web_stok.keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<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>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>27.782366</td>\n",
       "      <td>27.839401</td>\n",
       "      <td>27.603037</td>\n",
       "      <td>27.724083</td>\n",
       "      <td>27.692570</td>\n",
       "      <td>73129082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>27.770908</td>\n",
       "      <td>27.818977</td>\n",
       "      <td>27.520098</td>\n",
       "      <td>27.521841</td>\n",
       "      <td>27.490559</td>\n",
       "      <td>66917888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>27.721344</td>\n",
       "      <td>27.867046</td>\n",
       "      <td>27.557707</td>\n",
       "      <td>27.828691</td>\n",
       "      <td>27.797060</td>\n",
       "      <td>71037271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>28.019974</td>\n",
       "      <td>28.385853</td>\n",
       "      <td>27.924334</td>\n",
       "      <td>28.365179</td>\n",
       "      <td>28.332937</td>\n",
       "      <td>102486711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>28.543015</td>\n",
       "      <td>28.575891</td>\n",
       "      <td>28.226450</td>\n",
       "      <td>28.424210</td>\n",
       "      <td>28.391901</td>\n",
       "      <td>90036218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close     Volume\n",
       "Date                                                                        \n",
       "2014-01-02  27.782366  27.839401  27.603037  27.724083  27.692570   73129082\n",
       "2014-01-03  27.770908  27.818977  27.520098  27.521841  27.490559   66917888\n",
       "2014-01-06  27.721344  27.867046  27.557707  27.828691  27.797060   71037271\n",
       "2014-01-07  28.019974  28.385853  27.924334  28.365179  28.332937  102486711\n",
       "2014-01-08  28.543015  28.575891  28.226450  28.424210  28.391901   90036218"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = '2014-01-01'\n",
    "end = '2024-07-09'\n",
    "\n",
    "stock = 'GOOG'\n",
    "\n",
    "data =  yf.download(stock,start,end)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.reset_index(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>27.782366</td>\n",
       "      <td>27.839401</td>\n",
       "      <td>27.603037</td>\n",
       "      <td>27.724083</td>\n",
       "      <td>27.692570</td>\n",
       "      <td>73129082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>27.770908</td>\n",
       "      <td>27.818977</td>\n",
       "      <td>27.520098</td>\n",
       "      <td>27.521841</td>\n",
       "      <td>27.490559</td>\n",
       "      <td>66917888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>27.721344</td>\n",
       "      <td>27.867046</td>\n",
       "      <td>27.557707</td>\n",
       "      <td>27.828691</td>\n",
       "      <td>27.797060</td>\n",
       "      <td>71037271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>28.019974</td>\n",
       "      <td>28.385853</td>\n",
       "      <td>27.924334</td>\n",
       "      <td>28.365179</td>\n",
       "      <td>28.332937</td>\n",
       "      <td>102486711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>28.543015</td>\n",
       "      <td>28.575891</td>\n",
       "      <td>28.226450</td>\n",
       "      <td>28.424210</td>\n",
       "      <td>28.391901</td>\n",
       "      <td>90036218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2640</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>184.479996</td>\n",
       "      <td>185.339996</td>\n",
       "      <td>182.729996</td>\n",
       "      <td>184.490005</td>\n",
       "      <td>184.490005</td>\n",
       "      <td>11815900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2641</th>\n",
       "      <td>2024-07-02</td>\n",
       "      <td>183.470001</td>\n",
       "      <td>186.949997</td>\n",
       "      <td>183.059998</td>\n",
       "      <td>186.610001</td>\n",
       "      <td>186.610001</td>\n",
       "      <td>12555500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2642</th>\n",
       "      <td>2024-07-03</td>\n",
       "      <td>186.300003</td>\n",
       "      <td>187.619995</td>\n",
       "      <td>185.384995</td>\n",
       "      <td>187.389999</td>\n",
       "      <td>187.389999</td>\n",
       "      <td>7409100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2643</th>\n",
       "      <td>2024-07-05</td>\n",
       "      <td>187.320007</td>\n",
       "      <td>192.259995</td>\n",
       "      <td>187.320007</td>\n",
       "      <td>191.960007</td>\n",
       "      <td>191.960007</td>\n",
       "      <td>14303400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2644</th>\n",
       "      <td>2024-07-08</td>\n",
       "      <td>191.365005</td>\n",
       "      <td>191.679001</td>\n",
       "      <td>189.320007</td>\n",
       "      <td>190.479996</td>\n",
       "      <td>190.479996</td>\n",
       "      <td>12097600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2645 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "0    2014-01-02   27.782366   27.839401   27.603037   27.724083   27.692570   \n",
       "1    2014-01-03   27.770908   27.818977   27.520098   27.521841   27.490559   \n",
       "2    2014-01-06   27.721344   27.867046   27.557707   27.828691   27.797060   \n",
       "3    2014-01-07   28.019974   28.385853   27.924334   28.365179   28.332937   \n",
       "4    2014-01-08   28.543015   28.575891   28.226450   28.424210   28.391901   \n",
       "...         ...         ...         ...         ...         ...         ...   \n",
       "2640 2024-07-01  184.479996  185.339996  182.729996  184.490005  184.490005   \n",
       "2641 2024-07-02  183.470001  186.949997  183.059998  186.610001  186.610001   \n",
