{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9151a000-1923-408b-bd86-16008dc95f97",
   "metadata": {},
   "source": [
    "[readme](readme.md)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cecbac86-abb3-4f6b-a101-2d9324d96274",
   "metadata": {},
   "source": [
    "# Cleaning\n",
    "\n",
    "Let's get this to something we can work with"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b67cb510-2df0-4ce4-a033-473710fdc749",
   "metadata": {},
   "source": [
    "Load file and set column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3c4bfade-d06d-4887-9eb4-ec7f5bc61625",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:18:59.316785Z",
     "iopub.status.busy": "2022-08-01T00:18:59.315438Z",
     "iopub.status.idle": "2022-08-01T00:19:00.307894Z",
     "shell.execute_reply": "2022-08-01T00:19:00.307130Z",
     "shell.execute_reply.started": "2022-08-01T00:18:59.316695Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('data/auto-mpg.data',header=None,delim_whitespace=True)\n",
    "df.columns = ['mpg','cylinders','displacement','horsepower','weight',\n",
    "              'acceleration','model_year','origin','car_name']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdcec7e3-c65e-4d66-9a10-b500fb940234",
   "metadata": {},
   "source": [
    "Attribute Information:\n",
    "\n",
    "    1. mpg:           continuous\n",
    "    2. cylinders:     multi-valued discrete\n",
    "    3. displacement:  continuous\n",
    "    4. horsepower:    continuous\n",
    "    5. weight:        continuous\n",
    "    6. acceleration:  continuous\n",
    "    7. model year:    multi-valued discrete\n",
    "    8. origin:        multi-valued discrete\n",
    "    9. car name:      string (unique for each instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62bbb6bd-b5b3-4d54-a132-23cd367c4570",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.311568Z",
     "iopub.status.busy": "2022-08-01T00:19:00.310921Z",
     "iopub.status.idle": "2022-08-01T00:19:00.322308Z",
     "shell.execute_reply": "2022-08-01T00:19:00.321851Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.311524Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 398 entries, 0 to 397\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   mpg           398 non-null    float64\n",
      " 1   cylinders     398 non-null    int64  \n",
      " 2   displacement  398 non-null    float64\n",
      " 3   horsepower    398 non-null    object \n",
      " 4   weight        398 non-null    float64\n",
      " 5   acceleration  398 non-null    float64\n",
      " 6   model_year    398 non-null    int64  \n",
      " 7   origin        398 non-null    int64  \n",
      " 8   car_name      398 non-null    object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a4028ed-eda3-4c50-aed0-d9503d41a8e1",
   "metadata": {},
   "source": [
    "No nulls, but why is horsepower not a number?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "58fa2876-4ccb-4ef5-bc16-d25b74efb457",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.323107Z",
     "iopub.status.busy": "2022-08-01T00:19:00.322921Z",
     "iopub.status.idle": "2022-08-01T00:19:00.333299Z",
     "shell.execute_reply": "2022-08-01T00:19:00.332860Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.323092Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['130.0', '165.0', '150.0', '140.0', '198.0', '220.0', '215.0',\n",
       "       '225.0', '190.0', '170.0', '160.0', '95.00', '97.00', '85.00',\n",
       "       '88.00', '46.00', '87.00', '90.00', '113.0', '200.0', '210.0',\n",
       "       '193.0', '?', '100.0', '105.0', '175.0', '153.0', '180.0', '110.0',\n",
       "       '72.00', '86.00', '70.00', '76.00', '65.00', '69.00', '60.00',\n",
       "       '80.00', '54.00', '208.0', '155.0', '112.0', '92.00', '145.0',\n",
       "       '137.0', '158.0', '167.0', '94.00', '107.0', '230.0', '49.00',\n",
       "       '75.00', '91.00', '122.0', '67.00', '83.00', '78.00', '52.00',\n",
       "       '61.00', '93.00', '148.0', '129.0', '96.00', '71.00', '98.00',\n",
       "       '115.0', '53.00', '81.00', '79.00', '120.0', '152.0', '102.0',\n",
       "       '108.0', '68.00', '58.00', '149.0', '89.00', '63.00', '48.00',\n",
       "       '66.00', '139.0', '103.0', '125.0', '133.0', '138.0', '135.0',\n",
       "       '142.0', '77.00', '62.00', '132.0', '84.00', '64.00', '74.00',\n",
