Bank-Marketing-Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ### About Dataset\n",
    "   \n",
    "   The data is related with direct marketing campaigns of a Portuguese banking institution. \n",
    "   The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, \n",
    "   in order to access if the product (bank term deposit) would be (or not) subscribed. \n",
    " \n",
    "  ### Features:\n",
    "   #### Bank client data:\n",
    "   * 1 - age (numeric)\n",
    "   * 2 - job : type of job (categorical: \"admin.\",\"unknown\",\"unemployed\",\"management\",\"housemaid\",\"entrepreneur\",\"student\",\"blue-collar\",\"self employed\",\"retired\",\"technician\",\"services\") \n",
    "   * 3 - marital :\n",
    "       marital status (categorical: \"married\",\"divorced\",\"single\"; note: \"divorced\" means divorced or widowed)\n",
    "   * 4 - education (categorical: \"unknown\",\"secondary\",\"primary\",\"tertiary\")\n",
    "   * 5 - default: has credit in default? (binary: \"yes\",\"no\")\n",
    "   * 6 - balance: average yearly balance, in euros (numeric) \n",
    "   * 7 - housing: has housing loan? (binary: \"yes\",\"no\")\n",
    "   * 8 - loan: has personal loan? (binary: \"yes\",\"no\")\n",
    "#### Related with the last contact of the current campaign:\n",
    "   * 9 - contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\") \n",
    "   * 10 - day: last contact day of the month (numeric)\n",
    "   * 11 - month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n",
    "   * 12 - duration: last contact duration, in seconds (numeric)\n",
    "#### Other attributes:\n",
    "   * 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)  \n",
    "   * 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric,\n",
    "   -1 means client was not previously contacted)\n",
    "   * 15 - previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "   * 16 - poutcome: outcome of the previous marketing campaign (categorical: \"unknown\",\"other\",\"failure\",\"success\")\n",
    "\n",
    "  #### Output variable (desired target):\n",
    "   * 17 - y - has the client subscribed a term deposit? (binary: \"yes\",\"no\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn import metrics\n",
    "\n",
    "np.set_printoptions(precision=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4521, 17)\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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>oct</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital  education default  balance housing loan  \\\n",
       "0   30   unemployed  married    primary      no     1787      no   no   \n",
       "1   33     services  married  secondary      no     4789     yes  yes   \n",
       "2   35   management   single   tertiary      no     1350     yes   no   \n",
       "3   30   management  married   tertiary      no     1476     yes  yes   \n",
       "4   59  blue-collar  married  secondary      no        0     yes   no   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  cellular   19   oct        79         1     -1         0  unknown  no  \n",
       "1  cellular   11   may       220         1    339         4  failure  no  \n",
       "2  cellular   16   apr       185         1    330         1  failure  no  \n",
       "3   unknown    3   jun       199         4     -1         0  unknown  no  \n",
       "4   unknown    5   may       226         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('bank/bank.csv', sep= ';')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4521 entries, 0 to 4520\n",
      "Data columns (total 17 columns):\n",
      "age          4521 non-null int64\n",
      "job          4521 non-null object\n",
      "marital      4521 non-null object\n",
      "education    4521 non-null object\n",
      "default      4521 non-null object\n",
      "balance      4521 non-null int64\n",
      "housing      4521 non-null object\n",
      "loan         4521 non-null object\n",
      "contact      4521 non-null object\n",
      "day          4521 non-null int64\n",
      "month        4521 non-null object\n",
      "duration     4521 non-null int64\n",
      "campaign     4521 non-null int64\n",
      "pdays        4521 non-null int64\n",
      "previous     4521 non-null int64\n",
      "poutcome     4521 non-null object\n",
      "y            4521 non-null object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 600.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     4000\n",
       "yes     521\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Target Variable')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['y'])\n",
    "plt.xlabel('Target Variable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4000\n",
       "1     521\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['y'] = np.where(data['y'] == 'yes', 1,0)\n",
    "data['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_copy = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_copy['job'] = data_copy['job'].astype('category').cat.codes\n",
    "data_copy['marital'] = data_copy['marital'].astype('category').cat.codes\n",
    "data_copy['education'] = data_copy['education'].astype('category').cat.codes\n",
    "data_copy['contact'] = data_copy['contact'].astype('category').cat.codes\n",
    "data_copy['poutcome'] = data_copy['poutcome'].astype('category').cat.codes\n",
    "data_copy['month'] = data_copy['month'].astype('category').cat.codes\n",
    "data_copy['default'] = data_copy['default'].astype('category').cat.codes\n",
    "data_copy['loan'] = data_copy['loan'].astype('category').cat.codes\n",
    "data_copy['housing'] = data_copy['housing'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_plotter(independent, dependent, df):\n",
    "    plt.figure(figsize=(21,7))\n",
    "    sns.countplot(df[independent], hue = df[dependent])\n",
    "    plt.title('Count Plot of ' + independent + ' vs target variable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('age', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('job', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('marital', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('education', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNAAAAG5CAYAAABP8cnQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzde9RnZV0//PdHBhxNjNOowKBDQhlUgo7oY48+HnoE+ZVoZYImKLioFv5+tioTf5aKxtPBzLTUHgoU1MBTBhqipJGdFAZF5JAxockIwnBU8xFl+Dx/fPfgl+GePTcw3/uew+u11nfde1/72nt/vntulrfvdV37qu4OAAAAADC3Byx2AQAAAACwJROgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAsE2qqqdV1ZoFutfDq+ozVfWtqnrzPPq/pKr+eZ7XflBVfbSqbquqD97POhfsmWzPqupFVfXJefZ9fVW9d+T4V6vqZzZfdQDAfSFAAwA2qapeWFWrqurbVXVdVX28qv7PBbhvV9V+I8dfUlXrhrq+WVWXVNXP3of7vLuqfu9+lHp8khuTPLS7f/N+XGcuv5jk4Ul27+7nb84LL1Q4s9gh0Gb4971Xuvt93f2shbofADB7AjQAYFRV/UaSP03y/2QS5DwyyTuSHLGYdU35t+5+SJJdkpya5ANVtdsC1/CoJFd0d8/o2v/R3XfM4NpbharaYbFrmK+qWrLYNQAAm58ADQDYqKr64SRvSHJCd/9Nd/93d3+/uz/a3a8c+jywqv60qq4dPn9aVQ8cjt1jquL0qLJhZNDbq+rvhumPn6uqRw/HPjOc8sVhhNkLxmrt7juTnJbkQUl+ZI7v8uNVdUFV3VpVl1fVc4b245O8KMlvD/f56EaexZOr6qJhKuVFVfXk9d8hyTFT599jpFVV7V5V5wyj5C5M8ugNjj+mqs6vqpur6stV9UtD+0lJXpvkBcO1j6uqR1fVp6vqpqq6sareV1W7zPV8p57xPUZfVdV7MglDPzpc+7fn6HPl9Ii+qloy3PNxVbW0qt471HHr8EwePt/7VNUHq+obw/P8TFUduEHN76yqc6vqv5M8fXiGHx2e4UVV9XvTv1sjz3CT/75V9RdV9ccbtJ09hMepqhOr6j+H39Erqup5U/1eUlX/UlVvqaqbk7x+w9/7qnprVV0z1H5xVT1lgxKWVtX7h+t/vqoeu2GNw3UeMFXLTVW1GGExAGyXBGgAwJj/I8nSJB8Z6fOaJE9KclCSxyY5JMnv3It7HJXkpCS7Jlmd5OQk6e6nDscf290P6e73j12kJiN/Xpbk20mu2uDYjkk+muSTSR6W5H8meV9V/Vh3n5LkfUn+aLjPz81x7d2S/F2StyXZPcmfJPm7qtq9u1+ywfl/P0d5b0/y3SR7Jjl2+Ky/9g8lOT/JXw+1HZXkHVV1YHe/LpORf+8frn1qkkry+0n2SvLjSfZJ8vqxZzOX7n5xkq8l+bnh2n80R7czh3rWOzTJjd39+UxCwx8e7r97kl9N8v/di/t8PMn+w3f+fCbPcNoLM/ld2DnJP2fyDP87ySOGex+zvuMmnuEm/32H815QVTVcb9ckz0py1nD8P5M8Zfi+JyV5b1XtOXX+E5NcPdz75Dmuf1Em/33sNtzrg1W1dOr4EUk+OHX8b4ff2Q39ryTPTfJ/ZfLvf8vwXACAGROgAQBjds8kMBmbPviiJG/o7hu6e20mAcOL78U9/qa7Lxzu8b5MgoZ740lVdWuSb2QSnDyvu2/bsE+ShyT5g+7+Xnd/OsnHcvdwaMz/SHJVd7+nu+/o7jOT/HuSucKYu6nJ9MNfSPLaYQTfZUlOn+rys0m+2t3vGq79+SQfzuTdZ/fQ3au7+/zuvn143n+SSaAyC3+d5DlV9eBh/4VDW5J8P5Pfj/26e113X9zd35zvhbv7tO7+VnffnkkA+NiajHhc7+zu/pdhZOH3M3mGr+vu73T3Fbkfz3AO/5SkMwnJMpz3b9197VDrB7v72u6+cwhyr8okKF7v2u7+s+Hec4WI7+3um4bjb07ywCQ/NtXl4u7+UHd/P5N/z6WZ/M5u6FeSvKa710w9t18s00YBYOb8jy0AMOamJHtU1ZKREG2vJP81tf9fQ9t8fWNq+zuZBF33xme7e1MLGuyV5JohjFnvv5LsPc97bPgd7835yzL5m+uaDc5d71FJnjiEgOstSfKeuS5WVQ/LZCTcUzIZnfWATEYibXbdvbqqrkzyc8PUx+ckOXg4/J5MRp+dNUwhfW8m4c73N3XdIVQ8OcnzM3k+6/9d9kiyPvycfl5zPcPp7Xv1DDfU3V1VZ2USqH4mk6DwrpUxq+roJL+RZMXQ9JCh1rlquYeq+s1MRkfulUlQ99CNnd/dd9ZkpdS5/ht6VJKPVNX07/G6TN5N+PWxGgCA+8cINABgzL9lMvXwuSN9rs3k/9iv98ihLZlMuVs/eilV9YjNXeA8XZtkn6qa/tvnkflB6LCpl/9v+B03PH/M2iR3ZBI2TZ+73jVJ/rG7d5n6PKS7f20j1/v9od6f6u6HJvnlTKZ1rvedTD3zTKY8bsx8Fj1YP43ziEwWSlidJMO78E7q7gOSPDmTUWBHz/M+Lxyu9zOZTItcMbTXRs5Z/wyXT7VNP89NPcP5fs9frKpHZTIl88NJMuz/ZZKXZ7IS6i5JLhup9W6G9529KskvJdl1OP+2Dc7fZ6r/A4bveW3u6Zokz97gey7tbuEZAMyYAA0A2KhhKuRrk7y9qp5bVQ+uqh2r6tlVtf5dVmcm+Z2qWlZVewz914/e+WKSA6vqoOGdT6+/lyVcnzkWBLgPPpdJmPfbQ/1Py2T65fp3XG3qPucm+dGqeuHwIv0XJDkgk2mgo7p7XZK/yeTl8g+uqgMy9f6u4Ro/WlUvHmrbsaqeUFU/vpFL7pzJe95uraq9k7xyg+OXJHlhVe1QVYdlfHrnfJ7vWZm8D+zX8oPpm6mqp1fVTw6jyb6ZyTTLdfO8z85Jbs9khOODM3nP20bN8Qwfk7uHdZt6hpv8nt39hUyCur9K8onuXj+a7YcyCcjWDt/7pUl+YuxaG9g5k/BvbZIlVfXaTEagTXt8Vf38MBXz1zN5Np+d41p/keTkIdTL8N/clrIaLgBs0wRoAMCo7v6TTKav/U4mIcA1mYzG+duhy+8lWZXk0iRfyuSF8L83nPsfmazi+feZvDfqbityzsPrk5xek1Uef+l+fIfvZTL98NlJbkzyjiRHd/e/D11OTXLAcJ+/neP8mzIZYfWbmYQ+v53kZ7v7xnmW8PJMpv19I8m7k7xr6trfyiSgOjKTUUffSPKHmbwnay4nJXlcJqOY/i6TYGnaKzIJB2/N5P109/g+U34/k/Dz1qr6rbk6dPd1mYxEfHKS6YUcHpHkQ5mEZ1cm+cdMTXvcxH3OyGQa69eTXJG5w6INvTyT0WrfyGRq5pmZBE3zeYaj/75TzsxkVNxdQeHwvrU3Z/IMrk/yk0n+ZR71rveJTBZM+I9MvvN3c88pn2cneUEmU3FfnOTnNzIV9q1Jzknyyar6VibP7Yn3ohYA4D6q7vmMaAcAgC1HVf1hkkd09zGb7AwAcD8ZgQYAwBavqh5TVT9VE4ckOS7JRxa7LgBg+2AVTgAAtgY7ZzLFcq8kN2QyrfLsRa0IANhumMIJAAAAACNM4QQAAACAEdvkFM499tijV6xYsdhlAAAAALCVuPjii2/s7mVzHdsmA7QVK1Zk1apVi10GAAAAAFuJqvqvjR0zhRMAAAAARgjQAAAAAGCEAA0AAAAARmyT70ADAAAAYOF9//vfz5o1a/Ld7353sUvZqKVLl2b58uXZcccd532OAA0AAACAzWLNmjXZeeeds2LFilTVYpdzD92dm266KWvWrMm+++477/NM4QQAAABgs/jud7+b3XfffYsMz5KkqrL77rvf6xFyAjQAAAAANpstNTxb777UN/MArap2qKovVNXHhv19q+pzVXVVVb2/qnYa2h847K8ejq+Yusarh/YvV9Whs64ZAAAAANZbiBFor0hy5dT+HyZ5S3fvn+SWJMcN7ccluaW790vylqFfquqAJEcmOTDJYUneUVU7LEDdAAAAADDbAK2qlif5H0n+ativJM9I8qGhy+lJnjtsHzHsZzj+zKH/EUnO6u7bu/srSVYnOWSWdQMAAADAerMegfanSX47yZ3D/u5Jbu3uO4b9NUn2Hrb3TnJNkgzHbxv639U+xzl3qarjq2pVVa1au3bt5v4eAAAAACyQ3/3d381b3/rWu/Zf85rX5G1ve9ui1TOzAK2qfjbJDd198XTzHF17E8fGzvlBQ/cp3b2yu