Bangla Text Classification With Logistic Regression & Multinomial Naive Bayes.ipynb

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     "text": [
      "/kaggle/input/bangla-stopwords/stopwords_bangla.xlsx\n",
      "/kaggle/input/banglamct7-bangla-multiclass-text-dataset-7-tags/BanglaMCT7/train.csv\n",
      "/kaggle/input/banglamct7-bangla-multiclass-text-dataset-7-tags/BanglaMCT7/test.csv\n",
      "/kaggle/working/__notebook__.ipynb\n"
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   "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\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 read-only \"../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",
    "for dirname, _, filenames in os.walk('/kaggle/working'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
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    },
    "tags": []
   },
   "source": [
    "# **Import Libraries**"
   ]
  },
  {
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   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import h5py\n",
    "import csv\n",
    "import math\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import read_excel\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM,Dense,Embedding,SpatialDropout1D,Flatten,Bidirectional\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras import regularizers\n",
    "#from bnltk.stemmer import BanglaStemmer\n",
    "#from bangla_stemmer.stemmer import stemmer\n",
    "from gensim.models import Word2Vec\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report \n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from scipy.sparse import hstack\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "import seaborn as sns\n",
    "import eli5\n",
    "#ç\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from re import sub\n",
    "import multiprocessing\n",
    "from unidecode import unidecode\n",
    "import os\n",
    "import glob\n",
    "from gensim.models.phrases import Phrases, Phraser\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.test.utils import get_tmpfile\n",
    "from gensim.models import KeyedVectors\n",
    "from gensim.models import Word2Vec\n",
    "from time import time \n",
    "from collections import defaultdict\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
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   "id": "027b1d27",
   "metadata": {
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     "exception": false,
     "start_time": "2021-10-19T14:03:16.409037",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Updating Libraries**"
   ]
  },
  {
   "cell_type": "code",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xlrd\r\n",
      "  Downloading xlrd-2.0.1-py2.py3-none-any.whl (96 kB)\r\n",
      "\u001b[K     |████████████████████████████████| 96 kB 979 kB/s \r\n",
      "\u001b[?25hInstalling collected packages: xlrd\r\n",
      "Successfully installed xlrd-2.0.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Collecting openpyxl\r\n",
      "  Downloading openpyxl-3.0.9-py2.py3-none-any.whl (242 kB)\r\n",
      "\u001b[K     |████████████████████████████████| 242 kB 889 kB/s \r\n",
      "\u001b[?25hCollecting et-xmlfile\r\n",
      "  Downloading et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)\r\n",
      "Installing collected packages: et-xmlfile, openpyxl\r\n",
      "Successfully installed et-xmlfile-1.1.0 openpyxl-3.0.9\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (0.23.2)\r\n",
      "Collecting scikit-learn\r\n",
      "  Downloading scikit_learn-1.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (23.1 MB)\r\n",
      "\u001b[K     |████████████████████████████████| 23.1 MB 786 kB/s \r\n",
      "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn) (1.0.1)\r\n",
      "Requirement already satisfied: scipy>=1.1.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn) (1.7.1)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn) (2.2.0)\r\n",
      "Requirement already satisfied: numpy>=1.14.6 in /opt/conda/lib/python3.7/site-packages (from scikit-learn) (1.19.5)\r\n",
      "Installing collected packages: scikit-learn\r\n",
      "  Attempting uninstall: scikit-learn\r\n",
      "    Found existing installation: scikit-learn 0.23.2\r\n",
      "    Uninstalling scikit-learn-0.23.2:\r\n",
      "      Successfully uninstalled scikit-learn-0.23.2\r\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.4.3 which is incompatible.\r\n",
      "hypertools 0.7.0 requires scikit-learn!=0.22,<0.24,>=0.19.1, but you have scikit-learn 1.0 which is incompatible.\u001b[0m\r\n",
      "Successfully installed scikit-learn-1.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install xlrd\n",
    "!pip install openpyxl\n",
    "!pip install -U scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b384aef",
   "metadata": {
    "papermill": {
     "duration": 0.049078,
     "end_time": "2021-10-19T14:03:50.702449",
     "exception": false,
     "start_time": "2021-10-19T14:03:50.653371",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Load Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3df9a10e",
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     "exception": false,
     "start_time": "2021-10-19T14:03:50.751530",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "      <th>category</th>\n",
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       "      <th>0</th>\n",
       "      <td>economy</td>\n",
       "      <td>বাংলাদেশে সফররত বিশ্ব বাণিজ্য সংস্থা (ডব্লিউট...</td>\n",
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       "      <th>1</th>\n",
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       "      <th>4</th>\n",
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       "      <td>‘ছাই থেকে ছাই, ধুলো থেকে ধুলো...’ আধ্যাত্মিক ক...</td>\n",
       "      <td>ছাই    ছাই    ধুলো    ধুলো    আধ্যাত্মিক    ...</td>\n",
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       "      <th>98879</th>\n",
