4_mpg_prediction_nn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3738c539",
   "metadata": {},
   "source": [
    "## Import needed libraries and pakages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7c63fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad0456af",
   "metadata": {},
   "source": [
    "## Read data and explore it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5807bca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the data: (398, 9)\n",
      "\n",
      "<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    int64  \n",
      " 5   acceleration  398 non-null    float64\n",
      " 6   model_year    398 non-null    int64  \n",
      " 7   origin        398 non-null    int64  \n",
      " 8   name          398 non-null    object \n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 28.1+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  model_year  \\\n",
       "0  18.0          8         307.0        130    3504          12.0          70   \n",
       "1  15.0          8         350.0        165    3693          11.5          70   \n",
       "2  18.0          8         318.0        150    3436          11.0          70   \n",
       "3  16.0          8         304.0        150    3433          12.0          70   \n",
       "4  17.0          8         302.0        140    3449          10.5          70   \n",
       "\n",
       "   origin                       name  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/mpg.csv\")\n",
    "print(f\"Shape of the data: {data.shape}\\n\")\n",
    "print(data.info())\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223860d9",
   "metadata": {},
   "source": [
    "### Notes: \n",
    "- there is no missing values \n",
    "- the `horsepower` column's data type is `object` and it should be numerical and the `origin` should be categorical value\n",
    "- we have one categorical value which is the `car name`, we will check its unique values and see if we could apply encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5abe9df4",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e3ad775",
   "metadata": {},
   "source": [
    "### Convert data type of the `horsepower` column to numerical value and `origin` to categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bd605c4d",
   "metadata": {},
   "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    392 non-null    float64\n",
      " 4   weight        398 non-null    int64  \n",
      " 5   acceleration  398 non-null    float64\n",
      " 6   model_year    398 non-null    int64  \n",
      " 7   origin        398 non-null    object \n",
      " 8   name          398 non-null    object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data[\"horsepower\"] = pd.to_numeric(data[\"horsepower\"], errors=\"coerce\")\n",
    "data[\"origin\"] = data[\"origin\"].astype(\"object\")\n",
    "\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47def1c6",
   "metadata": {},
   "source": [
    "#### Note: we have missing values in `horsepower` column now, we have to deal with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7046e5e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>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>NaN</td>\n",
       "      <td>2046</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>NaN</td>\n",
       "      <td>2875</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>NaN</td>\n",
       "      <td>1835</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>NaN</td>\n",
       "      <td>2905</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>NaN</td>\n",
       "      <td>2320</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>NaN</td>\n",
       "      <td>3035</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         NaN    2046          19.0   \n",
       "126  21.0          6         200.0         NaN    2875          17.0   \n",
       "330  40.9          4          85.0         NaN    1835          17.3   \n",
       "336  23.6          4         140.0         NaN    2905          14.3   \n",
       "354  34.5          4         100.0         NaN    2320          15.8   \n",
       "374  23.0          4         151.0         NaN    3035          20.5   \n",
       "\n",
       "     model_year origin                  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": [
    "data[pd.isnull(data).any(axis=1) == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "722f2a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([130., 165., 150., 140., 198., 220., 215., 225., 190., 170., 160.,\n",
       "        95.,  97.,  85.,  88.,  46.,  87.,  90., 113., 200., 210., 193.,\n",
       "        nan, 100., 105., 175., 153., 180., 110.,  72.,  86.,  70.,  76.,\n",
       "        65.,  69.,  60.,  80.,  54., 208., 155., 112.,  92., 145., 137.,\n",
       "       158., 167.,  94., 107., 230.,  49.,  75.,  91., 122.,  67.,  83.,\n",
       "        78.,  52.,  61.,  93., 148., 129.,  96.,  71.,  98., 115.,  53.,\n",
       "        81.,  79., 120., 152., 102., 108.,  68.,  58., 149.,  89.,  63.,\n",
       "        48.,  66., 139., 103., 125., 133., 138., 135., 142.,  77.,  62.,\n",
       "       132.,  84.,  64.,  74., 116.,  82.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"horsepower\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29dc71fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c2aa760310>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/y+WS1+uV1+sNTEEAAFjGqs+oAQCAP4IaAACLEdQAAFiMoAYAwGIdCupzzz1Xu3fvbte+Z88enXvuuSddFAAAOKhDQb1z504dOHCgXXtzc7O++eabky4KAAAcdEJ/nvXqq6/6fn7zzTcVFhbme3zgwAG99dZbGjJkSKcVBwDA6e6Egvr666+XdPDvm2fPnu3X17t3bw0ZMkSPPvpopxUHAMDp7oSCuq2tTZIUGxur4uJihYeHd0lRAADgoA59M1lZWVln1wEAAA6jw18h+tZbb+mtt95SdXW170z7kGefffakCwMAAB0M6uXLl+v+++/XhAkTFBkZKZfL1dl1AQAAdTCon3rqKa1Zs0a33HJLZ9cDAAB+pEN/R93S0qLExMTOrgUAAPxEh4J63rx5ysvL6+xaAADAT3Tore8ffvhBq1at0rp16zRmzBj17t3brz8rK6tTigMA4HTXoaD+5JNPNG7cOEnSp59+6tfHhWUAAHSeDgX1O++809l1AACAw+A2lwAAWKxDZ9SXXnrpUd/ifvvttztcEAAA+JcOBfWhz6cPaW1t1datW/Xpp5+2u1kHAADouA4F9WOPPXbYdq/Xq717955UQQAA4F869TPqWbNm8T3fAAB0ok4N6g8++EBnnHFGZz4lAACntQ699T1t2jS/x8YYVVZWauPGjbrvvvs6pTAAANDBoA4LC/N73KNHDw0bNkz333+/UlJSOqUwAADQwaBevXp1Z9cBAAAOo0NBfcimTZtUWloql8ulkSNHavz48Z1VFwAAUAeDurq6WtOnT9f69et15plnyhijuro6XXrppVq7dq0GDBjQ2XUCAHBa6tBV33fddZfq6+tVUlKi77//XrW1tfr0009VX1+vhQsXdnaNAACctjp0Rv3GG29o3bp1GjFihK9t5MiReuKJJ7iYDACATtShoG5ra2t3D2pJ6t27t9ra2k66KAD2Ki8vV01NTUDGCg8PV0xMTEDGAmzVoaC+7LLLdPfdd+svf/mLoqKiJEnffPONfv3rX+vyyy/v1AIB2KO8vFzDh49QU1NjQMYLCgrW55+XEtY4rXUoqHNycjR16lQNGTJE0dHRcrlcKi8v1+jRo/X88893do0ALFFTU6OmpkYlzF2m0MghXTpWfeVOffjsctXU1BDUOK11KKijo6O1efNmFRQU6PPPP5cxRiNHjtQVV1zR2fUBsFBo5BD1jxnmdBnAaeGErvp+++23NXLkSNXX10uSJk+erLvuuksLFy7UxIkTNWrUKL333ntdUigAAKejEwrq7Oxs3XbbbQoNDW3XFxYWpvnz5ysrK6vTigMA4HR3QkH98ccf6xe/+MUR+1NSUrRp06aTLgoAABx0QkH97bffHvbPsg7p1auXvvvuuw4VkpmZKZfLpbS0NF+bMUZer1dRUVEKCgpScnKySkpKOvT8AAB0RycU1Oecc462bdt2xP5PPvlEkZGRJ1xEcXGxVq1apTFjxvi1r1ixQllZWcrJyVFxcbE8Ho8mT56shoaGEx4DAIDu6ISC+qqrrtIf/vAH/fDDD+36mpqatGzZMl1zzTUnVMDevXs1c+ZMPf300zrrrLN87cYYZWdna+nSpZo2bZri4+OVm5urxsZG5eXlHfH5mpubVV9f77cAANBdnVBQ33vvvfr+++81dOhQrVixQq+88opeffVVPfzwwxo2bJi+//57LV269IQKWLBgga6++up2f9pVVlamqqoqv68kdbvdSkpKUlFR0RGfLzMzU2FhYb4lOjr6hOoBAMAmJ/R31BERESoqKtIdd9yh9PR0GWMkSS6XS1deeaWefPJJRUREHPfzrV27Vps3b1ZxcXG7vqqqKt+YP61h165dR3zO9PR0LVq0yPe4vr6esAYAdFsn/IUngwcP1uuvv67a2lp9+eWXMsYoLi7O723r41FRUaG7775b+fn5OuOMM464nsvl8ntsjGnX9mNut1tut/uEagEAwFYd+mYySTrrrLM0ceLEDg+8adMmVVdX68ILL/S1HThwQO+++65ycnK0fft2SQfPrH98gVp1dfUJnbUDANCddeh+1J3h8ssv17Zt27R161bfMmHCBM2cOVNbt27VueeeK4/Ho4KCAt82LS0tKiwsVGJiolNlAwAQUB0+oz5ZISEhio+P92vr27evzj77bF97WlqaMjIyFBcXp7i4OGVkZCg4OFgzZsxwomQAAALOsaA+HosXL1ZTU5NSU1NVW1urhIQE5efnKyQkxOnSAAAICKuCev369X6PXS6XvF6vvF6vI/UAAOA0xz6jBgAAx0ZQAwBgMYIaAACLEdQAAFiMoAYAwGIENQAAFiOoAQCwGEENAIDFCGoAACxGUAMAYDGCGgAAixHUAABYjKAGAMBiBDUAABYjqAEAsBhBDQCAxQhqAAAsRlADAGAxghoAAIsR1AAAWIygBgDAYgQ1AAAWI6gBALAYQQ0AgMUIagAALEZQAwBgMYIaAACLEdQAAFiMoAYAwGIENQAAFiOoAQCwGEENAIDFCGoAACxGUAMAYDGCGgAAixHUAABYjKAGAMBijgb1ypUrNWbMGIWGhio0NFSTJk3S3//+d1+/MUZer1dRUVEKCgpScnKySkpKHKwYAIDAcjSoBw0apIceekgbN27Uxo0bddlll2nq1Km+MF6xYoWysrKUk5Oj4uJieTweTZ48WQ0NDU6WDQBAwDga1Ndee62uuuoqDR06VEOHDtWDDz6ofv36acOGDTLGKDs7W0uXLtW0adMUHx+v3NxcNTY2Ki8vz8myAQAIGGs+oz5w4IDWrl2rffv2adKkSSorK1NVVZVSUlJ867jdbiUlJamoqOiIz9Pc3Kz6+nq/BQCA7srxoN62bZv69esnt9ut22+/XS+99JJGjhypqqoqSVJERITf+hEREb6+w8nMzFRYWJhviY6O7tL6AQDoSo4H9bBhw7R161Zt2LBBd9xxh2bPnq3PPvvM1+9yufzWN8a0a/ux9PR01dXV+ZaKioouqx0AgK7Wy+kC+vTpo/PPP1+SNGHCBBUXF+tPf/qTlixZIkmqqqpSZGSkb/3q6up2Z9k/5na75Xa7u7ZoAAACxPEz6p8yxqi5uVmxsbHyeDwqKCjw9bW0tKiwsFCJiYkOVggAQOA4ekb9+9//XlOmTFF0dLQaGhq0du1arV+/Xm+88YZcLpfS0tKUkZGhuLg4xcXFKSMjQ8HBwZoxY4aTZQMAEDCOBvW3336rW265RZWVlQoLC9OYMWP0xhtvaPLkyZKkxYsXq6mpSampqaqtrVVCQoLy8/MVEhLiZNkAAASMo0H9zDPPHLXf5XLJ6/XK6/UGpiAAACxj3WfUAADgXwhqAAAsRlADAGAxghoAAIsR1AAAWIygBgDAYgQ1AAAWI6gBALAYQQ0AgMUIagAALEZQAwBgMYIaAACLEdQAAFiMoAYAwGIENQAAFiOoAQCwGEENAIDFCGoAACxGUAMAYDGCGgAAixHUAABYjKAGAMBiBDUAABYjqAEAsBhBDQCAxQhqAAAsRlADAGAxghoAAIsR1AAAWIygBgDAYgQ1AAAWI6gBALAYQQ0AgMUIagAALEZQAwBgMYIaAACLEdQAAFjM0aDOzMzUxIkTFRISooEDB+r666/X9u3b/dYxxsjr9SoqKkpBQUFKTk5WSUmJQxUDABBYjgZ1YWGhFixYoA0bNqigoED79+9XSkqK9u3b51tnxYoVysrKUk5OjoqLi+XxeDR58mQ1NDQ4WDkAAIHRy8nB33jjDb/Hq1ev1sCBA7Vp0yZdcsklMsYoOztbS5cu1bRp0yRJubm5ioiIUF5enubPn+9E2QAABIxVn1HX1dVJkvr37y9JKisrU1VVlVJSUnzruN1uJSUlqaio6LDP0dzcrPr6er8FAIDuypqgNsZo0aJFuvjiixUfHy9JqqqqkiRFRET4rRsREeHr+6nMzEyFhYX5lujo6K4tHACALmRNUN9555365JNP9Je//KVdn8vl8ntsjGnXdkh6errq6up8S0VFRZfUCwBAIDj6GfUhd911l1599VW9++67GjRokK/d4/FIOnhmHRkZ6Wuvrq5ud5Z9iNvtltvt7tqCAQAIEEfPqI0xuvPOO/Xiiy/q7bffVmxsrF9/bGysPB6PCgoKfG0tLS0qLCxUYmJioMsFACDgHD2jXrBggfLy8vTKK68oJCTE97lzWFiYgoKC5HK5lJaWpoyMDMXFxSkuLk4ZGRkKDg7WjBkznCwdAICAcDSoV65cKUlKTk72a1+9erXmzJkjSVq8eLGampqUmpqq2tpaJSQkKD8/XyEhIQGuFgCAwHM0qI0xx1zH5XLJ6/XK6/V2fUEAAFjGmqu+AQBAewQ1AAAWI6gBALAYQQ0AgMUIagAALEZQAwBgMYIaAACLEdQAAFiMoAYAwGIENQAAFiOoAQCwGEENAIDFCGoAACxGUAMAYDGCGgAAixHUAABYrJfTBXQ35eXlqqmpCchYpaWlARkHXSNQ+y88PFwxMTEBGetUFshjW2K/4fgR1CegvLxcw4ePUFNTY0DHbW1uCeh4ODlNdbsluTRr1qyAjBcUFKzPPy/lH/2T4MSxzX7D8SKoT0BNTY2amhqVMHeZQiOHdPl4lds+0KevrtL+/fu7fCx0ntbGBklG42Ys0YDY4V06Vn3lTn347HLV1NTwD/5JCPSxzX7DiSCoOyA0coj6xwzr8nHqK3d2+RjoOv0GxgTkdYLOE6hjGzgRXEwGAIDFOKMGTgGBunCNCxyBwCOogW4s0BeuHcIFjkDgENRANxbIC9ckLnAEnEBQA6eAQF24xgWOQOBxMRkAABYjqAEAsBhBDQCAxQhqAAAsRlADAGAxghoAAIsR1AAAWIygBgDAYgQ1AAAWI6gBALAYQQ0AgMUIagAALEZQAwBgMUeD+t1339W1116rqKgouVwuvfzyy379xhh5vV5FRUUpKChIycnJKikpcaZYAAAc4GhQ79u3T2PHjlVOTs5h+1esWKGsrCzl5OSouLhYHo9HkydPVkNDQ4ArBQDAGY7ej3rKlCmaMmXKYfuMMcrOztbSpUs1bdo0SVJubq4iIiKUl5en+fPnH3a75uZmNTc3+x7X19d3fuEAAASItZ9Rl5WVqaqqSikpKb42t9utpKQkFRUVHXG7zMxMhYWF+Zbo6OhAlAsAQJewNqirqqokSREREX7tERERvr7DSU9PV11dnW+pqKjo0joBAOhKjr71fTxcLpffY2NMu7Yfc7vdcrvdXV0WAAABYe0ZtcfjkaR2Z8/V1dXtzrIBADhVWRvUsbGx8ng8Kigo8LW1tLSosLBQiYmJDlYGAEDgOPrW9969e/Xll1/6HpeVlWnr1q3q37+/YmJilJaWpoyMDMXFxSkuLk4ZGRkKDg7WjBkzHKwaAIDAcTSoN27cqEsvvdT3eNGiRZKk2bNna82aNVq8eLGampqUmpqq2tpaJSQkKD8/XyEhIU6VDABAQDka1MnJyTLGHLHf5XLJ6/XK6/UGrigAACxi7WfUAACgG/x5FoDTW2lp6SkxhpPjhoeHKyYmJiBjofMR1ACs1FS3W5JLs2bNCtiYrc0tARkn0HMLCgrW55+XEtbdFEENwEqtjQ2SjMbNWKIBscO7dKzKbR/o01dXaf/+/V06ziGBnFt95U59+Oxy1dTUENTdFEENwGr9Bsaof8ywLh2jvnJnlz7/kQRibuj+uJgMAACLEdQAAFiMoAYAwGIENQAAFiOoAQCwGEENAIDFCGoAACxGUAMAYDGCGgAAixHUAABYjKAGAMBifNc3AKBTlZeXq6amJiBjNTc3y+12B2QsyZlbhhLUAIBOU15eruHDR6ipqTEwA7pckjGBGUvO3DKUoAYAdJqamho1NTUqYe4yhUYO6dKxDt2eNBC3C5Wcu2UoQQ0A6HShkUMCdnvSU/12oQQ1AJwGSktLT6lxTicENQCcwprqdktyadasWQEdt7W5JaDjncoIagA4hbU2NkgyAfsc99Dnxvv37+/ysU4XBDUAnAYC9Tnuoc+N0Xn4whMAACxGUAMAYDGCGgAAixHUAABYjKAGAMBiBDUAABYjqAEAsBhBDQCAxQhqAAAsRlADAGAxghoAAIsR1AAAWIygBgDAYt0iqJ988knFxsbqjDPO0IUXXqj33nvP6ZIAAAgI64P6hRdeUFpampYuXaotW7bo5z//uaZMmaLy8nKnSwMAoMtZH9RZWVm69dZbNW/ePI0YMULZ2dmKjo7WypUrnS4NAIAu18vpAo6mpaVFmzZt0j333OPXnpKSoqKiosNu09zcrObmZt/juro6SVJ9ff1J17N3715J0ve7tmt/c9NJP9+x1FfukiTVfbNDvXu5TpmxAj0ec+ue452qYwV6PObWieNVHXwnd+/evZ2SKZIUEhIil+sYtRuLffPNN0aS+cc//uHX/uCDD5qhQ4cedptly5YZSSwsLCwsLNYvdXV1x8xCq8+oD/np/zaMMUf8H0h6eroWLVrke9zW1qbvv/9eZ5999rH/19IJ6uvrFR0drYqKCoWGhnb5eF2N+djtVJuPdOrNifnYzen5hISEHHMdq4M6PDxcPXv2VFVVlV97dXW1IiIiDruN2+2W2+32azvzzDO7qsQjCg0NPSVexIcwH7udavORTr05MR+72Twfqy8m69Onjy688EIVFBT4tRcUFCgxMdGhqgAACByrz6gladGiRbrllls0YcIETZo0SatWrVJ5ebluv/12p0sDAKDLWR/UN910k3bv3q37779flZWVio+P1+uvv67Bgwc7Xdphud1uLVu2rN3b790V87HbqTYf6dSbE/OxW3eYj8sYY5wuAgAAHJ7Vn1EDAHC6I6gBALAYQQ0AgMUIagAALEZQd4DX65XL5fJbPB6Pr98YI6/Xq6ioKAUFBSk5OVklJSUOVnx0Q4YMaTcfl8ulBQsWSJLmzJnTru9nP/uZw1X/y7vvvqtrr71WUVFRcrlcevnll/36j2d/NDc366677lJ4eLj69u2r6667Tl9//XUAZ+HvaHNqbW3VkiVLNHr0aPXt21dRUVH61a9+pX/+859+z5GcnNxuv02fPj3AMznoWPvoeF5jNu2jY83ncMeTy+XSI4884lvHpv2TmZmpiRMnKiQkRAMHDtT111+v7du3+63TnY6jY82nux1DBHUHjRo1SpWVlb5l27Ztvr4VK1YoKytLOTk5Ki4ulsfj0eTJk9XQ0OBgxUdWXFzsN5dDXzBzww03+Nb5xS9+4bfO66+/7lS57ezbt09jx45VTk7OYfuPZ3+kpaXppZde0tq1a/X+++9r7969uuaaa3TgwIFATcPP0ebU2NiozZs367777tPmzZv14osv6osvvtB1113Xbt3bbrvNb7/9+c9/DkT57RxrH0nHfo3ZtI+ONZ8fz6OyslLPPvusXC6XfvnLX/qtZ8v+KSws1IIFC7RhwwYVFBRo//79SklJ0b59+3zrdKfj6Fjz6XbH0EneN+O0tGzZMjN27NjD9rW1tRmPx2MeeughX9sPP/xgwsLCzFNPPRWgCk/O3Xffbc477zzT1tZmjDFm9uzZZurUqc4WdZwkmZdeesn3+Hj2x549e0zv3r3N2rVrfet88803pkePHuaNN94IWO1H8tM5Hc5HH31kJJldu3b52pKSkszdd9/dtcV1wOHmc6zXmM376Hj2z9SpU81ll13m12br/jHGmOrqaiPJFBYWGmO6/3H00/kcjs3HEGfUHbRjxw5FRUUpNjZW06dP11dffSVJKisrU1VVlVJSUnzrut1uJSUlHfHWnDZpaWnR888/r7lz5/rdxGT9+vUaOHCghg4dqttuu03V1dUOVnn8jmd/bNq0Sa2trX7rREVFKT4+vlvsM+ng7VxdLle777X/7//+b4WHh2vUqFH67W9/a+27OtLRX2PdeR99++23eu2113Trrbe267N1/xy6PXD//v0ldf/j6KfzOdI6th5D1n8zmY0SEhL03HPPaejQofr222/1wAMPKDExUSUlJb4biPz0piERERHatWuXE+WekJdffll79uzRnDlzfG1TpkzRDTfcoMGDB6usrEz33XefLrvsMm3atMnqb/ORdFz7o6qqSn369NFZZ53Vbp2f3hDGRj/88IPuuecezZgxw++mAjNnzlRsbKw8Ho8+/fRTpaen6+OPP2733fk2ONZrrDvvo9zcXIWEhGjatGl+7bbuH2OMFi1apIsvvljx8fGSuvdxdLj5/JTtxxBB3QFTpkzx/Tx69GhNmjRJ5513nnJzc30XwJzIrTlt8swzz2jKlCmKiorytd10002+n+Pj4zVhwgQNHjxYr732Wrt/fGzVkf3RHfZZa2urpk+frra2Nj355JN+fbfddpvv5/j4eMXFxWnChAnavHmzLrjggkCXelQdfY11h3307LPPaubMmTrjjDP82m3dP3feeac++eQTvf/+++36uuNxdLT5SN3jGOKt707Qt29fjR49Wjt27PBd/X0it+a0xa5du7Ru3TrNmzfvqOtFRkZq8ODB2rFjR4Aq67jj2R8ej0ctLS2qra094jo2am1t1Y033qiysjIVFBQc8xZ9F1xwgXr37t0t9ttPX2PddR+999572r59+zGPKcmO/XPXXXfp1Vdf1TvvvKNBgwb52rvrcXSk+RzSXY4hgroTNDc3q7S0VJGRkb63SX781khLS4sKCwutvzXn6tWrNXDgQF199dVHXW/37t2qqKhQZGRkgCrruOPZHxdeeKF69+7tt05lZaU+/fRTa/fZoX9gduzYoXXr1unss88+5jYlJSVqbW3tFvvtp6+x7riPpIPvUF144YUaO3bsMdd1cv8YY3TnnXfqxRdf1Ntvv63Y2Fi//u52HB1rPlI3O4acuoqtO/vNb35j1q9fb7766iuzYcMGc80115iQkBCzc+dOY4wxDz30kAkLCzMvvvii2bZtm7n55ptNZGSkqa+vd7jyIztw4ICJiYkxS5Ys8WtvaGgwv/nNb0xRUZEpKysz77zzjpk0aZI555xzrJlPQ0OD2bJli9myZYuRZLKyssyWLVt8V28ez/64/fbbzaBBg8y6devM5s2bzWWXXWbGjh1r9u/fb92cWltbzXXXXWcGDRpktm7daiorK31Lc3OzMcaYL7/80ixfvtwUFxebsrIy89prr5nhw4eb8ePHOzKno83neF9jNu2jY73mjDGmrq7OBAcHm5UrV7bb3rb9c8cdd5iwsDCzfv16v9dTY2Ojb53udBwdaz7d7RgiqDvgpptuMpGRkaZ3794mKirKTJs2zZSUlPj629razLJly4zH4zFut9tccsklZtu2bQ5WfGxvvvmmkWS2b9/u197Y2GhSUlLMgAEDTO/evU1MTIyZPXu2KS8vd6jS9t555x0jqd0ye/ZsY8zx7Y+mpiZz5513mv79+5ugoCBzzTXXODrHo82prKzssH2SzDvvvGOMMaa8vNxccsklpn///qZPnz7mvPPOMwsXLjS7d++2bj7H+xqzaR8d6zVnjDF//vOfTVBQkNmzZ0+77W3bP0d6Pa1evdq3Tnc6jo41n+52DHGbSwAALMZn1AAAWIygBgDAYgQ1AAAWI6gBALAYQQ0AgMUIagAALEZQAwBgMYIaAACLEdSAZZKTk5WWluZ0GQAsQVADAGAxgho4xbW2tjpdQsC0tLQ4XQLQ6QhqwEJtbW1avHix+vfvL4/HI6/X6+srLy/X1KlT1a9fP4WGhurGG2/Ut99+6+v3er0aN26cnn32WZ177rlyu90yxuivf/2rRo8eraCgIJ199tm64oortG/fPt92q1ev1ogRI3TGGWdo+PDhevLJJ319O3fulMvl0tq1a5WYmKgzzjhDo0aN0vr16/3qLiws1L/927/J7XYrMjJS99xzj/bv3y9J+p//+R+deeaZamtrkyRt3bpVLpdLv/vd73zbz58/XzfffLPvcVFRkS655BIFBQUpOjpaCxcu9Kt5yJAheuCBBzRnzhyFhYXptttuO7lfPGCjgN8GBMBRJSUlmdDQUOP1es0XX3xhcnNzjcvlMvn5+aatrc2MHz/eXHzxxWbjxo1mw4YN5oILLjBJSUm+7ZctW2b69u1rrrzySrN582bz8ccfm3/+85+mV69eJisry5SVlZlPPvnEPPHEE6ahocEYY8yqVatMZGSk+dvf/ma++uor87e//c3079/frFmzxhjzr7sNDRo0yPz1r381n332mZk3b54JCQkxNTU1xhhjvv76axMcHGxSU1NNaWmpeemll0x4eLhZtmyZMcaYPXv2mB49epiNGzcaY4zJzs424eHhZuLEib7ahw4d6rst5CeffGL69etnHnvsMfPFF1+Yf/zjH2b8+PFmzpw5vvUHDx5sQkNDzSOPPGJ27NhhduzY0WX7BXAKQQ1YJikpyVx88cV+bRMnTjRLliwx+fn5pmfPnn63DiwpKTGSzEcffWSMORjUvXv3NtXV1b51Nm3aZCT57pn+U9HR0SYvL8+v7T//8z/NpEmTjDH/CuqHHnrI19/a2moGDRpkHn74YWOMMb///e/NsGHDTFtbm2+dJ554wvTr188cOHDAGGPMBRdcYP74xz8aY4y5/vrrzYMPPmj69Olj6uvrTWVlpZFkSktLjTHG3HLLLeY//uM//Gp67733TI8ePUxTU5Mx5mBQX3/99Uf9fQLdHW99AxYaM2aM3+PIyEhVV1ertLRU0dHRio6O9vWNHDlSZ555pkpLS31tgwcP1oABA3yPx44dq8svv1yjR4/WDTfcoKefflq1tbWSpO+++04VFRW69dZb1a9fP9/ywAMP6P/+7//86pg0aZLv5169emnChAm+cUtLSzVp0iS5XC7fOhdddJH27t2rr7/+WtLBK9rXr18vY4zee+89TZ06VfHx8Xr//ff1zjvvKCIiQsOHD5ckbdq0SWvWrPGr6corr1RbW5vKysp8Y0yYMKFjv2Sgm+jldAEA2uvdu7ffY5fLpba2Nhlj/ILwkJ+29+3b16+/Z8+eKigoUFFRkfLz8/X4449r6dKl+vDDDxUcHCxJevrpp5WQkNBuu2M5NO7hajP//3b3h9qTk5P1zDPP6OOPP1aPHj00cuRIJSUlqbCwULW1tUpKSvJt29bWpvnz52vhwoXtxoyJiTniXIFTDWfUQDcycuRIlZeXq6Kiwtf22Wefqa6uTiNGjDjqti6XSxdddJGWL1+uLVu2qE+fPnrppZcUERGhc845R1999ZXOP/98vyU2NtbvOTZs2OD7ef/+/dq0aZPvDHjkyJEqKiryhbN08GKwkJAQnXPOOZKkSy65RA0NDcrOzlZSUpJcLpeSkpK0fv16rV+/3i+oL7jgApWUlLSr6fzzz1efPn06/ksEuhnOqIFu5IorrtCYMWM0c+ZMZWdna//+/UpNTVVSUtJR3wL+8MMP9dZbbyklJUUDBw7Uhx9+qO+++84X7l6vVwsXLlRoaKimTJmi5uZmbdy4UbW1tVq0aJHveZ544gnFxcVpxIgReuyxx1RbW6u5c+dKklJTU5Wdna277rpLd955p7Zv365ly5Zp0aJF6tHj4DlBWFiYxo0bp+eff15/+tOfJB0M7xtuuEGtra1KTk72jbVkyRL97Gc/04IFC3Tbbbepb9++Ki0tVUFBgR5//PHO/tUC1uKMGuhGXC6XXn75ZZ111lm65JJLdMUVV+jcc8/VCy+8cNTtQkND9e677+qqq67S0KFDde+99+rRRx/VlClTJEnz5s3Tf/3Xf2nNmjUaPXq0kpKStGbNmnZn1A899JAefvhhjR07Vu+9955eeeUVhYeHS5LOOeccvf766/roo480duxY3X777br11lt17733+j3HpZdeqgMHDvhC+ayzztLIkSM1YMAAv3cFxowZo8LCQu3YsUM///nPNX78eN13332KjIw82V8j0K24zI/fpwKAw9i5c6diY2O1ZcsWjRs3zulygNMKZ9QAAFiMoAYAwGK89Q0AgMU4owYAwGIENQAAFiOoAQCwGEENAIDFCGoAACxGUAMAYDGCGgAAixHUAABY7P8BjluFUame68cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data, x=\"horsepower\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25d2d283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    392.000000\n",
       "mean     104.469388\n",
       "std       38.491160\n",
