{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><div style=\"direction:rtl;font-family:B Lotus, B Nazanin, Tahoma\">به نام خدا</div></center>\n",
    "\n",
    "<h1><center><div style=\"direction:rtl;font-family:B Lotus, B Nazanin, Tahoma\">رگرسیون یا \n",
    "Regression داده های ساختار یافته\n",
    "</div></center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">معرفی مجموعه داده تخممین قیمت منازل </div>\n",
    "\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "این مجموعه داده در مقاله سال 2016 تحت عنوان 2016\n",
    "    House price estimation from visual and textual features\n",
    "معرفی و منتشر شده است.\n",
    "</div>\n",
    "<p>\n",
    "<a href=\"https://github.com/emanhamed/Houses-dataset\" target=\"_blank\">\n",
    "https://github.com/emanhamed/Houses-dataset\n",
    "</a>\n",
    "<br>\n",
    "<a href=\"https://arxiv.org/pdf/1609.08399.pdf\" target=\"_blank\">\n",
    "https://arxiv.org/pdf/1609.08399.pdf\n",
    "</a>\n",
    "    \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# import the necessary packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.optimizers import Adam\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import cv2\n",
    "import os\n",
    "import locale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\"bedrooms\", \"bathrooms\", \"area\", \"zipcode\", \"price\"]\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/emanhamed/Houses-dataset/master/Houses%20Dataset/HousesInfo.txt\", sep=\" \", header=None, names=cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4053</td>\n",
       "      <td>85255</td>\n",
       "      <td>869500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3343</td>\n",
       "      <td>36372</td>\n",
       "      <td>865200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3923</td>\n",
       "      <td>85266</td>\n",
       "      <td>889000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4022</td>\n",
       "      <td>85262</td>\n",
       "      <td>910000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4116</td>\n",
       "      <td>85266</td>\n",
       "      <td>971226.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  area  zipcode     price\n",
       "0         4        4.0  4053    85255  869500.0\n",
       "1         4        3.0  3343    36372  865200.0\n",
       "2         3        4.0  3923    85266  889000.0\n",
       "3         5        5.0  4022    85262  910000.0\n",
       "4         3        4.0  4116    85266  971226.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "کد پستی های منحصر به فرد  و تعدادشان\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "zipcodes, counts = np.unique(df[\"zipcode\"], return_counts=True)\n",
    "#dict(zip(zipcodes, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(535, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "    حذف رکوردهایی که zip code آن منزل ها zip code شان کمتر از 25 بار تکرا شده است\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loop over each of the unique zip codes and their corresponding\n",
    "# count\n",
    "for (zipcode, count) in zip(zipcodes, counts):\n",
    "    # the zip code counts for our housing dataset is *extremely*\n",
    "    # unbalanced (some only having 1 or 2 houses per zip code)\n",
    "    # so let's sanitize our data by removing any houses with less\n",
    "    # than 25 houses per zip code\n",
    "    if count < 25:\n",
    "        idxs = df[df[\"zipcode\"] == zipcode].index\n",
    "        df.drop(idxs, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(362, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "جدا کردن داده های موجود به داده های آموزش و آزمون</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(271, 5)\n",
      "(91, 5)\n"
     ]
    }
   ],
   "source": [
    "(train, test) = train_test_split(df, test_size=0.25, random_state=42)\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">پیش پردازش داده ها:</div>\n",
    "\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "نرمال سازی داده ها یا Scaling  داده ها بین 0 و 1\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the largest house price in the training set and use it to\n",
    "# scale our house prices to the range [0, 1] (this will lead to\n",
