D20019-kNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing relevant libraries\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#considering index to be zero initially\n",
    "index=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car</th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chevrolet Chevelle Malibu</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Buick Skylark 320</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Plymouth Satellite</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMC Rebel SST</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ford Torino</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Ford Mustang GL</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Volkswagen Pickup</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Dodge Rampage</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Ford Ranger</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Chevy S-10</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>406 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Car   MPG  Cylinders  Displacement  Horsepower  \\\n",
       "0    Chevrolet Chevelle Malibu  18.0          8         307.0         130   \n",
       "1            Buick Skylark 320  15.0          8         350.0         165   \n",
       "2           Plymouth Satellite  18.0          8         318.0         150   \n",
       "3                AMC Rebel SST  16.0          8         304.0         150   \n",
       "4                  Ford Torino  17.0          8         302.0         140   \n",
       "..                         ...   ...        ...           ...         ...   \n",
       "401            Ford Mustang GL  27.0          4         140.0          86   \n",
       "402          Volkswagen Pickup  44.0          4          97.0          52   \n",
       "403              Dodge Rampage  32.0          4         135.0          84   \n",
       "404                Ford Ranger  28.0          4         120.0          79   \n",
       "405                 Chevy S-10  31.0          4         119.0          82   \n",
       "\n",
       "     Weight  Acceleration  Model  Origin  \n",
       "0      3504          12.0     70      US  \n",
       "1      3693          11.5     70      US  \n",
       "2      3436          11.0     70      US  \n",
       "3      3433          12.0     70      US  \n",
       "4      3449          10.5     70      US  \n",
       "..      ...           ...    ...     ...  \n",
       "401    2790          15.6     82      US  \n",
       "402    2130          24.6     82  Europe  \n",
       "403    2295          11.6     82      US  \n",
       "404    2625          18.6     82      US  \n",
       "405    2720          19.4     82      US  \n",
       "\n",
       "[406 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the cars dataset from my system\n",
    "cars = pd.read_csv(r\"D:\\Stu\\PGP Data Science\\Semester_1\\IML\\cars.csv\")\n",
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car</th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chevrolet Chevelle Malibu</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Buick Skylark 320</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Plymouth Satellite</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMC Rebel SST</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ford Torino</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Car   MPG  Cylinders  Displacement  Horsepower  \\\n",
       "0  Chevrolet Chevelle Malibu  18.0          8         307.0         130   \n",
       "1          Buick Skylark 320  15.0          8         350.0         165   \n",
       "2         Plymouth Satellite  18.0          8         318.0         150   \n",
       "3              AMC Rebel SST  16.0          8         304.0         150   \n",
       "4                Ford Torino  17.0          8         302.0         140   \n",
       "\n",
       "   Weight  Acceleration  Model Origin  \n",
       "0    3504          12.0     70     US  \n",
       "1    3693          11.5     70     US  \n",
       "2    3436          11.0     70     US  \n",
       "3    3433          12.0     70     US  \n",
       "4    3449          10.5     70     US  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding first 5 values\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Considering the particular variable we are interested in\n",
    "cars2 = cars[[\"MPG\",\"Cylinders\",\"Weight\",\"Horsepower\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3504</td>\n",
       "      <td>130</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3693</td>\n",
       "      <td>165</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3436</td>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3433</td>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3449</td>\n",
       "      <td>140</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2790</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2130</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2295</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2625</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2720</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>406 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Weight  Horsepower\n",
       "0    18.0          8    3504         130\n",
       "1    15.0          8    3693         165\n",
       "2    18.0          8    3436         150\n",
       "3    16.0          8    3433         150\n",
       "4    17.0          8    3449         140\n",
       "..    ...        ...     ...         ...\n",
       "401  27.0          4    2790          86\n",
       "402  44.0          4    2130          52\n",
       "403  32.0          4    2295          84\n",
       "404  28.0          4    2625          79\n",