       "       '116.0', '82.00'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.horsepower.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2d99ea58-ca51-4461-a127-c6b389b056a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.334093Z",
     "iopub.status.busy": "2022-08-01T00:19:00.333926Z",
     "iopub.status.idle": "2022-08-01T00:19:00.347963Z",
     "shell.execute_reply": "2022-08-01T00:19:00.347305Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.334077Z"
    },
    "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>ford pinto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2875.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>ford maverick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>40.9</td>\n",
       "      <td>4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>?</td>\n",
       "      <td>1835.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>renault lecar deluxe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>23.6</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2905.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang cobra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>34.5</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>renault 18i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151.0</td>\n",
       "      <td>?</td>\n",
       "      <td>3035.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>amc concord dl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  \\\n",
       "32   25.0          4          98.0          ?  2046.0          19.0   \n",
       "126  21.0          6         200.0          ?  2875.0          17.0   \n",
       "330  40.9          4          85.0          ?  1835.0          17.3   \n",
       "336  23.6          4         140.0          ?  2905.0          14.3   \n",
       "354  34.5          4         100.0          ?  2320.0          15.8   \n",
       "374  23.0          4         151.0          ?  3035.0          20.5   \n",
       "\n",
       "     model_year  origin              car_name  \n",
       "32           71       1            ford pinto  \n",
       "126          74       1         ford maverick  \n",
       "330          80       2  renault lecar deluxe  \n",
       "336          80       1    ford mustang cobra  \n",
       "354          81       2           renault 18i  \n",
       "374          82       1        amc concord dl  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.horsepower == '?']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498d069d-b95e-43d6-bd3d-4b707fdd9635",
   "metadata": {},
   "source": [
    "I'll fill in what I can with what I can find online"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e53a2eaf-a8f9-4d7e-bf8b-07a125cf6f06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.348907Z",
     "iopub.status.busy": "2022-08-01T00:19:00.348680Z",
     "iopub.status.idle": "2022-08-01T00:19:00.352582Z",
     "shell.execute_reply": "2022-08-01T00:19:00.351931Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.348891Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 1971 pinto kent I4\n",
    "df.at[32,'horsepower'] = '75.0'\n",
    "# 1974 maverick 200 I6\n",
    "df.at[126,'horsepower'] = '85.0'\n",
    "# 1980 renault lecar deluxe 85ci I4\n",
    "df.at[330,'horsepower'] = '53.5'\n",
    "# 1980 ford mustang cobra\n",
    "# they seem confused between 2 different models\n",
    "# 1981 renault 18i\n",
    "df.at[354,'horsepower'] = '81.5'\n",
    "#1982 AMC concord dl 151\n",
    "df.at[374,'horsepower'] = '90'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d959c5-9628-437f-8f3f-0b4c7002b1f0",
   "metadata": {},
   "source": [
    "We'll ignore the mustang because it's too far off from realistic, it looks like they got confused between two different models.\n",
    "\n",
    "Anyway, drop all '?' horsepower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10400330-e6aa-43e0-910f-f97869c23d0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.353597Z",
     "iopub.status.busy": "2022-08-01T00:19:00.353430Z",
     "iopub.status.idle": "2022-08-01T00:19:00.360958Z",
     "shell.execute_reply": "2022-08-01T00:19:00.359990Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.353582Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(df[df.horsepower == '?'].index,inplace=True)\n",
    "df['horsepower'] = df.horsepower.astype(float)\n",
    "df.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2afc76d-c428-4b81-9882-5ea19ecd04bb",
   "metadata": {},
   "source": [
