Stock_price_prediction_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d89c9667",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from keras.models import Sequential\n",
    "from sklearn import preprocessing\n",
    "from keras.layers import Conv1D,Flatten,MaxPooling1D,Bidirectional,LSTM,Dropout,TimeDistributed,MaxPool2D\n",
    "from keras.layers import Dense,GlobalAveragePooling2D\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import pprint\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d33f671f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>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>1996-01-01</td>\n",
       "      <td>15.859429</td>\n",
       "      <td>15.944529</td>\n",
       "      <td>15.754989</td>\n",
       "      <td>15.917452</td>\n",
       "      <td>10.509537</td>\n",
       "      <td>48051995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1996-01-02</td>\n",
       "      <td>15.878770</td>\n",
       "      <td>15.956133</td>\n",
       "      <td>15.677626</td>\n",
       "      <td>15.793671</td>\n",
       "      <td>10.427811</td>\n",
       "      <td>77875009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1996-01-03</td>\n",
       "      <td>16.052837</td>\n",
       "      <td>16.783918</td>\n",
       "      <td>15.878770</td>\n",
       "      <td>15.913584</td>\n",
       "      <td>10.506985</td>\n",
       "      <td>96602936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1996-01-04</td>\n",
       "      <td>15.762726</td>\n",
       "      <td>15.813012</td>\n",
       "      <td>15.553845</td>\n",
       "      <td>15.766594</td>\n",
       "      <td>10.409932</td>\n",
       "      <td>100099436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1996-01-05</td>\n",
       "      <td>15.704703</td>\n",
       "      <td>15.704703</td>\n",
       "      <td>15.522900</td>\n",
       "      <td>15.658285</td>\n",
       "      <td>10.338422</td>\n",
       "      <td>76935930.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Open       High        Low      Close  Adj Close  \\\n",
       "0  1996-01-01  15.859429  15.944529  15.754989  15.917452  10.509537   \n",
       "1  1996-01-02  15.878770  15.956133  15.677626  15.793671  10.427811   \n",
       "2  1996-01-03  16.052837  16.783918  15.878770  15.913584  10.506985   \n",
       "3  1996-01-04  15.762726  15.813012  15.553845  15.766594  10.409932   \n",
       "4  1996-01-05  15.704703  15.704703  15.522900  15.658285  10.338422   \n",
       "\n",
       "        Volume  \n",
       "0   48051995.0  \n",
       "1   77875009.0  \n",
       "2   96602936.0  \n",
       "3  100099436.0  \n",
       "4   76935930.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filename = 'AAPL'\n",
    "stock = pd.read_csv(r\"C:\\Users\\karth\\Downloads\\Project_Finalyr\\RELIANCE.NS.csv\")\n",
    "stock.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "902dff21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6522, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a26e95d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6504.000000</td>\n",
       "      <td>6504.000000</td>\n",
       "      <td>6504.000000</td>\n",
       "      <td>6504.000000</td>\n",
       "      <td>6504.000000</td>\n",
       "      <td>6.504000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>458.859794</td>\n",
       "      <td>464.607796</td>\n",
       "      <td>452.564438</td>\n",
       "      <td>458.325896</td>\n",
       "      <td>439.181946</td>\n",
       "      <td>3.008009e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>533.712776</td>\n",
       "      <td>539.689633</td>\n",
       "      <td>526.903597</td>\n",
       "      <td>532.939318</td>\n",
       "      <td>532.885896</td>\n",
       "      <td>4.932459e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.836550</td>\n",
       "      <td>11.890704</td>\n",
       "      <td>11.763055</td>\n",
       "      <td>11.890704</td>\n",
       "      <td>7.850866</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.231842</td>\n",
       "      <td>51.059628</td>\n",
       "      <td>49.208717</td>\n",
       "      <td>50.258919</td>\n",
       "      <td>39.579466</td>\n",
       "      <td>7.011007e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>391.959610</td>\n",
       "      <td>396.330673</td>\n",
       "      <td>386.461716</td>\n",
       "      <td>391.773880</td>\n",
       "      <td>364.196426</td>\n",
       "      <td>1.312527e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>525.023315</td>\n",
       "      <td>530.001144</td>\n",
       "      <td>518.807236</td>\n",
       "      <td>523.704544</td>\n",
       "      <td>489.048843</td>\n",
       "      <td>3.230377e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2742.750000</td>\n",
       "      <td>2751.350098</td>\n",
       "      <td>2708.000000</td>\n",
       "      <td>2731.850098</td>\n",
       "      <td>2731.850098</td>\n",
       "      <td>6.686620e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close    Adj Close  \\\n",
       "count  6504.000000  6504.000000  6504.000000  6504.000000  6504.000000   \n",
       "mean    458.859794   464.607796   452.564438   458.325896   439.181946   \n",
       "std     533.712776   539.689633   526.903597   532.939318   532.885896   \n",
       "min      11.836550    11.890704    11.763055    11.890704     7.850866   \n",
       "25%      50.231842    51.059628    49.208717    50.258919    39.579466   \n",
       "50%     391.959610   396.330673   386.461716   391.773880   364.196426   \n",
       "75%     525.023315   530.001144   518.807236   523.704544   489.048843   \n",
       "max    2742.750000  2751.350098  2708.000000  2731.850098  2731.850098   \n",
       "\n",
       "             Volume  \n",
       "count  6.504000e+03  \n",
       "mean   3.008009e+07  \n",
       "std    4.932459e+07  \n",
       "min    0.000000e+00  \n",
       "25%    7.011007e+06  \n",
       "50%    1.312527e+07  \n",
       "75%    3.230377e+07  \n",
       "max    6.686620e+08  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e95970f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(\"checking if any null values are present\\n\", df.isna().sum())\n",
    "stock.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c20353a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock.fillna(value=None, method=None, axis=0, inplace=False, limit=None, downcast=None)\n",
    "stock = stock.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67dd1676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bff45c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock['Adj Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5f6b2d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=stock.drop(['Adj Close'],axis=1)\n",
    "X=X.drop(['Close'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0c48209e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Correlation with Adj Close'}>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X.corrwith(stock['Adj Close']).plot.bar(\n",
    "        figsize = (20, 10), title = \"Correlation with Adj Close\", fontsize = 20,\n",
    "        rot = 90, grid = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d806279e",
   "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>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>15.859429</td>\n",
       "      <td>15.944529</td>\n",
       "      <td>15.754989</td>\n",
       "      <td>15.917452</td>\n",
       "      <td>10.509537</td>\n",
       "      <td>48051995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1996-01-02</td>\n",
       "      <td>15.878770</td>\n",
       "      <td>15.956133</td>\n",
       "      <td>15.677626</td>\n",
       "      <td>15.793671</td>\n",
       "      <td>10.427811</td>\n",
       "      <td>77875009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1996-01-03</td>\n",
       "      <td>16.052837</td>\n",
       "      <td>16.783918</td>\n",
       "      <td>15.878770</td>\n",
       "      <td>15.913584</td>\n",
       "      <td>10.506985</td>\n",
       "      <td>96602936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1996-01-04</td>\n",
       "      <td>15.762726</td>\n",
       "      <td>15.813012</td>\n",
       "      <td>15.553845</td>\n",
       "      <td>15.766594</td>\n",
       "      <td>10.409932</td>\n",
       "      <td>100099436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1996-01-05</td>\n",
       "      <td>15.704703</td>\n",
       "      <td>15.704703</td>\n",
       "      <td>15.522900</td>\n",
       "      <td>15.658285</td>\n",
       "      <td>10.338422</td>\n",
