Project3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor \n",
    "from numpy import loadtxt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Set random seed\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_fwf(\"housing.data\", header = None, names=[\"CRIM\", \"ZN\", \"INDUS\", \"CHAS\", \"NOX\", \n",
    "                                                         \"RM\", \"AGE\", \"DIS\", \"RAD\", \"TAX\", \n",
    "                                                         \"PTRATIO\", \"B\", \"LSTAT\", \"MEDV\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data description from file\n",
    "1. CRIM      per capita crime rate by town\n",
    "2. ZN        proportion of residential land zoned for lots over 25,000 sq.ft.\n",
    "3. INDUS     proportion of non-retail business acres per town\n",
    "4. CHAS      Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
    "5. NOX       nitric oxides concentration (parts per 10 million)\n",
    "6. RM        average number of rooms per dwelling\n",
    "7. AGE       proportion of owner-occupied units built prior to 1940\n",
    "8. DIS       weighted distances to five Boston employment centres\n",
    "9. RAD       index of accessibility to radial highways\n",
    "10. TAX      full-value property-tax rate per 10,000 dollars\n",
    "11. PTRATIO  pupil-teacher ratio by town\n",
    "12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
    "13. LSTAT    lower status of the population\n",
    "14. MEDV     Median value of owner-occupied homes in 1000 dollars\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</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",
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       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
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       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
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       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
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       "    <tr>\n",
       "      <th>504</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
       "1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242.0   \n",
       "2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242.0   \n",
       "3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222.0   \n",
       "4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273.0   \n",
       "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273.0   \n",
       "503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273.0   \n",
       "504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273.0   \n",
       "505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV  \n",
       "0       15.3  396.90   4.98  24.0  \n",
       "1       17.8  396.90   9.14  21.6  \n",
       "2       17.8  392.83   4.03  34.7  \n",
       "3       18.7  394.63   2.94  33.4  \n",
       "4       18.7  396.90   5.33  36.2  \n",
       "..       ...     ...    ...   ...  \n",
       "501     21.0  391.99   9.67  22.4  \n",
       "502     21.0  396.90   9.08  20.6  \n",
       "503     21.0  396.90   5.64  23.9  \n",
       "504     21.0  393.45   6.48  22.0  \n",
       "505     21.0  396.90   7.88  11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    int64  \n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    float64\n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "#Descriptive statistics\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data preprocessing part \n",
    "#Check missing values\n",
    "data.isnull().values.any()\n",
    "#data.isnull().sum()\n",
    "#no missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>CRIM</th>\n",
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       "      <th>MEDV</th>\n",
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       "      <th>count</th>\n",
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       "      <th>mean</th>\n",
       "      <td>1.716290</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.696228</td>\n",
       "      <td>4.332016</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.653510</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>1.999689</td>\n",
