Homework_05.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='heading'>\n",
    "    <div style='float:left;'><h1>CPSC 4300/6300: Applied Data Science</h1></div>\n",
    "    <img style=\"float: right; padding-right: 10px; width: 65px\" src=\"https://raw.githubusercontent.com/bsethwalker/clemson-cs4300/main/images/clemson_paw.png\"> </div>\n",
    "\n",
    "\n",
    "## Homework 5: Regression Modeling to Forecast Bike Sharing Usage, Continued\n",
    "\n",
    "**Clemson University**<br>\n",
    "**Fall 2021**<br>\n",
    "**Instructor(s):** Nina Hubig <br>\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "blockquote { background: #AEDE94; }\n",
       "\n",
       "div.heading {\n",
       "margin-bottom: 25px;\n",
       "height: 75px;\n",
       "}\n",
       "\n",
       "h1 { \n",
       "    padding-top: 25px;\n",
       "    padding-bottom: 25px;\n",
       "    text-align: left; \n",
       "    padding-left: 10px;\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "h2 { \n",
       "    padding-top: 10px;\n",
       "    padding-bottom: 10px;\n",
       "    text-align: left; \n",
       "    padding-left: 5px;\n",
       "    background-color: #EEEEEE; \n",
       "    color: black;\n",
       "}\n",
       "\n",
       "div.exercise {\n",
       "    \n",
       "    background: rgba(245, 102, 0, .75);\n",
       "    border-color: #E9967A;\n",
       "    border-left: 5px solid #522D80; \n",
       "    padding: 0.5em;\n",
       "}\n",
       "\n",
       "div.exercise-r {\n",
       "    background-color: #fce8e8;\n",
       "    border-color: #E9967A; \t\n",
       "    border-left: 5px solid #800080; \n",
       "    padding: 0.5em;\n",
       "}\n",
       "\n",
       "span.sub-q {\n",
       "    font-weight: bold;\n",
       "}\n",
       "div.theme {\n",
       "    background-color: #DDDDDD;\n",
       "    border-color: #E9967A; \t\n",
       "    border-left: 5px solid #800080; \n",
       "    padding: 0.5em;\n",
       "    font-size: 18pt;\n",
       "}\n",
       "div.gc { \n",
       "    background-color: #AEDE94;\n",
       "    border-color: #E9967A; \t \n",
       "    border-left: 5px solid #800080; \n",
       "    padding: 0.5em;\n",
       "    font-size: 12pt;\n",
       "}\n",
       "p.q1 { \n",
       "    padding-top: 5px;\n",
       "    padding-bottom: 5px;\n",
       "    text-align: left; \n",
       "    padding-left: 5px;\n",
       "    background-color: #EEEEEE; \n",
       "    color: black;\n",
       "}\n",
       "header {\n",
       "   padding-top: 35px;\n",
       "    padding-bottom: 35px;\n",
       "    text-align: left; \n",
       "    padding-left: 10px;\n",
       "    background-color: #DDDDDD; \n",
       "    color: black;\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" RUN THIS CELL TO GET THE RIGHT FORMATTING \"\"\"\n",
    "import requests\n",
    "from IPython.core.display import HTML\n",
    "css_file = 'https://raw.githubusercontent.com/bsethwalker/clemson-cs4300/main/css/cpsc6300.css'\n",
    "styles = requests.get(css_file).text\n",
    "HTML(styles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### INSTRUCTIONS\n",
    "\n",
    "- To submit your assignment follow the instructions given in Canvas.\n",
    "- Restart the kernel and run the whole notebook again before you submit. \n",
    "- As much as possible, try and stick to the hints and functions we import at the top of the homework, as those are the ideas and tools the class supports and is aiming to teach. And if a problem specifies a particular library you're required to use that library, and possibly others from the import list.\n",
    "- Please use .head() when viewing data. Do not submit a notebook that is excessively long because output was not suppressed or otherwise limited. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<hr style=\"height:2pt\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {}
   },
   "source": [
    "<img src=\"https://raw.githubusercontent.com/bsethwalker/clemson-cs4300/main/images/BSS.png\">\n",
    "\n",
    "### Overview \n",
    "\n",
    "You are hired by the administrators of the [Capital Bikeshare program](https://www.capitalbikeshare.com) program in Washington D.C., to **help them predict the hourly demand for rental bikes** and **give them suggestions on how to increase their revenue**. Your task is to prepare a short report summarizing your findings and make recommendations.\n",
    "\n",
    "The predicted hourly demand could be used for planning the number of bikes that need to be available in the system at any given hour of the day. It costs the program money if bike stations are full and bikes cannot be returned, or empty and there are no bikes available. You will use multiple linear regression and polynomial regression and will explore ridge and lasso regression to predict bike usage. The goal is to build a regression model that can predict the total number of bike rentals in a given hour of the day, based on all available information given to you. \n",
    "\n",
    "An example of a suggestion to increase revenue might be to offer discounts during certain times of the day either during holidays or non-holidays. Your suggestions will depend on your observations of the seasonality of ridership.\n",
    "\n",
    "The data for this problem were collected from the Capital Bikeshare program over the course of two years (2011 and 2012).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {}
   },
   "source": [
    "### Use only the libraries below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.api import OLS\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {}
   },
   "source": [
    "### Overview\n",
    "The initial data set is provided in the file `data/BSS_hour_raw.csv`. You will first add features that will help with the analysis and then separate the data into training and test sets. Each row in this file represents the number of rides by registered users and casual users in a given hour of a specific date. There are 12 attributes in total describing besides the number of users the weather if it is a holiday or not etc: \n",
    "\n",
    "\n",
    "- `dteday` (date in the format YYYY-MM-DD, e.g. 2011-01-01)\n",
    "- `season` (1 = winter, 2 = spring, 3 = summer, 4 = fall)\n",
    "- `hour` (0 for 12 midnight, 1 for 1:00am, 23 for 11:00pm)\n",
    "- `weekday` (0 through 6, with 0 denoting Sunday)\n",
    "- `holiday` (1 = the day is a holiday, 0 = otherwise)\n",
    "- `weather`\n",
    "    - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "    - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "    - 3: Light Snow, Light Rain + Thunderstorm\n",
    "    - 4: Heavy Rain + Thunderstorm + Mist, Snow + Fog \n",
    "- `temp` (temperature in Celsius, normalized)\n",
    "- `atemp` (apparent temperature, or relative outdoor temperature, in Celsius, normalized)\n",
    "- `hum` (relative humidity, normalized)\n",
    "- `windspeed` (wind speed, normalized)\n",
    "- `casual` (number of rides that day made by casual riders, not registered in the system)\n",
    "- `registered` (number of rides that day made by registered riders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<div class=\"theme\">  Getting Started </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** This homework assignment is a continuation of Homework 4. \n",
    "\n",
    "Load `BSS_train.csv` and `BSS_test.csv` into dataframes `BSS_train` and `BSS_test`, respectively. Remove the `dteday`, `casual`, and `registered` columns from both the train and the test dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n",
    "BSS_train = pd.read_csv(\"BSS_train.csv\")\n",
    "BSS_test = pd.read_csv(\"BSS_test.csv\")\n",
    "Bikes_df_raw = pd.read_csv(\"BSS_hour_raw.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "BSS_train = BSS_train.drop(['dteday','casual','registered'], axis=1)\n",
    "BSS_test = BSS_test.drop(['dteday','casual','registered'], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>year</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>counts</th>\n",
       "      <th>spring</th>\n",
       "      <th>...</th>\n",
       "      <th>Dec</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Cloudy</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Storm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.5758</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>479</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.7273</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.6061</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>411</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.1970</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hour  holiday  year  workingday  temp   atemp   hum  windspeed  counts  \\\n",
       "0    21        0     1           1  0.24  0.2273  0.60     0.2537      63   \n",
       "1    17        0     0           1  0.62  0.5758  0.83     0.2537     479   \n",
       "2     9        0     1           1  0.76  0.7273  0.66     0.1642     353   \n",
