AQI_FINAL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import keras\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Real_Combine.csv')"
   ]
  },
  {
   "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>T</th>\n",
       "      <th>TM</th>\n",
       "      <th>Tm</th>\n",
       "      <th>SLP</th>\n",
       "      <th>H</th>\n",
       "      <th>VV</th>\n",
       "      <th>V</th>\n",
       "      <th>VM</th>\n",
       "      <th>PM 2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>9.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>219.720833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>12.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1018.5</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.1</td>\n",
       "      <td>182.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.7</td>\n",
       "      <td>13.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>11.1</td>\n",
       "      <td>154.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.6</td>\n",
       "      <td>15.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1018.7</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>20.6</td>\n",
       "      <td>223.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.4</td>\n",
       "      <td>20.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1017.3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>200.645833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      T    TM   Tm     SLP     H   VV    V    VM      PM 2.5\n",
       "0   7.4   9.8  4.8  1017.6  93.0  0.5  4.3   9.4  219.720833\n",
       "1   7.8  12.7  4.4  1018.5  87.0  0.6  4.4  11.1  182.187500\n",
       "2   6.7  13.4  2.4  1019.4  82.0  0.6  4.8  11.1  154.037500\n",
       "3   8.6  15.5  3.3  1018.7  72.0  0.8  8.1  20.6  223.208333\n",
       "4  12.4  20.9  4.4  1017.3  61.0  1.3  8.7  22.2  200.645833"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1093, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for NaN values\n",
    "df.isnull().any().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=df.corr()\n",
    "fig=plt.figure(figsize=(10,10))\n",
    "sns.heatmap(corrmat,center=0,vmax=.8,square=True,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One NULL value found hence dropping it\n",
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T         False\n",
       "TM        False\n",
       "Tm        False\n",
       "SLP       False\n",
       "H         False\n",
       "VV        False\n",
       "V         False\n",
       "VM        False\n",
       "PM 2.5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000      36\n",
       "79.833333      7\n",
       "29.666667      7\n",
       "43.666667      7\n",
       "108.000000     7\n",
       "              ..\n",
       "121.208333     1\n",
       "19.541667      1\n",
       "231.125000     1\n",
       "25.791667      1\n",
       "32.583333      1\n",
       "Name: PM 2.5, Length: 555, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PM 2.5'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17bf3ce52c8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.scatterplot(x=\"PM 2.5\", y=\"TM\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17bf3c70188>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.scatterplot(x=\"PM 2.5\", y=\"SLP\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17bf2558e48>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.scatterplot(x=\"PM 2.5\", y=\"H\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1] \n",
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation,Dropout\n",
    "from tensorflow.keras.constraints import max_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "# input layer\n",
    "model.add(Dense(128,  activation='relu'))\n",
    "#model.add(Dropout(0.2))\n",
    "\n",
    "# hidden layer\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# hidden layer\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# output layer\n",
    "model.add(Dense(units=1,activation='linear'))\n",
    "\n",
    "# Compile model\n",
