ML_Assignment_on_Multi_Layer_Perceptron_120AD0015.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "137b8457",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow\n",
    "from keras.models import Sequentialuential\n",
    "from keras.optimizers import Adam\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4d40d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('E:\\\\Datasets\\\\Auto-mpg_Dataset\\\\auto-mpg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef1906c0",
   "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",
       "      <th>origin</th>\n",
       "      <th>car 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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model year  origin                   car name\n",
       "0    18.0          8         307.0        130    3504          12.0          70       1  chevrolet chevelle malibu\n",
       "1    15.0          8         350.0        165    3693          11.5          70       1          buick skylark 320\n",
       "2    18.0          8         318.0        150    3436          11.0          70       1         plymouth satellite\n",
       "3    16.0          8         304.0        150    3433          12.0          70       1              amc rebel sst\n",
       "4    17.0          8         302.0        140    3449          10.5          70       1                ford torino\n",
       "..    ...        ...           ...        ...     ...           ...         ...     ...                        ...\n",
       "393  27.0          4         140.0         86    2790          15.6          82       1            ford mustang gl\n",
       "394  44.0          4          97.0         52    2130          24.6          82       2                  vw pickup\n",
       "395  32.0          4         135.0         84    2295          11.6          82       1              dodge rampage\n",
       "396  28.0          4         120.0         79    2625          18.6          82       1                ford ranger\n",
       "397  31.0          4         119.0         82    2720          19.4          82       1                 chevy s-10\n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "229514ae",
   "metadata": {},
   "source": [
    "### Data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0472107c",
   "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    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   car name      398 non-null    object \n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "71ac77e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>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>2970.424623</td>\n",
       "      <td>15.568090</td>\n",
       "      <td>76.010050</td>\n",
       "      <td>1.572864</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>846.841774</td>\n",
       "      <td>2.757689</td>\n",
       "      <td>3.697627</td>\n",
       "      <td>0.802055</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>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>104.250000</td>\n",
       "      <td>2223.750000</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>17.175000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg   cylinders  displacement       weight  acceleration  model year      origin\n",
       "count  398.000000  398.000000    398.000000   398.000000    398.000000  398.000000  398.000000\n",
       "mean    23.514573    5.454774    193.425879  2970.424623     15.568090   76.010050    1.572864\n",
       "std      7.815984    1.701004    104.269838   846.841774      2.757689    3.697627    0.802055\n",
       "min      9.000000    3.000000     68.000000  1613.000000      8.000000   70.000000    1.000000\n",
       "25%     17.500000    4.000000    104.250000  2223.750000     13.825000   73.000000    1.000000\n",
       "50%     23.000000    4.000000    148.500000  2803.500000     15.500000   76.000000    1.000000\n",
       "75%     29.000000    8.000000    262.000000  3608.000000     17.175000   79.000000    2.000000\n",
       "max     46.600000    8.000000    455.000000  5140.000000     24.800000   82.000000    3.000000"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5ccff26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             0\n",
       "cylinders       0\n",
       "displacement    0\n",
       "horsepower      0\n",
       "weight          0\n",
       "acceleration    0\n",
       "model year      0\n",
       "origin          0\n",
       "car name        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "95cbd613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             129\n",
       "cylinders         5\n",
       "displacement     82\n",
       "horsepower       94\n",
       "weight          351\n",
       "acceleration     95\n",
       "model year       13\n",
       "origin            3\n",
       "car name        305\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "badbcfa5",
   "metadata": {},
   "source": [
    "### Cleaning the dataset by replacing the '?' values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0ec87211",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of NaN-equivalent values in each column: \n",
      "Column - 1 ---> 0\n",
      "Column - 2 ---> 0\n",
      "Column - 3 ---> 0\n",
      "Column - 4 ---> 6\n",
      "Column - 5 ---> 0\n",
      "Column - 6 ---> 0\n",
      "Column - 7 ---> 0\n",
      "Column - 8 ---> 0\n",
      "Column - 9 ---> 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of NaN-equivalent values in each column: \")\n",
    "for cols in range(data.shape[1]):\n",
    "    print(\"Column - \" + str(cols+1) + \" ---> \"+ str(((data[data.columns[cols]] == '?') | (data[data.columns[cols]] == ' ')).sum()), sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a879e5ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "393    False\n",
       "394    False\n",
       "395    False\n",
       "396    False\n",
       "397    False\n",
       "Name: horsepower, Length: 398, dtype: bool"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data[data.columns[3]] == \"?\") # 4th column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a8f93a00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "80c70ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Col1 Col2 Col3\n",
       "0     1    2    3\n",
       "1     5    4    6\n",
       "2     7    6    9\n",
       "3     9    ?    >"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = {\n",
    "    'Col1' : [1,5,7,9], 'Col2' : [2,4,6,'?'], 'Col3' : [3,6,9,'>']\n",
    "}\n",
    "df = pd.DataFrame(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "dc0c49a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Col2'].replace('?', 8, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f38e3232",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 59,
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   "cell_type": "code",
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    "df.replace('>', 12, inplace=True)"
   ]
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   "id": "b92590f1",
   "metadata": {},
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   "cell_type": "code",
   "execution_count": 70,
   "id": "6bcfc08e",
   "metadata": {},
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   "source": [
    "data1 = data[data['horsepower']!= '?']"
   ]
  },
  {
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   "execution_count": 76,
   "id": "942c207c",
   "metadata": {},
   "outputs": [
    {
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       "0      130.0\n",
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       "Name: horsepower, Length: 392, dtype: float32"
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     "execution_count": 76,
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  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "cb04e711",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_median = data1['horsepower'].median()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2612f08c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
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     "execution_count": 82,
     "metadata": {},
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   "source": [
    "type(my_median)"
   ]
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  {
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   "execution_count": 72,
   "id": "2afacea4",
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   "outputs": [
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       "      mpg  cylinders  displacement horsepower  weight  acceleration  model year  origin                   car name\n",
       "0    18.0          8         307.0        130    3504          12.0          70       1  chevrolet chevelle malibu\n",
       "1    15.0          8         350.0        165    3693          11.5          70       1          buick skylark 320\n",
       "2    18.0          8         318.0        150    3436          11.0          70       1         plymouth satellite\n",
       "3    16.0          8         304.0        150    3433          12.0          70       1              amc rebel sst\n",
       "4    17.0          8         302.0        140    3449          10.5          70       1                ford torino\n",
       "..    ...        ...           ...        ...     ...           ...         ...     ...                        ...\n",
       "393  27.0          4         140.0         86    2790          15.6          82       1            ford mustang gl\n",
       "394  44.0          4          97.0         52    2130          24.6          82       2                  vw pickup\n",
       "395  32.0          4         135.0         84    2295          11.6          82       1              dodge rampage\n",
       "396  28.0          4         120.0         79    2625          18.6          82       1                ford ranger\n",
