uci-car-01-updated.ipynb

{
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   "cell_type": "code",
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   "metadata": {
    "collapsed": true,
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   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import sys\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jupyter Notebook Version:3.5.2 |Enthought, Inc. (x86_64)| (default, Mar  2 2017, 16:37:47) [MSC v.1900 64 bit (AMD64)]\n",
      "Numpy Version:1.13.3:\n",
      "Pandas Version:0.20.3\n",
      "Matplotlib Version: 2.0.0\n",
      "Seaborn Version: 0.8.1\n"
     ]
    }
   ],
   "source": [
    "#display versions\n",
    "print( 'Jupyter Notebook Version:{}' .format(sys.version))\n",
    "print('Numpy Version:{}:'.format(np.__version__))\n",
    "print('Pandas Version:{}'.format(  pd.__version__))\n",
    "print('Matplotlib Version: {}'.format(matplotlib.__version__))\n",
    "print('Seaborn Version: {}'.format(sns.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#load dataset as a pandas dataframe\n",
    "#obtained from https://archive.ics.uci.edu/ml/datasets/Automobile\n",
    "dset = pd.read_csv(\"automobiles.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#df for encoded data , df_pure for non encoded\n",
    "df = pd.DataFrame(dset)\n",
    "#df_pure = pd.DataFrame(dset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 26)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df = df[df.price!= '?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201, 26)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 201 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      "symboling            201 non-null int64\n",
      "normalized-losses    201 non-null object\n",
      "make                 201 non-null object\n",
      "fuel-type            201 non-null object\n",
      "aspiration           201 non-null object\n",
      "num-of-doors         201 non-null object\n",
      "body-style           201 non-null object\n",
      "drive-wheels         201 non-null object\n",
      "engine-location      201 non-null object\n",
      "wheel-base           201 non-null float64\n",
      "length               201 non-null float64\n",
      "width                201 non-null float64\n",
      "height               201 non-null float64\n",
      "curb-weight          201 non-null int64\n",
      "engine-type          201 non-null object\n",
      "num-of-cylinders     201 non-null object\n",
      "engine-size          201 non-null int64\n",
      "fuel-system          201 non-null object\n",
      "bore                 201 non-null object\n",
      "stroke               201 non-null object\n",
      "compression-ratio    201 non-null float64\n",
      "horsepower           201 non-null object\n",
      "peak-rpm             201 non-null object\n",
      "city-mpg             201 non-null int64\n",
      "highway-mpg          201 non-null int64\n",
      "price                201 non-null object\n",
      "dtypes: float64(5), int64(5), object(16)\n",
      "memory usage: 42.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#getting types of attributes\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df = df.drop('normalized-losses',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
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      "text/plain": [
       "   symboling         make fuel-type aspiration num-of-doors   body-style  \\\n",
       "0          3  alfa-romero       gas        std          two  convertible   \n",
       "1          3  alfa-romero       gas        std          two  convertible   \n",
       "2          1  alfa-romero       gas        std          two    hatchback   \n",
       "3          2         audi       gas        std         four        sedan   \n",
       "4          2         audi       gas        std         four        sedan   \n",
       "\n",
       "  drive-wheels engine-location  wheel-base  length  ...    engine-size  \\\n",
       "0          rwd           front        88.6   168.8  ...            130   \n",
       "1          rwd           front        88.6   168.8  ...            130   \n",
       "2          rwd           front        94.5   171.2  ...            152   \n",
       "3          fwd           front        99.8   176.6  ...            109   \n",
       "4          4wd           front        99.4   176.6  ...            136   \n",
       "\n",
       "   fuel-system  bore stroke compression-ratio  horsepower peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47   2.68               9.0         111     5000       21   \n",
       "1         mpfi  3.47   2.68               9.0         111     5000       21   \n",
       "2         mpfi  2.68   3.47               9.0         154     5000       19   \n",
       "3         mpfi  3.19    3.4              10.0         102     5500       24   \n",
       "4         mpfi  3.19    3.4               8.0         115     5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    65\n",
       " 1    52\n",
       " 2    32\n",
       " 3    27\n",
       "-1    22\n",
       "-2     3\n",
       "Name: symboling, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['symboling'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "std      165\n",
       "turbo     36\n",
       "Name: aspiration, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['aspiration'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fwd    118\n",
       "rwd     75\n",
       "4wd      8\n",
       "Name: drive-wheels, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drive-wheels'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#convert type of price from object to float\n",
