Bharat intern - Task 1_Stock Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0107c846",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e0c3d79",
   "metadata": {},
   "source": [
    "## Step-1 Data Collection  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75e19c0d",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>325.25</td>\n",
       "      <td>332.83</td>\n",
       "      <td>324.97</td>\n",
       "      <td>663.59</td>\n",
       "      <td>7,380,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>331.27</td>\n",
       "      <td>333.87</td>\n",
       "      <td>329.08</td>\n",
       "      <td>666.45</td>\n",
       "      <td>5,749,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>329.83</td>\n",
       "      <td>330.75</td>\n",
       "      <td>326.89</td>\n",
       "      <td>657.21</td>\n",
       "      <td>6,590,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2012</td>\n",
       "      <td>328.34</td>\n",
       "      <td>328.77</td>\n",
       "      <td>323.68</td>\n",
       "      <td>648.24</td>\n",
       "      <td>5,405,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/9/2012</td>\n",
       "      <td>322.04</td>\n",
       "      <td>322.29</td>\n",
       "      <td>309.46</td>\n",
       "      <td>620.76</td>\n",
       "      <td>11,688,800</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>12/23/2016</td>\n",
       "      <td>790.90</td>\n",
       "      <td>792.74</td>\n",
       "      <td>787.28</td>\n",
       "      <td>789.91</td>\n",
       "      <td>623,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>12/27/2016</td>\n",
       "      <td>790.68</td>\n",
       "      <td>797.86</td>\n",
       "      <td>787.66</td>\n",
       "      <td>791.55</td>\n",
       "      <td>789,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>12/28/2016</td>\n",
       "      <td>793.70</td>\n",
       "      <td>794.23</td>\n",
       "      <td>783.20</td>\n",
       "      <td>785.05</td>\n",
       "      <td>1,153,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>12/29/2016</td>\n",
       "      <td>783.33</td>\n",
       "      <td>785.93</td>\n",
       "      <td>778.92</td>\n",
       "      <td>782.79</td>\n",
       "      <td>744,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>12/30/2016</td>\n",
       "      <td>782.75</td>\n",
       "      <td>782.78</td>\n",
       "      <td>770.41</td>\n",
       "      <td>771.82</td>\n",
       "      <td>1,770,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1258 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Open    High     Low   Close      Volume\n",
       "0       1/3/2012  325.25  332.83  324.97  663.59   7,380,500\n",
       "1       1/4/2012  331.27  333.87  329.08  666.45   5,749,400\n",
       "2       1/5/2012  329.83  330.75  326.89  657.21   6,590,300\n",
       "3       1/6/2012  328.34  328.77  323.68  648.24   5,405,900\n",
       "4       1/9/2012  322.04  322.29  309.46  620.76  11,688,800\n",
       "...          ...     ...     ...     ...     ...         ...\n",
       "1253  12/23/2016  790.90  792.74  787.28  789.91     623,400\n",
       "1254  12/27/2016  790.68  797.86  787.66  791.55     789,100\n",
       "1255  12/28/2016  793.70  794.23  783.20  785.05   1,153,800\n",
       "1256  12/29/2016  783.33  785.93  778.92  782.79     744,300\n",
       "1257  12/30/2016  782.75  782.78  770.41  771.82   1,770,000\n",
       "\n",
       "[1258 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_train = pd.read_csv(\"Google_Stock_Price_Train.csv\")\n",
    "ds_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67c5bf3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1258 entries, 0 to 1257\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Date    1258 non-null   object \n",
      " 1   Open    1258 non-null   float64\n",
      " 2   High    1258 non-null   float64\n",
      " 3   Low     1258 non-null   float64\n",
      " 4   Close   1258 non-null   object \n",
      " 5   Volume  1258 non-null   object \n",
      "dtypes: float64(3), object(3)\n",
      "memory usage: 59.1+ KB\n"
     ]
    }
   ],
   "source": [
    "ds_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c64c921",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>325.25</td>\n",
       "      <td>332.83</td>\n",
       "      <td>324.97</td>\n",
       "      <td>663.59</td>\n",
       "      <td>7,380,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>331.27</td>\n",
       "      <td>333.87</td>\n",
       "      <td>329.08</td>\n",
