New Notebook- LINEAR REGRESSION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define Imports\n",
    "import numpy \n",
    "import pandas as pd\n",
    "from pandas import read_csv\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>size</th>\n",
       "      <th>typeapartment</th>\n",
       "      <th>typeterraced</th>\n",
       "      <th>typesemidetached</th>\n",
       "      <th>typedetached</th>\n",
       "      <th>canalhouse</th>\n",
       "      <th>rooms</th>\n",
       "      <th>balconyroofterrace</th>\n",
       "      <th>noparking</th>\n",
       "      <th>parking</th>\n",
       "      <th>...</th>\n",
       "      <th>constryear_15001905</th>\n",
       "      <th>constryear_19061930</th>\n",
       "      <th>constryear_19311944</th>\n",
       "      <th>constryear_19451959</th>\n",
       "      <th>constryear_19601970</th>\n",
       "      <th>constryear_19711980</th>\n",
       "      <th>constryear_19811990</th>\n",
       "      <th>constryear_19912000</th>\n",
       "      <th>constryear_above2001</th>\n",
       "      <th>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.319588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.201805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.110238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>440</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.103365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.914123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   size  typeapartment  typeterraced  typesemidetached  typedetached  \\\n",
       "0   410              0             0                 0             1   \n",
       "1   375              0             0                 1             0   \n",
       "2   370              0             0                 0             1   \n",
       "3   440              0             0                 0             1   \n",
       "4   360              0             1                 0             0   \n",
       "\n",
       "   canalhouse  rooms  balconyroofterrace  noparking  parking    ...      \\\n",
       "0           0     10                   0          0        1    ...       \n",
       "1           0     13                   1          0        1    ...       \n",
       "2           0      9                   1          0        1    ...       \n",
       "3           0     12                   1          1        0    ...       \n",
       "4           0      7                   1          1        0    ...       \n",
       "\n",
       "   constryear_15001905  constryear_19061930  constryear_19311944  \\\n",
       "0                    0                    1                    0   \n",
       "1                    0                    1                    0   \n",
       "2                    0                    0                    1   \n",
       "3                    1                    0                    0   \n",
       "4                    1                    0                    0   \n",
       "\n",
       "   constryear_19451959  constryear_19601970  constryear_19711980  \\\n",
       "0                    0                    0                    0   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   constryear_19811990  constryear_19912000  constryear_above2001  log_price  \n",
       "0                    0                    0                     0  15.319588  \n",
       "1                    0                    0                     0  15.201805  \n",
       "2                    0                    0                     0  15.110238  \n",
       "3                    0                    0                     0  15.103365  \n",
       "4                    0                    0                     0  14.914123  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the dataset \n",
    "df = pd.read_csv(\"DataNVM-filtered-no-outliers.csv\") \n",
    "dataset = df.values\n",
    "\n",
    "\n",
    "#split data into X and Y variables:\n",
    "X = dataset[:,0:26]\n",
    "Y = dataset[:,27]\n",
    "\n",
    "X_table = dataset[0:20,0:26]\n",
    "Y_table = dataset[0:20,27]\n",
    "\n",
