From 3fcb82941e2623faf9d8463844c508a711d704a1 Mon Sep 17 00:00:00 2001
From: TiagoQuaresmaSimoes
 <138822196+TiagoQuaresmaSimoes@users.noreply.github.com>
Date: Wed, 10 Jan 2024 21:50:19 +0000
Subject: [PATCH] Add files via upload

---
 [lab-feature-engineering] Tiago.ipynb | 1430 +++++++++++++++++++++++++
 1 file changed, 1430 insertions(+)
 create mode 100644 [lab-feature-engineering] Tiago.ipynb

diff --git a/[lab-feature-engineering] Tiago.ipynb b/[lab-feature-engineering] Tiago.ipynb
new file mode 100644
index 0000000..7f4b905
--- /dev/null
+++ b/[lab-feature-engineering] Tiago.ipynb	
@@ -0,0 +1,1430 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3e671b9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import warnings \n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "import matplotlib.pyplot as plt \n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "8e1b92e9",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>ODATEDW</th>\n",
+       "      <th>OSOURCE</th>\n",
+       "      <th>TCODE</th>\n",
+       "      <th>STATE</th>\n",
+       "      <th>ZIP</th>\n",
+       "      <th>MAILCODE</th>\n",
+       "      <th>PVASTATE</th>\n",
+       "      <th>DOB</th>\n",
+       "      <th>NOEXCH</th>\n",
+       "      <th>RECINHSE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TARGET_D</th>\n",
+       "      <th>HPHONE_D</th>\n",
+       "      <th>RFA_2R</th>\n",
+       "      <th>RFA_2F</th>\n",
+       "      <th>RFA_2A</th>\n",
+       "      <th>MDMAUD_R</th>\n",
+       "      <th>MDMAUD_F</th>\n",
+       "      <th>MDMAUD_A</th>\n",
+       "      <th>CLUSTER2</th>\n",
+       "      <th>GEOCODE2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8901</td>\n",
+       "      <td>GRI</td>\n",
+       "      <td>0</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>61081</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>3712</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>L</td>\n",
+       "      <td>4</td>\n",
+       "      <td>E</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9401</td>\n",
+       "      <td>BOA</td>\n",
+       "      <td>1</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>91326</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>5202</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>L</td>\n",
+       "      <td>2</td>\n",
+       "      <td>G</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9001</td>\n",
+       "      <td>AMH</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NC</td>\n",
+       "      <td>27017</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>4</td>\n",
+       "      <td>E</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8701</td>\n",
+       "      <td>BRY</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>95953</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>2801</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>4</td>\n",
+       "      <td>E</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8601</td>\n",
+       "      <td></td>\n",
+       "      <td>0</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>33176</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>2001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>X</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>2</td>\n",
+       "      <td>F</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
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+       "      <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",
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+       "      <td>9601</td>\n",
+       "      <td>ASE</td>\n",
+       "      <td>1</td>\n",
+       "      <td>AK</td>\n",
+       "      <td>99504</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>L</td>\n",
+       "      <td>1</td>\n",
+       "      <td>G</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95408</th>\n",
+       "      <td>9601</td>\n",
+       "      <td>DCD</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TX</td>\n",
+       "      <td>77379</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>5001</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>1</td>\n",
+       "      <td>F</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95409</th>\n",
+       "      <td>9501</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>1</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>48910</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>3801</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>3</td>\n",
+       "      <td>E</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95410</th>\n",
+       "      <td>8601</td>\n",
+       "      <td>PRV</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>91320</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>4005</td>\n",
+       "      <td>0</td>\n",
+       "      <td>X</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>4</td>\n",
+       "      <td>F</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95411</th>\n",
+       "      <td>8801</td>\n",
+       "      <td>MCC</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NC</td>\n",
+       "      <td>28409</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>1801</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>1</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>C</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>95412 rows × 481 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ODATEDW OSOURCE  TCODE STATE    ZIP MAILCODE PVASTATE   DOB NOEXCH  \\\n",
+       "0         8901     GRI      0    IL  61081                    3712      0   \n",
+       "1         9401     BOA      1    CA  91326                    5202      0   \n",
+       "2         9001     AMH      1    NC  27017                       0      0   \n",
+       "3         8701     BRY      0    CA  95953                    2801      0   \n",
+       "4         8601              0    FL  33176                    2001      0   \n",
+       "...        ...     ...    ...   ...    ...      ...      ...   ...    ...   \n",
+       "95407     9601     ASE      1    AK  99504                       0      0   \n",
+       "95408     9601     DCD      1    TX  77379                    5001      0   \n",
+       "95409     9501     MBC      1    MI  48910                    3801      0   \n",
+       "95410     8601     PRV      0    CA  91320                    4005      0   \n",
+       "95411     8801     MCC      2    NC  28409                    1801      0   \n",
+       "\n",
+       "      RECINHSE  ... TARGET_D HPHONE_D RFA_2R RFA_2F RFA_2A MDMAUD_R  MDMAUD_F  \\\n",
+       "0               ...      0.0        0      L      4      E        X         X   \n",
+       "1               ...      0.0        0      L      2      G        X         X   \n",
+       "2               ...      0.0        1      L      4      E        X         X   \n",
+       "3               ...      0.0        1      L      4      E        X         X   \n",
+       "4            X  ...      0.0        1      L      2      F        X         X   \n",
