main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>Analyze Car Prices Dataset</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Set Information:\n",
    "\n",
    "This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process \"symboling\". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Several of the attributes in the database could be used as a \"class\" attribute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for more information [link](https://archive.ics.uci.edu/ml/datasets/Automobile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attribute Information:\n",
    "\n",
    "1. symboling: -3, -2, -1, 0, 1, 2, 3.\n",
    "2. normalized-losses: continuous from 65 to 256.\n",
    "3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo\n",
    "4. fuel-type: diesel, gas.\n",
    "5. aspiration: std, turbo.\n",
    "6. num-of-doors: four, two.\n",
    "7. body-style: hardtop, wagon, sedan, hatchback, convertible.\n",
    "8. drive-wheels: 4wd, fwd, rwd.\n",
    "9. engine-location: front, rear.\n",
    "10. wheel-base: continuous from 86.6 120.9.\n",
    "11. length: continuous from 141.1 to 208.1.\n",
    "12. width: continuous from 60.3 to 72.3.\n",
    "13. height: continuous from 47.8 to 59.8.\n",
    "14. curb-weight: continuous from 1488 to 4066.\n",
    "15. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor.\n",
    "16. num-of-cylinders: eight, five, four, six, three, twelve, two.\n",
    "17. engine-size: continuous from 61 to 326.\n",
    "18. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.\n",
    "19. bore: continuous from 2.54 to 3.94.\n",
    "20. stroke: continuous from 2.07 to 4.17.\n",
    "21. compression-ratio: continuous from 7 to 23.\n",
    "22. horsepower: continuous from 48 to 288.\n",
    "23. peak-rpm: continuous from 4150 to 6600.\n",
    "24. city-mpg: continuous from 13 to 49.\n",
    "25. highway-mpg: continuous from 16 to 54.\n",
    "26. price: continuous from 5118 to 45400"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install -r requirement.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import necessary tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "other_path = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/auto.csv\"\n",
    "df = pd.read_csv(other_path, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>9</th>\n",
       "      <th>...</th>\n",
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       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
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       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</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>...</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>?</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>...</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>?</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>...</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>164</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</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>164</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</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 × 26 columns</p>\n",
       "</div>"
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      "text/plain": [
       "   0    1            2    3    4     5            6    7      8     9   ...  \\\n",
       "0   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.6  ...   \n",
       "1   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.6  ...   \n",
       "2   1    ?  alfa-romero  gas  std   two    hatchback  rwd  front  94.5  ...   \n",
       "3   2  164         audi  gas  std  four        sedan  fwd  front  99.8  ...   \n",
       "4   2  164         audi  gas  std  four        sedan  4wd  front  99.4  ...   \n",
       "\n",
       "    16    17    18    19    20   21    22  23  24     25  \n",
       "0  130  mpfi  3.47  2.68   9.0  111  5000  21  27  13495  \n",
       "1  130  mpfi  3.47  2.68   9.0  111  5000  21  27  16500  \n",
       "2  152  mpfi  2.68  3.47   9.0  154  5000  19  26  16500  \n",
       "3  109  mpfi  3.19  3.40  10.0  102  5500  24  30  13950  \n",
       "4  136  mpfi  3.19  3.40   8.0  115  5500  18  22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding headrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create headers list\n",
    "headers = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\n",
    "         \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\n",
    "         \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\n",
    "         \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</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>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>highway-L/100km</th>\n",
       "      <th>price</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>fuel-type-diesel</th>\n",
       "      <th>fuel-type-gas</th>\n",
       "      <th>aspiration-std</th>\n",
       "      <th>aspiration-turbo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
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       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.886584</td>\n",
       "      <td>...</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.886584</td>\n",
       "      <td>...</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>0.905947</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.915629</td>\n",
       "      <td>...</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.918396</td>\n",
       "      <td>...</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>10.681818</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>audi</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.851994</td>\n",
       "      <td>0.917012</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>15250.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>6 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make num-of-doors   body-style  \\\n",
       "0          3                122  alfa-romero          two  convertible   \n",
       "1          3                122  alfa-romero          two  convertible   \n",
       "2          1                122  alfa-romero          two    hatchback   \n",
       "3          2                164         audi         four        sedan   \n",
       "4          2                164         audi         four        sedan   \n",
       "5          2                122         audi          two        sedan   \n",
       "\n",
       "  drive-wheels engine-location  wheel-base    length     width  ...  \\\n",
       "0          rwd           front        88.6  0.811148  0.886584  ...   \n",
       "1          rwd           front        88.6  0.811148  0.886584  ...   \n",
       "2          rwd           front        94.5  0.822681  0.905947  ...   \n",
       "3          fwd           front        99.8  0.848630  0.915629  ...   \n",
       "4          4wd           front        99.4  0.848630  0.918396  ...   \n",
       "5          fwd           front        99.8  0.851994  0.917012  ...   \n",
       "\n",
       "   horsepower  peak-rpm city-L/100km highway-L/100km    price  \\\n",
       "0         111    5000.0    11.190476        8.703704  13495.0   \n",
       "1         111    5000.0    11.190476        8.703704  16500.0   \n",
       "2         154    5000.0    12.368421        9.038462  16500.0   \n",