       "3    17        0     1           0  0.62  0.6061  0.22     0.2985     411   \n",
       "4     8        0     1           0  0.18  0.1970  0.40     0.1343      70   \n",
       "\n",
       "   spring  ...  Dec  Mon  Tue  Wed  Thu  Fri  Sat  Cloudy  Snow  Storm  \n",
       "0       0  ...    1    0    0    0    1    0    0       1     0      0  \n",
       "1       0  ...    0    0    0    0    1    0    0       0     1      0  \n",
       "2       0  ...    0    0    0    0    1    0    0       0     0      0  \n",
       "3       1  ...    0    0    0    0    0    0    0       0     0      0  \n",
       "4       0  ...    0    0    0    0    0    0    0       0     0      0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BSS_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.24\n",
       "1    0.62\n",
       "2    0.76\n",
       "3    0.62\n",
       "4    0.18\n",
       "Name: temp, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BSS_train['temp'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "---\n",
    "\n",
    "<div class=\"theme\">  Question 3 - Multiple Linear Regression to Predict Hourly Ridership </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 3.1 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Use statsmodels to fit a multiple linear regression model to the training set using all the useable predictors (nothing that `count` was derived of and no interactions or polynomial terms) to predict `counts`, and report its $R^2$ score on the train and test sets.  Call this set of predictors `X_train`. Reminder that you need to add a constant column when using statsmodels in order to fit the intercept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters: \n",
      " const         -30.407500\n",
      "hour            7.209247\n",
      "holiday       -19.151036\n",
      "year           78.121108\n",
      "workingday     10.948441\n",
      "temp          273.662515\n",
      "atemp         162.535266\n",
      "hum          -202.008009\n",
      "windspeed      19.382705\n",
      "spring         50.075412\n",
      "summer         22.687920\n",
      "fall           70.947417\n",
      "Feb           -10.719770\n",
      "Mar           -11.293840\n",
      "Apr           -44.832594\n",
      "May           -50.160547\n",
      "Jun           -72.204151\n",
      "Jul           -90.896453\n",
      "Aug           -58.324552\n",
      "Sept          -16.168637\n",
      "Oct           -15.883815\n",
      "Nov           -32.723179\n",
      "Dec           -19.208751\n",
      "Mon            -3.333610\n",
      "Tue            -0.007375\n",
      "Wed            -2.205852\n",
      "Thu            -5.569800\n",
      "Fri             2.914043\n",
      "Sat            12.746077\n",
      "Cloudy          6.916314\n",
      "Snow          -26.815450\n",
      "Storm          48.204539\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# multiple linear regression\n",
    "X_train = sm.add_constant(BSS_train.drop('counts', axis=1))\n",
    "X_test = sm.add_constant(BSS_test.drop('counts', axis=1))\n",
    "Y_train = BSS_train['counts'].values.reshape(-1,1)\n",
    "Y_test = BSS_test['counts'].values.reshape(-1,1)\n",
    "\n",
    "model = OLS(Y_train, X_train)\n",
    "results = model.fit()\n",
    "\n",
    "y_hat_train = results.predict()\n",
    "y_hat_test = results.predict(exog=X_test)\n",
    "\n",
    "print('Parameters: \\n', results.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.412</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.410</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   201.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 07 Oct 2021</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:39:53</td>     <th>  Log-Likelihood:    </th> <td> -55215.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  8689</td>      <th>  AIC:               </th> <td>1.105e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  8658</td>      <th>  BIC:               </th> <td>1.107e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    30</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>      <td>  -30.4075</td> <td>   10.908</td> <td>   -2.788</td> <td> 0.005</td> <td>  -51.789</td> <td>   -9.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour</th>       <td>    7.2092</td> <td>    0.233</td> <td>   30.975</td> <td> 0.000</td> <td>    6.753</td> <td>    7.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>holiday</th>    <td>  -19.1510</td> <td>    8.369</td> <td>   -2.288</td> <td> 0.022</td> <td>  -35.556</td> <td>   -2.746</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year</th>       <td>   78.1211</td> <td>    3.020</td> <td>   25.864</td> <td> 0.000</td> <td>   72.200</td> <td>   84.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>workingday</th> <td>   10.9484</td> <td>    3.512</td> <td>    3.118</td> <td> 0.002</td> <td>    4.065</td> <td>   17.832</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>temp</th>       <td>  273.6625</td> <td>   61.680</td> <td>    4.437</td> <td> 0.000</td> <td>  152.755</td> <td>  394.570</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>atemp</th>      <td>  162.5353</td> <td>   64.909</td> <td>    2.504</td> <td> 0.012</td> <td>   35.298</td> <td>  289.773</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hum</th>        <td> -202.0080</td> <td>    9.927</td> <td>  -20.349</td> <td> 0.000</td> <td> -221.468</td> <td> -182.548</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>windspeed</th>  <td>   19.3827</td> <td>   13.595</td> <td>    1.426</td> <td> 0.154</td> <td>   -7.268</td> <td>   46.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>spring</th>     <td>   50.0754</td> <td>    9.528</td> <td>    5.256</td> <td> 0.000</td> <td>   31.398</td> <td>   68.753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>summer</th>     <td>   22.6879</td> <td>   11.138</td> <td>    2.037</td> <td> 0.042</td> <td>    0.855</td> <td>   44.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>fall</th>       <td>   70.9474</td> <td>    9.448</td> <td>    7.509</td> <td> 0.000</td> <td>   52.426</td> <td>   89.469</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Feb</th>        <td>  -10.7198</td> <td>    7.590</td> <td>   -1.412</td> <td> 0.158</td> <td>  -25.598</td> <td>    4.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mar</th>        <td>  -11.2938</td> <td>    8.385</td> <td>   -1.347</td> <td> 0.178</td> <td>  -27.731</td> <td>    5.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Apr</th>        <td>  -44.8326</td> <td>   12.635</td> <td>   -3.548</td> <td> 0.000</td> <td>  -69.600</td> <td>  -20.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>May</th>        <td>  -50.1605</td> <td>   13.493</td> <td>   -3.718</td> <td> 0.000</td> <td>  -76.609</td> <td>  -23.712</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Jun</th>        <td>  -72.2042</td> <td>   13.700</td> <td>   -5.271</td> <td> 0.000</td> <td>  -99.058</td> <td>  -45.350</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Jul</th>        <td>  -90.8965</td> <td>   15.387</td> <td>   -5.907</td> <td> 0.000</td> <td> -121.058</td> <td>  -60.735</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Aug</th>        <td>  -58.3246</td> <td>   15.061</td> <td>   -3.873</td> <td> 0.000</td> <td>  -87.847</td> <td>  -28.802</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sept</th>       <td>  -16.1686</td> <td>   13.432</td> <td>   -1.204</td> <td> 0.229</td> <td>  -42.499</td> <td>   10.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Oct</th>        <td>  -15.8838</td> <td>   12.484</td> <td>   -1.272</td> <td> 0.203</td> <td>  -40.356</td> <td>    8.588</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Nov</th>        <td>  -32.7232</td> <td>   12.049</td> <td>   -2.716</td> <td> 0.007</td> <td>  -56.342</td> <td>   -9.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dec</th>        <td>  -19.2088</td> <td>    9.560</td> <td>   -2.009</td> <td> 0.045</td> <td>  -37.949</td> <td>   -0.469</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mon</th>        <td>   -3.3336</td> <td>    3.756</td> <td>   -0.888</td> <td> 0.375</td> <td>  -10.697</td> <td>    4.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tue</th>        <td>   -0.0074</td> <td>    4.105</td> <td>   -0.002</td> <td> 0.999</td> <td>   -8.055</td> <td>    8.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Wed</th>        <td>   -2.2059</td> <td>    4.082</td> <td>   -0.540</td> <td> 0.589</td> <td>  -10.207</td> <td>    5.795</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Thu</th>        <td>   -5.5698</td> <td>    4.013</td> <td>   -1.388</td> <td> 0.165</td> <td>  -13.436</td> <td>    2.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fri</th>        <td>    2.9140</td> <td>    4.015</td> <td>    0.726</td> <td> 0.468</td> <td>   -4.956</td> <td>   10.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sat</th>        <td>   12.7461</td> <td>    5.587</td> <td>    2.281</td> <td> 0.023</td> <td>    1.793</td> <td>   23.699</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cloudy</th>     <td>    6.9163</td> <td>    3.676</td> <td>    1.882</td> <td> 0.060</td> <td>   -0.289</td> <td>   14.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Snow</th>       <td>  -26.8155</td> <td>    6.094</td> <td>   -4.401</td> <td> 0.000</td> <td>  -38.760</td> <td>  -14.871</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Storm</th>      <td>   48.2045</td> <td>   98.839</td> <td>    0.488</td> <td> 0.626</td> <td> -145.543</td> <td>  241.952</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1691.839</td> <th>  Durbin-Watson:     </th> <td>   1.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3250.657</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.191</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 4.818</td>  <th>  Cond. No.          </th> <td>1.17e+16</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 1.18e-26. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.412\n",
