ProfAvery/Basic Linear Regression.ipynb

## Basic Linear Regression.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features = [1896, 1900, 1904, 1906, 1908, 1912, 1920, 1924, 1928, 1932, 1936, 1948, 1952, 1956, 1960, 1964, 1968, 1972, 1976, 1980, 1984, 1988, 1992, 1996, 2000, 2004, 2008]\n",
      "targets = [12, 11, 11, 11.2, 10.8, 10.8, 10.8, 10.6, 10.8, 10.3, 10.3, 10.3, 10.4, 10.5, 10.2, 10, 9.95, 10.14, 10.06, 10.25, 9.99, 9.92, 9.96, 9.84, 9.87, 9.85, 9.69]\n"
     ]
    }
   ],
   "source": [
    "features = [\n",
    "    1896, 1900, 1904, 1906, 1908, 1912, 1920, 1924, 1928, 1932, 1936, 1948, 1952, 1956, 1960, 1964, 1968, 1972, 1976, 1980, 1984, 1988, 1992, 1996, 2000, 2004, 2008,\n",
    "]\n",
    "\n",
    "targets = [\n",
    "    12, 11, 11, 11.2, 10.8, 10.8, 10.8, 10.6, 10.8, 10.3, 10.3, 10.3, 10.4, 10.5, 10.2, 10, 9.95, 10.14, 10.06, 10.25, 9.99, 9.92, 9.96, 9.84, 9.87, 9.85, 9.69\n",
    "]\n",
    "\n",
    "print(f'features = {features}')\n",
    "print(f'targets = {targets}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X = [[1896]\n",
      " [1900]\n",
      " [1904]\n",
      " [1906]\n",
      " [1908]\n",
      " [1912]\n",
      " [1920]\n",
      " [1924]\n",
      " [1928]\n",
      " [1932]\n",
      " [1936]\n",
      " [1948]\n",
      " [1952]\n",
      " [1956]\n",
      " [1960]\n",
      " [1964]\n",
      " [1968]\n",
      " [1972]\n",
      " [1976]\n",
      " [1980]\n",
      " [1984]\n",
      " [1988]\n",
      " [1992]\n",
      " [1996]\n",
      " [2000]\n",
      " [2004]\n",
      " [2008]]\n",
      "t = [12.   11.   11.   11.2  10.8  10.8  10.8  10.6  10.8  10.3  10.3  10.3\n",
      " 10.4  10.5  10.2  10.    9.95 10.14 10.06 10.25  9.99  9.92  9.96  9.84\n",
      "  9.87  9.85  9.69]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "X = np.array(features).reshape([-1, 1])\n",
    "t = np.array(targets)\n",
    "\n",
    "print(f'X = {X}')\n",
    "print(f't = {t}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w0 = 36.416455902501056\n",
      "w1 = -0.013330885710959679\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(X, t)\n",
    "\n",
    "print(f'w0 = {lm.intercept_}')\n",
    "print(f'w1 = {lm.coef_[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1896</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1900</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1904</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1906</td>\n",
       "      <td>11.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1908</td>\n",
       "      <td>10.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1912</td>\n",
       "      <td>10.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1920</td>\n",
       "      <td>10.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1924</td>\n",
       "      <td>10.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1928</td>\n",
       "      <td>10.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1932</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1936</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1948</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1952</td>\n",
       "      <td>10.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1956</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1960</td>\n",
       "      <td>10.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1964</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1968</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1972</td>\n",
       "      <td>10.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1976</td>\n",
       "      <td>10.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1980</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1984</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1988</td>\n",
       "      <td>9.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1992</td>\n",
       "      <td>9.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1996</td>\n",
       "      <td>9.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2000</td>\n",
       "      <td>9.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2004</td>\n",
       "      <td>9.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2008</td>\n",
       "      <td>9.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year   time\n",
       "0   1896  12.00\n",
       "1   1900  11.00\n",
       "2   1904  11.00\n",
       "3   1906  11.20\n",
       "4   1908  10.80\n",
       "5   1912  10.80\n",
       "6   1920  10.80\n",
       "7   1924  10.60\n",
       "8   1928  10.80\n",
       "9   1932  10.30\n",
       "10  1936  10.30\n",
       "11  1948  10.30\n",
       "12  1952  10.40\n",
       "13  1956  10.50\n",
       "14  1960  10.20\n",
       "15  1964  10.00\n",
       "16  1968   9.95\n",
       "17  1972  10.14\n",
       "18  1976  10.06\n",
       "19  1980  10.25\n",
       "20  1984   9.99\n",
       "21  1988   9.92\n",
       "22  1992   9.96\n",
       "23  1996   9.84\n",
       "24  2000   9.87\n",
       "25  2004   9.85\n",
       "26  2008   9.69"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(X, columns=['year'])\n",
    "df['time'] = t\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fe83d0a088>"
      ]
     },
     "execution_count": 53,
     "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": [
    "df.plot.scatter(x='year', y='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.14109659, 11.08777305, 11.03444951, 11.00778774, 10.98112597,\n",
       "       10.92780242, 10.82115534, 10.76783179, 10.71450825, 10.66118471,\n",
       "       10.60786117, 10.44789054, 10.39456699, 10.34124345, 10.28791991,\n",
       "       10.23459637, 10.18127282, 10.12794928, 10.07462574, 10.02130219,\n",
       "        9.96797865,  9.91465511,  9.86133157,  9.80800802,  9.75468448,\n",
       "        9.70136094,  9.64803739])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = lm.predict(X)\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fe83c80ec8>]"
      ]
     },
     "execution_count": 52,
     "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",
    "\n",
    "plt.scatter(X, t)\n",
    "plt.plot(X, y)"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}