+       "...        ...  ...      ...      ...    ...    ...    ...      ...       ...   \n",
+       "95407           ...      0.0        0      L      1      G        X         X   \n",
+       "95408           ...      0.0        1      L      1      F        X         X   \n",
+       "95409           ...      0.0        1      L      3      E        X         X   \n",
+       "95410        X  ...     18.0        1      L      4      F        X         X   \n",
+       "95411        X  ...      0.0        1      L      1      G        C         1   \n",
+       "\n",
+       "      MDMAUD_A CLUSTER2 GEOCODE2  \n",
+       "0            X     39.0        C  \n",
+       "1            X      1.0        A  \n",
+       "2            X     60.0        C  \n",
+       "3            X     41.0        C  \n",
+       "4            X     26.0        A  \n",
+       "...        ...      ...      ...  \n",
+       "95407        X     12.0        C  \n",
+       "95408        X      2.0        A  \n",
+       "95409        X     34.0        B  \n",
+       "95410        X     11.0        A  \n",
+       "95411        C     12.0        C  \n",
+       "\n",
+       "[95412 rows x 481 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data= pd.read_csv(r'C:\\Users\\Quaresma\\Documents\\IRONHACK\\Labs\\32 lab-feature-engineering\\learningSet.txt')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "aaa90ee5",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>columns_name</th>\n",
+       "      <th>nulls_percentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>RDATE_5</td>\n",
+       "      <td>0.999906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>RAMNT_5</td>\n",
+       "      <td>0.999906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>RDATE_3</td>\n",
+       "      <td>0.997464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>RAMNT_3</td>\n",
+       "      <td>0.997464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>RDATE_4</td>\n",
+       "      <td>0.997055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>ETHC3</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>ETHC2</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>ETHC1</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>HHD12</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>TPE11</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>481 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    columns_name  nulls_percentage\n",
+       "414      RDATE_5          0.999906\n",
+       "436      RAMNT_5          0.999906\n",
+       "412      RDATE_3          0.997464\n",
+       "434      RAMNT_3          0.997464\n",
+       "413      RDATE_4          0.997055\n",
+       "..           ...               ...\n",
+       "168        ETHC3          0.000000\n",
+       "167        ETHC2          0.000000\n",
+       "166        ETHC1          0.000000\n",
+       "165        HHD12          0.000000\n",
+       "240        TPE11          0.000000\n",
+       "\n",
+       "[481 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Lets deal with the aount of missing values\n",
+    "nulls_percent_df= data.isna().sum()\n",
+    "nulls_percent_df\n",
+    "\n",
+    "# Put it into a dataframe \n",
+    "nulls_percent_df=pd.DataFrame(data.isna().sum()/len(data))\n",
+    "nulls_percent_df\n",
+    "\n",
+    "# Take it out of the index\n",
+    "nulls_percent_df=pd.DataFrame(data.isna().sum()/len(data)).reset_index()\n",
+    "nulls_percent_df\n",
+    "\n",
+    "# Finally lets change column names \n",
+    "nulls_percent_df.columns= ['columns_name', 'nulls_percentage']\n",
+    "nulls_percent_df\n",
+    "\n",
+    "# Lets be super picky\n",
+    "nulls_percent_df.sort_values(by= ['nulls_percentage'], ascending= False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "34631d22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['NUMCHLD',\n",
+       " 'WEALTH1',\n",
+       " 'MBCRAFT',\n",
+       " 'MBGARDEN',\n",
+       " 'MBBOOKS',\n",
+       " 'MBCOLECT',\n",
+       " 'MAGFAML',\n",
+       " 'MAGFEM',\n",
+       " 'MAGMALE',\n",
+       " 'PUBGARDN',\n",
+       " 'PUBCULIN',\n",
+       " 'PUBHLTH',\n",
+       " 'PUBDOITY',\n",
+       " 'PUBNEWFN',\n",
+       " 'PUBPHOTO',\n",
+       " 'PUBOPP',\n",
+       " 'WEALTH2',\n",
+       " 'ADATE_5',\n",
+       " 'ADATE_10',\n",
+       " 'ADATE_13',\n",
+       " 'ADATE_15',\n",
+       " 'ADATE_17',\n",
+       " 'ADATE_19',\n",
+       " 'ADATE_20',\n",
+       " 'ADATE_21',\n",
+       " 'ADATE_22',\n",
+       " 'ADATE_23',\n",
+       " 'ADATE_24',\n",
+       " 'RDATE_3',\n",
+       " 'RDATE_4',\n",
+       " 'RDATE_5',\n",
+       " 'RDATE_6',\n",
+       " 'RDATE_7',\n",
+       " 'RDATE_8',\n",
+       " 'RDATE_9',\n",
+       " 'RDATE_10',\n",
+       " 'RDATE_11',\n",
+       " 'RDATE_12',\n",
+       " 'RDATE_13',\n",
+       " 'RDATE_14',\n",
+       " 'RDATE_15',\n",
+       " 'RDATE_16',\n",
+       " 'RDATE_17',\n",
+       " 'RDATE_18',\n",
+       " 'RDATE_19',\n",
+       " 'RDATE_20',\n",
+       " 'RDATE_21',\n",
+       " 'RDATE_22',\n",
+       " 'RDATE_23',\n",
+       " 'RDATE_24',\n",
+       " 'RAMNT_3',\n",
+       " 'RAMNT_4',\n",
+       " 'RAMNT_5',\n",
+       " 'RAMNT_6',\n",
+       " 'RAMNT_7',\n",
+       " 'RAMNT_8',\n",
+       " 'RAMNT_9',\n",
+       " 'RAMNT_10',\n",
+       " 'RAMNT_11',\n",
+       " 'RAMNT_12',\n",
+       " 'RAMNT_13',\n",
+       " 'RAMNT_14',\n",
+       " 'RAMNT_15',\n",
+       " 'RAMNT_16',\n",
+       " 'RAMNT_17',\n",
+       " 'RAMNT_18',\n",
+       " 'RAMNT_19',\n",
+       " 'RAMNT_20',\n",
+       " 'RAMNT_21',\n",
+       " 'RAMNT_22',\n",
+       " 'RAMNT_23',\n",
+       " 'RAMNT_24',\n",
+       " 'OSOURCE',\n",
+       " 'ZIP']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# First create the variable threshold\n",
+    "threshold  = 0.25\n",
+    "\n",
+    "#define the condition \n",
+    "condition= nulls_percent_df['nulls_percentage'] > threshold\n",
+    "columns_above_threshold= nulls_percent_df[condition]\n",
+    "columns_above_threshold\n",
+    "\n",
+    "#Create a list with a column names\n",
+    "drop_list= list(columns_above_threshold['columns_name'])\n",
+    "drop_list.extend(['OSOURCE','ZIP'])\n",
+    "drop_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "92248392",
+   "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>ODATEDW</th>\n",
+       "      <th>TCODE</th>\n",
+       "      <th>STATE</th>\n",
+       "      <th>MAILCODE</th>\n",
+       "      <th>PVASTATE</th>\n",
+       "      <th>DOB</th>\n",
+       "      <th>NOEXCH</th>\n",
+       "      <th>RECINHSE</th>\n",
+       "      <th>RECP3</th>\n",
+       "      <th>RECPGVG</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TARGET_D</th>\n",
+       "      <th>HPHONE_D</th>\n",
+       "      <th>RFA_2R</th>\n",
+       "      <th>RFA_2F</th>\n",
+       "      <th>RFA_2A</th>\n",
+       "      <th>MDMAUD_R</th>\n",
+       "      <th>MDMAUD_F</th>\n",
+       "      <th>MDMAUD_A</th>\n",
