ACB hacks final notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import re\n",
    "import nltk\n",
    "import scipy\n",
    "import scipy.sparse as sparse\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from numpy import asarray, zeros\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc, roc_auc_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from scipy.stats import randint\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"toxicity_parsed_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Text</th>\n",
       "      <th>ed_label_0</th>\n",
       "      <th>ed_label_1</th>\n",
       "      <th>oh_label</th>\n",
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       "      <td>This: :One can make an analogy in mathematical...</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>`  :Clarification for you  (and Zundark's righ...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Elected or Electoral? JHK</td>\n",
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       "      <td>`This is such a fun entry.   Devotchka  I once...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Please relate the ozone hole to increases in c...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   index                                               Text  ed_label_0  \\\n",
       "0      0  This: :One can make an analogy in mathematical...         0.9   \n",
       "1      1  `  :Clarification for you  (and Zundark's righ...         1.0   \n",
       "2      2                          Elected or Electoral? JHK         1.0   \n",
       "3      3  `This is such a fun entry.   Devotchka  I once...         1.0   \n",
       "4      4  Please relate the ozone hole to increases in c...         0.8   \n",
       "\n",
       "   ed_label_1  oh_label  \n",
       "0         0.1         0  \n",
       "1         0.0         0  \n",
       "2         0.0         0  \n",
       "3         0.0         0  \n",
       "4         0.2         0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = data['Text'].to_numpy()\n",
    "labels = data[['oh_label']].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = []\n",
    "for index in range(len(labels)):\n",
    "    num = np.count_nonzero(labels[index])\n",
    "    if(num == 0):\n",
    "        dataframe.append([text[index], 0])\n",
    "    else:\n",
    "        dataframe.append([text[index], 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(dataframe, columns = ['text', 'label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
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       "      <td>This: :One can make an analogy in mathematical...</td>\n",
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       "      <td>`This is such a fun entry.   Devotchka  I once...</td>\n",
       "      <td>0</td>\n",
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       "      <td>Please relate the ozone hole to increases in c...</td>\n",
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       "                                                     text  label\n",
       "0       This: :One can make an analogy in mathematical...      0\n",
       "1       `  :Clarification for you  (and Zundark's righ...      0\n",
       "2                               Elected or Electoral? JHK      0\n",
       "3       `This is such a fun entry.   Devotchka  I once...      0\n",
       "4       Please relate the ozone hole to increases in c...      0\n",
       "...                                                   ...    ...\n",
       "159681  `   These sources don't exactly exude a sense ...      0\n",
       "159682    The Institute for Historical Review is a pee...      0\n",
       "159683   :The way you're trying to describe it in this...      0\n",
       "159684    == Warning ==  There is clearly a protection...      0\n",
       "159685  Alternate option=== Is there perhaps enough ne...      0\n",
       "\n",
       "[159686 rows x 2 columns]"
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     "execution_count": 221,
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>`This is such a fun entry.   Devotchka  I once...</td>\n",
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       "      <td>Please relate the ozone hole to increases in c...</td>\n",
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      "text/plain": [
       "                                                text  label\n",
       "0  This: :One can make an analogy in mathematical...      0\n",
       "1  `  :Clarification for you  (and Zundark's righ...      0\n",
       "2                          Elected or Electoral? JHK      0\n",
       "3  `This is such a fun entry.   Devotchka  I once...      0\n",
       "4  Please relate the ozone hole to increases in c...      0"
      ]
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     "execution_count": 70,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
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       "  <tbody>\n",
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       "      <th>159681</th>\n",
       "      <td>`   These sources don't exactly exude a sense ...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>159682</th>\n",
       "      <td>The Institute for Historical Review is a pee...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>159683</th>\n",
       "      <td>:The way you're trying to describe it in this...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>159684</th>\n",
       "      <td>== Warning ==  There is clearly a protection...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>159685</th>\n",
       "      <td>Alternate option=== Is there perhaps enough ne...</td>\n",
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      "text/plain": [
       "                                                     text  label\n",
       "159681  `   These sources don't exactly exude a sense ...      0\n",
       "159682    The Institute for Historical Review is a pee...      0\n",
       "159683   :The way you're trying to describe it in this...      0\n",
       "159684    == Warning ==  There is clearly a protection...      0\n",
       "159685  Alternate option=== Is there perhaps enough ne...      0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    144324\n",
       "1     15362\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking imbalancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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+qFFVXCxO+tw2/kvoOdGHph+mSS+wJYQ7arb2twiOAX4IvGlmDwLzCG2mXQhNI6fS1J75AaF7abZHCF8s6QX8KTlCWk5Tk9HcEr5sUi7TCfdFejypxxya32so01xyp7vnO9qcRFMz2x1mlrlnkRF2Ul8jnIndTtNGks8MwmnznoQN5U6aNz/Nyr54l0/SJfQrhAuc2xKOnI82s1sI68FOhB1a5gDhQ+Asd1+fq7yO5O5rzOxrhHv5dAV+nuwApxF6uu1GaILMfEFpDS3cwbaxfo+Z2ecIodmbcB+qU8ws/cM0HxDazTM/TNOW79v8kqZ7JE1LPsPHCO97P8LtS3Yh9OYptBw+D3zXzBYSunq+QAjcHoQQGUnYb0DoKlufmnZ7wjeDx5vZ44Tupv9M6tCH8D7PTJ4D3OJZt+Z29z+Z2aWEHkYfAR4wsznAnwlfGN2YTD+IcPF5MGF7TIdTD8K6PNbMniZss38nbCfbE+49diZN3wKfQ9Mt+HMrdg+KrfVBaXfdSz9WEL7+v0MLyp6Z47UZJc7vbfLczTL5gN4qMO2I1LgjUsMnlLhspqSmGZDj9WZlErrDFroT4v3kuftoUp5R+E6XHxC6245KDRuVp6xh5L676hbvp9RlQ+jZ8UaRz+sd4IQCZUzI9fm0ddwSPsvTaLq/Tb7HYuCAAmUMSI07pczb4b6Eg4lSt8NnybFdFFtmyTo2tUjZdxMCv9D2+68S67koe5kSDipLfZ9/BLYtsNy+Tjj7LakuWdPu3oJ6zAB2LPY5VsUZQZYPCU0/qwkfZubH6+/z8vx4/WcIR/zHEnYwHydczHLCl9NeJKT7ZHdfmasAd3/dzA4gfKHkWMKG2osKXTx296lm9jzwn4Qznl0IO+N5hPdxa5HpPTl6fYBwMX5/QrfUpYQV8Wfu/jczG1VCXeaZ2YHAdwnLeVdK6+JaqMwnzGwvQhe60wjd6XYgrCP/JNxW+Lp8n1clufs9SRPdNwl3vfw4oWvgCsKNx+4BflOmdbs19fs7cHKyPp9G8x+v70LTb3Q8CUxz91ZdzPawB/yymd1P83XsLcJ6+jsPP3xDkRbFAwgHEMcQOi/sQTiK3kw4eHuBcBZ2c/YydffZFn6j+ZjkfQ4hnOX0JGwvrybv8yZ3n13k/fyPmU0j9O45gXBWk+lhtIKwXj5F2KZmZk27OGmGOzapx/6EM8RawkHX64Sm8Fvd/b5C9ciwJGFERCRSVX+xWEREClMQiIhETkEgIhI5BYGISOQ6Y68hXd0WEWm5vN2pdEYgIhI5BYGISOQUBCIikVMQdEJnn302dXV17Lffflu8NmnSJMyM5cub/5riq6++Sm1tLZMmNf3O+YknnsjQoUMZNGgQ3/jGN9i0Kfy+yDXXXMPAgQMZMmQIxx57LIsXL25W1urVq+nfvz/nn38+ItL5KQg6oVGjRvHAAw9sMfy1117joYceYrfdtrwJ5JgxYzjppJOaDbvtttt4/vnnmT9/Pm+//Ta33x7uyr3//vvT0NDACy+8wBlnnMG4ceOaTXfxxRdz1FFHlfEdiUglKQg6oSOPPJI+ffpsMXzMmDFceeWVW9xr5e6772aPPfZg0KDmP4q03XbhJ1k3btzIhg0bGqc7+uij6dkz3N5n+PDhLFmypHGaZ555hmXLlnH88ccjItVBQVAl7r33Xvr378/QoUObDX/vvfe44oorGD9+fM7pTjjhBOrq6ujduzdnnHHGFq9Pnjy58Uxi8+bNjB07lquuuqr8b0BEKkZBUAXWrVvHxIkTufTSS7d4bfz48YwZM4ba2tocU8KDDz7I0qVL+eCDD5gxY0az16ZOnUpDQwMXXRR+IOu6667j5JNPZtddC/72h4h0Mp3xC2WSZcGCBSxcuLDxbGDJkiUccMABzJ07l6eeeopp06Yxbtw4Vq5cSZcuXdhmm22aXejdZpttOPXUU7nnnnv49KfDr+c9/PDDTJw4kVmzZtGjRw8AnnjiCebMmcN1113H2rVr2bBhA7W1tVx++eUd/6ZFpGwUBFVg8ODBvPXWW43/DxgwgIaGBvr27cucOXMah0+YMIHa2lrOP/981q5dy5o1a+jXrx8bN25k+vTpHHFE+GG15557jnPPPZcHHniAurq6xulvuaXpFxynTJlCQ0ODQkCkCnTG3yNoU4UPvOjmctWjYhbedx1rXvsHG99fS7ee29HvsNPpO7ipF8/8+rHs8+UJ1PTs3Wy6Nx6/i67de7DTQSfz4XurWHDnT9m86UPwzfTebSAfO/pMrEtXXrntCt5fvoRuvXYAoPt2fdjz9Oa/Cf7O/Dmse3Mhux7XYb+O2K6euao63odIAXlvMaEgEEFBIFHQvYZERCQ3BYGISOQUBCIikVMQiIhErlMEgZmNNrMGM2uor6+vdHVERKpKp/gegbvXA5kE6HTdnEREtmad4oxARETaj4JARCRyCgIRkcgpCEREIqcgEBGJnIJARCRyCgIRkcgpCEREIqcgEBGJnIJARCRyCgIRkcgpCEREIqcgEBGJnIJARCRyCgIRkcgpCEREIqcgEBGJnIJARCRyCgIRkcgpCEREIqcgEBGJnIJARCRyCgIRkcgpCEREIqcgEBGJnIJARCRyCgIRkch1iiAws9Fm1mBmDfX19ZWujohIVampdAVK4e71QCYBvJJ1ERGpNp3ijEBERNqPgkBEJHIKAhGRyCkIREQipyAQEYmcgkBEJHIKAhGRyCkIREQipyAQEYmcgkBEJHIKAhGRyCkIREQipyAQEYmcgkBEJHIKAhGRyCkIREQipyAQEYmcgkBEJHIKAhGRyCkIREQipyAQEYmcgkBEJHIKAhGRyCkIREQipyAQEYmcgkBEJHKdIgjMbLSZNZhZQ319faWrIyJSVWoqXYFSuHs9kEkAr2RdRESqTac4IxARkfajIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcp0iCMxstJk1mFlDfX19pasjIlJVaipdgVK4ez2QSQCvZF1ERKpNpzgjEBGR9qMgEBGJnIJARCRyCgIRkcgpCEREIqcgEBGJnIJARCRyCgIRkcgpCEREIqcgEBGJnIJARCRyCgIRkcgpCEREIqcgEBGJnIJARCRyCgIRkcgpCEREIqcgEBGJnIJARCRyCgIRkcgpCEREIqcgEBGJnIJARCRyCgIRkcgpCEREIqcgEBGJnIJARCRynSIIzGy0mTWYWUN9fX2lqyMiUlVqKl2BUrh7PZBJAK9kXUREqk2nOCMQEZH2oyAQEYmcgkBEJHIKAhGRyCkIREQipyAQEYmcgkBEJHIKAhGRyCkIREQipyAQEYmcgkBEJHIKAhGRyCkIREQipyAQEYmcgkBEJHIKAhGRyCkIREQipyAQEYmcgkBEJHIKAhGRyCkIREQipyAQEYmcgkBEJHIKAhGRyCkIREQipyAQEYlcpwgCMxttZg1m1lBfX1/p6oiIVJWaSlegFO5eD2QSwCtZFxGRatMpzghERKT9KAhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiVynCAIzG21mDWbWUF9fX+nqiEiJzj77bOrq6thvv/0ah02YMIH+/fszbNgwhg0bxvTp0wGYO3du47ChQ4dy1113NU6zYcMGRo8ezd57780+++zDHXfcAcA111zDwIEDGTJkCMceeyyLFy/u2DdYJczdK12HlmpThQ+86OZy1UOqyDNXnVXpKlSl2bNnU1tby1lnncX8+fOBEAS1tbVceOGFzcZdt24d3bt3p6amhqVLlzJ06FDeeOMNampqGD9+PJs2beLHP/4xmzdv5t1336Vv3748+uijHHzwwfTs2ZPrr7+emTNn8sc//rESb7UzsHwv1HRkLUQkLkceeSSLFi0qadyePXs2Pl+/fj1mTfutG2+8kX/84x8AdOnShb59+wJw9NFHN44zfPhwpk6dWoZax6dTNA2JSHW59tprGTJkCGeffTYrVqxoHP7UU08xaNAgBg8ezA033EBNTQ0rV64E4OKLL+aAAw5g5MiRLFu2bIsyJ0+ezEknndRRb6GqKAhEpEOdd955LFiwgHnz5tGvXz/Gjh3b+NrBBx/Miy++yNNPP81PfvIT1q9fz8aNG1myZAmHHXYYzz77LIcccsgWzUpTp06loaGBiy66qKPfTlVQEIhIh9ppp53o2rUrXbp04ZxzzmHu3LlbjLPvvvvSq1cv5s+fz4477kjPnj05/fTTARg5ciTPPvts47gPP/wwEydO5N5776VHjx4d9j6qiYJARDrU0qVLG5/fddddjT2KFi5cyMaNGwFYvHgxL7/8MgMGDMDMOOWUU5g5cyYAjzzyCAMHDgTgueee49xzz+Xee++lrq6uY99IFVGvIRG2nl5Dr146uNJVKKsLbn+NJxa9x4p1G+lbW8OYEXU8ueg9XnpzPWbwsR26c9kpu7BT727c+fwKrpuznG5dDTP49lF1nLDvdgAsWbmBMXcuYfX6TfTpWcOkz/an/w7dOfOmhby8bD11vbsBsMv23Zh85u6VfMvtYrdL/laOYvL2GlIQiKAgkK1beweBmoZERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCKnIBARiZyCQEQkcgoCEZHIKQhERCJXUhCY2c5m9gczW2BmL5nZdDPbu1yVMLMRZnZoucoTEZHSFQ0CMzPgLmCmu+/p7gOBHwA7lbEeIwAFgYhIBZRyRnA08KG735AZ4O7zgMfM7Cozm29mfzOzL0Lj0f19mXHN7FozG5U8X2RmPzSzZ5Np9jGzAcA3gDFmNs/Mjijf2xMRkWJqShhnP+CZHMM/BwwDhgJ9gafNbHYJ5S139wPM7JvAhe7+dTO7AVjr7pNyTWBmo4HRyb/nunt9CfPJaWv5kfKtgZmNbsuylPIr04+UVwWtnx2nLReLDwd+7+6b3H0ZMAs4qITp7kz+PgMMKGVG7l7v7p9MHloxymd08VFEKkbrZwcpJQheBA7MMdzyjL8xq9xtsl7/IPm7idLOSEREpB2VEgQzgB5mdk5mgJkdBKwAvmhmXc3so8CRwFxgMTDQzHqY2fbAsSXMYw3Qu8W1FxGRNit6RO7ubmanAz8zs/8HrAcWAd8BaoHnAQfGufubAGZ2G/AC8ArwXAn1+BMwzcxOAy5w9zktfyvSCmpmk62Z1s8OYu5e6TqIiEgF6ZvFIiKRUxCIiEROQdBJmdkEM/PUY13yJb0Wd7kzs5lmNi2r7OXlrbF0Jqn168Ecr00zs5ktLK8uKXNACeOOylq3PzCzl83sB2bWtSXzTcqbYmYNOcqvbWlZ1UrdNzu3VcCJyfNewCnAr81srbvfWrlqSRU53swOcven21hOHTAemEnobFKKY4D3CV3QjwB+lAy/rI11kSwKgs5to7s/mfr/keTmfZ8FFATSVu8CS4D/IqxTHe1pd1+bPJ9pZoOTeigIykxNQ9VnDdAN8p8CJ/d8ynk7j2xmVmNmb5jZ+ByvzTKzO3NNJ1XBCTvdU5OdcF5mNszMHkmaKFeY2S1mtlPy2gAgc++MRzNNPq2oT+O6nZQ7Iilrv6y6NGvqLMbMnjaz3+YYfpOZPduKenY6CoJOLtlR15jZdmb2ZeAowt1iy8LdNwI3AaOSO9Fm5rsH4XR9iw1IqsrtwD8JZwU5JV8onQn0BM4ELiCshw+ZWXdgKfDvyejfAg5JHsV0TdbtXmZ2EvAFyrhup/wPMDJ9wJQ8/zyRrN8Kgs5tR+DD5LEK+B3wK3e/uczzuZFwX6gRqWGjgLeAP5d5XrIVcffNwOWEHWW+3yAZm/w9wd3vdvephJtSDgY+7+4fEL5gCvCSuz+Z1aSZz0rCur0WmA78hfZpFvo94ZY5I1PDvkA4+4iiiVVB0LmtItzo7yDCTQC/DXw1VzNOW7j7K8Bsws4/8xsVZwG/S84YpLpNBV4Fvp/n9U8Bf3H31ZkB7j6XcFH48DbM90jCun0I8B/AcOA3bSgvp6Te00jW78Qo4F53f6fc89sa6WJx57bR3RtS/z9uZt2Ay8zsl2We12TgejM7n7Dh704kp82xc/eNZnYl8Aszm5BjlH6Em1NmWwb0acOsn0tdLH7SzFYRbkVztbvPb0O5uUwmXJDeM/n/CODkMs9jq6UzgurzEtAd2JNwXyiS/9M+0opybyecpo8kHC095e4vtbKO0vncSGgK/F6O15YSuodm24nQ86hcMuvbvsnffOt3i8PH3WcT7o32VcL6/QahKSoKCoLqk+lB8Rqh6x80bTiY2cHAdi0t1N3fJ7SlfovQ/quzgYgk7fyTgLMJZwBpTwEnmFnjHYSTOxQPAB5LBm1I/mbflr4l0us25F6/dwU+0crybyQEwVnAze6+qZXldDpqGurcasxsePK8O+F3I/4buMfd3zSzd4HXCaf0FxOOlMYBq3OWVtxkws+Kvg/8oU01l87o14TfKz+U8ENUGdcA5wEPmtkVhLsSX07oMnpHMs6rhPXmq0kTz4dZzZq5HGRm7xP2U/sCPwQakgfuvsTMngZ+ZGbrCAe2P6D1ZyE3AT9O5jellWV0SgqCzm174Ink+YeE34K4gbAy4+4bkluIX0e4GPYyYYO9pTUzc/cGM3sdmOnuq9pYd+lk3H2dmf0UmJg1/G0zOxq4mnDWuIHQy2eMu29Ixlmf/KbJeEKIdCP/j1tlzEj+biIc/f8JuCSrg8KZhO6fU5NxxgFjWvn+3jSzp5LnL7emjM5Kt6GWkpnZQMJFwePc/ZFK10eknMysD+EM+nx3n1zp+nQkBYEUZWY7Etpdf0S4KDjEteJIlUiubQwkdL8+Dhjg7usqW6uOpYvFUopTCBf9+gGjFAJSZQ4EniRc+zgrthAAnRGIiERPZwQiIpFTEIiIRE5BICISOQWBiEjkFAQiIpH7X3+UYv8YFgR/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "\n",
    "g = sns.countplot(df.label)\n",
    "g.set_xticklabels(['Bully', 'Not Bully'])\n",
    "g.set_yticklabels(['Count'])\n",
    "\n",
    "# function to show values on bars\n",
    "def show_values_on_bars(axs):\n",
    "    def _show_on_single_plot(ax):        \n",
    "        for p in ax.patches:\n",
    "            _x = p.get_x() + p.get_width() / 2\n",
    "            _y = p.get_y() + p.get_height()\n",
    "            value = '{:.0f}'.format(p.get_height())\n",
    "            ax.text(_x, _y, value, ha=\"center\") \n",
    "\n",
    "    if isinstance(axs, np.ndarray):\n",
    "        for idx, ax in np.ndenumerate(axs):\n",
    "            _show_on_single_plot(ax)\n",
    "    else:\n",
    "        _show_on_single_plot(axs)\n",
    "show_values_on_bars(ax)\n",
    "\n",
    "sns.despine(left=True, bottom=True)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('Distribution of Classes', fontsize=30)\n",
    "plt.tick_params(axis = 'x', which='major', labelsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAD3CAYAAADsd3iFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcUElEQVR4nO3dYUxb1/3/8beJjZvVTIjNhoRlkba1mhR3iYSjLutmtmmKIeBmcRepgQVNW5UWbSntOiZCMlC0oqQRDUyaiDSt64Os3Ua31m6RMZvWkayjq4KlNmLN1C4K0QoNGJGN4ASwjX8P8sf/kLS9YJw5kM/riXOPz7W+h2vdj8+5vo4pmUwmERER+Qg52S5ARERufQoLERExpLAQERFDCgsRETGksBAREUPmbBeQabOzs0SjUSwWCyaTKdvliIgsC8lkklgsxp133klOzo3ziBUXFtFolHfeeSfbZYiILEt33303eXl5N7SvuLCwWCzA1QHn5uZmuZqVYWBgAKfTme0yRD6Q3p+ZMTMzwzvvvJM6h15vxYXF3NJTbm4uVqs1y9WsHPpbyq1M78/M+bDle13gFhERQwoLERExpLAQERFDCgsRETGksBAREUMKCxERMaSwEBERQwqLLJqJJbJdwoKUlJRku4QFWS5/T5HlaMXdlLec5FpW4X0ikO0yVoxXnt6e7RJEVizNLERExNCCwmJycpLKykree++9ee2//vWv2b17d2r7zJkz+Hw+PB4P+/fvJx6PAzA8PEx1dTVlZWXU1tYSjUYBmJiYYM+ePZSXl1NdXU0kEgGu/kZJfX095eXl7Nixg7Nnz2ZksCIikh7DsHjrrbfYtWsXg4OD89r/9a9/8Ytf/GJeW319PU1NTfT09JBMJuns7ATg4MGDVFVVEQqFcDqddHR0ANDe3o7L5aK7u5udO3fS0tICwPHjx1m9ejXd3d00Njayb9++TIxVRETSZBgWnZ2dNDc343A4Um0zMzM0NTXx6KOPptqGhoaYmppi06ZNAPh8PkKhELFYjFOnTuHxeOa1A/T29uL1egGorKzk5MmTxGIxent7uf/++wHYvHkz4+PjDA8PZ2bEIiKyaIYXuOc+7V/r6aef5oEHHuBTn/pUqm10dBS73Z7attvtjIyMcPHiRWw2G2azeV779fuYzWZsNhvj4+Mf+FoXLlxg7dq1Cx7YwMDAgvtmy3L5ltFyEg6Hs12CZIGO+8236G9D/e1vf+P9999n3759vPHGG6n22dnZeT9tm0wmMZlMqcdrfdhP4CaTSXJycm7YZ659MZxOp362+DakAL79hMNhHfcMmJ6e/sgP2Yv+NlRXVxfvvvsu27dv58CBAwwMDPDYY49RVFSUukANMDY2hsPhoKCggEuXLpFIXP0OfCQSSS1pORwOxsbGAIjH40SjUfLz8yksLGR0dPSG1xIRkexYdFgcOnSI7u5uAoEATz75JE6nk/b2doqLi7FaranpYCAQwO12Y7FYcLlcBINBAPx+P263G4DS0lL8fj8AwWAQl8uFxWKhtLSUQODq/Qf9/f1YrdZFLUGJiEhmZfQ+i9bWVg4dOkRZWRmXL1+mpqYGgObmZjo7O9m2bRv9/f089thjANTV1fHmm29SUVHB888/T1NTEwC7d+9mZmaGiooKWlpaOHLkSCbLFBGRRTIlk8lktovIpLl1t+VyzUJ3cGeO7uC+PemaRWYYnTt1B7eIiBhSWIiIiCGFhYiIGFJYiIiIIYWFiIgYUliIiIghhYWIiBhSWIiIiCGFhYiIGFJYiIiIIYWFiIgYUliIiIghhYWIiBhSWIiIiCGFhYiIGFJYiIiIIYWFiIgYUliIiIghhYWIiBhaUFhMTk5SWVnJe++9B8Dvfvc7Kisr8Xq97Nu3j5mZGQDOnDmDz+fD4/Gwf/9+4vE4AMPDw1RXV1NWVkZtbS3RaBSAiYkJ9uzZQ3l5OdXV1UQiEQBmZmaor6+nvLycHTt2cPbs2YwPXEREFs4wLN566y127drF4OAgAOfOneOZZ57ht7/9LS+//DKzs7M8//zzANTX19PU1ERPTw/JZJLOzk4ADh48SFVVFaFQCKfTSUdHBwDt7e24XC66u7vZuXMnLS0tABw/fpzVq1fT3d1NY2Mj+/btuxljFxGRBTIMi87OTpqbm3E4HADk5ubS3NyMzWbDZDJx9913Mzw8zNDQEFNTU2zatAkAn89HKBQiFotx6tQpPB7PvHaA3t5evF4vAJWVlZw8eZJYLEZvby/3338/AJs3b2Z8fJzh4eGMD15ERBbGbNRh7tP+nOLiYoqLiwEYHx/nueee49ChQ4yOjmK321P97HY7IyMjXLx4EZvNhtlsntcOzNvHbDZjs9kYHx//wNe6cOECa9euXeJwRUQkHYZh8WFGRkZ46KGHeOCBB7j33nsJh8OYTKbU88lkEpPJlHq81vXb1+6Tk5Nzwz5z7YsxMDCwqP7ZUFJSku0SVpxwOJztEiQLdNxvvrTC4uzZszz00EPs3r2b7373uwAUFRWlLlADjI2N4XA4KCgo4NKlSyQSCVatWkUkEkktaTkcDsbGxigqKiIejxONRsnPz6ewsJDR0VE+/elPz3utxXA6nVit1nSGJ8uYAvj2Ew6HddwzYHp6+iM/ZC/6q7OTk5N873vfo66uLhUUcHV5ymq1phI+EAjgdruxWCy4XC6CwSAAfr8ft9sNQGlpKX6/H4BgMIjL5cJisVBaWkogEACgv78fq9WqJSgRkSxadFj8/ve/Z2xsjGeffZbt27ezfft2fvaznwHQ2trKoUOHKCsr4/Lly9TU1ADQ3NxMZ2cn27Zto7+/n8ceewyAuro63nzzTSoqKnj++edpamoCYPfu3czMzFBRUUFLSwtHjhzJ0HBFRCQdpmQymcx2EZk0N5VaLstQ3icC2S5hxXjl6e3ZLkGyQMtQmWF07tQd3CIiYkhhISIihhQWIiJiSGEhIiKGFBYiImJIYSEiIoYUFiIiYkhhISIihhQWIiJiSGEhIiKGFBYiImJIYSEiIoYUFiIiYkhhISIihhQWIiJiSGEhIiKGFBYiImJIYSEiIoYUFiIiYkhhISIihhYUFpOTk1RWVvLee+8B0NfXh9frZevWrbS1taX6nTlzBp/Ph8fjYf/+/cTjcQCGh4eprq6mrKyM2tpaotEoABMTE+zZs4fy8nKqq6uJRCIAzMzMUF9fT3l5OTt27ODs2bMZHbSIiCyOYVi89dZb7Nq1i8HBQQCmpqZobGyko6ODYDDIwMAAJ06cAKC+vp6mpiZ6enpIJpN0dnYCcPDgQaqqqgiFQjidTjo6OgBob2/H5XLR3d3Nzp07aWlpAeD48eOsXr2a7u5uGhsb2bdv380Yu4iILJBhWHR2dtLc3IzD4QDg9OnTrF+/nnXr1mE2m/F6vYRCIYaGhpiammLTpk0A+Hw+QqEQsViMU6dO4fF45rUD9Pb24vV6AaisrOTkyZPEYjF6e3u5//77Adi8eTPj4+MMDw9nfPAiIrIwZqMOc5/254yOjmK321PbDoeDkZGRG9rtdjsjIyNcvHgRm82G2Wye1379a5nNZmw2G+Pj4x/4WhcuXGDt2rVLGKqIiKTLMCyuNzs7i8lkSm0nk0lMJtOHts89Xuv67Wv3ycnJuWGfufbFGBgYWFT/bCgpKcl2CStOOBzOdgmSBTruN9+iw6KoqCh1IRogEongcDhuaB8bG8PhcFBQUMClS5dIJBKsWrUq1R+uzkrGxsYoKioiHo8TjUbJz8+nsLCQ0dFRPv3pT897rcVwOp1YrdbFDk+WOQXw7SccDuu4Z8D09PRHfshe9FdnN27cyLlz5zh//jyJRIKuri7cbjfFxcVYrdZUwgcCAdxuNxaLBZfLRTAYBMDv9+N2uwEoLS3F7/cDEAwGcblcWCwWSktLCQQCAPT392O1WrUEJSKSRYueWVitVg4fPszevXuZnp6mtLSUsrIyAFpbWzlw4ACTk5Ns2LCBmpoaAJqbm2loaODYsWOsWbOGo0ePAlBXV0dDQwMVFRXk5eXR2toKwO7du2lqaqKiooLc3FyOHDmSqfGKiEgaTMlkMpntIjJpbiq1XJahvE8Esl3CivHK09uzXYJkgZahMsPo3Kk7uEVExJDCQkREDCksRETEkMJCREQMKSxERMSQwkJERAwpLERExJDCQkREDCksRETEkMJCREQMKSxERMSQwkJERAwpLERExJDCQkREDCksRETEkMJCREQMKSxERMSQwkJERAwpLERExJDCQkREDC0pLAKBABUVFVRUVPDUU08B0NfXh9frZevWrbS1taX6njlzBp/Ph8fjYf/+/cTjcQCGh4eprq6mrKyM2tpaotEoABMTE+zZs4fy8nKqq6uJRCJLKVVERJYg7bC4cuUKLS0tHD9+nEAgQH9/P6+++iqNjY10dHQQDAYZGBjgxIkTANTX19PU1ERPTw/JZJLOzk4ADh48SFVVFaFQCKfTSUdHBwDt7e24XC66u7vZuXMnLS0tGRiuiIikI+2wSCQSzM7OcuXKFeLxOPF4HJvNxvr161m3bh1msxmv10soFGJoaIipqSk2bdoEgM/nIxQKEYvFOHXqFB6PZ147QG9vL16vF4DKykpOnjxJLBZb4nBFRCQd5nR3tNls1NXVUV5ezurVq9m8eTOjo6PY7fZUH4fDwcjIyA3tdrudkZERLl68iM1mw2w2z2sH5u1jNpux2WyMj49TWFi4oPoGBgbSHdr/TElJSbZLWHHC4XC2S5As0HG/+dIOi3/+85/84Q9/4C9/+Qt5eXn86Ec/YnBwEJPJlOqTTCYxmUzMzs5+YPvc47Wu3752n5ychU+EnE4nVqt1kaOS5U4BfPsJh8M67hkwPT39kR+y016Geu2119iyZQuf+MQnyM3Nxefz8cYbb8y7EB2JRHA4HBQVFc1rHxsbw+FwUFBQwKVLl0gkEvP6w9VZydjYGADxeJxoNEp+fn665YqIyBKkHRaf//zn6evr4/LlyySTSV599VU2btzIuXPnOH/+PIlEgq6uLtxuN8XFxVit1tRUMRAI4Ha7sVgsuFwugsEgAH6/H7fbDUBpaSl+vx+AYDCIy+XCYrEscbgiIpKOtJehvvzlL/P222/j8/mwWCzcc8897N27l/vuu4+9e/cyPT1NaWkpZWVlALS2tnLgwAEmJyfZsGEDNTU1ADQ3N9PQ0MCxY8dYs2YNR48eBaCuro6GhgYqKirIy8ujtbU1A8MVEZF0mJLJZDLbRWTS3Lrbcrlm4X0ikO0SVoxXnt6e7RIkC3TNIjOMzp26g1tERAwpLERExJDCQkREDCksRETEkMJCREQMKSxERMSQwkJERAwpLERExJDCQkREDCksRETEkMJCREQMKSxERMSQwkJERAwpLERExJDCQkREDCksRETEkMJCREQMKSxERMSQwkJERAwpLERExNCSwuLVV1/F5/