       "      <td>666.45</td>\n",
       "      <td>5,749,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>329.83</td>\n",
       "      <td>330.75</td>\n",
       "      <td>326.89</td>\n",
       "      <td>657.21</td>\n",
       "      <td>6,590,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2012</td>\n",
       "      <td>328.34</td>\n",
       "      <td>328.77</td>\n",
       "      <td>323.68</td>\n",
       "      <td>648.24</td>\n",
       "      <td>5,405,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/9/2012</td>\n",
       "      <td>322.04</td>\n",
       "      <td>322.29</td>\n",
       "      <td>309.46</td>\n",
       "      <td>620.76</td>\n",
       "      <td>11,688,800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date    Open    High     Low   Close      Volume\n",
       "0  1/3/2012  325.25  332.83  324.97  663.59   7,380,500\n",
       "1  1/4/2012  331.27  333.87  329.08  666.45   5,749,400\n",
       "2  1/5/2012  329.83  330.75  326.89  657.21   6,590,300\n",
       "3  1/6/2012  328.34  328.77  323.68  648.24   5,405,900\n",
       "4  1/9/2012  322.04  322.29  309.46  620.76  11,688,800"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0447719",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>325.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>331.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>329.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>328.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>322.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>790.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>790.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>793.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>783.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>782.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1258 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Open\n",
       "0     325.25\n",
       "1     331.27\n",
       "2     329.83\n",
       "3     328.34\n",
       "4     322.04\n",
       "...      ...\n",
       "1253  790.90\n",
       "1254  790.68\n",
       "1255  793.70\n",
       "1256  783.33\n",
       "1257  782.75\n",
       "\n",
       "[1258 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set = ds_train.iloc[:,1:2]\n",
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3e2d3ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1258, 1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440be69b",
   "metadata": {},
   "source": [
    "## Step-2 Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e47f76ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08581368],\n",
       "       [0.09701243],\n",
       "       [0.09433366],\n",
       "       ...,\n",
       "       [0.95725128],\n",
       "       [0.93796041],\n",
       "       [0.93688146]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "MMscaler = MinMaxScaler(feature_range = (0,1))\n",
    "scaled_train_set = MMscaler.fit_transform(training_set)\n",
    "scaled_train_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ba690ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = []\n",
    "y_train = []\n",
    "for i in range(30,1258):\n",
    "    X_train.append(scaled_train_set[i-30:i,0])\n",
    "    y_train.append(scaled_train_set[i,0])\n",
    "X_train = np.array(X_train)\n",
    "y_train = np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f8585ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.08581368 0.09701243 0.09433366 ... 0.0440695  0.04648783 0.04745517]\n",
      " [0.09701243 0.09433366 0.09156187 ... 0.04648783 0.04745517 0.04873875]\n",
      " [0.09433366 0.09156187 0.07984225 ... 0.04745517 0.04873875 0.03936305]\n",
      " ...\n",
      " [0.88637547 0.87032145 0.88563137 ... 0.95475854 0.95204256 0.95163331]\n",
      " [0.87032145 0.88563137 0.90743359 ... 0.95204256 0.95163331 0.95725128]\n",
      " [0.88563137 0.90743359 0.91571173 ... 0.95163331 0.95725128 0.93796041]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1228, 30)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a896eba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04873875, 0.03936305, 0.04137213, ..., 0.95725128, 0.93796041,\n",
       "       0.93688146])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "34c72dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1228, 30, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train =  np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33938146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.08581368],\n",
       "        [0.09701243],\n",