       "min       46.000000\n",
       "25%       75.000000\n",
       "50%       93.500000\n",
       "75%      126.000000\n",
       "max      230.000000\n",
       "Name: horsepower, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"horsepower\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af5f6614",
   "metadata": {},
   "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    float64\n",
      " 4   weight        398 non-null    int64  \n",
      " 5   acceleration  398 non-null    float64\n",
      " 6   model_year    398 non-null    int64  \n",
      " 7   origin        398 non-null    object \n",
      " 8   name          398 non-null    object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# filling the null values with the median value \n",
    "data[\"horsepower\"].fillna(data[\"horsepower\"].median(), inplace=True)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93f9d0a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.514573</td>\n",
       "      <td>5.454774</td>\n",
       "      <td>193.425879</td>\n",
       "      <td>104.304020</td>\n",
       "      <td>2970.424623</td>\n",
       "      <td>15.568090</td>\n",
       "      <td>76.010050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.815984</td>\n",
       "      <td>1.701004</td>\n",
       "      <td>104.269838</td>\n",
       "      <td>38.222625</td>\n",
       "      <td>846.841774</td>\n",
       "      <td>2.757689</td>\n",
       "      <td>3.697627</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>104.250000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>2223.750000</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</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>3608.000000</td>\n",
       "      <td>17.175000</td>\n",
       "      <td>79.000000</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg   cylinders  displacement  horsepower       weight  \\\n",
       "count  398.000000  398.000000    398.000000  398.000000   398.000000   \n",
       "mean    23.514573    5.454774    193.425879  104.304020  2970.424623   \n",
       "std      7.815984    1.701004    104.269838   38.222625   846.841774   \n",
       "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
       "25%     17.500000    4.000000    104.250000   76.000000  2223.750000   \n",
       "50%     23.000000    4.000000    148.500000   93.500000  2803.500000   \n",
       "75%     29.000000    8.000000    262.000000  125.000000  3608.000000   \n",
       "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
       "\n",
       "       acceleration  model_year  \n",
       "count    398.000000  398.000000  \n",
       "mean      15.568090   76.010050  \n",
       "std        2.757689    3.697627  \n",
       "min        8.000000   70.000000  \n",
       "25%       13.825000   73.000000  \n",
       "50%       15.500000   76.000000  \n",
       "75%       17.175000   79.000000  \n",
       "max       24.800000   82.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91de5f3e",
   "metadata": {},
   "source": [
    "### Dealing with the categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45e2ae55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values of the categorical variables: \n",
      "origin 3\n",
      "name 305\n"
     ]
    }
   ],
   "source": [
    "cat_cols = [col for col in data.columns if data[col].dtype==\"object\"]\n",
    "print(\"Unique values of the categorical variables: \")\n",
    "\n",
    "for col in cat_cols:\n",
    "    print(col, data[col].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a547f0",
   "metadata": {},
   "source": [
    "#### encoding the `object` column using one hot encoding and dropping the `car name` column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef305eb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  0.0  0.0\n",
       "1  1.0  0.0  0.0\n",
       "2  1.0  0.0  0.0\n",
       "3  1.0  0.0  0.0\n",
       "4  1.0  0.0  0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer = make_column_transformer((OneHotEncoder(), [\"origin\"]), remainder=\"passthrough\")\n",
    "\n",
    "transformed = transformer.fit_transform(data[[\"origin\"]])\n",
    "transformed_df = pd.DataFrame(transformed, index=data.index)\n",
    "transformed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f2a7ba56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  model_year\n",
       "0  18.0          8         307.0       130.0    3504          12.0          70\n",
       "1  15.0          8         350.0       165.0    3693          11.5          70\n",
       "2  18.0          8         318.0       150.0    3436          11.0          70\n",
       "3  16.0          8         304.0       150.0    3433          12.0          70\n",
       "4  17.0          8         302.0       140.0    3449          10.5          70"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns=[\"name\", \"origin\"], axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b4c0011",
   "metadata": {},
   "source": [
    "### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ce83410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.090604</td>\n",
       "      <td>0.673118</td>\n",
       "      <td>0.630870</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.503514</td>\n",
       "      <td>1.589958</td>\n",
       "      <td>0.854333</td>\n",
       "      <td>-1.477038</td>\n",
       "      <td>-1.627426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.196232</td>\n",
       "      <td>1.197027</td>\n",
       "      <td>0.550470</td>\n",
       "      <td>-1.658577</td>\n",
       "      <td>-1.627426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.061796</td>\n",
       "      <td>1.197027</td>\n",
       "      <td>0.546923</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.042591</td>\n",
       "      <td>0.935072</td>\n",
       "      <td>0.565841</td>\n",
       "      <td>-1.840117</td>\n",
       "      <td>-1.627426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cylinders  displacement  horsepower    weight  acceleration  model_year\n",
       "0   1.498191      1.090604    0.673118  0.630870     -1.295498   -1.627426\n",
       "1   1.498191      1.503514    1.589958  0.854333     -1.477038   -1.627426\n",
       "2   1.498191      1.196232    1.197027  0.550470     -1.658577   -1.627426\n",
       "3   1.498191      1.061796    1.197027  0.546923     -1.295498   -1.627426\n",
       "4   1.498191      1.042591    0.935072  0.565841     -1.840117   -1.627426"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_cols = [\"cylinders\", \"displacement\", \"horsepower\", \"weight\", \"acceleration\", \"model_year\"]\n",
    "scaler_transformer = make_column_transformer((StandardScaler(), scaled_cols), remainder=\"passthrough\")\n",
    "\n",
    "scaled = scaler_transformer.fit_transform(data[scaled_cols])\n",
    "scaled_df = pd.DataFrame(scaled, index=data.index, columns=scaled_cols)\n",
    "scaled_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "246a1c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([scaled_df, transformed_df], axis=1)\n",
    "y = data.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "991e85e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.090604</td>\n",
       "      <td>0.673118</td>\n",
       "      <td>0.630870</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.503514</td>\n",
       "      <td>1.589958</td>\n",
       "      <td>0.854333</td>\n",
       "      <td>-1.477038</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.196232</td>\n",
       "      <td>1.197027</td>\n",
       "      <td>0.550470</td>\n",
       "      <td>-1.658577</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.061796</td>\n",
       "      <td>1.197027</td>\n",
       "      <td>0.546923</td>\n",
       "      <td>-1.295498</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.498191</td>\n",
       "      <td>1.042591</td>\n",
       "      <td>0.935072</td>\n",
       "      <td>0.565841</td>\n",
       "      <td>-1.840117</td>\n",
       "      <td>-1.627426</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cylinders  displacement  horsepower    weight  acceleration  model_year  \\\n",
       "0   1.498191      1.090604    0.673118  0.630870     -1.295498   -1.627426   \n",
       "1   1.498191      1.503514    1.589958  0.854333     -1.477038   -1.627426   \n",
       "2   1.498191      1.196232    1.197027  0.550470     -1.658577   -1.627426   \n",
       "3   1.498191      1.061796    1.197027  0.546923     -1.295498   -1.627426   \n",
       "4   1.498191      1.042591    0.935072  0.565841     -1.840117   -1.627426   \n",
       "\n",
       "     0    1    2  \n",
       "0  1.0  0.0  0.0  \n",
       "1  1.0  0.0  0.0  \n",
       "2  1.0  0.0  0.0  \n",
       "3  1.0  0.0  0.0  \n",
