RC_MODEL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Previsão de resistência à compressão de concreto usando aprendizado de máquina\n",
    "\n",
    "![image](https://www.dsigners.net/wp-content/uploads/2019/10/image1.jpg)\n",
    "\n",
    "Determinar a resistência à compressão do concreto implica a determinar a qualidade do concreto, normalmente para se obter a medida dessa resistência é realizado um teste de laboratório em que um equipamento mede essa resistência.\n",
    "\n",
    ">Os problemas nesses testes são normalmente:\n",
    ">\n",
    ">   * O tempo de espera para a cura do material coletado que é por volta de 28 dias;\n",
    ">   * A probabilidade de erro humano durante o processo.\n",
    "    \n",
    "Uma forma de amenizar esses problemas é tentando atravéz de uma combinação de várias simulações digitais utilizando dados já obtidos de resistência tentar prever qual seria a resistência final em uma situação real para o menor tempo possivel utilizando algoritmos de aprendizado de máquina."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análise, limpeza e pré-processamento dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cement (component 1)(kg in a m^3 mixture)</th>\n",
       "      <th>Blast Furnace Slag (component 2)(kg in a m^3 mixture)</th>\n",
       "      <th>Fly Ash (component 3)(kg in a m^3 mixture)</th>\n",
       "      <th>Water  (component 4)(kg in a m^3 mixture)</th>\n",
       "      <th>Superplasticizer (component 5)(kg in a m^3 mixture)</th>\n",
       "      <th>Coarse Aggregate  (component 6)(kg in a m^3 mixture)</th>\n",
       "      <th>Fine Aggregate (component 7)(kg in a m^3 mixture)</th>\n",
       "      <th>Age (day)</th>\n",
       "      <th>Concrete compressive strength(MPa, megapascals)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>79.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>61.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>270</td>\n",
       "      <td>40.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>365</td>\n",
       "      <td>41.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198.6</td>\n",
       "      <td>132.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.4</td>\n",
       "      <td>825.5</td>\n",
       "      <td>360</td>\n",
       "      <td>44.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cement (component 1)(kg in a m^3 mixture)  \\\n",
       "0                                      540.0   \n",
       "1                                      540.0   \n",
       "2                                      332.5   \n",
       "3                                      332.5   \n",
       "4                                      198.6   \n",
       "\n",
       "   Blast Furnace Slag (component 2)(kg in a m^3 mixture)  \\\n",
       "0                                                0.0       \n",
       "1                                                0.0       \n",
       "2                                              142.5       \n",
       "3                                              142.5       \n",
       "4                                              132.4       \n",
       "\n",
       "   Fly Ash (component 3)(kg in a m^3 mixture)  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
       "4                                         0.0   \n",
       "\n",
       "   Water  (component 4)(kg in a m^3 mixture)  \\\n",
       "0                                      162.0   \n",
       "1                                      162.0   \n",
       "2                                      228.0   \n",
       "3                                      228.0   \n",
       "4                                      192.0   \n",
       "\n",
       "   Superplasticizer (component 5)(kg in a m^3 mixture)  \\\n",
       "0                                                2.5     \n",
       "1                                                2.5     \n",
       "2                                                0.0     \n",
       "3                                                0.0     \n",
       "4                                                0.0     \n",
       "\n",
       "   Coarse Aggregate  (component 6)(kg in a m^3 mixture)  \\\n",
       "0                                             1040.0      \n",
       "1                                             1055.0      \n",
       "2                                              932.0      \n",
       "3                                              932.0      \n",
       "4                                              978.4      \n",
       "\n",
       "   Fine Aggregate (component 7)(kg in a m^3 mixture)  Age (day)  \\\n",
       "0                                              676.0         28   \n",
       "1                                              676.0         28   \n",
       "2                                              594.0        270   \n",