       "      <td>1.417166</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>0.585700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.081900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.073700</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.250895</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.107300</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.326718</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.112625</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.966540</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.222900</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     1.716290   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      2.653510   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.081900    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.250895    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      2.326718   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max      9.966540  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.696228    4.332016  408.237154   18.455534  356.674032   \n",
       "std     28.148861    1.999689    1.417166  168.537116    2.164946   91.294864   \n",
       "min      2.900000    0.585700    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.073700    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.107300    4.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.112625    5.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000    9.222900    8.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the summary of the data\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data Visualization Part\n",
    "#Scatter plot\n",
    "\n",
    "indx_length = len(data.columns)\n",
    "indx_length = indx_length - 1\n",
    "\n",
    "indx_length\n",
    "\n",
    "fig = plt.figure(figsize=(16, 10)) \n",
    "vector_names = data.columns[0:13]\n",
    "\n",
    "for i in range(0,indx_length):\n",
    "    ax = fig.add_subplot(4, 4, i+1)\n",
    "    plt.scatter(data.iloc[:, [i]], data.iloc[:,13], color='green')\n",
    "    ax.set_xlabel(vector_names[i])\n",
    "    ax.set_ylabel('MEDV')\n",
    "    fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#plt.savefig(\"Scatterplots\")\n",
    "#The prices increase as the value of RM increases linearly, however, there are few outliers.\n",
    "#The prices tend to decrease with an increase in LSTAT. However, the pattern doesn't follow exactly a linear line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation matrix\n",
    "\n",
    "correlation_matrix = data.corr().round(2)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "\n",
    "# annot = True to print the values inside the square\n",
    "sns.heatmap(data=correlation_matrix, annot=True, linewidths=.5, ax=ax)\n",
    "\n",
    "#As it can be seen from the correlation matrix, RM has a strong positive correlation with MEDV (0.7), while\n",
    "#LSTAT has a high negative correlation with MEDV(-0.74).\n",
    "\n",
    "\n",
    "#check for the multicollinearity\n",
    "#Moreover, it's essential to check features for multicollinearity in order to determine which features are \n",
    "#highly correlated with another ones. The features INDUS and TAX have a correlation of 0.72. Additionally, \n",
    "#CRIM and TAX have a correlation of 0.79. AGE and NOX have a correlation of 0.73. \n",
    "#These feature pairs are strongly correlated to each other. Thus, we should not select these features together \n",
    "#for training the model. \n",
    "#Similarly, features NOX and DIS have a high negative correlation of -0.75. INDUS and DIS have a negative \n",
    "#correlation of -0.68, and AGE and DIS = -0.71.\n",
    "\n",
    "\n",
    "#therefore, the following variables such as AGE, NOX, INDUS are not to be included into the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram of independent variables\n",
    "\n",
    "fig = plt.figure(figsize=(16, 10)) \n",
    "vector_names = data.columns[0:13]\n",
    "\n",
    "for i in range(0,indx_length):\n",
    "    ax = fig.add_subplot(4, 4, i+1)\n",
    "    plt.hist(data.iloc[:, i], bins = 30, color='green')\n",
    "    ax.set_xlabel(vector_names[i])\n",
    "    ax.set_ylabel('Frequency')\n",
    "    fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "#Plot the distribution of the target variable MEDV\n",
    "plt.hist(data.iloc[:, 13], bins = 30, color='green')\n",
    "plt.xlabel('Target price (in $1000)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title(\"Frequency of target variable\")\n",
    "plt.show()\n",
    "\n",
    "#As it can be seen, the values of MEDV are normally distributed with few outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
       "1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242.0   \n",
