Projeto 11.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset\n",
    "- Nesse trabalho vamos explorar algumas bases de dados variadas\n",
    "- Como irei abordar formas de pré-processamento foram necessários diferentes tipos de dados\n",
    "\n",
    "### Objetivo Geral\n",
    "- Trabalhar com Pré-processamento de dados e Pipelines\n",
    "\n",
    "### Objetivos Específicos\n",
    "- Tratamento de variáveis categóricas - Encoding\n",
    "- Como lidar com alguns tipos de Missing Data\n",
    "- Como estruturar uma Pipeline\n",
    "- Padronização de Dados e seus impactos na eficiência dos modelos\n",
    "\n",
    "### Fonte\n",
    "- Projeto pessoal com os assuntos abordados no módulo: <br>\n",
    "    'Supervised Learning with scikit-learn - Preprocessing and pipelines', da plataforma DataCamp, trilha 'Machine Learning Scientist with Python'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importando Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "import seaborn as sns\n",
    "\n",
    "# Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Pré-processamento\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "\n",
    "# Modelos\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Métricas\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Hyperparameter tuning\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gapminder Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compreensão Base\n",
    "- Vou dar maior atenção aos tipos de pré-processamento\n",
    "### Visualização Inicial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>population</th>\n",
       "      <th>fertility</th>\n",
       "      <th>HIV</th>\n",
       "      <th>CO2</th>\n",
       "      <th>BMI_male</th>\n",
       "      <th>GDP</th>\n",
       "      <th>BMI_female</th>\n",
       "      <th>life</th>\n",
       "      <th>child_mortality</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34811059</td>\n",
       "      <td>2.73</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.328945</td>\n",
       "      <td>24.59620</td>\n",
       "      <td>12314</td>\n",
       "      <td>129.9049</td>\n",
       "      <td>75.3</td>\n",
       "      <td>29.5</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19842251</td>\n",
       "      <td>6.43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.474353</td>\n",
       "      <td>22.25083</td>\n",
       "      <td>7103</td>\n",
       "      <td>130.1247</td>\n",
       "      <td>58.3</td>\n",
       "      <td>192.0</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40381860</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.785170</td>\n",
       "      <td>27.50170</td>\n",
       "      <td>14646</td>\n",
       "      <td>118.8915</td>\n",
       "      <td>75.5</td>\n",
       "      <td>15.4</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2975029</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.804106</td>\n",
       "      <td>25.35542</td>\n",
       "      <td>7383</td>\n",
       "      <td>132.8108</td>\n",
       "      <td>72.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21370348</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.1</td>\n",
       "      <td>18.016313</td>\n",
       "      <td>27.56373</td>\n",
       "      <td>41312</td>\n",
       "      <td>117.3755</td>\n",
       "      <td>81.5</td>\n",
       "      <td>5.2</td>\n",
       "      <td>East Asia &amp; Pacific</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>3350832</td>\n",
       "      <td>2.11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.489764</td>\n",
       "      <td>26.39123</td>\n",
       "      <td>15317</td>\n",
       "      <td>124.2604</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>26952719</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.476669</td>\n",
       "      <td>25.32054</td>\n",
       "      <td>3733</td>\n",
       "      <td>124.3462</td>\n",
       "      <td>68.7</td>\n",
       "      <td>49.2</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>86589342</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.479347</td>\n",
       "      <td>20.91630</td>\n",
       "      <td>4085</td>\n",
       "      <td>121.9367</td>\n",
       "      <td>75.4</td>\n",
       "      <td>26.2</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>13114579</td>\n",
       "      <td>5.88</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.148982</td>\n",
       "      <td>20.68321</td>\n",
       "      <td>3039</td>\n",
       "      <td>132.4493</td>\n",
       "      <td>52.0</td>\n",
       "      <td>94.9</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>13495462</td>\n",
       "      <td>3.85</td>\n",
       "      <td>15.1</td>\n",
       "      <td>0.654323</td>\n",
       "      <td>22.02660</td>\n",
       "      <td>1286</td>\n",
       "      <td>131.9745</td>\n",
       "      <td>49.0</td>\n",
       "      <td>98.3</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>139 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     population  fertility   HIV        CO2  BMI_male    GDP  BMI_female  \\\n",
       "0      34811059       2.73   0.1   3.328945  24.59620  12314    129.9049   \n",
       "1      19842251       6.43   2.0   1.474353  22.25083   7103    130.1247   \n",
       "2      40381860       2.24   0.5   4.785170  27.50170  14646    118.8915   \n",
       "3       2975029       1.40   0.1   1.804106  25.35542   7383    132.8108   \n",
       "4      21370348       1.96   0.1  18.016313  27.56373  41312    117.3755   \n",
       "..          ...        ...   ...        ...       ...    ...         ...   \n",
       "134     3350832       2.11   0.5   2.489764  26.39123  15317    124.2604   \n",
       "135    26952719       2.46   0.1   4.476669  25.32054   3733    124.3462   \n",
       "136    86589342       1.86   0.4   1.479347  20.91630   4085    121.9367   \n",
       "137    13114579       5.88  13.6   0.148982  20.68321   3039    132.4493   \n",
       "138    13495462       3.85  15.1   0.654323  22.02660   1286    131.9745   \n",
       "\n",
       "     life  child_mortality                      Region  \n",
       "0    75.3             29.5  Middle East & North Africa  \n",
       "1    58.3            192.0          Sub-Saharan Africa  \n",
       "2    75.5             15.4                     America  \n",
       "3    72.5             20.0       Europe & Central Asia  \n",
       "4    81.5              5.2         East Asia & Pacific  \n",
       "..    ...              ...                         ...  \n",
       "134  76.0             13.0                     America  \n",
       "135  68.7             49.2       Europe & Central Asia  \n",
       "136  75.4             26.2         East Asia & Pacific  \n",
       "137  52.0             94.9          Sub-Saharan Africa  \n",
       "138  49.0             98.3          Sub-Saharan Africa  \n",
