Motor Premium Dataset - Neural Network Model Application.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fa95b521",
   "metadata": {},
   "source": [
    "# Motor Premium Dataset - MLP Classifier Model Application"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb65a73b",
   "metadata": {},
   "source": [
    "This Jupyter Notebooks is an initial study of the application of sklearn neural network MLP Classifier model. <br>\n",
    "The model is applied to dataset MotorPremiums, which is supplied separately in .csv format. <br><br>\n",
    "This file contains 8 main sessions:\n",
    "- Data Exploration\n",
    "- Basic Data Cleaning\n",
    "- Definition of Target\n",
    "- Train, Test, Validate subsets split\n",
    "- Model Fitting\n",
    "- Predictions on Validation Subset\n",
    "- Predictions on Test Subset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8088003c",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "098c202a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries used in this Notebook\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1fea3da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the raw data into the programme and save as a Pandas dataframe to allow tabular visualisation\n",
    "dataFile = pd.read_csv('..\\MotorPremiums.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e131bdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4387 entries, 0 to 4386\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   ID              4387 non-null   object \n",
      " 1   Age             4387 non-null   int64  \n",
      " 2   Gender          4387 non-null   object \n",
      " 3   LicenceCountry  4387 non-null   object \n",
      " 4   Yrs DL          4387 non-null   int64  \n",
      " 5   Points          4387 non-null   int64  \n",
      " 6   NCD             4387 non-null   int64  \n",
      " 7   Engine cc       4387 non-null   int64  \n",
      " 8   Ins Group       4387 non-null   int64  \n",
      " 9   Stored          4387 non-null   object \n",
      " 10  Car Age         4387 non-null   int64  \n",
      " 11  Premium         4387 non-null   float64\n",
      "dtypes: float64(1), int64(7), object(4)\n",
      "memory usage: 411.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Visualise data information, which includes the data type of each field\n",
    "dataFile.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d56946f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>LicenceCountry</th>\n",
       "      <th>Yrs DL</th>\n",
       "      <th>Points</th>\n",
       "      <th>NCD</th>\n",
       "      <th>Engine cc</th>\n",
       "      <th>Ins Group</th>\n",
       "      <th>Stored</th>\n",
       "      <th>Car Age</th>\n",
       "      <th>Premium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B69193007</td>\n",
       "      <td>52</td>\n",
       "      <td>M</td>\n",
       "      <td>UK</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>4</td>\n",
       "      <td>Garage</td>\n",
       "      <td>2</td>\n",
       "      <td>195.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B58763002</td>\n",
       "      <td>46</td>\n",
       "      <td>F</td>\n",
       "      <td>UK</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1400</td>\n",
       "      <td>2</td>\n",
       "      <td>Garage</td>\n",
       "      <td>7</td>\n",
       "      <td>225.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A57010377</td>\n",
       "      <td>59</td>\n",
       "      <td>F</td>\n",
       "      <td>UK</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1400</td>\n",
       "      <td>5</td>\n",
       "      <td>Garage</td>\n",
       "      <td>14</td>\n",
       "      <td>201.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B61470950</td>\n",
       "      <td>62</td>\n",
       "      <td>M</td>\n",
       "      <td>UK</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1400</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>211.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B43586226</td>\n",
       "      <td>59</td>\n",
       "      <td>F</td>\n",
       "      <td>UK</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1400</td>\n",
       "      <td>5</td>\n",
       "      <td>Road</td>\n",
