Manutenção Preditiva.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f42d5f84",
   "metadata": {},
   "source": [
    "# Manutenção Preditiva\n",
    "Pergunta de Negócio: **Quais máquinas irão falhar e qual o tipo de falha?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79257a92",
   "metadata": {},
   "source": [
    "**Etapas**\n",
    "1. Manipular os dados\n",
    "2. Analisar os dados\n",
    "3. Desenvolver o modelo\n",
    "4. Criar um dashboard em Power BI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b6e1e5",
   "metadata": {},
   "source": [
    "## Instalando e configurando as bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "82611825",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "936e07e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "#\n",
    "from sklearn.metrics import roc_auc_score, confusion_matrix, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060c626c",
   "metadata": {},
   "source": [
    "## Manipulação de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef3f868d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "df_treino = pd.read_csv('df_treino_2.csv')\n",
    "df_teste = pd.read_csv('df_teste_2.csv')\n",
    "\n",
    "#\n",
    "df_raw = pd.concat([df_treino, df_teste])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c2b3718",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID Produto</th>\n",
       "      <th>Qualidade</th>\n",
       "      <th>Temperatura ar [K]</th>\n",
       "      <th>Temperatura processo [K]</th>\n",
       "      <th>Velocidade rotação [rpm]</th>\n",
       "      <th>Torque [Nm]</th>\n",
       "      <th>Desgaste [min]</th>\n",
       "      <th>Falha</th>\n",
       "      <th>Tipo falha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B57099</td>\n",
       "      <td>Baixa</td>\n",
       "      <td>298.5</td>\n",
       "      <td>308.8</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>58.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B50640</td>\n",
       "      <td>Baixa</td>\n",
       "      <td>301.5</td>\n",
       "      <td>310.3</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>41.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B55178</td>\n",
       "      <td>Baixa</td>\n",
       "      <td>300.9</td>\n",
       "      <td>312.1</td>\n",
       "      <td>1359.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B51337</td>\n",
       "      <td>Baixa</td>\n",
       "      <td>302.0</td>\n",
       "      <td>310.5</td>\n",
       "      <td>1263.0</td>\n",
       "      <td>60.6</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Superaquecimento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B56278</td>\n",
       "      <td>Baixa</td>\n",
       "      <td>297.3</td>\n",
       "      <td>308.1</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>49.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ID Produto Qualidade  Temperatura ar [K]  Temperatura processo [K]  \\\n",
       "0     B57099     Baixa               298.5                     308.8   \n",
       "1     B50640     Baixa               301.5                     310.3   \n",
       "2     B55178     Baixa               300.9                     312.1   \n",
       "3     B51337     Baixa               302.0                     310.5   \n",
       "4     B56278     Baixa               297.3                     308.1   \n",
       "\n",
       "   Velocidade rotação [rpm]  Torque [Nm]  Desgaste [min]  Falha  \\\n",
       "0                    1305.0         58.7            26.0    0.0   \n",
       "1                    1415.0         41.6            28.0    0.0   \n",
       "2                    1359.0         51.7           146.0    0.0   \n",
       "3                    1263.0         60.6            90.0    1.0   \n",
       "4                    1468.0         49.8             0.0    0.0   \n",
       "\n",
       "         Tipo falha  \n",
       "0         Sem falha  \n",
       "1         Sem falha  \n",
       "2         Sem falha  \n",
       "3  Superaquecimento  \n",
       "4         Sem falha  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eac628e",
   "metadata": {},
   "source": [
    "**Dicionário de variáveis**\n",
    "\n",
    "* *Qualidade*: nível de qualidade do equipamento\n",
    "* *Temperatura ar [K]*: temperatura do ar durante o processo\n",
    "* *Temperatura  processo [K]*: temperatura do processo\n",
