3_feature_adjustments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rc(\"font\", size=14)\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "sns.set(style=\"whitegrid\", color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41188 entries, 0 to 41187\n",
      "Data columns (total 22 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  campaign        41188 non-null  int64  \n",
      " 11  pdays           41188 non-null  int64  \n",
      " 12  previous        41188 non-null  int64  \n",
      " 13  poutcome        41188 non-null  object \n",
      " 14  emp.var.rate    41188 non-null  float64\n",
      " 15  cons.price.idx  41188 non-null  float64\n",
      " 16  cons.conf.idx   41188 non-null  float64\n",
      " 17  euribor3m       41188 non-null  float64\n",
      " 18  nr.employed     41188 non-null  float64\n",
      " 19  subscribed      41188 non-null  object \n",
      " 20  year            41188 non-null  int64  \n",
      " 21  date            41188 non-null  object \n",
      "dtypes: float64(5), int64(5), object(12)\n",
      "memory usage: 7.2+ MB\n"
     ]
    },
    {
     "data": {
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       "  <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>housing</th>\n",
       "      <th>loan</th>\n",
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       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  previous     poutcome  emp.var.rate cons.price.idx  \\\n",
       "0   may         mon  ...         0  nonexistent           1.1         93.994   \n",
       "1   may         mon  ...         0  nonexistent           1.1         93.994   \n",
       "2   may         mon  ...         0  nonexistent           1.1         93.994   \n",
       "3   may         mon  ...         0  nonexistent           1.1         93.994   \n",
       "4   may         mon  ...         0  nonexistent           1.1         93.994   \n",
       "\n",
       "   cons.conf.idx  euribor3m  nr.employed  subscribed  year        date  \n",
       "0          -36.4      4.857       5191.0          no  2008  2008-05-01  \n",
       "1          -36.4      4.857       5191.0          no  2008  2008-05-01  \n",
       "2          -36.4      4.857       5191.0          no  2008  2008-05-01  \n",
       "3          -36.4      4.857       5191.0          no  2008  2008-05-01  \n",
       "4          -36.4      4.857       5191.0          no  2008  2008-05-01  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"./data/after_first_process_data.csv\")\n",
    "data = data.dropna()\n",
    "data.rename(columns={'y': 'subscribed'}, inplace=True)\n",
    "\n",
    "data.info()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
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       "      <th>day_of_week</th>\n",
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       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>...</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>...</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "      <td>10570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>...</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>...</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age    job  marital  education  default  housing   loan  contact  \\\n",
       "campaign                                                                       \n",
       "1         17642  17642    17642      17642    17642    17642  17642    17642   \n",
       "2         10570  10570    10570      10570    10570    10570  10570    10570   \n",
       "3          5341   5341     5341       5341     5341     5341   5341     5341   \n",
       "4          7635   7635     7635       7635     7635     7635   7635     7635   \n",
       "\n",
       "          month  day_of_week  ...  previous  poutcome  emp.var.rate  \\\n",
       "campaign                      ...                                     \n",
       "1         17642        17642  ...     17642     17642         17642   \n",
       "2         10570        10570  ...     10570     10570         10570   \n",
       "3          5341         5341  ...      5341      5341          5341   \n",
       "4          7635         7635  ...      7635      7635          7635   \n",
       "\n",
       "          cons.price.idx  cons.conf.idx  euribor3m  nr.employed  subscribed  \\\n",
       "campaign                                                                      \n",
       "1                  17642          17642      17642        17642       17642   \n",
       "2                  10570          10570      10570        10570       10570   \n",
       "3                   5341           5341       5341         5341        5341   \n",
       "4                   7635           7635       7635         7635        7635   \n",
       "\n",
       "           year   date  \n",
       "campaign                \n",
       "1         17642  17642  \n",
       "2         10570  10570  \n",
       "3          5341   5341  \n",
       "4          7635   7635  \n",
       "\n",
       "[4 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Discretization of campaign variabile into 1, 2,3 and 3+\n",
    "data.loc[data.campaign > 3, 'campaign'] = 4\n",
    "data.groupby('campaign').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>39673</td>\n",
       "      <td>39673</td>\n",
       "      <td>39673</td>\n",
       "      <td>39673</td>\n",
       "      <td>39673</td>\n",
       "      <td>39673</td>\n",
       "      <td>39673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>...</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    age    job  marital  education  default  housing   loan  \\\n",
       "contacted_before                                                              \n",
       "0                 39673  39673    39673      39673    39673    39673  39673   \n",
       "1                  1515   1515     1515       1515     1515     1515   1515   \n",
       "\n",
       "                  contact  month  day_of_week  ...  previous  poutcome  \\\n",
       "contacted_before                               ...                       \n",
       "0                   39673  39673        39673  ...     39673     39673   \n",
       "1                    1515   1515         1515  ...      1515      1515   \n",
       "\n",
       "                  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "contacted_before                                                           \n",
       "0                        39673           39673          39673      39673   \n",
       "1                         1515            1515           1515       1515   \n",
       "\n",
       "                  nr.employed  subscribed   year   date  \n",
       "contacted_before                                         \n",
       "0                       39673       39673  39673  39673  \n",
       "1                        1515        1515   1515   1515  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert pdays into binary - 999 - 0 - never contacted, 1- contacted before\n",
    "data.groupby('pdays').count()\n",
    "\n",
    "data.loc[(data.pdays != 999), 'pdays'] = 1\n",
    "data.loc[(data.pdays == 999), 'pdays'] = 0\n",
    "data.rename(columns={'pdays': 'contacted_before'}, inplace=True)\n",
    "data.groupby('contacted_before').count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>contacted_before</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>...</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "      <td>35563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>...</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "      <td>5625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age    job  marital  education  default  housing   loan  contact  \\\n",
       "previous                                                                       \n",
       "0         35563  35563    35563      35563    35563    35563  35563    35563   \n",
       "1          5625   5625     5625       5625     5625     5625   5625     5625   \n",
       "\n",
       "          month  day_of_week  ...  contacted_before  poutcome  emp.var.rate  \\\n",
       "previous                      ...                                             \n",
       "0         35563        35563  ...             35563     35563         35563   \n",
       "1          5625         5625  ...              5625      5625          5625   \n",
       "\n",
       "          cons.price.idx  cons.conf.idx  euribor3m  nr.employed  subscribed  \\\n",
       "previous                                                                      \n",
       "0                  35563          35563      35563        35563       35563   \n",
       "1                   5625           5625       5625         5625        5625   \n",
       "\n",
       "           year   date  \n",
       "previous                \n",
       "0         35563  35563  \n",
       "1          5625   5625  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert previous into binary - 0 - never contacted, 1- contacted before\n",
    "data.groupby('previous').count()\n",
    "\n",
    "data.loc[(data.previous != 0), 'previous'] = 1\n",
    "data.loc[(data.previous == 0), 'previous'] = 0\n",
    "data.rename(columns={'previous': 'previous'}, inplace=True)\n",
    "data.groupby('previous').count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>contacted_before</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>cons.price.idx</th>\n",
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       "      <th>euribor3m</th>\n",