    "model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "3/3 [==============================] - 1s 218ms/step - loss: 110.1456 - mean_absolute_error: 110.1456 - val_loss: 106.2004 - val_mean_absolute_error: 106.2004\n",
      "Epoch 2/150\n",
      "3/3 [==============================] - 0s 79ms/step - loss: 109.4879 - mean_absolute_error: 109.4879 - val_loss: 105.3706 - val_mean_absolute_error: 105.3706\n",
      "Epoch 3/150\n",
      "3/3 [==============================] - 0s 82ms/step - loss: 108.5510 - mean_absolute_error: 108.5510 - val_loss: 104.1369 - val_mean_absolute_error: 104.1369\n",
      "Epoch 4/150\n",
      "3/3 [==============================] - 0s 81ms/step - loss: 107.2324 - mean_absolute_error: 107.2324 - val_loss: 102.2847 - val_mean_absolute_error: 102.2847\n",
      "Epoch 5/150\n",
      "3/3 [==============================] - 0s 67ms/step - loss: 105.1739 - mean_absolute_error: 105.1739 - val_loss: 99.5642 - val_mean_absolute_error: 99.5642\n",
      "Epoch 6/150\n",
      "3/3 [==============================] - 0s 76ms/step - loss: 102.1359 - mean_absolute_error: 102.1359 - val_loss: 95.6570 - val_mean_absolute_error: 95.6570\n",
      "Epoch 7/150\n",
      "3/3 [==============================] - 0s 73ms/step - loss: 98.0140 - mean_absolute_error: 98.0140 - val_loss: 90.2741 - val_mean_absolute_error: 90.2741\n",
      "Epoch 8/150\n",
      "3/3 [==============================] - 0s 80ms/step - loss: 92.0606 - mean_absolute_error: 92.0606 - val_loss: 83.1638 - val_mean_absolute_error: 83.1638\n",
      "Epoch 9/150\n",
      "3/3 [==============================] - 0s 69ms/step - loss: 85.2513 - mean_absolute_error: 85.2513 - val_loss: 76.5567 - val_mean_absolute_error: 76.5567\n",
      "Epoch 10/150\n",
      "3/3 [==============================] - 0s 81ms/step - loss: 78.5001 - mean_absolute_error: 78.5001 - val_loss: 71.4403 - val_mean_absolute_error: 71.4403\n",
      "Epoch 11/150\n",
      "3/3 [==============================] - 0s 74ms/step - loss: 74.2066 - mean_absolute_error: 74.2066 - val_loss: 68.3036 - val_mean_absolute_error: 68.3036\n",
      "Epoch 12/150\n",
      "3/3 [==============================] - 0s 99ms/step - loss: 71.9491 - mean_absolute_error: 71.9491 - val_loss: 67.3524 - val_mean_absolute_error: 67.3524\n",
      "Epoch 13/150\n",
      "3/3 [==============================] - 0s 77ms/step - loss: 72.3599 - mean_absolute_error: 72.3599 - val_loss: 67.2976 - val_mean_absolute_error: 67.2976\n",
      "Epoch 14/150\n",
      "3/3 [==============================] - 0s 77ms/step - loss: 72.4506 - mean_absolute_error: 72.4506 - val_loss: 66.3690 - val_mean_absolute_error: 66.3690\n",
      "Epoch 15/150\n",
      "3/3 [==============================] - 0s 79ms/step - loss: 71.6007 - mean_absolute_error: 71.6007 - val_loss: 64.8834 - val_mean_absolute_error: 64.8834\n",
      "Epoch 16/150\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 70.2878 - mean_absolute_error: 70.2878 - val_loss: 63.4663 - val_mean_absolute_error: 63.4663\n",
      "Epoch 17/150\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 68.5275 - mean_absolute_error: 68.5275 - val_loss: 62.4001 - val_mean_absolute_error: 62.4001\n",
      "Epoch 18/150\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 67.2091 - mean_absolute_error: 67.2091 - val_loss: 61.6509 - val_mean_absolute_error: 61.6509\n",
      "Epoch 19/150\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 66.2595 - mean_absolute_error: 66.2595 - val_loss: 60.8516 - val_mean_absolute_error: 60.8516\n",
      "Epoch 20/150\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 65.7256 - mean_absolute_error: 65.7256 - val_loss: 59.6403 - val_mean_absolute_error: 59.6403\n",
      "Epoch 21/150\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 64.1876 - mean_absolute_error: 64.1876 - val_loss: 58.1245 - val_mean_absolute_error: 58.1245\n",
      "Epoch 22/150\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 62.9366 - mean_absolute_error: 62.9366 - val_loss: 56.6120 - val_mean_absolute_error: 56.6120\n",
      "Epoch 23/150\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 61.6627 - mean_absolute_error: 61.6627 - val_loss: 55.1360 - val_mean_absolute_error: 55.1360\n",
