Quora_Incinsere_Question _with_ML.ipynb

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   "metadata": {
    "id": "QUbXNyBodlLS",
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   "source": [
    "##importing all the necessary files\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score,roc_curve,confusion_matrix,auc\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1oGceux1hRzX",
    "outputId": "10f53d3c-0b4c-44fa-8a47-5f588ad4e804"
   },
   "outputs": [],
   "source": [
    "import nltk\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C:\\\\Users\\\\sbana/nltk_data', 'C:\\\\Users\\\\sbana\\\\anaconda3\\\\envs\\\\py_gpu\\\\nltk_data', 'C:\\\\Users\\\\sbana\\\\anaconda3\\\\envs\\\\py_gpu\\\\share\\\\nltk_data', 'C:\\\\Users\\\\sbana\\\\anaconda3\\\\envs\\\\py_gpu\\\\lib\\\\nltk_data', 'C:\\\\Users\\\\sbana\\\\AppData\\\\Roaming\\\\nltk_data', 'C:\\\\nltk_data', 'D:\\\\nltk_data', 'E:\\\\nltk_data']\n"
     ]
    }
   ],
   "source": [
    "print(nltk.data.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\sbana\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers\\punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\sbana\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping corpora\\stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "jpY-qTw9jhzm"
   },
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "PdSC5rKu2QBs"
   },
   "outputs": [],
   "source": [
    "df_train=pd.read_csv(r'D:\\BANASHREE\\KAGGLE DATASETS\\quora\\train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "nXujlMeg261Y"
   },
   "outputs": [],
   "source": [
    "df_test=pd.read_csv(r'D:\\BANASHREE\\KAGGLE DATASETS\\quora\\test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FQARxbJt3GTH",
    "outputId": "ee820e8a-d2be-4bca-81d9-4946ec51cbd3"
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       "((1306122, 3), (375806, 2))"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_train.shape,df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
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    "id": "ui1OFlXN3MTk",
    "outputId": "44ff0aec-8599-4fce-fa96-75f6fd251020"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qid</th>\n",
       "      <th>question_text</th>\n",
       "      <th>target</th>\n",
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       "      <th>4</th>\n",
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       "      <td>Can I convert montra helicon D to a mountain b...</td>\n",
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       "  </tbody>\n",
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       "                    qid                                      question_text  \\\n",
       "0  00002165364db923c7e6  How did Quebec nationalists see their province...   \n",
       "1  000032939017120e6e44  Do you have an adopted dog, how would you enco...   \n",
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       "4  0000455dfa3e01eae3af  Can I convert montra helicon D to a mountain b...   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qid</th>\n",
       "      <th>question_text</th>\n",
       "      <th>target</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0000e91571b60c2fb487</td>\n",
       "      <td>Has the United States become the largest dicta...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>00013ceca3f624b09f42</td>\n",
       "      <td>Which babies are more sweeter to their parents...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>0004a7fcb2bf73076489</td>\n",
       "      <td>If blacks support school choice and mandatory ...</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "                      qid                                      question_text  \\\n",
       "22   0000e91571b60c2fb487  Has the United States become the largest dicta...   \n",
       "30   00013ceca3f624b09f42  Which babies are more sweeter to their parents...   \n",
       "110  0004a7fcb2bf73076489  If blacks support school choice and mandatory ...   \n",
       "\n",
       "     target  \n",
       "22        1  \n",
       "30        1  \n",
       "110       1  "
      ]
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#some sample incinsere questions\n",
    "df_train[df_train['target']==1].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
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     "height": 206
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    "id": "fCGry3Un3QJY",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qid</th>\n",
       "      <th>question_text</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000163e3ea7c7a74cd7</td>\n",
       "      <td>Why do so many women become so rude and arroga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00002bd4fb5d505b9161</td>\n",
       "      <td>When should I apply for RV college of engineer...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00007756b4a147d2b0b3</td>\n",
       "      <td>What is it really like to be a nurse practitio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000086e4b7e1c7146103</td>\n",
       "      <td>Who are entrepreneurs?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0000c4c3fbe8785a3090</td>\n",
       "      <td>Is education really making good people nowadays?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    qid                                      question_text\n",
       "0  0000163e3ea7c7a74cd7  Why do so many women become so rude and arroga...\n",
       "1  00002bd4fb5d505b9161  When should I apply for RV college of engineer...\n",
       "2  00007756b4a147d2b0b3  What is it really like to be a nurse practitio...\n",
       "3  000086e4b7e1c7146103                             Who are entrepreneurs?\n",
       "4  0000c4c3fbe8785a3090   Is education really making good people nowadays?"
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     "metadata": {},
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   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JWd12I7k3gJl"
   },
   "source": [
    "Imbalance Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "6ZA3_MpR3VH4",
    "outputId": "0f63a0dc-77f8-4b31-c817-9ff25adf72d3"
   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
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      "RangeIndex: 1306122 entries, 0 to 1306121\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count    Dtype \n",
      "---  ------         --------------    ----- \n",
      " 0   qid            1306122 non-null  object\n",
      " 1   question_text  1306122 non-null  object\n",
      " 2   target         1306122 non-null  int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 29.9+ MB\n"
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   ],
   "source": [
    "df_train.info()"
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  },
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   "cell_type": "code",
   "execution_count": 28,
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      "C:\\Users\\sbana\\anaconda3\\envs\\py_gpu\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\sbana\\anaconda3\\envs\\py_gpu\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\sbana\\anaconda3\\envs\\py_gpu\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of the data\n",
    "sns.countplot(x='target',data=df_train)\n",
    "plt.xlabel('Target')\n",
    "plt.ylabel('Value counts')\n",
    "plt.title('Distribution of the data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VZWOVjsJFQjI"
   },
   "source": [
    "Data is highly imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XAu_iUsMFOrl",
    "outputId": "182fe9f0-ada9-42c2-edc1-29410e71544b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    1225312\n",
       "1      80810\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1H-PrRo6Fw0A",
    "outputId": "dd98c433-d79b-4afe-ed3e-1af33a25d2a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.81298224821265 % of the data are sincere\n",
      "6.1870177517873515 % of data are insincere\n"
     ]
    }
   ],
   "source": [
    "print((df_train['target']==0).sum()/df_train.shape[0]*100,'% of the data are sincere')\n",
    "print(100-((df_train['target']==0).sum()/df_train.shape[0]*100),'% of data are insincere')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oZv5YbGxHw8V"
   },
   "source": [
    "Clearly the data is highly imbalanced. For the baseline model, even if predict always 0, we would be correct (1225312/1306122)*100 = 93.81 percent of time.!!!\n",
    "\n",
    "Also, using accuracy as a measure of correctness in such dataset can be higly misleading. F1 score given a more accurate measure in such cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "OwNumRfpHys-"
   },
   "outputs": [],
   "source": [
    "ros=RandomUnderSampler(random_state=42)\n",
    "X_resampled,Y_resampled=ros.fit_resample(df_train.drop('target',axis=1),df_train['target'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "apwAa6uwXVo9"
   },
   "outputs": [],
   "source": [
    "df_train_b=pd.concat([X_resampled,Y_resampled],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "MgG_MimCYkkH",
    "outputId": "776316ed-176e-4994-ecdc-ad03ac7c2d4a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sbana\\anaconda3\\envs\\py_gpu\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\sbana\\anaconda3\\envs\\py_gpu\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\sbana\\anaconda3\\envs\\py_gpu\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='target',data=df_train_b)\n",
    "plt.xlabel('target')\n",
    "plt.ylabel('value counts')\n",
    "plt.title('distribution of the data after upsampling')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "424_7gAyZOjF"
   },
   "source": [
