35 - ANN for Regression - Prediction of Continuous Well Logs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3dcafed",
   "metadata": {},
   "source": [
    "# Scikit Learn ANN for Predicting Continuous Well Measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33025840",
   "metadata": {},
   "source": [
    "## Importing Libraries and Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "731ac85c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db7159a5-126b-4ade-bfc5-82701c5f08e4",
   "metadata": {},
   "source": [
    "med_tutorial_scikit_learn_1.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c2fb85af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Data/Volve/volve_wells.csv', usecols=['WELL', 'DEPTH', 'RHOB', 'GR', 'NPHI', 'PEF', 'DT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "affa6ba8-c0db-409b-9af4-27c1ad175e98",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c86690f",
   "metadata": {},
   "source": [
    "## Create Training, Testing and Validation Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01aade55",
   "metadata": {},
   "source": [
    "Our dataset should have 4 wells within it. We can confirm this by calling upon the `unique()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c6c58be",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>DT</th>\n",
       "      <th>GR</th>\n",
       "      <th>NPHI</th>\n",
       "      <th>PEF</th>\n",
       "      <th>RHOB</th>\n",
       "      <th>WELL</th>\n",
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       "      <th>69703</th>\n",
       "      <td>2577.0</td>\n",
       "      <td>109.3850</td>\n",
       "      <td>74.6970</td>\n",
       "      <td>0.4030</td>\n",
       "      <td>8.1360</td>\n",
       "      <td>2.3470</td>\n",
       "      <td>15/9-F-11 A</td>\n",
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       "      <th>69704</th>\n",
       "      <td>2577.1</td>\n",
       "      <td>110.1320</td>\n",
       "      <td>72.7970</td>\n",
       "      <td>0.3990</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2.3570</td>\n",
       "      <td>15/9-F-11 A</td>\n",
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       "      <th>69705</th>\n",
       "      <td>2577.2</td>\n",
       "      <td>110.8430</td>\n",
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       "      <td>0.3900</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>2.3600</td>\n",
       "      <td>15/9-F-11 A</td>\n",
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       "      <th>69706</th>\n",
       "      <td>2577.3</td>\n",
       "      <td>111.0100</td>\n",
       "      <td>69.8990</td>\n",
       "      <td>0.3850</td>\n",
       "      <td>7.5920</td>\n",
       "      <td>2.3610</td>\n",
       "      <td>15/9-F-11 A</td>\n",
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       "      <th>69707</th>\n",
       "      <td>2577.4</td>\n",
       "      <td>110.7970</td>\n",
       "      <td>69.0410</td>\n",
       "      <td>0.3820</td>\n",
       "      <td>7.3800</td>\n",
       "      <td>2.3630</td>\n",
       "      <td>15/9-F-11 A</td>\n",
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       "      <th>149702</th>\n",
       "      <td>3641.7</td>\n",
       "      <td>74.4395</td>\n",
       "      <td>120.4854</td>\n",
       "      <td>0.1973</td>\n",
       "      <td>7.0911</td>\n",
       "      <td>2.6337</td>\n",
       "      <td>15/9-F-1 A</td>\n",
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       "    <tr>\n",
       "      <th>149703</th>\n",
       "      <td>3641.8</td>\n",
       "      <td>74.5584</td>\n",
       "      <td>120.2856</td>\n",
       "      <td>0.1930</td>\n",
       "      <td>7.0064</td>\n",
       "      <td>2.6295</td>\n",
       "      <td>15/9-F-1 A</td>\n",
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       "    <tr>\n",
       "      <th>149704</th>\n",
       "      <td>3641.9</td>\n",
       "      <td>74.6748</td>\n",
       "      <td>119.9561</td>\n",
       "      <td>0.1879</td>\n",
       "      <td>6.8993</td>\n",
       "      <td>2.6240</td>\n",
       "      <td>15/9-F-1 A</td>\n",
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       "    <tr>\n",
       "      <th>149705</th>\n",
       "      <td>3642.0</td>\n",
       "      <td>74.7656</td>\n",
       "      <td>122.5272</td>\n",
       "      <td>0.1825</td>\n",
       "      <td>6.8016</td>\n",
       "      <td>2.6194</td>\n",
       "      <td>15/9-F-1 A</td>\n",
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       "      <th>149706</th>\n",
       "      <td>3642.1</td>\n",
       "      <td>74.8246</td>\n",