1cuW7bsXtcLAAAAwJbhuOOOy+mnTyYq3nnnnTnrrLPyohe9aNHqWTLDa/90kudU1eFJliZ5aCYj0napqiXDKLPlSa4d+q9Jsk+SNVW1JMkPJ7l5qn296XMAAAAA2MasWLEiu+++e77whS/k+uuvz8EHH5zdd9990eqZ2Qi07n51dy/v7hWZLALw6e5+UZJ/SPKLQ7djkpw9bJ8z7Gc4/unu7qH9yGGVzn2T7J/kwlnVDQAAAMDie9nLXpZ3v/vdede73pVjjz12UWtZiFU4N/SqJL9RVaszecfZqUP7qUl2H9p/I8mJSdLdlyf5QJIrkpyX5ITuXrfgVQMAAACwYJ73vOflvPPOy0UXXZRDDz10UWuZ5RTOu3T3BUkuGLavzhyraHb3d5M8fyPnn5zk5NlVCAAAAMCWZKeddsrTn/707LLLLtlhhx0WtZYFCdCYnce/8ozFLgHYhIvfdPRilwAAALDVufPOO/PZz342H/zgBxe7lEWZwgkAAAAAG3XFFVdkv/32yzOf+czsv//+i12OEWgAAAAAbFkOOOCAXH311Ytdxl2MQAMAAACAEQI0AAAAABghQAMAAACAEQI0AAAAABhhEQEAAAAAZuLxrzxjs17v4jcdvck+5513Xl7xildk3bp1ednLXpYTTzzxft/XCDQAAAAAtgnr1q3LCSeckI9//OO54oorcuaZZ+aKK66439cVoAEAAACwTbjwwguz33775Ud+5Eey00475cgjj8zZZ599v68rQAMAAABgm/D1r389++yzz137y5cvz9e//vX7fV0BGgAAAADbhO6+R1tV3e/rCtAAAAAA2CYsX74811xzzV37a9asyV577XW/rytAAwAAAGCb8IQnPCFXXXVVvvKVr+R73/tezjrrrDznOc+539ddshlqAwAAAIB7uPhNRy/o/ZYsWZI///M/z6GHHpp169bl2GOPzYEHHnj/r7sZagMAAACALcLhhx+eww8/fLNe0xROAAAAABghQAMAAACAEQI0AAAAABghQAMAAACAEQI0AAAAABghQAMAAACAEUsWuwAAAAAAtk1fe8NPbtbrPfK1X9pkn2OPPTYf+9jH8rCHPSyXXXbZZrmvEWgAAAAAbDNe8pKX5Lzzztus1xSgAQAAALDNeOpTn5rddttts15TgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBiyWIXAAAAAMC26ZGv/dKC3/Ooo47KBRdckBtvvDHLly/PSSedlOOOO+5+XVOABgAAAMA248wzz9zs1zSFEwAAAABGCNAAAAAAYIQADQAAAIDNprsXu4RR96U+ARoAAAAAm8XSpUtz0003bbEhWnfnpptuytKlS+/VeRYRAAAAAGCzWL58edasWZO1a9cudikbtXTp0ixfvvxenSNAAwAAAGCz2HHHHbPvvvsudhmb3cymcFbV0qq6sKq+WFWXV9VJQ/u7q+orVXXJ8DloaK+qeltVra6qS6vqcVPXOqaqrho+x8yqZgAAAADY0CxHoN2e5Bnd/e2q2jHJP1fVx4djr+zuD23Q/9lJ9h8+T0zyziRPrKrdkrwuycokneTiqjqnu2+ZYe0AAAAAkGSGI9B64tvD7o7DZ+wNckckOWM477NJdqmqPZMcmuT87r55CM3OT3LYrOoGAAAAgGkzXYWzqnaoqkuS3JBJCPa54dDJwzTNt1TVA4e2vZNcM3X6mqFtY+0b3uv4qlpVVau25BfVAQAAALB1mWmA1t3ruvugJMuTHFJVP5Hk1Ukek+QJSXZL8qqhe811iZH2De91Snev7O6Vy5Yt2yz1AwAAAMBMA7T1uvvWJBckOay7rxumad6e5F1JDhm6rUmyz9Rpy5NcO9IOAAAAADM3y1U4l1XVLsP2g5L8TJJ/H95rlqqqJM9NctlwyjlJjh5W43xSktu6+7okn0jyrKratap2TfKsoQ0AAAAAZm6Wq3DumeT0qtohk6DuA939sar6dFUty2Rq5iVJfnXof26Sw5OsTvKdJC9Nku6+uaremOSiod8buvvmGdYNAAAAAHeZWYDW3ZcmOXiO9mdspH8nOWEjx05LctpmLRAAAAAA5mFB3oEGAAAAAFsrARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMCImQVoVbW0qi6sqi9W1eVVddLQvm9Vfa6qrqqq91fVTkP7A4f91cPxFVPXevXQ/uWqOnRWNQMAAADAhmY5Au32JM/o7scmOSjJYVX1pCR/mOQt3b1/kluSHDf0Py7JLd29X5K3DP1SVQckOTLJgUkOS/KOqtphhnUDAAAAwF1mFqD1xLeH3R2HTyd5RpIPDe2nJ3nusH3EsJ/h+DOrqob2s7r79u7+SpLVSQ6ZVd0AAAAAMG2m70Crqh2q6pIkNyQ5P8l/Jrm1u+8YuqxJsvewvXeSa5JkOH5bkt2n2+c4Z/pex1fVqqpatXbt2ll8HQAAAAC2QzMN0Lp7XXcflGR5JqPGfnyubsPP2sixjbVveK9Tuntld69ctmzZfS0ZAAAAAO5mQVbh7O5bk1yQ5ElJdqmqJcOh5UmuHbbXJNknSYbjP5zk5un2Oc4BAAAAgJma5Sqcy6pql2H7QUl+JsmVSf4hyS8O3Y5Jcvawfc6wn+H4p7u7h/Yjh1U6902yf5ILZ1U3AAAAAExbsuku99meSU4fVsx8QJIPdPfHquqKJGdV1e8l+UKSU4f+pyZ5T1WtzmTk2ZFJ0t2XV9UHklyR5I4kJ3T3uhnWDQAAAAB3mVmA1t2XJjl4jvarM8cqmt393STP38i1Tk5y8uauEQAAAAA2ZUHegQYAAAAAWysBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwIiZBWhVtU9V/UNVXVlVl1fVK4b211fV16vqkuFz+NQ5r66q1VX15ao6dKr9sKFtdVWdOKuaAQAAAGBDS2Z47TuS/GZ3f76qdk5ycVWdPxx7S3f/8XTnqjogyZFJDkyyV5K/r6ofHQ6/Pcn/nWRNkouq6pzuvmKGtQMAAABAkhkGaN19XZLrhu1vVdWVSfYeOeWIJGd19+1JvlJVq5McMhxb3d1XJ0lVnTX0FaABAAAAMHML8g60qlqR5OAknxuaXl5Vl1bVaVW169C2d5Jrpk5bM7RtrH3DexxfVauqatXatWs38zcAAAAAYHs18wCtqh6S5MNJfr27v5nknUkeneSgTEaovXl91zlO75H2uzd0n9LdK7t75bJlyzZL7QAAAAAwy3egpap2zCQ8e193/02SdPf1U8f/MsnHht01SfaZOn15kmuH7Y21AwAAAMBMzXIVzkpyapIru/tPptr3nOr2vCSXDdvnJDmyqh5YVfsm2T/JhUkuSrJ/Ve1bVTtlstDAObOqGwAAAACmzXIE2k8neXGSL1XVJUPb/05yVFUdlMk0zK8m+ZUk6e7Lq+oDmSwOcEeSE7p7XZJU1cuTfCLJDklO6+7LZ1g3AAAAANxllqtw/nPmfn/ZuSPnnJzk5Dnazx07DwAAAABmZUFW4QQAAACArZUADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGzCtAq6pPzacNAAAAALY1S8YOVtXSJA9OskdV7ZqkhkMPTbLXjGsDAAAAgEU3GqAl+ZUkv55JWHZxfhCgfTPJ22dYFwAAAABsEUYDtO5+a5K3VtX/7O4/W6CaAAAAAGCLsakRaEmS7v6zqnpykhXT53T3GTOqCwAAAAC2CPMK0KrqPUkeneSSJOuG5k4iQAMAAABgmzavAC3JyiQHdHfPshgAAAAA2NI8YJ79LkvyiFkWAgAAAABbovmOQNsjyRVVdWGS29c3dvdzZlIVAAAAAGwh5hugvX6WRQAAAADAlmq+q3D+46wLAQAAAIAt0XxX4fxWJqtuJslOSXZM8t/d/dBZFQYAAAAAW4L5jkDbeXq/qp6b5JCZVAQAAAAAW5D5rsJ5N939t0meMdanqvapqn+oqiur6vKqesXQvltVnV9VVw0/dx3aq6reVlWrq+rSqnrc1LWOGfpfVVXH3JeaAQAAAOC+mO8Uzp+f2n1AkpX5wZTOjbkjyW929+erauckF1fV+UlekuRT3f0HVXVikhOTvCrJs5PsP3yemOSdSZ5YVbsled3UPS+uqnO6+5Z5fkcAAAAAuM/muwrnz01t35Hkq0mOGDuhu69Lct2w/a2qujLJ3sN5Txu6nZ7kgkwCtCOSnNHdneSzVbVLVe059D2/u29OkiGEOyzJmfOsHQAAAADus/m+A+2l9+cmVbUiycFJPpfk4UO4lu6+rqoeNnTbO8k1U6etGdo21g4AAAAAMzevd6BV1fKq+khV3VBV11fVh6tq+TzPfUiSDyf59e7+5ljXOdp6pH3D+xxfVauqatXatWvnUxoAAAAAbNJ8FxF4V5JzkuyVyeivjw5to6pqx0zCs/d1998MzdcPUzMz/LxhaF+TZJ+p05cnuXak/W66+5TuXtndK5ctWzbPrwUAAAAA4+YboC3r7nd19x3D591JRlOqqqokpya5srv/ZOrQOUnWr6R5TJKzp9qPHlbjfFKS24apnp9I8qyq2nVYsfNZQxsAAAAAzNx8FxG4sap+OT94cf9RSW7axDk/neTFSb5UVZcMbf87yR8k+UBVHZfka0mePxw7N8nhSVYn+U6SlyZJd99cVW9MctHQ7w3rFxQAAAAAgFmbb4B2bJI/T/KWTN4/9q8ZAq6N6e5/ztzvL0uSZ87Rv5OcsJFrnZbktHnWCgAAAACbzXwDtDcmOaa7b0mSqtotyR9nEqwBAAAAwDZrvu9A+6n14VkymVaZ5ODZlAQAAAAAW475BmgPGF7gn+SuEWjzHb0GAAAAAFut+YZgb07yr1X1oUzegfZLSU6eWVUAAAAAsIWYV4DW3WdU1aokz8hkYYCf7+4rZloZAAAAAGwB5j0NcwjMhGYAAAAAbFfm+w40AAAAANguCdAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYMTMArSqOq2qbqiqy6baXl9VX6+qS4bP4VPHXl1Vq6vqy1V16FT7YUPb6qo6cVb1AgAAAMBcZjkC7d1JDpuj/S3dfdDwOTdJquqAJEcmOXA45x1VtUNV7ZDk7UmeneSAJEcNfQEAAABgQSyZ1YW7+zNVtWKe3Y9IclZ3357kK1W1Oskhw7HV3X11klTVWUPfKzZzuQAAAAAwp8V4B9rLq+rSYYrnrkPb3kmumeqzZmjbWPs9VNXxVbWqqlatXbt2FnUDAAAAsB1a6ADtnUkeneSgJNclefPQXnP07ZH2ezZ2n9LdK7t75bJlyzZHrQAAAAAwuymcc+nu69dvV9VfJvnYsLsmyT5TXZcnuXbY3lg7AAAAAMzcgo5Aq6o9p3afl2T9Cp3nJDmyqh5YVfsm2T/JhUkuSrJ/Ve1bVTtlstDAOQtZMwAAAADbt5mNQKuqM5M8LckeVbUmyeuSPK2qDspkGuZXk/xKknT35VX1gUwWB7gjyQndvW64zsuTfCLJDklO6+7LZ1UzAAAAAGxolqtwHjVH86kj/U9OcvIc7ecmOXczlgYAAAAA87YYq3ACAAAAwFZDgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAACUSfb4AAAu4SURBVAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBiZgFaVZ1WVTdU1WVTbbtV1flVddXwc9ehvarqbVW1uqourarHTZ1zzND/qqo6Zlb1AgAAAMBcZjkC7d1JDtug7cQkn+ru/ZN8athPkmcn2X/4HJ/knckkcEvyuiRPTHJIktetD90AAAAAYCHMLEDr7s8kuXmD5iOSnD5sn57kuVPtZ/TEZ5PsUlV7Jjk0yfndfXN335Lk/NwzlAMAAACAmVnod6A9vLuvS5Lh58OG9r2TXDPVb83QtrH2e6iq46tqVVWtWrt27WYvHAAAAIDt05ayiEDN0dYj7fds7D6lu1d298ply5Zt1uIAAAAA2H4tdIB2/TA1M8PPG4b2NUn2meq3PMm1I+0AAAAAsCAWOkA7J8n6lTSPSXL2VPvRw2qcT0py2zDF8xNJnlVVuw6LBzxraAMAAACABbFkVheuqjOTPC3JHlW1JpPVNP8gyQeq6rgkX0vy/KH7uUkOT7I6yXeSvDRJuvvmqnpjkouGfm/o7g0XJgAAAACAmZlZgNbdR23k0DPn6NtJTtjIdU5LctpmLA0AAAAA5m1LWUQAAAAAALZIAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARixKgVdVXq+pLVXVJVa0a2narqvOr6qrh565De1XV26pqdVVdWlWPW4yaAQAAANg+LeYItKd390HdvXLYPzHJp7p7/ySfGvaT5NlJ9h8+xyd554JXCgAAAMB2a8liFzDliCRPG7ZPT3JBklcN7Wd0dyf5bFXtUlV7dvd1i1IlwL30tTf85GKXAMzDI1/7pcUuAQCALdRijUDrJJ+sqour6vih7eHrQ7Hh58OG9r2TXDN17pqh7W6q6viqWlVVq9auXTvD0gEAAADYnizWCLSf7u5rq+phSc6vqn8f6VtztPU9GrpPSXJKkqxcufIexwEAAADgvliUEWjdfe3w84YkH0lySJLrq2rPJBl+3jB0X5Nkn6nTlye5duGqBQAAAGB7tuABWlX9UFXtvH47ybOSXJbknCTHDN2OSXL2sH1OkqOH1TiflOQ27z8DAAAAYKEsxhTOhyf5SFWtv/9fd/d5VXVRkg9U1XFJvpbk+UP/c5McnmR1ku8keenClwwAAADA9mrBA7TuvjrJY+dovynJM+do7yQnLEBpAAAAAHAPi7UKJwAAAABsFQRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBiyWIXAAAAs/D4V56x2CUAm3Dxm45e7BIA5sUINAAAAAAYIUADAAAAgBFbTYBWVYdV1ZeranVVnbjY9QAAAACwfdgqArSq2iHJ25M8O8kBSY6qqgMWtyoAAAAAtgdbyyIChyRZ3d1XJ0lVnZXkiCRXLGpVAAAA3Gdfe8NPLnYJwDw88rVfWuwSFt3WEqDtneSaqf01SZ443aGqjk9y/LD77ar68gLVBjDqUckeSW5c7DqATXhdLXYFANsdfyfBVmL7+TvpURs7sLUEaHP9S/XddrpPSXLKwpQDMH9Vtaq7Vy52HQAAWxp/JwFbi63iHWiZjDjbZ2p/eZJrF6kWAAAAALYjW0uAdlGS/atq36raKcmRSc5Z5JoAAAAA2A5sFVM4u/uOqnp5kk8k2SHJad19+SKXBTBfppcDAMzN30nAVqG6e9O9AAAAAGA7tbVM4QQAAACARSFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjSAzaiqVlTVlVX1l1V1eVV9sqoeVFUHVdVnq+rSqvpIVe262LUCAMxaVb2xql4xtX9yVf2vqnplVV00/G100nDsh6rq76rqi1V1WVW9YPEqB7g7ARrA5rd/krd394FJbk3yC0nOSPKq7v6pJF9K8rpFrA8AYKGcmuSYJKmqByQ5Msn1mfy9dEiSg5I8vqqemuSwJNd292O7+yeSnLc4JQPckwANYPP7SndfMmxfnOTRSXbp7n8c2k5P8tRFqQwAYAF191eT3FRVByd5VpIvJHnC1Pbnkzwmk0DtS0l+pqr+sKqe0t23LU7VAPe0ZLELANgG3T61vS7JLotVCADAFuCvkrwkySOSnJbkmUl+v7v/3w07VtXjkxye5Per6pPd/YaFLBRgY4xAA5i925LcUlVPGfZfnOQfR/oDAGxLPpLJ9MwnJPnE8Dm2qh6SJFW1d1U9rKr2SvKd7n5vkj9O8rjFKhhgQ0agASyMY5L8RVU9OMnVSV66yPUAACyI7v5eVf1Dklu7e12ST1bVjyf5t6pKkm8n+eUk+yV5U1XdmeT7SX5tsWoG2FB192LXAAAAwDZqWDzg80me391XLXY9APeFKZwAAADMRFUdkGR1kk8Jz4CtmRFoAAAAADDCCDQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwDYSlXV66vqt0aOL6uqz1XVF6rqKffh+i+pqj8ftp87vAwcAGC7I0ADANh2PTPJv3f3wd39T/fzWs9NIkADALZLAjQAgK1IVb2mqr5cVX+f5MeGtkdX1XlVdXFV/VNVPaaqDkryR0kOr6pLqupBVfXOqlpVVZdX1UlT1/xqVe0xbK+sqgs2uOeTkzwnyZuGaz16ob4vAMCWYMliFwAAwPxU1eOTHJnk4Ez+jvt8kouTnJLkV7v7qqp6YpJ3dPczquq1SVZ298uH81/T3TdX1Q5JPlVVP9Xdl27qvt39r1V1TpKPdfeHZvT1AAC2WAI0AICtx1OSfKS7v5MkQ6i1NMmTk3ywqtb3e+BGzv+lqjo+k78B98xkSuYmAzQAgO2dAA0AYOvSG+w/IMmt3X3Q2ElVtW+S30ryhO6+parenUn4liR35Aev9lg6x+kAANs170ADANh6fCbJ84b3me2c5OeSfCfJV6rq+UlSE4+d49yHJvnvJLdV1cOTPHvq2FeTPH7Y/oWN3PtbSXa+/18BAGDrI0ADANhKdPfnk7w/ySVJPpxk/cqaL0pyXFV9McnlSY6Y49wvJvnCcPy0JP8ydfikJG+tqn9Ksm4jtz8rySur6gsWEQAAtjfVveEsAAAAAABgPSPQAADg/2/HDgQAAAAABPlbbzBBYQQAMAQaAAAAAAyBBgAAAABDoAEAAADAEGgAAAAAMAQaAAAAAAyBBgAAAAAjCVhFH6s81IAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('default', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('housing', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNAAAAG5CAYAAABP8cnQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3da7RlVXkn/P8jxUUjyq00QKFFlCRiLqAlmpuvt1eU2KLppMWYgIIh6Rc7+sZhYpKO15BLG2M0MRpsiZAoNGqMFUWUmKhtdxQKJVxjUyKRAoTiqsaAUj79Ya/Sw+HU4kDVPvtU1e83xh5nrbnmWutZ+5wPZ/zHnGtWdwcAAAAAWNj9Zl0AAAAAACxnAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwB2SlX1pKrasET3emhVfaqqvlZVb1zg+Luq6neXoha2TlU9rKq+XlW7LKLv6N+Y3zsAbD8EaADAVquqn6+qdUOwcF1VfaSqfnIJ7ttV9ciR4y+sqk1DXV+tqgur6ln34T5bG3ScmOTGJA/q7pdvxXWWnVmHQMPv+NNLdb/u/nJ3P7C7Ny3VPQGA2ROgAQBbpap+LcmfJPm9JA9N8rAkf57k6FnWNcc/dfcDk+yV5J1JzqqqfZa4hocnuay7e4nvu+xV1YpZ17BY21OtAMC2JUADAO6zqnpwktclOam7/6a7/627v9Xdf9fdrxj67F5Vf1JV1w6fP6mq3Ydjdxs9NHdU2TC66a1V9eFh+uNnq+oRw7FPDaf88zDC7HljtXb3t5OcmuT+Sb5vgWd5VFV9oqpurapLq+rZQ/uJSV6Q5NeH+/zdFr6LH6+q86vqtuHnj29+hiTHzTn/aYv4Xn+pqtZX1c1VtbaqDphz7M1VdfUwou6CqvqpOcdeU1VnVdXpw/d1aVWt2cI93l5VfzSv7YNDIJqq+o2quma4zheq6qkLXGPB76aqXllVXxzOvayqnjvnnBdW1f+qqjdV1c1JXlNVu1TVG6vqxqr6UlW9ZPg7WDGc8+CqeucwuvGaqvrd4ZxHJXl7kh8b7n/rAjUeU1Xr5rX9/1W1dtj+6ar6/PB9Xl1Vr5nTb/VQxwlV9eUk/zCnbXNtL6qqy4dnvbKqfnmBGn5reLarquoFC/0+hn7PGkZJ3lpV/7uqfmRLfQGApSVAAwC2xo8l2SPJB0b6/HaSJyQ5LMmPJjkiyX+9F/d4fpLXJtk7yfokJydJdz9xOP6jw5S6/zF2kSHweHGSrye5Yt6xXZP8XZKPJXlIkv+S5N1V9QPdfUqSdyf5b8N9/sMC194nyYeTvCXJvkn+OMmHq2rf7n7hvPP//h7qfEqS30/yn5Lsn+Rfk5w5p8v5mXyX+yR5T5L3VtUec44/e+i/V5K1Sf5sC7d6T5LnVVUN9907ydOTnFlVP5DkJUke1917JjkyyVXzLzDy3XwxyU8leXAmv7u/rqr955z6+CRXZvJdn5zkl5I8c3iuxyR5zrxbnZbkziSPTHL4UOeLu/vyJL+SYZRhd++1wHOuTfIDVXXInLafH54/Sf4tybGZfF8/neQ/V9X8+/8/SR41fA/z3ZDkWUkelORFSd5UVY+Zc/x7k+yX5MBMgtRThu/3LoZzTk3yy5n8Df1FkrU1hM0AwGwJ0ACArbFvkhu7+86RPi9I8rruvqG7N2YSqPzivbjH33T3ecM93p1JyHJvPGEYmfSVTMK453b3bfP7JHlgkj/o7m929z8k+dDQfzF+OskV3f1X3X1nd5+R5F+S3C1sW4QXJDm1uz/X3Xck+c1MRlitTpLu/uvuvmm4zxuT7J5kbiDz6e4+e3hH119lElou5H8m6UyCriT52UyCqGuTbBque2hV7drdV3X3Fxf7AN393u6+tru/PQSbV2QSnG52bXf/6fAM/55JWPjm7t7Q3bck+YPNHavqoZmEay8bRjjekORNSY5ZZC3fSPLBDL/LIUj7wUyCtXT3J7r74qHWi5KckUlgNtdrhnv/+wLX/3B3f7EnPplJCPtT87r9TnffMRz/8PC88/1Skr/o7s9296buPi3JHZn8bQIAMyZAAwC2xk1J9qvxd0MdkMkoqs3+dWhbrK/M2f5GJkHXvfGZ7t6ru/fr7idsYQTYAUmuHqZ5zq3zwEXeY/4z3tvzt3it7v56Jt/zgUlSVS8fpgzeNgSDD85khNNm87+vPRb6/QzvYzsz3w0Jfz6TgDLdvT7Jy5K8JskNVXXm3Gmk96Sqjp0zFfHWJD80r8arF3jmq7dw/OFJdk1y3Zzr/UUmo9cW6z2563P+7RCspaoeX1X/WFUbq+q2TEa07Tfv/Pn1fkdVPbOqPjNMt701yVHzzr+lu/9tzv6W/v4fnuTlm59xuNZBW+gLACwxARoAsDX+KcntufuUu7muzSQc2OxhQ1symT73gM0Hqup7t3WBi3RtkoOqau7/Rg9Lcs2wfU8v/5//jPPPv7e1fOdaVfU9mYz0u2Z439lvZDKCae9hyuJtSeo+3CeZjLb62ap6eCbTKt+/+UB3v6e7f3KopZP84RaucZfvZrjWOzKZArrvUOMl82qc/31el2TVnP2D5mxfnclIrP2GIHSv7n5Qdz96C9dayMcyCXoPyyRIe8+cY+/JZDTaQd394EzeqTb/+1zwHsP0yvcn+aMkDx2e9ex55+89/A43m/v3P9fVSU6e84x7dfcDhtGMAMCMCdAAgPtsmAr5qiRvrarnVNUDqmrXYVTOfxu6nZHkv1bVyqrab+j/18Oxf07y6Ko6bHiP12vuZQnXZ4EFAe6Dz2YS5v36UP+TMpl+ufndY/d0n7OTfH9V/XxVrajJggaHZjIN9N56T5IXDd/J7pmsbvrZ7r4qyZ6ZvAtsY5IVVfWqTN69dZ909+eHa/33JB/t7luTpKp+oKqeMtz/9iT/nsm0zoXM/26+J5PAaeNwrRdlMgJtzFlJXlpVB1bVXpmEhJtrvC6TAOyNVfWgqrpfVT2iqjZPs7w+yaqq2m3kOe9M8r4kb8jk3XHnzjm8Z5Kbu/v2qjoikxFqi7VbJlNdNya5s6qemcn72eZ7bVXtNgSgz0ry3gX6vCPJrwwj4qqqvmdY4GDPe1EPADAlAjQAYKt09x8n+bVMFgbYmMlImpck+duhy+8mWZfkoiQXJ/nc0Jbu/j+ZrOL595m8J+suK3IuwmuSnDZMeVvovVKLfYZvZvLy/WcmuTHJnyc5trv/ZejyzkzeB3ZrVf3tAufflEkw8vJMplv+epJndfeN96GWjyf5nUxGNl2X5BH57vu+PprkI0n+TyZTAW/PyPTCRTojydNy11FZu2fyHrIbM5kS+pAkv7WF8+/y3XT3ZUnemMnoxOuT/HCS/3UPNbwjk5DsoiSfzySQvDPfDe2OzSSsuizJLZmEYZsXJfiHJJcm+UpVjX3f7xme873z3tn3/yV5XVV9LZNw96x7qPU7uvtrSX51OOeWTMK3tfO6fWU4dm0mU2R/Zc7f1dxrrcvkPWh/NvRfn+SFi60FAJiumrz+AgAAlodhJNfbu3v+tFgAgJkwAg0AgJmqqvtX1VHD9NcDk7w6yQdmXRcAwGZGoAEAMFNV9YAkn0zyg5m8b+3DSV7a3V+daWEAAAMBGgAAAACMmNoUzqrao6rOq6p/rqpLq+q1Q/u7qupLVXXh8DlsaK+qektVra+qi6rqMXOudVxVXTF8jptWzQAAAAAw34opXvuOJE/p7q9X1a5JPl1VHxmOvaK73zev/zOTHDJ8Hp/kbUkeX1X7ZPIejDWZLIl+QVWt7e5btnTj/fbbr1evXr1tnwYAAACAHdYFF1xwY3evXOjY1AK0nswN/fqwu+vwGZsvenSS04fzPlNVe1XV/kmelOTc7r45Sarq3CTPyGTJ9QWtXr0669at2/qHAAAAAGCnUFX/uqVjU12Fs6p2qaoLk9yQSQj22eHQycM0zTdV1e5D24FJrp5z+oahbUvt8+91YlWtq6p1Gzdu3ObPAgAAAMDOaaoBWndv6u7DkqxKckRV/VCS38xkhaXHJdknyW8M3WuhS4y0z7/XKd29prvXrFy54Gg7AAAAALjXphqgbdbdtyb5RJJndPd1PXFHkr9McsTQbUOSg+actirJtSPtAAAAADB1U3sHWlWtTPKt7r61qu6f5GlJ/rCq9u/u66qqkjwnySXDKWuTvKSqzsxkEYHbhn4fTfJ7VbX30O/pmYxiAwAAAGAZ+da3vpUNGzbk9ttvn3UpW7THHntk1apV2XXXXRd9zjRX4dw/yWlVtUsmI93O6u4PVdU/DOFaJbkwya8M/c9OclSS9Um+keRFSdLdN1fV65OcP/R73eYFBQAAAABYPjZs2JA999wzq1evzmTs1PLS3bnpppuyYcOGHHzwwYs+b5qrcF6U5PAF2p+yhf6d5KQtHDs1yanbtEAAAAAAtqnbb7992YZnSVJV2XfffXNvF6BcknegAQAAALBzWK7h2Wb3pT4BGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAsKz8zu/8Tt785jd/Z/+3f/u385a3vGVm9QjQAAAAAFhWTjjhhJx22mlJkm9/+9s588wz84IXvGBm9ayY2Z0BAAAAYAGrV6/Ovvvum89//vO5/vrrc/jhh2ffffedWT0CNAAAAACWnRe/+MV517vela985Ss5/vjjZ1qLKZwAAAAALDvPfe5zc8455+T888/PkUceOdNajEADAAAAYNnZbbfd8uQnPzl77bVXdtlll5nWIkDbzj32FafPugTgHlzwhmNnXQIAAMB259vf/nY+85nP5L3vfe+sSzGFEwAAAIDl5bLLLssjH/nIPPWpT80hhxwy63KMQAMAAABgeTn00ENz5ZVXzrqM7zACDQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIRFBAAAAACYise+4vRter0L3nDsPfY555xz8tKXvjSbNm3Ki1/84rzyla/c6vsagQYAAADADmHTpk056aST8pGPfCSXXXZZzjjjjFx22WVbfV0BGgAAAAA7hPPOOy+PfOQj833f933Zbbfdcswxx+SDH/zgVl9XgAYAAADADuGaa67JQQcd9J39VatW5Zprrtnq6wrQAAAAANghdPfd2qpqq68rQAMAAABgh7Bq1apcffXV39nfsGFDDjjggK2+rgANAAAAgB3C4x73uFxxxRX50pe+lG9+85s588wz8+xnP3urr7tiG9QGAAAAAHdzwRuOXdL7rVixIn/2Z3+WI488Mps2bcrxxx+fRz/60Vt/3W1QGwAAAAAsC0cddVSOOuqobXpNUzgBAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGrJh1AQAAAADsmL78uh/eptd72Ksuvsc+xx9/fD70oQ/lIQ95SC655JJtcl8j0AAAAADYYbzwhS/MOeecs02vKUADAAAAYIfxxCc+Mfvss882vaYADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYMSKWRcAAAAAwI7pYa+6eMnv+fznPz+f+MQncuONN2bVqlV57WtfmxNOOGGrrilAAwAAAGCHccYZZ2zza5rCCQAAAAAjBGgAAAAAMEKABgAAAMA2092zLmHUfalPgAYAAADANrHHHnvkpptuWrYhWnfnpptuyh577HGvzpvaIgJVtUeSTyXZfbjP+7r71VV1cJIzk+yT5HNJfrG7v1lVuyc5Pcljk9yU5HndfdVwrd9MckKSTUl+tbs/Oq26AQAAALhvVq1alQ0bNmTjxo2zLmWL9thjj6xatepenTPNVTjvSPKU7v56Ve2a5NNV9ZEkv5bkTd19ZlW9PZNg7G3Dz1u6+5FVdUySP0zyvKo6NMkxSR6d5IAkf19V39/dm6ZYOwAAAAD30q677pqDDz541mVsc1ObwtkTXx92dx0+neQpSd43tJ+W5DnD9tHDfobjT62qGtrP7O47uvtLSdYnOWJadQMAAADAXFN9B1pV7VJVFya5Icm5Sb6Y5NbuvnPosiHJgcP2gUmuTpLh+G1J9p3bvsA5c+91YlWtq6p1y3mYIAAAAADbl6kGaN29qbsPS7Iqk1Fjj1qo2/CztnBsS+3z73VKd6/p7jUrV668ryUDAAAAwF0sySqc3X1rkk8keUKSvapq87vXViW5dtjekOSgJBmOPzjJzXPbFzgHAAAAAKZqagFaVa2sqr2G7fsneVqSy5P8Y5KfHbodl+SDw/baYT/D8X/oyZqna5McU1W7Dyt4HpLkvGnVDQAAAABzTXMVzv2TnFZVu2QS1J3V3R+qqsuSnFlVv5vk80neOfR/Z5K/qqr1mYw8OyZJuvvSqjoryWVJ7kxykhU4AQAAAFgqUwvQuvuiJIcv0H5lFlhFs7tvT/JzW7jWyUlO3tY1AgAAAMA9WZJ3oAEAAADA9kqABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMGJqAVpVHVRV/1hVl1fVpVX10qH9NVV1TVVdOHyOmnPOb1bV+qr6QlUdOaf9GUPb+qp65bRqBgAAAID5Vkzx2ncmeXl3f66q9kxyQVWdOxx7U3f/0dzOVXVokmOSPDrJAUn+vqq+fzj81iT/b5INSc6vqrXdfdkUawcAAACAJFMM0Lr7uiTXDdtfq6rLkxw4csrRSc7s7juSfKmq1ic5Yji2vruvTJKqOnPoK0ADAAAAYOqW5B1oVbU6yeFJPjs0vaSqLqqqU6tq76HtwCRXzzltw9C2pXYAAAAAmLqpB2hV9cAk70/ysu7+apK3JXlEksMyGaH2xs1dFzi9R9rn3+fEqlpXVes2bty4TWoHAAAAgKkGaFW1aybh2bu7+2+SpLuv7+5N3f3tJO/Id6dpbkhy0JzTVyW5dqT9Lrr7lO5e091rVq5cue0fBgAAAICd0jRX4awk70xyeXf/8Zz2/ed0e26SS4bttUmOqardq+rgJIckOS/J+UkOqaqDq2q3TBYaWDutugEAAABgrmmuwvkTSX4xycVVdeHQ9ltJnl9Vh2UyDfOqJL+cJN19aVWdlcniAHcmOam7NyVJVb0kyUeT7JLk1O6+dIp1AwAAAMB3THMVzk9n4feXnT1yzslJTl6g/eyx8wAAAABgWpZkFU4AAAAA2F4J0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEZMLUCrqoOq6h+r6vKqurSqXjq071NV51bVFcPPvYf2qqq3VNX6qrqoqh4z51rHDf2vqKrjplUzAAAAAMw3zRFodyZ5eXc/KskTkpxUVYcmeWWSj3f3IUk+PuwnyTOTHDJ8TkzytmQSuCV5dZLHJzkiyas3h24AAAAAMG1TC9C6+7ru/tyw/bUklyc5MMnRSU4bup2W5DnD9tFJTu+JzyTZq6r2T3JkknO7++buviXJuUmeMa26AQAAAGCuJXkHWlWtTnJ4ks8meWh3X5dMQrYkDxm6HZjk6jmnbRjattQOAAAAAFM39QCtqh6Y5P1JXtbdXx3rukBbj7TPv8+JVbWuqtZt3LjxvhULAAAAAPNMNUCrql0zCc/e3d1/MzRfP0zNzPDzhqF9Q5KD5py+Ksm1I+130d2ndPea7l6zcuXKbfsgAAAAAOy0prkKZyV5Z5LLu/uP5xxam2TzSprHJfngnPZjh9U4n5DktmGK50eTPL2q9h4WD3j60AYAAAAAU7diitf+iSS/mOTiqrpwaPutJH+Q5KyqOiHJl5P83HDs7CRHJVmf5BtJXpQk3X1zVb0+yflDv9d1981TrBsAAAAAvmNqAVp3fzoLv78sSZ66QP9OctIWrnVqklO3XXUAAAAAsDhLsgonAAAAAGyvBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMGJRAVpVfXwxbQAAAACwo1kxdrCq9kjygCT7VdXeSWo49KAkB0y5NgAAAACYudEALckvJ3lZJmHZBflugPbVJG+dYl0AAAAAsCyMBmjd/eYkb66q/9Ldf7pENQEAAADAsnFPI9CSJN39p1X140lWzz2nu0+fUl0AAAAAsCwsKkCrqr9K8ogkFybZNDR3EgEaAAAAADu0RQVoSdYkObS7e5rFAAAAAMByc79F9rskyfdOsxAAAAAAWI4WOwJtvySXVdV5Se7Y3Njdz55KVQAAAACwTCw2QHvNNIsAAAAAgOVqsatwfnLahQAAAADAcrTYVTi/lsmqm0myW5Jdk/xbdz9oWoUBAAAAwHKw2BFoe87dr6rnJDliKhUBAAAAwDKy2FU476K7/zbJU7ZxLQAAAACw7Cx2CufPzNm9X5I1+e6UTgAAAADYYS12Fc7/MGf7ziRXJTl6m1cDAAAAAMvMYt+B9qJpFwIAAAAAy9Gi3oFWVauq6gNVdUNVXV9V76+qVdMuDgAAAABmbbGLCPxlkrVJDkhyYJK/G9oAAAAAYIe22ABtZXf/ZXffOXzelWTlFOsCAAAAgGVhsQHajVX1C1W1y/D5hSQ3TbMwAAAAAFgOFhugHZ/kPyX5SpLrkvxsEgsLAAAAALDDW9QqnElen+S47r4lSapqnyR/lEmwBgAAAAA7rMWOQPuRzeFZknT3zUkOn05JAAAAALB8LDZAu19V7b15ZxiBttjRawAAAACw3VpsCPbGJP+7qt6XpDN5H9rJU6sKAAAAAJaJRQVo3X16Va1L8pQkleRnuvuyqVYGAAAAAMvAoqdhDoGZ0AwAAACAncpi34EGAAAAADslARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjJhagFZVp1bVDVV1yZy211TVNVV14fA5as6x36yq9VX1hao6ck77M4a29VX1ymnVCwAAAAALmeYItHclecYC7W/q7sOGz9lJUlWHJjkmyaOHc/68qnapql2SvDXJM5McmuT5Q18AAAAAWBIrpnXh7v5UVa1eZPejk5zZ3Xck+VJVrU9yxHBsfXdfmSRVdebQ97JtXC4AAAAALGgW70B7SVVdNEzx3HtoOzDJ1XP6bBjattR+N1V1YlWtq6p1GzdunEbdAAAAAOyEljpAe1uSRyQ5LMl1Sd44tNcCfXuk/e6N3ad095ruXrNy5cptUSsAAAAATG8K50K6+/rN21X1jiQfGnY3JDloTtdVSa4dtrfUDgAAAABTt6Qj0Kpq/zm7z02yeYXOtUmOqardq+rgJIckOS/J+UkOqaqDq2q3TBYaWLuUNQMAAACwc5vaCLSqOiPJk5LsV1Ubkrw6yZOq6rBMpmFeleSXk6S7L62qszJZHODOJCd196bhOi9J8tEkuyQ5tbsvnVbNAAAAADDfNFfhfP4Cze8c6X9ykpMXaD87ydnbsDQAAAAAWLRZrMIJAAAAANsNARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMAIARoAAAAAjBCgAQAAAMCIqQVoVXVqVd1QVZfMadunqs6tqiuGn3sP7VVVb6mq9VV1UVU9Zs45xw39r6iq46ZVLwAAAAAsZJoj0N6V5Bnz2l6Z5OPdfUiSjw/7SfLMJIcMnxOTvC2ZBG5JXp3k8UmOSPLqzaEbAAAAACyFqQVo3f2pJDfPaz46yWnD9mlJnjOn/fSe+EySvapq/yRHJjm3u2/u7luSnJu7h3IAAAAAMDVL/Q60h3b3dUky/HzI0H5gkqvn9NswtG2p/W6q6sSqWldV6zZu3LjNCwcAAABg57RcFhGoBdp6pP3ujd2ndPea7l6zcuXKbVocAAAAADuvpQ7Qrh+mZmb4ecPQviHJQXP6rUpy7Ug7AAAAACyJpQ7Q1ibZvJLmcUk+OKf92GE1zickuW2Y4vnRJE+vqr2HxQOePrQBAAAAwJJYMa0LV9UZSZ6UZNSfOIQAAAlNSURBVL+q2pDJapp/kOSsqjohyZeT/NzQ/ewkRyVZn+QbSV6UJN19c1W9Psn5Q7/Xdff8hQkAAAAAYGqmFqB19/O3cOipC/TtJCdt4TqnJjl1G5YGAAAAAIu2XBYRAAAAAIBlSYAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACNWzLoAAACYhse+4vRZlwDcgwvecOysSwBYFCPQAAAAAGCEAA0AAAAARgjQAAAAAGCEAA0AAAAARgjQAAAAAGCEAA0AAAAARgjQAAAAAGCEAA0AAAAARgjQAAAAAGCEAA0AAAAARgjQAAAAAGCEAA0AAAAARswkQKuqq6rq4qq6sKrWDW37VNW5VXXF8HPvob2q6i1Vtb6qLqqqx8yiZgAAAAB2TrMcgfbk7j6su9cM+69M8vHuPiTJx4f9JHlmkkOGz4lJ3rbklQIAAACw01pOUziPTnLasH1akufMaT+9Jz6TZK+q2n8WBQIAAACw85lVgNZJPlZVF1TViUPbQ7v7uiQZfj5kaD8wydVzzt0wtN1FVZ1YVeuqat3GjRunWDoAAAAAO5MVM7rvT3T3tVX1kCTnVtW/jPStBdr6bg3dpyQ5JUnWrFlzt+MAAAAAcF/MJEDr7muHnzdU1QeSHJHk+qrav7uvG6Zo3jB035DkoDmnr0py7ZIWDLAVvvy6H551CcAiPOxVF8+6BAAAlqkln8JZVd9TVXtu3k7y9CSXJFmb5Lih23FJPjhsr01y7LAa5xOS3LZ5qicAAAAATNssRqA9NMkHqmrz/d/T3edU1flJzqqqE5J8OcnPDf3PTnJUkvVJvpHkRUtfMgAAAAA7qyUP0Lr7yiQ/ukD7TUmeukB7JzlpCUoDAAAAgLuZ1SqcAAAAALBdEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMWDHrAgAAANg5ffl1PzzrEoBFeNirLp51CTNnBBoAAAAAjNhuArSqekZVfaGq1lfVK2ddDwAAAAA7h+0iQKuqXZK8Nckzkxya5PlVdehsqwIAAABgZ7BdBGhJjkiyvruv7O5vJjkzydEzrgkAAACAncD2sojAgUmunrO/Icnj53aoqhOTnDjsfr2qvrBEtQGMeniyX5IbZ10HcA9eXbOuAGCn4/8k2E7sPP8nPXxLB7aXAG2h31TfZaf7lCSnLE05AItXVeu6e82s6wAAWG78nwRsL7aXKZwbkhw0Z39VkmtnVAsAAAAAO5HtJUA7P8khVXVwVe2W5Jgka2dcEwAAAAA7ge1iCmd331lVL0ny0SS7JDm1uy+dcVkAi2V6OQDAwvyfBGwXqrvvuRcAAAAA7KS2lymcAAAAADATAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAA9iGqmp1VV1eVe+oqkur6mNVdf+qOqyqPlNVF1XVB6pq71nXCgAwbVX1+qp66Zz9k6vqV6vqFVV1/vC/0WuHY99TVR+uqn+uqkuq6nmzqxzgrgRoANveIUne2t2PTnJrkv+Y5PQkv9HdP5Lk4iSvnmF9AABL5Z1JjkuSqrpfkmOSXJ/J/0tHJDksyWOr6olJnpHk2u7+0e7+oSTnzKZkgLsToAFse1/q7guH7QuSPCLJXt39yaHttCRPnEllAABLqLuvSnJTVR2e5OlJPp/kcXO2P5fkBzMJ1C5O8rSq+sOq+qnuvm02VQPc3YpZFwCwA7pjzvamJHvNqhAAgGXgvyd5YZLvTXJqkqcm+f3u/ov5HavqsUmOSvL7VfWx7n7dUhYKsCVGoAFM321Jbqmqnxr2fzHJJ0f6AwDsSD6QyfTMxyX56PA5vqoemCRVdWBVPaSqDkjyje7+6yR/lOQxsyoYYD4j0ACWxnFJ3l5VD0hyZZIXzbgeAIAl0d3frKp/THJrd29K8rGqelSSf6qqJPl6kl9I8sgkb6iqbyf5VpL/