       "4  1.0  0.0  0.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns = X.columns.astype(str)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f92b55d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    18.0\n",
       "1    15.0\n",
       "2    18.0\n",
       "3    16.0\n",
       "4    17.0\n",
       "Name: mpg, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a7ba41b",
   "metadata": {},
   "source": [
    "### NN model - single layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "06c447ff-8814-4e3b-a652-ed7abc5d9856",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 1)                 10        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10\n",
      "Trainable params: 10\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(1, input_dim=9, activation=\"linear\"))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "558dee0e-f838-4a21-b851-a511d7b7919e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"mse\", optimizer=\"sgd\", metrics=\"mae\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d86dff08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "10/10 [==============================] - 2s 66ms/step - loss: 453.2460 - mae: 20.3435 - val_loss: 332.0717 - val_mae: 17.3979\n",
      "Epoch 2/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 254.2135 - mae: 15.1755 - val_loss: 198.3885 - val_mae: 13.1808\n",
      "Epoch 3/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 149.7546 - mae: 11.3958 - val_loss: 123.8954 - val_mae: 10.0087\n",
      "Epoch 4/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 92.7899 - mae: 8.5745 - val_loss: 81.4048 - val_mae: 7.7220\n",
      "Epoch 5/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 60.7785 - mae: 6.5595 - val_loss: 57.3435 - val_mae: 6.1971\n",
      "Epoch 6/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 42.9833 - mae: 5.2883 - val_loss: 43.0985 - val_mae: 5.1986\n",
      "Epoch 7/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 32.7717 - mae: 4.5574 - val_loss: 34.3592 - val_mae: 4.5596\n",
      "Epoch 8/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 26.6624 - mae: 4.0684 - val_loss: 29.2071 - val_mae: 4.2061\n",
      "Epoch 9/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 23.1486 - mae: 3.8080 - val_loss: 25.6459 - val_mae: 3.9528\n",
      "Epoch 10/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 20.8750 - mae: 3.6076 - val_loss: 23.1476 - val_mae: 3.7928\n",
      "Epoch 11/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 19.3384 - mae: 3.4675 - val_loss: 21.4406 - val_mae: 3.6695\n",
      "Epoch 12/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 18.3667 - mae: 3.3613 - val_loss: 20.2252 - val_mae: 3.5803\n",
      "Epoch 13/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 17.5786 - mae: 3.2884 - val_loss: 19.2136 - val_mae: 3.4951\n",
      "Epoch 14/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 16.9899 - mae: 3.2275 - val_loss: 18.3182 - val_mae: 3.4155\n",
      "Epoch 15/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 16.5452 - mae: 3.1809 - val_loss: 17.6496 - val_mae: 3.3584\n",
      "Epoch 16/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 16.1132 - mae: 3.1319 - val_loss: 17.0575 - val_mae: 3.3039\n",
      "Epoch 17/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 15.7737 - mae: 3.0965 - val_loss: 16.5036 - val_mae: 3.2462\n",
      "Epoch 18/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 15.4630 - mae: 3.0550 - val_loss: 16.0812 - val_mae: 3.2028\n",
      "Epoch 19/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 15.2171 - mae: 3.0251 - val_loss: 15.7058 - val_mae: 3.1667\n",
      "Epoch 20/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 14.9219 - mae: 2.9888 - val_loss: 15.3969 - val_mae: 3.1372\n",
      "Epoch 21/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 14.7443 - mae: 2.9637 - val_loss: 15.0903 - val_mae: 3.1054\n",
      "Epoch 22/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 14.5144 - mae: 2.9358 - val_loss: 14.8412 - val_mae: 3.0814\n",
      "Epoch 23/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 14.2990 - mae: 2.9196 - val_loss: 14.4788 - val_mae: 3.0308\n",
      "Epoch 24/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 14.1185 - mae: 2.8846 - val_loss: 14.2708 - val_mae: 3.0101\n",
      "Epoch 25/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 13.9709 - mae: 2.8538 - val_loss: 14.1307 - val_mae: 3.0032\n",
      "Epoch 26/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 13.8258 - mae: 2.8472 - val_loss: 13.9290 - val_mae: 2.9811\n",
      "Epoch 27/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 13.6434 - mae: 2.8190 - val_loss: 13.7849 - val_mae: 2.9678\n",
      "Epoch 28/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 13.5694 - mae: 2.8183 - val_loss: 13.5487 - val_mae: 2.9318\n",
      "Epoch 29/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 13.4358 - mae: 2.7841 - val_loss: 13.4301 - val_mae: 2.9211\n",
      "Epoch 30/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 13.3035 - mae: 2.7658 - val_loss: 13.3345 - val_mae: 2.9133\n",
      "Epoch 31/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 13.2096 - mae: 2.7573 - val_loss: 13.2367 - val_mae: 2.9042\n",
      "Epoch 32/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 13.0373 - mae: 2.7357 - val_loss: 13.1278 - val_mae: 2.8906\n",
      "Epoch 33/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 12.9533 - mae: 2.7347 - val_loss: 12.9629 - val_mae: 2.8620\n",
      "Epoch 34/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 12.8735 - mae: 2.7141 - val_loss: 12.8803 - val_mae: 2.8521\n",
      "Epoch 35/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 12.8097 - mae: 2.6969 - val_loss: 12.8509 - val_mae: 2.8535\n",
      "Epoch 36/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 12.7032 - mae: 2.6885 - val_loss: 12.8101 - val_mae: 2.8508\n",
      "Epoch 37/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 12.6383 - mae: 2.6917 - val_loss: 12.6782 - val_mae: 2.8273\n",
      "Epoch 38/100\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 12.5657 - mae: 2.6700 - val_loss: 12.6558 - val_mae: 2.8281\n",
      "Epoch 39/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 12.4838 - mae: 2.6654 - val_loss: 12.5781 - val_mae: 2.8145\n",
      "Epoch 40/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 12.4181 - mae: 2.6524 - val_loss: 12.5633 - val_mae: 2.8157\n",
      "Epoch 41/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 12.3536 - mae: 2.6486 - val_loss: 12.5056 - val_mae: 2.8060\n",
      "Epoch 42/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 12.3056 - mae: 2.6430 - val_loss: 12.4487 - val_mae: 2.7984\n",
      "Epoch 43/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 12.2552 - mae: 2.6376 - val_loss: 12.3638 - val_mae: 2.7845\n",
      "Epoch 44/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 12.1933 - mae: 2.6275 - val_loss: 12.3246 - val_mae: 2.7804\n",
      "Epoch 45/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 12.1227 - mae: 2.6250 - val_loss: 12.2130 - val_mae: 2.7579\n",
      "Epoch 46/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 12.1126 - mae: 2.6145 - val_loss: 12.1893 - val_mae: 2.7592\n",
      "Epoch 47/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 12.0735 - mae: 2.6153 - val_loss: 12.1319 - val_mae: 2.7490\n",
      "Epoch 48/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 12.0170 - mae: 2.6094 - val_loss: 12.1153 - val_mae: 2.7517\n",
      "Epoch 49/100\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 11.9860 - mae: 2.5960 - val_loss: 12.1323 - val_mae: 2.7614\n",
      "Epoch 50/100\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 11.9399 - mae: 2.5895 - val_loss: 12.1122 - val_mae: 2.7618\n",
      "Epoch 51/100\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 11.9037 - mae: 2.5915 - val_loss: 12.1138 - val_mae: 2.7670\n",
      "Epoch 52/100\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 11.8525 - mae: 2.5867 - val_loss: 12.0440 - val_mae: 2.7538\n",
      "Epoch 53/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 11.8051 - mae: 2.5766 - val_loss: 11.9795 - val_mae: 2.7413\n",
      "Epoch 54/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.8066 - mae: 2.5754 - val_loss: 11.9401 - val_mae: 2.7318\n",
      "Epoch 55/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.7562 - mae: 2.5704 - val_loss: 11.9192 - val_mae: 2.7317\n",
      "Epoch 56/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.7498 - mae: 2.5623 - val_loss: 11.9365 - val_mae: 2.7406\n",
      "Epoch 57/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.7188 - mae: 2.5696 - val_loss: 11.8779 - val_mae: 2.7296\n",
      "Epoch 58/100\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 11.7082 - mae: 2.5550 - val_loss: 11.8860 - val_mae: 2.7356\n",
