aula01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning e Data Science com Python de A à Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parte 1 - Classificação"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pré-processamento com pandas e scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conteúdo\n",
    "<ul>\n",
    "    <li> Base de dados </li>\n",
    "        <ul>\n",
    "            <li> Dados de crédito </li>\n",
    "            <li> Censo </li>\n",
    "        </ul>\n",
    "    <li> Carregamento de bases de dados </li>\n",
    "    <li> Valores inconsistente </li>\n",
    "    <li> Valores faltantes </li>\n",
    "    <li> Escalonamento de atributos </li>\n",
    "    <li> Transformação de variáveis categóricas </li>\n",
    "    <li> Introdução a avaliação de algoritmos </li>\n",
    "    <li> Base de treinamento e base de teste </li>\n",
    "    <li> Vários recursos do pandas (localizar, remover linhas e colunas, alterar valores) </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variáveis\n",
    "Numéricas: Contínua (números reais, e. g. Temperatura, altura, peso, altura) e Discreta (conjunto finito de valores, inteiros, e. g. contagem de alguma coisa).\n",
    "Categóricas: Nominal (Dados não mensuráveis, sem ordenação: cor dos olhos, gênero) e Ordinal (categorizado sob uma ordenação. Tamanho P, M e G)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementação 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Importanto os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_credit = pd.read_csv('credit_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clientid</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>loan</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>66155.925095</td>\n",
       "      <td>59.017015</td>\n",
       "      <td>8106.532131</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34415.153966</td>\n",
       "      <td>48.117153</td>\n",
       "      <td>6564.745018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>57317.170063</td>\n",
       "      <td>63.108049</td>\n",
       "      <td>8020.953296</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>42709.534201</td>\n",
       "      <td>45.751972</td>\n",
       "      <td>6103.642260</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>66952.688845</td>\n",
       "      <td>18.584336</td>\n",
       "      <td>8770.099235</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1996</td>\n",
       "      <td>59221.044874</td>\n",
       "      <td>48.518179</td>\n",
       "      <td>1926.729397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1997</td>\n",
       "      <td>69516.127573</td>\n",
       "      <td>23.162104</td>\n",
       "      <td>3503.176156</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1998</td>\n",
       "      <td>44311.449262</td>\n",
       "      <td>28.017167</td>\n",
       "      <td>5522.786693</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1999</td>\n",
       "      <td>43756.056605</td>\n",
       "      <td>63.971796</td>\n",
       "      <td>1622.722598</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>2000</td>\n",
       "      <td>69436.579552</td>\n",
       "      <td>56.152617</td>\n",
       "      <td>7378.833599</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      clientid        income        age         loan  default\n",
       "0            1  66155.925095  59.017015  8106.532131        0\n",
       "1            2  34415.153966  48.117153  6564.745018        0\n",
       "2            3  57317.170063  63.108049  8020.953296        0\n",
       "3            4  42709.534201  45.751972  6103.642260        0\n",
       "4            5  66952.688845  18.584336  8770.099235        1\n",
       "...        ...           ...        ...          ...      ...\n",
       "1995      1996  59221.044874  48.518179  1926.729397        0\n",
       "1996      1997  69516.127573  23.162104  3503.176156        0\n",
       "1997      1998  44311.449262  28.017167  5522.786693        1\n",
       "1998      1999  43756.056605  63.971796  1622.722598        0\n",
       "1999      2000  69436.579552  56.152617  7378.833599        0\n",
       "\n",
       "[2000 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>age</th>\n",
       "      <th>loan</th>\n",
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       "      <td>42709.534201</td>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>24904.064140</td>\n",
       "      <td>57.471607</td>\n",
       "      <td>15.498598</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>48430.359613</td>\n",
       "      <td>26.809132</td>\n",
       "      <td>5722.581981</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>24500.141984</td>\n",
       "      <td>32.897548</td>\n",
       "      <td>2971.003310</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>40654.892537</td>\n",
       "      <td>55.496853</td>\n",
       "      <td>4755.825280</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>25075.872771</td>\n",
       "      <td>39.776378</td>\n",
       "      <td>1409.230371</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   clientid        income        age         loan  default\n",
       "0         1  66155.925095  59.017015  8106.532131        0\n",
       "1         2  34415.153966  48.117153  6564.745018        0\n",
       "2         3  57317.170063  63.108049  8020.953296        0\n",
       "3         4  42709.534201  45.751972  6103.642260        0\n",
       "4         5  66952.688845  18.584336  8770.099235        1\n",
       "5         6  24904.064140  57.471607    15.498598        0\n",
       "6         7  48430.359613  26.809132  5722.581981        0\n",
       "7         8  24500.141984  32.897548  2971.003310        1\n",
       "8         9  40654.892537  55.496853  4755.825280        0\n",
       "9        10  25075.872771  39.776378  1409.230371        0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_credit.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clientid</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>loan</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1993</td>\n",
       "      <td>30803.806165</td>\n",
       "      <td>23.250084</td>\n",