       "Model:                            OLS   Adj. R-squared:                  0.410\n",
       "Method:                 Least Squares   F-statistic:                     201.9\n",
       "Date:                Thu, 07 Oct 2021   Prob (F-statistic):               0.00\n",
       "Time:                        16:39:53   Log-Likelihood:                -55215.\n",
       "No. Observations:                8689   AIC:                         1.105e+05\n",
       "Df Residuals:                    8658   BIC:                         1.107e+05\n",
       "Df Model:                          30                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        -30.4075     10.908     -2.788      0.005     -51.789      -9.026\n",
       "hour           7.2092      0.233     30.975      0.000       6.753       7.665\n",
       "holiday      -19.1510      8.369     -2.288      0.022     -35.556      -2.746\n",
       "year          78.1211      3.020     25.864      0.000      72.200      84.042\n",
       "workingday    10.9484      3.512      3.118      0.002       4.065      17.832\n",
       "temp         273.6625     61.680      4.437      0.000     152.755     394.570\n",
       "atemp        162.5353     64.909      2.504      0.012      35.298     289.773\n",
       "hum         -202.0080      9.927    -20.349      0.000    -221.468    -182.548\n",
       "windspeed     19.3827     13.595      1.426      0.154      -7.268      46.033\n",
       "spring        50.0754      9.528      5.256      0.000      31.398      68.753\n",
       "summer        22.6879     11.138      2.037      0.042       0.855      44.521\n",
       "fall          70.9474      9.448      7.509      0.000      52.426      89.469\n",
       "Feb          -10.7198      7.590     -1.412      0.158     -25.598       4.158\n",
       "Mar          -11.2938      8.385     -1.347      0.178     -27.731       5.143\n",
       "Apr          -44.8326     12.635     -3.548      0.000     -69.600     -20.065\n",
       "May          -50.1605     13.493     -3.718      0.000     -76.609     -23.712\n",
       "Jun          -72.2042     13.700     -5.271      0.000     -99.058     -45.350\n",
       "Jul          -90.8965     15.387     -5.907      0.000    -121.058     -60.735\n",
       "Aug          -58.3246     15.061     -3.873      0.000     -87.847     -28.802\n",
       "Sept         -16.1686     13.432     -1.204      0.229     -42.499      10.162\n",
       "Oct          -15.8838     12.484     -1.272      0.203     -40.356       8.588\n",
       "Nov          -32.7232     12.049     -2.716      0.007     -56.342      -9.104\n",
       "Dec          -19.2088      9.560     -2.009      0.045     -37.949      -0.469\n",
       "Mon           -3.3336      3.756     -0.888      0.375     -10.697       4.029\n",
       "Tue           -0.0074      4.105     -0.002      0.999      -8.055       8.040\n",
       "Wed           -2.2059      4.082     -0.540      0.589     -10.207       5.795\n",
       "Thu           -5.5698      4.013     -1.388      0.165     -13.436       2.297\n",
       "Fri            2.9140      4.015      0.726      0.468      -4.956      10.784\n",
       "Sat           12.7461      5.587      2.281      0.023       1.793      23.699\n",
       "Cloudy         6.9163      3.676      1.882      0.060      -0.289      14.122\n",
       "Snow         -26.8155      6.094     -4.401      0.000     -38.760     -14.871\n",
       "Storm         48.2045     98.839      0.488      0.626    -145.543     241.952\n",
       "==============================================================================\n",
       "Omnibus:                     1691.839   Durbin-Watson:                   1.982\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3250.657\n",
       "Skew:                           1.191   Prob(JB):                         0.00\n",
       "Kurtosis:                       4.818   Cond. No.                     1.17e+16\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 1.18e-26. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train r2 = 0.4116\n",
      "test r2 = 0.4007\n"
     ]
    }
   ],
   "source": [
    "print('train r2 = {:.4}'.format( r2_score(BSS_train['counts'], y_hat_train)))\n",
    "print('test r2 = {:.4}'.format(r2_score(BSS_test['counts'], y_hat_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 3.2 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Examine the estimated coefficients and report which ones are statistically significant at a significance level of 5% (p-value < 0.05). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hour          6.967036e-200\n",
       "year          3.862196e-142\n",
       "workingday     1.828978e-03\n",
       "temp           9.243128e-06\n",
       "atemp          1.229662e-02\n",
       "spring         1.510519e-07\n",
       "summer         4.168321e-02\n",
       "fall           6.554799e-14\n",
       "Sat            2.255964e-02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "results.pvalues[((results.pvalues < 0.05) & (results.params > 0))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const      5.319401e-03\n",
       "holiday    2.214061e-02\n",
       "hum        5.926590e-90\n",
       "Apr        3.897799e-04\n",
       "May        2.023805e-04\n",
       "Jun        1.392848e-07\n",
       "Jul        3.606677e-09\n",
       "Aug        1.084419e-04\n",
       "Nov        6.623427e-03\n",
       "Dec        4.454012e-02\n",
       "Snow       1.092443e-05\n",
       "dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.pvalues[((results.pvalues < 0.05) & (results.params < 0))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 3.3 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "You should see some strange estimates for the predictors representing months.  Carefully interpret the `July` coefficient, and explain why this result is likely occurring (be as specific as possible). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*\n",
    "\n",
    "\n",
    "It implies negative correlation and it's relation is statistically insignificant. Here the independent variable might increases, the dependent variable might tends to decrease."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 3.4 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To diagnose the model, make two plots: first a histogram of the residuals, and second a plot of the residuals of the fitted model ${e} = y - \\hat{y}$ as a function of the predicted value $\\hat{y}$. Draw a horizontal line denoting the zero residual value on the Y-axis. What do the plots reveal about the OLS assumptions (linearity, constant variance, and normality)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your code here\n",
    "fig, axes = plt.subplots(1,2,figsize=(15,5))\n",
    "\n",
    "axes[0].set_title('Plot of Residuals')\n",
    "axes[0].scatter(y_hat_train, y_hat_train - Y_train[:,0], alpha=0.1)\n",
    "axes[0].axhline(0, c='black', lw=2)\n",
    "\n",
    "axes[1].set_title('Histogram of Residual Magnitudes')\n",
    "axes[1].hist(y_hat_train - Y_train[:,0], alpha=0.8)\n",
    "axes[1].axhline(0, c='black', lw=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*\n",
    "\n",
    "As y-hat increases, the variance of the residuals also increases. There is also a pattern to the residuals, suggesting more than linear behavior needs modeling. Hence, linear regression is a bad fit for this data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 3.5 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Perhaps we can do better via a model with polynomial terms. Build a dataset `X_train_poly` from your `X_train` data from question 3.1 with added $x^2$ terms for `temp`, `hour`, and `humidity`. Are these polynomial terms important? How does predicted ridership change as each of `temp`, `hour`, and `humidity` increase?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['const', 'hour', 'holiday', 'year', 'workingday', 'temp', 'atemp',\n",