      "Epoch 59/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 11.6547 - mae: 2.5546 - val_loss: 11.8544 - val_mae: 2.7306\n",
      "Epoch 60/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.6909 - mae: 2.5560 - val_loss: 11.8263 - val_mae: 2.7257\n",
      "Epoch 61/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 12ms/step - loss: 11.6149 - mae: 2.5520 - val_loss: 11.7915 - val_mae: 2.7211\n",
      "Epoch 62/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.6165 - mae: 2.5472 - val_loss: 11.7928 - val_mae: 2.7250\n",
      "Epoch 63/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.5861 - mae: 2.5433 - val_loss: 11.7405 - val_mae: 2.7154\n",
      "Epoch 64/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.5516 - mae: 2.5356 - val_loss: 11.7669 - val_mae: 2.7257\n",
      "Epoch 65/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.5302 - mae: 2.5359 - val_loss: 11.7540 - val_mae: 2.7246\n",
      "Epoch 66/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.5261 - mae: 2.5349 - val_loss: 11.7121 - val_mae: 2.7178\n",
      "Epoch 67/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.5140 - mae: 2.5265 - val_loss: 11.7613 - val_mae: 2.7350\n",
      "Epoch 68/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.4830 - mae: 2.5306 - val_loss: 11.7255 - val_mae: 2.7283\n",
      "Epoch 69/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.4908 - mae: 2.5323 - val_loss: 11.7018 - val_mae: 2.7256\n",
      "Epoch 70/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.5063 - mae: 2.5370 - val_loss: 11.6629 - val_mae: 2.7158\n",
      "Epoch 71/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.4594 - mae: 2.5247 - val_loss: 11.6762 - val_mae: 2.7247\n",
      "Epoch 72/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.4230 - mae: 2.5224 - val_loss: 11.6662 - val_mae: 2.7249\n",
      "Epoch 73/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.4225 - mae: 2.5245 - val_loss: 11.6544 - val_mae: 2.7254\n",
      "Epoch 74/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.4196 - mae: 2.5262 - val_loss: 11.6161 - val_mae: 2.7183\n",
      "Epoch 75/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.3809 - mae: 2.5171 - val_loss: 11.6289 - val_mae: 2.7256\n",
      "Epoch 76/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.3673 - mae: 2.5170 - val_loss: 11.5934 - val_mae: 2.7178\n",
      "Epoch 77/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.3561 - mae: 2.5142 - val_loss: 11.5843 - val_mae: 2.7155\n",
      "Epoch 78/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.3803 - mae: 2.5194 - val_loss: 11.5896 - val_mae: 2.7193\n",
      "Epoch 79/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.3233 - mae: 2.5123 - val_loss: 11.5765 - val_mae: 2.7192\n",
      "Epoch 80/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.3325 - mae: 2.5108 - val_loss: 11.5550 - val_mae: 2.7135\n",
      "Epoch 81/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.3091 - mae: 2.5088 - val_loss: 11.5736 - val_mae: 2.7221\n",
      "Epoch 82/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.3258 - mae: 2.5131 - val_loss: 11.5797 - val_mae: 2.7262\n",
      "Epoch 83/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.3093 - mae: 2.5116 - val_loss: 11.5669 - val_mae: 2.7243\n",
      "Epoch 84/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.3953 - mae: 2.5254 - val_loss: 11.5234 - val_mae: 2.7134\n",
      "Epoch 85/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2870 - mae: 2.5093 - val_loss: 11.5086 - val_mae: 2.7148\n",
      "Epoch 86/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2718 - mae: 2.5092 - val_loss: 11.4716 - val_mae: 2.7057\n",
      "Epoch 87/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2784 - mae: 2.5081 - val_loss: 11.4997 - val_mae: 2.7150\n",
      "Epoch 88/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2487 - mae: 2.4992 - val_loss: 11.5413 - val_mae: 2.7288\n",
      "Epoch 89/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2781 - mae: 2.5106 - val_loss: 11.4896 - val_mae: 2.7146\n",
      "Epoch 90/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2618 - mae: 2.5083 - val_loss: 11.4951 - val_mae: 2.7162\n",
      "Epoch 91/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2535 - mae: 2.5080 - val_loss: 11.4777 - val_mae: 2.7152\n",
      "Epoch 92/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2125 - mae: 2.4994 - val_loss: 11.4812 - val_mae: 2.7191\n",
      "Epoch 93/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2382 - mae: 2.5039 - val_loss: 11.4568 - val_mae: 2.7106\n",
      "Epoch 94/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2003 - mae: 2.5001 - val_loss: 11.4439 - val_mae: 2.7085\n",
      "Epoch 95/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2390 - mae: 2.5127 - val_loss: 11.4240 - val_mae: 2.7031\n",
      "Epoch 96/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.1948 - mae: 2.4998 - val_loss: 11.4282 - val_mae: 2.7088\n",
      "Epoch 97/100\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 11.1983 - mae: 2.5005 - val_loss: 11.4677 - val_mae: 2.7234\n",
      "Epoch 98/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.1787 - mae: 2.5020 - val_loss: 11.4182 - val_mae: 2.7093\n",
      "Epoch 99/100\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 11.2085 - mae: 2.4984 - val_loss: 11.4432 - val_mae: 2.7209\n",
      "Epoch 100/100\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 11.1638 - mae: 2.4983 - val_loss: 11.4168 - val_mae: 2.7145\n"
     ]
    }
   ],
   "source": [
    "result = model.fit(X_train, y_train, epochs=100, validation_data=(X_test, y_test), verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f39012c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 4ms/step\n",
      "Score of the single layer model: 77.96\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(X_test)\n",
    "score = r2_score(pred, y_test)\n",
    "print(f\"Score of the single layer model: {score * 100 :.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "44897f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c2ac053c10>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(result.history[\"loss\"])\n",
    "plt.plot(result.history[\"val_loss\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8300804d",
   "metadata": {},
   "source": [
    "### NN model - Multi layer perceptron model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "649ebe8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_4 (Dense)             (None, 16)                160       \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 8)                 136       \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 4)                 36        \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 1)                 5         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 337\n",
      "Trainable params: 337\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "mlp = Sequential()\n",
    "mlp.add(Dense(16, input_dim=9, activation=\"relu\"))\n",
    "mlp.add(Dense(8, activation=\"relu\"))\n",
    "mlp.add(Dense(4, activation=\"relu\"))\n",
    "mlp.add(Dense(1))\n",
    "mlp.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4340cba1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "10/10 [==============================] - 2s 62ms/step - loss: 591.0644 - mae: 22.9733 - val_loss: 571.9316 - val_mae: 22.4418\n",
      "Epoch 2/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 577.8822 - mae: 22.6773 - val_loss: 558.4274 - val_mae: 22.1177\n",
      "Epoch 3/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 563.4782 - mae: 22.3452 - val_loss: 543.0836 - val_mae: 21.7503\n",
      "Epoch 4/200\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 546.9094 - mae: 21.9637 - val_loss: 525.3038 - val_mae: 21.3247\n",
      "Epoch 5/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 527.2903 - mae: 21.5139 - val_loss: 504.4539 - val_mae: 20.8207\n",
      "Epoch 6/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 504.1667 - mae: 20.9706 - val_loss: 479.2599 - val_mae: 20.2064\n",
      "Epoch 7/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 476.2344 - mae: 20.3073 - val_loss: 449.3790 - val_mae: 19.4528\n",
      "Epoch 8/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 442.9585 - mae: 19.4886 - val_loss: 414.2233 - val_mae: 18.5234\n",
      "Epoch 9/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 405.2972 - mae: 18.5087 - val_loss: 374.0527 - val_mae: 17.4318\n",
      "Epoch 10/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 363.0222 - mae: 17.3833 - val_loss: 330.3343 - val_mae: 16.1763\n",
      "Epoch 11/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 316.7475 - mae: 16.1037 - val_loss: 284.3010 - val_mae: 14.8439\n",
      "Epoch 12/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 268.2381 - mae: 14.7190 - val_loss: 237.3780 - val_mae: 13.4902\n",