    "we can see that, now the data is balanced with both the classes having the same number of data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "5IM4ssZuZJwH",
    "outputId": "2a31c37e-31ec-4d3a-a8f1-700101bb1dc8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(161620, 4)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_b.sha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nfo2vceobp7S",
    "outputId": "3a2501b1-0b34-4e65-ff58-e8fabd442e6f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    80810\n",
       "1    80810\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_b['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gH3SyGKUcAkG"
   },
   "source": [
    "#Text preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "1evPKlF7bzHl"
   },
   "outputs": [],
   "source": [
    "text='Who are entrepreneurs?'\n",
    "words=word_tokenize(text)\n",
    "for num,i in enumerate(words):\n",
    "  words[num]=i.lower()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fSD7FSdMiUtl",
    "outputId": "09e0b60b-643b-4dcf-9142-ab0474673eba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['who', 'are', 'entrepreneurs', '?']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "NuDJ5oAckkKz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stopwords ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n",
      "punctuation !\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\n"
     ]
    }
   ],
   "source": [
    "#list of stopwords and punctuations, we can create our own list. I stick to the default lists.\n",
    "stopwords_english=stopwords.words('english')\n",
    "print('stopwords',stopwords_english)\n",
    "print('punctuation',string.punctuation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['entrepreneurs']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_words=[]\n",
    "for word in words:\n",
    "    if (word not in stopwords_english and word not in string.punctuation):\n",
    "        clean_words.append(word)\n",
    "clean_words        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer=PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['entrepreneur']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stem_words=[]\n",
    "for word in clean_words:\n",
    "    stem_word=stemmer.stem(word)\n",
    "    stem_words.append(stem_word)\n",
    "stem_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we write a general function which will preprocess the text data with all the previous steps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(text):\n",
    "    words=word_tokenize(text)\n",
    "    for num,i in enumerate(words):\n",
    "      words[num]=i.lower()\n",
    "    clean_words=[]\n",
    "    for word in words:\n",
    "        if (word not in stopwords_english and word not in string.punctuation):\n",
    "            clean_words.append(word)\n",
    "    stem_words=[]\n",
    "    for word in clean_words:\n",
    "        stem_word=stemmer.stem(word)\n",
    "        stem_words.append(stem_word)\n",
    "    return stem_words\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['entrepreneur']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess('Who are entrepreneurs?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_b['question_text']=df_train_b['question_text'].apply(lambda x:preprocess(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(161620, 4)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_b.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VECTORIZATION\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reduce test dataset for as it is exceeding system memory \n",
    "mini_df=df_train_b.sample(3000,random_state=42)\n",
    "\n",
    "# X=trad_data['question_text']\n",
    "# Y=trad_data['target']\n",
    "\n",
    "# train_X,val_X,train_y,val_y=train_test_split(X,Y,test_size=0.2,random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_b['processed_text']=df_train_b['question_text'].apply(lambda x:' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "mini_df=df_train_b.sample(3000,random_state=42)\n",
    "mini_df.head()\n",
    "X=mini_df['processed_text']\n",
    "Y=mini_df['target']\n",
    "train_X,val_X,train_y,val_y=train_test_split(X,Y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2400,), (600,))"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.shape,val_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2400,)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_train_b['processed_text'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2400, 5158)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer=TfidfVectorizer()\n",
    "X_train_tfidf=vectorizer.fit_transform(train_X)\n",
    "X_train_tfidf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(600, 5158)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_tfidf=vectorizer.transform(val_X)\n",
    "X_test_tfidf.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THUS WE HAVE 5158 NUMBER OF FEATURES, ITS A VERY LARGE NUMBER OF FEATURES. PCA is a option but PCA doesnt support sparse input, so I am using truncated SVD nad taking the top 50 components\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsvd=TruncatedSVD(n_components=50,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2400, 50), (600, 50))"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_reduced=tsvd.fit_transform(X_train_tfidf)\n",
    "X_test_reduced=tsvd.fit_transform(X_test_tfidf)\n",
    "X_train_reduced.shape,X_test_reduced.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODEL SELECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_predictions(model,train_X,train_Y,val_X, val_Y):\n",
    "    model.fit(train_X,train_Y)\n",
    "    pred_probs=model.predict_proba(val_X)[:,1]\n",
    "    pred_val=model.predict(val_X)\n",
    "    score=f1_score(val_Y,pred_val)\n",
    "    return pred_probs,pred_val,score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_confusion_matrix(val_Y,pred,title):\n",
    "    cm=confusion_matrix(val_Y,pred)\n",
    "    plt.figure(figsize=(6,5))\n",
    "    sns.heatmap(cm,annot=True)\n",
    "    plt.title(title)\n",
    "    plt.ylabel('True labels')\n",
    "    plt.xlabel('Predicted labels')\n",
    "    plt.show()\n",
    "    \n",
    "def roc_auc_curve(val_y,pred_probs,title):\n",
    "   \n",
    "    fpr, tpr, thresholds = roc_curve(val_y,pred_probs )\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title(title)\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1={'model':LR, 'name': 'logistic_regression'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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>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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1['model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score of Logistic_regression 0.8198347107438017\n"
     ]
    }
   ],
   "source": [
    "pred_probs_model1,pred_model1,f1_score_model1=get_predictions(model_1['model'],X_train_tfidf,train_y,X_test_tfidf, val_y)\n",
    "print('f1_score of Logistic_regression',f1_score_model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix(val_y,pred_model1,model_1['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EAO7uB2LPSZ3k/Z2ZK8lmE9Pk+DTEE1su1CyW1y9TjRAAyGQylWkhRL71lwIDAzF79uxiz0VERGVc8kPg4dHc5kVTYs4CFws3DpbyOhlZBUO39sO9Z9YA0otlG2WqEapUqRJiY2NVanFxcTAwMICNjU2+60yfPh3+/v7K6aSkJFSpUqVYcxIR0TvkZAJPL2u26XgfGYnAzo5SpygbavUD2i0t9s1kZORgcOPf8PBZCgDA3NwQycma306ZaoTc3Nywb98+ldqff/4JV1fXAscHyeVyyOXykohHRKQ7slIBkVO0dbMzgA21gfR4zWYq7VrPBexbSp3i/cgtATtXoICzMBrdlDmwbn1fdOmyBa1bV8GqVR3RsOEcjW9H0kboxYsXuH37tnI6MjISFy9eRPny5VG1alVMnz4djx49wi+//AIA8PHxwfLly+Hv749x48bh9OnTWLduHbZt2ybVWyAi0j2HPssdNItScjRH02zqAY0mavY17ZrlNkEl0ECUVUIIpKdnw8Tk1YGNzp2d8ccfQ9G+fXWkpr4olu1K2giFhYWhXbt2yumXp7BGjBiBjRs3IiYmBlFRUcr51atXx4EDB+Dn54cVK1bAwcEBy5Yt46XzRKS74q8Bf4wFku+XzPayMzR/JKfxp5p9vfdhXQtoMA4wNJE6iU5JSEiDj89+pKVlY+/ewSrjfjt3di7WbcuEKC0naEtGUlISLC0tkZiYCAsLC6njEBG9cmMHcGU9oMgq/DoPDhdfnsJw6lT0da1qAq3nACb5j/Ek3XD4cCSGDQvBo0e5A4BWrvTExInN8yxXXJ/fZWqMEBGR1spOB/4cA2SlvN/rmJfQxSCmFXMHzFZuXTLbI62TmZmDb775GwsXnlKOmbe2NkalSuVKNAcbISKiwoi7mHtpdXHJSn2/JqhWf6DnbxyDQmXCjRtP4eW1C+Hhr64Eb9++OjZt6gNHx5I9W8NGiIh0ixBAXDiQGlf4dZKjgNAJxZfpTU6dgd57Cr+8TAYYGBdbHCJNEUJgzZrz8Pf/A2lp2QAAQ0M9BAZ2gJ+fG/T0Sr6RZyNERGVLVgqQ+qTo658LBCJ+1lye4uDowcG6pHUyMrLx8ce/Yd++m8panTq2CA7uhyZN8j4ZoqSwESKisiP6DLCrM5BZDHdVKyyXAbmnoYqLWSXAsU3xvT6RRORyA5ibv7qvn6+vKxYs6AxTU2mfE8pGiIikF3MOOOAFJD94+3I5mZrdbqtvAJkavwZt6wE1+wJ6+prNQaQjVqzwxK1b8Zgxoy169HCROg4ANkJE9L4yEoFDnwJPI4r+Gk+KsG6FxkD52kXbnr4RUH8UUOWjoq1PRO8UEfEY0dHJ6Nr11cNSrayMcfbs2AKfDyoFNkJE9HZPLgMXlgKZSfnPv/mbZrdnageUc3jLAjLAuSfgPkuz2yUijVAoBJYuPYOAgEMwMzNERMRElSvBSlMTBLARIqJ3+ftT4OGxwi9vUMRBvjJ9oN4IoAOf1E1UVkVHJ2PkyD0IDb0LIPdeQd9/fxwrV3aXOFnB2AgRUcGEAkiKevdyAGBZHRh2EZDzju1EumjPnhsYO3Yv4uPTlLWpU90wb157CVO9GxshorIq8V7xPL07IwmIOQNEnwSiTwPpCbl1Y2tg2KWC1yvnwEHERDooJSUTfn5/ICjogrJmb18Ov/zSFx071pAwWeGwESIqi84vBo74l+w2bRsCFiX0+AYiKhPCwqLh7b0bN2+++qOsb9/aCArqCRsbUwmTFR4bIaKy6FZIyWzH2Cb3WVIOrYGG40pmm0RUJqSnZ6NXr22IiXkBADA1NcSyZV0xenSTUjcg+m3YCBGVFSdnAFc3AiJH9c7KjSYCMj3NbUfPAKjQCHBwB6xd+OwqIsqXsbEBVq7sjr59d6B5cwcEB/dDrVo2UsdSGxshotIk5ixw7gcgM1G1nhoHPL2Sd3kDU6D9TxybQ0QlIjMzB0ZGr37f9OlTGyEhg9C9ey0YGpbN30NshIik8uwWEBEEZL14Vbu0qnDrlnMEDORAUz82QURU7BIT0/HppweRkZGNHTsGqJz66tOniDc2LSXYCBFJ5Y/RwKMT6q1jYgv0/I13RCaiEnPyZBSGDg3BvXvPAQDdu1/CiBGNJc2kSWyEiKSSeLfgeXWHAR1X563rG+WO4SEiKmZZWTmYM+cY5s07DoVCAAAsLOQwNtau30Ha9W6ISjuhePVgUUV27n9N7YB+B18tY2gGlC8dDyMkIt10+3YChg7djbNnHylrrVtXwZYt/VCtmpV0wYoBGyGikpKdAWxuAiRcV63rGwF2TaTJRET0GiEENm68iM8+O4iUlCwAgL6+DLNmfYSAgA9hYKDBK1RLCTZCRCXhwk/A4Un5zyvnWLJZiIjykZ6ejWHDQrBz5zVlzdnZGsHB/dCypfb+nmIjRFQSzn2ft/bBIMDIHGjyWcnnISJ6g1yuj6ysHOX0mDFNsGRJV5QrZyRhquLHRoioKJIfAv8sAFJiC7d82tNX338wKPfeP6YViicbEVERyGQyrF3bC7dvb8Ts2R+hf/+6UkcqEWyEiIrizBwg4mf117OpB/TYrvk8RERqunHjKR4/foG2baspa7a2poiImAg9Pd25o7z2jXoiKgnJD9VfR6YH1Bup8ShEROoQQmD16jA0bboGAwfuxOPHL1Tm61ITBPCIEFHhCUXuYy4UWUDGa4/AGH4JkFu9e33DcoBJ+WKLR0T0LnFxKRg7di/27bsJAEhLy8acOcewfLmnxMmkw0aIqDBysoDtrYHYf/LOK+fIBoeISr2DB29h1Kj/4vHjFGXtk0+aY/78ThKmkh4bIaJ3+fc3YP/A/OeZ2OZe+UVEVEqlpWXhyy//wk8/nVPWKlY0w/r1vdC9O2/eykaI6HX3/gSOfg6kJ7yqvXiUd7nGnwB6hkDtwYC+YcnlIyJSw6VLsfD23o2rV58oa56etbB+fS/Y2ZWTMFnpwUaIKGItcPPX3DFAUYfevuwHg4HWcwDrmiWTjYioiNLSstC58xbExeWeCjM2NsDChZ3g69tc5enxuo6NEGmv23uBe3+8fZnUx8CtXfnPe/2OzwZyoMlkoClvfkhEZYOJiSEWL+4Cb+/daNTIDlu39kfdurx/2ZvYCFHZlvoUuL0HyE5TrSfdA87/WLTX1DMAmk8DPpz3vumIiEpUTo4C+vqv7ozj5dUAQggMGFAXcjk/8vPDnwqVXfHXgI31NPd6nluBmr0AmT5gYKy51yUiKmYpKZnw8/sDWVkKbNjQW2Wet3dDiVKVDWyEqGy6vB74c0zhlnWbCTj3evsy5RwAs0rvn4uIqISFhUXD23s3bt6MBwB4etbExx9r8I9ELcdGiMqm+6F5a57BeWvlPwDsmhV/HiKiEpaTo8D8+ScxY8YRZGcrAACmpobIyMh5x5r0OjZCVHplJAEhPYDH+dzEMCfz1fdNpwCtvgFMbEosGhGRlKKiEjFsWAiOHbuvrLm6OiA4uB9cXPi7UB1shKh0yEoBDn0KxIW/qj259O71ZHpsgohIp2zffgU+PvuRmJgBAJDJgK++8sDMmW1haKgvcbqyh40QSefZ7dwru9ITgH93vHv5Co1Up2X6QL3hbIKISCekpWVhwoT92Lw5QlmrWtUSW7b0hYeHk4TJyjY2QiSd418Ct3bnP09f/up7O1eg737A2KpEYhERlUZyuYHKc8K8vBpgxQpPWFnxKtf3wUaIpPHkcv5NUDlHYPhFHuUhInqDnp4MGzf2hofHBsye/REvi9cQNkJUshQ5wMUVwOHJqvVx93L/W65y7g0NiYh03O3bCYiPT0XLlq/ucm9vb44bNz6FgYHeW9YkdfATh0pOdgbwSwPg2S3VesWmgAXPbxMRAYAQAhs3XsRnnx2ElZUxIiImonx5E+V8NkGaxZ8mlYyItcBS47xNUONPgf4HpclERFTKJCSkYeDAnRg9ei9SUrLw6FEyZs8+InUsrcYjQlQyLq7IW+uxA/hgYMlnISIqhQ4fjsSwYSF49ChZWRszpgnmzesgYSrtx0aISobitRsg1hsJtJ4DmDsWuDgRka7IzMzBN9/8jYULT0GI3Jq1tTGCgnqif/+60obTAWyEqGQZlgO6bpA6BRFRqXDjxlN4ee1CeHissta+fXVs2tQHjo4WEibTHWyEqHgJBXD/UO6T4omISCk1NQtt2mzAkyepAABDQz0EBnaAn58b9PRkEqfTHRwsTcXr9h5gV2epUxARlTqmpoaYN689AKBOHVucOzcOU6e6swkqYTwiRMXr9WeHAYCDuzQ5iIhKASEEZLJXjc7YsU0hBDB0aEOYmhpKmEx38YgQlZxGvkDvPVKnICIqcWlpWZg06SAmTVK9XYhMJsP48c3YBEmIR4So5NTqCxiavHs5IiItculSLLy9d+Pq1ScAgK5da6J7dxeJU9FLPCJERERUDBQKgcWLT6NFi7XKJsjY2EA5OJpKBx4RIiIi0rDo6GSMHLkHoaF3lbVGjeywdWt/1K1bQcJk9CY2QlR8Yv8BzsyVOgURUYkKCbmOceP2IT4+TVmbOtUN8+a1h1zOj93ShnuEikf6M2BHG6lTEBGVmPT0bEyadBBBQReUNQcHc2za1AcdO9aQMBm9DRshKh6J94Ds9FfTRhZApeaSxSEiKm6Ghnq4ceOpcrpv39oICuoJGxtTCVPRu3CwNBU/yxrAuPuA3FLqJERExUZfXw+bN/dF5crmWLu2J3btGsgmqAzgESEqftW6AMZWUqcgItKo+/ef49mzdDRuXElZc3Kywp07kzgWqAzhniLNSbwH/DEaeH4byMl85+JERGXVtm2XMXHi/1C+vAkuXvSBhYVcOY9NUNnCU2OkGReWAmurAw8OA8kPgNTHr+YZmUuXi4hIgxIT0zFsWAi8vHYjMTEDkZHPMXv2Ealj0XuQvBFauXIlqlevDmNjYzRr1gzHjx9/6/LBwcFo1KgRTE1NYW9vj1GjRiE+Pr6E0lIeCTeB//YDDk/JO6+cA1D5Q6DB2BKPRUSkaSdPRqFx4zXYsiVCWfPyaoAZM9pKmIrel6TH73bs2IEpU6Zg5cqVaN26NdasWYNu3brh2rVrqFq1ap7lT5w4geHDh2Px4sXo2bMnHj16BB8fH4wdOxYhISESvAMdkxaf+zT57Ff3xsDfn+Vdrmp7YEAoIJO8zyYiem9ZWTmYM+cY5s07DoVCAAAsLORYudIT3t4NJU5H70smhBBSbbxly5Zo2rQpVq1apazVqVMHffr0QWBgYJ7lFy5ciFWrVuHOnTvK2k8//YT58+fjwYMHhdpmUlISLC0tkZiYCAsLi/d/E9ouLf7VE+R3dnr38v0OANW7FW8mIqIScudOAry9d+Ps2UfK2ocfVsXmzX1RrZqVdMF0UHF9fkv2J3tmZibOnz+Pzp07q9Q7d+6MU6dO5buOu7s7Hj58iAMHDkAIgcePH2Pnzp3o3r17gdvJyMhAUlKSyhcV0pMIYKVtbgP0riaofG1gYhybICLSGikpmWjVap2yCdLXl2Hu3HY4cmQEmyAtItmpsadPnyInJwd2dnYqdTs7O8TGxua7jru7O4KDgzFo0CCkp6cjOzsbvXr1wk8//VTgdgIDAzF79myNZtcJf44DLq8teL7nllffG1kC1ToD+kbFn4uIqISYmRnhm288MGXKH3B2tsbWrf3RokVlqWORhkl+jZ9MJlOZFkLkqb107do1TJo0CTNmzECXLl0QExODL774Aj4+Pli3bl2+60yfPh3+/v7K6aSkJFSpUkVzb0DbPDwO/D4SSLyrWrdyBmoPAeTWQN1hgCkfGkhE2ufNz6DPPmsJhUJg3LhmKFeOf+xpI8kaIVtbW+jr6+c5+hMXF5fnKNFLgYGBaN26Nb744gsAQMOGDWFmZgYPDw/MnTsX9vb2edaRy+WQy+V56lSAiyvzNkGt5wCuXwAG/DkSkXbKzMzBN9/8DT09Gf7zn47Kup6eDH5+bhImo+ImWSNkZGSEZs2aITQ0FH379lXWQ0ND0bt373zXSU1NhYGBamR9fX0AuV08vQehAMJ/Av7d/qpmUxfosh6wbyldLiKiYnb9+hN4e+9GeHgsZDKgSxdntGtXXepYVEIkvb7Z398fa9euxfr163H9+nX4+fkhKioKPj4+AHJPaw0fPly5fM+ePbF7926sWrUKd+/excmTJzFp0iS0aNECDg4OUr2Nsi/hJrCzS957AQ08yiaIiLSWEAKrVv2DZs1+Rnh47tkJAwM93LnzTOJkVJIkHSM0aNAgxMfH47vvvkNMTAzq16+PAwcOwMnJCQAQExODqKgo5fIjR45EcnIyli9fjqlTp8LKygrt27fHDz/8INVbKLsS/s29LF7kAAeG5p1fowdgalvyuYiISkBcXArGjNmL/ftvKmt16thi69b+Ks8OI+0n6X2EpKDT9xFKigLSE3IboD9GF7xc/z+BaoW4ZxARURl08OAtjBz5X8TFpShrvr6uWLCgM0xNDSVMRm9TXJ/fkl81RsUkJwtQZL+aPr8IOPnt29exbQD0/z330RhERFomPT0b06aF4qefzilrFSqYYv363ujRw0XCZCQlNkLa6MqG3EdfZKW8e1mXAYBDa8DUDqjVFzAwLv58REQS0NeX4cyZh8ppT89aWL++F+zsykmYiqTGRkibpMTm3gPo3h9vX65R7mB02LcC6g4HCrhvExGRNjE01EdwcD+4u6/HrFlt4evbvMD71pHuYCOkLSJ+BkIn5K1XaffqewNjoJEv4Nyj5HIREUkkOjoZiYnpqFPn1Q1ga9Wywb17k2FmxpsjUi42Qtog+REQ6pO33nkt0GBMyechIpJYSMh1jBu3DxUrmiEsbLzKIGg2QfQ6NkJllVAA9/8Cnt0CXjwE8NrFf/YtgUHH+OwvItI5KSmZ8PP7A0FBFwAA8fFp+O67oyp3iyZ6HRuhsurGduCAd956bS+ge3DJ5yEiklhYWDS8vXfj5s14Za1v39r44gt3CVNRacdGqKx6fCH/uhPv/0NEuiUnR4H5809ixowjyM5WAABMTQ2xbFlXjB7dhAOi6a3YCGmDVt8C1i6AZXXAgX/5EJHuiIpKxLBhITh27L6y1ry5A4KD+6FWLRsJk1FZwUaorBEC+J+X6sNRq3UBKreWLhMRkQSSkzPg6voznjxJBZB7J5CvvvLAzJltYWioL3E6KiskfegqqSnyILDcUrUJAgBja2nyEBFJyNxcjilTWgEAqla1xNGjIzF3bns2QaQWHhEqS45MBTKTVWstvwJs6kqTh4hIYl9+2RoKhcCnn7aAlRXvjE/qYyNUlqQnvPrepi7QcxdgU1u6PEREJSQ7W4E5c47CwEAP337bVlnX19fDN9+0kTAZlXVshMoii2rAyKtSpyAiKhF37iTA23s3zp59BD09GTp2rAE3typSxyItwTFCRERUKgkhsHHjRTRuvAZnzz4CkDsg+tKlxxInI23CI0JERFTqJCSkYcKE/di585qy5uxsjeDgfmjZ0lHCZKRt2AgREVGpcvhwJIYNC8GjR68uDhkzpgmWLOmKcuX46CDSLDZCRERUKmRm5uDbb//GggWnIP7/8YnW1sYICuqJ/v15dSwVDzZCRERUKigUAgcP3lY2Qe3bV8emTX3g6GghbTDSahwsTUREpYKxsQG2bu0PCws5Fi7shNDQYWyCqNjxiFBZoMgB7v8JpPJKCSLSHnFxKUhOzoCzc3llrX79irh/fwpvjkglhkeEyoIr64HdnlKnICLSmIMHb6FBg1UYMOA3ZGRkq8xjE0QliY1QaScUwJ19qjX7VtJkISJ6T2lpWZg06SA8PbciLi4FFy/GYt6841LHIh3GU2OlWXY6sMUViH/tLtJus4AWAZJFIiIqqkuXYuHtvRtXrz5R1jw9a+GTT5pLmIp0HRuh0iz6tGoTJNMDGo4HDOTSZSIiUpNCIbB06RkEBBxCZmYOgNyB0QsXdoKvb3PIZDKJE5IuYyNUWt3+L/DfPqq1bluAcvaSxCEiKoro6GSMGLEHf/11V1lr1MgOW7f2R926FSRMRpSLjVBpk/oUCFsI/PODav3D74E6Q6TJRERUBImJ6WjceDWePElV1qZOdcO8ee0hl/Pjh0oHDpYubS4sztsE2TYAag+WJg8RURFZWhpj/PhmAAAHB3OEhg7DwoWd2QRRqcL/G0ub5Aeq020XAq5TpclCRPSeZs5sC4VCYOpUN9jYmEodhyiPIjVC2dnZOHLkCO7cuQMvLy+Ym5sjOjoaFhYWKFeunKYz6o7sdOBJxKvpwSeByu7S5SEiKqScHAXmzz8JY2MD+Pm5KeuGhvr4/vsOEiYjeju1G6H79++ja9euiIqKQkZGBjp16gRzc3PMnz8f6enpWL16dXHk1H7pz4ANtYHUuFc1Uw4kJKLSLyoqEcOGheDYsfswNNTDRx9VQ5MmvLCDyga1xwhNnjwZrq6uePbsGUxMTJT1vn374tChQxoNp1MeHlNtggxMANOK0uUhIiqE7duvoGHDVTh27D4AIDtbgVOnHrxjLaLSQ+0jQidOnMDJkydhZGSkUndycsKjR480FkynRAQBoeNVa332AXJLafIQEb1DUlIGPv30ADZvfnU6v2pVS2zZ0hceHk4SJiNSj9qNkEKhQE5OTp76w4cPYW5urpFQOufoG4OhO6wAnHhOnYhKp5MnozB0aAju3XuurHl5NcCKFZ58ThiVOWqfGuvUqROWLFminJbJZHjx4gVmzpwJT08+GLRIMpNffe/UCXAZKF0WIqICZGXlYMaMw2jTZqOyCbKwkGPLlr4IDu7HJojKJLWPCC1evBjt2rVD3bp1kZ6eDi8vL9y6dQu2trbYtm1bcWTUHfYtgQF/Sp2CiChfmZk52LHjKhQKAQD48MOq2Ly5L6pVs5I2GNF7ULsRcnBwwMWLF7F9+3acP38eCoUCY8aMgbe3t8rgaSIi0i5mZkYIDu6HNm024OuvPRAQ8CH09XlfXirbZEIIoc4Kx44dg7u7OwwMVHuo7OxsnDp1Cm3atNFoQE1LSkqCpaUlEhMTYWFhIXWcXIv+/4GD9i0BrzPSZiEi+n8JCWlISclElSqqF27ExaWgYkUziVKRriquz2+1W/l27dohISEhTz0xMRHt2rXTSCgiIpLW4cORaNhwFQYO3InsbIXKPDZBpE3UboSEEJDJZHnq8fHxMDPjPw4iorIsMzMH06aFokOHX/DoUTLOnHmIH344IXUsomJT6DFC/fr1A5B7ldjIkSMhl8uV83JychAREQF3dz4OgoiorLp+/Qm8vXcjPDxWWWvfvjpGjGgsXSiiYlboRsjSMvccsRAC5ubmKgOjjYyM0KpVK4wbN07zCYmIqFgJIbBmzXn4+/+BtLRsAIChoR6+/74D/P3doKeX9ywAkbYodCO0YcMGAEC1atXw+eef8zQYEZEWiItLwdixe7Fv301lrU4dWwQH9+PzwkgnqH35/MyZM4sjh+7IfAFEHQJyMqVOQkQ67vnzdDRqtBqxsS+UNV9fVyxY0BmmpoYSJiMqOWo3QgCwc+dO/Prrr4iKikJmpuoH+oULFzQSTCsJAexoA8SFS52EiAhWVsYYPLgeliw5iwoVTLF+fW/06OEidSyiEqX2VWPLli3DqFGjULFiRYSHh6NFixawsbHB3bt30a1bt+LIqD2y097eBFVoVHJZiIgABAZ2xKRJLXD58kQ2QaST1L6hYu3atTFz5kwMGTIE5ubmuHTpEmrUqIEZM2YgISEBy5cvL66sGiHpDRWzUoFl/z+2yqom0Gjiq3kmNkCt/oBRuZLNREQ6QaEQWLr0DMzMjDB+fDOp4xCprbg+v9U+NRYVFaW8TN7ExATJybkPDB02bBhatWpV6huhUsOiKuDqL3UKItIB0dHJGDlyD0JD78LY2AAeHlVRp04FqWMRlQpqnxqrVKkS4uPjAQBOTk44cyb3kRCRkZFQ8+ASEREVs5CQ62jYcBVCQ+8CANLTs5XfE1ERjgi1b98e+/btQ9OmTTFmzBj4+flh586dCAsLU950kYiIpJWSkgk/vz8QFPTqAhYHB3Ns2tQHHTvWkDAZUemidiP0888/Q6HIfe6Mj48PypcvjxMnTqBnz57w8fHReEAiIlJPWFg0vL134+bNeGWtb9/aCArqCRsbUwmTEZU+ajdCenp60NN7dUZt4MCBGDhwIADg0aNHqFy5subSERFRoeXkKDB//knMmHFE+aBUU1NDLFvWFaNHN8n3OZFEuk7tMUL5iY2NxWeffYaaNWtq4uWIiKgIUlKysGbNeWUT1Ly5Ay5enIAxY5qyCSIqQKEboefPn8Pb2xsVKlSAg4MDli1bBoVCgRkzZqBGjRo4c+YM1q9fX5xZiYjoLSws5Ni8uS8MDfXw9dceOHlyNGrVspE6FlGpVuhTY1999RWOHTuGESNG4Pfff4efnx9+//13pKen4+DBg2jbtm1x5iQiojckJWUgNTULlSq9uv+Yh4cT7tyZhCpVLCVMRlR2FLoR+t///ocNGzagY8eO8PX1Rc2aNeHi4oIlS5YUYzwtkJ0OHJ4CPD4PiByp0xCRljh5MgpDh4agenUr/PXXcJUnxLMJIiq8Qp8ai46ORt26dQEANWrUgLGxMcaOHVtswbTGnX1AxBrgcZjq4zX0jaXLRERlVlZWDmbMOIw2bTbi3r3nOHz4HhYvPi11LKIyq9BHhBQKBQwNXz2NWF9fH2ZmZsUSSmsk/Ascm6Zak+kBphWBJp9Kk4mIyqzbtxMwdOhunD37SFn78MOq6N+/roSpiMq2QjdCQgiMHDkScrkcAJCeng4fH588zdDu3bs1m7CsSfgXeHoZyMkCDnipzuuyAag/UpJYRFR2CSGwceNFfPbZQaSkZAEA9PVlmD37IwQEfAh9fY1cAEykkwr9r2fEiBGoWLEiLC0tYWlpiaFDh8LBwUE5/fJLXStXrkT16tVhbGyMZs2a4fjx429dPiMjA19//TWcnJwgl8vh7OxcOq5WEwrgykZgQ21g38d5myA9A8DRQ5JoRFR2JSSkYeDAnRg9eq+yCXJ2tsapU2Pw9ddt2AQRvadCHxHasGGDxje+Y8cOTJkyBStXrkTr1q2xZs0adOvWDdeuXUPVqlXzXWfgwIF4/Pgx1q1bh5o1ayIuLg7Z2dkaz6YWIYBt7kDM2fznV2wC9N0PlHMo2VxEVKY9e5aGRo1W4+HDJGVtzJgmWLKkK8qVM5IwGZH2kAkJn5TasmVLNG3aFKtWrVLW6tSpgz59+iAwMDDP8r///jsGDx6Mu3fvonz58kXaZlJSEiwtLZGYmAgLC4siZ1d90ftAUDXV2geDgErNATN7oFY/wICDo4lIfRMm7MPPP1+AtbUxgoJ6cjwQ6axi+fxGER6xoSmZmZk4f/48AgICVOqdO3fGqVOn8l1n7969cHV1xfz587F582aYmZmhV69emDNnDkxMTEoidv6EQnW66yag7jCAd3Ilovf0449dkJMjMGvWR3B01NwvfyLKJVkj9PTpU+Tk5MDOzk6lbmdnh9jY2HzXuXv3Lk6cOAFjY2OEhITg6dOn8PX1RUJCQoHjhDIyMpCRkaGcTkpKync5jak9BKg3vHi3QURaRwiBNWvOo1w5Iwwd2lBZNzMzwtq1vSRMRqTdJGuEXnrz+TdCiAKfiaNQKCCTyRAcHKwcmP3jjz9iwIABWLFiRb5HhQIDAzF79mzNByci0pC4uBSMHbsX+/bdRLlyRnBzc4Szc9FO/xOReiS73MDW1hb6+vp5jv7ExcXlOUr0kr29PSpXrqxydVqdOnUghMDDhw/zXWf69OlITExUfj148EBzb4KI6D0dPHgLDRuuwr59NwEAL15kYv/+mxKnItIdRWqENm/ejNatW8PBwQH3798HACxZsgT//e9/C/0aRkZGaNasGUJDQ1XqoaGhcHd3z3ed1q1bIzo6Gi9evFDWbt68CT09PTg6Oua7jlwuh4WFhcoXEZHU0tKyMGnSQXh6bsXjxykAgAoVTLFv3xBMntxK4nREukPtRmjVqlXw9/eHp6cnnj9/jpyc3OdnWVlZqf3cMX9/f6xduxbr16/H9evX4efnh6ioKPj4+ADIPZozfPir8TZeXl6wsbHBqFGjcO3aNRw7dgxffPEFRo8eLe1gaSIiNUREPEbz5kH46adzypqnZy1cvjwRPXq4SJiMSPeo3Qj99NNPCAoKwtdffw19fX1l3dXVFZcvX1brtQYNGoQlS5bgu+++Q+PGjXHs2DEcOHAATk5OAICYmBhERUUply9XrhxCQ0Px/PlzuLq6wtvbGz179sSyZcvUfRtERCVOoRBYvPg0mjcPwtWrTwAAxsYGWL68G/bvHwI7u3LveAUi0jS1B0tHRkaiSZMmeepyuRwpKSlqB/D19YWvr2++8zZu3JinVrt27Tyn04iIyoLExHQsWHAKmZm5R9IbNrTD1q39UK9eRYmTEekutY8IVa9eHRcvXsxTP3jwoPLp9Dol+gywq4vUKYioDLC2NsGmTX2gpyfD1KluOHduLJsgIompfUToiy++wCeffIL09HQIIXDu3Dls27YNgYGBWLt2bXFkLJ2y0oALS4ET01Xr+ryDNBHlSknJRHp6NmxsTJW1Tp2c8e+/n6JmTV4eT1QaqN0IjRo1CtnZ2Zg2bRpSU1Ph5eWFypUrY+nSpRg8eHBxZCyd/t2etwmycAIajpMmDxGVKmFh0fD23o2aNctj//4hKvdHYxNEVHoU6YaK48aNw7hx4/D06VMoFApUrKiDh3aT37gfUcMJQKfV0mQholIjJ0eB+fNPYsaMI8jOVuDmzXisWhUGX9/mUkcjonyoPUZo9uzZuHPnDoDcmyLqZBP0pk4/swkiIkRFJaJ9+1/w1Vd/Izs79xmEzZs7oFOnGhInI6KCqN0I7dq1Cy4uLmjVqhWWL1+OJ0+eFEeussU8/5s5EpHu2L79Cho2XIVjx3JvMqunJ8PXX3vg5MnRqFXLRuJ0RFQQtRuhiIgIREREoH379vjxxx9RuXJleHp6YuvWrUhNTS2OjEREpVZSUgaGDw/BkCG7kJiY+4DnqlUtceTICMyd2x6GhvrveAUiklKRHrFRr149fP/997h79y4OHz6M6tWrY8qUKahUqZKm8xERlVrx8alo3Hg1Nm+OUNa8vBrg0iUfeHg4SZiMiArrvR+6amZmBhMTExgZGSErK0sTmYiIygQbG1O0bl0VAGBhIceWLX0RHNwPVla8jQZRWVGkq8YiIyOxdetWBAcH4+bNm2jTpg1mzZqFjz/+WNP5iIhKteXLuyEnR4Hvv++AatWspI5DRGpSuxFyc3PDuXPn0KBBA4waNUp5HyEiIm0mhMCmTZdgYSFHv351lHVLS2Ns3dpfwmRE9D7UboTatWuHtWvXol69esWRh4io1ElISMOECfuxc+c1WFkZo3lzB1SpYil1LCLSALXHCH3//fdsgohIZxw+HImGDVdh585rAIDnz9OV3xNR2VeoI0L+/v6YM2cOzMzM4O/v/9Zlf/zxR40EIyKSUmZmDr755m8sXHgKQuTWrK2NERTUE/376+ADpom0VKEaofDwcOUVYeHh4cUaiIhIajduPIWX1y6Eh8cqa+3bV8emTX3g6GghYTIi0rRCNUKHDx/O93siIm0ihMCaNefh7/8H0tKyAQCGhnoIDOwAPz836OnJ3vEKRFTWqD1GaPTo0UhOTs5TT0lJwejRozUSiohICgkJafj228PKJqhOHVucOzcOU6e6swki0lJqN0KbNm1CWlpannpaWhp++eUXjYQiIpKCjY0p1q7tCQDw9XVFWNh4NG7MO+YTabNCXz6flJQEIQSEEEhOToax8as7p+bk5ODAgQN8Ej0RlSlpaVnIzMyBpeWr32e9e9dGRIQPGjSwkzAZEZWUQjdCVlZWkMlkkMlkcHFxyTNfJpNh9uzZGg1HRFRcIiIew8trF+rUqYBffx0AmezVqS82QUS6o9CN0OHDhyGEQPv27bFr1y6UL19eOc/IyAhOTk5wcHAolpBERJqiUAgsXXoGAQGHkJmZg6tXn2DTpksYObKx1NGISAKFboTatm0LIPc5Y1WrVlX564mIqCyIjk7GyJF7EBp6V1lr1MgOLVrwMUFEuqpQjVBERATq168PPT09JCYm4vLlywUu27BhQ42FK7VOfAOcnSd1CiJSQ0jIdYwbtw/x8a8u9pg61Q3z5rWHXF6k508TkRYo1L/+xo0bIzY2FhUrVkTjxo0hk8kgXt5q9TUymQw5OTkaD1mqJN3P2wQZmkmThYjeKSUlE35+fyAo6IKy5uBgjk2b+qBjxxoSJiOi0qBQjVBkZCQqVKig/F6nZb5xD6UPBgEOraXJQkRv9eRJCj78cANu3oxX1vr2rY2goJ6wsTGVMBkRlRaFaoScnJzy/V7nNRgLdA6SOgURFcDW1hT16lXAzZvxMDU1xLJlXTF6dBOOcSQipSLdUPF///ufcnratGmwsrKCu7s77t+/r9FwpY4QQNxFqVMQUSHJZDIEBfVEr14f4OLFCRgzpimbICJSoXYj9P3338PExAQAcPr0aSxfvhzz58+Hra0t/Pz8NB6wVDniBxwcJnUKIirA9u1XcPDgLZWajY0p/vvfwahVy0aiVERUmql9qcSDBw9Qs2ZNAMCePXswYMAAjB8/Hq1bt8ZHH32k6XylS+QB1WnrvDeWJKKSl5SUgU8/PYDNmyNQoYIpLl+eCDu7clLHIqIyQO0jQuXKlUN8fO7Awz///BMdO3YEABgbG+f7DDKt8PwuENwSePbaX5odVgKNP5EuExEBAE6ejEKjRquxeXMEAODJk1QEBxd8iw8iotepfUSoU6dOGDt2LJo0aYKbN2+ie/fuAICrV6+iWrVqms5XOlzbDMSeezVtWQNoPFG6PESErKwczJlzDPPmHYdCkXs7DwsLOVau9IS3tw7cz4yINELtI0IrVqyAm5sbnjx5gl27dsHGJve8+/nz5zFkyBCNBywVslNVp9sukCYHEQEAbt9OgIfHBsyZc0zZBH34YVVcuuTDJoiI1CIT+d0ZUYslJSXB0tISiYmJsLCwKNxKx74E/pmf+/2go4Bjm+ILSEQFEkJg48aL+Oyzg0hJyQIA6OvLMHv2RwgI+BD6+mr/bUdEZUSRPr8LoUj3lX/+/DnWrVuH69evQyaToU6dOhgzZgwsLS01FoyI6E1PnqTCz+8PZRPk7GyN4OB+aNnSUeJkRFRWqf3nU1hYGJydnbF48WIkJCTg6dOnWLx4MZydnXHhwoV3vwARURFVrGiG1at7AADGjGmCixd92AQR0XtR+4iQn58fevXqhaCgIBgY5K6enZ2NsWPHYsqUKTh27JjGQxKRbsrMzEFWVg7MzIyUtcGD66NGDWs+MZ6INKJIR4S+/PJLZRMEAAYGBpg2bRrCwsI0Go6IdNeNG0/h5rYOn3xyIM88NkFEpClqN0IWFhaIiorKU3/w4AHMzc01EoqIdJcQAqtXh6Fp0zW4cCEGmzZdwq+/XpU6FhFpKbVPjQ0aNAhjxozBwoUL4e7uDplMhhMnTuCLL77Q3svniahEPHmSgjFj9mLfvpvKWp06tqhVq7yEqYhIm6ndCC1cuBAymQzDhw9HdnY2AMDQ0BATJ07Ef/7zH40HJCLd8PvvtzFy5B48fpyirPn6umLBgs4wNTWUMBkRaTO1GyEjIyMsXboUgYGBuHPnDoQQqFmzJkxNTYsjHxFpubS0LAQE/IVly17dvb1CBVOsX98bPXrweX5EVLwK3Qilpqbiiy++wJ49e5CVlYWOHTti2bJlsLW1Lc58RKTF4uJS0KHDL7hyJU5Z8/SshfXre/GhqURUIgo9WHrmzJnYuHEjunfvjsGDByM0NBQTJ/J5W0RUdLa2pqhcOfciC2NjAyxf3g379w9hE0REJabQR4R2796NdevWYfDgwQCAoUOHonXr1sjJyYG+vn6xBSQi7aWnJ8OGDb0xfPgeLF3aFXXrVpA6EhHpmEIfEXrw4AE8PDyU0y1atICBgQGio6OLJRgRaZ89e27gyJF7KjV7e3OEhg5jE0REkih0I5STkwMjIyOVmoGBgfLKMa119j+vHrhKREWSkpKJ8eP3oW/fHRg6dDcSEtKkjkREBECNU2NCCIwcORJyuVxZS09Ph4+PD8zMzJS13bt3azahlJ7dBk5MV63pG0uThaiMCguLhrf3bty8GQ8AePQoGRs3XoS/v5vEyYiI1GiERowYkac2dOhQjYYpddLjVaerewKVXKXJQlTG5OQoMH/+ScyYcQTZ2QoAgKmpIZYt64rRo5tInI6IKFehG6ENGzYUZ47Sr+lkoN0SqVMQlQlRUYkYNiwEx47dV9ZcXR0QHNwPLi42EiYjIlKl9g0ViYjeZvv2K/Dx2Y/ExAwAgEwGfPWVB2bObAtDQ15hSkSlCxshItKY2NgXGDt2L1JSsgAAVataYsuWvvDwcJI4GRFR/tR++jwRUUEqVSqHpUu7AgCGDKmPS5d82AQRUanGI0JEVGRZWTnIyREwNn71q2T06CaoUcMa7dpVlzAZEVHh8IgQERXJ7dsJ8PDYgKlT/1Cpy2QyNkFEVGYUqRHavHkzWrduDQcHB9y/n3tVyJIlS/Df//5Xo+GIqPQRQmDDhnA0brwaZ88+wsqVYdi//6bUsYiIikTtRmjVqlXw9/eHp6cnnj9/jpycHACAlZUVlixZoul8RFSKJCSkYeDAnRg9+tWAaGdna1SsaPaONYmISie1G6GffvoJQUFB+Prrr1Ueturq6orLly9rNBwRlR6HD0eiYcNV2LnzmrI2ZkwTXLzogxYtKkuYjIio6NQeLB0ZGYkmTfLeFVYulyMlJUUjoYio9MjMzME33/yNhQtPQYjcmrW1MYKCeqJ//7rShiMiek9qN0LVq1fHxYsX4eSkeknswYMHUbcufykSaZO4uBR07boF4eGxylqHDtWxaVMfVK5sIWEyIiLNULsR+uKLL/DJJ58gPT0dQgicO3cO27ZtQ2BgINauXVscGYlIIjY2JjA3z33QsqGhHgIDO8DPzw16ejKJkxERaYbaY4RGjRqFmTNnYtq0aUhNTYWXlxdWr16NpUuXYvDgwWoHWLlyJapXrw5jY2M0a9YMx48fL9R6J0+ehIGBARo3bqz2NomocPT19bB5c1+4u1fBuXPjMHWqO5sgItIqRbp8fty4cbh//z7i4uIQGxuLBw8eYMyYMWq/zo4dOzBlyhR8/fXXCA8Ph4eHB7p164aoqKi3rpeYmIjhw4ejQ4cORYlPRAU4ePAWzpx5qFKrWtUSJ06MQuPGlSRKRURUfN7rhoq2traoWLFikdf/8ccfMWbMGIwdOxZ16tTBkiVLUKVKFaxateqt602YMAFeXl5wc3Mr8raJ6JW0tCxMmnQQnp5b4eW1C0lJGSrzZTIeBSIi7VSkwdJv+6V49+7dQr1OZmYmzp8/j4CAAJV6586dcerUqQLX27BhA+7cuYMtW7Zg7ty579xORkYGMjJe/VJPSkoqVD4iXXHpUiy8vXfj6tUnAIDIyOdYt+4C/Pz4hwYRaT+1G6EpU6aoTGdlZSE8PBy///47vvjii0K/ztOnT5GTkwM7OzuVup2dHWJjY/Nd59atWwgICMDx48dhYFC46IGBgZg9e3ahcxHpCoVCYOnSMwgIOITMzNwboxobG2DRos6YONFV4nRERCVD7UZo8uTJ+dZXrFiBsLAwtQO8eXRJCJHvEaecnBx4eXlh9uzZcHFxKfTrT58+Hf7+/srppKQkVKlSRe2cRNokOjoZI0fuQWjoqyO4jRrZYevW/qhbt4KEyYiISpbGHrrarVs37Nq1q9DL29raQl9fP8/Rn7i4uDxHiQAgOTkZYWFh+PTTT2FgYAADAwN89913uHTpEgwMDPD333/nux25XA4LCwuVLyJdFhJyHQ0brlJpgqZOdcPZs2PZBBGRzlH7iFBBdu7cifLlyxd6eSMjIzRr1gyhoaHo27evsh4aGorevXvnWd7CwiLPIzxWrlyJv//+Gzt37kT16nzaNdG7REcnY8iQXcjIyD0V5uBgjk2b+qBjxxoSJyMikobajVCTJk1UTl0JIRAbG4snT55g5cqVar2Wv78/hg0bBldXV7i5ueHnn39GVFQUfHx8AOSe1nr06BF++eUX6OnpoX79+irrV6xYEcbGxnnqRJQ/BwdzLFjQCZMm/Y6+fWsjKKgnbGxMpY5FRCQZtRuhPn36qEzr6emhQoUK+Oijj1C7dm21XmvQoEGIj4/Hd999h5iYGNSvXx8HDhxQPr4jJibmnfcUIqKC5eQooFAIGBq+ekDyp5+2QI0a1vD0rMXL4olI58mEePkYxXfLzs5GcHAwunTpgkqVyubN1ZKSkmBpaYnExMR3jxeKOQtsbZX7fdPJQLslxZ6PSFOiohIxbFgIWrasjPnzO0kdh4jovaj1+a0GtQZLGxgYYOLEiSr35SGi0mf79ito2HAVjh27jwULTuHQocLd34uISNeofdVYy5YtER4eXhxZiOg9JSVlYPjwEAwZsguJibl/sFStagljY41dF0FEpFXU/u3o6+uLqVOn4uHDh2jWrBnMzMxU5jds2FBj4Yio8E6ejMLQoSG4d++5subl1QArVnjCyspYumBERKVYoRuh0aNHY8mSJRg0aBAAYNKkScp5MplMeSPEnJwczaeUwrXNwMHhUqcgeqesrBzMmXMM8+Ydh0KRO+TPwkKOlSs94e3NP0yIiN6m0I3Qpk2b8J///AeRkZHFmaf0OP2d6rSRpTQ5iN4iLi4FvXptw9mzj5S1Dz+sis2b+6JaNSvpghERlRGFboReXlz28tJ2rZeV8ur76t2ABmOky0JUAGtrY7y87lNfX4bZsz9CQMCH0NfX2E3jiYi0mlpjhHTyniPmVYF+B6ROQZQvQ0N9BAf3w8cf/4Y1a3qgRYvKUkciIipT1GqEXFxc3tkMJSQkvFcgIirY4cORsLY2QePGr+7jVbNmeVy4MF43/1AhInpPajVCs2fPhqUlx8oQlbTMzBx8883fWLjwFD74wBbnz4+Hqamhcj6bICKiolGrERo8eDAqVqxYXFmIKB83bjyFl9cuhIfHKqeDgs5j8uRWEicjIir7Cj2ikn9xEpUsIQRWrw5D06ZrlE2QoaEeFi7shM8+aylxOiIi7aD2VWNEVPzi4lIwduxe7Nt3U1mrU8cWW7f2VxkfRERE76fQjZBCoSjOHET0/w4evIVRo/6Lx49f3cLB19cVCxZ0VhkXRERE748PICIqRR4+TELv3tuRlZX7h0eFCqZYv743evRwkTgZEZF24l3XiEoRR0cLfPddOwBAt241cfnyRDZBRETFiEeEiCSkUAgIIVTuBP3FF+5wdrbGgAF1eZECEVEx4xGh1wkBpD7N/RJa8vBYKrWio5PRtesWzJlzTKWur6+Hjz+uxyaIiKgE8IjQSzmZwDZ34PF5qZOQDggJuY5x4/YhPj4Nhw5FonNnZ7i7V5E6FhGRzmEj9FLMmfyboHL2JZ+FtFZKSib8/P5AUNAFZc3OzgxZWTwCSUQkBTZCL+VkvfreyhkoXxswNAdcp0qXibRKWFg0vL134+bNeGWtb9/aCArqCRsbUwmTERHpLjZC+flgMPDhXKlTkJbIyVFg/vyTmDHjCLKzcy+LNzU1xLJlXTF6dBOOBSIikhAbIaJiFBeXgo8//g3Hjt1X1po3d0BwcD/UqmUjYTIiIgJ41RhRsbKwkOP583QAgEwGfP21B06eHM0miIiolGAjRFSMjI0NsHVrP3zwgQ2OHh2JuXPbw9BQX+pYRET0/3hqjEiDTp6MgrW1CerWraCs1atXEVev+qrcNJGIiEoH/mYm0oCsrBzMmHEYbdpshJfXLmRkZKvMZxNERFQ68bcz0Xu6cycBHh4bMGfOMSgUApcuPcbPP/PGnEREZQFPjREVkRACmzZdwmefHcSLF5kAAH19GWbP/gi+vs2lDUdERIXCRoioCBIS0jBhwn7s3HlNWXN2tsbWrf3RokVlCZMREZE62AgRqenvvyMxfHgIHj1KVtbGjGmCJUu6olw5IwmTERGRutgIEakhKioRXbpsUd4h2traGEFBPdG/f12JkxERUVFwsDSRGqpWtcT06R8CANq3r46IiIlsgoiIyjAeESJ6CyEEhAD09F49D+zbb9vA2dkaw4Y1UqkTEVHZwyNCRAWIi0tB797bsWjRKZW6oaE+RoxozCaIiEgL8IgQUT4OHryFUaP+i8ePU/D777fRoUMNNG1qL3UsIiLSMDZCRK9JS8vCl1/+hZ9+OqesWVkZ49mzNAlTERFRcWEjRPT/Ll2Khbf3bly9+kRZ69atJjZs6A07u3ISJiMiouLCRoh0nkIhsHTpGQQEHEJmZg6A3KfGL1jQCZ980hwyGccCERFpKzZCpNOePEmBl9du/PXXXWWtYUM7bN3aD/XqVZQwGRERlQReNUY6zdTUEFFRicrpqVPdcO7cWDZBREQ6go0Q6TQzMyNs3doP1apZITR0GBYu7Ay5nAdKiYh0BX/jk04JC4uGtbUxnJ3LK2vNmjng5s1PYWioL2EyIiKSAo8IkU7IyVEgMPA43NzWwdt7N7KyclTmswkiItJNbIRI60VFJaJ9+1/w1Vd/IztbgbNnH2Ht2gtSxyIiolKAp8ZIq23ffgU+PvuRmJgBAJDJgK++8sDYsU0lTkZERKUBGyHSSklJGfj00wPYvDlCWata1RJbtvSFh4eThMmIiKg0YSNEWufUqQcYOnQ3IiOfK2teXg2wYoUnrKyMpQtGRESlDhsh0ir37j1H27YbkZ2tAABYWMixcqUnvL0bSpyMiIhKIw6WJq1SrZoVPvusBQCgdesquHTJh00QEREViEeEqEwTQgCAyvPAvv++A2rWLI/x45vBwIC9PhERFYyfElRmJSSkYeDAnVi58h+VurGxAXx9m7MJIiKid+IRISqTDh+OxLBhIXj0KBn799/ERx9V4/PBiIhIbfyTmcqUzMwcTJsWig4dfsGjR8kAABMTA+X3RERE6uARISozrl9/Am/v3QgPj1XW2revjk2b+sDR0ULCZEREVFaxEaJSTwiB1avDMHXqn0hLywYAGBrqITCwA/z83KCnJ3vHKxAREeWPjRCVavHxqRg58r/Yv/+mslanji2Cg/uhSRN7CZMREZE24BghKtUMDPRw+fJj5bSvryvCwsazCSIiIo1gI0SlmqWlMbZs6Qd7+3LYt28IVqzoDlNTQ6ljERGRluCpMSpVLl2KRfnyJqhSxVJZ+/DDqrh7dzKMjfm/KxERaZbkR4RWrlyJ6tWrw9jYGM2aNcPx48cLXHb37t3o1KkTKlSoAAsLC7i5ueGPP/4owbRUXBQKgcWLT6NFi7UYNiwEOTkKlflsgoiIqDhI2gjt2LEDU6ZMwddff43w8HB4eHigW7duiIqKynf5Y8eOoVOnTjhw4ADOnz+Pdu3aoWfPnggPDy/h5KRJ0dHJ6Np1C/z9/0RmZg6OHr2P9eu5T4mIqPjJxMuHNUmgZcuWaNq0KVatWqWs1alTB3369EFgYGChXqNevXoYNGgQZsyYUajlk5KSYGlpicTERFhYvHbvmfuHgJ0d/z/Y18CHcwv9PqjoQkKuY9y4fYiPT1PWpk51w7x57SGX8ygQERHlKvDz+z1J9kmTmZmJ8+fPIyAgQKXeuXNnnDp1qlCvoVAokJycjPLlyxe4TEZGBjIyMpTTSUlJRQtMGpWSkgk/vz8QFHRBWXNwMMemTX3QsWMNCZMREZEukezU2NOnT5GTkwM7OzuVup2dHWJjYwtYS9WiRYuQkpKCgQMHFrhMYGAgLC0tlV9VqlR5r9z0/sLCotG06c8qTVC/fnUQEeHDJoiIiEqU5IOlZTLVuwILIfLU8rNt2zbMmjULO3bsQMWKBT9sc/r06UhMTFR+PXjw4L0zU9HdvfsMbm7rcPNmPADAzMwQ69b1ws6dH8PGxlTidEREpGska4RsbW2hr6+f5+hPXFxcnqNEb9qxYwfGjBmDX3/9FR07dnzrsnK5HBYWFipfJJ0aNawxZkwTAEDz5g4ID5+A0aObFKr5JSIi0jTJGiEjIyM0a9YMoaGhKvXQ0FC4u7sXuN62bdswcuRIbN26Fd27dy/umFQMFi3qjIULO+HkydGoVctG6jhERKTDJD015u/vj7Vr12L9+vW4fv06/Pz8EBUVBR8fHwC5p7WGDx+uXH7btm0YPnw4Fi1ahFatWiE2NhaxsbFITEyU6i3QWyQlZWD48BBs2KB6KbyZmRGmTnWHoaG+RMmIiIhySXp98qBBgxAfH4/vvvsOMTExqF+/Pg4cOAAnJycAQExMjMo9hdasWYPs7Gx88skn+OSTT5T1