       "      <td>13</td>\n",
       "      <td>251.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Age Gender LicenceCountry  Yrs DL  Points  NCD  Engine cc  \\\n",
       "0  B69193007   52      M             UK      16       0    0       1200   \n",
       "1  B58763002   46      F             UK      22       8    1       1400   \n",
       "2  A57010377   59      F             UK       9       0    0       1400   \n",
       "3  B61470950   62      M             UK      35       0    0       1400   \n",
       "4  B43586226   59      F             UK       3       0    4       1400   \n",
       "\n",
       "   Ins Group  Stored  Car Age  Premium  \n",
       "0          4  Garage        2   195.32  \n",
       "1          2  Garage        7   225.09  \n",
       "2          5  Garage       14   201.08  \n",
       "3          4       0       12   211.76  \n",
       "4          5    Road       13   251.49  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualise the data in tabular format and assess if you agree with the data types\n",
    "dataFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87961283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Yrs DL</th>\n",
       "      <th>Points</th>\n",
       "      <th>NCD</th>\n",
       "      <th>Engine cc</th>\n",
       "      <th>Ins Group</th>\n",
       "      <th>Car Age</th>\n",
       "      <th>Premium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.639519</td>\n",
       "      <td>0.024085</td>\n",
       "      <td>-0.004407</td>\n",
       "      <td>0.013961</td>\n",
       "      <td>-0.021256</td>\n",
       "      <td>0.019851</td>\n",
       "      <td>0.402763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yrs DL</th>\n",
       "      <td>0.639519</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.001876</td>\n",
       "      <td>0.009164</td>\n",
       "      <td>-0.019748</td>\n",
       "      <td>0.022919</td>\n",
       "      <td>0.442921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Points</th>\n",
       "      <td>0.024085</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.017312</td>\n",
       "      <td>0.007375</td>\n",
       "      <td>-0.023043</td>\n",
       "      <td>-0.006144</td>\n",
       "      <td>0.330168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCD</th>\n",
       "      <td>-0.004407</td>\n",
       "      <td>0.001876</td>\n",
       "      <td>-0.017312</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009170</td>\n",
       "      <td>0.004559</td>\n",
       "      <td>-0.025132</td>\n",
       "      <td>0.634157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engine cc</th>\n",
       "      <td>0.013961</td>\n",
       "      <td>0.009164</td>\n",
       "      <td>0.007375</td>\n",
       "      <td>0.009170</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.230337</td>\n",
       "      <td>-0.016065</td>\n",
       "      <td>0.085644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ins Group</th>\n",
       "      <td>-0.021256</td>\n",
       "      <td>-0.019748</td>\n",
       "      <td>-0.023043</td>\n",
       "      <td>0.004559</td>\n",
       "      <td>0.230337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013587</td>\n",
       "      <td>0.211748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car Age</th>\n",
       "      <td>0.019851</td>\n",
       "      <td>0.022919</td>\n",
       "      <td>-0.006144</td>\n",
       "      <td>-0.025132</td>\n",
       "      <td>-0.016065</td>\n",
       "      <td>0.013587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.171124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>0.402763</td>\n",
       "      <td>0.442921</td>\n",
       "      <td>0.330168</td>\n",
       "      <td>0.634157</td>\n",
       "      <td>0.085644</td>\n",
       "      <td>0.211748</td>\n",
       "      <td>0.171124</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age    Yrs DL    Points       NCD  Engine cc  Ins Group  \\\n",
       "Age        1.000000  0.639519  0.024085 -0.004407   0.013961  -0.021256   \n",
       "Yrs DL     0.639519  1.000000  0.006567  0.001876   0.009164  -0.019748   \n",
       "Points     0.024085  0.006567  1.000000 -0.017312   0.007375  -0.023043   \n",
