labs/04_knn_lab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Lab 3, predicting types of movement using accelerometer data\n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nDataset Information\\n   --- Data are separated by participant\\n   --- Each file contains the following information\\n       ---- sequential number, x acceleration, y acceleration, z acceleration, label \\n   --- Labels are codified by numbers\\n       --- 1: Working at Computer\\n       --- 2: Standing Up, Walking and Going up\\\\down stairs\\n       --- 3: Standing\\n       --- 4: Walking\\n       --- 5: Going Up\\\\Down Stairs\\n       --- 6: Walking and Talking with Someone\\n       --- 7: Talking while Standing\\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "https://archive.ics.uci.edu/ml/datasets/Activity+Recognition+from+Single+Chest-Mounted+Accelerometer\n",
    "\n",
    "\n",
    "Dataset Information\n",
    "   --- Data are separated by participant\n",
    "   --- Each file contains the following information\n",
    "       ---- sequential number, x acceleration, y acceleration, z acceleration, label \n",
    "   --- Labels are codified by numbers\n",
    "       --- 1: Working at Computer\n",
    "       --- 2: Standing Up, Walking and Going up\\down stairs\n",
    "       --- 3: Standing\n",
    "       --- 4: Walking\n",
    "       --- 5: Going Up\\Down Stairs\n",
    "       --- 6: Walking and Talking with Someone\n",
    "       --- 7: Talking while Standing\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "accel = pd.read_csv('../data/accelerometer_1.csv', header=None) # there is no header column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>1957</td>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "     0     1     2     3  4\n",
       "0  0.0  1502  2215  2153  1\n",
       "1  1.0  1667  2072  2047  1\n",
       "2  2.0  1611  1957  1906  1\n",
       "3  3.0  1601  1939  1831  1\n",
       "4  4.0  1643  1965  1879  1"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(162500, 5)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Beginner Level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 0. This dataframe has no headers, change that so the column names are\n",
    "# ['id', 'x', 'y', 'z', 'label']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1502</td>\n",
       "      <td>2215</td>\n",
       "      <td>2153</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1667</td>\n",
       "      <td>2072</td>\n",
       "      <td>2047</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1611</td>\n",
       "      <td>1957</td>\n",
       "      <td>1906</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1601</td>\n",
       "      <td>1939</td>\n",
       "      <td>1831</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1643</td>\n",
       "      <td>1965</td>\n",
       "      <td>1879</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id     x     y     z  label\n",
       "0  0.0  1502  2215  2153      1\n",
       "1  1.0  1667  2072  2047      1\n",
       "2  2.0  1611  1957  1906      1\n",
       "3  3.0  1601  1939  1831      1\n",
       "4  4.0  1643  1965  1879      1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              0\n",
       "x               0\n",
       "y               0\n",
       "z               0\n",
       "label           0\n",
       "binary_label    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Do we have any missing values?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    83748\n",
       "1    33677\n",
       "4    26860\n",
       "3    11179\n",
       "5     3191\n",
       "6     2917\n",
       "2      928\n",
       "0        1\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. print the value counts for the albel column of accel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 3. Remove any rows where the label column is not 1 - 7\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    83748\n",
       "1    33677\n",
       "4    26860\n",
       "3    11179\n",
       "5     3191\n",
       "6     2917\n",
       "2      928\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accel['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 4. replace the numbers in the label column with the word counterpart:\n",
    "    # example 1's become 'Working at Computer', 4's become \"Walking\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>label</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1502</td>\n",
       "      <td>2215</td>\n",
       "      <td>2153</td>\n",
       "      <td>Working at Computer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1667</td>\n",
       "      <td>2072</td>\n",
       "      <td>2047</td>\n",
       "      <td>Working at Computer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1611</td>\n",
       "      <td>1957</td>\n",
       "      <td>1906</td>\n",
       "      <td>Working at Computer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1601</td>\n",
       "      <td>1939</td>\n",
       "      <td>1831</td>\n",
       "      <td>Working at Computer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1643</td>\n",
       "      <td>1965</td>\n",
       "      <td>1879</td>\n",
       "      <td>Working at Computer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id     x     y     z                label\n",
       "0  0.0  1502  2215  2153  Working at Computer\n",
       "1  1.0  1667  2072  2047  Working at Computer\n",
       "2  2.0  1611  1957  1906  Working at Computer\n",
       "3  3.0  1601  1939  1831  Working at Computer\n",
       "4  4.0  1643  1965  1879  Working at Computer"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116f70fd0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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IiOiO8fFxxsfHBzpW9uA38JJ2B95lex9JxwL32v6QpPcCG9k+qnYMnwa8iNLM83VgG9uW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\njP0J7i3HHaztQqQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f70510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5. Plot the number of times each label occurs using a bar char\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Talking while Standing                           0.515372\n",