    "* *Velocidade rotação [rpm]*: velocidade de rotação do maquinário\n",
    "* *Torque [Nm]*: torque do maquinário\n",
    "* *Desgaste [min]*: tempo de duração do processo\n",
    "* *Tipo falha*: qual o tipo de falha que o equipamento apresentou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ae46b99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9973 entries, 0 to 3291\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   ID Produto                9973 non-null   object \n",
      " 1   Qualidade                 9973 non-null   object \n",
      " 2   Temperatura ar [K]        9973 non-null   float64\n",
      " 3   Temperatura processo [K]  9973 non-null   float64\n",
      " 4   Velocidade rotação [rpm]  9973 non-null   float64\n",
      " 5   Torque [Nm]               9973 non-null   float64\n",
      " 6   Desgaste [min]            9973 non-null   float64\n",
      " 7   Falha                     9973 non-null   float64\n",
      " 8   Tipo falha                9973 non-null   object \n",
      "dtypes: float64(6), object(3)\n",
      "memory usage: 779.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Verificação de valores nulos no dataset\n",
    "df_raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6cbaaa70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificação de linhas duplicadas no dataset\n",
    "df_raw.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4fab83c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperatura ar [K]</th>\n",
       "      <th>Temperatura processo [K]</th>\n",
       "      <th>Velocidade rotação [rpm]</th>\n",
       "      <th>Torque [Nm]</th>\n",
       "      <th>Desgaste [min]</th>\n",
       "      <th>Falha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9973.000000</td>\n",
       "      <td>9973.000000</td>\n",
       "      <td>9973.000000</td>\n",
       "      <td>9973.000000</td>\n",
       "      <td>9973.000000</td>\n",
       "      <td>9973.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>300.003259</td>\n",
       "      <td>310.004031</td>\n",
       "      <td>1538.893212</td>\n",
       "      <td>39.978993</td>\n",
       "      <td>107.921087</td>\n",
       "      <td>0.033089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.000548</td>\n",
       "      <td>1.483692</td>\n",
       "      <td>179.412171</td>\n",
       "      <td>9.966805</td>\n",
       "      <td>63.649152</td>\n",
       "      <td>0.178879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>295.300000</td>\n",
       "      <td>305.700000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>298.300000</td>\n",
       "      <td>308.800000</td>\n",
       "      <td>1423.000000</td>\n",
       "      <td>33.200000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>300.100000</td>\n",
       "      <td>310.100000</td>\n",
       "      <td>1503.000000</td>\n",
       "      <td>40.100000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>301.500000</td>\n",
       "      <td>311.100000</td>\n",
       "      <td>1612.000000</td>\n",
       "      <td>46.700000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>304.500000</td>\n",
       "      <td>313.800000</td>\n",
       "      <td>2886.000000</td>\n",
       "      <td>76.600000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperatura ar [K]  Temperatura processo [K]  Velocidade rotação [rpm]  \\\n",
       "count         9973.000000               9973.000000               9973.000000   \n",
       "mean           300.003259                310.004031               1538.893212   \n",
       "std              2.000548                  1.483692                179.412171   \n",
       "min            295.300000                305.700000               1168.000000   \n",
       "25%            298.300000                308.800000               1423.000000   \n",
       "50%            300.100000                310.100000               1503.000000   \n",
       "75%            301.500000                311.100000               1612.000000   \n",
       "max            304.500000                313.800000               2886.000000   \n",
       "\n",
       "       Torque [Nm]  Desgaste [min]        Falha  \n",
       "count  9973.000000     9973.000000  9973.000000  \n",
       "mean     39.978993      107.921087     0.033089  \n",
       "std       9.966805       63.649152     0.178879  \n",