      "Epoch 24/150\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 60.6709 - mean_absolute_error: 60.6709 - val_loss: 53.7490 - val_mean_absolute_error: 53.7490\n",
      "Epoch 25/150\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 59.2383 - mean_absolute_error: 59.2383 - val_loss: 52.4478 - val_mean_absolute_error: 52.4478\n",
      "Epoch 26/150\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 57.9518 - mean_absolute_error: 57.9518 - val_loss: 51.2433 - val_mean_absolute_error: 51.2433\n",
      "Epoch 27/150\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 56.7143 - mean_absolute_error: 56.7143 - val_loss: 50.0411 - val_mean_absolute_error: 50.0411\n",
      "Epoch 28/150\n",
      "3/3 [==============================] - 0s 66ms/step - loss: 55.3348 - mean_absolute_error: 55.3348 - val_loss: 48.7009 - val_mean_absolute_error: 48.7009\n",
      "Epoch 29/150\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 54.4748 - mean_absolute_error: 54.4748 - val_loss: 47.3521 - val_mean_absolute_error: 47.3521\n",
      "Epoch 30/150\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 52.7503 - mean_absolute_error: 52.7503 - val_loss: 45.9319 - val_mean_absolute_error: 45.9319\n",
      "Epoch 31/150\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 51.4602 - mean_absolute_error: 51.4602 - val_loss: 44.6276 - val_mean_absolute_error: 44.6276\n",
      "Epoch 32/150\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 50.4622 - mean_absolute_error: 50.4622 - val_loss: 43.6380 - val_mean_absolute_error: 43.6380\n",
      "Epoch 33/150\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 48.3528 - mean_absolute_error: 48.3528 - val_loss: 43.0682 - val_mean_absolute_error: 43.0682\n",
      "Epoch 34/150\n",
      "3/3 [==============================] - 0s 60ms/step - loss: 48.1552 - mean_absolute_error: 48.1552 - val_loss: 42.9975 - val_mean_absolute_error: 42.9975\n",
      "Epoch 35/150\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 47.7463 - mean_absolute_error: 47.7463 - val_loss: 42.9196 - val_mean_absolute_error: 42.9196\n",
      "Epoch 36/150\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 47.4661 - mean_absolute_error: 47.4661 - val_loss: 42.9719 - val_mean_absolute_error: 42.9719\n",
      "Epoch 37/150\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 47.5490 - mean_absolute_error: 47.5490 - val_loss: 43.2723 - val_mean_absolute_error: 43.2723\n",
      "Epoch 38/150\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 47.1261 - mean_absolute_error: 47.1261 - val_loss: 42.9521 - val_mean_absolute_error: 42.9521\n",
      "Epoch 39/150\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 46.8545 - mean_absolute_error: 46.8545 - val_loss: 42.6190 - val_mean_absolute_error: 42.6190\n",
      "Epoch 40/150\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 46.4210 - mean_absolute_error: 46.4210 - val_loss: 42.3083 - val_mean_absolute_error: 42.3083\n",
      "Epoch 41/150\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 45.9448 - mean_absolute_error: 45.9448 - val_loss: 42.2104 - val_mean_absolute_error: 42.2104\n",
      "Epoch 42/150\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 46.0499 - mean_absolute_error: 46.0499 - val_loss: 41.9600 - val_mean_absolute_error: 41.9600\n",
      "Epoch 43/150\n",
      "3/3 [==============================] - 0s 26ms/step - loss: 45.7647 - mean_absolute_error: 45.7647 - val_loss: 41.8281 - val_mean_absolute_error: 41.8281\n",
      "Epoch 44/150\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 45.1607 - mean_absolute_error: 45.1607 - val_loss: 41.7101 - val_mean_absolute_error: 41.7101\n",
      "Epoch 45/150\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 45.0566 - mean_absolute_error: 45.0566 - val_loss: 41.4826 - val_mean_absolute_error: 41.4826\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 46/150\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 45.2980 - mean_absolute_error: 45.2980 - val_loss: 41.1905 - val_mean_absolute_error: 41.1905\n",
