Automobile_Dataset_Cleaning_Data_in_Python.ipynb

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    "# Automobile Dataset\n",
    "1. This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. \n",
    "\n",
    "2. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process \"symboling\". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.\n",
    "\n",
    "3. The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc…), and represents the average loss per car per year.\n",
    "\n",
    "### Project goal: cleaning the dataset."
   ]
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   "source": [
    "# 1. Download libraries"
   ]
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   "source": [
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot"
   ]
  },
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   "source": [
    "# 2. Import dataset "
   ]
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   "source": [
    "url = \"/kaggle/input/automobile-dataset/Automobile_data.csv\""
   ]
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   "execution_count": 3,
   "id": "32cde8df",
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   "source": [
    "df = pd.read_csv(url)"
   ]
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   "source": [
    "# 3. Identify and handle missing values"
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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",
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       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
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       "      <th>highway-mpg</th>\n",
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       "      <td>3.47</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
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       "      <td>std</td>\n",
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       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
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       "      <td>mpfi</td>\n",
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       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
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       "      <td>94.5</td>\n",
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       "      <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",
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       "    <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>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
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       "      <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>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
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       "   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     3.4              10.0        102      5500       24   \n",
       "4         mpfi  3.19     3.4               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
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   "source": [
    "# Display the first five rows of the data:\n",
    "\n",
    "df.head(5)"
   ]
  },
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   "source": [
    "# Convert \"?\" to NaN:\n",
    "\n",
    "df.replace(\"?\", np.nan, inplace = True)"
   ]
  },
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       "      <td>hatchback</td>\n",
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       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
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       "      <td>9.0</td>\n",
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       "      <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>3.4</td>\n",
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       "      <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>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 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     3.4              10.0        102      5500       24   \n",
       "4         mpfi  3.19     3.4               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "314d243a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:21.802830Z",
     "iopub.status.busy": "2024-01-13T07:58:21.802255Z",
     "iopub.status.idle": "2024-01-13T07:58:21.807183Z",
     "shell.execute_reply": "2024-01-13T07:58:21.806213Z"
    },
    "papermill": {
     "duration": 0.019446,
     "end_time": "2024-01-13T07:58:21.809021",
     "exception": false,
     "start_time": "2024-01-13T07:58:21.789575",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Evaluate for Missing Data:\n",
    "\n",
    "missing_data = df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "77e8aceb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:21.833362Z",
     "iopub.status.busy": "2024-01-13T07:58:21.833034Z",
     "iopub.status.idle": "2024-01-13T07:58:21.852800Z",
     "shell.execute_reply": "2024-01-13T07:58:21.851948Z"
    },
    "papermill": {
     "duration": 0.034238,
     "end_time": "2024-01-13T07:58:21.854730",
     "exception": false,
     "start_time": "2024-01-13T07:58:21.820492",
     "status": "completed"
    },
    "tags": []
   },
   "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>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <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",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "missing_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69b9c28c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:21.880859Z",
     "iopub.status.busy": "2024-01-13T07:58:21.880116Z",
     "iopub.status.idle": "2024-01-13T07:58:21.904828Z",
     "shell.execute_reply": "2024-01-13T07:58:21.903816Z"
    },
    "papermill": {
     "duration": 0.039991,
     "end_time": "2024-01-13T07:58:21.907016",
     "exception": false,
     "start_time": "2024-01-13T07:58:21.867025",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symboling\n",
      "symboling\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "normalized-losses\n",
      "normalized-losses\n",
      "False    164\n",
      "True      41\n",
      "Name: count, dtype: int64\n",
      "\n",
      "make\n",
      "make\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "fuel-type\n",
      "fuel-type\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "aspiration\n",
      "aspiration\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "num-of-doors\n",
      "num-of-doors\n",
      "False    203\n",
      "True       2\n",
      "Name: count, dtype: int64\n",
      "\n",
      "body-style\n",
      "body-style\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "drive-wheels\n",
      "drive-wheels\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "engine-location\n",
      "engine-location\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "wheel-base\n",
      "wheel-base\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "length\n",
      "length\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "width\n",
      "width\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "height\n",
      "height\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "curb-weight\n",
      "curb-weight\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "engine-type\n",
      "engine-type\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "num-of-cylinders\n",
      "num-of-cylinders\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "engine-size\n",
