Deep_Learning_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVR\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas.__version__ 1.0.3\n",
      "numpy.__version__ 1.19.2\n",
      "sklearn.__version__ 0.23.2\n",
      "joblib.__version__ 0.17.0\n"
     ]
    }
   ],
   "source": [
    "print(\"pandas.__version__\", pd.__version__, flush=True)\n",
    "print(\"numpy.__version__\", np.__version__, flush=True)\n",
    "print(\"sklearn.__version__\", sklearn.__version__, flush=True)\n",
    "print(\"joblib.__version__\", joblib.__version__, flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_file_name = \"Used_Car_Price_Pipeline_SVR.pkl\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data and Select Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "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>id</th>\n",
       "      <th>region</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>title_status</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>posting_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7316356412</td>\n",
       "      <td>auburn</td>\n",
       "      <td>15000</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f-150 xlt</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>rwd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>truck</td>\n",
       "      <td>black</td>\n",
       "      <td>al</td>\n",
       "      <td>32.592000</td>\n",
       "      <td>-85.518900</td>\n",
       "      <td>2021-05-03T14:02:03-0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7314560853</td>\n",
       "      <td>auburn</td>\n",
       "      <td>19900</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f250 super duty</td>\n",
       "      <td>good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>diesel</td>\n",
       "      <td>88000.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>pickup</td>\n",
       "      <td>blue</td>\n",
       "      <td>al</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "      <td>2021-04-29T17:19:18-0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7313406529</td>\n",
       "      <td>auburn</td>\n",
       "      <td>14000</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>honda</td>\n",
       "      <td>odyssey</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>fwd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>mini-van</td>\n",
       "      <td>silver</td>\n",
       "      <td>al</td>\n",
       "      <td>32.628739</td>\n",
       "      <td>-85.461820</td>\n",
       "      <td>2021-04-27T12:20:01-0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7312847466</td>\n",
       "      <td>auburn</td>\n",
       "      <td>22500</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f450</td>\n",
       "      <td>good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>diesel</td>\n",
       "      <td>144700.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>manual</td>\n",
       "      <td>rwd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>truck</td>\n",
       "      <td>white</td>\n",
       "      <td>al</td>\n",
       "      <td>32.630400</td>\n",
       "      <td>-85.401600</td>\n",
       "      <td>2021-04-26T11:15:36-0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7312144944</td>\n",
       "      <td>auburn</td>\n",
       "      <td>15000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>dodge</td>\n",
       "      <td>charger rt 4dr sedan</td>\n",
       "      <td>excellent</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>rebuilt</td>\n",
       "      <td>automatic</td>\n",
       "      <td>rwd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>sedan</td>\n",
       "      <td>grey</td>\n",
       "      <td>al</td>\n",
       "      <td>32.822400</td>\n",
       "      <td>-85.770400</td>\n",
       "      <td>2021-04-24T18:39:59-0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71901</th>\n",
       "      <td>7303112347</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>15999</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>cruze, lt</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>36465.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>fwd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>sedan</td>\n",
       "      <td>black</td>\n",
       "      <td>wy</td>\n",
       "      <td>41.138284</td>\n",
       "      <td>-104.784799</td>\n",
       "      <td>2021-04-07T09:03:17-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71902</th>\n",
       "      <td>7302963673</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>18000</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>silverado 1500 lt 4x4</td>\n",
       "      <td>excellent</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>lien</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>truck</td>\n",
       "      <td>blue</td>\n",
       "      <td>wy</td>\n",
       "      <td>43.452800</td>\n",
       "      <td>-110.739300</td>\n",
       "      <td>2021-04-06T21:04:03-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71903</th>\n",