       "\n",
       "[139 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importando Base\n",
    "gapminder_df = pd.read_csv('datacamp_workspace_export_2022-03-29 11_09_13.csv')\n",
    "gapminder_df = gapminder_df.drop('index', axis = 1)\n",
    "display(gapminder_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Precisaremos tratar uma coluna não numérica, 'Region'\n",
    "- Normalmente Scikit-learn não aceita features categóricas, é necessário uma etapa de encode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise Inicial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region             0\n",
       "child_mortality    0\n",
       "life               0\n",
       "BMI_female         0\n",
       "GDP                0\n",
       "BMI_male           0\n",
       "CO2                0\n",
       "HIV                0\n",
       "fertility          0\n",
       "population         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder_df.isnull().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 139 entries, 0 to 138\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   population       139 non-null    int64  \n",
      " 1   fertility        139 non-null    float64\n",
      " 2   HIV              139 non-null    float64\n",
      " 3   CO2              139 non-null    float64\n",
      " 4   BMI_male         139 non-null    float64\n",
      " 5   GDP              139 non-null    int64  \n",
      " 6   BMI_female       139 non-null    float64\n",
      " 7   life             139 non-null    float64\n",
      " 8   child_mortality  139 non-null    float64\n",
      " 9   Region           139 non-null    object \n",
      "dtypes: float64(7), int64(2), object(1)\n",
      "memory usage: 11.0+ KB\n"
     ]
    }
   ],
   "source": [
    "gapminder_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Aparentemente não existem dados nulos\n",
    "- A coluna 'Region' precisa ser transformada em uma coluna numérica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot\n",
    "gapminder_df.boxplot('life', 'Region', rot=60)\n",
    "\n",
    "# Plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing\n",
    "### Encoding\n",
    "- Esse tipo de pré-processamento é utilizado quando estamos trabalhando com colunas textuais\n",
    "- O processo de encoding cria para cada categoria da coluna textual uma nova coluna, veja:\n",
    "![image.png](attachment:image.png)\n",
    "- É necessário tomar cuidado quando temos muitas categorias! Isso implica na criação de muitas outras colunas... Isso pode ser prejudicial para o modelo (pode deixar ele lento por exemplo)\n",
    "- Opções para diminuirmos o número de categorias:\n",
    "    1. Excluir a primeira coluna com o parâmetro 'drop_first'\n",
    "    \n",
    "    Ela é considerada 'informação repetida', se classificamos as amostras com todas as categorias menos uma, então os pontos amostrais que não foram classificados em nenhuma categoria são extamente dessa categoria que foi excluída! Sabemos implicitamente seus valores, sendo assim uma 'informação repetida'. Veja:\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "    2. Agrupar categorias com poucas aparições em 'Outras'\n",
    "    \n",
    "    Nessa abordagem vamos diminuir o número de categorias juntando aparições pouco relevantes em uma nova categoria\n",
    "    \n",
    "    \n",
    "- Para realizar o processo de encoding temos 2 opções mais comuns:\n",
    "    1. scikit-learn: OneHotEncoder()\n",
    "    2. pandas: get_dummies()\n",
    "    \n",
    "    \n",
    "- Como ja estamos trabalhando com a biblioteca pandas usarei o comando 'get_dummies()'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['population', 'fertility', 'HIV', 'CO2', 'BMI_male', 'GDP',\n",
      "       'BMI_female', 'life', 'child_mortality', 'Region_America',\n",
      "       'Region_East Asia & Pacific', 'Region_Europe & Central Asia',\n",
      "       'Region_Middle East & North Africa', 'Region_South Asia',\n",
      "       'Region_Sub-Saharan Africa'],\n",
      "      dtype='object')\n",
      "Index(['population', 'fertility', 'HIV', 'CO2', 'BMI_male', 'GDP',\n",
      "       'BMI_female', 'life', 'child_mortality', 'Region_East Asia & Pacific',\n",
      "       'Region_Europe & Central Asia', 'Region_Middle East & North Africa',\n",
      "       'Region_South Asia', 'Region_Sub-Saharan Africa'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Dummy variables\n",
    "df_region1 = pd.get_dummies(gapminder_df)\n",
    "\n",
    "print(df_region1.columns)\n",
    "\n",
    "# Dummy variables descartando a primeira coluna\n",
    "df_region2 = pd.get_dummies(gapminder_df, drop_first=True)\n",
    "\n",
    "print(df_region2.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Observe o parâmetro 'drop_first'\n",
    "- Quando fazemos o encoding de uma coluna categórica com por exemplo 4 categorias, nesse caso seriam criadas 4 outras colunas pela função 'get_dummies'. Mas pense comigo, uma dessas colunas é desnecessária. Quando você sabe que existem 4 opções e sei que uma categoria não é nenhuma das outras 3, então ela só poderia ser a 4! Nesse caso não é necessário passar as 4 colunas de encoding, por isso excluímos 1 delas pois não traz informações novas para nossa base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Region_America'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coluna_excluida = df_region1.columns.difference(df_region2.columns)\n",
    "coluna_excluida"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Veja que a coluna excluída foi a 'Region_America'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Treinamento Modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Separando as valores independentes em X e o target em y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_region2.copy()\n",
    "\n",
    "X = df.drop('life', axis = 1)\n",
    "y = df['life']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Como só fizemos o processo de encoding não há problemas em fazer o pré-processamento antes de separar os dados em treino e teste (Considerando que nunca vamos ter outra classe nova da coluna 'Region')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8345330688799243\n",
      "[0.86808336 0.80623545 0.84004203 0.7754344  0.87503712]\n"
     ]
    }
   ],
   "source": [
    "# Gerando Modelo\n",
    "ridge = Ridge(alpha = 0.5, normalize = True)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3, random_state=42)\n",
    "\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "print(ridge.score(X_test, y_test))\n",
    "\n",
    "# Cross-validation\n",
    "ridge_cv = cross_val_score(ridge, X, y, cv = 5)\n",
    "\n",
    "# Cross-validated scores\n",
    "print(ridge_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Voting Dataset\n",
    "# Compreensão Base\n",
    "- Vou dar maior atenção em quais tipos de pré-processamento são necessários\n",
