Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22364626",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:07.491395Z",
     "iopub.status.busy": "2022-11-18T06:37:07.490921Z",
     "iopub.status.idle": "2022-11-18T06:37:07.511331Z",
     "shell.execute_reply": "2022-11-18T06:37:07.509958Z"
    },
    "papermill": {
     "duration": 0.035736,
     "end_time": "2022-11-18T06:37:07.514573",
     "exception": false,
     "start_time": "2022-11-18T06:37:07.478837",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/language-detection/Language Detection.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\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",
    "\n",
    "# You can write up to 20GB 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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9dd874",
   "metadata": {
    "papermill": {
     "duration": 0.008947,
     "end_time": "2022-11-18T06:37:07.533499",
     "exception": false,
     "start_time": "2022-11-18T06:37:07.524552",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# IMPORTING PACKAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1cb89575",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:07.557144Z",
     "iopub.status.busy": "2022-11-18T06:37:07.556782Z",
     "iopub.status.idle": "2022-11-18T06:37:08.594940Z",
     "shell.execute_reply": "2022-11-18T06:37:08.593943Z"
    },
    "papermill": {
     "duration": 1.051613,
     "end_time": "2022-11-18T06:37:08.597681",
     "exception": false,
     "start_time": "2022-11-18T06:37:07.546068",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99fdfe90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:08.618147Z",
     "iopub.status.busy": "2022-11-18T06:37:08.617521Z",
     "iopub.status.idle": "2022-11-18T06:37:08.731660Z",
     "shell.execute_reply": "2022-11-18T06:37:08.730448Z"
    },
    "papermill": {
     "duration": 0.127546,
     "end_time": "2022-11-18T06:37:08.734480",
     "exception": false,
     "start_time": "2022-11-18T06:37:08.606934",
     "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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[2] In ancient philosophy, natura is mostly us...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[3][4] \\nThe concept of nature as a whole, the...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>During the advent of modern scientific method ...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[5][6] With the Industrial revolution, nature ...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>However, a vitalist vision of nature, closer t...</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\n",
       "5  [2] In ancient philosophy, natura is mostly us...  English\n",
       "6  [3][4] \\nThe concept of nature as a whole, the...  English\n",
       "7  During the advent of modern scientific method ...  English\n",
       "8  [5][6] With the Industrial revolution, nature ...  English\n",
       "9  However, a vitalist vision of nature, closer t...  English"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_csv(\"/kaggle/input/language-detection/Language Detection.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c3e90da",
   "metadata": {
    "papermill": {
     "duration": 0.009241,
     "end_time": "2022-11-18T06:37:08.755124",
     "exception": false,
     "start_time": "2022-11-18T06:37:08.745883",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# ANALYSING DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e182f683",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:08.776117Z",
     "iopub.status.busy": "2022-11-18T06:37:08.775321Z",
     "iopub.status.idle": "2022-11-18T06:37:08.782451Z",
     "shell.execute_reply": "2022-11-18T06:37:08.781557Z"
    },
    "papermill": {
     "duration": 0.02029,
     "end_time": "2022-11-18T06:37:08.784782",
     "exception": false,
     "start_time": "2022-11-18T06:37:08.764492",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10337, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe5f0302",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:08.805678Z",
     "iopub.status.busy": "2022-11-18T06:37:08.804895Z",
     "iopub.status.idle": "2022-11-18T06:37:08.815037Z",
     "shell.execute_reply": "2022-11-18T06:37:08.813925Z"
    },
    "papermill": {
     "duration": 0.023333,
     "end_time": "2022-11-18T06:37:08.817439",
     "exception": false,
     "start_time": "2022-11-18T06:37:08.794106",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of                                                     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\n",
       "...                                                  ...      ...\n",
       "10332  ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...  Kannada\n",
       "10333  ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...  Kannada\n",
       "10334  ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...  Kannada\n",
       "10335  ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...  Kannada\n",
       "10336  ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...  Kannada\n",
       "\n",
       "[10337 rows x 2 columns]>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d3eddf4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:08.838888Z",
     "iopub.status.busy": "2022-11-18T06:37:08.838014Z",
     "iopub.status.idle": "2022-11-18T06:37:08.848649Z",
     "shell.execute_reply": "2022-11-18T06:37:08.847622Z"
    },
    "papermill": {
     "duration": 0.023921,
     "end_time": "2022-11-18T06:37:08.850929",
     "exception": false,
     "start_time": "2022-11-18T06:37:08.827008",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text        0\n",
       "Language    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b93df250",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:08.872495Z",
     "iopub.status.busy": "2022-11-18T06:37:08.871843Z",
     "iopub.status.idle": "2022-11-18T06:37:08.905768Z",
     "shell.execute_reply": "2022-11-18T06:37:08.904626Z"
    },
    "papermill": {
     "duration": 0.04752,
     "end_time": "2022-11-18T06:37:08.908116",
     "exception": false,
     "start_time": "2022-11-18T06:37:08.860596",
     "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>Text</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>i'm sorry.</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>oh my god.</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>i'm sorry.</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1724</th>\n",
       "      <td>എന്നോട് ക്ഷമിക്കൂ.</td>\n",
       "      <td>Malayalam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>ഓ എന്റെ ദൈവമേ.</td>\n",
       "      <td>Malayalam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9706</th>\n",
       "      <td>Ach du lieber Gott.</td>\n",
       "      <td>German</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9726</th>\n",
       "      <td>Es tut mir Leid.</td>\n",
       "      <td>German</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10081</th>\n",
       "      <td>ನನ್ನನ್ನು ಕ್ಷಮಿಸು.</td>\n",
       "      <td>Kannada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10125</th>\n",
       "      <td>ಓ ದೇವರೇ.</td>\n",
       "      <td>Kannada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10141</th>\n",
       "      <td>ನನ್ನನ್ನು ಕ್ಷಮಿಸು.</td>\n",
       "      <td>Kannada</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Text   Language\n",
       "1141            i'm sorry.    English\n",
       "1180            oh my god.    English\n",
       "1196            i'm sorry.    English\n",
       "1724   എന്നോട് ക്ഷമിക്കൂ.  Malayalam\n",
       "1767        ഓ എന്റെ ദൈവമേ.  Malayalam\n",
       "...                    ...        ...\n",
       "9706   Ach du lieber Gott.     German\n",
       "9726      Es tut mir Leid.     German\n",
       "10081    ನನ್ನನ್ನು ಕ್ಷಮಿಸು.    Kannada\n",
       "10125             ಓ ದೇವರೇ.    Kannada\n",
       "10141    ನನ್ನನ್ನು ಕ್ಷಮಿಸು.    Kannada\n",
       "\n",
       "[66 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4f0773e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:08.930987Z",
     "iopub.status.busy": "2022-11-18T06:37:08.930321Z",
     "iopub.status.idle": "2022-11-18T06:37:08.946289Z",
     "shell.execute_reply": "2022-11-18T06:37:08.945200Z"
    },
    "papermill": {
     "duration": 0.03042,
     "end_time": "2022-11-18T06:37:08.948543",
     "exception": false,
     "start_time": "2022-11-18T06:37:08.918123",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df.duplicated()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "738b687a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:08.971125Z",
     "iopub.status.busy": "2022-11-18T06:37:08.970439Z",