       "397  31.0          4         119.0         82    2720          19.4          82       1                 chevy s-10\n",
       "\n",
       "[398 rows x 9 columns]"
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  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0da2210a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_replaced = data.replace('?', my_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9e2e3c3f",
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model year  origin                   car name\n",
       "0    18.0          8         307.0        130    3504          12.0          70       1  chevrolet chevelle malibu\n",
       "1    15.0          8         350.0        165    3693          11.5          70       1          buick skylark 320\n",
       "2    18.0          8         318.0        150    3436          11.0          70       1         plymouth satellite\n",
       "3    16.0          8         304.0        150    3433          12.0          70       1              amc rebel sst\n",
       "4    17.0          8         302.0        140    3449          10.5          70       1                ford torino\n",
       "..    ...        ...           ...        ...     ...           ...         ...     ...                        ...\n",
       "393  27.0          4         140.0         86    2790          15.6          82       1            ford mustang gl\n",
       "394  44.0          4          97.0         52    2130          24.6          82       2                  vw pickup\n",
       "395  32.0          4         135.0         84    2295          11.6          82       1              dodge rampage\n",
       "396  28.0          4         120.0         79    2625          18.6          82       1                ford ranger\n",
       "397  31.0          4         119.0         82    2720          19.4          82       1                 chevy s-10\n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_replaced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "b6632b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    398\n",
       "Name: horsepower, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data_replaced['horsepower'] == '?').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d47ed6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "(data_replaced['horsepower'] == '?').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "61c8cee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    392\n",
       "True       6\n",
       "Name: horsepower, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data['horsepower']=='?').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ccdd9714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_replaced['origin'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3928113a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_replaced.loc[data_replaced['origin'] == 1, 'origin'] = 'USA'\n",
    "data_replaced.loc[data_replaced['origin'] == 2, 'origin'] = 'Europe'\n",
    "data_replaced.loc[data_replaced['origin'] == 3, 'origin'] = 'Asia/Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "34f255d5",
   "metadata": {},
   "outputs": [
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       "      <td>ford mustang gl</td>\n",
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       "      <td>2130</td>\n",
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       "      <td>USA</td>\n",
       "      <td>chevy s-10</td>\n",
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       "      mpg  cylinders  displacement horsepower  weight  acceleration  model year  origin                   car name\n",
       "0    18.0          8         307.0        130    3504          12.0          70     USA  chevrolet chevelle malibu\n",
       "1    15.0          8         350.0        165    3693          11.5          70     USA          buick skylark 320\n",
       "2    18.0          8         318.0        150    3436          11.0          70     USA         plymouth satellite\n",
       "3    16.0          8         304.0        150    3433          12.0          70     USA              amc rebel sst\n",
       "4    17.0          8         302.0        140    3449          10.5          70     USA                ford torino\n",
       "..    ...        ...           ...        ...     ...           ...         ...     ...                        ...\n",
       "393  27.0          4         140.0         86    2790          15.6          82     USA            ford mustang gl\n",
       "394  44.0          4          97.0         52    2130          24.6          82  Europe                  vw pickup\n",
       "395  32.0          4         135.0         84    2295          11.6          82     USA              dodge rampage\n",
       "396  28.0          4         120.0         79    2625          18.6          82     USA                ford ranger\n",
       "397  31.0          4         119.0         82    2720          19.4          82     USA                 chevy s-10\n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "d95cc67b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cleaned = data_replaced.drop('car name', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "23fc6199",
   "metadata": {},
   "outputs": [
    {
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       "      <td>USA</td>\n",
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      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model year  origin\n",
       "0    18.0          8         307.0        130    3504          12.0          70     USA\n",
       "1    15.0          8         350.0        165    3693          11.5          70     USA\n",
       "2    18.0          8         318.0        150    3436          11.0          70     USA\n",
       "3    16.0          8         304.0        150    3433          12.0          70     USA\n",
       "4    17.0          8         302.0        140    3449          10.5          70     USA\n",
       "..    ...        ...           ...        ...     ...           ...         ...     ...\n",
       "393  27.0          4         140.0         86    2790          15.6          82     USA\n",
       "394  44.0          4          97.0         52    2130          24.6          82  Europe\n",
       "395  32.0          4         135.0         84    2295          11.6          82     USA\n",
       "396  28.0          4         120.0         79    2625          18.6          82     USA\n",
       "397  31.0          4         119.0         82    2720          19.4          82     USA\n",
       "\n",
       "[398 rows x 8 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "a42af1cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cleaned['origin'] = data_cleaned['origin'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "2100f7f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['Asia/Others', 'Europe', 'USA'], ordered=False)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned['origin'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "999f8da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         USA\n",
       "1         USA\n",
       "2         USA\n",
       "3         USA\n",
       "4         USA\n",
       "        ...  \n",
       "393       USA\n",
       "394    Europe\n",
       "395       USA\n",
       "396       USA\n",
       "397       USA\n",
       "Name: origin, Length: 398, dtype: category\n",
       "Categories (3, object): ['Asia/Others', 'Europe', 'USA']"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "edc9542f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model year  origin\n",
       "0    18.0          8         307.0        130    3504          12.0          70     USA\n",
       "1    15.0          8         350.0        165    3693          11.5          70     USA\n",
       "2    18.0          8         318.0        150    3436          11.0          70     USA\n",
       "3    16.0          8         304.0        150    3433          12.0          70     USA\n",
       "4    17.0          8         302.0        140    3449          10.5          70     USA\n",
       "..    ...        ...           ...        ...     ...           ...         ...     ...\n",
       "393  27.0          4         140.0         86    2790          15.6          82     USA\n",
       "394  44.0          4          97.0         52    2130          24.6          82  Europe\n",
       "395  32.0          4         135.0         84    2295          11.6          82     USA\n",
       "396  28.0          4         120.0         79    2625          18.6          82     USA\n",
       "397  31.0          4         119.0         82    2720          19.4          82     USA\n",
       "\n",
       "[398 rows x 8 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "eb2f3933",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cleaned['horsepower'] = data_cleaned['horsepower'].astype('float32')\n",
    "data_cleaned['cylinders'] = data_cleaned['cylinders'].astype('float32')\n",
    "data_cleaned['weight'] = data_cleaned['weight'].astype('float32')\n",
    "data_cleaned['model year'] = data_cleaned['model year'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "17b4b527",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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       "      <td>12.0</td>\n",
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       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
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       "    <tr>\n",
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       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70.0</td>\n",
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       "      <th>394</th>\n",
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       "      <td>4.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Europe</td>\n",
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       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