       "       'hum', 'windspeed', 'spring', 'summer', 'fall', 'Feb', 'Mar', 'Apr',\n",
       "       'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec', 'Mon', 'Tue',\n",
       "       'Wed', 'Thu', 'Fri', 'Sat', 'Cloudy', 'Snow', 'Storm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_poly = X_train.copy()\n",
    "X_train_poly['hour2'] = X_train_poly['hour']**2\n",
    "X_train_poly['temp'] = X_train_poly['temp']**2\n",
    "X_train_poly['hum2'] = X_train_poly['hum']**2\n",
    "X_train_poly.head()\n",
    "x = sm.add_constant(X_train_poly)\n",
    "poly_model = OLS(endog = Y_train, exog = x, hasconst = True).fit()\n",
    "poly_model_prediction = poly_model.predict(X_train_poly)\n",
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor_columns = ['hour', 'temp', 'hum']\n",
    "\n",
    "fig, ax = plt.subplots(3, figsize = (15,15))\n",
    "title_array = ['Riders vs Hours', 'Riders vs Temp', 'Riders vs Humidity']\n",
    "print(type(X_train_poly['hour']))\n",
    "for i in range(len(cor_columns)):\n",
    "    new_db = X_train_poly\n",
    "    new_db['count'] = poly_model_prediction\n",
    "    new_db = new_db.groupby(cor_columns[i]).mean().reset_index()\n",
    "    \n",
    "    rects1 = ax[i].scatter(new_db[cor_columns[i]], new_db['count'])\n",
    "    \n",
    "    ax[i].set_xlabel(cor_columns[i])\n",
    "    ax[i].set_ylabel('Predicted riders')\n",
    "    ax[i].set_title(title_array[i])\n",
    "    \n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*\n",
    "\n",
    "From the graphs above, the ridership in a day and temperatur show a significant positive or nonlinear impact on bicycling, while Humidity has a moderate negative impact on ridership."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 3.6 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "You should have received a warning regarding the presence of _multicolinearity_ in the model in 3.1.  Create a new dataset `X_train_drop` by dropping the following predictors from `X_train`:  `workingday` `atemp` `spring` `summer` and `fall` and fit a multiple linear regression model to the variables the remain.  Are the estimates more sensible in this model?  And explain in your own words why multicolinearity can lead to erroneous coefficient values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.407</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.405</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   220.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 08 Oct 2021</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:37:06</td>     <th>  Log-Likelihood:    </th> <td> -55250.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  8689</td>      <th>  AIC:               </th> <td>1.106e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  8661</td>      <th>  BIC:               </th> <td>1.108e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    27</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>  -26.0042</td> <td>   10.757</td> <td>   -2.417</td> <td> 0.016</td> <td>  -47.091</td> <td>   -4.917</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour</th>      <td>    7.2029</td> <td>    0.234</td> <td>   30.830</td> <td> 0.000</td> <td>    6.745</td> <td>    7.661</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>holiday</th>   <td>  -37.1695</td> <td>    9.377</td> <td>   -3.964</td> <td> 0.000</td> <td>  -55.550</td> <td>  -18.789</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year</th>      <td>   77.6699</td> <td>    3.031</td> <td>   25.624</td> <td> 0.000</td> <td>   71.728</td> <td>   83.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>temp</th>      <td>  430.2041</td> <td>   16.925</td> <td>   25.418</td> <td> 0.000</td> <td>  397.027</td> <td>  463.381</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hum</th>       <td> -199.2249</td> <td>    9.931</td> <td>  -20.060</td> <td> 0.000</td> <td> -218.693</td> <td> -179.757</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>windspeed</th> <td>    3.0755</td> <td>   13.147</td> <td>    0.234</td> <td> 0.815</td> <td>  -22.696</td> <td>   28.847</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Feb</th>       <td>  -10.6710</td> <td>    7.613</td> <td>   -1.402</td> <td> 0.161</td> <td>  -25.594</td> <td>    4.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mar</th>       <td>   -8.8311</td> <td>    8.277</td> <td>   -1.067</td> <td> 0.286</td> <td>  -25.055</td> <td>    7.393</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Apr</th>       <td>  -34.8773</td> <td>   11.605</td> <td>   -3.005</td> <td> 0.003</td> <td>  -57.625</td> <td>  -12.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>May</th>       <td>  -43.4220</td> <td>   12.643</td> <td>   -3.434</td> <td> 0.001</td> <td>  -68.206</td> <td>  -18.638</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Jun</th>       <td>  -63.2655</td> <td>   11.993</td> <td>   -5.275</td> <td> 0.000</td> <td>  -86.776</td> <td>  -39.756</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Jul</th>       <td>  -74.8504</td> <td>   11.578</td> <td>   -6.465</td> <td> 0.000</td> <td>  -97.546</td> <td>  -52.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Aug</th>       <td>  -43.1533</td> <td>   11.036</td> <td>   -3.910</td> <td> 0.000</td> <td>  -64.786</td> <td>  -21.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sept</th>      <td>   14.2565</td> <td>   10.050</td> <td>    1.418</td> <td> 0.156</td> <td>   -5.445</td> <td>   33.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Oct</th>       <td>   52.9882</td> <td>    8.677</td> <td>    6.107</td> <td> 0.000</td> <td>   35.980</td> <td>   69.996</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Nov</th>       <td>   37.7815</td> <td>    7.785</td> <td>    4.853</td> <td> 0.000</td> <td>   22.520</td> <td>   53.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dec</th>       <td>   25.9209</td> <td>    7.571</td> <td>    3.423</td> <td> 0.001</td> <td>   11.079</td> <td>   40.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>spring</th>    <td>   39.3742</td> <td>    7.777</td> <td>    5.063</td> <td> 0.000</td> <td>   24.130</td> <td>   54.618</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mon</th>       <td>    9.0752</td> <td>    5.795</td> <td>    1.566</td> <td> 0.117</td> <td>   -2.283</td> <td>   20.434</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tue</th>       <td>   11.6160</td> <td>    5.697</td> <td>    2.039</td> <td> 0.041</td> <td>    0.448</td> <td>   22.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Wed</th>       <td>    8.2531</td> <td>    5.668</td> <td>    1.456</td> <td> 0.145</td> <td>   -2.858</td> <td>   19.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Thu</th>       <td>    5.7258</td> <td>    5.633</td> <td>    1.016</td> <td> 0.309</td> <td>   -5.317</td> <td>   16.769</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fri</th>       <td>   13.3911</td> <td>    5.632</td> <td>    2.378</td> <td> 0.017</td> <td>    2.352</td> <td>   24.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sat</th>       <td>   11.9456</td> <td>    5.607</td> <td>    2.131</td> <td> 0.033</td> <td>    0.955</td> <td>   22.936</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cloudy</th>    <td>    6.6667</td> <td>    3.688</td> <td>    1.808</td> <td> 0.071</td> <td>   -0.562</td> <td>   13.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Snow</th>      <td>  -28.1551</td> <td>    6.105</td> <td>   -4.612</td> <td> 0.000</td> <td>  -40.122</td> <td>  -16.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Storm</th>     <td>   45.4112</td> <td>   99.207</td> <td>    0.458</td> <td> 0.647</td> <td> -149.058</td> <td>  239.881</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1707.399</td> <th>  Durbin-Watson:     </th> <td>   1.977</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3290.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.200</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 4.824</td>  <th>  Cond. No.          </th> <td>    898.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.407\n",
       "Model:                            OLS   Adj. R-squared:                  0.405\n",
       "Method:                 Least Squares   F-statistic:                     220.0\n",
       "Date:                Fri, 08 Oct 2021   Prob (F-statistic):               0.00\n",
       "Time:                        19:37:06   Log-Likelihood:                -55250.\n",
       "No. Observations:                8689   AIC:                         1.106e+05\n",
       "Df Residuals:                    8661   BIC:                         1.108e+05\n",
       "Df Model:                          27                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        -26.0042     10.757     -2.417      0.016     -47.091      -4.917\n",
       "hour           7.2029      0.234     30.830      0.000       6.745       7.661\n",