       "      <td>7302963273</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>18000</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>jeep</td>\n",
       "      <td>gand wagoneer</td>\n",
       "      <td>good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>114400.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>SUV</td>\n",
       "      <td>black</td>\n",
       "      <td>wy</td>\n",
       "      <td>43.452800</td>\n",
       "      <td>-110.739300</td>\n",
       "      <td>2021-04-06T21:02:26-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71904</th>\n",
       "      <td>7302384818</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>9800</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>300zx coupe with t-tops</td>\n",
       "      <td>like new</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>rwd</td>\n",
       "      <td>sub-compact</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>red</td>\n",
       "      <td>wy</td>\n",
       "      <td>41.143700</td>\n",
       "      <td>-104.796200</td>\n",
       "      <td>2021-04-05T18:10:52-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71905</th>\n",
       "      <td>7302338378</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>6800</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>jaguar</td>\n",
       "      <td>xk8 convertible</td>\n",
       "      <td>good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>69550.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>rwd</td>\n",
       "      <td>compact</td>\n",
       "      <td>convertible</td>\n",
       "      <td>white</td>\n",
       "      <td>wy</td>\n",
       "      <td>43.642930</td>\n",
       "      <td>-108.222976</td>\n",
       "      <td>2021-04-05T16:29:38-0600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71906 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id   region  price    year manufacturer  \\\n",
       "0      7316356412   auburn  15000  2013.0         ford   \n",
       "1      7314560853   auburn  19900  2004.0         ford   \n",
       "2      7313406529   auburn  14000  2012.0        honda   \n",
       "3      7312847466   auburn  22500  2001.0         ford   \n",
       "4      7312144944   auburn  15000  2017.0        dodge   \n",
       "...           ...      ...    ...     ...          ...   \n",
       "71901  7303112347  wyoming  15999  2018.0    chevrolet   \n",
       "71902  7302963673  wyoming  18000  2005.0    chevrolet   \n",
       "71903  7302963273  wyoming  18000  1990.0         jeep   \n",
       "71904  7302384818  wyoming   9800  1985.0       nissan   \n",
       "71905  7302338378  wyoming   6800  1997.0       jaguar   \n",
       "\n",
       "                         model  condition  cylinders    fuel  odometer  \\\n",
       "0                    f-150 xlt  excellent        6.0     gas  128000.0   \n",
       "1              f250 super duty       good        8.0  diesel   88000.0   \n",
       "2                      odyssey  excellent        6.0     gas   95000.0   \n",
       "3                         f450       good        8.0  diesel  144700.0   \n",
       "4         charger rt 4dr sedan  excellent        8.0     gas   90000.0   \n",
       "...                        ...        ...        ...     ...       ...   \n",
       "71901                cruze, lt  excellent        4.0     gas   36465.0   \n",
       "71902    silverado 1500 lt 4x4  excellent        8.0     gas  130000.0   \n",
       "71903            gand wagoneer       good        8.0     gas  114400.0   \n",
       "71904  300zx coupe with t-tops   like new        6.0     gas  115000.0   \n",
       "71905          xk8 convertible       good        8.0     gas   69550.0   \n",
       "\n",
       "      title_status transmission drive         size         type paint_color  \\\n",
       "0            clean    automatic   rwd    full-size        truck       black   \n",
       "1            clean    automatic   4wd    full-size       pickup        blue   \n",
       "2            clean    automatic   fwd    full-size     mini-van      silver   \n",
       "3            clean       manual   rwd    full-size        truck       white   \n",
       "4          rebuilt    automatic   rwd     mid-size        sedan        grey   \n",
       "...            ...          ...   ...          ...          ...         ...   \n",
       "71901        clean    automatic   fwd     mid-size        sedan       black   \n",
       "71902         lien    automatic   4wd    full-size        truck        blue   \n",
       "71903        clean    automatic   4wd    full-size          SUV       black   \n",
       "71904        clean    automatic   rwd  sub-compact    hatchback         red   \n",
       "71905        clean    automatic   rwd      compact  convertible       white   \n",
       "\n",
       "      state        lat        long              posting_date  \n",
       "0        al  32.592000  -85.518900  2021-05-03T14:02:03-0500  \n",
       "1        al  32.547500  -85.468200  2021-04-29T17:19:18-0500  \n",