       "      <td>entertainment</td>\n",
       "      <td>অক্টোবরের ১১ তারিখে ৭৫ বছর পূর্ণ করবেন অমিতাভ ...</td>\n",
       "      <td>অক্টোবরের    তারিখে    বছর    পূর্ণ    অমিতা...</td>\n",
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       "    <tr>\n",
       "      <th>98880</th>\n",
       "      <td>technology</td>\n",
       "      <td>কাজী নাঈম আল রশিদ। সফটওয়্যার প্রকৌশলী। কাজ করে...</td>\n",
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       "            category                                               text  \\\n",
       "0            economy   বাংলাদেশে সফররত বিশ্ব বাণিজ্য সংস্থা (ডব্লিউট...   \n",
       "1             sports  ইউরোপিয়ান ফুটবলে আজ ঝড় বয়ে যাচ্ছে। একদিকে সংবা...   \n",
       "2      international  বিজেপির নেতৃত্বে ভারতের উত্তর প্রদেশ থেকে শুরু...   \n",
       "3              state  নারায়ণগঞ্জের সোনারগাঁ উপজেলার একটি সরকারি প্র...   \n",
       "4             sports  ‘ছাই থেকে ছাই, ধুলো থেকে ধুলো...’ আধ্যাত্মিক ক...   \n",
       "...              ...                                                ...   \n",
       "98878      education    সহকারী অধ্যাপক  টিচার্স ট্রেনিং কলেজ (মহিলা)...   \n",
       "98879  entertainment  অক্টোবরের ১১ তারিখে ৭৫ বছর পূর্ণ করবেন অমিতাভ ...   \n",
       "98880     technology  কাজী নাঈম আল রশিদ। সফটওয়্যার প্রকৌশলী। কাজ করে...   \n",
       "98881     technology  ইয়াহুকে মার্কিন টেলিকম প্রতিষ্ঠান ভেরাইজন কিনে...   \n",
       "98882      education  অধ্যায়-১১প্রিয় শিক্ষার্থী, আজ ফিন্যান্স ও ব্যা...   \n",
       "\n",
       "                                               cleanText  \n",
       "0        বাংলাদেশে    সফররত    বিশ্ব    বাণিজ্য    সং...  \n",
       "1        ইউরোপিয়ান    ফুটবলে    বয়ে    একদিকে    সংবা...  \n",
       "2        বিজেপির    নেতৃত্বে    ভারতের    উত্তর    প্...  \n",
       "3        নারায়ণগঞ্জের    সোনারগাঁ    উপজেলার    সরকা...  \n",
       "4        ছাই    ছাই    ধুলো    ধুলো    আধ্যাত্মিক    ...  \n",
       "...                                                  ...  \n",
       "98878    সহকারী    অধ্যাপক    টিচার্স    ট্রেনিং    ক...  \n",
       "98879    অক্টোবরের    তারিখে    বছর    পূর্ণ    অমিতা...  \n",
       "98880    কাজী    নাঈম    রশিদ    সফটওয়্যার    প্রকৌশল...  \n",
       "98881    ইয়াহুকে    মার্কিন    টেলিকম    প্রতিষ্ঠান  ...  \n",
       "98882    অধ্যায়    প্রিয়    শিক্ষার্থী    ফিন্যান্স  ...  \n",
       "\n",
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       "      <th>98880</th>\n",
       "      <td>entertainment</td>\n",
       "      <td>গানের দল সহজিয়ার প্রথম অ্যালবাম ‘রঙমিস্ত্রী’র ...</td>\n",
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       "            category                                               text  \\\n",
       "0          education  অধ্যায়-২প্রিয় পরীক্ষার্থী, আজ তথ্য ও যোগাযোগ প...   \n",
       "1             sports  স্পিন, স্পিন, স্পিন। লর্ডসে ইয়াসির শাহ্‌র লেগ ...   \n",
       "2      international  সিরিয়ার উত্তর-পশ্চিমাঞ্চলে গতকাল রোববার বিমান...   \n",
       "3      international  দক্ষিণ আফ্রিকার কেপটাউন বিশ্ববিদ্যালয়ের (ইউসিট...   \n",
       "4         technology  সহজে ডিজিটাল আর্থিক সেবা দিতে অ্যাপ ও ডিজিটাল ...   \n",
       "...              ...                                                ...   \n",
       "98879         sports  ১৯৫৯ সালে ইউরোপিয়ান ক্লাব কাপে জাতীয় দল পাঠান...   \n",
       "98880  entertainment  গানের দল সহজিয়ার প্রথম অ্যালবাম ‘রঙমিস্ত্রী’র ...   \n",
       "98881  international  ফেসবুকের ৮১ হাজার ব্যবহারকারীর তথ্য চুরি করে স...   \n",
       "98882        economy   পেট্রোল পাম্পে তেল বা সিএনজির বিল বিকাশের মাধ...   \n",
       "98883        economy  বছর শেষে ঢাকা স্টক এক্সচেঞ্জের (ডিএসই) দুটি সূ...   \n",
       "\n",
       "                                               cleanText  \n",
       "0        অধ্যায়    প্রিয়    পরীক্ষার্থী    তথ্য    যো...  \n",
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       "2        সিরিয়ার    উত্তর    পশ্চিমাঞ্চলে    গতকাল  ...  \n",
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       "4        সহজে    ডিজিটাল    আর্থিক    সেবা    অ্যাপ  ...  \n",
       "...                                                  ...  \n",
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       "98883    বছর    শেষে    ঢাকা    স্টক    এক্সচেঞ্জের  ...  \n",
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   "source": [
    "df_train=pd.read_csv(\"/kaggle/input/banglamct7-bangla-multiclass-text-dataset-7-tags/BanglaMCT7/train.csv\")\n",
    "df_test=pd.read_csv(\"/kaggle/input/banglamct7-bangla-multiclass-text-dataset-7-tags/BanglaMCT7/test.csv\")\n",
    "\n",
    "display(df_train)\n",
    "display(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "716529c1",
   "metadata": {
    "papermill": {
     "duration": 0.051036,
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     "exception": false,
     "start_time": "2021-10-19T14:04:27.705785",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Train/Test Split**\n",
    "**We have seen on EDA that,Train and Test set are almost equal in size and classwise ratio is identical. So to build a robust model i have used full train set for training the model and test set for testing. So test and train ratio can be called 50-50.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad77b383",
   "metadata": {
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     "duration": 0.050838,
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     "start_time": "2021-10-19T14:04:27.807775",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "affd3d4b",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def text_to_word_list(text):\n",
    "    text = text.split()\n",
    "    return text\n",
    "\n",
    "def replace_strings(text):\n",
    "    emoji_pattern = re.compile(\"[\"\n",
    "                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "                           u\"\\U00002702-\\U000027B0\"\n",
