baseline-urgency.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline for Urgency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from collections import Counter\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold, StratifiedKFold\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, roc_auc_score\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'D://Dataset/support-ticket-classification/'\n",
    "\n",
    "ddata = pd.read_csv(data_path + 'all_tickets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>title</th>\n",
       "      <th>body</th>\n",
       "      <th>ticket_type</th>\n",
       "      <th>category</th>\n",
       "      <th>sub_category1</th>\n",
       "      <th>sub_category2</th>\n",
       "      <th>business_service</th>\n",
       "      <th>urgency</th>\n",
       "      <th>impact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>hi since recruiter lead permission approve req...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>connection with icon</td>\n",
       "      <td>icon dear please setup icon per icon engineers...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>work experience user</td>\n",
       "      <td>work experience user hi work experience studen...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>requesting for meeting</td>\n",
       "      <td>requesting meeting hi please help follow equip...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>reset passwords for external accounts</td>\n",
       "      <td>re expire days hi ask help update passwords co...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   title  \\\n",
       "0                                    NaN   \n",
       "1                   connection with icon   \n",
       "2                   work experience user   \n",
       "3                 requesting for meeting   \n",
       "4  reset passwords for external accounts   \n",
       "\n",
       "                                                body  ticket_type  category  \\\n",
       "0  hi since recruiter lead permission approve req...            1         4   \n",
       "1  icon dear please setup icon per icon engineers...            1         6   \n",
       "2  work experience user hi work experience studen...            1         5   \n",
       "3  requesting meeting hi please help follow equip...            1         5   \n",
       "4  re expire days hi ask help update passwords co...            1         4   \n",
       "\n",
       "   sub_category1  sub_category2  business_service  urgency  impact  \n",
       "0              2             21                71        3       4  \n",
       "1             22              7                26        3       4  \n",
       "2             13              7                32        3       4  \n",
       "3             13              7                32        3       4  \n",
       "4              2             76                 4        3       4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f4d6d0400>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y = ddata.urgency.value_counts(), x = ddata.urgency.value_counts().index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleansing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopword = stopwords.words('english')\n",
    "\n",
    "stem = PorterStemmer()\n",
    "\n",
    "lemma = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleansing(sentence):\n",
    "    word_list = word_tokenize(sentence)\n",
    "    word_list = [word for word in word_list if word not in stopword]\n",
    "    word_list = [word for word in word_list if len(word) > 2]\n",
    "    word_list = [stem.stem(word) for word in word_list]\n",
    "    word_list = [lemma.lemmatize(word) for word in word_list]\n",
    "    \n",
    "    text = \" \".join(word_list)\n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddata['body_cleansing'] = ddata.body.apply(cleansing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cv.fit_transform(ddata.body_cleansing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, ddata.urgency, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnb = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = mnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval(y_true, y_pred):\n",
    "    f1score = f1_score(y_true, y_pred, average='macro')\n",
    "    recall = recall_score(y_true, y_pred, average='macro')\n",
    "    precision = precision_score(y_true, y_pred, average = 'macro')\n",
    "    \n",
    "    return {'f1-score': f1score,\n",
    "            'recall': recall,\n",
    "            'precision': precision\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'f1-score': 0.5888329234753351,\n",
       " 'recall': 0.608404225903844,\n",
       " 'precision': 0.5808597474617259}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}