Notebooks/Regressão_Linear_02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "from sklearn.datasets import make_regression\n",
    "import matplotlib.pyplot as plt\n",
    "# Gerando uma massa de dados :\n",
    "x, y = make_regression(n_samples=200, n_features=1, noise=30)\n",
    "# Mostrando no gráfico\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.9272185400568205"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "# Criação do modelo:\n",
    "modelo = LinearRegression()\n",
    "modelo.fit(x, y)\n",
    "modelo.intercept_ # Coeficiente Linear da reta.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([76.69073053])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo.coef_ # Coeficiente Angular da reta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "import numpy as np\n",
    "plt.scatter(x_teste, y_teste)\n",
    "xreg = np.arange(-3, 4, 1)\n",
    "plt.plot(xreg, 93.97228432*xreg-3.4527390148985115, color='red') # gráfico regressão\n",
    "plt.show()     # y = m.x + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treinando e testando o modelo\n",
    "\n",
    "A função `train_test_split()` vai pegar os dados criados para x e y e vai dividir eles entre dados de treino e dados de test.\n",
    "\n",
    "O `test_size=30` indica que eu estou separando 30% dos meus dados para teste e o outros 70% para treino.\n",
    "\n",
    "O `modelo.score(x_test, y_teste)` Vai calcular o coeficiente de determinação R2 em relação aos dados de teste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo.fit(x_treino, y_treino) # Criando a reta "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultado = modelo.score(x_teste, y_teste) # Determinando o Coeficiente R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8824685626203148\n"
     ]
    }
   ],
   "source": [
    "print(resultado) # Resultado do Coeficiente de Determinação R2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotando os dados de teste com a reta gerada com os dados de Treino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "plt.scatter(x_teste, y_teste)\n",
    "xreg = np.arange(-3, 4, 1)\n",
    "plt.plot(xreg, 93.97228432*xreg-3.4527390148985115, color='red') # gráfico regressão\n",
    "plt.show()     # y = m.x + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}