+       "      <th>CLUSTER2</th>\n",
+       "      <th>GEOCODE2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8901</td>\n",
+       "      <td>0</td>\n",
+       "      <td>IL</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>3712</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>L</td>\n",
+       "      <td>4</td>\n",
+       "      <td>E</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9401</td>\n",
+       "      <td>1</td>\n",
+       "      <td>CA</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>5202</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>L</td>\n",
+       "      <td>2</td>\n",
+       "      <td>G</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NC</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>4</td>\n",
+       "      <td>E</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8701</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CA</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>2801</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>4</td>\n",
+       "      <td>E</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8601</td>\n",
+       "      <td>0</td>\n",
+       "      <td>FL</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>2001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>2</td>\n",
+       "      <td>F</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>A</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>95407</th>\n",
+       "      <td>9601</td>\n",
+       "      <td>1</td>\n",
+       "      <td>AK</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>L</td>\n",
+       "      <td>1</td>\n",
+       "      <td>G</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95408</th>\n",
+       "      <td>9601</td>\n",
+       "      <td>1</td>\n",
+       "      <td>TX</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>5001</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>1</td>\n",
+       "      <td>F</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95409</th>\n",
+       "      <td>9501</td>\n",
+       "      <td>1</td>\n",
+       "      <td>MI</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>3801</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td>X</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>3</td>\n",
+       "      <td>E</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95410</th>\n",
+       "      <td>8601</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CA</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>4005</td>\n",
+       "      <td>0</td>\n",
+       "      <td>X</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>4</td>\n",
+       "      <td>F</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>X</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95411</th>\n",
+       "      <td>8801</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NC</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>1801</td>\n",
+       "      <td>0</td>\n",
+       "      <td>X</td>\n",
+       "      <td></td>\n",
+       "      <td>X</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>L</td>\n",
+       "      <td>1</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>C</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>95412 rows × 407 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ODATEDW  TCODE STATE MAILCODE PVASTATE   DOB NOEXCH RECINHSE RECP3  \\\n",
+       "0         8901      0    IL                    3712      0                  \n",
+       "1         9401      1    CA                    5202      0                  \n",
+       "2         9001      1    NC                       0      0                  \n",
+       "3         8701      0    CA                    2801      0                  \n",
+       "4         8601      0    FL                    2001      0        X     X   \n",
+       "...        ...    ...   ...      ...      ...   ...    ...      ...   ...   \n",
+       "95407     9601      1    AK                       0      0                  \n",
+       "95408     9601      1    TX                    5001      0                  \n",
+       "95409     9501      1    MI                    3801      0              X   \n",
+       "95410     8601      0    CA                    4005      0        X         \n",
+       "95411     8801      2    NC                    1801      0        X         \n",
+       "\n",
+       "      RECPGVG  ... TARGET_D HPHONE_D RFA_2R RFA_2F  RFA_2A MDMAUD_R MDMAUD_F  \\\n",
+       "0              ...      0.0        0      L      4       E        X        X   \n",
+       "1              ...      0.0        0      L      2       G        X        X   \n",
+       "2              ...      0.0        1      L      4       E        X        X   \n",
+       "3              ...      0.0        1      L      4       E        X        X   \n",
+       "4              ...      0.0        1      L      2       F        X        X   \n",
+       "...       ...  ...      ...      ...    ...    ...     ...      ...      ...   \n",
+       "95407          ...      0.0        0      L      1       G        X        X   \n",
+       "95408          ...      0.0        1      L      1       F        X        X   \n",
+       "95409          ...      0.0        1      L      3       E        X        X   \n",
+       "95410          ...     18.0        1      L      4       F        X        X   \n",
+       "95411       X  ...      0.0        1      L      1       G        C        1   \n",
+       "\n",
+       "      MDMAUD_A CLUSTER2 GEOCODE2  \n",
+       "0            X     39.0        C  \n",
+       "1            X      1.0        A  \n",
+       "2            X     60.0        C  \n",
+       "3            X     41.0        C  \n",
+       "4            X     26.0        A  \n",
+       "...        ...      ...      ...  \n",
+       "95407        X     12.0        C  \n",
+       "95408        X      2.0        A  \n",
+       "95409        X     34.0        B  \n",
+       "95410        X     11.0        A  \n",
+       "95411        C     12.0        C  \n",
+       "\n",
+       "[95412 rows x 407 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = data.drop(drop_list,axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8e22526d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['F', 'M', ' ', 'C', 'U', 'J', 'A'], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['GENDER'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d823afc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['F', 'M', 'other'], dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['GENDER'] = df['GENDER'].apply(lambda x: 'M' if x == 'M' else ('F' if x == 'F' else 'other'))\n",
+    "df['GENDER'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "544beb4b",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>columns_name</th>\n",
+       "      <th>nulls_percentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AGE</td>\n",
+       "      <td>0.248030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>INCOME</td>\n",