       "        [0.09433366],\n",
       "        ...,\n",
       "        [0.0440695 ],\n",
       "        [0.04648783],\n",
       "        [0.04745517]],\n",
       "\n",
       "       [[0.09701243],\n",
       "        [0.09433366],\n",
       "        [0.09156187],\n",
       "        ...,\n",
       "        [0.04648783],\n",
       "        [0.04745517],\n",
       "        [0.04873875]],\n",
       "\n",
       "       [[0.09433366],\n",
       "        [0.09156187],\n",
       "        [0.07984225],\n",
       "        ...,\n",
       "        [0.04745517],\n",
       "        [0.04873875],\n",
       "        [0.03936305]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.88637547],\n",
       "        [0.87032145],\n",
       "        [0.88563137],\n",
       "        ...,\n",
       "        [0.95475854],\n",
       "        [0.95204256],\n",
       "        [0.95163331]],\n",
       "\n",
       "       [[0.87032145],\n",
       "        [0.88563137],\n",
       "        [0.90743359],\n",
       "        ...,\n",
       "        [0.95204256],\n",
       "        [0.95163331],\n",
       "        [0.95725128]],\n",
       "\n",
       "       [[0.88563137],\n",
       "        [0.90743359],\n",
       "        [0.91571173],\n",
       "        ...,\n",
       "        [0.95163331],\n",
       "        [0.95725128],\n",
       "        [0.93796041]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5db3bd4d",
   "metadata": {},
   "source": [
    "## step-3 Apllying LSTM method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "64f62c15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras in c:\\users\\chatt\\anaconda3\\lib\\site-packages (2.14.0)\n",
      "Requirement already satisfied: tensorflow in c:\\users\\chatt\\anaconda3\\lib\\site-packages (2.14.0)\n",
      "Requirement already satisfied: tensorflow-intel==2.14.0 in c:\\users\\chatt\\anaconda3\\lib\\site-packages (from tensorflow) (2.14.0)\n",
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     ]
    }
   ],
   "source": [
    "!pip install keras\n",
    "!pip install tensorflow\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dfabc60e",
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = Sequential()\n",
    "regressor.add(LSTM(units = 50, return_sequences= True, input_shape = (X_train.shape[1],1)))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units = 50, return_sequences= True))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units = 50, return_sequences= True))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units = 50))\n",
    "regressor.add(Dropout(0.2))\n",
    "\n",
    "regressor.add(Dense(units=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "474b521a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "20/20 [==============================] - 11s 89ms/step - loss: 0.0761\n",
      "Epoch 2/100\n",
      "20/20 [==============================] - 2s 84ms/step - loss: 0.0107\n",
      "Epoch 3/100\n",
      "20/20 [==============================] - 2s 86ms/step - loss: 0.0058\n",
      "Epoch 4/100\n",
      "20/20 [==============================] - 2s 84ms/step - loss: 0.0056\n",
      "Epoch 5/100\n",
      "20/20 [==============================] - 2s 86ms/step - loss: 0.0049\n",
      "Epoch 6/100\n",
      "20/20 [==============================] - 2s 84ms/step - loss: 0.0049\n",
      "Epoch 7/100\n",
      "20/20 [==============================] - 2s 82ms/step - loss: 0.0052\n",
      "Epoch 8/100\n",
      "20/20 [==============================] - 2s 81ms/step - loss: 0.0048\n",
      "Epoch 9/100\n",
      "20/20 [==============================] - 2s 83ms/step - loss: 0.0050\n",
      "Epoch 10/100\n",
      "20/20 [==============================] - 2s 82ms/step - loss: 0.0044\n",
      "Epoch 11/100\n",
      "20/20 [==============================] - 2s 91ms/step - loss: 0.0043\n",
      "Epoch 12/100\n",
      "20/20 [==============================] - 2s 85ms/step - loss: 0.0048\n",
      "Epoch 13/100\n",
      "20/20 [==============================] - 2s 87ms/step - loss: 0.0044\n",
      "Epoch 14/100\n",
      "20/20 [==============================] - 2s 87ms/step - loss: 0.0043\n",
      "Epoch 15/100\n",
      "20/20 [==============================] - 2s 93ms/step - loss: 0.0038\n",
      "Epoch 16/100\n",
      "20/20 [==============================] - 2s 93ms/step - loss: 0.0045\n",
      "Epoch 17/100\n",
      "20/20 [==============================] - 2s 92ms/step - loss: 0.0038\n",
      "Epoch 18/100\n",
      "20/20 [==============================] - 2s 89ms/step - loss: 0.0040\n",