       "      <td>76935930.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Open       High        Low      Close  Adj Close  \\\n",
       "0  1996-01-01  15.859429  15.944529  15.754989  15.917452  10.509537   \n",
       "1  1996-01-02  15.878770  15.956133  15.677626  15.793671  10.427811   \n",
       "2  1996-01-03  16.052837  16.783918  15.878770  15.913584  10.506985   \n",
       "3  1996-01-04  15.762726  15.813012  15.553845  15.766594  10.409932   \n",
       "4  1996-01-05  15.704703  15.704703  15.522900  15.658285  10.338422   \n",
       "\n",
       "        Volume  \n",
       "0   48051995.0  \n",
       "1   77875009.0  \n",
       "2   96602936.0  \n",
       "3  100099436.0  \n",
       "4   76935930.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = stock\n",
    "# Target column\n",
    "target_adj_close = pd.DataFrame(test['Adj Close'])\n",
    "display(test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b606207d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting Feature Columns\n",
    "feature_columns = ['Open', 'High', 'Low', 'Volume']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e11d6aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001473</td>\n",
       "      <td>0.001480</td>\n",
       "      <td>0.001481</td>\n",
       "      <td>0.071863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001480</td>\n",
       "      <td>0.001484</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.116464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001544</td>\n",
       "      <td>0.001786</td>\n",
       "      <td>0.001526</td>\n",
       "      <td>0.144472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001438</td>\n",
       "      <td>0.001432</td>\n",
       "      <td>0.001406</td>\n",
       "      <td>0.149701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001416</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.115060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Open      High       Low    Volume\n",
       "0  0.001473  0.001480  0.001481  0.071863\n",
       "1  0.001480  0.001484  0.001452  0.116464\n",
       "2  0.001544  0.001786  0.001526  0.144472\n",
       "3  0.001438  0.001432  0.001406  0.149701\n",
       "4  0.001416  0.001392  0.001394  0.115060"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "feature_minmax_transform_data = scaler.fit_transform(test[feature_columns])\n",
    "feature_minmax_transform = pd.DataFrame(columns=feature_columns, data=feature_minmax_transform_data, index=test.index)\n",
    "feature_minmax_transform.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fca384b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>High</th>\n",
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       "      <th>Volume</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001480</td>\n",
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       "      <td>0.001452</td>\n",
       "      <td>0.116464</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.001526</td>\n",
       "      <td>0.144472</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.001438</td>\n",
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       "      <td>0.001406</td>\n",
       "      <td>0.149701</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001416</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.115060</td>\n",
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      ],
      "text/plain": [
       "       Open      High       Low    Volume\n",
       "0  0.001473  0.001480  0.001481  0.071863\n",
       "1  0.001480  0.001484  0.001452  0.116464\n",
       "2  0.001544  0.001786  0.001526  0.144472\n",
       "3  0.001438  0.001432  0.001406  0.149701\n",
       "4  0.001416  0.001392  0.001394  0.115060"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of features :  (6522, 4)\n",
      "Shape of target :  (6522, 1)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6516</th>\n",
       "      <td>0.925776</td>\n",
       "      <td>0.933983</td>\n",
       "      <td>0.936041</td>\n",
       "      <td>0.007400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6517</th>\n",
       "      <td>0.934143</td>\n",
       "      <td>0.944296</td>\n",
       "      <td>0.943996</td>\n",
       "      <td>0.006013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6518</th>\n",