NRXl7Ok08+CUBfXx9er5etW7fS1taW6nvmzBl8Ph8ej4f9+/cTj8cBGB4eprq6mrKyMmpra4lGowBMTEywZ88eysvLqa6uJhKJLKVUERFZgrTD4t///jfNzc10dHTw8ssv8/bbb3PixAkaGxvp6OggGAwyMDDAiRMnAKivr6epqYmenh6SySSdnZ0AHDx4kKqqKkKhEE6nk46ODgDa29txuVx0d3ezc+dOWlpaMjBcERFJR9ph8ac//Ylt27ZRVFSExWKhra2N1atXs379etatW4fZbMbr9RIKhRgaGmJqaopNmzYB4PP5CIVCxGIxTp06hcfjmdcO0Nvbi9frBaCyspKTJ08Si8WWOFwREUmHOd0dz58/j8Vi4ZFHHuH999/nq1/9KnfddRd2uz3Vx+FwMDIywujo6Lx2u93OyMgIFy9exGazYTab57UD8/Yxm83YbDbGx8cpLCxcUH0DAwPpDu1/pqSkJNslrDjhcDjbJUgW6LjffGmHRSKRoL+/n+PHj/Oxj32M2tpa7rjjDkwmU6pPMpnEZDIxOzv7ge1zj9e6fvvafXJyFj4RcjqdWK3WRY5KljsF8O0nHA7ruGfA9PT0R37ITnsZ6pOf/CRbtmyhoKCAO+64g2984xv09fXNuxAdiURwOBwUFRXNax8bG8PhcFBQUMClS5dIJBLz+sPVWcnY2BgA8XicaDRKfn5+uuWKiMgSpB0WX/va13jttdeYmJggkUjw17/+lbKyMs6dO8f58+dJJBJ0dXXhdrspLi7GarWmpoqBQAC3243FYsHlchEMBgHw+/243W4ASktL8fv9AASDQVwuFxaLZYnDFRGRdKS9DLVx40YeeughqqqqiMVi3HfffezatYvPfOYz7N27l+npaUpLSykrKwOgtbWVAwcOMDk5yYYNG6ipqQGgubmZhoYGjh07xpo1azh69CgAdXV1NDQ0UFFRQV5eHq2trRkYroiIpMOUTCaT2S4ik+bW3ZbLNQvvE4Fsl7BivPL09myXIFmgaxaZYXTu1B3cIiJiSGEhIiKGFBYiImJIYSEiIoYUFiIiYkhhISIihhQWIiJiSGEhIiKGFBYiImJIYSEiIoYUFiIiYkhhISIihhQWIiJiSGEhIiKGFBYiImJIYSEiIoYUFiIiYkhhISIihhQWIiJiKCNh8dRTT9HQ0ABAX18fXq+XrVu30tbWlupz5swZfD4fHo+H/fv3E4/HARgeHqa6upqysjJqa2uJRqMATExMsGfPHsrLy6muriYSiWSiVBERScOSw+L111/npZdeAmBqaorGxkY6OjoIBoMMDAxw4sQJAOrr62lqaqKnp4dkMklnZycABw8epKqqilAohNPppKOjA4D29nZcLhfd3d3s3LmTlpaWpZYqIiJpWlJY/Oc//6GtrY1HHnkEgNOnT7N+/XrWrVuH2WzG6/USCoUYGhpiamqKTZs2AeDz+QiFQsRiMU6dOoXH45nXDtDb24vX6wWgsrKSkydPEovFllKuiIikaUlh0dTUxOOPP87HP/5xAEZHR7Hb7annHQ4HIyMjN7Tb7XZGRka4ePEiNpsNs9k8r/361zKbzdhsNsbHx5dSroiIpMmc7o4vvPACa9asYcuWLbz44osAzM7OYjKZUn2SySQmk+lD2+cer3X99rX75OQsPNsGBgYWM5ysKCkpyXYJK044HM52CZIFOu43X9phEQwGiUQibN++nf/+979cvnyZoaEhVq1aleoTiURwOBwUFRXNu0A9NjaGw+GgoKCAS5cukUgkWLVqVao/XJ2VjI2NUVRURDweJxqNkp+fv+D6nE4nVqs13eHJMqUAvv2Ew2Ed9wyYnp7+yA/ZaS9DPfvss3R1dREIBHj00Uf5+te/zi9/+UvOnTvH+fPnSSQSdHV14Xa7KS4uxmq1ptI/EAjgdruxWCy4XC6CwSAAfr8ft9sNQGlpKX6/H7gaTC6XC4vFkm65IiKyBGnPLD6I1Wrl8OHD7N27l+npaUpLSykrKwOgtbWVAwcOMDk5yYYNG6ipqQGgubmZhoYGjh07xpo1azh69CgAdXV1NDQ0UFFRQV5eHq2trZksVUREFsGUTCaT2S4ik+amUstlGcr7RCDbJawYrzy9PdslSBZoGSozjM6duoNbREQMKSxERMSQwkJERAwpLERExJDCQkREDCksRETEkMJCREQMKSxERMSQwkJERAwpLERExJDCQkREDCksRETEkMJCREQMKSxERMSQwkJERAwpLERExJDCQkREDCksRETEkMJCREQMKSxERMTQksLi5z//ORUVFVRUVHDkyBEA+vr68Hq9bN26lba2tlTfM2fO4PP58Hg87N+/n3g8DsDw8DDV1dWUlZVRW1tLNBoFYGJigj179lBeXk51dTWRSGQppYqIyBKkHRZ9fX289tprvPTSS/j9fv7xj3/Q1dVFY2MjHR0dBINBBgYGOHHiBAD19fU0NTXR09NDMpmks7MTgIMHD1JVVUUoFMLpdNLR0QFAe3s7LpeL7u5udu7cSUtLSwaGKyIi6Ug7LOx2Ow0NDeTm5mKxWPjsZz/L4OAg69evZ926dZjNZrxeL6FQiKGhIaampti0aRMAPp+PUChELBbj1KlTeDyeee0Avb29eL1eACorKzl58iSxWGyJwxURkXSY093xrrvuSv17cHCQ7u5uvv3tb2O321PtDoeDkZERRkdH57Xb7XZGRka4ePEiNpsNs9k8rx2Yt4/ZbMZmszE+Pk5hYeGC6hsYGEh3aP8zJSUl2S5hxQmHw9kuQbJAx/3mSzss5rz77rs8/PDD/PjHP2bVqlUMDg6mnksmk5hMJmZnZzGZTDe0zz1e6/rta/fJyVn4RMjpdGK1Whc3GFn2FMC3n3A4rOOeAdPT0x/5IXtJF7jD4TDf+c53eOKJJ9ixYwdFRUXzLkRHIhEcDscN7WNjYzgcDgoKCrh06RKJRGJef7g6KxkbGwMgHo8TjUbJz89fSrkiIpKmtMPi/fff5/vf/z6tra1UVFQAsHHjRs6dO8f58+dJJBJ0dXXhdrspLi7GarWmpoqBQAC3243FYsHlchEMBgHw+/243W4ASktL8fv9AASDQVwuFxaLZSljFRGRNKW9DPXMM88wPT3N4cOHU20PPvgghw8fZu/evUxPT1NaWkpZWRkAra2tHDhwgMnJSTZs2EBNTQ0Azc3NNDQ0cOzYMdasWcPRo0cBqKuro6GhgYqKCvLy8mhtbV3KOEVEZAlMyWQyme0iMmlu3W25XLPwPhHIdgkrxitPb892CZIFumaRGUbnTt3BLSIihhQWIvKBZmKJbJewIMthVrFc/pYfZclfnRWRlSnXskrLpBmyEpZINbMQERFDCgsRETGksBAREUMKCxERMaSwEBERQwoLERExpLAQERFDCgsRETGksBAREUMKCxERMaSwEBERQwoLERExpLAQERFDCgsRETGksBAREUMKCxERMaSwEBERQ7d0WLzyyits27aNrVu38txzz2W7HBGR29Yt+9+qjoyM0NbWxosvvkhubi4PPvgg9957L5/73OeyXZqIyG3nlg2Lvr4+vvjFL5Kfnw+Ax+MhFArxgx/84CP3SyaTAMzMzNzsEjMi/85V2S5hxZiens52CSuO3p+ZsRzem3PnzLlz6PVu2bAYHR3Fbrenth0OB6dPnzbcLxaLAfDOO+/ctNoy6bHta7JdwooxMDCQ7RJWHL0/M2M5vTdjsRh33HHHDe23bFjMzs5iMplS28lkct72h7nzzju5++67sVgsC+ovIiJXz7GxWIw777zzA5+/ZcOiqKiI/v7+1HYkEsHhcBjul5OTQ15e3s0sTURkRfqgGcWcW/bbUF/60pd4/fXXGR8f58qVK/zxj3/E7XZnuywRkdvSLTuzKCws5PHHH6empoZYLMa3vvUtvvCFL2S7LBGR25Ip+WGXvkVERP6fW3YZSkREbh0KCxERMaSwEBERQwoLERExpLAQERFDt+xXZyV7zp49S09PDxcuXCAnJweHw8FXvvIV7rnnnmyXJiJZopmFzPPcc8/xwx/+EIB77rmHDRs2APCTn/yEX/3qV9ksTUSySPdZyDwejwe/38/q1avntV+5coUdO3YQCoWyVJkIDA8Pf+Tza9eu/R9VcvvRMpTMYzabicfjN7RPTU1hsViyUJHI//fwww8zODiIw+G44ae0TSYTf/7zn7NU2cqnsJB5HnnkEb75zW+yZcsW7HY7JpOJ0dFR/v73v/P4449nuzy5zf3mN7+hqqqK5uZmSkpKsl3ObUXLUHKDkZERXn/9dUZHR5mdnaWoqIgtW7ZQWFiY7dJEOH36NC+88AI//elPs13KbUVhISIihvRtKBERMaSwEBERQwoLERExpLAQERFDCgsRETH0f1h39aVxhnEgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.label.value_counts().plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    144324\n",
       "1     15362\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considerable imbalance {0 : 143729, 1 : 15842}\n",
    "\n",
    "Now lets check the performance of provided data without training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary library\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "# here we are removing the stop words from the given text\n",
    "set_stopwords = set(stopwords.words(\"english\"))\n",
    "\n",
    "# we further use regex functions to further map them into the function and remove the desired unncessary words\n",
    "def clean_txt(text): # define the fuction with tokenization/string cleaning for all datasets \n",
    "                        \n",
    "    text = re.sub(r\"[^A-Za-z,!?]\", \" \", text)     \n",
    "    text = re.sub(r'\\[[^]]*\\]',\" \", text) \n",
    "    text = re.sub(r\"\\'s\", \"\", text) \n",
    "    text = re.sub(r\"\\'t\", \"\", text ) \n",
    "    text = re.sub(r\"\\'re\", \"\",text) \n",
    "    text = re.sub(r\"\\'d\", \"\", text) \n",
    "    text = re.sub(r\"\\'ll\", \" \",text) \n",
    "    text = re.sub(r\",\", \" \", text) \n",
    "    text = re.sub(r\"\\(\", \" \", text) \n",
    "    text = re.sub(r\"\\)\", \" \", text) \n",
    "    text = re.sub(r\"\\'\", \" \", text)\n",
    "    text = re.sub(r\"aa\", \"\", text)\n",
    "    text = re.sub(r\"zz\", \"\", text)\n",
    "    text = re.sub(r\"[0-9]\", ' ', text)\n",
    "    text = text.lower()\n",
    "    text = ' '.join(word for word in text.split() if word not in set_stopwords)\n",
    "    return text\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['text'] = df1['text'].apply(clean_txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one make analogy mathematical terms envisionin...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clarification zundark right checked wikipedia ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elected electoral? jhk</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fun entry devotchka coworker korea tell differ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>please relate ozone hole increases cancer prov...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159681</th>\n",
       "      <td>sources exactly exude sense impartiality newsw...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159682</th>\n",
       "      <td>institute historical review peer reviewed jour...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159683</th>\n",
       "      <td>way trying describe article serious step where...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159684</th>\n",
       "      <td>warning clearly protectionist regime going wit...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159685</th>\n",
       "      <td>alternate option perhaps enough newsworthy inf...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>159686 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     text  label\n",
       "0       one make analogy mathematical terms envisionin...      0\n",
       "1       clarification zundark right checked wikipedia ...      0\n",
       "2                                  elected electoral? jhk      0\n",
       "3       fun entry devotchka coworker korea tell differ...      0\n",
       "4       please relate ozone hole increases cancer prov...      0\n",
       "...                                                   ...    ...\n",
       "159681  sources exactly exude sense impartiality newsw...      0\n",
       "159682  institute historical review peer reviewed jour...      0\n",
       "159683  way trying describe article serious step where...      0\n",
       "159684  warning clearly protectionist regime going wit...      0\n",
       "159685  alternate option perhaps enough newsworthy inf...      0\n",
       "\n",
       "[159686 rows x 2 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter #import Counter for finding most common words\n",
    "import seaborn as sns #import searbon for vizualization result\n",
    "\n",
    "text = df1['text']\n",
    "words = text.str.split(expand=True).unstack()\n",
    "result_count = Counter(words).most_common()\n",
    "result_df = pd.DataFrame(result_count).reset_index().drop(0) #converting to Dataframe and drop the Nones values\n",
    "#result_df\n",
    "#vizualize result\n",
    "sns.set_theme(style=\"whitegrid\") \n",
    "f, ax = plt.subplots(figsize=(15, 8)) \n",
    "sns.barplot(y=result_df[0][0:5], x=result_df[1][0:5], data=result_df, palette=None)\n",
    "plt.ylabel('Words', color=\"blue\")  # Add an x-label to the axes.\n",
    "plt.xlabel('Count', color=\"blue\")  # Add a y-label to the axes.\n",
    "plt.title(\"Frequent Occuring words in Headlines\", color=\"blue\") \n",
    "plt.xticks(rotation=50);\n",
    "ax.tick_params(axis='x', colors='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "bully_words = pd.DataFrame(df1[df1['label']==1]['text'].str.split(expand=True).unstack().value_counts()).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#vizualize result\n",
    "sns.set_theme(style=\"whitegrid\") \n",
    "f, ax = plt.subplots(figsize=(15, 8)) \n",
    "sns.barplot(y=bully_words['index'][0:5], x=bully_words[0][0:5], data=result_df, palette=None)\n",
    "plt.ylabel('Words', color=\"blue\")  # Add an x-label to the axes.\n",
    "plt.xlabel('Count', color=\"blue\")  # Add a y-label to the axes.\n",
