Stock market prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dcc692a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb10832e",
   "metadata": {},
   "outputs": [],
   "source": [
    "stock=pd.read_csv('stock.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ce3fc64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Close</th>\n",
       "      <th>Total Trade Quantity</th>\n",
       "      <th>Turnover (Lacs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>234.05</td>\n",
       "      <td>235.95</td>\n",
       "      <td>230.20</td>\n",
       "      <td>233.50</td>\n",
       "      <td>233.75</td>\n",
       "      <td>3069914</td>\n",
       "      <td>7162.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>234.55</td>\n",
       "      <td>236.80</td>\n",
       "      <td>231.10</td>\n",
       "      <td>233.80</td>\n",
       "      <td>233.25</td>\n",
       "      <td>5082859</td>\n",
       "      <td>11859.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>240.00</td>\n",
       "      <td>240.00</td>\n",
       "      <td>232.50</td>\n",
       "      <td>235.00</td>\n",
       "      <td>234.25</td>\n",
       "      <td>2240909</td>\n",
       "      <td>5248.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>233.30</td>\n",
       "      <td>236.75</td>\n",
       "      <td>232.00</td>\n",
       "      <td>236.25</td>\n",
       "      <td>236.10</td>\n",
       "      <td>2349368</td>\n",
       "      <td>5503.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>233.55</td>\n",
       "      <td>239.20</td>\n",
       "      <td>230.75</td>\n",
       "      <td>234.00</td>\n",
       "      <td>233.30</td>\n",
       "      <td>3423509</td>\n",
       "      <td>7999.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    Open    High     Low    Last   Close  Total Trade Quantity  \\\n",
       "0  2018-09-28  234.05  235.95  230.20  233.50  233.75               3069914   \n",
       "1  2018-09-27  234.55  236.80  231.10  233.80  233.25               5082859   \n",
       "2  2018-09-26  240.00  240.00  232.50  235.00  234.25               2240909   \n",
       "3  2018-09-25  233.30  236.75  232.00  236.25  236.10               2349368   \n",
       "4  2018-09-24  233.55  239.20  230.75  234.00  233.30               3423509   \n",
       "\n",
       "   Turnover (Lacs)  \n",
       "0          7162.35  \n",
       "1         11859.95  \n",
       "2          5248.60  \n",
       "3          5503.90  \n",
       "4          7999.55  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "96963d18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2035 entries, 0 to 2034\n",
      "Data columns (total 8 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Date                  2035 non-null   object \n",
      " 1   Open                  2035 non-null   float64\n",
      " 2   High                  2035 non-null   float64\n",
      " 3   Low                   2035 non-null   float64\n",
      " 4   Last                  2035 non-null   float64\n",
      " 5   Close                 2035 non-null   float64\n",
      " 6   Total Trade Quantity  2035 non-null   int64  \n",
      " 7   Turnover (Lacs)       2035 non-null   float64\n",
      "dtypes: float64(6), int64(1), object(1)\n",
      "memory usage: 127.3+ KB\n"
     ]
    }
   ],
   "source": [
    "stock.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f0280a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Close</th>\n",
       "      <th>Total Trade Quantity</th>\n",
       "      <th>Turnover (Lacs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2035.000000</td>\n",
       "      <td>2035.000000</td>\n",
       "      <td>2035.000000</td>\n",
       "      <td>2035.000000</td>\n",
       "      <td>2035.00000</td>\n",
       "      <td>2.035000e+03</td>\n",
       "      <td>2035.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>149.713735</td>\n",
       "      <td>151.992826</td>\n",
       "      <td>147.293931</td>\n",
       "      <td>149.474251</td>\n",
       "      <td>149.45027</td>\n",
       "      <td>2.335681e+06</td>\n",
       "      <td>3899.980565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>48.664509</td>\n",
       "      <td>49.413109</td>\n",
       "      <td>47.931958</td>\n",
       "      <td>48.732570</td>\n",
       "      <td>48.71204</td>\n",
       "      <td>2.091778e+06</td>\n",
       "      <td>4570.767877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>81.100000</td>\n",
       "      <td>82.800000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>80.95000</td>\n",
       "      <td>3.961000e+04</td>\n",
       "      <td>37.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>120.025000</td>\n",
       "      <td>122.100000</td>\n",
       "      <td>118.300000</td>\n",
       "      <td>120.075000</td>\n",
       "      <td>120.05000</td>\n",
       "      <td>1.146444e+06</td>\n",
       "      <td>1427.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>141.500000</td>\n",
       "      <td>143.400000</td>\n",
       "      <td>139.600000</td>\n",
       "      <td>141.100000</td>\n",
       "      <td>141.25000</td>\n",
       "      <td>1.783456e+06</td>\n",
       "      <td>2512.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>157.175000</td>\n",
       "      <td>159.400000</td>\n",
       "      <td>155.150000</td>\n",
       "      <td>156.925000</td>\n",
       "      <td>156.90000</td>\n",
       "      <td>2.813594e+06</td>\n",
       "      <td>4539.015000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>327.700000</td>\n",
       "      <td>328.750000</td>\n",
       "      <td>321.650000</td>\n",
       "      <td>325.950000</td>\n",
       "      <td>325.75000</td>\n",
       "      <td>2.919102e+07</td>\n",
       "      <td>55755.080000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low         Last       Close  \\\n",
       "count  2035.000000  2035.000000  2035.000000  2035.000000  2035.00000   \n",
       "mean    149.713735   151.992826   147.293931   149.474251   149.45027   \n",
       "std      48.664509    49.413109    47.931958    48.732570    48.71204   \n",
