regression_exercise_soln.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Travel times\n",
    "##Description:\t\n",
    "A driver uses an app to track GPS coordinates as he drives to work and back each day. The app collects the location and elevation data. Data for about 200 trips are summarized in this data set.\n",
    "##Data source:\t\n",
    "* Date of travel\n",
    "* StartTime: when getting into the car\n",
    "* DayOfWeek: the day name\n",
    "* GoingTo: direction of travel\n",
    "* Distance travelled in kilometers\n",
    "* MaxSpeed: fastest speed recorded (all trips are on the 407 highway for some portion)\n",
    "* AvgSpeed: the average speed for the entire trip\n",
    "* AvgMovingSpeed: the average speed recorded only while the car is moving\n",
    "* FuelEconomy: a rough estimate of fuel economy (it is inaccurate)\n",
    "* TotalTime: duration of the entire trip, in minutes\n",
    "* MovingTime: duration when the car was considered to be moving (i.e. not counting traffic delays, accidents, or time while the car is stationary)\n",
    "* Take407All: is Yes if the 407 toll highway was taken for the entire trip. I try to avoid taking the 407, taking slower back routes to save costs. But some days I'm running late, or just lazy, and take it all the way.\n",
    "\n",
    "##Comments\n",
    "* Data shape:\t205 rows and 13 columns\n",
    "* Usage restrictions:\tNone\n",
    "* Contact person:\tKevin Dunn\n",
    "* Contact details:\tdatasets@connectmv.com\n",
    "* Added here on:\t17 September 2015\n",
    "* Last updated:\t09 January 2012 13:21\n",
    "\n",
    "#Task\n",
    "\n",
    "Build a regression model to predict the total travel time.\n",
    "\n",
    "##Extension\n",
    "Many of the data points in this data set are only available _after_ the drive. But what if we want to predict how long it will take _before_ we leave?\n",
    "\n",
    "Build a model that makes a prediction based only on data available to the driver _before_ they leave?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##1. Read the data into a data frame using pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>StartTime</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>GoingTo</th>\n",
       "      <th>Distance</th>\n",
       "      <th>MaxSpeed</th>\n",
       "      <th>AvgSpeed</th>\n",
       "      <th>AvgMovingSpeed</th>\n",
       "      <th>FuelEconomy</th>\n",
       "      <th>TotalTime</th>\n",
       "      <th>MovingTime</th>\n",
       "      <th>Take407All</th>\n",
       "      <th>Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1/6/2012</td>\n",
       "      <td> 16:37</td>\n",
       "      <td>    Friday</td>\n",
       "      <td> Home</td>\n",
       "      <td> 51.29</td>\n",
       "      <td> 127.4</td>\n",
       "      <td> 78.3</td>\n",
       "      <td> 84.8</td>\n",
       "      <td> NaN</td>\n",
       "      <td> 39.3</td>\n",
       "      <td> 36.3</td>\n",
       "      <td> No</td>\n",
       "      <td> NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1/6/2012</td>\n",
       "      <td> 08:20</td>\n",
       "      <td>    Friday</td>\n",
       "      <td>  GSK</td>\n",
       "      <td> 51.63</td>\n",
       "      <td> 130.3</td>\n",
       "      <td> 81.8</td>\n",
       "      <td> 88.9</td>\n",
       "      <td> NaN</td>\n",
       "      <td> 37.9</td>\n",
       "      <td> 34.9</td>\n",
       "      <td> No</td>\n",
       "      <td> NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 1/4/2012</td>\n",
       "      <td> 16:17</td>\n",
       "      <td> Wednesday</td>\n",
       "      <td> Home</td>\n",
       "      <td> 51.27</td>\n",
       "      <td> 127.4</td>\n",
       "      <td> 82.0</td>\n",
       "      <td> 85.8</td>\n",
       "      <td> NaN</td>\n",
       "      <td> 37.5</td>\n",
       "      <td> 35.9</td>\n",
       "      <td> No</td>\n",
       "      <td> NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 1/4/2012</td>\n",
       "      <td> 07:53</td>\n",
       "      <td> Wednesday</td>\n",
       "      <td>  GSK</td>\n",
       "      <td> 49.17</td>\n",
       "      <td> 132.3</td>\n",
       "      <td> 74.2</td>\n",
       "      <td> 82.9</td>\n",
       "      <td> NaN</td>\n",
       "      <td> 39.8</td>\n",
       "      <td> 35.6</td>\n",
       "      <td> No</td>\n",
       "      <td> NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 1/3/2012</td>\n",
       "      <td> 18:57</td>\n",
       "      <td>   Tuesday</td>\n",
       "      <td> Home</td>\n",
       "      <td> 51.15</td>\n",
       "      <td> 136.2</td>\n",
       "      <td> 83.4</td>\n",
       "      <td> 88.1</td>\n",
       "      <td> NaN</td>\n",
       "      <td> 36.8</td>\n",
       "      <td> 34.8</td>\n",
       "      <td> No</td>\n",
       "      <td> NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date StartTime  DayOfWeek GoingTo  Distance  MaxSpeed  AvgSpeed  \\\n",
       "0  1/6/2012     16:37     Friday    Home     51.29     127.4      78.3   \n",
       "1  1/6/2012     08:20     Friday     GSK     51.63     130.3      81.8   \n",
       "2  1/4/2012     16:17  Wednesday    Home     51.27     127.4      82.0   \n",
       "3  1/4/2012     07:53  Wednesday     GSK     49.17     132.3      74.2   \n",
       "4  1/3/2012     18:57    Tuesday    Home     51.15     136.2      83.4   \n",
       "\n",
       "   AvgMovingSpeed FuelEconomy  TotalTime  MovingTime Take407All Comments  \n",
       "0            84.8         NaN       39.3        36.3         No      NaN  \n",
       "1            88.9         NaN       37.9        34.9         No      NaN  \n",