      "Epoch 13/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 219.5633 - mae: 13.2467 - val_loss: 191.8618 - val_mae: 12.0928\n",
      "Epoch 14/200\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 173.9151 - mae: 11.7316 - val_loss: 149.3493 - val_mae: 10.6179\n",
      "Epoch 15/200\n",
      "10/10 [==============================] - 0s 11ms/step - loss: 130.9547 - mae: 10.1623 - val_loss: 113.7631 - val_mae: 9.2532\n",
      "Epoch 16/200\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 99.1374 - mae: 8.7314 - val_loss: 83.7997 - val_mae: 7.9734\n",
      "Epoch 17/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 71.2870 - mae: 7.2674 - val_loss: 62.6404 - val_mae: 6.8563\n",
      "Epoch 18/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 53.1199 - mae: 6.1063 - val_loss: 48.0979 - val_mae: 5.9484\n",
      "Epoch 19/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 41.3830 - mae: 5.2112 - val_loss: 39.1266 - val_mae: 5.2695\n",
      "Epoch 20/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 34.8856 - mae: 4.6357 - val_loss: 33.8426 - val_mae: 4.7848\n",
      "Epoch 21/200\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 31.1360 - mae: 4.3025 - val_loss: 31.0461 - val_mae: 4.5199\n",
      "Epoch 22/200\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 29.2039 - mae: 4.1590 - val_loss: 29.1350 - val_mae: 4.3616\n",
      "Epoch 23/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 27.8879 - mae: 4.0734 - val_loss: 27.7857 - val_mae: 4.2482\n",
      "Epoch 24/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 26.8390 - mae: 4.0038 - val_loss: 26.6652 - val_mae: 4.1588\n",
      "Epoch 25/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 25.7522 - mae: 3.9191 - val_loss: 25.5863 - val_mae: 4.0719\n",
      "Epoch 26/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 24.7522 - mae: 3.8389 - val_loss: 24.7215 - val_mae: 4.0059\n",
      "Epoch 27/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 23.8664 - mae: 3.7660 - val_loss: 23.8831 - val_mae: 3.9392\n",
      "Epoch 28/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 22.9714 - mae: 3.6965 - val_loss: 23.2012 - val_mae: 3.8887\n",
      "Epoch 29/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 22.1861 - mae: 3.6348 - val_loss: 22.4279 - val_mae: 3.8225\n",
      "Epoch 30/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 21.3710 - mae: 3.5716 - val_loss: 21.7478 - val_mae: 3.7742\n",
      "Epoch 31/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 20.6415 - mae: 3.5129 - val_loss: 21.0920 - val_mae: 3.7207\n",
      "Epoch 32/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 19.9558 - mae: 3.4562 - val_loss: 20.4569 - val_mae: 3.6646\n",
      "Epoch 33/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 19.3306 - mae: 3.4015 - val_loss: 19.8319 - val_mae: 3.6096\n",
      "Epoch 34/200\n",
      "10/10 [==============================] - 0s 12ms/step - loss: 18.6850 - mae: 3.3461 - val_loss: 19.3482 - val_mae: 3.5691\n",
      "Epoch 35/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 18.0921 - mae: 3.2935 - val_loss: 18.8525 - val_mae: 3.5238\n",
      "Epoch 36/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 17.5258 - mae: 3.2392 - val_loss: 18.3079 - val_mae: 3.4699\n",
      "Epoch 37/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 17.0051 - mae: 3.1872 - val_loss: 17.7896 - val_mae: 3.4181\n",
      "Epoch 38/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 16.4974 - mae: 3.1379 - val_loss: 17.3234 - val_mae: 3.3688\n",
      "Epoch 39/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 16.0395 - mae: 3.0896 - val_loss: 16.9335 - val_mae: 3.3309\n",
      "Epoch 40/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 15.5355 - mae: 3.0401 - val_loss: 16.5388 - val_mae: 3.2874\n",
      "Epoch 41/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 15.1199 - mae: 2.9929 - val_loss: 16.1319 - val_mae: 3.2435\n",
      "Epoch 42/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 14.7064 - mae: 2.9474 - val_loss: 15.7433 - val_mae: 3.2028\n",
      "Epoch 43/200\n",
      "10/10 [==============================] - 0s 22ms/step - loss: 14.2980 - mae: 2.9038 - val_loss: 15.3818 - val_mae: 3.1587\n",
      "Epoch 44/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 13.9111 - mae: 2.8600 - val_loss: 15.0216 - val_mae: 3.1178\n",
      "Epoch 45/200\n",
      "10/10 [==============================] - 0s 26ms/step - loss: 13.5909 - mae: 2.8204 - val_loss: 14.6873 - val_mae: 3.0812\n",
      "Epoch 46/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 13.2399 - mae: 2.7843 - val_loss: 14.3840 - val_mae: 3.0486\n",
      "Epoch 47/200\n",
      "10/10 [==============================] - 0s 27ms/step - loss: 12.9315 - mae: 2.7487 - val_loss: 14.1412 - val_mae: 3.0198\n",
      "Epoch 48/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 12.6366 - mae: 2.7142 - val_loss: 13.8500 - val_mae: 2.9849\n",
      "Epoch 49/200\n",
      "10/10 [==============================] - 0s 24ms/step - loss: 12.3548 - mae: 2.6805 - val_loss: 13.5725 - val_mae: 2.9519\n",
      "Epoch 50/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 12.0817 - mae: 2.6459 - val_loss: 13.3007 - val_mae: 2.9139\n",
      "Epoch 51/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 11.8423 - mae: 2.6184 - val_loss: 13.0687 - val_mae: 2.8837\n",
      "Epoch 52/200\n",
      "10/10 [==============================] - 0s 22ms/step - loss: 11.6181 - mae: 2.5899 - val_loss: 12.8612 - val_mae: 2.8577\n",
      "Epoch 53/200\n",
      "10/10 [==============================] - 0s 24ms/step - loss: 11.3838 - mae: 2.5566 - val_loss: 12.6025 - val_mae: 2.8125\n",
      "Epoch 54/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 11.1528 - mae: 2.5258 - val_loss: 12.4052 - val_mae: 2.7781\n",
      "Epoch 55/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 10.9416 - mae: 2.4948 - val_loss: 12.2481 - val_mae: 2.7584\n",
      "Epoch 56/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 10.7435 - mae: 2.4682 - val_loss: 12.0538 - val_mae: 2.7280\n",
      "Epoch 57/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 10.5567 - mae: 2.4441 - val_loss: 11.8963 - val_mae: 2.7043\n",
      "Epoch 58/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 10.3796 - mae: 2.4172 - val_loss: 11.7120 - val_mae: 2.6780\n",
      "Epoch 59/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 10.1949 - mae: 2.3930 - val_loss: 11.5336 - val_mae: 2.6510\n",
      "Epoch 60/200\n",
      "10/10 [==============================] - 0s 21ms/step - loss: 10.0540 - mae: 2.3721 - val_loss: 11.3517 - val_mae: 2.6213\n",
      "Epoch 61/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 16ms/step - loss: 9.9036 - mae: 2.3476 - val_loss: 11.2357 - val_mae: 2.6109\n",
      "Epoch 62/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 9.7397 - mae: 2.3253 - val_loss: 11.0962 - val_mae: 2.5878\n",
      "Epoch 63/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 9.6069 - mae: 2.3036 - val_loss: 10.9520 - val_mae: 2.5669\n",
      "Epoch 64/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 9.4824 - mae: 2.2842 - val_loss: 10.8037 - val_mae: 2.5395\n",
      "Epoch 65/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 9.3470 - mae: 2.2656 - val_loss: 10.7121 - val_mae: 2.5283\n",
      "Epoch 66/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 9.2446 - mae: 2.2496 - val_loss: 10.6009 - val_mae: 2.5117\n",
      "Epoch 67/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 9.1218 - mae: 2.2285 - val_loss: 10.5296 - val_mae: 2.4995\n",
      "Epoch 68/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 9.0305 - mae: 2.2145 - val_loss: 10.4032 - val_mae: 2.4784\n",
      "Epoch 69/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 8.9239 - mae: 2.1989 - val_loss: 10.2759 - val_mae: 2.4607\n",
      "Epoch 70/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 8.8189 - mae: 2.1787 - val_loss: 10.2465 - val_mae: 2.4556\n",
      "Epoch 71/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 8.7975 - mae: 2.1717 - val_loss: 10.1140 - val_mae: 2.4310\n",
      "Epoch 72/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 8.6708 - mae: 2.1525 - val_loss: 10.1183 - val_mae: 2.4298\n",
      "Epoch 73/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 8.5682 - mae: 2.1344 - val_loss: 10.0380 - val_mae: 2.4140\n",
      "Epoch 74/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 8.4921 - mae: 2.1234 - val_loss: 9.9582 - val_mae: 2.4003\n",
      "Epoch 75/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 8.4245 - mae: 2.1126 - val_loss: 9.8547 - val_mae: 2.3857\n",
      "Epoch 76/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 8.3909 - mae: 2.1101 - val_loss: 9.7618 - val_mae: 2.3664\n",
