Language Detection Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3952d15a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import string \n",
    "import re \n",
    "import codecs\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import feature_extraction\n",
    "from sklearn import linear_model\n",
    "from sklearn import pipeline\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b679eeed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "      <th>Language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nature, in the broadest sense, is the natural...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"Nature\" can refer to the phenomena of the phy...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The study of nature is a large, if not the onl...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Although humans are part of nature, human acti...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1] The word nature is borrowed from the Old F...</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text Language\n",
       "0   Nature, in the broadest sense, is the natural...  English\n",
       "1  \"Nature\" can refer to the phenomena of the phy...  English\n",
       "2  The study of nature is a large, if not the onl...  English\n",
       "3  Although humans are part of nature, human acti...  English\n",
       "4  [1] The word nature is borrowed from the Old F...  English"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(r\"C:\\Users\\Abdullah\\ISLR\\Great Learning\\Language Detection.csv\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dce5a34",
   "metadata": {},
   "source": [
    "# Split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc71c0fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = dataset.iloc[:,0], dataset.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2530f444",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00d1322e",
   "metadata": {},
   "source": [
    "# Vectorizer & Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89473ace",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ngram_range = words appear in sets of 1, 2 and 3\n",
    "vectorizer = feature_extraction.text.TfidfVectorizer(ngram_range=(1,3), analyzer='char')\n",
    "\n",
    "pipe_lr_r13 = pipeline.Pipeline([\n",
    "    ('vectorizer', vectorizer),\n",
    "    ('clf', linear_model.LogisticRegression())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ea9305",
   "metadata": {},
   "source": [
    "# Model Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0258cfa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;,\n",
       "                 TfidfVectorizer(analyzer=&#x27;char&#x27;, ngram_range=(1, 3))),\n",
       "                (&#x27;clf&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;vectorizer&#x27;,\n",
       "                 TfidfVectorizer(analyzer=&#x27;char&#x27;, ngram_range=(1, 3))),\n",
       "                (&#x27;clf&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(analyzer=&#x27;char&#x27;, ngram_range=(1, 3))</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('vectorizer',\n",
       "                 TfidfVectorizer(analyzer='char', ngram_range=(1, 3))),\n",
       "                ('clf', LogisticRegression())])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr_r13.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49037a95",
   "metadata": {},
   "source": [
    "# Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f6f0b2bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pipe_lr_r13.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d70f3ee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['English'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr_r13.predict([\"This is to inform you that you have a meeting scheduled today\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "54c34f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Spanish'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr_r13.predict([\"Buenas tardes\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "548a0dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Spanish'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr_r13.predict([\"¿Cómo estás?\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c686e2fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Arabic'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr_r13.predict([\"شکریہ\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b576947",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "870cb5ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98.25918762088975 %\n"