       "2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242.0   \n",
       "3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222.0   \n",
       "4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273.0   \n",
       "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273.0   \n",
       "503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273.0   \n",
       "504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273.0   \n",
       "505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "0       15.3  396.90   4.98  \n",
       "1       17.8  396.90   9.14  \n",
       "2       17.8  392.83   4.03  \n",
       "3       18.7  394.63   2.94  \n",
       "4       18.7  396.90   5.33  \n",
       "..       ...     ...    ...  \n",
       "501     21.0  391.99   9.67  \n",
       "502     21.0  396.90   9.08  \n",
       "503     21.0  396.90   5.64  \n",
       "504     21.0  393.45   6.48  \n",
       "505     21.0  396.90   7.88  \n",
       "\n",
       "[418 rows x 13 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding outliers using Z score\n",
    "#Formula for Z score = (Observation — Mean)/Standard Deviation\n",
    "#Find the outliers in the dataset without the 'Grade' column\n",
    "\n",
    "data2 = data.loc[:, data.columns != 'MEDV']\n",
    "z_scores = stats.zscore(data2)\n",
    "abs_z_scores = np.abs(z_scores)\n",
    "filtered_entries = (abs_z_scores < 3).all(axis=1)\n",
    "new_df = data2[filtered_entries]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict the column 13 which is “MEDV - Median value of owner-occupied homes in $1000's” (target variable)\n",
    "\n",
    "# split into input (X) and output (y) variables\n",
    "data3 = data[filtered_entries]\n",
    "x = new_df\n",
    "y = data3.iloc[:,13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>CHAS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CHAS\n",
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "..    ...\n",
       "501     0\n",
       "502     0\n",
       "503     0\n",
       "504     0\n",
       "505     0\n",
       "\n",
       "[418 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the columns containing only 0 for all rows\n",
    "data3.loc[:, (data3 == 0).all()] #CHAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing CHAS from dataset\n",
    "data3 = data3.drop(columns=['CHAS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = data3.corr().round(2)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "\n",
    "# annot = True to print the values inside the square\n",
    "sns.heatmap(data=correlation_matrix, annot=True, linewidths=.5, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</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>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS    NOX     RM   AGE    TAX  PTRATIO  LSTAT\n",
       "0    0.00632  18.0   2.31  0.538  6.575  65.2  296.0     15.3   4.98\n",
       "1    0.02731   0.0   7.07  0.469  6.421  78.9  242.0     17.8   9.14\n",
       "2    0.02729   0.0   7.07  0.469  7.185  61.1  242.0     17.8   4.03\n",
       "3    0.03237   0.0   2.18  0.458  6.998  45.8  222.0     18.7   2.94\n",
       "4    0.06905   0.0   2.18  0.458  7.147  54.2  222.0     18.7   5.33\n",
       "..       ...   ...    ...    ...    ...   ...    ...      ...    ...\n",
       "501  0.06263   0.0  11.93  0.573  6.593  69.1  273.0     21.0   9.67\n",
       "502  0.04527   0.0  11.93  0.573  6.120  76.7  273.0     21.0   9.08\n",
       "503  0.06076   0.0  11.93  0.573  6.976  91.0  273.0     21.0   5.64\n",
       "504  0.10959   0.0  11.93  0.573  6.794  89.3  273.0     21.0   6.48\n",
       "505  0.04741   0.0  11.93  0.573  6.030  80.8  273.0     21.0   7.88\n",
       "\n",
       "[418 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ATTEMPT2 (without AGE, NOX, INDUS)\n",
    "x = new_df.drop(columns = ['RAD','CHAS','DIS','B'])\n",
    "y = data3.iloc[:,-1]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(280, 9)\n",
      "(138, 9)\n"
     ]
    }
   ],
   "source": [
    "#Split into test and training datasets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=40)\n",
    "print(x_train.shape); \n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>0.31827</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.90</td>\n",
       "      <td>0.544</td>\n",
       "      <td>5.914</td>\n",
       "      <td>83.2</td>\n",
       "      <td>304.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>18.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.21977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.91</td>\n",