      "Epoch 19/100\n",
      "20/20 [==============================] - 2s 96ms/step - loss: 0.0041\n",
      "Epoch 20/100\n",
      "20/20 [==============================] - 2s 87ms/step - loss: 0.0040\n",
      "Epoch 21/100\n",
      "20/20 [==============================] - 2s 90ms/step - loss: 0.0035\n",
      "Epoch 22/100\n",
      "20/20 [==============================] - 2s 103ms/step - loss: 0.0038\n",
      "Epoch 23/100\n",
      "20/20 [==============================] - 2s 88ms/step - loss: 0.0038\n",
      "Epoch 24/100\n",
      "20/20 [==============================] - 2s 87ms/step - loss: 0.0038\n",
      "Epoch 25/100\n",
      "20/20 [==============================] - 2s 87ms/step - loss: 0.0037\n",
      "Epoch 26/100\n",
      "20/20 [==============================] - 2s 86ms/step - loss: 0.0036\n",
      "Epoch 27/100\n",
      "20/20 [==============================] - 2s 81ms/step - loss: 0.0038\n",
      "Epoch 28/100\n",
      "20/20 [==============================] - 2s 83ms/step - loss: 0.0040\n",
      "Epoch 29/100\n",
      "20/20 [==============================] - 2s 101ms/step - loss: 0.0037\n",
      "Epoch 30/100\n",
      "20/20 [==============================] - 2s 92ms/step - loss: 0.0034\n",
      "Epoch 31/100\n",
      "20/20 [==============================] - 2s 90ms/step - loss: 0.0036\n",
      "Epoch 32/100\n",
      "20/20 [==============================] - 2s 97ms/step - loss: 0.0041\n",
      "Epoch 33/100\n",
      "20/20 [==============================] - 2s 91ms/step - loss: 0.0040\n",
      "Epoch 34/100\n",
      "20/20 [==============================] - 2s 92ms/step - loss: 0.0032\n",
      "Epoch 35/100\n",
      "20/20 [==============================] - 2s 83ms/step - loss: 0.0039\n",
      "Epoch 36/100\n",
      "20/20 [==============================] - 2s 81ms/step - loss: 0.0035\n",
      "Epoch 37/100\n",
      "20/20 [==============================] - 2s 84ms/step - loss: 0.0031\n",
      "Epoch 38/100\n",
      "20/20 [==============================] - 2s 85ms/step - loss: 0.0033\n",
      "Epoch 39/100\n",
      "20/20 [==============================] - 2s 84ms/step - loss: 0.0032\n",
      "Epoch 40/100\n",
      "20/20 [==============================] - 2s 85ms/step - loss: 0.0032\n",
      "Epoch 41/100\n",
      "20/20 [==============================] - 2s 85ms/step - loss: 0.0032\n",
      "Epoch 42/100\n",
      "20/20 [==============================] - 2s 84ms/step - loss: 0.0031\n",
      "Epoch 43/100\n",
      "20/20 [==============================] - 2s 83ms/step - loss: 0.0033\n",
      "Epoch 44/100\n",
      "20/20 [==============================] - 2s 80ms/step - loss: 0.0032\n",
      "Epoch 45/100\n",
      "20/20 [==============================] - 2s 80ms/step - loss: 0.0032\n",
      "Epoch 46/100\n",
      "20/20 [==============================] - 2s 80ms/step - loss: 0.0031\n",
      "Epoch 47/100\n",
      "20/20 [==============================] - 2s 87ms/step - loss: 0.0030\n",
      "Epoch 48/100\n",
      "20/20 [==============================] - 2s 86ms/step - loss: 0.0037\n",
      "Epoch 49/100\n",
      "20/20 [==============================] - 2s 87ms/step - loss: 0.0030\n",
      "Epoch 50/100\n",
      "20/20 [==============================] - 2s 86ms/step - loss: 0.0032\n",
      "Epoch 51/100\n",
      "20/20 [==============================] - 2s 85ms/step - loss: 0.0030\n",
      "Epoch 52/100\n",
      "20/20 [==============================] - 2s 87ms/step - loss: 0.0029\n",
      "Epoch 53/100\n",
      "20/20 [==============================] - 2s 82ms/step - loss: 0.0030\n",
      "Epoch 54/100\n",
      "20/20 [==============================] - 2s 87ms/step - loss: 0.0032\n",
      "Epoch 55/100\n",
      "20/20 [==============================] - 2s 90ms/step - loss: 0.0030\n",
      "Epoch 56/100\n",
      "20/20 [==============================] - 2s 87ms/step - loss: 0.0029\n",
      "Epoch 57/100\n",
      "20/20 [==============================] - 2s 90ms/step - loss: 0.0033\n",
      "Epoch 58/100\n",
      "20/20 [==============================] - 2s 99ms/step - loss: 0.0033\n",
      "Epoch 59/100\n",
      "20/20 [==============================] - 2s 110ms/step - loss: 0.0031\n",
      "Epoch 60/100\n",
      "20/20 [==============================] - 2s 94ms/step - loss: 0.0029\n",
      "Epoch 61/100\n",
      "20/20 [==============================] - 2s 93ms/step - loss: 0.0030\n",
      "Epoch 62/100\n",
      "20/20 [==============================] - 2s 88ms/step - loss: 0.0028\n",
