Part 4.1: Text_Classification_using_TFIDF_AutoML_H2O_scikit_learn.ipynb

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      "name": "Text_Classification_using_TFIDF_AutoML/H2O_scikit-learn.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k3i3PCwNLJGU"
      },
      "source": [
        "Details of the code, how it works is available in on video - https://youtu.be/EHt_x8r1exU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBO2bfrW_AQz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d381ed56-91b9-4bf7-e704-90f1ad6cb80a"
      },
      "source": [
        "!apt-get install default-jre\n",
        "!java -version"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "default-jre is already the newest version (2:1.11-68ubuntu1~18.04.1).\n",
            "default-jre set to manually installed.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 15 not upgraded.\n",
            "openjdk version \"11.0.9.1\" 2020-11-04\n",
            "OpenJDK Runtime Environment (build 11.0.9.1+1-Ubuntu-0ubuntu1.18.04)\n",
            "OpenJDK 64-Bit Server VM (build 11.0.9.1+1-Ubuntu-0ubuntu1.18.04, mixed mode, sharing)\n"
          ],
          "name": "stdout"
        }
      ]
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      "cell_type": "code",
      "metadata": {
        "id": "VlsUTZSy_FWU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "823039b5-77b5-4497-c11e-2795e954c279"
      },
      "source": [
        "!pip install h2o"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting h2o\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/26/c5/d63a8bfdbeb4ebfb709c010af3e061d89a363204c437cb5527431f6de3d2/h2o-3.32.0.2.tar.gz (164.6MB)\n",
            "\u001b[K     |████████████████████████████████| 164.6MB 72kB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from h2o) (2.23.0)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.6/dist-packages (from h2o) (0.8.7)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from h2o) (0.16.0)\n",
            "Collecting colorama>=0.3.8\n",
            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->h2o) (3.0.4)\n",
            "Building wheels for collected packages: h2o\n",
            "  Building wheel for h2o (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for h2o: filename=h2o-3.32.0.2-py2.py3-none-any.whl size=164620456 sha256=468153db8f83394ba5846b6281ad8adf78bce71060b617389d2fc4c3b45c708d\n",
            "  Stored in directory: /root/.cache/pip/wheels/42/bd/ea/218fd15724eddf6fa7fc8fab802b6fa592e623d87199679721\n",
            "Successfully built h2o\n",
            "Installing collected packages: colorama, h2o\n",
            "Successfully installed colorama-0.4.4 h2o-3.32.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Tib-VoNf3SU"
      },
      "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"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrG3q2dy_VSE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "ffd42491-4264-44ea-ec8d-89616419349e"
      },
      "source": [
        "h2o.init()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
            "Attempting to start a local H2O server...\n",
            "  Java Version: openjdk version \"11.0.9.1\" 2020-11-04; OpenJDK Runtime Environment (build 11.0.9.1+1-Ubuntu-0ubuntu1.18.04); OpenJDK 64-Bit Server VM (build 11.0.9.1+1-Ubuntu-0ubuntu1.18.04, mixed mode, sharing)\n",
            "  Starting server from /usr/local/lib/python3.6/dist-packages/h2o/backend/bin/h2o.jar\n",
            "  Ice root: /tmp/tmp1tcj0juc\n",
            "  JVM stdout: /tmp/tmp1tcj0juc/h2o_unknownUser_started_from_python.out\n",
            "  JVM stderr: /tmp/tmp1tcj0juc/h2o_unknownUser_started_from_python.err\n",
            "  Server is running at http://127.0.0.1:54321\n",
            "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
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              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
              "<td>02 secs</td></tr>\n",
              "<tr><td>H2O_cluster_timezone:</td>\n",
              "<td>Etc/UTC</td></tr>\n",
              "<tr><td>H2O_data_parsing_timezone:</td>\n",
              "<td>UTC</td></tr>\n",
              "<tr><td>H2O_cluster_version:</td>\n",
              "<td>3.32.0.2</td></tr>\n",
              "<tr><td>H2O_cluster_version_age:</td>\n",
              "<td>1 month and 12 days </td></tr>\n",
              "<tr><td>H2O_cluster_name:</td>\n",
              "<td>H2O_from_python_unknownUser_lmxvnn</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>3.180 Gb</td></tr>\n",
              "<tr><td>H2O_cluster_total_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_allowed_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_status:</td>\n",
              "<td>accepting new members, healthy</td></tr>\n",
              "<tr><td>H2O_connection_url:</td>\n",
              "<td>http://127.0.0.1:54321</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, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
              "<tr><td>Python_version:</td>\n",
              "<td>3.6.9 final</td></tr></table></div>"
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            "text/plain": [
              "--------------------------  ------------------------------------------------------------------\n",
              "H2O_cluster_uptime:         02 secs\n",
              "H2O_cluster_timezone:       Etc/UTC\n",
              "H2O_data_parsing_timezone:  UTC\n",
              "H2O_cluster_version:        3.32.0.2\n",
              "H2O_cluster_version_age:    1 month and 12 days\n",
              "H2O_cluster_name:           H2O_from_python_unknownUser_lmxvnn\n",
              "H2O_cluster_total_nodes:    1\n",
              "H2O_cluster_free_memory:    3.180 Gb\n",
              "H2O_cluster_total_cores:    2\n",
              "H2O_cluster_allowed_cores:  2\n",
              "H2O_cluster_status:         accepting new members, healthy\n",
              "H2O_connection_url:         http://127.0.0.1:54321\n",
              "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
              "H2O_internal_security:      False\n",
              "H2O_API_Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
              "Python_version:             3.6.9 final\n",
              "--------------------------  ------------------------------------------------------------------"
            ]
          },
          "metadata": {
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WShP9UhDdqqz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b2fb5c9-e97d-4fa6-a33b-9aa516663985"
      },
      "source": [
        "nltk.download('punkt') #punkt is used for sentence tokenization"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJLUmACgf8oQ"
      },
      "source": [
        "df=pd.read_csv('https://github.com/srivatsan88/YouTubeLI/blob/master/dataset/consumer_compliants.zip?raw=true', compression='zip', sep=',', quotechar='\"')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YZbkRxzg6KI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "51d0e297-bb96-460c-cd58-c87f20708520"
      },
      "source": [
        "df"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Date received</th>\n",
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              "      <th>Timely response?</th>\n",
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              "      <th>Complaint ID</th>\n",
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              "      <th>0</th>\n",
              "      <td>4/3/2020</td>\n",
              "      <td>Vehicle loan or lease</td>\n",
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              "      <td>Getting a loan or lease</td>\n",
              "      <td>Fraudulent loan</td>\n",
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              "      <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",
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              "    <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",
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              "      <td>3564184</td>\n",
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              "      <th>2</th>\n",
              "      <td>2/6/2020</td>\n",
              "      <td>Vehicle loan or lease</td>\n",
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              "      <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",
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              "      <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",
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              "    <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",
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              "    <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",
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              "    <tr>\n",
              "      <th>57449</th>\n",
              "      <td>2/11/2020</td>\n",
              "      <td>Debt collection</td>\n",
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              "      <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",
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              "    <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",
              "</table>\n",
              "<p>57453 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Date received  ... Complaint ID\n",
              "0          4/3/2020  ...      3591341\n",
              "1         3/12/2020  ...      3564184\n",
              "2          2/6/2020  ...      3521949\n",
              "3          3/6/2020  ...      3556237\n",
              "4         2/14/2020  ...      3531704\n",
              "...             ...  ...          ...\n",
              "57448     2/29/2020  ...      3549178\n",
              "57449     2/11/2020  ...      3527928\n",
              "57450     2/29/2020  ...      3549238\n",
              "57451     1/16/2020  ...      3498566\n",
              "57452     1/16/2020  ...      3498524\n",
              "\n",
              "[57453 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iu0aXVAqg73M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6360fe6-90ff-4c3b-bebe-d1b401ff4abc"
      },
      "source": [
        "df['Product'].value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Debt collection                21772\n",
              "Credit card or prepaid card    13193\n",
              "Mortgage                        9799\n",
              "Checking or savings account     7003\n",
              "Student loan                    2950\n",
              "Vehicle loan or lease           2736\n",
              "Name: Product, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54Da_-V33gQ5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "37483bdf-512a-449a-dc33-0bab6339ff80"
      },
      "source": [
        "df['Product'].value_counts().plot(kind='bar')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd081f3e6a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIOA2D7qhPIM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "186dedd3-94a7-48c9-f03a-8c4b9a827ade"
      },
      "source": [
        "df['Company'].value_counts()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "CrossCheck, Inc.                            1\n",
              "RABOBANK, NATIONAL ASSOCIATION              1\n",
              "Rocky Mountain Recovery Group               1\n",
              "Premier Lending and Real Estate             1\n",
              "AGN Auto Gallery Inc                        1\n",
              "Name: Company, Length: 2197, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RyAy7ALMkDK-"
      },
      "source": [
        "complaints_df=df[['Consumer complaint narrative','Product','Company']].rename(columns={'Consumer complaint narrative':'complaints'})"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j2e_-PbHkgcC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2266517e-c817-4449-aa57-50448d494202"
      },
      "source": [
        "pd.set_option('display.max_colwidth', -1)\n",
        "complaints_df"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>This auto loan was opened on XX/XX/2020 in XXXX, NC with BB &amp; T in my name. I have NEVER been to North Carolina and I have NEVER been a resident. I have filed a dispute twice through my credit bureaus but both times BB &amp; T has claimed that this is an accurate loan. Which I wasn't aware of until today. I have tried to contact BB &amp; T multiple times but I have never gotten through to a live person. I do n't drive and I have never owned a car before. I didn't have any knowledge of this account until I checked XXXXXXXX XXXX  and noticed it. I've tried twice to dispute it. Additionally I never received any bills or information about this account. This is my last resort in trying to remove this fraudulent loan off of my account.</td>\n",