       "      <td>0.448</td>\n",
       "      <td>5.602</td>\n",
       "      <td>62.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>16.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0.01501</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.435</td>\n",
       "      <td>6.635</td>\n",
       "      <td>29.7</td>\n",
       "      <td>280.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>2.04820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.614</td>\n",
       "      <td>5.648</td>\n",
       "      <td>87.6</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>14.10</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>350</th>\n",
       "      <td>0.06211</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.429</td>\n",
       "      <td>6.490</td>\n",
       "      <td>44.4</td>\n",
       "      <td>335.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0.05425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.510</td>\n",
       "      <td>6.315</td>\n",
       "      <td>73.4</td>\n",
       "      <td>296.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>6.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.14455</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.172</td>\n",
       "      <td>96.1</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>19.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>0.21409</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.86</td>\n",
       "      <td>0.431</td>\n",
       "      <td>6.438</td>\n",
       "      <td>8.9</td>\n",
       "      <td>330.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>0.08490</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.700</td>\n",
       "      <td>4.368</td>\n",
       "      <td>91.2</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>30.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>280 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS    NOX     RM   AGE    TAX  PTRATIO  LSTAT\n",
       "316  0.31827   0.0   9.90  0.544  5.914  83.2  304.0     18.4  18.33\n",
       "49   0.21977   0.0   6.91  0.448  5.602  62.0  233.0     17.9  16.20\n",
       "502  0.04527   0.0  11.93  0.573  6.120  76.7  273.0     21.0   9.08\n",
       "348  0.01501  80.0   2.01  0.435  6.635  29.7  280.0     17.0   5.99\n",
       "422  2.04820   0.0  18.10  0.614  5.648  87.6  666.0     20.2  14.10\n",
       "..       ...   ...    ...    ...    ...   ...    ...      ...    ...\n",
       "350  0.06211  40.0   1.25  0.429  6.490  44.4  335.0     19.7   5.98\n",
       "177  0.05425   0.0   4.05  0.510  6.315  73.4  296.0     16.6   6.29\n",
       "7    0.14455  12.5   7.87  0.524  6.172  96.1  311.0     15.2  19.15\n",
       "251  0.21409  22.0   5.86  0.431  6.438   8.9  330.0     19.1   3.59\n",
       "384  0.08490   0.0  18.10  0.700  4.368  91.2  666.0     20.2  30.63\n",
       "\n",
       "[280 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#standardized dataset\n",
    "# transform data\n",
    "\n",
    "x_scaled = StandardScaler().fit_transform(x[['CRIM', 'ZN', 'INDUS', 'NOX', \n",
    "                                              'RM', 'AGE', 'TAX', 'PTRATIO', 'LSTAT']])\n",
    "\n",
    "x_scaled = pd.DataFrame(data=x_scaled, columns=['CRIM', 'ZN', 'INDUS', 'NOX', \n",
    "                                                'RM', 'AGE', 'TAX', 'PTRATIO', 'LSTAT'])\n",
    "\n",
    "y_df = pd.DataFrame(data=y, columns=['MEDV'])\n",
    "\n",
    "y_scaled = StandardScaler().fit_transform(y_df)\n",
    "y_scaled = pd.DataFrame(data=y_scaled, columns=['MEDV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>TAX</th>\n",
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       "      <td>-0.591596</td>\n",
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       "      <td>0.102865</td>\n",
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       "      <th>4</th>\n",
       "      <td>-0.576273</td>\n",
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       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>-0.578955</td>\n",
       "      <td>-0.498598</td>\n",
       "      <td>0.176728</td>\n",
       "      <td>0.253010</td>\n",
       "      <td>0.542183</td>\n",
       "      <td>0.077685</td>\n",
       "      <td>-0.750334</td>\n",
       "      <td>1.177800</td>\n",
       "      <td>-0.394163</td>\n",
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       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>-0.586207</td>\n",