       "3                                              594.0        365   \n",
       "4                                              825.5        360   \n",
       "\n",
       "   Concrete compressive strength(MPa, megapascals)   \n",
       "0                                             79.99  \n",
       "1                                             61.89  \n",
       "2                                             40.27  \n",
       "3                                             41.05  \n",
       "4                                             44.30  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importando biblioteca pandas para leitura dos dados\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#importando dataset\n",
    "df = pd.read_csv('Concrete_Data.csv')\n",
    "\n",
    "#imprimindo dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descrição dos dados utilizados:\n",
    "\n",
    "Este dataset contém 1030 itens com 9 classes de atributos e como vamos verificar mas na frente não possui valores nulos ou faltantes (_nullvalues, missigin_values_), ele possui 8 atributos de entrada para a o algoritmo e 1 de saida que no caso é :\n",
    "\n",
    "`Concrete compressive strenght(Mpa,megapascals)`. \n",
    "\n",
    "Sete dos valores são medidos em kg/m3 (Kilogramas por metro cúbico) o tempo de cura são representado em dias e a nossa variável target ou seja a variável que desejamos obter está em (MPa -- Mega Pascal)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rearranjo de nomes das colunas : Traduzindo o dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Cimento(kg/m3)', 'Brita(kg/m3)', 'Cinzas Volante(kg/m3)',\n",
       "       'Agua(kg/m3)', 'Superplastificante(kg/m3)', 'Agregado Grosso(kg/m3)',\n",
       "       'Agregado Fino(kg/m3)', 'Idade(dias)',\n",
       "       'Resistencia a Compressao do Concreto(MPa)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imprimindo o nome das colunas em inglês\n",
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alterando o nome das colunas:\n",
    "df.rename(columns={\"Cement (component 1)(kg in a m^3 mixture)\":\"Cimento(kg/m3)\",\n",
    "                   \"Blast Furnace Slag (component 2)(kg in a m^3 mixture)\":\"Brita(kg/m3)\",\n",
    "                   \"Fly Ash (component 3)(kg in a m^3 mixture)\":\"Cinzas Volante(kg/m3)\",\n",
    "                   \"Water  (component 4)(kg in a m^3 mixture)\":\"Agua(kg/m3)\",\n",
    "                   \"Superplasticizer (component 5)(kg in a m^3 mixture)\":\"Superplastificante(kg/m3)\",\n",
    "                   \"Coarse Aggregate  (component 6)(kg in a m^3 mixture)\":\"Agregado Grosso(kg/m3)\",\n",
    "                   \"Fine Aggregate (component 7)(kg in a m^3 mixture)\":\"Agregado Fino(kg/m3)\",\n",
    "                   \"Age (day)\":\"Idade(dias)\",\n",
    "                   \"Concrete compressive strength(MPa, megapascals) \":\"Resistencia a Compressao do Concreto(MPa)\"},\n",
    "         inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este passo apesar de simples é importante pois facilita muito na hora de trabalhar com os dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cimento(kg/m3)               0\n",
      "Brita(kg/m3)                 0\n",
      "Cinzas Volante(kg/m3)        0\n",
      "Agua(kg/m3)                  0\n",
      "Superplastificante(kg/m3)    0\n",
      "Agregado Grosso(kg/m3)       0\n",
      "Agregado Fino(kg/m3)         0\n",
      "Idade(dias)                  0\n",
      "fck(Mpa)                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Verificando dados nulos\n",
    "missing = df.isnull().sum()\n",
    "print(missing)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como dito anteriormente os dados não possuem nenhum item faltante.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1030 entries, 0 to 1029\n",
      "Data columns (total 9 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Cimento(kg/m3)             1030 non-null   float64\n",
      " 1   Brita(kg/m3)               1030 non-null   float64\n",
      " 2   Cinzas Volante(kg/m3)      1030 non-null   float64\n",
      " 3   Agua(kg/m3)                1030 non-null   float64\n",
      " 4   Superplastificante(kg/m3)  1030 non-null   float64\n",
      " 5   Agregado Grosso(kg/m3)     1030 non-null   float64\n",
      " 6   Agregado Fino(kg/m3)       1030 non-null   float64\n",
      " 7   Idade(dias)                1030 non-null   int64  \n",
      " 8   fck(Mpa)                   1030 non-null   float64\n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 72.6 KB\n"
     ]
    }
   ],
   "source": [
    "# Verificando o dtype de casa coluna\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análise exploratória dos dados\n",
    "\n",