     "iopub.status.idle": "2022-11-18T06:37:08.986178Z",
     "shell.execute_reply": "2022-11-18T06:37:08.985305Z"
    },
    "papermill": {
     "duration": 0.030067,
     "end_time": "2022-11-18T06:37:08.988752",
     "exception": false,
     "start_time": "2022-11-18T06:37:08.958685",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(df[df.duplicated()].index, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0a21b9ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:09.011704Z",
     "iopub.status.busy": "2022-11-18T06:37:09.010999Z",
     "iopub.status.idle": "2022-11-18T06:37:09.020534Z",
     "shell.execute_reply": "2022-11-18T06:37:09.019447Z"
    },
    "papermill": {
     "duration": 0.023721,
     "end_time": "2022-11-18T06:37:09.023074",
     "exception": false,
     "start_time": "2022-11-18T06:37:08.999353",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Language\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07541476",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:09.046207Z",
     "iopub.status.busy": "2022-11-18T06:37:09.045811Z",
     "iopub.status.idle": "2022-11-18T06:37:09.056570Z",
     "shell.execute_reply": "2022-11-18T06:37:09.055416Z"
    },
    "papermill": {
     "duration": 0.024652,
     "end_time": "2022-11-18T06:37:09.058681",
     "exception": false,
     "start_time": "2022-11-18T06:37:09.034029",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "English       1382\n",
       "French        1007\n",
       "Spanish        816\n",
       "Portugeese     736\n",
       "Italian        694\n",
       "Russian        688\n",
       "Sweedish       673\n",
       "Malayalam      591\n",
       "Dutch          542\n",
       "Arabic         532\n",
       "Turkish        471\n",
       "German         465\n",
       "Tamil          464\n",
       "Danish         424\n",
       "Kannada        366\n",
       "Greek          358\n",
       "Hindi           62\n",
       "Name: Language, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Language\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34eed21",
   "metadata": {
    "papermill": {
     "duration": 0.009941,
     "end_time": "2022-11-18T06:37:09.079043",
     "exception": false,
     "start_time": "2022-11-18T06:37:09.069102",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# ANALYSING VIA PLOTS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28525f19",
   "metadata": {
    "papermill": {
     "duration": 0.00999,
     "end_time": "2022-11-18T06:37:09.099819",
     "exception": false,
     "start_time": "2022-11-18T06:37:09.089829",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8cb99665",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:09.122258Z",
     "iopub.status.busy": "2022-11-18T06:37:09.121844Z",
     "iopub.status.idle": "2022-11-18T06:37:09.603716Z",
     "shell.execute_reply": "2022-11-18T06:37:09.602458Z"
    },
    "papermill": {
     "duration": 0.496488,
     "end_time": "2022-11-18T06:37:09.606631",
     "exception": false,
     "start_time": "2022-11-18T06:37:09.110143",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "total= float(len(df['Language']))\n",
    "ax= sns.countplot(x= 'Language', data= df, order= df['Language'].value_counts().index, palette= 'magma')\n",
    "\n",
    "for p in ax.patches:\n",
    "    percentage= '{:.2f}%'.format(100 * p.get_height()/total)\n",
    "    x= p.get_x() + p.get_width()\n",
    "    y= p.get_height()\n",
    "    ax.annotate(percentage, (x, y), fontsize=16, ha=\"right\")\n",
    "    \n",
    "plt.title('Counts and Percentages of Languages', fontsize=24)\n",
    "plt.xlabel(\"Language\",fontsize=20)\n",
    "plt.ylabel(\"Count\", fontsize=20)\n",
    "plt.xticks(size= 18, rotation=90) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fb037ba1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:09.631978Z",
     "iopub.status.busy": "2022-11-18T06:37:09.631098Z",
     "iopub.status.idle": "2022-11-18T06:37:09.646413Z",
     "shell.execute_reply": "2022-11-18T06:37:09.644423Z"
    },
    "papermill": {
     "duration": 0.031959,
     "end_time": "2022-11-18T06:37:09.650289",
     "exception": false,
     "start_time": "2022-11-18T06:37:09.618330",
     "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>index</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>English</td>\n",
       "      <td>1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>French</td>\n",
       "      <td>1007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spanish</td>\n",
       "      <td>816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Portugeese</td>\n",
       "      <td>736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Italian</td>\n",
       "      <td>694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Russian</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sweedish</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Malayalam</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dutch</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Turkish</td>\n",
       "      <td>471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>German</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Tamil</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Danish</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kannada</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Greek</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hindi</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         index  Language\n",
       "0      English      1382\n",
       "1       French      1007\n",
       "2      Spanish       816\n",
       "3   Portugeese       736\n",
       "4      Italian       694\n",
       "5      Russian       688\n",
       "6     Sweedish       673\n",
       "7    Malayalam       591\n",
       "8        Dutch       542\n",
       "9       Arabic       532\n",
       "10     Turkish       471\n",
       "11      German       465\n",
       "12       Tamil       464\n",
       "13      Danish       424\n",
       "14     Kannada       366\n",
       "15       Greek       358\n",
       "16       Hindi        62"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "language= df['Language'].value_counts().reset_index()\n",
    "language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cef17b4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:09.680521Z",
     "iopub.status.busy": "2022-11-18T06:37:09.679712Z",
     "iopub.status.idle": "2022-11-18T06:37:09.984738Z",
     "shell.execute_reply": "2022-11-18T06:37:09.983553Z"
    },
    "papermill": {
     "duration": 0.324281,
     "end_time": "2022-11-18T06:37:09.987078",
     "exception": false,
     "start_time": "2022-11-18T06:37:09.662797",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "#create pie chart\n",
    "labels= language['index']\n",
    "\n",
    "plt.pie(language[\"Language\"], labels= labels, autopct='%.1f%%', textprops={'fontsize': 15})\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d2ee72",
   "metadata": {
    "papermill": {
     "duration": 0.012706,
     "end_time": "2022-11-18T06:37:10.013495",
     "exception": false,
     "start_time": "2022-11-18T06:37:10.000789",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# CLEANING THE TEXT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a6da7c7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:10.042692Z",
     "iopub.status.busy": "2022-11-18T06:37:10.040917Z",
     "iopub.status.idle": "2022-11-18T06:37:10.057235Z",
     "shell.execute_reply": "2022-11-18T06:37:10.055996Z"
    },
    "papermill": {
     "duration": 0.033387,
     "end_time": "2022-11-18T06:37:10.059903",
     "exception": false,
     "start_time": "2022-11-18T06:37:10.026516",
     "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>Text</th>\n",
       "      <th>Language</th>\n",
       "      <th>cleaned_Text</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",
       "      <td></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",
       "      <td></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",
       "      <td></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",
       "      <td></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",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10332</th>\n",
       "      <td>ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...</td>\n",
       "      <td>Kannada</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10333</th>\n",
       "      <td>ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...</td>\n",
       "      <td>Kannada</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10334</th>\n",
       "      <td>ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...</td>\n",
