Multi_text_Classification.ipynb

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  "metadata": {
    "colab": {
      "name": "Multi-text_Classification.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rXQ9ta_Z12eJ",
        "outputId": "197756ca-064b-4727-abce-ee8732f22e45"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-DjMN8t2CjM",
        "outputId": "b1a63251-5449-4882-ea1b-3849f8b12f30"
      },
      "source": [
        "!unzip /content/drive/MyDrive/rows.csv.zip"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/drive/MyDrive/rows.csv.zip\n",
            "  inflating: rows.csv                \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMKXdzY2zlIG"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy.stats import randint\n",
        "import seaborn as sns # used for plot interactive graph. \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from io import StringIO\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_selection import chi2\n",
        "from IPython.display import display\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import metrics"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gzxJopN7zwbt",
        "outputId": "b43a185c-e6ac-4d9a-f8e4-973dd36b2868"
      },
      "source": [
        "print(os.listdir())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['.config', 'rows.csv', 'drive', 'sample_data']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20dVD9rm2mPA",
        "outputId": "f6fd8182-2660-4b4c-c2a8-ba2e54933672"
      },
      "source": [
        "# loading data\n",
        "df = pd.read_csv('/content/rows.csv')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (4,5,6,11,16) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVNe-ydh3J12",
        "outputId": "7d2b8826-b3ed-47c6-b933-15c6d2763e54"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1282355, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "nNu5Wq2I3KvC",
        "outputId": "011eddc3-3dc6-404e-ea4f-d4c15383e718"
      },
      "source": [
        "df.head(3).T"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
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              "<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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Date received</th>\n",
              "      <td>05/10/2019</td>\n",
              "      <td>05/10/2019</td>\n",
              "      <td>05/10/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <td>Checking or savings account</td>\n",
              "      <td>Checking or savings account</td>\n",
              "      <td>Debt collection</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-product</th>\n",
              "      <td>Checking account</td>\n",
              "      <td>Other banking product or service</td>\n",
              "      <td>Payday loan debt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Issue</th>\n",
              "      <td>Managing an account</td>\n",
              "      <td>Managing an account</td>\n",
              "      <td>Communication tactics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-issue</th>\n",
              "      <td>Problem using a debit or ATM card</td>\n",
              "      <td>Deposits and withdrawals</td>\n",
              "      <td>Frequent or repeated calls</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer complaint narrative</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company public response</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <td>NAVY FEDERAL CREDIT UNION</td>\n",
              "      <td>BOEING EMPLOYEES CREDIT UNION</td>\n",
              "      <td>CURO Intermediate Holdings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>State</th>\n",
              "      <td>FL</td>\n",
              "      <td>WA</td>\n",
              "      <td>TX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZIP code</th>\n",
              "      <td>328XX</td>\n",
              "      <td>98204</td>\n",
              "      <td>751XX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tags</th>\n",
              "      <td>Older American</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer consent provided?</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Submitted via</th>\n",
              "      <td>Web</td>\n",
              "      <td>Referral</td>\n",
              "      <td>Web</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date sent to company</th>\n",
              "      <td>05/10/2019</td>\n",
              "      <td>05/10/2019</td>\n",
              "      <td>05/10/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company response to consumer</th>\n",
              "      <td>In progress</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Closed with explanation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Timely response?</th>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Complaint ID</th>\n",
              "      <td>3238275</td>\n",
              "      <td>3238228</td>\n",
              "      <td>3237964</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                              0  ...                           2\n",
              "Date received                                        05/10/2019  ...                  05/10/2019\n",
              "Product                             Checking or savings account  ...             Debt collection\n",
              "Sub-product                                    Checking account  ...            Payday loan debt\n",