    "O primeiro passo é analisar os dados no modo geral para obter insights antes mesmo de gerar qualquer modelo.\n",
    "\n",
    "A primeira análise geral é a de correlação dos dados, essa análise nos ajuda a entender como os dados estão relacionados com cada feature(classes de variáveis).\n",
    "\n",
    "Para entender melhor o coeficiente de correlação classifica as features entre si e retorna uma valor entre **-1 e 1**, e quanto mais proxima de zero menos relação há entre as duas variáveis.\n",
    "\n",
    "![image2](https://i0.wp.com/www.abgconsultoria.com.br/blog/wp-content/uploads/img2-2.png?w=1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Abreviando o nome da classe\n",
    "df.rename(columns={\"Resistencia a Compressao do Concreto(MPa)\":\"fck(Mpa)\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesse gráfico podemos observar uma alta correlação positiva entre concreto e a resistencia a compressão do concreto, oque de fato está correto, a resistencia realmente aumenta com o aumento de cimento.\n",
    "\n",
    "> Se verificarmos na literatura o traço do concreto é fortemente influenciado pela combinação de cimento, areia brita e água.\n",
    "\n",
    "O superplastificante também possui uma correlação positiva forte justamente por sua utilização ser para dar uma resistência maior,  diminuindo a quantidade de água no concreto.\n",
    "\n",
    ">\"Os aditivos superplastificantes são utilizados na mistura com o concreto para diminuir a quantidade de água, aumentando a sua resistência e fluidez.\n",
    ">\n",
    ">Quanto maior a quantidade de água aplicada ao concreto, menos resistente ele terá, o que poderá ocasionar fissuras que aparecerão durante o endurecimento da estrutura\".\n",
    "                                 \n",
    "\n",
    "Oque justifica a correlação negativa forte da água em relação a resistencia a compressão do concreto."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Representação visual de cada correlação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Isg_df = df[[\"fck(Mpa)\",\"Idade(dias)\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x17627c1ea50>"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "corr_1 = sns.pairplot(Isg_df, height=3.0, diag_kind=\"kde\")\n",
    "corr_1.map_lower(sns.kdeplot, color=\"red\", levels=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verificando Outliers\n",
    "\n",
    "\n",
    "### Oque são os Outliers?\n",
    "\n",
    "Em poucas palavras outliers são os pontos que se distanciam muito dentro dos conjunto de valores.\n",
    "\n",
    "Esses outliers podem acabar causando um desequilibrio nos resultados obtidos.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Verificando para cada uma das colunas\n",
    "nome_colunas = [\"Cimento(kg/m3)\",\"Brita(kg/m3)\",\"Cinzas Volante(kg/m3)\",\n",
    "                \"Agua(kg/m3)\",\"Superplastificante(kg/m3)\",\n",
    "                \"Agregado Grosso(kg/m3)\",\"Agregado Fino(kg/m3)\",\n",
    "                \"Idade(dias)\",\"Resistencia a Compressao do Concreto(MPa)\"]\n",
    "\n",
    "def boxplot_plot(dataset_name, nome_colunas):\n",
    "    df = dataset_name\n",
    "    for colunas in nome_colunas:\n",
    "        df.boxplot(column=colunas)\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=df[\"fck(Mpa)\"], color=\"coral\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = sns.boxplot (x=df[\"Idade(dias)\"], palette=\"deep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax2 = sns.boxplot(data=Isg_df, orient=\"h\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O concreto, a partir de estudos, atinge a resistência máxima aos 28 dias logo para eliminar esses outliers vamos remover os dados que contenham resistencias com mais de 30 dias.\n",
    "\n",
    "\n",
    "> REFERÊNCIA BIBLIOGRÁFICA: YAZIGI, Walid. A Técnica de Edificar – Editora Pini - 14ª Ed - Páginas 263 e 264."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Idade: 10 dias  -->  fck Médio: 22.31 MPa \n",
      "Idade: 30 dias  -->  fck Médio: 31.04 MPa \n",
      "Idade: 50 dias  -->  fck Médio: 31.04 MPa \n",
      "Idade: 70 dias  -->  fck Médio: 33.29 MPa \n",
      "Idade: 90 dias  -->  fck Médio: 33.73 MPa \n",
      "Idade: 110 dias  -->  fck Médio: 35.30 MPa \n",
      "Idade: 130 dias  -->  fck Médio: 35.31 MPa \n",
      "Idade: 150 dias  -->  fck Médio: 35.31 MPa \n",
      "Idade: 170 dias  -->  fck Médio: 35.31 MPa \n",
      "Idade: 190 dias  -->  fck Médio: 35.48 MPa \n",
      "Idade: 210 dias  -->  fck Médio: 35.48 MPa \n",
      "Idade: 230 dias  -->  fck Médio: 35.48 MPa \n",
      "Idade: 250 dias  -->  fck Médio: 35.48 MPa \n",
      "Idade: 270 dias  -->  fck Médio: 35.68 MPa \n",
      "Idade: 290 dias  -->  fck Médio: 35.68 MPa \n",
      "Idade: 310 dias  -->  fck Médio: 35.68 MPa \n",
      "Idade: 330 dias  -->  fck Médio: 35.68 MPa \n"
     ]
    }
   ],
   "source": [