    "                           u\"\\U000024C2-\\U0001F251\"\n",
    "                           u\"\\u00C0-\\u017F\"          #latin\n",
    "                           u\"\\u2000-\\u206F\"          #generalPunctuations\n",
    "                               \n",
    "                           \"]+\", flags=re.UNICODE)\n",
    "    english_pattern=re.compile('[a-zA-Z0-9]+', flags=re.I)\n",
    "    #latin_pattern=re.compile('[A-Za-z\\u00C0-\\u00D6\\u00D8-\\u00f6\\u00f8-\\u00ff\\s]*',)\n",
    "    \n",
    "    text=emoji_pattern.sub(r'', text)\n",
    "    text=english_pattern.sub(r'', text)\n",
    "\n",
    "    return text\n",
    "\n",
    "def remove_punctuations(my_str):\n",
    "    # define punctuation\n",
    "    punctuations = '''```\u0012\u0010\u0002\b`\u0007\b£|¢|\u0007Ñ+-*/=EROero৳০১২৩৪৫৬৭৮৯012–34567•89।!()-[]{};:'\"“\\’,<>./?@#$%^&*_~‘—॥”‰🤣⚽️✌�￰৷￰'''\n",
    "    \n",
    "    no_punct = \"\"\n",
    "    for char in my_str:\n",
    "        if char not in punctuations:\n",
    "            no_punct = no_punct + char\n",
    "\n",
    "    # display the unpunctuated string\n",
    "    return no_punct\n",
    "\n",
    "\n",
    "\n",
    "def joining(text):\n",
    "    out=' '.join(text)\n",
    "    return out\n",
    "\n",
    "def preprocessing(text):\n",
    "    out=remove_punctuations(replace_strings(text))\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ea5f066",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:04:28.083809Z",
     "iopub.status.busy": "2021-10-19T14:04:28.083057Z",
     "iopub.status.idle": "2021-10-19T14:04:28.085267Z",
     "shell.execute_reply": "2021-10-19T14:04:28.085879Z",
     "shell.execute_reply.started": "2021-10-19T13:50:33.129409Z"
    },
    "papermill": {
     "duration": 0.058331,
     "end_time": "2021-10-19T14:04:28.086053",
     "exception": false,
     "start_time": "2021-10-19T14:04:28.027722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df['cleanText'] = df.cleanText.apply(lambda x: preprocessing(str(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e0632a4",
   "metadata": {
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     "duration": 0.050745,
     "end_time": "2021-10-19T14:04:28.187943",
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     "start_time": "2021-10-19T14:04:28.137198",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Stopwords Removal**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b43d8073",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-10-19T13:50:33.147114Z"
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     "duration": 0.373277,
     "end_time": "2021-10-19T14:04:28.613549",
     "exception": false,
     "start_time": "2021-10-19T14:04:28.240272",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>অথচ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>727</td>\n",
       "      <td>হয়েই</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>728</td>\n",
       "      <td>হয়েছিল</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>729</td>\n",
       "      <td>হয়েছে</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>730</td>\n",
       "      <td>হয়েছেন</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>731</td>\n",
       "      <td>অংশ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>732 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0   words\n",
       "0             0      অই\n",
       "1             1  অগত্যা\n",
       "2             2  অত: পর\n",
       "3             3    অতএব\n",
       "4             4     অথচ\n",
       "..          ...     ...\n",
       "727         727    হয়েই\n",
       "728         728  হয়েছিল\n",
       "729         729   হয়েছে\n",
       "730         730  হয়েছেন\n",
       "731         731     অংশ\n",
       "\n",
       "[732 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1 =pd.read_excel('/kaggle/input/bangla-stopwords/stopwords_bangla.xlsx')\n",
    "display(data1)\n",
    "stop = data1['words'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f15afbd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:04:28.726738Z",
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     "shell.execute_reply": "2021-10-19T14:04:28.728686Z",
     "shell.execute_reply.started": "2021-10-19T13:50:33.502535Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def stopwordRemoval(text):    \n",
    "    x=str(text)\n",
    "    l=x.split()\n",
    "\n",
    "    stm=[elem for elem in l if elem not in stop]\n",
    "    \n",
    "    out=' '.join(stm)\n",
    "    \n",
    "    return str(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2cf7dd48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:04:28.834818Z",
     "iopub.status.busy": "2021-10-19T14:04:28.834165Z",
     "iopub.status.idle": "2021-10-19T14:10:07.235686Z",
     "shell.execute_reply": "2021-10-19T14:10:07.235048Z",
     "shell.execute_reply.started": "2021-10-19T13:50:33.511766Z"
    },
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     "exception": false,
     "start_time": "2021-10-19T14:04:28.780316",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train['cleanText'] = df_train.cleanText.apply(lambda x: stopwordRemoval(str(x)))\n",
    "df_test['cleanText'] = df_test.cleanText.apply(lambda x: stopwordRemoval(str(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5131a15",
   "metadata": {
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     "exception": false,
     "start_time": "2021-10-19T14:10:07.289050",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Stemming**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4b8bab8",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2021-10-19T14:10:07.393794",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bangla-stemmer\r\n",
      "  Downloading bangla_stemmer-1.0-py3-none-any.whl (9.1 kB)\r\n",
      "Installing collected packages: bangla-stemmer\r\n",
      "Successfully installed bangla-stemmer-1.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "#make sure to turn on internet on your kernel\n",