       "Working at Computer                              0.207243\n",
       "Walking                                          0.165292\n",
       "Standing                                         0.068794\n",
       "Going Up\\Down Stairs                             0.019637\n",
       "Walking and Talking with Someone                 0.017951\n",
       "Standing Up, Walking and Going up\\down stairs    0.005711\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6. Talking while Standing is the most common label, \n",
    "# what is the null accuracy rate for this data set\n",
    "# The null accuracy is equal to the number of times the most \n",
    "# common response element occurs divided by the total number of rows\n",
    "# Talking while Standing                           0.515372"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 7. Make an X and a y variable with X being only the x, y, and z columns\n",
    "# while y is the label column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88682461538461543"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8. Fit a KNN model with 3 neighbors and print the accuracy of that model\n",
    "# Did you do better than the null model?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x accuracy: 0.648049230769\n",
      "y accuracy: 0.642196923077\n",
      "z accuracy: 0.625636923077\n"
     ]
    }
   ],
   "source": [
    "# 9. print the accuracy of predicting the label column using each column individually\n",
    "# Ie fit 3 different KNN models, \n",
    "# a KNN model with K=3 for each of the x, y, and z columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Intermediate Level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 10. Create a list called scores that holds 29 different accuracys, \n",
    "# one for each value of K between 1 and 29\n",
    "# Use all three columns for each model\n",
    "scores = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11c6b6050>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c793dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 11. Make a graph that plots the number of K on the x axis and the accuracy of a KNN model on the y axis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Why do you think that the accuracy keeps going down? We will discuss this phenomenom more on Wednesday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ADVANCED Level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-10.05261103,  -3.94236067,   1.87782031],\n",
       "       [ -5.99388975,  -7.35315447,   0.09718564],\n",
       "       [ -7.37139516, -10.09610053,  -2.27139443],\n",
       "       [ -7.61737826, -10.52543121,  -3.53127745],\n",
       "       [ -6.58424921,  -9.90528689,  -2.72495232]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 12. create a new dataframe called X_scaled which\n",
    "# is the scaled version of the X dataframe\n",
    "\n",
    "X_scaled[:5,] # first five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88682461538461543"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not scaled\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X, y)\n",
    "knn.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88762461538461535"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not scaled\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_scaled, y)\n",
    "knn.score(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 13. Why do you think there was very little difference?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 14. binarize the response (turn it into a binary response problem)\n",
    "# create a new column called \"binary_label\" which is either \"stationary\", \"mobile\n",
    "# \"binary_label is \"stationary\" if the person is stationary (1, 3, 7)\n",
    "# \"binary_label is \"mobile\" if the person is mobile (2, 4, 5, 6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>label</th>\n",
       "      <th>binary_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1502</td>\n",
       "      <td>2215</td>\n",
       "      <td>2153</td>\n",
       "      <td>Working at Computer</td>\n",
       "      <td>stationary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1667</td>\n",
       "      <td>2072</td>\n",
       "      <td>2047</td>\n",
       "      <td>Working at Computer</td>\n",
       "      <td>stationary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1611</td>\n",
       "      <td>1957</td>\n",
       "      <td>1906</td>\n",
       "      <td>Working at Computer</td>\n",
       "      <td>stationary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1601</td>\n",
       "      <td>1939</td>\n",
       "      <td>1831</td>\n",
       "      <td>Working at Computer</td>\n",
       "      <td>stationary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1643</td>\n",
       "      <td>1965</td>\n",
       "      <td>1879</td>\n",
       "      <td>Working at Computer</td>\n",
       "      <td>stationary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id     x     y     z                label binary_label\n",
       "0  0.0  1502  2215  2153  Working at Computer   stationary\n",
       "1  1.0  1667  2072  2047  Working at Computer   stationary\n",
       "2  2.0  1611  1957  1906  Working at Computer   stationary\n",
       "3  3.0  1601  1939  1831  Working at Computer   stationary\n",
       "4  4.0  1643  1965  1879  Working at Computer   stationary"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stationary    0.791409\n",
       "mobile        0.208591\n",
       "Name: binary_label, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 15. find the null accuracy of the new binary problem\n",
    "\n",
    "# stationary    0.791409"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 16. create a new variable, \"y_binary\" which is the binary_label column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95848"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 17. Fit a KNN with K=3 to this binary problem and print out the accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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