    "## IMPORTA: Verificar se esse algoritmo ta realmente coletando a média\n",
    "## entre os intervalos solicitados.\n",
    "\n",
    "seq_val = np.arange(10,350,20)\n",
    "\n",
    "t_idade = []\n",
    "results = []\n",
    "qnt = []\n",
    "for values in seq_val:\n",
    "    new_df = df.loc[df[\"Idade(dias)\"] <= values]\n",
    "    results.append(new_df[\"fck(Mpa)\"].mean())\n",
    "    qnt.append(len(df[\"Idade(dias)\"]))\n",
    "    t_idade.append(values)\n",
    "\n",
    "for value, result in zip(seq_val, results):\n",
    "    print(f'Idade: {value} dias  -->  fck Médio: {result:.2f} MPa ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_d18a6_row6_col1, #T_d18a6_row7_col1, #T_d18a6_row8_col1 {\n",
       "  background-color: green;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d18a6\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d18a6_level0_col0\" class=\"col_heading level0 col0\" >dias</th>\n",
       "      <th id=\"T_d18a6_level0_col1\" class=\"col_heading level0 col1\" >fck Médio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_d18a6_row0_col0\" class=\"data row0 col0\" >10</td>\n",
       "      <td id=\"T_d18a6_row0_col1\" class=\"data row0 col1\" >22.308397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_d18a6_row1_col0\" class=\"data row1 col0\" >30</td>\n",
       "      <td id=\"T_d18a6_row1_col1\" class=\"data row1 col1\" >31.035407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_d18a6_row2_col0\" class=\"data row2 col0\" >50</td>\n",
       "      <td id=\"T_d18a6_row2_col1\" class=\"data row2 col1\" >31.035407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_d18a6_row3_col0\" class=\"data row3 col0\" >70</td>\n",
       "      <td id=\"T_d18a6_row3_col1\" class=\"data row3 col1\" >33.294679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_d18a6_row4_col0\" class=\"data row4 col0\" >90</td>\n",
       "      <td id=\"T_d18a6_row4_col1\" class=\"data row4 col1\" >33.728714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_d18a6_row5_col0\" class=\"data row5 col0\" >110</td>\n",
       "      <td id=\"T_d18a6_row5_col1\" class=\"data row5 col1\" >35.297562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_d18a6_row6_col0\" class=\"data row6 col0\" >130</td>\n",
       "      <td id=\"T_d18a6_row6_col1\" class=\"data row6 col1\" >35.310999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_d18a6_row7_col0\" class=\"data row7 col0\" >150</td>\n",
       "      <td id=\"T_d18a6_row7_col1\" class=\"data row7 col1\" >35.310999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_d18a6_row8_col0\" class=\"data row8 col0\" >170</td>\n",
       "      <td id=\"T_d18a6_row8_col1\" class=\"data row8 col1\" >35.310999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_d18a6_row9_col0\" class=\"data row9 col0\" >190</td>\n",
       "      <td id=\"T_d18a6_row9_col1\" class=\"data row9 col1\" >35.478405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_d18a6_row10_col0\" class=\"data row10 col0\" >210</td>\n",
       "      <td id=\"T_d18a6_row10_col1\" class=\"data row10 col1\" >35.478405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_d18a6_row11_col0\" class=\"data row11 col0\" >230</td>\n",
       "      <td id=\"T_d18a6_row11_col1\" class=\"data row11 col1\" >35.478405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_d18a6_row12_col0\" class=\"data row12 col0\" >250</td>\n",
       "      <td id=\"T_d18a6_row12_col1\" class=\"data row12 col1\" >35.478405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_d18a6_row13_col0\" class=\"data row13 col0\" >270</td>\n",
       "      <td id=\"T_d18a6_row13_col1\" class=\"data row13 col1\" >35.681693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_d18a6_row14_col0\" class=\"data row14 col0\" >290</td>\n",
       "      <td id=\"T_d18a6_row14_col1\" class=\"data row14 col1\" >35.681693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_d18a6_row15_col0\" class=\"data row15 col0\" >310</td>\n",
       "      <td id=\"T_d18a6_row15_col1\" class=\"data row15 col1\" >35.681693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d18a6_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_d18a6_row16_col0\" class=\"data row16 col0\" >330</td>\n",
       "      <td id=\"T_d18a6_row16_col1\" class=\"data row16 col1\" >35.681693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17629144bd0>"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "mini_df = pd.DataFrame({'dias':t_idade,\n",
    "                      'fck Médio':results,\n",
    "                      })\n",
    "\n",
    "def highlight_median(median):\n",
    "    if median == mini_df['fck Médio'].median():\n",
    "        return 'background-color:green'\n",
    "    else:\n",
    "        return ''\n",
    "    \n",
    "mini_df.style.applymap(highlight_median, subset=['fck Médio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idade(dias)</th>\n",
