Week_5/Day_2/GLM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>High Temp (°F)</th>\n",
       "      <th>Low Temp (°F)</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Brooklyn Bridge</th>\n",
       "      <th>Manhattan Bridge</th>\n",
       "      <th>Williamsburg Bridge</th>\n",
       "      <th>Queensboro Bridge</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>2016-04-01 00:00:00</td>\n",
       "      <td>78.1</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1704.0</td>\n",
       "      <td>3126</td>\n",
       "      <td>4115.0</td>\n",
       "      <td>2552.0</td>\n",
       "      <td>11497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-02 00:00:00</td>\n",
       "      <td>2016-04-02 00:00:00</td>\n",
       "      <td>55.0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>0.15</td>\n",
       "      <td>827.0</td>\n",
       "      <td>1646</td>\n",
       "      <td>2565.0</td>\n",
       "      <td>1884.0</td>\n",
       "      <td>6922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-04-03 00:00:00</td>\n",
       "      <td>2016-04-03 00:00:00</td>\n",
       "      <td>39.9</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>526.0</td>\n",
       "      <td>1232</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>1306.0</td>\n",
       "      <td>4759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-04 00:00:00</td>\n",
       "      <td>2016-04-04 00:00:00</td>\n",
       "      <td>44.1</td>\n",
       "      <td>33.1</td>\n",
       "      <td>0.47 (S)</td>\n",
       "      <td>521.0</td>\n",
       "      <td>1067</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>4335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-05 00:00:00</td>\n",
       "      <td>2016-04-05 00:00:00</td>\n",
       "      <td>42.1</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1416.0</td>\n",
       "      <td>2617</td>\n",
       "      <td>3081.0</td>\n",
       "      <td>2357.0</td>\n",
       "      <td>9471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date                  Day  High Temp (°F)  Low Temp (°F)  \\\n",
       "0  2016-04-01 00:00:00  2016-04-01 00:00:00            78.1           66.0   \n",
       "1  2016-04-02 00:00:00  2016-04-02 00:00:00            55.0           48.9   \n",
