Placement_Prediction-1.1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>9 hours 26 mins</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>America/New_York</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.30.0.1</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>2 months and 13 days </td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_vishw_zfbpz4</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>1.893 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, \"no\": \"192.168.99.100,192.168.99.101\"}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O_API_Extensions:</td>\n",
       "<td>Amazon S3, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.6.8 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  --------------------------------------------------------------------\n",
       "H2O_cluster_uptime:         9 hours 26 mins\n",
       "H2O_cluster_timezone:       America/New_York\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.30.0.1\n",
       "H2O_cluster_version_age:    2 months and 13 days\n",
       "H2O_cluster_name:           H2O_from_python_vishw_zfbpz4\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    1.893 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, \"no\": \"192.168.99.100,192.168.99.101\"}\n",
       "H2O_internal_security:      False\n",
       "H2O_API_Extensions:         Amazon S3, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
       "Python_version:             3.6.8 final\n",
       "--------------------------  --------------------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=\"C:/Users/vishw/Downloads/datasets_596958_1073629_Placement_Data_Full_Class.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "campus=h2o.import_file(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  sl_no</th><th>gender  </th><th style=\"text-align: right;\">  ssc_p</th><th>ssc_b  </th><th style=\"text-align: right;\">  hsc_p</th><th>hsc_b  </th><th>hsc_s   </th><th style=\"text-align: right;\">  degree_p</th><th>degree_t  </th><th>workex  </th><th style=\"text-align: right;\">  etest_p</th><th>specialisation  </th><th style=\"text-align: right;\">  mba_p</th><th>status    </th><th style=\"text-align: right;\">  salary</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">      1</td><td>M       </td><td style=\"text-align: right;\">  67   </td><td>Others </td><td style=\"text-align: right;\">  91   </td><td>Others </td><td>Commerce</td><td style=\"text-align: right;\">     58   </td><td>Sci&amp;Tech  </td><td>No      </td><td style=\"text-align: right;\">    55   </td><td>Mkt&amp;HR          </td><td style=\"text-align: right;\">  58.8 </td><td>Placed    </td><td style=\"text-align: right;\">  270000</td></tr>\n",
       "<tr><td style=\"text-align: right;\">      2</td><td>M       </td><td style=\"text-align: right;\">  79.33</td><td>Central</td><td style=\"text-align: right;\">  78.33</td><td>Others </td><td>Science </td><td style=\"text-align: right;\">     77.48</td><td>Sci&amp;Tech  </td><td>Yes     </td><td style=\"text-align: right;\">    86.5 </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  66.28</td><td>Placed    </td><td style=\"text-align: right;\">  200000</td></tr>\n",
       "<tr><td style=\"text-align: right;\">      3</td><td>M       </td><td style=\"text-align: right;\">  65   </td><td>Central</td><td style=\"text-align: right;\">  68   </td><td>Central</td><td>Arts    </td><td style=\"text-align: right;\">     64   </td><td>Comm&amp;Mgmt </td><td>No      </td><td style=\"text-align: right;\">    75   </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  57.8 </td><td>Placed    </td><td style=\"text-align: right;\">  250000</td></tr>\n",
       "<tr><td style=\"text-align: right;\">      4</td><td>M       </td><td style=\"text-align: right;\">  56   </td><td>Central</td><td style=\"text-align: right;\">  52   </td><td>Central</td><td>Science </td><td style=\"text-align: right;\">     52   </td><td>Sci&amp;Tech  </td><td>No      </td><td style=\"text-align: right;\">    66   </td><td>Mkt&amp;HR          </td><td style=\"text-align: right;\">  59.43</td><td>Not Placed</td><td style=\"text-align: right;\">       0</td></tr>\n",
       "<tr><td style=\"text-align: right;\">      5</td><td>M       </td><td style=\"text-align: right;\">  85.8 </td><td>Central</td><td style=\"text-align: right;\">  73.6 </td><td>Central</td><td>Commerce</td><td style=\"text-align: right;\">     73.3 </td><td>Comm&amp;Mgmt </td><td>No      </td><td style=\"text-align: right;\">    96.8 </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  55.5 </td><td>Placed    </td><td style=\"text-align: right;\">  425000</td></tr>\n",
       "<tr><td style=\"text-align: right;\">      6</td><td>M       </td><td style=\"text-align: right;\">  55   </td><td>Others </td><td style=\"text-align: right;\">  49.8 </td><td>Others </td><td>Science </td><td style=\"text-align: right;\">     67.25</td><td>Sci&amp;Tech  </td><td>Yes     </td><td style=\"text-align: right;\">    55   </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  51.58</td><td>Not Placed</td><td style=\"text-align: right;\">       0</td></tr>\n",
       "<tr><td style=\"text-align: right;\">      7</td><td>F       </td><td style=\"text-align: right;\">  46   </td><td>Others </td><td style=\"text-align: right;\">  49.2 </td><td>Others </td><td>Commerce</td><td style=\"text-align: right;\">     79   </td><td>Comm&amp;Mgmt </td><td>No      </td><td style=\"text-align: right;\">    74.28</td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  53.29</td><td>Not Placed</td><td style=\"text-align: right;\">       0</td></tr>\n",
       "<tr><td style=\"text-align: right;\">      8</td><td>M       </td><td style=\"text-align: right;\">  82   </td><td>Central</td><td style=\"text-align: right;\">  64   </td><td>Central</td><td>Science </td><td style=\"text-align: right;\">     66   </td><td>Sci&amp;Tech  </td><td>Yes     </td><td style=\"text-align: right;\">    67   </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  62.14</td><td>Placed    </td><td style=\"text-align: right;\">  252000</td></tr>\n",