    "# better training and convergence)\n",
    "maxPrice = train[\"price\"].max()\n",
    "trainY = train[\"price\"] / maxPrice\n",
    "testY = test[\"price\"] / maxPrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alire\\Miniconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "# initialize the column names of the continuous data\n",
    "continuous = [\"bedrooms\", \"bathrooms\", \"area\"]\n",
    "\n",
    "# performin min-max scaling each continuous feature column to\n",
    "# the range [0, 1]\n",
    "cs = MinMaxScaler()\n",
    "trainContinuous = cs.fit_transform(train[continuous])\n",
    "testContinuous = cs.transform(test[continuous])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "تبدیل مقادیر گسسته به one-hot</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one-hot encode the zip code categorical data (by definition of\n",
    "# one-hot encoing, all output features are now in the range [0, 1])\n",
    "zipBinarizer = LabelBinarizer().fit(df[\"zipcode\"])\n",
    "trainCategorical = zipBinarizer.transform(train[\"zipcode\"])\n",
    "testCategorical = zipBinarizer.transform(test[\"zipcode\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "    ترکیب ویژگی‌های گستته و پیوسته\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(271, 10)\n",
      "(91, 10)\n"
     ]
    }
   ],
   "source": [
    "# construct our training and testing data points by concatenating\n",
    "# the categorical features with the continuous features\n",
    "trainX = np.hstack([trainCategorical, trainContinuous])\n",
    "testX = np.hstack([testCategorical, testContinuous])\n",
    "\n",
    "print(trainX.shape)\n",
    "print(testX.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">تعریف معماری مدل (model architecture)</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim = trainX.shape[1]\n",
    "# define our MLP network\n",
    "model = Sequential()\n",
    "model.add(Dense(8, input_dim=dim, activation=\"relu\"))\n",
    "model.add(Dense(4, activation=\"relu\"))\n",
    "model.add(Dense(1, activation=\"linear\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">Compile model</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = Adam(lr=1e-3, decay=1e-3 / 200)\n",
    "model.compile(loss=\"mean_absolute_percentage_error\", optimizer=opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">آموش مدل با داده‌های آموزشی</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 271 samples, validate on 91 samples\n",
      "Epoch 1/200\n",
      "271/271 [==============================] - 3s 11ms/step - loss: 274.3629 - val_loss: 109.6800\n",
      "Epoch 2/200\n",
      "271/271 [==============================] - 0s 852us/step - loss: 81.8187 - val_loss: 79.1573\n",
      "Epoch 3/200\n",
      "271/271 [==============================] - 0s 856us/step - loss: 57.5993 - val_loss: 63.2764\n",
      "Epoch 4/200\n",
      "271/271 [==============================] - 0s 915us/step - loss: 46.6169 - val_loss: 58.3518\n",
      "Epoch 5/200\n",
      "271/271 [==============================] - 0s 896us/step - loss: 46.4683 - val_loss: 55.3489\n",
      "Epoch 6/200\n",
      "271/271 [==============================] - 0s 815us/step - loss: 41.2374 - val_loss: 52.9153\n",
      "Epoch 7/200\n",
      "271/271 [==============================] - 0s 752us/step - loss: 41.2185 - val_loss: 50.9960\n",
      "Epoch 8/200\n",
      "271/271 [==============================] - 0s 749us/step - loss: 40.7949 - val_loss: 48.3711\n",
      "Epoch 9/200\n",
      "271/271 [==============================] - 0s 755us/step - loss: 37.9040 - val_loss: 47.3093\n",
      "Epoch 10/200\n",
      "271/271 [==============================] - 0s 734us/step - loss: 37.5179 - val_loss: 46.0542\n",
      "Epoch 11/200\n",
      "271/271 [==============================] - 0s 848us/step - loss: 38.1799 - val_loss: 42.2384\n",
      "Epoch 12/200\n",
      "271/271 [==============================] - 0s 841us/step - loss: 33.0743 - val_loss: 40.7507\n",
      "Epoch 13/200\n",
      "271/271 [==============================] - 0s 856us/step - loss: 32.9037 - val_loss: 40.1074\n",
      "Epoch 14/200\n",
      "271/271 [==============================] - 0s 859us/step - loss: 32.4340 - val_loss: 38.6366\n",