    "#importing stemmer\n",
    "!pip install bangla-stemmer\n",
    "from bangla_stemmer.stemmer import stemmer\n",
    "## stemmer function\n",
    "def stem_text (x):\n",
    "  stmr = stemmer.BanglaStemmer()\n",
    "  words=x.split(' ')\n",
    "  stm = stmr.stem(words)\n",
    "  words=(' ').join(stm)\n",
    "  return words"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4fdb73a",
   "metadata": {
    "papermill": {
     "duration": 0.05321,
     "end_time": "2021-10-19T14:10:16.671476",
     "exception": false,
     "start_time": "2021-10-19T14:10:16.618266",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**I have noticed stemming doesn't improve accuracy in this dataset, so i have commented it out.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "afc7eb0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:10:16.782100Z",
     "iopub.status.busy": "2021-10-19T14:10:16.781072Z",
     "iopub.status.idle": "2021-10-19T14:10:16.784034Z",
     "shell.execute_reply": "2021-10-19T14:10:16.783500Z",
     "shell.execute_reply.started": "2021-10-19T13:56:19.485225Z"
    },
    "papermill": {
     "duration": 0.06013,
     "end_time": "2021-10-19T14:10:16.784211",
     "exception": false,
     "start_time": "2021-10-19T14:10:16.724081",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df_train['cleanText']=df_train['cleanText'].apply(stem_text)\n",
    "#df_test['cleanText']=df_test['cleanText'].apply(stem_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1979879",
   "metadata": {
    "papermill": {
     "duration": 0.06003,
     "end_time": "2021-10-19T14:10:16.899433",
     "exception": false,
     "start_time": "2021-10-19T14:10:16.839403",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Changing Labels to Numbers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c00774a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:10:17.026005Z",
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     "iopub.status.idle": "2021-10-19T14:10:17.133632Z",
     "shell.execute_reply": "2021-10-19T14:10:17.132958Z",
     "shell.execute_reply.started": "2021-10-19T13:56:19.493887Z"
    },
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     "exception": false,
     "start_time": "2021-10-19T14:10:16.952904",
     "status": "completed"
    },
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   "outputs": [
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       "      category                                               text  \\\n",
       "0            0   বাংলাদেশে সফররত বিশ্ব বাণিজ্য সংস্থা (ডব্লিউট...   \n",
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       "3            3  নারায়ণগঞ্জের সোনারগাঁ উপজেলার একটি সরকারি প্র...   \n",
       "4            1  ‘ছাই থেকে ছাই, ধুলো থেকে ধুলো...’ আধ্যাত্মিক ক...   \n",
       "...        ...                                                ...   \n",
       "98878        6    সহকারী অধ্যাপক  টিচার্স ট্রেনিং কলেজ (মহিলা)...   \n",
       "98879        5  অক্টোবরের ১১ তারিখে ৭৫ বছর পূর্ণ করবেন অমিতাভ ...   \n",
       "98880        4  কাজী নাঈম আল রশিদ। সফটওয়্যার প্রকৌশলী। কাজ করে...   \n",
       "98881        4  ইয়াহুকে মার্কিন টেলিকম প্রতিষ্ঠান ভেরাইজন কিনে...   \n",
       "98882        6  অধ্যায়-১১প্রিয় শিক্ষার্থী, আজ ফিন্যান্স ও ব্যা...   \n",
       "\n",
       "                                               cleanText  \n",
       "0      বাংলাদেশে সফররত বাণিজ্য সংস্থা ডব্লিউটিও মহাপর...  \n",
       "1      ইউরোপিয়ান ফুটবলে বয়ে একদিকে সংবাদমাধ্যমগুলো নে...  \n",
       "2      বিজেপির নেতৃত্বে ভারতের প্রদেশ ঐতিহাসিক স্থানে...  \n",
       "3      নারায়ণগঞ্জের সোনারগাঁ উপজেলার সরকারি বিদ্যালয...  \n",
       "4      ছাই ছাই ধুলো ধুলো আধ্যাত্মিক বাক্যের অর্থটা সে...  \n",
       "...                                                  ...  \n",
       "98878  সহকারী অধ্যাপক টিচার্স ট্রেনিং কলেজ মহিলা ময়মন...  \n",
       "98879  অক্টোবরের তারিখে পূর্ণ অমিতাভ বচ্চন জন্মদিন এক...  \n",
       "98880  কাজী নাঈম রশিদ সফটওয়্যার প্রকৌশলী মাইক্রোসফটে ...  \n",
       "98881  ইয়াহুকে মার্কিন টেলিকম প্রতিষ্ঠান ভেরাইজন কিনে...  \n",
       "98882  অধ্যায় প্রিয় শিক্ষার্থী ফিন্যান্স ব্যাংকিং বহু...  \n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>অধ্যায়-২প্রিয় পরীক্ষার্থী, আজ তথ্য ও যোগাযোগ প...</td>\n",
       "      <td>অধ্যায় প্রিয় পরীক্ষার্থী তথ্য যোগাযোগ প্রযুক্ত...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>স্পিন, স্পিন, স্পিন। লর্ডসে ইয়াসির শাহ্‌র লেগ ...</td>\n",
       "      <td>স্পিন স্পিন স্পিন লর্ডসে ইয়াসির শাহ্ র লেগ স্প...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>সিরিয়ার উত্তর-পশ্চিমাঞ্চলে গতকাল রোববার বিমান...</td>\n",
       "      <td>সিরিয়ার পশ্চিমাঞ্চলে গতকাল রোববার বিমান হামলা...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>দক্ষিণ আফ্রিকার কেপটাউন বিশ্ববিদ্যালয়ের (ইউসিট...</td>\n",
       "      <td>দক্ষিণ আফ্রিকার কেপটাউন বিশ্ববিদ্যালয়ের ইউসিটি...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>সহজে ডিজিটাল আর্থিক সেবা দিতে অ্যাপ ও ডিজিটাল ...</td>\n",
       "      <td>সহজে ডিজিটাল আর্থিক সেবা অ্যাপ ডিজিটাল কেওয়াইস...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98879</th>\n",
       "      <td>1</td>\n",
       "      <td>১৯৫৯ সালে ইউরোপিয়ান ক্লাব কাপে জাতীয় দল পাঠান...</td>\n",
       "      <td>সালে ইউরোপিয়ান ক্লাব কাপে জাতীয় পাঠানোর প্রস...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98880</th>\n",
       "      <td>5</td>\n",
       "      <td>গানের দল সহজিয়ার প্রথম অ্যালবাম ‘রঙমিস্ত্রী’র ...</td>\n",
       "      <td>গানের সহজিয়ার অ্যালবাম রঙমিস্ত্রী প্রকাশনা উত্...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98881</th>\n",
       "      <td>2</td>\n",
       "      <td>ফেসবুকের ৮১ হাজার ব্যবহারকারীর তথ্য চুরি করে স...</td>\n",
       "      <td>ফেসবুকের ব্যবহারকারীর তথ্য চুরি বিক্রির বিজ্ঞা...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98882</th>\n",
       "      <td>0</td>\n",
