2. Supervised_Learning_and_K_Nearest_Neighbors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aprendizado Supervisionado e K Vizinhos mais Próximos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introdução\n",
    "\n",
    "Nós utilizaremos a base de dados de taxa de rejeição da indústria de telecomunicações para essa lista de exercícios. A base de dados tem o nome `Orange_Telecom_Churn_Data.csv`. Nesse notebook carregaremos a base de dados, faremos algum pré-processamento e usaremos a técnica k-NN para predizer a taxa de rejeição baseado nas características mensuradas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 1\n",
    "\n",
    "* Comece importando os dados através do pandas. Examine as colunas e os dados\n",
    "* Note que os dados contêm estado, código de área e telefone. Você acha que esses atributos são interessantes para construir nosso modelo de classificação? Por que?\n",
    "\n",
    "Não utilizaremos esses atributos para essa base, então eles podem ser removidos do dataframe (método `drop`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>state</th>\n",
       "      <th>account_length</th>\n",
       "      <th>area_code</th>\n",
       "      <th>phone_number</th>\n",
       "      <th>intl_plan</th>\n",
       "      <th>voice_mail_plan</th>\n",
       "      <th>number_vmail_messages</th>\n",
       "      <th>total_day_minutes</th>\n",
       "      <th>total_day_calls</th>\n",
       "      <th>total_day_charge</th>\n",
       "      <th>...</th>\n",
       "      <th>total_eve_calls</th>\n",
       "      <th>total_eve_charge</th>\n",
       "      <th>total_night_minutes</th>\n",
       "      <th>total_night_calls</th>\n",
       "      <th>total_night_charge</th>\n",
       "      <th>total_intl_minutes</th>\n",
       "      <th>total_intl_calls</th>\n",
       "      <th>total_intl_charge</th>\n",
       "      <th>number_customer_service_calls</th>\n",
       "      <th>churned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>382-4657</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  account_length  area_code phone_number intl_plan voice_mail_plan  \\\n",
       "0    KS             128        415     382-4657        no             yes   \n",
       "1    OH             107        415     371-7191        no             yes   \n",
       "2    NJ             137        415     358-1921        no              no   \n",
       "3    OH              84        408     375-9999       yes              no   \n",
       "4    OK              75        415     330-6626       yes              no   \n",
       "\n",
       "   number_vmail_messages  total_day_minutes  total_day_calls  \\\n",
       "0                     25              265.1              110   \n",
       "1                     26              161.6              123   \n",
       "2                      0              243.4              114   \n",
       "3                      0              299.4               71   \n",
       "4                      0              166.7              113   \n",
       "\n",
       "   total_day_charge   ...     total_eve_calls  total_eve_charge  \\\n",
       "0             45.07   ...                  99             16.78   \n",
       "1             27.47   ...                 103             16.62   \n",
       "2             41.38   ...                 110             10.30   \n",
       "3             50.90   ...                  88              5.26   \n",
       "4             28.34   ...                 122             12.61   \n",
       "\n",
       "   total_night_minutes  total_night_calls  total_night_charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   total_intl_minutes  total_intl_calls  total_intl_charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "1                13.7                 3               3.70   \n",
       "2                12.2                 5               3.29   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "\n",
       "   number_customer_service_calls  churned  \n",
       "0                              1    False  \n",
       "1                              1    False  \n",
       "2                              0    False  \n",
       "3                              2    False  \n",
       "4                              3    False  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "arquivo = 'data/Orange_Telecom_Churn_Data.csv'\n",
    "\n",
    "data = pd.read_csv(arquivo)\n",
    "data.iloc[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resposta: Não, pois eles são irrelevantes para a classificação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>account_length</th>\n",
       "      <th>intl_plan</th>\n",
       "      <th>voice_mail_plan</th>\n",
       "      <th>number_vmail_messages</th>\n",
       "      <th>total_day_minutes</th>\n",
       "      <th>total_day_calls</th>\n",
       "      <th>total_day_charge</th>\n",
       "      <th>total_eve_minutes</th>\n",
       "      <th>total_eve_calls</th>\n",
       "      <th>total_eve_charge</th>\n",
       "      <th>total_night_minutes</th>\n",
       "      <th>total_night_calls</th>\n",
       "      <th>total_night_charge</th>\n",
       "      <th>total_intl_minutes</th>\n",
       "      <th>total_intl_calls</th>\n",
