Text Classification using TFIDF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import re\n",
    "import nltk\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from sklearn.model_selection import train_test_split\n",
    "import h2o\n",
    "from h2o.automl import H2OAutoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>1 min 13 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Asia/Kolkata</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.30.1.3</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 15 days </td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_Varun_Vaibhav_Jha_tsh72x</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>1.973 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>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>3.7.6 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ---------------------------------------------------------\n",
       "H2O_cluster_uptime:         1 min 13 secs\n",
       "H2O_cluster_timezone:       Asia/Kolkata\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.30.1.3\n",
       "H2O_cluster_version_age:    1 month and 15 days\n",
       "H2O_cluster_name:           H2O_from_python_Varun_Vaibhav_Jha_tsh72x\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    1.973 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",
       "H2O_API_Extensions:         Amazon S3, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
       "Python_version:             3.7.6 final\n",
       "--------------------------  ---------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to C:\\Users\\Varun Vaibhav\n",
      "[nltk_data]     Jha\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers\\punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('consumer_complaints.csv', sep = ',', quotechar = '\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Date received</th>\n",
       "      <th>Product</th>\n",
       "      <th>Sub-product</th>\n",
       "      <th>Issue</th>\n",
       "      <th>Sub-issue</th>\n",
       "      <th>Consumer complaint narrative</th>\n",
       "      <th>Company public response</th>\n",
       "      <th>Company</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP code</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Consumer consent provided?</th>\n",
       "      <th>Submitted via</th>\n",
       "      <th>Date sent to company</th>\n",
       "      <th>Company response to consumer</th>\n",
       "      <th>Timely response?</th>\n",
       "      <th>Consumer disputed?</th>\n",
       "      <th>Complaint ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4/3/2020</td>\n",
       "      <td>Vehicle loan or lease</td>\n",
       "      <td>Loan</td>\n",
       "      <td>Getting a loan or lease</td>\n",
       "      <td>Fraudulent loan</td>\n",
       "      <td>This auto loan was opened on XX/XX/2020 in XXX...</td>\n",
       "      <td>Company has responded to the consumer and the ...</td>\n",
       "      <td>TRUIST FINANCIAL CORPORATION</td>\n",
       "      <td>PA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Consent provided</td>\n",
       "      <td>Web</td>\n",
       "      <td>4/3/2020</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3591341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3/12/2020</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>Payday loan debt</td>\n",
       "      <td>Attempts to collect debt not owed</td>\n",
       "      <td>Debt is not yours</td>\n",
       "      <td>In XXXX of 2019 I noticed a debt for {$620.00}...</td>\n",
       "      <td>None</td>\n",
       "      <td>CURO Intermediate Holdings</td>\n",
       "      <td>CO</td>\n",
       "      <td>806XX</td>\n",
       "      <td>None</td>\n",
       "      <td>Consent provided</td>\n",
       "      <td>Web</td>\n",
       "      <td>3/12/2020</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3564184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/6/2020</td>\n",
       "      <td>Vehicle loan or lease</td>\n",
       "      <td>Loan</td>\n",
       "      <td>Getting a loan or lease</td>\n",
       "      <td>Credit denial</td>\n",
       "      <td>As stated from Capital One, XXXX XX/XX/XXXX an...</td>\n",
       "      <td>None</td>\n",
       "      <td>CAPITAL ONE FINANCIAL CORPORATION</td>\n",
       "      <td>OH</td>\n",
       "      <td>430XX</td>\n",
       "      <td>None</td>\n",
       "      <td>Consent provided</td>\n",
       "      <td>Web</td>\n",
       "      <td>2/6/2020</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3521949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3/6/2020</td>\n",
       "      <td>Checking or savings account</td>\n",
       "      <td>Savings account</td>\n",
       "      <td>Managing an account</td>\n",
       "      <td>Banking errors</td>\n",
       "      <td>Please see CFPB case XXXX. \\n\\nCapital One, in...</td>\n",
       "      <td>None</td>\n",
       "      <td>CAPITAL ONE FINANCIAL CORPORATION</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Consent provided</td>\n",
       "      <td>Web</td>\n",
       "      <td>3/6/2020</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3556237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2/14/2020</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>Medical debt</td>\n",
       "      <td>Attempts to collect debt not owed</td>\n",
       "      <td>Debt is not yours</td>\n",
       "      <td>This debt was incurred due to medical malpract...</td>\n",
       "      <td>Company believes it acted appropriately as aut...</td>\n",
       "      <td>Merchants and Professional Bureau, Inc.</td>\n",
       "      <td>OH</td>\n",
       "      <td>432XX</td>\n",
       "      <td>None</td>\n",
       "      <td>Consent provided</td>\n",
       "      <td>Web</td>\n",
       "      <td>2/14/2020</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3531704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57448</th>\n",
       "      <td>2/29/2020</td>\n",
       "      <td>Student loan</td>\n",
       "      <td>Federal student loan servicing</td>\n",
       "      <td>Dealing with your lender or servicer</td>\n",
       "      <td>Trouble with how payments are being handled</td>\n",
       "      <td>I am attempting to make a payment toward my st...</td>\n",
       "      <td>None</td>\n",
       "      <td>Nelnet, Inc.</td>\n",
       "      <td>KS</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Consent provided</td>\n",
       "      <td>Web</td>\n",
       "      <td>2/29/2020</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3549178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57449</th>\n",
       "      <td>2/11/2020</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>Other debt</td>\n",
       "      <td>Attempts to collect debt not owed</td>\n",
       "      <td>Debt was paid</td>\n",
       "      <td>Received letter for {$480.00}. Original credit...</td>\n",
       "      <td>Company has responded to the consumer and the ...</td>\n",
       "      <td>The Receivable Management Services LLC, New Yo...</td>\n",
       "      <td>AZ</td>\n",
       "      <td>853XX</td>\n",
       "      <td>None</td>\n",
       "      <td>Consent provided</td>\n",
       "      <td>Web</td>\n",
       "      <td>2/18/2020</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3527928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57450</th>\n",
       "      <td>2/29/2020</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>Other debt</td>\n",
       "      <td>Communication tactics</td>\n",
       "      <td>Used obscene, profane, or other abusive language</td>\n",
       "      <td>entire time 10 years until XX/XX/2020. XXXX ma...</td>\n",
       "      <td>Company has responded to the consumer and the ...</td>\n",
       "      <td>Convergent Resources, Inc.</td>\n",
       "      <td>NJ</td>\n",
