main_arvore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
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       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
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       "      <td>83.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>125.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>19723</td>\n",
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       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.6</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
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       "      <td>0</td>\n",
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       "      <td>Rural</td>\n",
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       "      <td>25.6</td>\n",
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       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5110 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  gender   age  hypertension  heart_disease ever_married   \n",
       "0      9046    Male  67.0             0              1          Yes  \\\n",
       "1     51676  Female  61.0             0              0          Yes   \n",
       "2     31112    Male  80.0             0              1          Yes   \n",
       "3     60182  Female  49.0             0              0          Yes   \n",
       "4      1665  Female  79.0             1              0          Yes   \n",
       "...     ...     ...   ...           ...            ...          ...   \n",
       "5105  18234  Female  80.0             1              0          Yes   \n",
       "5106  44873  Female  81.0             0              0          Yes   \n",
       "5107  19723  Female  35.0             0              0          Yes   \n",
       "5108  37544    Male  51.0             0              0          Yes   \n",
       "5109  44679  Female  44.0             0              0          Yes   \n",
       "\n",
       "          work_type Residence_type  avg_glucose_level   bmi   smoking_status   \n",
       "0           Private          Urban             228.69  36.6  formerly smoked  \\\n",
       "1     Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2           Private          Rural             105.92  32.5     never smoked   \n",
       "3           Private          Urban             171.23  34.4           smokes   \n",
       "4     Self-employed          Rural             174.12  24.0     never smoked   \n",
       "...             ...            ...                ...   ...              ...   \n",
       "5105        Private          Urban              83.75   NaN     never smoked   \n",
       "5106  Self-employed          Urban             125.20  40.0     never smoked   \n",
       "5107  Self-employed          Rural              82.99  30.6     never smoked   \n",
       "5108        Private          Rural             166.29  25.6  formerly smoked   \n",
       "5109       Govt_job          Urban              85.28  26.2          Unknown   \n",
       "\n",
       "      stroke  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "...      ...  \n",
       "5105       0  \n",
       "5106       0  \n",
       "5107       0  \n",
       "5108       0  \n",
       "5109       0  \n",
       "\n",
       "[5110 rows x 12 columns]"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4909 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      hypertension  heart_disease  stroke  gender_Female  gender_Male   \n",
       "0              0.0            1.0     1.0            0.0          1.0  \\\n",
       "2              0.0            1.0     1.0            0.0          1.0   \n",
       "3              0.0            0.0     1.0            1.0          0.0   \n",
       "4              1.0            0.0     1.0            1.0          0.0   \n",
       "5              0.0            0.0     1.0            0.0          1.0   \n",
       "...            ...            ...     ...            ...          ...   \n",
       "5104           0.0            0.0     0.0            1.0          0.0   \n",
       "5106           0.0            0.0     0.0            1.0          0.0   \n",
       "5107           0.0            0.0     0.0            1.0          0.0   \n",
       "5108           0.0            0.0     0.0            0.0          1.0   \n",
       "5109           0.0            0.0     0.0            1.0          0.0   \n",
       "\n",
       "      gender_Other  ever_married_No  ever_married_Yes  work_type_Govt_job   \n",
       "0              0.0              0.0               1.0                 0.0  \\\n",
       "2              0.0              0.0               1.0                 0.0   \n",
       "3              0.0              0.0               1.0                 0.0   \n",
       "4              0.0              0.0               1.0                 0.0   \n",
       "5              0.0              0.0               1.0                 0.0   \n",
       "...            ...              ...               ...                 ...   \n",
       "5104           0.0              1.0               0.0                 0.0   \n",
       "5106           0.0              0.0               1.0                 0.0   \n",
       "5107           0.0              0.0               1.0                 0.0   \n",
       "5108           0.0              0.0               1.0                 0.0   \n",
       "5109           0.0              0.0               1.0                 1.0   \n",
       "\n",
       "      work_type_Never_worked  work_type_Private  work_type_Self-employed   \n",
       "0                        0.0                1.0                      0.0  \\\n",
       "2                        0.0                1.0                      0.0   \n",
       "3                        0.0                1.0                      0.0   \n",
       "4                        0.0                0.0                      1.0   \n",
       "5                        0.0                1.0                      0.0   \n",
       "...                      ...                ...                      ...   \n",
       "5104                     0.0                0.0                      0.0   \n",
       "5106                     0.0                0.0                      1.0   \n",
       "5107                     0.0                0.0                      1.0   \n",
       "5108                     0.0                1.0                      0.0   \n",
       "5109                     0.0                0.0                      0.0   \n",
       "\n",
       "      work_type_children  Residence_type_Rural  Residence_type_Urban   \n",
       "0                    0.0                   0.0                   1.0  \\\n",
       "2                    0.0                   1.0                   0.0   \n",
       "3                    0.0                   0.0                   1.0   \n",
       "4                    0.0                   1.0                   0.0   \n",
       "5                    0.0                   0.0                   1.0   \n",
       "...                  ...                   ...                   ...   \n",
       "5104                 1.0                   1.0                   0.0   \n",
       "5106                 0.0                   0.0                   1.0   \n",
       "5107                 0.0                   1.0                   0.0   \n",
       "5108                 0.0                   1.0                   0.0   \n",
       "5109                 0.0                   0.0                   1.0   \n",
       "\n",
       "      smoking_status_Unknown  smoking_status_formerly smoked   \n",