              "      <td>Vehicle loan or lease</td>\n",
              "      <td>TRUIST FINANCIAL CORPORATION</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>In XXXX of 2019 I noticed a debt for {$620.00} on my credit which i believed was mine I thought speedy cash had bought one of my old debts and sold it to XXXX XXXX XXXX XXXX. I contacted XXXX XXXX XXXX XXXX and after several attempts of giving my full name, nothing came up in their system. I gave my social and the rep said the account popped up but DID NOT tell me that the account was under someone elses name and continued to let me make a payment. The payment was for {$120.00}. Confirmation number-XXXX. After realizing it was not my account, I called back to get my money back and inform them of the mistake. I was told i needed to mail them an FTC report and dispute letter to get my money back. I completed all of this and when i called again they said they transferred the account back to speedy cash for fraud review and I would need to contact them. After contacting them i was again told that i can not get my money back. The issue im having is this representative at XXXX XXXX played blind to obvious fraud and let an innocent person make a payment on someone elses debt and i want my money back.</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>CURO Intermediate Holdings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>As stated from Capital One, XXXX XX/XX/XXXX and XXXX 2018, My wife and I went to several car dealerships to request for a car loan to get a used car. However, according to their credit requirements unfortunately my credit score was insufficient for the car loan approval at that time. It seemed as though they pulled my credit report multiple times.</td>\n",
              "      <td>Vehicle loan or lease</td>\n",
              "      <td>CAPITAL ONE FINANCIAL CORPORATION</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Please see CFPB case XXXX. \\n\\nCapital One, in the letter they provided ( and attached to that case as their response ) said this : \" The funds were reversed and sent back to XXXX XXXX XXXX  on XX/XX/XXXX ''. \\n\\nXXXX XXXX XXXX  ( now XXXX XXXX  ) has not received these funds. Staff at XXXX XXXX  - and also staff at the account-holder 's business - have looked for return of my money ( {$650.00} ) and find nothing. \\n\\nCapital One needs to document - actually prove - they returned the funds, as stated in their letter. Capital One must provide electronic information, if the return was made that way, or document the paper check they sent back to XXXX XXXX. \\n\\nI've left 3 messages about this problem for the person who signed the letter ( XXXX ) from Capital One. I have received no call-backs. \\n\\nSummary : Capital One said they returned my money on XX/XX/XXXX : they did not. If they continue claim they did, then they need to prove that.</td>\n",
              "      <td>Checking or savings account</td>\n",
              "      <td>CAPITAL ONE FINANCIAL CORPORATION</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>This debt was incurred due to medical malpractice ( XXXX XXXX XXXX, XXXX, TX ). I asked the doctor to turn over my claim to his malpractice insurance company. This has cost me thousands of dollars to XXXX XXXX XXXX. I am still trying to collect damages from this doctor. He never responded and turned over me to collections Merchants and Professional Collection Bureau , Inc. I sent them a letter describing exactly this issue and instead of not contacting me and verifying my debt they start reporting this debt to the credit reporting agencies. They never verified the debt, like I asked and they never stopped it from being reported when I specifically told them not to, due to the circumstances above.</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Merchants and Professional Bureau, Inc.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\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 student loans on the Nelnet website today, XX/XX/20, and Nelnet will not allow me to post the payment sooner than XX/XX/20. By the time the payment posts, 2-3 days of additional interest will have accrued and my payments will apply more to interest than is due today, the day that I'm attempting to pay. My understanding was that I could make a payment at any time but this does not appear to be true. The funds are available in my bank account today regardless of whether Nelnet can collect over the weekend. I should not be penalized for this. \\n\\nI submitted complaint XXXX in XXXX for other deceptive practices with Nelnet. They have not yet resolved the issue identified in that complaint or contacted me as they said they would in their response. I believe this new issue is just one more deceptive practice by this company that causes financial harm to borrowers.</td>\n",
              "      <td>Student loan</td>\n",
              "      <td>Nelnet, Inc.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57449</th>\n",
              "      <td>Received letter for {$480.00}. Original creditor didnt contact me until past statute of limitations for insurance company recoupment per Arizona law. Debt collection is illegal for phantom debt. Additionally they are phoning my office excessively.</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>The Receivable Management Services LLC, New York, NY Branch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57450</th>\n",
              "      <td>entire time 10 years until XX/XX/2020. XXXX makes my blood boil. I have called and was lied to told to provide my checking account information over the phone in order to turn my cell phone back on. i called at XXXX them at XXXX {$300.00} was added to my bill. \\n\\nScam scam scam I was told I can not call the office of the President just to write to XXXX XXXX XXXX XXXX XXXX XXXX XXXX, NM XXXX. I did three thousand times. the last letter I mailed on XX/XX/2020. Two collection agencies later. \\n\\nI chose to leave XXXX XXXX every time I called the XXXX supervisor would threaten me on a recorded line. I need peace of mind and a good Heart to beat inside of me. Im on a XXXX XXXX due to the stress at XXXX XXXX   taking all my money 4 10 years.</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Convergent Resources, Inc.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57451</th>\n",
              "      <td>I am a customer with Wells Fargo Bank. Recently money was withdrawn on a couple of occasions without my permission or consent to pay for a timeshare account that was never used by me nor anyone connected to me because of unfair policies pertaining to the fees of the said timeshare. I tried cancelling the said timeshare account several times because of these fees that were never mentioned at the initiation. My account was debited to pay for the timeshare fees without my knowledge or consent several times. I tried correcting this with Wells Fargo bank with no avail. I would appreciate it if you can look into this matter for me. I was left with no funds in my account and as such I could not take care of the basic necessities of my day to day life. \\nThanks in advance,</td>\n",
              "      <td>Checking or savings account</td>\n",
              "      <td>WELLS FARGO &amp; COMPANY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57452</th>\n",
              "      <td>I spoken with them several times in a year. An in XXXX they informed me yes they sold my information to a third party without my knowledge. Also, I told them that I dont owe them anything because my car was paid off by insurance XXXX an Gap an they said no. I also informed them that constantly calling me an telling me that my credit score would be damage was inappropriate. But on my credit they written several negative things. An I didnt give Exeter financial Corp no authority to sell my information to XXXX XXXX</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Exeter Finance Corp.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>57453 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   complaints  ...                                                      Company\n",
              "0      This auto loan was opened on XX/XX/2020 in XXXX, NC with BB & T in my name. I have NEVER been to North Carolina and I have NEVER been a resident. I have filed a dispute twice through my credit bureaus but both times BB & T has claimed that this is an accurate loan. Which I wasn't aware of until today. I have tried to contact BB & T multiple times but I have never gotten through to a live person. I do n't drive and I have never owned a car before. I didn't have any knowledge of this account until I checked XXXXXXXX XXXX  and noticed it. I've tried twice to dispute it. Additionally I never received any bills or information about this account. This is my last resort in trying to remove this fraudulent loan off of my account.                                                                                                                                                                                                                                                                                                                                                                                             ...  TRUIST FINANCIAL CORPORATION                               \n",
              "1      In XXXX of 2019 I noticed a debt for {$620.00} on my credit which i believed was mine I thought speedy cash had bought one of my old debts and sold it to XXXX XXXX XXXX XXXX. I contacted XXXX XXXX XXXX XXXX and after several attempts of giving my full name, nothing came up in their system. I gave my social and the rep said the account popped up but DID NOT tell me that the account was under someone elses name and continued to let me make a payment. The payment was for {$120.00}. Confirmation number-XXXX. After realizing it was not my account, I called back to get my money back and inform them of the mistake. I was told i needed to mail them an FTC report and dispute letter to get my money back. I completed all of this and when i called again they said they transferred the account back to speedy cash for fraud review and I would need to contact them. After contacting them i was again told that i can not get my money back. The issue im having is this representative at XXXX XXXX played blind to obvious fraud and let an innocent person make a payment on someone elses debt and i want my money back.  ...  CURO Intermediate Holdings                                 \n",
              "2      As stated from Capital One, XXXX XX/XX/XXXX and XXXX 2018, My wife and I went to several car dealerships to request for a car loan to get a used car. However, according to their credit requirements unfortunately my credit score was insufficient for the car loan approval at that time. It seemed as though they pulled my credit report multiple times.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           ...  CAPITAL ONE FINANCIAL CORPORATION                          \n",
              "3      Please see CFPB case XXXX. \\n\\nCapital One, in the letter they provided ( and attached to that case as their response ) said this : \" The funds were reversed and sent back to XXXX XXXX XXXX  on XX/XX/XXXX ''. \\n\\nXXXX XXXX XXXX  ( now XXXX XXXX  ) has not received these funds. Staff at XXXX XXXX  - and also staff at the account-holder 's business - have looked for return of my money ( {$650.00} ) and find nothing. \\n\\nCapital One needs to document - actually prove - they returned the funds, as stated in their letter. Capital One must provide electronic information, if the return was made that way, or document the paper check they sent back to XXXX XXXX. \\n\\nI've left 3 messages about this problem for the person who signed the letter ( XXXX ) from Capital One. I have received no call-backs. \\n\\nSummary : Capital One said they returned my money on XX/XX/XXXX : they did not. If they continue claim they did, then they need to prove that.                                                                                                                                                                     ...  CAPITAL ONE FINANCIAL CORPORATION                          \n",
