model1_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('Google_stock.xlsx')\n",
    "\n",
    "#Sort the data by date\n",
    "data = data.sort_values('Date')\n",
    "\n",
    "data = data.drop('Date', axis=1)\n",
    "\n",
    "# Scaling\n",
    "scaler = MinMaxScaler()\n",
    "data = scaler.fit_transform(data)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "data1 = pd.read_excel('Google_stock.xlsx')\n",
    "\n",
    "#Sort the data by date\n",
    "data1 = data1.sort_values('Date')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "to5=[]\n",
    "just6=[]\n",
    "for i in data:\n",
    "    to5.append(i[0:5])\n",
    "    just6.append(i[5])\n",
    "print(just6)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(to5, just6, test_size=0.1, random_state=32)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "X_train1, X_test1, Y_train1, Y_test1 = train_test_split(data1[['Open', 'High', 'Low', 'Close', 'Adj Close']], data1['Volume'], test_size=0.1, random_state=32)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train=np.array(X_train)\n",
    "X_test=np.array(X_test)\n",
    "Y_train=np.array(Y_train)\n",
    "\n",
    "\n",
    "\n",
    "X_train1=X_train1.to_numpy()\n",
    "X_test1=X_test1.to_numpy()\n",
    "Y_train1=Y_train1.to_numpy()\n",
    "Y_test1=Y_test1.to_numpy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape the input data to be 3D\n",
    "\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.LSTM(units=70, return_sequences=True, input_shape=(X_train.shape[1], 1)),\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.LSTM(units=70, return_sequences=True),\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.LSTM(units=70),\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.Dense(units=6)\n",
    "])\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Compile\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "39/39 [==============================] - 6s 12ms/step - loss: 0.0068\n",
      "Epoch 2/100\n",
      "39/39 [==============================] - 0s 12ms/step - loss: 0.0045\n",
      "Epoch 3/100\n",
      "39/39 [==============================] - 0s 13ms/step - loss: 0.0042\n",
      "Epoch 4/100\n",
      "39/39 [==============================] - 0s 13ms/step - loss: 0.0041\n",
      "Epoch 5/100\n",
      "39/39 [==============================] - 1s 14ms/step - loss: 0.0041\n",
      "Epoch 6/100\n",
      "39/39 [==============================] - 0s 13ms/step - loss: 0.0040\n",
      "Epoch 7/100\n",
      "39/39 [==============================] - 0s 12ms/step - loss: 0.0040\n",
      "Epoch 8/100\n",
      "39/39 [==============================] - 0s 12ms/step - loss: 0.0039\n",
      "Epoch 9/100\n",
      "39/39 [==============================] - 0s 12ms/step - loss: 0.0037\n",
      "Epoch 10/100\n",
      "39/39 [==============================] - 0s 12ms/step - loss: 0.0037\n",
      "Epoch 11/100\n",
      "39/39 [==============================] - 1s 15ms/step - loss: 0.0036\n",
      "Epoch 12/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0038\n",
      "Epoch 13/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0037\n",
      "Epoch 14/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0037\n",
      "Epoch 15/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0037\n",
      "Epoch 16/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 17/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0037\n",
      "Epoch 18/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0037\n",
      "Epoch 19/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 20/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 21/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 22/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 23/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 24/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 25/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 26/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0037\n",
      "Epoch 27/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0037\n",
      "Epoch 28/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0037\n",
      "Epoch 29/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 30/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 31/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 32/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 33/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 34/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 35/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 36/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 37/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0037\n",
      "Epoch 38/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 39/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 40/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 41/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 42/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 43/100\n",
      "39/39 [==============================] - 1s 23ms/step - loss: 0.0036\n",
      "Epoch 44/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 45/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 46/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 47/100\n",
      "39/39 [==============================] - 1s 23ms/step - loss: 0.0036\n",
      "Epoch 48/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 49/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0037\n",
      "Epoch 50/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 51/100\n",
      "39/39 [==============================] - 1s 23ms/step - loss: 0.0036\n",
      "Epoch 52/100\n",
      "39/39 [==============================] - 1s 23ms/step - loss: 0.0036\n",
      "Epoch 53/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 54/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 55/100\n",
      "39/39 [==============================] - 1s 23ms/step - loss: 0.0036\n",
      "Epoch 56/100\n",
      "39/39 [==============================] - 1s 23ms/step - loss: 0.0036\n",
      "Epoch 57/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 58/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 59/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 60/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 61/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 62/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0037\n",
      "Epoch 63/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 64/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 65/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 66/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 67/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 68/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 69/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 70/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 71/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0035\n",
      "Epoch 72/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 73/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 74/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 75/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 76/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 77/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 78/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 79/100\n",
      "39/39 [==============================] - 1s 23ms/step - loss: 0.0036\n",
      "Epoch 80/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 81/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 82/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 83/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 84/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 85/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0035\n",
      "Epoch 86/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0037\n",
      "Epoch 87/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0036\n",
      "Epoch 88/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0035\n",
      "Epoch 89/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0037\n",
      "Epoch 90/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 91/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 92/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 93/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 94/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0035\n",
      "Epoch 95/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 96/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0036\n",
      "Epoch 97/100\n",
      "39/39 [==============================] - 1s 26ms/step - loss: 0.0036\n",
      "Epoch 98/100\n",
      "39/39 [==============================] - 1s 23ms/step - loss: 0.0036\n",
      "Epoch 99/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0035\n",
      "Epoch 100/100\n",
      "39/39 [==============================] - 1s 27ms/step - loss: 0.0036\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c86138d510>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train\n",
    "model.fit(X_train, Y_train, epochs=100, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 2s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Make predictions\n",
    "predictions = model.predict(X_test)\n",
    "predictions = scaler.inverse_transform(predictions)\n",
    "predictions = predictions[:, -1]\n",
    "\n",
    "#X_train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(139,)\n",
      "(139,)\n"
     ]
    }
   ],
   "source": [
    "print(predictions.shape)\n",
    "print(Y_test1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(data1['Date'][len(data1)-len(Y_test1):], Y_test1, color='blue', label='Actual Google Stock Price')\n",
    "plt.plot(data1['Date'][len(data1)-len(predictions):], predictions, color='red', label='Predicted Google Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.10.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}