      "Epoch 63/100\n",
      "20/20 [==============================] - 2s 82ms/step - loss: 0.0028\n",
      "Epoch 64/100\n",
      "20/20 [==============================] - 6s 291ms/step - loss: 0.0026\n",
      "Epoch 65/100\n",
      "20/20 [==============================] - 4s 177ms/step - loss: 0.0029\n",
      "Epoch 66/100\n",
      "20/20 [==============================] - 3s 139ms/step - loss: 0.0026\n",
      "Epoch 67/100\n",
      "20/20 [==============================] - 2s 123ms/step - loss: 0.0028\n",
      "Epoch 68/100\n",
      "20/20 [==============================] - 2s 120ms/step - loss: 0.0027\n",
      "Epoch 69/100\n",
      "20/20 [==============================] - 3s 129ms/step - loss: 0.0026\n",
      "Epoch 70/100\n",
      "20/20 [==============================] - 3s 125ms/step - loss: 0.0023\n",
      "Epoch 71/100\n",
      "20/20 [==============================] - 2s 117ms/step - loss: 0.0028\n",
      "Epoch 72/100\n",
      "20/20 [==============================] - 2s 109ms/step - loss: 0.0024\n",
      "Epoch 73/100\n",
      "20/20 [==============================] - 2s 111ms/step - loss: 0.0028\n",
      "Epoch 74/100\n",
      "20/20 [==============================] - 2s 111ms/step - loss: 0.0026\n",
      "Epoch 75/100\n",
      "20/20 [==============================] - 2s 108ms/step - loss: 0.0029\n",
      "Epoch 76/100\n",
      "20/20 [==============================] - 2s 112ms/step - loss: 0.0029\n",
      "Epoch 77/100\n",
      "20/20 [==============================] - 2s 111ms/step - loss: 0.0025\n",
      "Epoch 78/100\n",
      "20/20 [==============================] - 2s 112ms/step - loss: 0.0025\n",
      "Epoch 79/100\n",
      "20/20 [==============================] - 2s 111ms/step - loss: 0.0024\n",
      "Epoch 80/100\n",
      "20/20 [==============================] - 2s 111ms/step - loss: 0.0024\n",
      "Epoch 81/100\n",
      "20/20 [==============================] - 2s 111ms/step - loss: 0.0021\n",
      "Epoch 82/100\n",
      "20/20 [==============================] - 2s 108ms/step - loss: 0.0024\n",
      "Epoch 83/100\n",
      "20/20 [==============================] - 2s 113ms/step - loss: 0.0024\n",
      "Epoch 84/100\n",
      "20/20 [==============================] - 2s 123ms/step - loss: 0.0023\n",
      "Epoch 85/100\n",
      "20/20 [==============================] - 2s 120ms/step - loss: 0.0026\n",
      "Epoch 86/100\n",
      "20/20 [==============================] - 2s 119ms/step - loss: 0.0022\n",
      "Epoch 87/100\n",
      "20/20 [==============================] - 2s 117ms/step - loss: 0.0024\n",
      "Epoch 88/100\n",
      "20/20 [==============================] - 2s 119ms/step - loss: 0.0023\n",
      "Epoch 89/100\n",
      "20/20 [==============================] - 2s 113ms/step - loss: 0.0022\n",
      "Epoch 90/100\n",
      "20/20 [==============================] - 2s 118ms/step - loss: 0.0023\n",
      "Epoch 91/100\n",
      "20/20 [==============================] - 2s 114ms/step - loss: 0.0027\n",
      "Epoch 92/100\n",
      "20/20 [==============================] - 2s 114ms/step - loss: 0.0025\n",
      "Epoch 93/100\n",
      "20/20 [==============================] - 2s 118ms/step - loss: 0.0021\n",
      "Epoch 94/100\n",
      "20/20 [==============================] - 2s 119ms/step - loss: 0.0023\n",
      "Epoch 95/100\n",
      "20/20 [==============================] - 2s 121ms/step - loss: 0.0021\n",
      "Epoch 96/100\n",
      "20/20 [==============================] - 2s 116ms/step - loss: 0.0020\n",
      "Epoch 97/100\n",
      "20/20 [==============================] - 2s 122ms/step - loss: 0.0021\n",
      "Epoch 98/100\n",
      "20/20 [==============================] - 2s 111ms/step - loss: 0.0022\n",
      "Epoch 99/100\n",
      "20/20 [==============================] - 2s 115ms/step - loss: 0.0023\n",
      "Epoch 100/100\n",
      "20/20 [==============================] - 2s 111ms/step - loss: 0.0021\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x196513ce4d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
    "regressor.fit(X_train,y_train, epochs=100, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dfc37ef",
   "metadata": {},
   "source": [
    "## Step-4 Preparing test data  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e452e9f",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>325.25</td>\n",
       "      <td>332.83</td>\n",
       "      <td>324.97</td>\n",
       "      <td>663.59</td>\n",
