resume_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resume Classifcation Model using NLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Dataset : https://www.kaggle.com/gauravduttakiit/resume-dataset\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "import re\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from scipy.sparse import hstack\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>Category</th>\n",
       "      <th>Resume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Skills * Programming Languages: Python (pandas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Education Details \\r\\nMay 2013 to May 2017 B.E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Areas of Interest Deep Learning, Control Syste...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Skills â¢ R â¢ Python â¢ SAP HANA â¢ Table...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Education Details \\r\\n MCA   YMCAUST,  Faridab...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Category                                             Resume\n",
       "0  Data Science  Skills * Programming Languages: Python (pandas...\n",
       "1  Data Science  Education Details \\r\\nMay 2013 to May 2017 B.E...\n",
       "2  Data Science  Areas of Interest Deep Learning, Control Syste...\n",
       "3  Data Science  Skills â¢ R â¢ Python â¢ SAP HANA â¢ Table...\n",
       "4  Data Science  Education Details \\r\\n MCA   YMCAUST,  Faridab..."
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/UpdatedResumeDataSet.csv')\n",
    "\n",
    "# df = pd.read_csv('./dataset/Resume.csv')\n",
    "# df.drop(columns=['ID', 'Resume_html'], inplace=True)\n",
    "# df.rename(columns={'Resume_str':'Resume'}, inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking duplicated\n",
    "# df.drop_duplicates(inplace=True)\n",
    "# df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>job</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Java Developer</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Testing</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DevOps Engineer</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Python Developer</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Web Designing</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                job  count\n",
       "0    Java Developer     84\n",
       "1           Testing     70\n",
       "2   DevOps Engineer     55\n",
       "3  Python Developer     48\n",
       "4     Web Designing     45"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category = df['Category'].value_counts().reset_index()\n",
    "category.rename(columns={'index':'job', 'Category':'count'}, inplace=True)\n",
    "category.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(x=category['count'], y=category['job'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Data Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\wish6\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\wish6\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing NLP tools\n",
    "import spacy # for lemmatization\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "import re   # regex\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "nltk.download('punkt') # for word_tokenize\n",
    "nltk.download('stopwords') # for stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean(text):\n",
    "    text = text.lower()\n",
    "\n",
    "    text = re.sub('^[\\w\\-\\.]+@([\\w-]+\\.)+[\\w-]{2,4}', ' ', text)  # remove email\n",
    "    text = re.sub('[(http(s)?):\\/\\/(www\\.)?a-zA-Z0-9@:%._\\+~#=]{2,256}\\.[a-z]{2,6}\\b([-a-zA-Z0-9@:%_\\+.~#\\?&//=]*)', ' ', text)  # remove URLs\n",
    "    text = re.sub('<.+?>', ' ', text)  # html tags\n",
    "    text = re.sub('[^a-z]', ' ', text) # remove non-characters\n",
    "    text = re.sub('\\s+', ' ', text)  # remove extra whitespace\n",
    "\n",
    "    doc = nlp(text) # change text into DOC object\n",
    "    text = ' '.join([token.lemma_ for token in doc]) # lemmatization\n",
    "     \n",
    "    text = text.lower() # to lowercase again\n",
    "     \n",
    "    text = word_tokenize(text) # tokenize word into list again\n",
    "    \n",
    "    stop_words = set(stopwords.words('english')) # get stopwords\n",
    "    text = [word for word in text if word not in stop_words] # remove stop words\n",
    "    \n",
    "    return ' '.join(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sly king fly'"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean(\"I'm sly king of the fly.\") # example after cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cleaned'] = df['Resume'].apply(lambda x : clean(x)) # apply clean to all resume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>Category</th>\n",
       "      <th>Resume</th>\n",
       "      <th>cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Skills * Programming Languages: Python (pandas...</td>\n",
       "      <td>skill programming language python panda numpy ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Education Details \\r\\nMay 2013 to May 2017 B.E...</td>\n",
       "      <td>education detail may may b e uit rgpv data sci...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Areas of Interest Deep Learning, Control Syste...</td>\n",
       "      <td>area interest deep learning control system des...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Skills â¢ R â¢ Python â¢ SAP HANA â¢ Table...</td>\n",
       "      <td>skill r python sap hana tableau sap hana sql s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Education Details \\r\\n MCA   YMCAUST,  Faridab...</td>\n",
       "      <td>education detail mca ymcaust faridabad haryana...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Category                                             Resume  \\\n",
       "0  Data Science  Skills * Programming Languages: Python (pandas...   \n",
       "1  Data Science  Education Details \\r\\nMay 2013 to May 2017 B.E...   \n",
       "2  Data Science  Areas of Interest Deep Learning, Control Syste...   \n",
       "3  Data Science  Skills â¢ R â¢ Python â¢ SAP HANA â¢ Table...   \n",
       "4  Data Science  Education Details \\r\\n MCA   YMCAUST,  Faridab...   \n",
       "\n",
       "                                             cleaned  \n",
       "0  skill programming language python panda numpy ...  \n",
       "1  education detail may may b e uit rgpv data sci...  \n",
       "2  area interest deep learning control system des...  \n",
       "3  skill r python sap hana tableau sap hana sql s...  \n",