              "4      This debt was incurred due to medical malpractice ( XXXX XXXX XXXX, XXXX, TX ). I asked the doctor to turn over my claim to his malpractice insurance company. This has cost me thousands of dollars to XXXX XXXX XXXX. I am still trying to collect damages from this doctor. He never responded and turned over me to collections Merchants and Professional Collection Bureau , Inc. I sent them a letter describing exactly this issue and instead of not contacting me and verifying my debt they start reporting this debt to the credit reporting agencies. They never verified the debt, like I asked and they never stopped it from being reported when I specifically told them not to, due to the circumstances above.                                                                                                                                                                                                                                                                                                                                                                                                                       ...  Merchants and Professional Bureau, Inc.                    \n",
              "...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  ...                                                                                                                                                                                                                                                                                                                                                                                                                       ...                                      ...                    \n",
              "57448  I am attempting to make a payment toward my student loans on the Nelnet website today, XX/XX/20, and Nelnet will not allow me to post the payment sooner than XX/XX/20. By the time the payment posts, 2-3 days of additional interest will have accrued and my payments will apply more to interest than is due today, the day that I'm attempting to pay. My understanding was that I could make a payment at any time but this does not appear to be true. The funds are available in my bank account today regardless of whether Nelnet can collect over the weekend. I should not be penalized for this. \\n\\nI submitted complaint XXXX in XXXX for other deceptive practices with Nelnet. They have not yet resolved the issue identified in that complaint or contacted me as they said they would in their response. I believe this new issue is just one more deceptive practice by this company that causes financial harm to borrowers.                                                                                                                                                                                                      ...  Nelnet, Inc.                                               \n",
              "57449  Received letter for {$480.00}. Original creditor didnt contact me until past statute of limitations for insurance company recoupment per Arizona law. Debt collection is illegal for phantom debt. Additionally they are phoning my office excessively.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ...  The Receivable Management Services LLC, New York, NY Branch\n",
              "57450  entire time 10 years until XX/XX/2020. XXXX makes my blood boil. I have called and was lied to told to provide my checking account information over the phone in order to turn my cell phone back on. i called at XXXX them at XXXX {$300.00} was added to my bill. \\n\\nScam scam scam I was told I can not call the office of the President just to write to XXXX XXXX XXXX XXXX XXXX XXXX XXXX, NM XXXX. I did three thousand times. the last letter I mailed on XX/XX/2020. Two collection agencies later. \\n\\nI chose to leave XXXX XXXX every time I called the XXXX supervisor would threaten me on a recorded line. I need peace of mind and a good Heart to beat inside of me. Im on a XXXX XXXX due to the stress at XXXX XXXX   taking all my money 4 10 years.                                                                                                                                                                                                                                                                                                                                                                               ...  Convergent Resources, Inc.                                 \n",
              "57451  I am a customer with Wells Fargo Bank. Recently money was withdrawn on a couple of occasions without my permission or consent to pay for a timeshare account that was never used by me nor anyone connected to me because of unfair policies pertaining to the fees of the said timeshare. I tried cancelling the said timeshare account several times because of these fees that were never mentioned at the initiation. My account was debited to pay for the timeshare fees without my knowledge or consent several times. I tried correcting this with Wells Fargo bank with no avail. I would appreciate it if you can look into this matter for me. I was left with no funds in my account and as such I could not take care of the basic necessities of my day to day life. \\nThanks in advance,                                                                                                                                                                                                                                                                                                                                                 ...  WELLS FARGO & COMPANY                                      \n",
              "57452  I spoken with them several times in a year. An in XXXX they informed me yes they sold my information to a third party without my knowledge. Also, I told them that I dont owe them anything because my car was paid off by insurance XXXX an Gap an they said no. I also informed them that constantly calling me an telling me that my credit score would be damage was inappropriate. But on my credit they written several negative things. An I didnt give Exeter financial Corp no authority to sell my information to XXXX XXXX                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   ...  Exeter Finance Corp.                                       \n",
              "\n",
              "[57453 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncJME1YZCDF2"
      },
      "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}"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSlF3GANCpca"
      },
      "source": [
        "complaints_df['target']=complaints_df['Product'].map(target)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2xqpcnRGvG1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5f9b28be-9372-4ac2-9932-972d4267eac8"
      },
      "source": [
        "complaints_df"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<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 XXXX, NC with BB &amp; T in my name. I have NEVER been to North Carolina and I have NEVER been a resident. I have filed a dispute twice through my credit bureaus but both times BB &amp; T has claimed that this is an accurate loan. Which I wasn't aware of until today. I have tried to contact BB &amp; T multiple times but I have never gotten through to a live person. I do n't drive and I have never owned a car before. I didn't have any knowledge of this account until I checked XXXXXXXX XXXX  and noticed it. I've tried twice to dispute it. Additionally I never received any bills or information about this account. This is my last resort in trying to remove this fraudulent loan off of my account.</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} on my credit which i believed was mine I thought speedy cash had bought one of my old debts and sold it to XXXX XXXX XXXX XXXX. I contacted XXXX XXXX XXXX XXXX and after several attempts of giving my full name, nothing came up in their system. I gave my social and the rep said the account popped up but DID NOT tell me that the account was under someone elses name and continued to let me make a payment. The payment was for {$120.00}. Confirmation number-XXXX. After realizing it was not my account, I called back to get my money back and inform them of the mistake. I was told i needed to mail them an FTC report and dispute letter to get my money back. I completed all of this and when i called again they said they transferred the account back to speedy cash for fraud review and I would need to contact them. After contacting them i was again told that i can not get my money back. The issue im having is this representative at XXXX XXXX played blind to obvious fraud and let an innocent person make a payment on someone elses debt and i want my money back.</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 and XXXX 2018, My wife and I went to several car dealerships to request for a car loan to get a used car. However, according to their credit requirements unfortunately my credit score was insufficient for the car loan approval at that time. It seemed as though they pulled my credit report multiple times.</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 the letter they provided ( and attached to that case as their response ) said this : \" The funds were reversed and sent back to XXXX XXXX XXXX  on XX/XX/XXXX ''. \\n\\nXXXX XXXX XXXX  ( now XXXX XXXX  ) has not received these funds. Staff at XXXX XXXX  - and also staff at the account-holder 's business - have looked for return of my money ( {$650.00} ) and find nothing. \\n\\nCapital One needs to document - actually prove - they returned the funds, as stated in their letter. Capital One must provide electronic information, if the return was made that way, or document the paper check they sent back to XXXX XXXX. \\n\\nI've left 3 messages about this problem for the person who signed the letter ( XXXX ) from Capital One. I have received no call-backs. \\n\\nSummary : Capital One said they returned my money on XX/XX/XXXX : they did not. If they continue claim they did, then they need to prove that.</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 malpractice ( XXXX XXXX XXXX, XXXX, TX ). I asked the doctor to turn over my claim to his malpractice insurance company. This has cost me thousands of dollars to XXXX XXXX XXXX. I am still trying to collect damages from this doctor. He never responded and turned over me to collections Merchants and Professional Collection Bureau , Inc. I sent them a letter describing exactly this issue and instead of not contacting me and verifying my debt they start reporting this debt to the credit reporting agencies. They never verified the debt, like I asked and they never stopped it from being reported when I specifically told them not to, due to the circumstances above.</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 student loans on the Nelnet website today, XX/XX/20, and Nelnet will not allow me to post the payment sooner than XX/XX/20. By the time the payment posts, 2-3 days of additional interest will have accrued and my payments will apply more to interest than is due today, the day that I'm attempting to pay. My understanding was that I could make a payment at any time but this does not appear to be true. The funds are available in my bank account today regardless of whether Nelnet can collect over the weekend. I should not be penalized for this. \\n\\nI submitted complaint XXXX in XXXX for other deceptive practices with Nelnet. They have not yet resolved the issue identified in that complaint or contacted me as they said they would in their response. I believe this new issue is just one more deceptive practice by this company that causes financial harm to borrowers.</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 creditor didnt contact me until past statute of limitations for insurance company recoupment per Arizona law. Debt collection is illegal for phantom debt. Additionally they are phoning my office excessively.</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>The Receivable Management Services LLC, New York, NY Branch</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57450</th>\n",