       "NCD       -0.004407  0.001876 -0.017312  1.000000   0.009170   0.004559   \n",
       "Engine cc  0.013961  0.009164  0.007375  0.009170   1.000000   0.230337   \n",
       "Ins Group -0.021256 -0.019748 -0.023043  0.004559   0.230337   1.000000   \n",
       "Car Age    0.019851  0.022919 -0.006144 -0.025132  -0.016065   0.013587   \n",
       "Premium    0.402763  0.442921  0.330168  0.634157   0.085644   0.211748   \n",
       "\n",
       "            Car Age   Premium  \n",
       "Age        0.019851  0.402763  \n",
       "Yrs DL     0.022919  0.442921  \n",
       "Points    -0.006144  0.330168  \n",
       "NCD       -0.025132  0.634157  \n",
       "Engine cc -0.016065  0.085644  \n",
       "Ins Group  0.013587  0.211748  \n",
       "Car Age    1.000000  0.171124  \n",
       "Premium    0.171124  1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualise the correlation coefficient between the different variables\n",
    "dataFile.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0942c527",
   "metadata": {},
   "source": [
    "Using histograms, the below code visualises the distribution of numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a1c6a746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Age'}>,\n",
       "        <AxesSubplot:title={'center':'Yrs DL'}>],\n",
       "       [<AxesSubplot:title={'center':'Points'}>,\n",
       "        <AxesSubplot:title={'center':'NCD'}>],\n",
       "       [<AxesSubplot:title={'center':'Engine cc'}>,\n",
       "        <AxesSubplot:title={'center':'Ins Group'}>],\n",
       "       [<AxesSubplot:title={'center':'Car Age'}>,\n",
       "        <AxesSubplot:title={'center':'Premium'}>]], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Define the number of subplots (4 rows with 2 columns) to plot all the 8 numerical variables\n",
    "fig, axis = plt.subplots(4,2, figsize=(8,10))\n",
    "\n",
    "#create histogram for each column in DataFrame\n",
    "dataFile.hist(ax=axis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc6cedd4",
   "metadata": {},
   "source": [
    "Using bar plots, the below code visualises the distribution of categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c9d1fbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(15,3))\n",
    "dataFile['Gender'].value_counts().plot(ax=axes[0], kind='bar')\n",
    "dataFile['LicenceCountry'].value_counts().plot(ax=axes[1], kind='bar')\n",
    "dataFile['Stored'].value_counts().plot(ax=axes[2], kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96037c8",
   "metadata": {},
   "source": [
    "## Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea650fab",
   "metadata": {},
   "source": [
    "In the below steps, 3 changes have been performed: <br>\n",
    "- The Gender column has been fixed on typos where Female is set to F and Male is set to M.\n",
    "- The dataset has been filtered to only values where LicenceCountry is equal to \"UK\".\n",
    "- The dataset has been filtered out any values where Stored is equal to \"0\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "45f278e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFile['Gender'] = np.where(dataFile['Gender']==\"Female\", \"F\",\n",
    "                             np.where(dataFile['Gender']==\"Male\", \"M\", dataFile['Gender']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "523c68e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFile = dataFile[dataFile.LicenceCountry=='UK']\n",
    "dataFile = dataFile[dataFile.Stored!='0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e99be7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(15,3))\n",
    "dataFile['Gender'].value_counts().plot(ax=axes[0], kind='bar')\n",
    "dataFile['LicenceCountry'].value_counts().plot(ax=axes[1], kind='bar')\n",
    "dataFile['Stored'].value_counts().plot(ax=axes[2], kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04df1ce8",
   "metadata": {},
   "source": [
    "Below, ID is removed from the data as this is not relevant to the model.<br>\n",
    "LicenceCountry feature is also removed as after the intial cleaning, the column had standard deviation of 0, with all values being \"UK\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "13c59513",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop IDs as this is irrelant\n",