       "<tr><td style=\"text-align: right;\">      9</td><td>M       </td><td style=\"text-align: right;\">  73   </td><td>Central</td><td style=\"text-align: right;\">  79   </td><td>Central</td><td>Commerce</td><td style=\"text-align: right;\">     72   </td><td>Comm&amp;Mgmt </td><td>No      </td><td style=\"text-align: right;\">    91.34</td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  61.29</td><td>Placed    </td><td style=\"text-align: right;\">  231000</td></tr>\n",
       "<tr><td style=\"text-align: right;\">     10</td><td>M       </td><td style=\"text-align: right;\">  58   </td><td>Central</td><td style=\"text-align: right;\">  70   </td><td>Central</td><td>Commerce</td><td style=\"text-align: right;\">     61   </td><td>Comm&amp;Mgmt </td><td>No      </td><td style=\"text-align: right;\">    54   </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  52.21</td><td>Not Placed</td><td style=\"text-align: right;\">       0</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "campus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "campus[campus[\"salary\"].isna(), \"salary\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_1 = campus[:, [1, 2,4,6,7,8,9,10,11,12,13,14]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>gender  </th><th style=\"text-align: right;\">  ssc_p</th><th style=\"text-align: right;\">  hsc_p</th><th>hsc_s   </th><th style=\"text-align: right;\">  degree_p</th><th>degree_t  </th><th>workex  </th><th style=\"text-align: right;\">  etest_p</th><th>specialisation  </th><th style=\"text-align: right;\">  mba_p</th><th>status    </th><th style=\"text-align: right;\">  salary</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>M       </td><td style=\"text-align: right;\">  67   </td><td style=\"text-align: right;\">  91   </td><td>Commerce</td><td style=\"text-align: right;\">     58   </td><td>Sci&amp;Tech  </td><td>No      </td><td style=\"text-align: right;\">    55   </td><td>Mkt&amp;HR          </td><td style=\"text-align: right;\">  58.8 </td><td>Placed    </td><td style=\"text-align: right;\">  270000</td></tr>\n",
       "<tr><td>M       </td><td style=\"text-align: right;\">  79.33</td><td style=\"text-align: right;\">  78.33</td><td>Science </td><td style=\"text-align: right;\">     77.48</td><td>Sci&amp;Tech  </td><td>Yes     </td><td style=\"text-align: right;\">    86.5 </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  66.28</td><td>Placed    </td><td style=\"text-align: right;\">  200000</td></tr>\n",
       "<tr><td>M       </td><td style=\"text-align: right;\">  65   </td><td style=\"text-align: right;\">  68   </td><td>Arts    </td><td style=\"text-align: right;\">     64   </td><td>Comm&amp;Mgmt </td><td>No      </td><td style=\"text-align: right;\">    75   </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  57.8 </td><td>Placed    </td><td style=\"text-align: right;\">  250000</td></tr>\n",
       "<tr><td>M       </td><td style=\"text-align: right;\">  56   </td><td style=\"text-align: right;\">  52   </td><td>Science </td><td style=\"text-align: right;\">     52   </td><td>Sci&amp;Tech  </td><td>No      </td><td style=\"text-align: right;\">    66   </td><td>Mkt&amp;HR          </td><td style=\"text-align: right;\">  59.43</td><td>Not Placed</td><td style=\"text-align: right;\">       0</td></tr>\n",
       "<tr><td>M       </td><td style=\"text-align: right;\">  85.8 </td><td style=\"text-align: right;\">  73.6 </td><td>Commerce</td><td style=\"text-align: right;\">     73.3 </td><td>Comm&amp;Mgmt </td><td>No      </td><td style=\"text-align: right;\">    96.8 </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  55.5 </td><td>Placed    </td><td style=\"text-align: right;\">  425000</td></tr>\n",
       "<tr><td>M       </td><td style=\"text-align: right;\">  55   </td><td style=\"text-align: right;\">  49.8 </td><td>Science </td><td style=\"text-align: right;\">     67.25</td><td>Sci&amp;Tech  </td><td>Yes     </td><td style=\"text-align: right;\">    55   </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  51.58</td><td>Not Placed</td><td style=\"text-align: right;\">       0</td></tr>\n",
       "<tr><td>F       </td><td style=\"text-align: right;\">  46   </td><td style=\"text-align: right;\">  49.2 </td><td>Commerce</td><td style=\"text-align: right;\">     79   </td><td>Comm&amp;Mgmt </td><td>No      </td><td style=\"text-align: right;\">    74.28</td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  53.29</td><td>Not Placed</td><td style=\"text-align: right;\">       0</td></tr>\n",
       "<tr><td>M       </td><td style=\"text-align: right;\">  82   </td><td style=\"text-align: right;\">  64   </td><td>Science </td><td style=\"text-align: right;\">     66   </td><td>Sci&amp;Tech  </td><td>Yes     </td><td style=\"text-align: right;\">    67   </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  62.14</td><td>Placed    </td><td style=\"text-align: right;\">  252000</td></tr>\n",
       "<tr><td>M       </td><td style=\"text-align: right;\">  73   </td><td style=\"text-align: right;\">  79   </td><td>Commerce</td><td style=\"text-align: right;\">     72   </td><td>Comm&amp;Mgmt </td><td>No      </td><td style=\"text-align: right;\">    91.34</td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  61.29</td><td>Placed    </td><td style=\"text-align: right;\">  231000</td></tr>\n",
       "<tr><td>M       </td><td style=\"text-align: right;\">  58   </td><td style=\"text-align: right;\">  70   </td><td>Commerce</td><td style=\"text-align: right;\">     61   </td><td>Comm&amp;Mgmt </td><td>No      </td><td style=\"text-align: right;\">    54   </td><td>Mkt&amp;Fin         </td><td style=\"text-align: right;\">  52.21</td><td>Not Placed</td><td style=\"text-align: right;\">       0</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_1df=cols_1.as_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = cols_1df['hsc_p'].quantile(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q3 = cols_1df['hsc_p'].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "IQR = Q3 - Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter = (cols_1df['hsc_p'] >= Q1 - 1.5 * IQR) & (cols_1df['hsc_p'] <= Q3 + 1.5 *IQR)\n",