       "      <td>122.0503</td>\n",
       "      <td>0.1774</td>\n",
       "      <td>6.7421</td>\n",
       "      <td>2.6169</td>\n",
       "      <td>15/9-F-1 A</td>\n",
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       "<p>24950 rows × 7 columns</p>\n",
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      "text/plain": [
       "         DEPTH        DT        GR    NPHI     PEF    RHOB         WELL\n",
       "69703   2577.0  109.3850   74.6970  0.4030  8.1360  2.3470  15/9-F-11 A\n",
       "69704   2577.1  110.1320   72.7970  0.3990  7.9250  2.3570  15/9-F-11 A\n",
       "69705   2577.2  110.8430   71.6290  0.3900  7.7750  2.3600  15/9-F-11 A\n",
       "69706   2577.3  111.0100   69.8990  0.3850  7.5920  2.3610  15/9-F-11 A\n",
       "69707   2577.4  110.7970   69.0410  0.3820  7.3800  2.3630  15/9-F-11 A\n",
       "...        ...       ...       ...     ...     ...     ...          ...\n",
       "149702  3641.7   74.4395  120.4854  0.1973  7.0911  2.6337   15/9-F-1 A\n",
       "149703  3641.8   74.5584  120.2856  0.1930  7.0064  2.6295   15/9-F-1 A\n",
       "149704  3641.9   74.6748  119.9561  0.1879  6.8993  2.6240   15/9-F-1 A\n",
       "149705  3642.0   74.7656  122.5272  0.1825  6.8016  2.6194   15/9-F-1 A\n",
       "149706  3642.1   74.8246  122.0503  0.1774  6.7421  2.6169   15/9-F-1 A\n",
       "\n",
       "[24950 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0607c9d1",
   "metadata": {},
   "source": [
    "As we are using measurements taken from multiple wells, one way to split our data into training and testing is to set aside a single well (blind test well) which will be used to see how our model performs on unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7114040",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training Wells\n",
    "training_wells = ['15/9-F-11 A', '15/9-F-1 A']\n",
    "\n",
    "# Test Well\n",
    "test_well = ['15/9-F-1 B']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd658cc",
   "metadata": {},
   "source": [
    "\"Extract\" the data from the main dataframe using the well lists above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6320823a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_val_df = df[df['WELL'].isin(training_wells)].copy()\n",
    "test_df = df[df['WELL'].isin(test_well)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "37d705af",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>DT</th>\n",
       "      <th>GR</th>\n",
       "      <th>NPHI</th>\n",
       "      <th>PEF</th>\n",
       "      <th>RHOB</th>\n",
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       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>21688.000000</td>\n",
       "      <td>21688.000000</td>\n",
       "      <td>21688.000000</td>\n",
       "      <td>21688.000000</td>\n",
       "      <td>21688.000000</td>\n",
       "      <td>21688.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3141.098875</td>\n",
       "      <td>77.235857</td>\n",
       "      <td>39.803246</td>\n",
       "      <td>0.166648</td>\n",
       "      <td>7.093603</td>\n",
       "      <td>2.475232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>314.723749</td>\n",
       "      <td>14.336048</td>\n",
       "      <td>57.907158</td>\n",
       "      <td>0.099200</td>\n",
       "      <td>1.188313</td>\n",
       "      <td>0.147635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2577.000000</td>\n",
       "      <td>53.165000</td>\n",
       "      <td>0.852000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>4.297800</td>\n",
       "      <td>1.980600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2869.475000</td>\n",
       "      <td>66.849300</td>\n",
       "      <td>9.416350</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>6.218475</td>\n",
       "      <td>2.379000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3140.550000</td>\n",
       "      <td>72.720750</td>\n",
       "      <td>27.552000</td>\n",
       "      <td>0.136000</td>\n",
       "      <td>7.487700</td>\n",
       "      <td>2.533000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3411.625000</td>\n",
       "      <td>86.093800</td>\n",
       "      <td>44.877425</td>\n",
       "      <td>0.217200</td>\n",
       "      <td>8.001000</td>\n",
       "      <td>2.581400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3723.300000</td>\n",
       "      <td>126.827000</td>\n",
       "      <td>1124.403000</td>\n",
       "      <td>0.593200</td>\n",
       "      <td>13.841000</td>\n",
       "      <td>3.025000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              DEPTH            DT            GR          NPHI           PEF  \\\n",