    "### Visualização Inicial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>infants</th>\n",
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       "      <th>salvador</th>\n",
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       "      <th>aid</th>\n",
       "      <th>missile</th>\n",
       "      <th>immigration</th>\n",
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       "      <th>education</th>\n",
       "      <th>superfund</th>\n",
       "      <th>crime</th>\n",
       "      <th>duty_free_exports</th>\n",
       "      <th>eaa_rsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>republican</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>?</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>republican</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
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       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>democrat</td>\n",
       "      <td>?</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>?</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>democrat</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
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       "      <td>y</td>\n",
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       "      <th>4</th>\n",
       "      <td>democrat</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
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       "      <td>y</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>republican</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
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       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>democrat</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
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       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>republican</td>\n",
       "      <td>n</td>\n",
       "      <td>?</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
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       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
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       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>republican</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
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       "      <td>y</td>\n",
       "      <td>y</td>\n",
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       "      <td>n</td>\n",
       "      <td>y</td>\n",
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       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>republican</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>?</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          index infants water budget physician salvador religious satellite  \\\n",
       "0    republican       n     y      n         y        y         y         n   \n",
       "1    republican       n     y      n         y        y         y         n   \n",
       "2      democrat       ?     y      y         ?        y         y         n   \n",
       "3      democrat       n     y      y         n        ?         y         n   \n",
       "4      democrat       y     y      y         n        y         y         n   \n",
       "..          ...     ...   ...    ...       ...      ...       ...       ...   \n",
       "430  republican       n     n      y         y        y         y         n   \n",
       "431    democrat       n     n      y         n        n         n         y   \n",
       "432  republican       n     ?      n         y        y         y         n   \n",
       "433  republican       n     n      n         y        y         y         ?   \n",
       "434  republican       n     y      n         y        y         y         n   \n",
       "\n",
       "    aid missile immigration synfuels education superfund crime  \\\n",
       "0     n       n           y        ?         y         y     y   \n",
       "1     n       n           n        n         y         y     y   \n",
       "2     n       n           n        y         n         y     y   \n",
       "3     n       n           n        y         n         y     n   \n",
       "4     n       n           n        y         ?         y     y   \n",
       "..   ..     ...         ...      ...       ...       ...   ...   \n",
       "430   n       y           y        n         y         y     y   \n",
       "431   y       y           y        n         n         n     n   \n",
       "432   n       n           n        y         y         y     y   \n",
       "433   ?       ?           ?        n         y         y     y   \n",
       "434   n       n           y        n         y         y     y   \n",
       "\n",
       "    duty_free_exports eaa_rsa  \n",
       "0                   n       y  \n",
       "1                   n       ?  \n",
       "2                   n       n  \n",
       "3                   n       y  \n",
       "4                   y       y  \n",
       "..                ...     ...  \n",
       "430                 n       y  \n",
       "431                 n       y  \n",
       "432                 n       y  \n",
       "433                 n       y  \n",
       "434                 ?       n  \n",
       "\n",
       "[435 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importando Base\n",
    "votes = pd.read_csv('datacamp_workspace_export_2022-03-29 12_16_04.csv')\n",
    "display(votes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Precisaremos trabalhar com a substituição de caractéres especiais '?'\n",
    "- Precisaremos redefinir a classificação y/n para numérica 1/0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise Inicial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eaa_rsa              0\n",
       "satellite            0\n",
       "infants              0\n",
       "water                0\n",
       "budget               0\n",
       "physician            0\n",
       "salvador             0\n",
       "religious            0\n",
       "aid                  0\n",
       "duty_free_exports    0\n",
       "missile              0\n",
       "immigration          0\n",
       "synfuels             0\n",
       "education            0\n",
       "superfund            0\n",
       "crime                0\n",
       "index                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes.isnull().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Aparentemente não há dados vazios, mas sabemos que alguns valores estão como '?'\n",
    "- Veja, por exemplo, na coluna 'education'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n    233\n",
       "y    171\n",