      "engine-size\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "fuel-system\n",
      "fuel-system\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "bore\n",
      "bore\n",
      "False    201\n",
      "True       4\n",
      "Name: count, dtype: int64\n",
      "\n",
      "stroke\n",
      "stroke\n",
      "False    201\n",
      "True       4\n",
      "Name: count, dtype: int64\n",
      "\n",
      "compression-ratio\n",
      "compression-ratio\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "horsepower\n",
      "horsepower\n",
      "False    203\n",
      "True       2\n",
      "Name: count, dtype: int64\n",
      "\n",
      "peak-rpm\n",
      "peak-rpm\n",
      "False    203\n",
      "True       2\n",
      "Name: count, dtype: int64\n",
      "\n",
      "city-mpg\n",
      "city-mpg\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "highway-mpg\n",
      "highway-mpg\n",
      "False    205\n",
      "Name: count, dtype: int64\n",
      "\n",
      "price\n",
      "price\n",
      "False    201\n",
      "True       4\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Count missing values in each column:\n",
    "\n",
    "for column in missing_data.columns.values.tolist():\n",
    "    print(column)\n",
    "    print (missing_data[column].value_counts())\n",
    "    print(\"\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "797949a5",
   "metadata": {
    "papermill": {
     "duration": 0.012872,
     "end_time": "2024-01-13T07:58:21.932576",
     "exception": false,
     "start_time": "2024-01-13T07:58:21.919704",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Conclusion:\n",
    "Based on the summary above, each column has 205 rows of data and seven of the columns containing missing data:\n",
    "\n",
    "1. \"normalized-losses\": 41 missing data\n",
    "2. \"num-of-doors\": 2 missing data\n",
    "3. \"bore\": 4 missing data\n",
    "4. \"stroke\" : 4 missing data\n",
    "5. \"horsepower\": 2 missing data\n",
    "6. \"peak-rpm\": 2 missing data\n",
    "7. \"price\": 4 missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0381a0a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:21.959882Z",
     "iopub.status.busy": "2024-01-13T07:58:21.959540Z",
     "iopub.status.idle": "2024-01-13T07:58:21.966794Z",
     "shell.execute_reply": "2024-01-13T07:58:21.965251Z"
    },
    "papermill": {
     "duration": 0.023703,
     "end_time": "2024-01-13T07:58:21.968940",
     "exception": false,
     "start_time": "2024-01-13T07:58:21.945237",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of normalized-losses: 122.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean value for the \"normalized-losses\" column:\n",
    "\n",
    "avg_norm_loss = df[\"normalized-losses\"].astype(\"float\").mean(axis=0)\n",
    "print(\"Average of normalized-losses:\", avg_norm_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22e3e40e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:21.995938Z",
     "iopub.status.busy": "2024-01-13T07:58:21.995135Z",
     "iopub.status.idle": "2024-01-13T07:58:22.000586Z",
     "shell.execute_reply": "2024-01-13T07:58:21.999346Z"
    },
    "papermill": {
     "duration": 0.02108,
     "end_time": "2024-01-13T07:58:22.002771",
     "exception": false,
     "start_time": "2024-01-13T07:58:21.981691",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Replace \"NaN\" with mean value in \"normalized-losses\" column:\n",
    "\n",
    "df[\"normalized-losses\"].replace(np.nan, avg_norm_loss, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "12444ae4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.030540Z",
     "iopub.status.busy": "2024-01-13T07:58:22.030027Z",
     "iopub.status.idle": "2024-01-13T07:58:22.035511Z",
     "shell.execute_reply": "2024-01-13T07:58:22.034096Z"
    },
    "papermill": {
     "duration": 0.022205,
     "end_time": "2024-01-13T07:58:22.037545",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.015340",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of bore: 3.3297512437810943\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean value for the \"bore\" column:\n",
    "\n",
    "avg_bore=df['bore'].astype('float').mean(axis=0)\n",
    "print(\"Average of bore:\", avg_bore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a1af0f0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.065763Z",
     "iopub.status.busy": "2024-01-13T07:58:22.065227Z",
     "iopub.status.idle": "2024-01-13T07:58:22.069222Z",
     "shell.execute_reply": "2024-01-13T07:58:22.068642Z"
    },
    "papermill": {
     "duration": 0.020751,
     "end_time": "2024-01-13T07:58:22.071279",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.050528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Replace \"NaN\" with the mean value in the \"bore\" column:\n",
    "\n",
    "df[\"bore\"].replace(np.nan, avg_bore, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b2cc0832",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.097706Z",
     "iopub.status.busy": "2024-01-13T07:58:22.097362Z",
     "iopub.status.idle": "2024-01-13T07:58:22.103475Z",
     "shell.execute_reply": "2024-01-13T07:58:22.102672Z"
    },
    "papermill": {
     "duration": 0.021642,
     "end_time": "2024-01-13T07:58:22.105646",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.084004",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of stroke: 3.255422885572139\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean value for the \"stroke\" column:\n",
    "\n",
    "avg_stroke=df['stroke'].astype('float').mean(axis=0)\n",
    "print(\"Average of stroke:\", avg_stroke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1e3d556d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.135123Z",
     "iopub.status.busy": "2024-01-13T07:58:22.134770Z",
     "iopub.status.idle": "2024-01-13T07:58:22.139732Z",
     "shell.execute_reply": "2024-01-13T07:58:22.138379Z"
    },
    "papermill": {
     "duration": 0.022351,
     "end_time": "2024-01-13T07:58:22.142025",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.119674",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Replace \"NaN\" with the mean value in the \"stroke\" column:\n",
    "\n",
    "df[\"stroke\"].replace(np.nan, avg_stroke, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99b5107d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.169698Z",
     "iopub.status.busy": "2024-01-13T07:58:22.169133Z",
     "iopub.status.idle": "2024-01-13T07:58:22.175562Z",
     "shell.execute_reply": "2024-01-13T07:58:22.173811Z"
    },
    "papermill": {
     "duration": 0.021798,
     "end_time": "2024-01-13T07:58:22.177494",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.155696",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average horsepower: 104.25615763546799\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean value for the \"horsepower\" column:\n",
    "\n",
    "avg_horsepower = df['horsepower'].astype('float').mean(axis=0)\n",
    "print(\"Average horsepower:\", avg_horsepower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3fa8dfc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.202959Z",
     "iopub.status.busy": "2024-01-13T07:58:22.202610Z",
     "iopub.status.idle": "2024-01-13T07:58:22.207865Z",
     "shell.execute_reply": "2024-01-13T07:58:22.206875Z"
    },
    "papermill": {
     "duration": 0.019717,