       "      <td>পেট্রোল পাম্পে তেল বা সিএনজির বিল বিকাশের মাধ...</td>\n",
       "      <td>পেট্রোল পাম্পে তেল সিএনজির বিল বিকাশের পরিশোধ ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98883</th>\n",
       "      <td>0</td>\n",
       "      <td>বছর শেষে ঢাকা স্টক এক্সচেঞ্জের (ডিএসই) দুটি সূ...</td>\n",
       "      <td>শেষে ঢাকা স্টক এক্সচেঞ্জের ডিএসই পুনঃসমন্বয় দু...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98884 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      category                                               text  \\\n",
       "0            6  অধ্যায়-২প্রিয় পরীক্ষার্থী, আজ তথ্য ও যোগাযোগ প...   \n",
       "1            1  স্পিন, স্পিন, স্পিন। লর্ডসে ইয়াসির শাহ্‌র লেগ ...   \n",
       "2            2  সিরিয়ার উত্তর-পশ্চিমাঞ্চলে গতকাল রোববার বিমান...   \n",
       "3            2  দক্ষিণ আফ্রিকার কেপটাউন বিশ্ববিদ্যালয়ের (ইউসিট...   \n",
       "4            4  সহজে ডিজিটাল আর্থিক সেবা দিতে অ্যাপ ও ডিজিটাল ...   \n",
       "...        ...                                                ...   \n",
       "98879        1  ১৯৫৯ সালে ইউরোপিয়ান ক্লাব কাপে জাতীয় দল পাঠান...   \n",
       "98880        5  গানের দল সহজিয়ার প্রথম অ্যালবাম ‘রঙমিস্ত্রী’র ...   \n",
       "98881        2  ফেসবুকের ৮১ হাজার ব্যবহারকারীর তথ্য চুরি করে স...   \n",
       "98882        0   পেট্রোল পাম্পে তেল বা সিএনজির বিল বিকাশের মাধ...   \n",
       "98883        0  বছর শেষে ঢাকা স্টক এক্সচেঞ্জের (ডিএসই) দুটি সূ...   \n",
       "\n",
       "                                               cleanText  \n",
       "0      অধ্যায় প্রিয় পরীক্ষার্থী তথ্য যোগাযোগ প্রযুক্ত...  \n",
       "1      স্পিন স্পিন স্পিন লর্ডসে ইয়াসির শাহ্ র লেগ স্প...  \n",
       "2      সিরিয়ার পশ্চিমাঞ্চলে গতকাল রোববার বিমান হামলা...  \n",
       "3      দক্ষিণ আফ্রিকার কেপটাউন বিশ্ববিদ্যালয়ের ইউসিটি...  \n",
       "4      সহজে ডিজিটাল আর্থিক সেবা অ্যাপ ডিজিটাল কেওয়াইস...  \n",
       "...                                                  ...  \n",
       "98879  সালে ইউরোপিয়ান ক্লাব কাপে জাতীয় পাঠানোর প্রস...  \n",
       "98880  গানের সহজিয়ার অ্যালবাম রঙমিস্ত্রী প্রকাশনা উত্...  \n",
       "98881  ফেসবুকের ব্যবহারকারীর তথ্য চুরি বিক্রির বিজ্ঞা...  \n",
       "98882  পেট্রোল পাম্পে তেল সিএনজির বিল বিকাশের পরিশোধ ...  \n",
       "98883  শেষে ঢাকা স্টক এক্সচেঞ্জের ডিএসই পুনঃসমন্বয় দু...  \n",
       "\n",
       "[98884 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train[\"category\"].replace({\"economy\": \"0\", \"sports\": \"1\",\"international\": \"2\",\"state\": \"3\",\"technology\": \"4\",\"entertainment\": \"5\",\"education\": \"6\"}, inplace=True)\n",
    "df_test[\"category\"].replace({\"economy\": \"0\", \"sports\": \"1\",\"international\": \"2\",\"state\": \"3\",\"technology\": \"4\",\"entertainment\": \"5\",\"education\": \"6\"}, inplace=True)\n",
    "display(df_train)\n",
    "display(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d45348f8",
   "metadata": {
    "papermill": {
     "duration": 0.054237,
     "end_time": "2021-10-19T14:10:17.243510",
     "exception": false,
     "start_time": "2021-10-19T14:10:17.189273",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Removing Null values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e6adf07c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:10:17.580426Z",
     "iopub.status.busy": "2021-10-19T14:10:17.579708Z",
     "iopub.status.idle": "2021-10-19T14:10:17.794860Z",
     "shell.execute_reply": "2021-10-19T14:10:17.795393Z",
     "shell.execute_reply.started": "2021-10-19T13:56:19.627600Z"
    },
    "papermill": {
     "duration": 0.497666,
     "end_time": "2021-10-19T14:10:17.795592",
     "exception": false,
     "start_time": "2021-10-19T14:10:17.297926",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#display non-null values\n",
    "df_train = df_train.dropna()\n",
    "df_test = df_test.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a518692b",
   "metadata": {
    "papermill": {
     "duration": 0.064509,
     "end_time": "2021-10-19T14:10:17.915557",
     "exception": false,
     "start_time": "2021-10-19T14:10:17.851048",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Removing Small Texts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c053675",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:10:18.096551Z",
     "iopub.status.busy": "2021-10-19T14:10:18.057111Z",
     "iopub.status.idle": "2021-10-19T14:10:26.194269Z",
     "shell.execute_reply": "2021-10-19T14:10:26.193584Z",
     "shell.execute_reply.started": "2021-10-19T13:56:20.109198Z"
    },
    "papermill": {
     "duration": 8.219598,
     "end_time": "2021-10-19T14:10:26.194417",
     "exception": false,
     "start_time": "2021-10-19T14:10:17.974819",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#counting text length\n",
    "df_train['count'] = df_train['cleanText'].str.split().str.len()\n",
    "df_test['count'] = df_test['cleanText'].str.split().str.len()\n",
    "# Remove the text with words less than 5\n",
    "df_train= df_train.loc[df_train['count']>5]\n",
    "df_test= df_test.loc[df_test['count']>5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65c7a7d0",
   "metadata": {
    "papermill": {
     "duration": 0.054319,
     "end_time": "2021-10-19T14:10:26.303659",
     "exception": false,
     "start_time": "2021-10-19T14:10:26.249340",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Feature Extraction with TFIDF**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ea6cb6",
   "metadata": {
    "papermill": {
     "duration": 0.054269,
     "end_time": "2021-10-19T14:10:26.412200",
     "exception": false,
     "start_time": "2021-10-19T14:10:26.357931",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**I have tried to tune parameters for max_features and 10000 seems to provide better result**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce36b995",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:10:26.574359Z",
     "iopub.status.busy": "2021-10-19T14:10:26.558547Z",
