drop.ipynb

{
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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   "version": "3.6.9"
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   "interpreter": {
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "#Importação das bibliotecas\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import mean_absolute_error as mae\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n"
   ]
  },
  {
   "source": [
    "#Load do nosso conjunto de dados\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carrega o conjunto de dados\n",
    "pima = pd.read_csv(\"speedDating_trab.csv\")"
   ]
  },
  {
   "source": [
    "#Imprime o nosso conjunto de dados\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Unnamed: 0    id  partner   age  age_o  goal  date  go_out  int_corr  \\\n",
       "0              1   1.0        1  21.0   27.0   2.0   7.0     1.0      0.14   \n",
       "1              2   1.0        2  21.0   22.0   2.0   7.0     1.0      0.54   \n",
       "2              3   1.0        3  21.0   22.0   2.0   7.0     1.0      0.16   \n",
       "3              4   1.0        4  21.0   23.0   2.0   7.0     1.0      0.61   \n",
       "4              5   1.0        5  21.0   24.0   2.0   7.0     1.0      0.21   \n",
       "...          ...   ...      ...   ...    ...   ...   ...     ...       ...   \n",
       "8373        8374  22.0       18  25.0   26.0   1.0   2.0     1.0      0.64   \n",
       "8374        8375  22.0       19  25.0   24.0   1.0   2.0     1.0      0.71   \n",
       "8375        8376  22.0       20  25.0   29.0   1.0   2.0     1.0     -0.46   \n",
       "8376        8377  22.0       21  25.0   22.0   1.0   2.0     1.0      0.62   \n",
       "8377        8378   NaN       22  25.0   22.0   1.0   2.0     1.0      0.01   \n",
       "\n",
       "      length  met  like  prob  match  \n",
       "0        2.0  1.0   7.0   6.0      0  \n",
       "1        2.0  1.0   7.0   5.0      0  \n",
       "2        2.0  1.0   7.0   NaN      1  \n",
       "3        2.0  1.0   7.0   6.0      1  \n",
       "4        2.0  1.0   6.0   6.0      1  \n",
       "...      ...  ...   ...   ...    ...  \n",
       "8373     1.0  0.0   2.0   5.0      0  \n",
       "8374     1.0  0.0   4.0   4.0      0  \n",
       "8375     1.0  0.0   6.0   5.0      0  \n",
       "8376     1.0  0.0   5.0   5.0      0  \n",
       "8377     1.0  0.0   4.0   5.0      0  \n",
       "\n",
       "[8378 rows x 14 columns]"
      ],
      "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>Unnamed: 0</th>\n      <th>id</th>\n      <th>partner</th>\n      <th>age</th>\n      <th>age_o</th>\n      <th>goal</th>\n      <th>date</th>\n      <th>go_out</th>\n      <th>int_corr</th>\n      <th>length</th>\n      <th>met</th>\n      <th>like</th>\n      <th>prob</th>\n      <th>match</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>21.0</td>\n      <td>27.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.14</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>21.0</td>\n      <td>22.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.54</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1.0</td>\n      <td>3</td>\n      <td>21.0</td>\n      <td>22.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.16</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>NaN</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1.0</td>\n      <td>4</td>\n      <td>21.0</td>\n      <td>23.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.61</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1.0</td>\n      <td>5</td>\n      <td>21.0</td>\n      <td>24.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.21</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>6.0</td>\n      <td>6.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>8373</th>\n      <td>8374</td>\n      <td>22.0</td>\n      <td>18</td>\n      <td>25.0</td>\n      <td>26.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.64</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8374</th>\n      <td>8375</td>\n      <td>22.0</td>\n      <td>19</td>\n      <td>25.0</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.71</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8375</th>\n      <td>8376</td>\n      <td>22.0</td>\n      <td>20</td>\n      <td>25.0</td>\n      <td>29.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>-0.46</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8376</th>\n      <td>8377</td>\n      <td>22.0</td>\n      <td>21</td>\n      <td>25.0</td>\n      <td>22.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.62</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8377</th>\n      <td>8378</td>\n      <td>NaN</td>\n      <td>22</td>\n      <td>25.0</td>\n      <td>22.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.01</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>8378 rows × 14 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "pima"