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       "      <th>396</th>\n",
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       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
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       "      <td>82.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82.0</td>\n",
       "      <td>USA</td>\n",
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       "</table>\n",
       "<p>398 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  model year  origin\n",
       "0    18.0        8.0         307.0       130.0  3504.0          12.0        70.0     USA\n",
       "1    15.0        8.0         350.0       165.0  3693.0          11.5        70.0     USA\n",
       "2    18.0        8.0         318.0       150.0  3436.0          11.0        70.0     USA\n",
       "3    16.0        8.0         304.0       150.0  3433.0          12.0        70.0     USA\n",
       "4    17.0        8.0         302.0       140.0  3449.0          10.5        70.0     USA\n",
       "..    ...        ...           ...         ...     ...           ...         ...     ...\n",
       "393  27.0        4.0         140.0        86.0  2790.0          15.6        82.0     USA\n",
       "394  44.0        4.0          97.0        52.0  2130.0          24.6        82.0  Europe\n",
       "395  32.0        4.0         135.0        84.0  2295.0          11.6        82.0     USA\n",
       "396  28.0        4.0         120.0        79.0  2625.0          18.6        82.0     USA\n",
       "397  31.0        4.0         119.0        82.0  2720.0          19.4        82.0     USA\n",
       "\n",
       "[398 rows x 8 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28991558",
   "metadata": {},
   "source": [
    "### Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "2df7a4d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_cleaned.drop('mpg', axis=1)\n",
    "y = pd.DataFrame(data_cleaned['mpg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f404aaa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8.0</td>\n",
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       "      <td>24.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Europe</td>\n",
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       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>4.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82.0</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>4.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82.0</td>\n",
       "      <td>USA</td>\n",
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       "<p>398 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cylinders  displacement  horsepower  weight  acceleration  model year  origin\n",
       "0          8.0         307.0       130.0  3504.0          12.0        70.0     USA\n",
       "1          8.0         350.0       165.0  3693.0          11.5        70.0     USA\n",
       "2          8.0         318.0       150.0  3436.0          11.0        70.0     USA\n",
       "3          8.0         304.0       150.0  3433.0          12.0        70.0     USA\n",
       "4          8.0         302.0       140.0  3449.0          10.5        70.0     USA\n",
       "..         ...           ...         ...     ...           ...         ...     ...\n",
       "393        4.0         140.0        86.0  2790.0          15.6        82.0     USA\n",
       "394        4.0          97.0        52.0  2130.0          24.6        82.0  Europe\n",
       "395        4.0         135.0        84.0  2295.0          11.6        82.0     USA\n",
       "396        4.0         120.0        79.0  2625.0          18.6        82.0     USA\n",
       "397        4.0         119.0        82.0  2720.0          19.4        82.0     USA\n",
       "\n",
       "[398 rows x 7 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "912c5949",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>397</th>\n",
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      "text/plain": [
       "      mpg\n",
       "0    18.0\n",
       "1    15.0\n",
       "2    18.0\n",
       "3    16.0\n",
       "4    17.0\n",
       "..    ...\n",
       "393  27.0\n",
       "394  44.0\n",
       "395  32.0\n",
       "396  28.0\n",
       "397  31.0\n",
       "\n",
       "[398 rows x 1 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "7556d8a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=data_cleaned['origin'], test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "8562e37a",
   "metadata": {},
   "outputs": [
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       "      <th>226</th>\n",
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       "    <tr>\n",
       "      <th>152</th>\n",
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       "      <td>95.0</td>\n",
       "      <td>3264.0</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>4.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>79.0</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>4.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>72.0</td>\n",
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       "      <td>455.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>4951.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>73.0</td>\n",
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       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>4.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3211.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>USA</td>\n",
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       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>6.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2725.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>81.0</td>\n",
       "      <td>USA</td>\n",
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       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2890.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>79.0</td>\n",
       "      <td>USA</td>\n",
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       "     cylinders  displacement  horsepower  weight  acceleration  model year  origin\n",
       "391        4.0         135.0        84.0  2370.0          13.0        82.0     USA\n",
       "226        6.0         231.0       105.0  3425.0          16.9        77.0     USA\n",
       "152        6.0         225.0        95.0  3264.0          16.0        75.0     USA\n",
       "302        4.0         105.0        70.0  2150.0          14.9        79.0     USA\n",
       "83         4.0          98.0        80.0  2164.0          15.0        72.0     USA\n",
       "..         ...           ...         ...     ...           ...         ...     ...\n",
       "95         8.0         455.0       225.0  4951.0          11.0        73.0     USA\n",
       "183        4.0         116.0        81.0  2220.0          16.9        76.0  Europe\n",
       "176        6.0         232.0        90.0  3211.0          17.0        75.0     USA\n",
       "341        6.0         173.0       110.0  2725.0          12.6        81.0     USA\n",
       "282        4.0         140.0        88.0  2890.0          17.3        79.0     USA\n",
       "\n",
       "[318 rows x 7 columns]"
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   "execution_count": 132,
   "id": "bd4ed116",
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   "outputs": [
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       "      mpg\n",
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       "302  34.5\n",
       "83   28.0\n",
       "..    ...\n",
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       "176  19.0\n",
       "341  23.5\n",
       "282  22.3\n",
       "\n",
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   "id": "6d7441b5",
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   "outputs": [
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       "      <td>1850.0</td>\n",
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       "      <th>164</th>\n",
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       "      <td>110.0</td>\n",
       "      <td>3039.0</td>\n",
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       "      <th>286</th>\n",
       "      <td>8.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>79.0</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71.0</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4668.0</td>\n",
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       "      <td>1760.0</td>\n",
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       "     cylinders  displacement  horsepower  weight  acceleration  model year       origin\n",
       "383        4.0          91.0        67.0  1965.0          15.0        82.0  Asia/Others\n",
       "7          8.0         440.0       215.0  4312.0           8.5        70.0          USA\n",
       "232        8.0         351.0       149.0  4335.0          14.5        77.0          USA\n",
       "167        4.0          97.0        75.0  2171.0          16.0        75.0  Asia/Others\n",
       "329        4.0          91.0        67.0  1850.0          13.8        80.0  Asia/Others\n",
       "..         ...           ...         ...     ...           ...         ...          ...\n",
       "164        6.0         231.0       110.0  3039.0          15.0        75.0          USA\n",
       "286        8.0         302.0       129.0  3725.0          13.4        79.0          USA\n",
       "32         4.0          98.0        93.5  2046.0          19.0        71.0          USA\n",
       "156        8.0         400.0       170.0  4668.0          11.5        75.0          USA\n",
       "345        4.0          81.0        60.0  1760.0          16.1        81.0  Asia/Others\n",
       "\n",
       "[80 rows x 7 columns]"
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     "execution_count": 133,
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   "execution_count": 134,