     "iopub.status.idle": "2021-10-19T14:13:46.333110Z",
     "shell.execute_reply": "2021-10-19T14:13:46.331841Z",
     "shell.execute_reply.started": "2021-10-19T13:56:29.749391Z"
    },
    "papermill": {
     "duration": 199.866162,
     "end_time": "2021-10-19T14:13:46.333528",
     "exception": false,
     "start_time": "2021-10-19T14:10:26.467366",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#max features 5000,10000,50000,100000\n",
    "text_transformer = TfidfVectorizer(ngram_range=(1, 3),lowercase=True, max_features=10000)\n",
    "#%%time\n",
    "X_train_text = text_transformer.fit_transform(df_train['cleanText'].values)\n",
    "X_test_text = text_transformer.transform(df_test['cleanText'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74207536",
   "metadata": {
    "papermill": {
     "duration": 0.055758,
     "end_time": "2021-10-19T14:13:46.449446",
     "exception": false,
     "start_time": "2021-10-19T14:13:46.393688",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Model Creation of Logistic Regression**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6031a7a7",
   "metadata": {
    "papermill": {
     "duration": 0.054289,
     "end_time": "2021-10-19T14:13:46.558713",
     "exception": false,
     "start_time": "2021-10-19T14:13:46.504424",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**All the parameters are optimized by grid search.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "34d2f2ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:13:46.673288Z",
     "iopub.status.busy": "2021-10-19T14:13:46.672585Z",
     "iopub.status.idle": "2021-10-19T14:13:46.677902Z",
     "shell.execute_reply": "2021-10-19T14:13:46.678480Z",
     "shell.execute_reply.started": "2021-10-19T13:59:58.316172Z"
    },
    "papermill": {
     "duration": 0.065256,
     "end_time": "2021-10-19T14:13:46.678650",
     "exception": false,
     "start_time": "2021-10-19T14:13:46.613394",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#C_param_range = [0.001,0.01,0.1,1,10,100]\n",
    "#saga is proved best for large dataset\n",
    "logit = LogisticRegression(C=.95,penalty='l2',solver='saga', multi_class='multinomial', random_state=17, n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5ac8f7b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:13:46.791793Z",
     "iopub.status.busy": "2021-10-19T14:13:46.791026Z",
     "iopub.status.idle": "2021-10-19T14:14:00.390370Z",
     "shell.execute_reply": "2021-10-19T14:14:00.389694Z",
     "shell.execute_reply.started": "2021-10-19T13:59:58.323964Z"
    },
    "papermill": {
     "duration": 13.656772,
     "end_time": "2021-10-19T14:14:00.390544",
     "exception": false,
     "start_time": "2021-10-19T14:13:46.733772",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model=logit.fit(X_train_text, df_train['category'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5162febb",
   "metadata": {
    "papermill": {
     "duration": 0.054201,
     "end_time": "2021-10-19T14:14:00.499196",
     "exception": false,
     "start_time": "2021-10-19T14:14:00.444995",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Model Creation of Multinomial Naive Bayes**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3583bb",
   "metadata": {
    "papermill": {
     "duration": 0.054429,
     "end_time": "2021-10-19T14:14:00.607975",
     "exception": false,
     "start_time": "2021-10-19T14:14:00.553546",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**All the parameters are optimized by grid search.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9b9c4931",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:14:00.722634Z",
     "iopub.status.busy": "2021-10-19T14:14:00.721909Z",
     "iopub.status.idle": "2021-10-19T14:14:00.723919Z",
     "shell.execute_reply": "2021-10-19T14:14:00.723307Z",
     "shell.execute_reply.started": "2021-10-19T14:00:11.502144Z"
    },
    "papermill": {
     "duration": 0.061692,
     "end_time": "2021-10-19T14:14:00.724056",
     "exception": false,
     "start_time": "2021-10-19T14:14:00.662364",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#0.5, 1.5, 6\n",
    "nb=MultinomialNB(alpha=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9a591233",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:14:00.838348Z",
     "iopub.status.busy": "2021-10-19T14:14:00.837653Z",
     "iopub.status.idle": "2021-10-19T14:14:01.514541Z",
     "shell.execute_reply": "2021-10-19T14:14:01.515113Z",
     "shell.execute_reply.started": "2021-10-19T14:00:11.508704Z"
    },
    "papermill": {
     "duration": 0.736028,
     "end_time": "2021-10-19T14:14:01.515314",
     "exception": false,
     "start_time": "2021-10-19T14:14:00.779286",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model=nb.fit(X_train_text, df_train['category'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13288ccd",
   "metadata": {
    "papermill": {
     "duration": 0.054564,
     "end_time": "2021-10-19T14:14:01.624411",
     "exception": false,
     "start_time": "2021-10-19T14:14:01.569847",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Logistic Regression Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a9e81302",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:14:01.736494Z",
     "iopub.status.busy": "2021-10-19T14:14:01.735849Z",
     "iopub.status.idle": "2021-10-19T14:14:03.507041Z",
     "shell.execute_reply": "2021-10-19T14:14:03.507735Z",
     "shell.execute_reply.started": "2021-10-19T14:00:12.189519Z"
    },
    "papermill": {
     "duration": 1.828902,
     "end_time": "2021-10-19T14:14:03.507916",
     "exception": false,
     "start_time": "2021-10-19T14:14:01.679014",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8858939893482121\n",
      "Testing Accuracy: 0.9156784174486432\n",
      "Precision :0.915713162947648\n",
      "Recall :0.9156784174486432\n",
      "fscore :0.9156376368750855\n"
     ]
    }
   ],
   "source": [