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       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>...</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "      <td>12090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>...</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "      <td>5101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>...</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "      <td>23997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                age    job  marital  education  default  housing   loan  \\\n",
       "emp.var.rate                                                              \n",
       "1.0           12090  12090    12090      12090    12090    12090  12090   \n",
       "2.0            5101   5101     5101       5101     5101     5101   5101   \n",
       "3.0           23997  23997    23997      23997    23997    23997  23997   \n",
       "\n",
       "              contact  month  day_of_week  ...  contacted_before  previous  \\\n",
       "emp.var.rate                               ...                               \n",
       "1.0             12090  12090        12090  ...             12090     12090   \n",
       "2.0              5101   5101         5101  ...              5101      5101   \n",
       "3.0             23997  23997        23997  ...             23997     23997   \n",
       "\n",
       "              poutcome  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \\\n",
       "emp.var.rate                                                                    \n",
       "1.0              12090           12090          12090      12090        12090   \n",
       "2.0               5101            5101           5101       5101         5101   \n",
       "3.0              23997           23997          23997      23997        23997   \n",
       "\n",
       "              subscribed   year   date  \n",
       "emp.var.rate                            \n",
       "1.0                12090  12090  12090  \n",
       "2.0                 5101   5101   5101  \n",
       "3.0                23997  23997  23997  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#bin emp.var.rate into 3 levels: smaller -1.8, (-1.8:-0.1), larger -0.1\n",
    "#df['Fee'] = np.where((df['Fee'] >= 22000) & (df['Courses'] == 'PySpark'), 14000, df['Fee'])\n",
    "\n",
    "data['emp.var.rate'] = np.where((data['emp.var.rate'] <= -1.8), -2, data['emp.var.rate'] )\n",
    "data['emp.var.rate'] = np.where((data['emp.var.rate'] > -1.8) & (data['emp.var.rate'] <=-0.1), -1.8, data['emp.var.rate'] )\n",
    "data['emp.var.rate'] = np.where((data['emp.var.rate'] > -0.1), -0.1, data['emp.var.rate'] )\n",
    "\n",
    "data.loc[(data['emp.var.rate'] == -2), 'emp.var.rate'] = 1\n",
    "data.loc[(data['emp.var.rate'] == -1.8), 'emp.var.rate'] = 2\n",
    "data.loc[(data['emp.var.rate'] == -0.1), 'emp.var.rate'] = 3\n",
    "\n",
    "data.groupby('emp.var.rate').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4963.6</th>\n",
       "      <td>635</td>\n",
       "      <td>635</td>\n",
       "      <td>635</td>\n",
       "      <td>635</td>\n",
       "      <td>635</td>\n",
       "      <td>635</td>\n",
       "      <td>635</td>\n",
       "      <td>635</td>\n",
       "      <td>635</td>\n",
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       "      <td>635</td>\n",
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       "      <td>635</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4991.6</th>\n",
       "      <td>773</td>\n",
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       "      <td>773</td>\n",
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       "      <td>773</td>\n",
       "      <td>773</td>\n",
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       "      <td>773</td>\n",
       "      <td>773</td>\n",
       "      <td>773</td>\n",
       "      <td>773</td>\n",
       "      <td>773</td>\n",
       "      <td>773</td>\n",
       "      <td>773</td>\n",
       "      <td>773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5008.7</th>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>...</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5017.5</th>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>...</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "      <td>1071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5023.5</th>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>...</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5076.2</th>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>...</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "      <td>1663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5099.1</th>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>...</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "      <td>8534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5176.3</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191.0</th>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>...</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "      <td>7763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5195.8</th>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>...</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "      <td>3683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5228.1</th>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>...</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "      <td>16234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               age    job  marital  education  default  housing   loan  \\\n",
       "nr.employed                                                              \n",
       "4963.6         635    635      635        635      635      635    635   \n",
       "4991.6         773    773      773        773      773      773    773   \n",
       "5008.7         650    650      650        650      650      650    650   \n",
       "5017.5        1071   1071     1071       1071     1071     1071   1071   \n",
       "5023.5         172    172      172        172      172      172    172   \n",
       "5076.2        1663   1663     1663       1663     1663     1663   1663   \n",
       "5099.1        8534   8534     8534       8534     8534     8534   8534   \n",
       "5176.3          10     10       10         10       10       10     10   \n",
       "5191.0        7763   7763     7763       7763     7763     7763   7763   \n",
       "5195.8        3683   3683     3683       3683     3683     3683   3683   \n",
       "5228.1       16234  16234    16234      16234    16234    16234  16234   \n",
       "\n",
       "             contact  month  day_of_week  ...  contacted_before  previous  \\\n",
       "nr.employed                               ...                               \n",
       "4963.6           635    635          635  ...               635       635   \n",
       "4991.6           773    773          773  ...               773       773   \n",
       "5008.7           650    650          650  ...               650       650   \n",
       "5017.5          1071   1071         1071  ...              1071      1071   \n",
       "5023.5           172    172          172  ...               172       172   \n",
       "5076.2          1663   1663         1663  ...              1663      1663   \n",
       "5099.1          8534   8534         8534  ...              8534      8534   \n",
       "5176.3            10     10           10  ...                10        10   \n",
       "5191.0          7763   7763         7763  ...              7763      7763   \n",
       "5195.8          3683   3683         3683  ...              3683      3683   \n",
       "5228.1         16234  16234        16234  ...             16234     16234   \n",
       "\n",
       "             poutcome  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "nr.employed                                                                     \n",
       "4963.6            635           635             635            635        635   \n",
       "4991.6            773           773             773            773        773   \n",
       "5008.7            650           650             650            650        650   \n",
       "5017.5           1071          1071            1071           1071       1071   \n",
       "5023.5            172           172             172            172        172   \n",
       "5076.2           1663          1663            1663           1663       1663   \n",
       "5099.1           8534          8534            8534           8534       8534   \n",
       "5176.3             10            10              10             10         10   \n",
       "5191.0           7763          7763            7763           7763       7763   \n",
       "5195.8           3683          3683            3683           3683       3683   \n",
       "5228.1          16234         16234           16234          16234      16234   \n",
       "\n",
       "             subscribed   year   date  \n",
       "nr.employed                            \n",
       "4963.6              635    635    635  \n",
       "4991.6              773    773    773  \n",
       "5008.7              650    650    650  \n",
       "5017.5             1071   1071   1071  \n",
       "5023.5              172    172    172  \n",
       "5076.2             1663   1663   1663  \n",
       "5099.1             8534   8534   8534  \n",
       "5176.3               10     10     10  \n",
       "5191.0             7763   7763   7763  \n",