       "3         102    5500.0     9.791667        7.833333  13950.0   \n",
       "4         115    5500.0    13.055556       10.681818  17450.0   \n",
       "5         110    5500.0    12.368421        9.400000  15250.0   \n",
       "\n",
       "  horsepower-binned  fuel-type-diesel  fuel-type-gas  aspiration-std  \\\n",
       "0               Low                 0              1               1   \n",
       "1               Low                 0              1               1   \n",
       "2            Medium                 0              1               1   \n",
       "3               Low                 0              1               1   \n",
       "4               Low                 0              1               1   \n",
       "5               Low                 0              1               1   \n",
       "\n",
       "   aspiration-turbo  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "5                 0  \n",
       "\n",
       "[6 rows x 29 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify and handle missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deleting the rows that have the missing value in price column, because the price car is our goal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simply drop whole row with NaN in \"price\" column\n",
    "df.dropna(subset=[\"price\"], axis=0, inplace=True)\n",
    "\n",
    "# reset index, because we droped two rows\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the car dataset, missing data comes with the question mark \"?\". We replace \"?\" with NaN (Not a Number), which is Python's default missing value marker, for reasons of computational speed and convenience. Here we use the function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>symboling</th>\n",
       "      <th>normalized-losses</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>...</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>NaN</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>...</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>NaN</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>...</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>NaN</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>...</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>164</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</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>164</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</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 × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3               NaN  alfa-romero       gas        std          two   \n",
       "1          3               NaN  alfa-romero       gas        std          two   \n",
       "2          1               NaN  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...          130   \n",
       "1  convertible          rwd           front        88.6  ...          130   \n",
       "2    hatchback          rwd           front        94.5  ...          152   \n",
       "3        sedan          fwd           front        99.8  ...          109   \n",
       "4        sedan          4wd           front        99.4  ...          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    3.40              10.0        102      5500       24   \n",
       "4         mpfi  3.19    3.40               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 26 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace(\"?\", np.nan, inplace = True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating for Missing Data<br>\n",
    "\"True\" stands for missing value, while \"False\" stands for not missing value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    }\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>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
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       "      <th>price</th>\n",
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       "      <th>0</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses   make  fuel-type  aspiration  num-of-doors  \\\n",
       "0      False               True  False      False       False         False   \n",
       "1      False               True  False      False       False         False   \n",
       "2      False               True  False      False       False         False   \n",
       "3      False              False  False      False       False         False   \n",
       "4      False              False  False      False       False         False   \n",
       "\n",
       "   body-style  drive-wheels  engine-location  wheel-base  ...  engine-size  \\\n",
       "0       False         False            False       False  ...        False   \n",
       "1       False         False            False       False  ...        False   \n",
       "2       False         False            False       False  ...        False   \n",
       "3       False         False            False       False  ...        False   \n",
       "4       False         False            False       False  ...        False   \n",
       "\n",
       "   fuel-system   bore  stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "0        False  False   False              False       False     False   \n",
       "1        False  False   False              False       False     False   \n",
       "2        False  False   False              False       False     False   \n",
       "3        False  False   False              False       False     False   \n",
       "4        False  False   False              False       False     False   \n",
       "\n",
       "   city-mpg  highway-mpg  price  \n",
       "0     False        False  False  \n",
       "1     False        False  False  \n",
       "2     False        False  False  \n",
       "3     False        False  False  \n",
       "4     False        False  False  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data = df.isnull()\n",
    "missing_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count missing values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symboling\n",
      "False    205\n",
      "Name: symboling, dtype: int64\n",
      "\n",
      "normalized-losses\n",
      "False    164\n",
      "True      41\n",
      "Name: normalized-losses, dtype: int64\n",
      "\n",
      "make\n",
      "False    205\n",
      "Name: make, dtype: int64\n",
      "\n",
      "fuel-type\n",
      "False    205\n",
      "Name: fuel-type, dtype: int64\n",
      "\n",
      "aspiration\n",
      "False    205\n",
      "Name: aspiration, dtype: int64\n",
      "\n",
      "num-of-doors\n",
      "False    203\n",
      "True       2\n",
      "Name: num-of-doors, dtype: int64\n",
      "\n",
      "body-style\n",
      "False    205\n",
      "Name: body-style, dtype: int64\n",
      "\n",
      "drive-wheels\n",
      "False    205\n",
      "Name: drive-wheels, dtype: int64\n",
      "\n",
      "engine-location\n",
      "False    205\n",
      "Name: engine-location, dtype: int64\n",
      "\n",
      "wheel-base\n",
      "False    205\n",
      "Name: wheel-base, dtype: int64\n",
      "\n",
      "length\n",
      "False    205\n",
      "Name: length, dtype: int64\n",
      "\n",
      "width\n",
      "False    205\n",
      "Name: width, dtype: int64\n",
      "\n",
      "height\n",
      "False    205\n",