+       "      <td>0.223096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>ADATE_18</td>\n",
+       "      <td>0.222855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>ADATE_16</td>\n",
+       "      <td>0.213432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>ADATE_14</td>\n",
+       "      <td>0.197742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>HVP2</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>HVP1</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>ETHC6</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>ETHC5</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>TPE2</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>335 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    columns_name  nulls_percentage\n",
+       "3            AGE          0.248030\n",
+       "4         INCOME          0.223096\n",
+       "310     ADATE_18          0.222855\n",
+       "309     ADATE_16          0.213432\n",
+       "308     ADATE_14          0.197742\n",
+       "..           ...               ...\n",
+       "111         HVP2          0.000000\n",
+       "110         HVP1          0.000000\n",
+       "109        ETHC6          0.000000\n",
+       "108        ETHC5          0.000000\n",
+       "167         TPE2          0.000000\n",
+       "\n",
+       "[335 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Check for null values in the numerical columns.\n",
+    "numerical= df.select_dtypes(include=np.number)\n",
+    "numerical.isna().sum()\n",
+    "\n",
+    "numerical=pd.DataFrame(numerical.isna().sum()/len(numerical)).reset_index()\n",
+    "numerical.columns= ['columns_name', 'nulls_percentage']\n",
+    "numerical.sort_values(by= ['nulls_percentage'], ascending= False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4c88bfd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        NaN\n",
+       "1        9.0\n",
+       "2        1.0\n",
+       "3        4.0\n",
+       "4        2.0\n",
+       "        ... \n",
+       "95407    NaN\n",
+       "95408    9.0\n",
+       "95409    NaN\n",
+       "95410    NaN\n",
+       "95411    8.0\n",
+       "Name: WEALTH1, Length: 95412, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f27e63a8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A    34803\n",
+       "B    28505\n",
+       "D    16580\n",
+       "C    15524\n",
+       "Name: GEOCODE2, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Use appropriate methods to clean the columns GEOCODE2, WEALTH1, ADI, DMA,and MSA.\n",
+    "data['GEOCODE2'].unique()\n",
+    "data['GEOCODE2']= data['GEOCODE2'].fillna(' ')\n",
+    "data['GEOCODE2'].replace(' ', 'A', inplace = True)\n",
+    "data['GEOCODE2'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "2da9a1fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4688299165723389\n"
+     ]
+    }
+   ],
+   "source": [
+    "data['WEALTH1'].unique()\n",
+    "data['WEALTH1'].isna().sum()\n",
+    "print(data['WEALTH1'].isna().sum()/len(data['WEALTH1']))\n",
+    "\n",
+    "#I should drop this column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "ffe284ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.001383473776883411\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data['ADI'])\n",
+    "plt.show()\n",
+    "print(data['ADI'].isna().sum()/len(data['ADI']))\n",
+    "\n",
+    "data['ADI'].isna().sum() #migalhas \n",
+    "data['ADI']= data['ADI'].fillna(np.mean(data['ADI']))\n",
+    "\n",
+    "sns.distplot(data['ADI'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "7085cc78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.001383473776883411\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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rryExMRGvvPIKFixYII2ZMWMGtm/fjueeew7PP/88JkyYgB07dmD69OnSmO985zvYuXMn8vLysGrVKqSkpKCgoAAPPfRQ4N48ERGRDw1U00vuU7RP0mDGPklERHbs2xNYcp/3VxeasOOLKvz0O8lITYzk5+3EoOiTRERERL5xssYEi01Aybl6pacypDBIIiIiGuSutNkbKpfXt6Kjy6rwbIYOBklERESDXFO7PUiyCcA3dS0Kz2boYJBEREQ0iNkEAU2OTBIAnKptVnA2QwuDJCIiokGsucMCa489WF9fbIbNxj1ZvsAgiYiIaBBrausEAEQYtTBo1Wg1W3C8uknhWQ0NDJKIiIgGsSuOeqTYMD2uHRUOAPjo6zolpzRkMEgiIiIaxBod9UgjQvWYmBABADj4DVsB+AKDJCIiokGs0bHcNiJEh7hwAwDgYnOHklMaMhgkERERDWI9M0lhevtpY1dau5w9hVzEIImIiGgQa2x3ZJJCdQg1aAAALWYLzBY2lfQWgyQiIqJBTMokhehg1Gmguuo6eY5BEhER0SDV3mmF2WIDYF9uU6tUCNXbs0mXWzuVnNqQwCCJiIhokBKX2kL1Gui19q/0UIOjLqmNQZK3GCQRERENUt1F2zrpWpgjk8Tibe8xSCIiIhqkurf/66VroY4dbpeZSfIagyQiIqJBSjaTZBAzSQySvMUgiYiIaJBqbO/ukSSSMkkMkrzGIImIiGiQ6tltWyTVJHG5zWsMkoiIiAaptk57w8hwx442oHt3GzNJ3mOQRERENEiJPZIMuu6vc2aSfEc78BAiIiLPHK9uQnSPomLyLfHoEaNWI10L5fltPsNMEhER+UVlQyu2fV6JrSUVsNkEpacz5FhtArqs9s+1VyaJy20+wyCJiIj84nh1EwD7YaunLjYrPJuhp+cBtoZemST7/27vsqKji4fceoNBEhER+ZwgCPiqxiT9ueRsg4KzGZrMXfZ6JJ1GBY1aJV03aNXQOv7MuiTvMEgiIiKfu9DY0esU+pJzDJJ8rcORSdL3yCIBgEqlQnSYvW8Sl9y8w8JtIqIg9vahSqeP/2x6coBm4p6vLtiX2mLD9Gho7cShcw2w2oReGQ/yjphJMmr75jtiQvW41Gxm8baXmEkiIiKf++qCfant+xNHwaBVw9Rhwckey2/kPbEmqWfRtig6zL6jkOe3eYdBEhER+dRFUwcutZihUaswaXQkxsWGAWBdkq91iD2SrlpuA4AYx3Ibz2/zDoMkIiLyqW/qWgAAE0aGwajTYPxIR5DEuiSfcrbcFh3KmiRfYJBEREQ+1eQ4dHVUhBEAMD4uHADwefllWKw2xeY11HQvtznJJHG5zSsMkoiIyKdMHfYgKcJo3xs0eoQR4QYtWswWnL3UquTUhhTpSBJmkvyGQRIREflUc4cFABBhtBcPq1UqJMWEAgAuNLUrNq+hxuxoFGlkJslvGCQREZFPdQdJ3V1mRkfZl95qGjsUmdNQ1OEskyT1SWILAG8wSCIiIp9qMfdebgOABEeQVMtMks+ImSTZmqRQ7m7zBcWDpLVr1yIlJQVGoxFpaWnYv3+/0/HFxcVIS0uD0WjE+PHjsX79+j5jCgsLkZqaCoPBgNTUVOzcubPX47/73e+gUql6/SQkJPj0fRERDUddVhs6HLuuIh3LbQAwOtKRSWpiJslXxJok2d1tPfokCQIPF/aUokHSjh07sHz5cjz77LMoKyvDzJkzMXfuXFRWyneYLS8vx7x58zBz5kyUlZVh5cqVePLJJ1FYWCiNKSkpQVZWFrKzs3Hs2DFkZ2fjgQcewKFDh3rda/LkyaipqZF+jh8/7tf3SkQ0HIhLbVq1qtcy0OgRIQCAWhODJF8RD6+V65MkFm53Wmxo6+Qht55S9FiSNWvW4Be/+AUee+wxAEBBQQE+/PBDrFu3Dvn5+X3Gr1+/HsnJySgoKAAATJo0CYcPH8bLL7+MBQsWSPe4++67kZeXBwDIy8tDcXExCgoKsG3bNuleWq2W2SMiIh9r7rGzTaXqPoLkhKMD99e1zbJHrQTr8SrBTNrdJtNxO1SvgVatgsUmoLnDgjADTyHzhGKZpM7OTpSWliIzM7PX9czMTBw8eFD2OSUlJX3Gz5kzB4cPH0ZXV5fTMVff88yZM0hMTERKSgp++tOf4ty5c07nazabYTKZev0QEVFvpqt2tomiQux/bmrv4vKPj3Qvt/XNJKlUKoQ7asLEGjFyn2JBUn19PaxWK+Lj43tdj4+PR21trexzamtrZcdbLBbU19c7HdPzntOnT8fWrVvx4Ycf4vXXX0dtbS1mzJiBhob+u8Hm5+cjKipK+klKSnLr/RIRDQfNHX2LtgEgMsT+506LTfpyJ+9Iy20ymSQACHdkj8QlUHKf4oXbPdOxACAIQp9rA42/+vpA95w7dy4WLFiAG2+8EXfddRf+8Y9/AADefPPNfl83Ly8PTU1N0k9VVdUA74yIaPi5ukeSyKDVwOj4Mhc7cpPnbIKATictAIDuIKnFzCDJU4otUsbFxUGj0fTJGtXV1fXJBIkSEhJkx2u1WsTGxjod0989ASAsLAw33ngjzpw50+8Yg8EAg8Hg9D0REQ13YpAUaez79TIiRI/arg40tXch3rHbjTzTZbFBXLSUayYJdGfzWphJ8phimSS9Xo+0tDQUFRX1ul5UVIQZM2bIPicjI6PP+D179iA9PR06nc7pmP7uCdjrjU6ePInRo0d78laIiMihv+U2oHvJzcRMktfERpJqlX0noRwxm8flNs8putyWm5uLN954A5s2bcLJkyexYsUKVFZWIicnB4B9ieuRRx6Rxufk5KCiogK5ubk4efIkNm3ahI0bN+Kpp56Sxixbtgx79uzB6tWr8fXXX2P16tXYu3cvli9fLo156qmnUFxcjPLychw6dAgLFy6EyWTCokWLAvbeiYiGInFp5+rlNqB38TZ5x9xj+39/JSpSTRKX2zym6J7ArKwsNDQ0YNWqVaipqcGUKVOwe/dujB07FgBQU1PTq2dSSkoKdu/ejRUrVuC1115DYmIiXnnlFWn7PwDMmDED27dvx3PPPYfnn38eEyZMwI4dOzB9+nRpzLfffosHH3wQ9fX1GDlyJL773e/is88+k16XiIg8I+5uC5fZch7JIMlnpJ1t/RRtA+je3cZMkscUb5ywdOlSLF26VPaxLVu29Lk2e/ZsHDlyxOk9Fy5ciIULF/b7+Pbt292aIxERDcxqE9Bm7ntum2gEgySfkXokyWz/F0UY2ALAW4rvbiMioqGhxWyBAHudjFzzQmaSfKe727aTTBJ3t3mNQRIREfmEWLQdbtBCLVMnE+WoUzJ1MEjylrNu2yJxuY2F255jkERERD7RX48kkVi43dFlkwqPyTNmS//ntom4u817DJKIiMgnuoMk+XJXg04jLQ9xyc07HV0uFG5zuc1rDJKIiAaphhYzTtYEzzmSznokiaQ2AFxy84prmSTubvMWgyQiokHoUrMZ/++TbzD/tX/ioqlD6ekAGHi5DegRJLUxSPKGucuFmiRmkrzGIImIaJDpstqw7fNKdFps6LTY8MnXdUpPCUB300K5HkmiSEcAxS9u73Q4MklGJ5mk7sJtBqSeYpBERDTI/O+xC6jtkT365FRwBEltnfbAR277v4g7rnxDyiQ5aQEgLbeZLdJh8OQeBklERIPIhcZ2HK64AhWAOZMTAAAHztRLJ8IrSezdE9LPgasAl4B8RapJcvJZRxjsWTubALR1cjehJxgkERENItWN7QCA8SPDMPO6OMSF69HaacXh85cVnhnQ3ulCkGRkkOQL0rEkTjJJRp0aGsfht/y8PcMgiYhoEKlzLLPFRxqhVqkw+/pRAIJjyc2tbelcbvOK1HHbSUCqUqm6D7nl5+0RBklERINIXbMZABAfYQQA3DFxJABg36lLis0JsJ/b1mm1B0kh+oGX25p5nphXus9uc/41zuVN7zBIIiIaRMTt/qMiDQCAmdeOhEatwpm6FlRdblNsXu09Omi7cuhqR5cNFqvydVSDlbTc5iSTBLBXkrc8CpLKy8t9PQ8iIhpAR5cVJseX3ShHJikqVIepSSMAACXnGpSaWq8DV8U6GDlGvQbiw8xueMZitcFqs+9WGyiT1L3DjZk7T3gUJF177bW444478NZbb6GjIziamBERDXViPVKkUdtrSWvS6EgAwPn6VkXmBbhWtA0A6h51MgySPNPRYyej3sXlNhMzSR7xKEg6duwYpk6div/zf/4PEhIS8Mtf/hKff/65r+dGREQ9iPVIoyKNva6PiwsDAJxvUC5IEjNJAy3/ANzh5i2x3YNOo4Ja1X/WDgDCxeadDJI84lGQNGXKFKxZswbV1dXYvHkzamtrcfvtt2Py5MlYs2YNLl1StoCQiGgokuqRIgy9ro+LDQUAnK9XvibJpSCJO9y8IhbI6zUDf4Uza+cdrwq3tVotfvzjH+N//ud/sHr1apw9exZPPfUUxowZg0ceeQQ1NTW+micR0bB39c42Uc9MklKdlcUgydnONlG4gUeTeKPLkUkaaKkN6N11m9znVZB0+PBhLF26FKNHj8aaNWvw1FNP4ezZs/j4449RXV2N+fPn+2qeRETDXvdyW+9MUlJ0KNQqe1flS44xgSb2SApx0iNJJH5xN/OL2yNiJknnRiaJfZI80/8BO06sWbMGmzdvxqlTpzBv3jxs3boV8+bNg1pt/w+WkpKCP//5z5g4caJPJ0tENFx1dFnR1G7foTTqqkySXqvGNdEhqLrcjvMNbX1qlgI1P4DLbYHQyUxSwHgUJK1btw6LFy/Gz3/+cyQkJMiOSU5OxsaNG72aHBER2fW3s000LjbMHiTVt+LWlJhAT8/l3W0A62S85U6Q1J1JYgsAT3gUJBUVFSE5OVnKHIkEQUBVVRWSk5Oh1+uxaNEin0ySiGi4629nm2hcbBj2n6lXbIebW4XbbHDoFXcKt9lM0jse1SRNmDAB9fX1fa5fvnwZKSkpXk+KiIh6q2/pBADEhRtkH1e6DUCHW4XbzCR5w71MEovkveFRkNTf7omWlhYYjYFfCyciGupMjuWSESE62ceVbgMg7W5zIZMkHk3S3mWFxcajSdzV5U4LACMLt73h1nJbbm4uAPvJwr/5zW8QGhoqPWa1WnHo0CHccsstPp0gEREBJkfRtrh8crWr2wCoBmgy6GvuFG6LR5PYBKDVbEVUCI8RdYfUTNKNmiRmkjzjVpBUVlYGwJ5JOn78OPR6vfSYXq/HzTffjKeeesq3MyQiIulYich+MklXtwEI9A63dqkFwMBBkng0ianDguaOLkT1855IntmNTFJkj91tSgTPg51bQdInn3wCAPj5z3+OP/3pT4iMjPTLpIiIqDdxuS3KKB9QKNkGQBAEdHSKmSTXskJikMQMh/vEZpIDHW4LdC+3WW0C2rusCNV7tF9r2PIox7l582YGSEREAdLRZZWWWCJC+v+SGxfrWHIL8EG3XVYBVketqiuZJIA73LzhTjPJEJ19aRPgZ+0Jl0PK+++/H1u2bEFkZCTuv/9+p2PfffddrydGRER2YhbJoFXDoO0/CFGqDYBYtK1WubbjCuCuK2+4s7tN1XNp02zBKH9PbohxOUiKioqS1jKjoqL8NiEiIurN1O68Hkk01rHDreJyYHe49SzadrXmRWpyyCDJbe70SQKACKPOvrTJTJLbXA6SNm/eLPu/iYjIvwaqRxKNiQ4BAFRfaff7nHrqcGP7v4jLbZ5z54BbgEeTeMOjmqT29na0tXX/S6WiogIFBQXYs2ePzyZGRER24vb/SCf1SABwzQh7Jqm6MbBBUnun69v/Rdya7jmzxfWaJIBHk3jDoyBp/vz52Lp1KwCgsbERt956K/74xz9i/vz5WLdunU8nSEQ03Inb/yMGyCQljrDvaLvUbIbZYvX7vETuNJIUMbvhObGZpCu72wAgTApIA/d3YqjwKEg6cuQIZs6cCQD461//ioSEBFRUVGDr1q145ZVXfDpBIqLhrjuT5DxIignTS1vwaxo7/D4vkVST5MKRJCIpk8TlNre500wS6P6sWxmQus2jIKmtrQ0REREAgD179uD++++HWq3Gd7/7XVRUVPh0gkREw113TZLz5TaVSoVrRjjqkgK45NadSXL9KyWcR5N4zN3C7TCDPXhl1s59HgVJ1157Lf72t7+hqqoKH374ITIzMwEAdXV1bvdPWrt2LVJSUmA0GpGWlob9+/c7HV9cXIy0tDQYjUaMHz8e69ev7zOmsLAQqampMBgMSE1Nxc6dO/u9X35+PlQqFZYvX+7WvImIAqXZxeU2AEhUIEjqcHTbdqcmKUTf3b+nlctALrPZBHRZ7T2p2G7B/zwKkn7zm9/gqaeewrhx4zB9+nRkZGQAsGeVpk6d6vJ9duzYgeXLl+PZZ59FWVkZZs6ciblz56KyslJ2fHl5OebNm4eZM2eirKwMK1euxJNPPonCwkJpTElJCbKyspCdnY1jx44hOzsbDzzwAA4dOtTnfl988QU2bNiAm266yc1PgIgoMGyCIBXcDrTcBiizw82TmiTxaBKAS27uED9rwPVMUrgjk8TlNvd5FCQtXLgQlZWVOHz4MD744APp+p133on/+q//cvk+a9aswS9+8Qs89thjmDRpEgoKCpCUlNRv8ff69euRnJyMgoICTJo0CY899hgWL16Ml19+WRpTUFCAu+++G3l5eZg4cSLy8vJw5513oqCgoNe9Wlpa8NBDD+H1119HdHT0gHM1m80wmUy9foiI/K3FbIFNAFToXqJyRlxuuxDI5TYPdrcBPXe4cdeVq9o6u4Mkrca1nlRhDEY95vHRywkJCZg6dSrU6u5b3HrrrZg4caJLz+/s7ERpaam0VCfKzMzEwYMHZZ9TUlLSZ/ycOXNw+PBhdHV1OR1z9T0ff/xx3Hvvvbjrrrtcmm9+fj6ioqKkn6SkJJeeR0TkDbFoO8KohUbd90vx7UOVvX4qGuztWUorr+DtQ/JZeV+T+iS5UbgNdPdKauaXt8vEgFSvUUPtpHFnz78TJ2rs/6j/urZZukau8eiku9bWVrz00kv46KOPUFdXB9tVRXfnzp0b8B719fWwWq2Ij4/vdT0+Ph61tbWyz6mtrZUdb7FYUF9fj9GjR/c7puc9t2/fjiNHjuCLL74YcJ6ivLw85ObmSn82mUwMlIjI79ypRwKAEaF6AEBjW+CyM540kwTYK8kTbV32z8rVnW0ApKNsAtkWYqjwKEh67LHHUFxcjOzsbIwePdrlNvRyrn6uIAhO7yc3/urrzu5ZVVWFZcuWYc+ePTAaXT8l22AwwGAwuDyeiMgXmlzc/i8a4RjX1N4Fm+P/H/2tvcvb5TYGSa5qkzJJrn/viv2UxCaU5DqPgqT3338f//jHP3Dbbbd5/MJxcXHQaDR9skZ1dXV9MkGihIQE2fFarRaxsbFOx4j3LC0tRV1dHdLS0qTHrVYrPv30U7z66qswm83QaNz7RSci8hdx+3/kANv/RZEhOqgAWG1CwIIPTwq3ASDcyF1X7pKW29zKJDmCpC4GSe7yqCYpOjoaMTExXr2wXq9HWloaioqKel0vKirCjBkzZJ+TkZHRZ/yePXuQnp4OnU7ndIx4zzvvvBPHjx/H0aNHpZ/09HQ89NBDOHr0KAMkIgoqzS4ebivSqFXS2KYALLnZbIL05Wt0o08SwIaSnhB3qLm6sw0ADDout3nKo0zSH/7wB/zmN7/Bm2++idDQUI9fPDc3F9nZ2UhPT0dGRgY2bNiAyspK5OTkALDXAVVXV0tHoOTk5ODVV19Fbm4ulixZgpKSEmzcuBHbtm2T7rls2TLMmjULq1evxvz587Fr1y7s3bsXBw4cAABERERgypQpveYRFhaG2NjYPteJiJTWnUlyLUgC7EtuTe1duNLW6a9pSVo7LRAX9TxdbmtmJsllYtbOvZokLrd5yqMg6Y9//CPOnj2L+Ph4jBs3TsriiI4cOeLSfbKystDQ0IBVq1ahpqYGU6ZMwe7duzF27FgAQE1NTa+eSSkpKdi9ezdWrFiB1157DYmJiXjllVewYMECacyMGTOwfft2PPfcc3j++ecxYcIE7NixA9OnT/fkrRIRKaq7cNv1/7seEapDxeXAFG+LS2UalQpamd13zoi725hJcp1Yk2RwJ5PkCJI6LbYB636pN4+CpPvuu89nE1i6dCmWLl0q+9iWLVv6XJs9e/aAQdjChQuxcOFCl+ewb98+l8cSEQVSa6c9gAhzoUeSSNrh1u7/IEkM4gw6tdtfvj2PJumy2lw+1X44E4MkT3a3CbAfaSL+mQbmUZD029/+1tfzICKiqwiCINWguNJIUjQi1J7dbwzAcpsYJLm71AYAoY6jSWwC0NDSiYQo13ccD1ftne7XJOk0KqhgD5LMFgZJ7vA4bG9sbMQbb7yBvLw8XL58GYB9ma26utpnkyMiGs5M7fZu2wAQ5kajxhEh9kxSU0AySfbXMLqR2RCpVSopQ3ap2ezTeQ1VrR7sblOpVDA4iuo7ucPNLR5lkr788kvcddddiIqKwvnz57FkyRLExMRg586dqKiokAqtiYjIc/Wt9sDBoFVD60bmQMwkBaJwu3u5zbPsRLhBi+YOC+pbGCS5omfHbXcYtBp0dNnQwR1ubvEok5Sbm4tHH30UZ86c6dWQce7cufj00099NjkiouHscqs9yHGnHgkAoh01SR1dNr+3ARALtz3JJAHdy4iXGCS5pE1cbnPz89Zzh5tHPPpb/cUXX+CXv/xln+vXXHNNv0eKEBGRexocgYM79UiA/QsxwvGcysttPp9XT+Jym6eZJHHXHjNJrmnzYLkN6A5iOxkkucWjIMloNMJkMvW5furUKYwcOdLrSREREdAgZpLcPDgWAKLD7Nmkqiv+DZJapMJt7zJJ9c3+XxocCrxZbgO6z9kj13j0t3r+/PlYtWoVurrs/4JQqVSorKzEM88806tnERERea6hxbPlNgCIcQRJ/s4kmcSaJA93THG5zT1iSwh3WgAAXG7zlEdB0ssvv4xLly5h1KhRaG9vx+zZs3HttdciIiICL7zwgq/nSEQ0LHlakwQA0Y7i7So/B0lSTZKnhdvicht3t7nE80wSl9s84dHutsjISBw4cACffPIJSktLYbPZMG3aNNx1112+nh8R0bBV72FNEhC4TJJUk+Rx4bY9mGNNkms8rUni+W2ecfs3z2azYcuWLXj33Xdx/vx5qFQqpKSkICEhge3OiYh8qDuT5EVN0iDJJHG5zTVtXmaSOphJcotbn7IgCPjRj36Exx57DNXV1bjxxhsxefJkVFRU4NFHH8WPf/xjf82TiGjY8aomydEGoLqxHVaxI6UfNHtZuB3pCJIa27pYVOwC8YBbtzNJWjaT9IRbv3lbtmzBp59+io8++gh33HFHr8c+/vhj3Hfffdi6dSseeeQRn06SiGg46t7d5n6QFBmig0alQpdVQK2pA9eMCPH19AD0aCbpYeF2iE4DncY+z5qmDqTEhflyekNOmwfHkgBcbvOUW5/ytm3bsHLlyj4BEgB8//vfxzPPPIO//OUvPpscEdFwZbMJUsdsT2qS1CqV1Hnbn0tu3maSVCoVohzHqNQ0tvtsXkOR1Sagw5EJcnd3m4G72zzi1qf85Zdf4p577un38blz5+LYsWNeT4qIaLhrau+SlslCPahJAgJTvN19dpvnh6aKwVw1gySn2nssR3pak8QgyT1ufcqXL19GfHx8v4/Hx8fjypUrXk+KiGi4E5fajDo1tGrPsjT+Lt7utNikL11PC7cBICrEHiTVNHX4ZF5DlbjUpgKg07i3SUpcDuVym3vc+s2zWq3QavtP+2o0GlgsFq8nRUQ03IlHknhSjyQSi7f9FSSJO9sA9wuJexohBUnMJDkj9kjSadRu7yRnJskzbv32CYKARx99FAaDQfZxs5lbOImIfEHc/u9JPZIo2s/LbeKRJHqNGhq15+1fxExSdSMzSc542iMJ6BEkcXebW9z67Vu0aNGAY7izjYjIe/VedNsWSZmkK/7J0JjEeiQPi7ZFUY6aJBZuOyftbPMkSOqxu00Q/NcSYqhx67dv8+bN/poHERH1cNmLHkkisXD7UrMZ7Z1WhHhwUK4z3m7/F41w7G670NjOpsROeNpIEujOJNkEwOLHvllDjXfhPxER+UVDq6MmycOdbQAQotdIzRr9seTW3W3by0ySY7mttdMqHZhLfbVJNUnuB5E9s0+sS3IdgyQioiDkTSPJniaMCgcAnKlr9npOV5PObfNiZxtg/wIXD+Rl8Xb/2r2oSVKrVFIGyszO5i5jkEREFIQavDjctqcb4iMAAKdrfR8kSZkkL3a2iUZH2TuCX2BdUr9aO71b3uQON/cxSCIiCkKXfVC4DQDXiUHSxRav53Q1qSbJy0wSACSOMAIALnCHW7/azJ5nkno+j0GS6xgkEREFoe7Dbb0LQKRM0kV/LLf5LpOUOIKZpIG0erG7Dehu+MmGkq5jkEREFGSsPc5t8zaTdH28vSbpfEMrOnxci+KrmiSge7mNXbf7JxZuGzzY3QYwk+QJBklEREGmqb0L4i5tbwu3R0YYMCJUB5sAnL3k2yW37t1tvlxuYyapP61m7zJJYk1SJxtKuoxBEhFRkBGzSBEGrVedrAFApVLh+lH+WXLzy3Ibd7f1y1dBUgeX21zGIImIKMg0OoKkEWE6n9zv+gT7kpuvi7d9u9xmzyTVNnXAxmaHslrF5TZPd7dJNUnMJLmKQRIRUZC50moPPqIdx4p463o/tQGQMkleNpMEgPhII9QqoMsqoL6F54DK8eZYEqDHchuDJJcxSCIiCjLictsIXwdJPm4o2b3c5n0mSadRY1SEPZtUzbokWa1mMZPkaZBk/+/k6wL+oYxBEhFRkGlss2eSRoT4aLnNESRVXW6X6lp8QSzcNvggkwQAY2NDAQDl9a0+ud9Q420mScz4cbnNdQySiIiCjJhJEo/q8FZMmB5x4QYAwDd1vqlLEgShR8dt3xycKx6hcu4SgyQ5YibJkwNuge5MEvskuY5BEhFRkLkiZpJ8tNwGADc4irdP+aguqb3LCqujwNoXLQAAYHxcGADgXL3vu4MPBb7KJHWwBYDLGCQREQWZpnbfZpIAYFJCJADgeHWTT+4n1iNp1CqPTqWXM2GkPZA7W8dMkpzu3W2sSQoUxYOktWvXIiUlBUajEWlpadi/f7/T8cXFxUhLS4PRaMT48eOxfv36PmMKCwuRmpoKg8GA1NRU7Ny5s9fj69atw0033YTIyEhERkYiIyMD77//vk/fFxGRp6TdbWHeZ5LePlSJtw9VSktjH39dJ10Tfzwhbv8PN2ihUvk2SCpvaJWyVGTXZbVJu9I8bQHAmiT3edfK1Us7duzA8uXLsXbtWtx2223485//jLlz5+LEiRNITk7uM768vBzz5s3DkiVL8NZbb+Gf//wnli5dipEjR2LBggUAgJKSEmRlZeEPf/gDfvzjH2Pnzp144IEHcODAAUyfPh0AMGbMGLz00ku49tprAQBvvvkm5s+fj7KyMkyePDlwHwARkYyeu9tazb7Z6ZUUbS+Krm3qQJfVBp2HdS0iMZMUYfTd18g10SHQa9XotNhwobEdSTGhPrt3oAwUdP5set/vNleIh9sCgE7rWVBqZCbJbYpmktasWYNf/OIXeOyxxzBp0iQUFBQgKSkJ69atkx2/fv16JCcno6CgAJMmTcJjjz2GxYsX4+WXX5bGFBQU4O6770ZeXh4mTpyIvLw83HnnnSgoKJDG/PCHP8S8efNw/fXX4/rrr8cLL7yA8PBwfPbZZ/5+y0REAxJ3t/lyuW1EqA5hBi2sgoAaH2yxF4OkcC/PlutJo1YhJdZel/SNj49QGeykw201amjVHi636br7JLFhp2sUC5I6OztRWlqKzMzMXtczMzNx8OBB2eeUlJT0GT9nzhwcPnwYXV1dTsf0d0+r1Yrt27ejtbUVGRkZ/c7XbDbDZDL1+iEi8ofu3W2+K9xWqVRIirYf/VF1xfsgqand/v+5UT5qUyAaP9JRvM0dbr2IRduhBs+L5MUCewHdQRc5p1iQVF9fD6vVivj4+F7X4+PjUVtbK/uc2tpa2fEWiwX19fVOx1x9z+PHjyM8PBwGgwE5OTnYuXMnUlNT+51vfn4+oqKipJ+kpCSX3ysRkavaO61SzcgIH2aSAGCMY8mt6kqb1/cydfg3SPL1YbyDnbj935sDj7VqFTSO+jExE0jOKV64fXXBnyAITosA5cZffd2Ve95www04evQoPvvsM/zbv/0bFi1ahBMnTvT7unl5eWhqapJ+qqqqnL8xIiIPiFkkrVrl06UsAEiKsWeSvvVhJinSx0GSWLx9jkFSL2LmJ1TveSZJpVJJS24tPmwqOpQpVrgdFxcHjUbTJ8NTV1fXJxMkSkhIkB2v1WoRGxvrdMzV99Tr9VLhdnp6Or744gv86U9/wp///GfZ1zYYDDAYDK6/QSIiD/Qs2vbVrjHRmBH2TNLl1k60mi0I8yIIM7Xbv2R9n0liQ0k5YuF2qJeBs1GnQVunVdqdSM4plknS6/VIS0tDUVFRr+tFRUWYMWOG7HMyMjL6jN+zZw/S09Oh0+mcjunvniJBEGA281BFIlKWP4q2RSF6jdR5+1svl9ykTJLRN/MU2xEcqbgCAKhrNmPTgXKv2hQMJWImKcyLTBLQ3WOJy22uUbQFQG5uLrKzs5Geno6MjAxs2LABlZWVyMnJAWBf4qqursbWrVsBADk5OXj11VeRm5uLJUuWoKSkBBs3bsS2bdukey5btgyzZs3C6tWrMX/+fOzatQt79+7FgQMHpDErV67E3LlzkZSUhObmZmzfvh379u3DBx98ENgPgIjoKv4o2u4pKToE9S1mVF1pxw2OBpOeMEmF2779GjHqNIgwaNFstqC+xSzVUQ13Uk2SDzJJAIMkVykaJGVlZaGhoQGrVq1CTU0NpkyZgt27d2Ps2LEAgJqaGlRWdv8LIiUlBbt378aKFSvw2muvITExEa+88orUIwkAZsyYge3bt+O5557D888/jwkTJmDHjh1SjyQAuHjxIrKzs1FTU4OoqCjcdNNN+OCDD3D33XcH7s0TEcnoPpLE95kkABgTE4qyqkavM0li4XZkiM7nx1zERRjQbLbgUjODJFGbjzNJrElyjaJBEgAsXboUS5culX1sy5Ytfa7Nnj0bR44ccXrPhQsXYuHChf0+vnHjRrfmSEQUKI2t/s8kAUDV5fYBN8o407MFQEeXb0sVRkYYUF7firpmlkCIWn1YkwSANUkuUnx3GxERdZMySWH+ySQlRBmhVavQ3mVFgyMg84TJT7vbACAxyh7IVfug6eVQ4fNMEpfbXMIgiYgoiDT6uSZJq1ZjdJQRgHfF2/5qJgnYjycBgOor7VKbl+GuuwWAbzJJJgZJLmGQREQURLoLt/2TSQIgnYlWddmzTI0gCNKXrK92t/UUH2mAxpHtEjNrw12bVLjtXSbJyJoktzBIIiIKIt2F2/7JJAHdh9162nm7tdMKq+PsL39kknyV7RpKfJVJMrAmyS0MkoiIgoi/l9sAYIxjOaumqQMWq/s708R6JJ1GBaPOP18jiSPsc7zAuiQAQFunPZPkbRd2cbmNmSTXMEgiIgoiV/zYTFIUE6ZHqF4Dq01ATVOH28/vWY/k667gojGOIOlbBkkAuoMab44lAbqX29gnyTUMkoiIgoTVJkj9h/y53KZSqbxacjP5uNu2HLF4+0JjO2ws3u5Rk+Sb5TbubnMNgyQioiDR1N4FMR7wVzNJkbjk5slht/463LanURH2VgUdXTZc9qJVwVDhiwNuAUjLo9zd5hoGSUREQULc2RZh0EKn8e//PXfvcPMgkyTubPNjkKRRq6Ti7WoPArmhRqxJ8vpYEq1Yk8TCbVcwSCIiChJi0ba/Gkn2JNb8NLR2otXNIl5/9kjqSeqXxLok6b+Rt5kkgyOT1NFlQ5cHRfvDDYMkIqIgcaVVLNr2Xz2SKNSglXZKfVPX4tZzu2uS/Huy1TUj7Nmu4d4GwGK1wWyxBzRh3rYA0HYHWaxLGhiDJCKiICEut/mzaLunUZEGAMDpi81uPS9QmaSxsWKQ1I6OLqtfXyuYtfV4794ut2nUKug09h2J3OE2MAZJRERBojEA2/97io+w1/yccTeT1OH/wm0AiA3TI8KghcUm4GhVo19fK5iJO9t0GhX0Wu+/tqVDblmXNCAGSUREQeJKABpJ9uRpJskUoEySSqXCuLgwAMChc5f9+lrBrLtHkm+WN8UlN2aSBsYgiYgoSHQfSRKYTNIoMZN00d2aJP+d23a1FDFIKm/w+2sFqzbH9v8wL4u2RWIbANYkDYxBEhFRkAjEkSQ9xUfYM0nVje1u7XALVE0S0B0kHam8gk7L8NyN1epYbgv1sh5JJLYB4HLbwBgkEREFie7C7cBkknrucHOnLqm7Jsm/u9sAYFSEAaF6DTq6bDhe3ej31wtGvs4kGZhJchmDJCKiINFduB2YTBLgWV1SIDNJKpVKyiZ9NkzrklodjSR9VZMkZpLYdXtgDJKIiIJEoAu3ge4dbq72Suqy2qTuz4GoSQKAcbFiXdLwDJLaHEuhYQYf1yS52UR0OGKQREQUJBoDXLgNuJ9JEne2AUCEn5tJisRMUun5y8OyS3Srj44kEYmH3DZ3sCZpIAySiIiCQHunVeqqHNAgyc0dbuISTbhBC62fz5cTJUQZEROmR2unFaUVVwLymsGkzcctAIxa1iS5ikESEVEQEJfatGqVVEwdCPGR3TvcXFl+CWQ9kkitUmHWdXEAgH2nLgXsdYOFlEnyWeE2+yS5ikESEVEQ6HkkiUqlCtjrhuq1GOloBeBKXZIYJAVqqU10x8RRAIB9p+oC+rrBQDrc1lctAKSO2wySBsIgiYgoCAT6SJKexjtqfsrrBw6SAtVt+2ozrxsJlQr4urYZNU3tAX1tpYkZvnAfFW4bHMttzCQNjEESEVEQUGJnmyhFCpLaBhwrZpL8fW7b1WLC9LglaQSA4bfk5uslTjGT1LMIn+QFNl9KRESStw9VSv/7s3P2YzdazJZe1wNBPB/tfH3rgGPFRpKBziQBwPeuH4WyykbsO1WHB29NDvjrK6U7SPJNAB3KIMllzCQREQWBNqlhoG+WVNwh9iE63zBwkCQuCyoRJN0xcSQA4MCZ+mF1RIl4XI2vPvMQfXdN0nBsqeAOBklEREGgvVPc5h34IKl7ua0VgiA4HVtn6gBgPy4k0KYkRiEu3N4K4PNh1FiyyXGgsK+DJIDZpIEwSCIiCgJtPj56wh1jY0MB2At5L7d2Oh1b12wG0N2EMpDUahXuTo0HALx3rDrgr68EQRC6i+V9VNSvVqkQ6dideKWNQZIzrEkiIgoCSi63GXUaJEYZcaGpA+cbWhEb3n8AdNGRSRKPMwm0+bdcg22fV+H947VYNX+KVIQMYMBarp9NH3x1TB1dNnQ6lsR8ucQ5IlQPU4cFTe3Og+LhjpkkIqIg0KbgchvQXbw90A43JTNJAHDruBgkRhnRbLbg46+Hfs+kxvbuJqO+aiYJdHd1b2QmySkGSUREQUDMJIUosNwGuLbDraPLKvXWGalQJkmtVuFHt1wDAPhb2dBfcuu5/d+XTUZHOFpNMEhyjkESEVEQUHK5DQBSHDvcyp3scKsz2bNIBq1aqmlRwn1TEwHY+yU1DYIv+dqmDryx/xzOXnLtfLyemvy0m3CE435ify6SxyCJiEhhNkFAR5eyQZIrmaS6ZsfOtkhDQI9OudrEhEhMTIhAp9WGfxyvUWwerhAEAbuOVeNcfSt2H68ZcPfg1fzVvFNcbmvi7janGCQRESmsvdMK8atTid1tAJASZ9/hdt5JGwCpHkmhpbae7p9mX3Lb9M9y2GzuBR6BdKauBRUN9jqvmqYOHK1qdOv5jY4gZoSPj6sRM0lcbnNO8SBp7dq1SElJgdFoRFpaGvbv3+90fHFxMdLS0mA0GjF+/HisX7++z5jCwkKkpqbCYDAgNTUVO3fu7PV4fn4+vvOd7yAiIgKjRo3Cfffdh1OnTvn0fRERuUpcajNo1dColcnQJMWEQq2ynzh/qcUsO0bJHklX++mtyYgwaPFNXQuKTl5UejqyBEFA0Qn73HQa+39Xd7up++usPKkmiZkkpxQNknbs2IHly5fj2WefRVlZGWbOnIm5c+eislL+L1F5eTnmzZuHmTNnoqysDCtXrsSTTz6JwsJCaUxJSQmysrKQnZ2NY8eOITs7Gw888AAOHTokjSkuLsbjjz+Ozz77DEVFRbBYLMjMzERr68DdZomIfE3pnW0AYNBqkDgiBABwvp8dbt2ZJOWDpEijDo/MGAsAWPvJN24vYwXCiRoTqhvbodeokZWeBAD43y8vuLXE5etz20Tdu9tYk+SMokHSmjVr8Itf/AKPPfYYJk2ahIKCAiQlJWHdunWy49evX4/k5GQUFBRg0qRJeOyxx7B48WK8/PLL0piCggLcfffdyMvLw8SJE5GXl4c777wTBQUF0pgPPvgAjz76KCZPnoybb74ZmzdvRmVlJUpLS/udq9lshslk6vVDROQLSjaS7CllgLqk7u3/yi+3AcDPb0uBQavGsW+bcPBsg9LT6ePQOXtX8BkTYjFpdCTiIw3o6LJh55FvXb6H/4MkZpKcUSxI6uzsRGlpKTIzM3tdz8zMxMGDB2WfU1JS0mf8nDlzcPjwYXR1dTkd0989AaCpqQkAEBMT0++Y/Px8REVFST9JSUn9vzkiIjcovbNNJJ7hdm6gICkIMkkAEBdukA66/dPeM0GVTRIEARea2gEAkxOjoFKpcGtKLADgnVLXgyR/nZUnHpbbyGaSTikWJNXX18NqtSI+Pr7X9fj4eNTW1so+p7a2Vna8xWJBfX290zH93VMQBOTm5uL222/HlClT+p1vXl4empqapJ+qqqoB3yMRkSuCYbkNAK6LDwcAfFPXLPu4VJMUJJkkAPj/Zo2HQavG5+cv418XgifD32y2oK3TChW6G2+mjo4EAHxd2yztZhyIvzJJ0cwkuUTxwu2rt5EKguB0a6nc+Kuvu3PPJ554Al9++SW2bdvmdJ4GgwGRkZG9foiIfCFYltuuGxUBADh9Ub6fT7BlkgAgcUQIcmZPAAC8f7wGnZbgONX+YpM9oIwN10OnsX/VRhq1GBGqg9Um4Js613om+W+5zZ5Jau6wwGINjs8sGCkWJMXFxUGj0fTJ8NTV1fXJBIkSEhJkx2u1WsTGxjodI3fPX/3qV3jvvffwySefYMyYMd68HSIijwXLctv1jkxS1ZU2tHf2znR0WmzS4bfBFCQBQM7sCbhmRAga27vw6ZlLSk8HAFDryLol9Mi6qVQqTEywB6Jf18pn667mryCpZzNQ9krqn2JBkl6vR1paGoqKinpdLyoqwowZM2Sfk5GR0Wf8nj