       "count  21688.000000  21688.000000  21688.000000  21688.000000  21688.000000   \n",
       "mean    3141.098875     77.235857     39.803246      0.166648      7.093603   \n",
       "std      314.723749     14.336048     57.907158      0.099200      1.188313   \n",
       "min     2577.000000     53.165000      0.852000      0.010000      4.297800   \n",
       "25%     2869.475000     66.849300      9.416350      0.096000      6.218475   \n",
       "50%     3140.550000     72.720750     27.552000      0.136000      7.487700   \n",
       "75%     3411.625000     86.093800     44.877425      0.217200      8.001000   \n",
       "max     3723.300000    126.827000   1124.403000      0.593200     13.841000   \n",
       "\n",
       "               RHOB  \n",
       "count  21688.000000  \n",
       "mean       2.475232  \n",
       "std        0.147635  \n",
       "min        1.980600  \n",
       "25%        2.379000  \n",
       "50%        2.533000  \n",
       "75%        2.581400  \n",
       "max        3.025000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_val_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fc094c52",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>DT</th>\n",
       "      <th>GR</th>\n",
       "      <th>NPHI</th>\n",
       "      <th>PEF</th>\n",
       "      <th>RHOB</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3262.000000</td>\n",
       "      <td>3262.000000</td>\n",
       "      <td>3262.000000</td>\n",
       "      <td>3262.000000</td>\n",
       "      <td>3262.000000</td>\n",
       "      <td>3262.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3261.550000</td>\n",
       "      <td>84.236378</td>\n",
       "      <td>72.114743</td>\n",
       "      <td>0.204948</td>\n",
       "      <td>6.448994</td>\n",
       "      <td>2.452392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>94.180262</td>\n",
       "      <td>14.488623</td>\n",
       "      <td>61.620077</td>\n",
       "      <td>0.097875</td>\n",
       "      <td>0.830665</td>\n",
       "      <td>0.133218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3098.500000</td>\n",
       "      <td>58.631800</td>\n",
       "      <td>8.001500</td>\n",
       "      <td>0.059500</td>\n",
       "      <td>4.729900</td>\n",
       "      <td>2.111800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3180.025000</td>\n",
       "      <td>74.450700</td>\n",
       "      <td>38.552300</td>\n",
       "      <td>0.146300</td>\n",
       "      <td>5.917400</td>\n",
       "      <td>2.364200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3261.550000</td>\n",
       "      <td>81.407800</td>\n",
       "      <td>50.796200</td>\n",
       "      <td>0.172400</td>\n",
       "      <td>6.345200</td>\n",
       "      <td>2.483300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3343.075000</td>\n",
       "      <td>88.037350</td>\n",
       "      <td>64.980325</td>\n",
       "      <td>0.225450</td>\n",
       "      <td>6.910000</td>\n",
       "      <td>2.539875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3424.600000</td>\n",
       "      <td>125.982700</td>\n",
       "      <td>297.767300</td>\n",
       "      <td>0.557600</td>\n",
       "      <td>10.987600</td>\n",
       "      <td>3.051700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DEPTH           DT           GR         NPHI          PEF  \\\n",
       "count  3262.000000  3262.000000  3262.000000  3262.000000  3262.000000   \n",
       "mean   3261.550000    84.236378    72.114743     0.204948     6.448994   \n",
       "std      94.180262    14.488623    61.620077     0.097875     0.830665   \n",
       "min    3098.500000    58.631800     8.001500     0.059500     4.729900   \n",
       "25%    3180.025000    74.450700    38.552300     0.146300     5.917400   \n",
       "50%    3261.550000    81.407800    50.796200     0.172400     6.345200   \n",
       "75%    3343.075000    88.037350    64.980325     0.225450     6.910000   \n",
       "max    3424.600000   125.982700   297.767300     0.557600    10.987600   \n",
       "\n",
       "              RHOB  \n",
       "count  3262.000000  \n",
       "mean      2.452392  \n",
       "std       0.133218  \n",
       "min       2.111800  \n",
       "25%       2.364200  \n",
       "50%       2.483300  \n",
       "75%       2.539875  \n",
       "max       3.051700  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495f5c4b-25ee-4f22-837a-1977f5abd3df",
   "metadata": {},
   "source": [
    "## Proprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e55a73d",