    "df.head() #verifying that we could read the data correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[410.   0.   0. ...   0.   0.   0.]\n",
      " [375.   0.   0. ...   0.   0.   0.]\n",
      " [370.   0.   0. ...   0.   0.   0.]\n",
      " ...\n",
      " [ 35.   1.   0. ...   0.   1.   0.]\n",
      " [ 30.   1.   0. ...   0.   1.   0.]\n",
      " [ 33.   1.   0. ...   0.   1.   0.]]\n",
      "[15.31958795 15.20180492 15.11023773 ... 11.00209984 11.00209984\n",
      " 11.00209984]\n",
      "[[410.   0.   0.   0.   1.   0.  10.   0.   0.   1.   0.   0.   0.   1.\n",
      "    0.   0.   0.   0.   0.   1.   0.   0.   0.   0.   0.   0.]\n",
      " [375.   0.   0.   1.   0.   0.  13.   1.   0.   1.   0.   1.   1.   0.\n",
      "    1.   0.   0.   0.   0.   1.   0.   0.   0.   0.   0.   0.]\n",
      " [370.   0.   0.   0.   1.   0.   9.   1.   0.   1.   0.   1.   1.   0.\n",
      "    1.   0.   1.   0.   0.   0.   1.   0.   0.   0.   0.   0.]\n",
      " [440.   0.   0.   0.   1.   0.  12.   1.   1.   0.   0.   0.   1.   0.\n",
      "    1.   1.   0.   0.   1.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [360.   0.   1.   0.   0.   0.   7.   1.   1.   0.   0.   0.   1.   1.\n",
      "    1.   1.   0.   0.   1.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [350.   0.   0.   1.   0.   0.   9.   1.   0.   1.   0.   1.   0.   0.\n",
      "    1.   0.   0.   0.   0.   1.   0.   0.   0.   0.   0.   0.]\n",
      " [400.   0.   0.   1.   0.   0.  13.   1.   1.   0.   0.   0.   0.   1.\n",
      "    1.   0.   1.   0.   1.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [450.   0.   1.   0.   0.   0.  11.   1.   1.   0.   0.   0.   1.   0.\n",
      "    1.   1.   0.   0.   0.   1.   0.   0.   0.   0.   0.   0.]\n",
      " [275.   0.   0.   1.   0.   0.   7.   1.   0.   1.   0.   1.   1.   0.\n",
      "    1.   0.   0.   0.   0.   1.   0.   0.   0.   0.   0.   0.]\n",
      " [300.   0.   0.   0.   1.   1.   6.   0.   0.   1.   0.   0.   1.   1.\n",
      "    1.   1.   0.   0.   0.   0.   0.   0.   0.   0.   0.   1.]\n",
      " [300.   0.   0.   0.   1.   1.   6.   0.   0.   1.   0.   0.   1.   1.\n",
      "    1.   1.   0.   0.   0.   0.   0.   0.   0.   0.   0.   1.]\n",
      " [400.   0.   0.   1.   0.   0.  13.   1.   0.   1.   0.   1.   1.   1.\n",
      "    1.   0.   0.   0.   0.   1.   0.   0.   0.   0.   0.   0.]\n",
      " [350.   0.   0.   0.   1.   0.   8.   1.   0.   1.   0.   1.   0.   0.\n",
      "    1.   0.   0.   0.   0.   0.   0.   1.   0.   0.   0.   0.]\n",
      " [400.   0.   1.   0.   0.   0.  10.   1.   1.   0.   0.   0.   1.   1.\n",
      "    1.   1.   0.   0.   1.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [380.   0.   0.   0.   1.   0.   6.   0.   0.   1.   0.   1.   0.   0.\n",
      "    1.   1.   0.   0.   0.   0.   0.   1.   0.   0.   0.   0.]\n",
      " [300.   0.   0.   0.   1.   0.   9.   1.   0.   1.   0.   1.   1.   1.\n",
      "    1.   1.   0.   0.   0.   0.   0.   1.   0.   0.   0.   0.]\n",
      " [380.   0.   1.   0.   0.   0.  13.   1.   1.   0.   0.   0.   1.   0.\n",
      "    0.   0.   0.   0.   0.   1.   0.   0.   0.   0.   0.   0.]\n",
      " [380.   0.   1.   0.   0.   0.  13.   1.   1.   0.   0.   0.   1.   0.\n",
      "    0.   0.   0.   0.   0.   1.   0.   0.   0.   0.   0.   0.]\n",
      " [400.   0.   0.   0.   1.   0.  10.   0.   0.   1.   0.   1.   0.   0.\n",
      "    1.   1.   0.   0.   0.   0.   1.   0.   0.   0.   0.   0.]]\n",
      "[15.31958795 15.20180492 15.11023773 15.10336485 14.91412285 14.83616131\n",
      " 14.80876233 14.80132735 14.77677466 14.73180129 14.73180129 14.73180129\n",
      " 14.70134208 14.69720985 14.6909793  14.66992589 14.66992589 14.6592306\n",
      " 14.65707774]\n"
     ]
    }
   ],
   "source": [
    "print(X)\n",
    "\n",
    "print(Y)\n",
    "\n",
    "print(X_table)\n",
    "print(Y_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "#Import for splitting data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the MAE:\n",
      "0.1857251692229057\n",
      "This is the MSE:\n",
      "0.05593308395650694\n",
      "The Following is the RMSE:\n",
      "0.23650176311500712\n",
      "The Following is the R^2 Score:\n",