     ]
    }
   ],
   "source": [
    "acc = (metrics.accuracy_score(y_test, y_pred))*100\n",
    "print(acc, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f62f14c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      " [[117   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0  72   0   2   0   0   0   0   0   0   0   0   0   0   3   0   0]\n",
      " [  0   1 103   2   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   1 256   1   0   0   0   1   0   0   0   0   1   0   0   0]\n",
      " [  0   0   0   1 194   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   2   3   0   0 100   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  70   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  18   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0 145   0   0   3   0   4   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0  77   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0 114   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0 149   0   1   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0 142   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   2   0   0   1   1 153   1   0   0]\n",
      " [  0   1   1   2   0   0   0   0   0   0   0   0   0   0 135   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  91   0]\n",
      " [  0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0  96]]\n"
     ]
    }
   ],
   "source": [
    "matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "print('Confusion Matrix: \\n', matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e4e7d314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1i4uHXspj4x+lfv36AefZqtnmWGjNzs21me3EmgGioqL4bvG3rMr9mcWLFvPjip8CznTSWERMQ3O5XOStzSu+nZ+XT4u45gFlLvwqm9btk4ht0rB42aypnzP/84WMfPr2gF4YG/WWVFRUxMVDL+XCi4aScX5GUDJt1WxzLLRm5+bazHZizSU1aNCA3qf25rOszwLOctJYRExDS++aRk7OanLX5FJYWMjUKdMYOGhgQJlffjib0zP+Orhj4VfZvP3sVB58eQzH1jo25Oo9xBjDP666juS2ydx48w1ByQR7NdscC63Zubk2s51Y88aNG9m2bRsAe/fu5asvviI5uU3AuU4ai7CYPqYioqOjmTBxPIMGZOB2u8kcPoyU1JRK5+3bu4/F33zPzY/+1RCeuvs5igqLuOOiUQC065J82P3VWW9J8+fN56033iL1pFS6p/UA4N4Hx9Cv/zkB5dqq2eZYaM3OzbWZ7cSa16/bwNVXXIPb7eagOcgFg/+P/gP7h2y9NrIj5rD9ytD50A4XTvMmKeU0Oh+ahx62r5RSKuxpQ1NKKRUWtKEppZQKC9rQlFJKhQVtaEoppcKCNjSllFJhIWLOQ6sMW4fWA7z884tWcjOTL7eSq5SqXk47vL466AgppZQKC9rQlFJKhQVtaEoppcKCNjSllFJhQRuaUkqpsKANTSmlVFiIqIaWNTOLDimdSE1uz7ixjwcl85oR13J83AmkdUyv1ONfu/sN7ugzkgf+9kjxst3bd/PkiGcYM+ABnhzxDHu27wEgd/nvPHzBWB6+YCwP/d+j/PD50ko9Z97aPPqfOYAu7dNI79iVZ558tlI5vtgYY5u5NrO1Zvu5NrO1Zvu5wc4OueljRKQl8CrQHDgITDLGTCzrMRWZPsbtdtO+XUc+nvkRrngXvbr35pXXJ9MupV1A9c79Zi516tZhxOVXsXhpdoUfd+g8tF+zc6hZuyavjHyd0R/cBcB74z+kznG1OWfEWcx64TP27NjD+bdkULi3kKgaUURFR7F943YeumAsj3z5AFHRUcW5FTkPbd269axft57OXTqxc+dOenXrzdvT3qZdStsyH1feeTC2xthWrtbs7Fyt2dm5lc122vQxB4BbjTHtgO7AdSIS8GxyixZmk5SUSEJiAjExMQwZOpgZ02cEXGyvPr2IjY2t9ONbp59IneNqH7Zs2VfL6Z5xMgDdM05m6ZfLAYipFVPcvIr2H0Dw+ZqWKy6uOZ27dAKgXr16JLdNpqCgoJLfwV9sjbGtXJvZWrP9XJvZWrP9XBvZIdfQjDHrjDFLvF/vBFYCrkBzCwoKiG8ZX3zbFe8iv2BdoLFW7Ny8k+OaHAfAcU2OY+eWncX3rVmWywMZD/PQ+Y9w0T1DD9s6q4zfc39n6Q/L6Hpy5XaZlmRrjG2+dlqzc3NtZmvN9nNtZIf0pa9EpBXQGVjg476rgasBWh7fstwsX7tWRSq3hVOdEjq0YvSHI1m3ej2vjnqd1N4p1KhZo1JZu3bt4uKhl/LY+EepX79+wLXZGmObr53W7Nxcm9las/1cG9kht4V2iIjUBd4FbjLG7Ch9vzFmkjEm3RiT3qRJ43LzXC4XeWvzim/n5+XTIq55MEsOmnqN6rF943YAtm/cTr3YekesE5fUnJhaMRT8Wrn/ZoqKirh46KVceNFQMs7PCKjeQ2yNsc3XTmt2bq7NbK3Zfq6N7JBsaCJSA08ze8MY814wMtO7ppGTs5rcNbkUFhYydco0Bg4aGIzooOvQ9yS++3AhAN99uJAOp7UHYFPeZtwH3ABsLtjCn7l/0sjl//t3xhj+cdV1JLdN5sabbwha3bbG2OZrpzU7N9dmttZsP9dGdsjtchTP9uaLwEpjzH+ClRsdHc2EieMZNCADt9tN5vBhpKQGfKwJwy7JZM7sOWzatJmkE1ozeszdDL8is8KPf+n2yaxalMOubbsYecZoBv5zAGePOIsXb32Zb9/7jti4hoz4j+fIxdVLVpP14udERUchxwgX3j2Uug3r+l3z/HnzeeuNt0g9KZXuaT0AuPfBMfTrf47fWSXZGmNbuTaztWb7uTaztWb7uTayQ/Gw/V7AHGA5nsP2AUYaYz452mMqcth+qHHi9DE6fYVSqrqVddh+yG2hGWPmQiWPR1dKKRWx9F9upZRSYUEbmlJKqbCgDU0ppVRY0IamlFIqLGhDU0opFRZC7ijHSHF52yut5Nbq18ZKLsDemausZSulVKB0C00ppVRY0IamlFIqLGhDU0opFRa0oSmllAoL2tCUUkqFBW1oSimlwoI2NKWUUmEhohpa1swsOqR0IjW5PePGPh7yudeMuJbj404grWO634+NbxLHl+Om8NOLX7Hi+S+48XzPeW9jLruFvLey+f6/s/j+v7Pof/LpxY9pn9CObyd+yIrnv2DZpM+pWaNmldZcFltjbDM7FH8uyuO0sbCZra+f/dxgZ4fcfGiVUZH50NxuN+3bdeTjmR/hinfRq3tvXnl9Mu1S2gX03LZyAeZ+M5c6desw4vKrWLw0u0KPOXRidfPYpsTFNuX7nBXUrVWHxc9+yt/GXMnQUwexa+9uxk/732GPizomiiXPzeSysTey7LeVxNZrwLbdOzh48GDxOhU5sboyNZfH5hhHys9FRThxLJxYs75+gWWXNR9axGyhLVqYTVJSIgmJCcTExDBk6GBmTJ8RsrkAvfr0IjY2tlKPXb/lT77PWQHArr27WfnHr7gaNz/q+menn8qy31ay7LeVAGzZue2wZlYVNR+NzTGOtJ+LsjhxLJxYs75+9rIjpqEVFBQQ3zK++LYr3kV+wbqQzQ2mE5rF0/nEk1jw8/cAXJ8xnKX/+4wXb32cBnWPA6CNKwGDYeYjr7P42U+5feg/qrPkw9gc40j+uSjNiWPhxJptceJYBDs7Yhqar12rIoFPjG0rN1jqHFubd++ZxE3P3cvOPbt47qNXScrsSadrz2bdlj8Zf81oAKKjoumV2pVLHrmBXjefz/k9+3F6557VXL2HzTGO1J8LX5w4Fk6s2RYnjkWwsyOmoblcLvLW5hXfzs/Lp0Xc0XfBVXduMERHRfPumEm88eX7vD/3UwD+3LaJgwcPYozh+U/e5OTkTgDkbVrH7OXfsXnHVvbu38cnC7+ky4ntq7H6v9gc40j8uTgaJ46FE2u2xYljEezsiGlo6V3TyMlZTe6aXAoLC5k6ZRoDBw0M2dxgePHWx1n5Rw4T3n2+eFnz2KbFX5/fsx8rcn8BYFb2bDoktKNWzWOJOiaKUzt056ffQ+Pq+jbHOBJ/Lo7GiWPhxJptceJYBDs7YqaPiY6OZsLE8QwakIHb7SZz+DBSUlNCNhdg2CWZzJk9h02bNpN0QmtGj7mb4VdkVuixPVO7MuyswSz7bSXf/3cWACNfGstFp2XQKSkVYwy5G9ZyzRP/BmDbru38593nWfT0xxhj+GThV3yy8MsqrflobI5xpP1clMWJY+HEmvX1s5cdMYftRwqdD00pFc70sH2llFJhTxuaUkqpsKANTSmlVFjQhqaUUiosaENTSikVFrShKaWUCgsRcx5apLB5aH3bxwdZyf35to+s5CqlIotuoSmllAoL2tCUUkqFBW1oSimlwoI2NKWUUmFBG5pSSqmwoA1NKaVUWIiohpY1M4sOKZ1ITW7PuLGPR2zuNSOu5fi4E0jrmF6pxz/W/zayr5/GrCteKF7Wrkki7136FDOveJ4XLniQujG1D3tMi3pN+fHmGVx18pBKPaetsbCZrTXbz7WZrTXbzw12dsg2NBGJEpHvRWRGMPLcbjc33XgLH854n++XL2bqO1NZ+dPKiMsFuGzYpXz48QeVfvy05bPInHrXYcse7X8rY2c/T7+XrmLWqrlc3W3oYfePPuMffP3bwko9n82xcOLr57SadSyqJttpuTayQ7ahAf8CgjNqwKKF2SQlJZKQmEBMTAxDhg5mxvTAe6XTcgF69elFbGxspR+/MG852/fuOGxZYmxLFqxdBsDc3MX0b9On+L6zW/fkj23r+HVTbqWez+ZYOPH1c1rNOhZVk+20XBvZIdnQRCQeGAi8UN66FVVQUEB8y/ji2654F/kF6yIu15ZVm3I568QeAAxoeypx9ZoAUKvGsVzb7e9MnPdqpbNtjoUTXz+n1axjUTXZTsu1kR2SDQ14ArgDOHi0FUTkahHJFpHsjRs3lRvoa2ZuEZ+TnvrFabm23PHJOC7rksFHmc9RN6YWRQcPAHBzr0xezJ7GnqJ9lc62ORZOfP2cVrOORdVkOy3XRnbIXctRRM4F/jTGLBaRvkdbzxgzCZgEkJbe5chRKcXlcpG3Nq/4dn5ePi3imgdcr9NybVm9ZS3DptwJQELDeE5L7A5Ap7h2DEjuw119r6Z+zbocNAfZf6CQV5d8WOFsm2PhxNfPaTXrWFRNttNybWSH4hZaT+A8EckF3gZOF5HXAw1N75pGTs5qctfkUlhYyNQp0xg4aGCgsY7LtaVR7QYACML1PS7hjR88Fxwe+uZN9PrvJfT67yW8lP0uz3z3pl/NDOyOhRNfP6fVrGNRNdlOy7WRHXJbaMaYu4C7ALxbaLcZYy4NNDc6OpoJE8czaEAGbrebzOHDSElNCTTWcbkAwy7JZM7sOWzatJmkE1ozeszdDL8is8KPf3LQKLof35GGtY5j/j/fZsLcV6hToxaXdckAYNaqOUxdPjMotYLdsXDi6+e0mnUsqibbabk2ssXXPsxQUaKhnVvWemnpXcy8BXOrpKZIptPHKKWqW89uvVicvcTnG20ht4VWkjHma+Drai5DKaWUA4Tie2hKKaWU37ShKaWUCgva0JRSSoUFbWhKKaXCgjY0pZRSYSGkj3KsbgfNUa+8FbKOEXv/o9g6vP61VZOt5F7WZriVXKVUaNItNKWUUmFBG5pSSqmwoA1NKaVUWNCGppRSKixoQ1NKKRUWtKEppZQKCxHV0LJmZtEhpROpye0ZN/bxoGTmrc2j/5kD6NI+jfSOXXnmyWeDkrtv3z76nNKXbl1OIb1jVx6876Gg5B5iYyyCkTt51Gvc2utO7j3vweJlu7ftZsKVT3F3v3uZcOVT7N6+p/i+TyfNYtQ5Yxg94D5+nPtTtdRc1bk2s52WazNba7afG+zsiGlobrebm268hQ9nvM/3yxcz9Z2prPxpZcC5UdHRPPzYwyxZvpiv5n7JpP9OYuVPPwecW7NmTT75bAYLlsxnfva3fDbrcxZ+tzDgXLA3FsHI7XF+d26cdN1hyz59IYu23ZN5cOa9tO2ezMwXsgAoyFnHok8Xc+9Hd/OvSdfxxgPvcNDt37mDoTwWVZ3ttFyb2Vqz/Vwb2RHT0BYtzCYpKZGExARiYmIYMnQwM6bPCDg3Lq45nbt0AqBevXokt02moKAg4FwRoW7dugAUFRVRVFSEiM8pgPxmayyCkdsmvTV1jqtz2LKlXy7jlL91A+CUv3Xjhy+WFi/v2j+NGjE1aBzfmKbHN2HN8twqr7kqc21mOy3XZrbWbD/XRnbENLSCggLiW8YX33bFu8gvWBfU5/g993eW/rCMrienByXP7XbTPa0HrVokcvqZp9G1W9eg5NoaC1u5OzbvpEGT4wBo0OQ4dm7ZCcDWP7fRsHnD4vUaNmvAtg3bQqJmmz9vTqtZx6Jqsp2WayM7Yhqar5m5g7XFA7Br1y4uHnopj41/lPr16wclMyoqiu8Wf8uq3J9ZvGgxP66o3HtEpdkaC9tjfOQT+ljm5/M5cSycVrOORdVkOy3XRnbENDSXy0Xe2rzi2/l5+bSIax6U7KKiIi4eeikXXjSUjPMzgpJZUoMGDeh9am8+y/osKHm2xsJWbv1G9di2cTsA2zZup15sPcCzRbZ1/dbi9bZu2EaDpseFRM02f96cVrOORdVkOy3XRnbENLT0rmnk5Kwmd00uhYWFTJ0yjYGDBgaca4zhH1ddR3LbZG68+YYgVOqxceNGtm3bBsDevXv56ouvSE5uE5RsW2NhK7fjae2Z/8ECAOZ/sICOp3coXr7o08UUFRaxKW8Tf/7+JwntW4VEzbZybWY7LddmttZsP9dGdsRcbT86OpoJE8czaEAGbrebzOHDSElNCTh3/rz5vPXGW6SelEr3tB4A3PvgGPr1Pyeg3PXrNnD1Fdfgdrs5aA5yweD/o//A/gHXC/bGIhi5z9/2Er8s/JVd23Zxx2mjOO/6gfS76mwm3fwi8979lti4hlwzYQQALVq3IO2cLowZ9CBRUcdw0d0XckyUf/+jhfJYVHW203JtZmvN9nNtZIuvfZhOk5bexcxbMDfouTp9TNXQ6WOUUhXVs1svFmcv8flGm/P++imllFI+aENTSikVFrShKaWUCgva0JRSSoUFbWhKKaXCgjY0pZRSYSFizkOrDCceAu9Etg6v/27DHCu5AN2b9baWrZSqHP2LrZRSKixoQ1NKKRUWtKEppZQKC9rQlFJKhQVtaEoppcKCNjSllFJhQRuaUkqpsBBRDS1rZhYdUjqRmtyecWMfj9hcm9lOyP0jZy0jzvpn8cfA5P9j2vPvs2PrTm77+11c2vMKbvv7XezctjNkaq6qbKfl2szWmu3nBjs7JOdDE5EGwAvASYABrjDGzD/a+hWZD83tdtO+XUc+nvkRrngXvbr35pXXJ9MupV1AtTot14k1Vza3IidWu91uhqRdyrMznuCDyR9Rv0E9Lr7+Qt58+h12bt/FNaOu9Pm48k6s1tfPfq7W7OzcymY7cT60icBMY0xboCOwMtDARQuzSUpKJCExgZiYGIYMHcyM6TMCLtRpuTaznZYLsGTuD7Q4IY7m8c34dtZ8zhlyJgDnDDmTeTO/DcmanTbOOhZVk+20XBvZIdfQRKQ+0Ad4EcAYU2iM2RZobkFBAfEt44tvu+Jd5BesCzTWcbk2s52WC/Dlh7M54299AdiyaRuNmjUCoFGzRmzdvL3Sufr62c+1ma0128+1kR1yDQ1IBDYCL4vI9yLygojUKb2SiFwtItkikr1x46ZyQ33tWhXxudXqF6fl2sx2Wm5RYRHfZn3HqecG/7qM+vrZz7WZrTXbz7WRHYoNLRroAjxnjOkM7Ab+XXolY8wkY0y6MSa9SZPG5Ya6XC7y1uYV387Py6dFXPOAi3Vars1sp+Uu+CqbNu1PJLZJQwBiGzdg84bNAGzesJmGjY6rdLa+fvZzbWZrzfZzbWSHYkPLA/KMMQu8t6fhaXABSe+aRk7OanLX5FJYWMjUKdMYOGhgoLGOy7WZ7bTcLz/4mtO9uxsBepzdnVlTPwdg1tTP6XHOKZXO1tfPfq7NbK3Zfq6N7JCbPsYYs15E1opIsjHmF+AM4KdAc6Ojo5kwcTyDBmTgdrvJHD6MlNSUgOt1Wq7NbCfl7tu7j8XfLOGWsTcWL7vougu579qH+eStWTR1NeXe/40KqZptZzst12a21mw/10Z2qB623wnPYfsxwG/A5caYrUdbvyKH7avIo/OhKRV+yjpsP+S20ACMMT8A6dVdh1JKKecIxffQlFJKKb9pQ1NKKRUWtKEppZQKC9rQlFJKhQVtaEoppcJCSB7lqFQw2Dy0fveBXVZy60TXtZKrVCTQLTSllFJhQRuaUkqpsKANTSmlVFiocEMTkZNF5KpSyzJEZLmI5IvIw8EvTymllKoYf7bQxgDnHbohIscDbwHNge3AnSJyeXDLU0oppSrGn4bWEZhX4vbfAQE6GWNSgCzg6iDWppRSSlWYPw2tEbC+xO1zgG+MMfne29OB1sEqzIasmVl0SOlEanJ7xo19PGJzbWY7LTfY2Tde/S/atkyhV5c+xcuWL13BOX360/fk0zmjx9ksWbQk0JIdN85Oef2qItdmttNyg55tjKnQB7AOuNP7dU08M0mPKnH/tcDuiuYF86NLWmez98DuMj927d9hEhITzE+rVpjte7aa9h1OMkuWZZf7uHDLdWLNoTgWm/Zt8Pkx/bMPzBfzPzNtU5KLl/U941Tz9odvmk37Npi3PnjD9Ojd46iPD8dxDsXXT2uu/tzKZndJ62yO1gv82UL7ARghImnAaOBYYFaJ+xOADYG1V3sWLcwmKSmRhMQEYmJiGDJ0MDOmz4i4XJvZTsu1kd2j9yk0bNjgsGUiws4dOwHYsX0HzeOaBVKy48bZSa+f7Vyb2U7LtZHtT0N7AIgDFgIjgc+NMdkl7j8XWFDpSiwrKCggvmV88W1XvIv8gnURl2sz22m5trMPeejxB7j3rvvpkNSZMXfdx+gHKj8TNjhvnJ34+mnN9nNtZFe4oRljvgW6ADcBw4FBh+4TkUZ4Dgp5rtKVWOZrZm4Rn5OehnWuzWyn5drOPuTlSZN5cNz9LFv9PQ8+dj//uvbmgPKcNs5OfP20Zvu5NrL9OrHaGLPKGPOUMeZVY0xhieWbjTE3G2O+qXQllrlcLvLW5hXfzs/Lp0Vc84jLtZnttFzb2Ye8/foUzv3bQAAyLjiPJdnfB5TntHF24uunNdvPtZEdMVcKSe+aRk7OanLX5FJYWMjUKdMYOGhgxOXazHZaru3sQ5rHNWfeN98CMOerOSSemBhQntPG2Ymvn9ZsP9dG9lGvti8iX1Yizxhjzqh0NRZFR0czYeJ4Bg3IwO12kzl8GCmpKRGXazPbabk2sq+67BrmzfmWLZu20D6pE3fefTsTnh3PyNvuxn3gADWPrcl/ngns0GSnjbOTXj/buTaznZZrI1t87cMEEJFcwPedZTDGJFS6mkpKS+9i5i2YW9VPqyKYTh+jVPXo2a0Xi7OX+Hyj7ahbaMaYVtYqUkoppYIsYt5DU0opFd60oSmllAoLR93l6IuINASuBLoBDTmyIYbsQSFKKaXCW4UbmoicgOdq+y3wTBdTH9jCX41tE57rOyqllFJVzp9djg8CDYAz8FxVX4AL8TS2R4CdQO8g16eUUkpViD+7HM8AnjfGfOW91BV4DvvfA4wSkVRgLHBJsItUKtTYOrz+8qxbreQCvHz2eGvZNhw0B61lHyN2Dh9wG7eVXIAoibKWHS78nQ9thffrIu/nWiXu/ww4KxhFKaWUUv7yp6FtBGK9X+8E9gGtStwfw+ENTimllKoy/jS0H4GO4DmUEc80Mv8UkeNFpBVwNfBz0CtUSimlKsCf99A+BG4VkVrGmL3A/Xgm+Fzjvd8A/xfk+pRSSqkKqXBDM8Y8Czxb4vaXInIKcDHgBt73zpmmlFJKVTm/TqwuzTtjdXa5KyqllFKWRdSlr7JmZtEhpROpye0ZNzawKTycnGsz22m5NrMDzb2m/WX89/THeKzX6CPuG5hwJm/1f456NeoA0LhWLK+cPZFHeo7kkZ4juTL1omqpuapz9+3bR59T+tKtyymkd+zKg/c9FLRsmz9zbrebHum9GJwxJKi5Tnv9gp3tz5VCXqrAasYYc2UA9Rx6rpuBEXjel1sOXG6M2RdIptvt5qYbb+HjmR/hinfRq3tvzh00kHYp7QKq1Wm5Tqw5Usdidt58Zv3+Nf/sMPyw5bHHNqR9o3Zs3Lv5sOUb9mzirnkPV2vNVZkLULNmTT75bAZ169alqKiIM089m7PPOYuTu58csjUDPPvkcyS3a8POHTuDkgfOfP2Cne3PFtrwCn4ERERcwI1AujHmJCAK+HuguYsWZpOUlEhCYgIxMTEMGTqYGdNnBBrruFyb2U7LtZkdjNyft+awq+jIq8kNazeYN395rxKzFZYtlMfiaESEunU9J7kXFRVRVFSEiM+psvxis+b8vHxmfjqLzCsyg5J3iBNfv2BnV7ihGWOOKf0B1ACSgeeB7/Bc1zEYooFaIhIN1AYKAg0sKCggvmV88W1XvIv8gnWBxjou12a203JtZtvKTWvagS37tvHHzvwj7mtSqxGP9BzJPd1uJrnhiX5nO20sDnG73XRP60GrFomcfuZpdO3WNeBMmzXfceu/efCR+znmmOC+4+PE1y/Y2QGNqDHGbYz51RhzDbAZz6WvAmKMyQceB/4A1gHbjTFZQcg9Ylkw/pNzWq7NbKfl2sy2kRtzTA3+ltSPqb9+dMR92/bv4IavR3HXvId5beW73NDxcmpFH+tXvpPGoqSoqCi+W/wtq3J/ZvGixfy44qeAM23V/OnHn9KkSWM6p3UOOKs0J75+wc4O5r8InwIXBBrinaImA0jAc2X/OiJyqY/1rhaRbBHJ3rhxU7m5LpeLvLV5xbfz8/JpEdc80HIdl2sz22m5NrNt5Dar3YQmtRoztufdPHnqg8Qe24CHe47kuJj6HDh4oHj35Jodf7Bhzybiajet9ppt5pbWoEEDep/am8+yPgs4y1bN3327gE9mfErKiScx/JLLmf3VN1w5bETAueDM1y/Y2cFsaI2AYFyx9UxgjTFmozGmCHgP6FF6JWPMJGNMujEmvUmTxuWGpndNIydnNblrciksLGTqlGkMHDQw4GKdlmsz22m5NrNt5K7dVcC1X97BjbPv5sbZd7Nl3zZGznuY7YU7qBdTF8Hzn23TWo1pXqcpG/aW/4+e7Zpt5gJs3LiRbdu2AbB3716++uIrkpPbBJxrq+b