       "?     31\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes['education'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Existem várias formas de mascarar 'missing values'. Podemos colocá-los como números absurdos como 9999 ou mesmo 0 em categorias em que seria impossível ter esse valor, como por exemplo peso. Outros algarismos podem fazer esse papel como #, @, $ e vários outros "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Data\n",
    "- Um ponto que não dei tanta atenção em trabalhos passados foi em relação a missing data\n",
    "- Normalmente sempre avaliei quando haviam fatores não preenchidos (null values / nan)\n",
    "- Porém essa não é a única forma de se mostrar valores faltando, eles podem estar mascarados!\n",
    "- Uma possibilidade é possuírem valor 0 quando na verdade essa medida seria impossível, como por exemplo: Idade, peso, altura etc.\n",
    "- Vamos agora explorar mais afundo sobre esse tema e como agir em determinadas situações"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Claramente existem 2 problemas:\n",
    "    1. Apesar de não termos valores faltantes temos valores representados por '?'\n",
    "    2. As informações 'n' e 'y' precisam ser transformadas em informações numéricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "votes1 = votes.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituindo os '?' por NaN\n",
    "votes1[votes1 == '?'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>democrat</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>republican</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>republican</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>republican</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          index infants water budget physician salvador religious satellite  \\\n",
       "0    republican       n     y      n         y        y         y         n   \n",
       "1    republican       n     y      n         y        y         y         n   \n",
       "2      democrat     NaN     y      y       NaN        y         y         n   \n",
       "3      democrat       n     y      y         n      NaN         y         n   \n",
       "4      democrat       y     y      y         n        y         y         n   \n",
       "..          ...     ...   ...    ...       ...      ...       ...       ...   \n",
       "430  republican       n     n      y         y        y         y         n   \n",
       "431    democrat       n     n      y         n        n         n         y   \n",
       "432  republican       n   NaN      n         y        y         y         n   \n",
       "433  republican       n     n      n         y        y         y       NaN   \n",
       "434  republican       n     y      n         y        y         y         n   \n",
       "\n",
       "     aid missile immigration synfuels education superfund crime  \\\n",
       "0      n       n           y      NaN         y         y     y   \n",
       "1      n       n           n        n         y         y     y   \n",
       "2      n       n           n        y         n         y     y   \n",
       "3      n       n           n        y         n         y     n   \n",
       "4      n       n           n        y       NaN         y     y   \n",
       "..   ...     ...         ...      ...       ...       ...   ...   \n",
       "430    n       y           y        n         y         y     y   \n",
       "431    y       y           y        n         n         n     n   \n",
       "432    n       n           n        y         y         y     y   \n",
       "433  NaN     NaN         NaN        n         y         y     y   \n",
       "434    n       n           y        n         y         y     y   \n",
       "\n",
       "    duty_free_exports eaa_rsa  \n",
       "0                   n       y  \n",
       "1                   n     NaN  \n",
       "2                   n       n  \n",
       "3                   n       y  \n",
       "4                   y       y  \n",
       "..                ...     ...  \n",
       "430                 n       y  \n",
       "431                 n       y  \n",
       "432                 n       y  \n",
       "433                 n       y  \n",
       "434               NaN       n  \n",
       "\n",
       "[435 rows x 17 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Veja que o caractér '?' está como NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eaa_rsa              104\n",
       "water                 48\n",
       "education             31\n",
       "duty_free_exports     28\n",
       "superfund             25\n",
       "missile               22\n",
       "synfuels              21\n",
       "crime                 17\n",
       "salvador              15\n",
       "aid                   15\n",
       "satellite             14\n",
       "infants               12\n",
       "religious             11\n",
       "physician             11\n",
       "budget                11\n",
       "immigration            7\n",
       "index                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes1.isnull().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Temos vários valores faltantes agora\n",
    "- Como tratá-los?\n",
    "- Uma alternativa é excluir esses valores, vamos avaliar esse caminho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(435, 17)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "votes1 = votes1.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(232, 17)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Observe que ao excluir as linhas que continham valores 'NaN' perdemos uma parcela expressiva dos nossos dados\n",
    "- Isso é um grande problema, precisamos de outro caminho, vamos avaliar isso posteriormente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>democrat</td>\n",
       "      <td>?</td>\n",
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       "      <th>...</th>\n",
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       "      <th>430</th>\n",
       "      <td>republican</td>\n",
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       "      <td>republican</td>\n",
       "      <td>n</td>\n",
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       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>republican</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>?</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          index infants water budget physician salvador religious satellite  \\\n",
       "0    republican       n     y      n         y        y         y         n   \n",
       "1    republican       n     y      n         y        y         y         n   \n",
       "2      democrat       ?     y      y         ?        y         y         n   \n",
       "3      democrat       n     y      y         n        ?         y         n   \n",
       "4      democrat       y     y      y         n        y         y         n   \n",
       "..          ...     ...   ...    ...       ...      ...       ...       ...   \n",