    "test_preds = logit.predict(X_test_text)\n",
    "test_labels=df_test['category'].values\n",
    "#print(accuracy_score(test_labels,test_preds))\n",
    "test_result=accuracy_score(test_labels,test_preds)\n",
    "\n",
    "precision, recall, fscore, _ = precision_recall_fscore_support(test_labels,test_preds, average='weighted')\n",
    "\n",
    "\n",
    "score = model.score(X_test_text,test_labels)\n",
    "print(score)\n",
    "\n",
    "\n",
    "print(\"Testing Accuracy: \"+str(test_result))\n",
    "\n",
    "print(\"Precision :\"+str(precision))\n",
    "print(\"Recall :\"+str(recall))\n",
    "print(\"fscore :\"+str(fscore))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d31c4535",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:14:03.620964Z",
     "iopub.status.busy": "2021-10-19T14:14:03.620323Z",
     "iopub.status.idle": "2021-10-19T14:14:11.002670Z",
     "shell.execute_reply": "2021-10-19T14:14:11.003210Z",
     "shell.execute_reply.started": "2021-10-19T14:00:13.966089Z"
    },
    "papermill": {
     "duration": 7.440455,
     "end_time": "2021-10-19T14:14:11.003384",
     "exception": false,
     "start_time": "2021-10-19T14:14:03.562929",
     "status": "completed"
    },
    "tags": []
   },
   "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>economy</th>\n",
       "      <td>88.73</td>\n",
       "      <td>89.76</td>\n",
       "      <td>89.25</td>\n",
       "      <td>14479.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sports</th>\n",
       "      <td>97.64</td>\n",
       "      <td>96.34</td>\n",
       "      <td>96.99</td>\n",
       "      <td>15865.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>international</th>\n",
       "      <td>90.74</td>\n",
       "      <td>92.76</td>\n",
       "      <td>91.74</td>\n",
       "      <td>15313.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>89.04</td>\n",
       "      <td>86.97</td>\n",
       "      <td>87.99</td>\n",
       "      <td>15183.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technology</th>\n",
       "      <td>88.51</td>\n",
       "      <td>87.08</td>\n",
       "      <td>87.79</td>\n",
       "      <td>9935.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entertainment</th>\n",
       "      <td>93.57</td>\n",
       "      <td>94.59</td>\n",
       "      <td>94.08</td>\n",
       "      <td>15242.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>91.23</td>\n",
       "      <td>91.61</td>\n",
       "      <td>91.42</td>\n",
       "      <td>12558.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>91.57</td>\n",
       "      <td>91.57</td>\n",
       "      <td>91.57</td>\n",
       "      <td>0.915678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>91.35</td>\n",
       "      <td>91.30</td>\n",
       "      <td>91.32</td>\n",
       "      <td>98575.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>91.57</td>\n",
       "      <td>91.57</td>\n",
       "      <td>91.56</td>\n",
       "      <td>98575.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               precision  recall  f1-score       support\n",
       "economy            88.73   89.76     89.25  14479.000000\n",
       "sports             97.64   96.34     96.99  15865.000000\n",
       "international      90.74   92.76     91.74  15313.000000\n",
       "state              89.04   86.97     87.99  15183.000000\n",
       "technology         88.51   87.08     87.79   9935.000000\n",
       "entertainment      93.57   94.59     94.08  15242.000000\n",
       "education          91.23   91.61     91.42  12558.000000\n",
       "accuracy           91.57   91.57     91.57      0.915678\n",
       "macro avg          91.35   91.30     91.32  98575.000000\n",
       "weighted avg       91.57   91.57     91.56  98575.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix(test_labels,test_preds)\n",
    "plt.figure(figsize=(9,9))\n",
    "cm_df = pd.DataFrame(cm,index = ['economy' ,'sports' ,'international', 'state','technology', 'entertainment', 'education'], \n",
    "                     columns = ['economy' ,'sports' ,'international', 'state','technology', 'entertainment', 'education'])\n",
    "\n",
    "sns.heatmap(cm_df, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score Logistic Regression: {0}'.format(score)\n",
    "plt.title(all_sample_title, size = 15);\n",
    "\n",
    "report = pd.DataFrame(classification_report(y_true = test_labels, y_pred = test_preds, output_dict=True)).transpose()\n",
    "report = report.rename(index={'0': 'economy','1':'sports','2':'international','3':'state','4':'technology','5':'entertainment','6':'education'})\n",
    "report[['precision','recall','f1-score']]=report[['precision','recall','f1-score']].apply(lambda x: round(x*100,2))\n",
    "display(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d52ed2be",
   "metadata": {
    "papermill": {
     "duration": 0.059941,
     "end_time": "2021-10-19T14:14:11.121924",
     "exception": false,
     "start_time": "2021-10-19T14:14:11.061983",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Multinomial Naive Bayes Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1c1d4a96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-19T14:14:11.244880Z",
     "iopub.status.busy": "2021-10-19T14:14:11.243841Z",
     "iopub.status.idle": "2021-10-19T14:14:16.626160Z",
     "shell.execute_reply": "2021-10-19T14:14:16.626682Z",
     "shell.execute_reply.started": "2021-10-19T14:00:21.248822Z"
    },
    "papermill": {
     "duration": 5.44609,
     "end_time": "2021-10-19T14:14:16.626849",
     "exception": false,
     "start_time": "2021-10-19T14:14:11.180759",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8858939893482121\n",
      "Testing Accuracy: 0.8858939893482121\n",
      "Precision :0.8863059851681149\n",
      "Recall :0.8858939893482121\n",
      "fscore :0.8859202000415151\n"
     ]
    },
    {
     "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>economy</th>\n",