              "Issue                                       Managing an account  ...       Communication tactics\n",
              "Sub-issue                     Problem using a debit or ATM card  ...  Frequent or repeated calls\n",
              "Consumer complaint narrative                                NaN  ...                         NaN\n",
              "Company public response                                     NaN  ...                         NaN\n",
              "Company                               NAVY FEDERAL CREDIT UNION  ...  CURO Intermediate Holdings\n",
              "State                                                        FL  ...                          TX\n",
              "ZIP code                                                  328XX  ...                       751XX\n",
              "Tags                                             Older American  ...                         NaN\n",
              "Consumer consent provided?                                  NaN  ...                         NaN\n",
              "Submitted via                                               Web  ...                         Web\n",
              "Date sent to company                                 05/10/2019  ...                  05/10/2019\n",
              "Company response to consumer                        In progress  ...     Closed with explanation\n",
              "Timely response?                                            Yes  ...                         Yes\n",
              "Consumer disputed?                                          NaN  ...                         NaN\n",
              "Complaint ID                                            3238275  ...                     3237964\n",
              "\n",
              "[18 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "RFvjU4KB4BgB",
        "outputId": "7c8d8420-ed0b-4069-f71d-80d56561371e"
      },
      "source": [
        "# Create a new dataframe with two columns\n",
        "df1 = df[['Product', 'Consumer complaint narrative']].copy()\n",
        "\n",
        "# Remove missing values (NaN)\n",
        "df1 = df1[pd.notnull(df1['Consumer complaint narrative'])]\n",
        "\n",
        "# Renaming second column for a simpler name\n",
        "df1.columns = ['Product', 'Consumer_complaint'] \n",
        "\n",
        "print(df1.shape)\n",
        "\n",
        "df1.head(2).T"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(383564, 2)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>29904</th>\n",
              "      <th>30629</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <td>Credit reporting, credit repair services, or o...</td>\n",
              "      <td>Credit reporting, credit repair services, or o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer_complaint</th>\n",
              "      <td>The Summer of XX/XX/2018 I was denied a mortga...</td>\n",
              "      <td>There are many mistakes appear in my report wi...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                29904                                              30629\n",
              "Product             Credit reporting, credit repair services, or o...  Credit reporting, credit repair services, or o...\n",
              "Consumer_complaint  The Summer of XX/XX/2018 I was denied a mortga...  There are many mistakes appear in my report wi..."
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qf8O4Xa77laL",
        "outputId": "3f3c5e97-05ee-4391-b22b-3641bcde4998"
      },
      "source": [
        "pd.DataFrame(df1.Product.unique()).values"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['Credit reporting, credit repair services, or other personal consumer reports'],\n",
              "       ['Debt collection'],\n",
              "       ['Student loan'],\n",
              "       ['Mortgage'],\n",
              "       ['Credit card or prepaid card'],\n",
              "       ['Checking or savings account'],\n",
              "       ['Money transfer, virtual currency, or money service'],\n",
              "       ['Vehicle loan or lease'],\n",
              "       ['Payday loan, title loan, or personal loan'],\n",
              "       ['Credit card'],\n",
              "       ['Bank account or service'],\n",
              "       ['Credit reporting'],\n",
              "       ['Consumer Loan'],\n",
              "       ['Prepaid card'],\n",
              "       ['Payday loan'],\n",
              "       ['Money transfers'],\n",
              "       ['Other financial service'],\n",
              "       ['Virtual currency']], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fa3A_pim8VZu"
      },
      "source": [
        "# Because the computation is time consuming (in terms of CPU), the data was sampled\n",
        "df2 = df1.sample(10000, random_state=1).copy()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcFwMo8WA_20"
      },
      "source": [
        "# Renaming categories\n",
        "df2.replace({'Product': \n",
        "             {'Credit reporting, credit repair services, or other personal consumer reports': \n",
        "              'Credit reporting, repair, or other', \n",
        "              'Credit reporting': 'Credit reporting, repair, or other',\n",
        "             'Credit card': 'Credit card or prepaid card',\n",
        "             'Prepaid card': 'Credit card or prepaid card',\n",
        "             'Payday loan': 'Payday loan, title loan, or personal loan',\n",