    "dataFile = dataFile.drop([\"ID\",\"LicenceCountry\"], axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cc385f8",
   "metadata": {},
   "source": [
    "In the following step we apply one-hot encoding to the categorical features of the dataset so the data is ready to be fitted to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3711e888",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Yrs DL</th>\n",
       "      <th>Points</th>\n",
       "      <th>NCD</th>\n",
       "      <th>Engine cc</th>\n",
       "      <th>Ins Group</th>\n",
       "      <th>Car Age</th>\n",
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       "<p>4374 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Yrs DL  Points  NCD  Engine cc  Ins Group  Car Age  Premium  \\\n",
       "0      52      16       0    0       1200          4        2   195.32   \n",
       "1      46      22       8    1       1400          2        7   225.09   \n",
       "2      59       9       0    0       1400          5       14   201.08   \n",
       "4      59       3       0    4       1400          5       13   251.49   \n",
       "5      78      57       4    1       1800          3        1   229.46   \n",
       "...   ...     ...     ...  ...        ...        ...      ...      ...   \n",
       "4382   28       8       0    3       1200          4        1   214.03   \n",
       "4383   76      41       0    4       1600          5        1   265.50   \n",
       "4384   50      32       0    4       1200          2        1   250.51   \n",
       "4385   36      12       8    3       1200          5        1   264.92   \n",
       "4386   36       2       0    3       1200          4        1   208.95   \n",
       "\n",
       "      Gender_F  Gender_M  Stored_Drive  Stored_Garage  Stored_Road  \n",
       "0            0         1             0              1            0  \n",
       "1            1         0             0              1            0  \n",
       "2            1         0             0              1            0  \n",
       "4            1         0             0              0            1  \n",
       "5            0         1             0              1            0  \n",
       "...        ...       ...           ...            ...          ...  \n",
       "4382         0         1             0              1            0  \n",
       "4383         1         0             0              0            1  \n",
       "4384         0         1             0              0            1  \n",
       "4385         1         0             0              0            1  \n",
       "4386         1         0             1              0            0  \n",
       "\n",
       "[4374 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Implement One Hot Encoding to the data\n",
    "dataFile_encoded = pd.get_dummies(dataFile, columns = ['Gender', 'Stored'])\n",
    "dataFile_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6df877",
   "metadata": {},
   "source": [
    "## Define the Target column and separate from rest of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6266b5e0",
   "metadata": {},
   "source": [
    "Define the target variable (column Premium) and features to be used for training the model (all other columns except target column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16bad7f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_column = 'Premium'\n",
    "X = dataFile_encoded.drop(columns=[target_column])\n",
    "y = dataFile_encoded[target_column]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bdcebb9",
   "metadata": {},
   "source": [
    "## Split dataset into Training, Validation and Test Subsets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db7419c8",
   "metadata": {},
   "source": [
    "The next step is then to split the training, validation and testing data. We first separate training and testing, then further subsegment the training into final training subset and validation subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc1c0f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training (80%) and test (20%) sets\n",
    "train_data, test_data, train_labels, test_labels = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c6d4de31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set number of rows:  2799\n",