    "df.price = df.price.astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 201 entries, 0 to 204\n",
      "Data columns (total 25 columns):\n",
      "symboling            201 non-null int64\n",
      "make                 201 non-null object\n",
      "fuel-type            201 non-null object\n",
      "aspiration           201 non-null object\n",
      "num-of-doors         201 non-null object\n",
      "body-style           201 non-null object\n",
      "drive-wheels         201 non-null object\n",
      "engine-location      201 non-null object\n",
      "wheel-base           201 non-null float64\n",
      "length               201 non-null float64\n",
      "width                201 non-null float64\n",
      "height               201 non-null float64\n",
      "curb-weight          201 non-null int64\n",
      "engine-type          201 non-null object\n",
      "num-of-cylinders     201 non-null object\n",
      "engine-size          201 non-null int64\n",
      "fuel-system          201 non-null object\n",
      "bore                 201 non-null object\n",
      "stroke               201 non-null object\n",
      "compression-ratio    201 non-null float64\n",
      "horsepower           201 non-null object\n",
      "peak-rpm             201 non-null object\n",
      "city-mpg             201 non-null int64\n",
      "highway-mpg          201 non-null int64\n",
      "price                201 non-null float64\n",
      "dtypes: float64(6), int64(5), object(14)\n",
      "memory usage: 40.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>wheel-base</th>\n",
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       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
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       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>130</td>\n",
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       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>152</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>109</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>136</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  wheel-base  length  width  height  curb-weight  engine-size  \\\n",
       "0          3        88.6   168.8   64.1    48.8         2548          130   \n",
       "1          3        88.6   168.8   64.1    48.8         2548          130   \n",
       "2          1        94.5   171.2   65.5    52.4         2823          152   \n",
       "3          2        99.8   176.6   66.2    54.3         2337          109   \n",
       "4          2        99.4   176.6   66.4    54.3         2824          136   \n",
       "\n",
       "   compression-ratio  city-mpg  highway-mpg    price  \n",
       "0                9.0        21           27  13495.0  \n",
       "1                9.0        21           27  16500.0  \n",
       "2                9.0        19           26  16500.0  \n",
       "3               10.0        24           30  13950.0  \n",
       "4                8.0        18           22  17450.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#obtaining numerical attributes for correlation\n",
    "df_numeric=df.select_dtypes(include=['float64', 'int64'])\n",
    "\n",
    "df_numeric.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#obtain correlation matrix of the numeric dataset\n",
    "cor = df_numeric.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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OMpc/DVQHPICdWut48/TlQL3CFlZKjQJGAdCgIvgWHK4o7t7qX9ayYdUmAOo3qkt0VEzu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\nEU+5/wd498++BhpT0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0bc93c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure size\n",
    "plt.figure(figsize=(16,8))\n",
    "\n",
    "# heatmap to check the relationship between different numerical attributes\n",
    "sns.heatmap(cor, cmap=\"Greens\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#df = df.drop('symboling',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 201 entries, 0 to 204\n",
      "Data columns (total 25 columns):\n",
      "symboling            201 non-null int64\n",
      "make                 201 non-null object\n",
      "fuel-type            201 non-null object\n",
      "aspiration           201 non-null object\n",
      "num-of-doors         201 non-null object\n",
      "body-style           201 non-null object\n",
      "drive-wheels         201 non-null object\n",
      "engine-location      201 non-null object\n",
      "wheel-base           201 non-null float64\n",
      "length               201 non-null float64\n",
      "width                201 non-null float64\n",
      "height               201 non-null float64\n",
      "curb-weight          201 non-null int64\n",
      "engine-type          201 non-null object\n",
      "num-of-cylinders     201 non-null object\n",
      "engine-size          201 non-null int64\n",
      "fuel-system          201 non-null object\n",
      "bore                 201 non-null object\n",
      "stroke               201 non-null object\n",
      "compression-ratio    201 non-null float64\n",
      "horsepower           201 non-null object\n",
      "peak-rpm             201 non-null object\n",
      "city-mpg             201 non-null int64\n",
      "highway-mpg          201 non-null int64\n",
      "price                201 non-null float64\n",
      "dtypes: float64(6), int64(5), object(14)\n",
      "memory usage: 40.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     alfa-romero\n",
       "1     alfa-romero\n",
       "2     alfa-romero\n",
       "3            audi\n",
       "4            audi\n",