   ]
  },
  {
   "source": [
    "#Vai contar o número de NaN que existe em cada coluna\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Unnamed: 0      0\n",
       "id              1\n",
       "partner         0\n",
       "age            95\n",
       "age_o         104\n",
       "goal           79\n",
       "date           97\n",
       "go_out         79\n",
       "int_corr      158\n",
       "length        915\n",
       "met           375\n",
       "like          240\n",
       "prob          309\n",
       "match           0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "pima.isnull().sum()\n"
   ]
  },
  {
   "source": [
    "#A estratégia vai ser retirar todas as linhas em que se encontram NaN"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pima=pima.dropna()"
   ]
  },
  {
   "source": [
    "#Imprime o conjunto de dados depois da remoção"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Unnamed: 0    id  partner   age  age_o  goal  date  go_out  int_corr  \\\n",
       "0              1   1.0        1  21.0   27.0   2.0   7.0     1.0      0.14   \n",
       "1              2   1.0        2  21.0   22.0   2.0   7.0     1.0      0.54   \n",
       "3              4   1.0        4  21.0   23.0   2.0   7.0     1.0      0.61   \n",
       "4              5   1.0        5  21.0   24.0   2.0   7.0     1.0      0.21   \n",
       "5              6   1.0        6  21.0   25.0   2.0   7.0     1.0      0.25   \n",
       "...          ...   ...      ...   ...    ...   ...   ...     ...       ...   \n",
       "8372        8373  22.0       17  25.0   24.0   1.0   2.0     1.0      0.28   \n",
       "8373        8374  22.0       18  25.0   26.0   1.0   2.0     1.0      0.64   \n",
       "8374        8375  22.0       19  25.0   24.0   1.0   2.0     1.0      0.71   \n",
       "8375        8376  22.0       20  25.0   29.0   1.0   2.0     1.0     -0.46   \n",
       "8376        8377  22.0       21  25.0   22.0   1.0   2.0     1.0      0.62   \n",
       "\n",
       "      length  met  like  prob  match  \n",
       "0        2.0  1.0   7.0   6.0      0  \n",
       "1        2.0  1.0   7.0   5.0      0  \n",
       "3        2.0  1.0   7.0   6.0      1  \n",
       "4        2.0  1.0   6.0   6.0      1  \n",
       "5        2.0  1.0   6.0   5.0      0  \n",
       "...      ...  ...   ...   ...    ...  \n",
       "8372     1.0  0.0   4.0   4.0      0  \n",
       "8373     1.0  0.0   2.0   5.0      0  \n",
       "8374     1.0  0.0   4.0   4.0      0  \n",
       "8375     1.0  0.0   6.0   5.0      0  \n",
       "8376     1.0  0.0   5.0   5.0      0  \n",
       "\n",
       "[6878 rows x 14 columns]"
      ],
      "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>Unnamed: 0</th>\n      <th>id</th>\n      <th>partner</th>\n      <th>age</th>\n      <th>age_o</th>\n      <th>goal</th>\n      <th>date</th>\n      <th>go_out</th>\n      <th>int_corr</th>\n      <th>length</th>\n      <th>met</th>\n      <th>like</th>\n      <th>prob</th>\n      <th>match</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>21.0</td>\n      <td>27.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.14</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>21.0</td>\n      <td>22.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.54</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1.0</td>\n      <td>4</td>\n      <td>21.0</td>\n      <td>23.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.61</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1.0</td>\n      <td>5</td>\n      <td>21.0</td>\n      <td>24.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.21</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>6.0</td>\n      <td>6.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>1.0</td>\n      <td>6</td>\n      <td>21.0</td>\n      <td>25.0</td>\n      <td>2.