       "2        al  32.628739  -85.461820  2021-04-27T12:20:01-0500  \n",
       "3        al  32.630400  -85.401600  2021-04-26T11:15:36-0500  \n",
       "4        al  32.822400  -85.770400  2021-04-24T18:39:59-0500  \n",
       "...     ...        ...         ...                       ...  \n",
       "71901    wy  41.138284 -104.784799  2021-04-07T09:03:17-0600  \n",
       "71902    wy  43.452800 -110.739300  2021-04-06T21:04:03-0600  \n",
       "71903    wy  43.452800 -110.739300  2021-04-06T21:02:26-0600  \n",
       "71904    wy  41.143700 -104.796200  2021-04-05T18:10:52-0600  \n",
       "71905    wy  43.642930 -108.222976  2021-04-05T16:29:38-0600  \n",
       "\n",
       "[71906 rows x 20 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the raw data\n",
    "raw_df = pd.read_csv(\"clean-testing-data.csv\") \n",
    "raw_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process Data for Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_df = raw_df.set_index(\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_category_map(df, column, category_map):\n",
    "    if (not pd.api.types.is_numeric_dtype(df[column])):\n",
    "        df[column] = df[column].map(category_map)\n",
    "    print(f\"Column {column}: {category_map}\")\n",
    "    print(df[column].describe())\n",
    "    print(\"\")\n",
    "    return df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column condition: {'new': 1, 'like new': 2, 'excellent': 3, 'good': 4, 'fair': 5, 'salvage': 6}\n",
      "count    71906.000000\n",
      "mean         3.260006\n",
      "std          0.752632\n",
      "min          1.000000\n",
      "25%          3.000000\n",
      "50%          3.000000\n",
      "75%          4.000000\n",
      "max          5.000000\n",
      "Name: condition, dtype: float64\n",
      "\n",
      "Column size: {'compact': 1, 'sub-compact': 2, 'mid-size': 3, 'full-size': 4}\n",
      "count    71906.000000\n",
      "mean         3.296206\n",
      "std          0.990295\n",
      "min          1.000000\n",
      "25%          3.000000\n",
      "50%          4.000000\n",
      "75%          4.000000\n",
      "max          4.000000\n",
      "Name: size, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# convert condition into numbers\n",
    "from mapping_values import vehicle_condition_map\n",
    "training_df = simple_category_map(training_df, \"condition\", vehicle_condition_map)\n",
    "\n",
    "# convert size into numbers\n",
    "from mapping_values import vehicle_size_map\n",
    "training_df = simple_category_map(training_df, \"size\", vehicle_size_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column fwd\n",
      "count    71906.000000\n",
      "mean         0.779671\n",
      "std          0.414472\n",
      "min          0.000000\n",
      "25%          1.000000\n",
      "50%          1.000000\n",
      "75%          1.000000\n",
      "max          1.000000\n",
      "Name: fwd, dtype: float64\n",
      "\n",
      "Column rwd\n",
      "count    71906.000000\n",
      "mean         0.633911\n",
      "std          0.481738\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          1.000000\n",
      "75%          1.000000\n",
      "max          1.000000\n",
      "Name: rwd, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# convert drive into category\n",
    "if (\"fwd\" not in training_df.columns or \"rwd\" not in training_df.columns):      \n",
    "    fwd = (training_df[\"drive\"] == \"fwd\") | (training_df[\"drive\"] == \"4wd\")\n",
    "    rwd = (training_df[\"drive\"] == \"rwd\") | (training_df[\"drive\"] == \"4wd\")\n",
    "    training_df[\"fwd\"] = (fwd * 1)\n",
    "    training_df[\"rwd\"] = (rwd * 1)\n",
    "    \n",
    "if (\"drive\" in training_df.columns):    \n",
    "    training_df = training_df.drop([\"drive\"], axis=1)\n",
    "\n",
    "print(f\"Column fwd\")\n",
    "print(training_df[\"fwd\"].describe())\n",
    "print(\"\")\n",
    "print(f\"Column rwd\")\n",
    "print(training_df[\"rwd\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['region', 'price', 'year', 'manufacturer', 'model', 'condition', 'cylinders', 'fuel', 'odometer', 'title_status', 'transmission', 'size', 'type', 'paint_color', 'state', 'lat', 'long', 'posting_date', 'fwd', 'rwd']\n"
     ]
    },
    {
     "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>region</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>title_status</th>\n",
       "      <th>transmission</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>fwd</th>\n",
       "      <th>rwd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7316356412</th>\n",
       "      <td>auburn</td>\n",
       "      <td>15000</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f-150 xlt</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4</td>\n",
       "      <td>truck</td>\n",
       "      <td>black</td>\n",
       "      <td>al</td>\n",
       "      <td>32.592000</td>\n",
       "      <td>-85.518900</td>\n",