       "      <th>Qnt.(itens)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>90</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>91</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>180</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>270</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>360</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>365</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Idade(dias)  Qnt.(itens)\n",
       "0             1            2\n",
       "1             3          134\n",
       "2             7          126\n",
       "3            14           62\n",
       "4            28          425\n",
       "5            56           91\n",
       "6            90           54\n",
       "7            91           22\n",
       "8           100           52\n",
       "9           120            3\n",
       "10          180           26\n",
       "11          270           13\n",
       "12          360            6\n",
       "13          365           14"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dias = [1, 3, 7, 14, 28, 56, 90, 91, 100, 120, 180, 270, 360, 365]\n",
    "qnt = [2, 134, 126, 62, 425, 91, 54, 22, 52, 3, 26, 13, 6, 14]\n",
    "\n",
    "meta_v = pd.DataFrame({\"Idade(dias)\":dias,\n",
    "                     'Qnt.(itens)':qnt})\n",
    "meta_v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Idade(dias)', ylabel='Qnt.(itens)'>"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.barplot(data=meta_v, x=meta_v[\"Idade(dias)\"], y=meta_v[\"Qnt.(itens)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# Novo dataset com a delimitação de até 80 dias\n",
    "new_df = df.loc[df[\"Idade(dias)\"] <= 10]\n",
    "\n",
    "ax = sns.boxplot(x=new_df[\"Idade(dias)\"])\n",
    "new_df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(840, 9)"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameter = 78\n",
    "new_df = df.loc[df[\"Idade(dias)\"] <= parameter]\n",
    "new_df1 = df.loc[df[\"fck(Mpa)\"] <= parameter]\n",
    "\n",
    "\n",
    "ax1 = sns.boxplot(x=new_df1[\"fck(Mpa)\"])\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=new_df[\"Idade(dias)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Verificano outliers com a nova delimitação de dados:\n",
    "nome_colunas = [\"Cimento(kg/m3)\",\"Brita(kg/m3)\",\"Cinzas Volante(kg/m3)\",\n",
    "                \"Agua(kg/m3)\",\"Superplastificante(kg/m3)\",\n",
    "                \"Agregado Grosso(kg/m3)\",\"Agregado Fino(kg/m3)\",\n",
    "                \"Idade(dias)\",\"fck(Mpa)\"]\n",
    "boxplot_plot(new_df, nome_colunas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(840, 9)"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df = df.loc[df[\"Idade(dias)\"] <= 70]\n",
    "\n",
    "ax = sns.boxplot(x=new_df[\"Idade(dias)\"])\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encontrando o melhor modelo\n",
    "\n",
    "Modelos Utilizados:\n",
    "\n",
    "* Regressão Linear;\n",
    "* Regressão Ridge;\n",
    "* Regressão Lasso;\n",
    "* Regressão Elastic Net;\n",
    "* Arvore de Decisão."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cimento(kg/m3)</th>\n",
       "      <th>Brita(kg/m3)</th>\n",
       "      <th>Cinzas Volante(kg/m3)</th>\n",
       "      <th>Agua(kg/m3)</th>\n",
       "      <th>Superplastificante(kg/m3)</th>\n",
       "      <th>Agregado Grosso(kg/m3)</th>\n",
       "      <th>Agregado Fino(kg/m3)</th>\n",
       "      <th>Idade(dias)</th>\n",
       "      <th>fck(Mpa)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>79.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>61.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>380.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>28</td>\n",
       "      <td>36.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>266.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>28</td>\n",
       "      <td>45.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>475.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>28</td>\n",
       "      <td>39.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cimento(kg/m3)  Brita(kg/m3)  Cinzas Volante(kg/m3)  Agua(kg/m3)  \\\n",
       "0           540.0           0.0                    0.0        162.0   \n",
       "1           540.0           0.0                    0.0        162.0   \n",
       "7           380.0          95.0                    0.0        228.0   \n",
       "8           266.0         114.0                    0.0        228.0   \n",
       "9           475.0           0.0                    0.0        228.0   \n",
       "\n",
       "   Superplastificante(kg/m3)  Agregado Grosso(kg/m3)  Agregado Fino(kg/m3)  \\\n",
       "0                        2.5                  1040.0                 676.0   \n",
