>\n",
       "<td>0.5120749</td>\n",
       "<td>0.7515391</td>\n",
       "<td>0.5951989</td>\n",
       "<td>0.5638314</td>\n",
       "<td>1.1127511</td>\n",
       "<td>0.4764985</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:51</td>\n",
       "<td> 9.863 sec</td>\n",
       "<td>95.0</td>\n",
       "<td>0.5120170</td>\n",
       "<td>0.7513862</td>\n",
       "<td>0.5953313</td>\n",
       "<td>0.5639338</td>\n",
       "<td>1.1127511</td>\n",
       "<td>0.4767453</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:51</td>\n",
       "<td> 9.905 sec</td>\n",
       "<td>96.0</td>\n",
       "<td>0.5120785</td>\n",
       "<td>0.7499115</td>\n",
       "<td>0.5950313</td>\n",
       "<td>0.5638208</td>\n",
       "<td>1.1225984</td>\n",
       "<td>0.4780784</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:51</td>\n",
       "<td> 9.954 sec</td>\n",
       "<td>97.0</td>\n",
       "<td>0.5120833</td>\n",
       "<td>0.7499233</td>\n",
       "<td>0.5950820</td>\n",
       "<td>0.5639031</td>\n",
       "<td>1.1324458</td>\n",
       "<td>0.4776340</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:51</td>\n",
       "<td>10.001 sec</td>\n",
       "<td>98.0</td>\n",
       "<td>0.5120461</td>\n",
       "<td>0.7483438</td>\n",
       "<td>0.5952236</td>\n",
       "<td>0.5638544</td>\n",
       "<td>1.1422932</td>\n",
       "<td>0.4783253</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:51</td>\n",
       "<td>10.053 sec</td>\n",
       "<td>99.0</td>\n",
       "<td>0.5120013</td>\n",
       "<td>0.7482479</td>\n",
       "<td>0.5952382</td>\n",
       "<td>0.5638735</td>\n",
       "<td>1.1324458</td>\n",
       "<td>0.4785721</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2023-02-12 16:54:51</td>\n",
       "<td>10.109 sec</td>\n",
       "<td>100.0</td>\n",
       "<td>0.5120117</td>\n",
       "<td>0.7482544</td>\n",
       "<td>0.5951508</td>\n",
       "<td>0.5639439</td>\n",
       "<td>1.1422932</td>\n",
       "<td>0.4791152</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "<pre style='font-size: smaller; margin-bottom: 1em;'>[101 rows x 10 columns]</pre></div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-8.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-8 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-8 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-8 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-8 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-8 .h2o-table th,\n",
       "#h2o-table-8 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-8 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-8\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Variable Importances: </caption>\n",
       "    <thead><tr><th>variable</th>\n",
       "<th>relative_importance</th>\n",
       "<th>scaled_importance</th>\n",
       "<th>percentage</th></tr></thead>\n",
       "    <tbody><tr><td>salary</td>\n",
       "<td>112947.2734375</td>\n",
       "<td>1.0</td>\n",
       "<td>0.5518554</td></tr>\n",
       "<tr><td>seniority</td>\n",
       "<td>49155.5429688</td>\n",
       "<td>0.4352079</td>\n",
       "<td>0.2401718</td></tr>\n",
       "<tr><td>company_id</td>\n",
       "<td>31751.4667969</td>\n",
       "<td>0.2811176</td>\n",
       "<td>0.1551363</td></tr>\n",
       "<tr><td>dept</td>\n",
       "<td>10813.9501953</td>\n",
       "<td>0.0957433</td>\n",
       "<td>0.0528365</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div><pre style=\"font-size: smaller; margin: 1em 0 0 0;\">\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section.</pre>"
      ],
      "text/plain": [
       "Model Details\n",
       "=============\n",
       "H2ORandomForestEstimator : Distributed Random Forest\n",
       "Model Key: DRF_model_python_1676249291128_1\n",
       "\n",
       "\n",
       "Model Summary: \n",
       "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
       "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
       "    100                100                         5.41477e+06            20           20           20            3559          5220          4309.58\n",
       "\n",
       "ModelMetricsBinomial: drf\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.2621560047840295\n",
       "RMSE: 0.5120117232876895\n",
       "LogLoss: 0.7482543588963848\n",
       "Mean Per-Class Error: 0.4793378168889879\n",
       "AUC: 0.5951507723031288\n",