        "             'Money transfer': 'Money transfer, virtual currency, or money service',\n",
        "             'Virtual currency': 'Money transfer, virtual currency, or money service'}}, \n",
        "            inplace= True)\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "uJuu1fg8A_6m",
        "outputId": "688bd0ff-088b-47df-950b-9347db9ad828"
      },
      "source": [
        "pd.DataFrame(df2.Product.unique())"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "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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              "        vertical-align: top;\n",
              "    }\n",
              "\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Credit reporting, repair, or other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Debt collection</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Consumer Loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Credit card or prepaid card</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mortgage</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Vehicle loan or lease</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Student loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Payday loan, title loan, or personal loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Checking or savings account</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bank account or service</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Money transfer, virtual currency, or money ser...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Money transfers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Other financial service</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    0\n",
              "0                  Credit reporting, repair, or other\n",
              "1                                     Debt collection\n",
              "2                                       Consumer Loan\n",
              "3                         Credit card or prepaid card\n",
              "4                                            Mortgage\n",
              "5                               Vehicle loan or lease\n",
              "6                                        Student loan\n",
              "7           Payday loan, title loan, or personal loan\n",
              "8                         Checking or savings account\n",
              "9                             Bank account or service\n",
              "10  Money transfer, virtual currency, or money ser...\n",
              "11                                    Money transfers\n",
              "12                            Other financial service"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "RLSUbBPvBACO",
        "outputId": "78c9dff3-5cce-489b-eac4-02cb8d2b6b93"
      },
      "source": [
        "# Create a new column 'category_id' with encoded categories \n",
        "df2['category_id'] = df2['Product'].factorize()[0]\n",
        "category_id_df = df2[['Product', 'category_id']].drop_duplicates()\n",
        "\n",
        "\n",
        "# Dictionaries for future use\n",
        "category_to_id = dict(category_id_df.values)\n",
        "id_to_category = dict(category_id_df[['category_id', 'Product']].values)\n",
        "\n",
        "# New dataframe\n",
        "df2.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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>Product</th>\n",
              "      <th>Consumer_complaint</th>\n",
              "      <th>category_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>310399</th>\n",
              "      <td>Credit reporting, repair, or other</td>\n",
              "      <td>Bayview completely ignored laws and is reporti...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186155</th>\n",
              "      <td>Credit reporting, repair, or other</td>\n",
              "      <td>inaccurate information no knowledge of account...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>651135</th>\n",
              "      <td>Debt collection</td>\n",
              "      <td>On XXXX/XXXX/2016 I received a notice from Con...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>515233</th>\n",
              "      <td>Consumer Loan</td>\n",
              "      <td>I have a loan with  Kia Motor Finance. I  rece...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>641112</th>\n",
              "      <td>Credit card or prepaid card</td>\n",
              "      <td>I have a XXXX BIG card and made my payment whe...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   Product  ... category_id\n",
              "310399  Credit reporting, repair, or other  ...           0\n",
              "186155  Credit reporting, repair, or other  ...           0\n",
              "651135                     Debt collection  ...           1\n",
              "515233                       Consumer Loan  ...           2\n",
              "641112         Credit card or prepaid card  ...           3\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "PItp5LKABAEq",
        "outputId": "718012c1-e2fa-4c6a-edbd-8a68bbe2856d"
      },
      "source": [
        "fig = plt.figure(figsize=(8,6))\n",
        "colors = ['grey','grey','grey','grey','grey','grey','grey','grey','grey',\n",
        "    'grey','darkblue','darkblue','darkblue']\n",
        "df2.groupby('Product').Consumer_complaint.count().sort_values().plot.barh(\n",