       "      <td>2021-05-03T14:02:03-0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7314560853</th>\n",
       "      <td>auburn</td>\n",
       "      <td>19900</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f250 super duty</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>diesel</td>\n",
       "      <td>88000.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4</td>\n",
       "      <td>pickup</td>\n",
       "      <td>blue</td>\n",
       "      <td>al</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "      <td>2021-04-29T17:19:18-0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7313406529</th>\n",
       "      <td>auburn</td>\n",
       "      <td>14000</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>honda</td>\n",
       "      <td>odyssey</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4</td>\n",
       "      <td>mini-van</td>\n",
       "      <td>silver</td>\n",
       "      <td>al</td>\n",
       "      <td>32.628739</td>\n",
       "      <td>-85.461820</td>\n",
       "      <td>2021-04-27T12:20:01-0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7312847466</th>\n",
       "      <td>auburn</td>\n",
       "      <td>22500</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f450</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>diesel</td>\n",
       "      <td>144700.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>manual</td>\n",
       "      <td>4</td>\n",
       "      <td>truck</td>\n",
       "      <td>white</td>\n",
       "      <td>al</td>\n",
       "      <td>32.630400</td>\n",
       "      <td>-85.401600</td>\n",
       "      <td>2021-04-26T11:15:36-0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7312144944</th>\n",
       "      <td>auburn</td>\n",
       "      <td>15000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>dodge</td>\n",
       "      <td>charger rt 4dr sedan</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>rebuilt</td>\n",
       "      <td>automatic</td>\n",
       "      <td>3</td>\n",
       "      <td>sedan</td>\n",
       "      <td>grey</td>\n",
       "      <td>al</td>\n",
       "      <td>32.822400</td>\n",
       "      <td>-85.770400</td>\n",
       "      <td>2021-04-24T18:39:59-0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7303112347</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>15999</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>cruze, lt</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>36465.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>3</td>\n",
       "      <td>sedan</td>\n",
       "      <td>black</td>\n",
       "      <td>wy</td>\n",
       "      <td>41.138284</td>\n",
       "      <td>-104.784799</td>\n",
       "      <td>2021-04-07T09:03:17-0600</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7302963673</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>18000</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>silverado 1500 lt 4x4</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>lien</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4</td>\n",
       "      <td>truck</td>\n",
       "      <td>blue</td>\n",
       "      <td>wy</td>\n",
       "      <td>43.452800</td>\n",
       "      <td>-110.739300</td>\n",
       "      <td>2021-04-06T21:04:03-0600</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7302963273</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>18000</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>jeep</td>\n",
       "      <td>gand wagoneer</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>114400.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4</td>\n",
       "      <td>SUV</td>\n",
       "      <td>black</td>\n",
       "      <td>wy</td>\n",
       "      <td>43.452800</td>\n",
       "      <td>-110.739300</td>\n",
       "      <td>2021-04-06T21:02:26-0600</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7302384818</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>9800</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>300zx coupe with t-tops</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>2</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>red</td>\n",
       "      <td>wy</td>\n",
       "      <td>41.143700</td>\n",
       "      <td>-104.796200</td>\n",
       "      <td>2021-04-05T18:10:52-0600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7302338378</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>6800</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>jaguar</td>\n",
       "      <td>xk8 convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>69550.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>1</td>\n",
       "      <td>convertible</td>\n",
       "      <td>white</td>\n",
       "      <td>wy</td>\n",
       "      <td>43.642930</td>\n",
       "      <td>-108.222976</td>\n",
       "      <td>2021-04-05T16:29:38-0600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71906 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             region  price    year manufacturer                    model  \\\n",