PKuaAear7p51DQAAAOyghsUDPpfk57r7ilnXA3BfmMIJAADAVFTVoUnWJ/m48AzYnhmBBgAAAAAjjEADAAAAgBECNAAAAAAYIUADAAAAgBECNACAHURVfX3WNQAA7IgEaAAAAAAwQoAGALCDqYk3VNUlVXVxVT1vaH9gVX28qj43tB89tK+uqsur6h1VdWlVfayq7j/bpwAAWD4EaAAAO56fSXJYkh9N8rQkb6iq/ZPcnuS53f2YJE9O8saqquGcQ5K8tbsfneTWJP9x6csGAFieBGgAADuen0xyRndv6u7rk3wyyeOSVJLfq6qLkvx9kgOTPHQ450vdfeGwfUGS1UtbMgDA8rVi1gUAALDN1RbaX5BkZZLHdve3quqqJHsMx+6Y029TElM4AQAGRqABAOx4PpXkeVW1S1WtTPLEJOcleXCSG4bw7MlJHj7LIgEAthdGoAEA7Hg+kOTHkvxzkk7y6939lap6d5K/q6p1SS5M8i8zrBEAYLtR3T3rGgAAAABg2TKFEwAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABG/F9YTnnqNAUIPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('loan', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('contact', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('day', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNAAAAG5CAYAAABP8cnQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzde5xtdV0//tdbDkjeQOBowkEPKmlgKoJ4ya9fFX+pVILlBSVBwR+VlpZJ8c1v3srS6KZp9vOrJhSCSl7QjCQNTQuVo8hNDb7eOKJyQMBbqMD798deR8dhzjoDZ2b2nJnn8/GYx+z1WZ+19nvvNbP37Nd8PmtVdwcAAAAAmNutpl0AAAAAACxnAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGCFAAwBWtKp6RFVtXKL7unNVfbiqvlVVf74U9zlfS/k8rGZVdVFVPWKefbuq7rmFdc+oqo8saHEAwC0mQAMA5q2qnlZV51bVt6vqq1X1z1X1sCW43y0GDcP6Z1TVDUNd36yq86rqF27B/by5qv5oG0o9LsmVSe7Q3b+zDfvZZlt7zpaohqmGQFW1fnge1izVfXb3/t199lLdHwCwNARoAMC8VNXzk/xVkj9Ocuckd03yN0kOm2ZdM/xnd98uya5J3pjkbVW12xLXcLckF3d3L/H9rkhLGXxtq+2pVgDg5hOgAQBbVVW7JHlZkud09zu6+zvd/YPufk93Hz/0uXVV/VVVXT58/VVV3XpYd5ORSDNHSA0jv15bVf80TH/8WFXdY1j34WGTTw8jzJ4yVmt335jkTUl+Isnd53gsP11VZ1fVNcN0u8cP7cclOTLJ7w73854tPBcPrapPVNW1w/eHbn4MSY6esf2j59j2zVX1N8PIvW9X1Uer6ieH5+rqqvpsVR2wtVq35Tmrqt+pqiuGEYTP3MJjPKKqzp3V9ttVdcZw+9Cquni4369U1Qvmep6T/G2Shww1XDO0/3xVfWoYKXhZVb1kxjabR4wdW1VfTvLBof2oqvpSVV1VVX9QVV/c/PxW1a2q6oSq+r/D+pnB6ebn4ZqhhofMqnHPqvrvmUFrVR1QVVdW1Y5VdY+q+uCw3yur6pSq2nVG3y9W1e9V1flJvlNVa2bVdnBV/edw/L5aVa+pqp1mPVWHVtXnh/2fWFVz/n1eVfeuqrOq6htV9bmqevJc/QCAxSFAAwDm4yFJdk7yzpE+L0zy4CT3T3K/JAcn+d834z6emuSlSe6Y5NIkL0+S7n74sP5+3X277n7r2E5qMhLoWUm+neSSWet2TPKeJO9Pcqckv5nklKq6V3e/PskpSf50uJ9fnGPfuyX5pySvTrJ7kr9I8k9VtXt3P2PW9v+6hRKfnMnzskeS7yX5zySfHJZPH/Y5Wus2PGc/mWSXJHslOTbJa6vqjnPUeEaSe1XVvjPanpbkLcPtNyb51e6+fZL7ZAi6ZuruzyT5tQwjA7t7c/D0nSRHZTJS8OeT/HpVHT5r8/+Z5KeTPKaq9stkpOORSe4yo/7Nnpvk8GGbPZNcneS1w7rNz8OuQw3/OavGyzN5/n951uM8vbt/kKSS/Mmw359OsneSl8yq9anD49i1u6+fte6GJL+dybF9SJJDkjx7Vp8nJDkoyQMyGc15zKz1qarbJjkrk+f/TsN9/k1V7T+7LwCwOARoAMB87J7kyjkCgpmOTPKy7r6iuzdlEuw8/Wbcxzu6++PDfZySSRB3czx4GOX0tUwChid097Wz+yS5XZJXdPf3u/uDSd479J+Pn09ySXf/fXdf392nJvlskpuEbSPe2d0buvu6TALJ67r75O6+Iclbk2wegTafWm/uc/aDTI7RD7r7fZmEjPea3am7v5vk3ZvvawjS7p1JsLZ5P/tV1R26++ru/uR8H3x3n93dF3T3jd19fpJTMwm/ZnrJMMrxv5M8Mcl7uvsj3f39JC9KMnOK7K8meWF3b+zu72UScD2x5j+l8i0zHmclOWJoS3df2t1ndff3hp/pv5ij1ld392VDrbMf64buPmf4Wflikv9vju1f2d3f6O4vZzJFeq6fxV9I8sXu/rthX59M8o/DcwMALAEBGgAwH1cl2WMrocSeSb40Y/lLQ9t8fW3G7e9mEh7dHOd0967dvUd3P3gLI8D2THLZMM1zZp17zdF3LrMf483dPkm+PuP2f8+xvPlxz6fWm/ucXTUrBB3b5ofBUiajst41BGvJZMTWoUm+VFUfmj01ckxVPaiq/q2qNlXVtZmMUttjVrfLZtzec+byUMNVM9bfLck7h2mS1yT5TCYjv+48z5JOz2Sa6Z6ZjFjrJP8+1HqnqjptmKb6zST/sJVaZz/Wn6qq91bV14bt/3gr22/pd+ZuSR60+TEOj/PITEYUAgBLQIAGAMzHfya5LpOpcltyeSYf9De769CWTKbt3Wbziqqa1gf/y5PsPes8U3dN8pXh9tZO/j/7Mc7efiFtrdbF9v5MQtP7ZxKkbZ6+me7+RHcflsl0wncledsW9jHX8/mWTEay7d3du2RynrQa2e6rSdZtXqiqn8hkRORmlyV53BCebv7aubu/soX7//E76r5meKxPziQoPHXGRSD+ZNjHfbv7Dkl+ZSu1zva6TEYo7jts//tzbL/3jNszf2dmuizJh2Y9xtt1969v7fEBAAtDgAYAbNUwFfJFmZwz6/Cqus1wkvXHVdWfDt1OTfK/q2ptVe0x9P+HYd2nk+xfVfevqp1z0/NIbc3XM8cFAW6Bj2US5v3uUP8jMpl+edo87+d9SX6qqp42nDD+KUn2y2Rq5ULbWq1bs03P2TBS7fQkJybZLZNzcKWqdqqqI6tql+E8Yd/MZMTXlmpYN+vE+bdP8o3uvq6qDs4ktBpzepJfrMnFG3bKZGrwzBDqb5O8vKruNtS3tqo2Xxl2U5Ibs/Xn4S2ZnJftlzMjKBxq/XYmFyHYK8nxW9nPbLfP5Pn5dlXdO8lcgdfxVXXHqto7yfMymcY723sz+bl7+vCzsGNVPbAmF2oAAJaAAA0AmJfu/oskz8/kBPibMhkV8xuZjEBKkj9Kcm6S85NckMmJ8f9o2Pa/MrmK579mcmL/H7si5zy8JMlJw/S1W3z1weEcWo9P8rgkV2ZycvqjuvuzQ5c3ZnJur2uq6l1zbH9VJuej+p1MphH+bpJf6O4rb2lN21Dr1rwk2/6cvSXJo5O8fdbUz6cn+eIwLfHXMhmZNZcPJrkoydeqavNz9OwkL6uqb2USsm5p9FqSpLsvyuQCCqdlMhrtW0muyOQCDEnyqkxGtL1/2Oc5SR40bPvdTC6s8NHheXjwFu7mjCT7Jvl6d396RvtLMzm5/7WZXDziHWO1zuEFmQSE30ryfzJ3OPbuJBuSnDfcxxtnd+jubyX5uUzOz3Z5JlN3X5nk1jezHgDgFqofjVAHAIDlrapul+SaTKZFfmHa9QAAq4MRaAAALGtV9YvDtOHbJvmzTEY4fnG6VQEAq4kADQCA5e6wTKYuXp7JVMsj2jQKAGAJmcIJAAAAACOMQAMAAACAEWumXcBi2GOPPXr9+vXTLgMAAACA7cSGDRuu7O61c61bkQHa+vXrc+655067DAAAAAC2E1X1pS2tM4UTAAAAAEYI0AAAAABghAANAAAAAEasyHOgAQAAALD0fvCDH2Tjxo257rrrpl3KFu28885Zt25ddtxxx3lvI0ADAAAAYEFs3Lgxt7/97bN+/fpU1bTLuYnuzlVXXZWNGzdmn332mfd2pnACAAAAsCCuu+667L777ssyPEuSqsruu+9+s0fICdAAAAAAWDDLNTzb7JbUJ0ADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAACWlT/4gz/Iq171qh8uv/CFL8yrX/3qqdUjQAMAAABgWTn22GNz0kknJUluvPHGnHbaaTnyyCOnVs+aqd0zAAAAAMxh/fr12X333fOpT30qX//613PAAQdk9913n1o9AjQAAAAAlp1nPetZefOb35yvfe1rOeaYY6ZaiymcAAAAACw7T3jCE3LmmWfmE5/4RB7zmMdMtRYj0AAAAABYdnbaaac88pGPzK677poddthhqrUI0Ji6A48/edolbNGGE4+adgkAAACwKt14440555xz8va3v33apZjCCQAAAMDycvHFF+ee97xnDjnkkOy7777TLscINAAAAACWl/322y+f//znp13GDxmBBgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMMJFBAAAAABYFAcef/KC7m/DiUdttc+ZZ56Z5z3vebnhhhvyrGc9KyeccMI2368RaAAAAACsCDfccEOe85zn5J//+Z9z8cUX59RTT83FF1+8zfsVoAEAAACwInz84x/PPe95z9z97nfPTjvtlCOOOCLvfve7t3m/AjQAAAAAVoSvfOUr2XvvvX+4vG7dunzlK1/Z5v0K0AAAAABYEbr7Jm1Vtc37FaABAAAAsCKsW7cul1122Q+XN27cmD333HOb9ytAAwAAAGBFeOADH5hLLrkkX/jCF/L9738/p512Wh7/+Mdv837XLEBtAAAAAHATG048aknvb82aNXnNa16TxzzmMbnhhhtyzDHHZP/999/2/S5AbQAAAACwLBx66KE59NBDF3SfpnACAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMWDPtAgAAAABYmb78sp9Z0P3d9UUXbLXPMccck/e+9725053ulAsvvHBB7tcINAAAAABWjGc84xk588wzF3SfAjQAAAAAVoyHP/zh2W233RZ0nwI0AAAAABixaAFaVb2pqq6oqgtntJ1YVZ+tqvOr6p1VteuMdf+rqi6tqs9V1WNmtD92aLu0qk5YrHoBAAAAYC6LOQLtzUkeO6vtrCT36e77JvmvJP8rSapqvyRHJNl/2OZvqmqHqtohyWuTPC7JfkmeOvQFAAAAgCWxaAFad384yTdmtb2/u68fFs9Jsm64fViS07r7e939hSSXJjl4+Lq0uz/f3d9PctrQFwAAAACWxJop3vcxSd463N4rk0Bts41DW5JcNqv9QYtfGgAAAADb6q4vumDJ7/OpT31qzj777Fx55ZVZt25dXvrSl+bYY4/dpn1OJUCrqhcmuT7JKZub5ujWmXuEXG9hn8clOS5J7nrXuy5AlQAAAABsb0499dQF3+eSX4Wzqo5O8gtJjuzuzWHYxiR7z+i2LsnlI+030d2v7+6DuvugtWvXLnzhAAAAAKxKSxqgVdVjk/xeksd393dnrDojyRFVdeuq2ifJvkk+nuQTSfatqn2qaqdMLjRwxlLWDAAAAMDqtmhTOKvq1CSPSLJHVW1M8uJMrrp56yRnVVWSnNPdv9bdF1XV25JcnMnUzud09w3Dfn4jyb8k2SHJm7r7osWqGQAAAIBt090Zcp9l6UcTIudv0QK07n7qHM1vHOn/8iQvn6P9fUnet4ClAQAAALAIdt5551x11VXZfffdl2WI1t256qqrsvPOO9+s7aZ5FU4AAAAAVpB169Zl48aN2bRp07RL2aKdd94569atu1nbCNAAAAAAWBA77rhj9tlnn2mXseCW/CqcAAAAALA9EaABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMEKABAAAAwAgBGgAAAACMWLQArareVFVXVNWFM9p2q6qzquqS4fsdh/aqqldX1aVVdX5VPWDGNkcP/S+pqqMXq14AAAAAmMtijkB7c5LHzmo7IckHunvfJB8YlpPkcUn2Hb6OS/K6ZBK4JXlxkgclOTjJizeHbgAAAACwFBYtQOvuDyf5xqzmw5KcNNw+KcnhM9pP7olzkuxaVXdJ8pgkZ3X3N7r76iRn5aahHAAAAAAsmqU+B9qdu/urSTJ8v9PQvleSy2b02zi0ban9JqrquKo6t6rO3bRp04IXDgAAAMDqtFwuIlBztPVI+00bu1/f3Qd190Fr165d0OIAAAAAWL2WOkD7+jA1M8P3K4b2jUn2ntFvXZLLR9oBAAAAYEksdYB2RpLNV9I8Osm7Z7QfNVyN88FJrh2meP5Lkp+rqjsOFw/4uaENAAAAAJbEmsXacVWdmuQRSfaoqo2ZXE3zFUneVlXHJvlykicN3d+X5NAklyb5bpJnJkl3f6Oq/jDJJ4Z+L+vu2RcmAAAAAIBFs2gBWnc/dQurDpmjbyd5zhb286Ykb1rA0gAAAABg3pbLRQQAAAAAYFkSoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIwQoAEAAADACAEaAAAAAIyYSoBWVb9dVRdV1YVVdWpV7VxV+1TVx6rqkqp6a1XtNPS99bB86bB+/TRqBgAAAGB1WvIArar2SvLcJAd1932S7JDkiCSvTPKX3b1vkquTHDtscmySq7v7nkn+cugHAAAAAEtiWlM41yT5iapak+Q2Sb6a5FFJTh/Wn5Tk8OH2YcNyhvWHVFUtYa0AAAAArGJLHqB191eS/FmSL2cSnF2bZEOSa7r7+qHbxiR7Dbf3SnLZsO31Q//dZ++3qo6rqnOr6txNmzYt7oMAAAAAYNWYxhTOO2YyqmyfJHsmuW2Sx83RtTdvMrLuRw3dr+/ug7r7oLVr1y5UuQAAAACsctOYwvnoJF/o7k3d/YMk70jy0CS7DlM6k2RdksuH2xuT7J0kw/pdknxjaUsGAAAAYLWaRoD25SQPrqrbDOcyOyTJxUn+LckThz5HJ3n3cPuMYTnD+g92901GoAEAAADAYpjGOdA+lsnFAD6Z5IKhhtcn+b0kz6+qSzM5x9kbh03emGT3of35SU5Y6poBAAAAWL3WbL3LwuvuFyd58azmzyc5eI6+1yV50lLUBQAAAACzTWMKJwAAAABsNwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAI+YVoFXVB+bTBgAAAAArzZqxlVW1c5LbJNmjqu6YpIZVd0iy5yLXBgAAAABTNxqgJfnVJL+VSVi2IT8K0L6Z5LWLWBcAAAAALAujAVp3vyrJq6rqN7v7r5eoJgAAAABYNrY2Ai1J0t1/XVUPTbJ+5jbdffIi1QUAAAAAy8K8ArSq+vsk90hyXpIbhuZOIkADAAAAYEWbV4CW5KAk+3V3L2YxAAAAALDc3Gqe/S5M8pOLWQgAAAAALEfzHYG2R5KLq+rjSb63ubG7H78oVQEAAADAMjHfAO0li1kEAAAAACxX870K54cWuxAAAAAAWI7mexXOb2Vy1c0k2SnJjkm+0913WKzCAAAAAGA5mO8ItNvPXK6qw5McvCgVAQAAAMAyMt+rcP6Y7n5XkkctcC0AAAAAsOzMdwrnL81YvFWSg/KjKZ0AAAAAsGLN9yqcvzjj9vVJvpjksAWvBgAAAACWmfmeA+2Zi10IAAAAACxH8zoHWlWtq6p3VtUVVfX1qvrHqlq32MUBAAAAwLTN9yICf5fkjCR7JtkryXuGNgAAAABY0eYboK3t7r/r7uuHrzcnWbuIdQEAAADAsjDfAO3KqvqVqtph+PqVJFctZmEAAAAAsBzMN0A7JsmTk3wtyVeTPDGJCwsAAAAAsOLN6yqcSf4wydHdfXWSVNVuSf4sk2ANAABYBAcef/K0S9iiDSceNe0SAGDJzHcE2n03h2dJ0t3fSHLA4pQEAAAAAMvHfAO0W1XVHTcvDCPQ5jt6DQAAAAC2W/MNwf48yX9U1elJOpPzob180aoCAAAAgGViXgFad59cVecmeVSSSvJL3X3xolYGAAAAAMvAvKdhDoGZ0AwAAACAVWW+50ADAAAAgFVJgAYAAAAAIwRoAAAAADBiKgFaVe1aVadX1Wer6jNV9ZCq2q2qzqqqS4bvdxz6VlW9uqourarzq+oB06gZAAAAgNVpWiPQXpXkzO6+d5L7JflMkhOSfKC7903ygWE5SR6XZN/h67gkr1v6cgEAAABYrZY8QKuqOyR5eJI3Jkl3f7+7r0lyWJKThm4nJTl8uH1YkpN74pwku1bVXZa4bAAAAABWqWmMQLt7kk1J/q6qPlVVb6iq2ya5c3d/NUmG73ca+u+V5LIZ228c2n5MVR1XVedW1bmbNm1a3EcAAAAAwKoxjQBtTZIHJHlddx+Q5Dv50XTNudQcbX2Thu7Xd/dB3X3Q2rVrF6ZSAAAAAFa9aQRoG5Ns7O6PDcunZxKofX3z1Mzh+xUz+u89Y/t1SS5foloBAAAAWOWWPEDr7q8luayq7jU0HZLk4iRnJDl6aDs6ybuH22ckOWq4GueDk1y7eaonAAAAACy2NVO6399MckpV7ZTk80memUmY97aqOjbJl5M8aej7viSHJrk0yXeHvgAAAACwJKYSoHX3eUkOmmPVIXP07STPWfSiAAAAAGAO0zgHGgAAAABsNwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAIwRoAAAAADBCgAYAAAAAI9ZMuwAAgJkOPP7kaZcwasOJR027BAAAlpgRaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwQoAGAAAAACMEaAAAAAAwYs20CwBg4sDjT552CVu04cSjpl0CAADA1BiBBgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAAAAMEKABgAAAAAjphagVdUOVfWpqnrvsLxPVX2sqi6pqrdW1U5D+62H5UuH9eunVTMAAAAAq880R6A9L8lnZiy/Mslfdve+Sa5OcuzQfmySq7v7nkn+cugHAAAAAEtiKgFaVa1L8vNJ3jAsV5JHJTl96HJSksOH24cNyxnWHzL0BwAAAIBFN60RaH+V5HeT3Dgs757kmu6+fljemGSv4fZeSS5LkmH9tUP/H1NVx1XVuVV17qZNmxazdgAAAABWkSUP0KrqF5Jc0d0bZjbP0bXnse5HDd2v7+6DuvugtWvXLkClAAAAAJCsmcJ9/mySx1fVoUl2TnKHTEak7VpVa4ZRZuuSXD7035hk7yQbq2pNkl2SfGPpywYAAABgNVryEWjd/b+6e113r09yRJIPdveRSf4tyROHbkcnefdw+4xhOcP6D3b3TUagAQAAAMBimOZVOGf7vSTPr6pLMznH2RuH9jcm2X1of36SE6ZUHwAAAACr0DSmcP5Qd5+d5Ozh9ueTHDxHn+uSPGlJCwMAAACAwXIagQYAAAAAy44ADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGCNAAAAAAYIQADQAAAABGrJl2AQAAAMvVgcefPO0StmjDiUdNuwSAVcMINAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBECNAAAAAAYIUADAAAAgBFrpl0AsDwcePzJ0y5hizaceNS0SwAAAGAVMwINAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABgxJIHaFW1d1X9W1V9pqouqqrnDe27VdVZVXXJ8P2OQ3tV1aur6tKqOr+qHrDUNQMAAACwek1jBNr1SX6nu386yYOTPKeq9ktyQpIPdPe+ST4wLCfJ45LsO3wdl+R1S18yAAAAAKvVkgdo3f3V7v7kcPtbST6TZK8khyU5aeh2UpLDh9uHJTm5J85JsmtV3WWJywYAAABglZrqOdCqan2SA5J8LMmdu/urySRkS3KnodteSS6bsdnGoW32vo6rqnOr6txNmzYtZtkAAAAArCJTC9Cq6nZJ/jHJb3X3N8e6ztHWN626RBUAAA5NSURBVGnofn13H9TdB61du3ahygQAAABglZtKgFZVO2YSnp3S3e8Ymr++eWrm8P2KoX1jkr1nbL4uyeVLVSsAAAAAq9s0rsJZSd6Y5DPd/RczVp2R5Ojh9tFJ3j2j/ajhapwPTnLt5qmeAAAAALDY1kzhPn82ydOTXFBV5w1tv5/kFUneVlXHJvlykicN696X5NAklyb5bpJnLm25AAAAAKxmSx6gdfdHMvd5zZLkkDn6d5LnLGpRADA48PiTp13CFm048ahplwAAAKvSVK/CCQAAAADLnQANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABghAANAAAAAEYI0AAAAABgxJppFwAAwPJz4PEnT7uELdpw4lHTLgEAWGWMQAMAAACAEQI0AAAAABghQAMAAACAEQI0AAAAABghQAMAAACAEQI0AAAAABghQAMAAACAEQI0AAAAABghQAMAAACAEQI0AAAAABixZtoFwHL25Zf9zLRL2KK7vuiCaZcAAAAAq4IRaAAAAAAwQoAGAAAAACMEaAAAAAAwwjnQAABuBufHBABYfYxAAwAAAIARAjQAAAAAGCFAAwAAAIARAjQAAAAAGOEiAgAAwM3mghoArCYCNGDZ8wc6AAAA0yRAA2CrhJgAAMBq5hxoAAAAADBCgAYAAAAAI1b1FM4Djz952iVs0YYTj5p2CQAAAABklQdoALA9cS46AGZazu8LifcGYGURoC1Ty/nN0BshAACwlMweAqbNOdAAAAAAYIQRaAAAAMBWGQnIamYEGgAAAACMMAINAAAA2K45jziLTYAGAMB2xYckYDnxmgSrgymcAAAAADBiuxmBVlWPTfKqJDskeUN3v2LKJQEAAAAsmeV8IYd33v7EaZewRQsxGnO7GIFWVTskeW2SxyXZL8lTq2q/6VYFAAAAwGqwXQRoSQ5Ocml3f767v5/ktCSHTbkmAAAAAFaB6u5p17BVVfXEJI/t7mcNy09P8qDu/o0ZfY5LctyweK8kn1vyQhfWHkmunHYROA7LhOMwfY7B8uA4LA+Ow/Q5BsuD4zB9jsHy4DgsD47D9K2EY3C37l4714rt5RxoNUfbjyV/3f36JK9fmnIWX1Wd290HTbuO1c5xWB4ch+lzDJYHx2F5cBymzzFYHhyH6XMMlgfHYXlwHKZvpR+D7WUK58Yke89YXpfk8inVAgAAAMAqsr0EaJ9Ism9V7VNVOyU5IskZU64JAAAAgFVgu5jC2d3XV9VvJPmXJDskeVN3XzTlshbbipmOup1zHJYHx2H6HIPlwXFYHhyH6XMMlgfHYfocg+XBcVgeHIfpW9HHYLu4iAAAAAAATMv2MoUTAAAAAKZCgAYAAAAAIwRo24mqOryq9pt2HcDKVlX/Me0aVruqem5VfaaqTtnC+mdU1WuWui6YBq9Jy8vWjkdVnV1VBy1VPQBMV1W9pKpeMO06looAbftxeBIBGsxSVdvFxVC2F9390GnXQJ6d5NDuPnLahcC0eU1aXhwPYHvnswPbQoA2ZVX1/Kq6cPj6raHtqKo6v6o+XVV/X1UPTfL4JCdW1XlVdY/pVr39q6r1VfXZqnrD8NyfUlWPrqqPVtUlVXXw8PUfVfWp4fu9hm3/varuP2NfH62q+07v0awMVfWuqtpQVRdV1XFD27er6s+r6pNV9YGqWju0n11Vf1xVH0ryvKkWvsIMz/kjquq9M9peU1XPGG5/sapeOhyTC6rq3lMrdgWqqr9NcvckZ1TVC6vqTVX1ieF16LAZXfeuqjOr6nNV9eIplbtiben1aMb6J1bVm4fb96iqc4bj9LKZ/dh2w2vS7Yb3gM2vO4cN69YPozX/z3Cs3l9VPzHtmleyrb1HsPC29HNeVfcfXnvOr6p3VtUdq+qnq+rjs7Y9f5r1rxRVdduq+qfh89mFVfWUqjqwqj40vF/8S1XdZeh7dlX91fD54cKqOnja9a8E2/j57RlV9faqek+S90/5oWz3hr9RP1dV/5pk83N8j+Fv0w3D5+V7D+13Hl6jPj18bdf/iBGgTVFVHZjkmUkelOTBSf7fqvrZJC9M8qjuvl+S53X3fyQ5I8nx3X3/7v6/Uyt6ZblnklcluW+Seyd5WpKHJXlBkt9P8tkkD+/uA5K8KMkfD9u9IckzkqSqfirJrbvbHyfb7pjuPjDJQUmeW1W7J7ltkk929wOSfCjJzKBg1+7+n93951OodbW7cjgmr8vk94UF0t2/luTyJI/M5Of/g939wGH5xKq67dD14CRHJrl/kieVKVMLba7Xoy15VZJXDcfp8iWpbvW5LskThtedRyb586qqYd2+SV7b3fsnuSbJL0+pRlhMc/2cn5zk97r7vkkuSPLi7v5Mkp2q6u7Ddk9J8rZpFLwCPTbJ5d19v+6+T5Izk/x1kicO7xdvSvLyGf1vO4zYfPawjoVxSz+/JclDkhzd3Y9a0opXmCHDOCLJAUl+KckDh1WvT/Kbw+/DC5L8zdD+6iQfGrKNByS5aGkrXliGL07Xw5K8s7u/kyRV9Y5M/lg/vbuvTJLu/sYU61vpvtDdFyRJVV2U5APd3VV1QZL1SXZJclJV7Zukk+w4bPf2JH9QVccnOSbJm5e68BXquVX1hOH23pn8sXhjkrcObf+Q5B0z+r81TMvm47AhkzdOFsfPJXl8/ei8Ejsnuetw+6zuvir54XvHw5Kcu/QlrlhzvR5tyUMyOc1CkrwlyZ8tZmGrVCX546p6eCbvC3slufOw7gvdfd5we0Mm79+w0sz+Ob9HJv9I/NDQdlImf58mk8DsyUlekUmA9pSlLHQFuyDJn1XVK5O8N8nVSe6T5Kwhz98hyVdn9D81Sbr7w1V1h6ratbuvWeKaV6Jb+vktmfzt5LP1tvsfmWQY302Sqjojk79RH5rk7T/6/1ZuPXx/VJKjkqS7b0hy7ZJWu8AEaNNVc7T18MXi+96M2zfOWL4xk9+NP0zyb939hKpan+TsJOnu71bVWUkOy+QPFCM/tlFVPSLJo5M8ZHh+z87khXi2mb8b31mC0lar6/PjI5RnH4vNvys3xPvIYqokv9zdn/uxxqoH5abvE943FsjI69HM53iu1ycWz5FJ1iY5sLt/UFVfzI+Owcz38huSmMK5+Lb2HsHCm/1zvutI37dm8iH2HUm6uy9Z1MpWie7+r2HkzaFJ/iTJWUku6u6HbGmTrSxzy9yiz28Dnx0Wzuyf51sluaa77z9X55XEFM7p+nCSw6vqNsO0nCdk8l+lJ2+eLlJVuw19v5Xk9tMpc9XaJclXhtvPmLXuDZkMR/2E/2QsiF2SXD18WL13JlOak8lr1BOH209L8pFpFLcKfSnJflV166raJckh0y5olfqXJL+5eapaVR0wY93/U1W7Ded7OjzJR6dR4Aq1pdejrw/nF7pVJu/Xm52TH00bPGIJ61xNdklyxRCePTLJ3aZd0CrnPWL6rk1ydVX9j2H56Zmc6iLDqV5uSPIHMVp/wVTVnkm+293/kMlI4wclWVtVDxnW71hV+8/Y5ClD+8OSXNvd2/Wom+3I2Oc3FsaHkzxhOBfj7ZP8YpLvJvlCVT0pSWrifkP/DyT59aF9h6q6wzSKXigCtCnq7k9mMv3v40k+luQN3f3RTObPf6iqPp3kL4bupyU5fjghoosILI0/TfInVfXRTIZl/1B3b0jyzSR/N43CVqAzk6ypyYlu/zCTD6TJ5D9F+1fVhkyG/75sSvWtJt3dl2UyBeT8JKck+dR0S1q1/jCTqQfnV9WFw/JmH0ny90nOS/KP3W365sLZ0uvRCZlM2/lgfnyazm8leX5NTtx9l2znUxOWoc7kdeigqjo3k9Fon51uSaua94jl4+hMzo15fibnw5z5N9Jbk/xKnP9sIf1Mko9X1XmZnK/6RZn8k/eVw2e28zKZwrbZ1VX1H0n+NsmxS13sKrbFz28sjCHDeGuGv0GT/Puw6sgkxw6/DxdlMlsrmVzw7ZHDNNsNSfbPdqy6jSaFm2v4L9TZSe7d3TdOuZwVq6q+3d23m3Ydq8Uw8vWT3W10B8xTVd0myX8P52A5IslTu/uwrW3H1nlNWl4cD5ifYer/C/xzC1Ye566Bm6mqjspklODzhWesFDNCYSdAh5vnwCSvGabaXpPJxWXYRl6TlhfHAwCMQAMAAACAUc6BBgAAAAAjBGgAAAAAMEKABgAAAAAjBGgAAKSqdq2qZ89YfkRVvXeaNQEALBcCNAAAkmTXJM/eai8AgFVIgAYAsJ2pqvVV9dmqekNVXVhVp1TVo6vqo1V1SVUdXFW7VdW7qur8qjqnqu47bPuSqnpTVZ1dVZ+vqucOu31FkntU1XlVdeLQdruqOn24r1OqqqbygAEApmzNtAsAAOAWuWeSJyU5LsknkjwtycOSPD7J7ye5LMmnuvvwqnpUkpOT3H/Y9t5JHpnk9kk+V1WvS3JCkvt09/2TyRTOJAck2T/J5Uk+muRnk3xkKR4cAMByYgQaAMD26QvdfUF335jkoiQf6O5OckGS9ZmEaX+fJN39wSS7V9Uuw7b/1N3f6+4rk1yR5M5buI+Pd/fG4T7OG/YLALDqCNAAALZP35tx+8YZyzdmMstgrumWPce2N2TLsxLm2w8AYEUToAEArEwfTnJk8sPpmP9/e3dsgzAMhGH096SswiaZArEHyhikCRLtpQhVhK5LAXqvvsL1Z539rKq1mX9lX+kEAODALSIAwH+6JpnGGI8k7ySXbriqls8nBHOSe5Lb+UcEAPgNY38qAwAAAAD4xgonAAAAADQENAAAAABoCGgAAAAA0BDQAAAAAKAhoAEAAABAQ0ADAAAAgIaABgAAAACNDdX0JuVamLFaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('month', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('duration', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('campaign', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('previous', 'y', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plotter('poutcome', 'y', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1a19f20990>,\n",
       "  <matplotlib.axis.YTick at 0x1a19f20150>,\n",
       "  <matplotlib.axis.YTick at 0x1a19f18d10>,\n",
       "  <matplotlib.axis.YTick at 0x1a17e85210>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a26f5d0>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a26fbd0>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a279290>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a2798d0>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a279f10>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a27c590>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a27cb90>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a27c490>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a279110>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a284210>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a2848d0>,\n",
       "  <matplotlib.axis.YTick at 0x1a1a284f10>],\n",
       " <a list of 16 Text yticklabel objects>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(data_copy.drop('y', 1), data_copy['y'].values.ravel())\n",
    "importance = clf.feature_importances_\n",
    "feature_names = data_copy.drop('y', axis=1).columns\n",
    "\n",
    "plt.barh(range(len(np.argsort(importance))), importance[np.argsort(importance)])\n",
    "plt.yticks(range(len(np.argsort(importance))), feature_names[np.argsort(importance)])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are considering following features based on the above graph:\n",
    "* duration\n",
    "* day\n",
    "* balance\n",
    "* age\n",
    "* pdays\n",
    "* month\n",
    "* poutcome\n",
    "* job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration', 'balance', 'age', 'day', 'pdays', 'y'], dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_filtered = data[['duration', 'balance', 'month', 'age', 'day', 'pdays', 'poutcome', 'job', 'y']]\n",
    "    \n",
    "num_cols = data_filtered.columns[data_filtered.dtypes.apply(lambda c: np.issubdtype(c, np.number))]\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting data into Dependent and Independent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_y = data_filtered['y'].values.ravel()\n",
    "data_x = data_filtered.drop('y', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(data_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating dummies for categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>duration</th>\n",
       "      <th>balance</th>\n",
       "      <th>age</th>\n",
       "      <th>day</th>\n",
       "      <th>pdays</th>\n",
       "      <th>month_apr</th>\n",
       "      <th>month_aug</th>\n",
       "      <th>month_dec</th>\n",
       "      <th>month_feb</th>\n",
       "      <th>month_jan</th>\n",
       "      <th>...</th>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <th>job_housemaid</th>\n",
       "      <th>job_management</th>\n",
       "      <th>job_retired</th>\n",
       "      <th>job_self-employed</th>\n",
       "      <th>job_services</th>\n",
       "      <th>job_student</th>\n",
       "      <th>job_technician</th>\n",
       "      <th>job_unemployed</th>\n",
       "      <th>job_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>1787</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>220</td>\n",
       "      <td>4789</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>339</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>185</td>\n",
       "      <td>1350</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>199</td>\n",
       "      <td>1476</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  balance  age  day  pdays  month_apr  month_aug  month_dec  \\\n",
       "0        79     1787   30   19     -1          0          0          0   \n",
       "1       220     4789   33   11    339          0          0          0   \n",
       "2       185     1350   35   16    330          1          0          0   \n",
       "3       199     1476   30    3     -1          0          0          0   \n",
       "4       226        0   59    5     -1          0          0          0   \n",
       "\n",
       "   month_feb  month_jan  ...  job_entrepreneur  job_housemaid  job_management  \\\n",
       "0          0          0  ...                 0              0               0   \n",
       "1          0          0  ...                 0              0               0   \n",
       "2          0          0  ...                 0              0               1   \n",
       "3          0          0  ...                 0              0               1   \n",
       "4          0          0  ...                 0              0               0   \n",
       "\n",
       "   job_retired  job_self-employed  job_services  job_student  job_technician  \\\n",
       "0            0                  0             0            0               0   \n",
       "1            0                  0             1            0               0   \n",
       "2            0                  0             0            0               0   \n",
       "3            0                  0             0            0               0   \n",
