3.4.4 SVM Regression Guided Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4.4 SVM Regression Guided Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Now it's time for another guided example. This time we're going to look at recipes. Specifically we'll use the epicurious dataset, which has a collection of recipes, key terms and ingredients, and their ratings.\n",
    "\n",
    "What we want to see is if we can use the ingredient and keyword list to predict the rating. For someone writing a cookbook this could be really useful information that could help them choose which recipes to include because they're more likely to be enjoyed and therefore make the book more likely to be successful.\n",
    "\n",
    "First let's load the dataset. It's [available on Kaggle](https://www.kaggle.com/hugodarwood/epirecipes). We'll use the csv file here and as pull out column names and some summary statistics for ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('epi_r.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>title</th>\n",
       "      <th>rating</th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>#cakeweek</th>\n",
       "      <th>#wasteless</th>\n",
       "      <th>22-minute meals</th>\n",
       "      <th>3-ingredient recipes</th>\n",
       "      <th>...</th>\n",
       "      <th>yellow squash</th>\n",
       "      <th>yogurt</th>\n",
       "      <th>yonkers</th>\n",
       "      <th>yuca</th>\n",
       "      <th>zucchini</th>\n",
       "      <th>cookbooks</th>\n",
       "      <th>leftovers</th>\n",
       "      <th>snack</th>\n",
       "      <th>snack week</th>\n",
       "      <th>turkey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lentil, Apple, and Turkey Wrap</td>\n",
       "      <td>2.500</td>\n",
       "      <td>426.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boudin Blanc Terrine with Red Onion Confit</td>\n",
       "      <td>4.375</td>\n",
       "      <td>403.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1439.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 680 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         title  rating  calories  protein  \\\n",
       "0              Lentil, Apple, and Turkey Wrap    2.500     426.0     30.0   \n",
       "1  Boudin Blanc Terrine with Red Onion Confit    4.375     403.0     18.0   \n",
       "\n",
       "    fat  sodium  #cakeweek  #wasteless  22-minute meals  3-ingredient recipes  \\\n",
       "0   7.0   559.0        0.0         0.0              0.0                   0.0   \n",
       "1  23.0  1439.0        0.0         0.0              0.0                   0.0   \n",
       "\n",
       "    ...    yellow squash  yogurt  yonkers  yuca  zucchini  cookbooks  \\\n",
       "0   ...              0.0     0.0      0.0   0.0       0.0        0.0   \n",
       "1   ...              0.0     0.0      0.0   0.0       0.0        0.0   \n",
       "\n",
       "   leftovers  snack  snack week  turkey  \n",
       "0        0.0    0.0         0.0     1.0  \n",
       "1        0.0    0.0         0.0     0.0  \n",
       "\n",
       "[2 rows x 680 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20052, 680)\n"
     ]
    }
   ],
   "source": [
    "display(df.head(2))\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X = df[list(set(list(df.columns)) - set(['rating', 'title']))]\n",
    "y = df['rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    10738\n",
       "0.0     9314\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Classifies high ratings (>4) and low raings (<4)\n",
    "y_class = y.copy()\n",
    "y_class[y_class < 4] = 0\n",
    "y_class[y_class > 4] = 1\n",
    "y_class.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reducing number of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jerusalem artichoke</th>\n",
       "      <th>wheat/gluten-free</th>\n",
       "      <th>lunar new year</th>\n",
       "      <th>snack week</th>\n",
       "      <th>lemongrass</th>\n",
       "      <th>omelet</th>\n",
       "      <th>poppy</th>\n",
       "      <th>utah</th>\n",
       "      <th>indiana</th>\n",
       "      <th>goat cheese</th>\n",
       "      <th>...</th>\n",
       "      <th>lime juice</th>\n",
       "      <th>missouri</th>\n",
       "      <th>quinoa</th>\n",
       "      <th>raw</th>\n",
       "      <th>trout</th>\n",
       "      <th>fruit</th>\n",
       "      <th>pistachio</th>\n",
       "      <th>illinois</th>\n",
       "      <th>biscuit</th>\n",
       "      <th>lasagna</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "      <td>20052.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000948</td>\n",
       "      <td>0.244664</td>\n",
       "      <td>0.002294</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>0.004189</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.015310</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006633</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.002543</td>\n",
       "      <td>0.003890</td>\n",
       "      <td>0.002693</td>\n",
       "      <td>0.097646</td>\n",
       "      <td>0.008777</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.000150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.030768</td>\n",
       "      <td>0.429898</td>\n",
       "      <td>0.047842</td>\n",
       "      <td>0.030768</td>\n",
       "      <td>0.064589</td>\n",
       "      <td>0.009987</td>\n",
       "      <td>0.031567</td>\n",
       "      <td>0.012231</td>\n",
       "      <td>0.015789</td>\n",
       "      <td>0.122787</td>\n",
       "      <td>...</td>\n",
       "      <td>0.081173</td>\n",
       "      <td>0.026415</td>\n",
       "      <td>0.050369</td>\n",
       "      <td>0.062249</td>\n",
       "      <td>0.051825</td>\n",
       "      <td>0.296843</td>\n",
       "      <td>0.093277</td>\n",
       "      <td>0.019971</td>\n",
       "      <td>0.018681</td>\n",
       "      <td>0.012231</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>0.000000</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>...</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>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",
