1_linear_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se lee el data set\n",
    "df = pd.read_csv('datasets/dataset1.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17742c77788>"
      ]
     },
     "execution_count": 33,
     "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",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "df.plot.scatter(x='x',y='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Para este ejemplo la variable 'x' será nuestra variable independiente y la variable 'y' sera nuestra variable dependiente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.asarray(df[['x']])\n",
    "y = np.asarray(df[['y']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Ahora instanciaremos el modelo lineal y entrenaremos de forma supervisada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17742badec8>]"
      ]
     },
     "execution_count": 35,
     "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": [
    "model = LinearRegression()\n",
    "model.fit(x,y)\n",
    "y_pred = model.predict(x)\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,y_pred,'--r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Score para el modelo de regresión lineal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9890019087694607"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Hagamos lo mismo pero con el modelo KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17742a78fc8>]"
      ]
     },
     "execution_count": 37,
     "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": [
    "knn = KNeighborsRegressor(n_neighbors=5)\n",
    "knn.fit(x,y)\n",
    "y_pred = knn.predict(x)\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,y_pred,'--r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9911049821509849"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = [1,2,3,4,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = lambda x : x*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a(lista)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(lista)*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "3    8\n",
       "4    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,2,3,4,4]) * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1     4\n",
       "2     6\n",
       "3     8\n",
       "4    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(lista) * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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