       "id                                                                         \n",
       "7316356412   auburn  15000  2013.0         ford                f-150 xlt   \n",
       "7314560853   auburn  19900  2004.0         ford          f250 super duty   \n",
       "7313406529   auburn  14000  2012.0        honda                  odyssey   \n",
       "7312847466   auburn  22500  2001.0         ford                     f450   \n",
       "7312144944   auburn  15000  2017.0        dodge     charger rt 4dr sedan   \n",
       "...             ...    ...     ...          ...                      ...   \n",
       "7303112347  wyoming  15999  2018.0    chevrolet                cruze, lt   \n",
       "7302963673  wyoming  18000  2005.0    chevrolet    silverado 1500 lt 4x4   \n",
       "7302963273  wyoming  18000  1990.0         jeep            gand wagoneer   \n",
       "7302384818  wyoming   9800  1985.0       nissan  300zx coupe with t-tops   \n",
       "7302338378  wyoming   6800  1997.0       jaguar          xk8 convertible   \n",
       "\n",
       "            condition  cylinders    fuel  odometer title_status transmission  \\\n",
       "id                                                                             \n",
       "7316356412          3        6.0     gas  128000.0        clean    automatic   \n",
       "7314560853          4        8.0  diesel   88000.0        clean    automatic   \n",
       "7313406529          3        6.0     gas   95000.0        clean    automatic   \n",
       "7312847466          4        8.0  diesel  144700.0        clean       manual   \n",
       "7312144944          3        8.0     gas   90000.0      rebuilt    automatic   \n",
       "...               ...        ...     ...       ...          ...          ...   \n",
       "7303112347          3        4.0     gas   36465.0        clean    automatic   \n",
       "7302963673          3        8.0     gas  130000.0         lien    automatic   \n",
       "7302963273          4        8.0     gas  114400.0        clean    automatic   \n",
       "7302384818          2        6.0     gas  115000.0        clean    automatic   \n",
       "7302338378          4        8.0     gas   69550.0        clean    automatic   \n",
       "\n",
       "            size         type paint_color state        lat        long  \\\n",
       "id                                                                       \n",
       "7316356412     4        truck       black    al  32.592000  -85.518900   \n",
       "7314560853     4       pickup        blue    al  32.547500  -85.468200   \n",
       "7313406529     4     mini-van      silver    al  32.628739  -85.461820   \n",
       "7312847466     4        truck       white    al  32.630400  -85.401600   \n",
       "7312144944     3        sedan        grey    al  32.822400  -85.770400   \n",
       "...          ...          ...         ...   ...        ...         ...   \n",
       "7303112347     3        sedan       black    wy  41.138284 -104.784799   \n",
       "7302963673     4        truck        blue    wy  43.452800 -110.739300   \n",
       "7302963273     4          SUV       black    wy  43.452800 -110.739300   \n",
       "7302384818     2    hatchback         red    wy  41.143700 -104.796200   \n",
       "7302338378     1  convertible       white    wy  43.642930 -108.222976   \n",
       "\n",
       "                        posting_date  fwd  rwd  \n",
       "id                                              \n",
       "7316356412  2021-05-03T14:02:03-0500    0    1  \n",
       "7314560853  2021-04-29T17:19:18-0500    1    1  \n",
       "7313406529  2021-04-27T12:20:01-0500    1    0  \n",
       "7312847466  2021-04-26T11:15:36-0500    0    1  \n",
       "7312144944  2021-04-24T18:39:59-0500    0    1  \n",
       "...                              ...  ...  ...  \n",
       "7303112347  2021-04-07T09:03:17-0600    1    0  \n",
       "7302963673  2021-04-06T21:04:03-0600    1    1  \n",
       "7302963273  2021-04-06T21:02:26-0600    1    1  \n",
       "7302384818  2021-04-05T18:10:52-0600    0    1  \n",
       "7302338378  2021-04-05T16:29:38-0600    0    1  \n",
       "\n",
       "[71906 rows x 20 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(list(training_df.columns))\n",
    "training_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select Training Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the training data to better train\n",
    "def scale_data(y, y_train, y_test):\n",
    "    '''\n",
    "        return the scaled training and testing data with the scaler\n",
    "    '''\n",
    "    y_scaler = MinMaxScaler().fit(y)\n",
    "    # apply the scale to training and testing data sets\n",
    "    y_train_scaled = y_scaler.transform(y_train)\n",
    "    y_test_scaled = y_scaler.transform(y_test)\n",
    "    #\n",
    "    return y_train_scaled, y_test_scaled, y_scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle our data to remove any potential bias\n",