       "      <td>0.947728</td>\n",
       "      <td>0.945555</td>\n",
       "      <td>0.948818</td>\n",
       "      <td>0.003315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6519</th>\n",
       "      <td>0.937493</td>\n",
       "      <td>0.935626</td>\n",
       "      <td>0.921001</td>\n",
       "      <td>0.007825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6520</th>\n",
       "      <td>0.910726</td>\n",
       "      <td>0.907865</td>\n",
       "      <td>0.908391</td>\n",
       "      <td>0.007041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Open      High       Low    Volume\n",
       "6516  0.925776  0.933983  0.936041  0.007400\n",
       "6517  0.934143  0.944296  0.943996  0.006013\n",
       "6518  0.947728  0.945555  0.948818  0.003315\n",
       "6519  0.937493  0.935626  0.921001  0.007825\n",
       "6520  0.910726  0.907865  0.908391  0.007041"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6516</th>\n",
       "      <td>2593.100098</td>\n",
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       "    <tr>\n",
       "      <th>6517</th>\n",
       "      <td>2577.800049</td>\n",
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       "    <tr>\n",
       "      <th>6518</th>\n",
       "      <td>2517.899902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6519</th>\n",
       "      <td>2464.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6520</th>\n",
       "      <td>2473.300049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Adj Close\n",
       "6516  2593.100098\n",
       "6517  2577.800049\n",
       "6518  2517.899902\n",
       "6519  2464.000000\n",
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      ]
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -----After process------ \n",
      "\n",
      "Shape of features :  (6432, 4)\n",
      "Shape of target :  (6432, 1)\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6427</th>\n",
       "      <td>2150.199951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6428</th>\n",
       "      <td>2124.800049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6429</th>\n",
       "      <td>2110.050049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6430</th>\n",
       "      <td>2092.600098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6431</th>\n",
       "      <td>2071.199951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Adj Close\n",
       "6427  2150.199951\n",
       "6428  2124.800049\n",
       "6429  2110.050049\n",
       "6430  2092.600098\n",
       "6431  2071.199951"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(feature_minmax_transform.head())\n",
    "print('Shape of features : ', feature_minmax_transform.shape)\n",
    "print('Shape of target : ', target_adj_close.shape)\n",
    "\n",
    "# Shift target array because we want to predict the n + 1 day value\n",
    "\n",
    "\n",
    "target_adj_close = target_adj_close.shift(-1)\n",
    "validation_y = target_adj_close[-90:-1]\n",
    "target_adj_close = target_adj_close[:-90]\n",
    "\n",
    "# Taking last 90 rows of data to be validation set\n",
    "validation_X = feature_minmax_transform[-90:-1]\n",
    "feature_minmax_transform = feature_minmax_transform[:-90]\n",
    "display(validation_X.tail())\n",
    "display(validation_y.tail())\n",
    "\n",
    "print(\"\\n -----After process------ \\n\")\n",
    "print('Shape of features : ', feature_minmax_transform.shape)\n",
    "print('Shape of target : ', target_adj_close.shape)\n",
    "display(target_adj_close.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "91171562",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaler = preprocessing.MinMaxScaler()\n",
    "# scaled_values = scaler.fit_transform(stock.iloc[:,1:4])\n",
    "# stock.iloc[:,1:4] = scaled_values\n",
    "#\n",
    "# y_scaler = preprocessing.MinMaxScaler()\n",
    "# scaled_values = y_scaler.fit_transform(np.array(stock.iloc[:,4]).reshape(-1,1))\n",
    "# stock.iloc[:,4] = scaled_values\n",
    "#\n",
    "# scaler = preprocessing.MinMaxScaler()\n",
    "# scaled_values = scaler.fit_transform(stock.iloc[:,5:])\n",
    "# stock.iloc[:,5:] = scaled_values\n",
    "window_size = 50\n",
    "week = 7\n",
    "X = []\n",
    "Y = []\n",
    "\n",
    "for i in range(0 , len(stock) - window_size -1 , 1):\n",
    "    first = stock.iloc[i, 4]\n",