      "Epoch 47/150\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 45.4109 - mean_absolute_error: 45.4109 - val_loss: 41.0688 - val_mean_absolute_error: 41.0688\n",
      "Epoch 48/150\n",
      "3/3 [==============================] - 0s 31ms/step - loss: 44.5371 - mean_absolute_error: 44.5371 - val_loss: 41.1263 - val_mean_absolute_error: 41.1263\n",
      "Epoch 49/150\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 44.4369 - mean_absolute_error: 44.4369 - val_loss: 41.3870 - val_mean_absolute_error: 41.3870\n",
      "Epoch 50/150\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 45.2016 - mean_absolute_error: 45.2016 - val_loss: 41.1639 - val_mean_absolute_error: 41.1639\n",
      "Epoch 51/150\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 44.2780 - mean_absolute_error: 44.2780 - val_loss: 40.7344 - val_mean_absolute_error: 40.7344\n",
      "Epoch 52/150\n",
      "3/3 [==============================] - 0s 27ms/step - loss: 44.4453 - mean_absolute_error: 44.4453 - val_loss: 40.4869 - val_mean_absolute_error: 40.4869\n",
      "Epoch 53/150\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 44.5551 - mean_absolute_error: 44.5551 - val_loss: 40.3189 - val_mean_absolute_error: 40.3189\n",
      "Epoch 54/150\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 43.5336 - mean_absolute_error: 43.5336 - val_loss: 40.1670 - val_mean_absolute_error: 40.1670\n",
      "Epoch 55/150\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 44.0383 - mean_absolute_error: 44.0383 - val_loss: 40.1849 - val_mean_absolute_error: 40.1849\n",
      "Epoch 56/150\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 43.7610 - mean_absolute_error: 43.7610 - val_loss: 39.8142 - val_mean_absolute_error: 39.8142\n",
      "Epoch 57/150\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 43.3157 - mean_absolute_error: 43.3157 - val_loss: 39.5140 - val_mean_absolute_error: 39.5140\n",
      "Epoch 58/150\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 43.4059 - mean_absolute_error: 43.4059 - val_loss: 39.2096 - val_mean_absolute_error: 39.2096\n",
      "Epoch 59/150\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 42.8691 - mean_absolute_error: 42.8691 - val_loss: 39.1081 - val_mean_absolute_error: 39.1081\n",
      "Epoch 60/150\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 42.9169 - mean_absolute_error: 42.9169 - val_loss: 39.0831 - val_mean_absolute_error: 39.0831\n",
      "Epoch 61/150\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 43.2191 - mean_absolute_error: 43.2191 - val_loss: 38.8535 - val_mean_absolute_error: 38.8535\n",
      "Epoch 62/150\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 43.1946 - mean_absolute_error: 43.1946 - val_loss: 38.5976 - val_mean_absolute_error: 38.5976\n",
      "Epoch 63/150\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 43.3472 - mean_absolute_error: 43.3472 - val_loss: 38.4790 - val_mean_absolute_error: 38.4790\n",
      "Epoch 64/150\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 42.1147 - mean_absolute_error: 42.1147 - val_loss: 38.8738 - val_mean_absolute_error: 38.8738\n",
      "Epoch 65/150\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 43.2846 - mean_absolute_error: 43.2846 - val_loss: 39.2196 - val_mean_absolute_error: 39.2196\n",
      "Epoch 66/150\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 42.6786 - mean_absolute_error: 42.6786 - val_loss: 38.5741 - val_mean_absolute_error: 38.5741\n",
      "Epoch 67/150\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 42.6419 - mean_absolute_error: 42.6419 - val_loss: 38.5545 - val_mean_absolute_error: 38.5545\n",
      "Epoch 68/150\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 42.7574 - mean_absolute_error: 42.7574 - val_loss: 38.3942 - val_mean_absolute_error: 38.3942\n",
      "Epoch 69/150\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 42.3618 - mean_absolute_error: 42.3618 - val_loss: 38.4317 - val_mean_absolute_error: 38.4317\n",
      "Epoch 70/150\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 42.3413 - mean_absolute_error: 42.3413 - val_loss: 38.2696 - val_mean_absolute_error: 38.2696\n",
