Py_ChatGPT_PCA2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neste exemplo vamos verificar o uso do nosso dataset \"cb_br_2022.csv\" para verificarmos o 'custo benefício', ou, talvez, o motivo que levou determinado veículo ao topo das vendas.\n",
    "### Vamos usar dados reais dos seis automóveis \"populares\" mais vendidos no Brasil em 2022, sendo estes os carros de entrada em suas variações e seus preços médios. Vamos analisar a capacidade dos motores, como consumo, torque e potência. É um exemplo simples, apenas para entender a PCA, suas aplicações, resultados e a necessidade de se aplicar ou não."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando nossas bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>modelo</th>\n",
       "      <th>motor</th>\n",
       "      <th>km/litro_gas_cid</th>\n",
       "      <th>km/litro_gas_est</th>\n",
       "      <th>km/litro_alc_cid</th>\n",
       "      <th>km/litro_alc_est</th>\n",
       "      <th>peso/kg</th>\n",
       "      <th>torque_gas</th>\n",
       "      <th>torque_alc</th>\n",
       "      <th>cavalos_gas</th>\n",
       "      <th>cavalos_alc</th>\n",
       "      <th>valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Strada CS</td>\n",
       "      <td>1.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>12.3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.151</td>\n",
       "      <td>13.7</td>\n",
       "      <td>14.2</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
       "      <td>93.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gol msi</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>14.4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1.001</td>\n",
       "      <td>9.7</td>\n",
       "      <td>10.4</td>\n",
       "      <td>75</td>\n",
       "      <td>84</td>\n",
       "      <td>63.738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Onix hb</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>16.7</td>\n",
       "      <td>9.9</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1.037</td>\n",
       "      <td>9.6</td>\n",
       "      <td>10.6</td>\n",
       "      <td>78</td>\n",
       "      <td>82</td>\n",
       "      <td>63.430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ka hb</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>15.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>10.7</td>\n",
       "      <td>1.037</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.7</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>49.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hb20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>14.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1.091</td>\n",
       "      <td>9.4</td>\n",
       "      <td>10.2</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>63.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kwid</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>15.6</td>\n",
       "      <td>10.3</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.779</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.8</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>42.690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      modelo  motor  km/litro_gas_cid  km/litro_gas_est  km/litro_alc_cid  \\\n",
       "0  Strada CS    1.4              10.8              12.3               7.4   \n",
       "1    Gol msi    1.0              13.3              14.4               9.1   \n",
       "2    Onix hb    1.0              13.9              16.7               9.9   \n",
       "3      Ka hb    1.0              13.4              15.5               9.2   \n",
       "4       Hb20    1.0              12.8              14.6               9.1   \n",
       "5       Kwid    1.0              14.9              15.6              10.3   \n",
       "\n",
       "   km/litro_alc_est  peso/kg  torque_gas  torque_alc  cavalos_gas  \\\n",
       "0               8.5    1.151        13.7        14.2           85   \n",
       "1              10.1    1.001         9.7        10.4           75   \n",
       "2              11.7    1.037         9.6        10.6           78   \n",
       "3              10.7    1.037        10.2        10.7           80   \n",
       "4              10.1    1.091         9.4        10.2           75   \n",