      "Epoch 15/200\n",
      "271/271 [==============================] - 0s 852us/step - loss: 32.3434 - val_loss: 38.7734\n",
      "Epoch 16/200\n",
      "271/271 [==============================] - 0s 786us/step - loss: 31.7143 - val_loss: 38.4141\n",
      "Epoch 17/200\n",
      "271/271 [==============================] - 0s 727us/step - loss: 31.3374 - val_loss: 38.3170\n",
      "Epoch 18/200\n",
      "271/271 [==============================] - 0s 771us/step - loss: 31.3158 - val_loss: 37.9615\n",
      "Epoch 19/200\n",
      "271/271 [==============================] - 0s 786us/step - loss: 31.1686 - val_loss: 37.5458\n",
      "Epoch 20/200\n",
      "271/271 [==============================] - 0s 800us/step - loss: 30.6506 - val_loss: 38.1518\n",
      "Epoch 21/200\n",
      "271/271 [==============================] - 0s 830us/step - loss: 30.3962 - val_loss: 37.4007\n",
      "Epoch 22/200\n",
      "271/271 [==============================] - 0s 918us/step - loss: 30.1059 - val_loss: 38.0858\n",
      "Epoch 23/200\n",
      "271/271 [==============================] - 0s 892us/step - loss: 30.5002 - val_loss: 38.4182\n",
      "Epoch 24/200\n",
      "271/271 [==============================] - 0s 863us/step - loss: 30.7216 - val_loss: 37.2208\n",
      "Epoch 25/200\n",
      "271/271 [==============================] - 0s 730us/step - loss: 29.9758 - val_loss: 37.7301\n",
      "Epoch 26/200\n",
      "271/271 [==============================] - 0s 852us/step - loss: 30.2822 - val_loss: 38.1261\n",
      "Epoch 27/200\n",
      "271/271 [==============================] - 0s 719us/step - loss: 31.1608 - val_loss: 36.6486\n",
      "Epoch 28/200\n",
      "271/271 [==============================] - 0s 774us/step - loss: 29.1486 - val_loss: 36.3479\n",
      "Epoch 29/200\n",
      "271/271 [==============================] - 0s 845us/step - loss: 29.1896 - val_loss: 35.6131\n",
      "Epoch 30/200\n",
      "271/271 [==============================] - 0s 845us/step - loss: 30.0241 - val_loss: 36.8921\n",
      "Epoch 31/200\n",
      "271/271 [==============================] - 0s 859us/step - loss: 30.0597 - val_loss: 36.7154\n",
      "Epoch 32/200\n",
      "271/271 [==============================] - 0s 848us/step - loss: 29.9695 - val_loss: 36.1278\n",
      "Epoch 33/200\n",
      "271/271 [==============================] - 0s 826us/step - loss: 28.5626 - val_loss: 35.8382\n",
      "Epoch 34/200\n",
      "271/271 [==============================] - 0s 734us/step - loss: 29.4202 - val_loss: 35.6527\n",
      "Epoch 35/200\n",
      "271/271 [==============================] - 0s 811us/step - loss: 28.9315 - val_loss: 36.7020\n",
      "Epoch 36/200\n",
      "271/271 [==============================] - 0s 837us/step - loss: 29.5862 - val_loss: 36.0291\n",
      "Epoch 37/200\n",
      "271/271 [==============================] - 0s 852us/step - loss: 30.0635 - val_loss: 36.1580\n",
      "Epoch 38/200\n",
      "271/271 [==============================] - 0s 1ms/step - loss: 30.1257 - val_loss: 35.6447\n",
      "Epoch 39/200\n",
      "271/271 [==============================] - 0s 966us/step - loss: 28.5957 - val_loss: 35.4753\n",
      "Epoch 40/200\n",
      "271/271 [==============================] - 0s 970us/step - loss: 29.1557 - val_loss: 36.6803\n",
      "Epoch 41/200\n",
      "271/271 [==============================] - 0s 937us/step - loss: 29.9257 - val_loss: 35.9405\n",
      "Epoch 42/200\n",
      "271/271 [==============================] - 0s 922us/step - loss: 28.8982 - val_loss: 35.5165\n",
      "Epoch 43/200\n",
      "271/271 [==============================] - 0s 973us/step - loss: 28.7194 - val_loss: 36.8679\n",
      "Epoch 44/200\n",
      "271/271 [==============================] - 0s 896us/step - loss: 29.6274 - val_loss: 35.4251\n",
      "Epoch 45/200\n",
      "271/271 [==============================] - 0s 837us/step - loss: 28.5909 - val_loss: 34.9274\n",
      "Epoch 46/200\n",
      "271/271 [==============================] - 0s 977us/step - loss: 28.5849 - val_loss: 35.4455\n",
      "Epoch 47/200\n",
      "271/271 [==============================] - 0s 985us/step - loss: 29.8543 - val_loss: 35.1163\n",
      "Epoch 48/200\n",