       "      <td>Kannada</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10335</th>\n",
       "      <td>ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...</td>\n",
       "      <td>Kannada</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10336</th>\n",
       "      <td>ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...</td>\n",
       "      <td>Kannada</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10271 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Text Language cleaned_Text\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             \n",
       "...                                                  ...      ...          ...\n",
       "10332  ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...  Kannada             \n",
       "10333  ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...  Kannada             \n",
       "10334  ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...  Kannada             \n",
       "10335  ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...  Kannada             \n",
       "10336  ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...  Kannada             \n",
       "\n",
       "[10271 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1= df.copy()\n",
    "df1['cleaned_Text']= \"\"\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "428389ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:10.089101Z",
     "iopub.status.busy": "2022-11-18T06:37:10.088729Z",
     "iopub.status.idle": "2022-11-18T06:37:10.096917Z",
     "shell.execute_reply": "2022-11-18T06:37:10.095420Z"
    },
    "papermill": {
     "duration": 0.026411,
     "end_time": "2022-11-18T06:37:10.099699",
     "exception": false,
     "start_time": "2022-11-18T06:37:10.073288",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import re\n",
    "def clean_function(Text):\n",
    "    # removing the symbols and numbers\n",
    "    Text = re.sub(r'[\\([{})\\]!@#$,\"%^*?:;~`0-9]', ' ', Text)\n",
    "    \n",
    "    # converting the text to lower case\n",
    "    Text = Text.lower()\n",
    "    Text = re.sub('http\\S+\\s*', ' ', Text)  # remove URLs\n",
    "    Text = re.sub('RT|cc', ' ', Text)  # remove RT and cc\n",
    "    Text = re.sub('#\\S+', '', Text)  # remove hashtags\n",
    "    Text = re.sub('@\\S+', '  ', Text)  # remove mentions\n",
    "    Text = re.sub('\\s+', ' ', Text)  # remove extra whitespace\n",
    "    \n",
    "    return Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2d5187eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:10.133653Z",
     "iopub.status.busy": "2022-11-18T06:37:10.132627Z",
     "iopub.status.idle": "2022-11-18T06:37:10.354209Z",
     "shell.execute_reply": "2022-11-18T06:37:10.352820Z"
    },
    "papermill": {
     "duration": 0.240292,
     "end_time": "2022-11-18T06:37:10.356775",
     "exception": false,
     "start_time": "2022-11-18T06:37:10.116483",
     "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>Text</th>\n",
       "      <th>Language</th>\n",
       "      <th>cleaned_Text</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",
       "      <td>nature in the broadest sense is the natural p...</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",
       "      <td>nature can refer to the phenomena of the phys...</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",
       "      <td>the study of nature is a large if not the only...</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",
       "      <td>although humans are part of nature human activ...</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",
       "      <td>the word nature is borrowed from the old fren...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10332</th>\n",
       "      <td>ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...</td>\n",
       "      <td>Kannada</td>\n",
       "      <td>ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10333</th>\n",
       "      <td>ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...</td>\n",
       "      <td>Kannada</td>\n",
       "      <td>ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10334</th>\n",
       "      <td>ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...</td>\n",
       "      <td>Kannada</td>\n",
       "      <td>ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10335</th>\n",
       "      <td>ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...</td>\n",
       "      <td>Kannada</td>\n",
       "      <td>ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10336</th>\n",
       "      <td>ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...</td>\n",
       "      <td>Kannada</td>\n",
       "      <td>ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10271 rows × 3 columns</p>\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   \n",
       "...                                                  ...      ...   \n",
       "10332  ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...  Kannada   \n",
       "10333  ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...  Kannada   \n",
       "10334  ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...  Kannada   \n",
       "10335  ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...  Kannada   \n",
       "10336  ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...  Kannada   \n",
       "\n",
       "                                            cleaned_Text  \n",
       "0       nature in the broadest sense is the natural p...  \n",
       "1       nature can refer to the phenomena of the phys...  \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       the word nature is borrowed from the old fren...  \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 ಎ...  \n",
       "10335  ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...  \n",
       "10336  ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...  \n",
       "\n",
       "[10271 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['cleaned_Text'] = df1['Text'].apply(lambda x: clean_function(x))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "116dbc99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:10.385751Z",
     "iopub.status.busy": "2022-11-18T06:37:10.385246Z",
     "iopub.status.idle": "2022-11-18T06:37:10.390586Z",
     "shell.execute_reply": "2022-11-18T06:37:10.389231Z"
    },
    "papermill": {
     "duration": 0.02275,
     "end_time": "2022-11-18T06:37:10.393058",
     "exception": false,
     "start_time": "2022-11-18T06:37:10.370308",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X=df1[\"cleaned_Text\"]\n",
    "y=df1[\"Language\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "04babccc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:10.422040Z",
     "iopub.status.busy": "2022-11-18T06:37:10.421660Z",
     "iopub.status.idle": "2022-11-18T06:37:10.569972Z",
     "shell.execute_reply": "2022-11-18T06:37:10.568928Z"
    },
    "papermill": {
     "duration": 0.165899,
     "end_time": "2022-11-18T06:37:10.572706",
     "exception": false,
     "start_time": "2022-11-18T06:37:10.406807",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lb=LabelEncoder()\n",
    "y=lb.fit_transform(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "346ccffd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:10.602598Z",
     "iopub.status.busy": "2022-11-18T06:37:10.602143Z",
     "iopub.status.idle": "2022-11-18T06:37:11.295186Z",
     "shell.execute_reply": "2022-11-18T06:37:11.293911Z"
    },
    "papermill": {
     "duration": 0.711409,
     "end_time": "2022-11-18T06:37:11.298178",
     "exception": false,
     "start_time": "2022-11-18T06:37:10.586769",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10271, 39370)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "CV= CountVectorizer()\n",
    "X= CV.fit_transform(X).toarray()\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8be2d75",
   "metadata": {
    "papermill": {
     "duration": 0.013643,
     "end_time": "2022-11-18T06:37:11.326350",
     "exception": false,
     "start_time": "2022-11-18T06:37:11.312707",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# SPLITTING DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c3c248fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:11.355419Z",
     "iopub.status.busy": "2022-11-18T06:37:11.354992Z",
     "iopub.status.idle": "2022-11-18T06:37:13.874726Z",
     "shell.execute_reply": "2022-11-18T06:37:13.873558Z"
    },
    "papermill": {
     "duration": 2.537875,
     "end_time": "2022-11-18T06:37:13.877767",
     "exception": false,
     "start_time": "2022-11-18T06:37:11.339892",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test= train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d451d9d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:13.909312Z",