       "5              10.6    0.779         9.4         9.8           66   \n",
       "\n",
       "   cavalos_alc   valor  \n",
       "0           88  93.890  \n",
       "1           84  63.738  \n",
       "2           82  63.430  \n",
       "3           85  49.890  \n",
       "4           80  63.690  \n",
       "5           70  42.690  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importando nosso dataset\n",
    "df = pd.read_csv('cb_br_2022.csv')\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6 entries, 0 to 5\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   modelo            6 non-null      object \n",
      " 1   motor             6 non-null      float64\n",
      " 2   km/litro_gas_cid  6 non-null      float64\n",
      " 3   km/litro_gas_est  6 non-null      float64\n",
      " 4   km/litro_alc_cid  6 non-null      float64\n",
      " 5   km/litro_alc_est  6 non-null      float64\n",
      " 6   peso/kg           6 non-null      float64\n",
      " 7   torque_gas        6 non-null      float64\n",
      " 8   torque_alc        6 non-null      float64\n",
      " 9   cavalos_gas       6 non-null      int64  \n",
      " 10  cavalos_alc       6 non-null      int64  \n",
      " 11  valor             6 non-null      float64\n",
      "dtypes: float64(9), int64(2), object(1)\n",
      "memory usage: 704.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# Apesar deste exemplo ser pequeno, sempre precisamos fazer o trivial e verificarmos os se dados estão corretos.\n",
    "# Precisamos verificar se os dados estão com seu \"tipos (Dtype) corretos\", se não há \"nulos (Null)\", entre outros.\n",
    "df.info() # Verificando as informções "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>motor</th>\n",
       "      <th>km/litro_gas_cid</th>\n",
       "      <th>km/litro_gas_est</th>\n",
       "      <th>km/litro_alc_cid</th>\n",
       "      <th>km/litro_alc_est</th>\n",
       "      <th>peso/kg</th>\n",
       "      <th>torque_gas</th>\n",
       "      <th>torque_alc</th>\n",
       "      <th>cavalos_gas</th>\n",
       "      <th>cavalos_alc</th>\n",
       "      <th>valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.066667</td>\n",
       "      <td>13.183333</td>\n",
       "      <td>14.850000</td>\n",
       "      <td>9.166667</td>\n",
       "      <td>10.283333</td>\n",
       "      <td>1.016000</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>10.983333</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>81.500000</td>\n",
       "      <td>62.888000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.163299</td>\n",
       "      <td>1.367358</td>\n",
       "      <td>1.494992</td>\n",
       "      <td>0.995322</td>\n",
       "      <td>1.051507</td>\n",
       "      <td>0.127378</td>\n",
       "      <td>1.675311</td>\n",
       "      <td>1.608001</td>\n",
       "      <td>6.348228</td>\n",
       "      <td>6.252999</td>\n",
       "      <td>17.548166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>42.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.925000</td>\n",
       "      <td>14.450000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>9.450000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>80.500000</td>\n",
       "      <td>53.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>15.050000</td>\n",
       "      <td>9.150000</td>\n",
       "      <td>10.350000</td>\n",
       "      <td>1.037000</td>\n",
       "      <td>9.650000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>63.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.775000</td>\n",
       "      <td>15.575000</td>\n",
       "      <td>9.725000</td>\n",
       "      <td>10.675000</td>\n",
       "      <td>1.077500</td>\n",
       "      <td>10.075000</td>\n",
       "      <td>10.675000</td>\n",
       "      <td>79.500000</td>\n",
       "      <td>84.750000</td>\n",
       "      <td>63.726000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>1.151000</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>93.890000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          motor  km/litro_gas_cid  km/litro_gas_est  km/litro_alc_cid  \\\n",
       "count  6.000000          6.000000          6.000000          6.000000   \n",
       "mean   1.066667         13.183333         14.850000          9.166667   \n",