17kJ6eDp1O53RMz3sKgoAnnngC7777Lj7++GOkpKT44i0REXlEXG4LMyibSYoNNyAmTA9BQJ9MR72jLYBWrUK0IwsRLEL0Gjx77yQAwKenLwXFji3pIOCo3kuTExMcS241ri0NNkl9knz7mWs1akQ4AiW2Aeifosttubm5eOONN7Bp0yacPHkSK1asQGVlJXJycgDY64AeeeQRaXxOTg4qKiqQm5uLkydPYtOmTdi4cSOeeuopacyyZcuwZ88erF69Gl9//TVWr16NvXv3Yvny5dKYxx9/HG+99RbefvttREREoLa2FrW1tWhvbw/YeyciErWagyOTBADXjbJnk85cVZckLrWNjDBArVAvJ2fmTknAuNgwWGwCPgqCg2/lMkkAMGm065kkQRD8lkkCIAW7rEvqn6JBUlZWFgoKCrBq1Srccsst+PTTT7F7926MHWvvfVFTU9OrZ1JKSgp2796Nffv24ZZbbsEf/vAHvPLKK1iwYIE0ZsaMGdi+fTs2b96Mm266CVu2bMGOHTswffp0acy6devQ1NSE733vexg9erT0s2PHjsC9eSIih+7CbWUzSQBwfbx8XVIwFm33pFKpcM+UBADAkYorUiZHCVabIJ1zd3WQJGWSXAiSWjutsDq6ifsjSOo+mkT5zFuwUvw3cunSpVi6dKnsY1u2bOlzbfbs2Thy5IjTey5cuBALFy7s9/Fg2iZKRNTqqP8JMwRBJslRl3TmonwmKdjqkXpKjglF6uhInKgxoejERTz83bGKzKOh1QyLTYBOo0J0WO9lsuvjI6BS2ZcvLzWbMdLJ5ykuG+o1ahh1vs9piIHXlVZmkvqj+O42IqLhzCYIaA+iTJK0w62f5bZgDpIAIDM1HirYu11XXZbvHO5vFx1ZpPhII9RX7awO0WukflSnBsgmSUttoTq/HCjMo0kGxiCJiEhB5i4bxPNZg6EmSdrhdrldWgYEgEvN4rltwbncJhoVacQtSSMAAJ+dU6YLd22TfD2SqHuHm/PibX/WIwHdvZKagqDQPVgxSCIiUlCrIxDRa9VSPx0lxYYbEOtYIjpb1915W6yxERsjBrPvjre3hDle3YQ2s2WA0b4n7WzrN0hyrS7JX4fbikY47stMUv+Uz+0SEQ1j4pd4WBBkkUTXjgpHQ/llnL7YjBvHRAEAvrlkL+QeHaVMJuntQ/IHn8sZEx2CxCgjLjR14EhVI26/Ns6PM+tL2tnWz2c1cbRrmSR/HUkiinIst13h7rZ+Kf/PFiKiYaw1SLpt9yTtcHPUJZ2vb0VFQxu0ahXSxkYrOTWXqFQqfCfFfhbn5+UNAd2s02W14Yqj6Wb/maTuHYTOul1LPZL8nUniclu/GCQRESmou5Fk8GSSxLqkr2vsQZLYxTptbDQijP75wva1W8aMgF6rRn1LZ78H9vrD5dZOCACMOnW/2cGk6FCE6jXotNhwvqH/4nIxSIr0V01SmNgCgJmk/jBIIiJSUHcjyeDJJKWNtWdhDp6tR11zB/adsgdJ37thlJLTcotBp5EKuI9UXAnY6za02LMysWGGfnekqdUqXDtKPEy4/zPc/F24HRXCZpIDYZBERKQgKZMURDVJqYmRmJo8Al1WAW8ePI+Ss/ZdYrOvH6nwzNxzy5gRAICTtSZYbIE5xLWh1V7gHhPm/BiRa0eKQVL/xduN/i7cduxuu8Lltn4xSCIiUpBUk6TwuW1XW5QxDgDw5+JzaO+yYlSEQTpSY7BIjg1FuEGLji4byi8FZsmtwVGPFBs+QJAUP3AmySSd2+bfmqTmDovT2qjhLLh+K4mIhhlxd5uSPZLkdo5ZrDaEGbRodcxv9vUj/dLQ0J/UKhUmjY7EF+cv4ysXD5T11uUey23OSJmkS0out3Xf19RhGTD7NRwxk0REpCDpSJIgqkkC7KfE3zqueyfbYKpH6mlyor0n0YkLJukcNH9yebmtR02SrZ95+bsFgFajlu7d0GL2y2sMdgySiIgUJB1uG0S720S3psRCo1YhRKcJeK8hXxk/MgxGnRotZgvKKv1bwN1psUmBzUDLbckxodBr1OjosqG6sb3P41abIHXu7q+VgC+IXcFrFTwQOJgxSCIiUpC4uy3YMkmAPYPxy1nj8T+/zECUn+pi/E2rVksdrj/8qtavr/XtlTYIAHQaFSIGqDHTatRIibOf4Sa35FZr6kCn1QadRoXEESH+mC4AIN7R8LKmiUGSHAZJREQKsdoEdHSJLQCCL5MEAGOiQ6Wu24NV6mgxSLro18aSFY6eR862//ckLbld7BskVTTYC83HRIdCo/ZfLdhoRybpIoMkWQySiIgU0t5lhfiVHUx9koaa6+LDoVGrUHm5DWedFEp767wjsBloqU00wUmvJDHgSo4J9dHs5EmZJC63yWKQRESkEHFnm1Gn9mu2YLgzaDUY71ja+vjrOr+9TncmybUg6bpR/e9wE+81Lta/QZJ4Fh8zSfIYJBERKSRYd7YNRTc4zkv76KT/giQpkzTA9n+RuNx25mJzn2XAysv2eyXHhvlwhn2JhdusSZLHIImISCHSzrYgrUcaSsTi7cMVV9Dkp2M4xOxPjIvLbSlxYVCr7D2KLl21BV+811h/L7eJNUlcbpPFIImISCFt4s62IOu2PRTFhOlx7ahwWG2CdGCvL1msNlRddm+5zajTIMkRBPWsSxIEoTtICtByW0NrJ8wWq19fazBikEREpJBWKZPEICkQ7pxob4jpj7qkC40dsNgEaNUqRLrR/FGsSzpZ032G2+XWTrSYLVCpIAVR/jIiVAe91h4K1JnYUPJqDJKIiBTSJtUkcbktEO5wBEn7TtX5vPt2uaMeKTpMD7Ubx7dMG2vvav55eYN0rcKRkUqINMKo8+/fDZVKJWWTWJfUF4MkIiKFdHfbZiYpENLGRiPSqMWVti4crfJt921xuWxUhGtF26LpKbEAgM/LL0vHk1QGaPu/KJ5dt/vF30wiIoV0d9tmJikQdBo1Zl0/En//sgYfnaxD2tgYn937mzr7cpm7QdJNY6IQotPgSlsXTtc1Y2JCZMDqkURiJqm2qft4FLlDj6/2s+nJfptTsGAmiYhIIW2sSQq4Oyf5py7pzEUxk+TeOWs6jRrpjoOED527DKC72/ZYP2//F0nntzWxJulqDJKIiBTS4mgmGRaEh9sOVbOvHwWVCvi6tln2YFlPCIKAM+JyW6R7mSQAmJ5iz2gdctQliTVJgcokJYiZJJNvPo+hhEESEZECBEFAc4c9SIowDs7DYwejmDA9piXbMzef+CibdKnFjKb2LqhVQFy4B0HSeHtd0qFzl3tv/48JdCaJNUlXY5BERKSAZrMFFkehbjgLtwPq+z5uBSAWbSfHhEKncf9r9aYxUTDq1Gho7UTx6UuodzSWTA50JolBUh8MkoiIFHCp2f5FaNCqpT41FBhikPTPb+rR3ul9A0UxSLp2VIRHzzdoNVJ269HNXwCwd+OOcqPfkjfEIKmu2ezz1giDHX8ziYgUIAZJEUZmkQJtYkIEEqOMMFtsOHi23uv7iUXb18WHe3yP7zqW3AB7q4I/Z6d5PS9XjQw3QK0CLDYBDS0DF293dFnxz2/qUV7fGoDZKYtBEhGRAuocQVK4gfVIgaZSqXB3ajwAYNfRC17f74xj+7/YPdsTP701Cffdkoj/XHgT3vllBq6P9ywr5QmtRo2RjtYFA/VKqm82Y92+s/jH8Ro8/MYhaYfmUMUgiYhIAcwkKev+aWMAAB9+VYumdu8OvBWX267zcLkNsLcOKPjpVPwkPQlqtesdu31FLN521nW7sqEVa4u/kQ7jrW5sx5/2ngnI/JTCIImISAEMkpR105goXDcqHGaLDf/4ssbj+1xu7UR9SycAYMKowOxG84cx0fYi8XOX+l9C+8fxGnR02ZAcE4r7p14DAHjjQDlOXDAFZI5KYJBERKQAKUjizjZFqFQqLEyzZ5P+Wlrl8X3ELNKY6JBB3RT05qQoAEBphfxxLRca21F1pR0alQoPTU9G+rgYzLsxAVabgOf+djyQUw0oBklERAqoa7Yva4SzR5Jifjz1GmjUKhypbMTZSy0e3cMX9UjBQDyi5UjlFQhC3x1uYqPL1MRIqa/Xb384GVrH53d+iBZxKx4krV27FikpKTAajUhLS8P+/fudji8uLkZaWhqMRiPGjx+P9evX9xlTWFiI1NRUGAwGpKamYufOnb0e//TTT/HDH/4QiYmJUKlU+Nvf/ubLt0RENCAutylvVKQRs68fCQD4n8OeZZP+Vd0EAAEttPaHKddEQq9V43JrZ59dax1dVhytagTQexdefKQRaWPtrQs+PXMpYHMNJEWDpB07dmD58uV49tlnUVZWhpkzZ2Lu3LmorJQ/WK+8vBzz5s3DzJkzUVZWhpUrV+LJJ59EYWGhNKakpARZWVnIzs7GsWPHkJ2djQceeACHDh2SxrS2tuLmm2/Gq6++6vf3SEQkR2wYyCBJWVnfSQIAvFVSIQWurhIEAZ+etrcQ+O6E2AFGBzeDVoObrpFfciurvIIuq4BREQaMu6rB5ewb7EHmp6eHZpCk6G/nmjVr8Itf/AKPPfYYAKCgoAAffvgh1q1bh/z8/D7j169fj+TkZBQUFAAAJk2ahMOHD+Pll1/GggULpHvcfffdyMvLAwDk5eWhuLgYBQUF2LZtGwBg7ty5mDt3bgDeIRFRXxarDQ2t9mJfdttW1t2T4nHzmCgc+7YJT7x9BPNvucbp+J4n359vaEN1Yzv0GrV0/tpA3j4knwTo7zUCKW1cNA5XXMGRyiu48ZoRAACbIOCzcvvBu9NTYqBS9d55N/v6kfi/H5zCwbMNMFusMGiH1jmEimWSOjs7UVpaiszMzF7XMzMzcfDgQdnnlJSU9Bk/Z84cHD58GF1dXU7H9HdPV5nNZphMpl4/RESeaGjthCAAKgBhDJIUpVar8MzcSQCAL85fRr0b2aT9jiWm9HHRg7poW5Tm6Pp9+Hx3Jun0xWZcajbDoFVjquPxniYlRCIu3IC2TitKz8sXfQ9migVJ9fX1sFqtiI+P73U9Pj4etbW1ss+pra2VHW+xWFBfX+90TH/3dFV+fj6ioqKkn6SkJK/uR0TDl7isE27UQq0KfE8c6i1jQiy+P3EUbALw/le1soXLcsSltpnXjfTn9AJGrC86U9ciHdcivsdbx8XAqOubJVKrVZh1fRwAoHgILrkpXrh9depOEIQ+1wYaf/V1d+/piry8PDQ1NUk/VVWebxklouGN2/+Dz9P3TIRaBZysMWGfC1/2nRYbSs6KQVKcv6cXELHhBqTE2Xs9VV5uRdXlNpxvaIVGpcKMa/t/j2LxO4MkH4qLi4NGo+mT4amrq+uTCRIlJCTIjtdqtYiNjXU6pr97uspgMCAyMrLXDxGRJ3pmkig43JAQgR/clAgAKDpxEUernC8dlVVeQWunFbFheqSOHjrfB2I2qfj0Jez+l73J5s1JUU4P25153UioVMDXtc2oddKxezBSLEjS6/VIS0tDUVFRr+tFRUWYMWOG7HMyMjL6jN+zZw/S09Oh0+mcjunvnkREgSb2SIrguW1B5bvjYzHTkTEpLK3G4fOX+x27/4w9i3T7dXGKHCPiL2JW7HxDGyoa2gAAt/eznPj2oUq8fagSH/yrFolRIQCANUWnpetDgaL/jMnNzUV2djbS09ORkZGBDRs2oLKyEjk5OQDsS1zV1dXYunUrACAnJwevvvoqcnNzsWTJEpSUlGDjxo3SrjUAWLZsGWbNmoXVq1dj/vz52LVrF/bu3YsDBw5IY1paWvDNN99Ify4vL8fRo0cRExOD5GRldhUQ0fDBHknKcOWLe86UBDSbLTha1Yh3y6pR09SBeTeOhqZHINTU1oWdZdUAgNudLEMNRj+6ORExYXq8/uk5VF5uQ0pcmHSumzPjYkNR3diOioZW3JI0wv8TDRBFf0OzsrLQ0NCAVatWoaamBlOmTMHu3bsxduxYAEBNTU2vnkkpKSnYvXs3VqxYgddeew2JiYl45ZVXpO3/ADBjxgxs374dzz33HJ5//nlMmDABO3bswPTp06Uxhw8fxh133CH9OTc3FwCwaNEibNmyxc/vmoiGO/GAUC63BR+147iS2HA9PjpZh5JzDai43IoF08ZgdFQIbDYBy3eUobqxHUkxIbhnSoLSU/YplUqFmdeNRNXldreeNzY2DP882yBln4YKxX9Dly5diqVLl8o+JhewzJ49G0eOHHF6z4ULF2LhwoX9Pv69733P5d0LRES+1p1J4nJbMFKrVLhzYjxGRxpReKQaFxo7sPaTs7guPhwff12HT05dgkGrxrqH0vjf0GGso8nkRVMHOrqssjvhBiPFd7cREQ03ddzdNiikJkZh+V3XYdLoSFgFAV/XNmPvyYsAgBd+fCOmODpUkz3gjwnTQwBQeXnoZJP4G0pEFGCsSRo8Iow6PDw9GVVX2nGhsR0xYXpcFx+OhWljlJ5a0BkbE4rLrZ2oaGgd9GfZifgbSkQUQK1mC9ocjfpYkzQ4qFQqJMeEIjkmNCBHhgzWnWFjY8NQVtU4pOqSuNxGRBRAFxrtBbERBu2QO+eKhjexLqnqShustqFR98sgiYgogMR6jeSrTlMnGuxGRhgQotOgyyqgpsm93XHBikESEVEASUFSDIMkGlrUjmVJAENmyY1BEhFRAIlfHgySaCga58iQnm9oVXgmvsEgiYgogKocmaQkBkk0BCXHOg7IbWgbEv0IGSQREQWQuNw2ljVJNASNiQ6BRqVCs9nidtfuYMQgiYgoQARBYE0SDWk6jRrXRNsPu/3CyQHBgwWDJCKiAKlrNsNssUGtAhJHhCg9HSK/GOv4B8DhiisKz8R7DJKIiAJEzCIljgiBTsP/+6WhaayjLqm0gpkkIiJyUWUD65Fo6BN7gJ2+2ILGtk6FZ+MdBklERAHCeiQaDsINWsSF6wEARyoH95IbgyQiogCp5PZ/GibEJbfD5xkkERGRC5hJouFCLN7+vHxw1yUxSCIiChCpR1JMmMIzIfKvCSPDAQBlVY1oau9SeDaeY5BERBQA7Z1WXGo2A2AmiYa+6DA9JowMg9Um4J/f1Cs9