kernel299814175e.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/myccds/all.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File b'../input/all.csv' does not exist: b'../input/all.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-b47cd9a63af7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../input/all.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    683\u001b[0m         )\n\u001b[1;32m    684\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 685\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1135\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1136\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1137\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1915\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1917\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1918\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File b'../input/all.csv' does not exist: b'../input/all.csv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('../input/all.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../input/myccds/all.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product</th>\n",
       "      <th>Consumer_complaint_narrative</th>\n",
       "      <th>category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Debt collection</td>\n",
       "      <td>transworld systems inc. \\nis trying to collect...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Credit reporting, credit repair services, or o...</td>\n",
       "      <td>I would like to request the suppression of the...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Debt collection</td>\n",
       "      <td>Over the past 2 weeks, I have been receiving e...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Money transfer, virtual currency, or money ser...</td>\n",
       "      <td>I was sold access to an event digitally, of wh...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Debt collection</td>\n",
       "      <td>While checking my credit report I noticed thre...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Product  \\\n",
       "0                                     Debt collection   \n",
       "2   Credit reporting, credit repair services, or o...   \n",
       "3                                     Debt collection   \n",
       "11  Money transfer, virtual currency, or money ser...   \n",
       "12                                    Debt collection   \n",
       "\n",
       "                         Consumer_complaint_narrative  category_id  \n",
       "0   transworld systems inc. \\nis trying to collect...            0  \n",
       "2   I would like to request the suppression of the...            1  \n",
       "3   Over the past 2 weeks, I have been receiving e...            0  \n",
       "11  I was sold access to an event digitally, of wh...            2  \n",
       "12  While checking my credit report I noticed thre...            0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from io import StringIO\n",
    "col = ['Product', 'Consumer complaint narrative']\n",
    "df = df[col]\n",
    "df = df[pd.notnull(df['Consumer complaint narrative'])]\n",
    "df.columns = ['Product', 'Consumer_complaint_narrative']\n",
    "df['category_id'] = df['Product'].factorize()[0]\n",
    "category_id_df = df[['Product', 'category_id']].drop_duplicates().sort_values('category_id')\n",
    "category_to_id = dict(category_id_df.values)\n",
    "id_to_category = dict(category_id_df[['category_id', 'Product']].values)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "df.groupby('Product').Consumer_complaint_narrative.count().plot.bar(ylim=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27881, 71827)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words='english')\n",
    "features = tfidf.fit_transform(df.Consumer_complaint_narrative).toarray()\n",
    "labels = df.category_id\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27881, 71827)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words='english')\n",
    "features = tfidf.fit_transform(df.Consumer_complaint_narrative).toarray()\n",
    "labels = df.category_id\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# 'Bank account or service':\n",
      "  . Most correlated unigrams:\n",
      ". organized\n",
      ". chip\n",
      "  . Most correlated bigrams:\n",
      ". chip card\n",
      ". service claims\n",
      "# 'Checking or savings account':\n",
      "  . Most correlated unigrams:\n",
      ". overdraft\n",
      ". deposit\n",
      "  . Most correlated bigrams:\n",
      ". savings account\n",
      ". checking account\n",
      "# 'Consumer Loan':\n",
      "  . Most correlated unigrams:\n",
      ". unorganized\n",
      ". temp\n",
      "  . Most correlated bigrams:\n",
      ". application fraudulent\n",
      ". loan opportunity\n",
      "# 'Credit card':\n",
      "  . Most correlated unigrams:\n",
      ". darn\n",
      ". macy\n",
      "  . Most correlated bigrams:\n",
      ". department receive\n",