    "And set to floats, like the rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0fd9a7b-6cdf-4346-8c8d-6c5f36e167f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.365129Z",
     "iopub.status.busy": "2022-08-01T00:19:00.364725Z",
     "iopub.status.idle": "2022-08-01T00:19:00.373554Z",
     "shell.execute_reply": "2022-08-01T00:19:00.372817Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.365100Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 397 entries, 0 to 396\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   mpg           397 non-null    float64\n",
      " 1   cylinders     397 non-null    int64  \n",
      " 2   displacement  397 non-null    float64\n",
      " 3   horsepower    397 non-null    float64\n",
      " 4   weight        397 non-null    float64\n",
      " 5   acceleration  397 non-null    float64\n",
      " 6   model_year    397 non-null    int64  \n",
      " 7   origin        397 non-null    int64  \n",
      " 8   car_name      397 non-null    object \n",
      "dtypes: float64(5), int64(3), object(1)\n",
      "memory usage: 28.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "097c4cec-eb77-46f6-8eef-89eb7c47b425",
   "metadata": {},
   "source": [
    "Looks good"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "151e5f1b-6409-4972-9c79-a26d132eedf5",
   "metadata": {},
   "source": [
    "### Min/Max to check range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "769f33e7-2f2e-46e8-b6dd-8f8fb79d13b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.374606Z",
     "iopub.status.busy": "2022-08-01T00:19:00.374357Z",
     "iopub.status.idle": "2022-08-01T00:19:00.379646Z",
     "shell.execute_reply": "2022-08-01T00:19:00.379055Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.374591Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mpg\n",
      "Min: 9.0  \n",
      "Max: 46.6\n",
      "\n",
      "cylinders\n",
      "Min: 3  \n",
      "Max: 8\n",
      "\n",
      "displacement\n",
      "Min: 68.0  \n",
      "Max: 455.0\n",
      "\n",
      "horsepower\n",
      "Min: 46.0  \n",
      "Max: 230.0\n",
      "\n",
      "weight\n",
      "Min: 1613.0  \n",
      "Max: 5140.0\n",
      "\n",
      "acceleration\n",
      "Min: 8.0  \n",
      "Max: 24.8\n",
      "\n",
      "model_year\n",
      "Min: 70  \n",
      "Max: 82\n",
      "\n",
      "origin\n",
      "Min: 1  \n",
      "Max: 3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns[:-1]:\n",
    "    print(f'''{col}\n",
    "Min: {df[col].min()}  \n",
    "Max: {df[col].max()}\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59641984-e266-4eaa-a90d-af266cb95936",
   "metadata": {},
   "source": [
    "All of this makes sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7bac1a71-53d2-4081-b566-244bccd3a3c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.380599Z",
     "iopub.status.busy": "2022-08-01T00:19:00.380414Z",
     "iopub.status.idle": "2022-08-01T00:19:00.387779Z",
     "shell.execute_reply": "2022-08-01T00:19:00.387145Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.380583Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['datsun pl510', 'amc gremlin', 'chevrolet chevelle malibu',\n",
       "       'chevrolet impala', 'ford galaxie 500', 'plymouth fury iii',\n",
       "       'pontiac catalina', 'amc matador', 'amc hornet', 'ford maverick',\n",
       "       'plymouth duster', 'chevrolet vega', 'ford pinto',\n",
       "       'toyota corolla 1200', 'ford gran torino', 'ford gran torino (sw)',\n",
       "       'amc matador (sw)', 'opel manta', 'toyota corona', 'fiat 128',\n",
       "       'chevrolet nova', 'ford ltd', 'volkswagen dasher', 'datsun 710',\n",
       "       'audi 100ls', 'peugeot 504', 'saab 99le', 'opel 1900',\n",
       "       'dodge colt', 'chevrolet chevelle malibu classic',\n",
       "       'plymouth valiant', 'honda civic', 'volkswagen rabbit',\n",
       "       'toyota corolla', 'toyota mark ii', 'chevrolet caprice classic',\n",
       "       'chevrolet chevette', 'honda civic cvcc', 'chevrolet malibu',\n",
       "       'chevrolet monte carlo landau', 'buick estate wagon (sw)',\n",
       "       'ford country squire (sw)', 'oldsmobile cutlass salon brougham',\n",
       "       'vw rabbit', 'chevrolet citation', 'amc concord', 'dodge aspen',\n",
       "       'datsun 210', 'subaru dl', 'buick skylark', 'plymouth reliant',\n",
       "       'subaru', 'mazda 626', 'buick century', 'pontiac phoenix',\n",