       "      <td>8101</td>\n",
       "      <td>None</td>\n",
       "      <td>Consent provided</td>\n",
       "      <td>Web</td>\n",
       "      <td>2/29/2020</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3549238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57451</th>\n",
       "      <td>1/16/2020</td>\n",
       "      <td>Checking or savings account</td>\n",
       "      <td>Checking account</td>\n",
       "      <td>Problem with a lender or other company chargin...</td>\n",
       "      <td>Transaction was not authorized</td>\n",
       "      <td>I am a customer with Wells Fargo Bank. Recentl...</td>\n",
       "      <td>Company has responded to the consumer and the ...</td>\n",
       "      <td>WELLS FARGO &amp; COMPANY</td>\n",
       "      <td>AZ</td>\n",
       "      <td>852XX</td>\n",
       "      <td>None</td>\n",
       "      <td>Consent provided</td>\n",
       "      <td>Web</td>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3498566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57452</th>\n",
       "      <td>1/16/2020</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>Auto debt</td>\n",
       "      <td>Took or threatened to take negative or legal a...</td>\n",
       "      <td>Threatened or suggested your credit would be d...</td>\n",
       "      <td>I spoken with them several times in a year. An...</td>\n",
       "      <td>None</td>\n",
       "      <td>Exeter Finance Corp.</td>\n",
       "      <td>SC</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Consent provided</td>\n",
       "      <td>Web</td>\n",
       "      <td>1/16/2020</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3498524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>57453 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "      Date received                      Product  \\\n",
       "0          4/3/2020        Vehicle loan or lease   \n",
       "1         3/12/2020              Debt collection   \n",
       "2          2/6/2020        Vehicle loan or lease   \n",
       "3          3/6/2020  Checking or savings account   \n",
       "4         2/14/2020              Debt collection   \n",
       "...             ...                          ...   \n",
       "57448     2/29/2020                 Student loan   \n",
       "57449     2/11/2020              Debt collection   \n",
       "57450     2/29/2020              Debt collection   \n",
       "57451     1/16/2020  Checking or savings account   \n",
       "57452     1/16/2020              Debt collection   \n",
       "\n",
       "                          Sub-product  \\\n",
       "0                                Loan   \n",
       "1                    Payday loan debt   \n",
       "2                                Loan   \n",
       "3                     Savings account   \n",
       "4                        Medical debt   \n",
       "...                               ...   \n",
       "57448  Federal student loan servicing   \n",
       "57449                      Other debt   \n",
       "57450                      Other debt   \n",
       "57451                Checking account   \n",
       "57452                       Auto debt   \n",
       "\n",
       "                                                   Issue  \\\n",
       "0                                Getting a loan or lease   \n",
       "1                      Attempts to collect debt not owed   \n",
       "2                                Getting a loan or lease   \n",
       "3                                    Managing an account   \n",
       "4                      Attempts to collect debt not owed   \n",
       "...                                                  ...   \n",
       "57448               Dealing with your lender or servicer   \n",
       "57449                  Attempts to collect debt not owed   \n",
       "57450                              Communication tactics   \n",
       "57451  Problem with a lender or other company chargin...   \n",
       "57452  Took or threatened to take negative or legal a...   \n",
       "\n",
       "                                               Sub-issue  \\\n",
       "0                                        Fraudulent loan   \n",
       "1                                      Debt is not yours   \n",
       "2                                          Credit denial   \n",
       "3                                         Banking errors   \n",
       "4                                      Debt is not yours   \n",
       "...                                                  ...   \n",
       "57448        Trouble with how payments are being handled   \n",
       "57449                                      Debt was paid   \n",
       "57450   Used obscene, profane, or other abusive language   \n",
       "57451                     Transaction was not authorized   \n",
       "57452  Threatened or suggested your credit would be d...   \n",
       "\n",
       "                            Consumer complaint narrative  \\\n",
       "0      This auto loan was opened on XX/XX/2020 in XXX...   \n",
       "1      In XXXX of 2019 I noticed a debt for {$620.00}...   \n",
       "2      As stated from Capital One, XXXX XX/XX/XXXX an...   \n",
       "3      Please see CFPB case XXXX. \\n\\nCapital One, in...   \n",
       "4      This debt was incurred due to medical malpract...   \n",
       "...                                                  ...   \n",
       "57448  I am attempting to make a payment toward my st...   \n",
       "57449  Received letter for {$480.00}. Original credit...   \n",
       "57450  entire time 10 years until XX/XX/2020. XXXX ma...   \n",
       "57451  I am a customer with Wells Fargo Bank. Recentl...   \n",
       "57452  I spoken with them several times in a year. An...   \n",
       "\n",
       "                                 Company public response  \\\n",
       "0      Company has responded to the consumer and the ...   \n",
       "1                                                   None   \n",
       "2                                                   None   \n",
       "3                                                   None   \n",
       "4      Company believes it acted appropriately as aut...   \n",
       "...                                                  ...   \n",
       "57448                                               None   \n",
       "57449  Company has responded to the consumer and the ...   \n",
       "57450  Company has responded to the consumer and the ...   \n",
       "57451  Company has responded to the consumer and the ...   \n",
       "57452                                               None   \n",
       "\n",
       "                                                 Company State ZIP code  Tags  \\\n",
       "0                           TRUIST FINANCIAL CORPORATION    PA     None  None   \n",
       "1                             CURO Intermediate Holdings    CO    806XX  None   \n",
       "2                      CAPITAL ONE FINANCIAL CORPORATION    OH    430XX  None   \n",
       "3                      CAPITAL ONE FINANCIAL CORPORATION    CA     None  None   \n",
       "4                Merchants and Professional Bureau, Inc.    OH    432XX  None   \n",
       "...                                                  ...   ...      ...   ...   \n",
       "57448                                       Nelnet, Inc.    KS     None  None   \n",
       "57449  The Receivable Management Services LLC, New Yo...    AZ    853XX  None   \n",
       "57450                         Convergent Resources, Inc.    NJ     8101  None   \n",
       "57451                              WELLS FARGO & COMPANY    AZ    852XX  None   \n",
       "57452                               Exeter Finance Corp.    SC     None  None   \n",
       "\n",
       "      Consumer consent provided? Submitted via Date sent to company  \\\n",