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       "      <td>0.176728</td>\n",
       "      <td>0.253010</td>\n",
       "      <td>-0.229408</td>\n",
       "      <td>0.346931</td>\n",
       "      <td>-0.750334</td>\n",
       "      <td>1.177800</td>\n",
       "      <td>-0.484884</td>\n",
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       "    <tr>\n",
       "      <th>415</th>\n",
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       "      <td>-0.498598</td>\n",
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       "      <td>1.177800</td>\n",
       "      <td>-1.013833</td>\n",
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       "      <th>416</th>\n",
       "      <td>-0.559338</td>\n",
       "      <td>-0.498598</td>\n",
       "      <td>0.176728</td>\n",
       "      <td>0.253010</td>\n",
       "      <td>0.870069</td>\n",
       "      <td>0.793312</td>\n",
       "      <td>-0.750334</td>\n",
       "      <td>1.177800</td>\n",
       "      <td>-0.884671</td>\n",
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       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>-0.585313</td>\n",
       "      <td>-0.498598</td>\n",
       "      <td>0.176728</td>\n",
       "      <td>0.253010</td>\n",
       "      <td>-0.376223</td>\n",
       "      <td>0.492182</td>\n",
       "      <td>-0.750334</td>\n",
       "      <td>1.177800</td>\n",
       "      <td>-0.669401</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CRIM        ZN     INDUS       NOX        RM       AGE       TAX  \\\n",
       "0   -0.602478  0.380992 -1.235202 -0.065391  0.512820 -0.060480 -0.606517   \n",
       "1   -0.593709 -0.498598 -0.536575 -0.693095  0.261605  0.424871 -0.944175   \n",
       "2   -0.593718 -0.498598 -0.536575 -0.693095  1.507896 -0.205731 -0.944175   \n",
       "3   -0.591596 -0.498598 -1.254282 -0.793163  1.202848 -0.747765 -1.069233   \n",
       "4   -0.576273 -0.498598 -1.254282 -0.793163  1.445908 -0.450178 -1.069233   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "413 -0.578955 -0.498598  0.176728  0.253010  0.542183  0.077685 -0.750334   \n",
       "414 -0.586207 -0.498598  0.176728  0.253010 -0.229408  0.346931 -0.750334   \n",
       "415 -0.579736 -0.498598  0.176728  0.253010  1.166960  0.853538 -0.750334   \n",
       "416 -0.559338 -0.498598  0.176728  0.253010  0.870069  0.793312 -0.750334   \n",
       "417 -0.585313 -0.498598  0.176728  0.253010 -0.376223  0.492182 -0.750334   \n",
       "\n",
       "      PTRATIO     LSTAT  \n",
       "0   -1.486170 -1.115317  \n",
       "1   -0.317762 -0.475658  \n",
       "2   -0.317762 -1.261393  \n",
       "3    0.102865 -1.428996  \n",
       "4    0.102865 -1.061500  \n",
       "..        ...       ...  \n",
       "413  1.177800 -0.394163  \n",
       "414  1.177800 -0.484884  \n",
       "415  1.177800 -1.013833  \n",
       "416  1.177800 -0.884671  \n",
       "417  1.177800 -0.669401  \n",
       "\n",
       "[418 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(280, 9)\n",
      "(138, 9)\n"
     ]
    }
   ],
   "source": [
    "#Split #standardized dataset into test and training datasets\n",
    "x_train_scaled, x_test_scaled, y_train_scaled, y_test_scaled = train_test_split(x_scaled, y_scaled, test_size=0.33, random_state=40)\n",
    "print(x_train_scaled.shape); \n",
    "print(x_test_scaled.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the model\n",
    "\n",
    "The model contains 9 inputs, 16 nodes in layer 1, 32 nodes in layer 2 as well as one output.\n",
    "\n",
    "**Optimizer** set to rmsprop\n",
    "**Loss** set to mse (mean squared error).\n",
    "**Metrics** set to mean average error.\n",
    "\n",
    "Fitting the model with training data and running for 100 epochs, with a validation split of 0.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define model1\n",
    "\n",
    "model = Sequential([\n",
    "    Dense(16, input_dim=9, activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(1),\n",
    "])\n",
    "#Comments: based on the results of all models developed\n",
    "#this model gives better results, meaning that the MSE is lower compared to the other ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='mse',metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 16)                160       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 737\n",
      "Trainable params: 737\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Print model Summary\n",