   "metadata": {},
   "source": [
    "## Implementing the Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fa08e1cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18c90d6",
   "metadata": {},
   "source": [
    "### Selecting Training and Target Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fd8dc2b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_val_df[['RHOB', 'GR', 'NPHI', 'PEF']]\n",
    "y = train_val_df['DT']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ec1cf5",
   "metadata": {},
   "source": [
    "Note that the name test used here is commonly used within machine learning. In this case the variables X_test and y_test are our validation data. In other words it is used to help tune our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fb9b92c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76c0fe11",
   "metadata": {},
   "source": [
    "Checking the shapes of X_train and X_test to make sure they have been split correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76569ed7-1f86-480b-911a-e98e7df1ea3f",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Applying Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3cb77c5f-7b63-4e1f-a708-7b7b56cc282c",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa6757ea-dd22-44c4-af8b-0d9ef6af6344",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d309579a-0122-4a20-b107-8e38c1b6deaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.45500068,  0.12198551,  0.03143028, -1.33766317],\n",
       "       [-0.04217912, -0.55153053, -0.21079483,  0.80663505],\n",
       "       [-1.46125324, -0.36535724,  0.03143028, -1.6310438 ],\n",
       "       ...,\n",
       "       [-1.82790398, -0.21927636,  0.68745661, -1.81045831],\n",
       "       [-1.98407003, -0.09303234,  0.88527378, -1.83058979],\n",
       "       [ 0.28848552, -0.28963525, -0.44292722,  0.49177522]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4334161f-a44a-4b21-ab10-62fcca63640e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = scaler.transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65f4c412-19f3-4aa1-813c-c8b53dd3dda4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.85407822, -0.60667942, -1.00811914,  1.09555138],\n",
       "       [ 1.26825775, -0.64234039, -1.4521985 ,  1.33814001],\n",
       "       [-1.66494809, -0.23387922,  0.74801289, -1.84246653],\n",
       "       ...,\n",
       "       [ 0.83031383, -0.42153199, -1.0131655 ,  0.53321745],\n",
       "       [-1.14892114,  2.80566018,  2.60507204,  0.35936226],\n",
       "       [ 0.2973123 , -0.53602045, -0.4227418 ,  1.07533566]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5cf39ac-6e08-4d5f-b905-4b597ce3e122",
   "metadata": {},
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform (X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6508af88",
   "metadata": {},
   "source": [
    "### Building the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d6904f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MLPRegressor(hidden_layer_sizes=(64, 64,64), \n",
    "                     activation=\"relu\" ,\n",
    "                     random_state=42, max_iter=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4a37dd73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(hidden_layer_sizes=(64, 64, 64), max_iter=2000, random_state=42)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8736e39b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4443b15",
   "metadata": {},
   "source": [
    "### Check the Prediction Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f11706f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mae = metrics.mean_absolute_error(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b225f0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = metrics.mean_squared_error(y_val, y_pred)\n",
    "rmse = mse**0.5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7387a999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.369147001404765"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8c236ab1-e77d-4b3b-b11b-be1948b1ad7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9455581622005285"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2 = metrics.r2_score(y_val, y_pred)\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8d1797f0-a04c-4c80-83a4-e6d9419ff0da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MAE: \t2.16\n",
      "RMSE: \t3.37\n",
      "r2: \t0.95\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "MAE: \\t{mae:.2f}\n",
    "RMSE: \\t{rmse:.2f}\n",
    "r2: \\t{r2:.2f}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328fca04",
   "metadata": {},
   "source": [