HY8xSCIiCgAxixRp1CIqVKfwbIj873s3jAIA7DtVp/BMPKdVegJERMNBhePAz2Ru/x/U3j5UqfQUBo07bhiFjQfKse/UJQiCAJVKpfSU3MZMEhFRAHx1wQQAuNZRq0E01H0nJRqheg3qms04UWNSejoeYSaJiCgAxH4x08ZGKzwT9zF7MvQE4r+pQavBjAmx2HuyDvtOXcLkxCi/v6avMZNERORnNpuAo1WNAIBpyYMvSCLy1GxHXVLxqUsKz8QzDJKIiPzs7KUWNHdYEKLTYGJChNLTIQqY710/EgBQWnkFDS1mhWfjPgZJRER+Ji613TQmCloebEvDSFJMKG4aEwWrTcDfjl5Qejpu428rEZGfHaloBDA465GIvPWT9CQAwDuHqyAIgsKzcQ+DJCIiP5OKtlmPRMPQj25KhF6rxte1zfhX9eDa5cYgiYjIj5rau3CmrgUAMDV5hLKTIVJAVKgOcyYnAADeKa1SeDbuYZBERORH4q62sbGhiAs3KDsZIoX8JG0MAGDX0Qvo6LIqPBvXMUgiIvKjIxVcaiO67do4jI4yoqm9CzvLqpWejssYJBER+YnNJuC9Y/YdPdNTYhSeDZFyNGoVHps5HgDwX0Wn0dZpUXhGrmGQRETkJ/tO16G8vhURRi1+eHOi0tMhUtTD301GUkwI6prNeGN/udLTcQmDJCIiP9l04DwA4MFbkxFm4ClQNLwZtBr8es5EAMCfi8/iUnPwN5dkkERE5Aenaptx4Jt6qFXAIxljlZ4OUVD4wU2jcXPSCLR2WvFM4ZewWG1KT8kp/tNmCLFYbTjf0Iby+lZ8evoStBoVYkL1iAnTQ6VS9Rn/s+nJCsySaHh4ff85AMA9UxIwJjpU4dkQBQeVSoU/zJ+Mn6wvwUdf1+H5XV/hxR9Pkf2OCgaKZ5LWrl2LlJQUGI1GpKWlYf/+/U7HFxcXIy0tDUajEePHj8f69ev7jCksLERqaioMBgNSU1Oxc+dOr183WNU0teO/S84je+MhTP7th7hrTTGWbD2M//6sApv/eR5/LDqNF3efxF8OVeBI5ZVBUyxHNJit3fcN/lr6LQBg8W0pCs+GKLjcNGYE/vTTqVCpgG2fV+KFf5wM2rYAigZJO3bswPLly/Hss8+irKwMM2fOxNy5c1FZWSk7vry8HPPmzcPMmTNRVlaGlStX4sknn0RhYaE0pqSkBFlZWcjOzsaxY8eQnZ2NBx54AIcOHfL4dYNJp8WGw+cv4/99dAbzXz2AjPyP8fyur7D/TD3MFhtCdBpMuSYSY6JDEB9pgEatQmunFV9dMOGvpd/ixd0n8caBczh4th6nLzbDZhtcLeKJgllzRxf+88Ov8X8/OAUAWHHX9Ugfx11tRFe7Z0oCfv+jyQCANw6UY+6f9mPPV7UwW4IrWFIJCh6kMn36dEybNg3r1q2Trk2aNAn33Xcf8vPz+4x/+umn8d577+HkyZPStZycHBw7dgwlJSUAgKysLJhMJrz//vvSmHvuuQfR0dHYtm2bR68rx2QyISoqCk1NTYiMjHTvjTvx7ZU2lFZcQYvZgpYOC1rNFjSbLbjUbMa5S604e6kFZkv3Gq5KZe+/kpkajzsnjcL4uHCo1Sq8fcge8FmsNlxobMfpuhacuGBCramj1+tFGLW4IT4CSTGhiI80IkyvQYheg1C9FqF6DdTq7hSoqsdrdl/r8biq9zhXuPqXz5W/pYILd3PtPq7x1a+Oq7fx2fsL8Gfg0r0C/Rm49nIu3ctsseKiyYxzl1pQfPqS9Pv573NuwON3XOv0ueLvKdFQ5EpJxz++rMHv//cr1DmKuEP1GtyaEoOxMaEYPSIEkxMjMfO6kT6dlzvf34rVJHV2dqK0tBTPPPNMr+uZmZk4ePCg7HNKSkqQmZnZ69qcOXOwceNGdHV1QafToaSkBCtWrOgzpqCgwOPXBQCz2QyzubsSv6mpCYD9w/alT/91Ac+8e9zpmOhQHdLHRSNjQhzuuGEkRkYYHY8IaGlpBgC0tTZL4+OMQFxyKGYkh+JySydOXzTh9MUW1Jo60NRkw+dNJnzu03dBNHylxIXi57el4P5powb8/4eev6dEQ40r348zx4Xh3SVT8efis9j9ZQ0uNbfh4y+7fy8yU0fh5vipfpmXK//IUyxIqq+vh9VqRXx8fK/r8fHxqK2tlX1ObW2t7HiLxYL6+nqMHj263zHiPT15XQDIz8/H73//+z7Xk5KS+n+TflIF4EsAmwL+ykQ0kCoAnwL4udITIVLYEh/cYyOAjf+fD24ko7m5GVFRUU7HKL677eqKdkEQnFa5y42/+ror93T3dfPy8pCbmyv92Waz4fLly4iNjQ26qnyTyYSkpCRUVVX5dCmQBsbPXjn87JXDz145/OzdJwgCmpubkZg4cINXxYKkuLg4aDSaPtmburq6PlkeUUJCgux4rVaL2NhYp2PEe3ryugBgMBhgMPQ+nHLEiBH9v8EgEBkZyV8ahfCzVw4/e+Xws1cOP3v3DJRBEim2u02v1yMtLQ1FRUW9rhcVFWHGjBmyz8nIyOgzfs+ePUhPT4dOp3M6RrynJ69LREREw4+iy225ubnIzs5Geno6MjIysGHDBlRWViInJweAfYmruroaW7duBWDfyfbqq68iNzcXS5YsQUlJCTZu3CjtWgOAZcuWYdasWVi9ejXmz5+PXbt2Ye/evThw4IDLr0tEREQEQWGvvfaaMHbsWEGv1wvTpk0TiouLpccWLVokzJ49u9f4ffv2CVOnThX0er0wbtw4Yd26dX3u+c477wg33HCDoNPphIkTJwqFhYVuve5g19HRIfz2t78VOjo6lJ7KsMPPXjn87JXDz145/Oz9S9E+SURERETBSvFjSYiIiIiCEYMkIiIiIhkMkoiIiIhkMEgiIiIiksEgaQhau3YtUlJSYDQakZaWhv379ys9pUEtPz8f3/nOdxAREYFRo0bhvvvuw6lTp3qNEQQBv/vd75CYmIiQkBB873vfw1dffdVrjNlsxq9+9SvExcUhLCwMP/rRj/Dtt98G8q0Mavn5+VCpVFi+fLl0jZ+7/1RXV+Phhx9GbGwsQkNDccstt6C0tFR6nJ+9f1gsFjz33HNISUlBSEgIxo8fj1WrVsFm6z7YnJ99ACm5tY58b/v27YJOpxNef/114cSJE8KyZcuEsLAwoaKiQumpDVpz5swRNm/eLPzrX/8Sjh49Ktx7771CcnKy0NLSIo156aWXhIiICKGwsFA4fvy4kJWVJYwePVowmUzSmJycHOGaa64RioqKhCNHjgh33HGHcPPNNwsWi0WJtzWofP7558K4ceOEm266SVi2bJl0nZ+7f1y+fFkYO3as8OijjwqHDh0SysvLhb179wrffPONNIafvX/8x3/8hxAbGyv8/e9/F8rLy4V33nlHCA8PFwoKCqQx/OwDh0HSEHPrrbcKOTk5va5NnDhReOaZZxSa0dBTV1cnAJB6a9lsNiEhIUF46aWXpDEdHR1CVFSUsH79ekEQBKGxsVHQ6XTC9u3bpTHV1dWCWq0WPvjgg8C+gUGmublZuO6664SioiJh9uzZUpDEz91/nn76aeH222/v93F+9v5z7733CosXL+517f777xcefvhhQRD42Qcal9uGkM7OTpSWliIzM7PX9czMTBw8eFChWQ09TU1NAICYmBgAQHl5OWpra3t97gaDAbNnz5Y+99LSUnR1dfUak5iYiClTpvC/zQAef/xx3Hvvvbjrrrt6Xefn7j/vvfce0tPT8ZOf/ASjRo3C1KlT8frrr0uP87P3n9tvvx0fffQRTp8+DQA4duwYDhw4gHnz5gHgZx9oih5LQr5VX18Pq9Xa56De+Pj4Pgf6kmcEQUBubi5uv/12TJkyBQCkz1buc6+oqJDG6PV6REdH9xnD/zb92759O44cOYIvvviiz2P83P3n3LlzWLduHXJzc7Fy5Up8/vnnePLJJ2EwGPDII4/ws/ejp59+Gk1NTZg4cSI0Gg2sViteeOEFPPjggwD49z7QGCQNQSqVqtefBUHoc40888QTT+DLL7/sdRagyJPPnf9t+ldVVYVly5Zhz549MBqN/Y7j5+57NpsN6enpePHFFwEAU6dOxVdffYV169bhkUcekcbxs/e9HTt24K233sLbb7+NyZMn4+jRo1i+fDkSExOxaNEiaRw/+8DgctsQEhcXB41G0+dfCnV1dX3+1UHu+9WvfoX33nsPn3zyCcaMGSNdT0hIAACnn3tCQgI6Oztx5cqVfsdQb6Wlpairq0NaWhq0Wi20Wi2Ki4vxyiuvQKvVSp8bP3ffGz16NFJTU3tdmzRpEiorKwHw77w//fu//zueeeYZ/PSnP8WNN96I7OxsrFixAvn5+QD42Qcag6QhRK/XIy0tDUVFRb2uFxUVYcaMGQrNavATBAFPPPEE3n33XXz88cdISUnp9XhKSgoSEhJ6fe6dnZ0oLi6WPve0tDTodLpeY2pqavCvf/2L/236ceedd+L48eM4evSo9JOeno6HHnoIR48exfjx4/m5+8ltt93Wp83F6dOnMXbsWAD8O+9PbW1tUKt7fzVrNBqpBQA/+wBTqGCc/ERsAbBx40bhxIkTwvLly4WwsDDh/PnzSk9t0Pq3f/s3ISoqSti3b59QU1Mj/bS1tUljXnrpJSEqKkp49913hePHjwsPPvig7JbcMWPGCHv37hWOHDkifP/73+eWXDf13N0mCPzc/eXzzz8XtFqt8MILLwhnzpwR/vKXvwihoaHCW2+9JY3hZ+8fixYtEq655hqpBcC7774rxMXFCb/+9a+lMfzsA4dB0hD02muvCWPHjhX0er0wbdo0aas6eQaA7M/mzZulMTabTfjtb38rJCQkCAaDQZg1a5Zw/PjxXvdpb28XnnjiCSEmJkYICQkRfvCDHwiVlZUBfjeD29VBEj93//nf//1fYcqUKYLBYBAmTpwobNiwodfj/Oz9w2QyCcuWLROSk5MFo9EojB8/Xnj22WcFs9ksjeFnHzgqQRAEJTNZRERERMGINUlEREREMhgkEREREclgkEREREQkg0ESERERkQwGSUREREQyGCQRERERyWCQRERERCSDQRIRERGRDAZJRERERDIYJBHRkPXoo49CpVJBpVJBp9MhPj4ed999NzZt2iQdGAoA48aNg0qlwvbt2/vcY/LkyVCpVNiyZUufx1588UVoNBq89NJL/nwbRKQQBklENKTdc889qKmpwfnz5/H+++/jjjvuwLJly/CDH/wAFotFGpeUlITNmzf3eu5nn32G2tpahIWFyd578+bN+PWvf41Nmzb59T0QkTIYJBHRkGYwGJCQkIBrrrkG06ZNw8qVK7Fr1y68//77vbJDDz30EIqLi1FVVSVd27RpEx566CFotdo+9y0uLkZ7eztWrVqF1tZWfPrpp4F4O0QUQAySiGjY+f73v4+bb74Z7777rnQtPj4ec+bMwZtvvgkAaGtrw44dO7B48WLZe2zcuBEPPvggdDodHnzwQWzcuDEgcyeiwGGQRETD0sSJE3H+/Ple1xYvXowtW7ZAEAT89a9/xYQJE3DLLbf0ea7JZEJhYSEefvhhAMDDDz+Mv/71rzCZTAGYOREFCoMkIhqWBEGASqXqde3ee+9FS0sLPv30U2zatKnfLNLbb7+N8ePH4+abbwYA3HLLLRg/frxs4TcRDV4MkohoWDp58iRSUlJ6XdNqtcjOzsZvf/tbHDp0CA899JDsczdt2oSvvvoKWq1W+vnqq6+45EY0xPStRiQiGuI+/vhjHD9+HCtWrOjz2OLFi/Hyyy8jKysL0dHRfR4/fvw4Dh8+jH379iEmJka63tjYiFmzZuFf//oXpkyZ4tf5E1FgMEgioiHNbDajtrYWVqsVFy9exAcffID8/Hz84Ac/wCOPPNJn/KRJk1BfX4/Q0FDZ+23cuBG33norZs2a1eexjIwMbNy4Ef/1X//l8/dBRIHH5TYiGtI++OADjB49GuPGjcM999yDTz75BK+88gp27doFjUYj+5zY2FiEhIT0ud7Z2Ym33noLCxYskH3eggUL8NZbb6Gzs9On74GIlKESBEFQehJEREREwYaZJCIiIiIZDJKIiIiIZDBIIiIiIpLBIImIiIhIBoMkIiIiIhkMkoiIiIhkMEgiIiIiksEgiYiIiEgGgyQiIiIiGQySiIiIiGQwSCIiIiKS8f8DkrDlzBK0Q9kAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data['DMA'])\n",
+    "plt.show()\n",
+    "print(data['DMA'].isna().sum()/len(data['DMA']))\n",
+    "\n",
+    "data['DMA'].isna().sum() #migalhas \n",
+    "data['DMA']= data['DMA'].fillna(np.mean(data['DMA']))\n",
+    "\n",
+    "sns.distplot(data['DMA'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "ce2e8bd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.001383473776883411\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data['MSA'])\n",
+    "plt.show()\n",
+    "print(data['MSA'].isna().sum()/len(data['MSA']))\n",
+    "\n",
+    "data['MSA'].isna().sum() #migalhas \n",
+    "data['MSA']= data['MSA'].fillna(np.mean(data['MSA']))\n",
+    "\n",
+    "sns.distplot(data['MSA'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e5b99d9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}