       "0               Consent provided           Web             4/3/2020   \n",
       "1               Consent provided           Web            3/12/2020   \n",
       "2               Consent provided           Web             2/6/2020   \n",
       "3               Consent provided           Web             3/6/2020   \n",
       "4               Consent provided           Web            2/14/2020   \n",
       "...                          ...           ...                  ...   \n",
       "57448           Consent provided           Web            2/29/2020   \n",
       "57449           Consent provided           Web            2/18/2020   \n",
       "57450           Consent provided           Web            2/29/2020   \n",
       "57451           Consent provided           Web            1/22/2020   \n",
       "57452           Consent provided           Web            1/16/2020   \n",
       "\n",
       "      Company response to consumer Timely response?  Consumer disputed?  \\\n",
       "0          Closed with explanation              Yes                 NaN   \n",
       "1          Closed with explanation              Yes                 NaN   \n",
       "2          Closed with explanation              Yes                 NaN   \n",
       "3          Closed with explanation              Yes                 NaN   \n",
       "4          Closed with explanation              Yes                 NaN   \n",
       "...                            ...              ...                 ...   \n",
       "57448      Closed with explanation              Yes                 NaN   \n",
       "57449      Closed with explanation              Yes                 NaN   \n",
       "57450      Closed with explanation              Yes                 NaN   \n",
       "57451      Closed with explanation              Yes                 NaN   \n",
       "57452      Closed with explanation              Yes                 NaN   \n",
       "\n",
       "       Complaint ID  \n",
       "0           3591341  \n",
       "1           3564184  \n",
       "2           3521949  \n",
       "3           3556237  \n",
       "4           3531704  \n",
       "...             ...  \n",
       "57448       3549178  \n",
       "57449       3527928  \n",
       "57450       3549238  \n",
       "57451       3498566  \n",
       "57452       3498524  \n",
       "\n",
       "[57453 rows x 18 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23fc70ea908>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Product'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CITIBANK, N.A.                           3226\n",
       "CAPITAL ONE FINANCIAL CORPORATION        2711\n",
       "BANK OF AMERICA, NATIONAL ASSOCIATION    2580\n",
       "JPMORGAN CHASE & CO.                     2409\n",
       "WELLS FARGO & COMPANY                    2001\n",
       "                                         ... \n",
       "Tormey Bewley Corporation                   1\n",
       "YKS Acceptance, Inc.                        1\n",
       "Jana Ferrell & Associates LLC               1\n",
       "Century Debt Solutions Inc.                 1\n",
       "Student Loan Care, LLC                      1\n",
       "Name: Company, Length: 2197, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Company'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "complaints_df=df[['Consumer complaint narrative','Product','Company']].rename(columns={'Consumer complaint narrative':'complaints'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = {'Debt collection':0,'Credit card or prepaid card':1,'Mortgage':2,'Checking or savings account':3,'Student loan':4,'Vehicle loan or lease':5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "complaints_df['target']=complaints_df['Product'].map(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>complaints</th>\n",
       "      <th>Product</th>\n",
       "      <th>Company</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This auto loan was opened on XX/XX/2020 in XXX...</td>\n",
       "      <td>Vehicle loan or lease</td>\n",
       "      <td>TRUIST FINANCIAL CORPORATION</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>In XXXX of 2019 I noticed a debt for {$620.00}...</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>CURO Intermediate Holdings</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>As stated from Capital One, XXXX XX/XX/XXXX an...</td>\n",
       "      <td>Vehicle loan or lease</td>\n",
       "      <td>CAPITAL ONE FINANCIAL CORPORATION</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Please see CFPB case XXXX. \\n\\nCapital One, in...</td>\n",
       "      <td>Checking or savings account</td>\n",
       "      <td>CAPITAL ONE FINANCIAL CORPORATION</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This debt was incurred due to medical malpract...</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>Merchants and Professional Bureau, Inc.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57448</th>\n",
       "      <td>I am attempting to make a payment toward my st...</td>\n",
       "      <td>Student loan</td>\n",
       "      <td>Nelnet, Inc.</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57449</th>\n",
       "      <td>Received letter for {$480.00}. Original credit...</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>The Receivable Management Services LLC, New Yo...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57450</th>\n",
       "      <td>entire time 10 years until XX/XX/2020. XXXX ma...</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>Convergent Resources, Inc.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57451</th>\n",
       "      <td>I am a customer with Wells Fargo Bank. Recentl...</td>\n",
       "      <td>Checking or savings account</td>\n",
       "      <td>WELLS FARGO &amp; COMPANY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57452</th>\n",
       "      <td>I spoken with them several times in a year. An...</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>Exeter Finance Corp.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57453 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              complaints  \\\n",
       "0      This auto loan was opened on XX/XX/2020 in XXX...   \n",
       "1      In XXXX of 2019 I noticed a debt for {$620.00}...   \n",
       "2      As stated from Capital One, XXXX XX/XX/XXXX an...   \n",
       "3      Please see CFPB case XXXX. \\n\\nCapital One, in...   \n",
       "4      This debt was incurred due to medical malpract...   \n",
       "...                                                  ...   \n",
       "57448  I am attempting to make a payment toward my st...   \n",
       "57449  Received letter for {$480.00}. Original credit...   \n",
       "57450  entire time 10 years until XX/XX/2020. XXXX ma...   \n",
       "57451  I am a customer with Wells Fargo Bank. Recentl...   \n",
       "57452  I spoken with them several times in a year. An...   \n",
       "\n",
       "                           Product  \\\n",
       "0            Vehicle loan or lease   \n",
       "1                  Debt collection   \n",
       "2            Vehicle loan or lease   \n",
       "3      Checking or savings account   \n",
       "4                  Debt collection   \n",
       "...                            ...   \n",
       "57448                 Student loan   \n",
       "57449              Debt collection   \n",
       "57450              Debt collection   \n",
       "57451  Checking or savings account   \n",
       "57452              Debt collection   \n",
       "\n",
       "                                                 Company  target  \n",
       "0                           TRUIST FINANCIAL CORPORATION       5  \n",
       "1                             CURO Intermediate Holdings       0  \n",