    "placement_filtered=cols_1df.loc[filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "object_cols=['gender','workex','specialisation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\vishw\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "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>gender</th>\n",
       "      <th>ssc_p</th>\n",
       "      <th>hsc_p</th>\n",
       "      <th>hsc_s</th>\n",
       "      <th>degree_p</th>\n",
       "      <th>degree_t</th>\n",
       "      <th>workex</th>\n",
       "      <th>etest_p</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>mba_p</th>\n",
       "      <th>status</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>67.00</td>\n",
       "      <td>91.00</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>58.80</td>\n",
       "      <td>Placed</td>\n",
       "      <td>270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>79.33</td>\n",
       "      <td>78.33</td>\n",
       "      <td>Science</td>\n",
       "      <td>77.48</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>1</td>\n",
       "      <td>86.5</td>\n",
       "      <td>0</td>\n",
       "      <td>66.28</td>\n",
       "      <td>Placed</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>65.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>Arts</td>\n",
       "      <td>64.00</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.80</td>\n",
       "      <td>Placed</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>56.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>Science</td>\n",
       "      <td>52.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.43</td>\n",
       "      <td>Not Placed</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>85.80</td>\n",
       "      <td>73.60</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>73.30</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>0</td>\n",
       "      <td>96.8</td>\n",
       "      <td>0</td>\n",
       "      <td>55.50</td>\n",
       "      <td>Placed</td>\n",
       "      <td>425000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  ssc_p  hsc_p     hsc_s  degree_p   degree_t  workex  etest_p  \\\n",
       "0       1  67.00  91.00  Commerce     58.00   Sci&Tech       0     55.0   \n",
       "1       1  79.33  78.33   Science     77.48   Sci&Tech       1     86.5   \n",
       "2       1  65.00  68.00      Arts     64.00  Comm&Mgmt       0     75.0   \n",
       "3       1  56.00  52.00   Science     52.00   Sci&Tech       0     66.0   \n",
       "4       1  85.80  73.60  Commerce     73.30  Comm&Mgmt       0     96.8   \n",
       "\n",
       "   specialisation  mba_p      status  salary  \n",
       "0               1  58.80      Placed  270000  \n",
       "1               0  66.28      Placed  200000  \n",
       "2               0  57.80      Placed  250000  \n",
       "3               1  59.43  Not Placed       0  \n",
       "4               0  55.50      Placed  425000  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "for col in object_cols:\n",
    "    placement_filtered[col] = label_encoder.fit_transform(placement_filtered[col])\n",
    "placement_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>gender</th>\n",
       "      <th>ssc_p</th>\n",
       "      <th>hsc_p</th>\n",
       "      <th>degree_p</th>\n",
       "      <th>workex</th>\n",
       "      <th>etest_p</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>mba_p</th>\n",
       "      <th>status</th>\n",
       "      <th>dummy_Arts</th>\n",
       "      <th>dummy_Commerce</th>\n",
       "      <th>dummy_Science</th>\n",
       "      <th>dummy_Comm&amp;Mgmt</th>\n",
       "      <th>dummy_Others</th>\n",
       "      <th>dummy_Sci&amp;Tech</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>67.00</td>\n",
       "      <td>91.00</td>\n",
       "      <td>58.00</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>58.80</td>\n",
       "      <td>Placed</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>79.33</td>\n",
       "      <td>78.33</td>\n",
       "      <td>77.48</td>\n",
       "      <td>1</td>\n",
       "      <td>86.5</td>\n",
       "      <td>0</td>\n",
       "      <td>66.28</td>\n",
       "      <td>Placed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>65.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.80</td>\n",
       "      <td>Placed</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>56.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.43</td>\n",
       "      <td>Not Placed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>85.80</td>\n",
       "      <td>73.60</td>\n",
       "      <td>73.30</td>\n",
       "      <td>0</td>\n",
       "      <td>96.8</td>\n",
       "      <td>0</td>\n",
       "      <td>55.50</td>\n",
       "      <td>Placed</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  ssc_p  hsc_p  degree_p  workex  etest_p  specialisation  mba_p  \\\n",
       "0       1  67.00  91.00     58.00       0     55.0               1  58.80   \n",
       "1       1  79.33  78.33     77.48       1     86.5               0  66.28   \n",
       "2       1  65.00  68.00     64.00       0     75.0               0  57.80   \n",
       "3       1  56.00  52.00     52.00       0     66.0               1  59.43   \n",
       "4       1  85.80  73.60     73.30       0     96.8               0  55.50   \n",
       "\n",
       "       status  dummy_Arts  dummy_Commerce  dummy_Science  dummy_Comm&Mgmt  \\\n",
       "0      Placed           0               1              0                0   \n",
       "1      Placed           0               0              1                0   \n",
       "2      Placed           1               0              0                1   \n",
       "3  Not Placed           0               0              1                0   \n",
       "4      Placed           0               1              0                1   \n",
       "\n",
       "   dummy_Others  dummy_Sci&Tech  \n",
       "0             0               1  \n",
       "1             0               1  \n",
       "2             0               0  \n",
       "3             0               1  \n",
       "4             0               0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_hsc_s=pd.get_dummies(placement_filtered['hsc_s'], prefix='dummy')\n",