      "Epoch 71/150\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 41.8698 - mean_absolute_error: 41.8698 - val_loss: 37.9957 - val_mean_absolute_error: 37.9957\n",
      "Epoch 72/150\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 42.0160 - mean_absolute_error: 42.0160 - val_loss: 37.9357 - val_mean_absolute_error: 37.9357\n",
      "Epoch 73/150\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 41.7586 - mean_absolute_error: 41.7586 - val_loss: 38.1246 - val_mean_absolute_error: 38.1246\n",
      "Epoch 74/150\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 42.2076 - mean_absolute_error: 42.2076 - val_loss: 37.8345 - val_mean_absolute_error: 37.8345\n",
      "Epoch 75/150\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 42.2040 - mean_absolute_error: 42.2040 - val_loss: 37.6482 - val_mean_absolute_error: 37.6482\n",
      "Epoch 76/150\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 42.6358 - mean_absolute_error: 42.6358 - val_loss: 37.5200 - val_mean_absolute_error: 37.5200\n",
      "Epoch 77/150\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 41.5627 - mean_absolute_error: 41.5627 - val_loss: 37.7398 - val_mean_absolute_error: 37.7398\n",
      "Epoch 78/150\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 42.2662 - mean_absolute_error: 42.2662 - val_loss: 37.7782 - val_mean_absolute_error: 37.7782\n",
      "Epoch 79/150\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 41.5957 - mean_absolute_error: 41.5957 - val_loss: 37.4715 - val_mean_absolute_error: 37.4715\n",
      "Epoch 80/150\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 42.2611 - mean_absolute_error: 42.2611 - val_loss: 37.4136 - val_mean_absolute_error: 37.4136\n",
      "Epoch 81/150\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 42.0280 - mean_absolute_error: 42.0280 - val_loss: 37.6861 - val_mean_absolute_error: 37.6861\n",
      "Epoch 82/150\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 41.3782 - mean_absolute_error: 41.3782 - val_loss: 37.6256 - val_mean_absolute_error: 37.6256\n",
      "Epoch 83/150\n",
      "3/3 [==============================] - 0s 49ms/step - loss: 41.3967 - mean_absolute_error: 41.3967 - val_loss: 37.2762 - val_mean_absolute_error: 37.2762\n",
      "Epoch 84/150\n",
      "3/3 [==============================] - 0s 47ms/step - loss: 41.6342 - mean_absolute_error: 41.6342 - val_loss: 37.2861 - val_mean_absolute_error: 37.2861\n",
      "Epoch 85/150\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 41.9224 - mean_absolute_error: 41.9224 - val_loss: 37.2903 - val_mean_absolute_error: 37.2903\n",
      "Epoch 86/150\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 41.2092 - mean_absolute_error: 41.2092 - val_loss: 37.3148 - val_mean_absolute_error: 37.3148\n",
      "Epoch 87/150\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 41.5275 - mean_absolute_error: 41.5275 - val_loss: 37.3280 - val_mean_absolute_error: 37.3280\n",
      "Epoch 88/150\n",
      "3/3 [==============================] - 0s 48ms/step - loss: 40.9368 - mean_absolute_error: 40.9368 - val_loss: 37.2370 - val_mean_absolute_error: 37.2370\n",
      "Epoch 89/150\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 41.0742 - mean_absolute_error: 41.0742 - val_loss: 36.9870 - val_mean_absolute_error: 36.9870\n",
      "Epoch 90/150\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 41.5784 - mean_absolute_error: 41.5784 - val_loss: 36.9263 - val_mean_absolute_error: 36.9263\n",
      "Epoch 91/150\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 40.9481 - mean_absolute_error: 40.9481 - val_loss: 37.2679 - val_mean_absolute_error: 37.2679\n",
      "Epoch 92/150\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 41.7228 - mean_absolute_error: 41.7228 - val_loss: 37.1821 - val_mean_absolute_error: 37.1821\n",
      "Epoch 93/150\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 40.6851 - mean_absolute_error: 40.6851 - val_loss: 37.2067 - val_mean_absolute_error: 37.2067\n",
      "Epoch 94/150\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 40.9241 - mean_absolute_error: 40.9241 - val_loss: 37.2711 - val_mean_absolute_error: 37.2711\n",