       "      <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>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",
       "      <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>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",
       "      <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>max</th>\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",
       "      <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 × 678 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       jerusalem artichoke  wheat/gluten-free  lunar new year    snack week  \\\n",
       "count         20052.000000       20052.000000    20052.000000  20052.000000   \n",
       "mean              0.000948           0.244664        0.002294      0.000948   \n",
       "std               0.030768           0.429898        0.047842      0.030768   \n",
       "min               0.000000           0.000000        0.000000      0.000000   \n",
       "25%               0.000000           0.000000        0.000000      0.000000   \n",
       "50%               0.000000           0.000000        0.000000      0.000000   \n",
       "75%               0.000000           0.000000        0.000000      0.000000   \n",
       "max               1.000000           1.000000        1.000000      1.000000   \n",
       "\n",
       "         lemongrass        omelet         poppy          utah       indiana  \\\n",
       "count  20052.000000  20052.000000  20052.000000  20052.000000  20052.000000   \n",
       "mean       0.004189      0.000100      0.000997      0.000150      0.000249   \n",
       "std        0.064589      0.009987      0.031567      0.012231      0.015789   \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",
       "        goat cheese      ...         lime juice      missouri        quinoa  \\\n",
       "count  20052.000000      ...       20052.000000  20052.000000  20052.000000   \n",
       "mean       0.015310      ...           0.006633      0.000698      0.002543   \n",
       "std        0.122787      ...           0.081173      0.026415      0.050369   \n",
       "min        0.000000      ...           0.000000      0.000000      0.000000   \n",
       "25%        0.000000      ...           0.000000      0.000000      0.000000   \n",
       "50%        0.000000      ...           0.000000      0.000000      0.000000   \n",
       "75%        0.000000      ...           0.000000      0.000000      0.000000   \n",
       "max        1.000000      ...           1.000000      1.000000      1.000000   \n",
       "\n",
       "                raw         trout         fruit     pistachio      illinois  \\\n",
       "count  20052.000000  20052.000000  20052.000000  20052.000000  20052.000000   \n",
       "mean       0.003890      0.002693      0.097646      0.008777      0.000399   \n",
       "std        0.062249      0.051825      0.296843      0.093277      0.019971   \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",
       "            biscuit       lasagna  \n",
       "count  20052.000000  20052.000000  \n",
       "mean       0.000349      0.000150  \n",
       "std        0.018681      0.012231  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.000000  \n",
       "75%        0.000000      0.000000  \n",
       "max        1.000000      1.000000  \n",
       "\n",
       "[8 rows x 678 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping 4 columns due to NaNs\n",
      "['fat', 'sodium', 'calories', 'protein']\n"
     ]
    }
   ],
   "source": [
    "# Drop NAN columns\n",
    "X_null = X.isnull().sum()\n",
    "null_cols = list(X_null[X_null > 0].index)\n",
    "print (\"Dropping {} columns due to NaNs\\n{}\".format(len(null_cols), null_cols))\n",
    "X_feat = X.drop(columns = null_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping 648 columns due to insignificant mean\n"
     ]
    }
   ],
   "source": [
    "# Drop heavily skewed means\n",
    "low_means = []\n",
    "for col in X_feat.columns:\n",
    "    if X_feat[col].mean() < 0.1:\n",
    "        low_means.append(col)\n",
    "print (\"Dropping {} columns due to insignificant mean\".format(len(low_means)))\n",
    "X_feat = X_feat.drop(columns = low_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features: 26\n"
     ]
    }
   ],
   "source": [
    "# Feature list\n",
    "print (\"Number of features: {}\".format(len(X_feat.columns)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime: 62.70 seconds\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "svc_model = svm.SVC()\n",
    "fit = svc_model.fit(X_feat, y_class)\n",
    "y_pred = svc_model.predict(X_feat)\n",
    "print (\"Runtime: %0.2f seconds\" % (time.time() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime: 23.82 seconds\n",
      "Baseline Score: 0.579\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "svc_score = svc_model.score(X_feat, y_class)\n",
    "print (\"Runtime: %0.2f seconds\" % (time.time() - start))\n",
    "print (\"Baseline Score: %0.3f\" % (svc_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Improving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime: 0.00 seconds\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "svc_iter_model = svm.SVC(C = 100)\n",
    "print (\"Runtime: %0.2f seconds\" % (time.time() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mache/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "# Add nutritional information, imputing nulls with median\n",
    "X_nut = df[list(set(list(df.columns)) - set(['rating', 'title']))]\n",
    "X_nut.drop(columns = low_means, inplace = True)\n",
    "X_nut = X_nut.fillna(X_nut.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime: 779.55 seconds\n",
      "Mean Accuracy: 0.603 (+/- 0.007)\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "svc_iter_cross_val_scores = cross_val_score(svc_iter_model, X_nut, y_class, cv=5)\n",
    "print (\"Runtime: %0.2f seconds\" % (time.time() - start))\n",
    "print (\"Mean Accuracy: %0.3f (+/- %0.3f)\" % (svc_iter_cross_val_scores.mean(), svc_iter_cross_val_scores.std()))"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.6.3"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "12px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}