analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carregando bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import statsmodels.api as sm\n",
    "from sklearn import metrics\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")\n",
    "\n",
    "# special matplotlib argument for improved plots\n",
    "from matplotlib import rcParams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carregando dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
      "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
      "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
      "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
      "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
      "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
      "\n",
      "   PTRATIO       B  LSTAT  PRICE  \n",
      "0     15.3  396.90   4.98   24.0  \n",
      "1     17.8  396.90   9.14   21.6  \n",
      "2     17.8  392.83   4.03   34.7  \n",
      "3     18.7  394.63   2.94   33.4  \n",
      "4     18.7  396.90   5.33   36.2  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n",
    "bos = pd.DataFrame(boston.data)\n",
    "bos.columns = boston.feature_names\n",
    "bos['PRICE'] = boston.target\n",
    "print(bos.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análises básicas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
      "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
      "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
      "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
      "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
      "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
      "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
      "\n",
      "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
      "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
      "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
      "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
      "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
      "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
      "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
      "\n",
      "            LSTAT       PRICE  \n",
      "count  506.000000  506.000000  \n",
      "mean    12.653063   22.532806  \n",
      "std      7.141062    9.197104  \n",
      "min      1.730000    5.000000  \n",
      "25%      6.950000   17.025000  \n",
      "50%     11.360000   21.200000  \n",
      "75%     16.955000   25.000000  \n",
      "max     37.970000   50.000000  \n"
     ]
    }
   ],
   "source": [
    "print(bos.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separando o dataset de treino do de teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(339, 13)\n",
      "(167, 13)\n",
      "(339,)\n",
      "(167,)\n"
     ]
    }
   ],
   "source": [
    "X = bos.drop('PRICE', axis = 1)\n",
    "Y = bos['PRICE']\n",
    "X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(X, Y, test_size = 0.33, random_state = 5)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iniciando a regressão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Preços originais vs Preços preditos:')"
      ]
     },
     "execution_count": 15,
     "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": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = lm.predict(X_test)\n",
    "\n",
    "plt.scatter(Y_test, Y_pred)\n",
    "plt.xlabel(\"Preços originais:\")\n",
    "plt.ylabel(\"Preços preditos:\")\n",
    "plt.title(\"Preços originais vs Preços preditos:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Original    Predito\n",
      "226      37.6  37.467236\n",
      "292      27.9  31.391547\n",
      "90       22.6  27.120196\n",
      "373      13.8   6.468433\n",
      "273      35.2  33.629667\n",
      "417      10.4   5.670680\n",
      "503      23.9  27.039467\n",
      "234      29.0  29.927047\n",
      "111      22.8  26.356613\n",
      "472      23.2  22.452460\n",
      "99       33.2  32.205044\n",
      "339      19.0  21.786417\n",
      "309      20.3  23.411384\n",
      "304      36.1  33.608944\n",
      "272      24.4  28.286195\n"
     ]
    }
   ],
   "source": [
    "diff = pd.DataFrame({'Original': Y_test, 'Predito': Y_pred})\n",
    "diff1 = diff.head(20)\n",
    "print(diff.head(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff1.plot(kind='bar',figsize=(10,8))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Original</th>\n",
       "      <th>Predito</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.523952</td>\n",
       "      <td>21.847663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.711260</td>\n",
       "      <td>8.394229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>-6.249525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.400000</td>\n",
       "      <td>17.425063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20.700000</td>\n",
       "      <td>21.725754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25.050000</td>\n",
       "      <td>27.079831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>42.756775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Original     Predito\n",
       "count  167.000000  167.000000\n",
       "mean    22.523952   21.847663\n",
       "std      9.711260    8.394229\n",
       "min      7.000000   -6.249525\n",
       "25%     16.400000   17.425063\n",
       "50%     20.700000   21.725754\n",
       "75%     25.050000   27.079831\n",
       "max     50.000000   42.756775"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erro absoluto médio: 3.4550349322483482\n",
      "Erro quadrático médio: 28.530458765974583\n",
      "Raiz quadrada do erro médio: 5.341391089030514\n"
     ]
    }
   ],
   "source": [
    "print('Erro absoluto médio:', metrics.mean_absolute_error(Y_test, Y_pred))  \n",
    "print('Erro quadrático médio:', metrics.mean_squared_error(Y_test, Y_pred))  \n",
    "print('Raiz quadrada do erro médio:', np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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