       "holiday      -37.1695      9.377     -3.964      0.000     -55.550     -18.789\n",
       "year          77.6699      3.031     25.624      0.000      71.728      83.612\n",
       "temp         430.2041     16.925     25.418      0.000     397.027     463.381\n",
       "hum         -199.2249      9.931    -20.060      0.000    -218.693    -179.757\n",
       "windspeed      3.0755     13.147      0.234      0.815     -22.696      28.847\n",
       "Feb          -10.6710      7.613     -1.402      0.161     -25.594       4.252\n",
       "Mar           -8.8311      8.277     -1.067      0.286     -25.055       7.393\n",
       "Apr          -34.8773     11.605     -3.005      0.003     -57.625     -12.129\n",
       "May          -43.4220     12.643     -3.434      0.001     -68.206     -18.638\n",
       "Jun          -63.2655     11.993     -5.275      0.000     -86.776     -39.756\n",
       "Jul          -74.8504     11.578     -6.465      0.000     -97.546     -52.155\n",
       "Aug          -43.1533     11.036     -3.910      0.000     -64.786     -21.520\n",
       "Sept          14.2565     10.050      1.418      0.156      -5.445      33.958\n",
       "Oct           52.9882      8.677      6.107      0.000      35.980      69.996\n",
       "Nov           37.7815      7.785      4.853      0.000      22.520      53.043\n",
       "Dec           25.9209      7.571      3.423      0.001      11.079      40.763\n",
       "spring        39.3742      7.777      5.063      0.000      24.130      54.618\n",
       "Mon            9.0752      5.795      1.566      0.117      -2.283      20.434\n",
       "Tue           11.6160      5.697      2.039      0.041       0.448      22.784\n",
       "Wed            8.2531      5.668      1.456      0.145      -2.858      19.364\n",
       "Thu            5.7258      5.633      1.016      0.309      -5.317      16.769\n",
       "Fri           13.3911      5.632      2.378      0.017       2.352      24.431\n",
       "Sat           11.9456      5.607      2.131      0.033       0.955      22.936\n",
       "Cloudy         6.6667      3.688      1.808      0.071      -0.562      13.896\n",
       "Snow         -28.1551      6.105     -4.612      0.000     -40.122     -16.188\n",
       "Storm         45.4112     99.207      0.458      0.647    -149.058     239.881\n",
       "==============================================================================\n",
       "Omnibus:                     1707.399   Durbin-Watson:                   1.977\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3290.114\n",
       "Skew:                           1.200   Prob(JB):                         0.00\n",
       "Kurtosis:                       4.824   Cond. No.                         898.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "X_train_drop=X_train[['const','hour','holiday','year','temp','hum','windspeed','Feb','Mar', 'Apr',\n",
    "                      'May','Jun','Jul','Aug','Sept','Oct','Nov','Dec','spring','Mon','Tue',\n",
    "                      'Wed','Thu','Fri','Sat','Cloudy','Snow','Storm']]\n",
    "X_train_drop=sm.add_constant(X_train_drop)\n",
    "model=OLS(endog=Y_train,exog=X_train_drop,hasconst=True).fit()\n",
    "model.summary()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*\n",
    "\n",
    "Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem because independent variables should be independent. If the degree of correlation between variables is high enough, it can cause problems when you fit the model and interpret the results. The coefficient values can lead to errors based on the other independent variables in the model. The coefficients are very sensitive to small changes in the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<div class=\"theme\">  Question 4 - Regression Models with Polynomial and Interaction Terms </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 4.1 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Drop the variables `workingday`, `atemp`, `spring`, `summer`, and `fall` from `BSS_train` (if any of them still remain)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>year</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>counts</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>...</th>\n",
       "      <th>Dec</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Cloudy</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Storm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>479</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2985</td>\n",
       "      <td>411</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hour  holiday  year  temp   hum  windspeed  counts  Feb  Mar  Apr  ...  \\\n",
       "0    21        0     1  0.24  0.60     0.2537      63    0    0    0  ...   \n",
       "1    17        0     0  0.62  0.83     0.2537     479    0    0    0  ...   \n",
       "2     9        0     1  0.76  0.66     0.1642     353    0    0    0  ...   \n",
       "3    17        0     1  0.62  0.22     0.2985     411    0    0    1  ...   \n",
       "4     8        0     1  0.18  0.40     0.1343      70    0    0    0  ...   \n",
       "\n",
       "   Dec  Mon  Tue  Wed  Thu  Fri  Sat  Cloudy  Snow  Storm  \n",
       "0    1    0    0    0    1    0    0       1     0      0  \n",
       "1    0    0    0    0    1    0    0       0     1      0  \n",
       "2    0    0    0    0    1    0    0       0     0      0  \n",
       "3    0    0    0    0    0    0    0       0     0      0  \n",
       "4    0    0    0    0    0    0    0       0     0      0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "\n",
    "BSS_train.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 4.2 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Write a function to standardize a provided subset of columns in your training/validation/test sets. Remember that while you will be scaling all of your data, you must learn the scaling parameters (mean and SD) from only the training set.\n",
    "\n",
    "Test your code by building a list of all non-binary columns from `X_train` into a dataset called `practice_X_train` and scaling only those columns. Call the result `practice_X_train_scaled`. Display the `.describe()` and verify that you have correctly scaled all columns.\n",
    "\n",
    "**Hint: employ the provided list of binary columns and use `pd.columns.difference()`**\n",
    "\n",
    "`binary_columns = [ 'holiday', 'workingday','Feb', 'Mar', 'Apr',\n",
    "       'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec', 'spring',\n",
    "       'summer', 'fall', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat',\n",
    "       'Cloudy', 'Snow', 'Storm']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n",
    "def standardize_cols(cols, df_train, df_test):\n",
    "    return_train = df_train.copy().reset_index().drop(\"index\", axis=1)\n",
    "    return_test = df_test.copy().reset_index().drop(\"index\", axis=1)\n",
    "    \n",
    "    df_train=df_train[cols]\n",
    "    df_test=df_test[cols]\n",
    "    \n",
    "    scaler=StandardScaler().fit(df_train)\n",
    "    \n",
    "    train_scaled=pd.DataFrame(scaler.transform(df_train), columns=cols)\n",
    "    test_scaled=pd.DataFrame(scaler.transform(df_test), columns=cols)\n",
    "    \n",
    "    for col in train_scaled.columns:\n",
    "        return_train[col]=train_scaled[col]\n",
    "        return_train[col]=train_scaled[col]\n",
    "        \n",
    "    return return_train, return_test\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>year</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>counts</th>\n",
       "      <th>spring</th>\n",
       "      <th>...</th>\n",
       "      <th>Dec</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Cloudy</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Storm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "      <td>8690.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.495972</td>\n",
       "      <td>0.028193</td>\n",
       "      <td>0.501726</td>\n",
       "      <td>0.682394</td>\n",
       "      <td>0.497570</td>\n",
       "      <td>0.476151</td>\n",
       "      <td>0.629201</td>\n",
       "      <td>0.189580</td>\n",
       "      <td>188.544764</td>\n",
       "      <td>0.257077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.085386</td>\n",
       "      <td>0.141082</td>\n",
       "      <td>0.144419</td>\n",
       "      <td>0.143038</td>\n",
       "      <td>0.139816</td>\n",
       "      <td>0.142232</td>\n",
       "      <td>0.143038</td>\n",
       "      <td>0.265362</td>\n",
       "      <td>0.079977</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.868329</td>\n",
       "      <td>0.165534</td>\n",
       "      <td>0.500026</td>\n",
       "      <td>0.465572</td>\n",
       "      <td>0.192561</td>\n",
       "      <td>0.171717</td>\n",
       "      <td>0.193568</td>\n",
       "      <td>0.121426</td>\n",
       "      <td>181.348797</td>\n",