    "plt.title(\"Frequent Occuring Bully Words in Text\", color=\"blue\") \n",
    "plt.xticks(rotation=70);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "nonbully_words = pd.DataFrame(df1[df1['label']==0]['text'].str.split(expand=True).unstack().value_counts()).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#vizualize result\n",
    "sns.set_theme(style=\"whitegrid\") \n",
    "f, ax = plt.subplots(figsize=(15, 8)) \n",
    "sns.barplot(y=nonbully_words['index'][0:5], x=nonbully_words[0][0:5], data=result_df, palette=None)\n",
    "plt.ylabel('Words', color=\"blue\")  # Add an x-label to the axes.\n",
    "plt.xlabel('Count', color=\"blue\")  # Add a y-label to the axes.\n",
    "plt.title(\"Frequent Occuring non-Bully Words in Text\", color=\"blue\") \n",
    "plt.xticks(rotation=70);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance of data\n",
    "\n",
    "For finding accuracy we need to perform- \n",
    "1. Data cleaning\n",
    "2. Vectorization\n",
    "3. Train test split\n",
    "\n",
    "## Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter = df[\"text\"] != \"\"\n",
    "df_clean = df[filter]\n",
    "df_clean = df_clean.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(sen):\n",
    "    # Remove punctuations and numbers\n",
    "    sentence = re.sub('[^a-zA-Z]', ' ', sen)\n",
    "\n",
    "    # Single character removal\n",
    "    sentence = re.sub(r\"\\s+[a-zA-Z]\\s+\", ' ', sentence)\n",
    "\n",
    "    # Removing multiple spaces\n",
    "    sentence = re.sub(r'\\s+', ' ', sentence)\n",
    "\n",
    "    return sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "sentences = list(df[\"text\"])\n",
    "for sen in sentences:\n",
    "    X.append(preprocess_text(sen))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_stopwords = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(stop_words = my_stopwords, use_idf = True)\n",
    "bag_of_words = vectorizer.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = bag_of_words\n",
    "y = df['label']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.25, random_state = 27)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.9012073543409649\n",
      "Test precision score: 0.0\n",
      "Test recall score: 0.0\n",
      "Test f1 score: 0.0\n"
     ]
    }
   ],
   "source": [
    "# dummy classifier\n",
    "dummy = DummyClassifier(strategy='most_frequent').fit(X_train, Y_train)\n",
    "dummy_predict = dummy.predict(X_test)\n",
    "\n",
    "print('Test score:', accuracy_score(Y_test, dummy_predict))\n",
    "print('Test precision score:', precision_score(Y_test, dummy_predict))\n",
    "print('Test recall score:', precision_score(Y_test, dummy_predict))\n",
    "print('Test f1 score:', f1_score(Y_test, dummy_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without training the model we have an accuracy of 0.8986. \n",
    "\n",
    "Now lets train some models namely - \n",
    "1. Decision Tree\n",
    "2. Logistic Regression\n",
    "3. K Nearest Neighbor Classifier\n",
    "\n",
    "# Algorithm1 - Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=25)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=25)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=25)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth = 25)\n",
    "clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_predict = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap = cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[35640   338]\n",
      " [ 1820  2124]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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l3JdVT87A8xp3PP/LjVlBN/LOh4eanTMibBJ/vfsWDDOWA9Cntz3rls5mzHWDsLPTEPtCOtt3H+7MavZ469a+xCsvr0Oj0XDNNcNZu/4VHBwceGju3/jxh+9paGjgzjvu4PnV4wA4fuwYjz48l+qqKjQaDc8sX8FkP/8urkX3Ia8ACQDGjhrMY+F3cNNfnqOiqobnHr+bpY8Es2bTJ5RXnGf8zBUtvvPy03P44ZcC7l/yFl++NZd/LZrB3kM/mRPqhDHX8MRfJ1N2rtr8ndh5gVSdr2XsPc8y2P0qdr/1P3x97HSzJCx+v6+zskhcncDBrO/o378/0YsW8nTcU/RycMDDYxDvbEmlurqa60eN4J4/hzF+wgQWRD7CfX99gPvuf4Bvv/kG/zsncaaw1DzLzJWu8cGNorrfltl29J3om+O5eE2Np6KqBodeOv6o70/ZuWrGj7kGk6mB/7z2GAe3LGbx3ADs7DRc5dyXO26+jmUvN44fNRqN+MxJoKyiMSHqXfuxKnoGMau3NrvOXbeP4Y0PvgAgt6Ccjw98zz1+/69zK9uD/b9x48g+/hP9+/enpqaG/PwzuP7hD/xr9QusWNk4RK3g7Fnq6uro378/ACaTifJfywGoqqqkd+/eXRZ/t6Rpw2bD5J/Cy1Bf30DIpBtIWhpGnbGep9d9iM+4EXx68AdiX0jHXqdl64sPUVlVw4HvTlJQUsH8e2/H/0//jecId8aOOszPp4uws9Pw5vK/siQxHWO9qdk1Bg24irzCcvPnM4W/4jHApZNr2rPZ29uzLT2NR+b9nV4ODiyNexqNRoNOp+P+8HvZ+kEqvr6+jLzuOgAS16xlit/tvPjCaoqLinh787vSivytHv4yeYe2JLdv305gYCB+fn5s3ry5Iy/VabbvPszg26N5dn0G29c+yptp+3nin+9xvqaOc1UXWLPpU+66fQz2Oi3DBl1NZXUNt9+/mpMnT/LP/wll7KjBPBN5F59//TOffPl9i/Pb2WlQftNz0Wg0mEwNnVjDK8NdU6eRV1BC7FP/ICTIn4aGxj/jN97eRF5BCRUVFSx/9mlqamqYM/svvPzam5zIyeOjT/YS+cg8cnNzu7gG3Ud7r3HT3XRYkiwsLGT16tUkJyeTlpbGli1b+Pnnnzvqch3umsFXc4v3NebPb6XvZ8hAV8KCb8JrxB/N5RoNGOtN5BefA+Dt9AMA1NbWsv/bExi8hjIr6Cam3jGGA+9Gs25pGNcMupoD70YDkHu2nIFu/c3nG+jWnzOFv3ZCDa8MJ37+mS8+/9z8+b77H+D0qVO8n/oe+fn5ADg5OeHv78+333zN0exszp8/T2BQMAA3jx/PqP8ezaGDX3ZJ/N1Re65x0x11WJLct28f48ePx8XFhb59++Lv729eZ8IWDby6P2+vuJ8/uDgCMDPwRo6eyGfUMHeWPhyEnZ2G3g72PPQXX1J3fc2p/FK+Pnaae0NuBhqnih8/5hq+Pnqaa/yWcPNfVjB+5goefjqZX/JKzA9+duw5zAP3/AkAD70Lk28ZRcZn2V1T6R7obMFZwu+daZ4h+93kzYwe7cXHH/2b5c/EoygKtbW1fPTRR/hOup3h115Lxblz7N+3D4BfTpzg++PH8PYe25XV6FZ6epLUKIrS+mMpK2zYsIHz58/z+OOPA/Dee+9x+PBhnnnmGdXv1dbWkp3dPZPC1VdfjV6vR1EUjEYjp0+fxmg0MmTIEBwdHdFoNJSXl5tbJPb29gwZMgQHBwegcX2Ni6evd3JyYsiQIRw7dgxofBI4ZMgQ+vbti0aj4ezZs5SVlXVuRXu41NRUUlJS0Ol0XH311Tz55JM4OzuzfPlyTpw4AcBtt93GvHnzsLOz46uvvmLNmjXU1tai1WqZO3cukyZN6tpK/E5eXl7m/x+t1fR39ZG0YoqrW78l5OZoR9I0t3a9dmfqsLvPra0/0VbTn3yPgtKqjgitS3z+6v1M/PsbXR1Guys/9FJXh3DZRo8ZR9wzz7Uo35Yxyfz7o99lMXrMOPPx9/1tXmeF1yHq6mr56XjHND56+nuSHdbdvnj9id+uMyGE6DkahyWqb7asw5LkLbfcwv79+ykrK+PChQv8+9//Nq8zIYToOXr6PckO624PGDCAxx9/nPDwcIxGI9OnT+eGG27oqMsJIbqQjedBVR36RmxISAghISEdeQkhRBezs9Ngp7IQmNo+WyDDBoQQVpEkKYQQKiw9nLH1rrgkSSGEVdr7FaCXXnqJnTt3Ao3rcC9atIjFixeTlZVFnz59AIiIiGDy5MkcP36cJUuWUF1djcFgID4+Hp1OR35+PlFRUZSWljJs2DASEhJwdHSkoqKChQsXkpubi6urK4mJibi5uanGI7MACSGs0p6vAO3bt4/PP/+crVu3kpaWxtGjR/noo4/Izs5m06ZNpKenk56ezuTJkwGIiopi6dKl7Nq1C0VRSElJASA+Pp6wsDAyMzPx8vIiKSkJgMTERAwGAzt37mTGjBksW7bMYkySJIUQVtFo7MyLgV1q02ga00xBQQF5eXnNtoqKimbncnNzIzo6ml69emFvb8/w4cPJz88nPz+fmJgYQkJCWLNmDQ0NDZw5c4aamhq8vb0BCA0NJTMzE6PRyKFDh/D3929WDrB7927zw+Tg4GD27t2L0WhUrZ90t4UQVmnrPcmwsLAWw3IjIiKIjIw0fx4xYoT59zk5OezcuZPNmzdz8OBB4uLi6NevH/PmzSM1NZURI0Y06yq7ublRWFhIeXk5Tk5O5unsmsqhcWhw03d0Oh1OTk6UlZUxYMCAVuOXJCmEsEpbFwJLTk5Gq9U22+fs7HzJ7/z000/MmzePRYsWcc0117B27Vrzvjlz5pCWlsbw4cMvOfT5UkOgW4tPURSLM6dLkhRCWKWtLUl3d/c2TXCRlZXF/PnziYmJISgoiB9++IGcnBxz91lRFHQ6XYuhzyUlJej1elxdXamsrMRkMqHVapsNidbr9ZSUlODu7k59fT3V1dW4uLioxiP3JIUQVmnPYYlnz57l0UcfJSEhgaCgIKAxKS5fvpxz585hNBrZsmULkydPxsPDAwcHB7KysgBIT0/Hx8cHe3t7DAYDGRmNS6ekpaWZh0T7+vqSlpYGQEZGBgaDAXt7e9WYpCUphLBK48vk6vvb6rXXXqO2tpYVK/5vYb2ZM2cyd+5cZs2aRX19PX5+fgQHN06CnJCQQGxsLFVVVYwePZrw8HAA4uLiiI6OZt26dQwcOJBVq1YBsGDBAqKjowkKCqJfv34kJCRYjEmSpBDCau31wnhsbCyxsbGX3Dd79uwWZZ6enqSmprYo9/DwYOPGjS3KXVxcWL9+/WXFJElSCGGVxi61+n5bJklSCGEVGZYohBAqpCUphBAq2vPBTXckSVIIYRXpbgshhArpbgshhCr1JAmSJIUQVzDpbgshhIq2TnBhqyRJCiGsYvnpdufF0hEkSQohrCLdbSGEUGGn0aD2KqSNvybZepL89ddfVb9oaQ42IcQVwtI6Nj01SY4fP948y+/FNBoNx48f79DAhBC2QWPhFSAbz5GtJ8nvv/++M+MQQtgorZ0GrcrDGbV9tsBi+A0NDbz22mtER0dTVVXFhg0bMJlMnRGbEMIGtOeSst2RxQc3K1eupKysjCNHjqAoCp999hnFxcWtTowphLiyaNCodqltPEdabknu37+fFStW4ODgQL9+/Xj99df54osvOiM2IYQN0Ggan2C3tvX4lqROp2u25GKvXr3M69kKIYTlCS46L5aOYDHbjRw5ks2bN2Mymfjll19488038fT07IzYhBA24Ip/cLNkyRKOHj1KaWkps2bNorq6mpiYmM6ITQhhAzRYeHDT1QFayWJL0snJieXLl3dGLEIIG2R5gouW71qreemll9i5cyfQuE72okWL2LdvH8899xy1tbVMmTKFxx9/HIDjx4+zZMkSqqurMRgMxMfHo9PpyM/PJyoqitLSUoYNG0ZCQgKOjo5UVFSwcOFCcnNzcXV1JTExETc3N9V4LLYkS0tLeeKJJ7j55puZOHEiMTExVFRUXFalhRA9V3u+ArRv3z4+//xztm7dSlpaGkePHmXHjh3ExMSQlJRERkYG2dnZ7NmzB4CoqCiWLl3Krl27UBSFlJQUAOLj4wkLCyMzMxMvLy+SkpIASExMxGAwsHPnTmbMmMGyZcssxmQxScbGxjJ48GBSU1PZtGkT/fv3Z+nSpW2vtRCiR9NoNP87fvvS2+XMTO7m5kZ0dDS9evXC3t6e4cOHk5OTw9ChQxk8eDA6nY6QkBAyMzM5c+YMNTU1eHt7AxAaGkpmZiZGo5FDhw7h7+/frBxg9+7dhISEABAcHMzevXsxGo2qMVnsbp85c4Z169aZPz/55JPmiwghRFMybH0/gEJBQQFarbbZPmdnZ5ydnc2fR4wYYf59Tk4OO3fu5N57723WJdbr9RQWFlJUVNSs3M3NjcLCQsrLy3FycjK/hdNUDjT7jk6nw8nJibKyMgYMGNBq/BaTpF6vJzc3l8GDBwNQUFBgsQ8vhLhyNL0PqbYfICwsjJKSkmb7IiIiiIyMbPGdn376iXnz5rFo0SK0Wi05OTnmfYqioNFoaGhoaNZKbSpv+vW3WmvNKorS7BXHS2k1ST700EMAlJWVMW3aNG655Rbs7Oz48ssvue6661RPKoS4cmgsdKmbdiUnJ1+yJXmxrKws5s+fT0xMDEFBQRw8eJDi4mLz/uLiYvR6Pe7u7s3KS0pK0Ov1uLq6UllZiclkQqvVmo+HxkZfSUkJ7u7u1NfXU11dbXFGs1aTZFN//mKTJk1SPaEQ4srS1kl33d3dcXBwUD3X2bNnefTRR1m9ejUTJkwAYMyYMZw8eZJTp04xaNAgduzYwT333IOHhwcODg5kZWUxbtw40tPT8fHxwd7eHoPBQEZGBiEhIaSlpeHj4wM0Pi1PS0vjoYceIiMjA4PBgL29vWpMrSbJu++++5LliqJw6tQp1ZMKIa4kl/dwRs1rr71GbW0tK1asMJfNnDmTFStWEBkZSW1tLb6+vgQEBACQkJBAbGwsVVVVjB49mvDwcADi4uKIjo5m3bp1DBw4kFWrVgGwYMECoqOjCQoKol+/fiQkJFiMyeI9yXfffZeVK1dy4cIFc5mrq6uM3xZCAE0jblpPkpcz4iY2NrbVyXO2bdvWoszT05PU1NQW5R4eHmzcuLFFuYuLC+vXr297QLQhSb788su88cYbrFu3jscee4xPP/2UgoKCy7qIEKLn0qA+qsbWR9xYzPEuLi6MGTOGUaNGUVpaysMPP8yhQ4c6IzYhhA1Qe0fS0utBtsBiktTpdJw7d46hQ4dy+PBhAJl0Vwhh1tMn3bWYJP/85z8zb948Jk2axJYtWwgNDeWaa67pjNiEELZA83+vAV1qs/X+tsV7ktOnTycwMJC+ffuyZcsWjhw5wq233toZsQkhbIDlBze2nSVbTZJvvPFGq19KTk7m/vvv75CAhBC2pWmqNLX9tqzVJPnjjz92ZhwtHExdin0v9RdPbcmJ499y9osXujqMdne+tr6rQ+gwPaluxrqOe45gecSNbafJVpPkc88915lxCCFslB3qDzdsfGJyy/ckhRBClaXp0HpqS1IIIdqirbMA2SpJkkIIq2jtNGiVnvt02+LtgoaGBl599VWefPJJqqqq2LBhg7xMLoQwU1tz21Ir0xZYbEmuXLmSsrIyjhw5AsBnn31GcXFxq4PQhRBXGEujamw8SVpsSe7fv58VK1bg4OCAk5MTr7/+uswAJIQws8PC2G0bz5IWW5I6na7Z9Oa9evUyrx0hhBBX/CtAI0eOZPPmzZhMJn755RfefPNNPD09OyM2IYQN0Npp0Kq0Fnv8g5slS5Zw9OhRSktLmTVrFtXV1cTExHRGbEIIW2BpBiDbzpGWW5JOTk4sX768M2IRQtigK/49yWefffaS5fJ0WwgBbVl327azZJtmJm/aHB0dOXjwYGfEJYSwET190l2LLcmIiIhmnx988EEefvjhDgtICGFb7DSgVW1JdmIwHeCy3+VxcnKiqKioI2IRQtggOyzck+y0SDqGxST5zDPPmGf4UBSFo0ePyvINQggzO42F9yR/R0uyqqqKmTNnsn79egYNGsTixYvJysqiT58+QGMPd/LkyRw/fpwlS5ZQXV2NwWAgPj4enU5Hfn4+UVFRlJaWMmzYMBISEnB0dKSiooKFCxeSm5uLq6sriYmJuLm5qdfPUrBXXXWV+Z7kVVddxV133cXzzz9/+bUWQvRIauvbWJqQ91K+++47Zs2aRU5OjrksOzubTZs2kZ6eTnp6OpMnTwYgKiqKpUuXsmvXLhRFISUlBYD4+HjCwsLIzMzEy8uLpKQkABITEzEYDOzcuZMZM2awbNkyi/FYTJKnT58mIiLCvIWFheHk5HRZlRZC9FxtneCioKCAvLy8ZltFRUWL86WkpBAXF4derwfgwoUL5OfnExMTQ0hICGvWrKGhoYEzZ85QU1ODt7c3AKGhoWRmZmI0Gjl06BD+/v7NygF2795NSEgIAMHBwezduxej0ahaP4vd7e+//x5FUWx+CnYhRMewszBVWtMrQGFhYZSUlDTbFxERQWRkZLOyi1t3JSUljB8/nri4OPr168e8efNITU1lxIgRzbrKbm5uFBYWUl5ejpOTk3n4dFM5QFFRkfk7Op0OJycnysrKGDBgQKvxW0ySbm5uBAUFMWbMGBwdHc3l8p6kEALafk8yOTkZrVbbbJ+zs7PF8w8ePJi1a9eaP8+ZM4e0tDSGDx/erPHW1Ji7VKOutUaeoijN5qa4lFaTZF1dHb169WLs2LGMHTvWYkWEEFcmS0trN+Und3d3HBwuf3G/H374gZycHHP3WVEUdDod7u7uFBcXm48rKSlBr9fj6upKZWUlJpMJrVZLcXGxueuu1+spKSnB3d2d+vp6qqurcXFxUb1+q0nyL3/5C1u3bm3xnqQQQvyWHerToVk7VZqiKCxfvpzx48fTt29ftmzZwt13342HhwcODg5kZWUxbtw40tPT8fHxwd7eHoPBQEZGBiEhIaSlpeHj4wOAr68vaWlpPPTQQ2RkZGAwGLC3t1e9fqtJUlEUqyomhLhydOQjC09PT+bOncusWbOor6/Hz8+P4OBgABISEoiNjaWqqorRo0cTHh4OQFxcHNHR0axbt46BAweyatUqABYsWEB0dDRBQUH069ePhIQEi9dvNUnW1tZy7NixVpPl6NGjL7uyQoieR2enoUGltaj7nS3JTz75xPz72bNnM3v27BbHeHp6kpqa2qLcw8ODjRs3tih3cXFh/fr1lxVHq0kyNzeXyMjISyZJjUbDxx9/fFkXEkL0TBbvSXZaJB2j1SR57bXXkpaW1omhCCFskaUlGnr88g1CCKHKQkvS1rWaJA0GQ2fGIYSwUVfsGjfysrgQoi00Gg0albak2j5bIN1tIYRVtBr1p9tqi4TZAkmSQgirWFrry7ZTpCRJIUQ7UH2Z3MbHpUiSFEJYpXEdG7V7kth0opQkKYSwyhX7dFsIIdrC4pKyaKQlKYS4cllaokFeARJCXNE0qHepbTtFSpIUQlhJWpJCCKFC3pMUQggV2sZ3gFrfb+NpUpKkEMIqFnKkjadISZJCCCtpkAkuhBBClQxLFEKIVthZeHQjM5MLIa5odhbGJdr6sERbj18I0cU0bfjvclVVVREcHExeXh4A+/btIyQkBD8/P1avXm0+7vjx44SGhuLv78+SJUuor68HID8/n9mzZxMQEMDDDz9MdXU1ABUVFcydO5cpU6Ywe/ZsiouLLcYiSdIKiqLw8IP382LivwAwmUw8seBRxo+7nvHjruepxVHm1SaPHj2K/x23cuv4cdxyozdb3tlsPs+mt95g/LjrGXeDJ08seBSj0dgl9enJUt7djM/4/4fvhHEE3HEr33z9lXnfmbxcRo8YSmlJibns6NGjTLnTB98J45h4kzcp725ucc71a1/gTzd6d0b43ZqdxvJ2Ob777jtmzZpFTk4OADU1NcTExJCUlERGRgbZ2dns2bMHgKioKJYuXcquXbtQFIWUlBQA4uPjCQsLIzMzEy8vL5KSkgBITEzEYDCwc+dOZsyYwbJlyyzX7/LCF01++P44UwMnsy3tfXPZluRN/PzjD3xx8Fs+O/A1X3z+Gelb30dRFBYtWsTiJXF8diCL99J2EBu9kBM//8Sxo9k8tyyeHZmfcOjbY5z79VeSXkzsuor1QD/9+AP/WBJNStqH7Nmfxf8siuG+sD8D8G7yRoL9b6fgbL75+Kaf15NL4tizP4stW3fwVHQUJ37+yXzMl/u/YM3qf3V6Xbqr9mxFpqSkEBcXh16vB+Dw4cMMHTqUwYMHo9PpCAkJITMzkzNnzlBTU4O3tzcAoaGhZGZmYjQaOXToEP7+/s3KAXbv3k1ISAgAwcHB7N2712KjRJLk7/Tqy+uY89e/MfXu6eYyU4OJ8+erqa2tpba2lrq6Ohx6O1BbW8uDDz7IpNvvBMDDYxBXu7mRfyaPjA+3MSUohKvd3LCzs+P+vz1IyrvJXVWtHsnBwYHEtRtwdx8IgPfYcRQVFpCXe5qM7dt4L+3DZsebf1633QE0/rz+cLUb+WfOAFBUWMiT/7OA+GUrOrci3VTTe5JqG0BBQQF5eXnNtoqKihbnW7ZsWbOFCIuKinBzczN/1uv1FBYWtih3c3OjsLCQ8vJynJyc0Ol0zcovPpdOp8PJyYmysjLV+smDm9/p+VVrAPj044/MZWH33kf6B6n894ghmOrrue2OyUwJbPxXa9q0aebj3nz9FSorKzHcNJ4P3n+PIUOGmvf90WMQ+fl5nVOJK8SQof/FkKH/BTS2EmMXLyQgMIRBg4fw9jvvtTi+d+/ezX5eb73+ClVVlRhuuhmTycTcB+YQ9+wK7HX2nVSD7k2r0aCojd3+331hYWGU/OaWBkBERASRkZGq529oaGg2NlxRFDQaTavlTb9eKoaLKYqCnZ16W7FDk2RVVRUzZ85k/fr1DBo0qCMv1S38c/nT/OFqN348mc+FCxe4d2YoL72wiogFT5iPWZ3wTzYkvch7aR/Sp08flEv8oLVabVeE3+NVV1cTMe9vnDmTy3tbP7T8BSDxXyt5OelFUtJ20KdPH/4RG80tf7qV226/k8/37ungiG2DxsIrQE1d7uTk5Bb/bzs7O1s8v7u7e7MHLMXFxej1+hblJSUl6PV6XF1dqaysxGQyodVqzcdDYyu0pKQEd3d36uvrqa6uxsXFRfX6Hdbdvvjm65Vge3oa94b/lV69etG/f39mhYXz2d7dANTV1fG3+2bz/ntb+Penn3P9DWMAGDR4MAVnz5rPUXA2nz/+sef/g9LZ8nJPM+WOW9FqtaRn/If+Fv5i1NXV8eBf7+WD994l85PP8Lq+8eeV8s5mdmzbiu+EcTwWMY+ckyfwnTCuE2rQfbW1u+3u7s6gQYOabW1JkmPGjOHkyZOcOnUKk8nEjh078PHxwcPDAwcHB7KysgBIT0/Hx8cHe3t7DAYDGRkZAKSlpeHj4wOAr68vaWlpAGRkZGAwGLC3V+8RdFiSvPjm65VgjPdYtn6QCoDRaGRnxnZuvOlmAJ566ikqKyvY9cln5q4fQEBgCDsztlNcVISiKLz5+qsEhdzVFeH3WJWVldw15U6C77qbV9/aTJ8+fSx+p+nntfPj5j+vYydy2Xvga/bszyLxpQ3817Dh7Nmf1YHRd3+aNmzWcHBwYMWKFURGRhIYGMg111xDQEAAAAkJCTz33HMEBARw/vx5wsPDAYiLiyMlJYXAwEC++uorHnvsMQAWLFjAt99+S1BQEMnJySxdutTi9Tusu92WR+s9zfJ//ouoJ+Zz09jRaLVafCbdzvzHozj45X4+/vhjrh0xkoA7fMzH/+OZ5dwx2Z9F0bHcFTiZ+noj4ww3seCJRV1Yi57n1Q1J5J4+xYfb0/hwe5q5fOuOf+P6hz+0OP7Q//68ho8YSeCd//fzinvmOW6/068zQrYpluaTVB+z2LpPPvnE/PsJEyawbdu2Fsd4enqSmpraotzDw4ONGze2KHdxcWH9+vWXFYdGaXqRr4PcfvvtvP32222+J1lbW0t2dnZHhiTEFcvLywsHB4d2OVfT31VjvyGgVemymozYV55u12t3pm77dHvI8FHY97K9P9DWnDj+LcNHeXd1GO2uoWP/je0yJ7//jmGeY7o6jHZjrKsj75fjHXJuSw9ubH2ytG6bJIUQtkHTs3OkJEkhhHVsPAda1OFJ8rc3X4UQPZC0JIUQonVyT1IIIVRYWuNGse0cKUlSCGGlnt2QlCQphLCO5SnRNDa9zI0kSSGEVSx1t9HY9lpgkiSFEFZpj/HZ3ZkkSSGEVTpq7HZ3IUlSCGGVtnS3bZkkSSGEVaS7LYQQlvTgLClJUghhNbVXgGz5yTZIkhRCWMni2to23sqUJCmEsI6MuBFCiNZZGnGjPhqn+5MkKYSwiqVXgGz8NUlJkkII6/Tw3rYkSSGEdSyNuFEdjXMJc+bMoaysDJ2uMT09/fTTVFdX89xzz1FbW8uUKVN4/PHHATh+/DhLliyhuroag8FAfHw8Op2O/Px8oqKiKC0tZdiwYSQkJODo6Pi76tdh624LIa4MTd1tta2tFEUhJyeH9PR083bdddcRExNDUlISGRkZZGdns2fPHgCioqJYunQpu3btQlEUUlJSAIiPjycsLIzMzEy8vLxISkr63fWTJCmEsJpGZbscv/zyCwAPPPAAd911F5s2beLw4cMMHTqUwYMHo9PpCAkJITMzkzNnzlBTU4O3tzcAoaGhZGZmYjQaOXToEP7+/s3Kfy/pbgshrNPGm5IFBQVotdpmu5ydnXF2djZ/rqioYMKECTz11FMYjUbCw8P5+9//jpubm/kYvV5PYWEhRUVFzcrd3NwoLCykvLwcJycnc3e9qfz3kiQphLBKW18BCgsLo6SkpNm+iIgIIiMjzZ/Hjh3L2LFjzZ+nT5/OmjVrGDdunLlMURQ0Gg0NDQ3N7nc2lTf92iwGKx6xS5IUQljF0oibpjVukpOTL9mS/K2vvvoKo9HIhAkTGr+rKHh4eFBcXGw+pri4GL1ej7u7e7PykpIS9Ho9rq6uVFZWYjKZ0Gq15uN/d/1+9zeFEKJJG25Kuru7M2jQoGbbxUmysrKSlStXUltbS1VVFVu3buWJJ57g5MmTnDp1CpPJxI4dO/Dx8cHDwwMHBweysrIASE9Px8fHB3t7ewwGAxkZGQCkpaXh4+Pzu6smLUkhhFXac8TNbbfdxnfffce0adNoaGggLCyMsWPHsmLFCiIjI6mtrcXX15eAgAAAEhISiI2NpaqqitGjRxMeHg5AXFwc0dHRrFu3joEDB7Jq1arfXz9FUbrVJB21tbVkZ2czZPgo7Hs5dHU47ebE8W8ZPsq7q8Nodw3d63+fdnPy++8Y5jmmq8NoN8a6OvJ+OY6XlxcODu3z96rp76rroJFo7Xu1epzJWEdZ3o/teu3OJC1JIYRVZMSNEEKokLHbQgihRhYCE0KI1kl3WwghVEh3WwghVMiku0IIoaaH97clSQohrGJpWKLqImE2QJKkEMIq0t0WQgg10t0WQojW9fAcKUlSCGE9W3/NR40kSSGEVRof3LSeJW39wY3MJymEECqkJSmEsIqMuBFCCBXyCpAQQqiQlqQQQqiQJCmEECqkuy2EEGostCRtPEdKkhRCWEdG3AghhJoeniW7XZJsWuG23ljXxZG0P2NdbVeH0O4aeuaKskDjMqw9RdPfp45YQdpkrKdepb9tMta3+zU7U7dLkkajEYD80ye6OJL2d/rE8a4OQVyGvF963s/LaDTSu3fvdjmXVqtFq9WSc+KHNh9rizRKR/zTYoWGhgaqq6uxt7dXX4FNCNFmiqJgNBpxdHTEzq79RiPX19djMpksHqfVatHpul2brE26XZIUQojuRCa4EEIIFZIkhRBChSRJIYRQIUlSCCFUSJIUQggVkiSFEEKFJEkhhFAhSbITbN++ncDAQPz8/Ni8eXNXhyMsqKqqIjg4mLy8vK4ORXQDkiQ7WGFhIatXryY5OZm0tDS2bNnCzz//3NVhiVZ89913zJo1i5ycnK4ORXQTkiQ72L59+xg/fjwuLi707dsXf39/MjMzuzos0YqUlBTi4uLQ6/VdHYroJmxzMKUNKSoqws3NzfxZr9dz+PDhLoxIqFm2bFlXhyC6GWlJdrCGhoZmE3UoiiITdwhhQyRJdjB3d3eKi4vNn4uLi6UrJ4QNkSTZwW655Rb2799PWVkZFy5c4N///jc+Pj5dHZYQoo3knmQHGzBgAI8//jjh4eEYjUamT5/ODTfc0NVhCSHaSOaTFEIIFdLdFkIIFZIkhRBChSRJIYRQIUlSCCFUSJIUQggVkiS7uby8PEaNGsXUqVPN21133UVqaqrV5543bx4ffPABAFOnTqWioqLVYysrKwkPD7/sa2RmZjJnzpwW5V9++SXBwcEWv3/ddddRVlZ2WdeMjo7mtddeu6zvCNEaeU/SBvTu3Zv09HTz58LCQoKDg/Hy8sLT07NdrvHb81/KuXPnOHLkSLtcSwhbIknSBg0YMIChQ4eSk5PDsWPHSE1N5cKFCzg5ObFx40bee+893nnnHRoaGnBxceGpp55i+PDhFBYWEh0dTVFREX/84x8pLS01n/O6665j//79uLq6smHDBrZu3YpOp2Po0KGsWLGCxYsXU1NTw9SpU/nggw/Iyclh2bJl/Prrr5hMJubMmcP06dMBeOGFF9i+fTsuLi4MHTrUYn1OnjzJ008/TXV1NcXFxXh6epKYmIiDgwMAiYmJHDlyhIaGBh577DFuu+02gFbrKUS7UkS3lpubq3h7ezcr+/rrr5Ubb7xRyc/PV95//33lxhtvVCorKxVFUZQvv/xSCQsLU86fP68oiqJ89tlnSkBAgKIoivLII48oq1evVhRFUXJychRvb2/l/fffVxRFUUaOHKmUlpYq//nPfxQ/Pz/l119/VRRFUZYvX64kJSU1i8NoNCqBgYFKdna2oiiKUlFRoUyZMkX55ptvlI8++kgJDAxUKisrFaPRqMydO1e59957W9TrwIEDSlBQkKIoirJixQolLS1NURRFqaurU4KDg5XMzExzXBs2bFAURVF++OEH5aabblJKS0tV6/nkk08qr776qlV/7kI0kZakDWhqwQGYTCauuuoqnn/+eQYOHAg0tgKdnJwA2L17N6dOnWLmzJnm71dUVPDrr7+yb98+nnzySQCGDh3KzTff3OJa+/fvJyAggP79+wOwePFigGazdOfk5HD69GliYmKaxXjs2DFOnDjB5MmTzfHcc889bNy4UbV+UVFRfPHFF7zyyivk5ORQVFTE+fPnzftnzZoFwMiRIxk+fDjffPMNWVlZrdZTiPYkSdIGXHxP8mJ9+/Y1/76hoYGpU6cSFRVl/lxUVET//v3RaDQovxmFqtO1/PFrtdpmU7lVVFS0eKBjMpno169fs5hKSkro168fK1eubHYNrVZrsX5PPPEEJpOJKVOmMGnSJM6ePdvsHHZ2//d8saGhAZ1Op1pPIdqTPN3uYSZOnMiHH35IUVERAO+88w733XcfALfeeitbtmwBID8/ny+//LLF92+55RY++ugjqqqqAHjxxRd588030el0mEwmFEVh2LBhzRL32bNnCQ4OJjs7Gx8fHzIzM6moqKChocHiAyGAzz//nEcffZTAwECgcQkFk8lk3r9161YAjh49yunTpxkzZoxqPYVoT9KS7GEmTpzIgw8+yAMPPIBGo8HJyYmXXnoJjUZDXFwcixcvZsqUKbi7u1/yybivry8///yzuYt77bXX8swzz9CnTx9uuOEGgoKC2Lx5M0lJSSxbtoxXX32V+vp6FixYwLhx4wD44YcfuOeee3B2dsbT05Py8nLVmB9//HEeffRR+vbti5OTEzfeeCOnT58278/NzWXatGloNBpWrVqFi4uLaj2FaE8yC5AQQqiQ7rYQQqiQJCmEECokSQohhApJkkIIoUKSpBBCqJAkKYQQKiRJCiGECkmSQgih4v8Dmmzs5S2P+PoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_cm = confusion_matrix(Y_test, dt_predict)\n",
    "plot_confusion_matrix(dt_cm, [0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.9459445919543109\n",
      "Precision: 0.8627132412672623\n",
      "Recall: 0.5385395537525355\n",
      "F1 score: 0.6631283172026224\n"
     ]
    }
   ],
   "source": [
    "acdt1 = accuracy_score(Y_test, dt_predict)\n",
    "print('Accuracy score:', acdt1)\n",
    "print('Precision:', precision_score(Y_test, dt_predict))\n",
    "print('Recall:', recall_score(Y_test, dt_predict))\n",
    "f1dt1 = f1_score(Y_test, dt_predict)\n",
    "print('F1 score:',f1dt1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "* Accuraccy increases\n",
    "* Precision can be improved\n",
    "* Recall score is not that good\n",
    "* F1 score needs improvement\n",
    "\n",
    "A possible solution can be training another model.\n",
    "\n",
    "Before training another model I will apply ensemble training method - Random Forest\n",
    "\n",
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 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-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 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-10 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-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 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-10 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-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 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-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 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-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 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-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=25)</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-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=25)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=25)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier(25)\n",
    "rf_clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_predict = rf_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[35733   245]\n",