      "0.7545459816728723\n"
     ]
    }
   ],
   "source": [
    "#Importing Linear Regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression(normalize=True)\n",
    "lr.fit(X,Y)\n",
    "\n",
    "#prediction\n",
    "y_pred = lr.predict(X)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#accuracy_score(Y, y_pred)\n",
    "\n",
    "#Compute the MAE\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"This is the MAE:\")\n",
    "print(mean_absolute_error(Y, y_pred))\n",
    "\n",
    "#Compute the MSE\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(\"This is the MSE:\")\n",
    "print(mean_squared_error(Y, y_pred))\n",
    "\n",
    "#Compute the RMSE\n",
    "print(\"The Following is the RMSE:\")\n",
    "print(numpy.sqrt(mean_squared_error(Y, y_pred)))\n",
    "\n",
    "#Compute the R^2 Score\n",
    "from sklearn.metrics import r2_score\n",
    "print(\"The Following is the R^2 Score:\")\n",
    "print(r2_score(Y,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.12070399e-03, -8.40303623e+11, -8.40303623e+11, -8.40303623e+11,\n",
       "       -8.40303623e+11,  9.94400053e-02,  1.32897305e-02,  1.33244356e-04,\n",
       "       -4.21489067e+10, -4.21489067e+10,  4.91005005e-02,  8.93628395e-02,\n",
       "        6.79687575e-02,  1.03156901e-01,  8.44059632e-02, -1.20955040e-02,\n",
       "        1.15195542e-01,  5.55169809e-02,  2.00125909e-01,  8.56166893e-02,\n",
       "        1.23403550e-02, -1.03743068e-01, -2.51913671e-01, -2.56804449e-01,\n",
       "       -1.70420418e-01, -8.77939441e-03])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "882452529813.4664"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network Part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Define Imports\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import metrics\n",
    "from keras.layers.core import Dense, Activation\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "\n",
    "#graphings\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Scalers\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "\n",
    "#Scaling X inputs\n",
    "scaler = StandardScaler()\n",
    "Xscaled = scaler.fit_transform(X)\n",
    "scaler2 = MinMaxScaler(feature_range=(-1,1))\n",
    "Xscaled2 = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot regression chart\n",
    "# Regression chart function\n",
    "def chart_regression(pred,y,sort=True):\n",
    "    t = pd.DataFrame({'pred' : pred, 'y' : y.flatten()})\n",
    "    if sort:\n",
    "        t.sort_values(by=['y'],inplace=True)\n",
    "    a = plt.plot(t['y'].tolist(),label='expected')\n",
    "    b = plt.plot(t['pred'].tolist(),label='prediction')\n",
    "    plt.ylabel('output')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 00009: early stopping\n",
      "Score (RMSE): 0.2396191310935969\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "model = Sequential()\n",
    "model.add(Dense(40, input_dim=26, activation='relu'))\n",
    "model.add(Dense(70, activation='relu'))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(1, activation = 'elu'))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=1, mode='auto')\n",
    "checkpointer = ModelCheckpoint(filepath=\"best_weightsNew2.hdf5\", verbose=0, save_best_only=True) # save best model\n",
    "model.fit(X,Y,validation_data=(X,Y),callbacks=[monitor,checkpointer],verbose=0,epochs=1000)\n",
    "model.load_weights('best_weightsNew2.hdf5') # load weights from best model\n",
    "\n",
    "# Predict and measure RMSE\n",
    "y_predNN = model.predict(X)\n",
    "score = numpy.sqrt(metrics.mean_squared_error(y_predNN,Y))\n",
    "print(\"Score (RMSE): {}\".format(score))\n",
    "\n",
    "# Plot the chart\n",
    "chart_regression(y_predNN.flatten(),Y)\n",
    "chart_regression(y_predNN.flatten(),Y,sort=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the MAE:\n",
      "0.18765845951206295\n",
      "This is the MSE:\n",
      "0.05741732798605038\n",
      "The Following is the RMSE:\n",
      "0.2396191310935969\n",
      "The Following is the R^2 Score:\n",
      "0.7480325975456397\n"
     ]
    }
   ],
   "source": [
    "#Scores for NN model\n",
    "\n",