       "AUCPR: 0.5639439211481407\n",
       "Gini: 0.1903015446062577\n",
       "\n",
       "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.13825023004727358\n",
       "       0    1      Error    Rate\n",
       "-----  ---  -----  -------  ----------------\n",
       "0      707  9415   0.9302   (9415.0/10122.0)\n",
       "1      289  9843   0.0285   (289.0/10132.0)\n",
       "Total  996  19258  0.4791   (9704.0/20254.0)\n",
       "\n",
       "Maximum Metrics: Maximum metrics at their respective thresholds\n",
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.13825      0.66982   362\n",
       "max f2                       0            0.83347   399\n",
       "max f0point5                 0.371614     0.581648  268\n",
       "max accuracy                 0.540558     0.573664  188\n",
       "max precision                0.933968     0.591623  11\n",
       "max recall                   0            1         399\n",
       "max specificity              0.998554     0.999605  0\n",
       "max absolute_mcc             0.324054     0.14779   289\n",
       "max min_per_class_accuracy   0.537125     0.571923  190\n",
       "max mean_per_class_accuracy  0.540558     0.573666  188\n",
       "max tns                      0.998554     10118     0\n",
       "max fns                      0.998554     10129     0\n",
       "max fps                      0            10122     399\n",
       "max tps                      0            10132     399\n",
       "max tnr                      0.998554     0.999605  0\n",
       "max fnr                      0.998554     0.999704  0\n",
       "max fpr                      0            1         399\n",
       "max tpr                      0            1         399\n",
       "\n",
       "Gains/Lift Table: Avg response rate: 50.02 %, avg score: 52.29 %\n",
       "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score     cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\n",
       "-------  --------------------------  -----------------  --------  -----------------  ---------------  --------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\n",
       "1        0.0100227                   0.929896           1.14229   1.14229            0.571429         0.95161   0.571429                    0.95161             0.0114489       0.0114489                  14.2293   14.2293            0.00285374\n",
       "2        0.0200454                   0.904752           1.09306   1.11767            0.546798         0.916379  0.559113                    0.933994            0.0109554       0.0224043                  9.30564   11.7675            0.00472001\n",
       "3        0.0300188                   0.885799           1.14795   1.12773            0.574257         0.895501  0.564145                    0.921205            0.0114489       0.0338531                  14.7948   12.7733            0.00767254\n",
       "4        0.0400415                   0.871678           1.26046   1.16096            0.630542         0.878761  0.580764                    0.910581            0.0126332       0.0464864                  26.0461   16.0956            0.0128962\n",
       "5        0.0500148                   0.858345           1.02919   1.13468            0.514851         0.865169  0.567621                    0.901526            0.0102645       0.0567509                  2.91948   13.4682            0.0134788\n",
       "6        0.10003                     0.806451           1.15047   1.14258            0.575518         0.831148  0.57157                     0.866337            0.0575405       0.114291                   15.0469   14.2575            0.0285376\n",
       "7        0.150044                    0.765975           1.22348   1.16954            0.612043         0.785935  0.585061                    0.839536            0.0611923       0.175484                   22.3483   16.9544            0.0509035\n",
       "8        0.20001                     0.73066            1.17728   1.17148            0.588933         0.747711  0.586028                    0.816597            0.0588235       0.234307                   17.7284   17.1478            0.0686284\n",
       "9        0.29999                     0.665446           1.17769   1.17355            0.589136         0.697703  0.587064                    0.776972            0.117746        0.352053                   17.769    17.3548            0.104177\n",