       "min       3.800000        0.000000     0.000000  \n",
       "25%      33.200000       53.000000     0.000000  \n",
       "50%      40.100000      108.000000     0.000000  \n",
       "75%      46.700000      162.000000     0.000000  \n",
       "max      76.600000      253.000000     1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descrição estatística básica dos dados\n",
    "df_raw.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c45f8995",
   "metadata": {},
   "source": [
    "## Análise dos dados\n",
    "- Identificar padrões e correlações nos dados\n",
    "- Formular e validar hipóteses sobre o problema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e2b5d1e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fazendo um cópia do dataset para Análise Exploratória dos Dados (EDA)\n",
    "df_eda = df_treino.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6575093",
   "metadata": {},
   "source": [
    "### Total de falhas e tipos de falhas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8c8b507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Proporção Sem Falha x Com Falha')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apresentação gráfica da proporção entre máquinas que falharam e que não falharam\n",
    "total_falhas = df_eda['Falha'].value_counts(normalize=True)\n",
    "\n",
    "plt.bar(x=total_falhas.index, height=total_falhas)\n",
    "plt.xticks([0,1],[\"Sem falha\", \"Com Falha\"])\n",
    "plt.ylabel(\"Total de Máquinas\")\n",
    "plt.title(\"Proporção Sem Falha x Com Falha\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "debeb98f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Frequência por Tipo de Falha')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apresentação gráfica dos tipos de falhas e em que frequência ocorrem\n",
    "df_falhas = df_eda.loc[df_eda['Falha'] == 1]\n",
    "df_sem_falhas = df_eda.loc[df_eda['Falha'] == 0]\n",
    "\n",
    "tipo_falhas = df_falhas['Tipo falha'].value_counts(normalize=True)\n",
    "\n",
    "plt.barh(y=tipo_falhas.index, width=tipo_falhas)\n",
    "plt.ylabel(\"Tipo de Falha\")\n",
    "plt.title(\"Frequência por Tipo de Falha\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f45ebc",
   "metadata": {},
   "source": [
    "### Relação entre falhas e qualidade do equipamento\n",
    "- As falhas acompanham a qualidade dos equipamentos?\n",
    "- Os componentes com baixa qualidade, são os que mais falham?\n",
    "\n",
    "Volume total e proporcionalmente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24a270c2",
   "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>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qualidade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alta</th>\n",
       "      <td>12.0</td>\n",
       "      <td>665</td>\n",
       "      <td>0.018045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baixa</th>\n",
       "      <td>162.0</td>\n",
       "      <td>3979</td>\n",
       "      <td>0.040714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Média</th>\n",
       "      <td>52.0</td>\n",
       "      <td>2037</td>\n",
       "      <td>0.025528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sum  count      mean\n",
       "Qualidade                        \n",
       "Alta        12.0    665  0.018045\n",
       "Baixa      162.0   3979  0.040714\n",
       "Média       52.0   2037  0.025528"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "falha_qualidade = df_eda.groupby(\"Qualidade\")['Falha'].agg(['sum', 'count', 'mean'])\n",
    "falha_qualidade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a17752b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total geral de componentes por qualidade\n",
    "ax = plt.figure(figsize=(16,5))\n",
    "ax = plt.subplot(1,3,1)\n",
    "ax = plt.pie(falha_qualidade['count'], labels=falha_qualidade.index, autopct=\"%.0f%%\")\n",
    "plt.title(\"Total Geral de Componentes por Qualidade\")\n",
    "\n",
    "# Total de falhas por tipo de componente - Volume total\n",
    "ax = plt.subplot(1,3,2)\n",
    "ax = plt.pie(falha_qualidade['sum'], labels=falha_qualidade.index, autopct=\"%.0f%%\")\n",
    "plt.title(\"Total de Falhas por Qualidade\")\n",
    "\n",
    "# Média de falhas por tipo de componente\n",
    "ax = plt.subplot(1,3,3)\n",
    "ax = plt.bar(x=falha_qualidade.index, height=falha_qualidade['mean'])\n",
