Lab-2_Data_Wrangling.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Data Wrangling**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data wrangling is the process of converting data from the initial format to a format that may be better for analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **What is the fuel consumption (L/100k) rate for the diesel car?**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Import Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/auto.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read csv and assign into dataframe\n",
    "df = pd.read_csv(filename, names = headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>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>?</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>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3                 ?  alfa-romero       gas        std          two   \n",
       "1          3                 ?  alfa-romero       gas        std          two   \n",
       "2          1                 ?  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": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see what the data set looks like, we'll use the head() method.\n",
    "df.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Identify & Handle missing values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
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       "      <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",
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       "      <th>0</th>\n",
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       "      <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",
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       "      <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",
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       "      <th>2</th>\n",
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       "      <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": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# replace \"?\" to NaN\n",
    "df.replace(\"?\", np.nan, inplace = True)\n",
    "df.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Evaluating for Missing Data**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To detect missing data in pandas, the `.isnull()` method returns `True` if a value is missing, while the `.notnull()` method returns `False` for missing values. \n",
    "- These boolean values indicate whether the data is missing or not, and can be used for filtering or further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</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",
       "    <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",
       "      <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",
       "    <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": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data = df.isnull()\n",
    "missing_data.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Count missing values in each column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "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(\"\")    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Deal with missing data** "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate mean for \"normalized-losses\" and replace NaN with mean**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of normalized-losses: 122.0\n"
     ]
    }
   ],
   "source": [
    "avg_norm_loss = df[\"normalized-losses\"].astype(\"float\").mean(axis=0)\n",
    "print(\"Average of normalized-losses:\", avg_norm_loss)\n",
    "\n",
    "df[\"normalized-losses\"].replace(np.nan, avg_norm_loss, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate mean for \"bore\" and replace NaN with mean**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of bore: 3.3297512437810943\n"
     ]
    }
   ],
   "source": [
    "avg_bore=df['bore'].astype('float').mean(axis=0)\n",
    "print(\"Average of bore:\", avg_bore)\n",
    "\n",
    "df[\"bore\"].replace(np.nan, avg_bore, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate mean for \"stroke\" and replace NaN with mean**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of stroke: 3.255422885572139\n"
     ]
    }
   ],
   "source": [
    "avg_stroke=df['stroke'].astype('float').mean(axis=0)\n",
    "print(\"Average of stroke:\", avg_stroke)\n",
    "\n",
    "df[\"stroke\"].replace(np.nan, avg_stroke, inplace = True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate mean for \"horsepower\" and replace NaN with mean**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average horsepower: 104.25615763546799\n"
     ]
    }
   ],
   "source": [
    "avg_horsepower = df['horsepower'].astype('float').mean(axis=0)\n",
    "print(\"Average horsepower:\", avg_horsepower)\n",
    "\n",
    "df['horsepower'].replace(np.nan, avg_horsepower, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate mean for \"peak-rpm\" and replace NaN with mean**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average peak rpm: 5125.369458128079\n"
     ]
    }
   ],
   "source": [
    "avg_peakrpm=df['peak-rpm'].astype('float').mean(axis=0)\n",
    "print(\"Average peak rpm:\", avg_peakrpm)\n",
    "\n",
    "df['peak-rpm'].replace(np.nan, avg_peakrpm, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "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": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    114\n",
       "two      89\n",
       "Name: num-of-doors, dtype: int64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num-of-doors'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "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 the most common type automatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'four'"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num-of-doors'].value_counts().idxmax()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Replace the missing 'num-of-doors' values by the most frequent** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df[\"num-of-doors\"].replace(np.nan, \"four\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "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>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122.0</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>122.0</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>122.0</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             122.0  alfa-romero       gas        std          two   \n",
       "1          3             122.0  alfa-romero       gas        std          two   \n",
       "2          1             122.0  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": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Correct data format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "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": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list data type for each column\n",
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convert data types to proper format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[[\"bore\", \"stroke\"]] = df[[\"bore\", \"stroke\"]].astype(\"float\")\n",
    "df[[\"normalized-losses\"]] = df[[\"normalized-losses\"]].astype(\"int\")\n",
    "df[[\"price\"]] = df[[\"price\"]].astype(\"float\")\n",
    "df[[\"peak-rpm\"]] = df[[\"peak-rpm\"]].astype(\"float\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**List the columns after the conversion**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses      int32\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": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Data Standardization**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardization is the process of transforming data into a common format, allowing the researcher to make the meaningful comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "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>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>21</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>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>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>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>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>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>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>18</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-mpg  highway-mpg    price  \n",