       "std    0.163299          1.367358          1.494992          0.995322   \n",
       "min    1.000000         10.800000         12.300000          7.400000   \n",
       "25%    1.000000         12.925000         14.450000          9.100000   \n",
       "50%    1.000000         13.350000         15.050000          9.150000   \n",
       "75%    1.000000         13.775000         15.575000          9.725000   \n",
       "max    1.400000         14.900000         16.700000         10.300000   \n",
       "\n",
       "       km/litro_alc_est   peso/kg  torque_gas  torque_alc  cavalos_gas  \\\n",
       "count          6.000000  6.000000    6.000000    6.000000     6.000000   \n",
       "mean          10.283333  1.016000   10.333333   10.983333    76.500000   \n",
       "std            1.051507  0.127378    1.675311    1.608001     6.348228   \n",
       "min            8.500000  0.779000    9.400000    9.800000    66.000000   \n",
       "25%           10.100000  1.010000    9.450000   10.250000    75.000000   \n",
       "50%           10.350000  1.037000    9.650000   10.500000    76.500000   \n",
       "75%           10.675000  1.077500   10.075000   10.675000    79.500000   \n",
       "max           11.700000  1.151000   13.700000   14.200000    85.000000   \n",
       "\n",
       "       cavalos_alc      valor  \n",
       "count     6.000000   6.000000  \n",
       "mean     81.500000  62.888000  \n",
       "std       6.252999  17.548166  \n",
       "min      70.000000  42.690000  \n",
       "25%      80.500000  53.275000  \n",
       "50%      83.000000  63.560000  \n",
       "75%      84.750000  63.726000  \n",
       "max      88.000000  93.890000  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # Para termos um panorama de como os dados se coomportam. É possível verificar a média(mean)\n",
    "# o desvio padrão(std), os quartis(25%, 50%, 75%), entre outras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
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       "      <th>motor</th>\n",
       "      <th>km/litro_gas_cid</th>\n",
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       "      <th>motor</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.853903</td>\n",
       "      <td>-0.835617</td>\n",
       "      <td>-0.869554</td>\n",
       "      <td>-0.830857</td>\n",
       "      <td>0.519212</td>\n",
       "      <td>0.984488</td>\n",
       "      <td>0.979999</td>\n",
       "      <td>0.655952</td>\n",
       "      <td>0.509249</td>\n",
       "      <td>0.865493</td>\n",
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       "    <tr>\n",
       "      <th>km/litro_gas_cid</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.868315</td>\n",
       "      <td>0.985578</td>\n",
       "      <td>0.827430</td>\n",
       "      <td>-0.849163</td>\n",
       "      <td>-0.864055</td>\n",
       "      <td>-0.891583</td>\n",
       "      <td>-0.830617</td>\n",
       "      <td>-0.777770</td>\n",
       "      <td>-0.926853</td>\n",
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       "    <tr>\n",
       "      <th>km/litro_gas_est</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.911291</td>\n",
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       "      <td>-0.505699</td>\n",
       "      <td>-0.815308</td>\n",
       "      <td>-0.792446</td>\n",
       "      <td>-0.494176</td>\n",
       "      <td>-0.486726</td>\n",
       "      <td>-0.779379</td>\n",
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       "      <td>-0.807149</td>\n",
       "      <td>-0.774453</td>\n",
       "      <td>-0.890952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km/litro_alc_est</th>\n",
       "      <td>-0.830857</td>\n",
       "      <td>0.827430</td>\n",
       "      <td>0.993006</td>\n",
       "      <td>0.876499</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.430494</td>\n",
       "      <td>-0.806842</td>\n",
       "      <td>-0.771419</td>\n",
       "      <td>-0.429949</td>\n",
       "      <td>-0.403037</td>\n",
       "      <td>-0.722239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peso/kg</th>\n",
       "      <td>0.519212</td>\n",
       "      <td>-0.849163</td>\n",
       "      <td>-0.505699</td>\n",