    "plt.title(\"Média de Falhas por Qualidade\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96130d2",
   "metadata": {},
   "source": [
    "- As falhas acompanham a qualidade dos equipamentos?\n",
    "\n",
    "**Sim, a qualidade impacta na falha de um componente.**\n",
    "\n",
    "- Os componentes com baixa qualidade, são os que mais falham?\n",
    "\n",
    "**Sim, os componentes de baixa qualidade são os equipamentos que mais sofreram falhas.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10625f52",
   "metadata": {},
   "source": [
    "### Correlação entre variáveis\n",
    "- Existem variáveis relacionadas entre si?\n",
    "- Como elas se relacionam com a falha do equipamento?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75917398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "corr_falhas = df_eda.corr()\n",
    "\n",
    "# Apresentação gráfica da correlação entre as variáveis\n",
    "ax = plt.figure(figsize=(16,6))\n",
    "ax = plt.subplot(1,2,1)\n",
    "ax = sns.heatmap(corr_falhas, annot=True, cmap= \"Greens\")\n",
    "plt.title(\"Correlação entre as Variáveis\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ef9e77",
   "metadata": {},
   "source": [
    "- Existem variáveis relacionadas entre si?\n",
    "\n",
    "**Sim, existe correlação entre algumas variáveis.**\n",
    "\n",
    "- Como elas se relacionam com a falha do equipamento?\n",
    "\n",
    "**Forte correção negativa entre as variáveis de: toruqe x velocidade de rotação e correlação positiva entre: temperatura do ar x temperatura do processo.**\n",
    "**Demais variáveis não possuem correlação aparente.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e786ba1d",
   "metadata": {},
   "source": [
    "### Temperatura\n",
    "- Equipamentos sujeitos a maiores temperaturas tem mais falhas?\n",
    "- Equipamentos que possuem maior diferença entre a temperatura do ar (ambiente) e temperatura de processo possuem mais falhas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bc453c01",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luis1\\AppData\\Local\\Temp/ipykernel_2696/1430274132.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_temp['Variação Temperatura'] = df_temp['Temperatura processo [K]'] - df_temp['Temperatura ar [K]']\n"
     ]
    }
   ],
   "source": [
    "df_temp = df_eda[['Temperatura ar [K]', 'Temperatura processo [K]', 'Falha']]\n",
    "\n",
    "# Criar coluna com valor da diferença entre as temperaturas (ar x processo)\n",
    "df_temp['Variação Temperatura'] = df_temp['Temperatura processo [K]'] - df_temp['Temperatura ar [K]'] \n",
    "\n",
    "#\n",
    "df_temp_falha = df_temp[df_temp['Falha'] == 1]\n",
    "df_temp_semfalha = df_temp[df_temp['Falha'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bef7026b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Análise com base na temperatura do processo\n",
    "ax = plt.figure(figsize=(10,4))\n",
    "\n",
    "# Total de componentes sem falha\n",
    "ax = plt.subplot(1,2,1)\n",
    "ax = plt.hist(df_temp_semfalha['Temperatura processo [K]'])\n",
    "plt.xlabel(\"Temperatura processo [K]\")\n",
    "plt.ylabel(\"Quantidade Equipamentos\")\n",
    "plt.title(\"Temperatura de processo de Componentes Sem Falha\")\n",
    "\n",
    "# Média de falhas por componente\n",
    "ax = plt.subplot(1,2,2)\n",
    "ax = plt.hist(df_temp_falha['Temperatura processo [K]'])\n",
    "plt.xlabel(\"Temperatura processo [K]\")\n",
    "plt.ylabel(\"Quantidade Equipamentos\")\n",
    "plt.title(\"Temperatura de processo de Componentes Com Falha\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5815738a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Análise com base na temperatura do ambiente\n",
    "ax = plt.figure(figsize=(10,4))\n",
    "\n",
    "# Total de componentes sem falha\n",
    "ax = plt.subplot(1,2,1)\n",
    "ax = plt.hist(df_temp_semfalha['Temperatura ar [K]'])\n",
    "plt.xlabel(\"Temperatura ambiente [K]\")\n",
    "plt.ylabel(\"Quantidade Equipamentos\")\n",
    "plt.title(\"Temperatura do ambiente de Componentes Sem Falha\")\n",
    "\n",
    "# Média de falhas por componente\n",
    "ax = plt.subplot(1,2,2)\n",
    "ax = plt.hist(df_temp_falha['Temperatura ar [K]'])\n",
    "plt.xlabel(\"Temperatura ambiente [K]\")\n",
    "plt.ylabel(\"Quantidade Equipamentos\")\n",