      "Epoch 77/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 8.3005 - mae: 2.0963 - val_loss: 9.7839 - val_mae: 2.3752\n",
      "Epoch 78/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 8.2358 - mae: 2.0821 - val_loss: 9.7001 - val_mae: 2.3596\n",
      "Epoch 79/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 8.1791 - mae: 2.0716 - val_loss: 9.6357 - val_mae: 2.3499\n",
      "Epoch 80/200\n",
      "10/10 [==============================] - 0s 13ms/step - loss: 8.1304 - mae: 2.0637 - val_loss: 9.6147 - val_mae: 2.3415\n",
      "Epoch 81/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 8.0762 - mae: 2.0572 - val_loss: 9.5946 - val_mae: 2.3360\n",
      "Epoch 82/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 8.0266 - mae: 2.0462 - val_loss: 9.5619 - val_mae: 2.3318\n",
      "Epoch 83/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 7.9877 - mae: 2.0403 - val_loss: 9.4903 - val_mae: 2.3201\n",
      "Epoch 84/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 7.9512 - mae: 2.0380 - val_loss: 9.3789 - val_mae: 2.2970\n",
      "Epoch 85/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 7.9103 - mae: 2.0301 - val_loss: 9.2988 - val_mae: 2.2861\n",
      "Epoch 86/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 7.8701 - mae: 2.0256 - val_loss: 9.3645 - val_mae: 2.2954\n",
      "Epoch 87/200\n",
      "10/10 [==============================] - 0s 22ms/step - loss: 7.8305 - mae: 2.0170 - val_loss: 9.3109 - val_mae: 2.2838\n",
      "Epoch 88/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 7.8117 - mae: 2.0173 - val_loss: 9.2379 - val_mae: 2.2685\n",
      "Epoch 89/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 7.7621 - mae: 2.0077 - val_loss: 9.2870 - val_mae: 2.2751\n",
      "Epoch 90/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 7.7524 - mae: 2.0045 - val_loss: 9.2167 - val_mae: 2.2628\n",
      "Epoch 91/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 7.6989 - mae: 2.0008 - val_loss: 9.1629 - val_mae: 2.2517\n",
      "Epoch 92/200\n",
      "10/10 [==============================] - 0s 23ms/step - loss: 7.6800 - mae: 1.9985 - val_loss: 9.1684 - val_mae: 2.2491\n",
      "Epoch 93/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 7.6584 - mae: 1.9952 - val_loss: 9.1452 - val_mae: 2.2466\n",
      "Epoch 94/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 7.6282 - mae: 1.9895 - val_loss: 9.1589 - val_mae: 2.2467\n",
      "Epoch 95/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 7.5952 - mae: 1.9828 - val_loss: 9.1124 - val_mae: 2.2399\n",
      "Epoch 96/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 7.5776 - mae: 1.9809 - val_loss: 9.0432 - val_mae: 2.2321\n",
      "Epoch 97/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 7.5346 - mae: 1.9775 - val_loss: 8.9514 - val_mae: 2.2185\n",
      "Epoch 98/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 7.5210 - mae: 1.9778 - val_loss: 8.9611 - val_mae: 2.2215\n",
      "Epoch 99/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 7.4896 - mae: 1.9744 - val_loss: 8.9436 - val_mae: 2.2193\n",
      "Epoch 100/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 7.4875 - mae: 1.9732 - val_loss: 8.8578 - val_mae: 2.2075\n",
      "Epoch 101/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 7.4475 - mae: 1.9683 - val_loss: 8.9162 - val_mae: 2.2159\n",
      "Epoch 102/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 7.4395 - mae: 1.9641 - val_loss: 8.9645 - val_mae: 2.2229\n",
      "Epoch 103/200\n",
      "10/10 [==============================] - 0s 27ms/step - loss: 7.4174 - mae: 1.9600 - val_loss: 8.9472 - val_mae: 2.2226\n",
      "Epoch 104/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 7.3855 - mae: 1.9581 - val_loss: 8.8020 - val_mae: 2.2005\n",
      "Epoch 105/200\n",
      "10/10 [==============================] - 0s 21ms/step - loss: 7.3640 - mae: 1.9582 - val_loss: 8.8075 - val_mae: 2.2005\n",
      "Epoch 106/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 7.3552 - mae: 1.9577 - val_loss: 8.7459 - val_mae: 2.1943\n",
      "Epoch 107/200\n",
      "10/10 [==============================] - 0s 24ms/step - loss: 7.3559 - mae: 1.9530 - val_loss: 8.8469 - val_mae: 2.2105\n",
      "Epoch 108/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 7.3665 - mae: 1.9557 - val_loss: 8.7096 - val_mae: 2.1869\n",
      "Epoch 109/200\n",
      "10/10 [==============================] - 0s 23ms/step - loss: 7.2921 - mae: 1.9488 - val_loss: 8.7626 - val_mae: 2.1978\n",
      "Epoch 110/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 7.3117 - mae: 1.9501 - val_loss: 8.8346 - val_mae: 2.2125\n",
      "Epoch 111/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 7.2672 - mae: 1.9432 - val_loss: 8.7764 - val_mae: 2.2011\n",
      "Epoch 112/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 7.2441 - mae: 1.9470 - val_loss: 8.6853 - val_mae: 2.1858\n",
      "Epoch 113/200\n",
      "10/10 [==============================] - 0s 23ms/step - loss: 7.2320 - mae: 1.9429 - val_loss: 8.6689 - val_mae: 2.1845\n",
      "Epoch 114/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 7.2217 - mae: 1.9375 - val_loss: 8.7491 - val_mae: 2.1980\n",
      "Epoch 115/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 7.1967 - mae: 1.9363 - val_loss: 8.6515 - val_mae: 2.1822\n",
      "Epoch 116/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 7.1760 - mae: 1.9349 - val_loss: 8.6584 - val_mae: 2.1847\n",
      "Epoch 117/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 7.1640 - mae: 1.9319 - val_loss: 8.6155 - val_mae: 2.1799\n",
      "Epoch 118/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 7.1549 - mae: 1.9285 - val_loss: 8.6211 - val_mae: 2.1804\n",
      "Epoch 119/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 7.1304 - mae: 1.9272 - val_loss: 8.5993 - val_mae: 2.1754\n",
      "Epoch 120/200\n",
      "10/10 [==============================] - 0s 21ms/step - loss: 7.1393 - mae: 1.9252 - val_loss: 8.6513 - val_mae: 2.1855\n",
      "Epoch 121/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 7.1423 - mae: 1.9292 - val_loss: 8.5209 - val_mae: 2.1628\n",
      "Epoch 122/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 21ms/step - loss: 7.1007 - mae: 1.9312 - val_loss: 8.5249 - val_mae: 2.1670\n",
      "Epoch 123/200\n",
      "10/10 [==============================] - 0s 29ms/step - loss: 7.1139 - mae: 1.9237 - val_loss: 8.7191 - val_mae: 2.1942\n",
      "Epoch 124/200\n",
      "10/10 [==============================] - 0s 23ms/step - loss: 7.0800 - mae: 1.9206 - val_loss: 8.5273 - val_mae: 2.1643\n",
      "Epoch 125/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 7.0480 - mae: 1.9186 - val_loss: 8.5137 - val_mae: 2.1630\n",
      "Epoch 126/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 7.0374 - mae: 1.9168 - val_loss: 8.5334 - val_mae: 2.1665\n",
      "Epoch 127/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 7.0497 - mae: 1.9165 - val_loss: 8.5608 - val_mae: 2.1724\n",
      "Epoch 128/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 7.0180 - mae: 1.9123 - val_loss: 8.4759 - val_mae: 2.1591\n",
      "Epoch 129/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 7.0114 - mae: 1.9100 - val_loss: 8.5130 - val_mae: 2.1625\n",
      "Epoch 130/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.9891 - mae: 1.9069 - val_loss: 8.4998 - val_mae: 2.1615\n",
      "Epoch 131/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 6.9783 - mae: 1.9091 - val_loss: 8.4507 - val_mae: 2.1527\n",
      "Epoch 132/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.9555 - mae: 1.9081 - val_loss: 8.4001 - val_mae: 2.1459\n",
      "Epoch 133/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 7.0143 - mae: 1.9121 - val_loss: 8.5039 - val_mae: 2.1607\n",
      "Epoch 134/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.9776 - mae: 1.9040 - val_loss: 8.2891 - val_mae: 2.1328\n",
      "Epoch 135/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.9367 - mae: 1.9099 - val_loss: 8.3625 - val_mae: 2.1444\n",
      "Epoch 136/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.9138 - mae: 1.8988 - val_loss: 8.4136 - val_mae: 2.1518\n",
      "Epoch 137/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 6.9163 - mae: 1.8986 - val_loss: 8.4702 - val_mae: 2.1556\n",
      "Epoch 138/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 6.9037 - mae: 1.9014 - val_loss: 8.3141 - val_mae: 2.1372\n",
      "Epoch 139/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.8782 - mae: 1.8996 - val_loss: 8.3759 - val_mae: 2.1453\n",
      "Epoch 140/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.8785 - mae: 1.8979 - val_loss: 8.3329 - val_mae: 2.1397\n",
      "Epoch 141/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.8624 - mae: 1.8927 - val_loss: 8.3612 - val_mae: 2.1419\n",
      "Epoch 142/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.8702 - mae: 1.8908 - val_loss: 8.2955 - val_mae: 2.1360\n",