       "5            audi\n",
       "6            audi\n",
       "7            audi\n",
       "8            audi\n",
       "10            bmw\n",
       "11            bmw\n",
       "12            bmw\n",
       "13            bmw\n",
       "14            bmw\n",
       "15            bmw\n",
       "16            bmw\n",
       "17            bmw\n",
       "18      chevrolet\n",
       "19      chevrolet\n",
       "20      chevrolet\n",
       "21          dodge\n",
       "22          dodge\n",
       "23          dodge\n",
       "24          dodge\n",
       "25          dodge\n",
       "26          dodge\n",
       "27          dodge\n",
       "28          dodge\n",
       "29          dodge\n",
       "30          honda\n",
       "Name: make, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting carname\n",
    "\n",
    "# Method 1: str.split() by space\n",
    "carnames = df['make'].apply(lambda x: x.split(\" \")[0])\n",
    "carnames[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# regex: any alphanumeric sequence before a space, may contain a hyphen\n",
    "p = re.compile(r'\\w+-?\\w+')\n",
    "carnames = df['make'].apply(lambda x: re.findall(p, x)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    alfa-romero\n",
       "1    alfa-romero\n",
       "2    alfa-romero\n",
       "3           audi\n",
       "4           audi\n",
       "Name: car_company, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carnames = df['make'].apply(lambda x: x.split(\" \")[0])\n",
    "df['car_company'] = df['make'].apply(lambda x: re.findall(p, x)[0])\n",
    "df['car_company'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# replacing misspelled car_company names\n",
    "\n",
    "# volkswagen\n",
    "df.loc[(df['car_company'] == \"vw\") | \n",
    "         (df['car_company'] == \"vokswagen\")\n",
    "         , 'car_company'] = 'volkswagen'\n",
    "\n",
    "# porsche\n",
    "df.loc[df['car_company'] == \"porcshce\", 'car_company'] = 'porsche'\n",
    "\n",
    "# toyota\n",
    "df.loc[df['car_company'] == \"toyouta\", 'car_company'] = 'toyota'\n",
    "\n",
    "# nissan\n",
    "df.loc[df['car_company'] == \"Nissan\", 'car_company'] = 'nissan'\n",
    "\n",
    "# mazda\n",
    "df.loc[df['car_company'] == \"maxda\", 'car_company'] = 'mazda'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toyota           32\n",
       "nissan           18\n",
       "mazda            17\n",
       "honda            13\n",
       "mitsubishi       13\n",
       "subaru           12\n",
       "volkswagen       12\n",
       "volvo            11\n",
       "peugot           11\n",
       "dodge             9\n",
       "mercedes-benz     8\n",
       "bmw               8\n",
       "plymouth          7\n",
       "audi              6\n",
       "saab              6\n",
       "porsche           4\n",
       "chevrolet         3\n",
       "alfa-romero       3\n",
       "jaguar            3\n",
       "isuzu             2\n",
       "renault           2\n",
       "mercury           1\n",
       "Name: car_company, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['car_company'].astype('category').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>...</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>car_company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>...</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling fuel-type aspiration num-of-doors   body-style drive-wheels  \\\n",
       "0          3       gas        std          two  convertible          rwd   \n",
       "1          3       gas        std          two  convertible          rwd   \n",
       "2          1       gas        std          two    hatchback          rwd   \n",
       "3          2       gas        std         four        sedan          fwd   \n",
       "4          2       gas        std         four        sedan          4wd   \n",
       "\n",
       "  engine-location  wheel-base  length  width     ...       fuel-system  bore  \\\n",
       "0           front        88.6   168.8   64.1     ...              mpfi  3.47   \n",
       "1           front        88.6   168.8   64.1     ...              mpfi  3.47   \n",
       "2           front        94.5   171.2   65.5     ...              mpfi  2.68   \n",
       "3           front        99.8   176.6   66.2     ...              mpfi  3.19   \n",
       "4           front        99.4   176.6   66.4     ...              mpfi  3.19   \n",
       "\n",
       "  stroke compression-ratio  horsepower peak-rpm city-mpg highway-mpg    price  \\\n",
       "0   2.68               9.0         111     5000       21          27  13495.0   \n",
       "1   2.68               9.0         111     5000       21          27  16500.0   \n",
       "2   3.47               9.0         154     5000       19          26  16500.0   \n",
       "3    3.4              10.0         102     5500       24          30  13950.0   \n",
       "4    3.4               8.0         115     5500       18          22  17450.0   \n",
       "\n",
       "   car_company  \n",
       "0  alfa-romero  \n",
       "1  alfa-romero  \n",
       "2  alfa-romero  \n",
       "3         audi  \n",
       "4         audi  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop('make', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 201 entries, 0 to 204\n",
      "Data columns (total 25 columns):\n",
      "symboling            201 non-null int64\n",
      "fuel-type            201 non-null object\n",
      "aspiration           201 non-null object\n",
      "num-of-doors         201 non-null object\n",
      "body-style           201 non-null object\n",
      "drive-wheels         201 non-null object\n",