       "4            0                  0             0            0               0   \n",
       "\n",
       "   job_unemployed  job_unknown  \n",
       "0               1            0  \n",
       "1               0            0  \n",
       "2               0            0  \n",
       "3               0            0  \n",
       "4               0            0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x = pd.get_dummies(data_x, sparse=True)\n",
    "data_x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting data into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3390, 33)\n",
      "(1131, 33)\n"
     ]
    }
   ],
   "source": [
    "data_train_x1, data_test_x, data_train_y1, data_test_y = train_test_split(data_x, data_y, random_state = 10)\n",
    "print(data_train_x1.shape)\n",
    "print(data_test_x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "smote = SMOTE(random_state=0)\n",
    "data_train_x, data_train_y = smote.fit_resample(data_train_x1, data_train_y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['No','Yes'], [sum(data_train_y), len(data_train_y)-sum(data_train_y)], facecolor = '#2ab0ee', edgecolor='#167aaa', linewidth=0.5)\n",
    "plt.title('Balanced Target Variable', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardizing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "data_train_x = scaler.fit_transform(data_train_x)\n",
    "data_test_x = scaler.transform(data_test_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Different Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'C': 0.1, 'max_iter': 10000}\n",
      "Best estimtor: SVC(C=0.1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
      "    max_iter=10000, probability=False, random_state=None, shrinking=True,\n",
      "    tol=0.001, verbose=False)\n",
      "Best score: 0.9158248540450374\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "params = {'C': [10**i for i in range(-4, 5)] ,'max_iter':[1000,10000]}\n",
    "grid = GridSearchCV(SVC(kernel = 'linear'), params, cv = 5)\n",
    "grid.fit(data_train_x, data_train_y)\n",
    "print('Best parameters:  {}'.format(grid.best_params_))\n",
    "print('Best estimtor: {}'.format(grid.best_estimator_))\n",
    "print('Best score: {}'.format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[987  14]\n",
      " [109  21]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.99      0.94      1001\n",
      "           1       0.60      0.16      0.25       130\n",
      "\n",
      "    accuracy                           0.89      1131\n",
      "   macro avg       0.75      0.57      0.60      1131\n",
      "weighted avg       0.87      0.89      0.86      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svc_linear = SVC(kernel = 'linear', C = 0.1, degree = 3, random_state = 1)\n",
    "svc_linear.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = svc_linear.predict(data_train_x)\n",
    "pred_test = svc_linear.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a dataframe to store all the model outputs\n",
    "model_output = pd.DataFrame(index=None, columns=['Model','Train_Accuracy', 'Test_Accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_output = model_output.append(pd.Series({'Model' : 'SVM Linear', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'C': 1, 'max_iter': 10000}\n",
      "Best estimtor: SVC(C=1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
      "    max_iter=10000, probability=False, random_state=None, shrinking=True,\n",
      "    tol=0.001, verbose=False)\n",
      "Best score: 0.9209920767306088\n"
     ]
    }
   ],
   "source": [
    "params = {'C': [10**i for i in range(-4, 5)] ,'max_iter':[1000,10000]}\n",
    "grid = GridSearchCV(SVC(kernel = 'rbf'), params, cv = 5)\n",
    "grid.fit(data_train_x, data_train_y)\n",
    "print('Best parameters:  {}'.format(grid.best_params_))\n",
    "print('Best estimtor: {}'.format(grid.best_estimator_))\n",
    "print('Best score: {}'.format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[978  23]\n",
      " [107  23]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.98      0.94      1001\n",
      "           1       0.50      0.18      0.26       130\n",
      "\n",
      "    accuracy                           0.89      1131\n",
      "   macro avg       0.70      0.58      0.60      1131\n",
      "weighted avg       0.86      0.89      0.86      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svc_rbf = SVC(kernel = 'rbf', C = 1, degree = 3,  random_state = 1)\n",
    "svc_rbf.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = svc_rbf.predict(data_train_x)\n",
    "pred_test = svc_rbf.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "model_output = model_output.append(pd.Series({'Model' : 'SVM RBF', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'criterion': 'entropy', 'max_depth': 14}\n",
      "Best estimtor: DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
      "                       max_depth=14, max_features=None, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
      "                       random_state=None, splitter='best')\n",
      "Best score: 0.9116497080900752\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "params = {'criterion':['gini','entropy'],'max_depth': np.arange(3, 15)}\n",
    "grid = GridSearchCV(DecisionTreeClassifier(), params, cv = 5)\n",
    "grid.fit(data_train_x, data_train_y)\n",
    "print('Best parameters:  {}'.format(grid.best_params_))\n",
    "print('Best estimtor: {}'.format(grid.best_estimator_))\n",
    "print('Best score: {}'.format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[911  90]\n",
      " [ 80  50]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.91      0.91      1001\n",
      "           1       0.36      0.38      0.37       130\n",
      "\n",
      "    accuracy                           0.85      1131\n",
      "   macro avg       0.64      0.65      0.64      1131\n",
      "weighted avg       0.85      0.85      0.85      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "decision_tree = DecisionTreeClassifier(max_depth = 14, criterion = 'entropy',  random_state = 1)\n",
    "decision_tree.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = decision_tree.predict(data_train_x)\n",
    "pred_test = decision_tree.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "model_output = model_output.append(pd.Series({'Model' : 'Decision Tree', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'C': 100.0, 'penalty': 'l2'}\n",
      "Best estimtor: LogisticRegression(C=100.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n",
      "Best score: 0.9186576313594662\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model  import LogisticRegression\n",
    "params = {\"C\":np.logspace(-3,3,7), \"penalty\":[\"l1\",\"l2\"]}\n",
    "grid = GridSearchCV(LogisticRegression(), params, cv = 5)\n",
    "grid.fit(data_train_x, data_train_y)\n",
    "print('Best parameters:  {}'.format(grid.best_params_))\n",
    "print('Best estimtor: {}'.format(grid.best_estimator_))\n",
    "print('Best score: {}'.format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[966  35]\n",
      " [ 84  46]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.97      0.94      1001\n",
      "           1       0.57      0.35      0.44       130\n",
      "\n",
      "    accuracy                           0.89      1131\n",
      "   macro avg       0.74      0.66      0.69      1131\n",
      "weighted avg       0.88      0.89      0.88      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "logistic = LogisticRegression(C = 100, penalty = 'l2',  random_state = 1)\n",
    "logistic.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = logistic.predict(data_train_x)\n",
    "pred_test = logistic.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "model_output = model_output.append(pd.Series({'Model' : 'Logistic Regression', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'max_features': 'log2', 'max_depth': 14, 'criterion': 'entropy'}\n",
      "Best estimtor: RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
      "                       criterion='entropy', max_depth=14, max_features='log2',\n",
      "                       max_leaf_nodes=None, max_samples=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
      "                       n_jobs=None, oob_score=False, random_state=None,\n",
      "                       verbose=0, warm_start=False)\n",
      "Best score: 0.9309892966360855\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "params = {'criterion':['gini','entropy'],'max_depth': np.arange(3, 15), 'max_features' : [\"auto\", \"sqrt\", \"log2\"],}\n",
    "grid = RandomizedSearchCV(RandomForestClassifier(n_estimators = 1000), params, cv = 5)\n",
    "grid.fit(data_train_x, data_train_y)\n",
    "print('Best parameters:  {}'.format(grid.best_params_))\n",
    "print('Best estimtor: {}'.format(grid.best_estimator_))\n",
    "print('Best score: {}'.format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[935  66]\n",
      " [ 66  64]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.93      0.93      1001\n",
      "           1       0.49      0.49      0.49       130\n",
      "\n",
      "    accuracy                           0.88      1131\n",
      "   macro avg       0.71      0.71      0.71      1131\n",
      "weighted avg       0.88      0.88      0.88      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(max_depth = 14, criterion = 'entropy', max_features = 'log2', \n",
    "                                       random_state = 1)\n",
    "random_forest.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = random_forest.predict(data_train_x)\n",
    "pred_test = random_forest.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "model_output = model_output.append(pd.Series({'Model' : 'Random Forest', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'weights': 'distance', 'n_neighbors': 19, 'metric': 'minkowski'}\n",
      "Best estimtor: KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=19, p=2,\n",
      "                     weights='distance')\n",
      "Best score: 0.9293269391159299\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "params = {'metric': ['minkowski','euclidean','manhattan'], 'weights' : ['uniform','distance'], \n",
    "          'n_neighbors'  : range(5,100) }\n",
    "grid = RandomizedSearchCV(KNeighborsClassifier(), params, cv = 5)\n",
    "grid.fit(data_train_x, data_train_y)\n",