       "2642 2024-07-03  186.300003  187.619995  185.384995  187.389999  187.389999   \n",
       "2643 2024-07-05  187.320007  192.259995  187.320007  191.960007  191.960007   \n",
       "2644 2024-07-08  191.365005  191.679001  189.320007  190.479996  190.479996   \n",
       "\n",
       "         Volume  \n",
       "0      73129082  \n",
       "1      66917888  \n",
       "2      71037271  \n",
       "3     102486711  \n",
       "4      90036218  \n",
       "...         ...  \n",
       "2640   11815900  \n",
       "2641   12555500  \n",
       "2642    7409100  \n",
       "2643   14303400  \n",
       "2644   12097600  \n",
       "\n",
       "[2645 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            NaN\n",
       "1            NaN\n",
       "2            NaN\n",
       "3            NaN\n",
       "4            NaN\n",
       "          ...   \n",
       "2640    160.7522\n",
       "2641    161.1515\n",
       "2642    161.5532\n",
       "2643    161.9706\n",
       "2644    162.3881\n",
       "Name: Close, Length: 2645, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#moving avg\n",
    "ma_100_days = data.Close.rolling(100).mean()\n",
    "ma_100_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(ma_100_days,'r',label = 'predicated Price')\n",
    "plt.plot(data.Close,'g',label = 'original Price')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ma_200_days = data.Close.rolling(200).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(ma_100_days)\n",
    "plt.plot(ma_200_days,'r')\n",
    "plt.plot(data.Close,'g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 80% data will be train dataset remaining 20% testdataset\n",
    "data_train =pd.DataFrame(data.Close[0: int(len(data)*0.80)])\n",
    "data_test = pd.DataFrame(data.Close[int(len(data)*0.80): len(data)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2116"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "529"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>112.799004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2117</th>\n",
       "      <td>114.039001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2118</th>\n",
       "      <td>114.137001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2119</th>\n",
       "      <td>117.746002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2120</th>\n",
       "      <td>114.564003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Close\n",
       "2116  112.799004\n",
       "2117  114.039001\n",
       "2118  114.137001\n",
       "2119  117.746002\n",
       "2120  114.564003"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scalling \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_scale = scaler.fit_transform(data_train)\n",
    "#data_train_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y = []\n",
    "\n",
    "for i in range(100,data_train_scale.shape[0]):\n",
    "    x.append(data_train_scale[i-100:i])\n",
    "    y.append(data_train_scale[i,0])\n",
    "\n",
    "#100 days of data is x train and 101 days data is y train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = np.array(x), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from  keras.layers import Dense, Dropout, LSTM\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\DELL\\Downloads\\python\\.venv\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "#4 LSTM layers each layer will have diffrent neurons\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=50, activation='relu' ,return_sequences= True,\n",
    "               input_shape = ((x.shape[1],1))))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units = 60,activation='relu',return_sequences=True))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(LSTM(units = 80, activation='relu',return_sequences=True))\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(LSTM(units=120,activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(units=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compile for time series analysis mean square error\n",
    "model.compile(optimizer=  'adam', loss ='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 224ms/step - loss: 0.0653\n",
      "Epoch 2/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 218ms/step - loss: 0.0059\n",
      "Epoch 3/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 212ms/step - loss: 0.0061\n",
      "Epoch 4/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 203ms/step - loss: 0.0044\n",
      "Epoch 5/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 202ms/step - loss: 0.0052\n",
      "Epoch 6/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 204ms/step - loss: 0.0048\n",
      "Epoch 7/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 200ms/step - loss: 0.0045\n",
      "Epoch 8/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 211ms/step - loss: 0.0044\n",
      "Epoch 9/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 203ms/step - loss: 0.0039\n",
      "Epoch 10/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 203ms/step - loss: 0.0043\n",
      "Epoch 11/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 283ms/step - loss: 0.0040\n",
      "Epoch 12/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 221ms/step - loss: 0.0034\n",
      "Epoch 13/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 223ms/step - loss: 0.0039\n",
      "Epoch 14/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 216ms/step - loss: 0.0041\n",
      "Epoch 15/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 220ms/step - loss: 0.0037\n",
      "Epoch 16/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 215ms/step - loss: 0.0029\n",
      "Epoch 17/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 210ms/step - loss: 0.0030\n",
      "Epoch 18/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 209ms/step - loss: 0.0034\n",