    "dummy_degree_t=pd.get_dummies(placement_filtered['degree_t'], prefix='dummy')\n",
    "placement_coded = pd.concat([placement_filtered,dummy_hsc_s,dummy_degree_t],axis=1)\n",
    "placement_coded.drop(['hsc_s','degree_t','salary'],axis=1, inplace=True)\n",
    "placement_coded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "placement_h2o  = h2o.H2OFrame(placement_coded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  gender</th><th style=\"text-align: right;\">  ssc_p</th><th style=\"text-align: right;\">  hsc_p</th><th style=\"text-align: right;\">  degree_p</th><th style=\"text-align: right;\">  workex</th><th style=\"text-align: right;\">  etest_p</th><th style=\"text-align: right;\">  specialisation</th><th style=\"text-align: right;\">  mba_p</th><th>status    </th><th style=\"text-align: right;\">  dummy_Arts</th><th style=\"text-align: right;\">  dummy_Commerce</th><th style=\"text-align: right;\">  dummy_Science</th><th style=\"text-align: right;\">  dummy_Comm&amp;Mgmt</th><th style=\"text-align: right;\">  dummy_Others</th><th style=\"text-align: right;\">  dummy_Sci&amp;Tech</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">  67   </td><td style=\"text-align: right;\">  91   </td><td style=\"text-align: right;\">     58   </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">    55   </td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">  58.8 </td><td>Placed    </td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">                0</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">               1</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">  79.33</td><td style=\"text-align: right;\">  78.33</td><td style=\"text-align: right;\">     77.48</td><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">    86.5 </td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">  66.28</td><td>Placed    </td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">                0</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">               1</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">  65   </td><td style=\"text-align: right;\">  68   </td><td style=\"text-align: right;\">     64   </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">    75   </td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">  57.8 </td><td>Placed    </td><td style=\"text-align: right;\">           1</td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">                1</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">               0</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">  56   </td><td style=\"text-align: right;\">  52   </td><td style=\"text-align: right;\">     52   </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">    66   </td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">  59.43</td><td>Not Placed</td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">                0</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">               1</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">  85.8 </td><td style=\"text-align: right;\">  73.6 </td><td style=\"text-align: right;\">     73.3 </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">    96.8 </td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">  55.5 </td><td>Placed    </td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">                1</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">               0</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">  55   </td><td style=\"text-align: right;\">  49.8 </td><td style=\"text-align: right;\">     67.25</td><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">    55   </td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">  51.58</td><td>Not Placed</td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">                0</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">               1</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">  46   </td><td style=\"text-align: right;\">  49.2 </td><td style=\"text-align: right;\">     79   </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">    74.28</td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">  53.29</td><td>Not Placed</td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">                1</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">               0</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">  82   </td><td style=\"text-align: right;\">  64   </td><td style=\"text-align: right;\">     66   </td><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">    67   </td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">  62.14</td><td>Placed    </td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">              1</td><td style=\"text-align: right;\">                0</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">               1</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">  73   </td><td style=\"text-align: right;\">  79   </td><td style=\"text-align: right;\">     72   </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">    91.34</td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">  61.29</td><td>Placed    </td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">                1</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">               0</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">  58   </td><td style=\"text-align: right;\">  70   </td><td style=\"text-align: right;\">     61   </td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">    54   </td><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">  52.21</td><td>Not Placed</td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">                1</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">               0</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "placement_h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = placement_h2o.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=\"status\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.remove(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gender',\n",
       " 'ssc_p',\n",
       " 'hsc_p',\n",
       " 'degree_p',\n",
       " 'workex',\n",
       " 'etest_p',\n",
       " 'specialisation',\n",
       " 'mba_p',\n",