       "2  2016-04-03 00:00:00  2016-04-03 00:00:00            39.9           34.0   \n",
       "3  2016-04-04 00:00:00  2016-04-04 00:00:00            44.1           33.1   \n",
       "4  2016-04-05 00:00:00  2016-04-05 00:00:00            42.1           26.1   \n",
       "\n",
       "  Precipitation  Brooklyn Bridge  Manhattan Bridge  Williamsburg Bridge  \\\n",
       "0          0.01           1704.0              3126               4115.0   \n",
       "1          0.15            827.0              1646               2565.0   \n",
       "2          0.09            526.0              1232               1695.0   \n",
       "3      0.47 (S)            521.0              1067               1440.0   \n",
       "4             0           1416.0              2617               3081.0   \n",
       "\n",
       "   Queensboro Bridge  Total  \n",
       "0             2552.0  11497  \n",
       "1             1884.0   6922  \n",
       "2             1306.0   4759  \n",
       "3             1307.0   4335  \n",
       "4             2357.0   9471  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing bikes dataset\n",
    "bikes = pd.read_csv(\"nyc-east-river-bicycle-counts.csv\", header = 0)\n",
    "bikes.drop('Unnamed: 0', axis = 1, inplace = True)\n",
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                    object\n",
       "Day                     object\n",
       "High Temp (°F)         float64\n",
       "Low Temp (°F)          float64\n",
       "Precipitation           object\n",
       "Brooklyn Bridge        float64\n",
       "Manhattan Bridge         int64\n",
       "Williamsburg Bridge    float64\n",
       "Queensboro Bridge      float64\n",
       "Total                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: -0.242981\n",
      "Kurtosis: -0.907534\n"
     ]
    },
    {
     "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": [
    "sns.distplot(bikes['Total']);\n",
    "\n",
    "#skewness and kurtosis\n",
    "print(\"Skewness: %f\" % bikes['Total'].skew())\n",
    "print(\"Kurtosis: %f\" % bikes['Total'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikes['Average Temp'] = (bikes['High Temp (°F)'] + bikes['Low Temp (°F)'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f62ce8e48e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Average Temp', 'Total', data=bikes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "X = bikes['Average Temp']\n",
