Notebooks/Regressão_Linear_04.ipynb

{
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    "## Feature Selection : correlação  Correlação \n",
    "\n",
    "### Interdependência entre duas ou mais variáveis como:\n",
    "\n",
    "    - Massa corporal e altura;\n",
    "    - Consumo de combústivel e distância percorrida;\n",
    "    - Tempo de estudo e desempenho na avaliação;\n",
    "    - etc...\n",
    "    \n",
    "Dentro os métodos mais comuns temos os índices de Pearson, Spearman e Kendall.\n",
    "\n",
    "### Coeficiente de Correlação de Pearson\n",
    "\n",
    "Também chamado de \"*Coeficiente de correlação produto-momento*\" mede o grau da correlação(e a direção dessa correlação -- se positiva ou negativa) entre duas variáveis. Os valores assumem representação que variam entre -1 e 1.\n",
    "\n",
    "### Coeficiente de Correlação de Spearman\n",
    "\n",
    "O coeficiente de correlação de Spearman é indicado para o cálculo da correlação entre variáveis aleatórias x e y relacionadas monotonicamente entre si, mas não necessariamente de maneira linear. Se a relação é linear o método de Pearson é o mais indicado.\n",
    "\n",
    "![image](https://miro.medium.com/max/542/1*UItO0nCkSMNRZW7SZHMKaA.png)\n",
    "\n",
    "Se as variáveis aleatórias seguitem, á perfeição, a função y=log(x), o coeficiente de correlação de Pearson não será igual a 1. Já o coeficiente de Spearman será 1, pois reflete a relação que não é linear.\n",
    "\n",
    "### Coeficiente de Correlaçção tau de Kendall\n",
    "\n",
    "É uma medida de correlação de postos, ou seja, verifica a semelhança entre as ordens dos dados quando classificadospor cada uma das quantidades.\n",
    "\n",
    "Recebe este nome em homenagem ao estatístico britânico Maurice Kendall, que o desenvolveu em 1938.\n",
    "\n",
    "A correlação de Kendall leva em consideração apenas a concordância direcional dos chamados pares concordantes e discordantes.\n",
    "\n",
    "![FORMULA](https://miro.medium.com/max/587/1*rPQsIVhiD3izV5sIiewMBg.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculando Correlação com Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.width', 90)\n",
    "# Importando os dados\n",
    "dados = pd.read_csv(\"/home/gab/MachineLearning-Files/Exercicios/Datasets/diabetes.csv\")\n",
    "dados.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin  \\\n",
       "Pregnancies                  1.000000  0.129459       0.141282      -0.081672 -0.073535   \n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328  0.331357   \n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371  0.088933   \n",
       "SkinThickness               -0.081672  0.057328       0.207371       1.000000  0.436783   \n",
       "Insulin                     -0.073535  0.331357       0.088933       0.436783  1.000000   \n",
       "BMI                          0.017683  0.221071       0.281805       0.392573  0.197859   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928  0.185071   \n",
       "Age                          0.544341  0.263514       0.239528      -0.113970 -0.042163   \n",
       "Outcome                      0.221898  0.466581       0.065068       0.074752  0.130548   \n",
       "\n",
       "                               BMI  DiabetesPedigreeFunction       Age   Outcome  \n",
       "Pregnancies               0.017683                 -0.033523  0.544341  0.221898  \n",
       "Glucose                   0.221071                  0.137337  0.263514  0.466581  \n",
       "BloodPressure             0.281805                  0.041265  0.239528  0.065068  \n",
       "SkinThickness             0.392573                  0.183928 -0.113970  0.074752  \n",
       "Insulin                   0.197859                  0.185071 -0.042163  0.130548  \n",
       "BMI                       1.000000                  0.140647  0.036242  0.292695  \n",
       "DiabetesPedigreeFunction  0.140647                  1.000000  0.033561  0.173844  \n",
       "Age                       0.036242                  0.033561  1.000000  0.238356  \n",
       "Outcome                   0.292695                  0.173844  0.238356  1.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Buscando a correlação dos dados pelo método de Pearson\n",
    "corr_matrix = dados.corr(method = 'pearson')\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizando as Correlações com HeatMap\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(dados.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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