       "2                      CAPITAL ONE FINANCIAL CORPORATION       5  \n",
       "3                      CAPITAL ONE FINANCIAL CORPORATION       3  \n",
       "4                Merchants and Professional Bureau, Inc.       0  \n",
       "...                                                  ...     ...  \n",
       "57448                                       Nelnet, Inc.       4  \n",
       "57449  The Receivable Management Services LLC, New Yo...       0  \n",
       "57450                         Convergent Resources, Inc.       0  \n",
       "57451                              WELLS FARGO & COMPANY       3  \n",
       "57452                               Exeter Finance Corp.       0  \n",
       "\n",
       "[57453 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complaints_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test = train_test_split(complaints_df, test_size = 0.2, random_state = 111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = nltk.stem.SnowballStemmer('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to C:\\Users\\Varun Vaibhav\n",
      "[nltk_data]     Jha\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "stop_words = set(nltk.corpus.stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(text):\n",
    "    tokens = [word for word in nltk.word_tokenize(text) if (len(word)>3 and len(word.strip('Xx/'))>2 and len(re.sub('\\d+','',word.strip('Xx/')))>3)]\n",
    "    tokens = map(str.lower,tokens)\n",
    "    stems = [stemmer.stem(item) for item in tokens if(item not in stop_words)]\n",
    "    return stems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer_tf = TfidfVectorizer(tokenizer=tokenize,stop_words = None, max_df=0.75, max_features=1000, lowercase = False, ngram_range = (1,2))\n",
    "train_vectors = vectorizer_tf.fit_transform(X_train.complaints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.05982061, 0.        , ..., 0.        , 0.        ,\n",
       "        0.04799437],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.09583077],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_vectors.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       " 'valid debt',\n",
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       " 'victim ident',\n",
       " 'violat',\n",
       " 'visa',\n",
       " 'visit',\n",
       " 'voicemail',\n",
       " 'wage',\n",
       " 'wait',\n",
       " 'waiv',\n",
       " 'want',\n",
       " 'wasnt',\n",
       " 'websit',\n",
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       " 'week later',\n",
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       " 'well fargo',\n",
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       " 'whole',\n",
       " 'wife',\n",
       " 'will',\n",
       " 'wire',\n",
       " 'withdraw',\n",
       " 'within',\n",
       " 'within day',\n",
       " 'without',\n",
       " 'word',\n",
       " 'work',\n",
       " 'would',\n",
       " 'would call',\n",
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       " 'written',\n",
       " 'wrong',\n",
       " 'wrote',\n",
       " 'year']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer_tf.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_vectors = vectorizer_tf.transform(X_test.complaints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45962, 4)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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       "\n",
       "[45962 rows x 1001 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df=pd.DataFrame(train_vectors.toarray(),columns=vectorizer_tf.get_feature_names())\n",
    "train_df=pd.concat([train_df,X_train['target'].reset_index(drop=True)],axis=1)\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "       accord   account  account account  ...  would like  would need  \\\n",
       "0         0.0  0.348196              0.0  ...         0.0    0.000000   \n",
       "1         0.0  0.124611              0.0  ...         0.0    0.000000   \n",
       "2         0.0  0.220511              0.0  ...         0.0    0.000000   \n",
       "3         0.0  0.000000              0.0  ...         0.0    0.000000   \n",
       "4         0.0  0.040013              0.0  ...         0.0    0.000000   \n",
       "...       ...       ...              ...  ...         ...         ...   \n",
       "11486     0.0  0.020535              0.0  ...         0.0    0.129507   \n",
       "11487     0.0  0.183318              0.0  ...         0.0    0.000000   \n",
       "11488     0.0  0.139306              0.0  ...         0.0    0.000000   \n",
       "11489     0.0  0.000000              0.0  ...         0.0    0.000000   \n",
       "11490     0.0  0.054797              0.0  ...         0.0    0.000000   \n",
       "\n",
       "       would receiv  would take  write  written  wrong  wrote      year  \\\n",
       "0          0.000000         0.0    0.0      0.0    0.0    0.0  0.000000   \n",
       "1          0.000000         0.0    0.0      0.0    0.0    0.0  0.000000   \n",
       "2          0.000000         0.0    0.0      0.0    0.0    0.0  0.000000   \n",
       "3          0.045317         0.0    0.0      0.0    0.0    0.0  0.070974   \n",
       "4          0.000000         0.0    0.0      0.0    0.0    0.0  0.128219   \n",
       "...             ...         ...    ...      ...    ...    ...       ...   \n",
       "11486      0.000000         0.0    0.0      0.0    0.0    0.0  0.032901   \n",
       "11487      0.000000         0.0    0.0      0.0    0.0    0.0  0.000000   \n",
       "11488      0.000000         0.0    0.0      0.0    0.0    0.0  0.000000   \n",
       "11489      0.000000         0.0    0.0      0.0    0.0    0.0  0.077802   \n",
       "11490      0.000000         0.0    0.0      0.0    0.0    0.0  0.000000   \n",
       "\n",
       "       target  \n",
       "0           3  \n",
       "1           2  \n",
       "2           1  \n",
       "3           4  \n",
       "4           1  \n",
       "...       ...  \n",
       "11486       1  \n",
       "11487       4  \n",
       "11488       2  \n",
       "11489       0  \n",
       "11490       0  \n",
       "\n",
       "[11491 rows x 1001 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df=pd.DataFrame(test_vectors.toarray(),columns=vectorizer_tf.get_feature_names())\n",
    "test_df=pd.concat([test_df,X_test['target'].reset_index(drop=True)],axis=1)\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "h2o_train_df = h2o.H2OFrame(train_df)\n",
    "h2o_test_df = h2o.H2OFrame(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'abil': 'real',\n",
       " 'abl': 'real',\n",
       " 'absolut': 'real',\n",
       " 'abus': 'real',\n",
       " 'accept': 'real',\n",
       " 'access': 'real',\n",
       " 'access account': 'real',\n",
       " 'accord': 'real',\n",
       " 'account': 'real',\n",
       " 'account account': 'real',\n",
       " 'account balanc': 'real',\n",
       " 'account bank': 'real',\n",
       " 'account call': 'real',\n",
       " 'account close': 'real',\n",
       " 'account credit': 'real',\n",
       " 'account inform': 'real',\n",
       " 'account number': 'real',\n",
       " 'account open': 'real',\n",
       " 'account report': 'real',\n",
       " 'account would': 'real',\n",
       " 'accru': 'real',\n",
       " 'acct': 'real',\n",
       " 'accur': 'real',\n",
       " 'acknowledg': 'real',\n",
       " 'act': 'real',\n",
       " 'action': 'real',\n",
       " 'activ': 'real',\n",
       " 'actual': 'real',\n",
       " 'ad': 'real',\n",
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       " 'address': 'real',\n",
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       " 'admit': 'real',\n",
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       " 'affidavit': 'real',\n",
       " 'afford': 'real',\n",
       " 'agenc': 'real',\n",