   "id": "42b9f467",
   "metadata": {},
   "outputs": [
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       "      mpg\n",
       "383  38.0\n",
       "7    14.0\n",
       "232  16.0\n",
       "167  29.0\n",
       "329  44.6\n",
       "..    ...\n",
       "164  21.0\n",
       "286  17.6\n",
       "32   25.0\n",
       "156  16.0\n",
       "345  35.1\n",
       "\n",
       "[80 rows x 1 columns]"
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     "execution_count": 134,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "6c7edbf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_encoded = pd.get_dummies(data=X_train, columns=['origin'])\n",
    "X_test_encoded = pd.get_dummies(data=X_test, columns=['origin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "ed281a04",
   "metadata": {},
   "outputs": [
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       "      <th>391</th>\n",
       "      <td>4.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2370.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>6.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>3425.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>6.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3264.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>4.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>4.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>8.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>4951.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>4.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3211.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>6.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2725.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2890.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>318 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cylinders  displacement  horsepower  weight  ...  model year  origin_Asia/Others  origin_Europe  origin_USA\n",
       "391        4.0         135.0        84.0  2370.0  ...        82.0                   0              0           1\n",
       "226        6.0         231.0       105.0  3425.0  ...        77.0                   0              0           1\n",
       "152        6.0         225.0        95.0  3264.0  ...        75.0                   0              0           1\n",
       "302        4.0         105.0        70.0  2150.0  ...        79.0                   0              0           1\n",
       "83         4.0          98.0        80.0  2164.0  ...        72.0                   0              0           1\n",
       "..         ...           ...         ...     ...  ...         ...                 ...            ...         ...\n",
       "95         8.0         455.0       225.0  4951.0  ...        73.0                   0              0           1\n",
       "183        4.0         116.0        81.0  2220.0  ...        76.0                   0              1           0\n",
       "176        6.0         232.0        90.0  3211.0  ...        75.0                   0              0           1\n",
       "341        6.0         173.0       110.0  2725.0  ...        81.0                   0              0           1\n",
       "282        4.0         140.0        88.0  2890.0  ...        79.0                   0              0           1\n",
       "\n",
       "[318 rows x 9 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "f2b5d94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>horsepower</th>\n",
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       "      <th>acceleration</th>\n",
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       "      <th>167</th>\n",
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       "      <th>329</th>\n",
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       "      <td>67.0</td>\n",
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       "      <th>164</th>\n",
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       "      <td>231.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>3039.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>8.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>13.4</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
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       "      <td>98.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71.0</td>\n",
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       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>8.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4668.0</td>\n",
       "      <td>11.5</td>\n",
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       "<p>80 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "     cylinders  displacement  horsepower  weight  ...  model year  origin_Asia/Others  origin_Europe  origin_USA\n",
       "383        4.0          91.0        67.0  1965.0  ...        82.0                   1              0           0\n",
       "7          8.0         440.0       215.0  4312.0  ...        70.0                   0              0           1\n",
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       "345        4.0          81.0        60.0  1760.0  ...        81.0                   1              0           0\n",
       "\n",
       "[80 rows x 9 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "ebac515b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_encoded['origin_Asia/Others'] = X_train_encoded['origin_Asia/Others'].astype('float32')\n",
    "X_train_encoded['origin_USA'] = X_train_encoded['origin_USA'].astype('float32')\n",
    "X_train_encoded['origin_Europe'] = X_train_encoded['origin_Europe'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "9b00c1ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled_std = X_train_encoded.copy()\n",
    "X_test_scaled_std = X_test_encoded.copy()\n",
    "\n",
    "X_train_scaled_minmax = X_train_encoded.copy()\n",
    "X_test_scaled_minmax = X_test_encoded.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "a06417d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "6b2366b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "minmax_scaler = MinMaxScaler()\n",
    "std_scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "53710b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "minmax_scaler.fit(X_train_scaled_minmax.iloc[:, :6])\n",
    "X_train_scaled_minmax.iloc[:, :6]=minmax_scaler.transform(X_train_scaled_minmax.iloc[:, :6])\n",
    "X_test_scaled_minmax.iloc[:, :6]=minmax_scaler.transform(X_test_scaled_minmax.iloc[:, :6])\n",
    "\n",
    "std_scaler.fit(X_train_scaled_std.iloc[:, :6])\n",
    "X_train_scaled_std.iloc[:, :6]=std_scaler.transform(X_train_scaled_std.iloc[:, :6])\n",
    "X_test_scaled_std.iloc[:, :6]=std_scaler.transform(X_test_scaled_std.iloc[:, :6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "8f6f54f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.6</td>\n",
       "      <td>0.423773</td>\n",
       "      <td>0.239130</td>\n",
       "      <td>0.453076</td>\n",
       "      <td>0.521472</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.271318</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.315282</td>\n",
       "      <td>0.251534</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.186047</td>\n",
       "      <td>0.228261</td>\n",
       "      <td>0.362064</td>\n",
       "      <td>0.539877</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>318 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cylinders  displacement  horsepower    weight  ...  model year  origin_Asia/Others  origin_Europe  origin_USA\n",
       "391        0.2      0.173127    0.206522  0.214630  ...    1.000000                 0.0            0.0         1.0\n",
       "226        0.6      0.421189    0.320652  0.513751  ...    0.583333                 0.0            0.0         1.0\n",
       "152        0.6      0.405685    0.266304  0.468103  ...    0.416667                 0.0            0.0         1.0\n",
       "302        0.2      0.095607    0.130435  0.152254  ...    0.750000                 0.0            0.0         1.0\n",
       "83         0.2      0.077519    0.184783  0.156223  ...    0.166667                 0.0            0.0         1.0\n",
       "..         ...           ...         ...       ...  ...         ...                 ...            ...         ...\n",
       "95         1.0      1.000000    0.972826  0.946413  ...    0.250000                 0.0            0.0         1.0\n",
       "183        0.2      0.124031    0.190217  0.172101  ...    0.500000                 0.0            1.0         0.0\n",
       "176        0.6      0.423773    0.239130  0.453076  ...    0.416667                 0.0            0.0         1.0\n",
       "341        0.6      0.271318    0.347826  0.315282  ...    0.916667                 0.0            0.0         1.0\n",
       "282        0.2      0.186047    0.228261  0.362064  ...    0.750000                 0.0            0.0         1.0\n",
       "\n",
       "[318 rows x 9 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled_minmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "8cdfd749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>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_Asia/Others</th>\n",
       "      <th>origin_Europe</th>\n",
       "      <th>origin_USA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>-0.852161</td>\n",
       "      <td>-0.560972</td>\n",
       "      <td>-0.531057</td>\n",
       "      <td>-0.708476</td>\n",
       "      <td>-0.959069</td>\n",
       "      <td>1.585994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.331189</td>\n",
       "      <td>0.363299</td>\n",
       "      <td>0.013370</td>\n",
       "      <td>0.537464</td>\n",
       "      <td>0.467286</td>\n",
       "      <td>0.253156</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0.331189</td>\n",
       "      <td>0.305532</td>\n",
       "      <td>-0.245881</td>\n",
       "      <td>0.347325</td>\n",
       "      <td>0.138127</td>\n",