       "1                        2.5                  1055.0                 676.0   \n",
       "7                        0.0                   932.0                 594.0   \n",
       "8                        0.0                   932.0                 670.0   \n",
       "9                        0.0                   932.0                 594.0   \n",
       "\n",
       "   Idade(dias)  fck(Mpa)  \n",
       "0           28     79.99  \n",
       "1           28     61.89  \n",
       "7           28     36.45  \n",
       "8           28     45.85  \n",
       "9           28     39.29  "
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = new_df\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 840 entries, 0 to 1029\n",
      "Data columns (total 9 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Cimento(kg/m3)             840 non-null    float64\n",
      " 1   Brita(kg/m3)               840 non-null    float64\n",
      " 2   Cinzas Volante(kg/m3)      840 non-null    float64\n",
      " 3   Agua(kg/m3)                840 non-null    float64\n",
      " 4   Superplastificante(kg/m3)  840 non-null    float64\n",
      " 5   Agregado Grosso(kg/m3)     840 non-null    float64\n",
      " 6   Agregado Fino(kg/m3)       840 non-null    float64\n",
      " 7   Idade(dias)                840 non-null    int64  \n",
      " 8   fck(Mpa)                   840 non-null    float64\n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 65.6 KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividindo as variáveis em treino e variavel target:\n",
    "\n",
    "y = df1['fck(Mpa)']\n",
    "x = df1.drop('fck(Mpa)', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coef R2 (Linear): 0.802532468413681\n",
      "RMSE (linear): 7.161701547957985\n"
     ]
    }
   ],
   "source": [
    "# Regressão Linear\n",
    "\n",
    "from sklearn.linear_model    import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics         import r2_score, mean_squared_error\n",
    "\n",
    "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size=0.30)\n",
    "\n",
    "modelo = LinearRegression()\n",
    "modelo.fit(x_treino, y_treino)\n",
    "\n",
    "# Instanciando valores de predições realizadas nos dados de teste:\n",
    "#y_train_pred = modelo.predict(x_treino) \n",
    "\n",
    "y_test_pred_reg = modelo.predict(x_teste) \n",
    "\n",
    "r2_linear = r2_score(y_teste, y_test_pred_reg)\n",
    "rmse_linear = np.sqrt(mean_squared_error(y_teste, y_test_pred_reg))\n",
    "\n",
    "print(f'Coef R2 (Linear): {r2_linear}')\n",
    "print(f'RMSE (linear): {rmse_linear}')\n",
    "\n",
    "\n",
    "\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "caso deseje verificar os valores para os dados de treino utilize:\n",
    "\n",
    "`y_test_pred = modelo.predict(x_treino)`.\n",
    "\n",
    "Essa situação é interessante pois assim é possivel verificar se não houve um overfitting aos dados de treino."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coef R2 (RIDGE): 0.8024412395839362\n",
      "RMSE (RIDGE): 7.163355688772935\n"
     ]
    }
   ],
   "source": [
    "# Regressão Ridge\n",
    "\n",
    "from sklearn.linear_model    import Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Definindo os valores de alpha a serem avaliados\n",
    "variation = np.arange(1, 370)\n",
    "param={'alpha':variation}\n",
    "\n",
    "# Instanciando a função do modelo a ser utilizado\n",
    "ModeloRidge = Ridge()\n",
    "grid = GridSearchCV(ModeloRidge, param_grid=param, cv=5, scoring=\"r2\")\n",
    "\n",
    "# Dandos fit aos dados para encontrar o melhor valor de alpha\n",
    "ModeloRidge.fit(x_treino, y_treino)\n",
    "\n",
    "# Obtendo o melhor valor de alpha\n",
    "hyp_rid = grid.fit(x_treino, y_treino)\n",
    "hyp_rid.best_params_\n",
    "\n",
    "# Inserindo o valor obtido de alpha e dando fit aos dados\n",
    "rid = Ridge(alpha=hyp_rid.best_params_['alpha'])\n",
    "rid.fit(x_treino, y_treino)\n",
    "\n",
    "# Obtendo os resultados\n",
    "y_test_pred = rid.predict(x_teste) \n",
    "r2_ridge = r2_score(y_teste, y_test_pred)\n",
    "rmse_ridge = np.sqrt(mean_squared_error(y_teste, y_test_pred))\n",
    "\n",
    "# Imprimindo os resultados\n",
    "print(f'Coef R2 (RIDGE): {r2_ridge}')\n",
    "print(f'RMSE (RIDGE): {rmse_ridge}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coef R2 (LASSO): -0.004184865556444484\n",
      "RMSE (LASSO): 16.15009962492293\n"
     ]
    }
   ],
   "source": [
    "# Regressão Lasso\n",
    "\n",
    "from sklearn.linear_model import Lasso \n",
    "\n",
    "# Dando fit aos dados de treino e teste\n",
    "lasso = Lasso(alpha=1000, tol=0.1, max_iter=1000 )\n",
    "lasso.fit(x_treino, y_treino)\n",
    "\n",
    "# Obtendo os resultados:\n",
    "y_teste_pred = lasso.predict(x_teste)\n",
    "r2_lasso = r2_score(y_teste, y_teste_pred)\n",