      " [ 1687  2257]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_cm = confusion_matrix(Y_test, rf_predict)\n",
    "plot_confusion_matrix(rf_cm, [0, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performance Measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9516056309804118\n",
      "Prercision: 0.9020783373301359\n",
      "Recall: 0.572261663286004\n",
      "F1 Score: 0.7002792429413589\n"
     ]
    }
   ],
   "source": [
    "acrf1 = accuracy_score(Y_test, rf_predict)\n",
    "print('Accuracy:',acrf1)\n",
    "print('Prercision:', precision_score(Y_test, rf_predict))\n",
    "print('Recall:', recall_score(Y_test, rf_predict))\n",
    "f1rf1 = f1_score(Y_test, rf_predict)\n",
    "print('F1 Score:', f1rf1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "\n",
    "* Accuracy increases\n",
    "* Precision increases \n",
    "* Recall decreases \n",
    "* F1 score increases\n",
    "\n",
    "## Algorithm2 - Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#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(max_iter=1000, 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(max_iter=1000, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, solver='liblinear')"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(max_iter = 1000, solver = 'liblinear')\n",
    "lr.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[35809   169]\n",
      " [ 1675  2269]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_regression_cm = confusion_matrix(Y_test, y_predict)\n",
    "plot_confusion_matrix(logistic_regression_cm, [0, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9538099293622564\n",
      "Precision: 0.9306808859721083\n",
      "Recall: 0.5753042596348884\n",
      "F1 score: 0.711062362895644\n"
     ]
    }
   ],
   "source": [
    "aclr1 = accuracy_score(Y_test, y_predict)\n",
    "print('Accuracy:', aclr1)\n",
    "print('Precision:', precision_score(Y_test, y_predict))\n",
    "print('Recall:', recall_score(Y_test, y_predict))\n",
    "f1lr1 = f1_score(Y_test, y_predict)\n",
    "print('F1 score:', f1lr1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "\n",
    "* Accuracy is greater than Random Forest\n",
    "* Precision is lower than Random forest\n",
    "* Recall improves greater than Decision Tree\n",
    "* F1 score is also greater than Random Forest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic Models:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decicion Tree</td>\n",
       "      <td>0.945945</td>\n",
       "      <td>0.663128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.951606</td>\n",
       "      <td>0.700279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.953810</td>\n",
       "      <td>0.711062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model Name  Accuracy Score  F1 Score\n",
       "0        Decicion Tree        0.945945  0.663128\n",
       "1        Random Forest        0.951606  0.700279\n",
       "2  Logistic Regression        0.953810  0.711062"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summry1 = pd.DataFrame(columns=[\"Model Name\", \"Accuracy Score\", \"F1 Score\"])\n",
    "e1 = {\"Model Name\": \"Decicion Tree\", \"Accuracy Score\":acdt1, \"F1 Score\":f1dt1}\n",
    "e2 = {\"Model Name\": \"Random Forest\", \"Accuracy Score\":acrf1, \"F1 Score\":f1rf1}\n",
    "e3 = {\"Model Name\": \"Logistic Regression\", \"Accuracy Score\":aclr1, \"F1 Score\":f1lr1}\n",
    "\n",
    "summry1 = summry1.append(e1,ignore_index=True)\n",
    "summry1 = summry1.append(e2,ignore_index=True)\n",
    "summry1 = summry1.append(e3,ignore_index=True)\n",
    "print(\"Basic Models:\")\n",
    "display(summry1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "* Accuracy lower than Logistic Regression\n",
    "* Precision lower than Random Forest\n",
    "* Recall is not upto the mark\n",
    "* F1 score is terrible\n",
    "\n",
    "**Conclusion of algorithms**:\n",
    "1. Most Accurate - Logistic Regression\n",
    "2. Most Precise - Random Forest\n",
    "3. Best Recall - Logistic Regression\n",
    "4. Best F1 score - Logistic Regression\n",
    "\n",
    "It can be concluded that **Logistic Regression** is thus far the best algorithm\n",
    "\n",
    "# Resampling Data\n",
    "\n",
    "## Oversampling minority class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_not_bully, count_bully = df.label.value_counts()\n",
    "\n",
    "not_bully = df[df['label'] == 0]\n",
    "bully = df[df['label'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
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       "                                                     text  label\n",
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       "159684    == Warning ==  There is clearly a protection...      0\n",
       "159685  Alternate option=== Is there perhaps enough ne...      0\n",
       "\n",
       "[144324 rows x 2 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "not_bully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
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       "                                                     text  label\n",
       "82      `  After the wasted bit on his sexuality, I ha...      1\n",
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       "245     BOOBS, BOOBS, BOOBS, BOOBS, BOOBS, BOOBS, BOOB...      1\n",
       "334                      ```Nazi filth`` is impolite    `      1\n",
       "483      Prior to Quickpolls, he would have been perma...      1\n",
       "...                                                   ...    ...\n",
       "159644  im soory since when is google images not allow...      1\n",
       "159645  what ever you fuggin fag Question how did you ...      1\n",
       "159646    == Nice try but no cigar........idiot ==  Th...      1\n",
       "159647  `  == kys ==  {| style=``background-color: #fd...      1\n",
       "159649   shut up mind your own business and go fuck so...      1\n",
       "\n",
       "[15362 rows x 2 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
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   ],
   "source": [
    "bully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "bully_over = bully.sample(count_not_bully, replace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
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       "                                                     text  label\n",
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       "\n",
       "[144324 rows x 2 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bully_over"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_over = pd.concat([not_bully, bully_over], axis = 0)\n",
    "#print(test_over.label.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288648, 2)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_over.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resampled data equally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                                                text  label\n",
       "0  This: :One can make an analogy in mathematical...      0\n",
       "1  `  :Clarification for you  (and Zundark's righ...      0\n",
       "2                          Elected or Electoral? JHK      0\n",
       "3  `This is such a fun entry.   Devotchka  I once...      0\n",
       "4  Please relate the ozone hole to increases in c...      0"
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     "metadata": {},
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   ],
   "source": [
    "test_over.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>If I wanted a kiss I'd call your mother</td>\n",
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       "      <th>36901</th>\n",
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       "      <th>74890</th>\n",
       "      <td>Fuck you, unpopular opinion, you disgusting ...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>64303</th>\n",
       "      <td>Unacceptable?  That I editted a nonsense sent...</td>\n",
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      "text/plain": [
       "                                                    text  label\n",
       "86892   ::::Tell that to the editors she drove off wi...      1\n",
       "82853       If I wanted a kiss I'd call your mother           1\n",
       "36901   stop catching feelings Durova, you dyke! You ...      1\n",
       "74890    Fuck you, unpopular opinion, you disgusting ...      1\n",
       "64303   Unacceptable?  That I editted a nonsense sent...      1"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "test_over.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now performing the following operations on resampled data:\n",
    "1. Data Cleaning\n",
    "2. Vectorization\n",
    "3. Train Test split\n",
    "\n",
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_filter = test_over[\"text\"] != \"\"\n",
    "test_over = test_over[new_filter]\n",
    "test_over = test_over.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing_text(sen):\n",
    "    # Remove punctuations and numbers\n",
    "    sent = re.sub('[^a-zA-Z]', ' ', sen)\n",
    "    # Single character removal\n",
    "    sent = re.sub(r\"\\s+[a-zA-Z]\\s+\", ' ', sent)\n",
    "    # Removing multiple spaces\n",
    "    sent = re.sub(r'\\s+', ' ', sent)\n",
    "    return sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = []\n",
    "new_sentences = list(test_over[\"text\"])\n",
    "for sents in new_sentences:\n",
    "    X_new.append(preprocessing_text(sents))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_stopwords = stopwords.words('english')\n",
    "\n",
    "vectorizer = TfidfVectorizer(stop_words = my_stopwords, use_idf = True)\n",
    "bag_of_words = vectorizer.fit_transform(X_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = bag_of_words\n",