       "      <th>total_intl_charge</th>\n",
       "      <th>number_customer_service_calls</th>\n",
       "      <th>churned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>107</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>137</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_length intl_plan voice_mail_plan  number_vmail_messages  \\\n",
       "0             128        no             yes                     25   \n",
       "1             107        no             yes                     26   \n",
       "2             137        no              no                      0   \n",
       "3              84       yes              no                      0   \n",
       "4              75       yes              no                      0   \n",
       "\n",
       "   total_day_minutes  total_day_calls  total_day_charge  total_eve_minutes  \\\n",
       "0              265.1              110             45.07              197.4   \n",
       "1              161.6              123             27.47              195.5   \n",
       "2              243.4              114             41.38              121.2   \n",
       "3              299.4               71             50.90               61.9   \n",
       "4              166.7              113             28.34              148.3   \n",
       "\n",
       "   total_eve_calls  total_eve_charge  total_night_minutes  total_night_calls  \\\n",
       "0               99             16.78                244.7                 91   \n",
       "1              103             16.62                254.4                103   \n",
       "2              110             10.30                162.6                104   \n",
       "3               88              5.26                196.9                 89   \n",
       "4              122             12.61                186.9                121   \n",
       "\n",
       "   total_night_charge  total_intl_minutes  total_intl_calls  \\\n",
       "0               11.01                10.0                 3   \n",
       "1               11.45                13.7                 3   \n",
       "2                7.32                12.2                 5   \n",
       "3                8.86                 6.6                 7   \n",
       "4                8.41                10.1                 3   \n",
       "\n",
       "   total_intl_charge  number_customer_service_calls  churned  \n",
       "0               2.70                              1    False  \n",
       "1               3.70                              1    False  \n",
       "2               3.29                              0    False  \n",
       "3               1.78                              2    False  \n",
       "4               2.73                              3    False  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['state','area_code','phone_number'],axis=1)\n",
    "data.iloc[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 2\n",
    "\n",
    "* Note que algumas das colunas são categóricas e algumas são *float*. Esses atributos precisam ser numéricos para usar os algoritmos que aprenderemos no curso.\n",
    "* Finalmente, o algoritmo K-Vizinhos mais próximos necessita de dados escalonados. Escalone os dados utilizando um dos métodos aprendidos em aula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>account_length</th>\n",
       "      <th>intl_plan</th>\n",
       "      <th>voice_mail_plan</th>\n",
       "      <th>number_vmail_messages</th>\n",
       "      <th>total_day_minutes</th>\n",
       "      <th>total_day_calls</th>\n",
       "      <th>total_day_charge</th>\n",
       "      <th>total_eve_minutes</th>\n",
       "      <th>total_eve_calls</th>\n",
       "      <th>total_eve_charge</th>\n",
       "      <th>total_night_minutes</th>\n",
       "      <th>total_night_calls</th>\n",
       "      <th>total_night_charge</th>\n",
       "      <th>total_intl_minutes</th>\n",
       "      <th>total_intl_calls</th>\n",
       "      <th>total_intl_charge</th>\n",
       "      <th>number_customer_service_calls</th>\n",
       "      <th>churned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.524793</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.480769</td>\n",
       "      <td>0.754196</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.754183</td>\n",
       "      <td>0.542755</td>\n",
       "      <td>0.582353</td>\n",
       "      <td>0.542866</td>\n",
       "      <td>0.619494</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.619584</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.438017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.459744</td>\n",
       "      <td>0.745455</td>\n",
       "      <td>0.459672</td>\n",
       "      <td>0.537531</td>\n",
       "      <td>0.605882</td>\n",
       "      <td>0.537690</td>\n",
       "      <td>0.644051</td>\n",
       "      <td>0.588571</td>\n",
       "      <td>0.644344</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.685185</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.561983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.692461</td>\n",
       "      <td>0.690909</td>\n",
       "      <td>0.692436</td>\n",
       "      <td>0.333242</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.333225</td>\n",