    "rmse_lasso = np.sqrt(mean_squared_error(y_teste, y_teste_pred))\n",
    "\n",
    "print(f'Coef R2 (LASSO): {r2_lasso}')\n",
    "print(f'RMSE (LASSO): {rmse_lasso}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coef R2 (ELASTICNET): 0.8022296722134468\n",
      "RMSE (ELASTICNET): 7.167190311929453\n"
     ]
    }
   ],
   "source": [
    "# Elastic Net\n",
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "# Instanciando a função para a varialvel 'elastic':\n",
    "elastic = ElasticNet(alpha=1, l1_ratio=0.7)\n",
    "elastic.fit(x_treino, y_treino)\n",
    "\n",
    "#Obtendo os resultados\n",
    "y_teste_pred = elastic.predict(x_teste)\n",
    "r2_elastic = r2_score(y_teste, y_teste_pred) \n",
    "rmse_elastic = np.sqrt(mean_squared_error(y_teste, y_teste_pred)) \n",
    "\n",
    "print(f'Coef R2 (ELASTICNET): {r2_elastic}')\n",
    "print(f'RMSE (ELASTICNET): {rmse_elastic}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ValueError: Unknown label type: 'continuous'\n"
     ]
    }
   ],
   "source": [
    "# Regressão Logistica\n",
    "\"\"\"\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Definindo Parametros:\n",
    "valores_C = np.array([0.01, 0.1, 0.5, 1, 2, 3, 5, 10, 20, 50, 100])\n",
    "penalty = ['l2']\n",
    "valores_grid = {'C':valores_C, 'penalty':penalty}\n",
    "\n",
    "# Criando o modelo:\n",
    "modelo = LogisticRegression()\n",
    "\n",
    "# Criando os Grids:\n",
    "RLgrid = GridSearchCV(estimator=modelo, param_grid=valores_grid, cv=5)\n",
    "RLgrid.fit(x_treino, y_treino)\n",
    "\n",
    "print(RLgrid.best_params_)\n",
    "\"\"\"\n",
    "print(\"ValueError: Unknown label type: 'continuous'\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_\"The algorith Does not Work, he expect an categorical value and i'm passing an float value\"_\n",
    "\n",
    "\n",
    "O algoritmo não funciona para esse conjunto de dados.\n",
    "\n",
    "Ele espear um valor target que seja categorico e estou passando valores flutuantes, o mesmo acontece para algoritmos de classificação como KNN.\n",
    "\n",
    "Solução parao problema pode ser encontrada no [stack overflow](https://stackoverflow.com/questions/46051985/tuning-elasticnet-parameters-sklearn-package-in-python)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coef R2 (Decision Tree Regressor): 0.8454248770955377\n",
      "RMSE (Decision Tree Regressor): 6.33633558226765\n"
     ]
    }
   ],
   "source": [
    "# Eliminando avisos do algoritmo:\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn\n",
    "\n",
    "\n",
    "# Decision Tree Regressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Definindo os valores de grid:\n",
    "min_split = np.arange(1,50)\n",
    "max_nivel = np.arange(1,100)\n",
    "algoritmo = [\"friedman_mse\", \"mae\", \"poisson\"]\n",
    "val_grid = {'min_samples_split':min_split, 'max_depth':max_nivel,\n",
    "            'criterion':algoritmo}\n",
    "\n",
    "\n",
    "#Creating the model:\n",
    "treeReg = DecisionTreeRegressor()\n",
    "\n",
    "#Creating the grids:\n",
    "gdt = GridSearchCV(treeReg, param_grid=val_grid, cv=5)\n",
    "gdt.fit(x_treino, y_treino)\n",
    "\n",
    "#Instanciando os melhores valores obtidos para serem inseridos na funçao:\n",
    "crit = gdt.best_params_['criterion']\n",
    "depth = gdt.best_params_['max_depth']\n",
    "splits = gdt.best_params_['min_samples_split']\n",
    "\n",
    "\n",
    "# Instanciando novamente com o melhor modelo encontrado:\n",
    "hyp_treeReg = DecisionTreeRegressor(criterion=crit, max_depth=depth,\n",
    "                                     min_samples_split=splits)\n",
    "hyp_treeReg.fit(x_treino, y_treino)\n",
    "\n",
    "\n",
    "#Obtendo os resultados\n",
    "y_teste_pred = hyp_treeReg.predict(x_teste)\n",
    "r2_tree = r2_score(y_teste, y_teste_pred) \n",
    "rmse_tree = np.sqrt(mean_squared_error(y_teste, y_teste_pred)) \n",
    "\n",
    "print(f'Coef R2 (Decision Tree Regressor): {r2_tree}')\n",
    "print(f'RMSE (Decision Tree Regressor): {rmse_tree}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Modelos</th>\n",
       "      <th>R²</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Regressão Linear</td>\n",
       "      <td>0.802532</td>\n",
       "      <td>7.161702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Regressão Ridge</td>\n",
       "      <td>0.802441</td>\n",
       "      <td>7.163356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Regressão Lasso</td>\n",
       "      <td>-0.004185</td>\n",
       "      <td>16.150100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Regressão Elastic Net</td>\n",
       "      <td>0.802230</td>\n",
       "      <td>7.167190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Regressão com Arvore de Decisão</td>\n",
       "      <td>0.845425</td>\n",