      "Epoch 143/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.8562 - mae: 1.8958 - val_loss: 8.2150 - val_mae: 2.1285\n",
      "Epoch 144/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 6.8135 - mae: 1.8898 - val_loss: 8.2769 - val_mae: 2.1369\n",
      "Epoch 145/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.8393 - mae: 1.8866 - val_loss: 8.3884 - val_mae: 2.1491\n",
      "Epoch 146/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.8105 - mae: 1.8884 - val_loss: 8.2069 - val_mae: 2.1267\n",
      "Epoch 147/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 6.8045 - mae: 1.8898 - val_loss: 8.2385 - val_mae: 2.1312\n",
      "Epoch 148/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 6.7901 - mae: 1.8836 - val_loss: 8.2792 - val_mae: 2.1366\n",
      "Epoch 149/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.7756 - mae: 1.8800 - val_loss: 8.1966 - val_mae: 2.1275\n",
      "Epoch 150/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.7543 - mae: 1.8816 - val_loss: 8.1600 - val_mae: 2.1238\n",
      "Epoch 151/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.7600 - mae: 1.8873 - val_loss: 8.1767 - val_mae: 2.1234\n",
      "Epoch 152/200\n",
      "10/10 [==============================] - 0s 24ms/step - loss: 6.7300 - mae: 1.8792 - val_loss: 8.2602 - val_mae: 2.1389\n",
      "Epoch 153/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.7310 - mae: 1.8736 - val_loss: 8.2551 - val_mae: 2.1403\n",
      "Epoch 154/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.7051 - mae: 1.8689 - val_loss: 8.1940 - val_mae: 2.1304\n",
      "Epoch 155/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.7074 - mae: 1.8723 - val_loss: 8.1623 - val_mae: 2.1286\n",
      "Epoch 156/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.6941 - mae: 1.8724 - val_loss: 8.1004 - val_mae: 2.1181\n",
      "Epoch 157/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.6752 - mae: 1.8729 - val_loss: 8.0849 - val_mae: 2.1173\n",
      "Epoch 158/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 6.6694 - mae: 1.8715 - val_loss: 8.1096 - val_mae: 2.1216\n",
      "Epoch 159/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 6.6674 - mae: 1.8696 - val_loss: 8.2148 - val_mae: 2.1327\n",
      "Epoch 160/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 6.6573 - mae: 1.8661 - val_loss: 8.0973 - val_mae: 2.1220\n",
      "Epoch 161/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 6.6391 - mae: 1.8648 - val_loss: 8.0971 - val_mae: 2.1222\n",
      "Epoch 162/200\n",
      "10/10 [==============================] - 0s 22ms/step - loss: 6.6216 - mae: 1.8630 - val_loss: 8.0955 - val_mae: 2.1209\n",
      "Epoch 163/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.6089 - mae: 1.8590 - val_loss: 8.1141 - val_mae: 2.1212\n",
      "Epoch 164/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.6116 - mae: 1.8608 - val_loss: 8.0116 - val_mae: 2.1114\n",
      "Epoch 165/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 6.6082 - mae: 1.8590 - val_loss: 8.0853 - val_mae: 2.1203\n",
      "Epoch 166/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.6175 - mae: 1.8615 - val_loss: 8.0645 - val_mae: 2.1149\n",
      "Epoch 167/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.5720 - mae: 1.8590 - val_loss: 8.0458 - val_mae: 2.1149\n",
      "Epoch 168/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.5880 - mae: 1.8633 - val_loss: 7.9689 - val_mae: 2.1035\n",
      "Epoch 169/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.5751 - mae: 1.8561 - val_loss: 8.1126 - val_mae: 2.1226\n",
      "Epoch 170/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 6.5446 - mae: 1.8513 - val_loss: 8.0483 - val_mae: 2.1143\n",
      "Epoch 171/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.5485 - mae: 1.8525 - val_loss: 8.0819 - val_mae: 2.1164\n",
      "Epoch 172/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 6.5248 - mae: 1.8484 - val_loss: 8.0114 - val_mae: 2.1121\n",
      "Epoch 173/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.5291 - mae: 1.8565 - val_loss: 7.9515 - val_mae: 2.1028\n",
      "Epoch 174/200\n",
      "10/10 [==============================] - 0s 20ms/step - loss: 6.5228 - mae: 1.8552 - val_loss: 7.9550 - val_mae: 2.1024\n",
      "Epoch 175/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.5133 - mae: 1.8485 - val_loss: 8.1158 - val_mae: 2.1223\n",
      "Epoch 176/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 6.4952 - mae: 1.8414 - val_loss: 8.0564 - val_mae: 2.1155\n",
      "Epoch 177/200\n",
      "10/10 [==============================] - 0s 21ms/step - loss: 6.4945 - mae: 1.8452 - val_loss: 7.9335 - val_mae: 2.1017\n",
      "Epoch 178/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.4950 - mae: 1.8533 - val_loss: 7.8842 - val_mae: 2.0934\n",
      "Epoch 179/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.4970 - mae: 1.8442 - val_loss: 8.0854 - val_mae: 2.1170\n",
      "Epoch 180/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.4994 - mae: 1.8447 - val_loss: 7.9381 - val_mae: 2.1033\n",
      "Epoch 181/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.4916 - mae: 1.8417 - val_loss: 8.0967 - val_mae: 2.1189\n",
      "Epoch 182/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.4581 - mae: 1.8433 - val_loss: 7.8845 - val_mae: 2.0934\n",
      "Epoch 183/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 15ms/step - loss: 6.4695 - mae: 1.8455 - val_loss: 8.0334 - val_mae: 2.1086\n",
      "Epoch 184/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.4344 - mae: 1.8412 - val_loss: 7.8981 - val_mae: 2.0970\n",
      "Epoch 185/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.4166 - mae: 1.8357 - val_loss: 7.9276 - val_mae: 2.0975\n",
      "Epoch 186/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 6.4088 - mae: 1.8359 - val_loss: 7.8681 - val_mae: 2.0922\n",
      "Epoch 187/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.4138 - mae: 1.8366 - val_loss: 7.8492 - val_mae: 2.0888\n",
      "Epoch 188/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 6.3954 - mae: 1.8351 - val_loss: 7.8926 - val_mae: 2.0928\n",
      "Epoch 189/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.3817 - mae: 1.8251 - val_loss: 7.9916 - val_mae: 2.1065\n",
      "Epoch 190/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.3876 - mae: 1.8255 - val_loss: 7.9683 - val_mae: 2.1057\n",
      "Epoch 191/200\n",
      "10/10 [==============================] - 0s 14ms/step - loss: 6.3600 - mae: 1.8235 - val_loss: 7.8799 - val_mae: 2.0949\n",
      "Epoch 192/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.3445 - mae: 1.8272 - val_loss: 7.8135 - val_mae: 2.0835\n",
      "Epoch 193/200\n",
      "10/10 [==============================] - 0s 17ms/step - loss: 6.3639 - mae: 1.8312 - val_loss: 7.8581 - val_mae: 2.0864\n",
      "Epoch 194/200\n",
      "10/10 [==============================] - 0s 22ms/step - loss: 6.3429 - mae: 1.8298 - val_loss: 7.7755 - val_mae: 2.0788\n",
      "Epoch 195/200\n",
      "10/10 [==============================] - 0s 19ms/step - loss: 6.3566 - mae: 1.8293 - val_loss: 7.8430 - val_mae: 2.0869\n",
      "Epoch 196/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 6.3343 - mae: 1.8260 - val_loss: 7.8482 - val_mae: 2.0900\n",
      "Epoch 197/200\n",
      "10/10 [==============================] - 0s 18ms/step - loss: 6.3381 - mae: 1.8246 - val_loss: 7.8019 - val_mae: 2.0834\n",
      "Epoch 198/200\n",
      "10/10 [==============================] - 0s 15ms/step - loss: 6.2996 - mae: 1.8181 - val_loss: 7.8880 - val_mae: 2.0921\n",
      "Epoch 199/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.3118 - mae: 1.8157 - val_loss: 7.9171 - val_mae: 2.0954\n",
      "Epoch 200/200\n",
      "10/10 [==============================] - 0s 16ms/step - loss: 6.3057 - mae: 1.8190 - val_loss: 7.8101 - val_mae: 2.0787\n"
     ]
    }
   ],
   "source": [
    "mlp.compile(loss=\"mse\", optimizer=\"adam\", metrics=\"mae\")\n",
    "result_mlp = mlp.fit(X_train, y_train, epochs=200, validation_data=(X_test, y_test), verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "621a574f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 6ms/step\n",
      "Score of the double layer model: 84.53\n"
     ]
    }
   ],
   "source": [
    "pred_mlp = mlp.predict(X_test)\n",
    "score_mlp = r2_score(pred_mlp, y_test)\n",
    "print(f\"Score of the double layer model: {score_mlp * 100 :.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6e79d68d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c2aea2c3a0>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(result_mlp.history[\"loss\"])\n",
    "plt.plot(result_mlp.history[\"val_loss\"])"
   ]
  },
  {
   "cell_type": "code",
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