       "430  republican       n     n      y         y        y         y         n   \n",
       "431    democrat       n     n      y         n        n         n         y   \n",
       "432  republican       n     ?      n         y        y         y         n   \n",
       "433  republican       n     n      n         y        y         y         ?   \n",
       "434  republican       n     y      n         y        y         y         n   \n",
       "\n",
       "    aid missile immigration synfuels education superfund crime  \\\n",
       "0     n       n           y        ?         y         y     y   \n",
       "1     n       n           n        n         y         y     y   \n",
       "2     n       n           n        y         n         y     y   \n",
       "3     n       n           n        y         n         y     n   \n",
       "4     n       n           n        y         ?         y     y   \n",
       "..   ..     ...         ...      ...       ...       ...   ...   \n",
       "430   n       y           y        n         y         y     y   \n",
       "431   y       y           y        n         n         n     n   \n",
       "432   n       n           n        y         y         y     y   \n",
       "433   ?       ?           ?        n         y         y     y   \n",
       "434   n       n           y        n         y         y     y   \n",
       "\n",
       "    duty_free_exports eaa_rsa  \n",
       "0                   n       y  \n",
       "1                   n       ?  \n",
       "2                   n       n  \n",
       "3                   n       y  \n",
       "4                   y       y  \n",
       "..                ...     ...  \n",
       "430                 n       y  \n",
       "431                 n       y  \n",
       "432                 n       y  \n",
       "433                 n       y  \n",
       "434                 ?       n  \n",
       "\n",
       "[435 rows x 17 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding\n",
    "- Transformar y/n em 1/0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Precisamos fazer o encoding\n",
    "for coluna in votes.columns:\n",
    "    votes.loc[votes[coluna]=='y', coluna] = 1\n",
    "    votes.loc[votes[coluna]=='n', coluna] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "votes[votes == '?'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>infants</th>\n",
       "      <th>water</th>\n",
       "      <th>budget</th>\n",
       "      <th>physician</th>\n",
       "      <th>salvador</th>\n",
       "      <th>religious</th>\n",
       "      <th>satellite</th>\n",
       "      <th>aid</th>\n",
       "      <th>missile</th>\n",
       "      <th>immigration</th>\n",
       "      <th>synfuels</th>\n",
       "      <th>education</th>\n",
       "      <th>superfund</th>\n",
       "      <th>crime</th>\n",
       "      <th>duty_free_exports</th>\n",
       "      <th>eaa_rsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>republican</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>republican</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>democrat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>democrat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>democrat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>republican</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>democrat</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>republican</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>republican</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>republican</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          index infants water budget physician salvador religious satellite  \\\n",
       "0    republican       0     1      0         1        1         1         0   \n",
       "1    republican       0     1      0         1        1         1         0   \n",
       "2      democrat     NaN     1      1       NaN        1         1         0   \n",
       "3      democrat       0     1      1         0      NaN         1         0   \n",
       "4      democrat       1     1      1         0        1         1         0   \n",
       "..          ...     ...   ...    ...       ...      ...       ...       ...   \n",
       "430  republican       0     0      1         1        1         1         0   \n",
       "431    democrat       0     0      1         0        0         0         1   \n",
       "432  republican       0   NaN      0         1        1         1         0   \n",
       "433  republican       0     0      0         1        1         1       NaN   \n",
       "434  republican       0     1      0         1        1         1         0   \n",
       "\n",
       "     aid missile immigration synfuels education superfund crime  \\\n",
       "0      0       0           1      NaN         1         1     1   \n",
       "1      0       0           0        0         1         1     1   \n",
       "2      0       0           0        1         0         1     1   \n",
       "3      0       0           0        1         0         1     0   \n",
       "4      0       0           0        1       NaN         1     1   \n",
       "..   ...     ...         ...      ...       ...       ...   ...   \n",
       "430    0       1           1        0         1         1     1   \n",
       "431    1       1           1        0         0         0     0   \n",
       "432    0       0           0        1         1         1     1   \n",
       "433  NaN     NaN         NaN        0         1         1     1   \n",
       "434    0       0           1        0         1         1     1   \n",
       "\n",
       "    duty_free_exports eaa_rsa  \n",
       "0                   0       1  \n",
       "1                   0     NaN  \n",
       "2                   0       0  \n",
       "3                   0       1  \n",
       "4                   1       1  \n",
       "..                ...     ...  \n",
       "430                 0       1  \n",
       "431                 0       1  \n",
       "432                 0       1  \n",
       "433                 0       1  \n",
       "434               NaN       0  \n",
       "\n",
       "[435 rows x 17 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratoty Data Analysis (EDA)\n",
    "- Vamos avaliar especificamente 3 colunas:\n",
    "    1. Education\n",
    "    2. Crime\n",
    "    3. Religious\n",
    "\n",
    "Queremos ver as diferenças de comportamento entre os dois partidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,5))\n",
    "x = ['education', 'crime', 'religious']\n",
    "for i, nome in enumerate(x):\n",
    "    plt.subplot(1,3,i+1)\n",
    "    sns.countplot(x=nome, hue='index', data=votes, palette='RdBu')\n",
    "\n",
    "    # Mudando a legenda do eixo x\n",
    "    # O parâmetro [0,1] é a posição da legenda 'Não' e 'Sim'\n",