              "      <td>entire time 10 years until XX/XX/2020. XXXX makes my blood boil. I have called and was lied to told to provide my checking account information over the phone in order to turn my cell phone back on. i called at XXXX them at XXXX {$300.00} was added to my bill. \\n\\nScam scam scam I was told I can not call the office of the President just to write to XXXX XXXX XXXX XXXX XXXX XXXX XXXX, NM XXXX. I did three thousand times. the last letter I mailed on XX/XX/2020. Two collection agencies later. \\n\\nI chose to leave XXXX XXXX every time I called the XXXX supervisor would threaten me on a recorded line. I need peace of mind and a good Heart to beat inside of me. Im on a XXXX XXXX due to the stress at XXXX XXXX   taking all my money 4 10 years.</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. Recently money was withdrawn on a couple of occasions without my permission or consent to pay for a timeshare account that was never used by me nor anyone connected to me because of unfair policies pertaining to the fees of the said timeshare. I tried cancelling the said timeshare account several times because of these fees that were never mentioned at the initiation. My account was debited to pay for the timeshare fees without my knowledge or consent several times. I tried correcting this with Wells Fargo bank with no avail. I would appreciate it if you can look into this matter for me. I was left with no funds in my account and as such I could not take care of the basic necessities of my day to day life. \\nThanks in advance,</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 in XXXX they informed me yes they sold my information to a third party without my knowledge. Also, I told them that I dont owe them anything because my car was paid off by insurance XXXX an Gap an they said no. I also informed them that constantly calling me an telling me that my credit score would be damage was inappropriate. But on my credit they written several negative things. An I didnt give Exeter financial Corp no authority to sell my information to XXXX XXXX</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  ... target\n",
              "0      This auto loan was opened on XX/XX/2020 in XXXX, NC with BB & T in my name. I have NEVER been to North Carolina and I have NEVER been a resident. I have filed a dispute twice through my credit bureaus but both times BB & T has claimed that this is an accurate loan. Which I wasn't aware of until today. I have tried to contact BB & T multiple times but I have never gotten through to a live person. I do n't drive and I have never owned a car before. I didn't have any knowledge of this account until I checked XXXXXXXX XXXX  and noticed it. I've tried twice to dispute it. Additionally I never received any bills or information about this account. This is my last resort in trying to remove this fraudulent loan off of my account.                                                                                                                                                                                                                                                                                                                                                                                             ...  5    \n",
              "1      In XXXX of 2019 I noticed a debt for {$620.00} on my credit which i believed was mine I thought speedy cash had bought one of my old debts and sold it to XXXX XXXX XXXX XXXX. I contacted XXXX XXXX XXXX XXXX and after several attempts of giving my full name, nothing came up in their system. I gave my social and the rep said the account popped up but DID NOT tell me that the account was under someone elses name and continued to let me make a payment. The payment was for {$120.00}. Confirmation number-XXXX. After realizing it was not my account, I called back to get my money back and inform them of the mistake. I was told i needed to mail them an FTC report and dispute letter to get my money back. I completed all of this and when i called again they said they transferred the account back to speedy cash for fraud review and I would need to contact them. After contacting them i was again told that i can not get my money back. The issue im having is this representative at XXXX XXXX played blind to obvious fraud and let an innocent person make a payment on someone elses debt and i want my money back.  ...  0    \n",
              "2      As stated from Capital One, XXXX XX/XX/XXXX and XXXX 2018, My wife and I went to several car dealerships to request for a car loan to get a used car. However, according to their credit requirements unfortunately my credit score was insufficient for the car loan approval at that time. It seemed as though they pulled my credit report multiple times.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           ...  5    \n",
              "3      Please see CFPB case XXXX. \\n\\nCapital One, in the letter they provided ( and attached to that case as their response ) said this : \" The funds were reversed and sent back to XXXX XXXX XXXX  on XX/XX/XXXX ''. \\n\\nXXXX XXXX XXXX  ( now XXXX XXXX  ) has not received these funds. Staff at XXXX XXXX  - and also staff at the account-holder 's business - have looked for return of my money ( {$650.00} ) and find nothing. \\n\\nCapital One needs to document - actually prove - they returned the funds, as stated in their letter. Capital One must provide electronic information, if the return was made that way, or document the paper check they sent back to XXXX XXXX. \\n\\nI've left 3 messages about this problem for the person who signed the letter ( XXXX ) from Capital One. I have received no call-backs. \\n\\nSummary : Capital One said they returned my money on XX/XX/XXXX : they did not. If they continue claim they did, then they need to prove that.                                                                                                                                                                     ...  3    \n",
              "4      This debt was incurred due to medical malpractice ( XXXX XXXX XXXX, XXXX, TX ). I asked the doctor to turn over my claim to his malpractice insurance company. This has cost me thousands of dollars to XXXX XXXX XXXX. I am still trying to collect damages from this doctor. He never responded and turned over me to collections Merchants and Professional Collection Bureau , Inc. I sent them a letter describing exactly this issue and instead of not contacting me and verifying my debt they start reporting this debt to the credit reporting agencies. They never verified the debt, like I asked and they never stopped it from being reported when I specifically told them not to, due to the circumstances above.                                                                                                                                                                                                                                                                                                                                                                                                                       ...  0    \n",
              "...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  ...                                                                                                                                                                                                                                                                                                                                                                                                                       ... ..    \n",
              "57448  I am attempting to make a payment toward my student loans on the Nelnet website today, XX/XX/20, and Nelnet will not allow me to post the payment sooner than XX/XX/20. By the time the payment posts, 2-3 days of additional interest will have accrued and my payments will apply more to interest than is due today, the day that I'm attempting to pay. My understanding was that I could make a payment at any time but this does not appear to be true. The funds are available in my bank account today regardless of whether Nelnet can collect over the weekend. I should not be penalized for this. \\n\\nI submitted complaint XXXX in XXXX for other deceptive practices with Nelnet. They have not yet resolved the issue identified in that complaint or contacted me as they said they would in their response. I believe this new issue is just one more deceptive practice by this company that causes financial harm to borrowers.                                                                                                                                                                                                      ...  4    \n",
              "57449  Received letter for {$480.00}. Original creditor didnt contact me until past statute of limitations for insurance company recoupment per Arizona law. Debt collection is illegal for phantom debt. Additionally they are phoning my office excessively.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ...  0    \n",
              "57450  entire time 10 years until XX/XX/2020. XXXX makes my blood boil. I have called and was lied to told to provide my checking account information over the phone in order to turn my cell phone back on. i called at XXXX them at XXXX {$300.00} was added to my bill. \\n\\nScam scam scam I was told I can not call the office of the President just to write to XXXX XXXX XXXX XXXX XXXX XXXX XXXX, NM XXXX. I did three thousand times. the last letter I mailed on XX/XX/2020. Two collection agencies later. \\n\\nI chose to leave XXXX XXXX every time I called the XXXX supervisor would threaten me on a recorded line. I need peace of mind and a good Heart to beat inside of me. Im on a XXXX XXXX due to the stress at XXXX XXXX   taking all my money 4 10 years.                                                                                                                                                                                                                                                                                                                                                                               ...  0    \n",
              "57451  I am a customer with Wells Fargo Bank. Recently money was withdrawn on a couple of occasions without my permission or consent to pay for a timeshare account that was never used by me nor anyone connected to me because of unfair policies pertaining to the fees of the said timeshare. I tried cancelling the said timeshare account several times because of these fees that were never mentioned at the initiation. My account was debited to pay for the timeshare fees without my knowledge or consent several times. I tried correcting this with Wells Fargo bank with no avail. I would appreciate it if you can look into this matter for me. I was left with no funds in my account and as such I could not take care of the basic necessities of my day to day life. \\nThanks in advance,                                                                                                                                                                                                                                                                                                                                                 ...  3    \n",
              "57452  I spoken with them several times in a year. An in XXXX they informed me yes they sold my information to a third party without my knowledge. Also, I told them that I dont owe them anything because my car was paid off by insurance XXXX an Gap an they said no. I also informed them that constantly calling me an telling me that my credit score would be damage was inappropriate. But on my credit they written several negative things. An I didnt give Exeter financial Corp no authority to sell my information to XXXX XXXX                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   ...  0    \n",
              "\n",
              "[57453 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SC6IhFa35ea5"
      },
      "source": [
        "X_train, X_test = train_test_split(complaints_df, test_size=0.2, random_state=111)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yOJI-TWxkXmH"
      },
      "source": [
        "#stemmer = PorterStemmer()\n",
        "stemmer = nltk.stem.SnowballStemmer('english') #it performs little better than poerterstemmer"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YklbjIF-GmSp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4ef2456-08c3-4a38-b218-1c55c202334f"
      },
      "source": [
        "nltk.download('stopwords')\n",
        "stop_words = set(nltk.corpus.stopwords.words('english'))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c33HdaMZkPlJ"
      },
      "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"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsWUO6c_Nqq4"
      },
      "source": [
        "#complaints_df['complaints_tok']=complaints_df['complaints'].apply(lambda x: tokenize(x))"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgZ6lFDEBz8k"
      },
      "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) "
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwxB1PqJPEJV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dd89722-07b7-497e-fea0-9f72f39524bf"
      },