       "      <td>7,380,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>331.27</td>\n",
       "      <td>333.87</td>\n",
       "      <td>329.08</td>\n",
       "      <td>666.45</td>\n",
       "      <td>5,749,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>329.83</td>\n",
       "      <td>330.75</td>\n",
       "      <td>326.89</td>\n",
       "      <td>657.21</td>\n",
       "      <td>6,590,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2012</td>\n",
       "      <td>328.34</td>\n",
       "      <td>328.77</td>\n",
       "      <td>323.68</td>\n",
       "      <td>648.24</td>\n",
       "      <td>5,405,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/9/2012</td>\n",
       "      <td>322.04</td>\n",
       "      <td>322.29</td>\n",
       "      <td>309.46</td>\n",
       "      <td>620.76</td>\n",
       "      <td>11,688,800</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>12/23/2016</td>\n",
       "      <td>790.90</td>\n",
       "      <td>792.74</td>\n",
       "      <td>787.28</td>\n",
       "      <td>789.91</td>\n",
       "      <td>623,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>12/27/2016</td>\n",
       "      <td>790.68</td>\n",
       "      <td>797.86</td>\n",
       "      <td>787.66</td>\n",
       "      <td>791.55</td>\n",
       "      <td>789,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>12/28/2016</td>\n",
       "      <td>793.70</td>\n",
       "      <td>794.23</td>\n",
       "      <td>783.20</td>\n",
       "      <td>785.05</td>\n",
       "      <td>1,153,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>12/29/2016</td>\n",
       "      <td>783.33</td>\n",
       "      <td>785.93</td>\n",
       "      <td>778.92</td>\n",
       "      <td>782.79</td>\n",
       "      <td>744,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>12/30/2016</td>\n",
       "      <td>782.75</td>\n",
       "      <td>782.78</td>\n",
       "      <td>770.41</td>\n",
       "      <td>771.82</td>\n",
       "      <td>1,770,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1258 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Open    High     Low   Close      Volume\n",
       "0       1/3/2012  325.25  332.83  324.97  663.59   7,380,500\n",
       "1       1/4/2012  331.27  333.87  329.08  666.45   5,749,400\n",
       "2       1/5/2012  329.83  330.75  326.89  657.21   6,590,300\n",
       "3       1/6/2012  328.34  328.77  323.68  648.24   5,405,900\n",
       "4       1/9/2012  322.04  322.29  309.46  620.76  11,688,800\n",
       "...          ...     ...     ...     ...     ...         ...\n",
       "1253  12/23/2016  790.90  792.74  787.28  789.91     623,400\n",
       "1254  12/27/2016  790.68  797.86  787.66  791.55     789,100\n",
       "1255  12/28/2016  793.70  794.23  783.20  785.05   1,153,800\n",
       "1256  12/29/2016  783.33  785.93  778.92  782.79     744,300\n",
       "1257  12/30/2016  782.75  782.78  770.41  771.82   1,770,000\n",
       "\n",
       "[1258 rows x 6 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_test = pd.read_csv(\"Google_Stock_Price_Train.csv\")\n",
    "actual_stock_price = ds_test.iloc[:500,1:2].values\n",
    "ds_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "62d56e3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chatt\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "dataset_total = pd.concat((ds_train['Open'],ds_test['Open']),axis=0)\n",
    "inputs = dataset_total[len(dataset_total)-len(ds_test)-30:].values\n",
    "\n",
    "inputs = inputs.reshape(-1,1)\n",
    "inputs = MMscaler.transform(inputs)\n",
    "\n",
    "x_test = []\n",
    "for i in range(30,500):\n",
    "    x_test.append(inputs[i-30:i,0])\n",
    "x_test = np.array(x_test)\n",
    "x_test = np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "00ca38a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 3s 29ms/step\n"
     ]
    }
   ],
   "source": [
    "pred_stock_price = regressor.predict(x_test)\n",
    "pred_stock_price = MMscaler.inverse_transform(pred_stock_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d3d615e",
   "metadata": {},
   "source": [
    "## Step-5 Visualizing the data (Actual Value vs Predicted Value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "261c41ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(actual_stock_price,color='red',label='Actual Stock Price')\n",
    "plt.plot(pred_stock_price,color='blue',label='Predicted Stock Price')\n",
    "plt.title('Stock Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80c97b78",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}