    "Simple metrics like above are a nice way to see how a model has performed, but you should always check the actual data. \n",
    "\n",
    "In the plot below, we are comparing the real data against the predicted data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6c5639e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s+oyShU9RU1FOzsBLyRl0JZKaFkrRfbKnQI0BK/3nB6p8Bw0ApzFHBIG6AQIIeURaMtHAoVQjFKmnXSsd5FaXTj1QVR10n5r9pZQsfIqKbz7D3uZo2lx6P2ltj7Fe4CCsTrVIEXymRvfwboryNUt80MTFfkekaeDQwKFUXAQKDOHMv/DHX1DIzTrctj9+WPegS6dKkBuxMYYDGxZTuvgZnI5D5A7+Ay0GjERs8bnFBKqRWElYGM6ItGSineNKxZh3Z7Qn06z33IFw5l/4M35Yd+w2qbd9/8FqZhcU1QawSkfNEbma6go0D7C6fDe7Z95H8QePYG/ZhQ7XTibnlMsaFDQCFCWo3Ey73458mwgPj+wTNAD76xzXTnMXrXGoJq8xdXLOLijiz2+urdf27t0M0pCnXV+/a3Zaar2+CYfTcNesdRx0OGv7QJwGbClieQKeMU72rXmfso9fBCDvnOtdSQml4c+jDRkWfMGJ7enfNd9np7eVoAG+a2GaWv0wDRyqSYtks0/d44aTA+quWev9dth6mpTCXUjI3+/qrwmq0keHgdWg4Sj+keK5UzhU9CUZR51Ey2E3k5rTxtJno+29tTtrV/ML94FBU6sHpoFDJSwrN+9AzT7h3gTCDUbBhr3axNVAE+7Trr/fNZJMTTV7P59F2bLXSLFn0PL8cWT3OguRhjQuRVZZpYPZBUUNTo2uqdX90z4OlZCC9RN4RKOTM9w+iGDnrDGGQRMXA/DwyD50zM1EcE28s9LEEu2O26qftrDzpT9RtvQlso79FR3GPEWz3mc3qqDh8cC7Gxk0cTFHTXj/iNQnKjK0xqESktWaRDTWjw43GFkZ9upZknXSpSeybMJZIZUrlHkWoTDVVZQte429K2dhy8qh9Yi7yep+asTPE0mlFY7aLL3RnIPRmPrPYkkDh0pIVm/eDenk9HdTsBKMfH12SI/WR8zi9ieUJVk957EyDyMcB7dvpHjuZKpLisjucw55Z43BltEsKueKpmjMwYhW/1ki0MChEpLVmkS4nZyBbgq+gpF3Dihfn719RiEpYn20kJWaw+yCIsa/tTbg/IpwOQ9VULb0RfateR9bTlvaXPYPMo/qF/HzxFKkm/Ki0X+WKDRwqIQUSk0inE7OQDeFZRPOCpgDyl8neKjLTHjP6/AV9B54d2NUgkbld6spnv84NXt/pvnJF5I7+PekpDW++QtZ9hQqQli1L9JzMJJ5kqAGDpWQoj1cMthNIVAOqEiuaeGtqKySP71ZyAPvbqSswhHRFOgANZV7KV08nQMbFmNv2ZnWV/+b9I7HR/gskeNrOLGHcGTtLhpzMKLRf5Yo/AYOEVlgjBkay8IoFYpoDpcMdlMIFFis5n4Kh9P4Xpq1IYwxVGxaRsnCqTgP7iPnlMvJOfUKJNV3yvFYqXvzryvYex1zM6PaaZ3MkwQD1ThaB3hPqSYt2E0hUGAZP6w742YURrxGEA3V+0soWfgUld8sJ63dsbS8/O+ktTk63sUi027j4pM7suTrPWEF4Y65mSGPSgtVMk8SDBQ4ckRkpL83jTGzolAepRqFYDeFQIHF1zoY4Fo4KDstlfJKBx1yM9m9t9JyttdIM8ZwYP1CShc/i6lxkHvmH2nxy98iKbbgHw5TKOlM6s5bGTRxcUgBJFZP/ck6STBg4AAuwHe+MQNo4FBNWqCbQt3AkpNpRwTGzShk0vxNDOnRmtwse22zUm6mnfsv7HXE8fr9fUHEm52scJTtomTe4xz8oZD0Tr1oee6t2POjf/OrcRrysuxkpaUGDAKeBIWDJi6uDdpDerTm7dVF9Uay+QpDuZn2pLyZx1KgwPGDMWZUzEqiVILxBJZ7Z68/onZRVFbJKyu2HbHvoer6VYuyGAcN46xh35r3KFv6EkgK+UNvpFnf4RFJSmhVWYWDgr8Npe8DC/wOOR7SozXjZ67F4a6dFJVVMuPzH7l8QGeWfL0nYDDJtNu4/8LYrTKYrAIFjgblERCR53DVWHYbY3q7t+UDM4BuwFbgMmNMqbhyFjwGnAdUAH80xqxpyPmVihRfk/mAkCbe+RrfH62Z3r5U/byNkrmTObTjazKOPtmVlLBF7LsxPYML7r+w1xHBwSM9NaVe0AXXpMj31u6k8L4jx+t4hj8nWx9DvInxk6lTRHobYzaISC5wnHvzN8aYcksHFhkM7Ade8goc/wZKjDETRWQCkGeMuVNEzgNuwRU4fgU8Zoz5VbBz9O/f36xatcpKcZQKS93JfODqq8BQ76ZnhUDt0/KML36MyjwMb6ammvKVb1H+2RukpGWRd/Z1ZPc8My75peqmNfcOyDmZdvYdqg7aB7J14vmxKGqTJyKrjTH9w/18oBrHtyLyAjAC+B7Xd76riLwD/J8xpirQgY0xS0WkW53NFwFnun9+EfgIuNO9/SXjimIrRCRXRNobY3aG9NsoFWG+JvM15GbvSchoJfVIQx3atZniDx7FsWcrWT1OJ/+c67Fl50b5rPV5B8tJ8zcxbkZhbe3AM/Jp0MTFMat9qYYLFDjuAexAZ2PMPgARaQ48AfzV/SdUbb2CwS6grfvnjsCPXvttd2+rFzhEZCwwFqBLly5hFEEp66I1CziaQcPpOET5stfY+/k72LJzaT3yXrKOGxjFM/qXm2mn8L6hQfM6WbnOeVnxnVeiDgsUOEYCA4wxFZ4Nxph9InIjsILwAkctY4wRkZD//xhjpgHTwNVU1ZAyKBVMNCfzRcPBbespnjeZ6tKdNDthKHlDRpESx6SEno5qfylcbnePQvMegeaL3Sbc9xvt9G4sAgUOp3fQ8DDG7A/nhu/2k6cJSkTaA7vd24uAzl77dXJvU41Q3c7iIT1aHzHapbF1UDYk9bWv+RqNkfNQBaUfPc/+wrmk5rajzeUPktmtb1TPmWlPIcNu83vDz8s6PCw2UI2iqKwSe4r4neeRIq7mQc96J43pu5WsAgUOIyJ5+B5dFe60pTnANcBE99//89p+s4i8gatzvFz7NxonX00O3qNgGltq6XBTX9ftuM2wp1BW4WiUNZDKLV9QPP8JavaX0PyXI8g97WpS0jKift787HTGD+vuM0OvPeXIGkKw6+ZwGnLdc2E8gSjTnkK109Qeu7F9t5JZoAHcOcBqP3+aBzuwiLwOLAe6i8h2ERmNK2D8WkS+Bc5xvwb4APgO2Aw8A9wY1m+joi7Y8qdgbTW8WAlntb66qwuWVTo46HDyyOV9WTbhrNoJavFWU1HOz+/+h91vPUBKWhbtrp5E/lljYhI0wFWLmDR/k8/BAs0yUo+4uY8f1p1Me+BZ6eWVrjkeWyeez9aJ55OfnV7v2I3pu5XM/NY4jDHdGnJgY8yVft4628e+BripIedTkROoacdqZ3FjSS1tNfV1sAWRvNvjh/Ro7XOuQawYY6j4+hNKFj2N8+B+cgZdSc7Ay2KelLCDO4mgL3UnN3rPtPd3jVNEatcKh+ROW97Y6Zrj6gjB1vK2mjK6saSW9leOuqv1eX7nYIrKKnl7dRH2OP3Pqd73M3tmPcjPc/5Naos2tP/jY+SedlXMg4YnL5eV6+sxol9Hlk04i0cv7+uz9lFjjKXvWmP5biUzDRzqCMGadqw0OQixSzIXjK/y1k19baX5zVulo4ZmGbG9URtj2Fc4jx3Tb+Tg1gLyhoyi3e//Q1rrblE9b8fcTLZOPJ9HL+9b20RnE6n9Tgzp0Trg9Z1dUMSgiYs5asL7DJq4uLZG8fDIPth8TEIM9l1LlrTljZ0u5KSOEKx5wEqTg6HxdF5aSX0dTtNHWYWDqwd2iclEPkfpTornTeHQtnWkd+lDy+G3YM/rEOWzutT9d6870ODt1UW16c/rXt9gAxPG1Vmoyt85NaVI4xNoIaf8QB80xpREvjgq3qysauZJ7ucv1XVj6Tz2CJb6OpyRUrlZdh4c0Qcgav0dxlnDvlVzKPvkFUixkT/sZpqdODSmSQm9/9391UaXfL3H59oXwdbkDuW7phqXQN/A1cAq9997gG+Ab90/r45+0VQ8hNI80FSaEqw0v9XlSfG25Os9USgRVO3Zyq5XxlO65Fkyup5Ah9FP0jzGmWwz7TaG9Ghd29TkL7iG2ontOY6/74/3OT3NW6pxCTSq6igAEXkGeMcY84H79bm48lepJiiU5oFEbUrwNWrs4pM71mt2CrR0abk7r1KkR/iYGgfly2dSvvxNUtKzaPWb8WQdPzgmSQnrLjTlK225L4E6sX0FG4EjRk/VnUzqfU6du9E4+c2OW7uDyHpjTJ9g2+JBs+OqUPnKdhsoQNhEqAnyfyRSDu3YRPHcyTh+/oGsnmeQf/ZYbFk5MTm3h/eSq1ZW3RPgkcv7+rypzy4o8ruErr+lXQM1f0Z7KdhkEs3suB47RORe4BX366uAHeGeUKl48tXuHigs1BhDpt0W1ZQjTsdByj95lb2r/octO4/WF/+NrGMHRO18gXhqULMLiiz1+3gGQvib+3N7kA7whm5X8WElcFwJ3Ae8g+t7stS9TamEE84NKJpB4+AP61xJCct20azvcPLOvJaU9OyonS+YDrmZtbUyKzp67e+reamjhQ7wuttD2V/FR9DA4R49dZuIZBtjDsSgTEqFLVhCw8aSa8p56AClS55n/9p5pOa2p+2VD5HR5YS4lskzsMHqvJZA+3tGT/lKEhloAEWo+6v4CDpEQ0ROFZEvga/cr08UkSejXjKlQhRs1juEN4Iq0io2r2TH9BvYv24BLQaMpP2oKXEPGjaR2tX5rNTKOuZmBt1/R1ll7WS/jrmZSJ3P+RLq/io+rDRVPQIMw5XBFmPMWveysKqJaEja8cYk2LwBODwy509vFhLGyq8NUlNRTsmiaVR89TH21t1oPfJe0tv/IraF8KHukq7BamUCR3RUB2teCnUuhs7daPwsDQo3xvxYZ1PjXpxAWWblKT1RWO1YHdGvY0yDhjGGA19+xI7pN1CxaRk5p11F+2seiWjQEFyr7Vkx6Jj8gE/0wWpldfsbmsp8HmWdlRrHjyJyKq71OezAbbibrVTis/KUHop41l6CPfl6ly1WqvfuoWTBk1Ru+YK09t1pee6tpLXuGvHzGEDE9SQYbLGcrcWVAYe2ev69Hnh3Y71FmnwFhESdz6PCZyVw/B/wGK41wIuABeh6GU1GJIc/hrtoUqQE6lj1NX8jmoxxsn/tfEqXPAfGSd5Z19H85AuQlOj1rwRaetWblX9bT3OR1QcBbV5KLlYCR3djzFXeG0RkELAsOkVSsRTJ4Y+Rrr2EyteT8kH3OhpWJvKlAP/v8r5+5x5Y5SgpciUl/HEDGV1PJH/4Ldhz2zXomJFk9d+2qfR9qciz0scxxeI2lYAi2T7dWCZvHXQcbqzxhAors7+d4DdjqxXGWUP5ylnsfP4WqnZ/T/7wW2lz+YONKmhY/bdtSn1fKvICZcc9BTgVaC0if/J6qwUQ3/GMKmIi1T49u6CIFD9P9bGcvBXq2hp1hdtnXrX7e4rnTqZq17dkHjeQ/F/fQGrzlmGXIxibCM0zUimrtNY8Ba6OcKv/tvGuParGLVBTVRrQzL2P9xrje4FLolkoFVsNbZ/2PJ36ChqxHl0T69qNqXZQvnwG5StmkpLRjFYX3klWj9OinpSwxhgOVFVjTxEcFoaIhZrrqbHUHlXjFCg77sfAxyLygjHmhxiWSSUYf0/53pPKYuHe2eujvqiSt0NFX7uSEhZvI7vXEPLOvg5bZosGHdNuE5qlp1rq6HbUGPKy7GSlpbKjrNJvjS+cFRk19YcKxEofx3QRyfW8EJE8EZkfvSKpROPvKdRpTEyDRrQWVKrLWXWQkg+fYdcr43FWVdLmkvtpdcGfGxw0UgQmXXIi9/2ml+XZ7WUVDpZNOIvvJ56P008/TjgrMurcDBWIlVFVrYwxZZ4XxphSEWkTvSKpRNMYnk5fX1l3jmp0VG4tpGTeFKrLf6JZv/PJO+MaUtKzGnzcTLuNi0/uWNvXlJtlJz01pXZtjIqqap+1EO9rbHX9Cyt0boYKxErgcIpIF2PMNgAR6Ur4fYiqCWoMiemivWaG8+B+Spc8x/51C0jN60Db300ko3PvsI/XtnkaqTab3wWMSiscZNpttWtd+JqHUvcajx/W3ef6Fwb485trGTejMKQAoHMzlD9WAsc9wKci8jGuh5fTgbFRLZVKKPF6Oo3VTPCKb5ZTsvApag6U0eJXl5Az6EpS7OkNOubufVVHLIA0aOLigKOYrFzjQOtfeAKrrqinIiHoCoAAItIKGOh+ucIY83NUS2WRrgCYvGIxE7zmQCklC5+mYtOn2NscRctzbyO93bERO75neOyk+ZsCJhV81M8Ke75YWbXPc25dUS95NXQFQL+BQ0R6GGO+FpGTfL1vjFkT7kkjRQNH8rJ6gwyHMYYDG5dQ+uEzOB2V5J56JS1+dTFis1JBD42V1QXtNsGeIlS4JzbmZtq5/8JefpdrtRJQBfh+4vlhl1sltmguHftn4Drgvz7eM4A+rqiICiXFRbSap6r37qZ4/hMc/G416R160PLc27C36hyVc9lELNWYHDUGR83hB7yySgfjZ64F6jc31W3SagyTMlXTE2gex3Xuv4fErjgqWYWaIDHSK/kZ42R/wVxKP34BjCHvnOtp3u+8qCUlbOg65g6n8TuL27tPxEqnulKhCpRyZGSgDxpjZkW+OCqZeNcwfD0ZV7oTFP75zbXUGFObqLCjj1FIDeEoKaJ47mQObd9IRrd+tBx+M6k5bRt83LpEAENtbSpY30YwVrPcgg6rVZEVqKnqN+6/2+DKWbXY/XoI8BmggUOFre6TcKDhtJ73vEcGvbJiG4OOyafwx3IOVIUXPIyzhr2fv0PZp6+SkppGy/NuJ7v32dFLF2Lq9yvUrQ3YbQIGS2lErDY36bBaFWmBmqquBRCRBUBPY8xO9+v2wAsxKZ1qshqajBBg2ZYSbCnh3eSrfvqO4rmPUfXTFjJ/cYorKWGz/AaVJ5i6N3p/tQHvbblZdsorHfVWLLSniDY3qbixMkyksydouP0EdIlSeVSSiFT/RE2Ia8Ca6irKPnuDvSveIiWrBa1G3EV290ERKUsgdpvvG72/2oD3ttkFRUesMRJoVJVSsWAlcHzozk31uvv15cCi6BVJJQMrCytF2sHtX1E89zGqS7aT3fts8s4agy2zefAPRkB2WmrYN3ptalKNTdDAYYy5WUR+Cwx2b5pmjHknusVSTZF3Z3gsQ4azqpKypS+xb/V72Fq0os2lD5B59MkxLAGUe62boSvrqURndUbTGmCfMWaRiGSJSHNjzL5oFkw1LbFe89uj8vs1FM97nJq9e2h+0vnkDv5DRJIShsrTvxHvddmVioSggUNErsOVmyofOAboCEwFzg73pCIyDhiDayLheuBaoD3wBtASWA383hhTFe45VOMSic7wUNRU7qN08bMc2LCI1PxOtL1qIhmdesXs/N68500EWlnP877WRFRjZ6XGcRMwAFgJYIz5tiFp1UWkI3ArrpFalSLyJnAFcB7wiDHmDRGZCowGngr3PCp80WhKieXKcQc2LaNk4VM4K/bSYuCl5A66EklNi9r53NMzfMrLsnPfbw53ZPu7Dp6ah9ZEVCKwspDTIe8nfxFJpeFp1VOBTPexsoCduFKYvOV+/0VgRAPPocLgaUopcvdDeG5gswuKGnTcWKS4qNlfyp53HuLn2Q+T2qwl7a95hLwzrolq0MjNtHPVwC71Fj0S4OqBXSj429DatOiDJi72+x/HV/oR75qIUo2JlRrHxyJyN64b/a+BG4F3wz2hMaZIRP4DbAMqgQW4mqbKjDHV7t2242oSq0dExuJO696li44KjrRATSnhPvnOLiiioqo6+I5hMsZwYMOHlC6ejtNxiNwzrqHFL38blaSEHnWHxPbvml87E9wzYmzJ13tqA26g/p1A6Ud0jW/VGFn5n3Unrv6I9cD1wAfA9HBPKCJ5wEXAUUAZMBMYbvXzxphpwDRwZccNtxzKN383qnBvYNHuFK8u/4nieY9zcGsB6Z160nL4rdhbdorKucBVk7hqYBceHNGn3nue4Og9w33cjEIy7SlUujPb1hUstbomI1SNUcDAISI2YKMxpgfwTITOeQ7wvTFmj/scs4BBQK6IpLprHZ2AhrWNqLBYXQbWXz9I3e0VVdVRCRrGONm35n3KPn4RRMj/9f/RrN95iFhpfW3AeYG3VxfRv2t+wESC3vtX+AkaAkesiaHJCFWiCBg4jDE1IrLJe+nYCNgGDBSRLFxNVWcDq4AlwCW4RlZdA/wvQudTIbCyDOzsgiLGz1xbm0+pqKyS8TPXsuqHEmZ8/uMR26PBUfwjxXOncKjoSzKOOomWw24mNSfs8Rohq9t0F+6IMe9grMkIVSKx0lSVB2wUkc+BA56NxpgLwzmhMWaliLyFa25INVCAq+npfeANEXnQve3ZcI6vGsbXDWxIj9ZMmr+pds3qkgOH6iXhczgNr6yI1LOFb6ammr2fz6Js2Wuk2DNoef44snudFb2khAF4N92F04znqzahM8RVorASOP4a6ZMaY+4D7quz+Ttcw35VI1JRVR2TWkQwh3ZtpnjuZBy7vyOr+2nk//p6bNl5ETl2pj2FDLutNheUFd61BStrg+Rl2clKS9XahGoSAq3HkQH8H3Asro7xZ71GPakmqm57fSg302hwOg5R/tnr7F05C1tWDq1/ezdZvzg1oue4+OROPDiiD0dNeN/SOPO6tQVfzXt19/eey6FUogtU43gRcACfAOcCPYHbYlEoFRu+OrhjPcM7kIPbN1I8dzLVJUVk9/k1eWeNxpbRLOLneW/tTpZ8vSdg0Mhyj4zyVVuo27yXk2lHBMoqHFq7UE2SGD8ZSkVkvTGmj/vnVOBzY8xJsSxcMP379zerVq2KdzESkq+RQIFmQMeS81AFZUtfZN+a97HltKXl8FvI7NY3LmUJNPxWqUQlIquNMf3D/XygGkdtG4UxpjoeHZAqenzVLEIJGln2FPKy02sXG9p/sNrSqnXBVG5ZRfH8J6jZ9zPNT76Q3MG/JyUtPnMZOmptQSmfAgWOE0Vkr/tnwTVzfK/7Z2OMaRH10qmoaeiM5EqHky+95