       " 'agent': 'real',\n",
       " 'agre': 'real',\n",
       " 'agreement': 'real',\n",
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       " 'alleg': 'real',\n",
       " 'alleg debt': 'real',\n",
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       " 'credit account': 'real',\n",
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       " 'credit bureaus': 'real',\n",
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       " 'list': 'real',\n",
       " 'littl': 'real',\n",
       " 'live': 'real',\n",
       " 'loan': 'real',\n",
       " 'loan modif': 'real',\n",
       " 'loan servic': 'real',\n",
       " 'loancar': 'real',\n",
       " 'local': 'real',\n",
       " 'locat': 'real',\n",
       " 'lock': 'real',\n",
       " 'log': 'real',\n",
       " 'long': 'real',\n",
       " 'longer': 'real',\n",
       " 'look': 'real',\n",
       " 'lose': 'real',\n",
       " 'loss': 'real',\n",
       " 'lost': 'real',\n",
       " 'lower': 'real',\n",
       " 'made': 'real',\n",
       " 'made payment': 'real',\n",
       " 'mail': 'real',\n",
       " 'maintain': 'real',\n",
       " 'major': 'real',\n",
       " 'make': 'real',\n",
       " 'make payment': 'real',\n",
       " 'make sure': 'real',\n",
       " 'manag': 'real',\n",
       " 'mani': 'real',\n",
       " 'mani time': 'real',\n",
       " 'manner': 'real',\n",
       " 'mark': 'real',\n",
       " 'market': 'real',\n",
       " 'mastercard': 'real',\n",
       " 'matter': 'real',\n",
       " 'mean': 'real',\n",
       " 'medic': 'real',\n",
       " 'meet': 'real',\n",
       " 'member': 'real',\n",
       " 'mention': 'real',\n",
       " 'merchant': 'real',\n",
       " 'messag': 'real',\n",
       " 'midland': 'real',\n",
       " 'might': 'real',\n",
       " 'mile': 'real',\n",
       " 'mine': 'real',\n",
       " 'minimum': 'real',\n",
       " 'minut': 'real',\n",
       " 'mislead': 'real',\n",
       " 'miss': 'real',\n",
       " 'miss payment': 'real',\n",
       " 'mistak': 'real',\n",
       " 'mobil': 'real',\n",
       " 'modif': 'real',\n",
       " 'monday': 'real',\n",
       " 'money': 'real',\n",
       " 'money back': 'real',\n",
       " 'month': 'real',\n",
       " 'month later': 'real',\n",
       " 'month payment': 'real',\n",
       " 'morn': 'real',\n",
       " 'mortgag': 'real',\n",
       " 'mortgag compani': 'real',\n",
       " 'mortgag loan': 'real',\n",
       " 'mortgag payment': 'real',\n",
       " 'mortgag servic': 'real',\n",
       " 'mother': 'real',\n",
       " 'move': 'real',\n",
       " 'much': 'real',\n",
       " 'multipl': 'real',\n",
       " 'multipl time': 'real',\n",
       " 'must': 'real',\n",
       " 'name': 'real',\n",
       " 'nation': 'real',\n",
       " 'navient': 'real',\n",
       " 'near': 'real',\n",
       " 'necessari': 'real',\n",
       " 'need': 'real',\n",
       " 'negat': 'real',\n",
       " 'neither': 'real',\n",
       " 'never': 'real',\n",
       " 'never receiv': 'real',\n",
       " 'next': 'real',\n",
       " 'none': 'real',\n",
       " 'note': 'real',\n",
       " 'noth': 'real',\n",
       " 'notic': 'real',\n",
       " 'notif': 'real',\n",
       " 'notifi': 'real',\n",
       " 'number': 'real',\n",
       " 'number call': 'real',\n",
       " 'numer': 'real',\n",
       " 'numer time': 'real',\n",
       " 'oblig': 'real',\n",
       " 'obtain': 'real',\n",
       " 'obvious': 'real',\n",
       " 'occas': 'real',\n",
       " 'occur': 'real',\n",
       " 'ocwen': 'real',\n",
       " 'offer': 'real',\n",
       " 'offic': 'real',\n",
       " 'onlin': 'real',\n",
       " 'open': 'real',\n",
       " 'open account': 'real',\n",
       " 'oper': 'real',\n",
       " 'opportun': 'real',\n",
       " 'option': 'real',\n",
       " 'order': 'real',\n",
       " 'origin': 'real',\n",
       " 'origin creditor': 'real',\n",
       " 'other': 'real',\n",
       " 'outstand': 'real',\n",
       " 'overdraft': 'real',\n",
       " 'owe': 'real',\n",
       " 'own': 'real',\n",
       " 'owner': 'real',\n",
       " 'packag': 'real',\n",
       " 'page': 'real',\n",
       " 'paid': 'real',\n",
       " 'paid full': 'real',\n",
       " 'pandem': 'real',\n",
       " 'paper': 'real',\n",
       " 'paperwork': 'real',\n",
       " 'part': 'real',\n",
       " 'parti': 'real',\n",
       " 'particular': 'real',\n",
       " 'pass': 'real',\n",
       " 'past': 'real',\n",
       " 'pay': 'real',\n",
       " 'payment': 'real',\n",
       " 'payment account': 'real',\n",
       " 'payment amount': 'real',\n",
       " 'payment date': 'real',\n",
       " 'payment histori': 'real',\n",
       " 'payment made': 'real',\n",
       " 'payment month': 'real',\n",
       " 'payment plan': 'real',\n",
       " 'payment receiv': 'real',\n",
       " 'payment time': 'real',\n",
       " 'payment would': 'real',\n",
       " 'payoff': 'real',\n",
       " 'paypal': 'real',\n",
       " 'penalti': 'real',\n",
       " 'pend': 'real',\n",
       " 'peopl': 'real',\n",
       " 'perform': 'real',\n",
       " 'period': 'real',\n",
       " 'permiss': 'real',\n",
       " 'person': 'real',\n",
       " 'person inform': 'real',\n",
       " 'phone': 'real',\n",
       " 'phone call': 'real',\n",
       " 'phone number': 'real',\n",
       " 'physic': 'real',\n",
       " 'pick': 'real',\n",
       " 'place': 'real',\n",
       " 'plan': 'real',\n",
       " 'pleas': 'real',\n",
       " 'pleas help': 'real',\n",
       " 'plus': 'real',\n",
       " 'point': 'real',\n",
       " 'polic': 'real',\n",
       " 'polic report': 'real',\n",
       " 'polici': 'real',\n",
       " 'portfolio': 'real',\n",
       " 'portfolio recoveri': 'real',\n",
       " 'posit': 'real',\n",
       " 'possibl': 'real',\n",
       " 'post': 'real',\n",
       " 'practic': 'real',\n",
       " 'present': 'real',\n",
       " 'prevent': 'real',\n",
       " 'previous': 'real',\n",
       " 'price': 'real',\n",
       " 'princip': 'real',\n",
       " 'prior': 'real',\n",
       " 'privat': 'real',\n",
       " 'problem': 'real',\n",
       " 'procedur': 'real',\n",
       " 'proceed': 'real',\n",
       " 'process': 'real',\n",
       " 'product': 'real',\n",
       " 'program': 'real',\n",
       " 'promis': 'real',\n",
       " 'promot': 'real',\n",
       " 'prompt': 'real',\n",
       " 'proof': 'real',\n",
       " 'proper': 'real',\n",
       " 'properti': 'real',\n",
       " 'protect': 'real',\n",
       " 'prove': 'real',\n",
       " 'provid': 'real',\n",
       " 'provid inform': 'real',\n",
       " 'provid proof': 'real',\n",
       " 'public': 'real',\n",
       " 'pull': 'real',\n",
       " 'purchas': 'real',\n",
       " 'purpos': 'real',\n",
       " 'put': 'real',\n",
       " 'qualifi': 'real',\n",
       " 'question': 'real',\n",
       " 'quick': 'real',\n",
       " 'rate': 'real',\n",
       " 'reach': 'real',\n",
       " 'read': 'real',\n",
       " 'real': 'real',\n",
       " 'realiz': 'real',\n",
       " 'realli': 'real',\n",
       " 'reason': 'real',\n",
       " 'receipt': 'real',\n",
       " 'receiv': 'real',\n",
       " 'receiv bill': 'real',\n",
       " 'receiv call': 'real',\n",
       " 'receiv email': 'real',\n",
       " 'receiv letter': 'real',\n",
       " 'receiv notic': 'real',\n",
       " 'receiv respons': 'real',\n",
       " 'receiv statement': 'real',\n",
       " 'recent': 'real',\n",
       " 'record': 'real',\n",
       " 'recoveri': 'real',\n",
       " 'reduc': 'real',\n",
       " 'refer': 'real',\n",
       " 'refin': 'real',\n",
       " 'refinanc': 'real',\n",
       " 'reflect': 'real',\n",
       " 'refund': 'real',\n",
       " 'refus': 'real',\n",
       " 'regard': 'real',\n",
       " 'regist': 'real',\n",
       " 'regul': 'real',\n",
       " 'regular': 'real',\n",
       " 'reimburs': 'real',\n",
       " 'reject': 'real',\n",
       " 'relat': 'real',\n",
       " 'releas': 'real',\n",