     "end_time": "2024-01-13T07:58:22.209378",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.189661",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Replace \"NaN\" with the mean value in the \"horsepower\" column:\n",
    "\n",
    "df['horsepower'].replace(np.nan, avg_horsepower, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "434f385e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.235647Z",
     "iopub.status.busy": "2024-01-13T07:58:22.234694Z",
     "iopub.status.idle": "2024-01-13T07:58:22.240658Z",
     "shell.execute_reply": "2024-01-13T07:58:22.239567Z"
    },
    "papermill": {
     "duration": 0.02045,
     "end_time": "2024-01-13T07:58:22.242265",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.221815",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average peak rpm: 5125.369458128079\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean value for the \"peak-rpm\" column:\n",
    "\n",
    "avg_peakrpm=df['peak-rpm'].astype('float').mean(axis=0)\n",
    "print(\"Average peak rpm:\", avg_peakrpm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d2f1aea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.273141Z",
     "iopub.status.busy": "2024-01-13T07:58:22.272312Z",
     "iopub.status.idle": "2024-01-13T07:58:22.276695Z",
     "shell.execute_reply": "2024-01-13T07:58:22.276066Z"
    },
    "papermill": {
     "duration": 0.023073,
     "end_time": "2024-01-13T07:58:22.278429",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.255356",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Replace \"NaN\" with the mean value in the \"peak-rpm\" column:\n",
    "\n",
    "df['peak-rpm'].replace(np.nan, avg_peakrpm, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0d10261e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.305000Z",
     "iopub.status.busy": "2024-01-13T07:58:22.304357Z",
     "iopub.status.idle": "2024-01-13T07:58:22.314787Z",
     "shell.execute_reply": "2024-01-13T07:58:22.313961Z"
    },
    "papermill": {
     "duration": 0.02594,
     "end_time": "2024-01-13T07:58:22.316893",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.290953",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'four'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the most common value in the \"num-of-doors\" column:\n",
    "\n",
    "df['num-of-doors'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3d1d3c6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.344527Z",
     "iopub.status.busy": "2024-01-13T07:58:22.343947Z",
     "iopub.status.idle": "2024-01-13T07:58:22.348270Z",
     "shell.execute_reply": "2024-01-13T07:58:22.347652Z"
    },
    "papermill": {
     "duration": 0.020421,
     "end_time": "2024-01-13T07:58:22.350367",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.329946",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Replace the missing 'num-of-doors' values by the most frequent:\n",
    "\n",
    "df[\"num-of-doors\"].replace(np.nan, \"four\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2fd2246c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.377042Z",
     "iopub.status.busy": "2024-01-13T07:58:22.376416Z",
     "iopub.status.idle": "2024-01-13T07:58:22.383812Z",
     "shell.execute_reply": "2024-01-13T07:58:22.383101Z"
    },
    "papermill": {
     "duration": 0.022321,
     "end_time": "2024-01-13T07:58:22.385450",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.363129",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Finally drop all rows with NaN in \"price\" column:\n",
    "\n",
    "df.dropna(subset=[\"price\"], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "941773c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.411938Z",
     "iopub.status.busy": "2024-01-13T07:58:22.411332Z",
     "iopub.status.idle": "2024-01-13T07:58:22.414884Z",
     "shell.execute_reply": "2024-01-13T07:58:22.414270Z"
    },
    "papermill": {
     "duration": 0.018352,
     "end_time": "2024-01-13T07:58:22.416366",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.398014",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Reset index, because we droped two rows:\n",
    "    \n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "700e05a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.441897Z",
     "iopub.status.busy": "2024-01-13T07:58:22.441376Z",
     "iopub.status.idle": "2024-01-13T07:58:22.463702Z",
     "shell.execute_reply": "2024-01-13T07:58:22.462682Z"
    },
    "papermill": {
     "duration": 0.038016,
     "end_time": "2024-01-13T07:58:22.466396",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.428380",
     "status": "completed"
    },
    "tags": []
   },
   "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>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>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>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 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     3.4              10.0        102      5500       24   \n",
       "4         mpfi  3.19     3.4               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d131f9",
   "metadata": {
    "papermill": {
     "duration": 0.012723,
     "end_time": "2024-01-13T07:58:22.492965",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.480242",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 4. Correct data format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a0659b10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.521260Z",
     "iopub.status.busy": "2024-01-13T07:58:22.520908Z",
     "iopub.status.idle": "2024-01-13T07:58:22.527376Z",
     "shell.execute_reply": "2024-01-13T07:58:22.526818Z"
    },
    "papermill": {
     "duration": 0.02294,
     "end_time": "2024-01-13T07:58:22.529142",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.506202",
     "status": "completed"
    },
    "tags": []
   },
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the data types for each column:\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "65fe1707",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.557867Z",
     "iopub.status.busy": "2024-01-13T07:58:22.557177Z",
     "iopub.status.idle": "2024-01-13T07:58:22.569773Z",
     "shell.execute_reply": "2024-01-13T07:58:22.568273Z"
    },
    "papermill": {
     "duration": 0.029239,
     "end_time": "2024-01-13T07:58:22.571946",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.542707",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert data types to proper format:\n",
    "\n",
    "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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3e5c59a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.601040Z",
     "iopub.status.busy": "2024-01-13T07:58:22.600630Z",
     "iopub.status.idle": "2024-01-13T07:58:22.608071Z",
     "shell.execute_reply": "2024-01-13T07:58:22.607117Z"
    },
    "papermill": {
     "duration": 0.024742,
     "end_time": "2024-01-13T07:58:22.610037",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.585295",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses      int64\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                 float64\n",
       "stroke               float64\n",
       "compression-ratio    float64\n",
       "horsepower            object\n",
       "peak-rpm             float64\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfa80ed8",
   "metadata": {
    "papermill": {
     "duration": 0.013855,