     "iopub.status.busy": "2022-11-18T06:37:13.908926Z",
     "iopub.status.idle": "2022-11-18T06:37:14.124932Z",
     "shell.execute_reply": "2022-11-18T06:37:14.123900Z"
    },
    "papermill": {
     "duration": 0.234261,
     "end_time": "2022-11-18T06:37:14.127501",
     "exception": false,
     "start_time": "2022-11-18T06:37:13.893240",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "models = {\n",
    "    'K-Nearest Neighbors' : KNeighborsClassifier(),\n",
    "    'Random Forest' : RandomForestClassifier(),\n",
    "    'MNB' : MultinomialNB()    \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea92402",
   "metadata": {
    "papermill": {
     "duration": 0.013348,
     "end_time": "2022-11-18T06:37:14.155208",
     "exception": false,
     "start_time": "2022-11-18T06:37:14.141860",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# TESTING DIFFERENT MODELS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49536ffd",
   "metadata": {
    "papermill": {
     "duration": 0.013261,
     "end_time": "2022-11-18T06:37:14.182486",
     "exception": false,
     "start_time": "2022-11-18T06:37:14.169225",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4486311b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:37:14.212009Z",
     "iopub.status.busy": "2022-11-18T06:37:14.211084Z",
     "iopub.status.idle": "2022-11-18T06:39:21.295877Z",
     "shell.execute_reply": "2022-11-18T06:39:21.294502Z"
    },
    "papermill": {
     "duration": 127.102451,
     "end_time": "2022-11-18T06:39:21.298564",
     "exception": false,
     "start_time": "2022-11-18T06:37:14.196113",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors training started...\n",
      "K-Nearest Neighbors trained\n",
      "Random Forest training started...\n",
      "Random Forest trained\n",
      "MNB training started...\n",
      "MNB trained\n",
      "CPU times: user 2min 6s, sys: 443 ms, total: 2min 6s\n",
      "Wall time: 2min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for name, model in models.items():\n",
    "    print(f'{name} training started...')\n",
    "    model.fit(X_train, y_train)\n",
    "    print(f'{name} trained')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23ceec3e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:39:21.328395Z",
     "iopub.status.busy": "2022-11-18T06:39:21.327977Z",
     "iopub.status.idle": "2022-11-18T06:39:21.334161Z",
     "shell.execute_reply": "2022-11-18T06:39:21.332889Z"
    },
    "papermill": {
     "duration": 0.023997,
     "end_time": "2022-11-18T06:39:21.336612",
     "exception": false,
     "start_time": "2022-11-18T06:39:21.312615",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.metrics import confusion_matrix as CM\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8272c1cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:39:21.366274Z",
     "iopub.status.busy": "2022-11-18T06:39:21.365887Z",
     "iopub.status.idle": "2022-11-18T06:39:49.213011Z",
     "shell.execute_reply": "2022-11-18T06:39:49.210914Z"
    },
    "papermill": {
     "duration": 27.868733,
     "end_time": "2022-11-18T06:39:49.219334",
     "exception": false,
     "start_time": "2022-11-18T06:39:21.350601",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors accuracy score :  0.524\n",
      "Random Forest accuracy score :  0.926\n",
      "MNB accuracy score :  0.981\n",
      "CPU times: user 1min 36s, sys: 2.38 s, total: 1min 38s\n",
      "Wall time: 27.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for name in models:\n",
    "    acc_score= round(accuracy_score(y_test, models.get(name).predict(X_test)), 3)\n",
    "    print(f'{name} accuracy score :  {acc_score}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac711cc3",
   "metadata": {
    "papermill": {
     "duration": 0.035597,
     "end_time": "2022-11-18T06:39:49.292304",
     "exception": false,
     "start_time": "2022-11-18T06:39:49.256707",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# The accuracy of the MNB model is 0.98 which is very good and our model is performing well.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0f49e0d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:39:49.342315Z",
     "iopub.status.busy": "2022-11-18T06:39:49.341923Z",
     "iopub.status.idle": "2022-11-18T06:40:17.089579Z",
     "shell.execute_reply": "2022-11-18T06:40:17.087825Z"
    },
    "papermill": {
     "duration": 27.768245,
     "end_time": "2022-11-18T06:40:17.094733",
     "exception": false,
     "start_time": "2022-11-18T06:39:49.326488",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors classification report\n",
      "-------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.35      0.44       146\n",
      "           1       0.76      0.55      0.64       103\n",
      "           2       0.87      0.46      0.60       133\n",
      "           3       0.97      0.59      0.74       361\n",
      "           4       0.89      0.56      0.69       255\n",
      "           5       0.84      0.46      0.59       114\n",
      "           6       1.00      0.48      0.65        79\n",
      "           7       1.00      0.33      0.50        12\n",
      "           8       0.97      0.45      0.61       159\n",
      "           9       0.08      0.97      0.16        86\n",
      "          10       0.81      0.72      0.76       161\n",
      "          11       0.90      0.48      0.63       178\n",
      "          12       0.79      0.29      0.43       150\n",
      "          13       0.79      0.55      0.65       205\n",
      "          14       0.99      0.49      0.66       182\n",
      "          15       0.41      0.86      0.55       111\n",
      "          16       1.00      0.20      0.34       133\n",
      "\n",
      "    accuracy                           0.52      2568\n",
      "   macro avg       0.80      0.52      0.57      2568\n",
      "weighted avg       0.83      0.52      0.60      2568\n",
      "\n",
      "******************************\n",
      " \n",
      "Random Forest classification report\n",
      "-------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.91      0.95       146\n",
      "           1       0.97      0.88      0.92       103\n",
      "           2       0.98      0.88      0.93       133\n",
      "           3       0.98      0.98      0.98       361\n",
      "           4       0.98      0.92      0.95       255\n",
      "           5       0.99      0.92      0.95       114\n",
      "           6       1.00      0.91      0.95        79\n",
      "           7       1.00      0.92      0.96        12\n",
      "           8       0.96      0.91      0.93       159\n",
      "           9       0.38      1.00      0.55        86\n",
      "          10       1.00      0.94      0.97       161\n",
      "          11       0.99      0.93      0.96       178\n",
      "          12       1.00      0.91      0.95       150\n",
      "          13       0.91      0.95      0.93       205\n",
      "          14       0.97      0.94      0.96       182\n",
      "          15       1.00      0.96      0.98       111\n",
      "          16       0.99      0.80      0.89       133\n",
      "\n",
      "    accuracy                           0.93      2568\n",
      "   macro avg       0.95      0.92      0.92      2568\n",
      "weighted avg       0.96      0.93      0.94      2568\n",
      "\n",
      "******************************\n",
      " \n",
      "MNB classification report\n",
      "-------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99       146\n",
      "           1       0.99      0.95      0.97       103\n",
      "           2       1.00      0.96      0.98       133\n",
      "           3       0.90      1.00      0.95       361\n",
      "           4       1.00      0.98      0.99       255\n",
      "           5       1.00      0.99      1.00       114\n",
      "           6       1.00      0.99      0.99        79\n",
      "           7       1.00      0.92      0.96        12\n",
      "           8       1.00      0.97      0.99       159\n",
      "           9       1.00      0.99      0.99        86\n",
      "          10       0.99      0.99      0.99       161\n",
      "          11       0.99      0.98      0.98       178\n",
      "          12       0.99      0.99      0.99       150\n",
      "          13       0.99      0.98      0.99       205\n",
      "          14       0.99      0.98      0.99       182\n",
      "          15       1.00      0.98      0.99       111\n",
      "          16       1.00      0.92      0.96       133\n",