      "271/271 [==============================] - 0s 951us/step - loss: 28.4919 - val_loss: 35.5199\n",
      "Epoch 49/200\n",
      "271/271 [==============================] - 0s 974us/step - loss: 28.6055 - val_loss: 36.7568\n",
      "Epoch 50/200\n",
      "271/271 [==============================] - 0s 892us/step - loss: 29.3425 - val_loss: 34.6022\n",
      "Epoch 51/200\n",
      "271/271 [==============================] - 0s 782us/step - loss: 28.8814 - val_loss: 34.8447\n",
      "Epoch 52/200\n",
      "271/271 [==============================] - 0s 856us/step - loss: 29.2900 - val_loss: 35.0079\n",
      "Epoch 53/200\n",
      "271/271 [==============================] - 0s 786us/step - loss: 29.5400 - val_loss: 34.9382\n",
      "Epoch 54/200\n",
      "271/271 [==============================] - 0s 874us/step - loss: 28.5073 - val_loss: 35.8380\n",
      "Epoch 55/200\n",
      "271/271 [==============================] - 0s 830us/step - loss: 28.7576 - val_loss: 34.2087\n",
      "Epoch 56/200\n",
      "271/271 [==============================] - 0s 867us/step - loss: 28.5596 - val_loss: 34.5693\n",
      "Epoch 57/200\n",
      "271/271 [==============================] - 0s 937us/step - loss: 29.4641 - val_loss: 34.5786\n",
      "Epoch 58/200\n",
      "271/271 [==============================] - 0s 944us/step - loss: 28.8239 - val_loss: 35.1018\n",
      "Epoch 59/200\n",
      "271/271 [==============================] - 0s 789us/step - loss: 28.2958 - val_loss: 35.3880\n",
      "Epoch 60/200\n",
      "271/271 [==============================] - 0s 760us/step - loss: 28.5673 - val_loss: 35.7874\n",
      "Epoch 61/200\n",
      "271/271 [==============================] - 0s 774us/step - loss: 29.4349 - val_loss: 36.7657\n",
      "Epoch 62/200\n",
      "271/271 [==============================] - 0s 767us/step - loss: 28.7991 - val_loss: 36.6966\n",
      "Epoch 63/200\n",
      "271/271 [==============================] - 0s 822us/step - loss: 29.0289 - val_loss: 34.5065\n",
      "Epoch 64/200\n",
      "271/271 [==============================] - 0s 896us/step - loss: 28.6727 - val_loss: 36.2448\n",
      "Epoch 65/200\n",
      "271/271 [==============================] - 0s 1ms/step - loss: 29.3485 - val_loss: 35.8350\n",
      "Epoch 66/200\n",
      "271/271 [==============================] - 0s 929us/step - loss: 29.2130 - val_loss: 35.9859\n",
      "Epoch 67/200\n",
      "271/271 [==============================] - 0s 863us/step - loss: 29.3722 - val_loss: 34.7409\n",
      "Epoch 68/200\n",
      "271/271 [==============================] - 0s 719us/step - loss: 29.1651 - val_loss: 35.7246\n",
      "Epoch 69/200\n",
      "271/271 [==============================] - 0s 822us/step - loss: 29.1330 - val_loss: 35.3774\n",
      "Epoch 70/200\n",
      "271/271 [==============================] - 0s 845us/step - loss: 28.1200 - val_loss: 35.1389\n",
      "Epoch 71/200\n",
      "271/271 [==============================] - 0s 826us/step - loss: 30.3745 - val_loss: 35.1621\n",
      "Epoch 72/200\n",
      "271/271 [==============================] - 0s 870us/step - loss: 29.7780 - val_loss: 36.2803\n",
      "Epoch 73/200\n",
      "271/271 [==============================] - 0s 915us/step - loss: 29.0088 - val_loss: 35.3808\n",
      "Epoch 74/200\n",
      "271/271 [==============================] - 0s 852us/step - loss: 28.7546 - val_loss: 35.0129\n",
      "Epoch 75/200\n",
      "271/271 [==============================] - 0s 922us/step - loss: 28.3806 - val_loss: 35.6722\n",
      "Epoch 76/200\n",
      "271/271 [==============================] - 0s 830us/step - loss: 28.4555 - val_loss: 35.6889\n",
      "Epoch 77/200\n",
      "271/271 [==============================] - 0s 811us/step - loss: 29.3141 - val_loss: 35.8317\n",
      "Epoch 78/200\n",
      "271/271 [==============================] - 0s 730us/step - loss: 28.5560 - val_loss: 34.8651\n",
      "Epoch 79/200\n",
      "271/271 [==============================] - 0s 734us/step - loss: 28.3928 - val_loss: 35.6066\n",
      "Epoch 80/200\n",
      "271/271 [==============================] - 0s 697us/step - loss: 29.1012 - val_loss: 36.1429\n",
      "Epoch 81/200\n",
