Build Natural Language Processing (NLP) model with Python| TF-IDF, N-gram, Text processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "20e13fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da334efb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Text</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nature, in the broadest sense, is the natural...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"Nature\" can refer to the phenomena of the phy...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The study of nature is a large, if not the onl...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Although humans are part of nature, human acti...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1] The word nature is borrowed from the Old F...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text Language\n",
       "0   Nature, in the broadest sense, is the natural...  English\n",
       "1  \"Nature\" can refer to the phenomena of the phy...  English\n",
       "2  The study of nature is a large, if not the onl...  English\n",
       "3  Although humans are part of nature, human acti...  English\n",
       "4  [1] The word nature is borrowed from the Old F...  English"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Language Detection.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f799ac97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3bd1fa05",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_pun(text):\n",
    "    for pun in string.punctuation:\n",
    "        text = text.replace(pun ,\"\")\n",
    "    text = text.lower()\n",
    "    return(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a6fdd86",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Text'] = df['Text'].apply(remove_pun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad550191",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cce47f4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         nature in the broadest sense is the natural p...\n",
      "1        nature can refer to the phenomena of the physi...\n",
      "2        the study of nature is a large if not the only...\n",
      "3        although humans are part of nature human activ...\n",
      "4        1 the word nature is borrowed from the old fre...\n",
      "                               ...                        \n",
      "10332    ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...\n",
      "10333    ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...\n",
      "10334    ಹೇಗೆ  ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎಲ...\n",
      "10335    ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...\n",
      "10336    ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...\n",
      "Name: Text, Length: 10337, dtype: object\n",
      "0        English\n",
      "1        English\n",
      "2        English\n",
      "3        English\n",
      "4        English\n",
      "          ...   \n",
      "10332    Kannada\n",
      "10333    Kannada\n",
      "10334    Kannada\n",
      "10335    Kannada\n",
      "10336    Kannada\n",
      "Name: Language, Length: 10337, dtype: object\n"
     ]
    }
   ],
   "source": [
    "X = df.iloc[:,0]\n",
    "Y = df.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d0f30e12",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c4c776e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import feature_extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "790dca63",
   "metadata": {},
   "outputs": [],
   "source": [
    "vec = feature_extraction.text.TfidfVectorizer(ngram_range=(1,2),analyzer='char')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9f1040af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import pipeline\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "27595b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pipe = pipeline.Pipeline([('vec',vec), ('clf',linear_model.LogisticRegression())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "153f7faf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vec', TfidfVectorizer(analyzer='char', ngram_range=(1, 2))),\n",
       "                ('clf', LogisticRegression())])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_pipe.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a889e716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Arabic', 'Danish', 'Dutch', 'English', 'French', 'German',\n",
       "       'Greek', 'Hindi', 'Italian', 'Kannada', 'Malayalam', 'Portugeese',\n",
       "       'Russian', 'Spanish', 'Sweedish', 'Tamil', 'Turkish'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_pipe.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "614f7bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_val = model_pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4b7876df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "084ecc49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.63056092843327"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(Y_test, predict_val) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1660a89b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['English'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_pipe.predict(['My name is Shubham'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ebdfd6a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['German'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_pipe.predict(['Mine name ist Shubham'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "109e532a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cf2779d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_file = open('language_detection_model.pckl', 'wb')\n",
    "pickle.dump(model_pipe , new_file)\n",
    "new_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "50030ee5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e1d5d83",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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