       "405  31.0          4    2720          82\n",
       "\n",
       "[406 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling the data\n",
    "cars2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3504</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3693</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3436</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3433</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3449</td>\n",
       "      <td>140</td>\n",
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      ],
      "text/plain": [
       "    MPG  Cylinders  Weight  Horsepower\n",
       "0  18.0          8    3504         130\n",
       "1  15.0          8    3693         165\n",
       "2  18.0          8    3436         150\n",
       "3  16.0          8    3433         150\n",
       "4  17.0          8    3449         140"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top 5 values\n",
    "cars2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>130</td>\n",
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       "      <td>15.0</td>\n",
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       "      <td>165</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3436</td>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
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       "      <td>3433</td>\n",
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       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
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       "      <td>140</td>\n",
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       "      <th>...</th>\n",
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       "      <th>402</th>\n",
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       "      <td>52</td>\n",
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       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>32.0</td>\n",
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       "      <td>84</td>\n",
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       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2720</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>406 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Weight  Horsepower\n",
       "0    18.0          8    3504         130\n",
       "1    15.0          8    3693         165\n",
       "2    18.0          8    3436         150\n",
       "3    16.0          8    3433         150\n",
       "4    17.0          8    3449         140\n",
       "..    ...        ...     ...         ...\n",
       "401  27.0          4    2790          86\n",
       "402  44.0          4    2130          52\n",
       "403  32.0          4    2295          84\n",
       "404  28.0          4    2625          79\n",
       "405  31.0          4    2720          82\n",
       "\n",
       "[406 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping if any missing values\n",
    "cars3=cars2.dropna()\n",
    "cars3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#zero values check\n",
    "cars4=cars3.loc[(cars3!=0).any(1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "      MPG  Cylinders  Weight  Horsepower\n",
       "0    18.0          8    3504         130\n",
       "1    15.0          8    3693         165\n",
       "2    18.0          8    3436         150\n",
       "3    16.0          8    3433         150\n",
       "4    17.0          8    3449         140\n",
       "..    ...        ...     ...         ...\n",
       "401  27.0          4    2790          86\n",
       "402  44.0          4    2130          52\n",
       "403  32.0          4    2295          84\n",
       "404  28.0          4    2625          79\n",
       "405  31.0          4    2720          82\n",
       "\n",
       "[406 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Weight  Horsepower\n",
       "0    18.0          8    3504         130\n",
       "1    15.0          8    3693         165\n",
       "2    18.0          8    3436         150\n",
       "3    16.0          8    3433         150\n",
       "4    17.0          8    3449         140\n",
       "..    ...        ...     ...         ...\n",
       "401  27.0          4    2790          86\n",
       "402  44.0          4    2130          52\n",
       "403  32.0          4    2295          84\n",
       "404  28.0          4    2625          79\n",
       "405  31.0          4    2720          82\n",
       "\n",
       "[406 rows x 4 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling the data\n",
    "cars4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>392 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "      MPG  Cylinders  Weight  Horsepower\n",
       "0    18.0          8    3504         130\n",
       "1    15.0          8    3693         165\n",
       "2    18.0          8    3436         150\n",
       "3    16.0          8    3433         150\n",
       "4    17.0          8    3449         140\n",
       "..    ...        ...     ...         ...\n",
       "401  27.0          4    2790          86\n",
       "402  44.0          4    2130          52\n",
       "403  32.0          4    2295          84\n",
       "404  28.0          4    2625          79\n",
       "405  31.0          4    2720          82\n",
       "\n",
       "[392 rows x 4 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we remove all the zero values \n",
    "cars5=cars4[(cars4['MPG']>0) & (cars4['Horsepower']>0)]\n",
    "cars5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Horsepower</th>\n",
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      "text/plain": [
       "    MPG  Cylinders  Weight  Horsepower\n",
       "0  18.0          8    3504         130\n",
       "1  15.0          8    3693         165\n",
       "2  18.0          8    3436         150\n",