    "model.summary() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.fit(x_train, y_train, epochs=100, validation_split=0.2,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 38.60\n",
      "Mean Absolute Error score: 4.59\n"
     ]
    }
   ],
   "source": [
    "val_loss_score, val_mae_score = model.evaluate(x_test, y_test, verbose=0);\n",
    "print(\"Mean Squared Error: {:.2f}\\nMean Absolute Error score: {:.2f}\".format(val_loss_score, val_mae_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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.plot(result.history['loss'], label='train')\n",
    "plt.plot(result.history['val_loss'], label='test')\n",
    "plt.title('Model Loss per epoch')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#plt.savefig('model_loss_100')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 24.99 ||| Real: 22.60 ||| Difference: 2.39\n",
      "Predicted: 23.67 ||| Real: 24.60 ||| Difference: -0.93\n",
      "Predicted: 18.17 ||| Real: 14.90 ||| Difference: 3.27\n",
      "Predicted: 25.89 ||| Real: 34.90 ||| Difference: -9.01\n",
      "Predicted: 22.19 ||| Real: 30.30 ||| Difference: -8.11\n",
      "Predicted: 21.98 ||| Real: 17.10 ||| Difference: 4.88\n",
      "Predicted: 22.45 ||| Real: 23.10 ||| Difference: -0.65\n",
      "Predicted: 12.27 ||| Real: 21.70 ||| Difference: -9.43\n",
      "Predicted: 16.13 ||| Real: 16.10 ||| Difference: 0.03\n",
      "Predicted: 20.02 ||| Real: 17.70 ||| Difference: 2.32\n",
      "Predicted: 21.65 ||| Real: 25.30 ||| Difference: -3.65\n",
      "Predicted: 31.21 ||| Real: 33.40 ||| Difference: -2.19\n",
      "Predicted: 21.44 ||| Real: 24.00 ||| Difference: -2.56\n",
      "Predicted: 29.24 ||| Real: 36.20 ||| Difference: -6.96\n",
      "Predicted: 15.95 ||| Real: 18.90 ||| Difference: -2.95\n",
      "Predicted: 5.69 ||| Real: 14.60 ||| Difference: -8.91\n",
      "Predicted: 29.03 ||| Real: 28.20 ||| Difference: 0.83\n",
      "Predicted: 18.52 ||| Real: 14.30 ||| Difference: 4.22\n",
      "Predicted: 17.79 ||| Real: 15.20 ||| Difference: 2.59\n",
      "Predicted: 25.32 ||| Real: 26.40 ||| Difference: -1.08\n",
      "Predicted: 27.01 ||| Real: 20.60 ||| Difference: 6.41\n",
      "Predicted: 24.27 ||| Real: 16.60 ||| Difference: 7.67\n",
      "Predicted: 12.18 ||| Real: 13.90 ||| Difference: -1.72\n",
      "Predicted: 30.50 ||| Real: 50.00 ||| Difference: -19.50\n",
      "Predicted: 28.38 ||| Real: 22.50 ||| Difference: 5.88\n",
      "Predicted: 20.53 ||| Real: 20.20 ||| Difference: 0.33\n",
      "Predicted: 23.31 ||| Real: 19.50 ||| Difference: 3.81\n",
      "Predicted: 24.04 ||| Real: 32.00 ||| Difference: -7.96\n",
      "Predicted: 33.01 ||| Real: 23.60 ||| Difference: 9.41\n",
      "Predicted: 14.53 ||| Real: 13.10 ||| Difference: 1.43\n",
      "Predicted: 21.97 ||| Real: 20.90 ||| Difference: 1.07\n",
      "Predicted: 21.59 ||| Real: 24.70 ||| Difference: -3.11\n",
      "Predicted: 21.83 ||| Real: 27.90 ||| Difference: -6.07\n",
      "Predicted: 11.23 ||| Real: 19.70 ||| Difference: -8.47\n",
      "Predicted: 19.11 ||| Real: 19.10 ||| Difference: 0.01\n",
      "Predicted: 26.97 ||| Real: 23.70 ||| Difference: 3.27\n",
      "Predicted: 25.22 ||| Real: 19.60 ||| Difference: 5.62\n",
      "Predicted: 23.27 ||| Real: 20.80 ||| Difference: 2.47\n",
      "Predicted: 14.14 ||| Real: 8.50 ||| Difference: 5.64\n",
      "Predicted: 18.68 ||| Real: 14.20 ||| Difference: 4.48\n",
      "Predicted: 29.06 ||| Real: 36.10 ||| Difference: -7.04\n",
      "Predicted: 23.88 ||| Real: 21.40 ||| Difference: 2.48\n",
      "Predicted: 21.97 ||| Real: 19.40 ||| Difference: 2.57\n",
      "Predicted: 18.79 ||| Real: 22.60 ||| Difference: -3.81\n",
      "Predicted: 14.84 ||| Real: 14.40 ||| Difference: 0.44\n",
      "Predicted: 28.65 ||| Real: 28.00 ||| Difference: 0.65\n",
      "Predicted: 12.63 ||| Real: 20.30 ||| Difference: -7.67\n",
      "Predicted: 27.12 ||| Real: 26.40 ||| Difference: 0.72\n",
      "Predicted: 13.10 ||| Real: 10.20 ||| Difference: 2.90\n",
      "Predicted: 31.69 ||| Real: 32.00 ||| Difference: -0.31\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test[:50])\n",
    "temp_label = y_test[:50].tolist()\n",
    "\n",
    "for i in range(len(predictions)):\n",
    "    print(\"Predicted: {:.2f} ||| Real: {:.2f} ||| Difference: {:.2f}\".format(predictions[i][0], temp_label[i], (predictions[i][0] - temp_label[i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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