ESNGYOPGjSUdn97i1KkHGDp0NyIjnyMk5AY8PJxQs2bBV/cRERFJQfIbtfj6+sLX1zffeW82N0eOHCn+QPResrMVmDPnKObOPQ6FIvcWVXp6Mty+ncBGiIiISh3JGyHSHnfuJMDbezfOnn2krH34YVVs3twX1apZSReMiIioAJJfPl8q3Nz56q7SpDYhBDZuvIjGjdcomyB9fRnmzm2HI0dGsAkiIqJSi0eEAODIVNVpQ97PprCePUvD+PH7sXPnNWXN2dkaW7f2R4sWlSVMRkRE9G5shAAg4/mr7ys1B+p4SxalrFEoBE6denWTyjFjmmDJkq4oV85IwlRERESFw1NjrytfB/A+B1g4SZ2kzLCxMcWmTX1gY2OCnTs/xtq1vdgEERFRmcEjQqSW69efoHx5E9jZlVPWOnasgcjIyTA3l0uYjIiISH08IkSFIoTA6tVhaNbsZ4wa9V8IIVTmswkiIqKyiI0QvVNcXAp6996OiRP/h7S0bBw8eBubNl2SOhYREdF746kxeqvff7+NkSP34PHjFGXN19cVAwfWkzAVERGRZuh2I5T0ADj5NZCZJHWSUictLQsBAX9h2bJzylqFCqZYv743evRwkTAZERGR5uh2IxS+DLi2+dW0Pse5AMDly4/h5bUbV67EKWuenrWwfn0vlUHSREREZZ1uN0JpT1Snm3wmTY5S5PbtBLi6BiEzMwcAYGxsgIULO8HXtzlkMpnE6YiIiDSLg6VfGnEFaDBa6hSSq1mzPAYNyh3/06iRHc6fH49PPmnBJoiIiLSSbh8Rep1MX+oEpcby5Z6oVas8pk1rDbmc/4sQEZH24hEhHZaSkonx4/dhx44rKnULCzm+/bYtmyAiItJ6/KTTUWFh0fD23o2bN+Px22/X4O5eBVWqWEodi4iIqETxiJCOyclRIDDwONzc1uHmzXgAQGZmDiIiHkucjIiIqOTxiJAOiYpKxLBhITh27L6y1ry5A4KD+6FWLRsJkxEREUmDjZCO2L79Cnx89iMxMQMAIJMBX33lgZkz28LQkAPFiYhIN7ER0nJJSRn49NMD2Lw5QlmrWtUSW7b0hYeHk4TJiIiIpMdGSMulpmbh4MHbyukhQ+pj5crusLIyljAVERFR6cDB0lquUqVyWLeuFyws5NiypS+2bu3PJoiIiOj/8YiQlrl9OwHW1sawsTFV1nr1+gCRkZNRvryJhMmIiIhKHx4R0hJCCGzYEI7GjVdjwoT9EEKozGcTRERElBcbIS2QkJCGgQN3YvTovUhJycKuXdexbduVd69IRESk43hqrIw7fDgSw4aF4NGjZGVtzJgm6NXrAwlTERERlQ1shMqozMwcfPPN31i48BRengWztjZGUFBP9O9fV9pwREREZQQboTLoxo2n8PLahfDwWGWtffvq2LSpDxwdLSRMRkREVLawESpj/v33KZo2XYO0tGwAgKGhHgIDO8DPzw16ejKJ0xEREZUtHCxdxri42KBbt1oAgDp1bHHu3DhMnerOJoiIiKgIeESojJHJZPj55x5wcSmPb79tC1NTQ6kjERERlVlshEqxtLQsfPnlX+jUqQZ69nx1FZiNjSkCAztKmIxItwghkJ2djZycHKmjEGk1Q0ND6OuX7IPAdbcRurQGuLpJ6hQFunQpFt7eu3H16hNs23YFly9PRKVK5aSORaRzMjMzERMTg9TUVKmjEGk9mUwGR0dHlCtXcp93utsIHZsGvP7ILb3S8aNQKASWLj2DgIBDyMzM/evzxYtMhIVFo0cPF4nTEekWhUKByMhI6Ovrw8HBAUZGRpDJOB6PqDgIIfDkyRM8fPgQtWrVKrEjQ6Xj019qVdsDVs5Sp0B0dDJGjtyD0NC7ylqjRnbYurU/6tatIGEyIt2UmZkJhUKBKlWqwNTU9N0rENF7qVChAu7du4esrCw2QiWmyWdAu6WAxH/lhYRcx7hx+xAfn6asTZ3qhnnz2kMu524ikpKeHi+wJSoJUhxx5Sesmb2kTdCLF5nw8/sda9eGK2sODubYtKkPOnasIVkuIiIiXcBGSGLPnqXht9+uKaf79q2NoKCesLHhYXgiIqLixuO9EqtSxRJr1vSAmZkh1q7tiV27BrIJIiKSSHx8PCpWrIh79+5JHUXrLF++HL169ZI6Rh5shEpYVFQikpIyVGqDBtXH7duTMGZMU16RQkTvbeTIkZDJZJDJZDAwMEDVqlUxceJEPHv2LM+yp06dgqenJ6ytrWFsbIwGDRpg0aJF+d4z6fDhw/D09ISNjQ1MTU1Rt25dTJ06FY8ePSqJt1UiAgMD0bNnT1SrVk3qKMXm6NGjaNasGYyNjVGjRg2sXr36nescOnQI7u7uMDc3h729Pb788ktkZ2cr56enp2PkyJFo0KABDAwM0KdPnzyvMW7cOPzzzz84ceKEJt/Oe2MjVIK2b7+Chg1X4bPPDuaZx3sEEZEmde3aFTExMbh37x7Wrl2Lffv2wdfXV2WZkJAQtG3bFo6Ojjh8+DBu3LiByZMnY968eRg8eDCEEMpl16xZg44dO6JSpUrYtWsXrl27htWrVyMxMRGLFi0qsfeVmZlZbK+dlpaGdevWYezYse/1OsWZ8X1FRkbC09MTHh4eCA8Px1dffYVJkyZh165dBa4TEREBT09PdO3aFeHh4di+fTv27t2LgIAA5TI5OTkwMTHBpEmT0LFj/jf8lcvl8PLywk8//aTx9/VehI5JTEwUAETiXAixEEKc+b4Etpkuhg3bLYBZyq+dO68W+3aJ6P2kpaWJa9euibS0NKmjqGXEiBGid+/eKjV/f39Rvnx55fSLFy+EjY2N6NevX5719+7dKwCI7du3CyGEePDggTAyMhJTpkzJd3vPnj0rMMuzZ8/EuHHjRMWKFYVcLhf16tUT+/btE0IIMXPmTNGoUSOV5RcvXiycnJzyvJfvv/9e2NvbCycnJxEQECBatmyZZ1sNGjQQM2bMUE6vX79e1K5dW8jlcvHBBx+IFStWFJhTCCF27dolbG1tVWrZ2dli9OjRolq1asLY2Fi4uLiIJUuWqCyTX0YhhHj48KEYOHCgsLKyEuXLlxe9evUSkZGRyvXOnTsnOnbsKGxsbISFhYVo06aNOH/+/Fszvq9p06aJ2rVrq9QmTJggWrVqVeA606dPF66uriq1kJAQYWxsLJKSkvIsn9//fy8dOXJEGBkZidTU1Hznv+3fnPLzOzGxwKxFwcHSxezkySgMHRqCe/eeK2tDhtRHhw68IoyoTNriCqTElvx2zSoBQ8OKtOrdu3fx+++/w9Dw1bMJ//zzT8THx+Pzzz/Ps3zPnj3h4uKCbdu2YdCgQfjtt9+QmZmJadOm5fv6VlZW+dYVCgW6deuG5ORkbNmyBc7Ozrh27Zra94c5dOgQLCwsEBoaqjxK9Z///Ad37tyBs3PuPeCuXr2Ky5cvY+fOnQCAoKAgzJw5E8uXL0eTJk0QHh6OcePGwczMDCNGjMh3O8eOHYOrq2ue9+Do6Ihff/0Vtra2OHXqFMaPHw97e3sMHDiwwIypqalo164dPDw8cOzYMRgYGGDu3Lno2rUrIiIiYGRkhOTkZIwYMQLLli0DACxatAienp64desWzM3N880YHByMCRMmvPXntWbNGnh7e+c77/Tp0+jcubNKrUuXLli3bh2ysrJU/h95KSMjA8bGxio1ExMTpKen4/z58/joo4/emud1rq6uyMrKwrlz59C2bdtCr1ec2AgVk6ysHMyZcwzz5h2HQpH7D9fCQo6VKz3h7d1Q4nREVGQpscCL0j8mZv/+/ShXrhxycnKQnp4OAPjxxx+V82/evAkAqFOnTr7r165dW7nMrVu3YGFhAXt7e7Uy/PXXXzh37hyuX78OF5fcO+PXqKH+H4FmZmZYu3YtjIyMlLWGDRti69at+PbbbwHkNgjNmzdXbmfOnDlYtGgR+vXrBwCoXr06rl27hjVr1hTYCN27dw8ODg4qNUNDQ8yePVs5Xb16dZw6dQq//vqrSiP0Zsb169dDT08Pa9euVY793LBhA6ysrHDkyBF07twZ7du3V9nWmjVrYG1tjaNHj6JHjx75ZuzVqxdatmz51p+XnZ1dgfNiY2PzzLezs0N2djaePn2a7z7u0qULlixZgm3btmHgwIGIjY3F3LlzAQAxMTFvzfImMzMzWFlZ4d69e2yEtNnt2wkYOnQ3zp599cuydesq2LKlH6pVs5IuGBG9P7NKZWK77dq1w6pVq5Camoq1a9fi5s2b+Oyzz/IsJ14bB/Rm/eUH+Ovfq+PixYtwdHRUNidF1aBBA5UmCAC8vb2xfv16fPvttxBCYNu2bZgyZQoA4MmTJ3jw4AHGjBmDcePGKdfJzs6GpaVlgdtJS0vLc+QDAFavXo21a9fi/v37SEtLQ2ZmJho3bvzWjOfPn8ft27fzHNlJT0/HnTt3AABxcXGYMWMG/v77bzx+/Bg5OTlITU1FVFRUgRnNzc0LPFpUWG/uy5f/DxS0jzt37owFCxbAx8cHw4YNg1wux7fffosTJ04U6e7PJiYmperZfWyENOz69Sdo3jwIKSlZAAB9fRlmzfoIAQEfwsCAY9OJyrwinp4qaWZmZqhZsyYAYNmyZWjXrh1mz56NOXPmAICyObl+/Trc3d3zrH/jxg3UrVtXuWxiYiJiYmLUOipkYmLy1vl6enp5GrGsrKx838ubvLy8EBAQgAsXLiAtLQ0PHjzA4MGDAeSezgJyT4+9efTkbR/ctra2ea6s+/XXX+Hn54dFixbBzc0N5ubmWLBgAc6ePfvWjAqFAs2aNUNwcHCe7VSokPvIpJEjR+LJkydYsmQJnJycIJfL4ebm9tbB1u97aqxSpUqIjVU9tRsXFwcDAwPY2NgU+Jr+/v7w8/NDTEwMrK2tce/ePUyfPh3Vq1d/a5b8JCQkKH8GpQEbIQ2rXdsWHh5O+P3323B2tkZwcD+0bOkodSwi0nEzZ85Et27dMHHiRDg4OKBz584oX748Fi1alKcR2rt3L27duqVsmgYMGICAgADMnz8fixcvzvPaz58/z3ecUMOGDfHw4UPcvHkz36NCFSpUQGxsrMoRp4sXLxbq/Tg6OqJNmzYIDg5GWloaOnbsqDzlY2dnh8qVK+Pu3bsFNgT5adKkCbZs2aJSO378ONzd3VWuuHt5ROdtmjZtih07dqBixYqwsLDId5njx49j5cqV8PT0BAA8ePAAT58+fevrvu+pMTc3N+zbt0+l9ueff8LV1TXf8UGvk8lkylOH27ZtQ5UqVdC0adO3rvOmO3fuID09HU2aNFFrvWKl0aHXZUBJXDUWE5MsJk8+KJKTMzT+2kRUcrTpqjEhhGjWrJn45JNPlNO//fab0NfXF+PGjROXLl0SkZGRYu3atcLa2loMGDBAKBQK5bIrVqwQMplMjB49Whw5ckTcu3dPnDhxQowfP174+/sXmOWjjz4S9evXF3/++ae4e/euOHDggDh48KAQQohr164JmUwm/vOf/4jbt2+L5cuXC2tr63yvGsvPzz//LBwcHIStra3YvHmzyrygoCBhYmIilixZIv79918REREh1q9fLxYtWlRg1oiICGFgYCASEhKUtSVLlggLCwvx+++/i3///Vd88803wsLCQuVqt/wypqSkiFq1aomPPvpIHDt2TNy9e1ccOXJETJo0STx48EAIIUTjxo1Fp06dxLVr18SZM2eEh4eHMDExEYsXLy4w4/u6e/euMDU1FX5+fuLatWti3bp1wtDQUOzcuVO5zO7du8UHH3ygst78+fNFRESEuHLlivjuu++EoaGhCAkJUVnm6tWrIjw8XPTs2VN89NFHIjw8XISHh6sss2HDBlGjRo0C80lx1RgbofdohDIyssW0aX+K0NA7GkxIRKWFtjVCwcHBwsjISERFRSlrx44dE127dhWWlpbCyMhI1K1bVyxcuFBkZ2fnWT80NFR06dJFWFtbC2NjY1G7dm3x+eefi+jo6AKzxMfHi1GjRgkbGxthbGws6tevL/bv36+cv2rVKlGlShVhZmYmhg8fLubNm1foRujZs2dCLpcLU1NTkZycnO/7bdy4sTAyMhLW1taiTZs2Yvfu3QVmFUKIVq1aidWrVyun09PTxciRI4WlpaWwsrISEydOFAEBAe9shIQQIiYmRgwfPlzY2toKuVwuatSoIcaNG6f8IL9w4YJwdXUVcrlc1KpVS/z222/CycmpWBshIXIvYW/SpIkwMjIS1apVE6tWrVKZv2HDBvHmcZJ27doJS0tLYWxsLFq2bCkOHDiQ53WdnJwEgDxfr+vcubMIDAwsMJsUjZBMiAJGymmppKQkWFpaInEuYGEM4MPvgZbT1X6dGzeewstrF8LDY+HgYI6ICB8+GoNIy6SnpyMyMhLVq1fPdxAtaZ8DBw7g888/x5UrV6Cnx3GdmnTlyhV06NABN2/eLHDQ+tv+zSk/vxMTCzzdWBTcy2oSQmD16jA0bboG4eG5A86ePEnBqVMPJE5GRETvy9PTExMmTNCqx4aUFtHR0fjll1/eeuWeFDhYWg1xcSkYO3Yv9u27qazVqWOLrVv7o3FjiS6pJSIijZo8ebLUEbTSmzdyLC3YCBXS77/fxsiRe/D4cYqy5uvrigULOsPU9O0j7YmIiKh0YiP0DmlpWQgI+AvLlp1T1ipUMMX69b3Ro8f73SSMiIiIpMVG6B2io5Oxbl24ctrTsxbWr+8FOzs+LZ5IV+jYNSVEkpHi3xoHS+sbvXW2s3N5LFvWDcbGBli+vBv27x/CJohIR7y8wVxpehwAkTZ7eVftojy6o6h0+4iQVU2gVn+VUnR0MqysjFXG/Ywa1RgdOlSHk5NVCQckIinp6+vDysoKcXFxAABTU9MiPXOLiN5NoVDgyZMnMDU1hYFBybUnutsI2dQBRl8FXvulFhJyHePG7cPHH9fFqlWvnvwrk8nYBBHpqEqVcq8IfdkMEVHx0dPTQ9WqVUv0Dw7dbYT0jZVN0IsXmfDz+x1r1+aOBVq9+jy6d3fhYGgigkwmg729PSpWrJjvA0GJSHOMjIxK/EaWkjdCK1euxIIFCxATE4N69ephyZIl8PDwKHD5o0ePwt/fH1evXoWDgwOmTZsGHx+fIm//n38ewdt7N27dSlDW+vatDTc3PiiViF7R19cv0XELRFQyJB0svWPHDkyZMgVff/01wsPD4eHhgW7duiEqKirf5SMjI+Hp6QkPDw+Eh4fjq6++wqRJk7Br1y61t52jAAIDj8Pdfb2yCTI1NcTatT2xa9dAPi6DiIhIB0j6rLGWLVuiadOmWLVqlbJWp04d9OnTB4GBgXmW//LLL7F3715cv35dWfPx8cGlS5dw+vTpQm3z5bNK3GtOwKnb9sp68+YOCA7uh1q1bN7jHREREVFx0LpnjWVmZuL8+fN5brnduXNnnDp1Kt91Tp8+nWf5Ll26ICwsTO1z96duWwMA9PRk+PprD5w8OZpNEBERkY6RbIzQ06dPkZOTAzs7O5W6nZ0dYmNj810nNjY23+Wzs7Px9OlT2Nvb51knIyMDGRkZyunExMSXc+DoaImgoB5wd6+KtLQUpKW933siIiKi4pGUlARA8zddlHyw9JuXyAkh3nrZXH7L51d/KTAwELNnz85nzmI8fAh06zZdvcBEREQkmfj4eI0+wV6yRsjW1hb6+vp5jv7ExcXlOerzUqVKlfJd3sDAADY2+Z/Wmj59Ovz9/ZXTz58/h5OTE6KiojT6g6SiSUpKQpUqVfDgwQONnvMl9XFflB7cF6UH90XpkZiYiKpVq6J8+fIafV3JGiEjIyM0a9YMoaGh6Nu3r7IeGhqK3r1757uOm5sb9u3bp1L7888/4erqqrwV/pvkcjnkcnmeuqWlJf+nLkUsLCy4P0oJ7ovSg/ui9OC+KD00fZ8hSS+f9/f3x9q1a7F+/Xpcv34dfn5+iIqKUt4XaPr06Rg+fLhyeR8fH9y/fx/+/v64fv061q9fj3Xr1uHzzz+X6i0QERFRGSbpGKFBgwYhPj4e3333HWJiYlC/fn0cOHAATk5OAICYmBiVewpVr14dBw4cgJ+fH1asWAEHBwcsW7YM/fv3L2gTRERERAWSfLC0r68vfH198523cePGPLW2bdviwoULRd6eXC7HzJkz8z1dRiWP+6P04L4oPbgvSg/ui9KjuPaFpDdUJCIiIpKSpGOEiIiIiKTERoiIiIh0FhshIiIi0llshIiIiEhnaWUjtHLlSlSvXh3GxsZo1qwZjh8//tbljx49imbNmsHY2Bg1atTA6tWrSyip9lNnX+zevRudOnVChQoVYGFhATc3N/zxxx8lmFb7qftv46WTJ0/CwMAAjRs3Lt6AOkTdfZGRkYGvv/4aTk5OkMvlcHZ2xvr160sorXZTd18EBwejUaNGMDU1hb29PUaNGoX4+PgSSqu9jh07hp49e8LBwQEymQx79ux55zoa+fwWWmb79u3C0NBQBAUFiWvXronJkycLMzMzcf/+/XyXv3v3rjA1NRWTJ08W165dE0FBQcLQ0FDs3LmzhJNrH3X3xeTJk8UPP/wgzp07J27evCmmT58uDA0NxYULF0o4uXZSd3+89Pz5c1GjRg3RuXNn0ahRo5IJq+WKsi969eolWrZsKUJDQ0VkZKQ4e/asOHnyZAmm1k7q7ovjx48LPT09sXTpUnH37l1x/PhxUa9ePdGnT58STq59Dhw4IL7++muxa9cuAUCEhIS8dXlNfX5rXSPUokUL4ePjo1KrXbu2CAgIyHf5adOmidq1a6vUJkyYIFq1alVsGXWFuvsiP3Xr1hWzZ8/WdDSdVNT9MWjQIPHNN9+ImTNnshHSEHX3xcGDB4WlpaWIj48viXg6Rd19sWDBAlGjRg2V2rJly4Sjo2OxZdRFhWmENPX5rVWnxjIzM3H+/Hl07txZpd65c2ecOnUq33VOnz6dZ/kuXbogLCwMWVlZxZZV2xVlX7xJoVAgOTlZ4w/Y00VF3R8bNmzAnTt3MHPmzOKOqDOKsi/27t0LV1dXzJ8/H5UrV4aLiws+//xzpKWllURkrVWUfeHu7o6HDx/iwIEDEELg8ePH2LlzJ7p3714Skek1mvr8lvzO0pr09OlT5OTk5Hl6vZ2dXZ6n1r8UGxub7/LZ2dl4+vQp7O3tiy2vNivKvnjTokWLkJKSgoEDBxZHRJ1SlP1x69YtBAQE4Pjx4zAw0KpfFZIqyr64e/cuTpw4AWNjY4SEhODp06fw9fVFQkICxwm9h6LsC3d3dwQHB2PQoEFIT09HdnY2evXqhZ9++qkkItNrNPX5rVVHhF6SyWQq00KIPLV3LZ9fndSn7r54adu2bZg1axZ27NiBihUrFlc8nVPY/ZGTkwMvLy/Mnj0bLi4uJRVPp6jzb0OhUEAmkyE4OBgtWrSAp6cnfvzxR2zcuJFHhTRAnX1x7do1TJo0CTNmzMD58+fx+++/IzIyUvmwcCpZmvj81qo/82xtbaGvr5+nk4+Li8vTNb5UqVKlfJc3MDCAjY1NsWXVdkXZFy/t2LEDY8aMwW+//YaOHTsWZ0ydoe7+SE5ORlhYGMLDw/Hpp58CyP0wFkLAwMAAf/75J9q3b18i2bVNUf5t2Nvbo3LlyrC0tFTW6tSpAyEEHj58iFq1ahVrZm1VlH0RGBiI1q1b44svvgAANGzYEGZmZvDw8MDcuXN5FqEEaerzW6uOCBkZGaFZs2YIDQ1VqYeGhsLd3T3fddzc3PIs/+eff8LV1RWGhobFllXbFWVfALlHgkaOHImtW7fynLsGqbs/LCwscPnyZVy8eFH55ePjgw8++AAXL15Ey5YtSyq61inKv43WrVsjOjoaL168UNZu3rwJPT09ODo6FmtebVaUfZGamgo9PdWPTn19fQCvjkZQydDY57daQ6vLgJeXQq5bt05cu3ZNTJkyRZiZmYl79+4JIYQICAgQw4YNUy7/8vI7Pz8/ce3aNbFu3TpePq8h6u6LrVu3CgMDA7FixQoRExOj/Hr+/LlUb0GrqLs/3sSrxjRH3X2RnJwsHB0dxYABA8TVq1fF0aNHRa1atcTYsWOlegtaQ919sWHDBmFgYCBWrlwp7ty5I06cOCFcXV1FixYtpHoLWiM5OVmEh4eL8PBwAUD8+OOPIjw8XHkrg+L6/Na6RkgIIVasWCGcnJyEkZGRaNq0qTh69Khy3ogRI0Tbtm1Vlj9y5Iho0qSJMDIyEtWqVROrVq0q4cTaS5190bZtWwEgz9eIESNKPriWUvffxuvYCGmWuvvi+vXromPHjsLExEQ4OjoKf39/kZqaWsKptZO6+2LZsmWibt26wsTERNjb2wtvb2/x8OHDEk6tfQ4fPvzWz4Di+vyWCcFjeURERKSbtGqMEBEREZE62AgRERGRzmIjRERERDqLjRARERHpLDZCREREpLPYCBEREZHOYiNEREREOouNEBGp2LhxI6ysrKSOUWTVqlXDkiVL3rrMrFmz0Lhx4xLJQ0SlGxshIi00cuRIyGSyPF+3b9+WOho2btyoksne3h4DBw5EZGSkRl7/n3/+wfjx45XTMpkMe/bsUVnm888/x6FDhzSyvYK8+T7t7OzQs2dPXL16Ve3XKcuNKVFpx0aISEt17doVMTExKl/Vq1eXOhaA3Ie6xsTEIDo6Glu3bsXFixfRq1cv5OTkvPdrV6hQAaampm9dply5cmo9nbqoXn+f//vf/5CSkoLu3bsjMzOz2LdNRIXDRohIS8nlclSqVEnlS19fHz/++CMaNGgAMzMzVKlSBb6+vipPNX/TpUuX0K5dO5ibm8PCwgLNmjVDWFiYcv6pU6fQpk0bmJiYoEqVKpg0aRJSUlLemk0mk6FSpUqwt7dHu3btMHPmTFy5ckV5xGrVqlVwdnaGkZERPvjgA2zevFll/VmzZqFq1aqQy+VwcHDApEmTlPNePzVWrVo1AEDfvn0hk8mU06+fGvvjjz9gbGyM58+fq2xj0qRJaNu2rcbep6urK/z8/HD//n38+++/ymXetj+OHDmCUaNGITExUXlkadasWQCAzMxMTJs2DZUrV4aZmRlatmyJI0eOvDUPEeXFRohIx+jp6WHZsmW4cuUKNm3ahL///hvTpk0rcHlvb284Ojrin3/+wfnz5xEQEABDQ0MAwOXLl9GlSxf069cPERER2LFjB06cOIFPP/1UrUwmJiYAgKysLISEhGDy5MmYOnUqrly5ggkTJmDUqFE4fPgwAGDnzp1YvHgx1qxZg1u3bmHPnj1o0KBBvq/7zz//AAA2bNiAmJgY5fTrOnbsCCsrK+zatUtZy8nJwa+//gpvb2+Nvc/nz59j69atAKD8+QFv3x/u7u5YsmSJ8shSTEwMPv/8cwDAqFGjcPLkSWzfvh0RERH4+OOP0bVrV9y6davQmYgI0MqnzxPpuhEjRgh9fX1hZmam/BowYEC+y/7666/CxsZGOb1hwwZhaWmpnDY3NxcbN27Md91hw4aJ8ePHq9SOHz8u9PT0RFpaWr7rvPn6Dx48EK1atRKOjo4iIyNDuLu7i3Hjxqms8/HHHwtPT08hhBCLFi0SLi4uIjMzM9/Xd3JyEosXL1ZOAxAhISEqy8ycOVM0atRIOT1p0iTRvn175fQff/whjIyMREJCwnu9TwDCzMxMmJqaKp+k3atXr3yXf+ld+0MIIW7fvi1kMpl49OiRSr1Dhw5i+vTpb319IlJlIG0bRkTFpV27dli1apVy2szMDABw+PBhfP/997h27RqSkpKQnZ2N9PR0pKSkKJd5nb+/P8aOHYvNmzejY8eO+Pjjj+Hs7AwAOH/+PG7fvo3g4GDl8kIIKBQKREZGok6dOvlmS0xMRLly5SCEQGpqKpo2bYrdu3fDyMgI169fVxnsDACtW7fG0qVLAQAff/wxlixZgho1aqBr167w9PREz549YWBQ9F9n3t7ecHNzQ3R0NBwcHBAcHAxPT09YW1u/1/s0NzfHhQsXkJ2djaNHj2LBggVYvXq1yjLq7g8AuHDhAoQQcHFxUalnZGSUyNgnIm3CRohIS5mZmaFmzZoqtfv378PT0xM+Pj6YM2cOypcvjxMnTmDMmDHIysrK93VmzZoFLy8v/O9//8PBgwcxc+ZMbN++HX379oVCocCECRNUxui8VLVq1QKzvWwQ9PT0YGdnl+cDXyaTqUwLIZS1KlWq4N9//0VoaCj++usv+Pr6YsGCBTh69KjKKSd1tGjRAs7Ozti+fTsmTpyIkJAQbNiwQTm/qO9TT09PuQ9q166N2NhYDBo0CMeOHQNQtP3xMo++vj7Onz8PfX19lXnlypVT670T6To2QkQ6JCwsDNnZ2Vi0aBH09HKHCP7666/vXM/FxQUuLi7w8/PDkCFDsGHDBvTt2xdNmzbF1atX8zRc7/J6g/CmOnXq4MSJExg+fLiydurUKZWjLiYmJujVqxd69eqFTz75BLVr18bly5fRtGnTPK9naGhYqKvRvLy8EBwcDEdHR+jp6aF79+7KeUV9n2/y8/PDjz/+iJCQEPTt27dQ+8PIyChP/iZNmiAnJwdxcXHw8PB4r0xEuo6DpYl0iLOzM7Kzs/HTTz/h7t272Lx5c55TNa9LS0vDp59+iiNHjuD+/fs4efIk/vnnH2VT8uWXX+L06dP45JNPcPHiRdy6dQt79+7FZ599VuSMX3zxBTZu3IjVq1fj1q1b+PHHH7F7927lIOGNGzdi3bp1uHLlivI9mJiYwMnJKd/Xq1atGg4dOoTY2Fg8e/aswO16e3vjwoULmDdvHgYMGABjY2PlPE29TwsLC4wdOxYzZ86EEKJQ+6NatWp48eIFDh06hKdPnyI1NRUuLi7w9vbG8OHDsXv3bkRGRuKff/7BDz/8gAMHDqiViUjnSTlAiYiKx4gRI0Tv3r3znffjjz8Ke3t7YWJiIrp06SJ++eUXAUA8e/ZMCKE6ODcjI0MMHjxYVKlSRRgZGQkHBwfx6aefqgwQPnfunOjUqZMoV66cMDMzEw0bNhTz5s0rMFt+g3/ftHLlSlGjRg1haGgoXFxcxC+//KKcFxISIlq2bCksLCyEmZmZaNWqlfjrr7+U898cLL13715Rs2ZNYWBgIJycnIQQeQdLv9S8eXMBQPz999955mnqfd6/f18YGBiIHTt2CCHevT+EEMLHx0fY2NgIAGLmzJlCCCEyMzPFjBkzRLVq1YShoaGoVKmS6Nu3r4iIiCgwExHlJRNCCGlbMSIiIiJp8NQYERER6Sw2QkRERKSz2AgRERGRzmIjRERERDqLjRARERHpLDZCREREpLPYCBEREZHOYiNEREREOouNEBEREeksNkJERESks9gIERERkc5iI0REREQ66/8ASdxy32Mx2WkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_auc_curve(val_y,pred_probs_model1,model_1['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "SVM=SVC(kernel='linear',probability=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;, probability=True)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, probability=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear', probability=True)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2={'model':SVM, 'name': 'SVM CLASSIFIER'}\n",
    "model_2['model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score of SVM 0.8202614379084968\n"
     ]
    }
   ],
   "source": [
    "pred_probs_model2,pred_model2,f1_score_model2=get_predictions(model_2['model'],X_train_tfidf,train_y,X_test_tfidf, val_y)\n",
    "print('f1_score of SVM',f1_score_model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix(val_y,pred_model2,model_2['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_auc_curve(val_y,pred_probs_model2,model_2['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "LC = LogisticRegression(penalty='l1', C=1.0,solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 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-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 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-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 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-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 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-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(penalty='l1', solver='liblinear')"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3={'model': LC, 'name': 'logistic with l1'}\n",
    "model_3['model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score of Ridge classifier 0.7741935483870968\n"
     ]
    }
   ],
   "source": [
    "pred_probs_model3,pred_model3,f1_score_model3=get_predictions(model_3['model'],X_train_tfidf,train_y,X_test_tfidf, val_y)\n",
    "print('f1_score of Ridge classifier',f1_score_model3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix(val_y,pred_model3,model_3['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_auc_curve(val_y,pred_probs_model3,model_3['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "DC=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {color: black;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 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-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 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-17 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-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 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-17 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-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 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-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 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-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 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-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_4={'model': DC, 'name': 'Decision Tree'}\n",
    "model_4['model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score of Decision Tree classifier 0.7623762376237625\n"
     ]
    }
   ],
   "source": [
    "pred_probs_model4,pred_model4,f1_score_model4=get_predictions(model_4['model'],X_train_tfidf,train_y,X_test_tfidf, val_y)\n",
    "print('f1_score of Decision Tree classifier',f1_score_model4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF=RandomForestClassifier(n_estimators=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-19 {color: black;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 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-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 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-19 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-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 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-19 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-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 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-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 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-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 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-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=150)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=150)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=150)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_5={'model': RF, 'name': 'Random Forest'}\n",
    "model_5['model']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score of Random Forest classifier 0.8184931506849316\n"
     ]
    }
   ],
   "source": [
    "pred_probs_model5,pred_model5,f1_score_model5=get_predictions(model_5['model'],X_train_tfidf,train_y,X_test_tfidf, val_y)\n",