      ". imagine surprise\n",
      "# 'Credit card or prepaid card':\n",
      "  . Most correlated unigrams:\n",
      ". citi\n",
      ". card\n",
      "  . Most correlated bigrams:\n",
      ". american express\n",
      ". credit card\n",
      "# 'Credit reporting':\n",
      "  . Most correlated unigrams:\n",
      ". examine\n",
      ". req\n",
      "  . Most correlated bigrams:\n",
      ". regarding information\n",
      ". date regarding\n",
      "# 'Credit reporting, credit repair services, or other personal consumer reports':\n",
      "  . Most correlated unigrams:\n",
      ". report\n",
      ". reporting\n",
      "  . Most correlated bigrams:\n",
      ". identity theft\n",
      ". credit report\n",
      "# 'Debt collection':\n",
      "  . Most correlated unigrams:\n",
      ". collection\n",
      ". debt\n",
      "  . Most correlated bigrams:\n",
      ". collection agency\n",
      ". collect debt\n",
      "# 'Money transfer, virtual currency, or money service':\n",
      "  . Most correlated unigrams:\n",
      ". tickets\n",
      ". paypal\n",
      "  . Most correlated bigrams:\n",
      ". western union\n",
      ". paypal account\n",
      "# 'Mortgage':\n",
      "  . Most correlated unigrams:\n",
      ". escrow\n",
      ". mortgage\n",
      "  . Most correlated bigrams:\n",
      ". mortgage company\n",
      ". escrow account\n",
      "# 'Payday loan':\n",
      "  . Most correlated unigrams:\n",
      ". yea\n",
      ". phantom\n",
      "  . Most correlated bigrams:\n",
      ". explanation asked\n",
      ". details asked\n",
      "# 'Payday loan, title loan, or personal loan':\n",
      "  . Most correlated unigrams:\n",
      ". borrowed\n",
      ". payday\n",
      "  . Most correlated bigrams:\n",
      ". took loan\n",
      ". payday loan\n",
      "# 'Student loan':\n",
      "  . Most correlated unigrams:\n",
      ". student\n",
      ". navient\n",
      "  . Most correlated bigrams:\n",
      ". student loan\n",
      ". loan forgiveness\n",
      "# 'Vehicle loan or lease':\n",
      "  . Most correlated unigrams:\n",
      ". vehicle\n",
      ". car\n",
      "  . Most correlated bigrams:\n",
      ". santander consumer\n",
      ". gm financial\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import chi2\n",
    "import numpy as np\n",
    "N = 2\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(\"# '{}':\".format(Product))\n",
    "  print(\"  . Most correlated unigrams:\\n. {}\".format('\\n. '.join(unigrams[-N:])))\n",
    "  print(\"  . Most correlated bigrams:\\n. {}\".format('\\n. '.join(bigrams[-N:])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "X_train, X_test, y_train, y_test = train_test_split(df['Consumer_complaint_narrative'], df['Product'], random_state = 0)\n",
    "count_vect = CountVectorizer()\n",
    "X_train_counts = count_vect.fit_transform(X_train)\n",
    "tfidf_transformer = TfidfTransformer()\n",
    "X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
    "clf = MultinomialNB().fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Debt collection']\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(count_vect.transform([\"This company refuses to provide me verification and validation of debt per my right under the FDCPA. I do not believe this debt is mine.\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Credit reporting, credit repair services, or other personal consumer reports']\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(count_vect.transform([\"I am disputing the inaccurate information the Chex-Systems has on my credit report. I initially submitted a police report on XXXX/XXXX/16 and Chex Systems only deleted the items that I mentioned in the letter and not all the items that were actually listed on the police report. In other words they wanted me to say word for word to them what items were fraudulent. The total disregard of the police report and what accounts that it states that are fraudulent. If they just had paid a little closer attention to the police report I would not been in this position now and they would n't have to research once again. I would like the reported information to be removed : XXXX XXXX XXXX\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Credit reporting, credit repair services, or other personal consumer reports']\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(count_vect.transform([\"i wont be able to your bedt, since i am not doing fine debt\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Credit reporting, credit repair services, or other personal consumer reports']\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(count_vect.transform([\"i wont be able to your bedt, since i am not doing collect debt\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Debt collection']\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(count_vect.transform([\"i wont be able to your debt, since i am not doing collect debt\"])))"
   ]
  }
 ],
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