      "Epoch 95/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 57ms/step - loss: 41.6406 - mean_absolute_error: 41.6406 - val_loss: 36.9627 - val_mean_absolute_error: 36.9627\n",
      "Epoch 96/150\n",
      "3/3 [==============================] - 0s 56ms/step - loss: 40.7506 - mean_absolute_error: 40.7506 - val_loss: 36.7683 - val_mean_absolute_error: 36.7683\n",
      "Epoch 97/150\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 41.1727 - mean_absolute_error: 41.1727 - val_loss: 36.6428 - val_mean_absolute_error: 36.6428\n",
      "Epoch 98/150\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 41.4115 - mean_absolute_error: 41.4115 - val_loss: 36.7561 - val_mean_absolute_error: 36.7561\n",
      "Epoch 99/150\n",
      "3/3 [==============================] - 0s 53ms/step - loss: 41.2884 - mean_absolute_error: 41.2884 - val_loss: 36.6098 - val_mean_absolute_error: 36.6098\n",
      "Epoch 100/150\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 40.8168 - mean_absolute_error: 40.8168 - val_loss: 36.4592 - val_mean_absolute_error: 36.4592\n",
      "Epoch 101/150\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 40.7763 - mean_absolute_error: 40.7763 - val_loss: 36.6449 - val_mean_absolute_error: 36.6449\n",
      "Epoch 102/150\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 40.9279 - mean_absolute_error: 40.9279 - val_loss: 36.4068 - val_mean_absolute_error: 36.4068\n",
      "Epoch 103/150\n",
      "3/3 [==============================] - 0s 40ms/step - loss: 40.7738 - mean_absolute_error: 40.7738 - val_loss: 36.3279 - val_mean_absolute_error: 36.3279\n",
      "Epoch 104/150\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 41.0363 - mean_absolute_error: 41.0363 - val_loss: 36.4917 - val_mean_absolute_error: 36.4917\n",
      "Epoch 105/150\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 40.6699 - mean_absolute_error: 40.6699 - val_loss: 36.5490 - val_mean_absolute_error: 36.5490\n",
      "Epoch 106/150\n",
      "3/3 [==============================] - 0s 52ms/step - loss: 40.3804 - mean_absolute_error: 40.3804 - val_loss: 36.7697 - val_mean_absolute_error: 36.7697\n",
      "Epoch 107/150\n",
      "3/3 [==============================] - 0s 57ms/step - loss: 40.7252 - mean_absolute_error: 40.7252 - val_loss: 36.8015 - val_mean_absolute_error: 36.8015\n",
      "Epoch 108/150\n",
      "3/3 [==============================] - 0s 51ms/step - loss: 40.2279 - mean_absolute_error: 40.2279 - val_loss: 36.5798 - val_mean_absolute_error: 36.5798\n",
      "Epoch 109/150\n",
      "3/3 [==============================] - 0s 46ms/step - loss: 40.3165 - mean_absolute_error: 40.3165 - val_loss: 36.4685 - val_mean_absolute_error: 36.4685\n",
      "Epoch 110/150\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 40.3435 - mean_absolute_error: 40.3435 - val_loss: 36.5999 - val_mean_absolute_error: 36.5999\n",
      "Epoch 111/150\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 40.4479 - mean_absolute_error: 40.4479 - val_loss: 36.6868 - val_mean_absolute_error: 36.6868\n",
      "Epoch 112/150\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 40.3791 - mean_absolute_error: 40.3791 - val_loss: 35.9340 - val_mean_absolute_error: 35.9340\n",
      "Epoch 113/150\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 40.2409 - mean_absolute_error: 40.2409 - val_loss: 35.8410 - val_mean_absolute_error: 35.8410\n",
      "Epoch 114/150\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 40.7477 - mean_absolute_error: 40.7477 - val_loss: 35.8162 - val_mean_absolute_error: 35.8162\n",
      "Epoch 115/150\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 40.7379 - mean_absolute_error: 40.7379 - val_loss: 36.4627 - val_mean_absolute_error: 36.4627\n",
      "Epoch 116/150\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 40.5256 - mean_absolute_error: 40.5256 - val_loss: 35.8430 - val_mean_absolute_error: 35.8430\n",
      "Epoch 117/150\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 39.9934 - mean_absolute_error: 39.9934 - val_loss: 35.9056 - val_mean_absolute_error: 35.9056\n",