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.25</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>8372</th>\n      <td>8373</td>\n      <td>22.0</td>\n      <td>17</td>\n      <td>25.0</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.28</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8373</th>\n      <td>8374</td>\n      <td>22.0</td>\n      <td>18</td>\n      <td>25.0</td>\n      <td>26.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.64</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8374</th>\n      <td>8375</td>\n      <td>22.0</td>\n      <td>19</td>\n      <td>25.0</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.71</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8375</th>\n      <td>8376</td>\n      <td>22.0</td>\n      <td>20</td>\n      <td>25.0</td>\n      <td>29.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>-0.46</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8376</th>\n      <td>8377</td>\n      <td>22.0</td>\n      <td>21</td>\n      <td>25.0</td>\n      <td>22.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.62</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>6878 rows × 14 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "pima"
   ]
  },
  {
   "source": [
    "#Convertemos todas as colunas exceto o partner, o prob, o like e o match para inteiro\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6878 entries, 0 to 8376\n",
      "Data columns (total 14 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  6878 non-null   int64  \n",
      " 1   id          6878 non-null   int64  \n",
      " 2   partner     6878 non-null   int64  \n",
      " 3   age         6878 non-null   int64  \n",
      " 4   age_o       6878 non-null   int64  \n",
      " 5   goal        6878 non-null   int64  \n",
      " 6   date        6878 non-null   int64  \n",
      " 7   go_out      6878 non-null   int64  \n",
      " 8   int_corr    6878 non-null   float64\n",
      " 9   length      6878 non-null   int64  \n",
      " 10  met         6878 non-null   int64  \n",
      " 11  like        6878 non-null   float64\n",
      " 12  prob        6878 non-null   float64\n",
      " 13  match       6878 non-null   int64  \n",
      "dtypes: float64(3), int64(11)\n",
      "memory usage: 806.0 KB\n",
      "/home/catarina/.local/lib/python3.6/site-packages/pandas/core/frame.py:3069: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#convertemos de float para int\n",
    "pima[['met','length','go_out','date','goal','age_o','age','id']]=  pima[['met','length','go_out','date','goal','age_o','age','id']].astype(int)\n",
    "pima.info()\n"
   ]
  },
  {
   "source": [
    "#Aqui vamos verificar a quantidade de zeros e uns que existe na coluna match\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "% 1's: 0.17112532712997963\n% 0's: 0.8288746728700204\n"
     ]
    }
   ],
   "source": [
    "#numero de 1's\n",
    "print(\"% 1's:\",pima['match'].mean())\n",
    "print(\"% 0's:\", 1-pima['match'].mean())\n"
   ]
  },
  {
   "source": [
    "#Confirma se todos os NaN foram retirados\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "id            0\n",
       "partner       0\n",
       "age           0\n",
       "age_o         0\n",
       "goal          0\n",
       "date          0\n",
       "go_out        0\n",
       "int_corr      0\n",
       "length        0\n",
       "met           0\n",
       "like          0\n",
       "prob          0\n",
       "match         0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "pima.isnull().sum()"
   ]
  },
  {
   "source": [
    "#Confirmar se a tabela foi convertida da maneira que queríamos\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    Unnamed: 0  id  partner  age  age_o  goal  date  go_out  int_corr  length  \\\n",
       "0            1   1        1   21     27     2     7       1      0.14       2   \n",
       "1            2   1        2   21     22     2     7       1      0.54       2   \n",
       "3            4   1        4   21     23     2     7       1      0.61       2   \n",
       "4            5   1        5   21     24     2     7       1      0.21       2   \n",
       "5            6   1        6   21     25     2     7       1      0.25       2   \n",
       "6            7   1        7   21     30     2     7       1      0.34       2   \n",
       "8            9   1        9   21     28     2     7       1      0.28       2   \n",
       "9           10   1       10   21     24     2     7       1     -0.36       2   \n",