       "10       0.40002                     0.599752           1.1406    1.16531            0.570582         0.632351  0.582942                    0.740808            0.114094        0.466147                   14.0602   16.531             0.13232\n",
       "11       0.5                         0.537147           1.07108   1.14647            0.535802         0.568618  0.573516                    0.706377            0.107086        0.573233                   7.10761   14.6467            0.146539\n",
       "12       0.59998                     0.47073            0.972359  1.11745            0.48642          0.504921  0.559003                    0.672806            0.0972167       0.67045                    -2.76406  11.7454            0.141009\n",
       "13       0.70001                     0.398377           0.964973  1.09566            0.482725         0.434858  0.548103                    0.638804            0.0965259       0.766976                   -3.50273  9.56644            0.133998\n",
       "14       0.79999                     0.316736           0.914117  1.07298            0.457284         0.359569  0.536752                    0.603906            0.0913936       0.85837                    -8.58834  7.29751            0.116816\n",
       "15       0.89997                     0.21208            0.767029  1.03899            0.383704         0.267319  0.51975                     0.566514            0.0766877       0.935057                   -23.2971  3.89867            0.0702084\n",
       "16       1                           0                  0.649235  1                  0.324778         0.13045   0.500247                    0.522895            0.0649428       1                          -35.0765  0                  0\n",
       "\n",
       "Scoring History: \n",
       "     timestamp            duration    number_of_trees    training_rmse       training_logloss    training_auc        training_pr_auc     training_lift       training_classification_error\n",
       "---  -------------------  ----------  -----------------  ------------------  ------------------  ------------------  ------------------  ------------------  -------------------------------\n",
       "     2023-02-12 16:54:42  0.228 sec   0.0                nan                 nan                 nan                 nan                 nan                 nan\n",
       "     2023-02-12 16:54:43  1.402 sec   1.0                0.6169863780015421  9.72018661533448    0.5413397607253667  0.5306769382066883  1.0887226085834054  0.499527983816588\n",
       "     2023-02-12 16:54:43  1.948 sec   2.0                0.6040381754411911  8.521891930670424   0.5482368908046438  0.5368070360270285  1.1021148905799885  0.49814218479068617\n",
       "     2023-02-12 16:54:44  2.237 sec   3.0                0.594713588842666   7.584356369688118   0.5495067205067546  0.5393545983707398  1.1073625204323165  0.4965016501650165\n",
       "     2023-02-12 16:54:44  2.433 sec   4.0                0.5851714793386487  6.7276339192872     0.5553452356159871  0.5442666437928853  1.1257281825522438  0.49625161063605483\n",
       "     2023-02-12 16:54:44  2.609 sec   5.0                0.5791221779729323  6.127218935757728   0.5580672759746025  0.5470898962422356  1.1349638913447346  0.4958510754449176\n",
       "     2023-02-12 16:54:44  2.770 sec   6.0                0.5697735614169906  5.277957656609023   0.5629566906025942  0.549588758546647   1.1434043389899242  0.4969795661081053\n",
       "     2023-02-12 16:54:44  2.929 sec   7.0                0.5626915134117331  4.633888442655284   0.5661425439177818  0.5503092081230635  1.145027007678933   0.49833136519997945\n",
       "     2023-02-12 16:54:44  3.077 sec   8.0                0.5571729042645066  4.12365147169532    0.5688951620535949  0.5512666751192741  1.1392816111147614  0.4988357967199838\n",
       "     2023-02-12 16:54:45  3.217 sec   9.0                0.5518210016685651  3.608896314095542   0.570107031158857   0.5505822476501401  1.125521882700083   0.49894736842105264\n",
       "---  ---                  ---         ---                ---                 ---                 ---                 ---                 ---                 ---\n",
       "     2023-02-12 16:54:51  9.674 sec   91.0               0.5121358644484013  0.7533704101780417  0.5952257702769208  0.5636860946214991  1.1127510944206425  0.47753530166880614\n",