       "      <td>-0.780866</td>\n",
       "      <td>-0.430494</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.559518</td>\n",
       "      <td>0.635374</td>\n",
       "      <td>0.903014</td>\n",
       "      <td>0.888140</td>\n",
       "      <td>0.783627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>torque_gas</th>\n",
       "      <td>0.984488</td>\n",
       "      <td>-0.864055</td>\n",
       "      <td>-0.815308</td>\n",
       "      <td>-0.892767</td>\n",
       "      <td>-0.806842</td>\n",
       "      <td>0.559518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991375</td>\n",
       "      <td>0.737171</td>\n",
       "      <td>0.603300</td>\n",
       "      <td>0.838635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>torque_alc</th>\n",
       "      <td>0.979999</td>\n",
       "      <td>-0.891583</td>\n",
       "      <td>-0.792446</td>\n",
       "      <td>-0.902646</td>\n",
       "      <td>-0.771419</td>\n",
       "      <td>0.635374</td>\n",
       "      <td>0.991375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.788601</td>\n",
       "      <td>0.657396</td>\n",
       "      <td>0.892887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cavalos_gas</th>\n",
       "      <td>0.655952</td>\n",
       "      <td>-0.830617</td>\n",
       "      <td>-0.494176</td>\n",
       "      <td>-0.807149</td>\n",
       "      <td>-0.429949</td>\n",
       "      <td>0.903014</td>\n",
       "      <td>0.737171</td>\n",
       "      <td>0.788601</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.944692</td>\n",
       "      <td>0.769190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cavalos_alc</th>\n",
       "      <td>0.509249</td>\n",
       "      <td>-0.777770</td>\n",
       "      <td>-0.486726</td>\n",
       "      <td>-0.774453</td>\n",
       "      <td>-0.403037</td>\n",
       "      <td>0.888140</td>\n",
       "      <td>0.603300</td>\n",
       "      <td>0.657396</td>\n",
       "      <td>0.944692</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.709915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valor</th>\n",
       "      <td>0.865493</td>\n",
       "      <td>-0.926853</td>\n",
       "      <td>-0.779379</td>\n",
       "      <td>-0.890952</td>\n",
       "      <td>-0.722239</td>\n",
       "      <td>0.783627</td>\n",
       "      <td>0.838635</td>\n",
       "      <td>0.892887</td>\n",
       "      <td>0.769190</td>\n",
       "      <td>0.709915</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     motor  km/litro_gas_cid  km/litro_gas_est  \\\n",
       "motor             1.000000         -0.853903         -0.835617   \n",
       "km/litro_gas_cid -0.853903          1.000000          0.868315   \n",
       "km/litro_gas_est -0.835617          0.868315          1.000000   \n",
       "km/litro_alc_cid -0.869554          0.985578          0.911291   \n",
       "km/litro_alc_est -0.830857          0.827430          0.993006   \n",
       "peso/kg           0.519212         -0.849163         -0.505699   \n",
       "torque_gas        0.984488         -0.864055         -0.815308   \n",
       "torque_alc        0.979999         -0.891583         -0.792446   \n",
       "cavalos_gas       0.655952         -0.830617         -0.494176   \n",
       "cavalos_alc       0.509249         -0.777770         -0.486726   \n",
       "valor             0.865493         -0.926853         -0.779379   \n",
       "\n",
       "                  km/litro_alc_cid  km/litro_alc_est   peso/kg  torque_gas  \\\n",
       "motor                    -0.869554         -0.830857  0.519212    0.984488   \n",
       "km/litro_gas_cid          0.985578          0.827430 -0.849163   -0.864055   \n",
       "km/litro_gas_est          0.911291          0.993006 -0.505699   -0.815308   \n",
       "km/litro_alc_cid          1.000000          0.876499 -0.780866   -0.892767   \n",
       "km/litro_alc_est          0.876499          1.000000 -0.430494   -0.806842   \n",
       "peso/kg                  -0.780866         -0.430494  1.000000    0.559518   \n",
       "torque_gas               -0.892767         -0.806842  0.559518    1.000000   \n",
       "torque_alc               -0.902646         -0.771419  0.635374    0.991375   \n",
       "cavalos_gas              -0.807149         -0.429949  0.903014    0.737171   \n",