       "2            85.8         NaN       37.5        35.9         No      NaN  \n",
       "3            82.9         NaN       39.8        35.6         No      NaN  \n",
       "4            88.1         NaN       36.8        34.8         No      NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/travel-times.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##2. Create X and y matrices\n",
    "\n",
    "Take only the columns which have numeric values and make that the `X` matrix. Make the `TotalTime` the `y` vector. Make sure that the Total Time *isn't* in your X matrix!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "regression_columns = ['Distance', 'MaxSpeed', 'AvgSpeed', 'AvgMovingSpeed', 'MovingTime']\n",
    "X = df[regression_columns].values\n",
    "y = df['TotalTime'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##3. Build a baseline KNN model with k=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_neighbors=10, p=2, weights='uniform')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "baseline = KNeighborsRegressor(n_neighbors=10)\n",
    "baseline.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Calculate the root mean squared error of the baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.7731230083629885"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "predict = baseline.predict(X)\n",
    "np.sqrt(mean_squared_error(predict, df.TotalTime))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge 1: Use Cross-Validation to compare performance of different values of k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1090c2bd0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109024a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cross_validation import KFold\n",
    "\n",
    "# function from lecture notes\n",
    "def kfold_cv_error(model, X, y, folds):\n",
    "    test_error = []\n",
    "    for train_index, test_index in folds:\n",
    "        model.fit(X[train_index], y[train_index])\n",
    "        predict = model.predict(X[test_index])\n",
    "        fold_error = np.sqrt(mean_squared_error(y[test_index], predict))\n",
    "        test_error.append(fold_error)\n",
    "    return np.mean(test_error)\n",
    "\n",
    "#What values of k to test.\n",
    "neighbors = range(1, 20)\n",
    "#What errors for those k\n",
    "folds = KFold(df.shape[0], n_folds=10)\n",
    "errors = [kfold_cv_error(KNeighborsRegressor(k), X, y, folds) for k in neighbors]\n",
    "\n",
    "plt.plot(neighbors, errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Challenge 2: Transform non-numeric data\n",
    "\n",
    "* Make the `Take407All` variable a boolean column.\n",
    "* Make the `GoingTo` column a boolean column.\n",
    "* Make the pandas function `get_dummies` to create dummy columns for the DayOfWeek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "      <th>MaxSpeed</th>\n",
       "      <th>AvgSpeed</th>\n",
       "      <th>AvgMovingSpeed</th>\n",
       "      <th>MovingTime</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 51.29</td>\n",
       "      <td> 127.4</td>\n",
       "      <td> 78.3</td>\n",
       "      <td> 84.8</td>\n",
       "      <td> 36.3</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 51.63</td>\n",
       "      <td> 130.3</td>\n",
       "      <td> 81.8</td>\n",
       "      <td> 88.9</td>\n",
       "      <td> 34.9</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 51.27</td>\n",
       "      <td> 127.4</td>\n",
       "      <td> 82.0</td>\n",
       "      <td> 85.8</td>\n",
       "      <td> 35.9</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 49.17</td>\n",
       "      <td> 132.3</td>\n",
       "      <td> 74.2</td>\n",
       "      <td> 82.9</td>\n",
       "      <td> 35.6</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 51.15</td>\n",
       "      <td> 136.2</td>\n",
       "      <td> 83.4</td>\n",
       "      <td> 88.1</td>\n",
       "      <td> 34.8</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Distance  MaxSpeed  AvgSpeed  AvgMovingSpeed  MovingTime  Friday  Monday  \\\n",
       "0     51.29     127.4      78.3            84.8        36.3       1       0   \n",
       "1     51.63     130.3      81.8            88.9        34.9       1       0   \n",
       "2     51.27     127.4      82.0            85.8        35.9       0       0   \n",
       "3     49.17     132.3      74.2            82.9        35.6       0       0   \n",
       "4     51.15     136.2      83.4            88.1        34.8       0       0   \n",
       "\n",
       "   Thursday  Tuesday  Wednesday  \n",
       "0         0        0          0  \n",
       "1         0        0          0  \n",
       "2         0        0          1  \n",
       "3         0        0          1  \n",
       "4         0        1          0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Take407All_bool'] = df['Take407All'] == 'Yes'\n",
    "df['GoingHome_bool'] = df['GoingTo'] == 'Home'\n",
    "columns = regression_columns + ['Take407All_bool', 'GoingHome_bool']\n",
    "tt_transformed = pd.concat([df[regression_columns],\n",
    "                            pd.get_dummies(df['DayOfWeek'])], axis=1)\n",
    "tt_transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x109322710>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10900d090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_transformed = tt_transformed.values\n",
    "\n",
    "neighbors = range(1, 20)\n",
    "folds = KFold(df.shape[0], n_folds=10)\n",
    "errors = [kfold_cv_error(KNeighborsRegressor(k), X_transformed, y, folds) for k in neighbors]\n",
    "\n",
    "plt.plot(neighbors, errors)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}