       "      <td>623.024153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>1994</td>\n",
       "      <td>54421.410155</td>\n",
       "      <td>26.821928</td>\n",
       "      <td>3273.631823</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>1995</td>\n",
       "      <td>24254.700791</td>\n",
       "      <td>37.751622</td>\n",
       "      <td>2225.284643</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1996</td>\n",
       "      <td>59221.044874</td>\n",
       "      <td>48.518179</td>\n",
       "      <td>1926.729397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1997</td>\n",
       "      <td>69516.127573</td>\n",
       "      <td>23.162104</td>\n",
       "      <td>3503.176156</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1998</td>\n",
       "      <td>44311.449262</td>\n",
       "      <td>28.017167</td>\n",
       "      <td>5522.786693</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1999</td>\n",
       "      <td>43756.056605</td>\n",
       "      <td>63.971796</td>\n",
       "      <td>1622.722598</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>2000</td>\n",
       "      <td>69436.579552</td>\n",
       "      <td>56.152617</td>\n",
       "      <td>7378.833599</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      clientid        income        age         loan  default\n",
       "1992      1993  30803.806165  23.250084   623.024153        0\n",
       "1993      1994  54421.410155  26.821928  3273.631823        0\n",
       "1994      1995  24254.700791  37.751622  2225.284643        0\n",
       "1995      1996  59221.044874  48.518179  1926.729397        0\n",
       "1996      1997  69516.127573  23.162104  3503.176156        0\n",
       "1997      1998  44311.449262  28.017167  5522.786693        1\n",
       "1998      1999  43756.056605  63.971796  1622.722598        0\n",
       "1999      2000  69436.579552  56.152617  7378.833599        0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_credit.tail(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clientid</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>loan</th>\n",
       "      <th>default</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1000.500000</td>\n",
       "      <td>45331.600018</td>\n",
       "      <td>40.807559</td>\n",
       "      <td>4444.369695</td>\n",
       "      <td>0.141500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>577.494589</td>\n",
       "      <td>14326.327119</td>\n",
       "      <td>13.624469</td>\n",
       "      <td>3045.410024</td>\n",
       "      <td>0.348624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20014.489470</td>\n",
       "      <td>-52.423280</td>\n",
       "      <td>1.377630</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>500.750000</td>\n",
       "      <td>32796.459717</td>\n",
       "      <td>28.990415</td>\n",
       "      <td>1939.708847</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1000.500000</td>\n",
       "      <td>45789.117313</td>\n",
       "      <td>41.317159</td>\n",
       "      <td>3974.719419</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1500.250000</td>\n",
       "      <td>57791.281668</td>\n",
       "      <td>52.587040</td>\n",
       "      <td>6432.410625</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>69995.685578</td>\n",
       "      <td>63.971796</td>\n",
       "      <td>13766.051239</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          clientid        income          age          loan      default\n",
       "count  2000.000000   2000.000000  1997.000000   2000.000000  2000.000000\n",
       "mean   1000.500000  45331.600018    40.807559   4444.369695     0.141500\n",
       "std     577.494589  14326.327119    13.624469   3045.410024     0.348624\n",
       "min       1.000000  20014.489470   -52.423280      1.377630     0.000000\n",
       "25%     500.750000  32796.459717    28.990415   1939.708847     0.000000\n",
       "50%    1000.500000  45789.117313    41.317159   3974.719419     0.000000\n",
       "75%    1500.250000  57791.281668    52.587040   6432.410625     0.000000\n",
       "max    2000.000000  69995.685578    63.971796  13766.051239     1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_credit.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>clientid</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>loan</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>423</td>\n",
       "      <td>69995.685578</td>\n",
       "      <td>52.719673</td>\n",
       "      <td>2084.370861</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     clientid        income        age         loan  default\n",
       "422       423  69995.685578  52.719673  2084.370861        0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_credit[base_credit['income'] >= 69995.685578]  # colocamos maior ou igual pois pode haver problemas de arredondamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>clientid</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>loan</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>28072.604355</td>\n",
       "      <td>54.142548</td>\n",
       "      <td>1.37763</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     clientid        income        age     loan  default\n",
       "865       866  28072.604355  54.142548  1.37763        0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_credit[base_credit['loan'] <= 1.377630]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Visualização dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1], dtype=int64), array([1717,  283], dtype=int64))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(base_credit['default'], return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = base_credit['default']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(base_credit['age']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(base_credit['income']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(base_credit['loan']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "grafico = px.scatter_matrix(base_credit, dimensions=['age', 'income', 'loan'], color = 'default')  # gráfico de dispersão\n",
    "grafico.show()"
   ]
  }
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