       "       'honda accord'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.car_name.duplicated()].car_name.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b8d5a5-d323-4a70-b951-b2fe4fb1e35f",
   "metadata": {},
   "source": [
    "There are some duplicate car names, honestly I wish there were more. If I had a bunch of data with lots of duplicate car names it'd actually be easier to predict MPG I imagine, I'll say more on this later but there are some big factors that aren't represented here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "87715776-3634-4ca7-bbb4-e04633fe4791",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.389256Z",
     "iopub.status.busy": "2022-08-01T00:19:00.388803Z",
     "iopub.status.idle": "2022-08-01T00:19:00.417680Z",
     "shell.execute_reply": "2022-08-01T00:19:00.417117Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.389227Z"
    },
    "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>397.000000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>397.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.514358</td>\n",
       "      <td>5.458438</td>\n",
       "      <td>193.560453</td>\n",
       "      <td>104.123426</td>\n",
       "      <td>2970.589421</td>\n",
       "      <td>15.571285</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.574307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.825846</td>\n",
       "      <td>1.701577</td>\n",
       "      <td>104.366796</td>\n",
       "      <td>38.396800</td>\n",
       "      <td>847.903955</td>\n",
       "      <td>2.760431</td>\n",
       "      <td>3.696846</td>\n",
       "      <td>0.802549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2223.000000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>2800.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>3609.000000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg   cylinders  displacement  horsepower       weight  \\\n",
       "count  397.000000  397.000000    397.000000  397.000000   397.000000   \n",
       "mean    23.514358    5.458438    193.560453  104.123426  2970.589421   \n",
       "std      7.825846    1.701577    104.366796   38.396800   847.903955   \n",
       "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
       "25%     17.500000    4.000000    104.000000   75.000000  2223.000000   \n",
       "50%     23.000000    4.000000    151.000000   92.000000  2800.000000   \n",
       "75%     29.000000    8.000000    262.000000  125.000000  3609.000000   \n",
       "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
       "\n",
       "       acceleration  model_year      origin  \n",
       "count    397.000000  397.000000  397.000000  \n",
       "mean      15.571285   76.000000    1.574307  \n",
       "std        2.760431    3.696846    0.802549  \n",
       "min        8.000000   70.000000    1.000000  \n",
       "25%       13.800000   73.000000    1.000000  \n",
       "50%       15.500000   76.000000    1.000000  \n",
       "75%       17.200000   79.000000    2.000000  \n",
       "max       24.800000   82.000000    3.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90fe9344-fe47-4503-be59-74d9d38cf1d3",
   "metadata": {},
   "source": [
    "Everything looks proportional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f68b5d6-15c7-4fe0-aa49-a04ab90c4efa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.418521Z",
     "iopub.status.busy": "2022-08-01T00:19:00.418342Z",
     "iopub.status.idle": "2022-08-01T00:19:00.645493Z",
     "shell.execute_reply": "2022-08-01T00:19:00.644587Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.418505Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df.mpg)\n",
    "plt.title('Distribution of MPG')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "506e5033-b3da-4624-bbc5-44f3d159d9e1",
   "metadata": {},
   "source": [
    "Most MPG is around 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c705ec7-9106-4f3c-a65b-5081fe1ded59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.647104Z",
     "iopub.status.busy": "2022-08-01T00:19:00.646709Z",
     "iopub.status.idle": "2022-08-01T00:19:00.732581Z",
     "shell.execute_reply": "2022-08-01T00:19:00.732044Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.647075Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df.mpg)\n",
    "plt.title('Boxplot of MPG')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab5a1cd-177f-4dcb-bd79-e9ad395d51d7",
   "metadata": {},
   "source": [
    "There's one value considered an outlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "655067f4-4c89-4e65-b65a-c1a1e4158535",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.733588Z",