      "Validation Set number of rows:  700\n",
      "Test Set number of rows:  875\n"
     ]
    }
   ],
   "source": [
    "# Further split the training set into training and validation sets (80% training, 20% validation)\n",
    "final_train_data, validation_data, final_train_labels, validation_labels = train_test_split(train_data, train_labels, \n",
    "                                                                                            test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Training Set number of rows: \",len(final_train_data))\n",
    "print(\"Validation Set number of rows: \",len(validation_data))\n",
    "print(\"Test Set number of rows: \",len(test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be39f36",
   "metadata": {},
   "source": [
    "## Definition of Hyperparameters and Training of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0b3515b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(max_iter=500, random_state=42)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_model = MLPRegressor(hidden_layer_sizes=(100,), max_iter=500, random_state=42)\n",
    "nn_model.fit(final_train_data, final_train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83af11b1",
   "metadata": {},
   "source": [
    "## Make predictions on validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5b7eac89",
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_predictions = nn_model.predict(validation_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9fb558c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error on Validation Set: 10.29\n",
      "R^2 Score on Validation Set: 0.98\n",
      "\n",
      "Validation Predictions:\n",
      "[253.51921352 180.47848543 302.80261759 227.90313431 208.67948825\n",
      " 210.09876057 226.63823238 201.73511906 237.71986597 233.68933645\n",
      " 230.92924296 218.33884759 214.99276035 217.66608453 251.44114553\n",
      " 200.59966546 205.53192333 212.05640514 182.44292107 246.30533725\n",
      " 213.16004725 243.7217009  186.23884175 230.18789499 220.45879988\n",
      " 200.70981863 198.0551369  206.95212413 235.49017801 196.65411417\n",
      " 246.6117247  228.28056711 249.53206058 237.59939256 246.66443436\n",
      " 220.06020915 237.21312431 222.01390875 197.82912831 208.44483425\n",
      " 203.14100568 230.92713935 212.39758979 207.04342421 217.9044363\n",
      " 225.80410801 194.03468634 198.02975188 207.91331133 194.29073427\n",
      " 207.38740406 253.11260692 238.67009843 226.39353601 186.27925791\n",
      " 223.74894718 198.43997648 211.963156   228.56740525 253.75414593\n",
      " 206.79848676 195.9526861  171.09755431 210.20376301 189.82353698\n",
      " 253.29138905 209.46382786 233.44903577 219.84303092 213.51956208\n",
      " 237.09423793 228.37717845 168.30354253 211.23213283 173.88504804\n",
      " 190.82613667 230.3564405  224.2819098  242.13313771 223.72082291\n",
      " 251.58969434 229.03990353 232.04940704 207.93590937 218.65242965\n",
      " 232.17367528 207.58924113 198.43320676 224.56160852 233.85507947\n",
      " 235.39160874 191.70397615 220.93370794 182.19919777 198.18580421\n",
      " 249.02429139 210.7622709  190.26596634 241.34419602 182.66916234\n",
      " 252.75825508 205.44383406 269.22092325 223.94657192 198.20430825\n",
      " 210.32851406 192.46790502 259.76799209 220.1394459  230.64849719\n",
      " 230.14509552 215.6336943  262.25067048 212.2864572  254.44699524\n",
      " 183.59421442 183.27596277 212.06791145 211.06127493 230.67101891\n",
      " 222.42127424 224.63331549 234.18697959 248.33190976 204.69654725\n",
      " 198.43127403 201.28848177 222.43126681 203.931573   223.47258482\n",
      " 206.30250625 227.7240797  200.19487506 231.85930599 218.78345158\n",
      " 232.03885713 186.48492036 219.31751445 245.27890338 239.1590985\n",
      " 198.98700483 223.29080739 200.69044656 230.64487187 175.74246628\n",
      " 217.46039306 232.50641772 224.22665852 240.50565913 188.28332582\n",
      " 239.27308973 215.56207631 202.69656813 243.45490661 227.89029421\n",
      " 222.59118436 173.49739658 198.94351409 230.3322417  203.10577498\n",
      " 220.11493914 205.11832244 265.23736973 206.53044671 242.87591778\n",