       " 'remain': 'real',\n",
       " 'remov': 'real',\n",
       " 'remov account': 'real',\n",
       " 'remov credit': 'real',\n",
       " 'rent': 'real',\n",
       " 'repair': 'real',\n",
       " 'repay': 'real',\n",
       " 'repeat': 'real',\n",
       " 'replac': 'real',\n",
       " 'repli': 'real',\n",
       " 'report': 'real',\n",
       " 'report account': 'real',\n",
       " 'report agenc': 'real',\n",
       " 'report credit': 'real',\n",
       " 'report debt': 'real',\n",
       " 'repres': 'real',\n",
       " 'request': 'real',\n",
       " 'request valid': 'real',\n",
       " 'requir': 'real',\n",
       " 'research': 'real',\n",
       " 'reserv': 'real',\n",
       " 'resid': 'real',\n",
       " 'resolut': 'real',\n",
       " 'resolv': 'real',\n",
       " 'resolv issu': 'real',\n",
       " 'respect': 'real',\n",
       " 'respond': 'real',\n",
       " 'respons': 'real',\n",
       " 'result': 'real',\n",
       " 'return': 'real',\n",
       " 'return call': 'real',\n",
       " 'revers': 'real',\n",
       " 'review': 'real',\n",
       " 'reward': 'real',\n",
       " 'right': 'real',\n",
       " 'rude': 'real',\n",
       " 'rule': 'real',\n",
       " 'said': 'real',\n",
       " 'said could': 'real',\n",
       " 'said would': 'real',\n",
       " 'sale': 'real',\n",
       " 'save': 'real',\n",
       " 'save account': 'real',\n",
       " 'say': 'real',\n",
       " 'scam': 'real',\n",
       " 'schedul': 'real',\n",
       " 'school': 'real',\n",
       " 'score': 'real',\n",
       " 'second': 'real',\n",
       " 'section': 'real',\n",
       " 'secur': 'real',\n",
       " 'secur number': 'real',\n",
       " 'seek': 'real',\n",
       " 'seem': 'real',\n",
       " 'select': 'real',\n",
       " 'sell': 'real',\n",
       " 'send': 'real',\n",
       " 'sens': 'real',\n",
       " 'sent': 'real',\n",
       " 'sent email': 'real',\n",
       " 'sent letter': 'real',\n",
       " 'separ': 'real',\n",
       " 'serious': 'real',\n",
       " 'serv': 'real',\n",
       " 'servic': 'real',\n",
       " 'servic repres': 'real',\n",
       " 'settl': 'real',\n",
       " 'settlement': 'real',\n",
       " 'sever': 'real',\n",
       " 'sever time': 'real',\n",
       " 'shall': 'real',\n",
       " 'share': 'real',\n",
       " 'shellpoint': 'real',\n",
       " 'short': 'real',\n",
       " 'show': 'real',\n",
       " 'sign': 'real',\n",
       " 'signatur': 'real',\n",
       " 'simpli': 'real',\n",
       " 'sinc': 'real',\n",
       " 'singl': 'real',\n",
       " 'site': 'real',\n",
       " 'situat': 'real',\n",
       " 'small': 'real',\n",
       " 'social': 'real',\n",
       " 'social secur': 'real',\n",
       " 'sold': 'real',\n",
       " 'solut': 'real',\n",
       " 'someon': 'real',\n",
       " 'someth': 'real',\n",
       " 'soon': 'real',\n",
       " 'speak': 'real',\n",
       " 'speak supervisor': 'real',\n",
       " 'special': 'real',\n",
       " 'specif': 'real',\n",
       " 'spend': 'real',\n",
       " 'spent': 'real',\n",
       " 'spoke': 'real',\n",
       " 'spoken': 'real',\n",
       " 'stand': 'real',\n",
       " 'standard': 'real',\n",
       " 'start': 'real',\n",
       " 'state': 'real',\n",
       " 'state would': 'real',\n",
       " 'statement': 'real',\n",
       " 'status': 'real',\n",
       " 'stay': 'real',\n",
       " 'step': 'real',\n",
       " 'still': 'real',\n",
       " 'stolen': 'real',\n",
       " 'stop': 'real',\n",
       " 'store': 'real',\n",
       " 'stress': 'real',\n",
       " 'student': 'real',\n",
       " 'student loan': 'real',\n",
       " 'subject': 'real',\n",
       " 'submit': 'real',\n",
       " 'subsequ': 'real',\n",
       " 'success': 'real',\n",
       " 'suggest': 'real',\n",
       " 'suit': 'real',\n",
       " 'suntrust': 'real',\n",
       " 'supervisor': 'real',\n",
       " 'support': 'real',\n",
       " 'suppos': 'real',\n",
       " 'sure': 'real',\n",
       " 'switch': 'real',\n",
       " 'synchroni': 'real',\n",
       " 'synchroni bank': 'real',\n",
       " 'system': 'real',\n",
       " 'take': 'real',\n",
       " 'taken': 'real',\n",
       " 'talk': 'real',\n",
       " 'tax': 'real',\n",
       " 'team': 'real',\n",
       " 'telephon': 'real',\n",
       " 'tell': 'real',\n",
       " 'temporari': 'real',\n",
       " 'term': 'real',\n",
       " 'texa': 'real',\n",
       " 'text': 'real',\n",
       " 'thank': 'real',\n",
       " 'theft': 'real',\n",
       " 'therefor': 'real',\n",
       " 'thing': 'real',\n",
       " 'think': 'real',\n",
       " 'third': 'real',\n",
       " 'third parti': 'real',\n",
       " 'though': 'real',\n",
       " 'thought': 'real',\n",
       " 'threaten': 'real',\n",
       " 'three': 'real',\n",
       " 'thus': 'real',\n",
       " 'ticket': 'real',\n",
       " 'time': 'real',\n",
       " 'time call': 'real',\n",
       " 'titl': 'real',\n",
       " 'today': 'real',\n",
       " 'told': 'real',\n",
       " 'told could': 'real',\n",
       " 'told would': 'real',\n",
       " 'took': 'real',\n",
       " 'total': 'real',\n",
       " 'toward': 'real',\n",
       " 'track': 'real',\n",
       " 'trade': 'real',\n",
       " 'transact': 'real',\n",
       " 'transfer': 'real',\n",
       " 'travel': 'real',\n",
       " 'treat': 'real',\n",
       " 'tri': 'real',\n",
       " 'tri collect': 'real',\n",
       " 'true': 'real',\n",
       " 'trust': 'real',\n",
       " 'turn': 'real',\n",
       " 'twice': 'real',\n",
       " 'type': 'real',\n",
       " 'u.s.': 'real',\n",
       " 'unabl': 'real',\n",
       " 'unauthor': 'real',\n",
       " 'understand': 'real',\n",
       " 'unemploy': 'real',\n",
       " 'unfair': 'real',\n",
       " 'unfortun': 'real',\n",
       " 'union': 'real',\n",
       " 'unit': 'real',\n",
       " 'unless': 'real',\n",
       " 'updat': 'real',\n",
       " 'upon': 'real',\n",
       " 'usaa': 'real',\n",
       " 'use': 'real',\n",
       " 'util': 'real',\n",
       " 'valid': 'real',\n",
       " 'valid debt': 'real',\n",
       " 'valu': 'real',\n",
       " 'vehicl': 'real',\n",
       " 'verif': 'real',\n",
       " 'verifi': 'real',\n",
       " 'victim': 'real',\n",
       " 'victim ident': 'real',\n",
       " 'violat': 'real',\n",
       " 'visa': 'real',\n",
       " 'visit': 'real',\n",
       " 'voicemail': 'real',\n",
       " 'wage': 'real',\n",
       " 'wait': 'real',\n",
       " 'waiv': 'real',\n",
       " 'want': 'real',\n",
       " 'wasnt': 'real',\n",
       " 'websit': 'real',\n",
       " 'week': 'real',\n",
       " 'week later': 'real',\n",
       " 'well': 'real',\n",
       " 'well fargo': 'real',\n",
       " 'went': 'real',\n",
       " 'whether': 'real',\n",
       " 'whole': 'real',\n",
       " 'wife': 'real',\n",
       " 'will': 'real',\n",
       " 'wire': 'real',\n",
       " 'withdraw': 'real',\n",
       " 'within': 'real',\n",
       " 'within day': 'real',\n",
       " 'without': 'real',\n",
       " 'word': 'real',\n",
       " 'work': 'real',\n",
       " 'would': 'real',\n",
       " 'would call': 'real',\n",
       " 'would like': 'real',\n",
       " 'would need': 'real',\n",
       " 'would receiv': 'real',\n",
       " 'would take': 'real',\n",
       " 'write': 'real',\n",
       " 'written': 'real',\n",
       " 'wrong': 'real',\n",
       " 'wrote': 'real',\n",
       " 'year': 'real',\n",
       " ...}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_train_df.types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2o_train_df['target'] = h2o_train_df['target'].asfactor()\n",
    "h2o_test_df['target'] = h2o_test_df['target'].asfactor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "aml = H2OAutoML(max_models = 10, seed = 10, exclude_algos = [\"StackedEnsemble\"],verbosity=\"info\",nfolds=0,balance_classes=True,max_after_balance_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = vectorizer_tf.get_feature_names()\n",