      "\n",
      "    accuracy                           0.98      2568\n",
      "   macro avg       0.99      0.97      0.98      2568\n",
      "weighted avg       0.98      0.98      0.98      2568\n",
      "\n",
      "******************************\n",
      " \n"
     ]
    }
   ],
   "source": [
    "for name in models:\n",
    "    print(f'{name} classification report')\n",
    "    print(\"-------------------------------\")\n",
    "    print(classification_report(y_test, models.get(name).predict(X_test)))\n",
    "    print(\"******************************\")\n",
    "    print(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e46fb5e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:40:17.174166Z",
     "iopub.status.busy": "2022-11-18T06:40:17.173202Z",
     "iopub.status.idle": "2022-11-18T06:41:43.068963Z",
     "shell.execute_reply": "2022-11-18T06:41:43.067650Z"
    },
    "papermill": {
     "duration": 85.941456,
     "end_time": "2022-11-18T06:41:43.073639",
     "exception": false,
     "start_time": "2022-11-18T06:40:17.132183",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors ConfusionMatrix\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************************\n",
      " \n",
      "Random Forest ConfusionMatrix\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************************\n",
      " \n",
      "MNB ConfusionMatrix\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEYCAYAAAB82RxTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAABWy0lEQVR4nO2dd3wVRfeHn5OEkoQaehMEUUCkKFWkqyAi4CuvFUXlldeG9bX3gmJBBDuIgA1BFOWnCCJNUKQIAanSJfRepSQ5vz92Ey8h92Zv9t5slsyTz3xy7+yeOWdumTs7O/MdUVUMBoPB4B9ivA7AYDAYDOFhGm6DwWDwGabhNhgMBp9hGm6DwWDwGabhNhgMBp9hGm6DwWDwGabhzmNEZL2IqIic5XUs+Q0RqS8i34jIVhH5236tvhCR+l7HlhtEpKSIjBCRvSKyX0Q+E5EyDuxKichHIrJHRA6JyA/ZfV5E5FoRWWifs1lEPhaRylnOuVNEvheR3fbnrl3kamjwCtNw5yEi0hKoYT+9zsNQ8h12w/QbUAK4G7gcGACUBRp4GJobxgLtgP8ANwNNgW8c2I0BOgH3AtcDZYCpIlIi4wQR6QaMBn4FugOPAG2A70Uk8Ht9E5AETHZVE0P+QlVNyqMEDAEOYTVQy72OJyCuWKCwxzH0B3YDRbI5JnngPz7C5bUEFGgTkNfMzrvYgV3HgLwKwBHgfwF5XwC/Z7HtZtvWDciLsf/Xt4+18/rzZpL7ZHrceYSIxAJXAxOAj4C6ItIwm/PaiMh0+/J3v4jMEJHGAceri8hoEdklIkdEZImIXG8fa2dfDtfPUuYMERkX8HykiCwQkR4isgw4CjQXkUr2Jfo6e6jiTxF5UUQKZykvXkReFZGNInLMHtJ42T72qm0vWWxuFpHjIlIuyEtUCtinqseyHlC75Qko60oRmWfHuFtEJopI9YDjHURkrogcFZHtIvKuiBQLOJ7xOnUSkQkicgh42z52hj08s8d+fSeLyDlBYg7FZcB2Vf05oB7zgPX2sWA0Ak4AMwLstgOLsa5CMigE7M9iuy+jigG26WFHbsj3mIY772iP1XP6AhiH9eU8abjEHn+cah/rDVwDzAKq2MfLA3OwLrn/B1wBDAeq5SKeGsCrwMtYDcl6rGGJPcADQGfgNeAW4K2AGAX4FrgDeAfoAjxj24L1o3Qm0DaLv1uA/1PVnUHiWQjUFJHBIlIvWNAiciPwNbAW64fwFuBPoJx9/FxgErALuMqO7Xqs1zwrw7EaxG7AcBFJAmYD5wC32+UnAj+JSHxADDNEZEawGG3qACuzyV9hHwtGUSBNVdOy5B8H6gY8/whoLSI3iUgJETkbeBGYpqrLc4jN4He87vIXlITVSOzFHpIAvgM2EDAMgNUoLyDI0ABWI3sYqBTkeDusy+H6WfJnAOMCno+0z2uUQ8xxWI3e0YC4O9m23ULYzQZGBTyvCaQDXXPwNcYuW7GGTT4BmgScEwNsBr4OUc4XwGogNiDvarvMlllep0FZbF+w/SYF5JXG6tneFZA3FZiaw2s3Bfgmm/xPgV9D2F1hx3ZeQF68HdfxLOfeYL83Ga/ZL0CpIOWaoZLTKJkedx5gDzX8Cxivqsft7C+A6lhjmohIItAcq8ELpvzVAZikqlsjENZmVU3OEqeIyH0islxE/sbq+X8GFAHOCIhhj6pOCFH2cOCqgOGJm4HtWD3hbFHVVFW9BmgIPAX8jtXgzhGRjCGCc4DKwIgQvpthvc6BPdavgFTgoiznfp/l+cVYDe4BEYkTkTjgoB1Lk4BYO6pqxxAxuGEy1tXPByJyjohUAt4HSmL9+AEgIu3t/MFYV3PXYt2EHG8PyxlOY0zDnTdchjWGO9Ge6lUKqxd8jH+GS0pjjU2GapTL5HA8HLZnk3cf8DowHmumQjPgLvtY0TBiGIvVyFxtD630Bj5W1dScglLVJar6oqpeitVQb8UaAsjwTQ7+K5GlbnYjvhurYQsk62tQFmt46kSW1J7wh6P2YjW2WSltH8sW+4f9WqxhtZXAFqwrlo+BbQGnDgQmqOojqjpDVccAPbCuJrqHGavBZ8R5HUABIaNx/jKbY/8WkfuwvszpWA1PMHbncPyo/b9wlvzSWGO+gWTXq/831pDKExkZ2Yw35xQDqnpYRL7A6mlvxOqth+olBytng4h8CdwZ4Jsc/G8Fygdm2D3QMljj9ye5yPJ8D9bN4xeyKfegk5gDWAm0zia/DjlMCVTVefb0yLOBVFVdKyLfYc1GCixndBa7VfaVUq0wYzX4DNPjjjL2EMgVWF+y9lnSA1g9qw6qehiYC9yUdUZGAFOBTiJSIcjxFPt/5k0sEalG6JthgcRjXQUEckM2MSSJSNccyhqO1XA9C/ymqtndqMvEvvGaHbX5p2e8CmuMu3eIouYCV2YZLvgXVidldg4xTwXOBZap6oIsaVUOtln5AagoIpnDMyLSBKv3/ENOxmqxym60a2MN4wwPOGUjcH6gjYjUxXoPN4QZq8FveD3IfronrJt7CjTP5lghrJ7wR/bzNlizByZhNTadsBq+rvbxcliN859YjVcHrKGMhwPKnA+sA67EmlXxO1YvNOvNyQXZxPMqVq/9Ttv3x3ZZmTc8sYZzJgEHsGa2dMBq3D/Ipryltm1fB6/TW8DPWLM52mLNVvnQtn8wm9fzM6Ar1hS5gdg3MbEa3uPA/9ll9MW6mpkUUEY7sr+JWxb4C+sm8fV2HFdjzZ65LuC8HG9O2udNtl+/f2ENY6wCZmU5ZziwJkveU1hXP+2Be4CdwMgs59yLdYU2EKtRv8Eufz2QGHBeE6AncL9d52fs501yit+k/Js8D+B0T3YD8meI4+9izb8tYj9vazdgR+z86QTM/sC6oTnGboyOYE1nuzbg+FlY4+eH7S9yd7KfVZJdw10Ma0hjj50+tBvHkxo5rF7d61g/IsfsxqJ/NuW9aMdYwsHr1ML2vdq22YW1KvDabM79F9YP0lGs4ZPvgeoBxzti9byPAjvs17hYwPF2WesUcCzj5ud2u24bsGaCnBtwzgxghoM6lbLL2of1Q/c5UDbLOSOBDVny3sS6sjgGrMFaFRmX5RzBmpK5xH6vN9ufi5rZlK/ZpJFefzdMyn0S+801GCKOiMwDVqnqjV7HYjCcTpibk4aIY4/ldsBaKHRXDqcbDIYwMQ23IRrMxxoeeExV53sci8Fw2mGGSgwGg8FnmOmABoPB4DPy7VDJx1V6uboUuHXn9EiFEjbBJmE7xVwDGQzhkXp8s9uvHSd2rXP81StUtqZrf27Itw23wWAw5CnpWQUZ8y+m4TYYDAYAH0mX59sx7gsH3sa/F7/DFVNfPuVYvf9exk2bP6VI6WIn5ZdpWJNeG0dxxuVNg5bb6dJ2LFv6MyuXz+bhh8KfqebGvmrVykz58UsWL55OcvI0+t3dJ0/9e1l3r+39HLtbez/HHgl7x6SnO08ek28b7jVjf2bqDa+dkp9QOYnKbc7jUMrJmkkSI5z/xDVsmflH0DJjYmIYMrg/Xa/oxXkN23PNNT2oW7e245jc2qempvLww8/RsGF7LrroCm6/4+Y88+913b2093Psbu39HHsk7MNBNd1xCoWIFLV3aFosIstE5Dk7f6S9W1SynRrZ+SIiQ0RkjVg7Wp0f0gH5uOHeMXcVx/YdOiW/6bO9+L3/F5BlGmOdWy/lr+/nc3T3gaBlNmvamLVrN7B+/V+cOHGCsWO/pdsVnRzH5NZ+27YdLEpeCsChQ4dZuXI1lStXzBP/XtfdS3s/x+7W3s+xR8I+LNJSnafQHMMSjmuItRVdZxFpYR97SFUb2SnZzrsMS0ytNpa2zns5OYhawy0idUTkEfuXZIj9uG7OlsGpdun5HNm6l73L/zopP75iaap1bsKqj6eGtK9cpSKbUrZkPk/ZvDWshtOtfSDVq1elUcP6zJu3KE/8e113L+39HLtbez/HHgn7sEhPc55CoBYZvc5Cdgo1Y6U7ll69qupvQCl7A42gRKXhFpFHsHZ4EWCenQQYLSKPhrDrK9YmtgumH1590rHYooWp368bya+funVg0+d6sfClU3vh+ZXExATGjhnGg/97hoMHT72qMBgMHqDpjlNgW2WnvoFFiUisiCRjiZxNUdW59qH+9nDIIBEpYudVATYFmKfYeUGJ1qySPlhqaicCM0XkDWAZMCA7I1UdCgyFU+dxF69RnmJnlOOKKS8BkFApia6TX+T7y5+hTIMzafPu3QAUSSpOlQ4N6bZnMRMmTD6p/C2bt1GtauXM51WrVGLLlm04xa09QFxcHGPHDGP06PF8802OsswR8+913b2093Psbu39HHsk7MMijJuOgW1VkONpQCN7t6vxIlIfeAxrF6PCtu0jwPO5CTVaQyXpWPKYWalEwL554bBvZQpfNryLr1vcz9ct7ufI1j181+lJju7cz/iWD2Tmb/x+HnMfH3lKow0wf0EyZ511JjVqVKNQoUJcfXV3/u+7Hx3H4NYeYNjQgaxcuYY3Bwd9z6Pi3+u6e2nv59jd2vs59kjYh0Okbk6eXKbuw5Jm7qyqW+3hkGNYcr/N7NM2c/LWeFXtvKBEq8d9HzBVRFbzzyXAGVha0Xc7KaD1O3dRoWVdiiYV46oFQ1j8+les+WKmq6DS0tK4974nmfj958TGxDBy1BiWL/8zz+xbXdiUXr168scfy1kw3/rwPfnUACZNmhZ1/17X3Ut7P8fu1t7PsUfCPiwiNM1PRMoBJ1R1n4jEA5cAr4hIJVXdau9w1QNroxGwtsu7297urzmwX3PYEDxqIlMiEoP1i5IxVrMZmK8n774dFLPk3WAwOCUSS96PrZzp+KtXpE7boP5EpAEwCojFGtUYq6rPi8g0rF2sBEgGblfVQ3ZD/jbQGWsTkVtUdUEo/1FbOanW9cRvOZ5oMBgM+YEIrZxU1SVA42zyOwQ5XwlTtz7fLnl322PuW7mVK/uhW37JtW3wvX6dYaR2DV5Q4K8U88GKSKfk24bbYDAY8hSjVRJdwtUuaH/LZTw5+XWe/HEg7W/tAkDVetV5aPyLPDbxVR6Z8DLVG9aKiu9AihQpwi+zv2PB/B9JXjSVp596MCx7t/691owweh3+q7vX+jqRsHeMj7RK8u0OOHGFq2QbWExMDCuWzaJzl+tISdnKb3Mm0uvGO1mx4uQFOxlDJZXOrkaft+7lle6Pk3YilbtHPc7oJ4Zx7Qv/YepH37N8RjLntmvMJbd3481rn8u0z26oxKnvmBBDJYmJCRw+fIS4uDhmTB/PAw8+w7x5C086Jz3Ie+LUf6Rt/W7v59jd2odjm92ntmLF8lSqWJ5FyUspViyRuXMn0bPnrdnaZ/epzau6R+Lm5NHk7xw3hkUbdfVUj9t3Pe5wtQsqnlWFDclrOHH0OOlp6ayeu4JGnZujKPHF4gGIL5HA/u17I+47Ow4fPgJAoUJxFCoUF9Z4ttGsMHXP67p7qa8TCfuw8FGP23cNd7jaBVtXbaJW0zoklipGoaKFObd9Y0pXKsO450Zx5WM30v/Xd/nX4zfy7aufR9x3dsTExDB/3mQ2pyxm6tRZzJ9vtEqibe/n2N3a+1lfJxL2YRHGknevOe1vTm5bu5kp739Lv0+e5NiRo6Qs30B6ejqte13KuBdGkTxpLudf3pJer9zOkF4vRj2e9PR0mjbrRMmSJfhy7IecW+8cli1fFXW/BoMbCoS+jo92wMnzHreI3BLiWKZwS3r64WzPyY12wa9jpzPgikcZdM2zHNl/mB3rttLiqrYkT7J0XxZ+P4fqDc/KMfZI6ibs33+AmTN/5dJO7RzbGM0KU/dw7f2srxMJ+7DwUY/bi6GS54IdUNWhqtpEVZvExCRme05utAuKlSkBQOnKZWjUuRnzJ8xm/4491G5RD4BzLqzPzg05fxjc6iaULZtEyZJWLEWLFqVjx9asWrXGsb3RrDB1z+u6g3f6OpGwDwsfjXFHZahERJYEOwRUcFN2brQL+r73IImli5OWmsqYp4bz94EjfPboB/z7mVuIiYvhxLETfPbYB1HxHUilihUYPnwQsbGxxMQI48Z9x8SJoTXEI+Xfa80Io9fhz7p7qa8TCfuwyHmDhHxDVKYDish2oBOQdaqGAL+qanbKgScRbDqgU7xcORlqOqATgk0HNBiiiZ9XTkZkOuCsT5xPB2x9o6fTAaN1c/I7oFjA1jyZiMiMKPk0GAyGXONQ/y5fEJWGW1WDLq9S1euj4dNgMBhckQ/Grp1y2k4HdDPUAfBKxfa5tn1s+wxXvg0GLyjwA3T5YLaIU07bhttgMBjCwkc9bt+tnIToC+4MGzqQOxa+Q+8pL2fmtXn8Om6Z9io3TX6JbkPvo0iJBABi4mLp/MZ/uenHl7l56is0u+uKHGOIiYlh3txJjB8/MqzYhw0dyJaUxSQvcj4TJRCvxX78KrTkd3s/xx4Je8ekpTpPHuO7hjsmJoYhg/vT9YpenNewPddc04O6dWtH1Pbjj8fy1U2vnZS3cdYfjLzkUT7u9Dh712/NbKDPvrwZsYXj+PjSx/j08qdocH0HqlevGjKOfv36sHKl8/nbgXFd3vWGsO3A3evmd3s/x+7W3s+xR8I+LMwCnOiRF4I7s2bP5ei+k5f1bpy1FE2z3rCtC9dSvGKSdUChUEIRJDaGuKKFSTuRyoEDwZcEV6lSicsu68hHI3LWRskurj1794VtB96L/fhZaMnP9n6OPRL2YeGjBThRa7hFpI6IdBSRYlnyO7spNz8I7tS/pg3rZ1hrjP6cOI8TR45x+4K36fvbmywYOpG9IRrXga8/y2OP9Sc9PW9vBXkt9uNnoSU/2/s59kjYh0VBb7hF5B7gW6AfsFREugccfimEXY5aJV7T/O5upKems2K8NWulYqOaaFo6HzTtx7BWD9Dkti6ceeYZ2dp26dKRHTt3sWjRH3kZssFgcIKPhkqiNavkNuACewfjGsA4EamhqoMJsUBLVYcCQyH4ykkvBXfO7dmamh0b8+V1/9y0rNv9QtbPXEJ6ahp/7z7AlgV/csH5DVi//q9T7C9s2ZSul19K504dKFq0CCVKFGfkiCHcfMs9jmPILV6L/fhZaMnP9n6OPRL2YZEPbjo6JVpDJTGqeghAVTcA7YDLROQNXK6s9Upwp0bbBjS9oyvf9HmD1KPHM/MPbNnNGReeC0BcfBEqnX8Wq1atzbaMJ58aQM1aTTn7nJb0uvEups/4JU8abfBe7MfPQkt+tvdz7JGwD4sIDZWISFERmScii0VkmYg8Z+efKSJzRWSNiIwRkcJ2fhH7+Rr7eI2cQo1Wj3u7iDTKWPJu97y7Ah8B57kpOC8Edz795B26tm9HfOli9J07hF/f+Ipmd3UjrnAcPT97FICti9bw0+MjSB41hU4D+9L7pwGICEvH/swfS1e4qWJQPv3kHdq