    "plt.title(\"Temperatura do ambiente de Componentes Com Falha\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1389304f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Análise com base na variação de temperatura (ar x processo)\n",
    "ax = plt.figure(figsize=(10,4))\n",
    "\n",
    "# Total de componentes sem falha\n",
    "ax = plt.subplot(1,2,1)\n",
    "ax = plt.hist(df_temp_semfalha['Variação Temperatura'])\n",
    "plt.xlabel(\"Variação de Temperatura\")\n",
    "plt.ylabel(\"Quantidade Equipamentos\")\n",
    "plt.title(\"Variação de Temperatura de Componentes Sem Falha\")\n",
    "\n",
    "# Média de falhas por componente\n",
    "ax = plt.subplot(1,2,2)\n",
    "ax = plt.hist(df_temp_falha['Variação Temperatura'])\n",
    "plt.xlabel(\"Variação de Temperatura\")\n",
    "plt.ylabel(\"Quantidade Equipamentos\")\n",
    "plt.title(\"Variação de Temperatura de Componentes Com Falha\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4af110e",
   "metadata": {},
   "source": [
    "- Equipamentos sujeitos a maiores temperaturas tem mais falhas?\n",
    "\n",
    "**Sim, quando as temperaturas são superiores à 310K.**\n",
    "\n",
    "- Equipamentos que possuem maior diferença entre a temperatura do ar (ambiente) e temperatura de processo possuem mais falhas?\n",
    "\n",
    "**Não, componentes com diferença baixa possuem maior tendência.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21dd553c",
   "metadata": {},
   "source": [
    "### Torque e Velocidade de rotação\n",
    "- Essas duas variáveis tem relação com falhas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "efe56ae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relação entre Torque e Velocidade de Rotação em ocorrer uma falha\n",
    "plt.figure(figsize=(8,4))\n",
    "sns.scatterplot(x='Velocidade rotação [rpm]', y='Torque [Nm]', palette='Greens', hue='Falha', data=df_eda)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0df370e",
   "metadata": {},
   "source": [
    "- Essas duas variáveis tem relação com falhas?\n",
    "\n",
    "**Sim. A maior parte das falhas ocorrem em componentes onde:**\n",
    "\n",
    "- Alta rotação e baixo torque;\n",
    "- Baixa rotação e alto torque."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0296df96",
   "metadata": {},
   "source": [
    "## Desenvolvendo o modelo ML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be9e47f3",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4e3fd03f",
   "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",
       "        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>ID Produto</th>\n",
       "      <th>Qualidade</th>\n",
       "      <th>Temperatura ar [K]</th>\n",
       "      <th>Temperatura processo [K]</th>\n",
       "      <th>Velocidade rotação [rpm]</th>\n",
       "      <th>Torque [Nm]</th>\n",
       "      <th>Desgaste [min]</th>\n",
       "      <th>Falha</th>\n",
       "      <th>Tipo falha</th>\n",
       "      <th>Variação Temperatura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B57099</td>\n",
       "      <td>Baixa</td>\n",
       "      <td>298.5</td>\n",
       "      <td>308.8</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>58.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B50640</td>\n",
       "      <td>Baixa</td>\n",
       "      <td>301.5</td>\n",
       "      <td>310.3</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>41.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B55178</td>\n",
       "      <td>Baixa</td>\n",
       "      <td>300.9</td>\n",
       "      <td>312.1</td>\n",
       "      <td>1359.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B51337</td>\n",
       "      <td>Baixa</td>\n",
       "      <td>302.0</td>\n",
       "      <td>310.5</td>\n",
       "      <td>1263.0</td>\n",
       "      <td>60.6</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Superaquecimento</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B56278</td>\n",
       "      <td>Baixa</td>\n",
       "      <td>297.3</td>\n",
       "      <td>308.1</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>49.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ID Produto Qualidade  Temperatura ar [K]  Temperatura processo [K]  \\\n",
       "0     B57099     Baixa               298.5                     308.8   \n",
       "1     B50640     Baixa               301.5                     310.3   \n",