       "     2023-02-12 16:54:51  9.720 sec   92.0               0.5121067344782826  0.7518641709201193  0.5953347252738852  0.5638120606694763  1.0930563847848789  0.47639972351140514\n",
       "     2023-02-12 16:54:51  9.764 sec   93.0               0.512079542814022   0.751675594189129   0.5952205536200946  0.5638486199306784  1.1127510944206425  0.47639972351140514\n",
       "     2023-02-12 16:54:51  9.817 sec   94.0               0.5120749432188113  0.7515391324343753  0.5951989361842372  0.5638313602665092  1.1127510944206425  0.4764984694381357\n",
       "     2023-02-12 16:54:51  9.863 sec   95.0               0.5120170370073744  0.7513862334796951  0.5953312978815966  0.5639337851325472  1.1127510944206425  0.47674533425496196\n",
       "     2023-02-12 16:54:51  9.905 sec   96.0               0.5120784822812615  0.74991150156904    0.5950312816095276  0.5638208366419363  1.1225984492385244  0.47807840426582404\n",
       "     2023-02-12 16:54:51  9.954 sec   97.0               0.5120833437546578  0.7499233225666733  0.595082005065247   0.5639031081048662  1.132445804056406   0.4776340475955367\n",
       "     2023-02-12 16:54:51  10.001 sec  98.0               0.5120461344384266  0.7483438068214187  0.5952235812312059  0.5638544219741348  1.1422931588742877  0.47832526908265033\n",
       "     2023-02-12 16:54:51  10.053 sec  99.0               0.5120012672438055  0.7482478539002292  0.5952381683687984  0.5638734706064864  1.132445804056406   0.47857213389947667\n",
       "     2023-02-12 16:54:51  10.109 sec  100.0              0.5120117232876895  0.7482543588963848  0.5951507723031288  0.5639439211481407  1.1422931588742877  0.4791152364964945\n",
       "[101 rows x 10 columns]\n",
       "\n",
       "\n",
       "Variable Importances: \n",
       "variable    relative_importance    scaled_importance    percentage\n",
       "----------  ---------------------  -------------------  ------------\n",
       "salary      112947                 1                    0.551855\n",
       "seniority   49155.5                0.435208             0.240172\n",
       "company_id  31751.5                0.281118             0.155136\n",
       "dept        10814                  0.0957433            0.0528365\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section."
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build random forest model\n",
    "model = H2ORandomForestEstimator(balance_classes=True, ntrees=100, max_depth=20, \n",
    "                                 mtries=-1, seed=42, score_each_iteration=True)\n",
    "\n",
    "model.train(x=feature, y=target, training_frame=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2o.plot._plot_result._MObject at 0x7fa4d35d8700>"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.varimp_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature importance\n",
    "importance = model.varimp(use_pandas=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.barplot(x='scaled_importance', y='variable', data=importance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import auc, roc_curve, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
      "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "# Make predictions\n",
    "\n",
    "train_true = train.as_data_frame()['quit'].values\n",
    "test_true = test.as_data_frame()['quit'].values\n",
    "train_pred = model.predict(train).as_data_frame()['p1'].values\n",
    "test_pred = model.predict(test).as_data_frame()['p1'].values\n",
    "\n",
    "train_fpr, train_tpr, _ = roc_curve(train_true, train_pred)\n",
    "test_fpr, test_tpr, _ = roc_curve(test_true, test_pred)\n",
    "\n",
    "train_auc = np.round(auc(train_fpr, train_tpr), 3)\n",
    "test_auc = np.round(auc(test_fpr, test_tpr), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.48      0.39      0.43      2798\n",
      "           1       0.56      0.64      0.60      3378\n",
      "\n",
      "    accuracy                           0.53      6176\n",
      "   macro avg       0.52      0.52      0.52      6176\n",
      "weighted avg       0.52      0.53      0.52      6176\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "print(classification_report(y_true=test_true, y_pred=(test_pred > 0.5).astype(int)))"
   ]
  },
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   "source": []
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