    "y = bikes['Total']\n",
    "\n",
    "# add intercept to input variable\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# fit poisson regression model \n",
    "model = sm.GLM(y, X, family=sm.families.Poisson()).fit()\n",
    "\n",
    "# add poisson fitted values to dataframe\n",
    "bikes['reg_fit'] = model.fittedvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bToTHO+zb1rYcpv+4eAIZdggcf3N9YrNezUnErEHqsuvgK4vhuzutXXbS3bDN8NKtD9/rYd3gJGLWIDMvOjTXxRKLJolxL3e+kfDt+k4e1n25JRFJ1wBHAEsiYtdU1gacCryQqn01Iu5Or50PnAKsBj4fEVNS+SjgMqAfcHVETEjlOwA3AFsBM4ETIuKNvD6PWTW6ShK5bFM75Wvw4BVrl7Unj2IJxDcKWg/kOcX3WmBUkfLvR8Tw9NWeQHYm23t9l3TMjyT1k9QP+L/AYcDOwHGpLsA307neA7xEloDMmkbdp/C+/krW+ihMIJ+cnH0vljy++g/fKGg9lltLJCIekDS0wupHAjdExOvAM5LmAnul1+ZGxDwASTcAR0p6AvgI8OlUZzLQBlS4J6dZ/uo6hbfU+MbNozuX/ef/wMBdah+DtaRG3Gx4lqQ5kq6RtGUq2xZ4rqDOglRWqnwr4OWIWNWhvChJYyTNkDTjhRdeKFXNrPcpNrtqmz1K129b7gRiNVXvJDIR2BEYDiwCvluPN42ISRExMiJGDhgwoB5vaZav1W9myePe89aUrbNe9v0fszrX99LslpOS3VmSyvw5AxFR5H9qeRGxuOD8VwF3pacLge0Kqg5OZZQoXwpsIWnd1BoprG/WFHKbwluq6+qtN4vUdeKwfJX731yulRBkYxLdImlQRCxKT48G/pIe3wn8QtL3gG2AYcCfAAHD0kyshWSD75+OiJB0H/AJshlao4EOd1KZNVbNp/A+dnvxMY5inDysTkomkYj4cE9OLOl64ABga0kLgHHAAZKGkyWh+cBp6b0ek3QT8DiwCjgzIlan85wFTCGb4ntNRDyW3uI84AZJlwCPAD/pSbxmeajJFN5im0SV4uRhdaaI6LqStCvZFNsN2ssi4roc48rNyJEjY8aMGY0Ow6wy5fbyWKuek4flS9LMiOi0CmeXnbOSxpG1KHYG7ia7Z+MPQK9MIma9wvw/wLWHd13PycMarJIRvk8AuwOPRMTJkgYCP8s3LLMWVknrw8nDmkQlSeSfEfGWpFWSNgOWsPaMKTOrBScP64UqSSIzJG0BXEW2RtWrwIN5BmXWMt54Db6xTdf1nDysSXWZRCLijPTwx5LuBTaLiDn5hmXWx906Bubc2HU9Jw9rcpUMrE+LiAMBImJ+xzIzq1ClrY7djoWPX5l/PGY1UO6O9Q2Ajcju89iS7MY/gM0os06VmXVw62kw54au6530axi6b/7xmNVQuZbIacA5ZHeQFy5xsgK4otgBZpa8sRK+Maiyul95Bjbqn288VrFcNwrrg7q82VDS5yLih3WKJ3e+2dBydfsZMPvnldW96CVYp+s1UP1LrX6K7QEDvubQg5sNgSslfR7YPz2/H7gyIoqs9mbWgrrT6oBuDZaX29iq1X+p5aGue8D0EZUkkR8B66XvACeQLen+2byCMusVbj8TZnfjvtsqZlr5l5o1u3ID6+3LrO8ZEbsXvPQ7SX/OPzSzJtTdVsfRk2D3T+UXj1mDlWuJ/AnYA1gtaceIeBpA0ruB1fUIzqxp3HEmPNKNVsfnH4H+767qrUr1y1v+ctsDpg8rd2Xap/R+CbhP0rz0fChwcp5BmTWFN/8Jl76re8d87XlYb8Oq37LSBOJfavmo+R4wLaDc/8QBkr6QHl9Jtp8HZK2QDwD35RmYtaam+AH+1dkw89ruHVOjO8srTSD+pZYfX9vuKZdE+gGbsKZFUnjMprlFZC2roTORqml1vP+T8B9X5xNPEfMnVLA0vFmdlUsiiyJifN0isZbXkJlI1bQ6Tv8jvGvXXMIx620qGROpiqRrgCOAJRGxayr7NvDvwBvA08DJEfGypKHAE8Df0uHTI+L0dMwI4FpgQ7JNsc5Oe6z3B24kG6OZDxwTES/1JGZrEW/+Cy4d2P3jxr0M6tGPRZc8sGu9TbnbZXu6wOK1wKgOZVOBXSNiN+BJ4PyC156OiOHp6/SC8onAqcCw9NV+zrHAtIgYBkxLz81K+/UXsz07uptA2pZnXzknEMj64zsmDI+BWDMr+edNRCzryYkj4oHUwigs+03B0+lkuyaWJGkQ2dLz09Pz64CjgHuAI8m27QWYTHYn/Xk9idkaK5e/wqttdfzHT+D9Zf975sYJw3qTRraRP0PWHdVuB0mPkC3weEFE/J5steAFBXUWsGYF4YERsSg9fh4o+ZtC0hhgDMCQIUNqE73VXE2nV/76S/DwVd0/7muLYb0Nun+cWYtqSBKR9DVgFdC+Ut0iYEhELE1jILdL2qXS86UxkpIrSUbEJGASZAswVh+55a1Hf4VX2+oAb/5kVqW6JxFJJ5ENuB8YaQnhiHgdeD09ninpaWAnYCEwuODwwakMYLGkQRGxKHV7LanTR7BmU22r4507wxne6dmsJ+qaRCSNAr4C/O+IWFlQPgBYFhGr07Iqw4B5EbFM0gpJ+wAPAScC7cvS3wmMBiak73fU8aPkpilutusNVr0Ol7yze8d8aS5sMiCfeMxaVG5JRNL1ZAPfW0taAIwjm421PjBV2UyX9qm8+wPjJb0JvAWcXjCwfwZrpvjek74gSx43SToFeBY4Jq/PUi9e9rsC94yFhyZ27xh3VZnlJrckEhHHFSn+SYm6twC3lHhtBtDpzq6IWErPpyE3FS/7XUI1rY7Be8Fnp+YTj5m9zXcwWfOqptVx7PXwvo/mE4+ZdeIkYs1l1RtwSRXjFucvgPW9pJtZR3mPszqJNJGWXvJiytfgwSu6f5zHO8xKqsc4awv8duo9Wm4vg2pbHTsfBcdMrnk4Zn1NPcZZnUSaTJ9NGIWqbXWcfA9s/8Hax2NmVXMSsfqottUBcMESWHf92sZjZjXhJGL5+s0F8D8/7LpeMR7vMOuReoyzOolY7a1+Ey7eurpjL3wR+q1X23jMWlQ9xlmdRKx2po6DP/6g+8ed8RC88301D8fM8h9ndRKxnqm21XHwxfChz9c+HjOrKycRq85v2+AP3+/eMZtvB+f+JZdwzKwxnESsctW2Oi56CdYptxOzmfVWTiLWtWkXw++/071jzn0MNh/cdT0z69WcRKy41avg4q26d8zRk2D3T+UTj5k1JScRW9vvLoEHvl15/e33hZN/nV88ZtbUnESsulaHbwQ0M5xEWlt3xzq+8gxs1D+/eMys18k1iUi6BjgCWBIRu6ay/sCNwFBgPnBMRLykbL/cy4CPAiuBkyJiVjpmNHBBOu0lETE5lY9gzda5dwNnR0Tk+Zl6ve62Oo6/BYYdlF88Ztar5T3v8lpgVIeyscC0iBgGTEvPAQ4DhqWvMcBEeDvpjAP2BvYCxknaMh0zETi14LiO72Xtbh0DbZtXlkB2Py7rrmpb7gRiZmXl2hKJiAckDe1QfCRwQHo8GbgfOC+VX5daEtMlbSFpUKo7NSKWAUiaCoySdD+wWURMT+XXAUcB9+T3iXqZ7rY6PM5hZt3UiDGRgRGxKD1+HhiYHm8LPFdQb0EqK1e+oEh5J5LGkLVuGDJkSA/D7wUmDIF/VZgQvroI3rFRvvGYWZ/V0IH1iAhJuY9hRMQkYBLAyJEj++aYycpl8K0dKqv72d/B4BH5xmN101K7YVrTaUQSWSxpUEQsSt1VS1L5QmC7gnqDU9lC1nR/tZffn8oHF6lfc039Q9q2eZnXlq+ps+8X4KBx9YnJ6qYee2ibldOIBY3uBEanx6OBOwrKT1RmH2B56vaaAhwiacs0oH4IMCW9tkLSPmlm14kF56qZcj+kDfPklCwxFEsgH7lgzaB4u7blTiB9VD320DYrJ+8pvteTtSK2lrSAbJbVBOAmSacAzwLHpOp3k03vnUs2xfdkgIhYJuli4OFUb3z7IDtwBmum+N5DDoPqTfND+tZqGF/mHg0PiptZA+Q9O+u4Ei8dWKRuAGeWOM81wDVFymcAu/YkxqZ3/XHwt7tLv+7kYWYN5DvWm9GLT8EVI8vX2eVo+OS1XZ6qqcdzrMfqsYe2WTn+n9aFuv2QVrJXx4l3