      "Name: height, dtype: int64\n",
      "\n",
      "curb-weight\n",
      "False    205\n",
      "Name: curb-weight, dtype: int64\n",
      "\n",
      "engine-type\n",
      "False    205\n",
      "Name: engine-type, dtype: int64\n",
      "\n",
      "num-of-cylinders\n",
      "False    205\n",
      "Name: num-of-cylinders, dtype: int64\n",
      "\n",
      "engine-size\n",
      "False    205\n",
      "Name: engine-size, dtype: int64\n",
      "\n",
      "fuel-system\n",
      "False    205\n",
      "Name: fuel-system, dtype: int64\n",
      "\n",
      "bore\n",
      "False    201\n",
      "True       4\n",
      "Name: bore, dtype: int64\n",
      "\n",
      "stroke\n",
      "False    201\n",
      "True       4\n",
      "Name: stroke, dtype: int64\n",
      "\n",
      "compression-ratio\n",
      "False    205\n",
      "Name: compression-ratio, dtype: int64\n",
      "\n",
      "horsepower\n",
      "False    203\n",
      "True       2\n",
      "Name: horsepower, dtype: int64\n",
      "\n",
      "peak-rpm\n",
      "False    203\n",
      "True       2\n",
      "Name: peak-rpm, dtype: int64\n",
      "\n",
      "city-mpg\n",
      "False    205\n",
      "Name: city-mpg, dtype: int64\n",
      "\n",
      "highway-mpg\n",
      "False    205\n",
      "Name: highway-mpg, dtype: int64\n",
      "\n",
      "price\n",
      "False    201\n",
      "True       4\n",
      "Name: price, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for column in missing_data.columns.values.tolist():\n",
    "    print(column)\n",
    "    print (missing_data[column].value_counts())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Deal with missing data\n",
    "How to deal with missing data ?\n",
    "\n",
    "<ol>\n",
    "    <li>drop data<br>\n",
    "        a. drop the whole row<br>\n",
    "        b. drop the whole column\n",
    "    </li>\n",
    "    <li>replace data<br>\n",
    "        a. replace it by mean<br>\n",
    "        b. replace it by frequency<br>\n",
    "        c. replace it based on other functions\n",
    "    </li>\n",
    "</ol>\n",
    "Whole columns should be dropped only if most entries in the column are empty. In our dataset, none of the columns are empty enough to drop entirely. We have some freedom in choosing which method to replace data; however, some methods may seem more reasonable than others. We will apply each method to many different columns:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Replace by mean:</b>\n",
    "<ul>\n",
    "    <li>\"normalized-losses\": 41 missing data, replace them with mean</li>\n",
    "    <li>\"stroke\": 4 missing data, replace them with mean</li>\n",
    "    <li>\"bore\": 4 missing data, replace them with mean</li>\n",
    "    <li>\"horsepower\": 2 missing data, replace them with mean</li>\n",
    "    <li>\"peak-rpm\": 2 missing data, replace them with mean</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Befor replacing the missing values</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>normalized-losses</th>\n",
       "      <th>stroke</th>\n",
       "      <th>bore</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.19</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>164</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.19</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>95</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.78</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>95</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.78</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>95</td>\n",
       "      <td>2.87</td>\n",
       "      <td>3.58</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>95</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.01</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>95</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.78</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    normalized-losses stroke  bore horsepower peak-rpm\n",
       "0                 NaN   2.68  3.47        111     5000\n",
       "1                 NaN   2.68  3.47        111     5000\n",
       "2                 NaN   3.47  2.68        154     5000\n",
       "3                 164   3.40  3.19        102     5500\n",
       "4                 164   3.40  3.19        115     5500\n",
       "..                ...    ...   ...        ...      ...\n",
       "200                95   3.15  3.78        114     5400\n",
       "201                95   3.15  3.78        160     5300\n",
       "202                95   2.87  3.58        134     5500\n",
       "203                95   3.40  3.01        106     4800\n",
       "204                95   3.15  3.78        114     5400\n",
       "\n",
       "[205 rows x 5 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"normalized-losses\", \"stroke\", \"bore\", \"horsepower\", \"peak-rpm\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>After replacing missing values</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>normalized-losses</th>\n",
       "      <th>stroke</th>\n",
       "      <th>bore</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>122</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>122</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.19</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>164</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.19</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>95</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.78</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>95</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.78</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>95</td>\n",
       "      <td>2.87</td>\n",
       "      <td>3.58</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>95</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.01</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>95</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.78</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    normalized-losses stroke  bore horsepower peak-rpm\n",
       "0                 122   2.68  3.47        111     5000\n",
       "1                 122   2.68  3.47        111     5000\n",
       "2                 122   3.47  2.68        154     5000\n",
       "3                 164   3.40  3.19        102     5500\n",
       "4                 164   3.40  3.19        115     5500\n",
       "..                ...    ...   ...        ...      ...\n",
       "200                95   3.15  3.78        114     5400\n",
       "201                95   3.15  3.78        160     5300\n",
       "202                95   2.87  3.58        134     5500\n",
       "203                95   3.40  3.01        106     4800\n",
       "204                95   3.15  3.78        114     5400\n",
       "\n",
       "[205 rows x 5 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_replaced_missing_data_with_mean = [\"normalized-losses\", \"stroke\", \"bore\", \"horsepower\", \"peak-rpm\"]\n",
    "for column in columns_replaced_missing_data_with_mean: \n",
    "    avg_column = df[column].astype(\"float\").mean(axis=0)\n",
    "    df[column].replace(np.nan, avg_column, inplace=True)\n",
    "df[[\"normalized-losses\", \"stroke\", \"bore\", \"horsepower\", \"peak-rpm\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Replace by frequency:</b>\n",
    "<ul>\n",