    "#Compute the MAE\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"This is the MAE:\")\n",
    "print(mean_absolute_error(Y, y_predNN))\n",
    "\n",
    "#Compute the MSE\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(\"This is the MSE:\")\n",
    "print(mean_squared_error(Y, y_predNN))\n",
    "\n",
    "#Compute the RMSE\n",
    "print(\"The Following is the RMSE:\")\n",
    "print(numpy.sqrt(mean_squared_error(Y, y_predNN)))\n",
    "\n",
    "#Compute the R^2 Score\n",
    "from sklearn.metrics import r2_score\n",
    "print(\"The Following is the R^2 Score:\")\n",
    "print(r2_score(Y,y_predNN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into train/test version 2, with Log_Price\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    X, Y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the MAE:\n",
      "0.1826023574662402\n",
      "This is the MSE:\n",
      "0.05424350775367815\n",
      "The Following is the RMSE:\n",
      "0.23290235669412654\n",
      "The Following is the R^2 Score:\n",
      "0.761806000727937\n"
     ]
    }
   ],
   "source": [
    "lr2 = LinearRegression(normalize=True)\n",
    "lr2.fit(x_train,y_train)\n",
    "\n",
    "#prediction\n",
    "y_pred2 = lr2.predict(x_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#accuracy_score(Y, y_pred)\n",
    "\n",
    "#Compute the MAE\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"This is the MAE:\")\n",
    "print(mean_absolute_error(y_test, y_pred2))\n",
    "\n",
    "#Compute the MSE\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(\"This is the MSE:\")\n",
    "print(mean_squared_error(y_test, y_pred2))\n",
    "\n",
    "#Compute the RMSE\n",
    "print(\"The Following is the RMSE:\")\n",
    "print(numpy.sqrt(mean_squared_error(y_test, y_pred2)))\n",
    "\n",
    "#Compute the R^2 Score\n",
    "from sklearn.metrics import r2_score\n",
    "print(\"The Following is the R^2 Score:\")\n",
    "print(r2_score(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 00011: early stopping\n",
      "Score (RMSE): 0.2261723514344\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "model2 = Sequential()\n",
    "model2.add(Dense(40, input_dim=26, activation='relu'))\n",
    "model2.add(Dense(70, activation='relu'))\n",
    "model2.add(Dense(100, activation='relu'))\n",
    "model2.add(Dense(1, activation = 'elu'))\n",
    "model2.compile(loss='mean_squared_error', optimizer='adam')\n",
    "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=1, mode='auto')\n",
    "checkpointer = ModelCheckpoint(filepath=\"best_weightsNew2.hdf5\", verbose=0, save_best_only=True) # save best model\n",
    "model.fit(x_train,y_train,validation_data=(x_test,y_test),callbacks=[monitor,checkpointer],verbose=0,epochs=1000)\n",
    "model.load_weights('best_weightsNew2.hdf5') # load weights from best model\n",
    "\n",
    "# Predict and measure RMSE\n",
    "y_predNN2 = model.predict(x_test)\n",
    "score = numpy.sqrt(metrics.mean_squared_error(y_predNN2,y_test))\n",
    "print(\"Score (RMSE): {}\".format(score))\n",
    "\n",
    "# Plot the chart\n",
    "chart_regression(y_predNN2.flatten(),y_test)\n",
    "chart_regression(y_predNN2.flatten(),y_test,sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the MAE:\n",
      "0.1763466686093723\n",
      "This is the MSE:\n",
      "0.05115393255336574\n",
      "The Following is the RMSE:\n",
      "0.2261723514344\n",
      "The Following is the R^2 Score:\n",
      "0.7753729381088312\n"
     ]
    }
   ],
   "source": [
    "#Scores for NN model\n",
    "\n",
    "#Compute the MAE\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"This is the MAE:\")\n",
    "print(mean_absolute_error(y_test, y_predNN2))\n",
    "\n",
    "#Compute the MSE\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(\"This is the MSE:\")\n",
    "print(mean_squared_error(y_test, y_predNN2))\n",
    "\n",
    "#Compute the RMSE\n",
    "print(\"The Following is the RMSE:\")\n",
    "print(numpy.sqrt(mean_squared_error(y_test, y_predNN2)))\n",
    "\n",
    "#Compute the R^2 Score\n",
    "from sklearn.metrics import r2_score\n",
    "print(\"The Following is the R^2 Score:\")\n",