       "      <td>-0.279980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>-0.852161</td>\n",
       "      <td>-0.849807</td>\n",
       "      <td>-0.894008</td>\n",
       "      <td>-0.968293</td>\n",
       "      <td>-0.264178</td>\n",
       "      <td>0.786291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>-0.852161</td>\n",
       "      <td>-0.917202</td>\n",
       "      <td>-0.634757</td>\n",
       "      <td>-0.951760</td>\n",
       "      <td>-0.227605</td>\n",
       "      <td>-1.079683</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.514540</td>\n",
       "      <td>2.519932</td>\n",
       "      <td>3.124382</td>\n",
       "      <td>2.339648</td>\n",
       "      <td>-1.690534</td>\n",
       "      <td>-0.813115</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>-0.852161</td>\n",
       "      <td>-0.743901</td>\n",
       "      <td>-0.608832</td>\n",
       "      <td>-0.885624</td>\n",
       "      <td>0.467286</td>\n",
       "      <td>-0.013412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0.331189</td>\n",
       "      <td>0.372927</td>\n",
       "      <td>-0.375506</td>\n",
       "      <td>0.284733</td>\n",
       "      <td>0.503859</td>\n",
       "      <td>-0.279980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>0.331189</td>\n",
       "      <td>-0.195115</td>\n",
       "      <td>0.142996</td>\n",
       "      <td>-0.289226</td>\n",
       "      <td>-1.105362</td>\n",
       "      <td>1.319426</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>-0.852161</td>\n",
       "      <td>-0.512833</td>\n",
       "      <td>-0.427356</td>\n",
       "      <td>-0.094364</td>\n",
       "      <td>0.613579</td>\n",
       "      <td>0.786291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>318 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cylinders  displacement  horsepower    weight  ...  model year  origin_Asia/Others  origin_Europe  origin_USA\n",
       "391  -0.852161     -0.560972   -0.531057 -0.708476  ...    1.585994                 0.0            0.0         1.0\n",
       "226   0.331189      0.363299    0.013370  0.537464  ...    0.253156                 0.0            0.0         1.0\n",
       "152   0.331189      0.305532   -0.245881  0.347325  ...   -0.279980                 0.0            0.0         1.0\n",
       "302  -0.852161     -0.849807   -0.894008 -0.968293  ...    0.786291                 0.0            0.0         1.0\n",
       "83   -0.852161     -0.917202   -0.634757 -0.951760  ...   -1.079683                 0.0            0.0         1.0\n",
       "..         ...           ...         ...       ...  ...         ...                 ...            ...         ...\n",
       "95    1.514540      2.519932    3.124382  2.339648  ...   -0.813115                 0.0            0.0         1.0\n",
       "183  -0.852161     -0.743901   -0.608832 -0.885624  ...   -0.013412                 0.0            1.0         0.0\n",
       "176   0.331189      0.372927   -0.375506  0.284733  ...   -0.279980                 0.0            0.0         1.0\n",
       "341   0.331189     -0.195115    0.142996 -0.289226  ...    1.319426                 0.0            0.0         1.0\n",
       "282  -0.852161     -0.512833   -0.427356 -0.094364  ...    0.786291                 0.0            0.0         1.0\n",
       "\n",
       "[318 rows x 9 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "f938f40a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(318, 9)\n",
      "(80, 9)\n",
      "(318, 9)\n",
      "(80, 9)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_scaled_std.shape)\n",
    "print(X_test_scaled_std.shape)\n",
    "print(X_train_scaled_minmax.shape)\n",
    "print(X_test_scaled_minmax.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "0fc40d3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For minmax scaled version\n",
    "X_train_1= np.array(X_train_scaled_minmax)\n",
    "X_test_1= np.array(X_test_scaled_minmax)\n",
    "\n",
    "# For standard scaled version\n",
    "X_train_2 = np.array(X_train_scaled_std)\n",
    "X_test_2 = np.array(X_test_scaled_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3a28cf",
   "metadata": {},
   "source": [
    "### Establishing the ANN Architeecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "fbdc0272",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For minmax scaled version\n",
    "model1 = Sequential([\n",
    "    Dense(units=50, input_shape=(9,), activation='linear'),\n",
    "    Dense(units=25, activation='elu'),\n",
    "    Dense(units=16, activation='elu'),\n",
    "    Dense(units=8, activation='elu'),\n",
    "    Dense(units=4, activation='linear'),\n",
    "    Dense(units=2, activation='linear'),\n",
    "    Dense(units=1, activation='linear')\n",
    "])\n",
    "\n",
    "# For standard scaled version\n",
    "model2 = Sequential([\n",
    "    Dense(units=50, input_shape=(9,), activation='linear'),\n",
    "    Dense(units=25, activation='elu'),\n",
    "    Dense(units=16, activation='elu'),\n",
    "    Dense(units=8, activation='elu'),\n",
    "    Dense(units=4, activation='linear'),\n",
    "    Dense(units=2, activation='linear'),\n",
    "    Dense(units=1, activation='linear')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "35d489aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/42\n",
      "10/10 [==============================] - 1s 25ms/step - loss: 580.1896 - val_loss: 558.2985\n",
      "Epoch 2/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 519.0006 - val_loss: 487.3487\n",
      "Epoch 3/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 436.3383 - val_loss: 389.3329\n",
      "Epoch 4/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 325.2852 - val_loss: 269.9108\n",
      "Epoch 5/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 205.9129 - val_loss: 159.6714\n",
      "Epoch 6/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 124.3871 - val_loss: 110.3274\n",
      "Epoch 7/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 95.8216 - val_loss: 98.4477\n",
      "Epoch 8/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 80.3407 - val_loss: 79.9241\n",
      "Epoch 9/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 65.3093 - val_loss: 66.5821\n",
      "Epoch 10/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 53.8154 - val_loss: 53.4920\n",
      "Epoch 11/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 41.7460 - val_loss: 42.1842\n",
      "Epoch 12/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 31.9608 - val_loss: 32.8846\n",
      "Epoch 13/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 24.6383 - val_loss: 26.2514\n",
      "Epoch 14/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 20.1457 - val_loss: 23.1430\n",
      "Epoch 15/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 18.3823 - val_loss: 22.3130\n",
      "Epoch 16/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 17.8485 - val_loss: 21.6265\n",
      "Epoch 17/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 17.4595 - val_loss: 21.0619\n",
      "Epoch 18/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 16.8477 - val_loss: 21.0935\n",
      "Epoch 19/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 16.4866 - val_loss: 20.5591\n",
      "Epoch 20/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 16.1654 - val_loss: 20.1808\n",
      "Epoch 21/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 15.8904 - val_loss: 19.9941\n",
      "Epoch 22/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 15.5743 - val_loss: 19.6101\n",
      "Epoch 23/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 15.2457 - val_loss: 19.4123\n",
      "Epoch 24/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 15.0335 - val_loss: 19.1748\n",
      "Epoch 25/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 14.8008 - val_loss: 18.7713\n",
      "Epoch 26/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 14.5465 - val_loss: 18.6556\n",
      "Epoch 27/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 14.4900 - val_loss: 18.6984\n",
      "Epoch 28/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 14.0860 - val_loss: 18.0052\n",
      "Epoch 29/42\n",
      "10/10 [==============================] - 0s 9ms/step - loss: 14.0033 - val_loss: 17.9097\n",
      "Epoch 30/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 13.8422 - val_loss: 17.6645\n",
      "Epoch 31/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 13.5156 - val_loss: 17.2994\n",
      "Epoch 32/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 13.2754 - val_loss: 17.2396\n",
      "Epoch 33/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 13.1075 - val_loss: 17.0194\n",
      "Epoch 34/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 12.9145 - val_loss: 16.8130\n",
      "Epoch 35/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 12.7682 - val_loss: 16.3843\n",
      "Epoch 36/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 12.6051 - val_loss: 16.4457\n",
      "Epoch 37/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 12.3136 - val_loss: 16.0302\n",
      "Epoch 38/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 12.1708 - val_loss: 15.7793\n",
      "Epoch 39/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 12.0210 - val_loss: 15.8283\n",
      "Epoch 40/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 11.9351 - val_loss: 15.3186\n",
      "Epoch 41/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 11.6495 - val_loss: 15.4200\n",
      "Epoch 42/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 11.4653 - val_loss: 14.9462\n"
     ]
    }
   ],
   "source": [
    "# For minmax scaled version\n",
    "model1.compile(optimizer = Adam(learning_rate=0.001), loss='mean_squared_error')\n",
    "history1 = model1.fit(X_train_1, y_train, validation_data=(X_test_1, y_test), epochs=42, verbose='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "7572bdef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/42\n",