    "    <li>\"num-of-doors\": 2 missing data, replace them with \"four\". \n",
    "        <ul>\n",
    "            <li>Reason: 84% sedans is four doors. Since four doors is most frequent, it is most likely to occur</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see which values are present in a particular column, we can use the \".value_counts()\" method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    114\n",
       "two      89\n",
       "Name: num-of-doors, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"num-of-doors\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that four doors are the most common type. We can also use the \".idxmax()\" method to calculate for us the most common type automatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'four'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num-of-doors'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Befor replacing the missing values</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</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>...</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>27</th>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.39</td>\n",
       "      <td>7.6</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>8558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>mazda</td>\n",
       "      <td>diesel</td>\n",
       "      <td>std</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>98.8</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.39</td>\n",
       "      <td>22.7</td>\n",
       "      <td>64</td>\n",
       "      <td>4650</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>10795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    symboling normalized-losses   make fuel-type aspiration num-of-doors  \\\n",
       "27          1               148  dodge       gas      turbo          NaN   \n",
       "63          0               122  mazda    diesel        std          NaN   \n",
       "\n",
       "   body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "27      sedan          fwd           front        93.7  ...           98   \n",
       "63      sedan          fwd           front        98.8  ...          122   \n",
       "\n",
       "    fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "27         mpfi  3.03    3.39               7.6        102      5500       24   \n",
       "63          idi  3.39    3.39              22.7         64      4650       36   \n",
       "\n",
       "   highway-mpg  price  \n",
       "27          30   8558  \n",
       "63          42  10795  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['num-of-doors'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"num-of-doors\"].replace(np.nan, \"four\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>After replacing missing values</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>symboling</th>\n",
       "      <th>normalized-losses</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>...</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [symboling, normalized-losses, make, fuel-type, aspiration, num-of-doors, body-style, drive-wheels, engine-location, wheel-base, length, width, height, curb-weight, engine-type, num-of-cylinders, engine-size, fuel-system, bore, stroke, compression-ratio, horsepower, peak-rpm, city-mpg, highway-mpg, price]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 26 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['num-of-doors'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      21\n",
       "1      21\n",
       "2      19\n",
       "3      24\n",
       "4      18\n",
       "       ..\n",
       "200    23\n",
       "201    19\n",
       "202    18\n",
       "203    26\n",
       "204    19\n",
       "Name: city-mpg, Length: 205, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"city-mpg\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Good!</b> Now, we obtain the dataset with no missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"correct_data_format\">Correct data format</h3>\n",
    "<b>We are almost there!</b>\n",
    "<p>The last step in data cleaning is checking and making sure that all data is in the correct format (int, float, text or other).</p>\n",
    "\n",
    "In Pandas, we use \n",
    "<p><b>.dtype()</b> to check the data type</p>\n",
    "<p><b>.astype()</b> to change the data type</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Lets list the data types for each column</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses     object\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                  object\n",
       "stroke                object\n",
       "compression-ratio    float64\n",
       "horsepower            object\n",
       "peak-rpm              object\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, some columns are not of the correct data type. Numerical variables should have type 'float' or 'int', and variables with strings such as categories should have type 'object'. For example, 'bore' and 'stroke' variables are numerical values that describe the engines, so we should expect them to be of the type 'float' or 'int'; however, they are shown as type 'object'. We have to convert data types into a proper format for each column using the \"astype()\" method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Convert data types to proper format</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[[\"bore\", \"stroke\", \"price\", \"peak-rpm\"]] = df[[\"bore\", \"stroke\", \"price\", \"peak-rpm\"]].astype(\"float\")\n",
    "df[[\"normalized-losses\"]] = df[[\"normalized-losses\"]].astype(\"int64\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Let us list the columns after the conversion</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses      int64\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                 float64\n",
       "stroke               float64\n",
       "compression-ratio    float64\n",
       "horsepower            object\n",
       "peak-rpm             float64\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Data Standardization</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform mpg to L/100km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</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>...</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-L/100km</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>122</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>...</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>5000.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</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>...</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>5000.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</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>...</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>5000.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make fuel-type aspiration  \\\n",
       "0          3                122  alfa-romero       gas        std   \n",
       "1          3                122  alfa-romero       gas        std   \n",
       "2          1                122  alfa-romero       gas        std   \n",
       "3          2                164         audi       gas        std   \n",
       "4          2                164         audi       gas        std   \n",
       "\n",