wLsPqPiUHnTt++qxh7ZZOU4iXcj1h3TRHLhyv/J1vjwPNu7m4ohJ04znWK6cMKyRnEQqUNMf0ptGw+O3l69zwRJYd/3avaeZWU6cROqh3L0cAJsPgXMfrU8sZmY15CSSh9dfgf/qYmvYE26DHT+SaxgedDWzvPm3Sa3Mug7u/Fz5OnXupvKgq5nlzUmkJ7rqpoKG38fhhGFmeXIS6Y4I+PoW5esccil88Ky6hGNm1mhOIpUq1+oY+3fYoIJWiZlZH+MkUi0vN2Jm5iRSMScNK8GTF6yVNWIpeLM+oym3CjCrIycRsx7w0jLW6pxEzMysak4iZmZWNScRsx4otYSMl5axVlH3JCLpvZJmF3ytkHSOpDZJCwvKP1pwzPmS5kr6m6RDC8pHpbK5ksbW+7OYzbzo0E4Jw7OzrJUo25W2QW8u9QMWAnuT7an+akR8p0OdnYHrgb2AbYDfAjull58EDgYWkO3BflxEPF7uPUeOHBkzZsyo5ccwM+vzJM2MiE5brja6zX0g8HREPCupVJ0jgRsi4nXgGUlzyRIKwNyImAcg6YZUt2wSMTOz2mn0mMixZK2MdmdJmiPpGklbprJtgecK6ixIZaXKO5E0RtIMSTNeeOGF2kVvZtbiGpZEJL0D+BhwcyqaCOwIDAcWAd+t1XtFxKSIGBkRIwcMGFCr05qZtbxGdmcdBsyKiMUA7d8BJF0F3JWeLgS2KzhucCqjTLmZmdVBI5PIcRR0ZUkaFBGL0tOjgb+kx3cCv5D0PbKB9WHAnwABwyTtQJY8jgU+XafYzSridbWsr2tId5akjclmVd1aUPwtSY9KmgN8GDgXICIeA24iGzC/FzgzIlZHxCrgLGAK8ARwU6pr1hS8rpa1goa0RCLiNWCrDmUnlKl/KXBpkfK7gbtrHqBZDXhdLWsFjZ6dZWZmvZiTiJmZVc1JxCwnXlfLWoGTiFlOvK6WtQL/SWSWIycM6+vcEjEzs6o5iZiZWdWcRMzMrGpOImZmVjUnETMzq5qTiJmZVc1JxMzMquYkYmZmVXMSMTOzqjmJmJlZ1ZxEzMysak4iZmZWtYYlEUnz03a4syXNSGX9JU2V9FT6vmUql6TLJc2VNEfSHgXnGZ3qPyVpdKM+j5lZK2p0S+TDETE8Ikam52OBaRExDJiWngMcBgxLX2OAiZAlHWAcsDewFzCuPfGYmVn+Gp1EOjoSmJweTwaOKii/LjLTgS0kDQIOBaZGxLKIeAmYCoyqc8xmZi2rkfuJBPAbSQFcGRGTgIERsSi9/jwwMD3eFniu4NgFqaxU+VokjSFrwTBkyJBafoYeGzF+CktXrnr7uTctMrPepJEtkX0jYg+yrqozJe1f+GJEBFmi6bGImBQRIyNi5IABA2pxypromEAAlq5cxYjxUxoUkZlZ9zSsJRIRC9P3JZJuIxvTWCxpUEQsSt1VS1L1hcB2BYcPTmULgQM6lN+fc+g10zGBdFVeC275mFktNaQlImljSZu2PwYOAf4C3Am0z7AaDdyRHt8JnJhmae0DLE/dXlOAQyRtmQbUD0llVoRbPmZWa41qiQwEbpPUHsMvIuJeSQ8DN0k6BXgWOCbVvxv4KDAXWAmcDBARyyRdDDyc6o2PiGX1+xiNUW1rohEtHzPr2xqSRCJiHrB7kfKlwIFFygM4s8S5rgGuqXWM9bDVRusW/QW+1Ual/1nKtSbcLWVm9dZsU3xbysyLDu2UMLpqVbg1YWbNpJFTfA3q2nqopuVjZlaOWyItpJqWj5lZOf4TtJfpaWvCCcPMasktkV7GrQkzayZuifRCThhm1izcEjEzs6o5iZiZWdWcRMzMrGpOImZmVjUnETMzq5qyZalah6QXyBZ3rLWtgRdzOG+tOL6ea/YYHV/POL7yto+IThsytVwSyYukGQV7xTcdx9dzzR6j4+sZx1cdd2eZmVnVnETMzKxqTiK1M6nRAXTB8fVcs8fo+HrG8VXBYyJmZlY1t0TMzKxqTiJmZlY1J5EqSNpA0p8k/VnSY5K+nsqvlfSMpNnpa3iD4+wn6RFJd6XnO0h6SNJcSTdKekeTxdc010/SfEmPpjhmpLL+kqZKeip937LJ4muTtLDg+n20gfFtIemXkv4q6QlJ/9Zk169YfE1x/SS9tyCG2ZJWSDqnma5fISeR6rwOfCQidgeGA6Mk7ZNe+3JEDE9fsxsVYHI28ETB828C34+I9wAvAac0JKo1OsYHzXX9PpziaJ+bPxaYFhHDgGnpeSN1jA+yf9/263d3wyKDy4B7I+J9wO5k/87NdP2KxQdNcP0i4m/tMQAjgJXAbTTX9Xubk0gVIvNqerpe+mqqGQqSBgOHA1en5wI+AvwyVZkMHNWQ4OgcXy9xJNl1gwZfv2YmaXNgf+AnABHxRkS8TJNcvzLxNaMDgacj4lma5Pp15CRSpdQVMxtYAkyNiIfSS5dKmiPp+5LWb1yE/AD4CvBWer4V8HJEtO+tuwDYtgFxtfsBa8fXrlmuXwC/kTRT0phUNjAiFqXHzwMDGxMaUDw+gLPS9bumgd0dOwAvAP+duiuvlrQxzXP9SsUHzXH9Ch0LXJ8eN8v1W4uTSJUiYnVqbg4G9pK0K3A+8D5gT6A/cF4jYpN0BLAkImY24v27Uia+prh+yb4RsQdwGHCmpP0LX4xsbnwjW5/F4psI7EjWxboI+G6DYlsX2AOYGBEfAF6jQ9dLg69fqfia5foBkMYsPwbc3PG1Jvj/9zYnkR5KzeD7gFERsSh1db0O/DewV4PC+hDwMUnzgRvIurEuA7aQ1L4l8mBgYWPC6xyfpJ810fUjIham70vI+qP3AhZLGgSQvi9ppvgiYnH64+Yt4Coad/0WAAsKWue/JPul3SzXr2h8TXT92h0GzIqIxel5s1y/tTiJVEHSAElbpMcbAgcDfy34BxZZf+VfGhFfRJwfEYMjYihZc/h3EXE8WbL7RKo2GrijieL7P81y/SRtLGnT9sfAISmWO8muGzTw+pWKr/36JUfTuP9/zwPPSXpvKjoQeJwmuX6l4muW61fgONZ0ZUGTXL+O1u26ihUxCJgsqR9ZIr4pIu6S9DtJAwABs4HTGxhjMecBN0i6BHiENLDYRH7eJNdvIHBblstYF/hFRNwr6WHgJkmnkG0ncEyTxffTNC06gPnAaQ2KD+BzZP+e7wDmASeTflaa4PqViu/yZrl+6Y+DgzvEMIHmuX5v87InZmZWNXdnmZlZ1ZxEzMysak4iZmZWNScRMzOrmpOImZlVzUnEDJB0lKSQ9L5Gx1KOslWYZ0v6u6QXClZ6Hdro2Kw1eYqvGSDpRmAbshsfx9XgfP0iYnXPIyt5/pOAkRFxVl7vYVYJt0Ss5UnaBNiXbGn8Y1PZKEk3F9Q5QGv2PTlE0oOSZkm6OR3fvsfHNyXNAj4p6VRJDyvbd+YWSRulejtKmq5sP5BLJL1a8D5fTsfMUdqnpoL4d5R0b1qM8fftrSll+7NMTO81L32Ga5Ttn3FtwfGvpgUvH5M0Ld3waVYRJxGzbInteyPiSWCppBHAb4G9C1Z3/RTZ3f5bAxcAB6UFEGcAXyg419KI2CMibgBujYg9074zT7Bm/5bLgMsi4v1k6zgBWXIChpGt2TQcGNFx4ccSJgGfi4gRwJeAHxW8tiXwb8C5ZMtmfB/YBXi/1mz6tTEwIyJ2Af4f0OOWmLUOJxGzbI2iG9LjG4Dj0pL59wL/nhatPJxsraJ9gJ2BPyrbCmA0sH3BuW4seLxrahk8ChxP9ssbsl/q7a2cXxTUPyR9PQLMIlvReFi5wFMr6IPAzSmeK8mW5Wn3q7Ti66PA4oh4NC0w+BgwNNV5qyDun5G1yswq4rWzrKVJ6k+2yvH7JQXQDwhJXyZLKGcBy8j+Un8lLQ45NSKOK3HK1woeXwscFRF/TmMYB3QVDvBfEXFlNz7COmT7xAwv8frr6ftbBY/bn5f6+fdAqVXMLRFrdZ8AfhoR20fE0IjYDngG2I+sa2cP4FTWtFSmAx+S9B54e0XdnUqce1NgkaT1yFoi7aYD/5EeH1tQPgX4TMEYy7aS3lku+IhYATwj6ZPpGEnavZIPXmAd1qzu/GngD9083lqYk4i1uuPI9uModAtZl9Zq4C6yfR3uAoiIF4CTgOslzQEeJOt2KuZC4CHgj8BfC8rPAb6Qjn8PsDyd+zdk3VsPpi6wX5Iloq4cD5wi6c9k3VRHVnBModfINlb7C1mrbHw3j7cW5im+ZnWWZmn9MyJC0rFkCau7v/hrGc+rEbFJo97fejePiZjV3wjgijS+8jLwmcaGY1Y9t0TMzKxqHhMxM7OqOYmYmVnVnETMzKxqTiJmZlY1JxEzM6va/wcP6/zfiSkb2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(bikes['Average Temp'], bikes['Total'], fit_reg=False)\n",
    "plt.plot(bikes['Average Temp'], bikes['reg_fit']);"
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}