       "cavalos_alc              -0.774453         -0.403037  0.888140    0.603300   \n",
       "valor                    -0.890952         -0.722239  0.783627    0.838635   \n",
       "\n",
       "                  torque_alc  cavalos_gas  cavalos_alc     valor  \n",
       "motor               0.979999     0.655952     0.509249  0.865493  \n",
       "km/litro_gas_cid   -0.891583    -0.830617    -0.777770 -0.926853  \n",
       "km/litro_gas_est   -0.792446    -0.494176    -0.486726 -0.779379  \n",
       "km/litro_alc_cid   -0.902646    -0.807149    -0.774453 -0.890952  \n",
       "km/litro_alc_est   -0.771419    -0.429949    -0.403037 -0.722239  \n",
       "peso/kg             0.635374     0.903014     0.888140  0.783627  \n",
       "torque_gas          0.991375     0.737171     0.603300  0.838635  \n",
       "torque_alc          1.000000     0.788601     0.657396  0.892887  \n",
       "cavalos_gas         0.788601     1.000000     0.944692  0.769190  \n",
       "cavalos_alc         0.657396     0.944692     1.000000  0.709915  \n",
       "valor               0.892887     0.769190     0.709915  1.000000  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr() # A correlação nos traz dados interessantes, por exemplo, linearidade entre \"valor x km/litro_\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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.figure(1, figsize = (15, 7));\n",
    "df.plot.scatter((['valor','km/litro_gas_cid']),(['valor','km/litro_alc_cid']));\n",
    "# A função scatter() nos traz um gráfico que corrobora com a linearidade que vimos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Começando nossa PCA\n",
    "# Selecionamos nossas colunas numéricas\n",
    "col_num = ['motor', 'km/litro_gas_cid', 'km/litro_gas_est', 'km/litro_alc_cid','km/litro_alc_est',\n",
    "           'peso/kg', 'torque_gas', 'torque_alc', 'cavalos_gas', 'cavalos_alc', 'valor']\n",
    "df_pca = df[col_num]\n",
    "# Instanciando o objeto PCA\n",
    "pca = PCA(n_components=2)\n",
    "# Ajustando o modelo PCA aos dados\n",
    "pca.fit(df_pca)\n",
    "# Tranformando os dados para novas dimensões\n",
    "df_pca_transformado = pca.transform(df_pca)\n",
    "# Plotando em um gráfico de dispersão com os dados transformados\n",
    "plt.figure(1, figsize=(10, 5))\n",
    "plt.scatter(df_pca_transformado[:, 0], df_pca_transformado[:, 1]) \n",
    "plt.xlabel('Componente Principal 1', color = \"orange\") \n",
    "plt.ylabel('Componente Principal 2', color = \"orange\") \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['motor', 'km/litro_gas_cid', 'km/litro_gas_est', 'km/litro_alc_cid','km/litro_alc_est','peso/kg', 'torque_gas', 'torque_alc', 'cavalos_gas', 'cavalos_alc']]\n",
    "Y = df['valor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.00730917 -0.06764998 -0.05985596 -0.04757287 -0.03871679  0.00555726\n",
      "   0.07437366  0.07600178  0.27818508  0.25686471  0.9128637 ]\n",
      " [-0.00591433 -0.0171997   0.06312993 -0.01668843  0.0527985   0.0091925\n",
      "  -0.009444   -0.00464452  0.6115249   0.69102911 -0.37541767]]\n"
     ]
    }
   ],
   "source": [
    "# Verificando nossa matriz\n",
    "print(pca.components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[364.4715113   29.50358537]\n",
      "[0.91352114 0.07394857]\n"
     ]
    }
   ],
   "source": [
    "# Verificando os autovalores e os autovetores e a redução dos componentes\n",
    "print(pca.explained_variance_)\n",
    "print(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.644494e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>1.644494e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              PC1           PC2\n",
       "PC1  1.000000e+00  1.644494e-16\n",
       "PC2  1.644494e-16  1.000000e+00"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando o coeficiente de correlação\n",
    "df_pca_transformado_df = pd.DataFrame(df_pca_transformado, columns=[\"PC1\", \"PC2\"])\n",
    "df_pca_transformado_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-11.91822714,   3.55702457],\n",
       "       [ -0.52442372,  -1.35284095],\n",
       "       [ -0.8162508 ,  -2.31832437],\n",
       "       [ -4.59726612,  -1.90941619],\n",
       "       [  2.18652962,   0.05153624],\n",