      "engine-location      201 non-null object\n",
      "wheel-base           201 non-null float64\n",
      "length               201 non-null float64\n",
      "width                201 non-null float64\n",
      "height               201 non-null float64\n",
      "curb-weight          201 non-null int64\n",
      "engine-type          201 non-null object\n",
      "num-of-cylinders     201 non-null object\n",
      "engine-size          201 non-null int64\n",
      "fuel-system          201 non-null object\n",
      "bore                 201 non-null object\n",
      "stroke               201 non-null object\n",
      "compression-ratio    201 non-null float64\n",
      "horsepower           201 non-null object\n",
      "peak-rpm             201 non-null object\n",
      "city-mpg             201 non-null int64\n",
      "highway-mpg          201 non-null int64\n",
      "price                201 non-null float64\n",
      "car_company          201 non-null object\n",
      "dtypes: float64(6), int64(5), object(14)\n",
      "memory usage: 40.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# creating dummy variables for categorical variables\n",
    "\n",
    "# subset all categorical variables\n",
    "# cars_categorical = df.select_dtypes(include=['object'])\n",
    "# cars_categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# cars_dummies = pd.get_dummies(cars_categorical, drop_first=True)\n",
    "# cars_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# drop categorical variables \n",
    "# df = df.drop(list(cars_categorical.columns), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# concat dummy variables with X\n",
    "# df = pd.concat([df, cars_dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Encoding the dataset\n",
    "from sklearn.preprocessing import LabelEncoder   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "for col in df.columns.values:\n",
    "    if df[col].dtypes=='object':\n",
    "        data = df[col]\n",
    "        le.fit(data.values)\n",
    "        df[col]=le.transform(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201, 25)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>...</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>car_company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  fuel-type  aspiration  num-of-doors  body-style  drive-wheels  \\\n",
       "0          3          1           0             2           0             2   \n",
       "1          3          1           0             2           0             2   \n",
       "2          1          1           0             2           2             2   \n",
       "3          2          1           0             1           3             1   \n",
       "4          2          1           0             1           3             0   \n",
       "\n",
       "   engine-location  wheel-base  length  width     ...       fuel-system  bore  \\\n",
       "0                0        88.6   168.8   64.1     ...                 5    23   \n",
       "1                0        88.6   168.8   64.1     ...                 5    23   \n",
       "2                0        94.5   171.2   65.5     ...                 5     1   \n",
       "3                0        99.8   176.6   66.2     ...                 5    13   \n",
       "4                0        99.4   176.6   66.4     ...                 5    13   \n",
       "\n",
       "   stroke  compression-ratio  horsepower  peak-rpm  city-mpg  highway-mpg  \\\n",
       "0       4                9.0           5        10        21           27   \n",
       "1       4                9.0           5        10        21           27   \n",
       "2      27                9.0          20        10        19           26   \n",
       "3      24               10.0           2        16        24           30   \n",
       "4      24                8.0           8        16        18           22   \n",
       "\n",
       "     price  car_company  \n",
       "0  13495.0            0  \n",
       "1  16500.0            0  \n",
       "2  16500.0            0  \n",
       "3  13950.0            1  \n",
       "4  17450.0            1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symboling', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style',\n",
       "       'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width',\n",
       "       'height', 'curb-weight', 'engine-type', 'num-of-cylinders',\n",
       "       'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-ratio',\n",
       "       'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price',\n",
       "       'car_company'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#import for dividing dataset into train and test samples\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#perform train test split\n",
    "x = df.drop('price',axis=1)\n",
    "y = df.price\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=0)\n",
    "x_train = pd.DataFrame(x_train)\n",
    "x_test = pd.DataFrame(x_test)\n",
    "y_train = pd.DataFrame(y_train)\n",
    "y_test = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# #scaling the features\n",
    "# from sklearn.preprocessing import scale\n",
    "\n",
    "# # storing column names in cols, since column names are (annoyingly) lost after \n",
    "# # scaling (the df is converted to a numpy array)\n",
    "# cols = x_train.columns\n",
    "# x_train = pd.DataFrame(scale(x_train))\n",
    "# x_train.columns = cols\n",
    "# x_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# #scaling the features\n",
    "# from sklearn.preprocessing import scale\n",
    "\n",