       "3  16.0          8    3433         150\n",
       "4  17.0          8    3449         140"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top 5 values after 0 is removed\n",
    "cars5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now to standardize all the elements on cars 5 datatset we do the following\n",
    "cars_sd=(cars5[['MPG','Cylinders','Weight','Horsepower']] - cars5[['MPG','Cylinders','Weight','Horsepower']].mean())/cars5[['MPG','Cylinders','Weight','Horsepower']].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.697747</td>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.619748</td>\n",
       "      <td>0.663285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.082115</td>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.842258</td>\n",
       "      <td>1.572585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.697747</td>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.539692</td>\n",
       "      <td>1.182885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.953992</td>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.536160</td>\n",
       "      <td>1.182885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.825870</td>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.554997</td>\n",
       "      <td>0.923085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>0.455359</td>\n",
       "      <td>-0.862911</td>\n",
       "      <td>-0.220842</td>\n",
       "      <td>-0.479835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>2.633448</td>\n",
       "      <td>-0.862911</td>\n",
       "      <td>-0.997859</td>\n",
       "      <td>-1.363154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1.095974</td>\n",
       "      <td>-0.862911</td>\n",
       "      <td>-0.803605</td>\n",
       "      <td>-0.531795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>0.583482</td>\n",
       "      <td>-0.862911</td>\n",
       "      <td>-0.415097</td>\n",
       "      <td>-0.661694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>0.967851</td>\n",
       "      <td>-0.862911</td>\n",
       "      <td>-0.303253</td>\n",
       "      <td>-0.583754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>392 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          MPG  Cylinders    Weight  Horsepower\n",
       "0   -0.697747   1.482053  0.619748    0.663285\n",
       "1   -1.082115   1.482053  0.842258    1.572585\n",
       "2   -0.697747   1.482053  0.539692    1.182885\n",
       "3   -0.953992   1.482053  0.536160    1.182885\n",
       "4   -0.825870   1.482053  0.554997    0.923085\n",
       "..        ...        ...       ...         ...\n",
       "401  0.455359  -0.862911 -0.220842   -0.479835\n",
       "402  2.633448  -0.862911 -0.997859   -1.363154\n",
       "403  1.095974  -0.862911 -0.803605   -0.531795\n",
       "404  0.583482  -0.862911 -0.415097   -0.661694\n",
       "405  0.967851  -0.862911 -0.303253   -0.583754\n",
       "\n",
       "[392 rows x 4 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Our standardized dataset\n",
    "cars_sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.697747</td>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.619748</td>\n",
       "      <td>0.663285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.082115</td>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.842258</td>\n",
       "      <td>1.572585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.697747</td>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.539692</td>\n",
       "      <td>1.182885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.953992</td>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.536160</td>\n",
       "      <td>1.182885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.825870</td>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.554997</td>\n",
       "      <td>0.923085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        MPG  Cylinders    Weight  Horsepower\n",
       "0 -0.697747   1.482053  0.619748    0.663285\n",
       "1 -1.082115   1.482053  0.842258    1.572585\n",
       "2 -0.697747   1.482053  0.539692    1.182885\n",
       "3 -0.953992   1.482053  0.536160    1.182885\n",
       "4 -0.825870   1.482053  0.554997    0.923085"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling top 5 values of standardized dataset\n",
    "cars_sd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining train X\n",
    "trainX = cars_sd.iloc[1:406, [1,2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.842258</td>\n",
       "      <td>1.572585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.539692</td>\n",
       "      <td>1.182885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.536160</td>\n",
       "      <td>1.182885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.554997</td>\n",
       "      <td>0.923085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.482053</td>\n",
       "      <td>1.605147</td>\n",
       "      <td>2.429924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>-0.862911</td>\n",
       "      <td>-0.220842</td>\n",
       "      <td>-0.479835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>-0.862911</td>\n",
       "      <td>-0.997859</td>\n",
       "      <td>-1.363154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>-0.862911</td>\n",
       "      <td>-0.803605</td>\n",
       "      <td>-0.531795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>-0.862911</td>\n",
       "      <td>-0.415097</td>\n",
       "      <td>-0.661694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>-0.862911</td>\n",
       "      <td>-0.303253</td>\n",
       "      <td>-0.583754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>391 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cylinders    Weight  Horsepower\n",