       "      <td>0.411646</td>\n",
       "      <td>0.594286</td>\n",
       "      <td>0.411930</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.609259</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.342975</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.851778</td>\n",
       "      <td>0.430303</td>\n",
       "      <td>0.851740</td>\n",
       "      <td>0.170195</td>\n",
       "      <td>0.517647</td>\n",
       "      <td>0.170171</td>\n",
       "      <td>0.498481</td>\n",
       "      <td>0.508571</td>\n",
       "      <td>0.498593</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.329630</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.305785</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.474253</td>\n",
       "      <td>0.684848</td>\n",
       "      <td>0.474230</td>\n",
       "      <td>0.407754</td>\n",
       "      <td>0.717647</td>\n",
       "      <td>0.407959</td>\n",
       "      <td>0.473165</td>\n",
       "      <td>0.691429</td>\n",
       "      <td>0.473270</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.505556</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_length  intl_plan  voice_mail_plan  number_vmail_messages  \\\n",
       "0        0.524793        0.0              1.0               0.480769   \n",
       "1        0.438017        0.0              1.0               0.500000   \n",
       "2        0.561983        0.0              0.0               0.000000   \n",
       "3        0.342975        1.0              0.0               0.000000   \n",
       "4        0.305785        1.0              0.0               0.000000   \n",
       "\n",
       "   total_day_minutes  total_day_calls  total_day_charge  total_eve_minutes  \\\n",
       "0           0.754196         0.666667          0.754183           0.542755   \n",
       "1           0.459744         0.745455          0.459672           0.537531   \n",
       "2           0.692461         0.690909          0.692436           0.333242   \n",
       "3           0.851778         0.430303          0.851740           0.170195   \n",
       "4           0.474253         0.684848          0.474230           0.407754   \n",
       "\n",
       "   total_eve_calls  total_eve_charge  total_night_minutes  total_night_calls  \\\n",
       "0         0.582353          0.542866             0.619494           0.520000   \n",
       "1         0.605882          0.537690             0.644051           0.588571   \n",
       "2         0.647059          0.333225             0.411646           0.594286   \n",
       "3         0.517647          0.170171             0.498481           0.508571   \n",
       "4         0.717647          0.407959             0.473165           0.691429   \n",
       "\n",
       "   total_night_charge  total_intl_minutes  total_intl_calls  \\\n",
       "0            0.619584               0.500              0.15   \n",
       "1            0.644344               0.685              0.15   \n",
       "2            0.411930               0.610              0.25   \n",
       "3            0.498593               0.330              0.35   \n",
       "4            0.473270               0.505              0.15   \n",
       "\n",
       "   total_intl_charge  number_customer_service_calls  churned  \n",
       "0           0.500000                       0.111111      0.0  \n",
       "1           0.685185                       0.111111      0.0  \n",
       "2           0.609259                       0.000000      0.0  \n",
       "3           0.329630                       0.222222      0.0  \n",
       "4           0.505556                       0.333333      0.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore', module='sklearn')\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelBinarizer\n",
    "\n",
    "bin_cols = ['intl_plan', 'voice_mail_plan', 'churned']\n",
    "\n",
    "lb = LabelBinarizer()\n",
    "\n",
    "for x in bin_cols: data[x] = lb.fit_transform(data[x])\n",
    "    \n",
    "y = MinMaxScaler()\n",
    "data_esc = y.fit_transform(data)\n",
    "\n",
    "data_esc = pd.DataFrame(data_esc, columns = data.columns)\n",
    "\n",
    "data_esc.iloc[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 3\n",
    "\n",
    "* Separe as colunas de atributos (todas menos `churned`) da  coluna de rótulo (`churned`). Isso criará duas tabelas.\n",
    "* Aplique o método `.fit()` do K-nearest neighbors com um valor de `n_neighbors=3` para essa base de dados e verifique o resultado com o método `.predict()` na mesma base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_cols = [x for x in data.columns if x != 'churned']\n",
    "\n",
    "X_data = data_esc[x_cols]\n",
    "y_data = data_esc['churned']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier as knn\n",
    "knn = knn(n_neighbors=3).fit(X_data, y_data)\n",
    "\n",
    "predict = knn.predict(X_data)\n",
    "\n",
    "print(predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 4\n",
    "\n",
    "Ainda não ensinamos formas de medir o erro, mas a acurácia é um conceito simples de entender--é a porcentagem de rótulos que foram corretamente classificados:\n",
    "\n",
    "$acc = \\frac{corretos}{total}$\n",