    "y = 'target'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoML progress: |█\n",
      "22:35:55.538: Project: AutoML_20201112_223555538\n",
      "22:35:55.538: Cross-validation disabled by user: no fold column nor nfolds > 1.\n",
      "22:35:55.554: Setting stopping tolerance adaptively based on the training frame: 0.00466445106034528\n",
      "22:35:55.554: Build control seed: 10\n",
      "22:35:55.554: Since cross-validation is disabled, and no leaderboard frame was provided, automatically split the training data into training and leaderboard frames in the ratio 90/10\n",
      "22:35:57.408: training frame: Frame key: automl_training_py_1_sid_99be    cols: 1001    rows: 41256  chunks: 53    size: 34317884  checksum: -7250044210537209784\n",
      "22:35:57.411: validation frame: Frame key: py_2_sid_99be    cols: 1001    rows: 11491  chunks: 14    size: 9650383  checksum: -3525458438983238450\n",
      "22:35:57.452: leaderboard frame: Frame key: automl_leaderboard_py_1_sid_99be    cols: 1001    rows: 4706  chunks: 53    size: 7551836  checksum: -6406089023458826254\n",
      "22:35:57.452: blending frame: NULL\n",
      "22:35:57.452: response column: target\n",
      "22:35:57.452: fold column: null\n",
      "22:35:57.452: weights column: null\n",
      "22:35:57.689: AutoML: XGBoost is not available; skipping it.\n",
      "22:35:57.783: 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",
      "22:35:57.794: Disabling Algo: XGBoost as requested by the user.\n",
      "22:35:57.794: Disabling Algo: StackedEnsemble as requested by the user.\n",
      "22:35:57.828: AutoML job created: 2020.11.12 22:35:55.538\n",
      "22:35:57.828: AutoML build started: 2020.11.12 22:35:57.828\n",
      "22:35:58.132: AutoML: starting GLM_1_AutoML_20201112_223555 model training\n",
      "\n",
      "████████Failed polling AutoML progress log: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\VARUNV~1\\\\AppData\\\\Local\\\\Temp\\\\tmpcxztb3cc.csv'\n",
      "███\n",
      "22:38:31.788: New leader: GLM_1_AutoML_20201112_223555, mean_per_class_error: 0.17281855715303787\n",
      "22:38:31.803: AutoML: starting DRF_1_AutoML_20201112_223555 model training\n",
      "\n",
      "█\n",
      "22:39:39.695: AutoML: starting GBM_1_AutoML_20201112_223555 model training\n",
      "\n",
      "████████████████████████\n",
      "22:45:12.128: New leader: GBM_1_AutoML_20201112_223555, mean_per_class_error: 0.1610331323675677\n",
      "22:45:12.128: AutoML: starting GBM_2_AutoML_20201112_223555 model training\n",
      "\n",
      "███████████\n",
      "22:49:13.765: New leader: GBM_2_AutoML_20201112_223555, mean_per_class_error: 0.15101860774855508\n",
      "22:49:13.765: AutoML: starting GBM_3_AutoML_20201112_223555 model training\n",
      "\n",
      "█████Failed polling AutoML progress log: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\VARUNV~1\\\\AppData\\\\Local\\\\Temp\\\\tmpvcalg5xm.csv'\n",
      "\n",
      "22:53:42.521: AutoML: starting GBM_4_AutoML_20201112_223555 model training\n",
      "\n",
      "█\n",
      "22:59:17.550: AutoML: starting GBM_5_AutoML_20201112_223555 model training\n",
      "\n",
      "█\n",
      "23:07:46.248: New leader: GBM_5_AutoML_20201112_223555, mean_per_class_error: 0.14496465615420198\n",
      "23:07:46.375: AutoML: starting DeepLearning_1_AutoML_20201112_223555 model training\n",
      "\n",
      "\n",
      "23:08:00.765: AutoML: starting XRT_1_AutoML_20201112_223555 model training\n",
      "\n",
      "Failed polling AutoML progress log: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\VARUNV~1\\\\AppData\\\\Local\\\\Temp\\\\tmpi32r6hrs.csv'\n",
      "\n",
      "23:09:26.441: AutoML: starting GBM_grid__1_AutoML_20201112_223555 hyperparameter search\n",
      "\n",
      "█| 100%\n",
      "\n",
      "23:17:41.153: Skipping StackedEnsemble 'best' due to the exclude_algos option.\n",
      "23:17:41.154: Skipping StackedEnsemble 'all' due to the exclude_algos option.\n",
      "23:17:41.155: Skipping StackedEnsemble 'monotonic' due to the exclude_algos option.\n",
      "23:17:41.155: Actual modeling steps: [{GLM : [def_1 (10)]}, {DRF : [def_1 (10)]}, {GBM : [def_1 (10), def_2 (10), def_3 (10), def_4 (10), def_5 (10)]}, {DeepLearning : [def_1 (10)]}, {DRF : [XRT (10)]}, {GBM : [grid_1 (60)]}]\n",
      "23:17:41.155: AutoML build stopped: 2020.11.12 23:17:41.155\n",
      "23:17:41.155: AutoML build done: built 10 models\n",
      "23:17:41.155: AutoML duration: 41 min 43.327 sec\n",
      "\n"
     ]
    }
   ],
   "source": [
    "aml.train(x = x, y = y, training_frame=h2o_train_df,validation_frame=h2o_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>model_id                                  </th><th style=\"text-align: right;\">  mean_per_class_error</th><th style=\"text-align: right;\">  logloss</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">     mse</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>GBM_5_AutoML_20201112_223555              </td><td style=\"text-align: right;\">              0.144965</td><td style=\"text-align: right;\"> 0.371045</td><td style=\"text-align: right;\">0.321411</td><td style=\"text-align: right;\">0.103305</td></tr>\n",
       "<tr><td>GBM_2_AutoML_20201112_223555              </td><td style=\"text-align: right;\">              0.151019</td><td style=\"text-align: right;\"> 0.379765</td><td style=\"text-align: right;\">0.333443</td><td style=\"text-align: right;\">0.111184</td></tr>\n",
       "<tr><td>GBM_4_AutoML_20201112_223555              </td><td style=\"text-align: right;\">              0.15297 </td><td style=\"text-align: right;\"> 0.386429</td><td style=\"text-align: right;\">0.334208</td><td style=\"text-align: right;\">0.111695</td></tr>\n",
       "<tr><td>GBM_grid__1_AutoML_20201112_223555_model_1</td><td style=\"text-align: right;\">              0.15505 </td><td style=\"text-align: right;\"> 0.406446</td><td style=\"text-align: right;\">0.338355</td><td style=\"text-align: right;\">0.114484</td></tr>\n",
       "<tr><td>GBM_3_AutoML_20201112_223555              </td><td style=\"text-align: right;\">              0.160114</td><td style=\"text-align: right;\"> 0.394517</td><td style=\"text-align: right;\">0.339311</td><td style=\"text-align: right;\">0.115132</td></tr>\n",
       "<tr><td>GBM_1_AutoML_20201112_223555              </td><td style=\"text-align: right;\">              0.161033</td><td style=\"text-align: right;\"> 0.404265</td><td style=\"text-align: right;\">0.343577</td><td style=\"text-align: right;\">0.118045</td></tr>\n",
       "<tr><td>GLM_1_AutoML_20201112_223555              </td><td style=\"text-align: right;\">              0.172819</td><td style=\"text-align: right;\"> 0.380815</td><td style=\"text-align: right;\">0.337484</td><td style=\"text-align: right;\">0.113896</td></tr>\n",
       "<tr><td>DeepLearning_1_AutoML_20201112_223555     </td><td style=\"text-align: right;\">              0.179605</td><td style=\"text-align: right;\"> 0.554205</td><td style=\"text-align: right;\">0.369377</td><td style=\"text-align: right;\">0.136439</td></tr>\n",
       "<tr><td>XRT_1_AutoML_20201112_223555              </td><td style=\"text-align: right;\">              0.273971</td><td style=\"text-align: right;\"> 0.604417</td><td style=\"text-align: right;\">0.448896</td><td style=\"text-align: right;\">0.201508</td></tr>\n",
       "<tr><td>DRF_1_AutoML_20201112_223555              </td><td style=\"text-align: right;\">              0.282182</td><td style=\"text-align: right;\"> 0.599123</td><td style=\"text-align: right;\">0.44671 </td><td style=\"text-align: right;\">0.19955 </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aml.leaderboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "pred=aml.leader.predict(h2o_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ModelMetricsMultinomial: gbm\n",