       "1     1.482053  0.842258    1.572585\n",
       "2     1.482053  0.539692    1.182885\n",
       "3     1.482053  0.536160    1.182885\n",
       "4     1.482053  0.554997    0.923085\n",
       "5     1.482053  1.605147    2.429924\n",
       "..         ...       ...         ...\n",
       "401  -0.862911 -0.220842   -0.479835\n",
       "402  -0.862911 -0.997859   -1.363154\n",
       "403  -0.862911 -0.803605   -0.531795\n",
       "404  -0.862911 -0.415097   -0.661694\n",
       "405  -0.862911 -0.303253   -0.583754\n",
       "\n",
       "[391 rows x 3 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calling trainX\n",
    "trainX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.842258</td>\n",
       "      <td>1.572585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.539692</td>\n",
       "      <td>1.182885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.536160</td>\n",
       "      <td>1.182885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.482053</td>\n",
       "      <td>0.554997</td>\n",
       "      <td>0.923085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.482053</td>\n",
       "      <td>1.605147</td>\n",
       "      <td>2.429924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cylinders    Weight  Horsepower\n",
       "1   1.482053  0.842258    1.572585\n",
       "2   1.482053  0.539692    1.182885\n",
       "3   1.482053  0.536160    1.182885\n",
       "4   1.482053  0.554997    0.923085\n",
       "5   1.482053  1.605147    2.429924"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top 5 values of trainX\n",
    "trainX.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cylinders     1.482053\n",
       "Weight        0.619748\n",
       "Horsepower    0.663285\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#defining and calling testX\n",
    "testX = cars_sd.iloc[0,[1,2,3]]\n",
    "testX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     -1.082115\n",
       "2     -0.697747\n",
       "3     -0.953992\n",
       "4     -0.825870\n",
       "5     -1.082115\n",
       "         ...   \n",
       "401    0.455359\n",
       "402    2.633448\n",
       "403    1.095974\n",
       "404    0.583482\n",
       "405    0.967851\n",
       "Name: MPG, Length: 391, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Defining and calling trainY\n",
    "trainY = cars_sd.MPG[1:406]\n",
    "trainY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   -1.082115\n",
       "2   -0.697747\n",
       "3   -0.953992\n",
       "4   -0.825870\n",
       "5   -1.082115\n",
       "Name: MPG, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 5 values of trainY\n",
    "trainY.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining eucledian for distance calculation\n",
    "def euclidean(p,q):\n",
    "    d = 0\n",
    "\n",
    "    for i in range(len(p)):\n",
    "        d = d + (p[i]-q[i])**2\n",
    "\n",
    "    return(np.sqrt(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "#finding the distance  \n",
    "dist = []\n",
    "\n",
    "for i in range(len(trainX)):\n",
    "    dist.append(euclidean(testX, trainX.iloc[i,[0,1,2]])) #taking0,1,2 because we have trainX and trainY as well\n",
    "    \n",
    "print(dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now define the knn function\n",
    "def knn(trainX,trainY,testX,k): \n",
    "    \n",
    "    \n",
    "    dist = []\n",
    "    \n",
    "\n",
    "    for i in range(0, len(trainX)):\n",
    "        dist.append(euclidean(testX,trainX.iloc[i,[0,1,2]]))\n",
    "\n",
    "    d = dist.copy()\n",
    "    dist.sort()\n",
    "    #print(d)\n",
    "\n",
    "    index = []\n",
    "    for i in range(0,k):\n",
    "        indx = d.index(dist[i])\n",
    "        index.append(indx)\n",
    "        d[indx] = -1\n",
    "        #print(d)\n",
    "        \n",
    "    #print(index)\n",
    "    \n",
    "\n",
    "    pred_y = np.mean(trainY.iloc[index])\n",
    "    return(pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5439992690143953"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running knn for k=1\n",
    "knn(trainX,trainY,testX,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4799378314231785"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running knn for k=2\n",
    "knn(trainX,trainY,testX,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5696238440508821"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running knn for k=3\n",
    "knn(trainX,trainY,testX,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.633685281642099"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running knn for k=4\n",
    "knn(trainX,trainY,testX,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.610623164109261"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running knn for k=5\n",
    "knn(trainX,trainY,testX,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7319128192819649"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running knn for k=6\n",
    "knn(trainX,trainY,testX,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7453352157296484"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running knn for k=7\n",
    "knn(trainX,trainY,testX,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7201682223902418"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running knn for k=8\n",
    "knn(trainX,trainY,testX,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7148297692576404"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running knn for k=9\n",
    "knn(trainX,trainY,testX,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7118402355033837"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running knn for k=10\n",
    "knn(trainX,trainY,testX,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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