    "print('Best parameters:  {}'.format(grid.best_params_))\n",
    "print('Best estimtor: {}'.format(grid.best_estimator_))\n",
    "print('Best score: {}'.format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[954  47]\n",
      " [ 97  33]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.95      0.93      1001\n",
      "           1       0.41      0.25      0.31       130\n",
      "\n",
      "    accuracy                           0.87      1131\n",
      "   macro avg       0.66      0.60      0.62      1131\n",
      "weighted avg       0.85      0.87      0.86      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(metric = 'minkowski', weights = 'distance', n_neighbors = 19)\n",
    "knn.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = knn.predict(data_train_x)\n",
    "pred_test = knn.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Model</th>\n",
       "      <th>Train_Accuracy</th>\n",
       "      <th>Test_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SVM Linear</td>\n",
       "      <td>0.929977</td>\n",
       "      <td>0.891247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>SVM RBF</td>\n",
       "      <td>0.949817</td>\n",
       "      <td>0.885057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.989330</td>\n",
       "      <td>0.849691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.935812</td>\n",
       "      <td>0.894783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.993831</td>\n",
       "      <td>0.883289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>KNN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Train_Accuracy  Test_Accuracy\n",
       "0           SVM Linear        0.929977       0.891247\n",
       "1              SVM RBF        0.949817       0.885057\n",
       "2        Decision Tree        0.989330       0.849691\n",
       "3  Logistic Regression        0.935812       0.894783\n",
       "4        Random Forest        0.993831       0.883289\n",
       "5                  KNN        1.000000       0.872679"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_output = model_output.append(pd.Series({'Model' : 'KNN', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)\n",
    "model_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Bagging & Boosting\n",
    "\n",
    "### Linear SVM with Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[921  80]\n",
      " [ 57  73]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.92      0.93      1001\n",
      "           1       0.48      0.56      0.52       130\n",
      "\n",
      "    accuracy                           0.88      1131\n",
      "   macro avg       0.71      0.74      0.72      1131\n",
      "weighted avg       0.89      0.88      0.88      1131\n",
      "\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</th>\n",
       "      <th>Train_Accuracy</th>\n",
       "      <th>Test_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SVM Linear</td>\n",
       "      <td>0.929977</td>\n",
       "      <td>0.891247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>SVM RBF</td>\n",
       "      <td>0.949817</td>\n",
       "      <td>0.885057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.989330</td>\n",
       "      <td>0.849691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.935812</td>\n",
       "      <td>0.894783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.993831</td>\n",
       "      <td>0.883289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>KNN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>SVM with Bagging</td>\n",
       "      <td>0.924308</td>\n",
       "      <td>0.878868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Train_Accuracy  Test_Accuracy\n",
       "0           SVM Linear        0.929977       0.891247\n",
       "1              SVM RBF        0.949817       0.885057\n",
       "2        Decision Tree        0.989330       0.849691\n",
       "3  Logistic Regression        0.935812       0.894783\n",
       "4        Random Forest        0.993831       0.883289\n",
       "5                  KNN        1.000000       0.872679\n",
       "6     SVM with Bagging        0.924308       0.878868"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "bagging = BaggingClassifier(svc_linear, max_features = 15, bootstrap = True, oob_score = True, n_estimators = 100, \n",
    "                           random_state = 1)\n",
    "bagging.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = bagging.predict(data_train_x)\n",
    "pred_test = bagging.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "\n",
    "model_output = model_output.append(pd.Series({'Model' : 'SVM with Bagging', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)\n",
    "model_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kernelized SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[946  55]\n",
      " [ 84  46]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.95      0.93      1001\n",
      "           1       0.46      0.35      0.40       130\n",
      "\n",
      "    accuracy                           0.88      1131\n",
      "   macro avg       0.69      0.65      0.66      1131\n",
      "weighted avg       0.87      0.88      0.87      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "bagging = BaggingClassifier(svc_rbf, max_features = 15, bootstrap = True, oob_score = True, n_estimators = 100, \n",
    "                           random_state = 1)\n",
    "bagging.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = bagging.predict(data_train_x)\n",
    "pred_test = bagging.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "\n",
    "model_output = model_output.append(pd.Series({'Model' : 'SVM-RBF with Bagging', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree with Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[929  72]\n",
      " [ 62  68]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.93      0.93      1001\n",
      "           1       0.49      0.52      0.50       130\n",
      "\n",
      "    accuracy                           0.88      1131\n",
      "   macro avg       0.71      0.73      0.72      1131\n",
      "weighted avg       0.89      0.88      0.88      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bagging = BaggingClassifier(decision_tree, max_features = 15, bootstrap = True, oob_score = True, n_estimators = 30, \n",
    "                           random_state = 1)\n",
    "bagging.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = bagging.predict(data_train_x)\n",
    "pred_test = bagging.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "\n",
    "model_output = model_output.append(pd.Series({'Model' : 'Decision Tree with Bagging', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression with Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[926  75]\n",
      " [ 61  69]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.93      0.93      1001\n",
      "           1       0.48      0.53      0.50       130\n",
      "\n",
      "    accuracy                           0.88      1131\n",
      "   macro avg       0.71      0.73      0.72      1131\n",
      "weighted avg       0.89      0.88      0.88      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bagging = BaggingClassifier(logistic, max_features = 15, bootstrap = True, oob_score = True, n_estimators = 30, \n",
    "                           random_state = 1)\n",
    "bagging.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = bagging.predict(data_train_x)\n",
    "pred_test = bagging.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "\n",
    "model_output = model_output.append(pd.Series({'Model' : 'Logistic Reg with Bagging', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest with Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[926  75]\n",
      " [ 67  63]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.93      0.93      1001\n",
      "           1       0.46      0.48      0.47       130\n",
      "\n",
      "    accuracy                           0.87      1131\n",
      "   macro avg       0.69      0.70      0.70      1131\n",
      "weighted avg       0.88      0.87      0.88      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bagging = BaggingClassifier(random_forest, max_features = 15, bootstrap = True, oob_score = True, n_estimators = 30, \n",
    "                           random_state = 1)\n",
    "bagging.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = bagging.predict(data_train_x)\n",
    "pred_test = bagging.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "\n",
    "model_output = model_output.append(pd.Series({'Model' : 'RF with Bagging', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN with Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[940  61]\n",
      " [ 72  58]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.94      0.93      1001\n",
      "           1       0.49      0.45      0.47       130\n",
      "\n",
      "    accuracy                           0.88      1131\n",
      "   macro avg       0.71      0.69      0.70      1131\n",
      "weighted avg       0.88      0.88      0.88      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bagging = BaggingClassifier(knn, max_features = 15, bootstrap = True, oob_score = True, n_estimators = 30, \n",
    "                           random_state = 1)\n",
    "bagging.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = bagging.predict(data_train_x)\n",
    "pred_test = bagging.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "\n",
    "model_output = model_output.append(pd.Series({'Model' : 'KNN with Bagging', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'learning_rate': 0.1, 'max_depth': 4, 'max_features': 'log2'}\n",
      "Best estimtor: GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
      "                           learning_rate=0.1, loss='deviance', max_depth=4,\n",
      "                           max_features='log2', max_leaf_nodes=None,\n",
      "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                           min_samples_leaf=1, min_samples_split=2,\n",
      "                           min_weight_fraction_leaf=0.0, n_estimators=200,\n",
      "                           n_iter_no_change=None, presort='deprecated',\n",
      "                           random_state=1, subsample=1.0, tol=0.0001,\n",
      "                           validation_fraction=0.1, verbose=0,\n",