    "y = test_over['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288648, 166830)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288648,)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_old, X_test_old, Y_train_old, Y_test_old = train_test_split(x, y, test_size = 0.20, random_state = 123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance of new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.49922050926727873\n",
      "Test precision score: 0.49922050926727873\n",
      "Test recall score: 0.49922050926727873\n",
      "Test f1 score: 0.6659734257654535\n"
     ]
    }
   ],
   "source": [
    "dummy_new = DummyClassifier(strategy='most_frequent').fit(X_train_old, Y_train_old)\n",
    "dummy_predict_new = dummy_new.predict(X_test_old)\n",
    "\n",
    "print('Test score:', accuracy_score(Y_test_old, dummy_predict_new))\n",
    "print('Test precision score:', precision_score(Y_test_old, dummy_predict_new))\n",
    "print('Test recall score:', precision_score(Y_test_old, dummy_predict_new))\n",
    "print('Test f1 score:', f1_score(Y_test_old, dummy_predict_new))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "\n",
    "Accuracy of resampled data is less than imbalanced data.\n",
    "\n",
    "Precision, recall and f1 score have increased.\n",
    "\n",
    "Now lets apply the algorithms\n",
    "\n",
    "# Algorithm1 - Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=25)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=25)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=25)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_clf_resampled = DecisionTreeClassifier(max_depth = 25)\n",
    "dt_clf_resampled.fit(X_train_old, Y_train_old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_predict_resampled = dt_clf_resampled.predict(X_test_old)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[28115   795]\n",
      " [11042 17778]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_cm_resampled = confusion_matrix(Y_test_old, dt_predict_resampled)\n",
    "plot_confusion_matrix(dt_cm_resampled, [0, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.7949592932617356\n",
      "Precision: 0.9571959295751898\n",
      "Recall: 0.6168632893823733\n",
      "F1 Score: 0.7502373768278014\n"
     ]
    }
   ],
   "source": [
    "acdt2 = accuracy_score(Y_test_old, dt_predict_resampled)\n",
    "print('Accuracy Score:',acdt2)\n",
    "print('Precision:', precision_score(Y_test_old, dt_predict_resampled))\n",
    "print('Recall:',recall_score(Y_test_old, dt_predict_resampled))\n",
    "f1dt2 = f1_score(Y_test_old, dt_predict_resampled)\n",
    "print('F1 Score:', f1dt2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "* Accuracy: Has improved but can be improved\n",
    "* Precision: Very good. Improved drastically\n",
    "* Recall: Better than last time can be improved further\n",
    "* f1 score: Improved.\n",
    "\n",
    "Now applying ensemble techniques on Decision Tree.\n",
    "\n",
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#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>RandomForestClassifier(n_estimators=25)</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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=25)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=25)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf_resampled = RandomForestClassifier(25)\n",
    "rf_clf_resampled.fit(X_train_old, Y_train_old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_predict_resampled = rf_clf_resampled.predict(X_test_old)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[28584   326]\n",
      " [   25 28795]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_cm_resampled = confusion_matrix(Y_test_old, rf_predict_resampled)\n",
    "plot_confusion_matrix(rf_cm_resampled, [0, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.8988047808764941\n",
      "Precision: 0.9483687168279737\n",
      "Recall: 0.8431991672449688\n",
      "F1 Score: 0.8926970832414959\n"
     ]
    }
   ],
   "source": [
    "acrf2 = accuracy_score(Y_test_old, rf_predict_resampled)\n",
    "print('Accuracy Score:', acrf2)\n",
    "print('Precision:', precision_score(Y_test_old, rf_predict_resampled))\n",
    "print('Recall:',recall_score(Y_test_old, rf_predict_resampled))\n",
    "f1rf2 = f1_score(Y_test_old, rf_predict_resampled)\n",
    "print('F1 Score:', f1rf2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "Accuracy , Precision , Recall score and F1 Score are very high\n",
    "\n",
    "# Algorithm2 - Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, 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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, solver='liblinear')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_resampled = LogisticRegression(max_iter=1000, penalty=\"l2\", solver = 'liblinear')\n",
    "lr_resampled.fit(X_train_old, Y_train_old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_resampled = lr_resampled.predict(X_test_old)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[27622  1288]\n",
      " [  995 27825]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_regression_cm_resampled = confusion_matrix(Y_test_old, y_predict_resampled)\n",
    "plot_confusion_matrix(logistic_regression_cm_resampled, [0, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.9604538368266066\n",
      "Precision: 0.955758595816302\n",
      "Recall: 0.9654753643303262\n",
      "F1 Score: 0.9605924084718555\n"
     ]
    }
   ],
   "source": [
    "aclr2 = accuracy_score(Y_test_old, y_predict_resampled)\n",
    "print('Accuracy Score:', aclr2)\n",
    "print('Precision:', precision_score(Y_test_old, y_predict_resampled))\n",
    "print('Recall:',recall_score(Y_test_old, y_predict_resampled))\n",
    "f1lr2 = f1_score(Y_test_old, y_predict_resampled)\n",
    "print('F1 Score:',f1lr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## build the lightgbm model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier()</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "clf = lgb.LGBMClassifier()\n",
    "clf.fit(X_train_old, Y_train_old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_predict_resampled = clf.predict(X_test_old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[27587  1323]\n",
      " [ 4519 24301]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_cm_resampled = confusion_matrix(Y_test_old, clf_predict_resampled)\n",
    "plot_confusion_matrix(clf_cm_resampled, [0, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.8988047808764941\n",
      "Precision: 0.9483687168279737\n",
      "Recall: 0.8431991672449688\n",
      "F1 Score: 0.8926970832414959\n"
     ]
    }
   ],
   "source": [
    "aclgb = accuracy_score(Y_test_old, clf_predict_resampled)\n",
    "print('Accuracy Score:', aclgb)\n",
    "print('Precision:', precision_score(Y_test_old, clf_predict_resampled))\n",
    "print('Recall:',recall_score(Y_test_old, clf_predict_resampled))\n",
    "f1lgb = f1_score(Y_test_old, clf_predict_resampled)\n",
    "print('F1 Score:', f1lgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**:\n",
    "1. Most accurate algorithm: Random Forest\n",
    "2. Most precise algorithm: Random Forest\n",
    "3. Best recall score: Random Forest\n",
    "4. Best f1 score: Random Forest\n",
    "\n",
    "**Random Forest** emerges as the best evaluation technique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Area under ROC curve for ideal classifier is 1. There is considerable evidence that after resampling **Random Forest** is the best classfier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Model:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decicion Tree</td>\n",
       "      <td>0.794959</td>\n",
       "      <td>0.750237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.898805</td>\n",
       "      <td>0.892697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.960454</td>\n",
       "      <td>0.960592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Light GBM</td>\n",
       "      <td>0.898805</td>\n",
       "      <td>0.892697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model Name  Accuracy Score  F1 Score\n",
       "0        Decicion Tree        0.794959  0.750237\n",
       "1        Random Forest        0.898805  0.892697\n",
       "2  Logistic Regression        0.960454  0.960592\n",
       "3            Light GBM        0.898805  0.892697"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summry2 = pd.DataFrame(columns=[\"Model Name\", \"Accuracy Score\", \"F1 Score\"])\n",
    "e1 = {\"Model Name\": \"Decicion Tree\", \"Accuracy Score\":acdt2, \"F1 Score\":f1dt2}\n",
    "e2 = {\"Model Name\": \"Random Forest\", \"Accuracy Score\":acrf2, \"F1 Score\":f1rf2}\n",
    "e3 = {\"Model Name\": \"Logistic Regression\", \"Accuracy Score\":aclr2, \"F1 Score\":f1lr2}\n",
    "e4 = {\"Model Name\": \"Light GBM\", \"Accuracy Score\":aclgb, \"F1 Score\":f1lgb}\n",
    "\n",
    "summry2 = summry2.append(e1,ignore_index=True)\n",
    "summry2 = summry2.append(e2,ignore_index=True)\n",
    "summry2 = summry2.append(e3,ignore_index=True)\n",
    "summry2 = summry2.append(e4,ignore_index=True)\n",
    "print(\"Tuned Model:\")\n",
    "display(summry2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper-parameter Tuning\n",
    "\n",
    "Thus far I have fit the model parameters by training a model with existing data. However, there are some parameters that cannot be directly learnt from the regular training process known as Hyperparameters.\n",
    "\n",
    "Hyperparameters express important properties such as complexity and how fast the algorithm should learn \n",
    "\n",
    "\n",
    "### Grid Search\n",
    "\n",
    "#### Applying Grid Search on Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = bag_of_words_new\n",
    "# y = test_over['label']\n",
    "# # X_train_old, X_test_old, Y_train_old, Y_test_old = train_test_split(x, y, test_size = 0.25, random_state = 27)\n",