       "2     B55178     Baixa               300.9                     312.1   \n",
       "3     B51337     Baixa               302.0                     310.5   \n",
       "4     B56278     Baixa               297.3                     308.1   \n",
       "\n",
       "   Velocidade rotação [rpm]  Torque [Nm]  Desgaste [min]  Falha  \\\n",
       "0                    1305.0         58.7            26.0    0.0   \n",
       "1                    1415.0         41.6            28.0    0.0   \n",
       "2                    1359.0         51.7           146.0    0.0   \n",
       "3                    1263.0         60.6            90.0    1.0   \n",
       "4                    1468.0         49.8             0.0    0.0   \n",
       "\n",
       "         Tipo falha  Variação Temperatura  \n",
       "0         Sem falha                  10.3  \n",
       "1         Sem falha                   8.8  \n",
       "2         Sem falha                  11.2  \n",
       "3  Superaquecimento                   8.5  \n",
       "4         Sem falha                  10.8  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criação de novas colunas no dataset de treino\n",
    "df_treino['Variação Temperatura'] = df_treino['Temperatura processo [K]'] - df_treino['Temperatura ar [K]']\n",
    "\n",
    "df_treino.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "46a9a75b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .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>Alta</th>\n",
       "      <th>Baixa</th>\n",
       "      <th>Média</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Alta  Baixa  Média\n",
       "0     0      1      0\n",
       "1     0      1      0\n",
       "2     0      1      0\n",
       "3     0      1      0\n",
       "4     0      1      0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manipulação de colunas para conversão de valores string para valores que o modelo compreenda\n",
    "dummy_treino = pd.get_dummies(df_treino['Qualidade'])\n",
    "\n",
    "dummy_treino.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "31a25b92",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "df_pbi = pd.concat([df_treino.drop(columns=['Tipo falha', 'Falha']), dummy_treino], axis=1)\n",
    "\n",
    "#\n",
    "file_name = \"df_pbi.xlsx\"\n",
    "df_pbi.to_excel(file_name, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "14a63bd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "def pipeline(df):\n",
    "    df['Variação Temperatura'] = df['Temperatura processo [K]'] - df['Temperatura ar [K]']\n",
    "    \n",
    "    dummy = pd.get_dummies(df['Qualidade'])\n",
    "    df = pd.concat([df.drop('Qualidade', axis=1), dummy], axis=1)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "324478d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>ID Produto</th>\n",
       "      <th>Temperatura ar [K]</th>\n",
       "      <th>Temperatura processo [K]</th>\n",
       "      <th>Velocidade rotação [rpm]</th>\n",
       "      <th>Torque [Nm]</th>\n",
       "      <th>Desgaste [min]</th>\n",
       "      <th>Falha</th>\n",
       "      <th>Tipo falha</th>\n",
       "      <th>Variação Temperatura</th>\n",
       "      <th>Alta</th>\n",
       "      <th>Baixa</th>\n",
       "      <th>Média</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B57099</td>\n",
       "      <td>298.5</td>\n",
       "      <td>308.8</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>58.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B50640</td>\n",
       "      <td>301.5</td>\n",
       "      <td>310.3</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>41.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B55178</td>\n",
       "      <td>300.9</td>\n",
       "      <td>312.1</td>\n",
       "      <td>1359.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B51337</td>\n",
       "      <td>302.0</td>\n",
       "      <td>310.5</td>\n",
       "      <td>1263.0</td>\n",
       "      <td>60.6</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Superaquecimento</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B56278</td>\n",
       "      <td>297.3</td>\n",
       "      <td>308.1</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>49.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sem falha</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ID Produto  Temperatura ar [K]  Temperatura processo [K]  \\\n",