       "  num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "0          two  convertible          rwd           front        88.6  ...   \n",
       "1          two  convertible          rwd           front        88.6  ...   \n",
       "2          two    hatchback          rwd           front        94.5  ...   \n",
       "3         four        sedan          fwd           front        99.8  ...   \n",
       "4         four        sedan          4wd           front        99.4  ...   \n",
       "\n",
       "   engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "0          130         mpfi  3.47    2.68               9.0        111   \n",
       "1          130         mpfi  3.47    2.68               9.0        111   \n",
       "2          152         mpfi  2.68    3.47               9.0        154   \n",
       "3          109         mpfi  3.19    3.40              10.0        102   \n",
       "4          136         mpfi  3.19    3.40               8.0        115   \n",
       "\n",
       "   peak-rpm city-L/100km  highway-mpg    price  \n",
       "0    5000.0    11.190476           27  13495.0  \n",
       "1    5000.0    11.190476           27  16500.0  \n",
       "2    5000.0    12.368421           26  16500.0  \n",
       "3    5500.0     9.791667           30  13950.0  \n",
       "4    5500.0    13.055556           22  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert mpg to L/100km by mathematical operation (235 divided by mpg)\n",
    "df['city-mpg'] = 235/df[\"city-mpg\"]\n",
    "df.rename(columns={\"city-mpg\":\"city-L/100km\"}, inplace=True)\n",
    "\n",
    "# check your transformed data \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "transform mpg to L/100km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</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>...</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-L/100km</th>\n",
       "      <th>highway-L/100km</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</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>...</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>5000.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</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>...</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>5000.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</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>...</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>5000.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</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>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</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>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>10.681818</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make fuel-type aspiration  \\\n",
       "0          3                122  alfa-romero       gas        std   \n",
       "1          3                122  alfa-romero       gas        std   \n",
       "2          1                122  alfa-romero       gas        std   \n",
       "3          2                164         audi       gas        std   \n",
       "4          2                164         audi       gas        std   \n",
       "\n",
       "  num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "0          two  convertible          rwd           front        88.6  ...   \n",
       "1          two  convertible          rwd           front        88.6  ...   \n",
       "2          two    hatchback          rwd           front        94.5  ...   \n",
       "3         four        sedan          fwd           front        99.8  ...   \n",
       "4         four        sedan          4wd           front        99.4  ...   \n",
       "\n",
       "   engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "0          130         mpfi  3.47    2.68               9.0        111   \n",
       "1          130         mpfi  3.47    2.68               9.0        111   \n",
       "2          152         mpfi  2.68    3.47               9.0        154   \n",
       "3          109         mpfi  3.19    3.40              10.0        102   \n",
       "4          136         mpfi  3.19    3.40               8.0        115   \n",
       "\n",
       "   peak-rpm city-L/100km  highway-L/100km    price  \n",
       "0    5000.0    11.190476         8.703704  13495.0  \n",
       "1    5000.0    11.190476         8.703704  16500.0  \n",
       "2    5000.0    12.368421         9.038462  16500.0  \n",
       "3    5500.0     9.791667         7.833333  13950.0  \n",
       "4    5500.0    13.055556        10.681818  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform mpg to L/100km by mathematical operation (235 divided by mpg)\n",
    "df[\"highway-mpg\"] = 235/df[\"highway-mpg\"]\n",
    "\n",
    "# rename column name from \"highway-mpg\" to \"highway-L/100km\"\n",
    "df.rename(columns={\"highway-mpg\":\"highway-L/100km\"}, inplace=True)\n",
    "\n",
    "# check your transformed data \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Why normalization?</b>\n",
    "<p>Normalization is the process of transforming values of several variables into a similar range. Typical normalizations include scaling the variable so the variable average is 0, scaling the variable so the variance is 1, or scaling variable so the variable values range from 0 to 1\n",
    "</p>\n",
    "\n",
    "<b>Example</b>\n",
    "<p>To demonstrate normalization, let's say we want to scale the columns \"length\", \"width\" and \"height\" </p>\n",
    "<p><b>Target:</b>would like to Normalize those variables so their value ranges from 0 to 1.</p>\n",
    "<p><b>Approach:</b> replace original value by (original value)/(maximum value)</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.886584</td>\n",
       "      <td>0.816054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.886584</td>\n",
       "      <td>0.816054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.822681</td>\n",
       "      <td>0.905947</td>\n",
       "      <td>0.876254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.915629</td>\n",
       "      <td>0.908027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.918396</td>\n",
       "      <td>0.908027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     length     width    height\n",
       "0  0.811148  0.886584  0.816054\n",
       "1  0.811148  0.886584  0.816054\n",
       "2  0.822681  0.905947  0.876254\n",
       "3  0.848630  0.915629  0.908027\n",
       "4  0.848630  0.918396  0.908027"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace (original value) by (original value)/(maximum value)\n",
    "df['length'] = df['length']/df['length'].max()\n",
    "df['width'] = df['width']/df['width'].max()\n",
    "df['height'] = df['height']/df['height'].max()\n",
    "\n",
    "df[[\"length\",\"width\",\"height\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binning\n",
    "<b>Why binning?</b>\n",
    "<p>\n",
    "    Binning is a process of transforming continuous numerical variables into discrete categorical 'bins', for grouped analysis.\n",
    "</p>\n",
    "\n",
    "<b>Example: </b>\n",
    "<p>In our dataset, \"horsepower\" is a real valued variable ranging from 48 to 288, it has 57 unique values. What if we only care about the price difference between cars with high horsepower, medium horsepower, and little horsepower (3 types)? Can we rearrange them into three ‘bins' to simplify analysis? </p>\n",