       "min      81.100000    82.800000    80.000000    81.000000    80.95000   \n",
       "25%     120.025000   122.100000   118.300000   120.075000   120.05000   \n",
       "50%     141.500000   143.400000   139.600000   141.100000   141.25000   \n",
       "75%     157.175000   159.400000   155.150000   156.925000   156.90000   \n",
       "max     327.700000   328.750000   321.650000   325.950000   325.75000   \n",
       "\n",
       "       Total Trade Quantity  Turnover (Lacs)  \n",
       "count          2.035000e+03      2035.000000  \n",
       "mean           2.335681e+06      3899.980565  \n",
       "std            2.091778e+06      4570.767877  \n",
       "min            3.961000e+04        37.040000  \n",
       "25%            1.146444e+06      1427.460000  \n",
       "50%            1.783456e+06      2512.030000  \n",
       "75%            2.813594e+06      4539.015000  \n",
       "max            2.919102e+07     55755.080000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6950a133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Close')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fif,ax=plt.subplots(figsize = (9,6))\n",
    "plt.title(' Stocks Closing Price')\n",
    "plt.plot(stock['Close'],'b')\n",
    "plt.xlabel('Date',fontsize=15)\n",
    "plt.ylabel('Close',fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1132e493",
   "metadata": {},
   "outputs": [],
   "source": [
    "smp=stock.reset_index()['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6159c2d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       233.75\n",
       "1       233.25\n",
       "2       234.25\n",
       "3       236.10\n",
       "4       233.30\n",
       "         ...  \n",
       "2030    118.65\n",
       "2031    117.60\n",
       "2032    120.65\n",
       "2033    120.90\n",
       "2034    121.55\n",
       "Name: Close, Length: 2035, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3339cde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ed219e1940>]"
      ]
     },
     "execution_count": 14,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(smp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26fd9291",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LSTM are sensitive to the scale of the data. so we apply MinMax scaler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7bbd7843",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler(feature_range=(0,1))\n",
    "smp=scaler.fit_transform(np.array(smp).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "850972e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.62418301]\n",
      " [0.62214052]\n",
      " [0.62622549]\n",
      " ...\n",
      " [0.1621732 ]\n",
      " [0.16319444]\n",
      " [0.16584967]]\n"
     ]
    }
   ],
   "source": [
    "print(smp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8244be14",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data set into train and test sets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f7ebe9b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_size=int(len(smp)*0.65)\n",
    "test_size=len(smp)-training_size\n",
    "train_data,test_data=smp[0:training_size,:],smp[training_size:len(smp),:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70566319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1322, 713)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_size,test_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "02503b15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.62418301],\n",
       "       [0.62214052],\n",
       "       [0.62622549],\n",
       "       ...,\n",
       "       [0.25347222],\n",
       "       [0.26041667],\n",
       "       [0.2563317 ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cfea35d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, time_step=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-time_step-1):\n",
    "        a = dataset[i:(i+time_step), 0]    \n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + time_step, 0])\n",
    "        return numpy.array(dataX), numpy.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "394eab24",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 100\n",
    "X_train, y_train = create_dataset(train_data, time_step)\n",
    "X_test, ytest = create_dataset(test_data, time_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "899e679d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100)\n",
      "(1,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train.shape), print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5e8aaa28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100)\n",
      "(1,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_test.shape), print(ytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "24098d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features] which is required for LSTM\n",
    "X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
    "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "da5c67eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create the Stacked LSTM model\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "063f4d5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(LSTM(50,return_sequences=True,input_shape=(100,1)))\n",
    "model.add(LSTM(50,return_sequences=True))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error',optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f259d35e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_3 (LSTM)               (None, 100, 50)           10400     \n",
      "                                                                 \n",
      " lstm_4 (LSTM)               (None, 100, 50)           20200     \n",
      "                                                                 \n",
