exercicio7 - aula 4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "    <h1>Regressão Linear - Exercício de previsão de preço de locação</h1>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Plot inicial - primeiras intuições\n",
    "\n",
    "from numpy import *\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Valores de x e y inventados para o exercício.\n",
    "x = array([40,50,80,110,140,160])\n",
    "y = array([700,800,1000,1500,2000,2500])\n",
    "#x = área em metros quadrados\n",
    "#y = preço de locação\n",
    "\n",
    "#Plot the dataset\n",
    "plt.scatter(x,y)\n",
    "plt.xlabel('Área Construída')\n",
    "plt.ylabel('Preça de Locação')\n",
    "plt.title('Conjunto de Dados - Preço de Locação')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha 5.113636363636488\n",
      "beta 14.602272727272727\n"
     ]
    }
   ],
   "source": [
    "#Exercício aula 04 parte 2\n",
    "#Determinação coeficientes Alfa e Beta da curva de melhor ajuste\n",
    "\n",
    "import numpy as np\n",
    "def solucao_analitica(x,y):\n",
    "    beta = np.corrcoef(x, y)[0,1] * np.std(y) / np.std(x)\n",
    "    alpha = mean(y) - beta * mean(x)\n",
    "    return alpha, beta\n",
    "\n",
    "alpha, beta = solucao_analitica(x, y)\n",
    "print(\"alpha\", alpha)\n",
    "print(\"beta\", beta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.98315288]\n",
      " [0.98315288 1.        ]]\n",
      "Correlacao_XY =  0.9832\n",
      "R^2 =  0.9666\n"
     ]
    }
   ],
   "source": [
    "#Exercício aula 04 parte 2\n",
    "#Determinação do coeficiente de correlação e R^2 do conjunto de dados\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "correlation_matrix = np.corrcoef(x, y)\n",
    "\n",
    "print(correlation_matrix)\n",
    "\n",
    "correlation_xy = correlation_matrix[0,1]\n",
    "\n",
    "print(\"Correlacao_XY = \", round(correlation_xy, 4))\n",
    "\n",
    "r_squared = correlation_xy**2\n",
    "print(\"R^2 = \", round(r_squared, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Com base na análise de regressão os valores de aluguel seriam de aproximadamente:\n",
      "60m² =  881.2500000000001\n",
      "120m² =  1757.3863636363637\n",
      "150m² =  2195.4545454545455\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-210.94, -83.74, 6.21, 11.61, 14.08, 9.04]\n"
     ]
    }
   ],
   "source": [
    "#Exercício aula 04 parte 2\n",
    "#Previsão de valores de aluguel para um imóvel com área de 60, 120 e 150 m^2\n",
    "\n",
    "x_reais = [40,50,80,110,140,160]\n",
    "y_reais = [700,800,1000,1500,2000,2500]\n",
    "\n",
    "m = (len(x)*cov(x, y)[0,1])/((len(x) - 1)*var(x))\n",
    "b = mean(y) - m * mean(x)\n",
    "\n",
    "y_regressao =  [m * x + b for x in x_reais]\n",
    "\n",
    "plt.scatter(x_reais, y_reais, c='b')\n",
    "plt.scatter(x_reais, y_regressao, c='k')\n",
    "\n",
    "print('Com base na análise de regressão os valores de aluguel seriam de aproximadamente:')\n",
    "\n",
    "#pred_formula = beta * x + alpha\n",
    "\n",
    "x1 = 60 \n",
    "x2 = 120\n",
    "x3 = 150\n",
    "\n",
    "pred_formula1 = beta * x1 + alpha\n",
    "print ('60m² = ', pred_formula1)\n",
    "\n",
    "pred_formula2 = beta * x2 + alpha\n",
    "print ('120m² = ', pred_formula2)\n",
    "\n",
    "pred_formula3 = beta * x3 + alpha\n",
    "print ('150m² = ', pred_formula3)\n",
    "\n",
    "plt.plot(x, pred, c='r')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "erro_proporcional = [round(100*(1 - i/j),2) for i, j in zip(y_reais, y_regressao)]\n",
    "print(erro_proporcional)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}