    "\n",
    "* Escreva uma função para calcular a acurácia usando os rótulos verdadeiros e os preditos.\n",
    "* Usando a função, calcule a acurácia do K-nn nos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9422"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def accuracy(real, predict):\n",
    "    calc = sum(y_data == predict) / real.shape[0]\n",
    "    return calc\n",
    "\n",
    "accuracy(y_data, predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 5\n",
    "\n",
    "* Repita a aplicação do modelo K-nearest neighbors mas com o parâmetro `weights=distance`. Calcule a acurácia da função criada por você.\n",
    "* Repita a aplicação do modelo com `n_neighbors=3`, `weights=uniform` e `p=1` para utilizar a distância de Manhattan. Verifique os resultados.\n",
    "\n",
    "Quando as distâncias ponderadas são utilizadas para a parte 1 dessa questão, vocês obterão acurácia de 1.0. Por que isso ocorre? *Dica:* o KNN usa diretamente os dados para fazer a predição."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier as knn\n",
    "knn = knn(n_neighbors=3, weights = 'distance').fit(X_data, y_data)\n",
    "\n",
    "predict = knn.predict(X_data)\n",
    "\n",
    "accuracy(y_data, predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9456"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier as knn\n",
    "knn = knn(n_neighbors=3, weights = 'uniform', p=1).fit(X_data, y_data)\n",
    "\n",
    "predict = knn.predict(X_data)\n",
    "\n",
    "accuracy(y_data, predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A acurácia é igual a 1 pois o mesmo conjunto de dados de treino é usado para o teste."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício 6\n",
    "\n",
    "* Teste o modelo KNN utilizando valores de `n_neighbors` na faixa de 1 a 20. Deixe o restante dos parâmetros como o padrão. Armazene os valores de vizinhos e acurácia em uma lista no formato [(k, acuracia)].\n",
    "* Plote um gráfico do tipo *scatter* da acurácia vs vizinhos. O que acontece com `n_neighbors=1`? Por que isso ocorre?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.9292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.9422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.9154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.9284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.9156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.9254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.9122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.9224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.9092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.9158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.9076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.9148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.9050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.9098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>0.9044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0.9080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>0.9028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0.9078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>0.9020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     k  accuracy\n",
       "0    1    1.0000\n",
       "1    2    0.9292\n",
       "2    3    0.9422\n",
       "3    4    0.9154\n",
       "4    5    0.9284\n",
       "5    6    0.9156\n",
       "6    7    0.9254\n",
       "7    8    0.9122\n",
       "8    9    0.9224\n",
       "9   10    0.9092\n",
       "10  11    0.9158\n",
       "11  12    0.9076\n",
       "12  13    0.9148\n",
       "13  14    0.9050\n",
       "14  15    0.9098\n",
       "15  16    0.9044\n",
       "16  17    0.9080\n",
       "17  18    0.9028\n",
       "18  19    0.9078\n",
       "19  20    0.9020"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "score_list = list()\n",
    "\n",
    "for x in range(1, 21):\n",
    "    knn = KNeighborsClassifier(n_neighbors=x).fit(X_data, y_data) \n",
    "    y_predict = knn.predict(X_data)\n",
    "    score_list.append((x, accuracy(y_data, y_predict)))\n",
    "\n",
    "\n",
    "score_df = pd.DataFrame(score_list, columns=['k', 'accuracy'])\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries to make the plot\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Acurácia x vizinhos')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7ff4bc6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('talk')\n",
    "sns.set_style('ticks')\n",
    "sns.set_palette('dark')\n",
    "\n",
    "ax = score_df.set_index('k').plot()\n",
    "\n",
    "ax.set(xlabel='k', ylabel='accuracy')\n",
    "ax.set_xticks(range(1, 21));\n",
    "plt.title('Acurácia x vizinhos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Acurácia x vizinhos')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7ff405f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(score_df['k'],score_df['accuracy'])\n",
    "plt.title('Acurácia x vizinhos')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para n_neighbours=1, a acurácia é 1."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "name": "Linear_Regression_and_K_Nearest_Neighbors_Exercises-ANSWERS",
  "notebookId": 2125319687183902
 },
 "nbformat": 4,
 "nbformat_minor": 1
}