    "print('f1_score of Random Forest classifier',f1_score_model5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fLA0wESAiIhljIsDSABERkZxxRoCIiGSLryFmIkBERHLG0gBLA0RERHLGGQEiIpIv7iPARICIiGSMpQGWBoiIiOSMMwJERCRfLA0wESAiIhljaYClASIiorKm0WjQuHFj2NjYwNnZGT169MDp06e11/Py8jB+/Hj4+PjA2toarq6uGDBgAP7991/JOH5+flAoFJIjODhYp1iYCBARkXyJQv0dOtizZw9GjhyJgwcPIi4uDvn5+QgICEBWVhYA4N69e0hKSsInn3yCpKQkbNiwAf/88w/eeOONImMNHToUKSkp2mP+/Pk6xcLSABERyZceSwM5OTnIycmRtCmVSiiVyiJ9t2/fLjmPiYmBs7MzEhMT0bp1a9jZ2SEuLk7S55tvvkGTJk1w6dIluLu7a9utrKygUqlKHTdnBIiIiPRAo9HAzs5Ocmg0mhLde+fOHQCAg4PDU/soFAq88sorkvYVK1bAyckJderUQXh4OO7evatT3AohhNDpjpdA3o3zxg6ByOAsXVsZOwQig8vPvWrQ8bO3ztbbWCbt3yvxjMCjhBDo3r070tPT8fvvvxfb5/79+3j99ddRs2ZNLF++XNu+YMECeHp6QqVS4cSJE4iIiMCrr75aZDbhaVgaICIi+dLj44Ml+aVfnFGjRuHYsWPYu3dvsdfz8vIQHByMwsJCREdHS64NHTpU+29vb294eXmhUaNGSEpKQoMGDUr0+SwNEBERGUloaCg2b96M3bt3o0qVKkWu5+XloXfv3khOTkZcXBxsbW2fOl6DBg1gZmaGM2fOlDgGzggQEZF8GWkfASEEQkNDERsbi/j4eHh6ehbp8zAJOHPmDHbv3g1HR8dnjnvy5Enk5eVBrVaXOBYmAkREJF9G2llw5MiRWLlyJTZt2gQbGxukpqYCAOzs7GBpaYn8/Hz07NkTSUlJ+Omnn1BQUKDt4+DgAHNzc5w7dw4rVqxA586d4eTkhL/++gtjx46Fr68vWrZsWeJYuFiQ6CXFxYIkBwZfLLhpmt7Gsuw+rsR9FQpFse0xMTEYNGgQLly4UOwsAQDs3r0bfn5+uHz5Mvr3748TJ04gMzMTbm5u6NKlCyIjI5/69MHjOCNARETyZcTSwNNUrVr1mX3c3NywZ8+e546FiQAREckXXzrEpwaIiIjkjDMCREQkX3z7IBMBIiKSMSYCLA0QERHJGWcEiIhIvsrfE/Q6YyJARETyxdIASwNERERyxhkBIiKSL84IMBEgIiIZ44ZCLA0QERHJGWcEiIhIvlgaYCJAREQyxscHWRogIiKSM84IEBGRfLE0wESAiIhkjIkASwNERERyxhkBIiKSL+4jwESAiIjkSxTyqQGWBoiIiGSMMwJERCRfXCzIRICIiGSMawRYGiAiIpIzzggQEZF8cbEgEwEiIpIxrhFgaYCIiEjOOCNARETyxRkBJgJERCRjfA0xSwNERERyxhkBIiKSL5YGOCNQHi1YugZ9QkajSfsgtO4SjNEffYbki1eeeV9ubi6+nr8YHYIGwtevGzr1GowNP/1i0Fj/OZeMQSM/REP/7mjbvT/mLVoB8chUXVz8Przz/sdo1aUPmnYIQr93P8C+PxINGhPRQ+PHjUJ+7lXM+GqypP3TT8bg0oVE3L1zFjvjfkTt2jWMFCE9t0Khv+MlxUSgHDp89DjeDuqGld/Pwvezo5BfUIB3P5iAe9n3n3rf2E80+OPwUXwWEYafVv2A6ZM/gqeHW6njuJpyDd4tA594PTMrC0PDJqCSkyNWL/waER+8h8Wr1mPJ6g3aPolHj6NFE19Ef/UZ1i76Bo0b1MPIcZNw6p+zpY6LqCQaNayHd0L64c9jf0naPwwfgbD338XosIlo1qILUq9dx/Ztq1CxorWRIiV6PiwNlEPzZ34hOf/i4w/Quuvb+Ov0GTSq71PsPXsPHsbho8ex/ccY2NnaAAAqq12K9Ivd+isWrViHqympqKxyQb9e3REc1LVUcf70627k5uZiyoQxMDc3h1e1qrh4+SqWro7FwOAgKBQKfBQ2XHJP2PBB2P37AcTv/QO1arxaqs8lehZrayssXToXw98bh48jRkuujQ59B5qpc7Bx488AgMFDwvDvlaN4O/hNLPhhuTHCpefBLYaNOyNw5coVTJgwAf7+/qhVqxZq164Nf39/TJgwAZcvXzZmaOVKZtY9AND+gi/O7r0HUaemFxat+BFtu/dHl+B3MH3uAtzPydH2Wbf5Z8yZvwSj3x2IzSu+x+hhg/DNgqXYtC2uVHH9eeJvNKrvA3Nzc21by6YNkHbjJq6mXCv2nsLCQmRlZz/1ZyF6Xt/MicLP23Zi567fJe2enu5Qq10Qt2OPti03Nxe//X4QzZs3KuswSR+MVBrQaDRo3LgxbGxs4OzsjB49euD06dOSPkIITJo0Ca6urrC0tISfnx9Onjwp6ZOTk4PQ0FA4OTnB2toab7zxBq5ceXYp+FFGSwT27t2LWrVqITY2FvXq1cOAAQPQv39/1KtXDxs3bkSdOnWwb9++Z46Tk5ODjIwMyZHzyC8vuRNCYNqc79Ggbh14Vav6xH5X/k1F0rGTOHP+Ir7WfILxo99F3O69+GLGt9o+3y1ehQ9Dh6KDX0tUcVWhg19LDOjzJtZu+rlUsd24eQuODq9I2hzt7R9cu5Ve7D2LV21AdvZ9dGzXulSfSfQsvXu/AV9fb3w8UVPkmsrFGQBw7doNSfu1a9ehcqlUJvFR+bBnzx6MHDkSBw8eRFxcHPLz8xEQEICsrCxtn2nTpmHmzJmYO3cuEhISoFKp0KFDB9y9e1fbJywsDLGxsVi9ejX27t2LzMxMdO3aFQUFBSWOxWilgQ8++ADvvPMOZs2a9cTrYWFhSEhIeOo4Go0GkydLF/JM/HA0Ph33vt5ifZlNmRmNf84lY+m8r57ar7CwEAoo8GXkONj8f63zw9w8jJk4BRPHjsS9e9lIvXYdn2pmI/LLr7X3FRQUoKL1f7XR7v2G4d9raQ9O/n/RX+P2b2qvu7o4Y9OK+dpzhUIhiUPgwT3S1ge2xcVj3qLlmDM1Eo72rzzzZyfSVZUqrpg14zMEdun71D8oxGPPnisUiiJt9HIQRnpqYPv27ZLzmJgYODs7IzExEa1bt4YQArNnz8aECRMQFBQEAFiyZAlcXFywcuVKDBs2DHfu3MHChQuxbNkytG/fHgCwfPlyuLm5YceOHejYsWOJYjFaInDixAksX/7ketqwYcPw3XffPXOciIgIjBkzRtJmcvfqc8dXHkTNjMbuvQex5NvpUDk//a+VSo4OcK7kqE0CAKBaVTcIIXAt7Qasra0AAJPGj0bdOjUl95qY/DexNG/GZ8jPf5CJXrt+A4NHjcf6xf/NKpiaVtD+28nRATduSv/yv5V+GwDg6GAvaf95xx58qpmNGV98jOaNfZ/1oxOVSoMGPnBxqYRDB/+b5TI1NUWrVs0wcsQg1PZ+MBOlUlVCamqato+zsxOupd0oMh69BPS42j8nJ6dIAqlUKqFUKp957507dwAADg4OAIDk5GSkpqYiICBAMlabNm2wf/9+DBs2DImJicjLy5P0cXV1hbe3N/bv31/iRMBopQG1Wo39+/c/8fqBAwegVqufOY5SqYStra3kKMmXXp4JITBlRjR27NmPRXOmooqr6pn3+Natjes3buHevWxt28XLV2FiYgIXZyc4OdjDpZIjrvybCvcqrpLj0fFdVS7adlfVg8WGj/Z92AYA9bxrIvHPE8jLy9O27T+UBGcnR8lCxW1x8Zg4ZSa+nDQObVo0ea7vhuhpdu3ai3q+bdGwcYD2SDh8FCtXxaJh4wCcP38RKSnX0P6R0pSZmRlat2qGAwcOGzFyehFoNBrY2dlJDo2maInpcUIIjBkzBq+//jq8vb0BAKmpqQAAFxfpom0XFxfttdTUVJibm8Pe3v6JfUrCaDMC4eHhGD58OBITE9GhQwe4uLhAoVAgNTUVcXFx+OGHHzB79mxjhfdS+2LGt9gWF485Uz+FtZUlbty8BQCoWNEaFv+fJM2aF4O0Gzeh+SQcANClgz++W7wKE6NmYmRIf6TfycCMbxfizS4B2nveG9IfU2d/B2trK7Rq1gi5eXk4+fcZZNzNxMDgIJ3j7NLBH/MWrcSEKTMxdEAfXLx8FQuWrsHwwX21JYNtcfH4+POv8FHYcNSrU1P7syiVSsnsBZE+ZGZm4eRJ6YKte1n3cPNmurZ9zjc/4KPxoThzNhlnzybjo/GhuHcvG6tWxxojZHpeenxqoLgZ6pL8YTpq1CgcO3YMe/fuLXKtSPlUiCJtjytJn0cZLREYMWIEHB0dMWvWLMyfP1+7sKFChQpo2LAhli5dit69exsrvJfamtitAIDBo8ZL2r/4eAx6dOkA4MFCvZRr/01tWllZYsHsKETNnIc+Ie/Dzs4Gndq2Rui7A7R9er7RCZYWSsSsXIeZ0QthaWGBGtWron/vHqWK06aiNRbMnoIpM6LRJ2Q0bG0qYkBwkCSpWLtpG/ILCvDFjG8lCxe7B7bHlIljS/W5RM9j+lfRsLS0wNw5UbC3t8OhQ0cQ2KUvMjOznn0zvXj0WBooaRngUaGhodi8eTN+++03VKlSRduuUj2YaU1NTZXMjqelpWlnCVQqFXJzc5Geni6ZFUhLS0OLFi1KHINCvAArXPLy8nDjxoP6mpOTE8zMzJ5vvBvn9REW0QvN0rWVsUMgMrj8XMOu+cr6rJ/exrL+dEWJ+wohEBoaitjYWMTHx8PLy6vIdVdXV3zwwQcYN24cgAePqjo7O+PLL7/ULhasVKkSli9frv3DOSUlBVWqVMG2bdte/MWCjzIzMyvRegAiIiK9MtJTAyNHjsTKlSuxadMm2NjYaGv6dnZ2sLS0hEKhQFhYGKKiouDl5QUvLy9ERUXBysoKffv21fYNCQnB2LFj4ejoCAcHB4SHh8PHx0f7FEFJvBCJABERkVEY6R0B8+bNAwD4+flJ2mNiYjBo0CAAwLhx45CdnY0RI0YgPT0dTZs2xa+//gobm/82VJs1axZMTU3Ru3dvZGdno127dli8eDEqVKiAknohSgP6xtIAyQFLAyQHBi8NfBqst7GsP1utt7HKEmcEiIhIvviuASYCREQkYy/x64P1ha8hJiIikjHOCBARkWwZ610DLxLOCBAREckYZwSIiEi+uEaAiQAREckYEwGWBoiIiOSMMwJERCRf3EeAiQAREckYSwMsDRAREckZZwSIiEi2BGcEmAgQEZGMMRFgaYCIiEjOOCNARETyxS2GmQgQEZGMsTTA0gAREZGccUaAiIjkizMCTASIiEi+hGAiwNIAERGRjHFGgIiI5IulASYCREQkY0wEWBogIiKSM84IEBGRbPFdA0wEiIhIzpgIsDRAREQkZ5wRICIi+eKrBpgIEBGRfHGNAEsDREREssYZASIiki/OCDARICIiGeMaAZYGiIiI5IwzAkREJFtcLMhEgIiI5IylAZYGiIiIytpvv/2Gbt26wdXVFQqFAhs3bpRcVygUxR7Tp0/X9vHz8ytyPTg4WOdYmAgQEZFsiUKht0MXWVlZqFevHubOnVvs9ZSUFMmxaNEiKBQKvPXWW5J+Q4cOlfSbP3++zt8BSwNERCRfRioNBAYGIjAw8InXVSqV5HzTpk3w9/dHtWrVJO1WVlZF+uqKMwJERER6kJOTg4yMDMmRk5Pz3ONeu3YNW7duRUhISJFrK1asgJOTE+rUqYPw8HDcvXtX5/GZCBARkWyJQv0dGo0GdnZ2kkOj0Tx3jEuWLIGNjQ2CgoIk7f369cOqVasQHx+PTz75BOvXry/SpyRYGiAiIvnSY2kgIiICY8aMkbQplcrnHnfRokXo168fLCwsJO1Dhw7V/tvb2xteXl5o1KgRkpKS0KBBgxKPz0SAiIhID5RKpV5+8T/q999/x+nTp7FmzZpn9m3QoAHMzMxw5swZJgJEREQlIV7wfQQWLlyIhg0bol69es/se/LkSeTl5UGtVuv0GUwEiIhIvoyUCGRmZuLs2bPa8+TkZBw9ehQODg5wd3cHAGRkZODHH3/EjBkzitx/7tw5rFixAp07d4aTkxP++usvjB07Fr6+vmjZsqVOsTARICIiKmOHDx+Gv7+/9vzh2oKBAwdi8eLFAIDVq1dDCIG33367yP3m5ubYuXMnvv76a2RmZsLNzQ1dunRBZGQkKlSooFMsCiFEudtoOe/GeWOHQGRwlq6tjB0CkcHl51416PjXO7TR21iV4vbobayyxBkBIiKSrRd9jUBZ4D4CREREMsYZASIiki3OCDARICIiORMKY0dgdCwNEBERyRhnBIiISLZYGtDDjEBBQQGOHj2K9PR0fcRDRERUZkShQm/Hy0rnRCAsLAwLFy4E8CAJaNOmDRo0aAA3NzfEx8frOz4iIiIyIJ0TgXXr1mn3PN6yZQuSk5Px999/IywsDBMmTNB7gERERIaiz9cQv6x0TgRu3LgBlUoFANi2bRt69eqFGjVqICQkBMePH9d7gERERIYihEJvx8tK50TAxcUFf/31FwoKCrB9+3a0b98eAHDv3j2d9zcmIiIi49L5qYHBgwejd+/eUKvVUCgU6NChAwDgjz/+QM2aNfUeIBERkaG8zFP6+qJzIjBp0iR4e3vj8uXL6NWrF5RKJQCgQoUK+Oijj/QeIBERkaG8zKv99YVvHyR6SfHtgyQHhn774OXG7fQ2llvCTr2NVZZKNCMwZ86cEg84evToUgdDRERUlsrfn8K6K1EiMGvWrBINplAomAgQEdFLg6WBEiYCycnJho6DiIiIjKDUWwzn5ubi9OnTyM/P12c8REREZYZbDJciEbh37x5CQkJgZWWFOnXq4NKlSwAerA2YOnWq3gMkIiIyFCH0d7ysdE4EIiIi8OeffyI+Ph4WFhba9vbt22PNmjV6DY6IiIgMS+d9BDZu3Ig1a9agWbNmUCj+mwqpXbs2zp07p9fgiIiIDOllntLXF50TgevXr8PZ2blIe1ZWliQxICIietG9zO8I0BedSwONGzfG1q1btecPf/kvWLAAzZs3119kREREZHA6zwhoNBp06tQJf/31F/Lz8/H111/j5MmTOHDgAPbs2WOIGImIiAyC7xooxYxAixYtsG/fPty7dw/Vq1fHr7/+ChcXFxw4cAANGzY0RIxEREQGUSgUejteVjrPCACAj48PlixZou9YiIiIqIyVKhEoKChAbGwsTp06BYVCgVq1aqF79+4wNS3VcEREREbBxYKlSAROnDiB7t27IzU1Fa+99hoA4J9//kGlSpWwefNm+Pj46D1IIiIiQ+Djg6VYI/DOO++gTp06uHLlCpKSkpCUlITLly+jbt26ePfddw0RIxERERmIzjMCf/75Jw4fPgx7e3ttm729PaZMmYLGjRvrNTgiIiJDepm3BtYXnWcEXnvtNVy7dq1Ie1paGl599VW9BEVERFQW+NKhEiYCGRkZ2iMqKgqjR4/GunXrcOXKFVy5cgXr1q1DWFgYvvzyS0PHS0RERHqkEOLZEyMmJiaS7YMf3vKw7dHzgoICQ8Spk7wb540dApHBWbq2MnYIRAaXn3vVoOOfqNZVb2N5n/9Jb2OVpRKtEdi9e7eh4yAiIipzfHywhIlAmzZtDB0HERGRbPz222+YPn06EhMTkZKSgtjYWPTo0UN7fdCgQUU27mvatCkOHjyoPc/JyUF4eDhWrVqF7OxstGvXDtHR0ahSpYpOsZR6B6B79+7h0qVLyM3NlbTXrVu3tEMSERGVKWM9NZCVlYV69eph8ODBeOutt4rt06lTJ8TExGjPzc3NJdfDwsKwZcsWrF69Go6Ojhg7diy6du2KxMREVKhQocSxlOo1xIMHD8bPP/9c7PUXYY0AERFRSRjrHQGBgYEIDAx8ah+lUgmVSlXstTt37mDhwoVYtmwZ2rdvDwBYvnw53NzcsGPHDnTs2LHEsej8+GBYWBjS09Nx8OBBWFpaYvv27ViyZAm8vLywefNmXYcjIiIqF3JyciRP2WVkZCAnJ6fU48XHx8PZ2Rk1atTA0KFDkZaWpr2WmJiIvLw8BAQEaNtcXV3h7e2N/fv36/Q5OicCu3btwqxZs9C4cWOYmJjAw8MD/fv3x7Rp06DRaHQdjoiIyGiEUOjt0Gg0sLOzkxyl/b0YGBiIFStWYNeuXZgxYwYSEhLQtm1bbWKRmpoKc3NzyeZ+AODi4oLU1FSdPkvn0kBWVhacnZ0BAA4ODrh+/Tpq1KgBHx8fJCUl6TocERGR0ehzjUBERATGjBkjaVMqlaUaq0+fPtp/e3t7o1GjRvDw8MDWrVsRFBT0xPuEEJLH/UuiVDsLnj59GgBQv359zJ8/H1evXsV3330HtVqt63BERETlglKphK2treQobSLwOLVaDQ8PD5w5cwYAoFKpkJubi/T0dEm/tLQ0uLi46DR2qdYIpKSkAAAiIyOxfft2uLu7Y86cOYiKitJ1OCIiIqMpFAq9HYZ08+ZNXL58WfsHd8OGDWFmZoa4uDhtn5SUFJw4cQItWrTQaWydSwP9+vXT/tvX1xcXLlzA33//DXd3dzg5Oek6nEE09O737E5EL7nMQ/ONHQLRS89YGwplZmbi7Nmz2vPk5GQcPXoUDg4OcHBwwKRJk/DWW29BrVbjwoUL+Pjjj+Hk5IQ333wTAGBnZ4eQkBCMHTsWjo6OcHBwQHh4OHx8fLRPEZRUqfcReMjKygoNGjR43mGIiIhk4/Dhw/D399eeP1xbMHDgQMybNw/Hjx/H0qVLcfv2bajVavj7+2PNmjWwsbHR3jNr1iyYmpqid+/e2g2FFi9erNMeAkAJ3zXw+OKHp5k5c6ZOARhCXVVzY4dAZHCHtk8wdghEBmdRX3/vAijOH65PXninq6b/btDbWGWpRDMCR44cKdFguq5UJCIiMiYjbSz4QuFLh4iIiGTsudcIEBERvayMtcXwi4SJABERyRZfQ1yKfQSIiIio/OCMABERyVahsQN4ATARICIi2RJgaaBUpYFly5ahZcuWcHV1xcWLFwEAs2fPxqZNm/QaHBERERmWzonAvHnzMGbMGHTu3Bm3b99GQUEBAOCVV17B7Nmz9R0fERGRwRQK/R0vK50TgW+++QYLFizAhAkTJNsYNmrUCMePH9drcERERIZUCIXejpeVzolAcnIyfH19i7QrlUpkZWXpJSgiIiIqGzonAp6enjh69GiR9p9//hm1a9fWR0xERERlQkCht+NlpfNTAx9++CFGjhyJ+/fvQwiBQ4cOYdWqVdBoNPjhhx8MESMREZFB8PHBUiQCgwcPRn5+PsaNG4d79+6hb9++qFy5Mr7++msEBwcbIkYiIiIykFLtIzB06FAMHToUN27cQGFhIZydnfUdFxERkcG9zFP6+vJcGwo5OTnpKw4iIqIyx9JAKRIBT09PKBRPzqDOnz//XAERERFR2dE5EQgLC5Oc5+Xl4ciRI9i+fTs+/PBDfcVFRERkcJwRKEUi8P777xfb/u233+Lw4cPPHRAREVFZ4RoBPb6GODAwEOvXr9fXcERERFQG9Pb2wXXr1sHBwUFfwxERERlcIScEdE8EfH19JYsFhRBITU3F9evXER0drdfgiIiIDOllfkeAvuicCPTo0UNybmJigkqVKsHPzw81a9bUV1xERERUBnRKBPLz81G1alV07NgRKpXKUDERERGViZf47cF6o9NiQVNTU7z33nvIyckxVDxERERlplCPx8tK56cGmjZtiiNHjhgiFiIiIipjOq8RGDFiBMaOHYsrV66gYcOGsLa2llyvW7eu3oIjIiIypMKn7JQrFyVOBIYMGYLZs2ejT58+AIDRo0drrykUCgghoFAoUFBQoP8oiYiIDIBrBHRIBJYsWYKpU6ciOTnZkPEQERFRGSpxIiDEg7zJw8PDYMEQERGVpZd5kZ++6LRG4GlvHSQiInrZcGdBHROBGjVqPDMZuHXr1nMFRERERGVHp0Rg8uTJsLOzM1QsREREZYpbDOuYCAQHB8PZ2dlQsRAREZUpPjWgw4ZCXB9ARESkH7/99hu6desGV1dXKBQKbNy4UXstLy8P48ePh4+PD6ytreHq6ooBAwbg33//lYzh5+cHhUIhOYKDg3WOpcSJwMOnBoiIiMqLQoX+Dl1kZWWhXr16mDt3bpFr9+7dQ1JSEj755BMkJSVhw4YN+Oeff/DGG28U6Tt06FCkpKRoj/nz5+v8HZS4NFBYyIcsiIiofDHWb7bAwEAEBgYWe83Ozg5xcXGStm+++QZNmjTBpUuX4O7urm23srJ67pcA6vyuASIiIioqJycHGRkZkkNfL+m7c+cOFAoFXnnlFUn7ihUr4OTkhDp16iA8PBx3797VeWwmAkREJFtCj4dGo4GdnZ3k0Gg0zx3j/fv38dFHH6Fv376wtbXVtvfr1w+rVq1CfHw8PvnkE6xfvx5BQUE6j6/zS4eIiIjKC31uKBQREYExY8ZI2pRK5XONmZeXh+DgYBQWFiI6OlpybejQodp/e3t7w8vLC40aNUJSUhIaNGhQ4s9gIkBERKQHSqXyuX/xPyovLw+9e/dGcnIydu3aJZkNKE6DBg1gZmaGM2fOMBEgIiIqiRd1GfzDJODMmTPYvXs3HB0dn3nPyZMnkZeXB7VardNnMREgIiLZMlYikJmZibNnz2rPk5OTcfToUTg4OMDV1RU9e/ZEUlISfvrpJxQUFCA1NRUA4ODgAHNzc5w7dw4rVqxA586d4eTkhL/++gtjx46Fr68vWrZsqVMsTASIiIjK2OHDh+Hv7689f7i2YODAgZg0aRI2b94MAKhfv77kvt27d8PPzw/m5ubYuXMnvv76a2RmZsLNzQ1dunRBZGQkKlSooFMsTASIiEi2hJE2zfXz83vqRn3P2sTPzc0Ne/bs0UssTASIiEi2XtQ1AmWJ+wgQERHJGGcEiIhItjgjwESAiIhkjK/TY2mAiIhI1jgjQEREsqXPLYZfVkwEiIhItrhGgKUBIiIiWeOMABERyRZnBJgIEBGRjPGpAZYGiIiIZI0zAkREJFt8aoCJABERyRjXCLA0QEREJGucESAiItniYkEmAkREJGOFTAVYGiAiIpIzzggQEZFscbEgEwEiIpIxFgZYGiAiIpI1zggQEZFssTTARICIiGSMOwuyNEBERCRrnBEgIiLZ4j4CTASIiEjGmAawNEBERCRrnBEgIiLZ4lMDTASIiEjGuEaApQEiIiJZ44wAERHJFucDmAgQEZGMcY0ASwNERESyxhkBIiKSLS4W5IwAERHJmNDjoYvffvsN3bp1g6urKxQKBTZu3CiNSwhMmjQJrq6usLS0hJ+fH06ePCnpk5OTg9DQUDg5OcHa2hpvvPEGrly5omMkTASIiIjKXFZWFurVq4e5c+cWe33atGmYOXMm5s6di4SEBKhUKnTo0AF3797V9gkLC0NsbCxWr16NvXv3IjMzE127dkVBQYFOsbA0QEREsmWsxYKBgYEIDAws9poQArNnz8aECRMQFBQEAFiyZAlcXFywcuVKDBs2DHfu3MHChQuxbNkytG/fHgCwfPlyuLm5YceOHejYsWOJY+GMABERyZbQ4//l5OQgIyNDcuTk5OgcU3JyMlJTUxEQEKBtUyqVaNOmDfbv3w8ASExMRF5enqSPq6srvL29tX1KiokAERGRHmg0GtjZ2UkOjUaj8zipqakAABcXF0m7i4uL9lpqairMzc1hb2//xD4lxdIAERHJlj5LAxERERgzZoykTalUlno8hUIhORdCFGl7XEn6PI4zAkREJFuFEHo7lEolbG1tJUdpEgGVSgUARf6yT0tL084SqFQq5ObmIj09/Yl9SoqJABER0QvE09MTKpUKcXFx2rbc3Fzs2bMHLVq0AAA0bNgQZmZmkj4pKSk4ceKEtk9JsTRARESyZazthDIzM3H27FnteXJyMo4ePQoHBwe4u7sjLCwMUVFR8PLygpeXF6KiomBlZYW+ffsCAOzs7BASEoKxY8fC0dERDg4OCA8Ph4+Pj/YpgpJiIkBERLJlrJ0FDx8+DH9/f+35w7UFAwcOxOLFizFu3DhkZ2djxIgRSE9PR9OmTfHrr7/CxsZGe8+sWbNgamqK3r17Izs7G+3atcPixYtRoUIFnWJRCCHK3f6KdVXNjR1CueSsqoSwiSPwetvmUFoocfH8JUSOicKpY6e1fTy9PPDBxJFo2NwXJiYKnDudjPB3JyL16jUjRl4+Hdo+wdghGNXC2J3Yeeg4kv9Ng9LcDPVreCCsX1dUdXUu0f1H/k5GyORovOqmwtppYw0a65lLKdAs2oATZy/BtqIVerZvjmFvddAu6trxxzH8GLcfpy/8i9z8fFSvosLwngFoWb+mQeN6GVjU72rQ8YdV7aW3seZf+FFvY5UlzghQidjY2WDJlvlI2JeIEf3G4NaNW3DzqIK7dzK1fap4VMaSTfMRu2oLoqf/gLsZmahWoypyc3KNGDmVV4dPnUOfji1Qp7o7CgoK8c2abRg+5XtsmPEhrCyevkDr7r1sTIxehSber+LWI/8ZLo2rabfQOXQK/lwzo9jrmffuY9gX89G4TnWsiArDxZTr+HTealgqzTGwmx8AIOnUeTTzqYHQ4M6wsbbEpvgEjJ62CMunjEYtzyrPFR89Hd8+yESASmjIqP64dvUaPg2bom3797J0RWtoxDD8vnM/Zn3+rbbt6qV/yyxGkpd5H78rOf/svWD4D43EqfNX0LB29afe+/n36xDY0hcVTEywO+FEkesbdx/C4s27cfX6LbhWskffTq3Qp2PLUsW5bW8ScvPy8PmIt2FuZgovdzUuplzHsq17MKBrGygUCowb1ENyz+i3O2P34RPYk/gXEwEDE3zpEJ8aoJLx69gKJ//8G18tmIL4E1uxJm4J3ur3hva6QqFA6/YtcPH8ZcxbNQvxJ7ZixbYf4N+ptRGjJjnJvHcfAGBb0eqp/TbuPoQr125ieM+AYq+v33kQc9f8jFHBgYidOQ6hwZ3x7drt2LwnoVRx/fnPBTSsXR3mZv/93dWi3mu4np6Bq9dvFXtPYWEh7mXnwO4ZPwuRPrz0iUBxWzoWCk726FsVd1f0HvgmLp2/jOHBH+DHpbEY/8UYdOv1YK9sByd7WFe0Rkjo/7Bv9x8Y1icMO7ftwaxFGjRs7mvk6Km8E0Lgq6Wb4FvTE17u6if2u5hyHV+v2oqo0H4wfcKCqu/Xx2Hs/7qhfdO6qOLsiPZN66J/59ZYt+NAqWK7cecuHO1sJG0Pz2/evlvcLVj60x5k5+QioHm9Un0mlVyhHo+X1QtdGrh8+TIiIyOxaNGiJ/bRaDSYPHmypM3ZujJcKroZOjxZMTExwck//8YczXcAgL9P/IPqr3mi98A3seXHn2Fi8iCn3L39dyz/fjUA4PTJM6jf2Ae9B/RA4oEjRoudyj/Nog04cykFiyePemKfgsJCRMxZgfd6dURV10rF9rmVkYnUm7cx6bu1mDz/R8m9Fa0stOdvjp2GlOsPNnJ5OLHcbECE9rq6kj1iZ4x7YiwP71Gg6A5wP+9Lwrx1v+Lr8MFFEgjSP5YGXvBE4NatW1iyZMlTE4HitnRs4dXB0KHJzvW0Gzj/T7KkLfnMBbTv8uDxl/Rbt5GXl49zj/U5f+YCfJvwrxoyHM2iDYhPPIlFk0bCxfGVJ/bLys7ByfOX8feFq5i6KBYAUCgEhBBo8PaHmDfhXbxa5cGObp++2ws+Xh6S+01M/vul/e1H7yC/4MHfgGm37iBkcrTkyQPTCv9NtjrZ2eDmHelf/rf+/9zhlYqS9u37j2DSd2sx/YMBaFa3Rkm/AqLnYtREYPPmzU+9fv78+WeOoVQqi2zhaKJ46SseL5yjh46janV3SZtHNXekXHmwYDA/Lx8nj556ah8ifRJCQBMTi12HjmNh5AhUcXZ8av+Klkqsmx4uaVv7634cOnkGX30wEJWdHWBloYSzgx2upN1Cl1YNnziWayUH7b8r/P9smLvKqdi+9WpUxZzV25CXnw8z0wf/k3vg2D+oZG+Lyo+M8/O+JETOW4Op7/dH6wa1n/7Dk968zFP6+mLURKBHjx5QKBR42lYGur48gQxj2fersXTL93hn9ED8snknfHxro+f/umNy+FRtn8XRKzB9/udIOngUh/YloWXbZmgT0BIhQSONGDmVV1ELN+DnfUmY/eEQWFsqceN2BgCgopUlLMzNAABfr9yKtFt3MGVUX5iYmBRZP+BgVxFKMzNJ+3s9A/Dl4o2oaKlEy/q1kJefj5PnLiMjKxsDurbROc7A133x3bpf8Un0aoT0aIdLqTewMHYn3n1kH4Gf9yVh4rerMG5gD9T18tD+LEpzM9hYWZbq+6GSKSx/W+nozKiJgFqtxrfffosePXoUe/3o0aNo2PDJWTmVnZNHT+GDIR/h/Y/fw7Axg3H1UgqmfTIb2zb8qu2z6+c9+Hz8NISEDsD4L8bgwrmLGBPyMY4cOmbEyKm8Whv34J3rIZOjJe2fvdcH3f2aAABu3M5A6s3bOo0b1K4ZLJTmWLxlN2at+AmWSnN4uavRr3PpnoCxsbLE/InDELVwA/p+PBu21pb4X5fWkqRi3Y6DyC8oRNSiDYhatEHb/kabRvh8xNul+lyikjLqzoJvvPEG6tevj88++6zY63/++Sd8fX1RWKjb5A13FiQ5kPvOgiQPht5ZsL9HkN7GWn5xw7M7vYCMOiPw4YcfIisr64nXX331VezevbsMIyIiIjkx1rsGXiRGTQRatWr11OvW1tZo00b3mhwRERGVzAv9+CAREZEhcR8BJgJERCRjfHywHGwxTERERKXHGQEiIpItLhbkjAAREZGscUaAiIhki4sFmQgQEZGMcbEgSwNERESyxhkBIiKSLSPusv/CYCJARESyxacGWBogIiKSNc4IEBGRbHGxIBMBIiKSMT4+yNIAERGRrHFGgIiIZIuLBZkIEBGRjPHxQZYGiIiIZI0zAkREJFt8aoCJABERyRifGmBpgIiISNY4I0BERLLFpwaYCBARkYzxqQGWBoiIiMpc1apVoVAoihwjR44EAAwaNKjItWbNmhkkFs4IEBGRbBmrNJCQkICCggLt+YkTJ9ChQwf06tVL29apUyfExMRoz83NzQ0SCxMBIiKSLWM9NVCpUiXJ+dSpU1G9enW0adNG26ZUKqFSqQweC0sDREREepCTk4OMjAzJkZOT88z7cnNzsXz5cgwZMgQKhULbHh8fD2dnZ9SoUQNDhw5FWlqaQeJmIkBERLJVKITeDo1GAzs7O8mh0WieGcPGjRtx+/ZtDBo0SNsWGBiIFStWYNeuXZgxYwYSEhLQtm3bEiUWulKIcrhksq6qubFDIDK4Q9snGDsEIoOzqN/VoOO3qtxOb2PtOL+tyC9qpVIJpVL51Ps6duwIc3NzbNmy5Yl9UlJS4OHhgdWrVyMoKEgv8T7ENQJERER6UJJf+o+7ePEiduzYgQ0bNjy1n1qthoeHB86cOfM8IRaLiQAREcmWsTcUiomJgbOzM7p06fLUfjdv3sTly5ehVqv1HgPXCBARkWwVQujt0PmzCwsRExODgQMHwtT0v7/LMzMzER4ejgMHDuDChQuIj49Ht27d4OTkhDfffFOfPz4AzggQEREZxY4dO3Dp0iUMGTJE0l6hQgUcP34cS5cuxe3bt6FWq+Hv7481a9bAxsZG73EwESAiItky5nr5gICAYj/f0tISv/zyS5nFwUSAiIhky9hrBF4EXCNAREQkY5wRICIi2TLWFsMvEiYCREQkW+VwTz2dsTRAREQkY5wRICIi2eJiQSYCREQkYywNsDRAREQka5wRICIi2WJpgIkAERHJGB8fZGmAiIhI1jgjQEREslXIxYJMBIiISL5YGmBpgIiISNY4I0BERLLF0gATASIikjGWBlgaICIikjXOCBARkWyxNMBEgIiIZIylAZYGiIiIZI0zAkREJFssDTARICIiGWNpgKUBIiIiWeOMABERyZYQhcYOweiYCBARkWwVsjTA0gAREZGccUaAiIhkS/CpASYCREQkXywNsDRAREQka5wRICIi2WJpgIkAERHJGHcWZGmAiIhI1jgjQEREssUthpkIEBGRjHGNAEsDREREZW7SpElQKBSSQ6VSaa8LITBp0iS4urrC0tISfn5+OHnypEFiYSJARESyVQiht0NXderUQUpKivY4fvy49tq0adMwc+ZMzJ07FwkJCVCpVOjQoQPu3r2rzx8fAEsDREQkY8YsDZiamkpmAR4SQmD27NmYMGECgoKCAABLliyBi4sLVq5ciWHDhuk1Ds4IEBER6UFOTg4yMjIkR05OzhP7nzlzBq6urvD09ERwcDDOnz8PAEhOTkZqaioCAgK0fZVKJdq0aYP9+/frPW4mAkREJFuFQujt0Gg0sLOzkxwajabYz23atCmWLl2KX375BQsWLEBqaipatGiBmzdvIjU1FQDg4uIiucfFxUV7TZ9YGiAiItnSZ2kgIiICY8aMkbQplcpi+wYGBmr/7ePjg+bNm6N69epYsmQJmjVrBgBQKBRFYn28TR84I0BERKQHSqUStra2kuNJicDjrK2t4ePjgzNnzmjXDTz+139aWlqRWQJ9YCJARESyZcynBh6Vk5ODU6dOQa1Ww9PTEyqVCnFxcdrrubm52LNnD1q0aPG8P3IRLA0QEZFsGeupgfDwcHTr1g3u7u5IS0vDF198gYyMDAwcOBAKhQJhYWGIioqCl5cXvLy8EBUVBSsrK/Tt21fvsTARICIiKmNXrlzB22+/jRs3bqBSpUpo1qwZDh48CA8PDwDAuHHjkJ2djREjRiA9PR1NmzbFr7/+ChsbG73HohDlcH/Fuqrmxg6ByOAObZ9g7BCIDM6ifleDjl/RylNvY2XeS9bbWGWJMwJERCRbfOkQFwsSERHJGmcEiIhItgrLX3VcZ0wEiIhItsrhMjmdsTRAREQkY5wRICIi2eJiQSYCREQkYywNsDRAREQka5wRICIi2eKMABMBIiKSMaYBLA0QERHJWrl81wCVrZycHGg0GkRERJT43dtELxv+55zKKyYC9NwyMjJgZ2eHO3fuwNbW1tjhEBkE/3NO5RVLA0RERDLGRICIiEjGmAgQERHJGBMBem5KpRKRkZFcQEXlGv9zTuUVFwsSERHJGGcEiIiIZIyJABERkYwxESAiIpIxJgJEREQyxkSAnlt0dDQ8PT1hYWGBhg0b4vfffzd2SER689tvv6Fbt25wdXWFQqHAxo0bjR0SkV4xEaDnsmbNGoSFhWHChAk4cuQIWrVqhcDAQFy6dMnYoRHpRVZWFurVq4e5c+caOxQig+Djg/RcmjZtigYNGmDevHnatlq1aqFHjx7QaDRGjIxI/xQKBWJjY9GjRw9jh0KkN5wRoFLLzc1FYmIiAgICJO0BAQHYv3+/kaIiIiJdMBGgUrtx4wYKCgrg4uIiaXdxcUFqaqqRoiIiIl0wEaDnplAoJOdCiCJtRET0YmIiQKXm5OSEChUqFPnrPy0trcgsARERvZiYCFCpmZubo2HDhoiLi5O0x8XFoUWLFkaKioiIdGFq7ADo5TZmzBj873//Q6NGjdC8eXN8//33uHTpEoYPH27s0Ij0IjMzE2fPntWeJycn4+jRo3BwcIC7u7sRIyPSDz4+SM8tOjoa06ZNQ0pKCry9vTFr1iy0bt3a2GER6UV8fDz8/f2LtA8cOBCLFy8u+4CI9IyJABERkYxxjQAREZGMMREgIiKSMSYCREREMsZEgIiISMaYCBAREckYEwEiIiIZYyJAREQkY0wEiIiIZIyJAJEeTZo0CfXr19eeDxo0CD169CjzOC5cuACFQoGjR48+sU/VqlUxe/bsEo+5ePFivPLKK88dm0KhwMaNG597HCLSDyYCVO4NGjQICoUCCoUCZmZmqFatGsLDw5GVlWXwz/76669LvA1tSX55ExHpG186RLLQqVMnxMTEIC8vD7///jveeecdZGVlYd68eUX65uXlwczMTC+fa2dnp5dxiIgMhTMCJAtKpRIqlQpubm7o27cv+vXrp52efjidv2jRIlSrVg1KpRJCCNy5cwfvvvsunJ2dYWtri7Zt2+LPP/+UjDt16lS4uLjAxsYGISEhuH//vuT646WBwsJCfPnll3j11VehVCrh7u6OKVOmAAA8PT0BAL6+vlAoFPDz89PeFxMTg1q1asHCwgI1a9ZEdHS05HMOHToEX19fWFhYoFGjRjhy5IjO39HMmTPh4+MDa2truLm5YcSIEcjMzCzSb+PGjahRowYsLCzQoUMHXL58WXJ9y5YtaNiwISwsLFCtWjVMnjwZ+fn5xX5mbm4uRo0aBbVaDQsLC1StWhUajUbn2Imo9DgjQLJkaWmJvLw87fnZs2exdu1arF+/HhUqVAAAdOnSBQ4ODti2bRvs7Owwf/58tGvXDv/88w8cHBywdu1aREZG4ttvv0WrVq2wbNkyzJkzB9WqVXvi50ZERGDBggWYNWsWXn/9daSkpODvv/8G8OCXeZMmTbBjxw7UqVMH5ubmAIAFCxYgMjISc+fOha+vL44cOYKhQ4fC2toaAwcORFZWFrp27Yq2bdti+fLlSE5Oxvvvv6/zd2JiYoI5c+agatWqSE5OxogRIzBu3DhJ0nHv3j1MmTIFS5Ysgbm5OUaMGIHg4GDs27cPAPDLL7+gf//+mDNnDlq1aoVz587h3XffBQBERkYW+cw5c+Zg8+bNWLt2Ldzd3XH58uUiiQURGZggKucGDhwounfvrj3/448/hKOjo+jdu7cQQojIyEhhZmYm0tLStH127twpbG1txf379yVjVa9eXcyfP18IIUTz5s3F8OHDJdebNm0q6tWrV+xnZ2RkCKVSKRYsWFBsnMnJyQKAOHLkiKTdzc1NrFy5UtL2+eefi+bNmwshhJg/f75wcHAQWVlZ2uvz5s0rdqxHeXh4iFmzZj3x+tq1a4Wjo6P2PCYmRgAQBw8e1LadOnVKABB//PGHEEKIVq1aiaioKMk4y5YtE2q1WnsOQMTGxgohhAgNDRVt27YVhYWFT4yDiAyLMwIkCz/99BMqVqyI/Px85OXloXv37vjmm2+01z08PFCpUiXteWJiIjIzM+Ho6CgZJzs7G+fOnQMAnDp1CsOHD5dcb968OXbv3l1sDKdOnUJOTg7atWtX4rivX7+Oy5cvIyQkBEOHDtW25+fna9cfnDp1CvXq1YOVlZUkDl3t3r0bUVFR+Ouvv5CRkYH8/Hzcv38fWVlZsLa2BgCYmpqiUaNG2ntq1qyJV155BadOnUKTJk2QmJiIhIQEbbkDAAoKCnD//n3cu3dPEiPwoHTSoUMHvPbaa+jUqRO6du2KgIAAnWMnotJjIkCy4O/vj3nz5sHMzAyurq5FFgM+/EX3UGFhIdRqNeLj44uMVdpH6CwtLXW+p7CwEMCD8kDTpk0l1x6WMIQQpYrnURcvXkTnzp0xfPhwfP7553BwcMDevXsREhIiKaEADx7/e9zDtsLCQkyePBlBQUFF+lhYWBRpa9CgAZKTk/Hzzz9jx44d6N27N9q3b49169Y9989ERCXDRIBkwdraGq+++mqJ+zdo0ACpqakwNTVF1apVi+1Tq1YtHDx4EAMGDNC2HTx48Iljenl5wdLSEjt37sQ777xT5PrDNQEFBQXaNhcXF1SuXBnnz59Hv379ih23du3aWLZsGbKzs7XJxtPiKM7hw4eRn5+PGTNmwMTkwRritWvXFumXn5+Pw4cPo0mTJgCA06dP4/bt26hZsyaAB9/b6dOndfqubW1t0adPH/Tp0wc9e/ZEp06dcOvWLTg4OOj0MxBR6TARICpG+/bt0bx5c/To0QNffvklXnvtNfz777/Ytm0bevTogUaNGuH999/HwIED0ahRI7z++utYsWIFTp48+cTFghYWFhg/fjzGjRsHc3NztGzZEtevX8fJkycREhICZ2dnWFpaYvv27ahSpQosLCxgZ2eHSZMmYfTo0bC1tUVgYCBycnJw+PBhpKenY8yYMejbty8mTJiAkJAQTJw4ERcuXMBXX32l089bvXp15Ofn45tvvkG3bt2wb98+fPfdd0X6mZmZITQ0FHPmzIGZmRlGjRqFZs2aaRODTz/9FF27doWbmxt69eoFExMTHDt2DMePH8cXX3xRZLxZs2ZBrVajfv36MDExwY8//giVSqWXjYuIqISMvUiByNAeXyz4uMjISMkCv4cyMjJEaGiocHV1FWZmZsLNzU3069dPXLp0SdtnypQpwsnJSVSsWFEMHDhQjBs37omLBYUQoqCgQHzxxRfCw8NDmJmZCXd3d8niugULFgg3NzdhYmIi2rRpo21fsWKFqF+/vjA3Nxf29vaidevWYsOGDdrrBw4cEPXq1RPm5uaifv36Yv369TovFpw5c6ZQq9XC0tJSdOzYUSxdulQAEOnp6UKIB4sF7ezsxPr160W1atWEubm5aNu2rbhw4YJk3O3bt4sWLVoIS0tLYWtrK5o0aSK+//577XU8sljw+++/F/Xr1xfW1tbC1tZWtGvXTiQlJT0xZiLSP4UQeigwEhER0UuJGwoRERHJGBMBIiIiGWMiQEREJGNMBIiIiGSMiQAREZGMMREgIiKSMSYCREREMsZEgIiISMaYCBAREckYEwEiIiIZYyJAREQkY/8HpqyX4qX+JSoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix(val_y,pred_model5,model_5['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
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