    "training_df = sklearn.utils.shuffle(training_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(71906, 9) (71906,)\n"
     ]
    }
   ],
   "source": [
    "# Select the features to train the data\n",
    "target_feature = \"price\"\n",
    "\n",
    "# no nulls for the selected features\n",
    "selected_features = [\n",
    "#     \"id\",\n",
    "#     \"region\",\n",
    "#     \"price\",\n",
    "    \"year\",\n",
    "#     \"manufacturer\",\n",
    "#     \"model\",\n",
    "    \"condition\",\n",
    "#     \"cylinders\",\n",
    "#     \"fuel\",\n",
    "    \"odometer\",\n",
    "#     \"title_status\",\n",
    "    \"transmission\",\n",
    "    \"size\",\n",
    "    \"type\",\n",
    "#     \"paint_color\",\n",
    "    \"state\",\n",
    "#     \"lat\",\n",
    "#     \"long\",\n",
    "#     \"posting_date\",\n",
    "    \"fwd\",\n",
    "    \"rwd\",\n",
    "]\n",
    "\n",
    "# just in case we left in the selected feature\n",
    "if target_feature in selected_features:\n",
    "    selected_features.remove(target_feature)\n",
    "    \n",
    "# just in case we loose a feature\n",
    "for feature in selected_features:\n",
    "    if (feature not in training_df.columns):\n",
    "        selected_features.remove(feature)\n",
    "        print(f\"Feature '{feature}' was dropped because it was missing from training dataframe!\")\n",
    "\n",
    "X = training_df[selected_features]\n",
    "y = training_df[target_feature].values.ravel()\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train\t (53929, 9) (53929,)\n",
      "test\t (17977, 9) (17977,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "print(\"train\\t\", X_train.shape, y_train.shape)\n",
    "print(\"test\\t\", X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select Column Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
       "                  transformers=[('onehotencoder',\n",
       "                                 OneHotEncoder(handle_unknown='ignore'),\n",
       "                                 ['transmission', 'type', 'state']),\n",
       "                                ('minmaxscaler', MinMaxScaler(),\n",
       "                                 ['year', 'condition', 'odometer', 'size'])],\n",
       "                  verbose=True)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoding_columns = [\n",
    "#     \"id\",\n",
    "#     \"region\",\n",
    "#     \"price\",\n",
    "#     \"year\",\n",
    "#     \"manufacturer\",\n",
    "#     \"model\",\n",
    "#     \"condition\",\n",
    "#     \"cylinders\",\n",
    "#     \"fuel\",\n",
    "#     \"odometer\",\n",
    "#     \"title_status\",\n",
    "    \"transmission\",\n",
    "#     \"size\",\n",
    "    \"type\",\n",
    "#     \"paint_color\",\n",
    "    \"state\",\n",
    "#     \"lat\",\n",
    "#     \"long\",\n",
    "#     \"posting_date\",\n",
    "#     \"fwd\",\n",
    "#     \"rwd\",\n",
    "]\n",
    "\n",
    "min_max_columns = [\n",
    "#     \"id\",\n",
    "#     \"region\",\n",
    "#     \"price\",\n",
    "    \"year\",\n",
    "#     \"manufacturer\",\n",
    "#     \"model\",\n",
    "    \"condition\",\n",
    "#     \"cylinders\",\n",
    "#     \"fuel\",\n",
    "    \"odometer\",\n",
    "#     \"title_status\",\n",
    "#     \"transmission\",\n",
    "    \"size\",\n",
    "#     \"type\",\n",
    "#     \"paint_color\",\n",
    "#     \"state\",\n",
    "#     \"lat\",\n",
    "#     \"long\",\n",
    "#     \"posting_date\",\n",
    "#     \"fwd\",\n",
    "#     \"rwd\",\n",
    "]\n",
    "\n",
    "column_transformer = make_column_transformer(\n",
    "    (\n",
    "        OneHotEncoder(\n",
    "            categories=\"auto\",\n",
    "            handle_unknown=\"ignore\"\n",
    "        ), \n",
    "        one_hot_encoding_columns\n",
    "    ),\n",
    "    (MinMaxScaler(), min_max_columns),\n",
    "    remainder=\"passthrough\",\n",
    "    n_jobs=-1,\n",
    "    verbose=True,\n",
    ")\n",
    "column_transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=2, estimator=Ridge(),\n",
       "             param_grid={'alpha': [0.1, 1.0, 10.0],\n",
       "                         'tol': [0.0001, 0.001, 0.01]})"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = GridSearchCV(\n",
    "    linear_model.Ridge(),\n",
    "    param_grid=\n",
    "    {    \n",
    "        'alpha': [0.1, 1.0, 10.0],\n",
    "        'tol': [0.0001, 0.001, 0.01],\n",
    "    },\n",
    "    cv=2,\n",
    "    refit=True, \n",
    "    verbose=0\n",
    ")\n",
    "reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=2, estimator=ElasticNet(),\n",
       "             param_grid={'alpha': [0.1, 1.0, 10.0]})"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = GridSearchCV(\n",
    "    ElasticNet(),\n",
    "    param_grid=\n",
    "    {    \n",
    "        'alpha': [0.1, 1.0, 10.0],\n",
    "#         'l1_ratio': [0.05, 0.5, 5.0],\n",
    "#         'tol': [0.0001, 0.001, 0.01],\n",
    "    },\n",
    "    cv=2,\n",
    "    refit=True, \n",
    "    verbose=0\n",
    ")\n",
    "regr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clfr = Ridge(\n",