       "5195.8             3683   3683   3683  \n",
       "5228.1            16234  16234  16234  \n",
       "\n",
       "[11 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('nr.employed').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.groupby('marital').count()\n",
    "\n",
    "#unknown marital status will be rolled into married\n",
    "\n",
    "data.loc[(data.marital == 'unknown'), 'marital'] = 'married'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.groupby('job').count()\n",
    "\n",
    "#unknown job category will be rolled into admin\n",
    "\n",
    "data.loc[(data.job == 'unknown'), 'job'] = 'admin.'\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>campaign</th>\n",
       "      <th>...</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>basic.4y</th>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>...</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basic.6y</th>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>...</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basic.9y</th>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>...</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high.school</th>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>...</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>professional.course</th>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>...</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>university.degree</th>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>...</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>...</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "      <td>1749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       age    job  marital  default  housing   loan  contact  \\\n",
       "education                                                                      \n",
       "basic.4y              4176   4176     4176     4176     4176   4176     4176   \n",
       "basic.6y              2292   2292     2292     2292     2292   2292     2292   \n",
       "basic.9y              6045   6045     6045     6045     6045   6045     6045   \n",
       "high.school           9515   9515     9515     9515     9515   9515     9515   \n",
       "professional.course   5243   5243     5243     5243     5243   5243     5243   \n",
       "university.degree    12168  12168    12168    12168    12168  12168    12168   \n",
       "unknown               1749   1749     1749     1749     1749   1749     1749   \n",
       "\n",
       "                     month  day_of_week  campaign  ...  previous  poutcome  \\\n",
       "education                                          ...                       \n",
       "basic.4y              4176         4176      4176  ...      4176      4176   \n",
       "basic.6y              2292         2292      2292  ...      2292      2292   \n",
       "basic.9y              6045         6045      6045  ...      6045      6045   \n",
       "high.school           9515         9515      9515  ...      9515      9515   \n",
       "professional.course   5243         5243      5243  ...      5243      5243   \n",
       "university.degree    12168        12168     12168  ...     12168     12168   \n",
       "unknown               1749         1749      1749  ...      1749      1749   \n",
       "\n",
       "                     emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "education                                                                     \n",
       "basic.4y                     4176            4176           4176       4176   \n",
       "basic.6y                     2292            2292           2292       2292   \n",
       "basic.9y                     6045            6045           6045       6045   \n",
       "high.school                  9515            9515           9515       9515   \n",
       "professional.course          5243            5243           5243       5243   \n",
       "university.degree           12168           12168          12168      12168   \n",
       "unknown                      1749            1749           1749       1749   \n",
       "\n",
       "                     nr.employed  subscribed   year   date  \n",
       "education                                                   \n",
       "basic.4y                    4176        4176   4176   4176  \n",
       "basic.6y                    2292        2292   2292   2292  \n",
       "basic.9y                    6045        6045   6045   6045  \n",
       "high.school                 9515        9515   9515   9515  \n",
       "professional.course         5243        5243   5243   5243  \n",
       "university.degree          12168       12168  12168  12168  \n",
       "unknown                     1749        1749   1749   1749  \n",
       "\n",
       "[7 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('education').count()\n",
    "\n",
    "#combine illiterare with unknown, illiterate is not significant by itself \n",
    "\n",
    "data.loc[(data.education == 'illiterate'), 'education'] = 'unknown'\n",
    "data.groupby('education').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6248</td>\n",
       "      <td>6248</td>\n",
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       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           age    job  marital  education  default  housing  contact  month  \\\n",
       "loan                                                                          \n",
       "no       33950  33950    33950      33950    33950    33950    33950  33950   \n",
       "unknown    990    990      990        990      990      990      990    990   \n",
       "yes       6248   6248     6248       6248     6248     6248     6248   6248   \n",
       "\n",
       "         day_of_week  campaign  ...  previous  poutcome  emp.var.rate  \\\n",
       "loan                            ...                                     \n",
       "no             33950     33950  ...     33950     33950         33950   \n",
       "unknown          990       990  ...       990       990           990   \n",
       "yes             6248      6248  ...      6248      6248          6248   \n",
       "\n",
       "         cons.price.idx  cons.conf.idx  euribor3m  nr.employed  subscribed  \\\n",
       "loan                                                                         \n",
       "no                33950          33950      33950        33950       33950   \n",
       "unknown             990            990        990          990         990   \n",
       "yes                6248           6248       6248         6248        6248   \n",
       "\n",
       "          year   date  \n",
       "loan                   \n",
       "no       33950  33950  \n",
       "unknown    990    990  \n",
       "yes       6248   6248  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('loan').count()\n",
    "\n",
    "#combine illiterare with unknown, illiterate is not significant by itself \n",
    "\n",
    "#data.loc[(data.loan == 'unknown'), 'loan'] = 'no'\n",
    "#data.groupby('loan').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>22566</td>\n",
       "      <td>22566</td>\n",
       "      <td>22566</td>\n",
       "      <td>22566</td>\n",
       "      <td>22566</td>\n",
       "      <td>22566</td>\n",
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       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           age    job  marital  education  default   loan  contact  month  \\\n",
       "housing                                                                     \n",
       "no       18622  18622    18622      18622    18622  18622    18622  18622   \n",
       "yes      22566  22566    22566      22566    22566  22566    22566  22566   \n",
       "\n",
       "         day_of_week  campaign  ...  previous  poutcome  emp.var.rate  \\\n",
       "housing                         ...                                     \n",
       "no             18622     18622  ...     18622     18622         18622   \n",
       "yes            22566     22566  ...     22566     22566         22566   \n",
       "\n",
       "         cons.price.idx  cons.conf.idx  euribor3m  nr.employed  subscribed  \\\n",
       "housing                                                                      \n",
       "no                18622          18622      18622        18622       18622   \n",
       "yes               22566          22566      22566        22566       22566   \n",
       "\n",
       "          year   date  \n",
       "housing                \n",
       "no       18622  18622  \n",
       "yes      22566  22566  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('housing').count()\n",
    "\n",
    "\n",
    "data.loc[(data.housing == 'unknown'), 'housing'] = 'yes'\n",
    "data.groupby('housing').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41185 entries, 0 to 41187\n",
      "Data columns (total 22 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   age               41185 non-null  int64  \n",
      " 1   job               41185 non-null  object \n",
      " 2   marital           41185 non-null  object \n",
      " 3   education         41185 non-null  object \n",
      " 4   default           41185 non-null  object \n",
      " 5   housing           41185 non-null  object \n",
      " 6   loan              41185 non-null  object \n",
      " 7   contact           41185 non-null  object \n",
      " 8   month             41185 non-null  object \n",
      " 9   day_of_week       41185 non-null  object \n",