       "10          11   2        1   24     27     1     5       1      0.29       2   \n",
       "11          12   2        2   24     22     1     5       1      0.18       2   \n",
       "\n",
       "    met  like  prob  match  \n",
       "0     1   7.0   6.0      0  \n",
       "1     1   7.0   5.0      0  \n",
       "3     1   7.0   6.0      1  \n",
       "4     1   6.0   6.0      1  \n",
       "5     1   6.0   5.0      0  \n",
       "6     1   6.0   5.0      0  \n",
       "8     1   7.0   7.0      1  \n",
       "9     1   6.0   6.0      0  \n",
       "10    1   6.0   4.0      0  \n",
       "11    1   7.0   3.0      0  "
      ],
      "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>Unnamed: 0</th>\n      <th>id</th>\n      <th>partner</th>\n      <th>age</th>\n      <th>age_o</th>\n      <th>goal</th>\n      <th>date</th>\n      <th>go_out</th>\n      <th>int_corr</th>\n      <th>length</th>\n      <th>met</th>\n      <th>like</th>\n      <th>prob</th>\n      <th>match</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>21</td>\n      <td>27</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.14</td>\n      <td>2</td>\n      <td>1</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>21</td>\n      <td>22</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.54</td>\n      <td>2</td>\n      <td>1</td>\n      <td>7.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>4</td>\n      <td>21</td>\n      <td>23</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.61</td>\n      <td>2</td>\n      <td>1</td>\n      <td>7.0</td>\n      <td>6.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1</td>\n      <td>5</td>\n      <td>21</td>\n      <td>24</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.21</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>6.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>1</td>\n      <td>6</td>\n      <td>21</td>\n      <td>25</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.25</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>1</td>\n      <td>7</td>\n      <td>21</td>\n      <td>30</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.34</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>1</td>\n      <td>9</td>\n      <td>21</td>\n      <td>28</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0.28</td>\n      <td>2</td>\n      <td>1</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>1</td>\n      <td>10</td>\n      <td>21</td>\n      <td>24</td>\n      <td>2</td>\n      <td>7</td>\n      <td>1</td>\n      <td>-0.36</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>6.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>11</td>\n      <td>2</td>\n      <td>1</td>\n      <td>24</td>\n      <td>27</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.29</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>4.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>2</td>\n      <td>2</td>\n      <td>24</td>\n      <td>22</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.18</td>\n      <td>2</td>\n      <td>1</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "pima.head(10)"
   ]
  },
  {
   "source": [
    "#Divisão dos casos variável objetivo com os restantes e o split do conjunto de dados em casos teste e treino\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#divisao do casos em q tem match e os q n tem match\n",
    "resultados = pima['match']\n",
    "dados = pima.drop(['match'],axis=1)\n",
    "\n",
    "\n",
    "# Separa o nosso conjunto de dados em treino e teste (30%/70%)\n",
    "dados_treino, dados_teste, resultados_treino, resultados_teste= train_test_split( dados, resultados, test_size=0.3,random_state=1)"
   ]
  },
  {
   "source": [
    "#ID3 COM ENTROPIA\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "##Método Holdout\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "###Aplicação, treino e predict do ID3\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#modelo a aplicar (ID3)\n",
    "clf = DecisionTreeClassifier(criterion=\"entropy\")\n",
    "\n",
    "#treina o nosso modelo\n",
    "clf = clf.fit(dados_treino, resultados_treino)\n",
    "\n",
    "#dá uma previsão \n",
    "predicted_test = clf.predict(dados_teste)\n"
   ]
  },
  {
   "source": [