    "print(r2_score(y_test,y_predNN2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12.443722]\n",
      " [12.824389]\n",
      " [12.716552]\n",
      " ...\n",
      " [12.181062]\n",
      " [12.291388]\n",
      " [12.417188]]\n"
     ]
    }
   ],
   "source": [
    "print(y_predNN2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12.47170019 12.72845745 12.63307428 ... 12.22683907 12.28098488\n",
      " 12.31928682]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12.43320822 12.9598468  12.73080117 ... 12.17561344 12.28995413\n",
      " 12.31043266]\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"get-plot-agehouse.csv\") \n",
    "dataset2 = df2.values\n",
    "\n",
    "X_age = dataset2[:,0:26]\n",
    "Y_age = dataset2[:,27]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_pred = lr.predict(X_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "yname = [\"u\",\"ref\", \"1500+\",\"1931+\",\"1945+\",\"1960+\",\"1971+\",\"1981+\",\"1991+\",\"2001+\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Construction year and log-house price')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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(yname,age_pred,linestyle='--', marker='o', color='b')\n",
    "plt.ylabel(\"log-price\")\n",
    "plt.title(\"Construction year and log-house price\")\n",
    "#plt.xlabel(\"year of construction, u = unknown, ref = reference/1906-1930\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases=[]\n",
    "for n in range(20):\n",
    "    cases.append(str(n))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19']\n"
     ]
    }
   ],
   "source": [
    "print(cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2e340bd6cf8>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "X_table_lr = lr.predict(X_table)\n",
    "X_table_NN = model.predict(X_table)\n",
    "\n",
    "plt.plot(cases,X_table_lr,linestyle='--', marker='o', color='b',label='LR')\n",
    "plt.plot(cases,X_table_NN, linestyle='--', marker='o', color='y',label='NN')\n",
    "plt.plot(cases,Y_table, linestyle='--', marker='o', color='r', label ='Actual')\n",
    "plt.title('Predicted Prices')\n",
    "plt.ylabel(\"log price\")\n",
    "plt.xlabel(\"Cases\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.4949951171875\n",
      "15.234375\n",
      "15.2974853515625\n",
      "15.9168701171875\n",
      "15.099853515625\n",
      "14.8851318359375\n",
      "15.60791015625\n",
      "15.7562255859375\n",
      "14.2425537109375\n",
      "14.583984375\n",
      "14.583984375\n",
      "15.5655517578125\n",
      "14.8026123046875\n",
      "15.5045166015625\n",
      "15.037353515625\n",
      "14.518798828125\n",
      "15.072021484375\n",
      "15.072021484375\n",
      "15.3890380859375\n",
      "14.9642333984375\n"
     ]
    }
   ],
   "source": [
    "for price in X_table_lr:\n",
    "    print(price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14.782414]\n",
      "[14.641139]\n",
      "[14.679446]\n",
      "[15.200257]\n",
      "[14.550961]\n",
      "[14.370049]\n",
      "[14.934865]\n",
      "[14.984293]\n",
      "[13.999337]\n",
      "[14.245799]\n",
      "[14.245799]\n",
      "[14.870002]\n",
      "[14.294585]\n",
      "[14.819451]\n",
      "[14.407308]\n",
      "[14.200945]\n",
      "[14.468351]\n",
      "[14.468351]\n",
      "[14.697535]\n",
      "[14.433842]\n"
     ]
    }
   ],
   "source": [
    "for price in X_table_NN:\n",
    "    print(price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.3195879547405\n",
      "15.201804919084198\n",
      "15.110237725558699\n",
      "15.1033648462709\n",
      "14.914122846632399\n",
      "14.8361613051627\n",
      "14.8087623309746\n",
      "14.801327352486998\n",
      "14.7767746554812\n",
      "14.7318012898384\n",
      "14.7318012898384\n",
      "14.7318012898384\n",
      "14.7013420823537\n",
      "14.6972098450688\n",
      "14.690979295318199\n",
      "14.6699258861203\n",
      "14.6699258861203\n",
      "14.659230597003601\n",
      "14.6570777436425\n",
      "14.6549202455025\n"
     ]
    }
   ],
   "source": [
    "for price in Y_table:\n",
    "    print(price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}