      " 10  campaign          41185 non-null  int64  \n",
      " 11  contacted_before  41185 non-null  int64  \n",
      " 12  previous          41185 non-null  int64  \n",
      " 13  poutcome          41185 non-null  object \n",
      " 14  emp.var.rate      41185 non-null  float64\n",
      " 15  cons.price.idx    41185 non-null  float64\n",
      " 16  cons.conf.idx     41185 non-null  float64\n",
      " 17  euribor3m         41185 non-null  float64\n",
      " 18  nr.employed       41185 non-null  float64\n",
      " 19  subscribed        41185 non-null  object \n",
      " 20  year              41185 non-null  int64  \n",
      " 21  date              41185 non-null  object \n",
      "dtypes: float64(5), int64(5), object(12)\n",
      "memory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.groupby('default').count()\n",
    "\n",
    "data = data[data['default'] != 'yes']\n",
    "# data.loc[(data.housing == 'unknown'), 'housing'] = 'no'\n",
    "data.groupby('default').count()\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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U2rdvr8KFC+v555+Xt7e3zb3HTp06adiwYerbt68+++wzffvttxo0aJCk28fOR4wYoQ8//FC3bt36w2VVqVJFgYGB6tSpk/z9/RUVFZXj+LuXl5eaNm2qCxcu3PcqB+n25YKRkZHWnaUmTZooIiJC0u0dq2PHjql79+4qXry4RowYoQEDBuSYv2/fvho9erR++eUXtWzZUkOGDJF0e+dj0KBBmjJlii5fvqzSpUtr+vTp1sMPMTExmjRpkmJjYxUYGKioqCjVrl3besz/QRo1aqQbN26offv2CgwM1Ny5c62HX/r06aObN29q/Pjxun79uvX7ULx4cUnSRx99pIiICHXu3FklS5ZUeHi4Vq1a9YfrRP5hsuTnO5sAdnLu3DldunTJeka7JI0fP143b97U5MmTHVgZfq9Hjx5q2bKltVcMgGF/4KGkpqaqZ8+e2rRpk86fP68tW7ZozZo1d115AMdJSEhQTEyMDh48eN9L/ACjYtgfeAhVqlTR2LFjNWPGDF24cEGBgYEaMWIE96t/gqxbt06bN2/W2LFj5efn5+hygCcKw/4AABgMw/4AABiMIYb9zWaz0tLS5O7uftclOgAA5EcWi0WZmZny9va+66ZQhgj/tLQ0HTt2zNFlAACQ5ypWrHjXI6ENEf53bqNZsWJFm287CgCAM8vIyNCxY8fueStpQ4T/naF+Dw+PB94rGwCA/OZeh7s54Q8AAIMh/AEAMBjCHwAAgzHEMX8AQP5jNpv1888/KyUlJcfjtY3Gy8tLQUFB9zyx734IfwCAU0pKSpLJZFLZsmUNex8Xi8Wiq1evKikpScHBwTbPx7A/AMAppaWlqWTJkvLw8DBk8Eu3z+QvUqSITY/A/i3CHwDgtH5/5zojepgdHz41AIBhzZo1S5GRkXZZ9muvvabjx48rISHhsTxW+k9/+pOSkpIeQ2Uc8wcAwC4WLFggSbp69aqDK7kb4Q8AyDfS0tI0YsQInTlzRi4uLqpWrZpat26tSZMmaf369ZKkhIQETZgwwfr6xIkT6tatm65du6YqVapo7Nix8vHx0dKlS7V8+XK5u7vL09NTkZGReuaZZ3Tq1CmNGTNGycnJcnFx0RtvvKFWrVqpadOmCgkJ0dGjR/Xee+8pOjpaM2fOlCT9+uuvGjhwoM6cOSM/Pz9FRkYqODhYGRkZmjZtmr799ltlZ2eratWqGj16tHx8fPTdd99pwoQJMplMqlGjhsxm82P7nBj2BwDkG19++aXS0tK0Zs0affbZZ5L0h0PlZ8+e1axZs7Ru3TpZLBbNmzdP2dnZioqK0sKFC/X555+rY8eO2rNnjyTpvffeU8uWLbVhwwbFxMRoxowZSk1NlSRVqFBBX3zxhZo3b55jHRcuXFDPnj21Zs0atWnTRkOHDpUkxcTEyNXVVStXrtTatWtVrFgxTZs2TRkZGXrnnXc0fPhwrV69WvXq1cv1SX0PQvgDAPKN0NBQHT9+XOHh4YqJidGrr76q0qVLP3Ce5s2bKyAgQCaTSS+99JJ27dolV1dXtWzZUp07d1ZkZKR8fX318ssvKyUlRUeOHNErr7wiSSpRooTi4+Pl4+MjSapTp84911GpUiXVrl1bktS+fXslJibqxo0b2rZtm/7zn/+oXbt2atu2reLj43XixAkdO3ZMbm5ueu655yRJbdq0kbe39+P6mBj2BwDkH6VKldKXX36phIQEffPNN+rVq5c6d+4si8VibZOZmZljHldXV+vPFotFbm63o3HatGk6duyYdu3apQULFuizzz7T1KlTJeU8w/7kyZMKDAyUJBUsWPCedf3+qgSTySQ3NzeZzWaNHDlSjRo1knT7sEV6erouXLiQo2ZJ1roeB3r+NsjINNado4y2vQDyj6VLl2rEiBGqX7++hgwZovr16+vHH3/UTz/9pKtXr8pisSg+Pj7HPP/5z3907do1ZWdnKzY2Vg0bNlRycrIaNWokf39/9ezZU4MGDdLRo0fl4+OjatWqafXq1ZJuD+d36dJFN27ceGBdR48e1eHDhyVJsbGxCg0NVYECBVS/fn0tWbJEGRkZMpvNioiI0IwZM1SxYkVZLBZ99dVXkqStW7fq2rVrj+1zoudvAw93V3UdusTRZeSZpVO6OboEAHgo7dq10+7du9WqVSsVKFBAgYGBmjRpkubPn6+XXnpJRYsWVePGjXPMU758efXv31/Xr19XaGio+vXrJ09PT73xxhvq2bOnvLy85OrqqokTJ0qSpk+frvHjx2vx4sUymUyaNGmSihYt+sC6ypUrp9mzZ+vcuXMqUqSIJk+eLEl688039cEHH6h9+/bKzs5WlSpVNHz4cLm7u2vOnDkaN26cZsyYoSpVqqhIkSKP7XMyWX4/rpAPpaenKzExUdWrV5enp+dDLYPwB4Any+HDh1WlShVHl/FEuNdn8aDsY9gfAACDIfwBADAYwh8AAIMh/AEAMBjCHwAAgyH8AQAwGMIfAACDIfwBAPmave5a6sx3Q+UOfwCAfM1ed2l15huiEf4AANhJQkKC/vGPf8jLy0snTpxQpUqVNG3aNK1bt07//Oc/ZTKZVK1aNUVERDzWp/b9EYb9AQCwo71792rMmDH64osv9NNPPyk2Nlbz58/X4sWLtW7dOhUoUECzZ8/O05oIfwAA7KhChQp6+umn5eLiovLly+vatWtq0qSJChcuLEnq1KmTvvnmmzytifAHAMCOfvtQHZPJJD8/vxzvWywWZWVl5WlNhD8AAHnsP//5j1JSUiRJK1asUL169fJ0/ZzwBwBAHvLx8VH//v0VHh6uzMxMVatWTePHj8/TGgh/AEC+lpGZbZfL8jIys+Xh7vrANvXq1cvRq588ebL151deeeWx12Qrhv0BAPnaHwX0k7bcvED4AwBgMIQ/AAAGQ/gDAGAwhD8AAAZD+AMAYDCEPwAgXzNnZTrVcvMC1/kDAPI1Fzd37ZnS97EvN3Towse+zLxCzx8AADsZMmSIYmNjra/Dw8O1b98+9erVS+3bt1eXLl106NAhSdK6devUtm1bdejQQQMHDlR6errd6iL8AQCwk5deeklr166VJJ0/f17JycmKjo7WkCFDtGrVKk2YMEHvvvuuJOmjjz7SJ598opUrVyo4OFgnT560W10M+wMAYCf16tVTRESEkpKStGbNGj3//POaP3++RowYYW3z66+/6pdfflGTJk3UpUsXNWvWTGFhYapSpYrd6rJL+GdmZmrkyJE6f/68MjIy9MYbb+iZZ57R8OHDZTKZVKFCBY0dO1YuLi6aPXu2tm3bJjc3N40cOVIhISE6c+aMzW0BAHhSmUwmtWvXThs2bNCmTZs0f/58ffLJJ1qzZo21zcWLF+Xv76/Ro0fryJEj+uqrrzRkyBANGDBAbdu2tUtddhn2X7t2rfz9/bV06VItXLhQEyZMUHR0tAYNGqSlS5fKYrFo69atOnjwoHbv3q24uDjNmDHD+lSj3LQFAOBJ1qFDBy1fvlxPP/20SpYsqbJly1rDf+fOnerWrZuysrLUokULFS5cWP3791fbtm11+PBhu9Vkl55/y5YtFRYWJkmyWCxydXXVwYMH9ec//1mS1LBhQ+3cuVPBwcGqX7++TCaTAgMDlZ2dreTk5Fy1DQgIsMcmAADwWJQoUUIlSpRQ+/btJUlTp07VuHHjtHDhQrm7u+vDDz+Uu7u7Bg4cqF69esnLy0t+fn764IMP7FaTXcLf29tbkpSamqqBAwdq0KBB+uCDD2Qymazv37hxQ6mpqfL3988x340bN2SxWGxuS/gDAB7EnJVpl8vyzFmZcnFzf2Abi8Wiy5cv6+eff9bf/vY3SVL58uW1ePHiu9q2adNGbdq0eex13ovdTvi7cOGC3nrrLXXt2lUvvPCCpk6dan0vLS1Nfn5+8vHxUVpaWo7pvr6+cnFxsbltbiQmJj7UtoSGhj7UfM5sz549ji4BAB7Izc0tRy48UHqGfYr4g+XGx8crKipKI0eOVGZmpjIz7XNjoIyMjFz93bZL+P/888/q3bu3xowZo+eee06SVLVqVSUkJKhevXravn27nn32WZUuXVpTp05Vnz59dPHiRZnNZgUEBOSqbW5Ur15dnp6e9tjkfMeIOzwAnMvhw4etI81PqrZt29rtpL3f8vDwUM2aNXNMS09Pv2+n1y7hP3/+fF2/fl1z587V3LlzJUmjRo3SxIkTNWPGDJUrV05hYWFydXVVnTp11KlTJ5nNZo0ZM0aSNGzYMEVERNjUFgAA5I7JYrFYHF2Evd3Z+3mUnn/XoUsec1VPrqVTujm6BAD4Q4cPH1alSpVyHCo2IovFoiNHjtx1X4AHZZ+xPzEAgNPy9va23k/GAP3Ye7JYLLp69aq8vLxyNR93+AMAOKWgoCD9/PPPOnPmjLKyshxdjsN4eXkpKCgoV/MQ/gAAp+Ti4qJixYqpWLFiji7F6TDsDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABiMXcN/3759Cg8PlyQdOnRIDRo0UHh4uMLDw7Vx40ZJ0uzZs/Xyyy+rc+fO2r9/vyTpzJkz6tKli7p27aqxY8fKbDbfty0AAMgdN3steMGCBVq7dq0KFCggSTp48KB69eql3r17W9scPHhQu3fvVlxcnC5cuKC3335bn3/+uaKjozVo0CDVq1dPY8aM0datWxUYGHjPtgAAIHfs1vMvXbq0Zs2aZX2dmJiobdu2qVu3bho5cqRSU1O1Z88e1a9fXyaTSYGBgcrOzlZycrIOHjyoP//5z5Kkhg0bateuXfdtCwAAcsdu4R8WFiY3t/8NLISEhGjo0KFasmSJSpUqpTlz5ig1NVU+Pj7WNt7e3rpx44YsFotMJlOOafdrCwAAcsduw/6/17x5c/n5+Vl/njBhgpo1a6a0tDRrm7S0NPn6+srFxSXHND8/P/n4+NyzbW4kJiY+VO2hoaEPNZ8z27Nnj6NLAADYSZ6Ff58+fRQREaGQkBB9/fXXqlatmmrXrq2pU6eqT58+unjxosxmswICAlS1alUlJCSoXr162r59u5599lmVLl36nm1zo3r16vL09LTTFuYvRtzhAYD8JD09/b6d3jwL/3HjxmnChAlyd3fXU089pQkTJsjHx0d16tRRp06dZDabNWbMGEnSsGHDFBERoRkzZqhcuXIKCwuTq6vrPdsCAIDcMVksFouji7C3O3s/j9Lz7zp0yWOu6sm1dEo3R5cAAHhED8o+bvIDAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABiMTeG/ePFipaam2rsWAACQB2wK/2PHjiksLEyjRo3SgQMH7F0TAACwI5vCf8KECdq8ebOqV6+u8ePH66WXXtJnn32m9PR0e9cHAAAeM5uP+fv4+Khly5Zq06aNUlJStHTpUrVs2VKbNm2yZ30AAOAxc7Ol0a5du7RixQp9/fXXCgsL05w5c1S5cmWdPXtWXbt2VcuWLe1dJwAAeExsCv/IyEh17dpVEyZMkK+vr3V66dKl1bFjR7sVBwAAHj+bhv3Xrl0rf39/+fr66sqVK1q0aJHMZrMkaeDAgXYtEAAAPF42n/C3bdu22zO4uGjPnj2KioqyZ10AAMBObBr237t3r9avXy9JKlKkiGbOnKm2bdvatTAAAGAfNvX8MzMzlZGRYX2dlZVlt4IAAIB92dTzb9y4sfr06aO2bdvKZDJp/fr1atSokb1rAwAAdmBT+A8dOlRLlizR1q1b5ebmpubNm6tz5872rg0AANiBTeHv6uqqHj16qEePHvauBwAA2JlN4b9x40ZNmzZN165dk8VisU7//vvv7VYYAACwD5vCf+bMmRo+fLiqVq0qk8lk75oAAIAd2RT+fn5+atGihb1rAQAAecCmS/1q1qypr776yt61AACAPGBTz/+rr77Sv//9b7m7u8vd3V0Wi0Umk4lj/gAAOCGbwn/RokV2LgMAAOQVm4b9S5YsqQMHDmjFihUKCAjQ3r17VbJkSXvXBgAA7MCm8I+JidGyZcu0adMm3bp1S7Nnz9acOXPsXRsAALADm8J/w4YNWrBggQoUKKDChQtrxYoV1gf9AAAA52JT+Lu5ucnDw8P62s/PT25uNp0uAAAAnjA2JXiJEiW0bds2mUwmZWRk6OOPP+aYPwAATsqm8I+IiNDQoUN19OhR1apVSzVr1tS0adPsXRsAALADm8K/ePHi+te//qWbN28qOztbPj4+9q4LAADYiU3h/89//vOe03v16vVYiwEAAPZnU/gfO3bM+nNGRob27NmjevXq2a0oAABgPzaFf3R0dI7XycnJGjp0qF0KAgAA9mXTpX6/FxAQoPPnzz/uWgAAQB7I9TF/i8WixMREFSlSxG5FAQAA+8n1MX/p9nX/DPsDAOCcHuqYPwAAcF42hX94eLhMJtN93//0008fW0EAAMC+bAr/6tWr68SJE+rYsaPc3d21Zs0aZWVlqXXr1vauDwAAPGY2hf/333+vpUuXytXVVZLUoEEDdezYUWFhYXYtDgAAPH42XeqXnJysjIwM6+u0tDTdunXLbkUBAAD7sann36ZNG3Xs2FHNmzeXxWLRF198oR49eti7NgAAYAc2hf8777yjqlWr6ptvvpGnp6ciIyP15z//2d61AQAAO7D5Dn/FixdXhQoVNGjQILm7u9uzJgAAYEc2hf/nn3+uESNGaOHChbpx44befPNNrVixwt61AQAAO7Ap/P/9738rNjZWPj4+KlKkiFauXKl//etf9q4NAADYgU3h7+LiIh8fH+vrEiVKWC/7AwAAzsWm8Pf399fhw4etd/lbu3atChUqZNfCAACAfdh0tv/IkSP1zjvv6OzZs6pfv748PT01d+5ce9cGAADswKbwv3XrltasWaPTp08rOztbwcHBnPEPAICTsmnY//3335erq6vKly+vihUrEvwAADgxm8K/UqVKWrdunX766SelpKRY/wEAAOdj07D/1q1btWnTphzTTCaTDh8+/MD59u3bp2nTpmnx4sU6c+aMhg8fLpPJpAoVKmjs2LFycXHR7NmztW3bNrm5uWnkyJEKCQnJVVsAAJA7NoX/gQMHcr3gBQsWaO3atSpQoIAkKTo6WoMGDVK9evU0ZswYbd26VYGBgdq9e7fi4uJ04cIFvf322/r8889z1RYAAOTOA4f9IyIirD8nJyfnasGlS5fWrFmzrK8PHjxofR5Aw4YNtWvXLu3Zs0f169eXyWRSYGCgsrOzlZycnKu2AAAgdx7Y809MTLT+3KdPH61atcrmBYeFhSkpKcn62mKxWO8T4O3trRs3big1NVX+/v7WNnem56ZtQECAzTX9dntyIzQ09KHmc2Z79uxxdAkAADt5YPhbLJZ7/vwwXFz+N8iQlpYmPz8/+fj4KC0tLcd0X1/fXLXNjerVq8vT0/MRtsI4jLjDk5cyMrPl4W6cu2QabXuBJ0F6evp9O702HfOXZO2JP6yqVasqISFB9erV0/bt2/Xss8+qdOnSmjp1qvr06aOLFy/KbDYrICAgV20BZ+Th7qquQ5c4uow8s3RKN0eXAOA3Hhj+ZrNZ165dk8ViUXZ2tvXnO347DP9Hhg0bpoiICM2YMUPlypVTWFiYXF1dVadOHXXq1Elms1ljxozJdVsAAJA7JssDxvMrV64sk8l0zyF/Wy71e1LcGfp4lGF/eml43PhOAbCnB2XfA3v+R44csWthAAAg79l0hz8AAJB/EP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYDOEPAIDBEP4AABgM4Q8AgMEQ/gAAGAzhDwCAwRD+AAAYjFter7B9+/by8fGRJAUFBalTp06aNGmSXF1dVb9+fQ0YMEBms1njxo3T0aNH5eHhoYkTJ6pMmTL64Ycf7moLAAByJ0/DPz09XRaLRYsXL7ZOa9u2rWbNmqVSpUqpX79+OnTokJKSkpSRkaHY2Fj98MMPmjx5subNm6exY8fe1bZq1ap5uQkAADi9PA3/I0eO6ObNm+rdu7eysrL09ttvKyMjQ6VLl5Yk1a9fX7t27dKVK1fUoEEDSVKtWrWUmJio1NTUe7Yl/AEAyJ08DX8vLy/16dNHr7zyik6fPq3XXntNfn5+1ve9vb117tw5paamWg8NSJKrq+td0+60zY3ExMSHqjs0NPSh5nNme/bscXQJ+RrfKQCOlKfhHxwcrDJlyshkMik4OFi+vr5KSUmxvp+WliY/Pz/dunVLaWlp1ulms1k+Pj45pt1pmxvVq1eXp6fnI2+HERgxnGBffKeAvJWenn7fTm+enu3/2WefafLkyZKkS5cu6ebNmypYsKDOnj0ri8WiHTt2qE6dOqpdu7a2b98uSfrhhx9UsWJF+fj4yN3d/a62AAAgd/K05//yyy9rxIgR6tKli0wmk6KiouTi4qL3339f2dnZql+/vmrWrKkaNWpo586d6ty5sywWi6KioiRJ48ePv6stAADInTwNfw8PD02fPv2u6StWrMjx2sXFRZGRkXe1q1Wr1l1tAQBA7nCTHwAADIbwBwDAYAh/AAAMhvAHAMBgCH8AAAyG8AcAwGAIfwAADIbwB4B8ICMz29El5Bkjbau95OlNfgAA9uHh7qquQ5c4uow8sXRKN0eX4PTo+QMAYDCEPwAABkP4AwBgMIQ/AAAGQ/gDAGAwhD8AAAZD+AMAYDCEPwAABkP4AwBgMIQ/AAAGQ/gDAGAwhD8AAAZD+AMAYDCEPwAABkP4AwBgMIQ/AAAGQ/gDAGAwhD8AAAZD+AMAYDCEPwAABkP44y7mrExHl5BnjLStAHCHm6MLwJPHxc1de6b0dXQZeSJ06EJHlwAAeY6ePwAABkP4AwBgMIQ/AAAGQ/gDAGAwhD8AAAZD+AMAYDCEPwAABkP4AwBgMIQ/AAAGQ/gDAGAwhD8AAAZD+AOwOyM9QMlI2wrnxYN9ANgdD4sCniz0/AEAMBjCHwAAgyH8AQAwGMIfAACDIfwBADAYwh8AAIMh/AEAMBjCHwAAgyH8AQAwGMIfAACDIfwBADAYwh8AAIMh/AEAMBjCHwAAgyH8AQBOxZyV6egS8pQ9ttftsS8RAAA7cnFz154pfR1dRp4JHbrwsS+Tnj8AAAbjdD1/s9mscePG6ejRo/Lw8NDEiRNVpkwZR5cFAIDTcLqef3x8vDIyMhQbG6vBgwdr8uTJji4JAACn4nQ9/z179qhBgwaSpFq1aikxMfEP57FYLJKkjIyMh16vX0H3h57X2aSnp0tevo4uI0+kp6c7bN18p/InvlP2Z6Tvk/Tw36k7mXcnA3/LZLnX1CfYqFGj1KJFCzVq1EiS1LhxY8XHx8vN7f77MTdu3NCxY8fyqkQAAJ4YFStWlK9vzp0lp+v5+/j4KC0tzfrabDY/MPglydvbWxUrVpS7u7tMJpO9SwQAwOEsFosyMzPl7e1913tOF/61a9fWf//7X7Vq1Uo//PCDKlas+IfzuLi43LXXAwBAfufl5XXP6U437H/nbP9jx47JYrEoKipK5cuXd3RZAAA4DacLfwAA8Gic7lI/AADwaAh/AAAMhvAHAMBgCH8AAAyG8AcAwGAIfwAADIbwBwDAYJzuDn94vFavXn3f99q1a5dndSB/OXbsmMaNG6fr16/rxRdfVIUKFdSkSRNHlwUn9O233973vbp16+ZhJfkL4W9wJ06ckCT98MMPKlCggP70pz/pwIEDysrKIvzx0CZNmqTo6GiNHj1aL7/8svr27Uv446EsW7ZMknT27FllZmaqRo0aOnTokLy9vbV48WIHV+e8CH+DGzx4sCSpT58+iomJsU7v3bu3o0pCPlGmTBmZTCYFBATc88EigC1mzJghSerXr5/mzp0rNzc3ZWdnq1+/fg6uzLlxzB+SpOTkZF2/fl2S9MsvvyglJcWxBcGpFSpUSMuXL9fNmze1YcMG+fn5ObokOLkrV65Yf87OzlZycrIDq3F+9PwhSXr99dfVrl07FSpUSDdu3FBERISjS4ITi4qK0vz581W4cGElJiZq0qRJji4JTu7ll19W69atVbFiRf3444967bXXHF2SU+PBPrDKysrSlStX9NRTT8nd3d3R5cAJnTp16r7vBQcH52ElyI+uXr2qs2fPqkyZMgoICHB0OU6N8Iek22fUjh8/XtnZ2WrZsqUCAwP1yiuvOLosOJnw8PB7TjeZTPr000/zuBrkJz/++KPGjh3LFSSPCeEPSVK3bt00Z84cvf3221q4cKG6dOmilStXOrosAJAkvfrqq4qMjNTo0aM1c+ZM9e3bl79Rj4Bj/pAkubi4yN/fXyaTSZ6enpydjUfSoEEDJScnq3DhwkpJSZGHh4eeeuopjR07Vn/9618dXR6cFFeQPD6c7Q9JUunSpTV9+nSlpKQoJiZGgYGBji4JTqxu3bpat26dduzYoY0bN+pvf/ubFixYoJkzZzq6NDgpriB5vAh/SJLGjx+vwMBAhYaGqmDBgpowYYKjS4ITu3jxosqVKyfp9o7lhQsXVKZMGbm6ujq4MjirqKgoJSUlcQXJY0L4G9yBAwckSd98841KlSqlZs2aqWzZstq9e7eDK4MzK1q0qKZNm6atW7dq2rRpeuqpp7Rz506uIsFDS05OVuXKlRUTEyNXV1elpqY6uiSnRvgb3Ndffy1J2rBhw13/gIc1ZcoUFStWTNu3b1eJEiU0efJkFSxY0Hq3NiC3hg4dqqCgIElSo0aNNGrUKAdX5Nw44c/g7twis1ChQho+fLiDq0F+4eHhoVq1aqlKlSqSpP379/MQFjyyWrVqSbp9TonZbHZsMU6O8Ick6fjx47p+/Ton0eCxGDBggH755ReVKFFCFotFJpOJ8Mcj8fPzU2xsrGrVqqX9+/dztv8j4jp/SJKaNm2qixcvqnDhwjKZTJKkHTt2OLgqOKvOnTtr+fLlji4D+UhycrLmzZunU6dO6ZlnnlG/fv24y98joOcPSbcfwfrcc885ugzkE8HBwbp06ZKKFy/u6FKQT0yaNEnTp093dBn5BuEPSdLs2bMJfzw2e/bsUZMmTXL0zBhJwqPIyMjQkSNHFBwcbB2d9PDwcHBVzothf0iSunfvrkKFCik4OFguLrcvAnnvvfccXBUA3PbCCy8oLS3N+tpkMmnr1q0OrMi50fOHJOmll15ydAnIB+bOnas333xT7733nrV3dgdDtngU69atk3T7yX7+/v7cMOoRcZ0/JN3eqy5btqyCgoJUsmRJbsaCh9K0aVNJt6/Drl27turWrasffvhBNWrUcHBlcHYJCQlq1qyZ+vTpo+bNm2vnzp2OLsmpMewPSdLrr7+uzMxMXb58WdnZ2SpWrJgWLVrk6LLgpLp3764BAwZo6dKlCgsL0/Lly7V48WJHlwUn1qVLF3300UcqXry4Ll26pAEDBiguLs7RZTktev6QJP3yyy/6+OOPFRISopUrVyo9Pd3RJcGJ3bmu//r162rdurX1PBLgYbm6ulqvHilevLg8PT0dXJFz45g/JEleXl6SpJs3b1p/Bh5WVlaWpk6dqjp16uibb75RZmamo0uCk/Px8dHixYtVt25dffvttypUqJCjS3JqDPtDkrRkyRKlpKTI3d1dW7duVYECBRj2x0M7ffq0du7cqVdeeUXx8fGqUaOGSpUq5eiy4MRu3LihuXPn6uTJkypfvrz69+/PDsAjoOcPSdLTTz+tHTt2KDMzU15eXpxJi0dStmxZlS1bVpLUqlUrxxaDfGHWrFnq2LGjnnnmGUeXki/Q84ckKSwsTJGRkTn2pCtXruzAigDgfzZv3qyVK1cqLS1NHTp0UKtWrThE+QgIf0i6/SCW2bNnO7oMAHigy5cvKzo6Wv/3f/+n7777ztHlOC2G/SFJatasmTp16qRy5cpZp0VHRzuwIgD4n59++kmrVq3Sli1bVLVqVS1YsMDRJTk1wh+SpMWLF6tv377y9fV1dCkAcJe3335br7zyipYsWSIfHx9Hl+P0GPaHJKlfv36KiYlxdBkAkMPFixf19NNP6+TJk3fdMjo4ONhBVTk/ev6QdPs6/z59+qhq1arW/2A82AeAo/3zn//UiBEjNG7cuLve+/TTT/O+oHyC8IckqUmTJo4uAQDu8uWXXyo+Pl53Bqnd3d2VmZnJHf4eEcP+AIAnVkZGhiwWi8aPH6/OnTsrJCREhw4d0rJlyzRhwgRHl+e06PkDAJ5YHh4ekqRz584pJCREklS1alWdPHnSkWU5PcIfAPDE8/X11UcffaSQkBDt3btXRYsWdXRJTo1hfwDAE+/XX3/V8uXLdfr0aZUvX15dunSxjgog9wh/AAAMhodsAwBgMIQ/AAAGQ/gDyDMzZ87U6tWrJUmzZ89WfHz8H85TqVIlJScn27kywFg42x9AnnnnnXesPyckJPBsdsBBCH8A95WQkKAZM2aoWLFi+vHHH1WgQAG9/fbbWrx4sU6dOqUWLVpo+PDhioqK0r59+5SWliaLxaKJEycqNDRUw4cPV0pKis6dO6fGjRvr6tWrqlChgry8vJSYmKgpU6bI1dVVzzzzjCIjI/Xrr7/q8uXLqly5sj766CPu4gbYCcP+AB7owIEDeuONN7Rp0yYVKVJEMTEx+sc//qGVK1dq6dKl2rt3ry5fvqzY2Fht3LhR7du3z/G41Vu3bmnDhg0aMmSIdVq3bt1UvXp1DR06VM2bN9eKFSvUrl07xcbGasuWLUpKStK2bdscsLWAMdDzB/BAQUFBqlq1qiSpdOnS8vX1lYeHhwICAuTt7S1fX18NGjRIy5cv17lz55SQkCBvb2/r/KGhoX+4jiFDhmjnzp1asGCBTp8+rcuXL+vXX3+12zYBRkfPH8AD/f5GKm5uOfsMX3/9tfr37y9Jatasmbp06ZLj/YIFC/7hOt577z2tWLFCJUuWVM+ePVWtWjVxCxLAfgh/AI/kv//9r5o0aaKuXbuqRo0aio+PV3Z29h/O5+rqqqysLEnSjh079NZbb6lVq1YymUzat2+fTcsA8HAY9gfwSEaOHKlhw4bphRdekKurq+rUqaMtW7bIbDY/cL4mTZrogw8+UGZmpt5991299dZbKlSokAoUKKC6devq7NmzebQFgPFwe18AAAyGYX8AAAyG8AcAwGAIfwAADIbwBwDAYAh/AAAMhvAHAMBgCH8AAAyG8AcAwGD+H9+AKx5T12tHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mTZqk83vS24/D/mT0zM3NsWTJEkyfPh3dunWDhYUFOnbsiGHDhgEAunbtir179+LDDz/E0aNHMXPmTERFRaF///5QKpVo164dJkyYAACwsrLC0qVLMXXqVKxfvx4NGzZEly5dXnkrnrm5OebMmYNZs2ZhzZo1qF69OoYMGYKFCxfi7NmzcHFxeaN98/f3x71797BgwQLcu3cPrq6u+N///gdnZ2edtxEYGIgJEybg4cOH6Ny5M8LDw6V9/frrrxEdHY1u3brB3t4eH330kXSet3nz5hgwYAAmT54MExMTfPzxx/Dy8pK226FDB6xbtw6dO3fGgQMHMGHCBMTExGDw4MFQKpXo0KGDdI3G2LFjIYTAyJEjoVar0aJFC3z33XevdQ65Y8eOuH//PuLi4nD37l04OTlh0aJFcHV1feU6gwcPxqRJk/DgwQMEBARg7NixBZZ36tQJmzZtKjL8+/btizt37mDChAnIysqCs7Mzli1bhgoVKqBChQoICwvDsmXLMH/+fHTv3v2lh0oFBgbixIkTWLp0qbRu/qmKoj6/RR1XXfTr1w/Dhg2DWq1Gr1690L9/fwDPrxVYtmwZ5s6di5UrV6Jy5coYPnw4+vTpA+D5tSxZWVmIiYlBTk4OhgwZUmAUiYybQpTE2CRRGXHt2jXcuXMHPj4+0rypU6fi6dOnmDlzpgErez316tXDypUr0aJFC0OX8lbbsmUL1qxZU+hjpYnKIo7hEL0gOzsb/fv3x549e3Djxg38/PPP2Lp160t3HlDZcO3aNezatQuLFi0q1qkUImPHYX+iF7i6umLy5MmIjY3FrVu34ODggAkTJqBNmzaGLo304Pr16wgPD0erVq34GwUkKxz2JyIikhlZ9Py1Wi1UKhXMzMwKvR+XiIiorBFCIDc3F1ZWVi/dqSGL8FepVAXudSUiIpILFxcX2NjYFJgni/DPf4qWi4tLkU8iIyIiKivUajXS0tIKPMI7nyzCP3+o39zcnM+mJiIiWSnsdDdv9SMiIpIZhj8REZHMMPyJiIhkRhbn/ImIqOzRarX466+/kJWVVayfuS5rLC0tUb169UIv7HsVhj8RERml69evQ6FQoHbt2rJ9josQAvfv38f169eL9VPfHPYnIiKjpFKpUK1aNZibm8sy+IHnV/JXrFgRz549K9Z6DH8iIjJaf39ynRy9zhcfHjUiIiKZYfgTEZFsLVy4ENOmTdPLtj/55BNcvHgRycnJ6Ny58xtvr3Hjxrh+/XoJVMYL/oiIiPRixYoVAID79+8buJKXMfyJiKjMUKlUmDBhAjIyMmBiYoIGDRqgU6dOiImJwY4dOwAAycnJiIqKkqbT09PRt29fPHz4EK6urpg8eTKsra2xdu1arFu3DmZmZrCwsMC0adNQt25dXL58GZGRkcjMzISJiQmGDh2Kjh074oMPPoCHhwcuXLiA0aNHY8aMGZg/fz4A4MmTJxgxYgQyMjJga2uLadOmwdHREWq1GnPnzsVvv/2GvLw8uLm5YdKkSbC2tsbvv/+OqKgoKBQKNGzYEFqttsSOE4f9iYiozNi3bx9UKhW2bt2KH3/8EQD+caj86tWrWLhwIbZv3w4hBJYsWYK8vDxMnz4dX3/9NTZt2oTevXsjJSUFADB69GgEBARg586dWL58OWJjY5GdnQ0AcHZ2xu7du9G+ffsC73Hr1i30798fW7duRefOnTFu3DgAwPLly6FUKrF582Zs27YNVapUwdy5c6FWqzFy5EiMHz8eW7Zsga+vb7Gv6C8Kw5+IiMoMb29vXLx4EaGhoVi+fDk+/vhj1KxZs8h12rdvD3t7eygUCvTo0QNJSUlQKpUICAhAcHAwpk2bBhsbG/Ts2RNZWVk4f/48evXqBQCoWrUq9u/fD2trawCAj49Poe9Rr149eHl5AQACAwORmpqKx48f4+DBgzhw4AC6deuGrl27Yv/+/UhPT0daWhpMTU3RvHlzAEDnzp1hZWVVUoeJw/66UOfmwdxMaegySo3c9peIyo4aNWpg3759SE5OxtGjRzFgwAAEBwdDCCG1yc3NLbCOUvn/f++EEDA1fR6Nc+fORVpaGpKSkrBixQr8+OOPmDNnDoCCt9ddunQJDg4OAIDy5csXWtffb0lUKBQwNTWFVqtFeHg4WrduDeD5aYucnBzcunWrQM0ApLpKAsNfB+ZmSvQZt8bQZZSatbP7GroEIqLXsnbtWqSkpGDu3Llo2bIl7t+/jz///BM3b97E/fv3YW9vj/379xdY58CBAxgyZAisra2xfv16tGrVCpmZmQgMDMTGjRvRv39/1K9fH+Hh4bC2tkaDBg2wZcsW9OrVC7du3UJISIh0/cCrXLhwAefOnYOrqyvWr18Pb29vlCtXDn5+flizZg2aN28OU1NTREREoHz58pg8eTKEEDh06BBat26N+Ph4PHz4sMSOE8OfiIjKjG7duuHYsWPo2LEjypUrBwcHB8TExGDp0qXo0aMHKleujDZt2hRYp06dOvj000/x6NEjeHt74z//+Q8sLCwwdOhQ9O/fH5aWllAqlYiOjgYAzJs3D1OnTsWqVaugUCgQExODypUrF1mXk5MT4uLicO3aNVSsWBEzZ84EAHz22WeYNWsWAgMDkZeXB1dXV4wfPx5mZmZYtGgRpkyZgtjYWLi6uqJixYoldpwU4u/jCmVQTk4OUlNT4e7uDgsLi9faBnv+RERvl/yeNBV+LIrKPl7wR0REJDMMfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGYY/ERGVaercPKPabmngQ36IiKhM09dTWo35mSjs+RMREckMe/5ERER6kpycjGXLlsHS0hLp6emoV68e5s6di+3bt2PlypVQKBRo0KABIiIiSvRX+/4Je/5ERER6dOLECURGRmL37t24efMm1q9fj6VLl2LVqlXYvn07ypUrh7i4uFKtieFPRESkR87OznjvvfdgYmKCOnXq4OHDh2jbti3eeecdAEBQUBCOHj1aqjUx/ImIiPToxR/VUSgUsLW1LbBcCAGNRlOqNTH8iYiIStmBAweQlZUFANiwYQN8fX1L9f15wR8REZVp6tw8vdyWp87Ng7mZstjrWVtb49NPP0VoaChyc3PRoEEDTJ06tcTrKwrDn4iIyrTXCeiS2q6vr2+BXv3MmTOl17169dJLXbrQW/jn5eVh0qRJuHz5MhQKBaZOnQoLCwuMHz8eCoUCzs7OmDx5MkxMTBAXF4eDBw/C1NQU4eHh8PDwQEZGhs5tiYiISHd6C/9ffvkFALBu3TokJyfjyy+/hBACo0aNgq+vLyIjIxEfHw8HBwccO3YMGzduxK1btzB8+HBs2rQJM2bM0LktERER6U5v4f+vf/0Lbdq0AQDcvHkTtra2SEpKQtOmTQEArVq1QmJiIhwdHeHn5weFQgEHBwfk5eUhMzMTZ86c0bmtvb29vnaDiIiozNHrOX9TU1OEhYVh3759WLBgARITE6FQKAAAVlZWePz4MbKzs2FnZyetkz9fCKFzW13DPzU19bX2w9vb+7XWM2YpKSmGLoGIqEimpqZQqVSGLuOtoFari/V3W+8X/M2aNQtjxoxB7969kZOTI81XqVSwtbWFtbV1gX88lUoFGxsbmJiY6NxWV+7u7gXut6RXk+MXHiIyLufOnSvVR+K+zczNzeHp6VlgXk5Ozis7vXq7z3/Lli1YtmwZAKBcuXJQKBRwd3dHcnIyAODw4cPw8fGBl5cXEhISoNVqcfPmTWi1Wtjb28PNzU3ntkRERKQ7vfX8O3TogAkTJqBv377QaDQIDw9HnTp1EBERgdjYWDg5OcHf3x9KpRI+Pj4ICgqCVqtFZGQkACAsLEzntkRERK+i1eTCxNTMaLZbGhRCCGHoIvQtf+jjTYb99fFb0G8rY/6NaiKSj3PnzsHV1VWntimzB5f4+3uP+7rEt/m6CjsWRWUfH+9LRESkJ2PHjsX69eul6dDQUJw6dQoDBgxAYGAgQkJCcPbsWQDA9u3b0bVrV3Tv3h0jRowocJ1cSWP4ExER6UmPHj2wbds2AMCNGzeQmZmJGTNmYOzYsfjpp58QFRWF//73vwCAr776Ct988w02b94MR0dHXLp0SW918fG+REREeuLr64uIiAhcv34dW7duxb///W8sXboUEyZMkNo8efIEDx48QNu2bRESEoJ27drB399f51Mar4PhT0REpCcKhQLdunXDzp07sWfPHixduhTffPMNtm7dKrW5ffs27OzsMGnSJJw/fx6HDh3C2LFj8fnnn6Nr1656qYvD/kRERHrUvXt3rFu3Du+99x6qVauG2rVrS+GfmJgo3RXXoUMHvPPOO/j000/RtWtXnDt3Tm81sedPRERlmlaTq5cr83W91a9q1aqoWrUqAgMDAQBz5szBlClT8PXXX8PMzAxffvklzMzMMGLECAwYMACWlpawtbXFrFmzSrzmfAx/IiIq0/R1L74u2xVC4O7du/jrr7/wr3/9CwBQp04drFq16qW2nTt3RufOnUu8zsJw2J+IiEhP9u7di65du2L06NEwNzc3dDkS9vyJiIj0JCAgAAEBAYYu4yXs+RMREckMw5+IiIyWVqs1dAkG9zpP6Wf4ExGRUbKyssKNGzegVqtfKwDLAiEE7t+/D0tLy2Ktx3P+RERklKpXr46//voLGRkZ0Gg0hi7HYCwtLVG9evVircPwJyIio2RiYoIqVaqgSpUqhi7F6HDYn4iISGYY/kRERDLD8CciIpIZhj8REZHMMPyJiIhkhuFPREQkMwx/IiIimWH4ExERyQzDn4iISGYY/kRERDLD8CciIpIZhj8REZHMMPyJiIhkhuFPREQkMwx/IiIimWH4ExERyQzDn4iISGYY/kRERDLD8CciIpIZhj8REZHMMPyJiIhkhuFPREQkMwx/IiIimWH4ExERyQzDn4iISGYY/kRERDJjqo+N5ubmIjw8HDdu3IBarcbQoUNRtWpVfPrpp6hduzYAICQkBB07dkRcXBwOHjwIU1NThIeHw8PDAxkZGRg/fjwUCgWcnZ0xefJkmJiYFNqWiIiIikcv4b9t2zbY2dlhzpw5yMrKQrdu3TBs2DAMGDAAAwcOlNqdOXMGx44dw8aNG3Hr1i0MHz4cmzZtwowZMzBq1Cj4+voiMjIS8fHxcHBwKLQtERERFY9ewj8gIAD+/v4AACEElEolUlNTcfnyZcTHx6NWrVoIDw9HSkoK/Pz8oFAo4ODggLy8PGRmZuLMmTNo2rQpAKBVq1ZITEyEo6NjoW3t7e31sQtERERlll7C38rKCgCQnZ2NESNGYNSoUVCr1ejVqxfc3d2xZMkSLFq0CDY2NrCzsyuw3uPHjyGEgEKhKDAvOzu70LbFCf/U1NTX2h9vb+/XWs+YpaSkGLoEIiLSE72EPwDcunULw4YNQ58+fdClSxc8evQItra2AID27dsjKioK7dq1g0qlktZRqVSwsbGBiYlJgXm2trawtrYutG1xuLu7w8LC4g33TB7k+IWHiKgsycnJeWWnVy9X+//1118YOHAgxo4di549ewIABg0ahNOnTwMAjhw5ggYNGsDLywsJCQnQarW4efMmtFot7O3t4ebmhuTkZADA4cOH4ePj88q2REREVDx66fkvXboUjx49wuLFi7F48WIAwPjx4zF9+nSYmZmhUqVKiIqKgrW1NXx8fBAUFAStVovIyEgAQFhYGCIiIhAbGwsnJyf4+/tDqVQW2paIiIiKRyGEEIYuQt/yhz7eZNi/z7g1JVzV22vt7L6GLoGIiN5QUdnHh/wQERHJDMOfiIhIZhj+REREMsPwJyIikhmGPxERkcww/ImIiGSG4U9ERCQzDH8iIiKZYfgTERHJDMOfiIhIZhj+REREMsPwJyIikhmGPxERkcww/ImIiGSG4U9ERCQzDH8iIiKZYfgTERHJDMOfiIhIZhj+REREMsPwJyIikhmGPxERkcww/ImIiGSG4U9ERCQzDH8iIiKZYfgTERHJDMOfiIhIZhj+REREMsPwJyIikhmGPxERkcww/ImIiGSG4U9ERCQzDH8iIiKZYfgTERHJDMOfiIhIZhj+REREMsPwJyIikhmGPxERkcww/ImIiGSG4U9ERCQzDH8iIiKZMdXHRnNzcxEeHo4bN25ArVZj6NChqFu3LsaPHw+FQgFnZ2dMnjwZJiYmiIuLw8GDB2Fqaorw8HB4eHggIyND57ZERERUPHoJ/23btsHOzg5z5sxBVlYWunXrhvr162PUqFHw9fVFZGQk4uPj4eDggGPHjmHjxo24desWhg8fjk2bNmHGjBk6tyUiIqLi0Uv4BwQEwN/fHwAghIBSqcSZM2fQtGlTAECrVq2QmJgIR0dH+Pn5QaFQwMHBAXl5ecjMzCxWW3t7e33sAhERUZmll/C3srICAGRnZ2PEiBEYNWoUZs2aBYVCIS1//PgxsrOzYWdnV2C9x48fQwihc9vihH9qaupr7Y+3t/drrWfMUlJSDF0CERHpiV7CHwBu3bqFYcOGoU+fPujSpQvmzJkjLVOpVLC1tYW1tTVUKlWB+TY2NjAxMdG5bXG4u7vDwsLiDfZKPuT4hYeIqCzJycl5ZadXL1f7//XXXxg4cCDGjh2Lnj17AgDc3NyQnJwMADh8+DB8fHzg5eWFhIQEaLVa3Lx5E1qtFvb29sVqS0RERMWjU89/1apVCAwMhLW1tU4bXbp0KR49eoTFixdj8eLFAICJEyciOjoasbGxcHJygr+/P5RKJXx8fBAUFAStVovIyEgAQFhYGCIiInRqS0RERMWjEEKIf2oUERGBAwcOoE2bNggODkbDhg1Lo7YSkz/08SbD/n3GrSnhqt5ea2f3NXQJRET0horKPp2G/aOiorB37164u7tj6tSp6NGjB3788Ufk5OTopWAiIiLSH53P+VtbWyMgIACdO3dGVlYW1q5di4CAAOzZs0ef9REREVEJ0+mcf1JSEjZs2IAjR47A398fixYtQv369XH16lX06dMHAQEB+q6TiIiISohO4T9t2jT06dMHUVFRBW6vq1mzJnr37q234oiIiKjk6TTsn/+4XhsbG9y7dw/ffvsttFotAGDEiBF6LZCIiIhKls4X/B08ePD5CiYmSElJwfTp0/VZFxEREemJTsP+J06cwI4dOwAAFStWxPz589G1a1e9FkZERET6oVPPPzc3F2q1WprWaDR6K4iIiIj0S6eef5s2bTBo0CB07doVCoUCO3bsQOvWrfVdGxEREemBTuE/btw4rFmzBvHx8TA1NUX79u0RHBys79qIiIhID3QKf6VSiY8++ggfffSRvushIiIiPdMp/Hft2oW5c+fi4cOHePGnAI4fP663woiIiEg/dAr/+fPnY/z48XBzc4NCodB3TURERKRHOoW/ra0tOnTooO9aiIiIqBTodKufp6cnDh06pO9aiIiIqBTo1PM/dOgQVq9eDTMzM5iZmUEIAYVCwXP+RERERkin8P/222/1XAYRERGVFp2G/atVq4Y//vgDGzZsgL29PU6cOIFq1arpuzYiIiLSA53Cf/ny5fjhhx+wZ88ePHv2DHFxcVi0aJG+ayMiIiI90Cn8d+7ciRUrVqBcuXJ45513sGHDBumHfoiIiMi46BT+pqamMDc3l6ZtbW1haqrT5QJERET0ltEpwatWrYqDBw9CoVBArVbjf//7H8/5ExERGSmdwj8iIgLjxo3DhQsX0KhRI3h6emLu3Ln6ro2IiIj0QKfwf/fdd/Hdd9/h6dOnyMvLg7W1tb7rIiIiIj3RKfxXrlxZ6PwBAwaUaDFERESkfzqFf1pamvRarVYjJSUFvr6+eiuKiIiI9Een8J8xY0aB6czMTIwbN04vBREREZF+6XSr39/Z29vjxo0bJV0LERERlYJin/MXQiA1NRUVK1bUW1FERESkP8U+5w88v++fw/5ERETG6bXO+RMREZHx0in8Q0NDoVAoXrn8+++/L7GCiIiISL90Cn93d3ekp6ejd+/eMDMzw9atW6HRaNCpUyd910dEREQlTKfwP378ONauXQulUgkAaNmyJXr37g1/f3+9FkdEREQlT6db/TIzM6FWq6VplUqFZ8+e6a0oIiIi0h+dev6dO3dG79690b59ewghsHv3bnz00Uf6ro2IiIj0QKfwHzlyJNzc3HD06FFYWFhg2rRpaNq0qb5rIyIiIj3Q+Ql/7777LpydnTFq1CiYmZnpsyYiIiLSI53Cf9OmTZgwYQK+/vprPH78GJ999hk2bNig79qIiIhID3QK/9WrV2P9+vWwtrZGxYoVsXnzZnz33Xf6ro2IiIj0QKfwNzExgbW1tTRdtWpV6ba/opw6dQqhoaEAgLNnz6Jly5YIDQ1FaGgodu3aBQCIi4tDz549ERwcjNOnTwMAMjIyEBISgj59+mDy5MnQarWvbEtERETFo9MFf3Z2djh37pz0lL9t27ahQoUKRa6zYsUKbNu2DeXKlQMAnDlzBgMGDMDAgQOlNmfOnMGxY8ewceNG3Lp1C8OHD8emTZswY8YMjBo1Cr6+voiMjER8fDwcHBwKbUtERETFo1P4h4eHY+TIkbh69Sr8/PxgYWGBxYsXF7lOzZo1sXDhQukHgFJTU3H58mXEx8ejVq1aCA8PR0pKCvz8/KBQKODg4IC8vDxkZmbizJkz0t0ErVq1QmJiIhwdHQtta29v/4aHgIiISF50Cv9nz55h69atuHLlCvLy8uDo6PiPV/z7+/vj+vXr0rSHhwd69eoFd3d3LFmyBIsWLYKNjQ3s7OykNlZWVnj8+DGEENIoQ/687OzsQtsWJ/xTU1N1bvsib2/v11rPmKWkpBi6BCIi0hOdwn/MmDHYvXs36tSp89pv1L59e9ja2kqvo6Ki0K5dO6hUKqmNSqWCjY0NTExMCsyztbWFtbV1oW2Lw93dHRYWFq+9D3Iixy88RERlSU5Ozis7vTpd8FevXj1s374dN2/eRFZWlvRfcQwaNEi6SO/IkSNo0KABvLy8kJCQAK1Wi5s3b0Kr1cLe3h5ubm5ITk4GABw+fBg+Pj6vbEtERETFo1PPPz4+Hnv27CkwT6FQ4Ny5czq/0ZQpUxAVFQUzMzNUqlQJUVFRsLa2ho+PD4KCgqDVahEZGQkACAsLQ0REBGJjY+Hk5AR/f38olcpC2xIREVHxKIQQwtBF6Fv+0MebDPv3GbemhKt6e62d3dfQJRAR0RsqKvuKHPaPiIiQXmdmZuqnOiIiIipVRYb/ixcKDBo0SO/FEBERkf4VGf4vnhGQwdkBIiIiWdD5V/3y77snIiIi41bk1f5arRYPHz6EEAJ5eXnS63wvPnSHiIiIjEOR4Z+WloZmzZpJge/r6ystK+6tfkRERPR2KDL8z58/X1p1EBERUSnR+Zw/ERERlQ0MfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZIbhT0REJDMMfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZIbhT0REJDMMfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZIbhT0REJDMMfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZIbhT0REJDN6Df9Tp04hNDQUAJCRkYGQkBD06dMHkydPhlarBQDExcWhZ8+eCA4OxunTp4vdloiIiIpHb+G/YsUKTJo0CTk5OQCAGTNmYNSoUVi7di2EEIiPj8eZM2dw7NgxbNy4EbGxsZg6dWqx2xIREVHx6C38a9asiYULF0rTZ86cQdOmTQEArVq1QlJSElJSUuDn5weFQgEHBwfk5eUhMzOzWG2JiIioeEz1tWF/f39cv35dmhZCQKFQAACsrKzw+PFjZGdnw87OTmqTP784be3t7XWuKTU19bX2xdvb+7XWM2YpKSmGLoGIiPREb+H/dyYm/z/IoFKpYGtrC2tra6hUqgLzbWxsitW2ONzd3WFhYfEGeyEfcvzCQ0RUluTk5Lyy01tqV/u7ubkhOTkZAHD48GH4+PjAy8sLCQkJ0Gq1uHnzJrRaLezt7YvVloiIiIqn1Hr+YWFhiIiIQGxsLJycnODv7w+lUgkfHx8EBQVBq9UiMjKy2G2JiIioeBRCCGHoIvQtf+jjTYb9+4xbU8JVvb3Wzu5r6BKIiOgNFZV9fMgPERGRzDD8iYiIZIbhT0REJDMMfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZIbhT0REJDMMfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZIbhT0REJDMMfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZIbhT0REJDMMfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZIbhT0REJDMMfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGdPSfsPAwEBYW1sDAKpXr46goCDExMRAqVTCz88Pn3/+ObRaLaZMmYILFy7A3Nwc0dHRqFWrFk6ePPlSWyIiIiqeUg3/nJwcCCGwatUqaV7Xrl2xcOFC1KhRA//5z39w9uxZXL9+HWq1GuvXr8fJkycxc+ZMLFmyBJMnT36prZubW2nuAhERkdEr1fA/f/48nj59ioEDB0Kj0WD48OFQq9WoWbMmAMDPzw9JSUm4d+8eWrZsCQBo1KgRUlNTkZ2dXWhbhj8REVHxlGr4W1paYtCgQejVqxeuXLmCTz75BLa2ttJyKysrXLt2DdnZ2dKpAQBQKpUvzctvWxypqamvVbe3t/drrWfMUlJSDF0CERHpSamGv6OjI2rVqgWFQgFHR0fY2NggKytLWq5SqWBra4tnz55BpVJJ87VaLaytrQvMy29bHO7u7rCwsHjj/ZADOX7hISIqS3Jycl7Z6S3Vq/1//PFHzJw5EwBw584dPH36FOXLl8fVq1chhEBCQgJ8fHzg5eWFw4cPAwBOnjwJFxcXWFtbw8zM7KW2REREVDyl2vPv2bMnJkyYgJCQECgUCkyfPh0mJiYYM2YM8vLy4OfnB09PTzRs2BCJiYkIDg6GEALTp08HAEydOvWltkRERFQ8CiGEMHQR+pY/9PEmw/59xq0p4areXmtn9zV0CURE9IaKyj4+5IeIiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZIbhT0REJDMMfyIiIplh+BMREckMw5/IANS5eYYuoVTJbX+J3nal+nhfInrO3EzJp0YSkcGw509ERCQzDH8iIiKZYfgTERHJDMOfXqLV5Bq6hFIjp30lIsrHC/7oJSamZkiZPdjQZZQK73FfG7oEIqJSx54/ERGRzDD8iYiIZIbhT0REJDMMfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZIbhT0REJDMMfyIiIplh+BMREckMw5+IiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZIbhT0REJDMMfyIiMipaTa6hSyhV+thf0xLfIhERkR6ZmJohZfZgQ5dRarzHfV3i22TPn4iISGYY/kSkd3IappXTvpLx4rA/EemdnIZp9TFEqwt1bh7MzZQGeW8yPgx/IqIywNxMiT7j1hi6jFKxdnZfQ5dg9Iwu/LVaLaZMmYILFy7A3Nwc0dHRqFWrlqHLIiIiMhpGd85///79UKvVWL9+Pb744gvMnDnT0CUREREZFaPr+aekpKBly5YAgEaNGiE1NfUf1xFCAADUavVrv69tebPXXtfY5OTkAJY2hi6jVOTk5BjsvfmZKpv4mdI/OX2egNf/TOVnXn4GvkghCpv7Fps4cSI6dOiA1q1bAwDatGmD/fv3w9T01d9jHj9+jLS0tNIqkYiI6K3h4uICG5uCX5aMrudvbW0NlUolTWu12iKDHwCsrKzg4uICMzMzKBQKfZdIRERkcEII5ObmwsrK6qVlRhf+Xl5e+OWXX9CxY0ecPHkSLi4u/7iOiYnJS996iIiIyjpLS8tC5xvdsH/+1f5paWkQQmD69OmoU6eOocsiIiIyGkYX/kRERPRmjO5WPyIiInozDH8iIiKZYfgTERHJDMOfiIhIZhj+REREMsPwJyIikhmGPwEAfvnllwLTu3btMlAlVJZkZWUZugQqg7RaraFLMHpG94Q/Klm//PILjh8/jp07d+LEiRMAgLy8PBw4cAAdO3Y0cHVkrI4dO4Zp06YhLy8PAQEBcHBwQK9evQxdFhmxbdu2QalUQq1WY/bs2Rg8eDAGDRpk6LKMFnv+Mle/fn04OTnBwsICjo6OcHR0hLOzM2JjYw1dGhmx+fPnY/Xq1ahUqRKGDBmCH374wdAlkZH7/vvv0aJFC2zbtg2HDh16abSSioc9f5mrWrUqAgMD0bVrV5iY8LsglQwTExPY2dlBoVDAwsKi0B8WISqO/GfUW1lZwdzcHBqNxsAVGTeGPwEAVqxYgRUrVhT4EYiEhAQDVkTGrGbNmpg3bx6ysrKwfPlyODg4GLokMnI1atRAUFAQJkyYgLi4ONSrV8/QJRk1PtufAAAffvgh1q9fj3Llyhm6FCoD1Go1Nm3ahLS0NDg5OSEoKAjm5uaGLouMnEqlgpWVFf766y9UqlTJ0OUYNfb8CQBQvXr1V/70I1FxDRkyBN98842hy6AyJCkpCRqNBkIIREVFYeTIkejSpYuhyzJaDH8CAOTm5qJLly5wcXGBQqEAAMybN8/AVZGxsrW1RXx8PGrXri1dS+Lo6GjgqsiYffnll5g3bx6mTp2KH374AaNGjWL4vwGGPwEAPvnkE0OXQGXI/fv38e2330rTCoUC33//veEKIqNnaWmJihUrwtTUFJUrV5Y6KfR6eM6fAADZ2dlYsWIF7t69i7Zt26JevXqoVauWocsiIgIADB06FFlZWQgKCoJKpUJycjIWLFhg6LKMFsOfAAAjRoxAq1atsHnzZowZMwaxsbFYvXq1ocsiI/XBBx8U6JnZ2Nhgy5YthiuIjJ5arcbVq1dRt25dpKWloXbt2ryI9A1w2J8APH8Ma8+ePbFt2zZ4eXnx8Zn0Rvbs2QMAEEIgNTVVmiZ6XQ8ePMDSpUuRmZmJgIAAPH36FJ6enoYuy2jxqS4kSU9PBwDcvn0bSqXSwNWQMTM3N4e5uTksLCzg7e2Ns2fPGrokMnIRERHo0aMHcnNz4ePjg5iYGEOXZNTY8ycAwKRJkxAeHo709HSMGDECU6ZMMXRJZMTmzZsnDfvfvXuXT4+kN/bs2TM0b94cS5YskR5JTq+P4U8AgBs3bmD9+vXS9K5du+Dm5mbAisiYOTk5Sa/r16+Pli1bGrAaKgssLCzw66+/QqvV4uTJkzzf/4Z4wZ/Mvfirfp07dwbw/Ocy4+PjsXv3bgNXR8amqEdC+/n5lWIlVNbcvn0bs2bNQlpaGurUqYOxY8eiRo0ahi7LaLHnL3P169dHVlaW9Kt+wPN7sjt16mTgysgY7dy585XLGP70JiwsLNCzZ0+8//77WL16NSpUqGDokowae/4EANLV/flDah4eHhxWo2LTaDQwNTWFWq1+aRk/T/QmBgwYgI8++ght27bF9u3bsWPHDixbtszQZRkt9vwJADBjxgzUqVMHN2/exJkzZ1CpUiXMmjXL0GWRkQkLC8O8efMQEBAgXfAnhIBCoUB8fLyBqyNj9vTpU7Rt2xYA0KVLF2zcuNHAFRk3hj8BAP744w9MnDgRoaGhWLVqFT7++GNDl0RGKP/3IA4cOGDgSqisMTMzQ2JiIjw9PfHHH3/wDpI3xPAnAM+H+1NTU1G9enWo1WqoVCpDl0RGLD4+HmvXrkVubi6EEMjKysL27dsNXRYZsejoaMyaNQvR0dGoW7cupk2bZuiSjBq/OhEAoGvXrpg6dSoGDhyIOXPmICgoyNAlkRH76quv8Pnnn6Nq1aoIDAyEi4uLoUsiI2dra4u+ffti9+7d8PX1hZ2dnaFLMmoMfwIA9O3bFxs3boSzszMmTpyIXr16GbokMmJVqlRB48aNAQDdu3fH3bt3DVwRGbvRo0dLF5JWqFABY8eONXBFxo3hL3MjRowA8Pw2rL//R1Rcjx8/BvD8/Oxvv/0GjUaDX3/9FQ8ePDBwZWTs/n7B39OnTw1ckXHjOX+Zy/9JzEOHDhV4nn92drahSiIj9p///Ac//PADKlasCI1Gg6FDh2LBggX47LPPDF0aGTle8FeyeJ8/AQD69euH2NhYVKlSBadPn0Z4eDh27Nhh6LLIyISGhuLJkyfIyMhA3bp1Afz/rX7r1q0zcHVkzDIyMjBr1ixcvnwZdevWxdixY1GzZk1Dl2W0GP4EADhy5AhmzZqFJk2aIDU1FdHR0ahTp46hyyIjk5eXhzt37mDKlCmYPHlygWXVqlUzUFVE9Hcc9icAgLOzMypWrIikpCT4+fnxGzW9FqVSCQcHByxfvtzQpVAZ8+J1SFlZWahRowZ/f+QN8KQJAXh+tX9ISAh27tyJKlWq8FY/InqrJCQkSP/t3bsXjRo1MnRJRo3D/gTg+S9mvffee9J0amoq3N3dDVgREdGrBQUFFfgZcioeDvsTgOe3aI0ePRqPHj3Chx9+CGdnZ0OXREQkGT16tPR7EXfv3kWlSpUMXJFx47A/AXj+6MwZM2bgnXfeQc+ePbFw4UJDl0REJGndujW8vLzQpEkTKBQKDBkyxNAlGTWGP0lq1aoFhUIBe3t7WFlZGbocIiLJxo0bUadOHSQlJSEoKAizZ882dElGjeFPAJ4/LnPdunV4+vQpdu7cCVtbW0OXREQkUSgUaNKkCR49eoROnTrxIT9viEePAADTp0/H9evX8c477yA1NRUxMTGGLomISKLRaDBnzhz4+Pjg6NGjyM3NNXRJRo1X+5Pk/v37yMnJkaYdHBwMWA0R0f+7cuUKEhMT0atXL+zfvx8NGzZEjRo1DF2W0WL4EwBgypQpOHz4MKpUqcLHsRIRlXG81Y8AAKdPn8b+/ft5Ho2ISAb4l54APL/S/8UhfyIiKrvY8ycAwK1bt9C2bVvUqlULADjsT0RUhvGcPwEAbty48dI8/gobEVHZxJ4/AXj+a2zTp09Heno6ateujQkTJhi6JCIi0hP2/AkAMHjwYISEhKBJkyY4duwYVq1ahe+++87QZRERkR7wgj8CAOTk5KBdu3awtbXFv/71L2g0GkOXREREesLwJwBAXl4eLly4AAC4cOGC9OtZRERU9nDYnwAA586dQ0REBO7evYsqVaogOjoa9evXN3RZRESkB+z5EwDg/PnzUKlUMDU1RWZmJoYNG2bokoiISE/Y8ycAQKdOnbB48WJUrVpVmmdubm7AioiISF94qx8BAGrUqCE94IeIiMo2hj8BACwtLTF48GC4urpKF/uNHj3awFUREZE+MPwJANC6dWtDl0BERKWE5/yJiIhkhlf7ExERyQzDn4iISGYY/kRERDLD8CeiEjNw4EBkZmYaugwi+gcMfyIqMYmJiYYugYh0wFv9iGQsOTkZs2fPxrvvvotr167B0tISM2fORJUqVTB16lScP38eCoUCLVu2xOjRo2Fqaop69erhyJEjsLe3BwBpes6cOQCAjz/+GMuXL8ezZ88QGRmJzMxMmJiYYOjQoejYsSP+/PNPTJs2DVlZWVAoFBg4cCC6deuG5ORkxMbGokqVKvjzzz9Rrlw5DB8+HKtWrcLly5fRoUMHhIeHAwAOHDiAJUuWIDc3F5aWlggLC0Pjxo0NdhyJjI4gItk6evSoqF+/vvjtt9+EEEKsXbtWBAYGinHjxomoqCih1WpFTk6OGDhwoFi2bJkQQggXFxdx//59aRsvTr/4ulu3bmL16tVCCCFu3rwp2rVrJx4/fizatWsn9u7dK4QQ4vbt26Jly5bi+PHj4ujRo8LV1VWcOXNGCCHEoEGDRFBQkMjJyRH3798XDRo0ELdv3xaXL18WnTt3FpmZmUIIIdLS0sT7778vVCpVKRwxorKBw/5EMle/fn34+PgAAHr06IFz585hx44d6NevHxQKBczNzREcHIzDhw/rvM2srCycP38evXr1AgBUrVoV+/fvx+3bt5GTk4MOHToAAN5991106NABv/76KwCgevXqcHNzAwDUrFkTvr6+MDc3h729PaysrPDw4UMkJibi7t276N+/P7p27YoxY8ZAoVDg6tWrJXlYiMo0DvsTyZxSqSwwLYSA+Nuzv7RaLTQazUvrqtXqQrdpavr8T0v+o6IB4NKlS8jLy3uprRBC2vbff0wqfzt/r6V58+b46quvpHm3bt1ClSpVCq2FiF7Gnj+RzJ0/fx7nz58HAKxfvx5eXl7497//jTVr1kAIAbVajQ0bNqBFixYAAHt7e/zxxx8AgH379hXYllKphEajgbW1NRo0aIAtW7YAeB7OISEhsLW1hZmZGX7++WcAwJ07d7B3715p27po1qwZEhMTkZ6eDgA4dOgQPvzwQ+Tk5LzRcSCSE/b8iWSuUqVK+Oqrr3Djxg3Y29tj9uzZsLKyQnR0NLp06YLc3Fy0bNkSQ4YMAQBMmjQJ06ZNg62tLVq0aIHKlStL22rfvj369OmDxYsXY968eZg6dSpWrVoFhUKBmJgYVK1aFYsXL0Z0dDQWLlyIvLw8DBs2DM2aNUNycrJO9To7O2PatGkYPXo0hBAwNTXFkiVLUL58eb0cH6KyiM/2J5Kx5ORkREVFYceOHYYuhYhKEYf9iYiIZIY9fyIiIplhz5+IiEhmGP5EREQyw/AnIiKSGYY/ERGRzDD8iYiIZOb/AKQx8aISkLQrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def separate_variables(df):\n",
    "    numeric_vars = df.select_dtypes(include=['float64', 'int64'])\n",
    "    categorical_vars = df.select_dtypes(include=['object'])\n",
    "    return numeric_vars, categorical_vars\n",
    "\n",
    "numeric_df, categorical_df = separate_variables(data)\n",
    "\n",
    "def plot_histogram(data, column, y):\n",
    "    plt.figure(figsize=(8, 6)) \n",
    "    sns.countplot(x=column,hue=y, data=data) \n",
    "    plt.title(f'Histogram of {column} by {y}', fontsize=14)  \n",
    "    plt.xlabel(column, fontsize=12) \n",
    "    plt.ylabel('Frequency', fontsize=12)  \n",
    "    plt.xticks(rotation='vertical',fontsize=10)  \n",
    "    plt.yticks(fontsize=10)  \n",
    "    plt.show()\n",
    "\n",
    "for column in categorical_df.columns:\n",
    "    plot_histogram(data, column, y='subscribed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41185 entries, 0 to 41187\n",
      "Data columns (total 22 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   age               41185 non-null  int64  \n",
      " 1   job               41185 non-null  object \n",
      " 2   marital           41185 non-null  object \n",
      " 3   education         41185 non-null  object \n",
      " 4   default           41185 non-null  object \n",
      " 5   housing           41185 non-null  object \n",
      " 6   loan              41185 non-null  object \n",
      " 7   contact           41185 non-null  object \n",
      " 8   month             41185 non-null  object \n",
      " 9   day_of_week       41185 non-null  object \n",
      " 10  campaign          41185 non-null  int64  \n",
      " 11  contacted_before  41185 non-null  int64  \n",
      " 12  previous          41185 non-null  int64  \n",
      " 13  poutcome          41185 non-null  object \n",
      " 14  emp.var.rate      41185 non-null  float64\n",
      " 15  cons.price.idx    41185 non-null  float64\n",
      " 16  cons.conf.idx     41185 non-null  float64\n",
      " 17  euribor3m         41185 non-null  float64\n",
      " 18  nr.employed       41185 non-null  float64\n",
      " 19  subscribed        41185 non-null  object \n",
      " 20  year              41185 non-null  int64  \n",
      " 21  date              41185 non-null  object \n",
      "dtypes: float64(5), int64(5), object(12)\n",
      "memory usage: 8.2+ MB\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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40553</th>\n",
       "      <td>64</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>sep</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.199</td>\n",
       "      <td>-37.5</td>\n",
       "      <td>0.886</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40554</th>\n",
       "      <td>72</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>sep</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.199</td>\n",
       "      <td>-37.5</td>\n",
       "      <td>0.886</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40555</th>\n",
       "      <td>34</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>sep</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.199</td>\n",
       "      <td>-37.5</td>\n",
       "      <td>0.886</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40556</th>\n",
       "      <td>69</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>sep</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.199</td>\n",
       "      <td>-37.5</td>\n",
       "      <td>0.886</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40557</th>\n",
       "      <td>33</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>sep</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.199</td>\n",
       "      <td>-37.5</td>\n",
       "      <td>0.886</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41183</th>\n",
       "      <td>73</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>yes</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41187</th>\n",
       "      <td>74</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>no</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-11-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>635 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital            education default housing loan  \\\n",
       "40553   64      retired  married  professional.course      no     yes   no   \n",
       "40554   72      retired  married  professional.course      no      no   no   \n",
       "40555   34       admin.  married    university.degree      no     yes   no   \n",
       "40556   69      retired  married          high.school      no     yes  yes   \n",
       "40557   33   technician  married  professional.course      no     yes   no   \n",
       "...    ...          ...      ...                  ...     ...     ...  ...   \n",
       "41183   73      retired  married  professional.course      no     yes   no   \n",
       "41184   46  blue-collar  married  professional.course      no      no   no   \n",
       "41185   56      retired  married    university.degree      no     yes   no   \n",
       "41186   44   technician  married  professional.course      no      no   no   \n",
       "41187   74      retired  married  professional.course      no     yes   no   \n",
       "\n",
       "        contact month day_of_week  ...  previous     poutcome  emp.var.rate  \\\n",
       "40553  cellular   sep         wed  ...         0  nonexistent           2.0   \n",
       "40554  cellular   sep         wed  ...         1      failure           2.0   \n",
       "40555  cellular   sep         wed  ...         1      failure           2.0   \n",
       "40556  cellular   sep         wed  ...         1      success           2.0   \n",
       "40557  cellular   sep         wed  ...         0  nonexistent           2.0   \n",
       "...         ...   ...         ...  ...       ...          ...           ...   \n",
       "41183  cellular   nov         fri  ...         0  nonexistent           2.0   \n",
       "41184  cellular   nov         fri  ...         0  nonexistent           2.0   \n",
       "41185  cellular   nov         fri  ...         0  nonexistent           2.0   \n",
       "41186  cellular   nov         fri  ...         0  nonexistent           2.0   \n",
       "41187  cellular   nov         fri  ...         1      failure           2.0   \n",
       "\n",
       "      cons.price.idx  cons.conf.idx  euribor3m  nr.employed  subscribed  year  \\\n",
       "40553         94.199          -37.5      0.886       4963.6         yes  2010   \n",
       "40554         94.199          -37.5      0.886       4963.6          no  2010   \n",
       "40555         94.199          -37.5      0.886       4963.6          no  2010   \n",
       "40556         94.199          -37.5      0.886       4963.6         yes  2010   \n",
       "40557         94.199          -37.5      0.886       4963.6         yes  2010   \n",
       "...              ...            ...        ...          ...         ...   ...   \n",
       "41183         94.767          -50.8      1.028       4963.6         yes  2010   \n",
       "41184         94.767          -50.8      1.028       4963.6          no  2010   \n",