     "iopub.status.busy": "2022-08-01T00:19:00.733352Z",
     "iopub.status.idle": "2022-08-01T00:19:00.742775Z",
     "shell.execute_reply": "2022-08-01T00:19:00.741517Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.733572Z"
    },
    "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>46.6</td>\n",
       "      <td>4</td>\n",
       "      <td>86.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>mazda glc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "322  46.6          4          86.0        65.0  2110.0          17.9   \n",
       "\n",
       "     model_year  origin   car_name  \n",
       "322          80       3  mazda glc  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.mpg > 45]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f89f5906-7b78-4268-933a-ccf02e151b85",
   "metadata": {},
   "source": [
    "I'm going to leave this in because it's a real value. I guess it appears as an outlier because the data set is so small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2720ba7e-272d-4cf7-810c-0a7ec7ad2e58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.744397Z",
     "iopub.status.busy": "2022-08-01T00:19:00.744001Z",
     "iopub.status.idle": "2022-08-01T00:19:00.899639Z",
     "shell.execute_reply": "2022-08-01T00:19:00.899044Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.744367Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df.cylinders)\n",
    "plt.title('Distribution of Cylinders')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d99f70d-728f-4df3-a178-023617e642af",
   "metadata": {},
   "source": [
    "4 Cylinder engines outnumber the others by a lot. It would be nice if we had more info to go off of, particularly if 6 cylinders could be split between inline and V configurations. It's less important for the others cause while inline-8s and V4s exist they're so uncommon in cars of this vintage that we can assume they don't exist. They'll get different fuel economy but not by enough to sway things at the level of accuracy we're at. Inline-6 vs V6 though I think there could be something to see there and it could improve accuracy slightly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8fecb4c7-cdda-4922-8018-3e5160f6ecf1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:00.900607Z",
     "iopub.status.busy": "2022-08-01T00:19:00.900368Z",
     "iopub.status.idle": "2022-08-01T00:19:01.039878Z",
     "shell.execute_reply": "2022-08-01T00:19:01.039298Z",
     "shell.execute_reply.started": "2022-08-01T00:19:00.900591Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df.displacement)\n",
    "plt.title('Distribution of Displacement')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3235dc9c-d7d4-4795-8fd2-1d208be182c7",
   "metadata": {},
   "source": [
    "Most engines in the data are smaller since most of our engines are 4 cylinders. The 3 groups seen here are the split between 4, 6, and 8 cylinders because they all come in generally the same sizes for automotive applications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7b4c6b4-c46c-4f61-9e2a-1243ccc39d0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:01.041100Z",
     "iopub.status.busy": "2022-08-01T00:19:01.040804Z",
     "iopub.status.idle": "2022-08-01T00:19:01.123396Z",
     "shell.execute_reply": "2022-08-01T00:19:01.122989Z",
     "shell.execute_reply.started": "2022-08-01T00:19:01.041075Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df.displacement)\n",
    "plt.title('Boxplot of Displacement')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78eff4eb-407f-4d7b-9c7d-42fde78b4cf4",
   "metadata": {},
   "source": [
    "Again most engines are on the smaller side of the spectrum ranging from around 100ci to around 260ci which is representative of the market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0235d7ee-8882-490c-b209-8d2d2fbacee5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:01.124186Z",
     "iopub.status.busy": "2022-08-01T00:19:01.124021Z",
     "iopub.status.idle": "2022-08-01T00:19:01.256006Z",
     "shell.execute_reply": "2022-08-01T00:19:01.255440Z",
     "shell.execute_reply.started": "2022-08-01T00:19:01.124159Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df.weight)\n",
    "plt.title('Distribution of Weight')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8bbd1af-1c95-4fde-916e-e757f95b2f04",
   "metadata": {},
   "source": [