       "      <td>0.437047</td>\n",
       "      <td>...</td>\n",
       "      <td>0.279471</td>\n",
       "      <td>0.348126</td>\n",
       "      <td>0.351534</td>\n",
       "      <td>0.350132</td>\n",
       "      <td>0.346816</td>\n",
       "      <td>0.349308</td>\n",
       "      <td>0.350132</td>\n",
       "      <td>0.441551</td>\n",
       "      <td>0.271273</td>\n",
       "      <td>0.010727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.030300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.484800</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.621200</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.253700</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850700</td>\n",
       "      <td>976.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              hour      holiday         year   workingday         temp  \\\n",
       "count  8690.000000  8690.000000  8690.000000  8690.000000  8690.000000   \n",
       "mean     11.495972     0.028193     0.501726     0.682394     0.497570   \n",
       "std       6.868329     0.165534     0.500026     0.465572     0.192561   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.020000   \n",
       "25%       6.000000     0.000000     0.000000     0.000000     0.340000   \n",
       "50%      11.000000     0.000000     1.000000     1.000000     0.500000   \n",
       "75%      17.000000     0.000000     1.000000     1.000000     0.660000   \n",
       "max      23.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "             atemp          hum    windspeed       counts       spring  ...  \\\n",
       "count  8690.000000  8690.000000  8690.000000  8690.000000  8690.000000  ...   \n",
       "mean      0.476151     0.629201     0.189580   188.544764     0.257077  ...   \n",
       "std       0.171717     0.193568     0.121426   181.348797     0.437047  ...   \n",
       "min       0.030300     0.000000     0.000000     1.000000     0.000000  ...   \n",
       "25%       0.333300     0.480000     0.104500    40.000000     0.000000  ...   \n",
       "50%       0.484800     0.630000     0.194000   141.000000     0.000000  ...   \n",
       "75%       0.621200     0.790000     0.253700   276.000000     1.000000  ...   \n",
       "max       1.000000     1.000000     0.850700   976.000000     1.000000  ...   \n",
       "\n",
       "               Dec          Mon          Tue          Wed          Thu  \\\n",
       "count  8690.000000  8690.000000  8690.000000  8690.000000  8690.000000   \n",
       "mean      0.085386     0.141082     0.144419     0.143038     0.139816   \n",
       "std       0.279471     0.348126     0.351534     0.350132     0.346816   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "               Fri          Sat       Cloudy         Snow        Storm  \n",
       "count  8690.000000  8690.000000  8690.000000  8690.000000  8690.000000  \n",
       "mean      0.142232     0.143038     0.265362     0.079977     0.000115  \n",
       "std       0.349308     0.350132     0.441551     0.271273     0.010727  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     1.000000     0.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_columns=[ 'const','holiday', 'workingday','Feb', 'Mar', 'Apr',\n",
    "       'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec', 'spring',\n",
    "       'summer', 'fall', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat',\n",
    "       'Cloudy', 'Snow', 'Storm']\n",
    "nonbinary_columns=X_train.columns.difference(binary_columns)\n",
    "practice_X_train=X_train[nonbinary_columns]\n",
    "\n",
    "practice_X_train_scaled, practice_X_test_scaled=standardize_cols(nonbinary_columns,X_train,BSS_test)\n",
    "practice_X_test_scaled.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 4.3 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Write a code to augment your a dataset with higher-order features for `temp`, `hum`,`windspeed`, and `hour`. You should include ONLY the pure powers of these columns. So with degree=2 you should produce `temp^2` and `hum^2` but not `temp*hum` or any other two-feature interactions. \n",
    "\n",
    "Encapsulate this process as a function with appropriate inputs and outputs, and test your code by producing `practice_X_train_poly`, a training dataset with quadratic and cubic features built from `practice_X_train_scaled`, and printing `practice_X_train_poly`'s column names and `.head()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['const', 'hour', 'holiday', 'year', 'workingday', 'temp', 'atemp',\n",
      "       'hum', 'windspeed', 'spring', 'summer', 'fall', 'Feb', 'Mar', 'Apr',\n",
      "       'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec', 'Mon', 'Tue',\n",
      "       'Wed', 'Thu', 'Fri', 'Sat', 'Cloudy', 'Snow', 'Storm'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>const</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>year</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>spring</th>\n",
       "      <th>...</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Cloudy</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Storm</th>\n",
       "      <th>temp^2</th>\n",
       "      <th>hum^2</th>\n",
       "      <th>windspeed^2</th>\n",
       "      <th>hour^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.350966</td>\n",
       "      <td>0</td>\n",
       "      <td>0.993233</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.331629</td>\n",
       "      <td>-1.442584</td>\n",
       "      <td>-0.131358</td>\n",
       "      <td>0.511860</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.773236</td>\n",
       "      <td>0.017255</td>\n",
       "      <td>0.262001</td>\n",
       "      <td>1.825110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.776238</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.006813</td>\n",
       "      <td>1</td>\n",
       "      <td>0.641888</td>\n",
       "      <td>0.583783</td>\n",
       "      <td>1.064879</td>\n",
       "      <td>0.511860</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.412021</td>\n",
       "      <td>1.133967</td>\n",
       "      <td>0.262001</td>\n",
       "      <td>0.602545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.373220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.993233</td>\n",
       "      <td>1</td>\n",
       "      <td>1.368974</td>\n",
       "      <td>1.464686</td>\n",
       "      <td>0.180704</td>\n",
       "      <td>-0.214334</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.874089</td>\n",
       "      <td>0.032654</td>\n",
       "      <td>0.045939</td>\n",
       "      <td>0.139293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.776238</td>\n",
       "      <td>0</td>\n",
       "      <td>0.993233</td>\n",
       "      <td>0</td>\n",
       "      <td>0.641888</td>\n",
       "      <td>0.759963</td>\n",
       "      <td>-2.107748</td>\n",
       "      <td>0.875363</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.412021</td>\n",
       "      <td>4.442601</td>\n",
       "      <td>0.766260</td>\n",
       "      <td>0.602545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.516902</td>\n",
       "      <td>0</td>\n",
       "      <td>0.993233</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.643237</td>\n",
       "      <td>-1.618765</td>\n",
       "      <td>-1.171563</td>\n",
       "      <td>-0.456939</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.700228</td>\n",
       "      <td>1.372560</td>\n",
       "      <td>0.208794</td>\n",
       "      <td>0.267188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   const      hour  holiday      year  workingday      temp     atemp  \\\n",
       "0    1.0  1.350966        0  0.993233           1 -1.331629 -1.442584   \n",
       "1    1.0  0.776238        0 -1.006813           1  0.641888  0.583783   \n",
       "2    1.0 -0.373220        0  0.993233           1  1.368974  1.464686   \n",
       "3    1.0  0.776238        0  0.993233           0  0.641888  0.759963   \n",
       "4    1.0 -0.516902        0  0.993233           0 -1.643237 -1.618765   \n",
       "\n",
       "        hum  windspeed  spring  ...  Thu  Fri  Sat  Cloudy  Snow  Storm  \\\n",
       "0 -0.131358   0.511860       0  ...    1    0    0       1     0      0   \n",
       "1  1.064879   0.511860       0  ...    1    0    0       0     1      0   \n",
       "2  0.180704  -0.214334       0  ...    1    0    0       0     0      0   \n",
       "3 -2.107748   0.875363       1  ...    0    0    0       0     0      0   \n",
       "4 -1.171563  -0.456939       0  ...    0    0    0       0     0      0   \n",
       "\n",
       "     temp^2     hum^2  windspeed^2    hour^2  \n",
       "0  1.773236  0.017255     0.262001  1.825110  \n",
       "1  0.412021  1.133967     0.262001  0.602545  \n",
       "2  1.874089  0.032654     0.045939  0.139293  \n",
       "3  0.412021  4.442601     0.766260  0.602545  \n",