      "source": [
        "train_vectors.A"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.05982223, 0.        , ..., 0.        , 0.        ,\n",
              "        0.04798975],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.09582682],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rl3k7xm0aKE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad11c5f1-b9c1-4e10-9759-a3032120297b"
      },
      "source": [
        "vectorizer_tf.get_feature_names()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NOeDMu380qFh"
      },
      "source": [
        "test_vectors = vectorizer_tf.transform(X_test.complaints)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHfDuFQrOaPx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b49a45fb-7de5-4a08-b4e2-55102632ea8c"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45962, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYApdx8w0qAc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "e618fc86-636b-42bd-d78a-faf0c46963cd"
      },
      "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"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.292819</td>\n",
              "      <td>0.363256</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.059822</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.075668</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.047990</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.031225</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.021576</td>\n",
              "      <td>0.041002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.032756</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.066071</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.046675</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.025086</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.021839</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.026262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.127564</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.321204</td>\n",
              "      <td>0.478162</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45957</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.056681</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.183791</td>\n",
              "      <td>0.065329</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>45962 rows × 1001 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       abil       abl  absolut  abus  ...  wrong  wrote      year  target\n",
              "0      0.0   0.000000  0.0      0.0   ...  0.0    0.0    0.000000  3     \n",
              "1      0.0   0.059822  0.0      0.0   ...  0.0    0.0    0.047990  0     \n",
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              "...    ...        ...  ...      ...   ...  ...    ...         ... ..     \n",
              "45957  0.0   0.000000  0.0      0.0   ...  0.0    0.0    0.000000  1     \n",
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              "45961  0.0   0.000000  0.0      0.0   ...  0.0    0.0    0.000000  2     \n",
              "\n",
              "[45962 rows x 1001 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hOiVsb6zRESm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "a795cc0c-b2c5-4eac-9138-a4d23c274cec"
      },
      "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"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.045103</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.043198</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.085755</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.078402</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.109801</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.124581</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.146798</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.220463</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.194989</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.239325</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.088469</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.033514</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.043696</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.058183</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.044707</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.037249</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04532</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.070970</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.079911</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.040004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.118409</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.128210</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11486</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.020516</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.100242</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11491 rows × 1001 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       abil       abl  absolut  abus  ...  wrong  wrote      year  target\n",
              "0      0.0   0.086928  0.0      0.0   ...  0.0    0.0    0.000000  3     \n",
              "1      0.0   0.000000  0.0      0.0   ...  0.0    0.0    0.000000  2     \n",
              "2      0.0   0.146798  0.0      0.0   ...  0.0    0.0    0.000000  1     \n",
              "3      0.0   0.088469  0.0      0.0   ...  0.0    0.0    0.070970  4     \n",
              "4      0.0   0.079911  0.0      0.0   ...  0.0    0.0    0.128210  1     \n",
              "...    ...        ...  ...      ...   ...  ...    ...         ... ..     \n",
              "11486  0.0   0.000000  0.0      0.0   ...  0.0    0.0    0.032876  1     \n",
              "11487  0.0   0.000000  0.0      0.0   ...  0.0    0.0    0.000000  4     \n",
              "11488  0.0   0.000000  0.0      0.0   ...  0.0    0.0    0.000000  2     \n",
              "11489  0.0   0.000000  0.0      0.0   ...  0.0    0.0    0.077801  0     \n",
              "11490  0.0   0.000000  0.0      0.0   ...  0.0    0.0    0.000000  0     \n",
              "\n",
              "[11491 rows x 1001 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7Lh45Tv6A0K"
      },
      "source": [
        "**How do you know, which model works better?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBsOidoLUIrC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc26cfc3-49dc-4f65-f1a0-4c03f6b79398"
      },
      "source": [
        "#Conert pd dataframe into h2o data frame\n",
        "h2o_train_df = h2o.H2OFrame(train_df) \n",
        "h2o_test_df = h2o.H2OFrame(test_df)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
            "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXN4B7t6Y3jL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00edbd65-bcab-4105-b28c-98c1ddc6dd74"
      },
      "source": [
        "h2o_train_df.types  "
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              " 'addit': 'real',\n",
              " 'address': 'real',\n",
              " 'adjust': 'real',\n",
              " 'admit': 'real',\n",
              " 'advanc': 'real',\n",
              " 'advantag': 'real',\n",
              " 'advertis': 'real',\n",
              " 'advis': 'real',\n",
              " 'affect': 'real',\n",
              " 'affidavit': 'real',\n",
              " 'afford': 'real',\n",
              " 'agenc': 'real',\n",
              " 'agent': 'real',\n",
              " 'agre': 'real',\n",
              " 'agreement': 'real',\n",
              " 'alert': 'real',\n",
              " 'alleg': 'real',\n",
              " 'alleg debt': 'real',\n",
              " 'alli': 'real',\n",
              " 'allow': 'real',\n",
              " 'almost': 'real',\n",
              " 'along': 'real',\n",
              " 'alreadi': 'real',\n",
              " 'also': 'real',\n",
              " 'although': 'real',\n",
              " 'alway': 'real',\n",
              " 'america': 'real',\n",
              " 'american': 'real',\n",
              " 'american express': 'real',\n",
              " 'amount': 'real',\n",
              " 'amount owe': 'real',\n",
              " 'and/or': 'real',\n",
              " 'annual': 'real',\n",
              " 'anoth': 'real',\n",
              " 'answer': 'real',\n",
              " 'anyon': 'real',\n",
              " 'anyth': 'real',\n",
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              " '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",
              " 'relationship': '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",
              " ...}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpow8QD77GB6",
        "outputId": "7a8618e8-589a-482c-c1f7-41c5494addb5"
      },
      "source": [
        "h2o_train_df['target'].types #target should not be integer otherwise h2o treat it as regression problem"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'target': 'int'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tki9co1ZKz3"
      },
      "source": [
        "h2o_train_df['target'] = h2o_train_df['target'].asfactor()\n",
        "h2o_test_df['target'] = h2o_test_df['target'].asfactor()"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WsyHArUw7bYP",
        "outputId": "b7ec931a-e40e-4cc3-866e-9aba3c682668"
      },
      "source": [
        "#target column type should be enumerous type for text classification\r\n",
        "h2o_train_df['target'].types "
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'target': 'enum'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WY7OVHAw7zAY"
      },
      "source": [
        "#H2O pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ab6V3ht4_wP8"
      },
      "source": [
        "aml = H2OAutoML(max_models = 5, seed = 10, exclude_algos = [\"StackedEnsemble\"], verbosity=\"info\", nfolds=0, balance_classes=True, max_after_balance_size=0.3)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QE1_UPcwAPUR"
      },
      "source": [
        "x=vectorizer_tf.get_feature_names()\n",
        "y='target'"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWAnP-go_wLH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83b327a3-e96a-46d1-ac63-2cb2e0f2e63a"
      },
      "source": [
        "aml.train(x = x, y = y, training_frame = h2o_train_df, validation_frame=h2o_test_df)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AutoML progress: |\n",
            "15:21:01.869: Project: AutoML_20201230_152101866\n",
            "15:21:01.870: Cross-validation disabled by user: no fold column nor nfolds > 1.\n",
            "15:21:01.900: Setting stopping tolerance adaptively based on the training frame: 0.00466445106034528\n",
            "15:21:01.900: Build control seed: 10\n",
            "15:21:01.901: 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",
            "15:21:07.390: training frame: Frame key: automl_training_py_1_sid_8928    cols: 1001    rows: 41253  chunks: 53    size: 34326323  checksum: -3708572512705517030\n",
            "15:21:07.395: validation frame: Frame key: py_2_sid_8928    cols: 1001    rows: 11491  chunks: 14    size: 9663839  checksum: 6186608125092982850\n",
            "15:21:07.730: leaderboard frame: Frame key: automl_leaderboard_py_1_sid_8928    cols: 1001    rows: 4709  chunks: 53    size: 7556253  checksum: 384529285802033387\n",
            "15:21:07.730: blending frame: NULL\n",
            "15:21:07.731: response column: target\n",
            "15:21:07.731: fold column: null\n",
            "15:21:07.731: weights column: null\n",
            "15:21:07.818: 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",
            "15:21:07.832: Disabling Algo: StackedEnsemble as requested by the user.\n",
            "15:21:07.836: AutoML job created: 2020.12.30 15:21:01.866\n",
            "15:21:07.837: AutoML build started: 2020.12.30 15:21:07.836\n",
            "15:21:07.851: AutoML: starting XGBoost_1_AutoML_20201230_152101 model training\n",
            "\n",
            "██\n",
            "15:23:18.584: New leader: XGBoost_1_AutoML_20201230_152101, mean_per_class_error: 0.154777443217031\n",
            "15:23:18.584: AutoML: starting XGBoost_2_AutoML_20201230_152101 model training\n",
            "\n",
            "██████████████████████████████████\n",
            "15:28:30.178: New leader: XGBoost_2_AutoML_20201230_152101, mean_per_class_error: 0.1516073069534709\n",
            "15:28:30.178: AutoML: starting XGBoost_3_AutoML_20201230_152101 model training\n",
            "\n",
            "█████\n",
            "15:30:01.495: AutoML: starting GLM_1_AutoML_20201230_152101 model training\n",
            "\n",
            "████████████\n",
            "15:38:19.647: AutoML: starting DRF_1_AutoML_20201230_152101 model training\n",