    "###Apresentação da Accuracy do modelo, da sua matriz de confusão e o seu classification report\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Accuracy: 0.7659883720930233\nMatriz de confusão:\n [[1454  241]\n [ 242  127]]\nClassification Report:\n               precision    recall  f1-score   support\n\n           0       0.86      0.86      0.86      1695\n           1       0.35      0.34      0.34       369\n\n    accuracy                           0.77      2064\n   macro avg       0.60      0.60      0.60      2064\nweighted avg       0.77      0.77      0.77      2064\n\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Accuracy:\",metrics.accuracy_score(resultados_teste, predicted_test))\n",
    "\n",
    "print(\"Matriz de confusão:\\n\",confusion_matrix(resultados_teste, clf.predict(dados_teste)))\n",
    "\n",
    "print(\"Classification Report:\\n\",classification_report(resultados_teste, clf.predict(dados_teste)))\n"
   ]
  },
  {
   "source": [
    "##Método Validação Cruzada (Cross-Validation)\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "###Vamos dividir o nosso conjunto de dados em 10 e cada uma das partições vai ser testada como teste e as restantes como treino.\n",
    "###Apresentação da Accuracy de todas as partições, a média dessas Accuracies, matriz de confusão de cada partição e soma total das matrizes de confusão"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cross Validation:\n",
      " [0.74273256 0.69331395 0.67587209 0.68604651 0.72819767 0.70784884\n",
      " 0.6744186  0.67877907 0.68267831 0.72925764]\n",
      "A média para Cross-Validation do K-fold é: 0.6999145255746251 \n",
      "\n",
      "MATRIZ DE CONFUSÃO DE CADA K-FOLD:\n",
      "[[405 181]\n",
      " [ 59  43]]\n",
      "[[482  73]\n",
      " [103  30]]\n",
      "[[448  94]\n",
      " [ 98  48]]\n",
      "[[477  99]\n",
      " [ 84  28]]\n",
      "[[508  68]\n",
      " [ 89  23]]\n",
      "[[496 108]\n",
      " [ 59  25]]\n",
      "[[440 115]\n",
      " [ 75  58]]\n",
      "[[459 100]\n",
      " [ 98  31]]\n",
      "[[472  98]\n",
      " [ 86  31]]\n",
      "[[412 166]\n",
      " [ 67  42]]\n",
      "\n",
      " SOMA DA MATRIZ DE CONFUSÃO DE TODOS OS K-FOLD:\n",
      " [[4599 1102]\n",
      " [ 818  359]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#divide o nosso conjunto de dados em 10\n",
    "kf = KFold(n_splits=10,shuffle=False)\n",
    "kf.split(dados)\n",
    "\n",
    "scores=cross_val_score(DecisionTreeClassifier(criterion=\"entropy\"), dados, resultados,cv=10,  scoring='accuracy')\n",
    "print(\"Cross Validation:\\n\", scores)\n",
    "print(\"A média para Cross-Validation do K-fold é: {}\".format(scores.mean()),\"\\n\")\n",
    "\n",
    "# Inicializa o array a zero que vai guardar a nossa matriz de confusão\n",
    "array = [[0,0],[0,0]]\n",
    "print(\"MATRIZ DE CONFUSÃO DE CADA K-FOLD:\")\n",
    "# para cada split train test vai treinar, prever e fazer a matriz de confusão\n",
    "for train_index, test_index in kf.split(dados):\n",
    "    # split train test\n",
    "    dados_treino, dados_teste = dados.iloc[train_index], dados.iloc[test_index]\n",
    "    resultados_treino, resultados_teste = resultados.iloc[train_index], resultados.iloc[test_index]\n",
    "    # treina o modelo\n",
    "    model = clf.fit(dados_treino, resultados_treino)\n",
    "    # calcula a matriz de confusão\n",
    "    score=confusion_matrix(resultados_teste, model.predict(dados_teste))\n",
    "    print(score )\n",
    "    c = score\n",
    "   # soma as matrizes de confusões\n",
    "    array = array + c\n",
    "print(\"\\n SOMA DA MATRIZ DE CONFUSÃO DE TODOS OS K-FOLD:\\n\",array,\"\\n\")"
   ]
  },
  {
   "source": [
    "#NAIVE BAYES\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "##Método Holdout\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "###Implementação do Gaussian Naive Bayes\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Chama o modelo Gaussian\n",
    "gnb = GaussianNB()\n",
    "\n",
    "#treina o modelo\n",
    "gnb.fit(dados_treino,resultados_treino)\n",
    "\n",
    "#dá uma previsão\n",
    "predicted3 = gnb.predict(dados_teste)\n",
    "\n"
   ]
  },
  {
   "source": [
    "###Apresentação da Accuracy, matriz de confusão e classification report\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Accuracy: 0.8471615720524017\nMatriz de confusão:\n [[567  11]\n [ 94  15]]\nClassification Report:\n               precision    recall  f1-score   support\n\n           0       0.86      0.98      0.92       578\n           1       0.58      0.14      0.22       109\n\n    accuracy                           0.85       687\n   macro avg       0.72      0.56      0.57       687\nweighted avg       0.81      0.85      0.81       687\n\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(resultados_teste, predicted3))\n",