      "271/271 [==============================] - 0s 749us/step - loss: 28.6560 - val_loss: 35.5072\n",
      "Epoch 82/200\n",
      "271/271 [==============================] - 0s 804us/step - loss: 28.8311 - val_loss: 36.2556\n",
      "Epoch 83/200\n",
      "271/271 [==============================] - 0s 811us/step - loss: 28.5464 - val_loss: 35.2115\n",
      "Epoch 84/200\n",
      "271/271 [==============================] - 0s 826us/step - loss: 28.0626 - val_loss: 37.0662\n",
      "Epoch 85/200\n",
      "271/271 [==============================] - 0s 767us/step - loss: 28.5353 - val_loss: 35.1223\n",
      "Epoch 86/200\n",
      "271/271 [==============================] - 0s 612us/step - loss: 28.2699 - val_loss: 36.5861\n",
      "Epoch 87/200\n",
      "271/271 [==============================] - 0s 664us/step - loss: 28.7683 - val_loss: 36.0658\n",
      "Epoch 88/200\n",
      "271/271 [==============================] - 0s 693us/step - loss: 28.6031 - val_loss: 37.1731\n",
      "Epoch 89/200\n",
      "271/271 [==============================] - 0s 734us/step - loss: 28.4059 - val_loss: 34.6264\n",
      "Epoch 90/200\n",
      "271/271 [==============================] - 0s 734us/step - loss: 29.3826 - val_loss: 36.8670\n",
      "Epoch 91/200\n",
      "271/271 [==============================] - 0s 760us/step - loss: 28.7343 - val_loss: 36.1997\n",
      "Epoch 92/200\n",
      "271/271 [==============================] - 0s 774us/step - loss: 27.9420 - val_loss: 35.6907\n",
      "Epoch 93/200\n",
      "271/271 [==============================] - 0s 789us/step - loss: 28.4056 - val_loss: 35.9340\n",
      "Epoch 94/200\n",
      "271/271 [==============================] - 0s 771us/step - loss: 29.8033 - val_loss: 35.0321\n",
      "Epoch 95/200\n",
      "271/271 [==============================] - 0s 822us/step - loss: 28.6592 - val_loss: 34.5973\n",
      "Epoch 96/200\n",
      "271/271 [==============================] - 0s 620us/step - loss: 28.2901 - val_loss: 35.5223\n",
      "Epoch 97/200\n",
      "271/271 [==============================] - 0s 730us/step - loss: 28.7182 - val_loss: 36.1289\n",
      "Epoch 98/200\n",
      "271/271 [==============================] - 0s 679us/step - loss: 29.0689 - val_loss: 36.2295\n",
      "Epoch 99/200\n",
      "271/271 [==============================] - 0s 734us/step - loss: 28.5465 - val_loss: 34.9149\n",
      "Epoch 100/200\n",
      "271/271 [==============================] - 0s 749us/step - loss: 28.0349 - val_loss: 35.6574\n",
      "Epoch 101/200\n",
      "271/271 [==============================] - 0s 708us/step - loss: 28.6254 - val_loss: 35.6179\n",
      "Epoch 102/200\n",
      "271/271 [==============================] - 0s 797us/step - loss: 28.1658 - val_loss: 34.7164\n",
      "Epoch 103/200\n",
      "271/271 [==============================] - 0s 774us/step - loss: 27.7563 - val_loss: 34.8238\n",
      "Epoch 104/200\n",
      "271/271 [==============================] - 0s 811us/step - loss: 28.8107 - val_loss: 35.2860\n",
      "Epoch 105/200\n",
      "271/271 [==============================] - 0s 804us/step - loss: 28.7350 - val_loss: 36.6296\n",
      "Epoch 106/200\n",
      "271/271 [==============================] - 0s 645us/step - loss: 28.2885 - val_loss: 37.2982\n",
      "Epoch 107/200\n",
      "271/271 [==============================] - 0s 649us/step - loss: 29.1292 - val_loss: 36.7109\n",
      "Epoch 108/200\n",
      "271/271 [==============================] - 0s 704us/step - loss: 29.5676 - val_loss: 34.8100\n",
      "Epoch 109/200\n",
      "271/271 [==============================] - 0s 704us/step - loss: 28.5612 - val_loss: 36.1396\n",
      "Epoch 110/200\n",
      "271/271 [==============================] - 0s 745us/step - loss: 28.2348 - val_loss: 34.9619\n",
      "Epoch 111/200\n",
      "271/271 [==============================] - 0s 786us/step - loss: 28.1678 - val_loss: 35.6697\n",
      "Epoch 112/200\n",
      "271/271 [==============================] - 0s 904us/step - loss: 29.4567 - val_loss: 34.8336\n",
      "Epoch 113/200\n",
      "271/271 [==============================] - 0s 826us/step - loss: 28.7854 - val_loss: 35.6134\n",
      "Epoch 114/200\n",