      "10/10 [==============================] - 1s 27ms/step - loss: 617.7740 - val_loss: 592.3104\n",
      "Epoch 2/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 561.2755 - val_loss: 541.6514\n",
      "Epoch 3/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 505.2898 - val_loss: 479.4923\n",
      "Epoch 4/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 433.6291 - val_loss: 399.4073\n",
      "Epoch 5/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 344.3719 - val_loss: 298.6833\n",
      "Epoch 6/42\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 237.5604 - val_loss: 183.7691\n",
      "Epoch 7/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 124.6811 - val_loss: 78.3168\n",
      "Epoch 8/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 42.7703 - val_loss: 22.9924\n",
      "Epoch 9/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 19.0330 - val_loss: 17.4300\n",
      "Epoch 10/42\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 16.9780 - val_loss: 14.6505\n",
      "Epoch 11/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 14.0351 - val_loss: 14.2426\n",
      "Epoch 12/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 12.3905 - val_loss: 13.3268\n",
      "Epoch 13/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 11.2771 - val_loss: 12.8246\n",
      "Epoch 14/42\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 10.6613 - val_loss: 12.3892\n",
      "Epoch 15/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 10.2058 - val_loss: 12.2394\n",
      "Epoch 16/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 9.6915 - val_loss: 11.9377\n",
      "Epoch 17/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 9.4251 - val_loss: 11.7479\n",
      "Epoch 18/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 9.1411 - val_loss: 11.6736\n",
      "Epoch 19/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 8.9155 - val_loss: 11.5273\n",
      "Epoch 20/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 8.7388 - val_loss: 11.4041\n",
      "Epoch 21/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 8.6039 - val_loss: 11.2526\n",
      "Epoch 22/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 8.4577 - val_loss: 11.1410\n",
      "Epoch 23/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 8.3430 - val_loss: 11.0473\n",
      "Epoch 24/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 8.2695 - val_loss: 11.0059\n",
      "Epoch 25/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 8.1765 - val_loss: 10.9232\n",
      "Epoch 26/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 8.0879 - val_loss: 10.8617\n",
      "Epoch 27/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 8.0367 - val_loss: 10.7441\n",
      "Epoch 28/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.9185 - val_loss: 10.7477\n",
      "Epoch 29/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.8720 - val_loss: 10.6608\n",
      "Epoch 30/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.8441 - val_loss: 10.5419\n",
      "Epoch 31/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.7861 - val_loss: 10.5160\n",
      "Epoch 32/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.7930 - val_loss: 10.5415\n",
      "Epoch 33/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 7.8169 - val_loss: 10.3639\n",
      "Epoch 34/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 7.7035 - val_loss: 10.3278\n",
      "Epoch 35/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 7.7248 - val_loss: 10.3556\n",
      "Epoch 36/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.6589 - val_loss: 10.2209\n",
      "Epoch 37/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.6053 - val_loss: 10.2549\n",
      "Epoch 38/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.6429 - val_loss: 10.2255\n",
      "Epoch 39/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.5041 - val_loss: 10.1092\n",
      "Epoch 40/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.5349 - val_loss: 10.0662\n",
      "Epoch 41/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.5000 - val_loss: 10.0528\n",
      "Epoch 42/42\n",
      "10/10 [==============================] - 0s 8ms/step - loss: 7.4801 - val_loss: 10.0841\n"
     ]
    }
   ],
   "source": [
    "# For standard scaled version\n",
    "model2.compile(optimizer = Adam(learning_rate=0.001), loss='mean_squared_error')\n",
    "history2 = model2.fit(X_train_2, y_train, validation_data=(X_test_2, y_test), epochs=42, verbose='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "497669d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = list(i for i in range(42))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "175832fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "validation_loss_minmax = history1.history['val_loss']\n",
    "validation_loss_std = history2.history['val_loss']\n",
    "\n",
    "plt.plot(epochs, validation_loss_minmax, label='MinMax Validation Loss')\n",
    "plt.plot(epochs, validation_loss_std, label='Standard scaled Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Validation Loss Metric')\n",
    "plt.title('Learning Curves for MinMax and Standard Scaling - Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "dfe2803a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_loss_minmax = history1.history['loss']\n",
    "training_loss_std = history2.history['loss']\n",
    "\n",
    "plt.plot(epochs, training_loss_minmax, label='MinMax Training Loss')\n",
    "plt.plot(epochs, training_loss_std, label='Standard scaled Training Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Training Loss Metric')\n",
    "plt.title('Learning Curves for MinMax and Standard Scaling - Training Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "87ad7014",
   "metadata": {},
   "outputs": [],
   "source": [
    "how_many_epochs = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "826530d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's go with the standard scaled version and train different models with different learning rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "0ed2cf25",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_optimizer = Adam(learning_rate=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "5da8e969",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/42\n",
      "10/10 [==============================] - 1s 26ms/step - loss: 244.3621 - val_loss: 47.8285\n",
      "Epoch 2/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 46.8989 - val_loss: 24.9856\n",
      "Epoch 3/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 20.6061 - val_loss: 14.0451\n",
      "Epoch 4/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 11.6695 - val_loss: 11.4027\n",
      "Epoch 5/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 9.4328 - val_loss: 11.1224\n",
      "Epoch 6/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.9107 - val_loss: 11.6383\n",
      "Epoch 7/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 8.0640 - val_loss: 10.5584\n",
      "Epoch 8/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.5296 - val_loss: 10.0002\n",
      "Epoch 9/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.1916 - val_loss: 10.5492\n",
      "Epoch 10/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 7.1735 - val_loss: 9.6984\n",
      "Epoch 11/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 6.8250 - val_loss: 10.8475\n",
      "Epoch 12/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.3615 - val_loss: 9.8310\n",
      "Epoch 13/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 7.1229 - val_loss: 9.6555\n",
      "Epoch 14/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.0633 - val_loss: 9.3061\n",
      "Epoch 15/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.0616 - val_loss: 9.7745\n",
      "Epoch 16/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.6974 - val_loss: 9.3671\n",
      "Epoch 17/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.7623 - val_loss: 9.3275\n",
      "Epoch 18/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.8872 - val_loss: 9.3889\n",
      "Epoch 19/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 6.9406 - val_loss: 9.2252\n",
      "Epoch 20/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.6955 - val_loss: 9.2722\n",
      "Epoch 21/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.6110 - val_loss: 9.2080\n",
      "Epoch 22/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.0227 - val_loss: 10.2512\n",
      "Epoch 23/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 7.1053 - val_loss: 11.0721\n",
      "Epoch 24/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 7.1608 - val_loss: 11.1001\n",
      "Epoch 25/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 6.6922 - val_loss: 9.9035\n",
      "Epoch 26/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.7667 - val_loss: 8.7272\n",
      "Epoch 27/42\n",
      "10/10 [==============================] - 0s 7ms/step - loss: 5.9283 - val_loss: 8.8144\n",
      "Epoch 28/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 6.3436 - val_loss: 8.9200\n",
      "Epoch 29/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.6796 - val_loss: 8.7209\n",
      "Epoch 30/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.3378 - val_loss: 8.8801\n",
      "Epoch 31/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.2812 - val_loss: 9.4853\n",
      "Epoch 32/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 5.9312 - val_loss: 9.2902\n",
      "Epoch 33/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 5.8902 - val_loss: 8.7711\n",
      "Epoch 34/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 5.7859 - val_loss: 8.6561\n",
      "Epoch 35/42\n",
      "10/10 [==============================] - 0s 7ms/step - loss: 7.4883 - val_loss: 10.6804\n",
      "Epoch 36/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.3423 - val_loss: 8.7523\n",
      "Epoch 37/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.0851 - val_loss: 9.2117\n",
      "Epoch 38/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.1365 - val_loss: 11.7466\n",
      "Epoch 39/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 6.7537 - val_loss: 15.4848\n",
      "Epoch 40/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 7.9858 - val_loss: 11.8170\n",
      "Epoch 41/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 7.0374 - val_loss: 9.3753\n",
      "Epoch 42/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 6.1978 - val_loss: 8.6999\n"
     ]
    }
   ],
   "source": [
    "m1 = Sequential([\n",
    "    Dense(units=50, input_shape=(9,), activation='linear'),\n",
    "    Dense(units=25, activation='elu'),\n",
    "    Dense(units=16, activation='elu'),\n",
    "    Dense(units=8, activation='elu'),\n",
    "    Dense(units=4, activation='linear'),\n",
    "    Dense(units=2, activation='linear'),\n",
    "    Dense(units=1, activation='linear')\n",
    "])\n",
    "\n",
    "m1.compile(optimizer = my_optimizer, loss='mean_squared_error')\n",
    "h1 = m1.fit(X_train_2, y_train, validation_data=(X_test_2, y_test), epochs=how_many_epochs, verbose='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "136e95a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = Sequential([\n",
    "    Dense(units=50, input_shape=(9,), activation='linear'),\n",
    "    Dense(units=25, activation='elu'),\n",
    "    Dense(units=16, activation='elu'),\n",
    "    Dense(units=8, activation='elu'),\n",
    "    Dense(units=4, activation='linear'),\n",
    "    Dense(units=2, activation='linear'),\n",
    "    Dense(units=1, activation='linear')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "4da068b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/42\n",
      "10/10 [==============================] - 1s 24ms/step - loss: 353.7922 - val_loss: 207.0000\n",
      "Epoch 2/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 119.9475 - val_loss: 39.9712\n",
      "Epoch 3/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 28.7291 - val_loss: 19.3482\n",
      "Epoch 4/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 11.6043 - val_loss: 13.0907\n",
      "Epoch 5/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 11.5469 - val_loss: 14.7754\n",
      "Epoch 6/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 10.4063 - val_loss: 14.0530\n",
      "Epoch 7/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 11.1518 - val_loss: 11.8582\n",
      "Epoch 8/42\n",
      "10/10 [==============================] - 0s 10ms/step - loss: 11.6220 - val_loss: 19.7699\n",
      "Epoch 9/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 10.8595 - val_loss: 10.9358\n",
      "Epoch 10/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 10.4358 - val_loss: 11.3997\n",
      "Epoch 11/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 11.7622 - val_loss: 12.6156\n",
      "Epoch 12/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 12.7238 - val_loss: 10.4714\n",
      "Epoch 13/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 12.1430 - val_loss: 12.3448\n",
      "Epoch 14/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 10.7494 - val_loss: 10.8743\n",
      "Epoch 15/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 8.8162 - val_loss: 11.5500\n",
      "Epoch 16/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 10.2751 - val_loss: 10.0120\n",
      "Epoch 17/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 8.5660 - val_loss: 12.3642\n",
      "Epoch 18/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 8.4650 - val_loss: 11.7847\n",
      "Epoch 19/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 8.4509 - val_loss: 9.8459\n",
      "Epoch 20/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 9.0626 - val_loss: 10.9260\n",
      "Epoch 21/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 8.7345 - val_loss: 9.5671\n",
      "Epoch 22/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 7.6517 - val_loss: 9.4241\n",
      "Epoch 23/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 9.2798 - val_loss: 9.4353\n",
      "Epoch 24/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 9.1518 - val_loss: 10.8405\n",
      "Epoch 25/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 10.9760 - val_loss: 10.0556\n",
      "Epoch 26/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 11.3110 - val_loss: 10.5636\n",
      "Epoch 27/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 9.3244 - val_loss: 14.4210\n",
      "Epoch 28/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 15.5670 - val_loss: 13.9648\n",
      "Epoch 29/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 11.7445 - val_loss: 13.1896\n",
      "Epoch 30/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 9.9618 - val_loss: 12.9589\n",
      "Epoch 31/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 30.9543 - val_loss: 173.5990\n",
      "Epoch 32/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 122.1164 - val_loss: 66.5832\n",
      "Epoch 33/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 62.1104 - val_loss: 66.5930\n",
      "Epoch 34/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 61.7348 - val_loss: 67.5072\n",
      "Epoch 35/42\n",
      "10/10 [==============================] - 0s 9ms/step - loss: 60.4112 - val_loss: 66.8616\n",
      "Epoch 36/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 59.9630 - val_loss: 66.5787\n",
      "Epoch 37/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 59.8741 - val_loss: 66.5934\n",
      "Epoch 38/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 60.1758 - val_loss: 66.8328\n",
      "Epoch 39/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 61.0244 - val_loss: 73.2007\n",
      "Epoch 40/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 63.5889 - val_loss: 66.5787\n",
      "Epoch 41/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 62.7255 - val_loss: 86.7045\n",
      "Epoch 42/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 68.2497 - val_loss: 67.4116\n"
     ]
    }
   ],
   "source": [
    "my_optimizer_2 = Adam(learning_rate=0.1)\n",
    "m2.compile(optimizer = my_optimizer_2, loss='mean_squared_error')\n",
    "h2 = m2.fit(X_train_2, y_train, validation_data=(X_test_2, y_test), epochs=how_many_epochs, verbose='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "232c144f",
   "metadata": {},
   "outputs": [],
   "source": [
    "m3 = Sequential([\n",
    "    Dense(units=50, input_shape=(9,), activation='linear'),\n",
    "    Dense(units=25, activation='elu'),\n",
    "    Dense(units=16, activation='elu'),\n",
    "    Dense(units=8, activation='elu'),\n",
    "    Dense(units=4, activation='linear'),\n",
    "    Dense(units=2, activation='linear'),\n",
    "    Dense(units=1, activation='linear')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "b38640aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/42\n",
      "10/10 [==============================] - 2s 33ms/step - loss: 9625.0303 - val_loss: 226.8596\n",
      "Epoch 2/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 290.7993 - val_loss: 139.4501\n",
      "Epoch 3/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 150.0388 - val_loss: 147.3532\n",
      "Epoch 4/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 112.5705 - val_loss: 121.4241\n",
      "Epoch 5/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 111.5952 - val_loss: 108.0003\n",
      "Epoch 6/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 100.3023 - val_loss: 95.5528\n",
      "Epoch 7/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 83.7967 - val_loss: 84.8514\n",
      "Epoch 8/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 73.9756 - val_loss: 80.8826\n",
      "Epoch 9/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 71.8296 - val_loss: 73.3151\n",
      "Epoch 10/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 62.2765 - val_loss: 81.3824\n",
      "Epoch 11/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 58.6542 - val_loss: 74.3012\n",
      "Epoch 12/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 60.7384 - val_loss: 65.3037\n",
      "Epoch 13/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 53.5652 - val_loss: 63.4179\n",
      "Epoch 14/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 54.5531 - val_loss: 60.8777\n",
      "Epoch 15/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 48.7174 - val_loss: 61.3688\n",
      "Epoch 16/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 45.2367 - val_loss: 55.6142\n",
      "Epoch 17/42\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 43.9134 - val_loss: 58.0563\n",
      "Epoch 18/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 39.6529 - val_loss: 46.6157\n",
      "Epoch 19/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 38.8025 - val_loss: 47.0543\n",
      "Epoch 20/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 38.0150 - val_loss: 40.8459\n",
      "Epoch 21/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 33.5674 - val_loss: 38.1304\n",
      "Epoch 22/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 33.5166 - val_loss: 39.3155\n",
      "Epoch 23/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 31.6748 - val_loss: 34.0433\n",
      "Epoch 24/42\n",
      "10/10 [==============================] - 0s 8ms/step - loss: 26.8297 - val_loss: 31.2329\n",
      "Epoch 25/42\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 25.8286 - val_loss: 31.9156\n",
      "Epoch 26/42\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 26.1206 - val_loss: 34.0598\n",
      "Epoch 27/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 23.8160 - val_loss: 29.1331\n",
      "Epoch 28/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 21.8069 - val_loss: 25.2896\n",
      "Epoch 29/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 20.1736 - val_loss: 23.1634\n",
      "Epoch 30/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 19.2442 - val_loss: 19.8304\n",