       "0    5000.0       21           27  13495.0  \n",
       "1    5000.0       21           27  16500.0  \n",
       "2    5000.0       19           26  16500.0  \n",
       "3    5500.0       24           30  13950.0  \n",
       "4    5500.0       18           22  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .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>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</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>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>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</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>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>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</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>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>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</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>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>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</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>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>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 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",
       "   fuel-system  bore  stroke  compression-ratio horsepower peak-rpm  city-mpg  \\\n",
       "0         mpfi  3.47    2.68                9.0        111   5000.0        21   \n",
       "1         mpfi  3.47    2.68                9.0        111   5000.0        21   \n",
       "2         mpfi  2.68    3.47                9.0        154   5000.0        19   \n",
       "3         mpfi  3.19    3.40               10.0        102   5500.0        24   \n",
       "4         mpfi  3.19    3.40                8.0        115   5500.0        18   \n",
       "\n",
       "  highway-mpg    price  city-L/100km  \n",
       "0          27  13495.0     11.190476  \n",
       "1          27  16500.0     11.190476  \n",
       "2          26  16500.0     12.368421  \n",
       "3          30  13950.0      9.791667  \n",
       "4          22  17450.0     13.055556  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert mpg to L/100km by mathematical operation (235 divided by mpg)\n",
    "df['city-L/100km'] = 235/df[\"city-mpg\"]\n",
    "\n",
    "# check your transformed data \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "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>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-L/100km</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</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>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>21</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</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>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>21</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</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>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>19</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</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>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>24</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</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>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>18</td>\n",
       "      <td>10.681818</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 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",
       "   fuel-system  bore  stroke  compression-ratio horsepower peak-rpm  city-mpg  \\\n",
       "0         mpfi  3.47    2.68                9.0        111   5000.0        21   \n",
       "1         mpfi  3.47    2.68                9.0        111   5000.0        21   \n",
       "2         mpfi  2.68    3.47                9.0        154   5000.0        19   \n",
       "3         mpfi  3.19    3.40               10.0        102   5500.0        24   \n",
       "4         mpfi  3.19    3.40                8.0        115   5500.0        18   \n",
       "\n",
       "  highway-L/100km    price  city-L/100km  \n",
       "0        8.703704  13495.0     11.190476  \n",
       "1        8.703704  16500.0     11.190476  \n",
       "2        9.038462  16500.0     12.368421  \n",
       "3        7.833333  13950.0      9.791667  \n",
       "4       10.681818  17450.0     13.055556  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert 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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Data Normalization**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalization is the process of adjusting variable values to a common range, such as scaling them to have an average of 0, a variance of 1, or values between 0 and 1."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Normalize \"length\", \"width\" & \"height\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Binning**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Binning is a process of transforming continuous numerical variables into discrete categorical 'bins' for grouped analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convert data to correct format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"horsepower\"]=df[\"horsepower\"].astype(int, copy=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Histogram of \"horsepower\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot\n",
    "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\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create 3 bins of equal size bandwidth in \"horsepower\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 48.        , 119.33333333, 190.66666667, 262.        ])"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linspace(start_value, end_value, numbers_generated)\n",
    "bins = np.linspace(min(df[\"horsepower\"]), max(df[\"horsepower\"]), 4)\n",
    "bins"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set group names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_names = ['Low', 'Medium', 'High']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We apply the function \"cut\" to determine what each value of df['horsepower'] belongs to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "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>5</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>140</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>121</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>121</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>121</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>182</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>182</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>182</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>48</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>70</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>70</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "5          110               Low\n",
       "6          110               Low\n",
       "7          110               Low\n",
       "8          140            Medium\n",
       "9          101               Low\n",
       "10         101               Low\n",
       "11         121            Medium\n",
       "12         121            Medium\n",
       "13         121            Medium\n",
       "14         182            Medium\n",
       "15         182            Medium\n",
       "16         182            Medium\n",
       "17          48               Low\n",
       "18          70               Low\n",
       "19          70               Low"
      ]
     },
     "execution_count": 280,
     "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']].head(20)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the number of vehicles in each bin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low       153\n",
       "Medium     43\n",
       "High        5\n",
       "Name: horsepower-binned, dtype: int64"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"horsepower-binned\"].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the distribution of each bin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot\n",
