MLP for Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP for Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import packages\n",
    "from numpy import sqrt\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from matplotlib import pyplot \n",
    "from tensorflow.keras.utils import plot_model\n",
    "import pydot \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the Boston housing regression dataset to demonstrate an MLP for regression predictive modeling.\n",
    "\n",
    "This problem involves predicting house value based on properties of the house and neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the dataset\n",
    "path = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.csv'\n",
    "df = read_csv(path, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</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",
       "      <td>24.0</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",
       "      <td>21.6</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",
       "      <td>34.7</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",
       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2   3      4      5   ...  8      9     10      11    12    13\n",
       "0  0.00632  18.0  2.31   0  0.538  6.575  ...   1  296.0  15.3  396.90  4.98  24.0\n",
       "1  0.02731   0.0  7.07   0  0.469  6.421  ...   2  242.0  17.8  396.90  9.14  21.6\n",
       "2  0.02729   0.0  7.07   0  0.469  7.185  ...   2  242.0  17.8  392.83  4.03  34.7\n",
       "3  0.03237   0.0  2.18   0  0.458  6.998  ...   3  222.0  18.7  394.63  2.94  33.4\n",
       "4  0.06905   0.0  2.18   0  0.458  7.147  ...   3  222.0  18.7  396.90  5.33  36.2\n",
       "\n",
       "[5 rows x 14 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first five row \n",
    "df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(339, 13) (167, 13) (339,) (167,)\n"
     ]
    }
   ],
   "source": [
    "# split into input and output columns\n",
    "X, y = df.values[:, :-1], df.values[:, -1]\n",
    "\n",
    "# split into train and test datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n",
    "# determine the number of input features\n",
    "n_features = X_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define model\n",
    "model = Sequential()\n",
    "model.add(Dense(10, activation='relu', kernel_initializer='he_normal', input_shape=(n_features,)))\n",
    "model.add(Dense(8, activation='relu', kernel_initializer='he_normal'))\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 10)                140       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 8)                 88        \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 237\n",
      "Trainable params: 237\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# summarize the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile the model\n",
    "model.compile(optimizer='adam', loss='mse')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit the model\n",
    "history = model.fit(X_train, y_train, epochs=150, batch_size=32,validation_split=0.2, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot learning curves\n",
    "pyplot.title('Learning Curves')\n",
    "pyplot.xlabel('Epoch')\n",
    "pyplot.ylabel('mse')\n",
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='val')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 69.641, RMSE: 8.345\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model\n",
    "error = model.evaluate(X_test, y_test, verbose=0)\n",
    "print('MSE: %.3f, RMSE: %.3f' % (error, sqrt(error)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 26.843\n"
     ]
    }
   ],
   "source": [
    "# make a prediction\n",
    "row = [0.00632,18.00,2.310,0,6.5380,6.5750,65.20,4.0900,1,296.0,15.30,396.90,44.98]\n",
    "result = model.predict([row])\n",
    "print('Predicted: %.3f' % result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}