       " 'dummy_Arts',\n",
       " 'dummy_Commerce',\n",
       " 'dummy_Science',\n",
       " 'dummy_Comm&Mgmt',\n",
       " 'dummy_Others',\n",
       " 'dummy_Sci&Tech']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test,valid=placement_h2o.split_frame(ratios=[.7,.15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from h2o.automl import H2OAutoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "aml = H2OAutoML(max_models = 15, seed = 10, verbosity=\"info\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoML progress: |█\n",
      "01:50:41.135: Project: AutoML_20200617_15041135\n",
      "01:50:41.135: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.\n",
      "01:50:41.135: Setting stopping tolerance adaptively based on the training frame: 0.05\n",
      "01:50:41.135: Build control seed: 10\n",
      "01:50:41.135: training frame: Frame key: automl_training_py_33_sid_ba8e    cols: 15    rows: 141  chunks: 1    size: 3294  checksum: -8775346951757672819\n",
      "01:50:41.135: validation frame: Frame key: py_35_sid_ba8e    cols: 15    rows: 31  chunks: 1    size: 2060  checksum: 8823566266234026082\n",
      "01:50:41.135: leaderboard frame: NULL\n",
      "01:50:41.135: blending frame: NULL\n",
      "01:50:41.135: response column: status\n",
      "01:50:41.135: fold column: null\n",
      "01:50:41.135: weights column: null\n",
      "01:50:41.142: AutoML: XGBoost is not available; skipping it.\n",
      "01:50:41.142: Loading execution steps: [{XGBoost : defaults}, {GLM : defaults}, {DRF : [def_1]}, {GBM : defaults}, {DeepLearning : defaults}, {DRF : [XRT]}, {XGBoost : grids}, {GBM : grids}, {DeepLearning : grids}, {GBM : [lr_annealing]}, {XGBoost : [lr_search]}, {StackedEnsemble : defaults}]\n",
      "01:50:41.142: Disabling Algo: XGBoost as requested by the user.\n",
      "01:50:41.142: AutoML job created: 2020.06.17 01:50:41.135\n",
      "01:50:41.142: AutoML build started: 2020.06.17 01:50:41.142\n",
      "01:50:41.142: AutoML: starting GLM_1_AutoML_20200617_015041 model training\n",
      "01:50:42.149: New leader: GLM_1_AutoML_20200617_015041, auc: 0.9292833412434742\n",
      "01:50:42.149: AutoML: starting DRF_1_AutoML_20200617_015041 model training\n",
      "01:50:43.158: AutoML: starting GBM_1_AutoML_20200617_015041 model training\n",
      "01:50:45.166: New leader: GBM_1_AutoML_20200617_015041, auc: 0.9477930707166587\n",
      "01:50:45.166: AutoML: starting GBM_2_AutoML_20200617_015041 model training\n",
      "\n",
      "██████████\n",
      "01:50:46.170: AutoML: starting GBM_3_AutoML_20200617_015041 model training\n",
      "01:50:47.176: AutoML: starting GBM_4_AutoML_20200617_015041 model training\n",
      "01:50:48.183: AutoML: starting GBM_5_AutoML_20200617_015041 model training\n",
      "01:50:48.187: Skipping training of model GBM_5_AutoML_20200617_015041 due to exception: water.exceptions.H2OModelBuilderIllegalArgumentException: Illegal argument(s) for GBM model: GBM_5_AutoML_20200617_015041.  Details: ERRR on field: _min_rows: The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 141.0.\n",
      "\n",
      "01:50:48.188: AutoML: starting DeepLearning_1_AutoML_20200617_015041 model training\n",
      "01:50:49.195: AutoML: starting XRT_1_AutoML_20200617_015041 model training\n",
      "01:50:50.200: AutoML: starting GBM_grid__1_AutoML_20200617_015041 hyperparameter search\n",
      "01:50:53.231: New leader: GBM_grid__1_AutoML_20200617_015041_model_4, auc: 0.9537256763170384\n",
      "01:50:53.235: AutoML: starting DeepLearning_grid__1_AutoML_20200617_015041 hyperparameter search\n",
      "01:51:13.803: AutoML: starting DeepLearning_grid__2_AutoML_20200617_015041 hyperparameter search\n",
      "\n",
      "███████████████████████████\n",
      "01:53:13.93: AutoML: starting DeepLearning_grid__3_AutoML_20200617_015041 hyperparameter search\n",
      "\n",
      "██████████████████\n",
      "01:56:40.577: AutoML: starting StackedEnsemble_BestOfFamily_AutoML_20200617_015041 model training\n",
      "01:56:41.586: AutoML: starting StackedEnsemble_AllModels_AutoML_20200617_015041 model training\n",
      "01:56:42.600: Actual modeling steps: [{GLM : [def_1 (10)]}, {DRF : [def_1 (10)]}, {GBM : [def_1 (10), def_2 (10), def_3 (10), def_4 (10)]}, {DeepLearning : [def_1 (10)]}, {DRF : [XRT (10)]}, {GBM : [grid_1 (60)]}, {DeepLearning : [grid_1 (20), grid_2 (20), grid_3 (20)]}, {StackedEnsemble : [best (10), all (10)]}]\n",
      "01:56:42.600: AutoML build stopped: 2020.06.17 01:56:42.600\n",
      "01:56:42.600: AutoML build done: built 17 models\n",
      "01:56:42.600: AutoML duration:  6 min  1.458 sec\n",
      "\n",
      "██| 100%\n"
     ]
    }
   ],
   "source": [
    "aml.train(x = x, y = y, training_frame = train, validation_frame=valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = aml.leaderboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>model_id                                           </th><th style=\"text-align: right;\">     auc</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">   aucpr</th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">      mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>GBM_grid__1_AutoML_20200617_015041_model_4         </td><td style=\"text-align: right;\">0.953726</td><td style=\"text-align: right;\"> 0.300801</td><td style=\"text-align: right;\">0.980177</td><td style=\"text-align: right;\">              0.178097</td><td style=\"text-align: right;\">0.310966</td><td style=\"text-align: right;\">0.0967   </td></tr>\n",
       "<tr><td>GBM_1_AutoML_20200617_015041                       </td><td style=\"text-align: right;\">0.947793</td><td style=\"text-align: right;\"> 0.303269</td><td style=\"text-align: right;\">0.976665</td><td style=\"text-align: right;\">              0.141789</td><td style=\"text-align: right;\">0.306504</td><td style=\"text-align: right;\">0.0939445</td></tr>\n",
       "<tr><td>GBM_4_AutoML_20200617_015041                       </td><td style=\"text-align: right;\">0.943522</td><td style=\"text-align: right;\"> 0.277775</td><td style=\"text-align: right;\">0.9745  </td><td style=\"text-align: right;\">              0.146891</td><td style=\"text-align: right;\">0.29802 </td><td style=\"text-align: right;\">0.0888161</td></tr>\n",