    "    plt.xticks([0,1], ['Não', 'Sim'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- No geral os republicanos são mais homogêneos quando vão votar, não foi observado votos divididos entre os partidos\n",
    "- No caso dos democratas, tanto no segundo como no terceiro gráfico houve muitos votos tanto positivos como negativos. \n",
    "- Dessa forma, seria possível supor que dentro dos democratas ainda existe muitas diferenças de opiniões na hora de votar "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputing missing data in a ML Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline\n",
    "- https://medium.com/analytics-vidhya/what-is-a-pipeline-in-machine-learning-how-to-create-one-bda91d0ceaca#\n",
    "- Sequência de passos que tem como objeto organizar as etapas em um modelo de machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Vamos avaliar uma forma de preencher os valores 'NaN'\n",
    "- Uma alternativa é preencher com a média"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp1 = SimpleImputer(missing_values=np.nan, strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = votes.drop('index', axis = 1)\n",
    "y = votes['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1 = X.copy()\n",
    "imp1.fit(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = imp1.transform(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.        , 0.        , ..., 1.        , 0.        ,\n",
       "        1.        ],\n",
       "       [0.        , 1.        , 0.        , ..., 1.        , 0.        ,\n",
       "        0.81268882],\n",
       "       [0.44208038, 1.        , 1.        , ..., 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.50387597, 0.        , ..., 1.        , 0.        ,\n",
       "        1.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 1.        , 0.        ,\n",
       "        1.        ],\n",
       "       [0.        , 1.        , 0.        , ..., 1.        , 0.42751843,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Veja que para treinar o modelo antes precisamos tratar os dados!\n",
    "- Os dados foram preenchidos com a média\n",
    "- Mas como estamos trabalhando com valores 0 e 1 talvez essa não seja a melhor saída\n",
    "- Vamos agora substituir com o valor mais frequente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp2 = SimpleImputer(missing_values=np.nan, strategy='most_frequent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='most_frequent')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2 = X.copy()\n",
    "imp2.fit(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2 = imp2.transform(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, ..., 1, 0, 1],\n",
       "       [0, 1, 0, ..., 1, 0, 1],\n",
       "       [0, 1, 1, ..., 1, 0, 0],\n",
       "       ...,\n",
       "       [0, 1, 0, ..., 1, 0, 1],\n",
       "       [0, 0, 0, ..., 1, 0, 1],\n",
       "       [0, 1, 0, ..., 1, 0, 0]], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Veja que agora só temos valores 0 e 1, o que faz mais sentido para esse caso\n",
    "- E se nós conseguíssemos 'imbutir' essa etapa de tratamento dentro do nosso modelo?\n",
    "- É exatamente isso que vamos tentar fazer!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pìpeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9618320610687023"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando o modelo\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "# Definindo uma sequência lógica\n",
    "# Primeiro substituímos todos os valores np.nan pela média --> comando imp\n",
    "# Segundo fazemos a regressão logística\n",
    "# Steps representa essa sequência de açoes\n",
    "steps = [('imputation', imp2),\n",
    "         ('logistic_regression', logreg)]\n",
    "\n",
    "# Definimos nossa Pipeline com essa sequência de ações\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "# Separamos os dados\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3, random_state=42)\n",
    "\n",
    "# Usamos a pipeline exatamente como se fosse um modelo de Machine Learning que importamos\n",
    "# Treinamos o modelo\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Previsão com dados nunca vistos\n",
    "y_pred = pipeline.predict(X_test)\n",
    "\n",
    "# Métrica\n",
    "pipeline.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wine Dataset\n",
    "# Compreensão Base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "    }\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>pregnancies</th>\n",
       "      <th>glucose</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>triceps</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>dpf</th>\n",
       "      <th>age</th>\n",
       "      <th>diabetes</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",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pregnancies  glucose  diastolic  triceps  insulin   bmi    dpf  age  \\\n",
       "0              6      148         72       35        0  33.6  0.627   50   \n",
       "1              1       85         66       29        0  26.6  0.351   31   \n",
       "2              8      183         64        0        0  23.3  0.672   32   \n",
       "3              1       89         66       23       94  28.1  0.167   21   \n",
       "4              0      137         40       35      168  43.1  2.288   33   \n",
       "..           ...      ...        ...      ...      ...   ...    ...  ...   \n",
       "763           10      101         76       48      180  32.9  0.171   63   \n",
       "764            2      122         70       27        0  36.8  0.340   27   \n",
       "765            5      121         72       23      112  26.2  0.245   30   \n",
       "766            1      126         60        0        0  30.1  0.349   47   \n",
       "767            1       93         70       31        0  30.4  0.315   23   \n",
       "\n",
       "     diabetes  \n",
       "0           1  \n",
       "1           0  \n",
       "2           1  \n",
       "3           0  \n",
       "4           1  \n",
       "..        ...  \n",
       "763         0  \n",
       "764         0  \n",
       "765         0  \n",
       "766         1  \n",
       "767         0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df = pd.read_csv('datacamp_workspace_export_2022-03-29 17_20_37.csv')\n",
    "wine_df = wine_df.drop('index', axis = 1)\n",
    "display(wine_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Aparentemente só temos colunas numéricas\n",
    "### Análise Inicial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diabetes       0\n",
       "age            0\n",
       "dpf            0\n",
       "bmi            0\n",
       "insulin        0\n",
       "triceps        0\n",
       "diastolic      0\n",
       "glucose        0\n",
       "pregnancies    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.isnull().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Apesar de não haver valores vazios, existem valores nulos\n",