    "\n",
    "<p>We will use the Pandas method 'cut' to segment the 'horsepower' column into 3 bins </p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Convert data to correct format "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"horsepower\"]=df[\"horsepower\"].astype(int, copy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the histogram of horspower, to see what the distribution of horsepower looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pyplot.hist(df[\"horsepower\"])\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.pyplot.xlabel(\"horsepower\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>We would like 3 bins of equal size bandwidth so we use numpy's <code>linspace(start_value, end_value, numbers_generated</code> function.</p>\n",
    "<p>Since we want to include the minimum value of horsepower we want to set start_value=min(df[\"horsepower\"]).</p>\n",
    "<p>Since we want to include the maximum value of horsepower we want to set end_value=max(df[\"horsepower\"]).</p>\n",
    "<p>Since we are building 3 bins of equal length, there should be 4 dividers, so numbers_generated=4.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We build a bin array, with a minimum value to a maximum value, with bandwidth calculated above. The bins will be values used to determine when one bin ends and another begins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 48., 128., 208., 288.])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.linspace(min(df[\"horsepower\"]), max(df[\"horsepower\"]), 4)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_names = ['Low', 'Medium', 'High']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>horsepower</th>\n",
       "      <th>horsepower-binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>111</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>115</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>114</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>160</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>134</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>106</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>114</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     horsepower horsepower-binned\n",
       "0           111               Low\n",
       "1           111               Low\n",
       "2           154            Medium\n",
       "3           102               Low\n",
       "4           115               Low\n",
       "..          ...               ...\n",
       "200         114               Low\n",
       "201         160            Medium\n",
       "202         134            Medium\n",
       "203         106               Low\n",
       "204         114               Low\n",
       "\n",
       "[205 rows x 2 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['horsepower-binned'] = pd.cut(df['horsepower'], bins, labels=group_names, include_lowest=True )\n",
    "df[['horsepower','horsepower-binned']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low       163\n",
       "Medium     40\n",
       "High        2\n",
       "Name: horsepower-binned, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"horsepower-binned\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the distribution of each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.bar(group_names, df[\"horsepower-binned\"].value_counts())\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.pyplot.xlabel(\"horsepower\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Bins visualization</h3>\n",
    "Normally, a histogram is used to visualize the distribution of bins we created above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RkWwMESIiko0hQkREsjFEiIhINoYIERHJxhAhIiLZ7PJkw02bNuH48ePQarVITU0FAOzYsQPHjh2Ds7Mz7rzzTiQlJcHLywsAkJGRgaysLKjVaiQmJiIiIsIeZRIR0W2yy5nI2LFjsWzZshbr7r33XqSmpmLt2rXo27cvMjIyAADnz59HTk4O0tLSsHz5cmzZsgU2m80eZRIR0W2yS4iEhYVBo9G0WDdkyBA4OTkBAO6++25YrVYAgMViQXR0NFxcXODr6ws/Pz8UFRXZo0wiIrpNdpnO+ilZWVmIjo4GAFitVoSGhkpf0+v1UsDcyGw2w2w2AwBSUlJgNBpl11AieyR1lY4cTzmcnZ3tvs/uwpF7B9h/RygeIrt27YKTkxNGjx4NABBCtHusyWSCyWSSlsvLyzu9PlKOvY+n0Wh02J8hR+4dcOz+/f39OzRe0XdnHTp0CMeOHcOCBQugUqkAAAaDARUVFdI2VqsVer1eqRKJiOgWFAuR3Nxc7N69G0uWLIGbm5u0PjIyEjk5OWhsbERpaSkuXryIkJAQpcokIqJbsMt01rp161BQUICamhrMmTMHCQkJyMjIQFNTE1555RUAQGhoKJ588kkEBQVhxIgRSE5OhlqtxqxZs6BW8+MsRETdkUrczkWIbu7ChQuyxzbPntSJlVBncHpnj13358jz4o7cO+DY/ffoayJERNSzMUSIiEg2hggREcnGECEiItkYIkREJBtDhIiIZGOIEBGRbAwRIiKSjSFCRESyMUSIiEg2hggREcnGECEiItkYIkREJBtDhIiIZGOIEBGRbAwRIiKSjSFCRESy2eXxuJs2bcLx48eh1WqRmpoKAKitrUV6ejrKysrg4+ODRYsWQaPRAAAyMjKQlZUFtVqNxMRERERE2KNMIiK6TXY5Exk7diyWLVvWYl1mZibCw8Oxfv16hIeHIzMzEwBw/vx55OTkIC0tDcuXL8eWLVtgs9nsUSYREd0mu4RIWFiYdJZxncViQUxMDAAgJiYGFotFWh8dHQ0XFxf4+vrCz88PRUVF9iiTiIhuk12ms1pTVVUFnU4HANDpdKiurgYAWK1WhIaGStvp9XpYrdZWX8NsNsNsNgMAUlJSYDQaZddTInskdZWOHE85nJ2d7b7P7sKRewfYf0coFiJtEUK0e1uTyQSTySQtl5eXd0VJpBB7H0+j0eiwP0OO3Dvg2P37+/t3aLxi787SarWorKwEAFRWVsLb2xsAYDAYUFFRIW1ntVqh1+sVqZGIiG5NsRCJjIxEdnY2ACA7OxtRUVHS+pycHDQ2NqK0tBQXL15ESEiIUmUSEdEttHs6a8+ePZg0adJN6z/77DNMnDjxlmPXrVuHgoIC1NTUYM6cOUhISEB8fDzS09ORlZUFo9GI5ORkAEBQUBBGjBiB5ORkqNVqzJo1C2o1P85CRNQdqUQ7L0JMnz4d27dvv2l9YmIitm3b1umFyXHhwgXZY5tn3xyQpCynd/bYdX+OPC/uyL0Djt1/R6+J/OSZyNdffw0AsNls0t+vKykpgYeHR4cKICKinusnQ+TNN98EADQ0NEh/BwCVSoU+ffpg5syZXVcdERF1az8ZIhs3bgQAbNiwAU8//XSXF0RERD1Huy+s/zhAbrwNCS98ExE5pnaHyLfffostW7bg+++/R0NDQ4uvffTRR51eGBERdX/tDpGNGzfi/vvvx9y5c+Hm5taVNRERUQ/R7hApLy/HlClToFKpurIeIiLqQdp9MSMqKgr/+c9/urIWIiLqYdp9JtLY2Ii1a9di0KBB6NOnT4uv8V1bRESOqd0hEhgYiMDAwK6shYiIeph2h8gjjzzSlXUQEVEP1O4QufGWJz92zz33dEoxRETUs7Q7RH58yxMAqK6uRlNTEwwGAzZs2NDphRERUfd3W58T+TGbzYb//u//5g0YiYgcmOz7lajVakyePBm7d+/uzHqIiKgH6dBNr/Ly8njfLCIiB9bu6ay5c+e2WG5oaEBDQwOeeOKJDhXw2WefISsrCyqVCkFBQUhKSkJDQwPS09NRVlYGHx8fLFq0CBqNpkP7ISKiztfuEJk/f36LZTc3N/Tt2xeenp6yd261WrFv3z6kp6fD1dUVaWlpyMnJwfnz5xEeHo74+HhkZmYiMzMT06ZNk70fIiLqGu2eiwoLC0NYWBgGDRqEvn37YsCAAR0KkOtsNhsaGhrQ3NyMhoYG6HQ6WCwWxMTEAABiYmJgsVg6vB8iIup87T4Tqaurw5YtW5CTk4Pm5mY4OTkhOjoaM2fOlB0mer0eDz30EObOnQtXV1cMGTIEQ4YMQVVVFXQ6HQBAp9Ohurq61fFmsxlmsxkAkJKSAqPRKKsOACiRPZK6SkeOpxzOzs5232d34ci9A+y/I9odIlu3bkV9fT3Wrl0LHx8flJWVYefOndi6davse2fV1tbCYrFg48aN8PT0RFpaGg4fPtzu8SaTCSaTSVouLy+XVQd1T/Y+nkaj0WF/hhy5d8Cx+/f39+/Q+HZPZ+Xm5mL+/Pnw9/eHi4sL/P39kZSU1KE7+544cQK+vr7w9vaGs7Mzhg0bhlOnTkGr1aKyshIAUFlZCW9vb9n7ICKirtPuEHF1db1pWqm6uhrOzu0+mbmJ0WjE6dOncfXqVQghcOLECQQEBCAyMhLZ2dkAgOzsbERFRcneBxERdZ12J0BsbCzWrFmDX/3qV9J01t69exEXFyd756GhoRg+fDiWLFkCJycn9O/fHyaTCfX19UhPT0dWVhaMRiOSk5Nl74OIiLqOSggh2rOhEAKHDh3Cl19+CavVCr1ej5EjRyI2Nrara2y3CxcuyB7bPHtSJ1ZCncHpnT123Z8jz4s7cu+AY/ff0Wsi7T4T2bZtG0aOHIkXX3xRWnfy5Em89957mDFjRoeKICKinqnd10SOHDmCgQMHtlgXHByML7/8stOLIiKinqHdIaJSqWCz2Vqss9lsaOdsGBER9ULtDpFBgwZh586dUpDYbDZ8/PHHGDRoUJcVR0RE3Vu7r4kkJiYiJSUFTz31lHQRSqfTYcmSJV1ZHxERdWPtDhGDwYA//OEPKCoqQkVFBQwGA0JCQngreCIiB3ZbnxRUq9W4++67u6oWIiLqYXgaQUREsjFEiIhINoYIERHJxhAhIiLZGCJERCQbQ4SIiGRjiBARkWwMESIiko0hQkREssl/tm0nuXz5MjZv3oxz585BpVJh7ty58Pf3R3p6OsrKyuDj44NFixZBo9EoXSoREd1A8RDZtm0bIiIisHjxYjQ1NeHq1avIyMhAeHg44uPjkZmZiczMTEybNk3pUomI6AaKTmdduXIFhYWF0iN2nZ2d4eXlBYvFgpiYGABATEwMLBaLkmUSEVEbFD0TKS0thbe3NzZt2oTvvvsOwcHBmDFjBqqqqqDT6QAAOp0O1dXVSpZJRERtUDREmpubcfbsWcycOROhoaHYtm0bMjMz2z3ebDbDbDYDAFJSUmA0GmXXUiJ7JHWVjhxPOZydne2+z+7CkXsH2H9HKBoiBoMBBoMBoaGhAIDhw4cjMzMTWq0WlZWV0Ol0qKyshLe3d6vjTSYTTCaTtFxeXm6Xusk+7H08rz9szRE5cu+AY/fv7+/fofGKXhPp06cPDAYDLly4AAA4ceIEAgMDERkZiezsbABAdnY2oqKilCyTiIjaoPi7s2bOnIn169ejqakJvr6+SEpKghAC6enpyMrKgtFoRHJystJlEhFRKxQPkf79+yMlJeWm9StXrlSgGiIiuh38xDoREcnGECEiItkYIkREJBtDhIiIZGOIEBGRbAwRIiKSjSFCRESyMUSIiEg2hggREcnGECEiItkYIkREJBtDhIiIZGOIEBGRbAwRIiKSjSFCRESyMUSIiEg2hggREcmm+JMNAcBms2Hp0qXQ6/VYunQpamtrkZ6ejrKyMvj4+GDRokXQaDRKl0lERDfoFmcin3/+OQICAqTlzMxMhIeHY/369QgPD0dmZqaC1RERUVsUD5GKigocP34ccXFx0jqLxYKYmBgAQExMDCwWi1LlERHRLSg+nfXee+9h2rRpqKurk9ZVVVVBp9MBAHQ6Haqrq1sdazabYTabAQApKSkwGo2y6yiRPZK6SvPsSXbdnyP/DLS39zszcrq0DqU4Ozt36PeHI1M0RI4dOwatVovg4GDk5+ff9niTyQSTySQtl5eXd2Z5RHSD3vpvzGg09trefoq/v3+HxisaIidPnsTRo0fx73//Gw0NDairq8P69euh1WpRWVkJnU6HyspKeHt7K1kmERG1QdEQmTp1KqZOnQoAyM/Px6effooFCxZgx44dyM7ORnx8PLKzsxEVFaVkmURE1AbFL6y3Jj4+Hnl5eViwYAHy8vIQHx+vdElERNQKlRBCKF1EZ7lw4YLssfa+iEvUEzm9s0fpEroEr4nI1y3PRIiIqGdgiBARkWwMESIiko0hQkREsjFEiIhINoYIERHJxhAhIiLZGCJERCQbQ4SIiGRjiBARkWwMESIiko0hQkREsjFEiIhINoYIERHJxhAhIiLZGCJERCSboo/HLS8vx8aNG3Hp0iWoVCqYTCZMmDABtbW1SE9PR1lZGXx8fLBo0SJoNBolSyUiolYoGiJOTk54/PHHERwcjLq6OixduhT33nsvDh06hPDwcMTHxyMzMxOZmZmYNm2akqUSEVErFJ3O0ul0CA4OBgB4eHggICAAVqsVFosFMTExAICYmBhYLBYlyyQiojYoeibyY6WlpTh79ixCQkJQVVUFnU4H4FrQVFdXtzrGbDbDbDYDAFJSUmA0GmXvv0T2SCLH0ZF/Y92Zs7Nzr+2tq3WLEKmvr0dqaipmzJgBT0/Pdo8zmUwwmUzScnl5eVeUR0T/T2/9N2Y0Gnttbz/F39+/Q+MVf3dWU1MTUlNTMXr0aAwbNgwAoNVqUVlZCQCorKyEt7e3kiUSEVEbFA0RIQQ2b96MgIAATJw4UVofGRmJ7OxsAEB2djaioqKUKpGIiG5B0emskydP4vDhw+jXrx+ee+45AMCUKVMQHx+P9PR0ZGVlwWg0Ijk5WckyiYioDSohhFC6iM5y4cIF2WObZ0/qxEqIeiend/YoXUKX4DUR+RS/JkJERD0XQ4SIiGRjiBARkWwMESIiko0hQkREsjFEiIhINoYIERHJxhAhIiLZGCJERCQbQ4SIiGRjiBARkWwMESIiko0hQkREsjFEiIhINoYIERHJxhAhIiLZGCJERCSboo/H/Sm5ubnYtm0bbDYb4uLiEB8fr3RJRA6ttz4BtETpAjpIySdOdtszEZvNhi1btmDZsmVIT0/HkSNHcP78eaXLIiKiH+m2IVJUVAQ/Pz/ceeedcHZ2RnR0NCwWi9JlERHRj3Tb6Syr1QqDwSAtGwwGnD59usU2ZrMZZrMZAJCSktKxB87vPSp/LBGRg+q2ZyJCiJvWqVSqFssmkwkpKSlISUnptP0uXbq0016rp3Hk3gHH7t+Rewccu/+O9t5tQ8RgMKCiokJarqiogE6nU7AiIiK6UbcNkYEDB+LixYsoLS1FU1MTcnJyEBkZqXRZRET0I06rV69erXQRrVGr1fDz88Of/vQnfPHFFxg9ejSGDx9ul30HBwfbZT/dkSP3Djh2/47cO+DY/Xekd5Vo7eIDERFRO3Tb6SwiIur+GCJERCRbt/2ciL3MmzcP7u7uUKvVcHJyQkpKCmpra5Geno6ysjL4+Phg0aJF0Gg0SpfaYZs2bcLx48eh1WqRmpoKALfsNSMjA1lZWVCr1UhMTERERISS5XdIa73/9a9/xYEDB+Dt7Q0AmDJlCn7+858D6F29l5eXY+PGjbh06RJUKhVMJhMmTJjgMMe+rf4d4fg3NDRg1apVaGpqQnNzM4YPH46EhITOPfbCwSUlJYmqqqoW63bs2CEyMjKEEEJkZGSIHTt2KFFap8vPzxdnzpwRycnJ0rq2ej137px49tlnRUNDgygpKRFPP/20aG5uVqTuztBa7x999JHYvXv3Tdv2tt6tVqs4c+aMEEKIK1euiAULFohz5845zLFvq39HOP42m03U1dUJIYRobGwUL7zwgjh58mSnHntOZ7XCYrEgJiYGABATE9NrbrcSFhZ20xlVW71aLBZER0fDxddwnasAAAhrSURBVMUFvr6+8PPzQ1FRkd1r7iyt9d6W3ta7TqeT3n3j4eGBgIAAWK1Whzn2bfXflt7Uv0qlgru7OwCgubkZzc3NUKlUnXrsHX46CwBeffVVAMADDzwAk8mEqqoq6YONOp0O1dXVSpbXpdrq1Wq1IjQ0VNpOr9ff8h9eT7V//34cPnwYwcHB+P3vfw+NRtOrey8tLcXZs2cREhLikMf+x/1/8803DnH8bTYblixZgv/93//F+PHjERoa2qnH3uFD5JVXXoFer0dVVRXWrFnTsftv9SLCAd75/eCDD+Lhhx8GAHz00Uf485//jKSkpF7be319PVJTUzFjxgx4enq2uZ2j9O8ox1+tVuOPf/wjLl++jLVr1+L7779vc1s5vTv8dJZerwcAaLVaREVFoaioCFqtFpWVlQCAyspK6cJbb9RWrzfedsZqtUrfq96iT58+UKvVUKvViIuLw5kzZwD0zt6bmpqQmpqK0aNHY9iwYQAc69i31r8jHX8A8PLyQlhYGHJzczv12Dt0iNTX16Ourk76e15eHvr164fIyEhkZ2cDALKzsxEVFaVkmV2qrV4jIyORk5ODxsZGlJaW4uLFiwgJCVGy1E53/R8RAHz11VcICgoC0Pt6F0Jg8+bNCAgIwMSJE6X1jnLs2+rfEY5/dXU1Ll++DODaO7VOnDiBgICATj32Dv2J9ZKSEqxduxbAtYtOo0aNwuTJk1FTU4P09HSUl5fDaDQiOTm5V7zFd926dSgoKEBNTQ20Wi0SEhIQFRXVZq+7du3CwYMHoVarMWPGDNx3330KdyBfa73n5+ejuLgYKpUKPj4+ePLJJ6V54t7U+zfffIOVK1eiX79+0p2wp0yZgtDQUIc49m31f+TIkV5//L/77jts3LgRNpsNQgiMGDECDz/88C1/x91u7w4dIkRE1DEOPZ1FREQdwxAhIiLZGCJERCQbQ4SIiGRjiBARkWwMEeq15s2bh7y8PKXLIOrVGCJERCQbQ4ToFpqbm5UuodP0pl6o+3D4GzBS71ZcXIw///nPKCsrQ0REBObNmwdXV1eYzWbs3r0btbW1GDRoEGbPni3dIyghIQEzZ87E559/jubmZmzYsAHbt2/Hl19+icbGRvj4+GDBggXo168fGhsb8eGHH+Kf//wnmpqaEBUVhRkzZsDV1RX5+fn405/+hAcffBB79+6Fu7s7HnvsMYwePRoAcOXKFWzduhX//ve/4ebmhri4OPzmN7+BWq1GUlISnn32WQQHB+Pw4cPYsGED0tLSEBgYiAMHDuDYsWN4/vnnYbPZsGfPHhw4cACXL1/GPffcgyeffBIajQalpaV4+umnMWfOHHz88cfw9fXFSy+9pOThoF6IIUK92j//+U8sW7YMrq6uePHFF3Ho0CH4+/vjww8/xPLlyxEUFIQdO3bgjTfeaPEL1mKx4LXXXoOrqyv+85//oLCwEG+88QY8PT3xww8/wMvLCwDwwQcfoKSkBH/84x/h5OSEN954A5988gmmTp0KALh06RJqamqwefNmnD59Gq+//joGDhwIf39/bN26FVeuXMGGDRtQU1ODV199FTqdDrGxsQgLC0N+fj6Cg4NRWFiIO++8EwUFBQgMDERhYSHCwsIAAPv27YPFYsHq1avh7e2Nbdu24d1338Uzzzwj9VJQUID09HSo1Zx4oM7Hnyrq1X75y19Cr9dDo9Hg/vvvR3FxMf7xj39g3LhxCA4OhouLC6ZOnYpTp06htLRUGveb3/wGGo0Grq6ucHZ2Rn19PX744QcIIRAYGAidTgchBA4cOIDp06dDo9HAw8MDkydPxpEjR1rU8Oijj8LFxQVhYWG47777kJOTA5vNhpycHEydOhUeHh7w9fXFxIkTcfjwYQDXHqJVUFAA4Nq9n+Lj46XlgoICKUTMZjMee+wxGAwGuLi44JFHHsH//M//tJi6euSRR+Du7g5XV9cu/V6TY+KZCPVqffr0kf7u6uoKq9WK2tpaDBgwQFrv7u4uPYzI19cXwLVbYl93zz33YPz48diyZQvKy8sxdOhQPP7442hsbMTVq1exdOlSaVshBGw2m7Ts5eUlPVkOAHx8fFBZWYnq6mo0NTXBaDS2+Nr1BwCFhYVhx44duHTpEmw2G6Kjo/HJJ5+gtLQUV65cQf/+/QEAZWVlWLt2rXRjQeDa8yOqqqqk5R/3QtTZGCLkcHQ6HcrLy6Xl+vp61NbWtnhuwo9/KQPAhAkTMGHCBFRVVSE9PR179uxBQkICXF1dkZaW1uYzFy5fvoz6+nopSMrLyxEUFARvb284OTmhvLwcgYGB0teuv46fnx9cXV3x+eefY/DgwfDw8ECfPn1gNpsxaNAgaWrKYDBg7ty5GDRo0E37vn5mdWMvRJ2J01nkcEaNGoWDBw+iuLhYujAeEhIinYXcqKioCKdPn0ZTUxPc3Nzg4uLS4mFG7733nvQ/f6vVitzc3Bbj//rXv6KpqQmFhYU4fvw4RowYAbVajREjRuDDDz9EXV0dysrK8Nlnn0kX3YFrZyP79++Xpq5uXAauPdJ5586dKCsrA3Dt+RHXn5dNZA88EyGHEx4ejkcffRSpqamora3Ff/3Xf7W4EH2juro6bN++HSUlJXB1dcWQIUMwadIkAMDvfvc7fPLJJ1i+fDlqamqg1+vxwAMPICIiAsC16TSNRoOnnnoKrq6umD17NgICAgAAM2fOxNatW/H000/D1dUVcXFxGDdunLTfsLAwHDlyBIMHD5aWP/30U2kZuHaGBABr1qxBZWUltFotRowY0asfpEbdC58nQtRFrr/Fd/PmzUqXQtRlOJ1FRESyMUSIiEg2TmcREZFsPBMhIiLZGCJERCQbQ4SIiGRjiBARkWwMESIiku3/AABnWLEz9JIEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw historgram of attribute \"horsepower\" with bins = 3\n",
    "plt.pyplot.hist(df[\"horsepower\"], bins = 3)\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.pyplot.xlabel(\"horsepower\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indicator variable (or dummy variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Example</b><br>\n",
    "We see the column \"fuel-type\" has two unique values, \"gas\" or \"diesel\". Regression doesn't understand words, only numbers. To use this attribute in regression analysis, we convert \"fuel-type\" into indicator variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get indicator variables and assign it to data frame \"dummy_variable_1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fuel-type-diesel</th>\n",
       "      <th>fuel-type-gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fuel-type-diesel  fuel-type-gas\n",
       "0                 0              1\n",
       "1                 0              1\n",
       "2                 0              1\n",
       "3                 0              1\n",
       "4                 0              1"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable_fuel = pd.get_dummies(df[\"fuel-type\"])\n",
    "dummy_variable_fuel.rename(columns={'gas':'fuel-type-gas', 'diesel':'fuel-type-diesel'}, inplace=True)\n",
    "dummy_variable_fuel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have the value 0 to represent \"gas\" and 1 to represent \"diesel\" in the column \"fuel-type\". We will now insert this column back into our original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</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>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>highway-L/100km</th>\n",
       "      <th>price</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>fuel-type-diesel</th>\n",