      " 209.94778167 215.38892697 219.20996508 189.32619049 197.00128948\n",
      " 244.18503604 208.04147191 224.2278075  268.53019219 246.3547478\n",
      " 213.97420593 221.75500021 220.35042534 237.31823777 205.23102322\n",
      " 213.03007579 208.37433384 168.75811788 247.36892731 222.5481769\n",
      " 261.1964728  225.73575158 209.47598048 257.40708039 231.63814929\n",
      " 200.86332747 223.63188781 205.51703743 232.79132522 216.51663119\n",
      " 203.86743852 256.40595304 205.96405295 232.42537056 226.25952704\n",
      " 204.02888824 215.82340795 220.65224852 195.84892825 208.92014079\n",
      " 234.03888063 216.94182673 223.82123911 214.17817401 229.28363525\n",
      " 198.00738829 201.24461854 254.9663455  215.41895477 167.18419531\n",
      " 227.02550657 211.24303716 252.69816181 241.23339633 248.57370639\n",
      " 216.82065917 225.24157775 204.0683681  231.61018886 247.40867843\n",
      " 193.84095096 211.09744821 219.58533933 187.31329241 232.95306937\n",
      " 256.14687817 235.50075481 238.96550005 252.76059497 213.72622719\n",
      " 216.97375567 247.03202225 218.06744458 192.40190799 222.52784276\n",
      " 195.74686375 256.10802636 194.2637296  213.8990339  247.14521107\n",
      " 255.68012657 225.63349035 220.36910802 222.86353976 227.9659394\n",
      " 242.8930683  228.4761532  195.54718978 196.87862892 176.77209468\n",
      " 219.4909233  184.51963152 235.83348785 209.34253054 212.04267559\n",
      " 176.30240799 209.77444439 199.96494359 199.29918825 213.09328645\n",
      " 222.41403584 205.41125399 221.3024178  244.83148667 256.73636583\n",
      " 193.47980644 227.03666274 186.69822803 212.46895783 216.90580113\n",
      " 240.95838159 206.89177806 224.22604495 209.97467526 253.07009323\n",
      " 223.34585712 223.58794698 243.80232816 253.93265892 200.19184097\n",
      " 216.46158645 228.35821721 247.29128765 277.74583251 244.02921988\n",
      " 247.39563554 257.06038166 219.72457287 245.91413832 205.67399217\n",
      " 223.63410321 178.22735803 197.62624173 188.55366931 214.28437784\n",
      " 261.6608297  245.35333143 257.0539136  185.63931443 241.16840048\n",
      " 227.59294855 197.67355096 177.8954342  202.0394902  224.69578514\n",
      " 214.62122314 243.9780675  229.31914815 211.011453   223.87891793\n",
      " 249.82102138 209.10862155 247.75758172 252.27792459 217.8022061\n",
      " 234.62375941 229.10166167 246.10912037 253.46981457 222.74627016\n",
      " 194.70676504 226.812887   258.5905054  214.18961185 191.43129052\n",
      " 212.24465637 207.76845154 205.51627222 197.81221176 217.95103325\n",
      " 232.37205277 259.398431   232.15569229 221.44220562 231.10340408\n",
      " 249.07224771 201.19039151 212.35387911 216.5765035  220.26841182\n",
      " 236.53597315 198.95511769 254.52865367 202.28477804 263.49649857\n",
      " 189.28829868 221.29918847 229.94140866 246.6427093  239.52657023\n",
      " 209.463521   195.19581836 237.86305031 185.18588223 203.89839739\n",
      " 219.21422987 231.00821704 239.79662606 229.21094413 202.03489132\n",
      " 205.3100988  168.29039544 227.2435582  209.84551438 200.86990323\n",
      " 198.29804318 207.94066019 264.36015686 213.88359794 208.04696986\n",
      " 200.47725282 241.99102791 210.54638371 182.578321   217.8441895\n",
      " 222.36448158 205.59526771 230.20527079 213.00803101 251.1164086\n",
      " 225.38908267 231.15323865 227.29324956 208.1980049  195.50763174\n",
      " 271.37057556 211.90723995 231.85562029 210.62938732 188.75018321\n",
      " 195.74572749 216.8774587  252.5115734  227.47215283 219.87644606\n",
      " 212.11083137 233.72215112 209.49867039 246.98190357 261.535476\n",
      " 230.31320864 200.6162739  167.21160159 192.24853792 196.94860666\n",
      " 227.22692948 180.70594672 223.22501852 236.53564829 230.39494176\n",
      " 219.19532595 221.54438371 218.8581613  220.23497938 245.19039531\n",
      " 223.70785126 246.2235901  216.81092339 239.04884434 206.13907421\n",
      " 215.28110777 231.66360757 209.00354213 207.88127296 189.93050411\n",
      " 213.41400286 192.96357563 258.33575116 212.37866684 213.72994062\n",
      " 209.87021847 224.40816507 205.93755905 190.75084847 228.14758503\n",
      " 180.41430385 215.28090239 201.96759263 227.36230477 234.50795823\n",