        "    ylim=0, color=colors, title= 'NUMBER OF COMPLAINTS IN EACH PRODUCT CATEGORY\\n')\n",
        "plt.xlabel('Number of ocurrences', fontsize = 10);"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J9j4zM7VBAHI",
        "outputId": "9ba03b09-2b39-4495-caa0-36463cf6917d"
      },
      "source": [
        "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5,\n",
        "                        ngram_range=(1, 2), \n",
        "                        stop_words='english')\n",
        "\n",
        "# We transform each complaint into a vector\n",
        "features = tfidf.fit_transform(df2.Consumer_complaint).toarray()\n",
        "\n",
        "labels = df2.category_id\n",
        "\n",
        "print(\"Each of the %d complaints is represented by %d features (TF-IDF score of unigrams and bigrams)\" %(features.shape))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Each of the 10000 complaints is represented by 27507 features (TF-IDF score of unigrams and bigrams)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imNSO4dEB46w",
        "outputId": "88ad1118-f6c2-4586-8612-050909507a86"
      },
      "source": [
        "# Finding the three most correlated terms with each of the product categories\n",
        "N = 3\n",
        "for Product, category_id in sorted(category_to_id.items()):\n",
        "  features_chi2 = chi2(features, labels == category_id)\n",
        "  indices = np.argsort(features_chi2[0])\n",
        "  feature_names = np.array(tfidf.get_feature_names())[indices]\n",
        "  unigrams = [v for v in feature_names if len(v.split(' ')) == 1]\n",
        "  bigrams = [v for v in feature_names if len(v.split(' ')) == 2]\n",
        "  print(\"\\n==> %s:\" %(Product))\n",
        "  print(\"  * Most Correlated Unigrams are: %s\" %(', '.join(unigrams[-N:])))\n",
        "  print(\"  * Most Correlated Bigrams are: %s\" %(', '.join(bigrams[-N:])))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Bank account or service:\n",
            "  * Most Correlated Unigrams are: overdraft, bank, scottrade\n",
            "  * Most Correlated Bigrams are: citigold checking, debit card, checking account\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Checking or savings account:\n",
            "  * Most Correlated Unigrams are: checking, branch, overdraft\n",
            "  * Most Correlated Bigrams are: 00 bonus, overdraft fees, checking account\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Consumer Loan:\n",
            "  * Most Correlated Unigrams are: dealership, vehicle, car\n",
            "  * Most Correlated Bigrams are: car loan, vehicle loan, regional acceptance\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Credit card or prepaid card:\n",
            "  * Most Correlated Unigrams are: express, citi, card\n",
            "  * Most Correlated Bigrams are: balance transfer, american express, credit card\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Credit reporting, repair, or other:\n",
            "  * Most Correlated Unigrams are: report, experian, equifax\n",
            "  * Most Correlated Bigrams are: credit file, equifax xxxx, credit report\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Debt collection:\n",
            "  * Most Correlated Unigrams are: collect, collection, debt\n",
            "  * Most Correlated Bigrams are: debt collector, collect debt, collection agency\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Money transfer, virtual currency, or money service:\n",
            "  * Most Correlated Unigrams are: ethereum, bitcoin, coinbase\n",
            "  * Most Correlated Bigrams are: account coinbase, coinbase xxxx, coinbase account\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Money transfers:\n",
            "  * Most Correlated Unigrams are: paypal, moneygram, gram\n",
            "  * Most Correlated Bigrams are: sending money, western union, money gram\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Mortgage:\n",
            "  * Most Correlated Unigrams are: escrow, modification, mortgage\n",
            "  * Most Correlated Bigrams are: short sale, mortgage company, loan modification\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Other financial service:\n",
            "  * Most Correlated Unigrams are: meetings, productive, vast\n",
            "  * Most Correlated Bigrams are: insurance check, check payable, face face\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Payday loan, title loan, or personal loan:\n",
            "  * Most Correlated Unigrams are: astra, ace, payday\n",
            "  * Most Correlated Bigrams are: 00 loan, applied payday, payday loan\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==> Student loan:\n",
            "  * Most Correlated Unigrams are: student, loans, navient\n",
            "  * Most Correlated Bigrams are: income based, student loan, student loans\n",
            "\n",
            "==> Vehicle loan or lease:\n",
            "  * Most Correlated Unigrams are: honda, car, vehicle\n",
            "  * Most Correlated Bigrams are: used vehicle, total loss, honda financial\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vbevVCbbB6UX"
      },
      "source": [
        "X = df2['Consumer_complaint'] # Collection of documents\n",
        "y = df2['Product'] # Target or the labels we want to predict (i.e., the 13 different complaints of products)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                                                    test_size=0.25,\n",