            "\n",
            "███| 100%\n",
            "\n",
            "15:42:26.502: Skipping StackedEnsemble 'best' due to the exclude_algos option.\n",
            "15:42:26.504: Skipping StackedEnsemble 'all' due to the exclude_algos option.\n",
            "15:42:26.505: Skipping StackedEnsemble 'monotonic' due to the exclude_algos option.\n",
            "15:42:26.505: Actual modeling steps: [{XGBoost : [def_1 (10), def_2 (10), def_3 (10)]}, {GLM : [def_1 (10)]}, {DRF : [def_1 (10)]}]\n",
            "15:42:26.505: AutoML build stopped: 2020.12.30 15:42:26.505\n",
            "15:42:26.505: AutoML build done: built 5 models\n",
            "15:42:26.505: AutoML duration: 21 min 18.669 sec\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBchFOXi_wGd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "f9cc044d-9c6a-4243-d922-eeeac5a98a76"
      },
      "source": [
        "aml.leaderboard"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "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>XGBoost_2_AutoML_20201230_152101</td><td style=\"text-align: right;\">              0.151607</td><td style=\"text-align: right;\"> 0.3315  </td><td style=\"text-align: right;\">0.307789</td><td style=\"text-align: right;\">0.0947342</td></tr>\n",
              "<tr><td>XGBoost_3_AutoML_20201230_152101</td><td style=\"text-align: right;\">              0.153793</td><td style=\"text-align: right;\"> 0.33175 </td><td style=\"text-align: right;\">0.313482</td><td style=\"text-align: right;\">0.0982707</td></tr>\n",
              "<tr><td>XGBoost_1_AutoML_20201230_152101</td><td style=\"text-align: right;\">              0.154777</td><td style=\"text-align: right;\"> 0.335565</td><td style=\"text-align: right;\">0.31218 </td><td style=\"text-align: right;\">0.0974565</td></tr>\n",
              "<tr><td>GLM_1_AutoML_20201230_152101    </td><td style=\"text-align: right;\">              0.170193</td><td style=\"text-align: right;\"> 0.379036</td><td style=\"text-align: right;\">0.335487</td><td style=\"text-align: right;\">0.112552 </td></tr>\n",
              "<tr><td>DRF_1_AutoML_20201230_152101    </td><td style=\"text-align: right;\">              0.277178</td><td style=\"text-align: right;\"> 0.592434</td><td style=\"text-align: right;\">0.443854</td><td style=\"text-align: right;\">0.197007 </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Crn9KnP_v-y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd27de5b-1f3c-408f-ee26-a6ce0db8e779"
      },
      "source": [
        "pred=aml.leader.predict(h2o_test_df)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "xgboost prediction progress: |████████████████████████████████████████████| 100%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKIvuGEX_v46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "outputId": "fccc4785-91bd-4595-c5be-75563e177478"
      },
      "source": [
        "aml.leader.model_performance(h2o_test_df)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "ModelMetricsMultinomial: xgboost\n",
            "** Reported on test data. **\n",
            "\n",
            "MSE: 0.09580208143934055\n",
            "RMSE: 0.3095191132052115\n",
            "LogLoss: 0.3330637004461808\n",
            "Mean Per-Class Error: 0.15823994814305678\n",
            "\n",
            "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>3996.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.069616</td>\n",
              "      <td>299 / 4,295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>159.0</td>\n",
              "      <td>2275.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.119241</td>\n",
              "      <td>308 / 2,583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>63.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>1855.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.079404</td>\n",
              "      <td>160 / 2,015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46.0</td>\n",
              "      <td>135.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1252.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.143053</td>\n",
              "      <td>209 / 1,461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>65.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>482.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.211129</td>\n",
              "      <td>129 / 611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>78.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>354.0</td>\n",
              "      <td>0.326996</td>\n",
              "      <td>172 / 526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4407.0</td>\n",
              "      <td>2627.0</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1416.0</td>\n",
              "      <td>544.0</td>\n",
              "      <td>475.0</td>\n",
              "      <td>0.111130</td>\n",
              "      <td>1,277 / 11,491</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        0       1       2       3      4      5     Error            Rate\n",
              "0  3996.0   126.0    58.0    30.0   40.0   45.0  0.069616     299 / 4,295\n",
              "1   159.0  2275.0    24.0   106.0    2.0   17.0  0.119241     308 / 2,583\n",
              "2    63.0    33.0  1855.0    18.0   12.0   34.0  0.079404     160 / 2,015\n",
              "3    46.0   135.0    21.0  1252.0    1.0    6.0  0.143053     209 / 1,461\n",
              "4    65.0    19.0    25.0     1.0  482.0   19.0  0.211129       129 / 611\n",
              "5    78.0    39.0    39.0     9.0    7.0  354.0  0.326996       172 / 526\n",
              "6  4407.0  2627.0  2022.0  1416.0  544.0  475.0  0.111130  1,277 / 11,491"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Top-6 Hit Ratios: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        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>0.888869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.963885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.985554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.993908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.998434</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   0.888869\n",
              "1  2   0.963885\n",
              "2  3   0.985554\n",
              "3  4   0.993908\n",
              "4  5   0.998434\n",
              "5  6   1.000000"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iRFCcUKqAVdo"
      },
      "source": [
        "Gives the hyper parameter that H2O uses..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwXZ0K6WvJ3B"
      },
      "source": [
        "model_ids = list(aml.leaderboard['model_id'].as_data_frame().iloc[:,0])\n",
        "out = h2o.get_model([mid for mid in model_ids if \"XGBoost\" in mid][0])"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SlON5YMd655",
        "outputId": "1c0c002b-b407-4466-fe6c-35458cd5ec22"
      },
      "source": [
        "model_ids"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['XGBoost_2_AutoML_20201230_152101',\n",
              " 'XGBoost_3_AutoML_20201230_152101',\n",
              " 'XGBoost_1_AutoML_20201230_152101',\n",
              " 'GLM_1_AutoML_20201230_152101',\n",
              " 'DRF_1_AutoML_20201230_152101']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFoB6mUqvf_e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cb09c2f0-806a-49d6-d056-bcf3c183d114"
      },
      "source": [
        "out"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Details\n",
            "=============\n",
            "H2OXGBoostEstimator :  XGBoost\n",
            "Model Key:  XGBoost_2_AutoML_20201230_152101\n",
            "\n",
            "\n",
            "Model Summary: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>number_of_trees</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     number_of_trees\n",
              "0               75.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "ModelMetricsMultinomial: xgboost\n",
            "** Reported on train data. **\n",
            "\n",
            "MSE: 0.027045769139722887\n",
            "RMSE: 0.1644559793370946\n",
            "LogLoss: 0.10444530084969711\n",
            "Mean Per-Class Error: 0.0336917353617761\n",
            "\n",
            "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "  <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>15478.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.012190</td>\n",
              "      <td>191 / 15,669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>174.0</td>\n",
              "      <td>9279.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.025315</td>\n",
              "      <td>241 / 9,520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>6891.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.016976</td>\n",
              "      <td>119 / 7,010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4891.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.021409</td>\n",
              "      <td>107 / 4,998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1994.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.047301</td>\n",
              "      <td>99 / 2,093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>82.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1808.0</td>\n",
              "      <td>0.078961</td>\n",
              "      <td>155 / 1,963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>15891.0</td>\n",
              "      <td>9557.0</td>\n",
              "      <td>6959.0</td>\n",
              "      <td>4976.0</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>1843.0</td>\n",
              "      <td>0.022107</td>\n",
              "      <td>912 / 41,253</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         0       1       2       3       4       5     Error          Rate\n",
              "0  15478.0    93.0    35.0    30.0    18.0    15.0  0.012190  191 / 15,669\n",
              "1    174.0  9279.0    12.0    41.0     7.0     7.0  0.025315   241 / 9,520\n",
              "2     61.0    43.0  6891.0     8.0     2.0     5.0  0.016976   119 / 7,010\n",
              "3     44.0    51.0     8.0  4891.0     0.0     4.0  0.021409   107 / 4,998\n",
              "4     52.0    36.0     6.0     1.0  1994.0     4.0  0.047301    99 / 2,093\n",
              "5     82.0    55.0     7.0     5.0     6.0  1808.0  0.078961   155 / 1,963\n",
              "6  15891.0  9557.0  6959.0  4976.0  2027.0  1843.0  0.022107  912 / 41,253"
            ]
          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Top-6 Hit Ratios: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "  <thead>\n",
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              "      <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>0.977893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.994449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.998012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.999394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.999806</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   0.977893\n",
              "1  2   0.994449\n",
              "2  3   0.998012\n",
              "3  4   0.999394\n",
              "4  5   0.999806\n",
              "5  6   1.000000"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "ModelMetricsMultinomial: xgboost\n",
            "** Reported on validation data. **\n",
            "\n",
            "MSE: 0.09580208143934055\n",
            "RMSE: 0.3095191132052115\n",
            "LogLoss: 0.3330637004461808\n",
            "Mean Per-Class Error: 0.15823994814305678\n",
            "\n",
            "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "      <th>3</th>\n",
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              "      <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>3996.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.069616</td>\n",
              "      <td>299 / 4,295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>159.0</td>\n",
              "      <td>2275.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.119241</td>\n",
              "      <td>308 / 2,583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>63.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>1855.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.079404</td>\n",
              "      <td>160 / 2,015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46.0</td>\n",
              "      <td>135.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1252.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.143053</td>\n",
              "      <td>209 / 1,461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>65.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>482.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.211129</td>\n",