    "    temp = []\n",
    "    temp2 = []\n",
    "    for j in range(window_size):\n",
    "        temp.append((stock.iloc[i + j, 4] - first) / first)\n",
    "    # for j in range(week):\n",
    "    temp2.append((stock.iloc[i +window_size, 4] - first) / first)\n",
    "    # X.append(np.array(stock.iloc[i:i+window_size,4]).reshape(50,1))\n",
    "    # Y.append(np.array(stock.iloc[i+window_size,4]).reshape(1,1))\n",
    "    # print(stock2.iloc[i:i+window_size,4])\n",
    "    X.append(np.array(temp).reshape(50, 1))\n",
    "    Y.append(np.array(temp2).reshape(1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b8130a4b",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_16600/4272426292.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "X[0]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "25f4de2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5823\n",
      "648\n"
     ]
    }
   ],
   "source": [
    "train_X,test_X,train_label,test_label = train_test_split(X, Y, test_size=0.1,shuffle=False)\n",
    "len_t = len(train_X)\n",
    "# train_X,valid_X,train_label,valid_label = train_test_split(train_X, train_label, test_size=0.2,shuffle=True)\n",
    "\n",
    "print(len(train_X))\n",
    "print(len(test_X))\n",
    "train_X = np.array(train_X)\n",
    "test_X = np.array(test_X)\n",
    "train_label = np.array(train_label)\n",
    "test_label = np.array(test_label)\n",
    "# valid_label = np.array(valid_label)\n",
    "# valid_X = np.array(valid_X)\n",
    "train_X = train_X.reshape(train_X.shape[0],1,50,1)\n",
    "test_X = test_X.reshape(test_X.shape[0],1,50,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a503fd4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "91/91 [==============================] - 4s 23ms/step - loss: 0.0118 - val_loss: 0.0041\n",
      "Epoch 2/40\n",
      "91/91 [==============================] - 1s 16ms/step - loss: 0.0079 - val_loss: 0.0041\n",
      "Epoch 3/40\n",
      "91/91 [==============================] - 2s 18ms/step - loss: 0.0074 - val_loss: 0.0040\n",
      "Epoch 4/40\n",
      "91/91 [==============================] - 1s 15ms/step - loss: 0.0070 - val_loss: 0.0038\n",
      "Epoch 5/40\n",
      "91/91 [==============================] - 1s 15ms/step - loss: 0.0068 - val_loss: 0.0035\n",
      "Epoch 6/40\n",
      "91/91 [==============================] - 1s 15ms/step - loss: 0.0067 - val_loss: 0.0034\n",
      "Epoch 7/40\n",
      "91/91 [==============================] - 1s 15ms/step - loss: 0.0066 - val_loss: 0.0034\n",
      "Epoch 8/40\n",
      "91/91 [==============================] - 1s 15ms/step - loss: 0.0063 - val_loss: 0.0034\n",
      "Epoch 9/40\n",
      "91/91 [==============================] - 1s 16ms/step - loss: 0.0062 - val_loss: 0.0034\n",
      "Epoch 10/40\n",
      "91/91 [==============================] - 1s 16ms/step - loss: 0.0063 - val_loss: 0.0033\n",
      "Epoch 11/40\n",
      "91/91 [==============================] - 1s 15ms/step - loss: 0.0062 - val_loss: 0.0032\n",
      "Epoch 12/40\n",
      "91/91 [==============================] - 1s 16ms/step - loss: 0.0059 - val_loss: 0.0034\n",
      "Epoch 13/40\n",
      "91/91 [==============================] - 1s 16ms/step - loss: 0.0061 - val_loss: 0.0033\n",
      "Epoch 14/40\n",
      "91/91 [==============================] - 1s 15ms/step - loss: 0.0059 - val_loss: 0.0033\n",
      "Epoch 15/40\n",
      "91/91 [==============================] - 1s 15ms/step - loss: 0.0060 - val_loss: 0.0034\n",
      "Epoch 16/40\n",
      "91/91 [==============================] - 1s 15ms/step - loss: 0.0061 - val_loss: 0.0032\n",
      "Epoch 17/40\n",
      "91/91 [==============================] - 1s 15ms/step - loss: 0.0059 - val_loss: 0.0032\n",
      "Epoch 18/40\n",
      "91/91 [==============================] - 2s 18ms/step - loss: 0.0058 - val_loss: 0.0032\n",
      "Epoch 19/40\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0059 - val_loss: 0.0032\n",
      "Epoch 20/40\n",
      "91/91 [==============================] - 2s 18ms/step - loss: 0.0058 - val_loss: 0.0031\n",
      "Epoch 21/40\n",
      "91/91 [==============================] - 2s 19ms/step - loss: 0.0058 - val_loss: 0.0031\n",
      "Epoch 22/40\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0057 - val_loss: 0.0032\n",
      "Epoch 23/40\n",
