Ayudantía 6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regresion lineal\n",
    "\n",
    "### Ejemplo 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn import datasets, linear_model, metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [5,7,8,7,2,17,2,9,4,11,12,9,6]\n",
    "y = [99,86,87,88,111,86,103,87,94,78,77,85,86]\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show() "
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "slope, intercept, r, p, std_err = stats.linregress(x, y)\n",
    "\n",
    "def myfunc(x):\n",
    "  return slope * x + intercept\n",
    "\n",
    "mymodel = list(map(myfunc, x))\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x, mymodel)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [938.23786125]\n",
      "Mean squared error: 2548.07\n",
      "Coefficient of determination: 0.47\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code source: Jaques Grobler\n",
    "# License: BSD 3 clause\n",
    "# Load the diabetes dataset\n",
    "diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)\n",
    "\n",
    "# Use only one feature\n",
    "diabetes_X = diabetes_X[:, np.newaxis, 2]\n",
    "\n",
    "# Split the data into training/testing sets\n",
    "diabetes_X_train = diabetes_X[:-20]\n",
    "diabetes_X_test = diabetes_X[-20:]\n",
    "\n",
    "# Split the targets into training/testing sets\n",
    "diabetes_y_train = diabetes_y[:-20]\n",
    "diabetes_y_test = diabetes_y[-20:]\n",
    "\n",
    "# Create linear regression object\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "regr.fit(diabetes_X_train, diabetes_y_train)\n",
    "\n",
    "# Make predictions using the testing set\n",
    "diabetes_y_pred = regr.predict(diabetes_X_test)\n",
    "\n",
    "# The coefficients\n",
    "print('Coefficients: \\n', regr.coef_)\n",
    "# The mean squared error\n",
    "print('Mean squared error: %.2f'\n",
    "      % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print('Coefficient of determination: %.2f'\n",
    "      % r2_score(diabetes_y_test, diabetes_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\n",
    "plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n",
    "\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [89,43,36,36,95,10,66,34,38,20,26,29,48,64,6,5,36,66,72,40]\n",
    "y = [21,46,3,35,67,95,53,72,58,10,26,34,90,33,38,20,56,2,47,15]\n",
    "\n",
    "slope, intercept, r, p, std_err = stats.linregress(x, y)\n",
    "\n",
    "def myfunc(x):\n",
    "  return slope * x + intercept\n",
    "\n",
    "mymodel = list(map(myfunc, x))\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x, mymodel)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [-8.95714048e-02  6.73132853e-02  5.04649248e-02  2.18579583e+00\n",
      " -1.72053975e+01  3.63606995e+00  2.05579939e-03 -1.36602886e+00\n",
      "  2.89576718e-01 -1.22700072e-02 -8.34881849e-01  9.40360790e-03\n",
      " -5.04008320e-01]\n",
      "Variance score: 0.7209056672661777\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load the boston dataset \n",
    "boston = datasets.load_boston(return_X_y=False) \n",
    "  \n",
    "# defining feature matrix(X) and response vector(y) \n",
    "X = boston.data \n",
    "y = boston.target \n",
    "  \n",
    "# splitting X and y into training and testing sets \n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, \n",
    "                                                    random_state=1) \n",
    "  \n",
    "# create linear regression object \n",
    "reg = linear_model.LinearRegression() \n",
    "  \n",
    "# train the model using the training sets \n",
    "reg.fit(X_train, y_train) \n",
    "  \n",
    "# regression coefficients \n",
    "print('Coefficients: \\n', reg.coef_) \n",
    "  \n",
    "# variance score: 1 means perfect prediction \n",
    "print('Variance score: {}'.format(reg.score(X_test, y_test))) \n",
    "  \n",
    "# plot for residual error \n",
    "  \n",
    "## setting plot style \n",
    "plt.style.use('fivethirtyeight') \n",
    "  \n",
    "## plotting residual errors in training data \n",
    "plt.scatter(reg.predict(X_train), reg.predict(X_train) - y_train, \n",
    "            color = \"green\", s = 10, label = 'Train data') \n",
    "  \n",
    "## plotting residual errors in test data \n",
    "plt.scatter(reg.predict(X_test), reg.predict(X_test) - y_test, \n",
    "            color = \"blue\", s = 10, label = 'Test data') \n",
    "  \n",
    "## plotting line for zero residual error \n",
    "plt.hlines(y = 0, xmin = 0, xmax = 50, linewidth = 2) \n",
    "  \n",
    "## plotting legend \n",
    "plt.legend(loc = 'upper right') \n",
    "  \n",
    "## plot title \n",
    "plt.title(\"Residual errors\") \n",
    "  \n",
    "## function to show plot \n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Referencias\n",
    "\n",
    "\n",
    "- [Supervised and unsupervised concepts](https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/)\n",
    "\n",
    "- [What is data mining?](https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/process.htm#DMCON046)\n",
    "\n",
    "- [Example of linear regression](https://www.geeksforgeeks.org/linear-regression-python-implementation/)\n",
    "\n",
    "- [A Beginner’s Guide to Linear Regression in Python with Scikit-Learn](https://www.kdnuggets.com/2019/03/beginners-guide-linear-regression-python-scikit-learn.html)\n",
    "\n",
    "- [Linear models  (Videos)](https://www.youtube.com/playlist?list=PLblh5JKOoLUIzaEkCLIUxQFjPIlapw8nU)\n",
    "\n",
    "- [When use regression Analysis](https://statisticsbyjim.com/regression/when-use-regression-analysis/)\n",
    "\n",
    "- [Linear Regression a detailed view](https://towardsdatascience.com/linear-regression-detailed-view-ea73175f6e86)\n",
    "\n",
    "- [A few things to know about Machine Learning](https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf)\n",
    "\n",
    "- [Scikit linear regression example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html)\n",
    "\n",
    "- [w3school's guide to linear regression](https://www.w3schools.com/python/python_ml_linear_regression.asp)"
   ]
  }
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