iDMLihi3IzCsGssNZV7Kf3wGQ5sXIK9ZWdaX/1v0jse36AyNkTdORZKqcP8zpYyxtiMMS3cf5obY1K9ftagkeAaOiPZAIMmLq5NqTGiX0fSUkOffGeM4cDXn7Jj+o0c+GopOadeQfs/To5Z0MhOs/ncrjO2lfIvesl8VKM2flh3bp9R2KBjeFJqrPqhhP5d8zlU7XuGtD/V+0soWfAkld+uIK3dsbS8/O+ktTm6QWUKld2WQqYdnbGtVAg0cCQhz2iqSDDAKyu2hTT5zxjDgfULKVn8LNQ4yD3zWlr8cgSS4vvpP5rKKx08cnlfnbGtVAg0cCSZcJMOdrQwyc0KR9kuSuY9zsEfCknv3JuWw2/Bnh+/m3SH3Eydsa1UiDRwJJlw5mlk2m10a9mwwGGcNexb8x5lS18CSSF/6I006zs8akkJrQwttqeINkkpFQYNHEnGymiquukxhvRo3aA8VFU/b6N47mNU7dhE5tH9yR92E6ktWod9vGDysuxBZ7xn2lN4eOQJWtNQKgwaOJJMsLxKddNjzC4o4k9vFoZ1LlPjoHzFW5Qvn0FKWhYtL/gz2T3PjHpSQitpUr76x7lRLYNSTZkGjiTjK6+Sp1mn7oQ3T39IOPP6Du38luK5j+HYs5Ws4weTf/ZYbNm5EfkdgvGswOdPRx1qq1SDaOBIMsHWffCsjR1uf4bTcYjyT19l7xezsWXn0nrkX8k67lcRK78VNcZgTxGfM9l1qK1SDaeBIwn5G0XU0GVeD25bT/G8yVSX7qTZicPIO/NaUiKUlHDQMflc2r+L9fKJa13wskpHbQ1EU4goFRkaOJoIf0u5WvncA+9ubFD6dOehCko/ep79hXNJzW1Hmyv+SWbXE8M+ni9rtpVzaX94eGQf/vzm2oBNUQCOGkN2eiqF9w2NaDmUUho4moS6NYWiskrumrUeIGDwuHf2+gav2lex5QtK5j9Bzf4Smv9yBLmnXU1KWkaDjumLZ7lWT/4oKzWPhubjUkr5poGjCbh/zsZ6N9G662J7eGomDZ3MV1NR7kpK+OVH2Ft1ofWIu0jvEN2+A08g8O6nCfR7aL4ppaJDA0eCshIAisoqmV1QVHujvXf2el5dsa1Ba24YY6j4aikli57GeaiCnEFXknPKZYjN3oCjWjw3rsSK3s1w/moe2gmuVPRo4EhAoXRie5qsgAYHjep9P1Oy4CkqN68krf1xtDz3NtJad2vAEUPn3Qznbxa8TYSHR/bRTnClokQDRwIKJW1IpaOG22cUkiLhr+5njGH/2vmULnkOnDXkDRlF8/4XRT0poWdUVF2eZjh/fRhOYzRoKBVF0UkUpKIqnP6JcBfnc5Tu5Kc37qFk/uOktTuG9qOm0GLAyJhksi28byj+5ph7Ro/5on0bSkWXBo4EZIvBMr7GWcPez99h53M3U7VrM/nDbqbtFf/EntchYuewpwi5mb77RjyzuwMFh/HDupNpPzKAad+GUtGnTVUJKNgchoaq2rOV4rmTqdr5DZnHDiB/6I2kNm8V0XN4kgxC/Q5u75u/rxQpnveDzYJXSkWHBo4EFKm1MeoyNQ7Kl8+kfPmbpKRn0eo348k6fnBUkhLmZ6cfcYP3d/MPFhx0LQ2lYk9MlJ9eo6l///5m1apV8S5GzDU0NYgvh3ZsonjuZBw//0BWzzNcSQmzciJ2/LoE+H7i+VE7vlLKPxFZbYzpH+7ntcaRgDxP2A1NFQLgdByk7JNX2LdqDrbsPFpf/Deyjh0QiWIG5GtOhlIqMWjgSGD7D1Y36PMHf1jnSkpYtotmfYe7khKmZ0eodMFZTY2ilGpcNHAkqEnzN/lMG26F89ABSpc8x/6180nNa0/bKx8io8sJEStbbqad8kpHbX+Ep7y++mX8pUZRSjVeGjgSVLgJ/Co2r3QlJTxQRosBI8k57Xek2COXlPDqgV14cESfettH9OvIURPe9zkJUZMRKpVYNHAkoNkFRYeX7bOopqKckkVPU/HVUuytu9F65F9Jb39cxMqUZhP+fcmJfpMq7iirJMXPynw6YU+pxKKBI8Fc9cxylm0psby/Kynhx5QsmuZKSnjaVeQMvCRiSQntKTDp0r6WFobyFTR0wp5SiUcDR4KYXVDE+JmFOJzWP1O9dw8lC56kcssXpLXvTstzbyWtddeIlWnQMfm8et0pft8PllMrN9PO/Rf20v4NpRKMBo4E4Aoaay0HDWOc7C+cR+lHz4NxknfWdTQ/+YKI5pcSCBg0IHjfxaHqEKKgUqrRiEvgEJFcYDrQG1dL/ShgEzAD6AZsBS4zxpTGo3zxEGjp11BGUDlKiiieN4VDP24go+uJ5A+/BXtuu4iXN1BpPL9LsBLriCqlElO8ahyPAfOMMZeISBqQBdwNfGiMmSgiE4AJwJ1xKl9MzS4oYvxba3HUuG61RWWV3D6jkFU/lPDgiD6W0osYZw17v5hN+aevgs1Oy3NvJbvPr6OSLgQOJyH0Fs765TqiSqnEE/PAISI5wGDgjwDGmCqgSkQuAs507/Yi8BFJEjgeeHdjbdDw9sqKbby2Mvia4FW7v3clJdz1LZnHDST/1zeQ2rxlNIoK+O7QDjcNio6oUirxxKPGcRSwB3heRE4EVgO3AW2NMTvd++wC2vr6sIiMBcYCdOnSJfqljYFAT+iBWqhMtYPy5TMoXzGTlIzmtLpoAlndB0WtlgGumoavNCFWFpfKtNv8ZsFVSiWOeASOVOAk4BZjzEoReQxXs1QtY4wREZ+3TGPMNGAauJIcRruwjdWhoq9cSQmLfyS71xDyzr4OW2aLqJ5TgGUTzvL5XrAmJ0/A0RToSiW+eASO7cB2Y8xK9+u3cAWOn0SkvTFmp4i0B3bHoWxx4W+JVF+cVQcpW/oS+1a/i615K9pccj+Zx4Sd5DIkgZqVOgRI9e69foYGCqUSX8xXADTG7AJ+FBFPG8XZwJfAHOAa97ZrgP/Fumzxcv+FvSztV7m1kB3P3cS+1XNo1u88Oox+ImZBI1izkq/V+MAVFB8e2UcDhlJNSLxGVd0CvOoeUfUdcC2uIPamiIwGfgAui1PZYsozdDUQ58H9lCx+lgPrF5Ka14G2v5tIRufeUS9bx9xMy81KuhqfUslDF3KKIysjkSq+WU7JwqdcSQl/NZKcU68kxZ4e9bLlZtopvG9o1M+jlIo9XcgpgQUaiVRzoJSShU9TselT7G2OovXFfyO93bEROa/dJmDwO6nQniKWm8+UUslHA0cc+RqJZIzhwMYllH74DE5HJbmD/0CLASMRW3j/VIOOyefS/l3qNSHB4WalnEw7IlBW4dAmJqVUUBo4YixQmvHqvbspnvcEB79fTXrH42k5/FbsrTqHfa40m9Tmk/IVCDQ4KKXCoYEjhvylGTfGyf6CDyj9+EUwhrxzrqf5SecjEv6gt0y7jYdH1l9QSSmlGkoDRwz56tNwFG+neN5kDm3/koxu/Wg5/GZSc3xOmg9o0DH5bC2u1BFNSqmo08ARQ959GsZZw97PZ1H26WukpKZx7CXjcRw9GFtKis8FjwJ59HLfCykppVQ0aOCIIc/s6qqfvqN47mNU/bSFrF+cylEX3kJOyzbsKKukeUYqB6qqj0h6mGm3kZ6a4nN2ecfcTA0aSqmY0sARQ6cf3YKpjz5N+Yq3SMlqQasRd5Hb8zQOGWrTdZRVOrCnCHlZ9iNGOQH15nxokkClVDxo4IiRh5+fzf+74xYcJdvJ7n0OeWeNxpbZ3Gc6dYfTkJWWSsHf6k/A05nZSql408ARZfv37+fuu+9myuOPY2vemjaX/Z3Mo04K+jlfczw0SaBSqjHQwBFFCxYsYOzYsWzbto3mJ11A7uA/kJJmbeEiXeBIKdVYxTw7bjIoKSnh2muvZdiwYWRkZPDJJ5+Qf871loOG9l0opRozDRwR9vbbb9OzZ09efvll7r77bgoLCxk0aBC5mXZLn++Ym6lpyJVSjZo2VUXIzp07ufnmm5k1axb9+vVj3rx59O3bt/b9+y/sxfiZa/0nFrQJky45UQOGUqrR0xpHAxljeOGFF+jZsyfvv/8+EydOZOXKlUcEDXB1bE+69EQ6uvsuvFcFz8uya9BQSiUMrXE0wNatWxk7diwLFy7ktNNOY/r06XTv7r9vQkdFKaWaAq1xhMHpdDJlyhR69+7N8uXLeeKJJ/j4448DBg2llGoqtMYRoq+++ooxY8bw2WefMXz4cKZOnUrXrl3jXSyllIoZrXFY5HA4eOihh+jbty9ff/01L730Eh988IEGDaVU0tEahwVr1qxh1KhRrF27lksvvZQpU6bQtm3oqc+VUqop0BpHAJWVlUyYMIEBAwbw008/MWvWLN58800NGkqppKY1Dj8++eQTxowZwzfffMPo0aOZNGkSeXl58S6WUkrFndY46ti3bx833XQTgwcPpqqqioULFzJ9+nQNGkop5aaBw8vcuXPp1asXTz31FLfffjsbNmzgnHPOiXexlFKqUdHAARQXF/OHP/yB8847j2bNmrFs2TIeeeQRsrOz4100pZRqdJI6cBhjePPNNzn++ON5/fXX+etf/0pBQQGnnHJKvIumlFKNVtJ2ju/YsYObbrqJ2bNnc/LJJ7No0SJOOOGEeBdLKaUavaSrcRhjePbZZ+nZsyfz5s3j3//+NytWrNCgoZRSFiVVjeO7775j7NixfPjhhwwePJjp06dz3HHHxbtYSimVUJKixlFTU8Ojjz5Knz59+Pzzz3nqqadYsmSJBg2llApDk69xfPnll4wePZoVK1Zw3nnnMXXqVDp37hzvYimlVMJqsjWOqqoq/vGPf9C3b1++/fZbXnnlFd577z0NGkop1UBNssbxxRdfMHr0aNavX88VV1zBY489Rps2beJdLKWUahKaVI2joqKCv/zlLwwcOJDi4mL+97//8frrr2vQUEqpCGoyNY6PP/6YMWPGsHnzZq677jomTZpETk5OvIullFJNTtxqHCJiE5ECEXnP/fooEVkpIptFZIaIpFk5zt69e7nhhhs488wzcTqdfPjhh0ybNk2DhlJKRUk8m6puA77yev0v4BFjzLFAKTA62AHKy8vp1asX06ZN409/+hPr16/nrLPOilJxlVJKQZwCh4h0As4HprtfC3AW8JZ7lxeBEcGOs3nzZnJycvjss8/473//S1ZWVpRKrJRSyiNefRyPAn8BmrtftwTKjDHV7tfbgY6+PigiY4Gx7peHNm7cuGHgwIFRLGrCaAX8HO9CNBJ6LQ7Ta3GYXovDujfkwzEPHCJyAbDbGLNaRM4M9fPGmGnANPexVhlj+ke2hIlJr8Vhei0O02txmF6Lw0RkVUM+H48axyDgQhE5D8gAWgCPAbkikuqudXQCiuJQNqWUUkHEvI/DGHOXMaaTMaYbcAWw2BhzFbAEuMS92zXA/2JdNqWUUsE1pgmAdwJ/EpHNuPo8nrXwmWnRLVJC0WtxmF6Lw/RaHKbX4rAGXQsxxkSqIEoppZJAY6pxKKWUSgAaOJRSSoUkoQJHpNKUNAUikisib4nI1yLylYicIiL5IrJQRL51/50X73JGm4iME5GNIrJBRF4XkYxk+l6IyHMisltENnht8/k9EJfJ7uuyTkROil/JI8/PtZjk/j+yTkTeEZFcr/fucl+LTSIyLC6FjhJf18LrvT+LiBGRVu7XIX8vEipwEIE0JU3IY8A8Y0wP4ERc12UC8KEx5jjgQ/frJktEOgK3Av2NMb0BG66Resn0vXgBGF5nm7/vwbnAce4/Y4GnYlTGWHmB+tdiIdDbGHMC8A1wF4CI9MT1Xenl/syTImKLXVGj7gXqXwtEpDMwFNjmtTnk70XCBI5IpSlpCkQkBxiMe+SZMabKGFMGXITrOkDyXI9UIFNEUoEsYCdJ9L0wxiwFSups9vc9uAh4ybiswDV3qn1MChoDvq6FMWaBV0aKFbjmiIHrWrxhjDlkjPke2AwMiFlho8zP9wLgEVxZO7xHRYX8vUiYwMHhNCVO92vLaUqaoKOAPcDz7qa76SKSDbQ1xux077MLaBu3EsaAMaYI+A+up6edQDmwmuT9Xnj4+x50BH702i/Zrs0oYK7756S7FiJyEVBkjFlb562Qr0VCBA7vNCXxLksjkQqcBDxljOkHHKBOs5RxjbNu0mOt3W33F+EKpB2AbHxUz5NZMnwPrBCRe4Bq4NV4lyUeRCQLuBv4WySOlxCBg8NpSrYCb+BqiqhNU+LeJ5nSlGwHthtjVrpfv4UrkPzkqWK6/94dp/LFyjnA98aYPcYYBzAL13clWb8XHv6+B0VAZ6/9kuLaiMgfgQuAq8zhiWvJdi2OwfWAtdZ9H+0ErBGRdoRxLRIicGiakiMZY3YBP4qIJ8Pl2cCXwBxc1wGS43psAwaKSJa7z8tzHZLye+HF3/dgDvAH9yiagUC5V5NWkyQiw3E1cV9ojKnwemsOcIWIpIvIUbg6hj+PRxljwRiz3hjTxhjTzX0f3Q6c5L6XhP69MMYk1B/gTOA9989H4/rH3gzMBNLjXb4YXoe+wCpgHTAbyMPV7/Mh8C2wCMiPdzljcB0eAL4GNgAvA+nJ9L0AXsfVv+Nw3wxG+/seAAI8AWwB1uMajRb33yHK12Izrvb7QvefqV773+O+FpuAc+Nd/mhfizrvbwVahfu90JQjSimlQpIQTVVKKaUaDw0cSimlQqKBQymlVEg0cCillAqJBg6llFIh0cChkpqIjHBnCu1hYd/b3TNwwz3XH0XkcT/b97jTx3wrIvNF5FT3e0+ISKGIfCkile6fC0XkkvpnUCo2NHCoZHcl8Kn772Bux5VIMRpmGGP6GVdG24nALBE53hhzkzGmL3AesMUY09f9562AR1MqijRwqKQlIs2A03BNFLvCa7tNRP7jXuNjnYjcIiK34sqHtURElrj32+/1mUtE5AX3z79xrwdSICKLRCSkZJPGmCW41oQe29DfUalo0MChktlFuNY0+QYoFpGT3dvHAt2Avsa1jsOrxpjJwA5giDFmSJDjfgoMNK4ElG/gSnkRqjVA0OYzpeIhNfguSjVZV+JKlgmuG/yVuNKyn4MrNUU1gDHG17oGgXQCZrgTDKYB34dRNgnjM0rFhAYOlZREJB9XluU+ImJwrR5oRGR8CIfxzteT4fXzFOD/GWPmiMiZwP1hFLEfR652qVSjoU1VKlldArxsjOlqXBlDO+OqGZyOa7nR6z2p2d1BBmAf0NzrGD+JyPEikgL81mt7DofTUl9DiETkDFzNZc+E+lmlYkEDh0pWVwLv1Nn2tnv7dFwp29eJyFrgd+73pwHzPJ3juBbPeg/4DFcmUo/7gZkishr42WJ5LncPs/0G14I7FxtjtMahGiXNjquUUiokWuNQSikVEg0cSimlQqKBQymlVEg0cCillAqJBg6llFIh0cChlFIqJBo4lFJKheT/Ax1g2PRgWsXrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_val, y_pred)\n",
    "plt.xlim(40, 140)\n",
    "plt.ylim(40, 140)\n",
    "plt.ylabel('Predicted DT')\n",
    "plt.xlabel('Actual DT')\n",
    "plt.plot([40,140], [40,140], 'black') #1 to 1 line\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742e3edd",
   "metadata": {},
   "source": [
    "## Test Well Prediction\n",
    "Once the model has been fine tuned, we can apply it to our blind test well and see how it performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9ffe8a16",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_well_x = test_df[['RHOB', 'GR', 'NPHI', 'PEF']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4c27a1c9-5728-46f9-a706-563f10d734ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_well_x = scaler.transform(test_well_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "af0d83da",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['TEST_DT'] = model.predict(test_well_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "71e6109f",
   "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": [
    "plt.scatter(test_df['DT'], test_df['TEST_DT'])\n",
    "plt.xlim(40, 140)\n",
    "plt.ylim(40, 140)\n",
    "plt.ylabel('Predicted DT')\n",
    "plt.xlabel('Actual DT')\n",
    "plt.plot([40,140], [40,140], 'black') #1 to 1 line\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ccb99db5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(test_df['DEPTH'], test_df['DT'], label='Actual DT')\n",
    "plt.plot(test_df['DEPTH'], test_df['TEST_DT'], label='Predicted DT')\n",
    "\n",
    "plt.xlabel('Depth (m)', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('DT', fontsize=14,fontweight='bold')\n",
    "\n",
    "plt.ylim(40, 140)\n",
    "plt.legend(fontsize=14)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1859d8ec",
   "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": "3.10.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}