              "      <td>129 / 611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>78.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>354.0</td>\n",
              "      <td>0.326996</td>\n",
              "      <td>172 / 526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4407.0</td>\n",
              "      <td>2627.0</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1416.0</td>\n",
              "      <td>544.0</td>\n",
              "      <td>475.0</td>\n",
              "      <td>0.111130</td>\n",
              "      <td>1,277 / 11,491</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        0       1       2       3      4      5     Error            Rate\n",
              "0  3996.0   126.0    58.0    30.0   40.0   45.0  0.069616     299 / 4,295\n",
              "1   159.0  2275.0    24.0   106.0    2.0   17.0  0.119241     308 / 2,583\n",
              "2    63.0    33.0  1855.0    18.0   12.0   34.0  0.079404     160 / 2,015\n",
              "3    46.0   135.0    21.0  1252.0    1.0    6.0  0.143053     209 / 1,461\n",
              "4    65.0    19.0    25.0     1.0  482.0   19.0  0.211129       129 / 611\n",
              "5    78.0    39.0    39.0     9.0    7.0  354.0  0.326996       172 / 526\n",
              "6  4407.0  2627.0  2022.0  1416.0  544.0  475.0  0.111130  1,277 / 11,491"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Top-6 Hit Ratios: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
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              "        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>0.888869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.963885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.985554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.993908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.998434</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   0.888869\n",
              "1  2   0.963885\n",
              "2  3   0.985554\n",
              "3  4   0.993908\n",
              "4  5   0.998434\n",
              "5  6   1.000000"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Scoring History: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>duration</th>\n",
              "      <th>number_of_trees</th>\n",
              "      <th>training_rmse</th>\n",
              "      <th>training_logloss</th>\n",
              "      <th>training_classification_error</th>\n",
              "      <th>validation_rmse</th>\n",
              "      <th>validation_logloss</th>\n",
              "      <th>validation_classification_error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:23:18</td>\n",
              "      <td>0.013 sec</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>1.791759</td>\n",
              "      <td>0.830073</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>1.791759</td>\n",
              "      <td>0.824645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:23:43</td>\n",
              "      <td>25.119 sec</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.448321</td>\n",
              "      <td>0.606908</td>\n",
              "      <td>0.114949</td>\n",
              "      <td>0.468508</td>\n",
              "      <td>0.656358</td>\n",
              "      <td>0.141502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:24:02</td>\n",
              "      <td>43.474 sec</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.334795</td>\n",
              "      <td>0.372666</td>\n",
              "      <td>0.093011</td>\n",
              "      <td>0.375110</td>\n",
              "      <td>0.454757</td>\n",
              "      <td>0.131407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:24:18</td>\n",
              "      <td>1 min  0.347 sec</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.291100</td>\n",
              "      <td>0.285404</td>\n",
              "      <td>0.078322</td>\n",
              "      <td>0.346171</td>\n",
              "      <td>0.391433</td>\n",
              "      <td>0.124445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:24:35</td>\n",
              "      <td>1 min 16.866 sec</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.265984</td>\n",
              "      <td>0.239520</td>\n",
              "      <td>0.067341</td>\n",
              "      <td>0.332545</td>\n",
              "      <td>0.363869</td>\n",
              "      <td>0.121225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:24:53</td>\n",
              "      <td>1 min 35.000 sec</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.247432</td>\n",
              "      <td>0.209249</td>\n",
              "      <td>0.058808</td>\n",
              "      <td>0.324798</td>\n",
              "      <td>0.349971</td>\n",
              "      <td>0.117048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:25:13</td>\n",
              "      <td>1 min 55.093 sec</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.234043</td>\n",
              "      <td>0.188900</td>\n",
              "      <td>0.052336</td>\n",
              "      <td>0.320734</td>\n",
              "      <td>0.343231</td>\n",
              "      <td>0.115482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:25:32</td>\n",
              "      <td>2 min 13.828 sec</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.222221</td>\n",
              "      <td>0.172429</td>\n",
              "      <td>0.047172</td>\n",
              "      <td>0.317288</td>\n",
              "      <td>0.338037</td>\n",
              "      <td>0.113741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:25:52</td>\n",
              "      <td>2 min 33.736 sec</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.212618</td>\n",
              "      <td>0.159591</td>\n",
              "      <td>0.042421</td>\n",
              "      <td>0.315460</td>\n",
              "      <td>0.335677</td>\n",
              "      <td>0.113567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:26:13</td>\n",
              "      <td>2 min 54.595 sec</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.203545</td>\n",
              "      <td>0.148131</td>\n",
              "      <td>0.038252</td>\n",
              "      <td>0.313812</td>\n",
              "      <td>0.334216</td>\n",
              "      <td>0.112523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:26:32</td>\n",
              "      <td>3 min 13.955 sec</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.195839</td>\n",
              "      <td>0.138780</td>\n",
              "      <td>0.034882</td>\n",
              "      <td>0.312776</td>\n",
              "      <td>0.333725</td>\n",
              "      <td>0.111827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:26:55</td>\n",
              "      <td>3 min 36.470 sec</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.188742</td>\n",
              "      <td>0.130405</td>\n",
              "      <td>0.032192</td>\n",
              "      <td>0.311812</td>\n",
              "      <td>0.333024</td>\n",
              "      <td>0.110869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:27:14</td>\n",
              "      <td>3 min 56.354 sec</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.181995</td>\n",
              "      <td>0.123020</td>\n",
              "      <td>0.029258</td>\n",
              "      <td>0.310459</td>\n",
              "      <td>0.332417</td>\n",
              "      <td>0.109825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:27:35</td>\n",
              "      <td>4 min 17.353 sec</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0.175508</td>\n",
              "      <td>0.116026</td>\n",
              "      <td>0.026204</td>\n",
              "      <td>0.310109</td>\n",
              "      <td>0.332346</td>\n",
              "      <td>0.109651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:27:55</td>\n",
              "      <td>4 min 36.985 sec</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.169759</td>\n",
              "      <td>0.109885</td>\n",
              "      <td>0.024022</td>\n",
              "      <td>0.309778</td>\n",
              "      <td>0.332401</td>\n",
              "      <td>0.109564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td></td>\n",
              "      <td>2020-12-30 15:28:16</td>\n",
              "      <td>4 min 58.277 sec</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0.164456</td>\n",
              "      <td>0.104445</td>\n",
              "      <td>0.022107</td>\n",
              "      <td>0.309519</td>\n",
              "      <td>0.333064</td>\n",
              "      <td>0.111130</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                timestamp  ... validation_logloss  validation_classification_error\n",
              "0     2020-12-30 15:23:18  ...           1.791759                         0.824645\n",
              "1     2020-12-30 15:23:43  ...           0.656358                         0.141502\n",
              "2     2020-12-30 15:24:02  ...           0.454757                         0.131407\n",
              "3     2020-12-30 15:24:18  ...           0.391433                         0.124445\n",
              "4     2020-12-30 15:24:35  ...           0.363869                         0.121225\n",
              "5     2020-12-30 15:24:53  ...           0.349971                         0.117048\n",
              "6     2020-12-30 15:25:13  ...           0.343231                         0.115482\n",
              "7     2020-12-30 15:25:32  ...           0.338037                         0.113741\n",
              "8     2020-12-30 15:25:52  ...           0.335677                         0.113567\n",
              "9     2020-12-30 15:26:13  ...           0.334216                         0.112523\n",
              "10    2020-12-30 15:26:32  ...           0.333725                         0.111827\n",
              "11    2020-12-30 15:26:55  ...           0.333024                         0.110869\n",
              "12    2020-12-30 15:27:14  ...           0.332417                         0.109825\n",
              "13    2020-12-30 15:27:35  ...           0.332346                         0.109651\n",
              "14    2020-12-30 15:27:55  ...           0.332401                         0.109564\n",
              "15    2020-12-30 15:28:16  ...           0.333064                         0.111130\n",
              "\n",
              "[16 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Variable Importances: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\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>variable</th>\n",
              "      <th>relative_importance</th>\n",
              "      <th>scaled_importance</th>\n",
              "      <th>percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>debt</td>\n",
              "      <td>15688.623047</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.102429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>card</td>\n",
              "      <td>12361.398438</td>\n",
              "      <td>0.787921</td>\n",
              "      <td>0.080706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mortgag</td>\n",
              "      <td>11345.549805</td>\n",
              "      <td>0.723171</td>\n",
              "      <td>0.074074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>loan</td>\n",
              "      <td>8262.434570</td>\n",
              "      <td>0.526651</td>\n",
              "      <td>0.053944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bank</td>\n",
              "      <td>7164.359863</td>\n",
              "      <td>0.456660</td>\n",
              "      <td>0.046775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>collect</td>\n",
              "      <td>4369.185059</td>\n",
              "      <td>0.278494</td>\n",
              "      <td>0.028526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>credit card</td>\n",
              "      <td>4071.709229</td>\n",
              "      <td>0.259533</td>\n",
              "      <td>0.026584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>payment</td>\n",
              "      <td>4004.601318</td>\n",
              "      <td>0.255255</td>\n",
              "      <td>0.026146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>account</td>\n",
              "      <td>3416.742676</td>\n",
              "      <td>0.217785</td>\n",
              "      <td>0.022307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>vehicl</td>\n",