7HrqXVbk/81POCia/8TKnntaHF199IeBccObrF+zsgM5DAxCRBnia0M3A4kDz8Oxq7C4itYG9eK7yH/C5btHR0UyYOJ5BAzJwu91kDh9GSmpKoLGOy7WZ7bRcm9nByL2h4xW0i21DvZi6PH3aw0z7dQZf5/m+dkG7hq0Z0vpc3OYgB81BXvzxTXYX7anymqsyF2D9ug1cfcU1uN1uDpqDXDD4/+g/sH/AuTZrtsWJr1+ws8XXPkyfK4oc5OjHVQmeyT77G2MWVbqav57rPjxzrR0AvgdGGGP2H239tPQuZt6CuYE+rVLVTqeP+YtOH3M4nT7Go2e3XizOXuLzjTZ/ttBe5ciGZvA0slXAW8aYoJxUYYwZA4wJRpZSSqnI4M+1HIdbrEMppZQKSIW3u0XkHhE5qYz7U0XknuCUpZRSSvnHnx3J9wIdyrj/JHQ3oVJKqWoSzHdGj8VzEIdSSilV5cp8D01E6gMNSixqJCLH+1g1FrgEWBu80pRSSqmKK++gkJuBQ++LGeAJ74cvAtwRlKqUUkopP5XX0L72fhY8je19YFmpdQywC/hOZ6xWKjA2zxX7uiDwS0L50rfFWVZybZ0rZpOeK1a9ymxoxpjZwGwAETkB+K8xZkFVFKaUUkr5w5/z0C63WYhSSikVCH/OQ7tORD4v4/4sEbkmOGUppZRS/vF3xupfy7h/FXBFQNUopZRSleRPQ2sNLC/j/h+96yillFJVzp+GVgPPydNHc2w59yullFLW+NPQVgFlHZ97NrA6sHLsypqZRYeUTqQmt2fc2McjNtdmttNybWY7oea81flc1++m4o//S7mI91+YziP/HFe8LLPHVVzX76aQqLcqs7Vm+7nBzvZnPrQ7gEeAh4EHjDGF3uU1gLsPfRhjHgmookqoyHxobreb9u068vHMj3DFu+jVvTevvD6ZdintAnpup+U6sWYdi+Bkl3cemtvt5rKTr2TCh4/RLL5p8fLnH3iJ2vXqcMlNF/p8XHnnoYXiWFRXrhNrDrWxKGs+NH+20CYA3wCjgAIRmSsic4B1wGhgLhCyMwguWphNUlIiCYkJxMTEMGToYGZMnxFxuTaznZZrM9uJNf8wbxlxxzc/rJkZY/hmxjz6ZvQOuXptZmvN9nNtZFe4oRljivDsVvw3kAd0BrrguX7jHcAZeK4oEpIKCgqIbxlffNsV7yK/YF3E5drMdlquzWwn1jx7+lxOLdW4Viz8iYaNG+BKaFHpXCeOhdZsP9dGtl/XljHGFBljHjPGdDLG1PF+dAa+Ap4ECipdiWW+dq2KBN5/nZZrM9tpuTaznVZzUWERCz5bSO+BPQ9b/vWHc45ocv5y2ljYzLWZ7bRcG9mVvliaiMSKyI0ishRYCFwLbKx0JZa5XC7y1uYV387Py6dFXPOIy7WZ7bRcm9lOqzn76yUknZRIwyYNipe5D7j5duZ8+gzqFVC208bCZq7NbKfl2sj2u6GJyDki8g6Qj+d9tRjgPqC9MaZtpSuxLL1rGjk5q8ldk0thYSFTp0xj4KCBEZdrM9tpuTaznVbz1x/OoW9Gn8OWfT93KfFJ8TSJaxxQttPGwmauzWyn5drIrtC1HEUkAbgcyATi8WyJTQMuBkYZY96rdAVVJDo6mgkTxzNoQAZut5vM4cNISU2JuFyb2U7LtZntpJr37d3P93OWcuMj/zhs+ezpc+h7XmC7G8FZY2E712a203JtZJd52L6IXAxcCZyKZzbqj4FXvJ8T8JybNri6G1pFDttXKtI5bfoYpXwp67D98rbQXgd+A24C3jTGbDl0h4hU7AQ2pZRSqgqU9x5aIdAKyAD6i0gt6xUppZRSlVBeQ2uOZ+usEfAasEFEXhSRPoTwOWdKKaUiT5kNzRizzRjztDGmC5COp6n9Dc95Z3MBAxxnu0illFKqPP5cKWSJMeY6oAVwGZ7pYgBeEJEfRORuEUm1UaRSSilVHr/PQzPG7DfGvGmMOQNIAh4CGgL3A0uDXJ9SSilVIRU6D+1ojDG5wD0iMgY4B52xWqmQZevw+id+eMJK7k2dbrKSq8JXQA3tEOM5mW2m90MppZSqcpW+lqNSSikVSrShKaWUCgva0JRSSoUFbWhKKaXCgjY0pZRSYSGiGlrWzCw6pHQiNbk948Y+HrG5NrOdlmszOxJrnnbfuzx41sM8MXRi8bLlny9nwtCJjOx6N3k/5R3xmG3rtzGm931889qcaqm5qnNtZjstN9jZ1dbQROQlEflTRFaUWBYrIp+JyK/ezw2D9Xxut5ubbryFD2e8z/fLFzP1nams/GllxOXazHZars3sSK05bVAXLn8q87BlzZKaceljF9Oqcyufj5kx/hPa9GhTbTVXZa7NbKfl2siuzi20yUC/Usv+DXxhjGkNfOG9HRSLFmaTlJRIQmICMTExDBk6mBnTZ0Rcrs1sp+XazI7UmhO6JFC7fu3DljVNaEqTVk18rv/j1z8RG9+QZolNq63mqsy1me20XBvZ1dbQjDHfAFtKLc7AM4Eo3s9/C9bzFRQUEN8yvvi2K95FfsG6iMu1me20XJvZWnP5CvcWMvuVbzjjqtMrneHEsXBazU4ai1B7D62ZMWYdgPfzUf9tE5GrRSRbRLI3btxUbrCvmblFAp8Bx2m5NrOdlmszW2su3+f/+4JeF/ekZu2alc5w4lg4rWYnjUVQLn1VHYwxk4BJAGnpXcqdPdvlcpG39q83pPPz8mkR1zzgOpyWazPbabk2s7Xm8q1dsZblX6zg0ydnsm/nPuQYITommh4XnlLhDCeOhdNqdtJYhNoW2gYRiQPwfv4zWMHpXdPIyVlN7ppcCgsLmTplGgMHDYy4XJvZTsu1ma01l++aF67mzo9u586PbqfnRT3oe/mpfjUzcOZYOK1mJ41FqG2hTQcygUe9nz8MVnB0dDQTJo5n0IAM3G43mcOHkZKaEnG5NrOdlmszO1JrfmvkO6xZ/Bu7t+3hkQFjOfPqM6h9XC2mj5vB7q27eeWmV4lrE8cVT18ecL3Bqrkqc21mOy3XRrb42odZFUTkLaAv0BjYAIwBPgCmAMcDfwBDjDGlDxw5Qlp6FzNvwVxrtSqljk6nj1FVqWe3XizOXuLzjbZq20Izxlx0lLvOqNJClFJKhYVQew9NKaWUqhRtaEoppcKCNjSllFJhQRuaUkqpsKANTSmlVFgItfPQlFIOY+vw+os/tZML8Gb/J6xlq+qjW2hKKaXCgjY0pZRSYUEbmlJKqbCgDU0ppVRY0IamlFIqLGhDU0opFRa0oSmllAoLEdXQsmZm0SGlE6nJ7Rk39vGIzbWZ7bRcm9lac/Byr+uQyctnPc4TfcYccV9G4lm8d+4k6tWoC0C0RHF9x0wm9BnDf/qMJrVRm2qp+WiuGXEtx8edQFrH9KBlHhKqr19VZUdMQ3O73dx04y18OON9vl++mKnvTGXlTysjLtdmttNybWZrzcHN/SrvWx5Y8OQRyxsd25AOjVPYuGdz8bIzj+8NwM3f3Md93z3B8HZDEHxOn2W15qO5bNilfPjxB0HJKimUX7+qyo6YhrZoYTZJSYkkJCYQExPDkKGDmTF9RsTl2sx2Wq7NbK05uLk/bfmVnUW7j1h+RepQXlv5Loa/JipuWS+OZZt+BmB74U52H9hDUoMTqrzmo+nVpxexsbFBySoplF+/qsqOmIZWUFBAfMv44tuueBf5BesiLtdmttNybWZrzfZzuzbryOZ928jdmXfY8twdeZzcrBPHyDE0rdWIpONOoPGx/jUQm6+fLU57/WxkR8y1HI0xRywT8W83RDjk2sx2Wq7NbK3Zbm7MMTFccOIA7l/wxBH3fbF2HvF14xjXaxQb927m562rcRu3X/k2Xz9bnPT62cqOmIbmcrnIW/vXf3L5efm0iGsecbk2s52WazNba7ab27xOE5rVbsR/+owGPO+lPd7nbu6c+zDb9u/g5Z+mFK/7cI87Wbf7z2qv2TYnvX62siNml2N61zRyclaTuyaXwsJCpk6ZxsBBAyMu12a203JtZmvNdnP/2JnP5Z/dxrVfjuTaL0eyed9WbvvmQbbt30HMMTHUjIoBoGPjdriNm7xd/u3Gsvn62eKk189WdsRsoUVHRzNh4ngGDcjA7XaTOXwYKakpEZdrM9tpuTaztebg5t7ceQQnNUqmXkxdnj9jLG+vms4Xa+f5XPe4mvW4p9u/MMawed82nvzhpWqp+WiGXZLJnNlz2LRpM0kntGb0mLsZfkVmwLmh/PpVVbb42ofpNGnpXcy8BXOruwylVBDpfGjKl57derE4e4nPN9oiZpejUkqp8KYNTSmlVFjQhqaUUiosaENTSikVFrShKaWUCgva0JRSSoWFiDkPTSnlLDYPrb/q89ut5D5/5jgruapidAtNKaVUWNCGppRSKixoQ1NKKRUWtKEppZQKC9rQlFJKhQVtaEoppcJCRDW0rJlZdEjpRGpye8aNfTxic21mOy3XZrbWbD830OwRqZfyTN9HeaTHqCPuG9DqDF475xnq1qgDwEmN2nJ/9zt5uMdI7u9+Jymxbaql5nDKDXZ2tTU0EXlJRP4UkRUllo0TkZ9FZJmIvC8iDYL1fG63m5tuvIUPZ7zP98sXM/Wdqaz8aWXE5drMdlquzWyt2X5uMLLnFHzHY4ufOWJ57LENSG3Ulk17txQv21m4i/98/19Gfvswk1a8yjXtKzeHmdPGOZRfv9KqcwttMtCv1LLPgJOMMR2AVcBdwXqyRQuzSUpKJCExgZiYGIYMHcyM6TMiLtdmttNybWZrzfZzg5H9y9YcdhftPmL5JcmDeWfVBxj+mi/y9515bNu/HYC8XeuocUw00eL/tSmcNs6h/PqVVm0NzRjzDbCl1LIsY8wB783vgPhgPV9BQQHxLf+Kc8W7yC/wb1r2cMi1me20XJvZWrP9XFvZnZu0Z+v+bfyxM/+o63Rt1pnfd+ZxoPjPVcU5bZyd9PqF8ntoVwCfHu1OEblaRLJFJHvjxk3lhvmamVvE56SnfnFars1sp+XazNaa7efayI45pgYZif14N+foWwmuOnFc2CaDl398q1LP4bRxdtLrF5INTURGAQeAN462jjFmkjEm3RiT3qRJ43IzXS4XeWvzim/n5+XTIq55wLU6LddmttNybWZrzfZzbWQ3rd2EJrUa8VCPkfynz/3E1mzAA6f8m+Ni6gPQsGYD/tX5Kv63/FX+3Fv+P9JVUbNTc21kh1xDE5FM4FzgEuOrfVdSetc0cnJWk7sml8LCQqZOmcbAQQMjLtdmttNybWZrzfZzbWTn7Srguq//zS3f3MMt39zDlv3bGD3/UbYX7qB2dC1uS/sHU36dzq/bfguZmp2aayM7pK62LyL9gDuBU40xe4KZHR0dzYSJ4xk0IAO3203m8GGkpKZEXK7NbKfl2szWmu3nBiP7nx0up11sa+rWqMvEUx/kvZyPmZ0/3+e6Zx1/Ks1qNeFvif35W2J/AB5b/BQ7CndVac3hkmsjW4K4EeTfE4u8BfQFGgMbgDF4jmqsCWz2rvadMeba8rLS0ruYeQvmWqpUKRVudPoY5+rZrReLs5f4fKOt2rbQjDEX+Vj8YpUXopRSKiyE3HtoSimlVGVoQ1NKKRUWtKEppZQKC9rQlFJKhQVtaEoppcJCSJ2HplSkO2gOWss+RvT/10NsHV4/eMb1VnIBpp37tLXscKE/4UoppcKCNjSllFJhQRuaUkqpsKANTSmlVFjQhqaUUiosaENTSikVFiKqoWXNzKJDSidSk9szbuzjEZtrM9tpuTazbeXmrc2j/5kD6NI+jfSOXXnmyWeDlu20sbhmxLUcH3cCaR3Tg5YZrOx/dbqc1/s9wTOn3V+87OLkDF45ezxP9r2XJ/veS3rT9gC0aZBQvOypvvdxSlyXKq+3LE75/au26WOCqSLTx7jdbtq368jHMz/CFe+iV/fevPL6ZNqltAvouZ2W68SaI2ksKnIe2rp161m/bj2du3Ri586d9OrWm7envU27lLZlPq6889BCbSwqYu43c6lTtw4jLr+KxUuzA84LNLvkeWipjdqw78A+bukyguu+ugfwNLS9B/bx/upZhz2uZlQMRQcPcNAcpGHN43jqtPsYNuuWw34eyjsPzdZYhNrvX1nTx0TMFtqihdkkJSWSkJhATEwMQ4YOZsb0GRGXazPbabk2s23WHBfXnM5dOgFQr149ktsmU1BQEHCuE8eiV59exMbGBiUr2Nk/bl7FzsLdFVp3v7uwuHnFRNWgMhsatsbCSb9/EdPQCgoKiG8ZX3zbFe8iv2BdxOXazHZars1smzWX9Hvu7yz9YRldTw58N5PTx8Ipzk08g6f63se/Ol1OnRq1i5e3aZjIM6c9wNOn3c+zy16zetUYfzjp9y9iLn3l6z8eEZ9brWGdazPbabk2s23WfMiuXbu4eOilPDb+UerXrx9wnpPHwik+yf2Kt3+ZjgEubXc+I1IvZOIPLwOwautvXPfVaOLrxnFLlyvJ3rCMooMHqrdgnPX7FzFbaC6Xi7y1ecW38/PyaRHXPOJybWY7Lddmts2aAYqKirh46KVceNFQMs7PCEqmU8fCSbbt38FBDAbDrNzZtGmYcMQ6ebvWse/Afk6oH+8joeo56fcvYhpaetc0cnJWk7sml8LCQqZOmcbAQQMjLtdmttNybWbbrNkYwz+uuo7ktsncePMNQckEZ46F0zSseVzx16fEdeH3HfkANKvduPignSa1GuGqF8efezZVS42lOen3L2J2OUZHRzNh4ngGDcjA7XaTOXwYKakpEZdrM9tpuTazbdY8f9583nrjLVJPSqV7Wg8A7n1wDP36nxNQrhPHYtglmcyZPYdNmzaTdEJrRo+5m+FXZIZE9u1p19C+cTL1Y+oy+ezHeePnD2nfOJnE447HGMOfezbx9NJXAUiJbc3g1gNwGzcHjeG5pa+xo3BXldZ7NE76/YuYw/aVcgKdPsbZdPoY+/SwfaWUUmFPG5pSSqmwoA1NKaVUWNCGppRSKixoQ1NKKRUWtKEppZQKC9rQlFJKhYWIObE6Uuh5TM6mY+xsNs8Vu/mbUVZyJ/R5yEpuddDfHqWUUmFBG5pSSqmwoA1NKaVUWNCGppRSKixoQ1NKKRUWtKEppZQKCxHV0LJmZtEhpROpye0ZN/bxoGReM+Jajo87gbSO6UHJq4rsvLV59D9zAF3ap5HesSvPPPls0LJtjLHNXJvZWrP9XJvZoVrzZW3/zmM972d01zuKlw1K6M+orrczMv02buh4LcfF1Acg9tiGTOwzlpHptzEy/TYuajOkyuutyuxqmw9NRF4CzgX+NMacVOq+24BxQBNjTLnTtlZkPjS32037dh35eOZHuOJd9Orem1den0y7lHYBfBcw95u51KlbhxGXX8XipdkBZQUjuyLnoa1bt57169bTuUsndu7cSa9uvXl72tu0S2lb5uPKO0fK1hjbytWanZ0baTUfOg/txOMS2e8uZHi7i3lg0WMAHBtVk33u/QCc5upN8zrNeWvVVGKPbch17a8qXs+X8s5DC7WxCNX50CYD/UovFJGWwFnAH8F8skULs0lKSiQhMYGYmBiGDB3MjOkzAs7t1acXsbGxQaiw6rLj4prTuUsnAOrVq0dy22QKCgoCzrU1xrZybWZrzfZzbWaHcs05239j94Hdhy071MwAYqJigOBtqITyWJRWbQ3NGPMNsMXHXROAOwjmKwIUFBQQ3zK++LYr3kV+wbpgPoUj/Z77O0t/WEbXkwPfrWlrjG2+dlqzc3NtZjux5vMSBvDQKfdwcrM0PlrzafHyRrViGZl+Kzd3vo4Tj0sMmXptZIfUe2gich6Qb4xZWoF1rxaRbBHJ3rix3L2S+Nq1KuJzqzVi7Nq1i4uHXspj4x+lfv36AefZGmObr53W7Nxcm9lOrHn6mk8YNf9+Fm5YTF9XbwB27N/BqG/v5+Hs8bz764dcnnIpx0bVDIl6bWSHTEMTkdrAKOCeiqxvjJlkjEk3xqQ3adK43PVdLhd5a/OKb+fn5dMirnlly3W8oqIiLh56KRdeNJSM8zOCkmlrjG2+dlqzc3NtZjux5kMWbVhC5yYdADhg3Ow+sAeAP3blsWnvZprWbhoy9QY7O2QaGpAEJABLRSQXiAeWiEhQRi69axo5OavJXZNLYWEhU6dMY+CggcGIdhxjDP+46jqS2yZz4803BC3X1hjbfO20Zufm2sx2Ws1Nav31T32Hxiexfs+fANStUQfBs8XT+NhGNK3dmE17N1d7vbayQ+Zq+8aY5UDxvw7eppZekaMcKyI6OpoJE8czaEAGbrebzOHDSElNCTh32CWZzJk9h02bNpN0QmtGj7mb4VdkBqFie9nz583nrTfeIvWkVLqn9QDg3gfH0K//OQHl2hpjW7k2s7Vm+7k2s0O55itSLqNNgxOpW6MOD58yhhm5Mzkpth3NajflIIYt+7by5i9TAWjdIIlzE/pz0Lg5aAxv/jKNPd4ttqqqtyqzq/Ow/beAvkBjYAMwxhjzYon7c6lgQ6vIYfuRQqePUSo86fQxHmUdtl9tW2jGmIvKub9VFZWilFIqDOi/3EoppcKCNjSllFJhQRuaUkqpsKANTSmlVFjQhqaUUioshMx5aCo49NB6pcKTrcPr318z1UouwPkJlZuuprL0r59SSqmwoA1NKaVUWNCGppRSKixoQ1NKKRUWtKEppZQKC9rQlFJKhYWIamhZM7PokNKJ1OT2jBv7eMTm2sx2Wq7NbK3Zfq7N7Eiv+dPJWdwxYBS39x/Jpy/PAuC7Txdye/+RXNLmcn5bviYYJQe15ohpaG63m5tuvIUPZ7zP98sXM/Wdqaz8aWXE5drMdlquzWyt2X6uzexIr3ntqjy+mjKbB969h0c/eoAlXy9lXe56WraO5+ZnbqBt1zYB1xvsmiGCGtqihdkkJSWSkJhATEwMQ4YOZsb0GRGXazPbabk2s7Vm+7k2syO95vzVBZzYKYmatWoSFR1Fu67JZGctwXViC1okxgVcq42aIYIaWkFBAfEt44tvu+Jd5Besi7hcm9lOy7WZrTXbz7WZHek1t2wdz8+LfmHn1l3s37ufH2YvY/P6zQHXWFqwxyJiLn3la2ZuEZ+TnoZ1rs1sp+XazNaa7efazI70ml0ntmDQ1QN4ZPg4jq1dkxPatiQqKirQEo8Q7LGImIbmcrnIW5tXfDs/L58Wcc0jLtdmttNybWZrzfZzbWZrzXDakFM5bcipALw9fhqNmjcMuMbSgl1zxOxyTO+aRk7OanLX5FJYWMjUKdMYOGhgxOXazHZars1srdl+rs1srRm2b94BwKaCzSzKyuaUc7sHXGNpwa45YrbQoqOjmTBxPIMGZOB2u8kcPoyU1JSIy7WZ7bRcm9las/1cm9laMzxx/dPs2rqLqBpRXD5mGHWPq8OirMW8cv/r7Niyk8eumsAJ7Y7nrpdvC5maxdc+TKdJS+9i5i2YW91lKKWU4zht+pie3XqxOHuJzzfaImaXo1JKqfCmDU0ppVRY0IamlFIqLGhDU0opFRa0oSmllAoL2tCUUkqFBW1oSimlwkLEnFitlFLqSDbOFTvknZw3gp65Zd+Wo96nW2hKKaXCgjY0pZRSYUEbmlJKqbCgDU0ppVRY0IamlFIqLGhDU0opFRYiqqFlzcyiQ0onUpPbM27s4xGbe82Iazk+7gTSOqYHLfMQp42FzWyt2X6uzWyt2V5u1qtfMGrQvYw8dwyzXvm8ePlnr33Jv/uNZuS5Y3hn3DS/c6ttPjQReQk4F/jTGHNSieU3ANcDB4CPjTF3lJdVkfnQ3G437dt15OOZH+GKd9Gre29eeX0y7VLaBfR9OC0XYO43c6lTtw4jLr+KxUuzA847xIljoTU7N1drDv1cX+eh5a3K57lbn+eeKXcRXSOa8VdNZNiYS9i6fisf/e8Tbv7fDdSIqcGOzTuo36j+EY+/94KHWLMiN+TmQ5sM9Cu5QEROAzKADsaYVCBo/2IsWphNUlIiCYkJxMTEMGToYGZMnxFxuQC9+vQiNjY2KFklOXEstGbn5trM1prt5Rb8to6kjonUrFWTqOgokru2Ycnn3/Pl27MZeFU/asTUAPDZzMpTbQ3NGPMNUPqU738Ajxpj9nvX+TNYz1dQUEB8y/ji2654F/kF6yIu1yYnjoXW7Nxcm9las73c+NYuflm0il1bd7F/736WzV7B5nVbWZ+7gVXZOdw/9GEeuXQcvy3P9Ts71C591QboLSIPAfuA24wxi3ytKCJXA1cDtDy+ZbnBvnativjcavWL03JtcuJYaM3OzbWZrTXby22RFMeAq/ox7soJ1Kx9LC3bxhMVfQwH3QfZvWMPo9+5izXLc3n2pv8x7vOH/XquUGto0UBDoDvQFZgiIonGx4gaYyYBk8DzHlp5wS6Xi7y1ecW38/PyaRHXPOCCnZZrkxPHQmt2bq7NbK3Zbu6pg3tx6uBeAEz7z/s0bN6QgtXrSTurMyJCYocE5Bhh59Zd1I+tV+HcUDvKMQ94z3gsBA4CjYMRnN41jZyc1eSuyaWwsJCpU6YxcNDAiMu1yYljoTU7N9dmttZsN3fH5h0AbC7YTPZnS+g+sCtdzuzEygU/A7B+zQbcRW7qNazrV26obaF9AJwOfC0ibYAYYFMwgqOjo5kwcTyDBmTgdrvJHD6MlNSUiMsFGHZJJnNmz2HTps0kndCa0WPuZvgVmQHnOnEstGbn5trM1prt5j5943/ZtW03UdFRDLvnYuocV4c+/9eTF0e9wqhB9xJdI4oRj17u967N6jxs/y2gL54tsA3AGOA14CWgE1CI5z20L8vLqshh+0oppaqWjeljyjpsv9q20IwxFx3lrkurtBCllFJhIdTeQ1NKKaUqRRuaUkqpsKANTSmlVFjQhqaUUiosaENTSikVFqrtsP1gEpGNwO8VXL0xQTq3rQppzfY5rV7QmquK1myfP/WeYIxp4uuOsGho/hCRbGNM8CcCs0hrts9p9YLWXFW0ZvuCVa/uclRKKRUWtKEppZQKC5HY0CZVdwGVoDXb57R6QWuuKlqzfUGpN+LeQ1NKKRWeInELTSmlVBjShqaUUiosRFRDE5F+IvKLiOSIyL+ru56yiEhLEflKRFaKyI8i8q/qrqmiRCRKRL4XkRnVXUtFiEgDEZkmIj97x/uU6q6pPCJys/fnYoWIvCUix1Z3TaWJyEsi8qeIrCixLFZEPhORX72fG1ZnjSUdpd5x3p+LZSLyvog0qMYSj+Cr5hL33SYiRkSCMklysBytZhG5wfv3+UcReawy2RHT0EQkCngG6A+kABeJSHBm7LPjAHCrMaYd0B24LsTrLelfwMrqLsIPE4GZxpi2QEdCvHYRcQE3AunGmJOAKODv1VuVT5OBfqWW/Rv4whjTGvjCeztUTObIej8DTjLGdABWAXdVdVHlmMyRNSMiLYGzgD+quqAKmEypmkXkNCAD6GCMSQUer0xwxDQ04GQgxxjzmzGmEHgbzwCGJGPMOmPMEu/XO/H8kXVVb1XlE5F4YCDwQnXXUhEiUh/oA7wIYIwpNMZsq9aiKiYaqCUi0UBtoKCa6zmCMeYbYEupxRnAK96vXwH+VpU1lcVXvcaYLGPMAe/N74D4Ki+sDEcZY4AJwB1AyB31d5Sa/wE8aozZ713nz8pkR1JDcwFrS9zOwwENAkBEWgGdgQXVXEpFPIHnF+lgNddRUYnARuBl727SF0SkTnUXVRZjTD6e/2D/ANYB240xWdVbVYU1M8asA88/bUDTaq7HH1cAn1Z3EeURkfOAfGPM0uquxQ9tgN4iskBEZotI18qERFJD8zVld8j991KaiNQF3gVuMsbsqO56yiIi5wJ/GmMWV3ctfogGugDPGWM6A7sJrd1gR/C+75QBJAAtgDoiojO9WyQio/C8DfBGdddSFhGpDYwC7qnuWvwUDTTE8/bK7cAUEfH1N7tMkdTQ8oCWJW7HE4K7aUoSkRp4mtkbxpj3qrueCugJnCciuXh26Z4uIq9Xb0nlygPyjDGHtn6n4WlwoexMYI0xZqMxpgh4D+hRzTVV1AYRiQPwfq7UrqWqJCKZwLnAJSb0T9xNwvOPzlLv72E8sEREmldrVeXLA94zHgvx7OHx+2CWSGpoi4DWIpIgIjF43kSfXs01HZX3v5MXgZXGmP9Udz0VYYy5yxgTb4xphWd8vzTGhPSWgzFmPbBWRJK9i84AfqrGkiriD6C7iNT2/pycQYgfyFLCdCDT+3Um8GE11lIuEekH3AmcZ4zZU931lMcYs9wY09QY08r7e5gHdPH+nIeyD4DTAUSkDRBDJWYLiJiG5n1j93pgFp5f/inGmB+rt6oy9QQuw7OV84P3Y0B1FxWmbgDeEJFlQCfg4eotp2zerclpwBJgOZ7f45C71JGIvAXMB5JFJE9ErgQeBc4SkV/xHIX3aHXWWNJR6n0aqAd85v0d/G+1FlnKUWoOaUep+SUg0Xso/9tAZmW2hvXSV0oppcJCxGyhKaWUCm/a0JRSSoUFbWhKKaXCgjY0pZRSYUEbmlJKqbCgDU2paiYirbxXRb+3rGW2nkupcKENTUUsEenr/eNe8mOXiCwWkX95Z2hwHG/TuldEOlV3LUpVpejqLkCpEPAW8Ame6322AIbjuchyKnB1NdX0O1ALz/UD/dUKGAPkAj8EMVepkKYNTSlYYowpvuakiDyH52oyI0RktDFmQ+kHiEg977Q+VnivkrDPKblKhQLd5ahUKd5ZDebj2WJLFJFcEflaRDqLyCwR2Q4sO7S+iLQWkddEZJ2IFHrXH+drGhoR6SUi80Rkr4hsEJGngbo+1jvqe10icoF4ZjPfJiJ7vLP8PikiMSIyHPjKu+rLJXalfl1WrohEi8idIvKTiOwTkc3imaG5/dHqEpFzRWSRd/113u85utT6qSIyVUTyRWS/iKz31j6wAi+FUn7RLTSlSvFe8PdE781DF0g9HvgSmIpnBoS63nXTvMu3Af8D8vHMen0j0FNETvVeER8R6QZ8DuwExnof83fgVT9qewgYiecCyhPwzIeWBFyAZ8qQb/Bci3Iknus7zvE+9IitzFLeAIbimaH5OaA5cB0wX0R6G2O+L7X+AOCfwH/xXIcvA7gN2Op9fkSkEZ6xwbve73iuoJ4OdAM+ruj3rVSFGGP0Qz8i8gPoi2dOvHvw/KFtAnQAnvcun+9dL9d7e4SPjKXAz0C9UsvP9z5meIll3wKFQJsSy2KAhd517y2xvJWPZSd7l30JHFvq+YS/rs3at/Rzl5N7lnfZO4cyvMs74HmvbY6Px+8GWpV6/hXAuhLLzvOuO7S6X2v9iIwP3eWoFNyHZ9bqP/E0qCvwTHPytxLrbAFeLvkg7+64DsCbQE0RaXzoA5iL54/+2d51mwKnAB8aY1YdyjDGFOLZ0qqIS7yf7zLGHPY+mPGqYE5p53s/P1QywxizDJgB9BKRJqUe84ExJrfk8+PZ1dlcPJPSAmz3fu4vIvUrWZtSFaYNTSnPrrmz8EyceQrQxBiTYQ4/GGS1McZd6nHtvJ8PNcSSH38CdYBm3nUSvZ9/9vH8FZ1/rTWeLZ6lFVy/ohLwTKjoa061FSXWKek3H+tu9n5uBGCMmY1nd+pwYJP3vcP7RCQl4IqV8kHfQ1MKfjXGfF7OOr4mdzw0Rfx4YOZRHre11Lq+tqIqOtW8HOXxgfJ7qnugdHP3mWeMyRSRcXjec+sF3AqMEpGbjDFPV+J5lToqbWhKVd6v3s/uCjTE1d7P7Xzc52uZL78A/fDs5lxYxnr+Nr3VwDneOpaVuu/Q1tQaPzP/KsaYFXi29B4TkQbAAuBREXkmgN2kSh1BdzkqVXnf4/lDfa2IJJa+03sofCyAMeZP4DsgQzxTzB9aJwa4uYLP96b388MiUtPH8x3aMtrl/RxbwdwPvJ/vKpGBiJyE58COucaYjRXMKllPrIgc9jfGGLMNT3OsDRzrb6ZSZdEtNKUqyRhjROQyPEcdLhORl4Af8fyxPhH4P+AuYLL3IbcAXwPzROQZ/jpsv0K/h8aYhSIyFrgTWCwi7wDr8by/NRjPUZDb8LwntxP4p4js8S770xjz5VFyPxORKd5aGorIDP46bH8fnlMQKmMYcLOIvA/kAEXAqXi2BqcYY/ZWMlcpn7ShKRUAY8wPItIZT+M6D7gWTzPJxdPIviix7nwROQt4FPg3sAPPeW3PAcsr+Hz/FpGlwPXAHXj2sqzFc+muPd519orI34EH8VzCqyYwm7/OCfPlEmAJngM4xuM5QnM2MNoYU6HafPga6AycC8Thed9tDZ7z1fT9MxV0oruwlVJKhQN9D00ppVRY0IamlFIqLGhDU0opFRa0oSmllAoL2tCUUkqFBW1oSimlwoI2NKWUUmFBG5pSSqmwoA1NKaVUWPh/tEYKQZYAWZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mlxtend.plotting import plot_confusion_matrix\n",
    " \n",
    "fig, ax = plot_confusion_matrix(conf_mat=conf_matrix, figsize=(7, 7), cmap=plt.cm.Greens)\n",
    "plt.xlabel('Predictions', fontsize=18)\n",
    "plt.ylabel('Actuals', fontsize=18)\n",
    "plt.title('Confusion Matrix', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29aaa703",
   "metadata": {},
   "source": [
    "# Model Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d53dcf47",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "lrFile = open('LRModel.pckl', 'wb')\n",
    "pickle.dump(pipe_lr_r13, lrFile)\n",
    "lrFile.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d90e51b",
   "metadata": {},
   "source": [
    "# Model Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4607eb9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "global lrLangDetectModel\n",
    "\n",
    "lrLangDetectFile = open('LRModel.pckl', 'rb')\n",
    "lrLangDetectModel = pickle.load(lrLangDetectFile)\n",
    "lrLangDetectFile.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a25285f3",
   "metadata": {},
   "source": [
    "# Call trained model & make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c571835a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lang_detect(text):\n",
    "    import numpy as np\n",
    "    import string\n",
    "    import re\n",
    "    import pickle\n",
    "    translate_table = dict((ord(char), None) for char in string.punctuation)\n",
    "    \n",
    "    global lrLangDetectModel\n",
    "    lrLangDetectFile = open('LRModel.pckl', 'rb')\n",
    "    lrLangDetectModel = pickle.load(lrLangDetectFile)\n",
    "    lrLangDetectFile.close()\n",
    "    \n",
    "    text = \" \".join(text.split())\n",
    "    text = text.lower()\n",
    "    text = re.sub(r\"\\d+\", \"\", text)\n",
    "    text = text.translate(translate_table)\n",
    "    \n",
    "    pred = lrLangDetectModel.predict([text])\n",
    "    prob = lrLangDetectModel.predict_proba([text])\n",
    "    \n",
    "    return pred[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "91deaabc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'German'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lang_detect(\"Sprechen Sie Englisch?\")"
   ]
  }
 ],
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