      "271/271 [==============================] - 0s 800us/step - loss: 27.9338 - val_loss: 35.0001\n",
      "Epoch 115/200\n",
      "271/271 [==============================] - 0s 826us/step - loss: 28.8813 - val_loss: 35.9869\n",
      "Epoch 116/200\n",
      "271/271 [==============================] - 0s 723us/step - loss: 28.8484 - val_loss: 35.4887\n",
      "Epoch 117/200\n",
      "271/271 [==============================] - 0s 653us/step - loss: 28.8066 - val_loss: 35.1312\n",
      "Epoch 118/200\n",
      "271/271 [==============================] - 0s 712us/step - loss: 28.4494 - val_loss: 34.7813\n",
      "Epoch 119/200\n",
      "271/271 [==============================] - 0s 730us/step - loss: 28.0864 - val_loss: 34.6969\n",
      "Epoch 120/200\n",
      "271/271 [==============================] - 0s 756us/step - loss: 28.3474 - val_loss: 35.3573\n",
      "Epoch 121/200\n",
      "271/271 [==============================] - 0s 804us/step - loss: 28.0311 - val_loss: 34.2595\n",
      "Epoch 122/200\n",
      "271/271 [==============================] - 0s 815us/step - loss: 28.2223 - val_loss: 34.6867\n",
      "Epoch 123/200\n",
      "271/271 [==============================] - 0s 833us/step - loss: 28.2812 - val_loss: 34.2276\n",
      "Epoch 124/200\n",
      "271/271 [==============================] - 0s 793us/step - loss: 27.9038 - val_loss: 34.9292\n",
      "Epoch 125/200\n",
      "271/271 [==============================] - 0s 752us/step - loss: 28.0054 - val_loss: 34.1833\n",
      "Epoch 126/200\n",
      "271/271 [==============================] - 0s 760us/step - loss: 28.3382 - val_loss: 34.9324\n",
      "Epoch 127/200\n",
      "271/271 [==============================] - 0s 616us/step - loss: 28.4073 - val_loss: 35.4488\n",
      "Epoch 128/200\n",
      "271/271 [==============================] - 0s 734us/step - loss: 27.5672 - val_loss: 34.4673\n",
      "Epoch 129/200\n",
      "271/271 [==============================] - 0s 723us/step - loss: 28.2534 - val_loss: 36.3752\n",
      "Epoch 130/200\n",
      "271/271 [==============================] - 0s 756us/step - loss: 28.3115 - val_loss: 34.3845\n",
      "Epoch 131/200\n",
      "271/271 [==============================] - 0s 830us/step - loss: 28.0234 - val_loss: 35.7207\n",
      "Epoch 132/200\n",
      "271/271 [==============================] - 0s 856us/step - loss: 30.7974 - val_loss: 35.5416\n",
      "Epoch 133/200\n",
      "271/271 [==============================] - 0s 782us/step - loss: 28.3088 - val_loss: 34.9732\n",
      "Epoch 134/200\n",
      "271/271 [==============================] - 0s 789us/step - loss: 27.9249 - val_loss: 35.0184\n",
      "Epoch 135/200\n",
      "271/271 [==============================] - 0s 738us/step - loss: 28.5611 - val_loss: 35.2841\n",
      "Epoch 136/200\n",
      "271/271 [==============================] - 0s 701us/step - loss: 27.9119 - val_loss: 35.5909\n",
      "Epoch 137/200\n",
      "271/271 [==============================] - 0s 690us/step - loss: 27.7818 - val_loss: 34.7712\n",
      "Epoch 138/200\n",
      "271/271 [==============================] - 0s 811us/step - loss: 28.2153 - val_loss: 34.8878\n",
      "Epoch 139/200\n",
      "271/271 [==============================] - 0s 620us/step - loss: 28.1936 - val_loss: 34.5620\n",
      "Epoch 140/200\n",
      "271/271 [==============================] - 0s 767us/step - loss: 29.1327 - val_loss: 34.3295\n",
      "Epoch 141/200\n",
      "271/271 [==============================] - 0s 811us/step - loss: 28.1130 - val_loss: 35.4334\n",
      "Epoch 142/200\n",
      "271/271 [==============================] - 0s 793us/step - loss: 27.6401 - val_loss: 35.8526\n",
      "Epoch 143/200\n",
      "271/271 [==============================] - 0s 797us/step - loss: 28.8755 - val_loss: 34.9421\n",
      "Epoch 144/200\n",
      "271/271 [==============================] - 0s 826us/step - loss: 28.4211 - val_loss: 35.3376\n",
      "Epoch 145/200\n",
      "271/271 [==============================] - 0s 712us/step - loss: 27.6346 - val_loss: 34.6086\n",
      "Epoch 146/200\n",
      "271/271 [==============================] - 0s 631us/step - loss: 27.7236 - val_loss: 34.6648\n",
      "Epoch 147/200\n",