       "4  2.700228  1.372560     0.208794  0.267188  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(practice_X_train_scaled.columns)\n",
    "def higher_order(df_scaled, degree, cols):\n",
    "    df=df_scaled.copy()\n",
    "    for col in cols:\n",
    "        for i in range(2, degree+1):\n",
    "            df['{}^{}'.format(col, i)]=df[col]**i\n",
    "    return df\n",
    "higher_order_feat=[\"temp\",\"hum\",\"windspeed\",\"hour\"]\n",
    "\n",
    "practice_X_train_poly=higher_order(practice_X_train_scaled, 2, higher_order_feat)\n",
    "practice_X_train_poly.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 4.4 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Write code to add interaction terms to the model. Specifically, we want interactions between the continuous predictors (`temp`, `hum`,`windspeed`) and the month and weekday dummies (`Feb`, `Mar`...`Dec`, `Mon`, `Tue`, ... `Sat`). That means you SHOULD build `temp*Feb` and `hum*Mon` and so on, but NOT `Feb*Mar` and NOT `Feb*Tue`. The interaction terms should always be a continuous feature times a month dummy or a continuous feature times a weekday dummy.\n",
    "\n",
    "Encapsulate this process as a function with appropriate inputs and outputs, and test your code by adding interaction terms to `practice_X_train_poly` and show its column names and `.head()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>atemp</th>\n",
       "      <th>const</th>\n",
       "      <th>fall</th>\n",
       "      <th>hour^2</th>\n",
       "      <th>hum^2</th>\n",
       "      <th>spring</th>\n",
       "      <th>summer</th>\n",
       "      <th>temp^2</th>\n",
       "      <th>windspeed^2</th>\n",
       "      <th>workingday</th>\n",
       "      <th>...</th>\n",
       "      <th>windspeed Sept</th>\n",
       "      <th>windspeed Oct</th>\n",
       "      <th>windspeed Nov</th>\n",
       "      <th>windspeed Dec</th>\n",
       "      <th>windspeed Mon</th>\n",
       "      <th>windspeed Tue</th>\n",
       "      <th>windspeed Wed</th>\n",
       "      <th>windspeed Thu</th>\n",
       "      <th>windspeed Fri</th>\n",
       "      <th>windspeed Sat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.442584</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.825110</td>\n",
       "      <td>0.017255</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.773236</td>\n",
       "      <td>0.262001</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.583783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.602545</td>\n",
       "      <td>1.133967</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.412021</td>\n",
       "      <td>0.262001</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.464686</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.139293</td>\n",
       "      <td>0.032654</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.874089</td>\n",
       "      <td>0.045939</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.759963</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.602545</td>\n",
       "      <td>4.442601</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.412021</td>\n",
       "      <td>0.766260</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.618765</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.267188</td>\n",
       "      <td>1.372560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.700228</td>\n",
       "      <td>0.208794</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      atemp  const  fall    hour^2     hum^2  spring  summer    temp^2  \\\n",
       "0 -1.442584    1.0     0  1.825110  0.017255       0       0  1.773236   \n",
       "1  0.583783    1.0     1  0.602545  1.133967       0       0  0.412021   \n",
       "2  1.464686    1.0     0  0.139293  0.032654       0       1  1.874089   \n",
       "3  0.759963    1.0     0  0.602545  4.442601       1       0  0.412021   \n",
       "4 -1.618765    1.0     0  0.267188  1.372560       0       0  2.700228   \n",
       "\n",
       "   windspeed^2  workingday  ...  windspeed Sept  windspeed Oct  windspeed Nov  \\\n",
       "0     0.262001           1  ...             0.0         0.0000            0.0   \n",
       "1     0.262001           1  ...             0.0         0.2537            0.0   \n",
       "2     0.045939           1  ...             0.0         0.0000            0.0   \n",
       "3     0.766260           0  ...             0.0         0.0000            0.0   \n",
       "4     0.208794           0  ...             0.0         0.0000            0.0   \n",
       "\n",
       "   windspeed Dec  windspeed Mon  windspeed Tue  windspeed Wed  windspeed Thu  \\\n",
       "0         0.2537            0.0            0.0            0.0         0.2537   \n",
       "1         0.0000            0.0            0.0            0.0         0.2537   \n",
       "2         0.0000            0.0            0.0            0.0         0.1642   \n",
       "3         0.0000            0.0            0.0            0.0         0.0000   \n",
       "4         0.0000            0.0            0.0            0.0         0.0000   \n",
       "\n",
       "   windspeed Fri  windspeed Sat  \n",
       "0            0.0            0.0  \n",
       "1            0.0            0.0  \n",
       "2            0.0            0.0  \n",
       "3            0.0            0.0  \n",
       "4            0.0            0.0  \n",
       "\n",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "continuous_predictors=[\"temp\",\"hum\",\"windspeed\"]\n",
    "dummies=['Feb', 'Mar', 'Apr',\n",
    "       'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec','Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']\n",
    "def higher_order_dummies(df,continuous,dummies):\n",
    "    df_copy=df.copy()\n",
    "    column_names_final=[]\n",
    "    for a in continuous:\n",
    "        for b in dummies:\n",
    "            column_names_final.append(a+\" \"+b)\n",
    "            df_copy[a+\" \"+b]=df_copy[a]*df_copy[b]\n",
    "    return df_copy\n",
    "return_df=higher_order_dummies(BSS_train,continuous_predictors,dummies)\n",
    "practice_X_train_poly=pd.concat([practice_X_train_poly[practice_X_train_poly.columns.difference(return_df.columns)],return_df], axis = 1)\n",
    "practice_X_train_poly.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 4.5 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Combine all your code so far into a function named `get_design_mats` that takes in `BSS_train`, `BSS_test`, the names of columns for polynomial, the target column, the columns to be dropped and produces computation-ready design matrices `X_train` and `X_test` and responses `y_train` and `y_test`. Your final function should build correct, scaled design matrices with the stated interaction terms and any polynomial degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_design_mats(train_df, val_df,  degree, \n",
    "                    columns_forpoly=['temp', 'hum','windspeed', 'hour'],\n",
    "                    target_col='counts', \n",
    "                    bad_columns=['counts']):\n",
    "    train_df = train_df.reset_index().drop('index', axis = 1)\n",
    "    val_df = val_df.reset_index().drop('index', axis = 1)\n",
    "    \n",
    "    y_train = train_df[target_col]\n",
    "    y_val = val_df[target_col]\n",
    "    x_train = train_df.drop(bad_columns, axis = 1)\n",
    "    x_val = val_df.drop(bad_columns, axis = 1)\n",
    "    \n",
    "    nonbinary_columns = x_train.columns.difference(binary_columns)\n",
    "    \n",
    "    x_train, x_val = standardize_cols(nonbinary_columns, x_train, x_val)\n",
    "    \n",
    "    x_train = higher_order(x_train, degree, columns_forpoly)\n",
    "    x_val = higher_order(x_val, dgree, columns_forpoly)\n",
    "    \n",
    "    dummy_cols = ['Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec', 'Mon', 'Tue', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n",
    "    \n",
    "    continuous_pred = ['temp', 'hum', 'windspeed']\n",
    "    \n",
    "    \"\"\"\n",
    "    :param train_df: The training data\n",
    "    :param val_df: Your test/validation data\n",
    "    :param columns_forpoly: List containing the column name for which we want to create \n",
    "                            polynomial terms\n",
    "    :param target_col: The response variable column\n",
    "    :param bad_columns: The columns you want to drop\n",
    "    \"\"\"\n",
    "    \n",
    "    # Add code here\n",
    "    x_train=higher_order_dummies(x_train,continous_pred, dummy_cols)\n",
    "    x_val=higher_order_dummies(X_val, continous_pred, dummy_cols)\n",
    "    \n",
    "    x_train[\"const\"]=1\n",
    "    x_val[\"const\"]=1\n",
    "    return x_train,y_train, x_val,y_val\n",
    "    return x_train,y_train, x_val,y_val\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 4.6 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "For each degree in 1 through 8:\n",
    "\n",