       "       [ 15.66963816,   1.9720207 ]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformando os valores de \"X\"\n",
    "X_pca = pca.fit_transform(X)\n",
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O melhor custo benefício é do  Ka hb\n"
     ]
    }
   ],
   "source": [
    "# Extraindo custo benefício\n",
    "# IMPORTANTE: Foi preciso incluir o parâmetro '-Y.mean()', para apontarmos qual automóvel está abaixo da\n",
    "# média do \"valor\"\n",
    "melhor_custo_beneficio = Y / (X_pca[:, 0] + X_pca[:, 1] - Y.mean())\n",
    "# encontrar o índice da amostra com o maior benefício\n",
    "melhor_custo = melhor_custo_beneficio.argmax()\n",
    "# imprimir a marca correspondente\n",
    "print('O melhor custo benefício é do ', df.iloc[melhor_custo]['modelo'])\n",
    "# Veja que é muito simples entender nosso objetivo, bastar olhar o dataset(pequeno) que fica fácil de perceber\n",
    "# que o 'Ka hb' tem o custo-benefício melhor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## O 'Ka hb' tem o melhor custo benefício, então porque não vende mais que o 'Gol' e o 'Onix'?\n",
    "### Apesar da média de preço do 'Ka' ser mais atraente que os concorrentes e alguns quesitos serem melhores, ainda assim existem outros fatores que influenciam nas venda do produto, o 'design' e a 'marca' poderiam ser alguns deles. seria necessário uma análise mais direcionada para identificarmos esses elementos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O pior custo benefício é do  Strada CS\n"
     ]
    }
   ],
   "source": [
    "# Extraindo custo benefício\n",
    "# Mudando o parâmetro 'Y' para '+'(acima da média)\n",
    "pior_custo_beneficio = Y / (X_pca[:, 0] + X_pca[:, 1] + Y.mean())\n",
    "# encontrar o índice da amostra com o maior benefício\n",
    "pior_custo = pior_custo_beneficio.argmax()\n",
    "# imprimir a marca correspondente\n",
    "print('O pior custo benefício é do ', df.iloc[pior_custo]['modelo'])\n",
    "# Veja que é muito simples entender nosso objetivo, bastar olhar o dataset(pequeno) e fica fácil de perceber\n",
    "# que a 'Strada CS' tem o custo-benefício inferior aos demais."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Então porque a 'Strada CS' vende mais? \n",
    "### A Strada perde na comparação de todos os quesitos, mas, é o único utilitário com carroceria aberta do grupo e também um motor 1.4, logo, esses poderiam vir a ser os diferenciais. Talvez fosse interessante descosidera-la da amostra e incluí-la em um grupo específico. Talvez a necessidade por este tipo de veículo pelas empresas possa explicar, mas, essa é outra análise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# No fim, pela simples análise do gráfico, claramente por conta do fator \"preço\", fica fácil de entender\n",
    "# o melhor custo benefício, mesmo que o \"Kwid\" seja mais barato, ele não tem algumas características,\n",
    "# como robustez, etc...talvez a marca influencie neste resultado.\n",
    "plt.rcParams.update({'font.size': 12, 'lines.linewidth': 2})\n",
    "plt.figure(1, figsize=(12, 5))\n",
    "plt.bar(df['modelo'], df['valor'], color = 'green')\n",
    "plt.xlabel ('Modelos', color = 'orange')\n",
    "plt.ylabel ('Valores', color = 'orange')\n",
    "plt.grid(True)\n",
    "plt.title(\"Modelos x Preços\", color = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lembrando que este trabalho é apenas um fragmento simples e diminuto do que poderia ser a PCA. Comprender o que são \"pca.explained_variance\" e \"pca.explained_variance_ratio\" no contexto das amostras é fundamental para sua aplicação correta.\n",
    "### Portanto, entender o objetivo e a necessidade da análise é fundamental para a correta aplicação das diversas ferramentas que temos disponíveis, não só em Python, mas, em outras plataformas e linguagens."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "varInspector": {
   "cols": {
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    "lenVar": 40
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
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    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
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    "_Feature"
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   "window_display": false
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}