      "Epoch 19/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 195ms/step - loss: 0.0033\n",
      "Epoch 20/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 194ms/step - loss: 0.0034\n",
      "Epoch 21/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 200ms/step - loss: 0.0032\n",
      "Epoch 22/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 199ms/step - loss: 0.0033\n",
      "Epoch 23/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 196ms/step - loss: 0.0030\n",
      "Epoch 24/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 196ms/step - loss: 0.0028\n",
      "Epoch 25/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 194ms/step - loss: 0.0031\n",
      "Epoch 26/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 198ms/step - loss: 0.0030\n",
      "Epoch 27/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 194ms/step - loss: 0.0028\n",
      "Epoch 28/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 194ms/step - loss: 0.0029\n",
      "Epoch 29/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 190ms/step - loss: 0.0031\n",
      "Epoch 30/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 193ms/step - loss: 0.0032\n",
      "Epoch 31/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 193ms/step - loss: 0.0036\n",
      "Epoch 32/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 197ms/step - loss: 0.0028\n",
      "Epoch 33/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 190ms/step - loss: 0.0028\n",
      "Epoch 34/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 194ms/step - loss: 0.0028\n",
      "Epoch 35/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 191ms/step - loss: 0.0030\n",
      "Epoch 36/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 192ms/step - loss: 0.0028\n",
      "Epoch 37/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 198ms/step - loss: 0.0028\n",
      "Epoch 38/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 190ms/step - loss: 0.0030\n",
      "Epoch 39/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 198ms/step - loss: 0.0026\n",
      "Epoch 40/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 217ms/step - loss: 0.0024\n",
      "Epoch 41/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 221ms/step - loss: 0.0026\n",
      "Epoch 42/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 214ms/step - loss: 0.0027\n",
      "Epoch 43/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 214ms/step - loss: 0.0026\n",
      "Epoch 44/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 198ms/step - loss: 0.0030\n",
      "Epoch 45/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 199ms/step - loss: 0.0021\n",
      "Epoch 46/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 199ms/step - loss: 0.0026\n",
      "Epoch 47/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 198ms/step - loss: 0.0026\n",
      "Epoch 48/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 196ms/step - loss: 0.0025\n",
      "Epoch 49/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 192ms/step - loss: 0.0031\n",
      "Epoch 50/50\n",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 194ms/step - loss: 0.0026\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x20047f32cf0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the model\n",
    "model.fit(x,y,epochs = 50,batch_size=32, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">26,640</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">45,120</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">96,480</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">121</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m60\u001b[0m)        │        \u001b[38;5;34m26,640\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m60\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m80\u001b[0m)        │        \u001b[38;5;34m45,120\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m80\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m)            │        \u001b[38;5;34m96,480\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m121\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">536,285</span> (2.05 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m536,285\u001b[0m (2.05 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">178,761</span> (698.29 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m178,761\u001b[0m (698.29 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">357,524</span> (1.36 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m357,524\u001b[0m (1.36 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "pas_100_days = data_train.tail(100)\n",
    "#past 100 days data from train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = pd.concat([pas_100_days,data_test],ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test_scale = scaler.fit_transform(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y = []\n",
    "\n",
    "for i in range(100,data_test_scale.shape[0]):\n",
    "    x.append(data_test_scale[i-100:i])\n",
    "    y.append(data_test_scale[i,0])\n",
    "\n",
    "x,y = np.array(x),np.array(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m17/17\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 159ms/step\n"
     ]
    }
   ],
   "source": [
    "y_predict = model.predict(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00921914])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = 1/scaler.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict= y_predict*scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y*scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(y_predict,'r',label='predicted price')\n",
    "plt.plot(y,'g',label = 'original Price')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('Stock_Predications_Model.keras')"
   ]
  }
 ],
 "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}