       "<tr><td>GBM_3_AutoML_20200617_015041                       </td><td style=\"text-align: right;\">0.93925 </td><td style=\"text-align: right;\"> 0.280362</td><td style=\"text-align: right;\">0.971495</td><td style=\"text-align: right;\">              0.146891</td><td style=\"text-align: right;\">0.298182</td><td style=\"text-align: right;\">0.0889126</td></tr>\n",
       "<tr><td>StackedEnsemble_BestOfFamily_AutoML_20200617_015041</td><td style=\"text-align: right;\">0.93664 </td><td style=\"text-align: right;\"> 0.304018</td><td style=\"text-align: right;\">0.971501</td><td style=\"text-align: right;\">              0.159943</td><td style=\"text-align: right;\">0.307168</td><td style=\"text-align: right;\">0.0943521</td></tr>\n",
       "<tr><td>GBM_2_AutoML_20200617_015041                       </td><td style=\"text-align: right;\">0.936402</td><td style=\"text-align: right;\"> 0.288928</td><td style=\"text-align: right;\">0.97006 </td><td style=\"text-align: right;\">              0.183199</td><td style=\"text-align: right;\">0.303862</td><td style=\"text-align: right;\">0.0923324</td></tr>\n",
       "<tr><td>GBM_grid__1_AutoML_20200617_015041_model_2         </td><td style=\"text-align: right;\">0.933792</td><td style=\"text-align: right;\"> 0.301244</td><td style=\"text-align: right;\">0.968904</td><td style=\"text-align: right;\">              0.141789</td><td style=\"text-align: right;\">0.30812 </td><td style=\"text-align: right;\">0.0949382</td></tr>\n",
       "<tr><td>StackedEnsemble_AllModels_AutoML_20200617_015041   </td><td style=\"text-align: right;\">0.930233</td><td style=\"text-align: right;\"> 0.307759</td><td style=\"text-align: right;\">0.96215 </td><td style=\"text-align: right;\">              0.166469</td><td style=\"text-align: right;\">0.305158</td><td style=\"text-align: right;\">0.0931217</td></tr>\n",
       "<tr><td>DeepLearning_grid__3_AutoML_20200617_015041_model_1</td><td style=\"text-align: right;\">0.929521</td><td style=\"text-align: right;\"> 0.32939 </td><td style=\"text-align: right;\">0.970788</td><td style=\"text-align: right;\">              0.185453</td><td style=\"text-align: right;\">0.33313 </td><td style=\"text-align: right;\">0.110975 </td></tr>\n",
       "<tr><td>GBM_grid__1_AutoML_20200617_015041_model_1         </td><td style=\"text-align: right;\">0.929283</td><td style=\"text-align: right;\"> 0.305849</td><td style=\"text-align: right;\">0.967053</td><td style=\"text-align: right;\">              0.194827</td><td style=\"text-align: right;\">0.313735</td><td style=\"text-align: right;\">0.0984297</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "churn_pred=aml.leader.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>predict   </th><th style=\"text-align: right;\">  Not Placed</th><th style=\"text-align: right;\">    Placed</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>Not Placed</td><td style=\"text-align: right;\">  0.996991  </td><td style=\"text-align: right;\">0.00300892</td></tr>\n",
       "<tr><td>Placed    </td><td style=\"text-align: right;\">  0.00110703</td><td style=\"text-align: right;\">0.998893  </td></tr>\n",
       "<tr><td>Placed    </td><td style=\"text-align: right;\">  0.00110691</td><td style=\"text-align: right;\">0.998893  </td></tr>\n",
       "<tr><td>Placed    </td><td style=\"text-align: right;\">  0.00318759</td><td style=\"text-align: right;\">0.996812  </td></tr>\n",
       "<tr><td>Placed    </td><td style=\"text-align: right;\">  0.00110696</td><td style=\"text-align: right;\">0.998893  </td></tr>\n",
       "<tr><td>Placed    </td><td style=\"text-align: right;\">  0.00212659</td><td style=\"text-align: right;\">0.997873  </td></tr>\n",
       "<tr><td>Placed    </td><td style=\"text-align: right;\">  0.00109167</td><td style=\"text-align: right;\">0.998908  </td></tr>\n",
       "<tr><td>Placed    </td><td style=\"text-align: right;\">  0.00134918</td><td style=\"text-align: right;\">0.998651  </td></tr>\n",
       "<tr><td>Placed    </td><td style=\"text-align: right;\">  0.00209949</td><td style=\"text-align: right;\">0.997901  </td></tr>\n",
       "<tr><td>Placed    </td><td style=\"text-align: right;\">  0.208396  </td><td style=\"text-align: right;\">0.791604  </td></tr>\n",
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      ]
     },
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    {
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     "execution_count": 65,
     "metadata": {},
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    }
   ],
   "source": [
    "churn_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ModelMetricsBinomial: gbm\n",
      "** Reported on test data. **\n",
      "\n",
      "MSE: 0.06433259332815834\n",
      "RMSE: 0.2536387062893957\n",
      "LogLoss: 0.23120704251697646\n",
      "Mean Per-Class Error: 0.07000000000000006\n",
      "AUC: 0.968\n",
      "AUCPR: 0.9867101406469265\n",
      "Gini: 0.9359999999999999\n",
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.46810376745154486: \n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Not Placed</th>\n",
       "      <th>Placed</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Not Placed</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>(1.0/10.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Placed</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>(1.0/25.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0571</td>\n",
       "      <td>(2.0/35.0)</td>\n",
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       "               Not Placed  Placed   Error         Rate\n",
       "0  Not Placed         9.0     1.0     0.1   (1.0/10.0)\n",
       "1      Placed         1.0    24.0    0.04   (1.0/25.0)\n",
       "2       Total        10.0    25.0  0.0571   (2.0/35.0)"
      ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Maximum Metrics: Maximum metrics at their respective thresholds\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>threshold</th>\n",
       "      <th>value</th>\n",
       "      <th>idx</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>max f1</td>\n",
       "      <td>0.468104</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>max f2</td>\n",