     "end_time": "2024-01-13T07:58:22.637420",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.623565",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 5. Data Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6633f1fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.667063Z",
     "iopub.status.busy": "2024-01-13T07:58:22.666691Z",
     "iopub.status.idle": "2024-01-13T07:58:22.672768Z",
     "shell.execute_reply": "2024-01-13T07:58:22.671644Z"
    },
    "papermill": {
     "duration": 0.023798,
     "end_time": "2024-01-13T07:58:22.674639",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.650841",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Transform 'mpg' to 'L/100km' in columns \"city-mpg\" and \"highway-mpg\" (the formula for unit conversion is: L/100km = 235 / mpg):\n",
    "\n",
    "df['city-L/100km'] = 235 / df[\"city-mpg\"]\n",
    "df['highway-L/100km'] = 235 / df[\"highway-mpg\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a42d8f93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.703360Z",
     "iopub.status.busy": "2024-01-13T07:58:22.703016Z",
     "iopub.status.idle": "2024-01-13T07:58:22.725022Z",
     "shell.execute_reply": "2024-01-13T07:58:22.724086Z"
    },
    "papermill": {
     "duration": 0.03797,
     "end_time": "2024-01-13T07:58:22.726711",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.688741",
     "status": "completed"
    },
    "tags": []
   },
   "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>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",
       "      <th>highway-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>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",
       "      <td>8.703704</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>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",
       "      <td>8.703704</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>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",
       "      <td>9.038462</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>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",
       "      <td>7.833333</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>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",
       "      <td>10.681818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 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",
       "   bore  stroke  compression-ratio  horsepower peak-rpm city-mpg  highway-mpg  \\\n",
       "0  3.47    2.68                9.0         111   5000.0       21           27   \n",
       "1  3.47    2.68                9.0         111   5000.0       21           27   \n",
       "2  2.68    3.47                9.0         154   5000.0       19           26   \n",
       "3  3.19    3.40               10.0         102   5500.0       24           30   \n",
       "4  3.19    3.40                8.0         115   5500.0       18           22   \n",
       "\n",
       "     price  city-L/100km  highway-L/100km  \n",
       "0  13495.0     11.190476         8.703704  \n",
       "1  16500.0     11.190476         8.703704  \n",
       "2  16500.0     12.368421         9.038462  \n",
       "3  13950.0      9.791667         7.833333  \n",
       "4  17450.0     13.055556        10.681818  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the transformed data:\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b91e6aab",
   "metadata": {
    "papermill": {
     "duration": 0.01411,
     "end_time": "2024-01-13T07:58:22.754320",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.740210",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 6. Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "266e1cee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.782307Z",
     "iopub.status.busy": "2024-01-13T07:58:22.781683Z",
     "iopub.status.idle": "2024-01-13T07:58:22.787440Z",
     "shell.execute_reply": "2024-01-13T07:58:22.786866Z"
    },
    "papermill": {
     "duration": 0.022131,
     "end_time": "2024-01-13T07:58:22.789570",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.767439",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Scale the columns \"length\", \"width\" and \"height\" (value ranges from 0 to 1):\n",
    "\n",
    "df['length'] = df['length'] / df['length'].max()\n",
    "df['width'] = df['width'] / df['width'].max()\n",
    "df['height'] = df['height'] / df['height'].max() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3aaa0e62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.818101Z",
     "iopub.status.busy": "2024-01-13T07:58:22.817742Z",
     "iopub.status.idle": "2024-01-13T07:58:22.828563Z",
     "shell.execute_reply": "2024-01-13T07:58:22.827433Z"
    },
    "papermill": {
     "duration": 0.027035,
     "end_time": "2024-01-13T07:58:22.830550",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.803515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>0.816054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>0.816054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.822681</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>0.876254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.919444</td>\n",
       "      <td>0.908027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.922222</td>\n",
       "      <td>0.908027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     length     width    height\n",
       "0  0.811148  0.890278  0.816054\n",
       "1  0.811148  0.890278  0.816054\n",
       "2  0.822681  0.909722  0.876254\n",
       "3  0.848630  0.919444  0.908027\n",
       "4  0.848630  0.922222  0.908027"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the normalized columns:\n",
    "\n",
    "df[[\"length\",\"width\",\"height\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27367d62",
   "metadata": {
    "papermill": {
     "duration": 0.015608,
     "end_time": "2024-01-13T07:58:22.860256",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.844648",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 7. Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3cf6668a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.889619Z",
     "iopub.status.busy": "2024-01-13T07:58:22.889268Z",
     "iopub.status.idle": "2024-01-13T07:58:22.894759Z",
     "shell.execute_reply": "2024-01-13T07:58:22.893782Z"
    },
    "papermill": {
     "duration": 0.021857,
     "end_time": "2024-01-13T07:58:22.896524",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.874667",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert data to correct format (before binning):\n",
    "\n",
    "df[\"horsepower\"]=df[\"horsepower\"].astype(int, copy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6377c7ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:22.927768Z",
     "iopub.status.busy": "2024-01-13T07:58:22.927377Z",
     "iopub.status.idle": "2024-01-13T07:58:23.184925Z",
     "shell.execute_reply": "2024-01-13T07:58:23.183926Z"
    },
    "papermill": {
     "duration": 0.275314,
     "end_time": "2024-01-13T07:58:23.186864",
     "exception": false,
     "start_time": "2024-01-13T07:58:22.911550",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 33,
     "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": [
    "# Plot the histogram of \"horsepower\" column to see what the distribution looks like:\n",
    "\n",
    "plt.pyplot.hist(df[\"horsepower\"])\n",
    "plt.pyplot.xlabel(\"horsepower\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "15583714",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:23.216002Z",
     "iopub.status.busy": "2024-01-13T07:58:23.215634Z",