    "- Valores '0' nas colunas 'glucose', 'diastolic', 'triceps', 'insulin', 'bmi', 'dpf', 'age' são equivalentes a valores nulos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>glucose</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>triceps</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>dpf</th>\n",
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       "      <th>diabetes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
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       "      <td>26.6</td>\n",
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       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pregnancies  glucose  diastolic  triceps  insulin   bmi    dpf  age  \\\n",
       "0              6    148.0       72.0     35.0      NaN  33.6  0.627   50   \n",
       "1              1     85.0       66.0     29.0      NaN  26.6  0.351   31   \n",
       "2              8    183.0       64.0      NaN      NaN  23.3  0.672   32   \n",
       "3              1     89.0       66.0     23.0     94.0  28.1  0.167   21   \n",
       "4              0    137.0       40.0     35.0    168.0  43.1  2.288   33   \n",
       "..           ...      ...        ...      ...      ...   ...    ...  ...   \n",
       "763           10    101.0       76.0     48.0    180.0  32.9  0.171   63   \n",
       "764            2    122.0       70.0     27.0      NaN  36.8  0.340   27   \n",
       "765            5    121.0       72.0     23.0    112.0  26.2  0.245   30   \n",
       "766            1    126.0       60.0      NaN      NaN  30.1  0.349   47   \n",
       "767            1     93.0       70.0     31.0      NaN  30.4  0.315   23   \n",
       "\n",
       "     diabetes  \n",
       "0           1  \n",
       "1           0  \n",
       "2           1  \n",
       "3           0  \n",
       "4           1  \n",
       "..        ...  \n",
       "763         0  \n",
       "764         0  \n",
       "765         0  \n",
       "766         1  \n",
       "767         0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colunas = ['glucose', 'diastolic', 'triceps', 'insulin', 'bmi', 'dpf', 'age']\n",
    "wine_df[wine_df[colunas] == 0] = np.nan\n",
    "wine_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Método 'replace'\n",
    "- Forma mais técnica de fazer a substituição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outra forma de substituir os valores em 0 por NaN seria usando o comando 'replace'\n",
    "\n",
    "#df.insulin.replace(0, np.nan, inplace=True)\n",
    "#df.triceps.replace(0, np.nan, inplace=True)\n",
    "#df.bmi.replace(0, np.nan, inplace=True)\n",
    "\n",
    "# Dessa forma os valores dessas 3 colunas seriam substituidos\n",
    "# O parâmetro 'inplace' = True aplica diretamente na base a substituição modificando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = wine_df.drop('diabetes', axis= 1)\n",
    "y = wine_df['diabetes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  6.   , 148.   ,  72.   , ...,  33.6  ,   0.627,  50.   ],\n",
       "       [  1.   ,  85.   ,  66.   , ...,  26.6  ,   0.351,  31.   ],\n",
       "       [  8.   , 183.   ,  64.   , ...,  23.3  ,   0.672,  32.   ],\n",
       "       ...,\n",
       "       [  5.   , 121.   ,  72.   , ...,  26.2  ,   0.245,  30.   ],\n",
       "       [  1.   , 126.   ,  60.   , ...,  30.1  ,   0.349,  47.   ],\n",
       "       [  1.   ,  93.   ,  70.   , ...,  30.4  ,   0.315,  23.   ]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "imp.fit(X)\n",
    "X = imp.transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Pronto, agora todos os valores foram corrigidos pela média"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputing Scalling data in a ML Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling the Data\n",
    "- Dados em escalas muito diferentes podem ter um impacto negativo no treino dos nossos modelos\n",
    "- Corrigimos isso através de métodos como padronização e normalização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled = scale(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.10110842140057\n",
      "61.90166864136243\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(X))\n",
    "print(np.std(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.9038590148845458e-16, 1.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(X_scaled), np.std(X_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Através do método 'scale' deixamos todos os dados com média 0 e desvio padrão 1\n",
    "- O comando 'scale' e o comando 'StandardScaler' fazem essencialmente a mesma operação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7207792207792207"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steps = [('scaler', StandardScaler()),\n",
    "         ('knn', KNeighborsClassifier())]\n",
    "\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2, random_state=21)\n",
    "                  \n",
    "knn_scaled = pipeline.fit(X_train, y_train)\n",
    "y_pred = pipeline.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6623376623376623"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_unscaled = KNeighborsClassifier().fit(X_train, y_train)\n",
    "knn_unscaled.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Vemos um resultado melhor para os dados com escala padronizada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline = Scalling + Hyperparameter tuning\n",
    "### Modelo SVM - SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7402597402597403\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.90      0.81        94\n",
      "           1       0.76      0.48      0.59        60\n",
      "\n",
      "    accuracy                           0.74       154\n",
      "   macro avg       0.75      0.69      0.70       154\n",
      "weighted avg       0.74      0.74      0.72       154\n",
      "\n",
      "Tuned Model Parameters: {'SVM__C': 1, 'SVM__gamma': 0.01}\n"
     ]
    }
   ],
   "source": [
    "# Definir Pipeline\n",
    "steps = [('scaler', StandardScaler()),\n",
    "         ('SVM', SVC())]\n",
    "\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "# Especificar intervalo dos hiperparametros\n",
    "parameters = {'SVM__C':[1, 10, 100],\n",
    "              'SVM__gamma':[0.1, 0.01]}\n",
    "\n",
    "# Split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 21)\n",
    "\n",
    "# GridSearchCV\n",
    "cv = GridSearchCV(pipeline, param_grid = parameters)\n",
    "\n",
    "# Treinar modelo\n",
    "cv.fit(X_train, y_train)\n",
    "\n",
    "# Previsão\n",
    "y_pred = cv.predict(X_test)\n",
    "\n",
    "# Métricas\n",
    "print(\"Accuracy: {}\".format(cv.score(X_test, y_test)))\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(\"Tuned Model Parameters: {}\".format(cv.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline = Missing Data + Scalling + Hyperparameter tuning\n",