        "                                                    random_state = 0)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vebCrd6DB6Wu",
        "outputId": "27c08ae0-2793-46ea-a9e1-a9f0a7ea355f"
      },
      "source": [
        "models = [\n",
        "    RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0),\n",
        "    LinearSVC(),\n",
        "    MultinomialNB(),\n",
        "    LogisticRegression(random_state=0),\n",
        "]\n",
        "\n",
        "# 5 Cross-validation\n",
        "CV = 5\n",
        "cv_df = pd.DataFrame(index=range(CV * len(models)))\n",
        "\n",
        "entries = []\n",
        "for model in models:\n",
        "  model_name = model.__class__.__name__\n",
        "  accuracies = cross_val_score(model, features, labels, scoring='accuracy', cv=CV)\n",
        "  for fold_idx, accuracy in enumerate(accuracies):\n",
        "    entries.append((model_name, fold_idx, accuracy))\n",
        "    \n",
        "cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "xXnlQU9fB6Y6",
        "outputId": "bb30e020-f73d-4a52-9c0a-118e93a51f4d"
      },
      "source": [
        "mean_accuracy = cv_df.groupby('model_name').accuracy.mean()\n",
        "std_accuracy = cv_df.groupby('model_name').accuracy.std()\n",
        "\n",
        "acc = pd.concat([mean_accuracy, std_accuracy], axis= 1, \n",
        "          ignore_index=True)\n",
        "acc.columns = ['Mean Accuracy', 'Standard deviation']\n",
        "acc"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "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>Mean Accuracy</th>\n",
              "      <th>Standard deviation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LinearSVC</th>\n",
              "      <td>0.7791</td>\n",
              "      <td>0.005561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.7641</td>\n",
              "      <td>0.009839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MultinomialNB</th>\n",
              "      <td>0.6475</td>\n",
              "      <td>0.004596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.3949</td>\n",
              "      <td>0.006665</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Mean Accuracy  Standard deviation\n",
              "model_name                                               \n",
              "LinearSVC                      0.7791            0.005561\n",
              "LogisticRegression             0.7641            0.009839\n",
              "MultinomialNB                  0.6475            0.004596\n",
              "RandomForestClassifier         0.3949            0.006665"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "-f03n6P8B6cV",
        "outputId": "ecf73791-aa32-4e6e-969b-832ad1eb74f5"
      },
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.boxplot(x='model_name', y='accuracy', \n",
        "            data=cv_df, \n",
        "            color='lightblue', \n",
        "            showmeans=True)\n",
        "plt.title(\"MEAN ACCURACY (cv = 5)\\n\", size=14);"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wk1yHgJUB6gX"
      },
      "source": [
        "X_train, X_test, y_train, y_test,indices_train,indices_test = train_test_split(features, \n",
        "                                                               labels, \n",
        "                                                               df2.index, test_size=0.25, \n",
        "                                                               random_state=1)\n",
        "model = LinearSVC()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B-k6K6CAB6iI",
        "outputId": "92d305bc-7f47-4111-9162-0f15530a60e8"
      },
      "source": [
        "# Classification report\n",
        "print('\\t\\t\\t\\tCLASSIFICATIION METRICS\\n')\n",
        "print(metrics.classification_report(y_test, y_pred, \n",
        "                                    target_names= df2['Product'].unique()))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t\t\t\tCLASSIFICATIION METRICS\n",
            "\n",
            "                                                    precision    recall  f1-score   support\n",
            "\n",
            "                Credit reporting, repair, or other       0.81      0.88      0.84       783\n",
            "                                   Debt collection       0.76      0.80      0.78       588\n",
            "                                     Consumer Loan       0.52      0.22      0.31        73\n",
            "                       Credit card or prepaid card       0.73      0.82      0.77       253\n",
            "                                          Mortgage       0.87      0.92      0.90       340\n",
            "                             Vehicle loan or lease       0.43      0.14      0.21        43\n",
            "                                      Student loan       0.85      0.85      0.85       155\n",
            "         Payday loan, title loan, or personal loan       0.50      0.17      0.26        40\n",
            "                       Checking or savings account       0.61      0.60      0.61        85\n",
            "                           Bank account or service       0.61      0.43      0.50        93\n",
            "Money transfer, virtual currency, or money service       0.83      0.62      0.71        32\n",
            "                                   Money transfers       0.60      0.23      0.33        13\n",