    "# # storing column names in cols, since column names are (annoyingly) lost after \n",
    "# # scaling (the df is converted to a numpy array)\n",
    "# cols = x_test.columns\n",
    "# x_test = pd.DataFrame(scale(x_test))\n",
    "# x_test.columns = cols\n",
    "# x_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>car_company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>106.7</td>\n",
       "      <td>187.5</td>\n",
       "      <td>70.3</td>\n",
       "      <td>...</td>\n",
       "      <td>183</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>21.5</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>93.7</td>\n",
       "      <td>157.3</td>\n",
       "      <td>63.8</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>107.9</td>\n",
       "      <td>186.7</td>\n",
       "      <td>68.4</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>21.0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>98.4</td>\n",
       "      <td>176.2</td>\n",
       "      <td>65.6</td>\n",
       "      <td>...</td>\n",
       "      <td>146</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>175.0</td>\n",
       "      <td>66.1</td>\n",
       "      <td>...</td>\n",
       "      <td>134</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  fuel-type  aspiration  num-of-doors  body-style  drive-wheels  \\\n",
       "69           0          0           1             2           1             2   \n",
       "27           1          1           1             0           3             1   \n",
       "116          0          0           1             1           3             2   \n",
       "172          2          1           0             2           0             2   \n",
       "66           0          0           0             1           3             2   \n",
       "\n",
       "     engine-location  wheel-base  length  width     ...       engine-size  \\\n",
       "69                 0       106.7   187.5   70.3     ...               183   \n",
       "27                 0        93.7   157.3   63.8     ...                98   \n",
       "116                0       107.9   186.7   68.4     ...               152   \n",
       "172                0        98.4   176.2   65.6     ...               146   \n",
       "66                 0       104.9   175.0   66.1     ...               134   \n",
       "\n",
       "     fuel-system  bore  stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "69             3    26      32               21.5          12         3   \n",
       "27             5     7      23                7.6           2        16   \n",
       "116            3    32      29               21.0          56         0   \n",
       "172            5    30      28                9.3           9         8   \n",
       "66             3    21      32               22.0          44         1   \n",
       "\n",
       "     city-mpg  highway-mpg  car_company  \n",
       "69         22           25            9  \n",
       "27         24           30            4  \n",
       "116        28           33           13  \n",
       "172        24           30           19  \n",
       "66         31           39            8  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>28176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>17950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>17669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>18344.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price\n",
       "69   28176.0\n",
       "27    8558.0\n",
       "116  17950.0\n",
       "172  17669.0\n",
       "66   18344.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 24)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 1)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# #plot the graphs of the variation in data\n",
    "# x_train[x_train.dtypes[(x_train.dtypes==\"float64\")|(x_train.dtypes==\"int64\")]\n",
    "#                         .index.values].hist(figsize=[11,11]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#performing linear regression\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "linearRegressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearRegressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#performing prediction\n",
    "y_predict = linearRegressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# #displaying output side by side\n",
    "# from IPython.display import display, HTML\n",
    "\n",
    "# CSS = \"\"\"\n",
    "# .output {\n",
    "#     flex-direction: row;\n",
    "# }\n",
    "# \"\"\"\n",
    "\n",
    "# HTML('<style>{}</style>'.format(CSS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'display' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-3758edcf234c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdisplay\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_predict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'display' is not defined"
     ]
    }
   ],
   "source": [
    "display(pd.DataFrame(y_test))\n",
    "display(pd.DataFrame(y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#import library for accuracy calculation\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "r2_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#0.8682... without dropping symboling\n",
    "#mean difference = 54.358"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#calculate the mean difference\n",
    "np.mean(y_test-y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#making a plot of the price distribution so as to show the mean difference is not much\n",
    "sns.distplot(df['price'],bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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