       "      <td>0.312316</td>\n",
       "      <td>0.976563</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>max f0point5</td>\n",
       "      <td>0.468104</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>max accuracy</td>\n",
       "      <td>0.468104</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>max precision</td>\n",
       "      <td>0.998908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>max recall</td>\n",
       "      <td>0.312316</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>max specificity</td>\n",
       "      <td>0.998908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max absolute_mcc</td>\n",
       "      <td>0.468104</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>max min_per_class_accuracy</td>\n",
       "      <td>0.626052</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>max mean_per_class_accuracy</td>\n",
       "      <td>0.468104</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>max tns</td>\n",
       "      <td>0.998908</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>max fns</td>\n",
       "      <td>0.998908</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>max fps</td>\n",
       "      <td>0.003009</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>max tps</td>\n",
       "      <td>0.312316</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>max tnr</td>\n",
       "      <td>0.998908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>max fnr</td>\n",
       "      <td>0.998908</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>max fpr</td>\n",
       "      <td>0.003009</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>max tpr</td>\n",
       "      <td>0.312316</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         metric  threshold      value   idx\n",
       "0                        max f1   0.468104   0.960000  24.0\n",
       "1                        max f2   0.312316   0.976563  27.0\n",
       "2                  max f0point5   0.468104   0.960000  24.0\n",
       "3                  max accuracy   0.468104   0.942857  24.0\n",
       "4                 max precision   0.998908   1.000000   0.0\n",
       "5                    max recall   0.312316   1.000000  27.0\n",
       "6               max specificity   0.998908   1.000000   0.0\n",
       "7              max absolute_mcc   0.468104   0.860000  24.0\n",
       "8    max min_per_class_accuracy   0.626052   0.900000  23.0\n",
       "9   max mean_per_class_accuracy   0.468104   0.930000  24.0\n",
       "10                      max tns   0.998908  10.000000   0.0\n",
       "11                      max fns   0.998908  24.000000   0.0\n",
       "12                      max fps   0.003009  10.000000  34.0\n",
       "13                      max tps   0.312316  25.000000  27.0\n",
       "14                      max tnr   0.998908   1.000000   0.0\n",
       "15                      max fnr   0.998908   0.960000   0.0\n",
       "16                      max fpr   0.003009   1.000000  34.0\n",
       "17                      max tpr   0.312316   1.000000  27.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gains/Lift Table: Avg response rate: 71.43 %, avg score: 71.56 %\n"
     ]
    },
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       "      <th>cumulative_gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.998905</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998908</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.998902</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998908</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>0.998900</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998904</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.08</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>0.998898</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998904</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>0.998897</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998904</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>0.998894</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998895</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998900</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.16</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>0.171429</td>\n",
       "      <td>0.998893</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998893</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998897</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.24</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>8</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.998699</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998893</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998897</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.28</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>9</td>\n",
       "      <td>0.314286</td>\n",
       "      <td>0.998583</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998649</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998807</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.44</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>10</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.997237</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998035</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998641</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.56</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>11</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.995547</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996080</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998072</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.72</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td>12</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.982166</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.986640</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.996439</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.80</td>\n",
