linear_reg_sample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa9749e3d50>"
      ]
     },
     "execution_count": 24,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "rng = np.random.RandomState(42)\n",
    "x = 10 * rng.rand(50)\n",
    "y = 2 * x - 1 + rng.rand(50)\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression(fit_intercept=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = x[: ,np.newaxis]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.00660766])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5350275750800026"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "xfit = np.linspace(-1, 11, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xfit = xfit[:, np.newaxis]\n",
    "yfit = model.predict(Xfit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa974a0b490>]"
      ]
     },
     "execution_count": 41,
     "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": [
    "plt.scatter(x, y)\n",
    "plt.plot(xfit, yfit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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