      "Epoch 118/150\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 40.8082 - mean_absolute_error: 40.8082 - val_loss: 35.9001 - val_mean_absolute_error: 35.9001\n",
      "Epoch 119/150\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 40.3154 - mean_absolute_error: 40.3154 - val_loss: 35.8741 - val_mean_absolute_error: 35.8741\n",
      "Epoch 120/150\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 40.0372 - mean_absolute_error: 40.0372 - val_loss: 35.7865 - val_mean_absolute_error: 35.7865\n",
      "Epoch 121/150\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 39.8722 - mean_absolute_error: 39.8722 - val_loss: 35.7935 - val_mean_absolute_error: 35.7935\n",
      "Epoch 122/150\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 40.3612 - mean_absolute_error: 40.3612 - val_loss: 35.5770 - val_mean_absolute_error: 35.5770\n",
      "Epoch 123/150\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 40.2311 - mean_absolute_error: 40.2311 - val_loss: 35.6513 - val_mean_absolute_error: 35.6513\n",
      "Epoch 124/150\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 39.6642 - mean_absolute_error: 39.6642 - val_loss: 36.0319 - val_mean_absolute_error: 36.0319\n",
      "Epoch 125/150\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 40.2669 - mean_absolute_error: 40.2669 - val_loss: 36.3945 - val_mean_absolute_error: 36.3945\n",
      "Epoch 126/150\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 39.9041 - mean_absolute_error: 39.9041 - val_loss: 35.8734 - val_mean_absolute_error: 35.8734\n",
      "Epoch 127/150\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 40.0373 - mean_absolute_error: 40.0373 - val_loss: 35.5544 - val_mean_absolute_error: 35.5544\n",
      "Epoch 128/150\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 39.1586 - mean_absolute_error: 39.1586 - val_loss: 35.3868 - val_mean_absolute_error: 35.3868\n",
      "Epoch 129/150\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 39.4685 - mean_absolute_error: 39.4685 - val_loss: 35.3004 - val_mean_absolute_error: 35.3004\n",
      "Epoch 130/150\n",
      "3/3 [==============================] - 0s 36ms/step - loss: 39.7377 - mean_absolute_error: 39.7377 - val_loss: 35.2678 - val_mean_absolute_error: 35.2678\n",
      "Epoch 131/150\n",
      "3/3 [==============================] - 0s 50ms/step - loss: 39.3617 - mean_absolute_error: 39.3617 - val_loss: 35.5335 - val_mean_absolute_error: 35.5335\n",
      "Epoch 132/150\n",
      "3/3 [==============================] - 0s 37ms/step - loss: 39.6570 - mean_absolute_error: 39.6570 - val_loss: 35.3958 - val_mean_absolute_error: 35.3958\n",
      "Epoch 133/150\n",
      "3/3 [==============================] - 0s 39ms/step - loss: 39.2928 - mean_absolute_error: 39.2928 - val_loss: 35.5508 - val_mean_absolute_error: 35.5508\n",
      "Epoch 134/150\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 39.8813 - mean_absolute_error: 39.8813 - val_loss: 35.8847 - val_mean_absolute_error: 35.8847\n",
      "Epoch 135/150\n",
      "3/3 [==============================] - 0s 32ms/step - loss: 39.8404 - mean_absolute_error: 39.8404 - val_loss: 36.2702 - val_mean_absolute_error: 36.2702\n",
      "Epoch 136/150\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 39.2975 - mean_absolute_error: 39.2975 - val_loss: 35.2659 - val_mean_absolute_error: 35.2659\n",
      "Epoch 137/150\n",
      "3/3 [==============================] - 0s 38ms/step - loss: 39.8053 - mean_absolute_error: 39.8053 - val_loss: 35.0689 - val_mean_absolute_error: 35.0689\n",
      "Epoch 138/150\n",
      "3/3 [==============================] - 0s 42ms/step - loss: 40.0147 - mean_absolute_error: 40.0147 - val_loss: 35.4785 - val_mean_absolute_error: 35.4785\n",
      "Epoch 139/150\n",
      "3/3 [==============================] - 0s 45ms/step - loss: 40.1542 - mean_absolute_error: 40.1542 - val_loss: 35.3521 - val_mean_absolute_error: 35.3521\n",
      "Epoch 140/150\n",
      "3/3 [==============================] - 0s 41ms/step - loss: 39.2123 - mean_absolute_error: 39.2123 - val_loss: 35.1189 - val_mean_absolute_error: 35.1189\n",