     "iopub.status.idle": "2024-01-13T07:58:23.221416Z",
     "shell.execute_reply": "2024-01-13T07:58:23.220111Z"
    },
    "papermill": {
     "duration": 0.022662,
     "end_time": "2024-01-13T07:58:23.223846",
     "exception": false,
     "start_time": "2024-01-13T07:58:23.201184",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Rearrange the values into three ‘bins':\n",
    "\n",
    "bins = np.linspace(min(df[\"horsepower\"]), max(df[\"horsepower\"]), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9a7f1e73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:23.254822Z",
     "iopub.status.busy": "2024-01-13T07:58:23.254274Z",
     "iopub.status.idle": "2024-01-13T07:58:23.260733Z",
     "shell.execute_reply": "2024-01-13T07:58:23.259287Z"
    },
    "papermill": {
     "duration": 0.024139,
     "end_time": "2024-01-13T07:58:23.262973",
     "exception": false,
     "start_time": "2024-01-13T07:58:23.238834",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 48.        , 119.33333333, 190.66666667, 262.        ])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the bins:\n",
    "\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3fe1b813",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:23.293584Z",
     "iopub.status.busy": "2024-01-13T07:58:23.293244Z",
     "iopub.status.idle": "2024-01-13T07:58:23.297264Z",
     "shell.execute_reply": "2024-01-13T07:58:23.296398Z"
    },
    "papermill": {
     "duration": 0.021014,
     "end_time": "2024-01-13T07:58:23.298967",
     "exception": false,
     "start_time": "2024-01-13T07:58:23.277953",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set group names:\n",
    "\n",
    "group_names = ['Low', 'Medium', 'High']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "64d76651",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:23.332434Z",
     "iopub.status.busy": "2024-01-13T07:58:23.332079Z",
     "iopub.status.idle": "2024-01-13T07:58:23.338924Z",
     "shell.execute_reply": "2024-01-13T07:58:23.337795Z"
    },
    "papermill": {
     "duration": 0.024958,
     "end_time": "2024-01-13T07:58:23.340885",
     "exception": false,
     "start_time": "2024-01-13T07:58:23.315927",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Determine what each value of df['horsepower'] belongs to:\n",
    "\n",
    "df['horsepower-binned'] = pd.cut(df['horsepower'], bins, labels=group_names, include_lowest=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4d8b25f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:23.372156Z",
     "iopub.status.busy": "2024-01-13T07:58:23.371786Z",
     "iopub.status.idle": "2024-01-13T07:58:23.383422Z",
     "shell.execute_reply": "2024-01-13T07:58:23.382184Z"
    },
    "papermill": {
     "duration": 0.029087,
     "end_time": "2024-01-13T07:58:23.385347",
     "exception": false,
     "start_time": "2024-01-13T07:58:23.356260",
     "status": "completed"
    },
    "tags": []
   },
   "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",
       "  </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"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "df[['horsepower','horsepower-binned']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2ffe0fef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:23.418064Z",
     "iopub.status.busy": "2024-01-13T07:58:23.416589Z",
     "iopub.status.idle": "2024-01-13T07:58:23.425656Z",
     "shell.execute_reply": "2024-01-13T07:58:23.424815Z"
    },
    "papermill": {
     "duration": 0.027259,
     "end_time": "2024-01-13T07:58:23.427891",
     "exception": false,
     "start_time": "2024-01-13T07:58:23.400632",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "horsepower-binned\n",
       "Low       153\n",
       "Medium     43\n",
       "High        5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of values in each bin:\n",
    "\n",
    "df[\"horsepower-binned\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d7c820e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:23.460901Z",
     "iopub.status.busy": "2024-01-13T07:58:23.460109Z",
     "iopub.status.idle": "2024-01-13T07:58:23.665681Z",
     "shell.execute_reply": "2024-01-13T07:58:23.664820Z"
    },
    "papermill": {
     "duration": 0.224826,
     "end_time": "2024-01-13T07:58:23.668023",
     "exception": false,
     "start_time": "2024-01-13T07:58:23.443197",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bG3vesX19fa1ZHGZzAACwN7dexiouLtauXbus9T179mjLli0KDg5WRESERo0apbvvvlsdOnTQbbfdpuXLl+ujjz5SWlqaJCkwMFADBw5UUlKSgoODFRAQoGHDhik2NpYn+wIAAElu/uh5WlqabrvttrPa+/Xrp/nz50uS5s6dq5SUFP3www9q3LixJk6cqG7dull9jx8/rscff1z//ve/VVJSosTERL366qsXvIz1W0VFRQoMDFRhYSGzPAAAXCEu9u+3x3zPjjsRdgAAuPJc7N9vj71nBwAAwBUIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNbc+iDQq0H9sR+7uwS42d7JXd1dAgBc1ZjZAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtkbYAQAAtubWsJORkaE777xTYWFhcjgcWrJkyXn7Pvzww3I4HJo2bZpT+6FDh9S3b18FBAQoKChIAwcOVHFxccUWDgAArhhuDTtHjx5VdHS0UlNTL9hv8eLFWrt2rcLCws7a1rdvX23dulUrV67UsmXLlJGRocGDB1dUyQAA4Arj484X79Kli7p06XLBPj/++KOGDRumzz77TF27dnXatn37di1fvlzr169Xq1atJEkzZszQHXfcoRdffPGc4QgAAFxdPPqenbKyMt13330aNWqUbrjhhrO2Z2ZmKigoyAo6kpSQkCAvLy9lZWVdzlIBAICHcuvMzu+ZMmWKfHx8NHz48HNuz83NVe3atZ3afHx8FBwcrNzc3POOW1JSopKSEmu9qKjINQUDAACP47EzOxs3btQ///lPzZ8/Xw6Hw6Vjp6SkKDAw0FrCw8NdOj4AAPAcHht21qxZo/z8fEVERMjHx0c+Pj7at2+fHn/8cdWvX1+SFBoaqvz8fKf9Tp48qUOHDik0NPS8YycnJ6uwsNBacnJyKvJQAACAG3nsZaz77rtPCQkJTm2JiYm677779MADD0iSYmNjVVBQoI0bN6ply5aSpM8//1xlZWWKiYk579i+vr7y9fWtuOIBAIDHcGvYKS4u1q5du6z1PXv2aMuWLQoODlZERIRq1Kjh1L9SpUoKDQ1V48aNJUlRUVHq3LmzHnzwQc2aNUulpaUaOnSo+vTpwyexAACAJDdfxtqwYYNuuukm3XTTTZKkpKQk3XTTTRo/fvxFj7Fw4UI1adJEnTp10h133KF27drp9ddfr6iSAQDAFcatMzvx8fEyxlx0/717957VFhwcrEWLFrmwKgAAYCcee4MyAACAKxB2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArbk17GRkZOjOO+9UWFiYHA6HlixZYm0rLS3VmDFj1KxZM1WtWlVhYWG6//77deDAAacxDh06pL59+yogIEBBQUEaOHCgiouLL/ORAAAAT+XWsHP06FFFR0crNTX1rG3Hjh3Tpk2bNG7cOG3atEkffvihsrOzdddddzn169u3r7Zu3aqVK1dq2bJlysjI0ODBgy/XIQAAAA/nMMYYdxchSQ6HQ4sXL1b37t3P22f9+vVq06aN9u3bp4iICG3fvl1NmzbV+vXr1apVK0nS8uXLdccdd+iHH35QWFjYRb12UVGRAgMDVVhYqICAAFccjqX+2I9dOh6uPHsnd3V3CQBgSxf79/uKumensLBQDodDQUFBkqTMzEwFBQVZQUeSEhIS5OXlpaysLDdVCQAAPImPuwu4WMePH9eYMWN0zz33WOktNzdXtWvXdurn4+Oj4OBg5ebmnneskpISlZSUWOtFRUUVUzQAAHC7K2Jmp7S0VL1795YxRjNnzrzk8VJSUhQYGGgt4eHhLqgSAAB4Io8PO2eCzr59+7Ry5Uqna3KhoaHKz8936n/y5EkdOnRIoaGh5x0zOTlZhYWF1pKTk1Nh9QMAAPfy6MtYZ4LOzp07tXr1atWoUcNpe2xsrAoKCrRx40a1bNlSkvT555+rrKxMMTEx5x3X19dXvr6+FVo7AADwDG4NO8XFxdq1a5e1vmfPHm3ZskXBwcGqU6eO/vrXv2rTpk1atmyZTp06Zd2HExwcrMqVKysqKkqdO3fWgw8+qFmzZqm0tFRDhw5Vnz59LvqTWAAAwN7cGnY2bNig2267zVpPSkqSJPXr109PP/20/vOf/0iSWrRo4bTf6tWrFR8fL0lauHChhg4dqk6dOsnLy0s9e/bU9OnTL0v9AADA87k17MTHx+tCX/NzMV8BFBwcrEWLFrmyLAAAYCMef4MyAADApSDsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAW3Nr2MnIyNCdd96psLAwORwOLVmyxGm7MUbjx49XnTp15O/vr4SEBO3cudOpz6FDh9S3b18FBAQoKChIAwcOVHFx8WU8CgAA4MncGnaOHj2q6OhopaamnnP7888/r+nTp2vWrFnKyspS1apVlZiYqOPHj1t9+vbtq61bt2rlypVatmyZMjIyNHjw4Mt1CAAAwMP5uPPFu3Tpoi5dupxzmzFG06ZN01NPPaVu3bpJkt58802FhIRoyZIl6tOnj7Zv367ly5dr/fr1atWqlSRpxowZuuOOO/Tiiy8qLCzssh0LAADwTB57z86ePXuUm5urhIQEqy0wMFAxMTHKzMyUJGVmZiooKMgKOpKUkJAgLy8vZWVlXfaaAQCA53HrzM6F5ObmSpJCQkKc2kNCQqxtubm5ql27ttN2Hx8fBQcHW33OpaSkRCUlJdZ6UVGRq8oGAAAexmNndipSSkqKAgMDrSU8PNzdJQEAgArisWEnNDRUkpSXl+fUnpeXZ20LDQ1Vfn6+0/aTJ0/q0KFDVp9zSU5OVmFhobXk5OS4uHoAAOApPDbsREZGKjQ0VKtWrbLaioqKlJWVpdjYWElSbGysCgoKtHHjRqvP559/rrKyMsXExJx3bF9fXwUEBDgtAADAntx6z05xcbF27dplre/Zs0dbtmxRcHCwIiIiNGLECE2aNEmNGjVSZGSkxo0bp7CwMHXv3l2SFBUVpc6dO+vBBx/UrFmzVFpaqqFDh6pPnz58EgsAAEhyc9jZsGGDbrvtNms9KSlJktSvXz/Nnz9fo0eP1tGjRzV48GAVFBSoXbt2Wr58ufz8/Kx9Fi5cqKFDh6pTp07y8vJSz549NX369Mt+LAAAwDM5jDHG3UW4W1FRkQIDA1VYWOjyS1r1x37s0vFw5dk7uau7SwAAW7rYv98ee88OAACAKxB2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArZUr7HTs2FEFBQVntRcVFaljx46XWhMAAIDLlCvspKWl6cSJE2e1Hz9+XGvWrLnkogAAAFzF5490/uabb6yft23bptzcXGv91KlTWr58ua699lrXVQcAAHCJ/lDYadGihRwOhxwOxzkvV/n7+2vGjBkuKw4AAOBS/aGws2fPHhljdN1112ndunWqVauWta1y5cqqXbu2vL29XV4kAABAef2hsFOvXj1JUllZWYUUAwAA4Gp/KOz82s6dO7V69Wrl5+efFX7Gjx9/yYUBAAC4QrnCzuzZs/XII4+oZs2aCg0NlcPhsLY5HA7CDgAA8BjlCjuTJk3Ss88+qzFjxri6HgAAAJcq1/fsHD58WL169XJ1LQAAAC5XrrDTq1cvrVixwtW1AAAAuFy5LmM1bNhQ48aN09q1a9WsWTNVqlTJafvw4cNdUhwAAMClchhjzB/dKTIy8vwDOhz6/vvvL6moy62oqEiBgYEqLCxUQECAS8euP/Zjl46HK8/eyV3dXQIA2NLF/v0u12WsPXv2nHdxZdA5deqUxo0bp8jISPn7+6tBgwZ65pln9Ot8ZozR+PHjVadOHfn7+yshIUE7d+50WQ0AAODKVq6wc7lMmTJFM2fO1CuvvKLt27drypQpev75550eSfH8889r+vTpmjVrlrKyslS1alUlJibq+PHjbqwcAAB4inLdszNgwIALbp87d265ivmtr776St26dVPXrqcvA9SvX1///ve/tW7dOkmnZ3WmTZump556St26dZMkvfnmmwoJCdGSJUvUp08fl9QBAACuXOX+6Pmvl/z8fH3++ef68MMPVVBQ4LLibr31Vq1atUo7duyQJH399df64osv1KVLF0mnL6fl5uYqISHB2icwMFAxMTHKzMx0WR0AAODKVa6ZncWLF5/VVlZWpkceeUQNGjS45KLOGDt2rIqKitSkSRN5e3vr1KlTevbZZ9W3b19JUm5uriQpJCTEab+QkBBr27mUlJSopKTEWi8qKnJZzQAAwLO47J4dLy8vJSUlaerUqa4aUu+++64WLlyoRYsWadOmTVqwYIFefPFFLViw4JLGTUlJUWBgoLWEh4e7qGIAAOBpXHqD8u7du3Xy5EmXjTdq1CiNHTtWffr0UbNmzXTfffdp5MiRSklJkSSFhoZKkvLy8pz2y8vLs7adS3JysgoLC60lJyfHZTUDAADPUq7LWElJSU7rxhj99NNP+vjjj9WvXz+XFCZJx44dk5eXcx7z9va2nrIeGRmp0NBQrVq1Si1atJB0+pJUVlaWHnnkkfOO6+vrK19fX5fVCQAAPFe5ws7mzZud1r28vFSrVi299NJLv/tJrT/izjvv1LPPPquIiAjdcMMN2rx5s15++WXrNRwOh0aMGKFJkyapUaNGioyM1Lhx4xQWFqbu3bu7rA4AAHDlKlfYWb16tavrOKcZM2Zo3LhxevTRR5Wfn6+wsDA99NBDGj9+vNVn9OjROnr0qAYPHqyCggK1a9dOy5cvl5+f32WpEQAAeLZyPS7ijIMHDyo7O1uS1LhxY9WqVctlhV1OPC4CFYnHRQBAxajQx0UcPXpUAwYMUJ06ddShQwd16NBBYWFhGjhwoI4dO1buogEAAFytXGEnKSlJ6enp+uijj1RQUKCCggItXbpU6enpevzxx11dIwAAQLmV656dDz74QO+//77i4+OttjvuuEP+/v7q3bu3Zs6c6ar6AAAALkm5ZnaOHTt21rcWS1Lt2rW5jAUAADxKucJObGysJkyY4PRk8V9++UUTJ05UbGysy4oDAAC4VOW6jDVt2jR17txZdevWVXR0tKTTD+n09fXVihUrXFogAADApShX2GnWrJl27typhQsX6rvvvpMk3XPPPerbt6/8/f1dWiAAAMClKFfYSUlJUUhIiB588EGn9rlz5+rgwYMaM2aMS4oDAAC4VOW6Z+e1115TkyZNzmq/4YYbNGvWrEsuCgAAwFXKFXZyc3NVp06ds9pr1aqln3766ZKLAgAAcJVyhZ3w8HB9+eWXZ7V/+eWXCgsLu+SiAAAAXKVc9+w8+OCDGjFihEpLS9WxY0dJ0qpVqzR69Gi+QRkAAHiUcoWdUaNG6X//+58effRRnThxQpLk5+enMWPGKDk52aUFAgAAXIpyhR2Hw6EpU6Zo3Lhx2r59u/z9/dWoUSP5+vq6uj4AAIBLUq6wc0a1atXUunVrV9UCAADgcuW6QRkAAOBKQdgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC25vFh58cff9S9996rGjVqyN/fX82aNdOGDRus7cYYjR8/XnXq1JG/v78SEhK0c+dON1YMAAA8iUeHncOHD6tt27aqVKmSPv30U23btk0vvfSSrrnmGqvP888/r+nTp2vWrFnKyspS1apVlZiYqOPHj7uxcgAA4Cl83F3AhUyZMkXh4eGaN2+e1RYZGWn9bIzRtGnT9NRTT6lbt26SpDfffFMhISFasmSJ+vTpc9lrBgAAnsWjZ3b+85//qFWrVurVq5dq166tm266SbNnz7a279mzR7m5uUpISLDaAgMDFRMTo8zMTHeUDAAAPIxHh53vv/9eM2fOVKNGjfTZZ5/pkUce0fDhw7VgwQJJUm5uriQpJCTEab+QkBBr27mUlJSoqKjIaQEAAPbk0ZexysrK1KpVKz333HOSpJtuukn//e9/NWvWLPXr16/c46akpGjixImuKhMAAHgwj57ZqVOnjpo2berUFhUVpf3790uSQkNDJUl5eXlOffLy8qxt55KcnKzCwkJrycnJcXHlAADAU3h02Gnbtq2ys7Od2nbs2KF69epJOn2zcmhoqFatWmVtLyoqUlZWlmJjY887rq+vrwICApwWAABgTx59GWvkyJG69dZb9dxzz6l3795at26dXn/9db3++uuSJIfDoREjRmjSpElq1KiRIiMjNW7cOIWFhal79+7uLR4AAHgEjw47rVu31uLFi5WcnKx//OMfioyM1LRp09S3b1+rz+jRo3X06FENHjxYBQUFateunZYvXy4/Pz83Vg4AADyFwxhj3F2EuxUVFSkwMFCFhYUuv6RVf+zHLh0PV569k7u6uwQAsKWL/fvt0ffsAAAAXCrCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDUfdxcAoGLVH/uxu0uAm+2d3NXdJQBuxcwOAACwtSsq7EyePFkOh0MjRoyw2o4fP64hQ4aoRo0aqlatmnr27Km8vDz3FQkAADzKFRN21q9fr9dee03Nmzd3ah85cqQ++ugjvffee0pPT9eBAwfUo0cPN1UJAAA8zRURdoqLi9W3b1/Nnj1b11xzjdVeWFioN954Qy+//LI6duyoli1bat68efrqq6+0du1aN1YMAAA8xRURdoYMGaKuXbsqISHBqX3jxo0qLS11am/SpIkiIiKUmZl5ucsEAAAeyOM/jfX2229r06ZNWr9+/VnbcnNzVblyZQUFBTm1h4SEKDc397xjlpSUqKSkxFovKipyWb0AAMCzePTMTk5Ojh577DEtXLhQfn5+Lhs3JSVFgYGB1hIeHu6ysQEAgGfx6LCzceNG5efn6+abb5aPj498fHyUnp6u6dOny8fHRyEhITpx4oQKCgqc9svLy1NoaOh5x01OTlZhYaG15OTkVPCRAAAAd/Hoy1idOnXSt99+69T2wAMPqEmTJhozZozCw8NVqVIlrVq1Sj179pQkZWdna//+/YqNjT3vuL6+vvL19a3Q2gEAgGfw6LBTvXp13XjjjU5tVatWVY0aNaz2gQMHKikpScHBwQoICNCwYcMUGxurW265xR0lAwAAD+PRYediTJ06VV5eXurZs6dKSkqUmJioV1991d1lAQAAD3HFhZ20tDSndT8/P6Wmpio1NdU9BQEAAI/m0TcoAwAAXCrCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDXCDgAAsDWPDzspKSlq3bq1qlevrtq1a6t79+7Kzs526nP8+HENGTJENWrUULVq1dSzZ0/l5eW5qWIAAOBJPD7spKena8iQIVq7dq1Wrlyp0tJS3X777Tp69KjVZ+TIkfroo4/03nvvKT09XQcOHFCPHj3cWDUAAPAUPu4u4PcsX77caX3+/PmqXbu2Nm7cqA4dOqiwsFBvvPGGFi1apI4dO0qS5s2bp6ioKK1du1a33HKLO8oGAAAewuNndn6rsLBQkhQcHCxJ2rhxo0pLS5WQkGD1adKkiSIiIpSZmemWGgEAgOfw+JmdXysrK9OIESPUtm1b3XjjjZKk3NxcVa5cWUFBQU59Q0JClJube85xSkpKVFJSYq0XFRVVWM0AAMC9rqiZnSFDhui///2v3n777UsaJyUlRYGBgdYSHh7uogoBAICnuWLCztChQ7Vs2TKtXr1adevWtdpDQ0N14sQJFRQUOPXPy8tTaGjoOcdKTk5WYWGhteTk5FRk6QAAwI08PuwYYzR06FAtXrxYn3/+uSIjI522t2zZUpUqVdKqVaustuzsbO3fv1+xsbHnHNPX11cBAQFOCwAAsCePv2dnyJAhWrRokZYuXarq1atb9+EEBgbK399fgYGBGjhwoJKSkhQcHKyAgAANGzZMsbGxfBILAAB4ftiZOXOmJCk+Pt6pfd68eerfv78kaerUqfLy8lLPnj1VUlKixMREvfrqq5e5UgAA4Ik8PuwYY363j5+fn1JTU5WamnoZKgIAAFcSj79nBwAA4FIQdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK35uLsAAIC91R/7sbtLgJvtndzVra/PzA4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA1wg4AALA124Sd1NRU1a9fX35+foqJidG6devcXRIAAPAAtgg777zzjpKSkjRhwgRt2rRJ0dHRSkxMVH5+vrtLAwAAbmaLsPPyyy/rwQcf1AMPPKCmTZtq1qxZqlKliubOnevu0gAAgJtd8WHnxIkT2rhxoxISEqw2Ly8vJSQkKDMz042VAQAAT3DFPy7i559/1qlTpxQSEuLUHhISou++++6c+5SUlKikpMRaLywslCQVFRW5vL6ykmMuHxNXloo4r/4IzkFwDsLdKuocPDOuMeaC/a74sFMeKSkpmjhx4lnt4eHhbqgGdhc4zd0V4GrHOQh3q+hz8MiRIwoMDDzv9is+7NSsWVPe3t7Ky8tzas/Ly1NoaOg590lOTlZSUpK1XlZWpkOHDqlGjRpyOBwVWu/VpqioSOHh4crJyVFAQIC7y8FViHMQ7sY5WHGMMTpy5IjCwsIu2O+KDzuVK1dWy5YttWrVKnXv3l3S6fCyatUqDR069Jz7+Pr6ytfX16ktKCiogiu9ugUEBPAfOdyKcxDuxjlYMS40o3PGFR92JCkpKUn9+vVTq1at1KZNG02bNk1Hjx7VAw884O7SAACAm9ki7Nx99906ePCgxo8fr9zcXLVo0ULLly8/66ZlAABw9bFF2JGkoUOHnveyFdzH19dXEyZMOOuyIXC5cA7C3TgH3c9hfu/zWgAAAFewK/5LBQEAAC6EsAMAAGyNsAMAAGyNsAPAltLS0uRwOFRQUCBJmj9/Pt+nhQpXnvOsf//+1vfEoWIQdvCH8B8lXKV///5yOBx6+OGHz9o2ZMgQORwO9e/f32Wvd/fdd2vHjh0uGw9Xn/P9/+/XwZrzzDMRdgC4TXh4uN5++2398ssvVtvx48e1aNEiRUREuPS1/P39Vbt2bZeOCfwW55lnIuzAZdLT09WmTRv5+vqqTp06Gjt2rE6ePClJWrZsmYKCgnTq1ClJ0pYtW+RwODR27Fhr/0GDBunee+91S+1wj5tvvlnh4eH68MMPrbYPP/xQERERuummm6y2srIypaSkKDIyUv7+/oqOjtb777/vNNYnn3yi66+/Xv7+/rrtttu0d+9ep+2/vbxwrn+ljxgxQvHx8dZ6fHy8hg0bphEjRuiaa65RSEiIZs+ebX1De/Xq1dWwYUN9+umnl/xewB7OdRlr0qRJql27tqpXr65BgwZp7NixatGixVn7vvjii6pTp45q1KihIUOGqLS09PIUfRUg7MAlfvzxR91xxx1q3bq1vv76a82cOVNvvPGGJk2aJElq3769jhw5os2bN0s6HYxq1qyptLQ0a4z09HSnPzS4OgwYMEDz5s2z1ufOnXvWo15SUlL05ptvatasWdq6datGjhype++9V+np6ZKknJwc9ejRQ3feeae2bNli/UFxhQULFqhmzZpat26dhg0bpkceeUS9evXSrbfeqk2bNun222/Xfffdp2PHjrnk9WAvCxcu1LPPPqspU6Zo48aNioiI0MyZM8/qt3r1au3evVurV6/WggULNH/+fM2fP//yF2xXBvgD+vXrZ7p163ZW+5NPPmkaN25sysrKrLbU1FRTrVo1c+rUKWOMMTfffLN54YUXjDHGdO/e3Tz77LOmcuXK5siRI+aHH34wksyOHTsuy3HA/c6cS/n5+cbX19fs3bvX7N271/j5+ZmDBw+abt26mX79+pnjx4+bKlWqmK+++spp/4EDB5p77rnHGGNMcnKyadq0qdP2MWPGGEnm8OHDxhhj5s2bZwIDA896/V977LHHTFxcnLUeFxdn2rVrZ62fPHnSVK1a1dx3331W208//WQkmczMzEt4N3Al6Nevn/H29jZVq1Z1Wvz8/Kxz7bfnWUxMjBkyZIjTOG3btjXR0dFO49arV8+cPHnSauvVq5e5++67K/qQrhrM7MAltm/frtjYWDkcDqutbdu2Ki4u1g8//CBJiouLU1pamowxWrNmjXr06KGoqCh98cUXSk9PV1hYmBo1auSuQ4Cb1KpVS127dtX8+fM1b948de3aVTVr1rS279q1S8eOHdOf/vQnVatWzVrefPNN7d69W9Lp8y8mJsZp3NjYWJfU17x5c+tnb29v1ahRQ82aNbPazjyDLz8/3yWvB8922223acuWLU7LnDlzzts/Oztbbdq0cWr77bok3XDDDfL29rbW69SpwznlQrZ5NhY8X3x8vObOnauvv/5alSpVUpMmTRQfH6+0tDQdPnxYcXFx7i4RbjJgwADr2XapqalO24qLiyVJH3/8sa699lqnbZfyrCEvLy+Z3zwt51z3SFSqVMlp3eFwOLWdCfhlZWXlrgVXjqpVq6phw4ZObWf+QXcpznWecU65DjM7cImoqChlZmY6/fH48ssvVb16ddWtW1fS/923M3XqVCvYnAk7aWlp3K9zFevcubNOnDih0tJSJSYmOm1r2rSpfH19tX//fjVs2NBpCQ8Pl3T6/Fu3bp3TfmvXrr3ga9aqVUs//fSTU9uWLVsu/WCAX2ncuLHWr1/v1PbbdVQ8wg7+sMLCwrOmcQcPHqycnBwNGzZM3333nZYuXaoJEyYoKSlJXl6nT7NrrrlGzZs318KFC61g06FDB23atEk7duxgZucq5u3tre3bt2vbtm1OU/mSVL16dT3xxBMaOXKkFixYoN27d2vTpk2aMWOGFixYIEl6+OGHtXPnTo0aNUrZ2dlatGjR797c2bFjR23YsEFvvvmmdu7cqQkTJui///1vRR0irlLDhg3TG2+8oQULFmjnzp2aNGmSvvnmG6dL/qh4XMbCH5aWlub0sWBJGjhwoD755BONGjVK0dHRCg4O1sCBA/XUU0859YuLi9OWLVussBMcHKymTZsqLy9PjRs3vlyHAA8UEBBw3m3PPPOMatWqpZSUFH3//fcKCgrSzTffrCeffFKSFBERoQ8++EAjR47UjBkz1KZNGz333HMaMGDAecdMTEzUuHHjNHr0aB0/flwDBgzQ/fffr2+//dblx4arV9++ffX999/riSee0PHjx9W7d2/179//rJlIVCyH+e1FawAAUGH+9Kc/KTQ0VG+99Za7S7lqMLMDAEAFOXbsmGbNmqXExER5e3vr3//+t/7f//t/WrlypbtLu6owswMAQAX55ZdfdOedd2rz5s06fvy4GjdurKeeeko9evRwd2lXFcIOAACwNT6NBQAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wA+CSxcfHa8SIEe4uAwDOibADAABsjbADwOOcOHHC3SVcNlfTsQLuQtgB4BJlZWUaPXq0goODFRoaqqefftratn//fnXr1k3VqlVTQECAevfurby8PGv7008/rRYtWmjOnDmKjIyUn5+fJOn9999Xs2bN5O/vrxo1aighIUFHjx619pszZ46ioqLk5+enJk2a6NVXX7W27d27Vw6HQ2+//bZuvfVW+fn56cYbb1R6erpT3enp6WrTpo18fX1Vp04djR07VidPnpQkLVu2TEFBQTp16pSk009FdzgcGjt2rLX/oEGDdO+991rrX3zxhdq3by9/f3+Fh4dr+PDhTjXXr19fzzzzjO6//34FBARo8ODBl/K2A7gYBgAuUVxcnAkICDBPP/202bFjh1mwYIFxOBxmxYoV5tSpU6ZFixamXbt2ZsOGDWbt2rWmZcuWJi4uztp/woQJpmrVqqZz585m06ZN5uuvvzYHDhwwPj4+5uWXXzZ79uwx33zzjUlNTTVHjhwxxhjzr3/9y9SpU8d88MEH5vvvvzcffPCBCQ4ONvPnzzfGGLNnzx4jydStW9e8//77Ztu2bWbQoEGmevXq5ueffzbGGPPDDz+YKlWqmEcffdRs377dLF682NSsWdNMmDDBGGNMQUGB8fLyMuvXrzfGGDNt2jRTs2ZNExMTY9XesGFDM3v2bGOMMbt27TJVq1Y1U6dONTt27DBffvmluemmm0z//v2t/vXq1TMBAQHmxRdfNLt27TK7du2qsN8LgNMIOwAuWVxcnGnXrp1TW+vWrc2YMWPMihUrjLe3t9m/f7+1bevWrUaSWbdunTHmdNipVKmSyc/Pt/ps3LjRSDJ79+4952s2aNDALFq0yKntmWeeMbGxscaY/ws7kydPtraXlpaaunXrmilTphhjjHnyySdN48aNTVlZmdUnNTXVVKtWzZw6dcoYY8zNN99sXnjhBWOMMd27dzfPPvusqVy5sjly5Ij54YcfjCSzY8cOY4wxAwcONIMHD3aqac2aNcbLy8v88ssvxpjTYad79+4XfD8BuBaXsQC4RPPmzZ3W69Spo/z8fG3fvl3h4eEKDw+3tjVt2lRBQUHavn271VavXj3VqlXLWo+OjlanTp3UrFkz9erVS7Nnz9bhw4clSUePHtXu3bs1cOBAVatWzVomTZqk3bt3O9URGxtr/ezj46NWrVpZr7t9+3bFxsbK4XBYfdq2bavi4mL98MMPkqS4uDilpaXJGKM1a9aoR48eioqK0hdffKH09HSFhYWpUaNGkqSvv/5a8+fPd6opMTFRZWVl2rNnj/UarVq1Kt+bDKBceOo5AJeoVKmS07rD4VBZWdlF71+1alWndW9vb61cuVJfffWVVqxYoRkzZujvf/+7srKyVKVKFUnS7NmzFRMTc9Z+rhQfH6+5c+fq66+/VqVKldSkSRPFx8crLS1Nhw8fVlxcnNW3uLhYDz30kIYPH37WOBEREdbPvz1WABWLmR0AFSoqKko5OTnKycmx2rZt26aCggI1bdr0gvs6HA61bdtWEydO1ObNm1W5cmUtXrxYISEhCgsL0/fff6+GDRs6LZGRkU5jrF271vr55MmT2rhxo6KioqzaMjMzZX71POQvv/xS1atXV926dSVJ7du315EjRzR16lQr2JwJO2lpaYqPj7f2vfnmm7Vt27azamrYsKEqV65cvjcQwCVjZgdAhUpISFCzZs3Ut29fTZs2TSdPntSjjz6quLi4C17OycrK0qpVq3T77berdu3aysrK0sGDB62gMnHiRA0fPlyBgYHq3LmzSkpKtGHDBh0+fFhJSUnWOKmpqWrUqJGioqI0depUHT58WAMGDJAkPfroo5o2bZqGDRumoUOHKjs7WxMmTFBSUpK8vE7/W/Caa65R8+bNtXDhQr3yyiuSpA4dOqh3794qLS11mtkZM2aMbrnlFg0dOlSDBg1S1apVtW3bNq1cudLaF8DlR9gBUKEcDoeWLl2qYcOGqUOHDvLy8lLnzp01Y8aMC+4XEBCgjIwMTZs2TUVFRapXr55eeukldenSRdLpj3xXqVJFL7zwgkaNGqWqVauqWbNmZ32T8+TJkzV58mRt2bJFDRs21H/+8x/VrFlTknTttdfqk08+0ahRoxQdHa3g4GANHDhQTz31lNMYcXFx2rJlizWLExwcrKZNmyovL0+NGze2+jVv3lzp6en6+9//rvbt28sYowYNGujuu+++xHcRwKVwmF/P3wKATezdu1eRkZHavHmzWrRo4e5yALgR9+wAAABbI+wAAABb4zIWAACwNWZ2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArf1/xpSqHLW/7WUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of each bin:\n",
    "\n",
    "pyplot.bar(group_names, df[\"horsepower-binned\"].value_counts())\n",
    "plt.pyplot.xlabel(\"horsepower\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5be51a30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:23.700704Z",
     "iopub.status.busy": "2024-01-13T07:58:23.700160Z",
     "iopub.status.idle": "2024-01-13T07:58:23.927332Z",
     "shell.execute_reply": "2024-01-13T07:58:23.926180Z"
    },
    "papermill": {
     "duration": 0.246532,
     "end_time": "2024-01-13T07:58:23.929718",
     "exception": false,
     "start_time": "2024-01-13T07:58:23.683186",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horsepower bins')"
      ]
     },
     "execution_count": 41,
     "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": [
    "# Draw historgram of attribute \"horsepower\" with bins = 3:\n",
    "\n",
    "plt.pyplot.hist(df[\"horsepower\"], bins = 3)\n",
    "plt.pyplot.xlabel(\"horsepower\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"horsepower bins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b088b58f",
   "metadata": {
    "papermill": {
     "duration": 0.014794,
     "end_time": "2024-01-13T07:58:23.960788",
     "exception": false,
     "start_time": "2024-01-13T07:58:23.945994",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 8. Indicator Variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3240b9d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:23.995277Z",
     "iopub.status.busy": "2024-01-13T07:58:23.994488Z",
     "iopub.status.idle": "2024-01-13T07:58:24.002372Z",
     "shell.execute_reply": "2024-01-13T07:58:24.000914Z"
    },
    "papermill": {
     "duration": 0.028241,
     "end_time": "2024-01-13T07:58:24.004824",
     "exception": false,
     "start_time": "2024-01-13T07:58:23.976583",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Column \"fuel-type\" has two unique values: \"gas\" or \"diesel\". Convert \"fuel-type\" to indicator variables and assign it to data \"dummy_variable_1\":\n",
    "\n",
    "dummy_variable_1 = pd.get_dummies(df[\"fuel-type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "98242211",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:24.038227Z",
     "iopub.status.busy": "2024-01-13T07:58:24.037175Z",
     "iopub.status.idle": "2024-01-13T07:58:24.042196Z",
     "shell.execute_reply": "2024-01-13T07:58:24.041266Z"
    },
    "papermill": {
     "duration": 0.022986,
     "end_time": "2024-01-13T07:58:24.044053",
     "exception": false,
     "start_time": "2024-01-13T07:58:24.021067",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Change the column names:\n",
    "\n",
    "dummy_variable_1.rename(columns={'gas':'fuel-type-gas', 'diesel':'fuel-type-diesel'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f7ed253c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:24.076670Z",
     "iopub.status.busy": "2024-01-13T07:58:24.075463Z",
     "iopub.status.idle": "2024-01-13T07:58:24.083790Z",
     "shell.execute_reply": "2024-01-13T07:58:24.082897Z"
    },
    "papermill": {
     "duration": 0.025982,
     "end_time": "2024-01-13T07:58:24.085439",
     "exception": false,
     "start_time": "2024-01-13T07:58:24.059457",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "<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>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   fuel-type-diesel  fuel-type-gas\n",
       "0             False           True\n",
       "1             False           True\n",
       "2             False           True\n",
       "3             False           True\n",
       "4             False           True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "dummy_variable_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bb78eddd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:24.117088Z",
     "iopub.status.busy": "2024-01-13T07:58:24.116414Z",
     "iopub.status.idle": "2024-01-13T07:58:24.123033Z",
     "shell.execute_reply": "2024-01-13T07:58:24.121809Z"
    },
    "papermill": {
     "duration": 0.024426,
     "end_time": "2024-01-13T07:58:24.125028",
     "exception": false,