    "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\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bins Visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot\n",
    "\n",
    "\n",
    "# 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\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Indicator Variable (Dummy Variable)**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Indicator variables, also called dummy variables, are numeric labels used to represent categories in statistical analysis. \n",
    "- They enable the utilization of categorical variables in regression analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convert \"fuel-type\" to indicator variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n",
       "       'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n",
       "       'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n",
       "       'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n",
       "       'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',\n",
       "       'highway-L/100km', 'price', 'city-L/100km', 'horsepower-binned'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the indicator variables and assign it to data frame \"dummy_variable_1\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "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>diesel</th>\n",
       "      <th>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": [
       "   diesel  gas\n",
       "0       0    1\n",
       "1       0    1\n",
       "2       0    1\n",
       "3       0    1\n",
       "4       0    1"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable_1 = pd.get_dummies(df[\"fuel-type\"])\n",
    "dummy_variable_1.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the column names for clarity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "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": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable_1.rename(columns={'gas':'fuel-type-gas', 'diesel':'fuel-type-diesel'}, inplace=True)\n",
    "dummy_variable_1.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the dataframe, column 'fuel-type' has values for 'gas' and 'diesel' as 0s and 1s now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge data frame \"df\" and \"dummy_variable_1\" \n",
    "df = pd.concat([df, dummy_variable_1], axis=1)\n",
    "\n",
    "# drop original column \"fuel-type\" from \"df\"\n",
    "df.drop(\"fuel-type\", axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-L/100km</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</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>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</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>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</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>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</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>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</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>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>10.681818</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 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",
       "   compression-ratio  horsepower  peak-rpm city-mpg highway-L/100km    price  \\\n",
       "0                9.0         111    5000.0       21        8.703704  13495.0   \n",
       "1                9.0         111    5000.0       21        8.703704  16500.0   \n",
       "2                9.0         154    5000.0       19        9.038462  16500.0   \n",
       "3               10.0         102    5500.0       24        7.833333  13950.0   \n",
       "4                8.0         115    5500.0       18       10.681818  17450.0   \n",
       "\n",
       "  city-L/100km  horsepower-binned  fuel-type-diesel  fuel-type-gas  \n",
       "0    11.190476                Low                 0              1  \n",
       "1    11.190476                Low                 0              1  \n",
       "2    12.368421             Medium                 0              1  \n",
       "3     9.791667                Low                 0              1  \n",
       "4    13.055556                Low                 0              1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convert \"aspiration\" to indicator variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <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": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get indicator variables of aspiration and assign it to data frame \"dummy_variable_2\"\n",
    "dummy_variable_2 = pd.get_dummies(df[\"aspiration\"])\n",
    "\n",
    "# change column names for clarity\n",
    "dummy_variable_2.rename(columns={'std': 'aspiration-std', 'turbo': 'aspiration-turbo'}, inplace=True)\n",
    "\n",
    "dummy_variable_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>num-of-doors</th>\n",
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       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>...</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-L/100km</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</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",
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       "      <td>alfa-romero</td>\n",
       "      <td>two</td>\n",
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       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>...</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</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>0.890278</td>\n",
       "      <td>...</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8.703704</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</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>0.909722</td>\n",
       "      <td>...</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>9.038462</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</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>0.919444</td>\n",
       "      <td>...</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</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>0.922222</td>\n",
       "      <td>...</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>10.681818</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</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 × 30 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  ...  peak-rpm  \\\n",
       "0          rwd           front        88.6  0.811148  0.890278  ...    5000.0   \n",
       "1          rwd           front        88.6  0.811148  0.890278  ...    5000.0   \n",
       "2          rwd           front        94.5  0.822681  0.909722  ...    5000.0   \n",
       "3          fwd           front        99.8  0.848630  0.919444  ...    5500.0   \n",
       "4          4wd           front        99.4  0.848630  0.922222  ...    5500.0   \n",
       "\n",
       "   city-mpg highway-L/100km    price  city-L/100km horsepower-binned  \\\n",
       "0        21        8.703704  13495.0     11.190476               Low   \n",
       "1        21        8.703704  16500.0     11.190476               Low   \n",
       "2        19        9.038462  16500.0     12.368421            Medium   \n",
       "3        24        7.833333  13950.0      9.791667               Low   \n",
       "4        18       10.681818  17450.0     13.055556               Low   \n",
       "\n",
       "   fuel-type-diesel  fuel-type-gas  aspiration-std  aspiration-turbo  \n",
       "0                 0              1               1                 0  \n",
       "1                 0              1               1                 0  \n",
       "2                 0              1               1                 0  \n",
       "3                 0              1               1                 0  \n",
       "4                 0              1               1                 0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge data frame \"df\" & \"dummy_variable_2\"\n",
    "df = pd.concat([df, dummy_variable_2], axis=1)\n",
    "\n",
    "# drop original column \"aspiration\" from \"df\"\n",
    "df.drop(\"aspiration\", axis=1, inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the new csv:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('clean_df.csv')"
   ]
  }
 ],
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