       "0                        0.0                             1.0  \\\n",
       "2                        0.0                             0.0   \n",
       "3                        0.0                             0.0   \n",
       "4                        0.0                             0.0   \n",
       "5                        0.0                             1.0   \n",
       "...                      ...                             ...   \n",
       "5104                     1.0                             0.0   \n",
       "5106                     0.0                             0.0   \n",
       "5107                     0.0                             0.0   \n",
       "5108                     0.0                             1.0   \n",
       "5109                     1.0                             0.0   \n",
       "\n",
       "      smoking_status_never smoked  smoking_status_smokes  \n",
       "0                             0.0                    0.0  \n",
       "2                             1.0                    0.0  \n",
       "3                             0.0                    1.0  \n",
       "4                             1.0                    0.0  \n",
       "5                             0.0                    0.0  \n",
       "...                           ...                    ...  \n",
       "5104                          0.0                    0.0  \n",
       "5106                          1.0                    0.0  \n",
       "5107                          1.0                    0.0  \n",
       "5108                          0.0                    0.0  \n",
       "5109                          0.0                    0.0  \n",
       "\n",
       "[4909 rows x 19 columns]"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean = pd.get_dummies(df).astype(float)\n",
    "df_clean = df_clean.drop(columns=['id','age','avg_glucose_level', 'bmi'])\n",
    "df_clean"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelo de classificação DecisionTreeClassifier"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explicação célula 0:\n",
    "\n",
    "Importância: \n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier: importa o modelo DecisionTreeClassifier, que é um modelo de classificação que será utilizado.\n",
    "\n",
    "from sklearn.model_selection import train_test_split: importa a função train_test_split, que será utilizada para separar os dados em treino e teste.\n",
    "\n",
    "Resultado:  Módulos necessários para o projeto importados ."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> célula 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explicação célula 1:\n",
    "\n",
    "Importância: Gerar as features. Para isso iremos remover a coluna stroke do nosso dataset, df_clean pois ela é o target, e não uma feature.\n",
    "\n",
    "Resultado: pandas DataFrame com as features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> célula 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataset \n",
    "# data, all columns except stroke\n",
    "X = df_clean.drop(columns=[\"stroke\"])\n",
    "type(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explicação célula 2:\n",
    "\n",
    "Importância: Gerar o target. Para isso iremos selecionar apenas a coluna stroke do nosso dataset, df_clean pois ela é o target, e não uma feature.\n",
    "\n",
    "Resultado: pandas Series com o target"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> célula 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "2       1.0\n",
       "3       1.0\n",
       "4       1.0\n",
       "5       1.0\n",
       "       ... \n",
       "5104    0.0\n",
       "5106    0.0\n",
       "5107    0.0\n",
       "5108    0.0\n",
       "5109    0.0\n",
       "Name: stroke, Length: 4909, dtype: float64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# target, only stroke column\n",
    "Y = df_clean[\"stroke\"]\n",
    "Y"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explicação célula 3:\n",
    "\n",
    "Importância: Para que o nosso modelo não seja treinado com os próprios dados que serão usados para avaliar sua performance, é necessário separar os dados em treino e teste. Para isso iremos utilizar a função train_test_split, que irá separar os dados em treino e teste, de acordo com os parâmetros passados.\n",
    "\n",
    "Resultado: \n",
    "\n",
    "X_train, (features de treino)\n",
    "\n",
    "X_test, (features de teste)\n",
    "\n",
    "y_train, (target de treino)\n",
    "\n",
    "y_test, (target de teste)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> célula 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split dataset into train and test\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explicação célula 4:\n",
    "\n",
    "Importância: Treinar o modelo. Para isso iremos utilizar a função fit, que irá treinar o modelo com os dados de treino. Iremos inicializar o modelo DecisionTreeClassifier, e passar os dados de treino para a função fit.\n",
    "\n",
    "Resultado: Modelo treinado"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> célula 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(X_train, Y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explicação célula 5:\n",
    "\n",
    "Importância: Avaliar o modelo. Para isso iremos utilizar a função score, que irá avaliar o modelo com os dados de teste. Iremos passar os dados de teste para a função score.\n",
    "\n",
    "Resultado: Score do modelo"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> célula 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9527494908350306\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy: {clf.score(X_test, Y_test)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explicação célula 6:\n",
    "\n",
    "Importância: Avaliar as features mais importantes para o modelo. Para isso iremos utilizar o atributo feature_importances_, que irá avaliar as features mais importantes para o modelo.\n",
    "\n",
    "\n",
    "Resultado: Lista com as features mais importantes para o modelo e o seu respectivo valor de importância. Ordenado de forma decrescente."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> célula 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('smoking_status_never smoked', 0.15261914571655094),\n",
       " ('gender_Male', 0.10150746687732964),\n",
       " ('heart_disease', 0.09351245008469822),\n",
       " ('gender_Female', 0.09154024993931627),\n",
       " ('hypertension', 0.08919579611001185),\n",
       " ('Residence_type_Rural', 0.07768998386151876),\n",
       " ('smoking_status_Unknown', 0.07252746989411972),\n",
       " ('work_type_Govt_job', 0.06111084226397998),\n",
       " ('work_type_Private', 0.05464165804554581),\n",
       " ('ever_married_No', 0.04793140506573098),\n",
       " ('smoking_status_formerly smoked', 0.042025348486053576),\n",
       " ('smoking_status_smokes', 0.04068730294506316),\n",
       " ('Residence_type_Urban', 0.036117225447184355),\n",
       " ('ever_married_Yes', 0.02038142108823984),\n",
       " ('work_type_Self-employed', 0.018512234174656785),\n",
       " ('gender_Other', 0.0),\n",
       " ('work_type_Never_worked', 0.0),\n",
       " ('work_type_children', 0.0)]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance = []\n",
    "for i in range(len(clf.feature_importances_)):\n",
    "   importance.append((X.columns[i],clf.feature_importances_[i]))\n",
    "\n",
    "# display the importance of each attribute, sorted \n",
    "importance.sort(key=lambda x: x[1], reverse=True)\n",
    "importance\n"
   ]
  }
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