    "\n",
    "print(\"Matriz de confusão:\\n\",confusion_matrix(resultados_teste, gnb.predict(dados_teste)))\n",
    "\n",
    "print(\"Classification Report:\\n\",classification_report(resultados_teste, gnb.predict(dados_teste)))\n"
   ]
  },
  {
   "source": [
    "##Método Validação Cruzada (Cross-Validation)\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "###Vamos dividir o nosso conjunto de dados em 10 e cada uma das partições vai ser testada como teste e as restantes como treino.\n",
    "###Apresentação da Accuracy de todas as partições, a média dessas Accuracies, matriz de confusão de cada partição e soma total das matrizes de confusão"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cross Validation:\n",
      " [0.81540698 0.81395349 0.83430233 0.74563953 0.84156977 0.7994186\n",
      " 0.8372093  0.80232558 0.81222707 0.83842795]\n",
      "A média para Cross-Validation do K-fold é: 0.814048060322941 \n",
      "\n",
      "MATRIZ DE CONFUSÃO DE CADA K-FOLD:\n",
      "[[517  69]\n",
      " [ 60  42]]\n",
      "[[524  31]\n",
      " [101  32]]\n",
      "[[511  31]\n",
      " [102  44]]\n",
      "[[482  94]\n",
      " [ 74  38]]\n",
      "[[563  13]\n",
      " [ 94  18]]\n",
      "[[554  50]\n",
      " [ 65  19]]\n",
      "[[542  13]\n",
      " [113  20]]\n",
      "[[506  53]\n",
      " [ 82  47]]\n",
      "[[526  44]\n",
      " [ 87  30]]\n",
      "[[567  11]\n",
      " [ 94  15]]\n",
      "\n",
      " SOMA DA MATRIZ DE CONFUSÃO DE TODOS OS K-FOLD:\n",
      " [[5292  409]\n",
      " [ 872  305]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#divide o nosso conjunto de dados em 10\n",
    "kf = KFold(n_splits=10,shuffle=False)\n",
    "kf.split(dados)\n",
    "\n",
    "scores=cross_val_score(GaussianNB(), dados, resultados, cv=10, scoring='accuracy')\n",
    "print(\"Cross Validation:\\n\", scores)\n",
    "print(\"A média para Cross-Validation do K-fold é: {}\".format(scores.mean()),\"\\n\")\n",
    "\n",
    "# Inicializa o array a zero que vai guardar a nossa matriz de confusão\n",
    "array = [[0,0],[0,0]]\n",
    "print(\"MATRIZ DE CONFUSÃO DE CADA K-FOLD:\")\n",
    "# para cada split train test vai treinar, prever e fazer a matriz de confusão\n",
    "for train_index, test_index in kf.split(dados):\n",
    "    # split train test\n",
    "    dados_treino, dados_teste = dados.iloc[train_index], dados.iloc[test_index]\n",
    "    resultados_treino, resultados_teste = resultados.iloc[train_index], resultados.iloc[test_index]\n",
    "     # treina o modelo\n",
    "    model = gnb.fit(dados_treino, resultados_treino)\n",
    "    # calcula a matriz de confusão\n",
    "    score=confusion_matrix(resultados_teste, model.predict(dados_teste))\n",
    "    print(score )\n",
    "    c = score\n",
    "    # soma as matrizes de confusões\n",
    "    array = array + c\n",
    "print(\"\\n SOMA DA MATRIZ DE CONFUSÃO DE TODOS OS K-FOLD:\\n\",array,\"\\n\")"
   ]
  },
  {
   "source": [
    "##Curva de aprendizagem para o Gaussian Naive Bayes com o método Cross-Validation\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "# cv é o número de folds enquanto faz Cross-Validation\n",
    "sizes, training_scores, testing_scores = learning_curve(GaussianNB(), dados, resultados, cv=10, scoring='accuracy', train_sizes=np.linspace(0.01, 1.0, 50))\n",
    "  \n",
    "# Média e desvio padrão do score do treino\n",
    "mean_training = np.mean(training_scores, axis=1)\n",
    "Standard_Deviation_training = np.std(training_scores, axis=1)\n",
    "  \n",
    "# Média e desvio padrão do score do teste\n",
    "mean_testing = np.mean(testing_scores, axis=1)\n",
    "Standard_Deviation_testing = np.std(testing_scores, axis=1)\n",
    "\n",
    "# azul trajecado é para os valores de treino e a linha verde para o valor cross.-validation\n",
    "plt.plot(sizes, mean_training, '--', color=\"b\",  label=\"Training score\")\n",
    "plt.plot(sizes, mean_testing, color=\"g\", label=\"Cross-validation score\")\n",
    "  \n",
    "# Desenha gráfico\n",
    "plt.title(\"LEARNING CURVE FOR GAUSSIAN\")\n",
    "plt.xlabel(\"Training Set Size\"), plt.ylabel(\"Accuracy Score\"), plt.legend(loc=\"best\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ]
}