2aUnZsklsWLeA555/nREjv3Bk67XYj5+Flvxs7+fYI2EfFpEbAjkGdLDbvULAbBH5AXgAGKSqX4jI+0Af4D37/15VPUtErgVeAa4J5SBaIlNVgVRVPeWaRkRaqWqOyxrdiky5xcuVk0ZkymAIj0iITP097kXHX7z4nk868iciCcBs4A7ge6CiqqaKSEvgWVXtJCKT7cdzRCQO2AaU0xCNc1SGSlQ1JbtG2z7mbi26wWAwRIMwhkoCJ1LYqW9gUSISKyLJwA5gCrAW2KeqGQPpKUAV+3EVYBOAfXw/UCZUqGbJu8FgMACEcaUbOJEiyPE0oJGIlALGA3XchheIabiD8Mi26bm2/XvLLFe+4yu3dmVvMBhyQWrkZ5Wo6j4RmQ60BEqJSJzdq64KbLZP2wxUA1LsoZKSwO5Q5fpu5aTBYDBEhQjN4xaRcnZPGxGJBy4BVgDTgZ72ab2x1roATLCfYx+fFmp8G3zacOd3wZ1jx45z7X/u5V+976T7Df/l7Q8/AUBVGfzBSC6/9j9ccX1fPv3Set/WbdzEDX3vp3G7Kxjx+bioxe+12I8RWjJ1z9ciU5FbOVkJmG5v4TgfmKKq3wGPAA+IyBqsMezh9vnDgTJ2/gPAozk5iMqskkgQbFZJTEwMK5bNonOX60hJ2cpvcybS68Y7WbFidY5lurENx/7I5p/5+++jJCTEcyI1lZvu+B+P3vtf1m3cxLyFS+j/xAPExMSwe+8+ypQuxe69+9iybTvTfp5DieLFuPN/gyMef17VPT/a+zl2t/Z+jj0c+4jMKhn1qPNZJb0HeLp1me963H4Q3BEREhLiAUhNTSU1NRURYcz477njluuJibFe9jKlS2X+P6/uOcTFhb7l4Ie650d7P8fu1t7PsUfCPiwKulYJgIg0E5Gm9uN6IvKAiHRxW65fBHfS0tK4qvddtOl6HS2bNqbBuXXYtHkrP0ydydW33sPtDz7Fxk2bs7WNRvxei/0YoSVTdy/sw6KgN9wi8gwwBHhPRF4G3gYSgUdF5IkQdvleZMopsbGxfDXqHaaO/4Q/lv/J6nUbOH7iBEUKF2bsR0O46orOPPXSIK/DNBgMNpqW5jh5TbR63D2BVkAb4C6gh6q+AHQixFJOVR2qqk1UtUlMTGK25/hNcKdE8WI0O78Bs39bQMVyZbm4bSsALm57IX+uXe/Yt9v4vRb7MUJLpu5e2IdFQe9xYy13T1PVI8BaVT0AoKp/A65q7QfBnT1793HgoLWZwtFjx5gzfxFnVq9GhzYtmbdwsVXWoj+oXq3KKbbRit9rsR8jtGTqnu9FpoysK8dFJMFuuC/IyBSRkrhsuP0guLNz916eePF10tLT0XSlU4fWtGvVnPMbnMsjz73KJ2O+ISG+KM89eh8Au3bv4Zo+93Do8BFiYmKoXi2Bv1KOnLKQyw91z4/2fo7drb2fY4+EfVjk8eYmboiWyFQRVT2WTX5ZoJKq5riTgNciU24wKycNhrwlEtMBj7x1p+M2J6Hfu55OB4xKjzu7RtvO3wXsioZPg8FgcEU+uOnoFKNVYjAYDJAvbjo6xTTcUSCxShtX9ntvb+zKvvT7i1zZGwwFEh+NcZuG22AwGCBfzBZxiu+WvIN/BXeKFCnCL7O/Y8H8H0leNJWnn3rwlHOqVq1EfL+XSHj8XRIee4dCbbsBUPiy60l8fhQJDw8h4eEhxNZrYhkkFCe+30sUe+1LivS8PWqx+93ez7G7tR82dCBbUhaTvGhq2H7d+s4P9o5JV+fJY4zIVBQEb2Ik+A3nxMQEDh8+QlxcHDOmj+eBB59h3ryFmccrVizPirtak56yForEk/jQm/z94YvENW6NHvubE9PGn1xg4SLEVq1FTKXqxFSqzrFx72c7VOIXsaBo2Ps59kjYt76oOYcOHWbEiME0atzRkU1+iT0vRaYOv9zbcWOY+NgoIzIVDn4W3AE4fPgIAIUKxVGoUBxZfzi3bdthNdoAx/4mbfsmpGSIXYyOHyNt3XL0xPHg50Qgdj/b+zn2SNjPmj2XPXv3OT4/kr69tg+LtDTnyWPyrOEWkY8jUY6fBXfA6kHMnzeZzSmLmTp1FvPnB7+RKEnlia1Sk7SNqwAo3LorCY+8RdHr74X47CUBohW7n+39HHsk7N3gdex5KzLln6GSqNycFJEJWbOA9hm7QqhqtyB2fYG+ABJbkmB6JX4mPT2dps06UbJkCb4c+yHn1juHZctXnXpi4aLE93mcY18Pg6N/c2L2RI5P+gJQCnfpRdEr/8PRz7PX7TYYDLnATAekKrAc+BBQrIa7CTAwlFHgBpzBxrj9LLgTyP79B5g581cu7dTu1IY7Jpb4Po9zYsEMUpfMAUAP7ss8fGLOZOL7PhOWP6/rboSW8sfnLly8jj1vRaa870k7JVpDJU2A34EngP2qOgP4W1VnqupMNwX7WXCnbNkkSpYsAUDRokXp2LE1q1atOeW8otffS/r2TZyY/k1mnpQonfk4rkFL0rdudOw3ErH72d7PsUfC3g1ex25EprInWkve04FBIvKl/X97pHz5WXCnUsUKDB8+iNjYWGJihHHjvmPixJOnaF14YVMKNetA2ub1JDw8BIBj331MoQvaEFOlJqiie3ZwdMzbmTaJzwxHiiZAXBxxDVpQd2bPU+66e113I7Tknf2nn7xD2zYtKVs2iQ3rFvDc868zYuQXvojdiExlT55MBxSRy4FWqvq4Uxs/i0yFmg7ohN3/beTK3qycNBQ0IjEd8NAj/3Lc5hR75evTT2QqK6r6PfB9XvgyGAyGXOGjHrdZ8h4F0l1exbjtMT9aua0r+wFbXN2GMBj8SYTGrkWkGvAxUAFrcsZQVR0sIs8CtwE77VMfV9WJts1jQB8gDbhHVSeH8mEaboPBYIBI9rhTgQdVdaGIFAd+F5Ep9rFBqvp64MkiUg+4FjgXqAz8JCJnq2rQlT6m4TYYDAZAI9Rwq+pWYKv9+KCIrABC7VPYHfjC3sdgvYisAZoBc4IZ+G7JO/hXbMit2I8T/8OGDuTRBe/Rb/IrmXnndmlOvx9f5fl1n1L5vDMz86s0rMVdE1+y0g8vU7dTE1e+3cYeTXs/x+7W3s+xR8LeMalpjpOI9BWRBQGpb3ZFikgNoDEw1866W0SWiMhHIpIxx7cKsCnALIXQDX14DbeIlBaRBuHYRJqYmBiGDO5P1yt6cV7D9lxzTQ/q1q0dddtI2H/88Vgu73qD4/Nz4//jj8cyqvcrJ+XtWLWJ0bcPYuO8lafkv3fFk7zT5XFG3fQK3fv3ISY2+4+E16+dn993U3fv7MMijCXvqjpUVZsEpKFZixORYsBXwH32hunvAbWARlg98pALEkORY8MtIjNEpISIJAELgWEi8kZuHbrFz2JDbsR+nPqfNXsuf+8/dFLezrVb2LVu6ynlnTh6nPQ064ZMoSKFrNsoLny7jT1a9n6O3a29n2OPhH1YRFCrREQKYTXan6nq1wCqul1V0+x1LsOwhkMANgPVAsyr2nlBcdLjLmn/WvwL+FhVmwMXO7ALrMRFIvKAiFwajl12+FlsyC3R8F+1US36/fgqd09+hW+fHJ7ZkEfatxGZMnX3wj4cVNVxCoWICDAcWKGqbwTkVwo47Upgqf14AnCtiBQRkTOB2sC8UD6c3JyMsx1ejbWEPUdEZJ6qNrMf3wbcBYwHnhGR81V1QBC7015kKr+RkryWty59mHK1KnPVwDtYPWOx1yEZDN4QuVklrYAbgT9EJNnOexy4TkQaYV3bbgD+C6Cqy0RkLJa+UypwV6gZJeCs4X4emAz8oqrzRaQmkJMKeqGAx32BS1R1p4i8DvwGZNtwFySRqdwQTf87127h+JGjlD+7KqyPvG8jMmXq7oV9WERuVslsLGG9rEwMYdMf6O/UR45DJar6pao2UNU77OfrVPWqnMq1b2SWwVpWv9O2PYz1i5Jr/Cw25JZI+y9dtVzmzchSVcpStlZl9qXsiopvIzJl6p7fv3Oamu44eU2OPW4RORvrbmgFVa1vzyrppqovhjAriaUOKICKSCVV3WrfZXW1xt/PYkNuxH6c+v/0k3e4vH1bEkoX56E5bzFt0Fcc2X+Irs/2JjGpBDd99DBbV2xk1E0DqN70HFrf0Y301FQ0Xfm/p0ZwZO/BqNTdiEyZuud/kanoFBsNchSZEpGZwEPAB6ra2M5bqqr1w3YmkoD1A5DNxfjJ+FlkymvMkndDQSMSIlP7bujguM0p9dm0fC8ylaCq8+RkxbtcDXeo6hGyHUE1GAwGjznNRKZ2iUgt7Fm+ItITezmnIX/itsfco9IFubb9ZuvvrnwbDJ7ho6ESJw33XVgzPeqIyGasHnOvqEZlMBgMeUyktEryAiezStap6sVAOaCOql6kqhuiHlkIjG5D3tS9cs0qvDbxzcz08dIvuPzWbtSodyYvjX+N1ya+ySv/N5CzGjpbguynuucne7caN36ueyTsnaKp6jh5jZObk09nl6+qz0clIptgNydjYmJYsWwWnbtcR0rKVn6bM5FeN955ylZdkbb1u304ttkNlcTExPDB3BE81uN/3DHgbr4b/i2LZiykcfsL6PHff/HMtdbarGBDJX6pe360b31Rcw4dOsyIEYNp1LijI5v8Ente2Ufi5uSe7m0dt8hJ38709OakkyXvhwNSGnAZUCOKMYXE6DZ4U/fzWjVg+1/b2LV5J6pKfLEEABKKJ7Jnx56o+y/I77sbjRuvY/faPhx8tFewo6GSgQGpP9AOqBn1yIJgdBu8qXurbm2YPeFnAEY8/yE3Pn4L788Zzk1P3MJnr3wcdf8F+X13g9exe20fFulhJI/JjR53ApZ6VVBEpLmIlLAfx4vIcyLyfyLyioiUDGGXqXGbnn44F6EZokFcoTiaXNyMOd//AkCnXpcx8oUPub1lH0Y+/yF3vtrP4wgNBvecVj1uEfnDFv5eIiLLgFXAmzmYfQQcsR8PxlpJ+YqdNyKYUaDGbTCBKaPbkPd1b9zuAtYvXcv+XfsAaHtVB+b+YG3OMef7Xzir4dlR9e/W3s/vm1u8jt1r+3DQVOfJa5z0uLsCV9jpUqCyqr6dU7mqmdVroqr3qepsVX0Ol8MsRrch7+t+UbfWmcMkAHt37OHcFtbC2fNaNWDrhi3BTCPi3629n983t3gdu9f24eCnHnfQedz2xgkAWcUrSogIqhrqjtRSEblFVUcAi0WkiaousHVPTrgJ2Og25G3di8QXoUHrRnzw+LuZee8/8ja3PHsbsbGxnDh2nA8efSeqsbu19/P7Bu40bryO3Wv7cMgPDbJTgk4HFJH1WKsls5v2oqoatOdsj2MPBloDu4DzsfZU24S19XyOos9Gq8Q7zMpJg9+IxHTA7e3aOW5zKsyYkT+1SlT1zGDHckJV9wM32zcoz7T9pKjq9tyWaTAYDNHETz1uJ0vesXcjrg0UzchT1Z+DW2SecwAwW6oYDIZ8j6Z72okOCyd63P8B7sWaApgMtADmAB2iGpnBM9wMdxSNK+zK99HU467sDYbckp7mn4bbyaySe4GmwEZVbQ80BvZFMyiDwWDIa/w0q8RJw31UVY8CiEgRVV0JnBPdsELjV7Eht2JBbv3ndd3fff8V1m+Yz7z5kzLzrryyC/MXTObAobU0Pv+8PIvfa6EjI7DlA5GpdHGcvMZJw50iIqWAb4ApIvItsDGaQYUiJiaGIYP70/WKXpzXsD3XXNODunWdqdO5sY2E/ccfj+Xyrjc4Pj+S/r2o+2effEWPHjeflLd8+Squv+4Ofpk9z7Fvt/F7/b57ae/n2CNhHw6qzpPXBG24ReQhEamqqleq6j5VfRZ4ChgO9Mij+E7Bz2JDbsSC3Pr3ou6//DKPvXv2nZS3atVaVq9e59hvJOL3+n03Alt+EZmKTI9bRKqJyHQRWS4iy0TkXjs/SUSmiMhq+39pO19EZIiIrLFXqJ+fU6yhetyVgTkiMktE7hSRcqo6U1UnqGrIO0gico+IVMvJeW7ws9iQW0zd/Vl3I7Dlj89depo4TjmQCjyoqvWwJnPcJSL1gEeBqapaG5hqPwdLcbW2nfpibc4ekqANt6reD5wBPAmcBywRkUki0ltEiudQ7gvA3MBGP6dAwIhMGQwG74hUj1tVt6rqQvvxQWAFUAXoDoyyTxvFPyMX3YGP1eI3oJSIVArlI+QYt13QTFW9A2s64CDgPiCnhTTr7PNfAC4Aljtp9E93kSm3mLr7s+5GYMsfnztVcZwCO5l26ptdmSJSA2sm3lyggqpm7Ne7DahgP66Ctao8gxQ7LyiOZF1F5DzgeeAd4BjwWA4mqqrpqvqjqvbBGnZ5F+iM1ajnGj+LDbnF1N2fdTcCW/743IUzHTCwk2mnoVnLE5FiwFfAffZixH98WVojub7NGUpkqjZwrZ3SgC+AS1XVScN70rWEqp4AJgATRCQht8GCv8WG3IgFufXvRd1HjBxM6zYtKFOmNKtW/0r/F99k7959vD7wWcqWTeKrrz5iyZLl9OjeO6rxe/2+G4Etf4hMpWvkpvmJSCGsRvszVf3azt4uIpVUdas9FLLDzt8MBN4TrGrnBS8/hMjUWmA08IWqLg0z6LNV1dWra0Sm/IlZOWnwgkiITK2qc5njNueclT8E9ScigjWGvUdV7wvIfw3YraoDRORRIElVHxaRy4G7gS5Ac2CIqjYL5T+UyFQtp5XIxjY6P4kGg8EQJSK45L0VcCPwh4gk23mPAwOAsSLSB2stzNX2sYlYjfYarM1mbsnJgSORKYPBYDjdidSKSFWdTfZy2AAdszlfgbCWhJqGOwq4ffv9PEbkdqhjbFJbV/ZX75npyt5QcInkGHe0MQ23wWAwYE0H9AtONgtuZS/P/FNE1onIehFxNaXPLX4V3KlatTJTfvySxYunk5w8jX5398lT/16L/TixP39QX7osfY+OM17JzKv7v6u4bNHbdPjpJTr89BIVOjYCIKFaWbqvH5mZ3+iVWz2NPRhei4u59e+Hz00k8JNWSdBZJZkniKwE7gd+x5oWCICq7o5mYMFmlcTExLBi2Sw6d7mOlJSt/DZnIr1uvJMVK1bnWKYb23Dsg/1uV6xYnkoVy7MoeSnFiiUyd+4keva89RT7YO+IH+ru1n5ql/+SdvgoF7x1B1PbPQJYDXfq4aOsfu/7k85NqFaWlp88lHkeZD9U4nXdW1/UnEOHDjNixGAaNT5liDNf+/f6tXNqH4lZJcnVuzlukhttnOBp99zJApz9qvqDqu5Q1d0ZKeqRBcHPgjvbtu1gUbI1s/LQocOsXLk6LN0FP9fdqf3u31ZyfN8hx+VG0ne07L0UF3Pr3+vXLi9FptLTxXHyGicN93QReU1EWorI+RkplIGIFBaRm0TkYvv59SLytojcZU9MzzV+FtwJpHr1qjRqWJ958xbliX+v6+7Wvuatl9Jx2gDOH9SXQiX/kUNIPKMcHaa8ROvxT1GmefYy8V7H7hYv/Xv92uWpyJSK4+Q1Tm5ONrf/NwnIU0JvXTbCLjtBRHoDxYCvsabCNAOyXSpnr/fvCyCxJQmmV+J3EhMTGDtmGA/+7xkOHoxs7/J0ZN3IKax442tQqPfIvznv2RtYeP9Qjm7fx6QL7uH43kOUanAmLUY8QPEGn5vX1JAr/HRzMseG296uLFzOU9UGIhKHtXSzsqqmicinhNg82F7vPxSCj3H7WXAHIC4ujrFjhjF69Hi++eaHsGz9XHc39sd2/SPzsOGzabT85CEA0o+ncvy41UjvW7Kewxu3c3btmvy+cEm+iT0SeOnf69cuL+ueH3rSTnEyq6SkiLwRoII1UERK5lSuiBQGigMJQMb5RQBXQyV+FtwB6w7/ypVreHPwKZo0UfXvdd3d2BctXyrzceXLmnJgZQoAhcsUhxjry5ZwRnmKnVmRdev/ylexRwIv/Xv92uWpyFQYyWucDJV8BCzln+WZN2INhfwrhM1wYCUQCzwBfGlPIWyBJVaVa/wsuNPqwqb06tWTP/5YzoL51ofvyacGMGnStKj797ruTu2bvnc35S6sS+Gk4ly28C2Wv/YV5S6sS8n61UHhyKadLHpoOABlW9Sh3sP/Jv1EKqQrix7+iL3Z3ITzuu5eiou59e/1a5eXIlNp6Y7EUvMFTqYDJqtqo5zysrGrDKCqW+w9Ky8G/lJVR5sN+llkqiCvnHSLWTlpyA2RmA44q2JPx1+91tvGeTqu4qTH/beIXGSvv0dEWgF/52SkqlsCHu8DxuU2SIPBYIg26rrLlXc4abjvAEbZ49oC7AFujmZQfqcg95jd4rbHfG2l5jmfFIIvts51ZW/wL+k++uI6mVWSDDQUkRL28wOhLQwGg8F/pJ8OPW4R6aWqn4rIA1nyAVDVN6Icm8FgMOQZfhoqCXUbNWP1S/FsUrEoxxUSvwoteS025LXYT17bd+7TlQFT3uTlH9/kriH3U6hIIfq+fjdvzH6P/hMH0n/iQM6oVyNfxh5Jez/HHgl7p6QhjpPXOJlV0kpVf8kpL9L4WWQqGF6KDXld97yyzxjjLl0hiae+6s8jHe/lxLHj9HvnQZKnL6Rui3NZNO135k+ck62f7Ma4/VL3/OY7L+0jMatkUoVrHY9yd97+Rb4XmXrLYV6e4GehJS/Fhryuuxf2sbGxFC5amJjYGArHF2Hv9j2O/Xkde6Ts/Rx7JOzDIT2M5DVBG25bVOpBoJyIPBCQnsVaWBMSEakpIv8TkcH2ysvbM25wusHPQktu8XPd89p+7/Y9TBz6LYPnfMDb84dz5OARls6y1Bau/t/1vDTpDW546hbiCuc8