       "      <td>6.336336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Modelos        R²       RMSE\n",
       "0                 Regressão Linear  0.802532   7.161702\n",
       "1                  Regressão Ridge  0.802441   7.163356\n",
       "2                  Regressão Lasso -0.004185  16.150100\n",
       "3            Regressão Elastic Net  0.802230   7.167190\n",
       "4  Regressão com Arvore de Decisão  0.845425   6.336336"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_results = {'Modelos':['Regressão Linear','Regressão Ridge','Regressão Lasso','Regressão Elastic Net','Regressão com Arvore de Decisão'],\n",
    "                'R²':[r2_linear, r2_ridge, r2_lasso, r2_elastic, r2_tree],\n",
    "                'RMSE':[rmse_linear, rmse_ridge, rmse_lasso, rmse_elastic, rmse_tree]}\n",
    "\n",
    "df_results = pd.DataFrame(models_results)\n",
    "\n",
    "df_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resultado:\n",
    "\n",
    "O algoritmo de **Regressão Linear** obteve o maior valor de Coeficiente de determinação r2 assim como o menor de desvio quadrático."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[26.1726435  23.42424258 27.76462865 44.77705869 42.93117597 58.29708454\n",
      " 39.4917629  42.87272442 29.88507445 22.2846156  45.56525637  5.85004851\n",
      " 47.29715154 57.34091495 36.94679824 35.66990262 25.00311968 31.85574916\n",
      " 12.63409149 14.28704463 22.02380331 39.53519578 18.96236561 34.81766152\n",
      " 32.4227453  32.78542283 40.38710656 52.44939522 23.38339834 44.20991889\n",
      " 21.77084407 38.56245246 23.89541501 32.57626023 29.9533666  21.1866297\n",
      " 18.3667059  11.63791276 37.0154214  40.46031772 21.92131221 32.65128572\n",
      " 19.6089155  24.8402648  27.00953333 43.14327764 21.9266016  27.43607042\n",
      " 35.55997726 30.7654743  25.30573339 24.66760023 26.08546968 18.36725486\n",
      " 46.8404085  54.77452523 35.8669001  24.20144687 18.15552953 43.64857579\n",
      " 42.82540626 24.12254408 24.91601249 27.75467498 26.76775425 27.76181473\n",
      " 38.37950795 24.77632928 16.97094065 18.8572787  28.64978646 25.99512759\n",
      " 38.13964087 25.44208168 35.7879309  21.96057517 30.2176708  27.74740981\n",
      " 32.36958369 43.33451886 35.11248135 27.04604621 35.58906858 45.63283199\n",
      " 18.32643724  7.98311821 35.89876917 25.02577716 28.70253061 21.62900937\n",
      " 18.95390806 24.12870161 50.34410056 26.74316441 48.18226053 72.95169223\n",
      " 38.1716388  18.12110899 18.93777577 32.52346472 65.24745688 35.57001729\n",
      " 30.64450096 32.18183455 33.0825121  65.68526561  8.44232575 44.03361953\n",
      " 43.06644227 26.0640592  53.75041815 17.29143723 30.4842145  50.19851969\n",
      " 28.96768634 19.24065757 10.90509609 65.24745688 27.65649079 42.18306277\n",
      " 28.58406125 48.49622544 18.13521774 36.26948673 38.01230885 20.65375862\n",
      " 42.11983916 42.8547884  19.76380264 28.99665486 35.6984856  46.61012083\n",
      " 13.78554759 36.71120264 36.77780161 15.30145037 21.37442914 15.27738694\n",
      " 38.18764831 35.98591491 18.70745944 24.7456156  66.714791   24.85721911\n",
      " 20.35562938 23.14583458 25.35961179 72.95169223 32.68866067 58.45893602\n",
      " 15.47937176 19.52764791 33.43111789 78.84151521 26.76654382 32.68365224\n",
      " 25.32520225 16.06159003 18.53479487 18.44371749 37.92859664  3.33797517\n",
      " 37.60557442 46.27246971 16.54368517 30.95824349 42.32707477 23.14117169\n",
      " 47.8900088  43.30467351 28.38718859 22.57566011 54.39851172 29.16628141\n",
      " 44.44602001 51.6742015  25.40678445 26.21947827 38.56773387 37.86379029\n",
      " 26.01985181 34.56006829 36.51136912 16.62558233 37.99244609 31.94358536\n",
      " 29.82919195 39.03062317 38.89553768 39.08776927 25.06499202 35.67095198\n",
      " 26.87465115 49.92410891 14.89704874 49.71172338 26.11494716 36.30329741\n",
      " 12.23611146 34.86086504 44.19483853 41.37426442 19.21766752  5.56808621\n",
      " 37.72793477 30.16839678 42.40373064 27.90969336 21.02985409 24.34512711\n",
      "  6.48859844 25.36601113 24.56076883 47.98100187 45.87868683 44.38000979\n",
      "  8.05571969 14.51139532 42.82540626 20.55638883 33.6334005  54.53348922\n",
      " 54.53348922 32.97787678 34.73332328 40.9131666  16.19963559 13.82369379\n",
      " 43.06644227 16.2794768  34.90675606 74.0697133  24.76609461 40.83633123\n",
      " 36.22879133 12.32815064 55.49635391 27.8350199  26.56197379 25.88843771\n",
      " 20.79512928 38.6696815  40.94111343 31.96070667 49.29254393 17.83436386\n",
      " 20.10430472 33.34409424 10.18435008 13.83147899 53.68212391 55.99151407]\n"
     ]
    }
   ],
   "source": [
    "# Visualizando as previsções para o conjunto de valores teste:\n",
    "\n",
    "print(y_test_pred_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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