KNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://cdn-images-1.medium.com/max/800/0*Sk18h9op6uK9EpT8.'/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Euclidean dist :  the Euclidean distance or Euclidean metric is the \"ordinary\" straight-line distance between two points \n",
    "\n",
    "- Hamming dist : the Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different\n",
    "    \n",
    "\"karolin\" and \"kathrin\" is 3.\n",
    "\n",
    "\"karolin\" and \"kerstin\" is 3.\n",
    "\n",
    "1011101 and 1001001 is 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df =pd.DataFrame(iris.data,columns = ['sepal_length','sepal_width','petal_length','petal_width'])\n",
    "df['class']=iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### species\n",
    "- setosa\n",
    "- versicolor\n",
    "- virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
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       "      <td>1.4</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width  class\n",
       "0           5.1          3.5           1.4          0.2      0\n",
       "1           4.9          3.0           1.4          0.2      0\n",
       "2           4.7          3.2           1.3          0.2      0\n",
       "3           4.6          3.1           1.5          0.2      0\n",
       "4           5.0          3.6           1.4          0.2      0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
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       "      <td>4.9</td>\n",
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       "      <td>0</td>\n",
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       "      <td>4.3</td>\n",
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       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width  class\n",
       "1             4.9          3.0           1.4          0.2      0\n",
       "2             4.7          3.2           1.3          0.2      0\n",
       "3             4.6          3.1           1.5          0.2      0\n",
       "4             5.0          3.6           1.4          0.2      0\n",
       "5             5.4          3.9           1.7          0.4      0\n",
       "6             4.6          3.4           1.4          0.3      0\n",
       "7             5.0          3.4           1.5          0.2      0\n",
       "8             4.4          2.9           1.4          0.2      0\n",
       "9             4.9          3.1           1.5          0.1      0\n",
       "10            5.4          3.7           1.5          0.2      0\n",
       "11            4.8          3.4           1.6          0.2      0\n",
       "12            4.8          3.0           1.4          0.1      0\n",
       "13            4.3          3.0           1.1          0.1      0\n",
       "14            5.8          4.0           1.2          0.2      0\n",
       "15            5.7          4.4           1.5          0.4      0\n",
       "16            5.4          3.9           1.3          0.4      0\n",
       "18            5.7          3.8           1.7          0.3      0\n",
       "20            5.4          3.4           1.7          0.2      0\n",
       "22            4.6          3.6           1.0          0.2      0\n",
       "24            4.8          3.4           1.9          0.2      0\n",
       "25            5.0          3.0           1.6          0.2      0\n",
       "26            5.0          3.4           1.6          0.4      0\n",
       "27            5.2          3.5           1.5          0.2      0\n",
       "28            5.2          3.4           1.4          0.2      0\n",
       "29            4.7          3.2           1.6          0.2      0\n",
       "30            4.8          3.1           1.6          0.2      0\n",
       "31            5.4          3.4           1.5          0.4      0\n",
       "32            5.2          4.1           1.5          0.1      0\n",
       "33            5.5          4.2           1.4          0.2      0\n",
       "34            4.9          3.1           1.5          0.1      0\n",
       "..            ...          ...           ...          ...    ...\n",
       "120           6.9          3.2           5.7          2.3      2\n",
       "121           5.6          2.8           4.9          2.0      2\n",
       "122           7.7          2.8           6.7          2.0      2\n",
       "123           6.3          2.7           4.9          1.8      2\n",
       "124           6.7          3.3           5.7          2.1      2\n",
       "125           7.2          3.2           6.0          1.8      2\n",
       "126           6.2          2.8           4.8          1.8      2\n",
       "127           6.1          3.0           4.9          1.8      2\n",
       "128           6.4          2.8           5.6          2.1      2\n",
       "129           7.2          3.0           5.8          1.6      2\n",
       "130           7.4          2.8           6.1          1.9      2\n",
       "131           7.9          3.8           6.4          2.0      2\n",
       "132           6.4          2.8           5.6          2.2      2\n",
       "133           6.3          2.8           5.1          1.5      2\n",
       "134           6.1          2.6           5.6          1.4      2\n",
       "135           7.7          3.0           6.1          2.3      2\n",
       "136           6.3          3.4           5.6          2.4      2\n",
       "137           6.4          3.1           5.5          1.8      2\n",
       "138           6.0          3.0           4.8          1.8      2\n",
       "139           6.9          3.1           5.4          2.1      2\n",
       "140           6.7          3.1           5.6          2.4      2\n",
       "141           6.9          3.1           5.1          2.3      2\n",
       "142           5.8          2.7           5.1          1.9      2\n",
       "143           6.8          3.2           5.9          2.3      2\n",
       "144           6.7          3.3           5.7          2.5      2\n",
       "145           6.7          3.0           5.2          2.3      2\n",
       "146           6.3          2.5           5.0          1.9      2\n",
       "147           6.5          3.0           5.2          2.0      2\n",
       "148           6.2          3.4           5.4          2.3      2\n",
       "149           5.9          3.0           5.1          1.8      2\n",
       "\n",
       "[141 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['sepal_length']!=5.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3,random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((105, 4), (45, 4))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape , x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = []\n",
    "for i in range(10):\n",
    "    model = KNeighborsClassifier(n_neighbors=i)\n",
    "    model.fit(x_train,y_train)\n",
    "    preds.append(model.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93333333333333335"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "\n",
    "kfold = model_selection.KFold(n_splits=10,random_state = 7)\n",
    "\n",
    "results = model_selection.cross_val_score(model,x,y,cv=kfold,scoring='accuracy')\n",
    "\n",
    "results.sum()/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        12\n",
      "          1       0.83      0.94      0.88        16\n",
      "          2       0.93      0.82      0.87        17\n",
      "\n",
      "avg / total       0.92      0.91      0.91        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame({'actual':y_test,'pred':pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>actual</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   actual  pred\n",
       "0       2     2\n",
       "1       1     1\n",
       "2       0     0\n",
       "3       1     1\n",
       "4       2     1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f69f3c095f8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P+3P9jcxKht6uXgbH1d8QKTcFiHhqORxhsbER//4rimqUq78h4h0FiHginckw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\nj6NDihOL6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69f3c83780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('actual','pred',data,hue='pred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}