      " lstm_5 (LSTM)               (None, 50)                20200     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0e3ce7a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1/1 [==============================] - 15s 15s/step - loss: 0.8437 - val_loss: 0.0887\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 0.6970 - val_loss: 0.0638\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 278ms/step - loss: 0.5658 - val_loss: 0.0416\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 274ms/step - loss: 0.4390 - val_loss: 0.0219\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 281ms/step - loss: 0.3115 - val_loss: 0.0064\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 267ms/step - loss: 0.1841 - val_loss: 4.2361e-05\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 275ms/step - loss: 0.0691 - val_loss: 0.0142\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 288ms/step - loss: 0.0021 - val_loss: 0.0652\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 300ms/step - loss: 0.0432 - val_loss: 0.1130\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 277ms/step - loss: 0.1110 - val_loss: 0.1137\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 287ms/step - loss: 0.0977 - val_loss: 0.0868\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 0.0493 - val_loss: 0.0565\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 0.0122 - val_loss: 0.0337\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 4.0967e-05 - val_loss: 0.0195\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 0.0060 - val_loss: 0.0119\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 276ms/step - loss: 0.0188 - val_loss: 0.0083\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 294ms/step - loss: 0.0301 - val_loss: 0.0071\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 297ms/step - loss: 0.0360 - val_loss: 0.0077\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 295ms/step - loss: 0.0356 - val_loss: 0.0100\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 283ms/step - loss: 0.0301 - val_loss: 0.0141\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 277ms/step - loss: 0.0215 - val_loss: 0.0206\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 299ms/step - loss: 0.0123 - val_loss: 0.0297\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 283ms/step - loss: 0.0048 - val_loss: 0.0413\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 291ms/step - loss: 6.2655e-04 - val_loss: 0.0548\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 324ms/step - loss: 3.3096e-04 - val_loss: 0.0685\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 292ms/step - loss: 0.0032 - val_loss: 0.0804\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 277ms/step - loss: 0.0073 - val_loss: 0.0885\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 275ms/step - loss: 0.0105 - val_loss: 0.0916\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 285ms/step - loss: 0.0114 - val_loss: 0.0896\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 0.0098 - val_loss: 0.0837\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 301ms/step - loss: 0.0067 - val_loss: 0.0755\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 299ms/step - loss: 0.0034 - val_loss: 0.0666\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 317ms/step - loss: 0.0011 - val_loss: 0.0583\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 289ms/step - loss: 3.9249e-05 - val_loss: 0.0512\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 300ms/step - loss: 3.0243e-04 - val_loss: 0.0458\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 0.0014 - val_loss: 0.0421\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 0.0026 - val_loss: 0.0401\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 276ms/step - loss: 0.0036 - val_loss: 0.0396\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 282ms/step - loss: 0.0039 - val_loss: 0.0405\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 0.0036 - val_loss: 0.0428\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 300ms/step - loss: 0.0028 - val_loss: 0.0462\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 291ms/step - loss: 0.0018 - val_loss: 0.0505\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 277ms/step - loss: 8.3023e-04 - val_loss: 0.0555\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 277ms/step - loss: 1.9404e-04 - val_loss: 0.0608\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 9.6301e-07 - val_loss: 0.0658\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 276ms/step - loss: 2.1199e-04 - val_loss: 0.0701\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 283ms/step - loss: 6.5476e-04 - val_loss: 0.0732\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 0.0011 - val_loss: 0.0748\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 332ms/step - loss: 0.0013 - val_loss: 0.0748\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 299ms/step - loss: 0.0013 - val_loss: 0.0734\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 0.0010 - val_loss: 0.0709\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 291ms/step - loss: 6.2980e-04 - val_loss: 0.0678\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 283ms/step - loss: 2.6557e-04 - val_loss: 0.0645\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 4.5671e-05 - val_loss: 0.0613\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 291ms/step - loss: 6.1713e-06 - val_loss: 0.0586\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 282ms/step - loss: 1.1138e-04 - val_loss: 0.0566\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 317ms/step - loss: 2.8203e-04 - val_loss: 0.0554\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 276ms/step - loss: 4.3238e-04 - val_loss: 0.0549\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 283ms/step - loss: 5.0134e-04 - val_loss: 0.0553\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 275ms/step - loss: 4.6878e-04 - val_loss: 0.0563\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 287ms/step - loss: 3.5524e-04 - val_loss: 0.0578\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 280ms/step - loss: 2.0787e-04 - val_loss: 0.0597\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 291ms/step - loss: 7.9626e-05 - val_loss: 0.0619\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 282ms/step - loss: 8.9890e-06 - val_loss: 0.0639\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 316ms/step - loss: 7.3670e-06 - val_loss: 0.0657\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 282ms/step - loss: 5.8159e-05 - val_loss: 0.0671\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 291ms/step - loss: 1.2699e-04 - val_loss: 0.0679\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 