      "Epoch 31/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 20.1398 - val_loss: 19.0512\n",
      "Epoch 32/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 15.2709 - val_loss: 20.6708\n",
      "Epoch 33/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 13.7009 - val_loss: 16.7533\n",
      "Epoch 34/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 13.6279 - val_loss: 19.2304\n",
      "Epoch 35/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 13.0145 - val_loss: 14.7296\n",
      "Epoch 36/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 10.8789 - val_loss: 14.1613\n",
      "Epoch 37/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 11.1504 - val_loss: 13.0543\n",
      "Epoch 38/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 10.9598 - val_loss: 12.7288\n",
      "Epoch 39/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 10.6045 - val_loss: 12.3907\n",
      "Epoch 40/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 10.8611 - val_loss: 17.1229\n",
      "Epoch 41/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 11.0444 - val_loss: 11.8960\n",
      "Epoch 42/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 9.6479 - val_loss: 11.7449\n"
     ]
    }
   ],
   "source": [
    "my_optimizer_3 = Adam(learning_rate=0.18)\n",
    "m3.compile(optimizer = my_optimizer_3, loss='mean_squared_error')\n",
    "h3 = m3.fit(X_train_2, y_train, validation_data=(X_test_2, y_test), epochs=how_many_epochs, verbose='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "1aac850b",
   "metadata": {},
   "outputs": [],
   "source": [
    "m4 = Sequential([\n",
    "    Dense(units=50, input_shape=(9,), activation='linear'),\n",
    "    Dense(units=25, activation='elu'),\n",
    "    Dense(units=16, activation='elu'),\n",
    "    Dense(units=8, activation='elu'),\n",
    "    Dense(units=4, activation='linear'),\n",
    "    Dense(units=2, activation='linear'),\n",
    "    Dense(units=1, activation='linear')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "717d2cbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/42\n",
      "10/10 [==============================] - 1s 26ms/step - loss: 2082.5315 - val_loss: 804.7235\n",
      "Epoch 2/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 608.8988 - val_loss: 344.1545\n",
      "Epoch 3/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 155.1532 - val_loss: 86.4282\n",
      "Epoch 4/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 71.5799 - val_loss: 80.7086\n",
      "Epoch 5/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 63.0498 - val_loss: 71.0368\n",
      "Epoch 6/42\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 56.4865 - val_loss: 60.2862\n",
      "Epoch 7/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 59.0230 - val_loss: 55.6663\n",
      "Epoch 8/42\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 52.6288 - val_loss: 69.1644\n",
      "Epoch 9/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 55.7857 - val_loss: 70.7690\n",
      "Epoch 10/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 62.7740 - val_loss: 73.8696\n",
      "Epoch 11/42\n",
      "10/10 [==============================] - 0s 4ms/step - loss: 61.7098 - val_loss: 69.5468\n",
      "Epoch 12/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 60.7464 - val_loss: 69.3094\n",
      "Epoch 13/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 60.5590 - val_loss: 69.1154\n",
      "Epoch 14/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 59.6958 - val_loss: 308.5260\n",
      "Epoch 15/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 204.3789 - val_loss: 71.0375\n",
      "Epoch 16/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 63.9093 - val_loss: 61.1511\n",
      "Epoch 17/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 47.0644 - val_loss: 55.6048\n",
      "Epoch 18/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 40.5443 - val_loss: 51.7663\n",
      "Epoch 19/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 37.4846 - val_loss: 49.3306\n",
      "Epoch 20/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 39.2271 - val_loss: 43.3773\n",
      "Epoch 21/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 38.2511 - val_loss: 42.6898\n",
      "Epoch 22/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 36.6707 - val_loss: 45.9789\n",
      "Epoch 23/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 38.5574 - val_loss: 42.8837\n",
      "Epoch 24/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 36.4706 - val_loss: 40.1785\n",
      "Epoch 25/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 34.6698 - val_loss: 40.1427\n",
      "Epoch 26/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 32.8060 - val_loss: 42.5194\n",
      "Epoch 27/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 35.0035 - val_loss: 41.2324\n",
      "Epoch 28/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 34.1151 - val_loss: 41.1522\n",
      "Epoch 29/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 33.8257 - val_loss: 40.8268\n",
      "Epoch 30/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 36.7818 - val_loss: 48.7331\n",
      "Epoch 31/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 35.0850 - val_loss: 40.1672\n",
      "Epoch 32/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 34.1577 - val_loss: 40.4379\n",
      "Epoch 33/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 34.8003 - val_loss: 41.3569\n",
      "Epoch 34/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 33.4900 - val_loss: 42.4084\n",
      "Epoch 35/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 35.3714 - val_loss: 40.9115\n",
      "Epoch 36/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 34.9938 - val_loss: 41.3910\n",
      "Epoch 37/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 34.0310 - val_loss: 40.8994\n",
      "Epoch 38/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 36.0575 - val_loss: 40.9118\n",
      "Epoch 39/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 33.7593 - val_loss: 40.1745\n",
      "Epoch 40/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 33.5817 - val_loss: 41.7242\n",
      "Epoch 41/42\n",
      "10/10 [==============================] - 0s 5ms/step - loss: 34.3153 - val_loss: 40.3309\n",
      "Epoch 42/42\n",
      "10/10 [==============================] - 0s 6ms/step - loss: 33.5422 - val_loss: 41.1750\n"
     ]
    }
   ],
   "source": [
    "my_optimizer_4 = Adam(learning_rate=0.25)\n",
    "m4.compile(optimizer = my_optimizer_4, loss='mean_squared_error')\n",
    "h4 = m4.fit(X_train_2, y_train, validation_data=(X_test_2, y_test), epochs=how_many_epochs, verbose='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "5db7470f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_loss_0 = history2.history['val_loss']\n",
    "val_loss_1 = h1.history['val_loss']\n",
    "val_loss_2 = h2.history['val_loss']\n",
    "val_loss_3 = h3.history['val_loss']\n",
    "val_loss_4 = h4.history['val_loss']\n",
    "\n",
    "plt.plot(epochs, val_loss_0, label='Learning Rate = 0.001')\n",
    "plt.plot(epochs, val_loss_1, label='Learning Rate = 0.01')\n",
    "plt.plot(epochs, val_loss_2, label='Learning Rate = 0.1')\n",
    "plt.plot(epochs, val_loss_3, label='Learning Rate = 0.18')\n",
    "plt.plot(epochs, val_loss_4, label='Learning Rate = 0.25')\n",
    "\n",
    "plt.title('Validation Loss Learning Curves for Different Learning Rates')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Validation Loss Metric')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "883ae96a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# From the above visualizaton, it's clear that 0.01 and 0.18 are good learning rates...with 0.01 being the best!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "ec4f0655",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import KFold, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "b6946039",
   "metadata": {},
   "outputs": [],
   "source": [
    "def baseline_model():\n",
    "    m = Sequential([\n",
    "    Dense(units=50, input_shape=(9,), activation='linear'),\n",
    "    Dense(units=25, activation='elu'),\n",
    "    Dense(units=16, activation='elu'),\n",
    "    Dense(units=8, activation='elu'),\n",
    "    Dense(units=4, activation='linear'),\n",
    "    Dense(units=2, activation='linear'),\n",
    "    Dense(units=1, activation='linear')\n",
    "    ])\n",
    "    \n",
    "    m.compile(optimizer=my_optimizer, loss='mean_squared_error')\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "638a43dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mvsg2\\AppData\\Local\\Temp\\ipykernel_26496\\1755685345.py:1: DeprecationWarning: KerasRegressor is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n",
      "  my_estimator = KerasRegressor(build_fn=baseline_model, nb_epoch= how_many_epochs)\n"
     ]
    }
   ],
   "source": [
    "my_estimator = KerasRegressor(build_fn=baseline_model, nb_epoch= how_many_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "7ec84509",
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8f227f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 1s 2ms/step - loss: 235.2469\n",
      "WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x00000292A2D758B0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "2/2 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "results = cross_val_score(estimator=my_estimator, X=X_train_2, y=y_train, cv=kfold, scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "b5e250ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -91.57188133,  -42.99743184, -305.98833061,  -64.63295209,\n",
       "        -53.309603  ])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "2586fd6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-111.70003977157964"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "1f3a60fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.48643044091381"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d953199",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}