    "1.  Build the training design matrix and validation design matrix using the function `get_design_mats` with polynomial terms up through the specified degree.\n",
    "\n",
    "2.  Fit a regression model to the training data.\n",
    "\n",
    "3.  Report the model's score on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 31 is different from 32)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-106-e09d5444bc5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \"\"\"\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mtrain_r2_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBikeOLSModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mtest_r2_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBikeOLSModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Train Set R^2 Score: {:.4f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_r2_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m         \"\"\"\n\u001b[0;32m--> 238\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0m_preprocess_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m         return safe_sparse_dot(X, self.coef_.T,\n\u001b[0m\u001b[1;32m    222\u001b[0m                                dense_output=True) + self.intercept_\n\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/extmath.py\u001b[0m in \u001b[0;36msafe_sparse_dot\u001b[0;34m(a, b, dense_output)\u001b[0m\n\u001b[1;32m    150\u001b[0m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m     if (sparse.issparse(a) and sparse.issparse(b)\n",
      "\u001b[0;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 31 is different from 32)"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "    :param train_df: The training data\n",
    "    :param val_df: Your test/validation data\n",
    "    :param columns_forpoly: List containing the column name for which we want to create \n",
    "                            polynomial terms\n",
    "    :param target_col: The response variable column\n",
    "    :param bad_columns: The columns you want to drop\n",
    "    \"\"\"\n",
    "\n",
    "train_r2_score = r2_score(y_train, BikeOLSModel.predict(X_train))\n",
    "test_r2_score = r2_score(y_test, BikeOLSModel.predict(X_test))\n",
    "print(\"Train Set R^2 Score: {:.4f}\".format(train_r2_score))\n",
    "print(\"Test Set R^2 Score: {:.4f}\".format(test_r2_score))\n",
    "\n",
    "BikeOLS_r2scores = {}\n",
    "BikeOLS_r2scores['training'] = train_r2_score\n",
    "BikeOLS_r2scores['test'] = test_r2_score    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 4.7 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Discuss patterns you see in the results from 4.5. Which model would you select, and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='exercise'> <b> Question 4.8 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reflect back on the `get_design_mats` function you built. In what way was writing this function useful in your analysis? What issues might you have encountered if you copy/pasted the model-building code instead of tying it together in a function? Does a `get_design_mat` function seem wise in general, or are there better options?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<div class=\"theme\">  Question 5 - Regularized Models </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 5.1 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's try regularizing our models via ridge regression. Split the training set, `BSS_train`, into an 80-20 train-validation split.  Build a table showing the validation set $R^2$ of polynomial models with degree from 1-8, using your `get_design_mats` function, regularized at the levels $\\lambda = (.01, .05, .1,.5, 1, 5, 10, 50, 100)$. Do not perform cross validation at this point, simply report performance on the single validation set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n",
    "# your code here\n",
    "from sklearn.model_selection import KFold\n",
    "lambdas = [.01, .05, 1, 5, 10, 50, 100]\n",
    "fold = KFold(5, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here\n",
    "# RidgeCV\n",
    "BikeRRModel = RidgeCV(cv=5, alphas=lambdas, fit_intercept=True)\n",
    "\n",
    "BikeRRModel.fit(X_train, y_train) \n",
    "\n",
    "BikeRR_shrinkage_parameter = BikeRRModel.alpha_\n",
    "\n",
    "Ridge_coefficients = BikeRRModel.coef_.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 5.2 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Find the best-scoring degree and regularization combination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 5.3 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "It's time to see how well our selected model will do on future data. Use the earlier created test dataset, do any required formatting, and report the best model's $R^2$ score. How does it compare to the validation set score that made us choose this model? Does the test set simply have observations that are more difficult or easier to predict?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 5.4 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Build a dataset with polynomial degree 1 and fit an OLS model, a Ridge model, and a Lasso model. Use `RidgeCV` and `LassoCV` to select the best regularization level from among `(.1,.5,1,5,10,50,100)`. \n",
    "\n",
    "Note: On the lasso model, you may need to increase `max_iter` (start with 100,000) for the optimization to converge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n",
    "lambdas = [.001, .005, 1, 5, 10, 50, 100, 500, 1000]\n",
    "fold = KFold(5, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 5.5 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Plot histograms of the coefficients found by each of OLS, ridge, and lasso. What trends do you see in the magnitude of the coefficients?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n",
    "si= np.argsort(np.abs(BikeOLSModel.coef_[0]))\n",
    "\n",
    "n_coeffs = len(feature_names)\n",
    "\n",
    "plt.figure(figsize=(14,8))\n",
    "plt.bar(np.arange(n_coeffs)-0.2, np.abs(Ridge_coefficients3)[si], alpha=0.4, label='Ridge')\n",
    "plt.bar(np.arange(n_coeffs)+0.2, np.abs(Lasso_coefficients3)[si], alpha=0.2, label='LASSO')\n",
    "plt.bar(np.arange(n_coeffs), -np.abs(OLS_coefficients)[si], color='purple', hatch='/', label='OLS')\n",
    "plt.xticks(range(n_coeffs), feature_names[si], rotation=60)\n",
    "plt.xlim(-1,n_coeffs)\n",
    "plt.ylim(-100,100)\n",
    "plt.legend(fontsize=16)\n",
    "plt.title(\"Model coefficients: 5-fold CV 'ideal' parameters\")\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"+/- Coefficient Magnitude\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 5.6 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The plots above show the overall distribution of coefficient values in each model, but do not show how each model treats individual coefficients. Build a plot which cleanly presents, for each feature in the data, 1) The coefficient assigned by OLS, 2) the coefficient assigned by ridge, and 3) the coefficient assigned by lasso.\n",
    "\n",
    "**Hint: Bar plots are a possible choice, but you are not required to use them**\n",
    "\n",
    "**Hint: use `xticks` to label coefficients with their feature names**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 5.7: </b> What trends do you see in the plot above? </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"theme\">  Question 6 - Reflection </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "These problems are open-ended, and you are not expected to write more than 2-3 sentences. We are interested in seeing that you have thought about these issues; you will be graded on how well you justify your conclusions here, not on what you conclude."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 6.1 </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "What are the costs and benefits of applying ridge/lasso regularization to an overfit OLS model, versus setting a specific degree of polynomial or forward selecting polynomial features for the model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 6.2 </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "This pset posed a purely predictive goal: forecast ridership as accurately as possible. How important is interpretability in this context? Considering, e.g., your lasso and ridge models from Question 5, how would you react if the models predicted well, but the coefficient values didn't make sense once interpreted?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 6.3 </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Reflect back on our original goal of helping BikeShare predict what demand will be like in the week ahead, and thus how many bikes they can bring in for maintenance. In your view, did we accomplish this goal? If yes, which model would you put into production and why? If not, which model came closest, what other analyses might you conduct, and how likely do you think they are to work?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*"
   ]
  },
  {
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}