276ms/step - loss: 1.7800e-04 - val_loss: 0.0681\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 283ms/step - loss: 1.8891e-04 - val_loss: 0.0677\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 292ms/step - loss: 1.5816e-04 - val_loss: 0.0669\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 291ms/step - loss: 1.0224e-04 - val_loss: 0.0657\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 268ms/step - loss: 4.5554e-05 - val_loss: 0.0643\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 299ms/step - loss: 8.7343e-06 - val_loss: 0.0630\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 307ms/step - loss: 6.4317e-07 - val_loss: 0.0619\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 1.6853e-05 - val_loss: 0.0610\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 289ms/step - loss: 4.3941e-05 - val_loss: 0.0605\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 6.6653e-05 - val_loss: 0.0603\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 7.4480e-05 - val_loss: 0.0605\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 276ms/step - loss: 6.5132e-05 - val_loss: 0.0611\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 274ms/step - loss: 4.4065e-05 - val_loss: 0.0618\n",
      "Epoch 81/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 284ms/step - loss: 2.0894e-05 - val_loss: 0.0627\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 307ms/step - loss: 4.6698e-06 - val_loss: 0.0635\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 283ms/step - loss: 7.9082e-08 - val_loss: 0.0643\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 283ms/step - loss: 6.0701e-06 - val_loss: 0.0649\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 299ms/step - loss: 1.7192e-05 - val_loss: 0.0653\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 274ms/step - loss: 2.6707e-05 - val_loss: 0.0654\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 268ms/step - loss: 2.9817e-05 - val_loss: 0.0653\n",
      "Epoch 88/100\n",
      "1/1 [==============================] - 0s 268ms/step - loss: 2.5530e-05 - val_loss: 0.0649\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 0s 292ms/step - loss: 1.6471e-05 - val_loss: 0.0644\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 297ms/step - loss: 7.0492e-06 - val_loss: 0.0639\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 274ms/step - loss: 1.1138e-06 - val_loss: 0.0633\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 276ms/step - loss: 2.7263e-07 - val_loss: 0.0628\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 283ms/step - loss: 3.5449e-06 - val_loss: 0.0625\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 268ms/step - loss: 8.2709e-06 - val_loss: 0.0623\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 279ms/step - loss: 1.1635e-05 - val_loss: 0.0623\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 269ms/step - loss: 1.1988e-05 - val_loss: 0.0624\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 284ms/step - loss: 9.4088e-06 - val_loss: 0.0627\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 307ms/step - loss: 5.3625e-06 - val_loss: 0.0631\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 298ms/step - loss: 1.7848e-06 - val_loss: 0.0634\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 290ms/step - loss: 7.5437e-08 - val_loss: 0.0638\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1ed21e24550>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=100,batch_size=64,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "17b8de53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 2s 2s/step\n",
      "1/1 [==============================] - 0s 81ms/step\n"
     ]
    }
   ],
   "source": [
    "### Lets Do the prediction and check performance metrics\n",
    "train_predict=model.predict(X_train)\n",
    "test_predict=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5a28ba2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Transformback to original form\n",
    "train_predict=scaler.inverse_transform(train_predict)\n",
    "test_predict=scaler.inverse_transform(test_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c6f4a4ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "293.5056507384855"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Calculate RMSE performance metrics\n",
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "math.sqrt(mean_squared_error(y_train,train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d4aec45b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "221.45504331900403"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Test Data RMSE\n",
    "math.sqrt(mean_squared_error(ytest,test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a8a2b5d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Visualise the Predictions\n",
    "look_back = 100\n",
    "train_num_pyredict_plot = np.empty_like(smp)\n",
    "train_num_pyredict_plot[:, :] = np.nan\n",
    "train_num_pyredict_plot[look_back : len(train_predict) + look_back, :] = train_predict\n",
    "test_predict_plot = np.empty_like(smp)\n",
    "test_predict_plot[:, :] = np.nan\n",
    "test_predict_plot[len(train_predict) + (look_back * 2) + 1 : len(smp) - 1, :] = test_predict\n",
    "plt.plot(scaler.inverse_transform(smp))\n",
    "plt.plot(train_num_pyredict_plot)\n",
    "plt.plot(test_predict_plot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e639db1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Future Prediction Model\n",
    "x_inum_pyut=test_data[307:].reshape(1, -1)\n",
    "x_inum_pyut.shape\n",
    "temp_inum_pyut = list(x_inum_pyut)\n",
    "temp_inum_pyut = temp_inum_pyut[0].tolist()\n",
    "temp_inum_pyut = list(x_inum_pyut)\n",
    "temp_inum_pyut = temp_inum_pyut[0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4f3a6f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ed2e82db50>]"
      ]
     },
     "execution_count": 39,
     "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": [
    "day_new = np.arange(1, 101)\n",
    "day_pred = np.arange(101, 131)\n",
    "plt.plot(day_new, scaler.inverse_transform(smp[1935 : ]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90ccec60",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}