scpvdc8vvvODfTgo4jh5Taged2Gssew4Th7fPgD0DFWoiNwDvA8UBZpiLXWvBvwmIu1C2PXNWFqfnn7YeS0MhiwklEjk/Eubcf9Fd9Cv2X8oEl+EVle2Yeyrn/FQh3483e1hipUqRtfbr/Q6VEM+IV2cJ68Jtcv7TGCmiIxU1Y1hlnsb0MgWlnoDmKiq7UTkA+BboHEQn6e9yJQb/Fz3vLavf1EDdm7azsE91uzVBZPmUvuCOvwy/mcAUo+n8vOX0+jSt3u+iz2S9n6OPRL24eCn6YBOxrg/tJesAyAipUVksgO7jB+FItizUFT1Lwq4yJQb/Fz3vLbfvWUXZzU+m8JFCwNwbqvz2LwmhVLlS2eec8GlzUlZdaooldexR9Lez7FHwj4c0sJIOSEiH4nIDhFZGpD3rIhsFpFkO3UJOPaYiKwRkVUikuMgvpOVk2XtJesAqOpeESmfg82HwHwRmQu0Bl6xgyuHtfIy1/hZaMlLsSGv657X9muTVzNv4hxe/P510tLS2bhsHdM//5GHRj1FiaQSIMJfy9fz0eMf5LvYI2nv59gjYR8O6RLRHvdI4G3g4yz5g1T19cAMEakHXAucC1QGfhKRs1U16G+Ek+mAvwNX2r1lRKQ6MF5Vc9oF51ygLrBUVVeGdJINfhaZMniHWfJeMInEdMAvK93guM3599bPcvQnIjWA71S1vv38WeBQNg33YwCq+rL9fDLwrKpmP2cVZz3uJ4DZIjITS6ukNfYuNaFQ1WXAMgflGwwGg+eEM80vcLcum6H2PbqcuFtEbgIWAA+q6l6gCvBbwDkpdl5QnGiVTLL3mGxhZ92nqrscBGgw5Dlue8zflm7jyr773p9d2Ru8I5zZIoETKcLgPeAFLB26F4CBwK1hlgGE1iqpo6orAzYGzphMeYaInKGqC3Pj0GAwGPIj0V7KrqrbMx6LyDDgO/vpZqzp0hlUtfOCEqrH/SDWtL6B2cVA6M2CDQaDwVdEe362iFRS1a320yuxdhYDmAB8bk+drgzUBkJuOBN0OqCq3mb/b59N8rTR9qvgjhGZyt9CS8OGDuTiZe/TZuarJ+XX6NOJtrNfp83M16jz1PWZ+bXu6U673wbR9peBlG3XIGqxu7X38/sWCXunRHLJu4iMBuYA54hIioj0AV4VkT9EZAnQHrgfMu8HjgWWA5OAu0LNKIEQs0pEJNSekqjq1w7izzVGZCqy/r2uux+Ellpf1JznY86h0dt38nPbhwEo06oeZ93Xg/k3vEr68VQKly3B8V0HKHZ2FRq/349fOj9JkYqlaf7lE8xoeT/dd5+6EYQf6u53+0jMKhlRpZfjWSW3bP4034pMXWGnPlib/95gpw/JYUDd3hl+gIisFJE9IrJbRFbYeaXcBOxnwR0jMpW/hZZmzZ7LiX2HTso7o/clrHlrAunHUwE4vstaiVmhcxO2fDOH9OOp/P3XTo6s30ap88/ybd1PV/tw8NOS91BDJbeo6i1YKx3rqepVqnoV1iTxnFY/jgX2Au1UNUlVy2BdGuy1j+UaPwvuuMXPdfer0FJirYokNa/DhT+8QIvxT1OyUU0AilYszdHNuzPPO7p1D0Urls62DL/W/XSwD4fTQh0wgGoBA+oA24EzcrCpoaqvqGqmqICqblPVV4DqwYyMyJQhvxETF0vh0sX49bKnWPH8Z5w/7F6vQzJEiTRxnrzGyQKcqfZKntH282uAn3Kw2SgiDwOjMqbAiEgFrE2GNwUzMiJT0fPvdd39KrT095Y9bPveusG/f9FaNF0pXKY4R7ftpWiVMpnnFa2UxNFteyPu3629n9+3SNiHQ37oSTslxx63qt6NJdHa0E5DVbVfDmbXAGWw1AX3iMgeYAaQBPzbTcB+Ftxxi5/r7lehpe0/LKBMq3oAJNasSEyhOI7vPsj2yb9TuUdLYgrHEX9GORJrVmTfwjUR9+/W3s/vWyTsw8FPQyVOetwAC4GDqvqTiCSISHFVPRjsZHsZ5yN2OgkRuQUYkato8bfgjhGZyt9CS59+8g4XtmtL4aTidFj0NqtfG8em0dNp+ObttJlpzSpZfM97ABxalcLWCb/RZtbraGoaSx8dAenZT0rwQ91PV/tw8JM4khORqduw1uQnqWotEakNvK+q4c9ns8r7S1VzGiM3IlMGTzBL3v1JJKYDDj7D+XTAe//ydjqgkx73XUAzYC6Aqq7OSdbVnmCe7SGgQlgRGgwGQx6QH4ZAnOKk4T6mqsfF1qoVkThyvqqoAHTCmv4XiAC/hhuk34hxqeubnsNVkCF6uO0xz0xqmWvbtnuCqnga8gAnGyTkF5w03DNF5HEgXkQuAe4E/i8Hm++AYqqanPWAiMwIN0iDwWCINvlhYY1TnMzjfgTYCfwB/BeYCDwZykBV+6jq7CDHrs8uPxz8rNsA1jLeeXMnMX78yDz173XdT3e9jmFDB9J06XAazXgjM++cD+6n4U+v0fCn17hg/rs0/Om1k2wKVylLi7WfUPmOblGL38/vWyTsneKnWSUhb06KSCywTFXr5F1IFn7WKslpqOTee2/jgvMbUrxEMa688uZTjgcbKvFD3fOjfV75bn1RcwbF1qL2W/1IbvfAKeXUePYm0g4cYdMb4zLzzvnwQVA4uHA1tfs/5tu6e20fiZuTL1d3fnPysY35V6sEW6FqlYjkOAskr/CzbgNAlSqVuOyyjnw04vOw7Nz697ruBUGvY9bsuaRm0ToJpOwVF7Jz/D8Xokmdm3Lsrx0cWRV0TZrr+P38vkXCPhzSUcfJa5wMlZQGlonIVBGZkJFy61BEfsitLfhbtwFg4OvP8thj/UkPMuc3Wv69rntB1usAKNGiLid27efoemvVX0xCUarc3YO/Xv8yqv69rrvX9uEQyV3eo42Tm5NPhVtowK45pxwCGoWwy9zHTWJLEhOTGK7rfE2XLh3ZsXMXixb9QZs2uZ99YPAfZa+86KTe9hkPXc2Wod+RfuSoh1EZAskPY9dOCbV1WVHgduAsrBuTw1U11WG584GMzYWzUiqY0emuVXJhy6Z0vfxSOnfqQNGiRShRojgjRwzh5lvuibp/r+tekPU6iI2hTJfmLL704cysYo1rU6ZrC2o8dSNxJRLR9HTu3LOKd98bGVH/Xtfda/twOF1mlYwCmmA12peR/RZmwVgB/De73XMAVxsN+1m34cmnBlCzVlPOPqclvW68i+kzfnHcaLv173XdC7JeR6k2Dfh7zWaOb92Tmbe0x1P83vROfm96J1uGfU/KkPHZNtpu/Xtdd6/tw8FPY9yhhkrqqep5ACIynBz2QMvCswT/UchJoCokftZtcIuf614Q9Do+/eQdGrRrS1xScZos/IC/XhvDjtHTKNujFbvG/+LYXyTj9/P7Fgn7cPC+OXZOqK3LFqrq+cGe59qhyC2qmqPIlJ+1SszKyYKLWTnpDZGYDvhYjesdf/Fe3vB5vtUqaSgiB+zHgrVy8oD9WFW1RC59PocLdUCDwWCIBmk+6nMHbbhVNTa3hRqRKYPB4DdOi1klLinQIlM5SeUaTl/cDHeMKtvele/eu6a7si/oRPKmo4h8BHQFdqhqfTsvCRgD1AA2AFer6l6xFPwGA12AI8DNqrowVPlOFuDkhgyRqY1Z0gasnXAMBoMhX6FhJAeMBDpnyXsUmKqqtYGp9nOwZu3VtlNf4L2cCo9Kw21EprKnatXKTPnxSxYvnk5y8jT63d0nT/17LfZzuotMubVv8cZtXLXkHS6f9vIpx+r89zJu2PIpRZKKAVCoeDxtRz1Alyn9uXz6AGpeE3wDCD/UPZr2TomkyJSq/gzsyZLdHWuaNfb/HgH5H6vFb0ApEakUqvxo9bijRkxMDEMG96frFb04r2F7rrmmB3Xr1o66bSTsU1NTefjh52jYsD0XXXQFt99xc57597ruXtr7JfZ1Y35m2g2vnZKfUDmJSm3P43DKP0sgzr75Evb/uZmJlzzBT1f15/ynr6dQoUKexZ5f7cMhDXWcckkFVd1qP97GP/f7qnDyJuopdl5QotJwi0gJEXlZRD4RkeuzHHvXTdl+FtzZtm0Hi5KXAnDo0GFWrlwdlu6Cn+teEESm3NrvmLuK43tPFam64NleLHrxi5PvnahSKDEegLjEohzfd5jU1FMXNvul7tGyD4dwFuCISF8RWRCQ+objS603M9e/ANHqcY/AuhH5FXCtiHwlIkXsYy3cFOxnwZ1AqlevSqOG9Zk3b1Ge+Pe67gVZZMqNfdVO53Nk2172Lf/rpPxVI6ZQonZl/rXobS6f9jILnv4k25vifq57JOzDIZwxblUdqqpNAtJQBy62ZwyB2P932PmbgWoB51W184ISrYa7lqo+qqrfqGo3rF3ip4lImVBGgb9i6emHoxSa9yQmJjB2zDAe/N8zHDwYXAbUULCJjS/Muf26seS1caccq9TuPPYu28jXje9m4iVP0LT/TRQvXsyDKE8f8mDJ+wSgt/24N/BtQP5NYtEC2B8wpJIt0Wq4i4hIZtmq2h8YBvwMBG28A3/FgikD+llwByAuLo6xY4YxevR4vvkmPIVbP9e9IItM5da+ePXyFDujHF1+eonucweRUCmJyya/SNFyJal1TVs2TVwAwKEN2zn0107qnHNWvok9v9iHQyRvTorIaGAOcI6IpIhIH2AAcImIrAYutp+DtavYOmANVjt5Z07lR6vh/j+gQ2CGqo4EHgSOuynYz4I7YG1vtXLlGt4c7OTKKnL+va57QRaZyq39vpUpfNXgLr5tfj/fNr+fI1v38EOnJzm6cz+HN++iYutzAShatgQlalVi3fqN+Sb2/GIfDhrGX45lqV6nqpVUtZCqVlXV4aq6W1U7qmptVb1YVffY56qq3qWqtVT1PFVdkFP5UVmAo6oPB8mfJCIvuSnbz4I7rS5sSq9ePfnjj+UsmG99+J58agCTJk2Lun+v614QRKbc2rd69y4qtKxLkaRiXLlgCEsGfsXa0TOzLXPpm9/Q8s3/cvnUl0FgUf8x7N6ddb2bf+oeLftw8NOS95B7TkbFochfqprjVmh+Fplyqz7j24obXGFWTuaeSIhM9a5xleOv3qgNX+VbkalcY7RKDAaD3/CTKqfRKjEYDAb8daUbrYY7Q6skOesBEZkRJZ/5BnGpx21Eqgomboc6/jznXFf2Z69a5sre7+SHnW2cEq2bk0FFOCKhVWIwGAyRxslskfyC77RKwL9iQ0WKFOGX2d+xYP6PJC+aytNPPZin/r0W+ymoIlPDhg5kS8pikhdNDduvU/9Vq1am0vBXqfrNMKqOH0qJG3oAEFOiOBWHDqDadyOoOHQAMSVskaozq1H50zc58/fvKNm7pyvfbmOPtr1TUlHHyWvyfFaJU4LNKomJiWHFsll07nIdKSlb+W3ORHrdeCcrVqzOsUw3tuHYh9q6LDExgcOHjxAXF8eM6eN54MFnmDfvZOndYDdJ/FD3/GjvdeytL2rOoUOHGTFiMI0ad3RkE67/ihXL81uLlhxfsQZJiKfKmHfYfu+zFO9+KWkHDrJ/+BhK9rmG2BLF2DNoODFJpShUqTwJHS4k/cAh9o8al+1QidevnVP7SMwq6Vm9m+PGcNzGCZ7OKomWyFRFEXlPRN4RkTIi8qyI/CEiY3OSK8wJP4sNARw+fASAQoXiKFQoLqzxbD/XvSCLTM2aPZc9e/c5Pj83/rdt28HxFWsA0CN/c2L9X8RVKEtC+5Yc+nYKAIe+nUJC+wsBSN+zj2PL/oTUNNe+87N9OERy5WS0idZQyUhgOZZU4XTgb6zdHWYB77sp2M9iQ2D1IObPm8zmlMVMnTqL+fONyFS07b2O3S3h+o+rXIEidc7i6JKVxJYpTdouSxY6bdceYsuUjqrv/GYfDqrqOHlNtBruCqr6lqoOAEqp6iuquklV3wKqBzMqCCJT6enpNG3WiTNrNqVJk0acW+8cr0MynEZIfFEqDHqaXa+8h9pXdyfjfaOTX8kDkamIEa2GO7Dcj7McC7oJcUEQmcpg//4DzJz5K5d2apcn/r2ue0EWmXKLY/9xsVQY9DSHvp/Gkam/AJC2ey+xZZMAiC2bRNrufdHxnU/twyEPNlKIGNFquL8VkWIAqvpkRqaInAWsclOwn8WGypZNomTJEgAULVqUjh1bs2rVmjzx73XdC7LIlFuc+i/33AOcWPcX+z/+KjPvyIzfKNb9EgCKdb+EI9PD28zY69cuL197P/W4ozWP++kg+WtE5Hs3ZftZbKhSxQoMHz6I2NhYYmKEceO+Y+JE51PE/Fz3giwy9ekn79C2TUvKlk1iw7oFPPf864wY+UVE/be6sCnFu13CsT/XUeVLa6/ZPUM+Yt/wL6jw+pOUuLIzJ7ZuZ8eD/QGILVOaKmPeJiYxAU1XSt54JcXrXXSKPrzXr11eikzlh7FrpxiRqSgQajqgE/ykmWDIPxTklZORmA7Yqdpljr94kzf9YESmDAaDwWv8tHLSiExFAT9dchlOH9z2mH8p29yVfatdc13Ze01+GLt2ihGZMhgMBiBN88PSGmcYkSmDwWDAX0MlBU5kyq3gjxvfVatWZsqPX7J48XSSk6fR7+6gv29R8e+12E9BFZny2t6J7bChAzl/yQjOm/ZmZt5Z7z9I/SkDqT9lII3mvk/9KQMBkEJx1Bx0N+dNHUT9KW9QvGXom6Jev3ZOSVd1nLwmz2aViEh5Vd3h9PxoiEyBO8Efp76D3W6uWLE8lSqWZ1HyUooVS2Tu3En07HnrKfbB3hE/Cy0VZJEpP9S99UXNGVKoJrUG38MfHe47pZwznr6ZtIOH2TzoSyrc3JnEhmex7v63iStTkjqfPcnSyx6m1c7fPKt7JGaVtK7S0XFjOGvz1NNSZCopSyoDzBOR0iKS5KZsLwV/3Pretm0Hi5KXAnDo0GFWrlwdlu6Cn4WWCrLIlB/qPmv2XFL3HgxaTlK3C9n1zWwA4s+uxoHZfwCQuns/qfsPk9iwVsRjj4R9OPhpAU60hkp2Ab8HpAVAFWCh/TjXeCn4E0nf1atXpVHD+sybZ0Smom3v59jd2kfiM1u8eT1O7NzHsfVbATi8bAOlLm0KsTEUqVaexAa1KFy5bFT85+X3PZINt4hssBVRk0VkgZ2XJCJTRGS1/T88xa8AojWr5CHgEuAhVf0DQETWq+qZoYxEpC/QF0BiSxJMr8TvJCYmMHbMMB783zOnrFQzGPIbZXpcxG67tw2w84upxNeuSv1Jr3E8ZSeHFqyEdP/MyAhGFGaVtFfVXQHPHwWmquoAEXnUfv5IbgqO1qySgSIyBhgkIpuAZ3AgS6aqQ4GhEHyM20vBn0j4jouLY+yYYYwePZ5vvvkhz/x7LfZTkEWm/Fx3YmNI6tKCpZ0f+icvLZ2/nh2R+bTehJc4unZLNsbev3bhkAezSroD7ezHo4AZ5LLhjtqsElVNUdV/YwU3BUiIRLleCv5EwvewoQNZuXINbw4emqf+vRb7KcgiU36ue8nWDfl7zWaOb92dmRcTX5iY+CIAlGjTEE1N4+/VKVHxn5ff93D0uAMlqO3UN2txwI8i8nvAsQqqutV+vA0Xq8ijNVSSiapOEJEpQC0AEblFVUfkYBYULwV/3PpudWFTevXqyR9/LGfBfOvD9+RTA5g0aVrU/Xst9lOQRab8UPdPP3mHc9u1JS6pOI0XDCNl4BfsHD2VMt1bsfubWSedG1emJHVGPw3pyvFtu1nbb0i+rHu4hHPTMXB0IAgXqepmESkPTBGRlVnsVURy3cU3IlNRwO08Id9W3OBr/LzkPRLTARtXbOX4q7do2y+O/YnIs8Ah4DagnaputbdwnKGqudpJxYhMGQwGA5AWod0kRSQRiFHVg/bjS4HngQlAb2CA/f/b3PowIlMGgwFw32N+sHKbXNsO3PKzK9+RIIIrIisA48WSd44DPlfVSSIyHxgrIn2AjcDVuXVgRKYMBoOByM0qUdV1QMNs8ncD4S3XDkJUZpWoah9VnR3kmGuRKb9qVhitkvyt13G62ueF72FDB/LUgve5f/KrmXnxJRP5zyeP89D0N/jPJ48TX8JalxFfIpEbP3iA+354hbu/eYEKZ1eNavxOMVolESAaWiV5pZtgtEryl72fY3drn1e+W1/UnB7x53DNG3cyqNPDAFz26PX8vf8QM96bQLs7uhFfMpEfBoymy2PXc/zIMX4a/BXlalWmx/O3UKd99svY81KrpE75po4bw5U75p9+WiXRxM+aFUarJH/rdZyO9nnle9bsufy9/+RVwOdecgG/j7PGrn8f9zPnXtIEgPK1q7LmV+t7sHPtFkpXLUf58tkvmc9TrRIf9bijJTLVOeBxSREZLiJLRORzEXE1q8TPmhWBGK0S/+h1+NneS9/FypXk4M59ABzcuY9i5UoCsHXFRup3bgZA1Ya1KFWlLFWrVIpK/OGQpumOk9dEq8f9UsDjgcBW4ApgPvBBlHz6BqNVYiiIZAzLznhvAvElErh34su06t2JLcs2kJYPtE40jD+vifrKSaCJqjayHw8Skd7BTnQiMuVnzQowWiVe2Ps5drf2Xvo+tHM/xcuV4uDOfRQvV4rDuw4AcOzQ33z50D/9t0dmD2Hduo1RiT8cNB/0pJ0SrR53eRF5QEQeBEqIPaExJ5+qOlRVm6hqk2DKgH7WrACjVeJHvQ4/23vpe/lPv3NBT2tu9wU927Bsyu8AFC2RQGyhWACaXduB9XNXBL3yzEutEj/pcUerxz0MKG4/HgWUBXaKSEUg2U3BftasMFol+Vuv43S0zyvfn37yDl3atyWxdHEen/M2UwaNY8Z7E7jhnXtpenU79m7exWd3DQag/FlVuPr1O0CV7atTGPdw8E5MXmqV5NcZdtnhhVaJI5Epo1ViMPgLL1dORmI6YNWk+o6/eil7lha46YDPeeDTYDAYQpKWnu44eY0RmTIYDAbyZCOFiGFEpqJAXKy7l/VEWmqEIjEY8g43wx0vVmofwUhyh5/GuI3IlMFgMBDeRgpeY0Sm8tA3QL9+ffj99yksWPAjo0YNoUiRInnm3+u6+1loadjQgWxJWUzyoqlh27r176XvvLLv8tpt9Pv9Hfr8+HJmXvvHr+O2qa9y66SX+NcH91GkhLX7YaWGNbllYn9umdifW3/oz9mdmoQdU3aEs3WZ1xiRqSgI7hQKMlRSuXIFpk79isaNO3L06DE+/fQdJk2azqefjjvpvGBDJX6oe360d+sbLBGlQ4cOM2LEYBo1Dk+Z061/L33nlf2o7rdz/Mgxur7xX4Zf+hgANVrXZ+Ovy9G0dNo9eg0AMwaMIa5oYdJOpKJp6SSWL8WtP/QnsWzJQoCrMcbSxc5y3BjuPbSmYMwqEZEykSjHD4I7oYiLiyU+viixsbHEx8ezdev2PPHvdd39LLQElojSnr37wrKJlH8vfeeV/aZ5qzi67+RFOBtmLUXTrBkcWxatpXilJABSjx7PzI8rUihi82f9tAAnWiJTA0SkrP24iYisA+aKyEYRaeumbL8K7gBs2bKdN98cyp9/zmH9+vkcOHCQqVNn5WwYAf9e193PQktu8dK/n9+3QBpc3YZ1M/6ZrFapUS36TBlAn8kvM/mJEeCytw3+GiqJVo/7clXdZT9+DbhGVc8CLsESncqWwC3v09MPRyk07yhVqgRdu15K3boXUbNmMxIT47n22iu9DstgyNe0vLsb6anpLBv/S2be1uS1DL/kUUZ1e5oWd14BUNStnwIv6wrEiUjGQG+8qs4HUNU/gaB345xolfhVcAegQ4eL2LBhE7t27SE1NZVvvplEixYX5Il/r+vuZ6Elt3jp38/vG8B5PVtzVsfGTLj33WyP716zhRNHjgLUd1xoEPykDhithvtdYKKIdAAmichgEWkrIs/hUqvEr4I7AJs2baFZs8bEx1udg/btW7Fq1Zo88e913f0stOQWL/37+X07s20Dmt/elXF93iD16PHM/JLVyiGxVtNVokoZkmpVBtjgOKgg+KnHHZV53Kr6loj8AdwBnG37qQ18A7zgpmw/CO4EY/78ZMaPn8icOd+TmprG4sXLGD788zzx73Xd/Sy0BJaIUts2LSlbNokN6xbw3POvM2LkF3ni30vfeWXfbchdnNGyLvGli3Hnb0OYPegrWt7ZjdjCcVz76aMAbFm0hslPjKBqk7NpcecVpJ9IQ1X58cmRXDXs/l2nFBom6T6SdTUiU1Eg2HRAp5iVk4aChtuVk49u/NT19LzCRao6bnOOH0sJ6c/eBWwwEAt8qKoDXIZ3EkZkymAwGIjcrBIRiQXeAS4D6gHXiUi9SMZqRKYMBoOBiMopNwPWqOo6ABH5AugOLI+Yh3B+ZcL4NdoONAKqZ0k1gC0R8tHXr/Z+jt3U3dTdb76jkbC2WFwQkPoGHOuJNTyS8fxG4O1I+o/WUEmGyNTGLGkDMCNCPvr62N7Psbu193Psbu39HLtbe69jjygaMHXZTuHvReiCaM0q6RPimGuRKYPBYMjHbAaqBTyvaudFDC9uThoMBsPpzHygtoicKSKFgWuBCZF0EC097rzA7aWJl/Z+jt2tvZ9jd2vv59jd2nsde56hqqkicjcwGWs64EequiySPvKtrKvBYDAYsscMlRgMBoPPMA23wWAw+AxfNtwi0llEVonIGhF5NEzbj0Rkh4gszYXfaiIyXUSWi8gyEbk3TPuiIjJPRBbb9mGvIhWRWBFZJCLf5cJ2g4j8ISLJIrIgF/alRGSciKwUkRUi0jIM23NsvxnpgIjcF6b/++3XbamIjBYRx1KeInKvbbfMid/sPicikiQiU0Rktf2/dJj2/7b9p4tIyP22gti/Zr/2S0RkvIiUCtP+Bds2WUR+FJHKTm0Djj0oIpqhtx+G72dFZHPA+98lHHs7v59d/2Ui8mow+wKB1xPZczHxPRZYC9QECgOLgXph2LcBzgeW5sJ3JeB8+3Fx4M8wfQvW/HaAQsBcoEWYMTwAfA58l4v4NwBlXbz2o4D/2I8LA6VcvIfbgOph2FQB1mPJBAOMBW52aFsfWAokYN2Q/wk4K9zPCfAq8Kj9+FHglTDt6wLnYK1laJIL/5cCcfbjV3Lhv0TA43uA953a2vnVsG64bQz1OQri+1ngfw7fr+zs29vvWxH7efncfo5Ph+THHnfmclJVPQ5kLCd1hKr+DOzJjWNV3aqqC+3HB4EVWA2KU3tV1Yz9mQrZyfHdYRGpClwOfOg46AghIiWxvlDDAVT1uKruy2VxHYG1qroxTLs4IN7Wek8AtuRwfgZ1gbmqekRVU4GZwL9CGQT5nHTH+vHC/t8jHHtVXaGqq5wEHMT+Rzt+gN+w5geHY38g4GkiQT57Ib4jg4CHg9k5sHdEEPs7gAGqesw+Z0duyz8d8GPDXQXYFPA8hTAaz0ghIjWAxli95nDsYkUkGdgBTFHVcOzfxPri5FZ/UoEfReR3EQl3JdqZwE5ghD1U86GIZL/bRc5cC4wOx0BVNwOvA38BW4H9qupUGHop0FpEyohIAtCFkxdIOKWCqm61H2/DW92dW4EfwjUSkf4isgm4AXg6DLvuwGZVXRyuzwDutodqPgo1zBSEs7Hew7kiMlNEmrqIw/f4seH2HBEpBnwF3JelF5Mjqpqmqo2wekvNRMTRzh0i0hXYoaq/hxtvABep6vlYqmV3iUibMGzjsC5f31PVxsBhrOGCsLAXJHQDvgzTrjRWj/dMoDKQKCK9nNiq6gqsoYUfgUlYm3mkheM/mzKViOoSOUdEnsDaY/GzcG1V9QlVrWbb3u3QXwLwOGE09NnwHlALS8NoKyG2MAxCHJAEtAAeAsaKiKc7rXuJHxvuqC8nDYWIFMJqtD9T1a9zW449zDAd6OzQpBXQTUQ2YA0PdRCRT8P0udn+vwMYjzXs5JQUICXgCmEcVkMeLpcBC1XV+fb2FhcD61V1p6qeAL4GLnRqrKrDVfUCVW0D7MW6PxEu20WkEoD9P88v10XkZqArcIP945FbPgOucnhuLawfzMX2568qsFBEHO/6q6rb7U5LOjCM8D57YH3+vraHG+dhXXUGvUF6uuPHhjvqy0mDYf/CDwdWqOobubAvlzETQETisTZPXunEVlUfU9WqqloDq87TVNVRj9P2lygixTMeY93ocjyzRlW3AZtE5Bw7qyO5k6m8jjCHSWz+AlqISIL9PnTEusfgCBEpb/8/A2t82/nWQ/8wAehtP+4NfJuLMnKNWOL8DwPdVPVILuxrBzztjvPP3h+qWl5Va9ifvxSsm/SON4/M+MGzuZIwPns232DdoEREzsa6Oe561xvf4vXd0dwkrDHKP7FmlzwRpu1orEu1E1gfwD5h2F6EdXm8BOtyOxnoEoZ9A2CRbb8UeDqX9W9HmLNKsGbhLLbTsnBfN7uMRlgSlkuwvkilw7RPBHYDJXNZ7+ewGpulwCfYMwwc2s7C+qFZDHTMzecEKANMBVZjzXBICtP+SvvxMSzp48lh2q/Bur+T8dnLdlZICPuv7NduCfB/QJXcfEfIYXZSEN+fAH/YvicAlcK0Lwx8ase/EOiQm8/Q6ZLMkneDwWDwGX4cKjEYDIYCjWm4DQaDwWeYhttgMBh8hmm4DQaDwWeYhttgMBh8hmm4DWEhIj1sdbg6Ds69z151l1tfN4vI21nyaohIiojEZMlPFpHmQcqpkZ3SncHgV0zDbQiX64DZ9v+cuA9LDCpiqOoGrMU4rTPy7B+R4hqe7ovB4FtMw21wjK3RchHWgohrA/JjReR1sfSul9i6yfdgaYpMF5Hp9nmHAmx6ishI+/EVtnjQIhH5SURyEm8aHejffvyF3bOeJSIL7XTKkvisvXgR+U5E2tmPLxWRObbtl3Z9EZEBYmmwLxGR152/YgZDdPDzZsGGvKc7MElV/xSR3SJygVqiV32BGkAjtTZKTVLVPSLyANBeVXNamjwbS5dcReQ/WMu6Hwxx/lggWUT6qSVzeg3wbyztkEtU9ai9vHs0EHLDggzE2hjgSeBiVT0sIo8AD4jIO1grHuvY8ZVyUp7BEE1Mw20Ih+uAwfbjL+znv2MJQL1vN6KoarhazFWBMbaeRWGsDROCoqrb7THrjiKyHUhV1aW2ZvjbItIIS/3v7DBiaAHUA36xRecKA3OA/cBRYLhYuw6FvfOQwRBpTMNtcISIJAEdgPNERLF2sVEReSiMYgL1FQK3HXsLeENVJ9jDFs86KCtjuGQ7/4hW3W8/b4g1DHg0G7tUTh4izIhDsPTRTxm7F5FmWKJWPbGkUDs4iM9giBpmjNvglJ7AJ6paXS2VuGpYPePWwBTgv2LtTJPRyAMcxNriLYPtIlLXnhFyZUB+Sf6R5u2NM77GEhu7Bqv3n1HOVrWkQ2/E+nHJygagkYjEiEg1/pEX/Q1oJSJn2XVIFJGz7XHukqo6EeuHoaHD+AyGqGEaboNTrsPS8A7kKzv/Q6yZHktEZDFwvX18KDAp4+Yk1sYL3wG/Yqm/ZfAs8KWI/I5DqU619MznANtVdZ2d/S7Q246hDtZmD1n5BesHZzkwBEtpDlXdCdwMjBaRJXbZdbB+eL6z82Zj7flpMHiKUQc0GAwGn2F63AaDweAzTMNtMBgMPsM03AaDweAzTMNtMBgMPsM03AaDweAzTMNtMBgMPsM03AaDweAz/h8Xn3gVlt4GwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************************\n",
      " \n"
     ]
    }
   ],
   "source": [
    "for name in models:\n",
    "    print(f'{name} ConfusionMatrix')\n",
    "    predictions= models.get(name).predict(X_test)\n",
    "    score = round(accuracy_score(y_test, models.get(name).predict(X_test)), 3)\n",
    "    confusionMatrix = CM(y_test, models.get(name).predict(X_test))\n",
    "    sns.heatmap(confusionMatrix, annot=True, fmt=\".0f\")\n",
    "    plt.xlabel('Actual Values')\n",
    "    plt.ylabel('Prediction Values')\n",
    "    plt.title('Accuracy Score: {0}'.format(score), size = 15)\n",
    "    plt.show()\n",
    "    print(\"******************************\")\n",
    "    print(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "789566dd",
   "metadata": {
    "papermill": {
     "duration": 0.016755,
     "end_time": "2022-11-18T06:41:43.108156",
     "exception": false,
     "start_time": "2022-11-18T06:41:43.091401",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e542c029",
   "metadata": {
    "papermill": {
     "duration": 0.016713,
     "end_time": "2022-11-18T06:41:43.142459",
     "exception": false,
     "start_time": "2022-11-18T06:41:43.125746",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# PREDICTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1f337545",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:41:43.178622Z",
     "iopub.status.busy": "2022-11-18T06:41:43.178228Z",
     "iopub.status.idle": "2022-11-18T06:41:43.185349Z",
     "shell.execute_reply": "2022-11-18T06:41:43.184106Z"
    },
    "papermill": {
     "duration": 0.028692,
     "end_time": "2022-11-18T06:41:43.188126",
     "exception": false,
     "start_time": "2022-11-18T06:41:43.159434",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "def prediction(text):\n",
    "    x= CV.transform([text]).toarray()\n",
    "    lang= model.predict(x)\n",
    "    lang= lb.inverse_transform(lang)\n",
    "    print(\"This word/sentence contains {} word(s).\".format(lang[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "edd8b9d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:41:43.225600Z",
     "iopub.status.busy": "2022-11-18T06:41:43.224612Z",
     "iopub.status.idle": "2022-11-18T06:41:43.238090Z",
     "shell.execute_reply": "2022-11-18T06:41:43.236385Z"
    },
    "papermill": {
     "duration": 0.036448,
     "end_time": "2022-11-18T06:41:43.242113",
     "exception": false,
     "start_time": "2022-11-18T06:41:43.205665",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This word/sentence contains English word(s).\n"
     ]
    }
   ],
   "source": [
    "prediction(\"THIS IS MY FIRST NOTEBOOK ON KAGGLE AND I HAVE TAKEN HELP FROM YASEMIN TURKER'S NOTEBOOK \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1177fa6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-18T06:41:43.329709Z",
     "iopub.status.busy": "2022-11-18T06:41:43.329267Z",
     "iopub.status.idle": "2022-11-18T06:41:43.369890Z",
     "shell.execute_reply": "2022-11-18T06:41:43.368850Z"
    },
    "papermill": {
     "duration": 0.087278,
     "end_time": "2022-11-18T06:41:43.372677",
     "exception": false,
     "start_time": "2022-11-18T06:41:43.285399",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(model, open(\"model.pkl\", \"wb\"))\n",
    "pickle.dump(CV, open(\"transform.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d8212ee",
   "metadata": {
    "papermill": {
     "duration": 0.016368,
     "end_time": "2022-11-18T06:41:43.406005",
     "exception": false,
     "start_time": "2022-11-18T06:41:43.389637",
     "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": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 285.413036,
   "end_time": "2022-11-18T06:41:44.348119",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-11-18T06:36:58.935083",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}