       "0     B57099               298.5                     308.8   \n",
       "1     B50640               301.5                     310.3   \n",
       "2     B55178               300.9                     312.1   \n",
       "3     B51337               302.0                     310.5   \n",
       "4     B56278               297.3                     308.1   \n",
       "\n",
       "   Velocidade rotação [rpm]  Torque [Nm]  Desgaste [min]  Falha  \\\n",
       "0                    1305.0         58.7            26.0    0.0   \n",
       "1                    1415.0         41.6            28.0    0.0   \n",
       "2                    1359.0         51.7           146.0    0.0   \n",
       "3                    1263.0         60.6            90.0    1.0   \n",
       "4                    1468.0         49.8             0.0    0.0   \n",
       "\n",
       "         Tipo falha  Variação Temperatura  Alta  Baixa  Média  \n",
       "0         Sem falha                  10.3     0      1      0  \n",
       "1         Sem falha                   8.8     0      1      0  \n",
       "2         Sem falha                  11.2     0      1      0  \n",
       "3  Superaquecimento                   8.5     0      1      0  \n",
       "4         Sem falha                  10.8     0      1      0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "df_treino = pipeline(df_treino)\n",
    "df_teste = pipeline(df_teste)\n",
    "\n",
    "df_treino.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0381c8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropando as colunas onde o modelo não pode compreender os valores\n",
    "# Após a etapa de conversão desses valores para outros que o modelo compreenda\n",
    "cols_drop = ['ID Produto', 'Tipo falha', 'Falha']\n",
    "\n",
    "x_treino = df_treino.drop(columns = cols_drop)\n",
    "x_teste = df_teste.drop(columns = cols_drop)\n",
    "\n",
    "#\n",
    "y_treino = df_treino['Tipo falha']\n",
    "y_teste = df_teste['Tipo falha']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6d535768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sem falha', 'Sem falha', 'Sem falha', ..., 'Sem falha',\n",
       "       'Sem falha', 'Sem falha'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modelo Random Forest\n",
    "forest = RandomForestClassifier(class_weight='balanced')\n",
    "forest.fit(x_treino, y_treino)\n",
    "\n",
    "forest.predict(x_teste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1cc2918b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9270607955846788"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando a precisão do modelo Random Forest\n",
    "y_proba = forest.predict_proba(x_teste)\n",
    "\n",
    "roc_auc_score(y_teste, y_proba, multi_class = 'ovo', average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8fed3382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sem falha', 'Sem falha', 'Sem falha', ..., 'Sem falha',\n",
       "       'Sem falha', 'Sem falha'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modelo KNN\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(x_treino, y_treino)\n",
    "\n",
    "knn.predict(x_teste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c3f33df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7648615314165327"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando a precisão do modelo KNN\n",
    "y_proba = knn.predict_proba(x_teste)\n",
    "\n",
    "roc_auc_score(y_teste, y_proba, multi_class = 'ovo', average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6764f3e3",
   "metadata": {},
   "source": [
    "### Avaliação dos modelos\n",
    "**RandomForest = 94,175%**\n",
    "\n",
    "**KNN = 76,486%**\n",
    "\n",
    "Modelo com maior performance é o **Random Forest**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7decb4f0",
   "metadata": {},
   "source": [
    "## Criar um dashboard em Power BI\n",
    "- Conversão de relatório em .sav\n",
    "- Conversão de relatório em .xlsx para criar o dashboard em Power BI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b9dbbcd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'modelo-treinado.sav'\n",
    "pickle.dump(forest, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70663e83",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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