       "      <td>-6.666667</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td>13</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>0.499693</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.341667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.799812</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.971861</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.92</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>34.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td>14</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.258040</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.400473</td>\n",
       "      <td>0.892857</td>\n",
       "      <td>0.890234</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-30.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td>15</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.014093</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.129032</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032955</td>\n",
       "      <td>0.806452</td>\n",
       "      <td>0.807271</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>12.903226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td></td>\n",
       "      <td>16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004847</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.715566</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      group  cumulative_data_fraction  lower_threshold      lift  \\\n",
       "0         1                  0.028571         0.998905  1.400000   \n",
       "1         2                  0.028571         0.998902  0.000000   \n",
       "2         3                  0.057143         0.998900  1.400000   \n",
       "3         4                  0.057143         0.998898  0.000000   \n",
       "4         5                  0.057143         0.998897  0.000000   \n",
       "5         6                  0.114286         0.998894  1.400000   \n",
       "6         7                  0.171429         0.998893  1.400000   \n",
       "7         8                  0.200000         0.998699  1.400000   \n",
       "8         9                  0.314286         0.998583  1.400000   \n",
       "9        10                  0.400000         0.997237  1.400000   \n",
       "10       11                  0.514286         0.995547  1.400000   \n",
       "11       12                  0.600000         0.982166  0.933333   \n",
       "12       13                  0.685714         0.499693  1.400000   \n",
       "13       14                  0.800000         0.258040  0.700000   \n",
       "14       15                  0.885714         0.014093  0.000000   \n",
       "15       16                  1.000000         0.003009  0.000000   \n",
       "\n",
       "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
       "0          1.400000       1.000000  0.998908                  1.000000   \n",
       "1          1.400000       0.000000  0.000000                  1.000000   \n",
       "2          1.400000       1.000000  0.998900                  1.000000   \n",
       "3          1.400000       0.000000  0.000000                  1.000000   \n",
       "4          1.400000       0.000000  0.000000                  1.000000   \n",
       "5          1.400000       1.000000  0.998895                  1.000000   \n",
       "6          1.400000       1.000000  0.998893                  1.000000   \n",
       "7          1.400000       1.000000  0.998893                  1.000000   \n",
       "8          1.400000       1.000000  0.998649                  1.000000   \n",
       "9          1.400000       1.000000  0.998035                  1.000000   \n",
       "10         1.400000       1.000000  0.996080                  1.000000   \n",
       "11         1.333333       0.666667  0.986640                  0.952381   \n",
       "12         1.341667       1.000000  0.799812                  0.958333   \n",
       "13         1.250000       0.500000  0.400473                  0.892857   \n",
       "14         1.129032       0.000000  0.032955                  0.806452   \n",
       "15         1.000000       0.000000  0.004847                  0.714286   \n",
       "\n",
       "    cumulative_score  capture_rate  cumulative_capture_rate        gain  \\\n",
       "0           0.998908          0.04                     0.04   40.000000   \n",
       "1           0.998908          0.00                     0.04 -100.000000   \n",
       "2           0.998904          0.04                     0.08   40.000000   \n",
       "3           0.998904          0.00                     0.08 -100.000000   \n",
       "4           0.998904          0.00                     0.08 -100.000000   \n",
       "5           0.998900          0.08                     0.16   40.000000   \n",
       "6           0.998897          0.08                     0.24   40.000000   \n",
       "7           0.998897          0.04                     0.28   40.000000   \n",
       "8           0.998807          0.16                     0.44   40.000000   \n",
       "9           0.998641          0.12                     0.56   40.000000   \n",
       "10          0.998072          0.16                     0.72   40.000000   \n",
       "11          0.996439          0.08                     0.80   -6.666667   \n",
       "12          0.971861          0.12                     0.92   40.000000   \n",
       "13          0.890234          0.08                     1.00  -30.000000   \n",
       "14          0.807271          0.00                     1.00 -100.000000   \n",
       "15          0.715566          0.00                     1.00 -100.000000   \n",
       "\n",
       "    cumulative_gain  \n",
       "0         40.000000  \n",
       "1         40.000000  \n",
       "2         40.000000  \n",
       "3         40.000000  \n",
       "4         40.000000  \n",
       "5         40.000000  \n",
       "6         40.000000  \n",
       "7         40.000000  \n",
       "8         40.000000  \n",
       "9         40.000000  \n",
       "10        40.000000  \n",
       "11        33.333333  \n",
       "12        34.166667  \n",
       "13        25.000000  \n",
       "14        12.903226  \n",
       "15         0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aml.leader.model_performance(test)"
   ]
  }
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