    "     alpha=1.0,\n",
    "#     fit_intercept=True,\n",
    "#     normalize=False,\n",
    "#     copy_X=True,\n",
    "#     max_iter=None,\n",
    "#     tol=0.001,\n",
    "#     solver='auto',\n",
    "#     random_state=None,\n",
    ")\n",
    "clfr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(kernel='linear', verbose=True)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SVR(\n",
    "    kernel=\"linear\", \n",
    "#     degree=3,\n",
    "#     gamma='scale',\n",
    "#     coef0=0.0,\n",
    "#     tol=0.001,\n",
    "#     C=1.0,\n",
    "#     epsilon=0.1,\n",
    "#     shrinking=True,\n",
    "#     cache_size=200,\n",
    "    verbose=True,\n",
    "#     max_iter=-1,\n",
    ")\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Deep Learning Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('columntransformer',\n",
      "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
      "                                   transformers=[('onehotencoder',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
      "                                                  ['transmission', 'type',\n",
      "                                                   'state']),\n",
      "                                                 ('minmaxscaler',\n",
      "                                                  MinMaxScaler(),\n",
      "                                                  ['year', 'condition',\n",
      "                                                   'odometer', 'size'])],\n",
      "                                   verbose=True)),\n",
      "                ('gridsearchcv',\n",
      "                 GridSearchCV(cv=2, estimator=Ridge(),\n",
      "                              param_grid={'alpha': [0.1, 1.0, 10.0],\n",
      "                                          'tol': [0.0001, 0.001, 0.01]}))],\n",
      "         verbose=True)\n",
      "[Pipeline] . (step 1 of 2) Processing columntransformer, total=   0.1s\n",
      "[Pipeline] ...... (step 2 of 2) Processing gridsearchcv, total=   0.5s\n",
      "Pipeline(steps=[('columntransformer',\n",
      "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
      "                                   transformers=[('onehotencoder',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
      "                                                  ['transmission', 'type',\n",
      "                                                   'state']),\n",
      "                                                 ('minmaxscaler',\n",
      "                                                  MinMaxScaler(),\n",
      "                                                  ['year', 'condition',\n",
      "                                                   'odometer', 'size'])],\n",
      "                                   verbose=True)),\n",
      "                ('svr', SVR())],\n",
      "         verbose=True)\n",
      "[Pipeline] . (step 1 of 2) Processing columntransformer, total=   0.1s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svr, total= 4.1min\n",
      "Pipeline(steps=[('columntransformer',\n",
      "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
      "                                   transformers=[('onehotencoder',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
      "                                                  ['transmission', 'type',\n",
      "                                                   'state']),\n",
      "                                                 ('minmaxscaler',\n",
      "                                                  MinMaxScaler(),\n",
      "                                                  ['year', 'condition',\n",
      "                                                   'odometer', 'size'])],\n",
      "                                   verbose=True)),\n",
      "                ('ridge', Ridge())],\n",
      "         verbose=True)\n",
      "[Pipeline] . (step 1 of 2) Processing columntransformer, total=   0.1s\n",
      "[Pipeline] ............. (step 2 of 2) Processing ridge, total=   0.0s\n",
      "Pipeline(steps=[('columntransformer',\n",
      "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
      "                                   transformers=[('onehotencoder',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
      "                                                  ['transmission', 'type',\n",
      "                                                   'state']),\n",
      "                                                 ('minmaxscaler',\n",
      "                                                  MinMaxScaler(),\n",
      "                                                  ['year', 'condition',\n",
      "                                                   'odometer', 'size'])],\n",
      "                                   verbose=True)),\n",
      "                ('svr', SVR())],\n",
      "         verbose=True)\n",
      "[Pipeline] . (step 1 of 2) Processing columntransformer, total=   0.1s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svr, total= 4.1min\n",