    "# X_train, X_test , y_train, y_test = train_test_split(x,y, test_size = 0.20, random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # Grid search cross validation\n",
    "# X_train, X_test , y_train, y_test = train_test_split(x,y, test_size = 0.20, random_state = 123)\n",
    "\n",
    "# from sklearn.model_selection import GridSearchCV\n",
    "# from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# grid={\"C\":np.logspace(-3,3,7), \"penalty\":[\"l1\",\"l2\"]}# l1 lasso l2 ridge\n",
    "# logreg=LogisticRegression()\n",
    "# logreg_cv=GridSearchCV(logreg,grid,cv=10)\n",
    "# logreg_cv.fit(X_train,y_train)\n",
    "\n",
    "# print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
    "# print(\"accuracy :\",logreg_cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, 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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, solver='liblinear')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tuned hpyerparameters :(best parameters)  {'C': 1000.0, 'penalty': 'l2'}\n",
    "# accuracy : 0.981175132456112\n",
    "\n",
    "# Training with best parameters:\n",
    "lr_hp = LogisticRegression(max_iter=1000, penalty=\"l2\", solver = 'liblinear')\n",
    "lr_hp.fit(X_train_old, Y_train_old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hp = lr_hp.predict(X_test_old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.9604538368266066\n",
      "Precision: 0.955758595816302\n",
      "Recall: 0.9654753643303262\n",
      "F1 Score: 0.9605924084718555\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy Score:', accuracy_score(Y_test_old, y_hp))\n",
    "print('Precision:', precision_score(Y_test_old, y_hp))\n",
    "print('Recall:',recall_score(Y_test_old, y_hp))\n",
    "print('F1 Score:', f1_score(Y_test_old, y_hp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.9604538368266066\n",
      "F1 Score: 0.9605924084718555\n"
     ]
    }
   ],
   "source": [
    "# Training with best parameters:\n",
    "lr_hp = LogisticRegression(max_iter=1000, penalty=\"l2\", solver = 'liblinear')\n",
    "lr_hp.fit(X_train_old, Y_train_old)\n",
    "y_hp = lr_hp.predict(X_test_old)\n",
    "print('Accuracy Score:', accuracy_score(Y_test_old, y_hp))\n",
    "print('F1 Score:', f1_score(Y_test_old, y_hp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Randomized Search\n",
    "\n",
    "#### Applying Randomized Search on Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-79-189792b283f4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m }\n\u001b[0;32m     11\u001b[0m \u001b[0mCV_rfc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrfc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mCV_rfc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[0mCV_rfc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    839\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    840\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 841\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    842\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    843\u001b[0m             \u001b[1;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1286\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1287\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1288\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1289\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    793\u001b[0m                               n_splits, n_candidates, n_candidates * n_splits))\n\u001b[0;32m    794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 795\u001b[1;33m                 out = parallel(delayed(_fit_and_score)(clone(base_estimator),\n\u001b[0m\u001b[0;32m    796\u001b[0m                                                        \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m                                                        \u001b[0mtrain\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1039\u001b[0m             \u001b[1;31m# remaining jobs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1040\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1041\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    857\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    860\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 777\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    778\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[0;32m    591\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 593\u001b[1;33m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    594\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    595\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    385\u001b[0m             \u001b[1;31m# parallel_backend contexts set at a higher level,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    386\u001b[0m             \u001b[1;31m# since correctness does not rely on using threads.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 387\u001b[1;33m             trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n\u001b[0m\u001b[0;32m    388\u001b[0m                              \u001b[1;33m**\u001b[0m\u001b[0m_joblib_parallel_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprefer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'threads'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    389\u001b[0m                 delayed(_parallel_build_trees)(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1044\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1045\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    857\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    860\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 777\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    778\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[1;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[0;32m    167\u001b[0m                                                         indices=indices)\n\u001b[0;32m    168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    170\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    896\u001b[0m         \"\"\"\n\u001b[0;32m    897\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 898\u001b[1;33m         super().fit(\n\u001b[0m\u001b[0;32m    899\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    900\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    387\u001b[0m                                            min_impurity_split)\n\u001b[0;32m    388\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 389\u001b[1;33m         \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    390\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Grid Search CV ~ Random Forest:\n",
    "X_train, X_test , y_train, y_test = train_test_split(x,y, test_size = 0.20, random_state = 123)\n",
    "\n",
    "rfc=RandomForestClassifier(random_state=42)\n",
    "param_grid = { \n",
    "    'n_estimators': [200, 500],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [4,5,6,7,8],\n",
    "    'criterion' :['gini', 'entropy']\n",
    "}\n",
    "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)\n",
    "CV_rfc.fit(X_train, y_train)\n",
    "CV_rfc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Best Parameters:\n",
    "rfc1=RandomForestClassifier(random_state=42, max_features='auto', n_estimators= 500, max_depth=8, criterion='gini')\n",
    "rfc1.fit(X_train, y_train)\n",
    "pred=rfc1.predict(X_test)\n",
    "ac_rfcv = accuracy_score(y_test,pred)\n",
    "print(\"Accuracy for Random Forest on CV data: \",round(ac_rfcv*100, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Work in progress..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PROTO TYPE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the text:you are a moron\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>you are a moron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              text\n",
       "0  you are a moron"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "user = pd.DataFrame()\n",
    "t1 = {\"text\":str(input(\"Enter the text:\"))}\n",
    "user = user.append(t1, ignore_index=True)\n",
    "# t2 = {\"text\":str(input(\"Enter the text:\"))}\n",
    "# user = user.append(t2, ignore_index=True)\n",
    "display(user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_over = test_over.append(t1, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_over.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_filter = test_over[\"text\"] != \"\"\n",
    "# test_over = test_over[new_filter]\n",
    "# test_over = test_over.dropna()\n",
    "\n",
    "X_new = []\n",
    "new_sentences = list(test_over[\"text\"])\n",
    "for sents in new_sentences:\n",
    "    X_new.append(preprocessing_text(sents))\n",
    "\n",
    "my_stopwords = stopwords.words('english')\n",
    "\n",
    "vectorizer = TfidfVectorizer(stop_words = my_stopwords, use_idf = True)\n",
    "bag_of_words = vectorizer.fit_transform(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1x166830 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 1 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_data = bag_of_words[-1]\n",
    "u_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are bullying\n"
     ]
    }
   ],
   "source": [
    "OP=lr_hp.predict(u_data)\n",
    "print(\"You are bullying\") if OP==1 else print(\"You are not bullying\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "#(test_over.text.apply(preprocessing_text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bag_of_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OP=rf_clf.predict(u_data)\n",
    "print(\"You are bullying\") if OP==1 else print(\"You are not bullying\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [],
   "source": [
    "OP=lr.predict(u_data)\n",
    "print(\"You are bullying\") if OP==1 else print(\"You are not bullying\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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