      " 237.09628014 235.80799673 200.2175159  217.66746489 216.26784084\n",
      " 190.79467057 238.61889735 231.17111413 219.618491   244.18931051\n",
      " 228.17923971 232.28228993 240.21287827 237.99403031 201.31089036\n",
      " 203.23037002 210.04690019 224.06568743 211.61328567 207.82177145\n",
      " 229.9612256  217.88603802 224.53142333 200.11988732 254.42581742\n",
      " 247.12147141 169.31460916 237.2555552  205.51469248 171.73620701\n",
      " 263.49134951 217.38296986 219.58572485 244.56833486 184.18298743\n",
      " 223.2300856  175.6570115  233.08383484 253.22009941 220.49307191\n",
      " 199.52020789 168.36920597 191.02713383 237.47609017 234.84809296\n",
      " 216.85746592 207.85766249 211.05431387 275.10297887 234.54314593\n",
      " 220.76392814 199.24647349 199.15994782 221.93809109 214.52641448\n",
      " 198.13386657 240.60122218 231.01345778 194.08568973 203.15632894\n",
      " 200.17093045 212.4398415  191.97367689 230.90024743 228.53342507\n",
      " 233.36956584 222.83252966 206.780482   255.80329161 238.3572297\n",
      " 226.25798904 207.30995418 203.63918891 262.74999542 214.3071757\n",
      " 215.17884257 236.4025977  247.99317904 220.01069651 183.02895855\n",
      " 250.28797964 195.82727724 230.04617246 234.45253357 215.9488384\n",
      " 240.07892501 217.43685914 237.22744657 239.83837572 190.86213549\n",
      " 268.78740519 176.44770786 225.72872933 232.29148391 198.97039697\n",
      " 190.30815977 180.50714756 234.59828279 211.22906237 258.179614\n",
      " 200.86771534 208.98488249 193.96084437 227.73028982 234.03212914\n",
      " 190.69324579 193.22854363 218.10579674 168.68890238 256.38637091\n",
      " 208.8517929  211.9348135  239.32022338 203.13002399 210.18357692\n",
      " 226.78706669 226.59224033 208.95845542 262.39430911 228.41677715\n",
      " 216.37647507 206.85654493 245.99310935 222.94635993 250.05733138\n",
      " 232.06082711 198.53193064 231.14306039 197.78625024 213.82552868\n",
      " 234.34517755 189.77721103 244.98235924 217.09341522 254.05709493\n",
      " 188.52687668 212.74417425 256.87710042 243.31300748 200.79399904\n",
      " 229.14356779 242.58548451 221.63093822 211.93493283 213.44138799\n",
      " 199.93888038 224.63312185 272.64796706 238.96113213 266.84628843\n",
      " 187.42775529 179.96923042 204.82116255 236.32346528 213.091987\n",
      " 240.54183146 232.15649795 232.16742305 202.03859178 245.87711158\n",
      " 215.05682359 227.3369164  193.16500162 184.71463303 209.21032515\n",
      " 203.80806237 181.41046403 205.51234261 240.11738286 184.95824508\n",
      " 215.08935826 213.68815219 226.07406048 180.21042486 213.88880125\n",
      " 205.10649456 220.86268513 214.28526838 199.84383245 225.21761137\n",
      " 196.57622538 236.40260778 183.49213472 199.28466048 215.39231552\n",
      " 246.03081741 182.38528439 241.91273708 200.30351256 184.46002066\n",
      " 237.6601845  213.88811788 215.33934905 196.42699132 222.41926885\n",
      " 206.88529912 234.19375372 180.74053572 227.81933673 183.78291522\n",
      " 200.68085606 239.25403709 215.01145491 231.70000407 191.83193184\n",
      " 238.77845035 229.0564166  199.61036403 256.24695128 164.16244476\n",
      " 218.1548681  224.54889944 214.00555753 175.33952677 233.45818343\n",
      " 210.88152028 216.20542129 242.71160089 217.36556699 234.97379392\n",
      " 251.63393488 236.62486705 221.54539155 230.6245292  236.26514559\n",
      " 220.30499821 197.01553055 209.8090433  243.25761989 278.39483931\n",
      " 230.28290592 196.14021742 218.29088782 254.91626588 215.66815088\n",
      " 226.33464891 204.92829035 215.23396728 228.43579154 220.32646268\n",
      " 184.91014525 189.53882069 202.48143062 209.76880236 210.86376871\n",
      " 215.75794591 214.28215088 210.24099373 244.71909132 190.94113342\n",
      " 187.8639315  252.27842171 245.39835513 239.66012541 263.03238494]\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the validation data\n",
    "mse = mean_squared_error(validation_labels, validation_predictions)\n",
    "r2 = r2_score(validation_labels, validation_predictions)\n",
    "\n",
    "print(f\"Mean Squared Error on Validation Set: {mse:.2f}\")\n",
    "print(f\"R^2 Score on Validation Set: {r2:.2f}\")\n",
    "\n",
    "# Display predictions\n",