    "Weight is a major player in fuel economy as it takes more energy to move a heavy car. An inefficient engine moving less weight than a highly efficient engine can end up burning more fuel but generally less weight means higher mpg. Most cars here are around 2000lbs, which makes sense because that's about the weight of a typical commuter/economy car from the 70s. They didn't have as much stuff packed into the interior that we have today so they're lighter. That's why some of these MPG numbers may seem high, but they're real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "762d3940-4f1e-40e1-9fef-67073f33791b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:01.256902Z",
     "iopub.status.busy": "2022-08-01T00:19:01.256753Z",
     "iopub.status.idle": "2022-08-01T00:19:01.325922Z",
     "shell.execute_reply": "2022-08-01T00:19:01.325506Z",
     "shell.execute_reply.started": "2022-08-01T00:19:01.256887Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df.weight)\n",
    "plt.title('Boxplot of Weight')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cfba8d4-ac12-44bb-8917-22ed65f513b2",
   "metadata": {},
   "source": [
    "Nothing out of the ordinary here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "acd58565-0abe-4245-b505-81fa56a0e106",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:01.326907Z",
     "iopub.status.busy": "2022-08-01T00:19:01.326552Z",
     "iopub.status.idle": "2022-08-01T00:19:01.473926Z",
     "shell.execute_reply": "2022-08-01T00:19:01.473358Z",
     "shell.execute_reply.started": "2022-08-01T00:19:01.326892Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df.acceleration)\n",
    "plt.title('Distribution of Acceleration')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dd0e9f92-fe24-4de5-ba3c-e0cdb9767e0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:01.474821Z",
     "iopub.status.busy": "2022-08-01T00:19:01.474614Z",
     "iopub.status.idle": "2022-08-01T00:19:01.558118Z",
     "shell.execute_reply": "2022-08-01T00:19:01.557560Z",
     "shell.execute_reply.started": "2022-08-01T00:19:01.474806Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df.acceleration);\n",
    "plt.title('Boxplot of Acceleration')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1087d45a-dc2f-47f9-be25-f1f28669c2ad",
   "metadata": {},
   "source": [
    "I'm not even sure what acceleration is supposed to be. I assume probably it's 0-60mph time in seconds.. While it's interesting I think it contributes nothing to calculating MPG and looks a bit less than ideal anyway. Everything is close to the same value and there are some outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "291e8653-3d29-4fb5-8105-2fea779cc5ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:01.559014Z",
     "iopub.status.busy": "2022-08-01T00:19:01.558801Z",
     "iopub.status.idle": "2022-08-01T00:19:01.693534Z",
     "shell.execute_reply": "2022-08-01T00:19:01.692969Z",
     "shell.execute_reply.started": "2022-08-01T00:19:01.558999Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=df.model_year);\n",
    "plt.title('Distribution of Model Year')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7addd67-2fba-4539-88e3-347164d3cfd7",
   "metadata": {},
   "source": [
    "Model year interestingly has 3 peaks. I'm not going to use this as a feature, not because of this, but because the data only spans 12 years. Model year could be a great indicator of tech but it won't work in this case because there's just not enough data and no real leaps in technology were had in these years anyway. To make predictions on unseen data if the model year is outside 1970-1982 like in the training set then it'll throw the prediction wildly off"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "042416c1-0e56-4269-96c8-6926392e11e7",
   "metadata": {},
   "source": [
    "### Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b3b42cca-6960-4d06-b7c4-1570f09e9fe0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T00:19:01.695975Z",
     "iopub.status.busy": "2022-08-01T00:19:01.695735Z",
     "iopub.status.idle": "2022-08-01T00:19:01.701574Z",
     "shell.execute_reply": "2022-08-01T00:19:01.700997Z",
     "shell.execute_reply.started": "2022-08-01T00:19:01.695960Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.to_csv('data/clean.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59524851-efe5-4042-8eee-d67038a13a77",
   "metadata": {},
   "source": [
    "[EDA](eda.ipynb)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}