       "41185         94.767          -50.8      1.028       4963.6          no  2010   \n",
       "41186         94.767          -50.8      1.028       4963.6         yes  2010   \n",
       "41187         94.767          -50.8      1.028       4963.6          no  2010   \n",
       "\n",
       "             date  \n",
       "40553  2010-09-01  \n",
       "40554  2010-09-01  \n",
       "40555  2010-09-01  \n",
       "40556  2010-09-01  \n",
       "40557  2010-09-01  \n",
       "...           ...  \n",
       "41183  2010-11-01  \n",
       "41184  2010-11-01  \n",
       "41185  2010-11-01  \n",
       "41186  2010-11-01  \n",
       "41187  2010-11-01  \n",
       "\n",
       "[635 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# At this point, data is ready for sampling\n",
    "\n",
    "# validation data - from 2010-09-01 to latest\n",
    "\n",
    "data.info()\n",
    "validation_data = data.loc[data['date'] >= '2010-09-01']\n",
    "\n",
    "validation_data.to_csv('./data/validation_set.csv', index=False)\n",
    "validation_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#recode yes/no in 1/0 for target\n",
    "data.subscribed.replace(('yes', 'no'), (1, 0), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>...</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "      <td>17640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>...</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "      <td>10569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>...</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "      <td>5341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>...</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "      <td>7635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age    job  marital  education  default  housing   loan  contact  \\\n",
       "campaign                                                                       \n",
       "1         17640  17640    17640      17640    17640    17640  17640    17640   \n",
       "2         10569  10569    10569      10569    10569    10569  10569    10569   \n",
       "3          5341   5341     5341       5341     5341     5341   5341     5341   \n",
       "4          7635   7635     7635       7635     7635     7635   7635     7635   \n",
       "\n",
       "          month  day_of_week  ...  previous  poutcome  emp.var.rate  \\\n",
       "campaign                      ...                                     \n",
       "1         17640        17640  ...     17640     17640         17640   \n",
       "2         10569        10569  ...     10569     10569         10569   \n",
       "3          5341         5341  ...      5341      5341          5341   \n",
       "4          7635         7635  ...      7635      7635          7635   \n",
       "\n",
       "          cons.price.idx  cons.conf.idx  euribor3m  nr.employed  subscribed  \\\n",
       "campaign                                                                      \n",
       "1                  17640          17640      17640        17640       17640   \n",
       "2                  10569          10569      10569        10569       10569   \n",
       "3                   5341           5341       5341         5341        5341   \n",
       "4                   7635           7635       7635         7635        7635   \n",
       "\n",
       "           year   date  \n",
       "campaign                \n",
       "1         17640  17640  \n",
       "2         10569  10569  \n",
       "3          5341   5341  \n",
       "4          7635   7635  \n",
       "\n",
       "[4 rows x 21 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#campaign into 4 ordinal bins\n",
    "data.groupby('campaign').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OptimalBinning(max_n_bins=5, name='cons.price.idx', outlier_detector='range')\n",
      "cons.price.idx\n",
      "SPLITS: [92.86800003 93.13749695 93.95600128 94.01050186]\n",
      "BINS: [-inf, 92.86800003051758, 93.13749694824219, 93.95600128173828, 94.01050186157227, inf]\n",
      "--------------------------------------------------------------\n",
      "OptimalBinning(max_n_bins=5, name='cons.conf.idx', outlier_detector='range')\n",
      "cons.conf.idx\n",
      "SPLITS: [-46.64999962 -36.95000076 -36.25       -35.44999886]\n",
      "BINS: [-inf, -46.64999961853027, -36.95000076293945, -36.25, -35.44999885559082, inf]\n",
      "--------------------------------------------------------------\n",
      "OptimalBinning(max_n_bins=5, name='euribor3m', outlier_detector='range')\n",
      "euribor3m\n",
      "SPLITS: [1.23949999 3.16750002 4.84100008 4.85749984]\n",
      "BINS: [-inf, 1.2394999861717224, 3.1675000190734863, 4.841000080108643, 4.857499837875366, inf]\n",
      "--------------------------------------------------------------\n",
      "OptimalBinning(max_n_bins=5, name='nr.employed', outlier_detector='range')\n",
      "nr.employed\n",
      "SPLITS: [5087.65014648 5183.64990234 5193.39990234]\n",
      "BINS: [-inf, 5087.650146484375, 5183.64990234375, 5193.39990234375, inf]\n",
      "--------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41185 entries, 0 to 41187\n",
      "Data columns (total 22 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   age               41185 non-null  int64  \n",
      " 1   job               41185 non-null  object \n",
      " 2   marital           41185 non-null  object \n",
      " 3   education         41185 non-null  object \n",
      " 4   default           41185 non-null  object \n",
      " 5   housing           41185 non-null  object \n",
      " 6   loan              41185 non-null  object \n",
      " 7   contact           41185 non-null  object \n",
      " 8   month             41185 non-null  object \n",
      " 9   day_of_week       41185 non-null  object \n",
      " 10  campaign          41185 non-null  int64  \n",
      " 11  contacted_before  41185 non-null  int64  \n",
      " 12  previous          41185 non-null  int64  \n",
      " 13  poutcome          41185 non-null  object \n",
      " 14  emp.var.rate      41185 non-null  float64\n",
      " 15  cons.price.idx    41185 non-null  int64  \n",
      " 16  cons.conf.idx     41185 non-null  int64  \n",
      " 17  euribor3m         41185 non-null  int64  \n",
      " 18  nr.employed       41185 non-null  int64  \n",
      " 19  subscribed        41185 non-null  int64  \n",
      " 20  year              41185 non-null  int64  \n",
      " 21  date              41185 non-null  object \n",
      "dtypes: float64(1), int64(10), object(11)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "### Discretization/binning\n",
    "from optbinning import OptimalBinning\n",
    "\n",
    "# This function takes in a dataframe and a set list of columns\n",
    "# returns a copy of the dataframe with discretized cols\n",
    "def find_optimal_binning_split(dataframe, cols):\n",
    "    df = dataframe.copy()\n",
    "    target = np.array(df['subscribed'])\n",
    "    for i in cols:\n",
    "        array = np.array(df[i])\n",
    "        optb = OptimalBinning(name=i, dtype=\"numerical\", solver=\"cp\", outlier_detector='range',max_n_bins=5)\n",
    "        print(optb)\n",
    "        optb.fit(array, target)\n",
    "        splits = optb.splits\n",
    "        print(i)\n",
    "        print('SPLITS:',splits)\n",
    "\n",
    "        # Converting the splits array back into list before passing it as param 'bins' to pd.cut\n",
    "        bins = splits.tolist()\n",
    "        # Add value -inf at the start of the array, and inf at the end \n",
    "        bins.insert(0,-np.inf)\n",
    "        bins.append(np.inf)\n",
    "\n",
    "        print('BINS:',bins)\n",
    "        print('--------------------------------------------------------------')\n",
    "        # Replace the column with the result of cut\n",
    "        df[i] = pd.cut(df[i],bins,include_lowest=True, labels=False )\n",
    "    return df\n",
    "\n",
    "#bureau_per included for binning\n",
    "disc_cols =  ['cons.price.idx','cons.conf.idx','euribor3m', 'nr.employed']\n",
    "# should only the purify_cols be passed here? why not the rest of the numeric cols\n",
    "data = find_optimal_binning_split(data,disc_cols )\n",
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of training examples: 28385\n",
      "No. of testing examples: 12165\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data.to_csv('data/after_feature_adjust_data.csv', index=False)\n",
    "\n",
    "#Sampling train/test ???\n",
    "#disc_model_data = disc_data[disc_data['date'] < '2010-09-01']\n",
    "\n",
    "#disc_model_data.to_csv('./data/disc_model_data.csv',index=False)\n",
    "\n",
    "model_data = data.loc[data['date'] < '2010-09-01']\n",
    "\n",
    "model_data = model_data.drop(columns=['date'])\n",
    "\n",
    "model_data.to_csv('./data/model_data.csv',index=False)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the dataset into training and test sets\n",
    "training_data, testing_data = train_test_split(model_data, test_size=0.3, random_state=42)\n",
    "\n",
    "# Print the shapes of the resulting sets\n",
    "print(f\"No. of training examples: {training_data.shape[0]}\")\n",
    "print(f\"No. of testing examples: {testing_data.shape[0]}\")\n",
    "\n",
    "testing_data.to_csv('./data/testing_set.csv', index=False)\n",
    "\n",
    "\n",
    "#Apply sampling techniques to training data\n",
    "\n",
    "training_data.to_csv('./data/training_set.csv', index=False)\n"
   ]
  }
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