      "                           warm_start=False)\n",
      "Best score: 0.9228229079788713\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "params = {'learning_rate': [1, 0.1, 0.01, 0.05],'max_depth': [2,3,4],'max_features':['auto', 'log2']}\n",
    "grid = GridSearchCV(GradientBoostingClassifier(n_estimators= 200,random_state = 1), params, cv=5)\n",
    "grid.fit(data_train_x, data_train_y)\n",
    "print('Best parameters:  {}'.format(grid.best_params_))\n",
    "print('Best estimtor: {}'.format(grid.best_estimator_))\n",
    "print('Best score: {}'.format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[917  84]\n",
      " [ 67  63]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.92      0.92      1001\n",
      "           1       0.43      0.48      0.45       130\n",
      "\n",
      "    accuracy                           0.87      1131\n",
      "   macro avg       0.68      0.70      0.69      1131\n",
      "weighted avg       0.87      0.87      0.87      1131\n",
      "\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</th>\n",
       "      <th>Train_Accuracy</th>\n",
       "      <th>Test_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SVM Linear</td>\n",
       "      <td>0.929977</td>\n",
       "      <td>0.891247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>SVM RBF</td>\n",
       "      <td>0.949817</td>\n",
       "      <td>0.885057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.989330</td>\n",
       "      <td>0.849691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.935812</td>\n",
       "      <td>0.894783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.993831</td>\n",
       "      <td>0.883289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>KNN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>SVM with Bagging</td>\n",
       "      <td>0.924308</td>\n",
       "      <td>0.878868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>SVM-RBF with Bagging</td>\n",
       "      <td>0.931811</td>\n",
       "      <td>0.877100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Decision Tree with Bagging</td>\n",
       "      <td>0.996999</td>\n",
       "      <td>0.881521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Logistic Reg with Bagging</td>\n",
       "      <td>0.923641</td>\n",
       "      <td>0.879752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>RF with Bagging</td>\n",
       "      <td>0.982494</td>\n",
       "      <td>0.874447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>KNN with Bagging</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.882405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.959153</td>\n",
       "      <td>0.866490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Model  Train_Accuracy  Test_Accuracy\n",
       "0                   SVM Linear        0.929977       0.891247\n",
       "1                      SVM RBF        0.949817       0.885057\n",
       "2                Decision Tree        0.989330       0.849691\n",
       "3          Logistic Regression        0.935812       0.894783\n",
       "4                Random Forest        0.993831       0.883289\n",
       "5                          KNN        1.000000       0.872679\n",
       "6             SVM with Bagging        0.924308       0.878868\n",
       "7         SVM-RBF with Bagging        0.931811       0.877100\n",
       "8   Decision Tree with Bagging        0.996999       0.881521\n",
       "9    Logistic Reg with Bagging        0.923641       0.879752\n",
       "10             RF with Bagging        0.982494       0.874447\n",
       "11            KNN with Bagging        1.000000       0.882405\n",
       "12           Gradient Boosting        0.959153       0.866490"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier(learning_rate = 0.1 , max_depth = 4 , max_features = 'log2')\n",
    "gbc.fit(data_train_x, np.ravel(data_train_y))\n",
    "pred_train = gbc.predict(data_train_x)\n",
    "pred_test = gbc.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "\n",
    "model_output = model_output.append(pd.Series({'Model' : 'Gradient Boosting', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)\n",
    "model_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "#Fitting the PCA algorithm with our Data\n",
    "pca = PCA().fit(data_train_x)\n",
    "#Plotting the Cumulative Summation of the Explained Variance\n",
    "plt.figure()\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Variance (%)') #for each component\n",
    "plt.title('Dataset Explained Variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PCA doesn't make sense here as the data doesn't have really high dimension."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ADABoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'learning_rate': 1}\n",
      "Best estimtor: AdaBoostClassifier(algorithm='SAMME',\n",
      "                   base_estimator=SVC(C=0.1, break_ties=False, cache_size=200,\n",
      "                                      class_weight=None, coef0=0.0,\n",
      "                                      decision_function_shape='ovr', degree=3,\n",
      "                                      gamma='scale', kernel='linear',\n",
      "                                      max_iter=-1, probability=False,\n",
      "                                      random_state=1, shrinking=True, tol=0.001,\n",
      "                                      verbose=False),\n",
      "                   learning_rate=1, n_estimators=50, random_state=1)\n",
      "Best score: 0.6321665276619405\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "param_grid = {'learning_rate':[1, 0.1, 0.01, 0.001]}\n",
    "grid = GridSearchCV(AdaBoostClassifier(base_estimator = svc_linear, random_state = 1, algorithm='SAMME'),\n",
    "                            param_grid, cv = 5,return_train_score=True)\n",
    "grid.fit(data_train_x, data_train_y)\n",
    "\n",
    "print('Best parameters:  {}'.format(grid.best_params_))\n",
    "print('Best estimtor: {}'.format(grid.best_estimator_))\n",
    "print('Best score: {}'.format(grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[995   6]\n",
      " [107  23]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.99      0.95      1001\n",
      "           1       0.79      0.18      0.29       130\n",
      "\n",
      "    accuracy                           0.90      1131\n",
      "   macro avg       0.85      0.59      0.62      1131\n",
      "weighted avg       0.89      0.90      0.87      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "adaboost = AdaBoostClassifier(base_estimator = svc_linear, random_state = 1, algorithm='SAMME', \n",
    "                                 learning_rate = 1)\n",
    "adaboost.fit(data_train_x, np.ravel(data_train_y))\n",
    "\n",
    "pred_train = adaboost.predict(data_train_x)\n",
    "pred_test = adaboost.predict(data_test_x)\n",
    "print('Confusion matrix:\\n', confusion_matrix(data_test_y, pred_test))\n",
    "train_accuracy = metrics.accuracy_score(data_train_y, pred_train)\n",
    "test_accuracy = metrics.accuracy_score(data_test_y, pred_test)\n",
    "print(classification_report(data_test_y,pred_test))\n",
    "\n",
    "model_output = model_output.append(pd.Series({'Model' : 'AdaBoost w Linear SVC', 'Train_Accuracy' : train_accuracy, \n",
    "                                              'Test_Accuracy' : test_accuracy}),ignore_index=True)"
   ]
  },
  {
   "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>Model</th>\n",
       "      <th>Train_Accuracy</th>\n",
       "      <th>Test_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SVM Linear</td>\n",
       "      <td>0.929977</td>\n",
       "      <td>0.891247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>SVM RBF</td>\n",
       "      <td>0.949817</td>\n",
       "      <td>0.885057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.989330</td>\n",
       "      <td>0.849691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.935812</td>\n",
       "      <td>0.894783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.993831</td>\n",
       "      <td>0.883289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>KNN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>SVM with Bagging</td>\n",
       "      <td>0.924308</td>\n",
       "      <td>0.878868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>SVM-RBF with Bagging</td>\n",
       "      <td>0.931811</td>\n",
       "      <td>0.877100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Decision Tree with Bagging</td>\n",
       "      <td>0.996999</td>\n",
       "      <td>0.881521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Logistic Reg with Bagging</td>\n",
       "      <td>0.923641</td>\n",
       "      <td>0.879752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>RF with Bagging</td>\n",
       "      <td>0.982494</td>\n",
       "      <td>0.874447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>KNN with Bagging</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.882405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.959153</td>\n",
       "      <td>0.866490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Model  Train_Accuracy  Test_Accuracy\n",
       "0                   SVM Linear        0.929977       0.891247\n",
       "1                      SVM RBF        0.949817       0.885057\n",
       "2                Decision Tree        0.989330       0.849691\n",
       "3          Logistic Regression        0.935812       0.894783\n",
       "4                Random Forest        0.993831       0.883289\n",
       "5                          KNN        1.000000       0.872679\n",
       "6             SVM with Bagging        0.924308       0.878868\n",
       "7         SVM-RBF with Bagging        0.931811       0.877100\n",
       "8   Decision Tree with Bagging        0.996999       0.881521\n",
       "9    Logistic Reg with Bagging        0.923641       0.879752\n",
       "10             RF with Bagging        0.982494       0.874447\n",
       "11            KNN with Bagging        1.000000       0.882405\n",
       "12           Gradient Boosting        0.959153       0.866490"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best classification model for this dataset is logistic regression. It gives the highest test accuracy compared to all other models."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}