     "start_time": "2024-01-13T07:58:24.100602",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Merge data \"df\" and \"dummy_variable_1\":\n",
    "\n",
    "df = pd.concat([df, dummy_variable_1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8315e991",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:24.157250Z",
     "iopub.status.busy": "2024-01-13T07:58:24.156266Z",
     "iopub.status.idle": "2024-01-13T07:58:24.164438Z",
     "shell.execute_reply": "2024-01-13T07:58:24.163141Z"
    },
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     "start_time": "2024-01-13T07:58:24.140042",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Drop original column \"fuel-type\" from \"df\":\n",
    "\n",
    "df.drop(\"fuel-type\", axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "07071360",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:24.197714Z",
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     "status": "completed"
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    {
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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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>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>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",
       "      <th>highway-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>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",
       "      <td>8.703704</td>\n",
       "      <td>Low</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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>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",
       "      <td>8.703704</td>\n",
       "      <td>Low</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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>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",
       "      <td>9.038462</td>\n",
       "      <td>Medium</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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>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",
       "      <td>7.833333</td>\n",
       "      <td>Low</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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>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",
       "      <td>10.681818</td>\n",
       "      <td>Low</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 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",
       "   horsepower  peak-rpm  city-mpg highway-mpg    price  city-L/100km  \\\n",
       "0         111    5000.0        21          27  13495.0     11.190476   \n",
       "1         111    5000.0        21          27  16500.0     11.190476   \n",
       "2         154    5000.0        19          26  16500.0     12.368421   \n",
       "3         102    5500.0        24          30  13950.0      9.791667   \n",
       "4         115    5500.0        18          22  17450.0     13.055556   \n",
       "\n",
       "  highway-L/100km  horsepower-binned  fuel-type-diesel  fuel-type-gas  \n",
       "0        8.703704                Low             False           True  \n",
       "1        8.703704                Low             False           True  \n",
       "2        9.038462             Medium             False           True  \n",
       "3        7.833333                Low             False           True  \n",
       "4       10.681818                Low             False           True  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "be948527",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:24.250967Z",
     "iopub.status.busy": "2024-01-13T07:58:24.250615Z",
     "iopub.status.idle": "2024-01-13T07:58:24.255942Z",
     "shell.execute_reply": "2024-01-13T07:58:24.254824Z"
    },
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     "end_time": "2024-01-13T07:58:24.257650",
     "exception": false,
     "start_time": "2024-01-13T07:58:24.234396",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get indicator variables of \"aspiration\" and assign it to data frame \"dummy_variable_2\":\n",
    "\n",
    "dummy_variable_2 = pd.get_dummies(df['aspiration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f0bb2d4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:24.288962Z",
     "iopub.status.busy": "2024-01-13T07:58:24.288563Z",
     "iopub.status.idle": "2024-01-13T07:58:24.293639Z",
     "shell.execute_reply": "2024-01-13T07:58:24.292777Z"
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     "start_time": "2024-01-13T07:58:24.272683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Change column names for clarity:\n",
    "\n",
    "dummy_variable_2.rename(columns={'std':'aspiration-std', 'turbo': 'aspiration-turbo'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f660f8ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:24.328739Z",
     "iopub.status.busy": "2024-01-13T07:58:24.328371Z",
     "iopub.status.idle": "2024-01-13T07:58:24.336185Z",
     "shell.execute_reply": "2024-01-13T07:58:24.335557Z"
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     "status": "completed"
    },
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   },
   "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>aspiration-std</th>\n",
       "      <th>aspiration-turbo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aspiration-std  aspiration-turbo\n",
       "0            True             False\n",
       "1            True             False\n",
       "2            True             False\n",
       "3            True             False\n",
       "4            True             False"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the result:\n",
    "\n",
    "dummy_variable_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fd7b8ce3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:24.369912Z",
     "iopub.status.busy": "2024-01-13T07:58:24.369515Z",
     "iopub.status.idle": "2024-01-13T07:58:24.374837Z",
     "shell.execute_reply": "2024-01-13T07:58:24.373955Z"
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     "start_time": "2024-01-13T07:58:24.353174",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Merge the new dataframe to the original dataframe:\n",
    "\n",
    "df = pd.concat([df, dummy_variable_2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b89b8e0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-13T07:58:24.411561Z",
     "iopub.status.busy": "2024-01-13T07:58:24.411188Z",
     "iopub.status.idle": "2024-01-13T07:58:24.418625Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Drop original column \"aspiration\" from \"df\":\n",
    "\n",
    "df.drop('aspiration', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b7b5ad4a",
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     "exception": false,
     "start_time": "2024-01-13T07:58:24.436470",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Save the file:\n",
    "\n",
    "df.to_csv('clean_df.csv')"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
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     "sourceType": "datasetVersion"
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   ],
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   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
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   "parameters": {},
   "start_time": "2024-01-13T07:58:18.510437",
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