       "4  education detail mca ymcaust faridabad haryana...  "
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corpus string length: 2306347\n"
     ]
    }
   ],
   "source": [
    "# corpus is all the texts in the dataset\n",
    "corpus = \"\"\n",
    "for resume in df['cleaned']:\n",
    "    corpus += resume\n",
    "\n",
    "print(f'Corpus string length: {len(corpus)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>word</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>project</td>\n",
       "      <td>4059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>exprience</td>\n",
       "      <td>3829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>company</td>\n",
       "      <td>3635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>month</td>\n",
       "      <td>3244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>detail</td>\n",
       "      <td>3039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        word  frequency\n",
       "0    project       4059\n",
       "1  exprience       3829\n",
       "2    company       3635\n",
       "3      month       3244\n",
       "4     detail       3039"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus_tokens = word_tokenize(corpus) # tokenize word in corpus\n",
    "\n",
    "word_freq = nltk.FreqDist(corpus_tokens) # get frequency of each word\n",
    "mostcommon = word_freq.most_common(30) # get top 30 common words\n",
    "\n",
    "common = pd.DataFrame(mostcommon)\n",
    "common.rename(columns={0:'word', 1:'frequency'}, inplace = True)\n",
    "common.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Most common words'}, xlabel='frequency', ylabel='word'>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Most common words')\n",
    "sns.barplot(common, x='frequency', y='word')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : Advocate\n",
      "1 : Arts\n",
      "2 : Automation Testing\n",
      "3 : Blockchain\n",
      "4 : Business Analyst\n",
      "5 : Civil Engineer\n",
      "6 : Data Science\n",
      "7 : Database\n",
      "8 : DevOps Engineer\n",
      "9 : DotNet Developer\n",
      "10 : ETL Developer\n",
      "11 : Electrical Engineering\n",
      "12 : HR\n",
      "13 : Hadoop\n",
      "14 : Health and fitness\n",
      "15 : Java Developer\n",
      "16 : Mechanical Engineer\n",
      "17 : Network Security Engineer\n",
      "18 : Operations Manager\n",
      "19 : PMO\n",
      "20 : Python Developer\n",
      "21 : SAP Developer\n",
      "22 : Sales\n",
      "23 : Testing\n",
      "24 : Web Designing\n"
     ]
    }
   ],
   "source": [
    "# Label Encoding\n",
    "le = LabelEncoder()\n",
    "df['Category_ID'] = le.fit_transform(df['Category'])\n",
    "\n",
    "for i,v in enumerate(le.classes_):\n",
    "    print(f'{i} : {v}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vectorizing (Feature Extraction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vectorizing the cleaned columns\n",
    "cleaned = df['cleaned'].to_numpy()\n",
    "Category_ID = df['Category_ID'].to_numpy()\n",
    "\n",
    "word_vectorizer = TfidfVectorizer(\n",
    "    sublinear_tf=True,\n",
    "    stop_words='english',\n",
    "    max_features=1500)\n",
    "\n",
    "word_vectorizer.fit(cleaned)\n",
    "WordFeatures = word_vectorizer.transform(cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(962, 1500)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WordFeatures.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split training and testing set\n",
    "X_train, X_test, y_train, y_test = train_test_split(WordFeatures, Category_ID, random_state=42, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((769, 1500), (193, 1500), (769,), (193,))"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=KNeighborsClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneVsRestClassifier</label><div class=\"sk-toggleable__content\"><pre>OneVsRestClassifier(estimator=KNeighborsClassifier())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneVsRestClassifier(estimator=KNeighborsClassifier())"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Training\n",
    "model = OneVsRestClassifier(KNeighborsClassifier())\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "| Training Accuracy   :- 99.09% |\n",
      "---------------------------------\n",
      "| Validation Accuracy :- 98.45% |\n",
      "---------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(f'---------------------------------\\n| Training Accuracy   :- {(model.score(X_train, y_train)*100).round(2)}% |')\n",
    "print(f'---------------------------------\\n| Validation Accuracy :- {(model.score(X_test, y_test)*100).round(2)}% |\\n---------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         3\n",
      "           1       1.00      1.00      1.00         6\n",
      "           2       1.00      1.00      1.00         5\n",
      "           3       1.00      1.00      1.00         7\n",
      "           4       1.00      1.00      1.00         4\n",
      "           5       1.00      1.00      1.00         9\n",
      "           6       1.00      0.60      0.75         5\n",
      "           7       1.00      1.00      1.00         8\n",
      "           8       1.00      0.93      0.96        14\n",
      "           9       1.00      1.00      1.00         5\n",
      "          10       1.00      1.00      1.00         7\n",
      "          11       1.00      1.00      1.00         6\n",
      "          12       1.00      1.00      1.00        12\n",
      "          13       1.00      1.00      1.00         4\n",
      "          14       1.00      1.00      1.00         7\n",
      "          15       1.00      1.00      1.00        15\n",
      "          16       1.00      1.00      1.00         8\n",
      "          17       1.00      1.00      1.00         3\n",
      "          18       1.00      1.00      1.00        12\n",
      "          19       0.88      1.00      0.93         7\n",
      "          20       1.00      1.00      1.00        10\n",
      "          21       0.78      1.00      0.88         7\n",
      "          22       1.00      1.00      1.00         8\n",
      "          23       1.00      1.00      1.00        16\n",
      "          24       1.00      1.00      1.00         5\n",
      "\n",
      "    accuracy                           0.98       193\n",
      "   macro avg       0.99      0.98      0.98       193\n",
      "weighted avg       0.99      0.98      0.98       193\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_pred))"
   ]
  }
 ],
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