            "                           Other financial service       0.00      0.00      0.00         2\n",
            "\n",
            "                                          accuracy                           0.78      2500\n",
            "                                         macro avg       0.62      0.51      0.54      2500\n",
            "                                      weighted avg       0.77      0.78      0.77      2500\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "id": "7I7eocNIB6kF",
        "outputId": "f8654ede-0b44-4505-f10d-3f7218427516"
      },
      "source": [
        "conf_mat = confusion_matrix(y_test, y_pred)\n",
        "fig, ax = plt.subplots(figsize=(8,8))\n",
        "sns.heatmap(conf_mat, annot=True, cmap=\"Blues\", fmt='d',\n",
        "            xticklabels=category_id_df.Product.values, \n",
        "            yticklabels=category_id_df.Product.values)\n",
        "plt.ylabel('Actual')\n",
        "plt.xlabel('Predicted')\n",
        "plt.title(\"CONFUSION MATRIX - LinearSVC\\n\", size=16);"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOCSfmJeQS0I"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                                                    test_size=0.25,\n",
        "                                                    random_state = 0)\n",
        "\n",
        "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5,\n",
        "                        ngram_range=(1, 2), \n",
        "                        stop_words='english')\n",
        "\n",
        "fitted_vectorizer = tfidf.fit(X_train)\n",
        "tfidf_vectorizer_vectors = fitted_vectorizer.transform(X_train)\n",
        "\n",
        "model = LinearSVC().fit(tfidf_vectorizer_vectors, y_train)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbiAB0zpQS2f",
        "outputId": "4d247936-31bd-4877-f453-3d1aebf02b47"
      },
      "source": [
        "complaint = \"\"\"I have received over 27 emails from XXXX XXXX who is a representative from Midland Funding LLC. \n",
        "From XX/XX/XXXX I received approximately 6 emails. From XX/XX/XXXX I received approximately 6 emails. \n",
        "From XX/XX/XXXX I received approximately 9 emails. From XX/XX/XXXX I received approximately 6 emails. \n",
        "All emails came from the same individual, XXXX XXXX. It is becoming a nonstop issue of harassment.\"\"\"\n",
        "\n",
        "print(model.predict(fitted_vectorizer.transform([complaint])))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Debt collection']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0HbKv90QS40",
        "outputId": "676a370b-4fc8-4f73-feac-b853015a3cc8"
      },
      "source": [
        "complaint = \"\"\"Respected Sir/ Madam, I am exploring the possibilities for financing my daughter 's \n",
        "XXXX education with private loan from bank. I am in the XXXX on XXXX visa. \n",
        "My daughter is on XXXX dependent visa. As a result, she is considered as international student. \n",
        "I am waiting in the Green Card ( Permanent Residency ) line for last several years. \n",
        "\n",
        "I checked with Discover, XXXX XXXX websites. While they allow international students to apply for loan, they need \n",
        "cosigners who are either US citizens or Permanent Residents. I feel that this is unfair. \n",
        "I had been given mortgage and car loans in the past which I closed successfully. I have good financial history. \n",
        "I think I should be allowed to remain cosigner on the student loan. I would be much obliged if you could look into it. \n",
        "Thanking you in advance. Best Regards\"\"\"\n",
        "\n",
        "print(model.predict(fitted_vectorizer.transform([complaint])))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Student loan']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7Z2oVSeQS7L",
        "outputId": "c43488bf-4ff2-442d-ee50-fea735127b3b"
      },
      "source": [
        "complaint = \"\"\"They make me look like if I was behind on my Mortgage on the month of XX/XX/2018 & XX/XX/XXXX \n",
        "when I was not and never was, when I was even giving extra money to the Principal. \n",
        "The Money Source Web site and the managers started a problem, when my wife was trying to increase the payment, \n",
        "so more money went to the Principal and two payments came out that month and because \n",
        "I reverse one of them thru my Bank as Fraud they took revenge and committed slander against me by reporting me late at the Credit Bureaus, \n",
        "for 45 and 60 days, when it was not thru. Told them to correct that and the accounting department or the company revert \n",
        "that letter from going to the Credit Bureaus to correct their injustice. The manager by the name XXXX requested this for \n",
        "the second time and nothing yet. I am a Senior of XXXX years old and a Retired XXXX Veteran and is a disgraced that Americans \n",
        "treat us that way and do not want to admit their injustice and lies to the Credit Bureau.\"\"\"\n",
        "print(model.predict(fitted_vectorizer.transform([complaint])))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Mortgage']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4Ol8CTyQS9Y"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQxHqlnbQS_c"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbCJN9eaQTUo"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIUQe0YTQTWd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}