       "      <th>fuel-type-gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</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>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</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>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</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>0.822681</td>\n",
       "      <td>...</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</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>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</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>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>10.681818</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make aspiration num-of-doors  \\\n",
       "0          3                122  alfa-romero        std          two   \n",
       "1          3                122  alfa-romero        std          two   \n",
       "2          1                122  alfa-romero        std          two   \n",
       "3          2                164         audi        std         four   \n",
       "4          2                164         audi        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base    length  ...  \\\n",
       "0  convertible          rwd           front        88.6  0.811148  ...   \n",
       "1  convertible          rwd           front        88.6  0.811148  ...   \n",
       "2    hatchback          rwd           front        94.5  0.822681  ...   \n",
       "3        sedan          fwd           front        99.8  0.848630  ...   \n",
       "4        sedan          4wd           front        99.4  0.848630  ...   \n",
       "\n",
       "   stroke  compression-ratio  horsepower peak-rpm city-L/100km  \\\n",
       "0    2.68                9.0         111   5000.0    11.190476   \n",
       "1    2.68                9.0         111   5000.0    11.190476   \n",
       "2    3.47                9.0         154   5000.0    12.368421   \n",
       "3    3.40               10.0         102   5500.0     9.791667   \n",
       "4    3.40                8.0         115   5500.0    13.055556   \n",
       "\n",
       "   highway-L/100km    price  horsepower-binned  fuel-type-diesel  \\\n",
       "0         8.703704  13495.0                Low                 0   \n",
       "1         8.703704  16500.0                Low                 0   \n",
       "2         9.038462  16500.0             Medium                 0   \n",
       "3         7.833333  13950.0                Low                 0   \n",
       "4        10.681818  17450.0                Low                 0   \n",
       "\n",
       "   fuel-type-gas  \n",
       "0              1  \n",
       "1              1  \n",
       "2              1  \n",
       "3              1  \n",
       "4              1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge data frame \"df\" and \"dummy_variable_1\" \n",
    "df = pd.concat([df, dummy_variable_fuel], axis=1)\n",
    "\n",
    "# drop original column \"fuel-type\" from \"df\"\n",
    "df.drop(\"fuel-type\", axis = 1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create indicator variable to the column of \"aspiration\": \"std\" to 0 and \"turbo\" to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aspiration-std</th>\n",
       "      <th>aspiration-turbo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aspiration-std  aspiration-turbo\n",
       "0               1                 0\n",
       "1               1                 0\n",
       "2               1                 0\n",
       "3               1                 0\n",
       "4               1                 0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable_aspiration = pd.get_dummies(df[\"aspiration\"])\n",
    "dummy_variable_aspiration.rename(columns={'std':'aspiration-std', 'turbo':'aspiration-turbo'}, inplace=True)\n",
    "dummy_variable_aspiration.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</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>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>highway-L/100km</th>\n",
       "      <th>price</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>fuel-type-diesel</th>\n",
       "      <th>fuel-type-gas</th>\n",
       "      <th>aspiration-std</th>\n",
       "      <th>aspiration-turbo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.886584</td>\n",
       "      <td>...</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.886584</td>\n",
       "      <td>...</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>0.905947</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.915629</td>\n",
       "      <td>...</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.918396</td>\n",
       "      <td>...</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>10.681818</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make num-of-doors   body-style  \\\n",
       "0          3                122  alfa-romero          two  convertible   \n",
       "1          3                122  alfa-romero          two  convertible   \n",
       "2          1                122  alfa-romero          two    hatchback   \n",
       "3          2                164         audi         four        sedan   \n",
       "4          2                164         audi         four        sedan   \n",
       "\n",
       "  drive-wheels engine-location  wheel-base    length     width  ...  \\\n",
       "0          rwd           front        88.6  0.811148  0.886584  ...   \n",
       "1          rwd           front        88.6  0.811148  0.886584  ...   \n",
       "2          rwd           front        94.5  0.822681  0.905947  ...   \n",
       "3          fwd           front        99.8  0.848630  0.915629  ...   \n",
       "4          4wd           front        99.4  0.848630  0.918396  ...   \n",
       "\n",
       "   horsepower  peak-rpm city-L/100km highway-L/100km    price  \\\n",
       "0         111    5000.0    11.190476        8.703704  13495.0   \n",
       "1         111    5000.0    11.190476        8.703704  16500.0   \n",
       "2         154    5000.0    12.368421        9.038462  16500.0   \n",
       "3         102    5500.0     9.791667        7.833333  13950.0   \n",
       "4         115    5500.0    13.055556       10.681818  17450.0   \n",
       "\n",
       "  horsepower-binned  fuel-type-diesel  fuel-type-gas  aspiration-std  \\\n",
       "0               Low                 0              1               1   \n",
       "1               Low                 0              1               1   \n",
       "2            Medium                 0              1               1   \n",
       "3               Low                 0              1               1   \n",
       "4               Low                 0              1               1   \n",
       "\n",
       "   aspiration-turbo  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge data frame \"df\" and \"dummy_variable_1\" \n",
    "df = pd.concat([df, dummy_variable_aspiration], axis=1)\n",
    "\n",
    "# drop original column \"fuel-type\" from \"df\"\n",
    "df.drop(\"aspiration\", axis = 1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symboling', 'normalized-losses', 'make', '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-L/100km', 'highway-L/100km', 'price',\n",
       "       'horsepower-binned', 'fuel-type-diesel', 'fuel-type-gas',\n",
       "       'aspiration-std', 'aspiration-turbo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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