      "271/271 [==============================] - 0s 679us/step - loss: 28.5931 - val_loss: 35.5566\n",
      "Epoch 148/200\n",
      "271/271 [==============================] - 0s 752us/step - loss: 27.7323 - val_loss: 34.5734\n",
      "Epoch 149/200\n",
      "271/271 [==============================] - 0s 704us/step - loss: 27.7599 - val_loss: 34.6223\n",
      "Epoch 150/200\n",
      "271/271 [==============================] - 0s 808us/step - loss: 28.7277 - val_loss: 36.7365\n",
      "Epoch 151/200\n",
      "271/271 [==============================] - 0s 774us/step - loss: 29.3655 - val_loss: 34.8086\n",
      "Epoch 152/200\n",
      "271/271 [==============================] - 0s 767us/step - loss: 28.5911 - val_loss: 34.3407\n",
      "Epoch 153/200\n",
      "271/271 [==============================] - 0s 815us/step - loss: 28.0147 - val_loss: 34.4950\n",
      "Epoch 154/200\n",
      "271/271 [==============================] - 0s 837us/step - loss: 28.4955 - val_loss: 34.7144\n",
      "Epoch 155/200\n",
      "271/271 [==============================] - 0s 679us/step - loss: 28.0506 - val_loss: 35.0608\n",
      "Epoch 156/200\n",
      "271/271 [==============================] - 0s 708us/step - loss: 28.4803 - val_loss: 34.4899\n",
      "Epoch 157/200\n",
      "271/271 [==============================] - 0s 752us/step - loss: 27.7605 - val_loss: 35.5914\n",
      "Epoch 158/200\n",
      "271/271 [==============================] - 0s 612us/step - loss: 27.6416 - val_loss: 34.4462\n",
      "Epoch 159/200\n",
      "271/271 [==============================] - 0s 749us/step - loss: 28.2755 - val_loss: 34.8035\n",
      "Epoch 160/200\n",
      "271/271 [==============================] - 0s 789us/step - loss: 28.0464 - val_loss: 34.8777\n",
      "Epoch 161/200\n",
      "271/271 [==============================] - 0s 786us/step - loss: 27.8735 - val_loss: 34.5049\n",
      "Epoch 162/200\n",
      "271/271 [==============================] - 0s 800us/step - loss: 27.5493 - val_loss: 34.6863\n",
      "Epoch 163/200\n",
      "271/271 [==============================] - 0s 822us/step - loss: 28.2082 - val_loss: 35.1853\n",
      "Epoch 164/200\n",
      "271/271 [==============================] - 0s 797us/step - loss: 29.6458 - val_loss: 34.2768\n",
      "Epoch 165/200\n",
      "271/271 [==============================] - 0s 697us/step - loss: 28.3591 - val_loss: 36.0058\n",
      "Epoch 166/200\n",
      "271/271 [==============================] - 0s 660us/step - loss: 28.7876 - val_loss: 36.5060\n",
      "Epoch 167/200\n",
      "271/271 [==============================] - 0s 712us/step - loss: 28.7109 - val_loss: 34.9671\n",
      "Epoch 168/200\n",
      "271/271 [==============================] - 0s 690us/step - loss: 28.0675 - val_loss: 36.1290\n",
      "Epoch 169/200\n",
      "271/271 [==============================] - 0s 727us/step - loss: 29.3283 - val_loss: 35.3012\n",
      "Epoch 170/200\n",
      "271/271 [==============================] - 0s 793us/step - loss: 28.2303 - val_loss: 34.7625\n",
      "Epoch 171/200\n",
      "271/271 [==============================] - 0s 763us/step - loss: 28.1528 - val_loss: 34.4396\n",
      "Epoch 172/200\n",
      "271/271 [==============================] - 0s 774us/step - loss: 27.3404 - val_loss: 34.5707\n",
      "Epoch 173/200\n",
      "271/271 [==============================] - 0s 800us/step - loss: 28.2543 - val_loss: 34.9200\n",
      "Epoch 174/200\n",
      "271/271 [==============================] - 0s 727us/step - loss: 27.7098 - val_loss: 34.5400\n",
      "Epoch 175/200\n",
      "271/271 [==============================] - 0s 642us/step - loss: 27.7488 - val_loss: 34.9666\n",
      "Epoch 176/200\n",
      "271/271 [==============================] - 0s 649us/step - loss: 28.2577 - val_loss: 34.5095\n",
      "Epoch 177/200\n",
      "271/271 [==============================] - 0s 708us/step - loss: 27.7456 - val_loss: 34.4621\n",
      "Epoch 178/200\n",
      "271/271 [==============================] - 0s 671us/step - loss: 28.0341 - val_loss: 34.4388\n",
      "Epoch 179/200\n",
      "271/271 [==============================] - 0s 719us/step - loss: 28.0120 - val_loss: 34.4689\n",
      "Epoch 180/200\n",