      "Pipeline(steps=[('columntransformer',\n",
      "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
      "                                   transformers=[('onehotencoder',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
      "                                                  ['transmission', 'type',\n",
      "                                                   'state']),\n",
      "                                                 ('minmaxscaler',\n",
      "                                                  MinMaxScaler(),\n",
      "                                                  ['year', 'condition',\n",
      "                                                   'odometer', 'size'])],\n",
      "                                   verbose=True)),\n",
      "                ('svr', SVR(kernel='linear', verbose=True))],\n",
      "         verbose=True)\n",
      "[Pipeline] . (step 1 of 2) Processing columntransformer, total=   0.1s\n",
      "[LibSVM][Pipeline] ............... (step 2 of 2) Processing svr, total= 5.4min\n"
     ]
    }
   ],
   "source": [
    "model = None\n",
    "score = 0.0\n",
    "\n",
    "for pipeline in [reg, regr, clfr, clf]:\n",
    "    pipeline = make_pipeline(column_transformer, pipeline, verbose=True)\n",
    "    print(pipeline)\n",
    "    pipeline.fit(X_train, y_train)\n",
    "    new_score = pipeline.score(X_test, y_test)\n",
    "    if (new_score > score):\n",
    "        score = new_score\n",
    "        model = pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('columntransformer',\n",
      "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
      "                                   transformers=[('onehotencoder',\n",
      "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
      "                                                  ['transmission', 'type',\n",
      "                                                   'state']),\n",
      "                                                 ('minmaxscaler',\n",
      "                                                  MinMaxScaler(),\n",
      "                                                  ['year', 'condition',\n",
      "                                                   'odometer', 'size'])],\n",
      "                                   verbose=True)),\n",
      "                ('gridsearchcv',\n",
      "                 GridSearchCV(cv=2, estimator=Ridge(),\n",
      "                              param_grid={'alpha': [0.1, 1.0, 10.0],\n",
      "                                          'tol': [0.0001, 0.001, 0.01]}))],\n",
      "         verbose=True)\n",
      "score: 0.5543524411667217\n"
     ]
    }
   ],
   "source": [
    "print(model)\n",
    "print(f\"score: {score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Deep Learning Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Used_Car_Price_Pipeline_SVR.pkl']"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model, pipeline_file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "When you send in the data to predict the value, use these columns in this order!\n",
      "['year', 'condition', 'odometer', 'transmission', 'size', 'type', 'state', 'fwd', 'rwd']\n"
     ]
    }
   ],
   "source": [
    "print(\"When you send in the data to predict the value, use these columns in this order!\")\n",
    "print(list(X.columns.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Loading the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "def submit_model(vehicle_as_dict, sorted_columns, pipeline_file):\n",
    "    # set the first value as a list\n",
    "    for key in vehicle_as_dict:\n",
    "        vehicle_as_dict[key] = [vehicle_as_dict[key]]\n",
    "        break\n",
    "    # create the the dataframe\n",
    "    testing_vehicle = pd.DataFrame.from_dict(vehicle_as_dict, orient='columns')\n",
    "    # sorting columns into order\n",
    "    testing_vehicle = testing_vehicle[sorted_columns]\n",
    "    # load the model from disk\n",
    "    testing_pipeline = joblib.load(pipeline_file)\n",
    "    vehicle_testing_results = testing_pipeline.predict(testing_vehicle)\n",
    "    testing_result = round(vehicle_testing_results[0], 2)\n",
    "    print(\"testing_result: \", testing_result, flush=True)\n",
    "    return testing_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing_result:  -5339.34\n",
      "-5339.34\n"
     ]
    }
   ],
   "source": [
    "print(submit_model(X.iloc[-1].to_dict(), X.columns.values, pipeline_file_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing_result:  14851.56\n",
      "14851.56\n"
     ]
    }
   ],
   "source": [
    "print(submit_model(X.iloc[-2].to_dict(), X.columns.values, pipeline_file_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing_result:  1930.06\n",
      "1930.06\n"
     ]
    }
   ],
   "source": [
    "print(submit_model(X.iloc[-3].to_dict(), X.columns.values, pipeline_file_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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