      "Epoch 141/150\n",
      "3/3 [==============================] - 0s 43ms/step - loss: 39.1598 - mean_absolute_error: 39.1598 - val_loss: 35.0339 - val_mean_absolute_error: 35.0339\n",
      "Epoch 142/150\n",
      "3/3 [==============================] - 0s 29ms/step - loss: 39.2935 - mean_absolute_error: 39.2935 - val_loss: 35.3266 - val_mean_absolute_error: 35.3266\n",
      "Epoch 143/150\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 39.5497 - mean_absolute_error: 39.5497 - val_loss: 35.4481 - val_mean_absolute_error: 35.4481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 144/150\n",
      "3/3 [==============================] - 0s 30ms/step - loss: 39.7941 - mean_absolute_error: 39.7941 - val_loss: 34.8379 - val_mean_absolute_error: 34.8379\n",
      "Epoch 145/150\n",
      "3/3 [==============================] - 0s 34ms/step - loss: 39.6508 - mean_absolute_error: 39.6508 - val_loss: 34.8150 - val_mean_absolute_error: 34.8150\n",
      "Epoch 146/150\n",
      "3/3 [==============================] - 0s 35ms/step - loss: 39.8174 - mean_absolute_error: 39.8174 - val_loss: 34.8745 - val_mean_absolute_error: 34.8745\n",
      "Epoch 147/150\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 38.8996 - mean_absolute_error: 38.8996 - val_loss: 36.0317 - val_mean_absolute_error: 36.0317\n",
      "Epoch 148/150\n",
      "3/3 [==============================] - 0s 28ms/step - loss: 39.4502 - mean_absolute_error: 39.4502 - val_loss: 34.9206 - val_mean_absolute_error: 34.9206\n",
      "Epoch 149/150\n",
      "3/3 [==============================] - 0s 33ms/step - loss: 39.5261 - mean_absolute_error: 39.5261 - val_loss: 34.9715 - val_mean_absolute_error: 34.9715\n",
      "Epoch 150/150\n",
      "3/3 [==============================] - 0s 63ms/step - loss: 39.2678 - mean_absolute_error: 39.2678 - val_loss: 35.1251 - val_mean_absolute_error: 35.1251\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1e98bc37908>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, \n",
    "          y_train, \n",
    "          epochs=150,\n",
    "          batch_size=256,\n",
    "          validation_split=0.30\n",
    "          #validation_data=(X_test, y_test), \n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"ANN1.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step - loss: 34.1502 - mean_absolute_error: 34.1502\n",
      "34.15023422241211 34.15023422241211\n"
     ]
    }
   ],
   "source": [
    "val_loss, val_mean_absolute_error= model.evaluate(X_test, y_test)\n",
    "print(val_loss, val_mean_absolute_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 34.15023303735928\n",
      "MSE: 2511.0025628016947\n",
      "RMSE: 50.10990483728436\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "prediction=model.predict(X_test)\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, prediction))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, prediction))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e98faf8a88>"
      ]
     },
     "execution_count": 35,
     "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.scatter(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-9fc32864077d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ANN1.pkl'\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "#pickle.dump(model,open('ANN1.pkl' , 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import dill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dill_file = open(\"ANN1_resultstatsDF\", \"wb\")\\ndill_file.write(dill.dumps(resultstatsDF))\\ndill_file.close()'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"dill_file = open(\"ANN1_resultstatsDF\", \"wb\")\n",
    "dill_file.write(dill.dumps(resultstatsDF))\n",
    "dill_file.close()\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model1.pkl']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(scaler, 'model1.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "import joblib\n",
    "import numpy as np\n",
    "\n",
    "a_model = load_model('ANN1.h5')\n",
    "a_scaler = joblib.load(\"model1.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}