    "### Importando Novamente a Base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "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>diastolic</th>\n",
       "      <th>triceps</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>dpf</th>\n",
       "      <th>age</th>\n",
       "      <th>diabetes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <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",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <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",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>766</th>\n",
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       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pregnancies  glucose  diastolic  triceps  insulin   bmi    dpf  age  \\\n",
       "0              6      148         72       35        0  33.6  0.627   50   \n",
       "1              1       85         66       29        0  26.6  0.351   31   \n",
       "2              8      183         64        0        0  23.3  0.672   32   \n",
       "3              1       89         66       23       94  28.1  0.167   21   \n",
       "4              0      137         40       35      168  43.1  2.288   33   \n",
       "..           ...      ...        ...      ...      ...   ...    ...  ...   \n",
       "763           10      101         76       48      180  32.9  0.171   63   \n",
       "764            2      122         70       27        0  36.8  0.340   27   \n",
       "765            5      121         72       23      112  26.2  0.245   30   \n",
       "766            1      126         60        0        0  30.1  0.349   47   \n",
       "767            1       93         70       31        0  30.4  0.315   23   \n",
       "\n",
       "     diabetes  \n",
       "0           1  \n",
       "1           0  \n",
       "2           1  \n",
       "3           0  \n",
       "4           1  \n",
       "..        ...  \n",
       "763         0  \n",
       "764         0  \n",
       "765         0  \n",
       "766         1  \n",
       "767         0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df = pd.read_csv('datacamp_workspace_export_2022-03-29 17_20_37.csv')\n",
    "wine_df = wine_df.drop('index', axis = 1)\n",
    "display(wine_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correção dos Valores 0 para NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>bmi</th>\n",
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       "      <td>70.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pregnancies  glucose  diastolic  triceps  insulin   bmi    dpf  age  \\\n",
       "0              6    148.0       72.0     35.0      NaN  33.6  0.627   50   \n",
       "1              1     85.0       66.0     29.0      NaN  26.6  0.351   31   \n",
       "2              8    183.0       64.0      NaN      NaN  23.3  0.672   32   \n",
       "3              1     89.0       66.0     23.0     94.0  28.1  0.167   21   \n",
       "4              0    137.0       40.0     35.0    168.0  43.1  2.288   33   \n",
       "..           ...      ...        ...      ...      ...   ...    ...  ...   \n",
       "763           10    101.0       76.0     48.0    180.0  32.9  0.171   63   \n",
       "764            2    122.0       70.0     27.0      NaN  36.8  0.340   27   \n",
       "765            5    121.0       72.0     23.0    112.0  26.2  0.245   30   \n",
       "766            1    126.0       60.0      NaN      NaN  30.1  0.349   47   \n",
       "767            1     93.0       70.0     31.0      NaN  30.4  0.315   23   \n",
       "\n",
       "     diabetes  \n",
       "0           1  \n",
       "1           0  \n",
       "2           1  \n",
       "3           0  \n",
       "4           1  \n",
       "..        ...  \n",
       "763         0  \n",
       "764         0  \n",
       "765         0  \n",
       "766         1  \n",
       "767         0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colunas = ['glucose', 'diastolic', 'triceps', 'insulin', 'bmi', 'dpf', 'age']\n",
    "wine_df[wine_df[colunas] == 0] = np.nan\n",
    "wine_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definindo Pipeline Completa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7402597402597403\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.90      0.81        94\n",
      "           1       0.76      0.48      0.59        60\n",
      "\n",
      "    accuracy                           0.74       154\n",
      "   macro avg       0.75      0.69      0.70       154\n",
      "weighted avg       0.74      0.74      0.72       154\n",
      "\n",
      "Tuned Model Parameters: {'SVM__C': 1, 'SVM__gamma': 0.01}\n"
     ]
    }
   ],
   "source": [
    "# Definir Pipeline\n",
    "steps = [('imputation', SimpleImputer(missing_values=np.nan, strategy='mean')),\n",
    "         ('scaler', StandardScaler()),\n",
    "         ('SVM', SVC())]\n",
    "\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "# Especificar intervalo dos hiperparametros\n",
    "parameters = {'SVM__C':[1, 10, 100],\n",
    "              'SVM__gamma':[0.1, 0.01]}\n",
    "\n",
    "# Split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 21)\n",
    "\n",
    "# GridSearchCV\n",
    "cv = GridSearchCV(pipeline, param_grid = parameters)\n",
    "\n",
    "# Treinar modelo\n",
    "cv.fit(X_train, y_train)\n",
    "\n",
    "# Previsão\n",
    "y_pred = cv.predict(X_test)\n",
    "\n",
    "# Métricas\n",
    "print(\"Accuracy: {}\".format(cv.score(X_test, y_test)))\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(\"Tuned Model Parameters: {}\".format(cv.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Veja que o resultado obtido foi exatamente o mesmo que fazendo a etapa do 'SimpleImputer' separada "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusão\n",
    "- Foram exploradas várias formas de pré-processamento\n",
    "    1. Colunas Categóricas: Normalmente é necessário transformar em informações testuais\n",
    "    2. Missing Data: Analisamos várias formas de identificar esse tidpo de dado, através de caractéres especiais, valores absurdos ou até mesmo de maneira mais direta quando identificando o valor como NaN\n",
    "    3. Scalling: Em muitos modelos a escala das features importa, caso as escalas entre as features sejam muito distintas isso deve ser corrigido\n",
    "- Também foi possível estudar de maneiras mais aprofundada como funciona a construção de pipelines, facilitando o workflow do modelo. Foi realizado pipelines incorporando desde o preenchimento de valores vazios e correção da escala até o treinamento do modelo associado ao processo de Hyperparameter tuning \n",
    "- Em todos os casos o pré-processamento teve impacto positivo nas métricas analisadas, mostrando assim sua importância"
   ]
  }
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