      "** Reported on test data. **\n",
      "\n",
      "MSE: 0.10664093624678768\n",
      "RMSE: 0.3265592384955411\n",
      "LogLoss: 0.3787255832863651\n",
      "Mean Per-Class Error: 0.15203211162433491\n",
      "\n",
      "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3885.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.095460</td>\n",
       "      <td>410 / 4,295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191.0</td>\n",
       "      <td>2227.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.137824</td>\n",
       "      <td>356 / 2,583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1835.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.089330</td>\n",
       "      <td>180 / 2,015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1259.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.138261</td>\n",
       "      <td>202 / 1,461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.171849</td>\n",
       "      <td>105 / 611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>69.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>0.279468</td>\n",
       "      <td>147 / 526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4319.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>1446.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>555.0</td>\n",
       "      <td>0.121834</td>\n",
       "      <td>1,400 / 11,491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0       1       2       3      4      5     Error            Rate\n",
       "0  3885.0   163.0    67.0    40.0   57.0   83.0  0.095460     410 / 4,295\n",
       "1   191.0  2227.0    18.0   120.0    3.0   24.0  0.137824     356 / 2,583\n",
       "2    74.0    35.0  1835.0    17.0   16.0   38.0  0.089330     180 / 2,015\n",
       "3    53.0   123.0    18.0  1259.0    0.0    8.0  0.138261     202 / 1,461\n",
       "4    47.0    18.0    14.0     3.0  506.0   23.0  0.171849       105 / 611\n",
       "5    69.0    44.0    21.0     7.0    6.0  379.0  0.279468       147 / 526\n",
       "6  4319.0  2610.0  1973.0  1446.0  588.0  555.0  0.121834  1,400 / 11,491"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top-6 Hit Ratios: \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>k</th>\n",
       "      <th>hit_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.878166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.959272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.983291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.993125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.997999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   k  hit_ratio\n",
       "0  1   0.878166\n",
       "1  2   0.959272\n",
       "2  3   0.983291\n",
       "3  4   0.993125\n",
       "4  5   0.997999\n",
       "5  6   1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aml.leader.model_performance(h2o_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GBM_5_AutoML_20201112_223555',\n",
       " 'GBM_2_AutoML_20201112_223555',\n",
       " 'GBM_4_AutoML_20201112_223555',\n",
       " 'GBM_grid__1_AutoML_20201112_223555_model_1',\n",
       " 'GBM_3_AutoML_20201112_223555',\n",
       " 'GBM_1_AutoML_20201112_223555',\n",
       " 'GLM_1_AutoML_20201112_223555',\n",
       " 'DeepLearning_1_AutoML_20201112_223555',\n",
       " 'XRT_1_AutoML_20201112_223555',\n",
       " 'DRF_1_AutoML_20201112_223555']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ids = list(aml.leaderboard['model_id'].as_data_frame().iloc[:,0])\n",
    "model_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm_clf = GradientBoostingClassifier(n_estimators=100,learning_rate=0.05,max_depth=6,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.05, loss='deviance', max_depth=6,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=10, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_clf.fit(train_vectors, X_train['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = gbm_clf.predict(test_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3960  148   65   27   42   53]\n",
      " [ 248 2222   11   82    3   17]\n",
      " [  97   29 1825   25   12   27]\n",
      " [  84  133   19 1217    0    8]\n",
      " [  75   26   27    1  463   19]\n",
      " [ 116   43   26   10    5  326]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "cm = confusion_matrix(X_test['target'],predictions)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.92      0.89      4295\n",
      "           1       0.85      0.86      0.86      2583\n",
      "           2       0.92      0.91      0.92      2015\n",
      "           3       0.89      0.83      0.86      1461\n",
      "           4       0.88      0.76      0.82       611\n",
      "           5       0.72      0.62      0.67       526\n",
      "\n",
      "    accuracy                           0.87     11491\n",
      "   macro avg       0.86      0.82      0.84     11491\n",
      "weighted avg       0.87      0.87      0.87     11491\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Classification Report:\\n',classification_report(X_test['target'],predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import class_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weights = list(class_weight.compute_class_weight('balanced',np.unique(X_train['target']),X_train['target']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.43830939711239536,\n",
       " 0.7219918316054037,\n",
       " 0.9841127098321343,\n",
       " 1.382232647660291,\n",
       " 3.275046316089497,\n",
       " 3.466214177978884]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., ..., 1., 1., 1.])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = np.ones(X_train.shape[0],dtype='float')\n",
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,val in enumerate(X_train['target']):\n",
    "    weights[i] = class_weights[val]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.38223265, 0.4383094 , 0.4383094 , ..., 0.72199183, 0.72199183,\n",
       "       0.98411271])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm_clf = GradientBoostingClassifier(n_estimators=100,learning_rate=0.05,max_depth=6,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.05, loss='deviance', max_depth=6,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=10, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_clf.fit(train_vectors, X_train['target'],sample_weight=weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = gbm_clf.predict(test_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3713  181   86   75   77  163]\n",
      " [ 143 2143   21  152    4  120]\n",
      " [  39   15 1808   27   39   87]\n",
      " [  26  101   15 1293    4   22]\n",
      " [  25    7    5    2  518   54]\n",
      " [  47   17   12   12   14  424]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(X_test['target'],predictions)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.86      0.90      4295\n",
      "           1       0.87      0.83      0.85      2583\n",
      "           2       0.93      0.90      0.91      2015\n",
      "           3       0.83      0.89      0.86      1461\n",
      "           4       0.79      0.85      0.82       611\n",
      "           5       0.49      0.81      0.61       526\n",
      "\n",
      "    accuracy                           0.86     11491\n",
      "   macro avg       0.81      0.86      0.82     11491\n",
      "weighted avg       0.88      0.86      0.87     11491\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Classification Report:\\n',classification_report(X_test['target'],predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score (test): 0.861\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy score (test): {0:.3f}\".format(gbm_clf.score(test_vectors, X_test['target'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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