       "      <td>86.60</td>\n",
       "      <td>85.93</td>\n",
       "      <td>86.26</td>\n",
       "      <td>14479.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sports</th>\n",
       "      <td>96.72</td>\n",
       "      <td>94.34</td>\n",
       "      <td>95.51</td>\n",
       "      <td>15865.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>international</th>\n",
       "      <td>87.87</td>\n",
       "      <td>90.51</td>\n",
       "      <td>89.17</td>\n",
       "      <td>15313.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>84.24</td>\n",
       "      <td>82.68</td>\n",
       "      <td>83.46</td>\n",
       "      <td>15183.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technology</th>\n",
       "      <td>82.73</td>\n",
       "      <td>86.08</td>\n",
       "      <td>84.37</td>\n",
       "      <td>9935.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entertainment</th>\n",
       "      <td>90.42</td>\n",
       "      <td>93.05</td>\n",
       "      <td>91.72</td>\n",
       "      <td>15242.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>89.47</td>\n",
       "      <td>85.76</td>\n",
       "      <td>87.58</td>\n",
       "      <td>12558.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>88.59</td>\n",
       "      <td>88.59</td>\n",
       "      <td>88.59</td>\n",
       "      <td>0.885894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>88.29</td>\n",
       "      <td>88.34</td>\n",
       "      <td>88.30</td>\n",
       "      <td>98575.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>88.63</td>\n",
       "      <td>88.59</td>\n",
       "      <td>88.59</td>\n",
       "      <td>98575.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               precision  recall  f1-score       support\n",
       "economy            86.60   85.93     86.26  14479.000000\n",
       "sports             96.72   94.34     95.51  15865.000000\n",
       "international      87.87   90.51     89.17  15313.000000\n",
       "state              84.24   82.68     83.46  15183.000000\n",
       "technology         82.73   86.08     84.37   9935.000000\n",
       "entertainment      90.42   93.05     91.72  15242.000000\n",
       "education          89.47   85.76     87.58  12558.000000\n",
       "accuracy           88.59   88.59     88.59      0.885894\n",
       "macro avg          88.29   88.34     88.30  98575.000000\n",
       "weighted avg       88.63   88.59     88.59  98575.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_preds = nb.predict(X_test_text)\n",
    "test_labels=df_test['category'].values\n",
    "#print(accuracy_score(test_labels,test_preds))\n",
    "test_result=accuracy_score(test_labels,test_preds)\n",
    "\n",
    "precision, recall, fscore, _ = precision_recall_fscore_support(test_labels,test_preds, average='weighted')\n",
    "\n",
    "\n",
    "score = model.score(X_test_text,test_labels)\n",
    "print(score)\n",
    "\n",
    "\n",
    "print(\"Testing Accuracy: \"+str(test_result))\n",
    "\n",
    "print(\"Precision :\"+str(precision))\n",
    "print(\"Recall :\"+str(recall))\n",
    "print(\"fscore :\"+str(fscore))\n",
    "\n",
    "\n",
    "cm = metrics.confusion_matrix(test_labels,test_preds)\n",
    "cm_df = pd.DataFrame(cm,index = ['economy' ,'sports' ,'international', 'state','technology', 'entertainment', 'education'], \n",
    "                     columns = ['economy' ,'sports' ,'international', 'state','technology', 'entertainment', 'education'])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(cm_df, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score Multinomial NB: {0}'.format(score)\n",
    "plt.title(all_sample_title, size = 15);\n",
    "\n",
    "report = pd.DataFrame(classification_report(y_true = test_labels, y_pred = test_preds, output_dict=True)).transpose()\n",
    "report = report.rename(index={'0': 'economy','1':'sports','2':'international','3':'state','4':'technology','5':'entertainment','6':'education'})\n",
    "report[['precision','recall','f1-score']]=report[['precision','recall','f1-score']].apply(lambda x: round(x*100,2))\n",
    "display(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "321bedcb",
   "metadata": {
    "papermill": {
     "duration": 0.061162,
     "end_time": "2021-10-19T14:14:16.749931",
     "exception": false,
     "start_time": "2021-10-19T14:14:16.688769",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Conclusion**\n",
    "**After Hyperparameter Tuning and comparing the evaluation measures, we can say that Logistic Regression provides better result.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f48ca85",
   "metadata": {
    "papermill": {
     "duration": 0.06684,
     "end_time": "2021-10-19T14:14:16.878579",
     "exception": false,
     "start_time": "2021-10-19T14:14:16.811739",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e80ed08",
   "metadata": {
    "papermill": {
     "duration": 0.064669,
     "end_time": "2021-10-19T14:14:17.004729",
     "exception": false,
     "start_time": "2021-10-19T14:14:16.940060",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad77d89a",
   "metadata": {
    "papermill": {
     "duration": 0.063641,
     "end_time": "2021-10-19T14:14:17.134541",
     "exception": false,
     "start_time": "2021-10-19T14:14:17.070900",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62e6b0aa",
   "metadata": {
    "papermill": {
     "duration": 0.06131,
     "end_time": "2021-10-19T14:14:17.257598",
     "exception": false,
     "start_time": "2021-10-19T14:14:17.196288",
     "status": "completed"
    },
    "tags": []
   },
   "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": "3.7.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 680.74411,
   "end_time": "2021-10-19T14:14:20.136520",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-10-19T14:02:59.392410",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}