      "271/271 [==============================] - 0s 760us/step - loss: 27.4960 - val_loss: 34.3378\n",
      "Epoch 181/200\n",
      "271/271 [==============================] - 0s 789us/step - loss: 28.2026 - val_loss: 34.6268\n",
      "Epoch 182/200\n",
      "271/271 [==============================] - 0s 767us/step - loss: 27.4339 - val_loss: 34.6973\n",
      "Epoch 183/200\n",
      "271/271 [==============================] - 0s 815us/step - loss: 28.4992 - val_loss: 34.7024\n",
      "Epoch 184/200\n",
      "271/271 [==============================] - 0s 749us/step - loss: 28.3122 - val_loss: 34.5392\n",
      "Epoch 185/200\n",
      "271/271 [==============================] - 0s 653us/step - loss: 28.6185 - val_loss: 35.5946\n",
      "Epoch 186/200\n",
      "271/271 [==============================] - 0s 701us/step - loss: 28.5614 - val_loss: 35.1319\n",
      "Epoch 187/200\n",
      "271/271 [==============================] - 0s 693us/step - loss: 27.9678 - val_loss: 36.6366\n",
      "Epoch 188/200\n",
      "271/271 [==============================] - 0s 668us/step - loss: 28.0846 - val_loss: 36.4053\n",
      "Epoch 189/200\n",
      "271/271 [==============================] - 0s 723us/step - loss: 28.3186 - val_loss: 34.6923\n",
      "Epoch 190/200\n",
      "271/271 [==============================] - 0s 741us/step - loss: 27.6466 - val_loss: 34.3430\n",
      "Epoch 191/200\n",
      "271/271 [==============================] - 0s 822us/step - loss: 28.0437 - val_loss: 35.4831\n",
      "Epoch 192/200\n",
      "271/271 [==============================] - 0s 845us/step - loss: 28.7008 - val_loss: 34.6764\n",
      "Epoch 193/200\n",
      "271/271 [==============================] - 0s 771us/step - loss: 28.3251 - val_loss: 34.0047\n",
      "Epoch 194/200\n",
      "271/271 [==============================] - 0s 804us/step - loss: 28.0629 - val_loss: 35.7091\n",
      "Epoch 195/200\n",
      "271/271 [==============================] - 0s 686us/step - loss: 27.6946 - val_loss: 35.1283\n",
      "Epoch 196/200\n",
      "271/271 [==============================] - 0s 671us/step - loss: 27.9952 - val_loss: 34.9689\n",
      "Epoch 197/200\n",
      "271/271 [==============================] - 0s 719us/step - loss: 28.2978 - val_loss: 33.7529\n",
      "Epoch 198/200\n",
      "271/271 [==============================] - 0s 752us/step - loss: 27.6831 - val_loss: 33.7525\n",
      "Epoch 199/200\n",
      "271/271 [==============================] - 0s 690us/step - loss: 28.2086 - val_loss: 35.0329\n",
      "Epoch 200/200\n",
      "271/271 [==============================] - 0s 786us/step - loss: 27.7026 - val_loss: 34.3507\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x214f418deb8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(trainX, trainY, validation_data=(testX, testY),\n",
    "    epochs=200, batch_size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg. house price: $533,388.27, std house price: $493,403.08\n",
      "mean: 34.35%, std: 34.27%\n"
     ]
    }
   ],
   "source": [
    "# make predictions on the testing data\n",
    "preds = model.predict(testX)\n",
    " \n",
    "# compute the difference between the *predicted* house prices and the\n",
    "# *actual* house prices, then compute the percentage difference and\n",
    "# the absolute percentage difference\n",
    "diff = preds.flatten() - testY\n",
    "percentDiff = (diff / testY) * 100\n",
    "absPercentDiff = np.abs(percentDiff)\n",
    " \n",
    "# compute the mean and standard deviation of the absolute percentage\n",
    "# difference\n",
    "mean = np.mean(absPercentDiff)\n",
    "std = np.std(absPercentDiff)\n",
    " \n",
    "# finally, show some statistics on our model\n",
    "locale.setlocale(locale.LC_ALL, \"en_US.UTF-8\")\n",
    "print(\"avg. house price: {}, std house price: {}\".format(\n",
    "    locale.currency(df[\"price\"].mean(), grouping=True),\n",
    "    locale.currency(df[\"price\"].std(), grouping=True)))\n",
    "print(\"mean: {:.2f}%, std: {:.2f}%\".format(mean, std))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">‌منبع</div>   \n",
    "\n",
    "https://www.pyimagesearch.com/2019/01/21/regression-with-keras/"
   ]
  }
 ],
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