              "      <td>2746.517578</td>\n",
              "      <td>0.175064</td>\n",
              "      <td>0.017932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>deposit</td>\n",
              "      <td>2714.503174</td>\n",
              "      <td>0.173024</td>\n",
              "      <td>0.017723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>student</td>\n",
              "      <td>2348.206299</td>\n",
              "      <td>0.149676</td>\n",
              "      <td>0.015331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>student loan</td>\n",
              "      <td>2346.772705</td>\n",
              "      <td>0.149584</td>\n",
              "      <td>0.015322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>credit</td>\n",
              "      <td>2259.807129</td>\n",
              "      <td>0.144041</td>\n",
              "      <td>0.014754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>home</td>\n",
              "      <td>1898.884277</td>\n",
              "      <td>0.121036</td>\n",
              "      <td>0.012398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>navient</td>\n",
              "      <td>1813.252808</td>\n",
              "      <td>0.115578</td>\n",
              "      <td>0.011838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>report</td>\n",
              "      <td>1795.553223</td>\n",
              "      <td>0.114449</td>\n",
              "      <td>0.011723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>call</td>\n",
              "      <td>1519.958862</td>\n",
              "      <td>0.096883</td>\n",
              "      <td>0.009924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>late</td>\n",
              "      <td>1316.758179</td>\n",
              "      <td>0.083931</td>\n",
              "      <td>0.008597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>escrow</td>\n",
              "      <td>1175.678223</td>\n",
              "      <td>0.074938</td>\n",
              "      <td>0.007676</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        variable  relative_importance  scaled_importance  percentage\n",
              "0           debt         15688.623047           1.000000    0.102429\n",
              "1           card         12361.398438           0.787921    0.080706\n",
              "2        mortgag         11345.549805           0.723171    0.074074\n",
              "3           loan          8262.434570           0.526651    0.053944\n",
              "4           bank          7164.359863           0.456660    0.046775\n",
              "5        collect          4369.185059           0.278494    0.028526\n",
              "6    credit card          4071.709229           0.259533    0.026584\n",
              "7        payment          4004.601318           0.255255    0.026146\n",
              "8        account          3416.742676           0.217785    0.022307\n",
              "9         vehicl          2746.517578           0.175064    0.017932\n",
              "10       deposit          2714.503174           0.173024    0.017723\n",
              "11       student          2348.206299           0.149676    0.015331\n",
              "12  student loan          2346.772705           0.149584    0.015322\n",
              "13        credit          2259.807129           0.144041    0.014754\n",
              "14          home          1898.884277           0.121036    0.012398\n",
              "15       navient          1813.252808           0.115578    0.011838\n",
              "16        report          1795.553223           0.114449    0.011723\n",
              "17          call          1519.958862           0.096883    0.009924\n",
              "18          late          1316.758179           0.083931    0.008597\n",
              "19        escrow          1175.678223           0.074938    0.007676"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "See the whole table with table.as_data_frame()\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SvQ5skTjAcNK"
      },
      "source": [
        "Convert H2O parameters into XGB parameters so that we can use sklearn on that"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOKgQZ16vfsI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8442098-4cac-4d0b-aed5-14a18d1a8f10"
      },
      "source": [
        "out.convert_H2OXGBoostParams_2_XGBoostParams()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'alpha': 0.0,\n",
              "  'booster': 'gbtree',\n",
              "  'colsample_bylevel': 0.8,\n",
              "  'colsample_bytree': 0.8,\n",
              "  'eta': 0.3,\n",
              "  'gamma': 0.0,\n",
              "  'grow_policy': 'depthwise',\n",
              "  'lambda': 1.0,\n",
              "  'max_delta_step': 0.0,\n",
              "  'max_depth': 20,\n",
              "  'min_child_weight': 10.0,\n",
              "  'nround': 10000,\n",
              "  'nthread': 2,\n",
              "  'num_class': 6,\n",
              "  'objective': 'multi:softprob',\n",
              "  'seed': 11,\n",
              "  'silent': True,\n",
              "  'subsample': 0.6,\n",
              "  'tree_method': 'exact'},\n",
              " 10000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyIUFqxB0p8e"
      },
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NEcr79py0o9o"
      },
      "source": [
        "xgb_clf = xgb.XGBClassifier(n_estimators=100, learning_rate=0.05, max_depth=6, objective='multi:softprob', random_state=10, **{\"updater\": \"grow_gpu\"})"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9zF6yS1u3rHP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e4ff6ae-bc2f-44a6-e520-d4a5ab472444"
      },
      "source": [
        "xgb_clf.fit(train_vectors, X_train['target'])"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[15:42:33] WARNING: /workspace/src/learner.cc:194: DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.05, max_delta_step=0, max_depth=6,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='multi:softprob', random_state=10,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, updater='grow_gpu', verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7DrYcwtQzWdv"
      },
      "source": [
        "predictions = xgb_clf.predict(test_vectors)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IpymHqCY6-IO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "547429a3-b5cb-44bf-ef26-3b512abe70d0"
      },
      "source": [
        "cm = confusion_matrix(X_test['target'], predictions)\n",
        "print(cm)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3941  156   63   37   46   52]\n",
            " [ 282 2177   13   99    1   11]\n",
            " [  97   38 1822   24    8   26]\n",
            " [  89  131   21 1213    0    7]\n",
            " [  77   23   29    2  461   19]\n",
            " [ 107   53   35    9    4  318]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_PwacHgMgqm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66a88855-74a0-4af9-ca17-b3d203e944c6"
      },
      "source": [
        "print('classification_report :\\n',classification_report(X_test['target'], predictions))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "classification_report :\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.92      0.89      4295\n",
            "           1       0.84      0.84      0.84      2583\n",
            "           2       0.92      0.90      0.91      2015\n",
            "           3       0.88      0.83      0.85      1461\n",
            "           4       0.89      0.75      0.82       611\n",
            "           5       0.73      0.60      0.66       526\n",
            "\n",
            "    accuracy                           0.86     11491\n",
            "   macro avg       0.85      0.81      0.83     11491\n",
            "weighted avg       0.86      0.86      0.86     11491\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSlvYmCs69_p"
      },
      "source": [
        "from sklearn.utils import class_weight\n",
        "class_weights = list(class_weight.compute_class_weight('balanced',\n",
        "                                             np.unique(X_train['target']),\n",
        "                                             X_train['target']))"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4P8CgUeU696B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5659052f-ffc6-4255-a3fb-8a96fe067b76"
      },
      "source": [
        "class_weights"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.43830939711239536,\n",
              " 0.7219918316054037,\n",
              " 0.9841127098321343,\n",
              " 1.382232647660291,\n",
              " 3.275046316089497,\n",
              " 3.466214177978884]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6RgPVE-69z_"
      },
      "source": [
        "weights = np.ones(X_train.shape[0], dtype = 'float')"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9nr9u5Yr8-Lj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2913d269-31bb-404e-b55e-2eb322bc954a"
      },
      "source": [
        "weights"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., ..., 1., 1., 1.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JExLyKUycPdR"
      },
      "source": [
        "for i, val in enumerate(X_train['target']):\n",
        "    weights[i] = class_weights[val]"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0F6FpqCKIgPs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c0bb361-1121-4f57-e445-287685e95450"
      },
      "source": [
        "weights"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.38223265, 0.4383094 , 0.4383094 , ..., 0.72199183, 0.72199183,\n",
              "       0.98411271])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_G1BOCj9QTI"
      },
      "source": [
        "xgb_clf = xgb.XGBClassifier(n_estimators=100, learning_rate=0.05, max_depth=6, objective='multi:softprob', random_state=10, **{\"updater\": \"grow_gpu\"})"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZglQvABJVOW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bcd421f-fc5e-4bdc-b106-1c01212074de"
      },
      "source": [
        "xgb_clf.fit(train_vectors, X_train['target'], sample_weight=weights)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[15:42:52] WARNING: /workspace/src/learner.cc:194: DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.05, max_delta_step=0, max_depth=6,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='multi:softprob', random_state=10,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, updater='grow_gpu', verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXkq2Mg7Kbua"
      },
      "source": [
        "#print(\"Accuracy score (test): {0:.3f}\".format(xgb_clf.score(test_vectors, X_test['target'])))"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0u1RqMpKr9p"
      },
      "source": [
        "predictions = xgb_clf.predict(test_vectors)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uq402RW8K-Wn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f8c62ae-e93a-46f0-9e0e-ed1e13748914"
      },
      "source": [
        "cm = confusion_matrix(X_test['target'], predictions)\n",
        "print(cm)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3649  186   81  110   90  179]\n",
            " [ 167 2107   18  178    2  111]\n",
            " [  41   20 1784   30   52   88]\n",
            " [  33   94   16 1291    5   22]\n",
            " [  25    6    4    2  515   59]\n",
            " [  48   21    9   13   21  414]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G29xRuJfLXOb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c81fbf6d-3316-42e6-e623-e6c1892aadf5"
      },
      "source": [
        "print('classification_report :\\n',classification_report(X_test['target'], predictions))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "classification_report :\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.85      0.88      4295\n",
            "           1       0.87      0.82      0.84      2583\n",
            "           2       0.93      0.89      0.91      2015\n",
            "           3       0.79      0.88      0.84      1461\n",
            "           4       0.75      0.84      0.79       611\n",
            "           5       0.47      0.79      0.59       526\n",
            "\n",
            "    accuracy                           0.85     11491\n",
            "   macro avg       0.79      0.84      0.81     11491\n",
            "weighted avg       0.87      0.85      0.85     11491\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vypbeX0T-UM-"
      },
      "source": [
        "-----------------"
      ]
    }
  ]
}