    "print(\"\\nValidation Predictions:\")\n",
    "print(validation_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8df61753",
   "metadata": {},
   "source": [
    "### Visualisation of Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "05d209f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between true and predicted premiums: -0.02\n",
      "\n",
      "True vs Predicted Premiums:\n",
      "      True Premium  Predicted Premium\n",
      "4319        255.81         253.519214\n",
      "3759        184.65         180.478485\n",
      "25          306.07         302.802618\n",
      "1453        226.33         227.903134\n",
      "3073        212.41         208.679488\n"
     ]
    }
   ],
   "source": [
    "# Visualise predictions vs. true values using a scatter plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(validation_labels, validation_predictions, alpha=0.5, color='blue')\n",
    "plt.plot([validation_labels.min(), validation_labels.max()], \n",
    "         [validation_labels.min(), validation_labels.max()], 'r--', lw=2)\n",
    "plt.title('True Premium vs. Predicted Premium (Neural Network)')\n",
    "plt.xlabel('True Premium')\n",
    "plt.ylabel('Predicted Premium')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Compute the correlation between true and predicted values\n",
    "correlation = pd.Series(validation_labels).corr(pd.Series(validation_predictions))\n",
    "print(f\"Correlation between true and predicted premiums: {correlation:.2f}\")\n",
    "\n",
    "# Display the predictions and true values in a data table\n",
    "comparison_df = pd.DataFrame({\n",
    "    'True Premium': validation_labels,\n",
    "    'Predicted Premium': validation_predictions\n",
    "})\n",
    "\n",
    "print(\"\\nTrue vs Predicted Premiums:\")\n",
    "print(comparison_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1702a4d7",
   "metadata": {},
   "source": [
    "## Final Assessment: Predictions on Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3b0df73b",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = nn_model.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c9648422",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error on Validation Set: 10.50\n",
      "R^2 Score on Validation Set: 0.98\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the validation data\n",
    "mse_test = mean_squared_error(test_labels, test_predictions)\n",
    "r2_test = r2_score(test_labels, test_predictions)\n",
    "\n",
    "print(f\"Mean Squared Error on Validation Set: {mse_test:.2f}\")\n",
    "print(f\"R^2 Score on Validation Set: {r2_test:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fea898d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between true and predicted premiums: 0.03\n",
      "\n",
      "True vs Predicted Premiums:\n",
      "      True Premium  Predicted Premium\n",
      "1573        206.38         200.178625\n",
      "464         243.79         245.085531\n",
      "1617        249.69         244.656212\n",
      "1486        219.36         220.610708\n",
      "3815        234.75         236.379139\n"
     ]
    }
   ],
   "source": [
    "# Visualise predictions vs. true values using a scatter plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(test_labels, test_predictions, alpha=0.5, color='blue')\n",
    "plt.plot([test_labels.min(), test_predictions.max()], \n",
    "         [test_labels.min(), test_predictions.max()], 'r--', lw=2)\n",
    "plt.title('True Premium vs. Predicted Premium (Neural Network)')\n",
    "plt.xlabel('True Premium')\n",
    "plt.ylabel('Predicted Premium')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Compute the correlation between true and predicted values\n",
    "correlation_test = pd.Series(test_labels).corr(pd.Series(test_predictions))\n",
    "print(f\"Correlation between true and predicted premiums: {correlation_test:.2f}\")\n",
    "\n",
    "# Display the predictions and true values in a data table\n",
    "comparison_df_test = pd.DataFrame({\n",
    "    'True Premium': test_labels,\n",
    "    'Predicted Premium': test_predictions\n",
    "})\n",
    "\n",
    "print(\"\\nTrue vs Predicted Premiums:\")\n",
    "print(comparison_df_test.head())"
   ]
  }
 ],
 "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
}