      "91/91 [==============================] - 2s 18ms/step - loss: 0.0054 - val_loss: 0.0033\n",
      "Epoch 24/40\n",
      "91/91 [==============================] - 2s 18ms/step - loss: 0.0058 - val_loss: 0.0031\n",
      "Epoch 25/40\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0058 - val_loss: 0.0030\n",
      "Epoch 26/40\n",
      "91/91 [==============================] - 1s 16ms/step - loss: 0.0057 - val_loss: 0.0030\n",
      "Epoch 27/40\n",
      "91/91 [==============================] - 1s 16ms/step - loss: 0.0056 - val_loss: 0.0031\n",
      "Epoch 28/40\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0057 - val_loss: 0.0030\n",
      "Epoch 29/40\n",
      "91/91 [==============================] - 1s 16ms/step - loss: 0.0057 - val_loss: 0.0032\n",
      "Epoch 30/40\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0057 - val_loss: 0.0031\n",
      "Epoch 31/40\n",
      "91/91 [==============================] - 2s 18ms/step - loss: 0.0055 - val_loss: 0.0030\n",
      "Epoch 32/40\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0056 - val_loss: 0.0031\n",
      "Epoch 33/40\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0056 - val_loss: 0.0032\n",
      "Epoch 34/40\n",
      "91/91 [==============================] - 2s 18ms/step - loss: 0.0054 - val_loss: 0.0030\n",
      "Epoch 35/40\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0055 - val_loss: 0.0030\n",
      "Epoch 36/40\n",
      "91/91 [==============================] - 2s 18ms/step - loss: 0.0056 - val_loss: 0.0030\n",
      "Epoch 37/40\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0056 - val_loss: 0.0030\n",
      "Epoch 38/40\n",
      "91/91 [==============================] - 2s 17ms/step - loss: 0.0056 - val_loss: 0.0030\n",
      "Epoch 39/40\n",
      "91/91 [==============================] - 2s 23ms/step - loss: 0.0056 - val_loss: 0.0031\n",
      "Epoch 40/40\n",
      "91/91 [==============================] - 2s 23ms/step - loss: 0.0054 - val_loss: 0.0031\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0031\n",
      "0.0030814858619123697\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "#add model layers\n",
    "model.add(TimeDistributed(Conv1D(128, kernel_size=1, activation='relu', input_shape=(None,50,1))))\n",
    "model.add(TimeDistributed(MaxPooling1D(2)))\n",
    "model.add(TimeDistributed(Conv1D(256, kernel_size=1, activation='relu')))\n",
    "model.add(TimeDistributed(MaxPooling1D(2)))\n",
    "model.add(TimeDistributed(Conv1D(512, kernel_size=1, activation='relu')))\n",
    "model.add(TimeDistributed(MaxPooling1D(2)))\n",
    "model.add(TimeDistributed(Flatten()))\n",
    "# model.add(Bidirectional(LSTM(200,return_sequences=True)))\n",
    "model.add(Dropout(0.25))\n",
    "# model.add(Bidirectional(LSTM(200,return_sequences=False)))\n",
    "# model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "model.compile(optimizer='RMSprop', loss='mse')\n",
    "model.fit(train_X, train_label, validation_data=(test_X,test_label), epochs=40,batch_size=64,shuffle =False)\n",
    "print(model.evaluate(test_X,test_label))\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2709b023",
   "metadata": {},
   "outputs": [],
   "source": [
    "def validate_result(model, model_name):\n",
    "    predicted = model.predict(validation_X)\n",
    "    RSME_score = np.sqrt(mean_squared_error(validation_y, predicted))\n",
    "    print('RMSE: ', RSME_score)\n",
    "    \n",
    "    R2_score = r2_score(validation_y, predicted)\n",
    "    print('R2 score: ', R2_score)\n",
    "\n",
    "    plt.plot(validation_y.index, predicted,'r', label='Predict')\n",
    "    plt.plot(validation_y.index, validation_y,'b', label='Actual')\n",
    "    plt.ylabel('Price')\n",
    "    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "    plt.gca().xaxis.set_major_locator(mdates.MonthLocator())\n",
    "    plt.title(model_name + ' Predict vs Actual')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "13641bc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0031\n"
     ]
    }
   ],
   "source": [
    "# y_pred_test_lstm = model.predict(test_X)\n",
    "# y_train_pred_lstm = model.predict(train_X)\n",
    "# print(\"The R2 score on the Train set is:\\t{:0.3f}\".format(r2_score(train_label, y_train_pred_lstm)))\n",
    "# r2_train = r2_score(y_train, y_train_pred_lstm)\n",
    "\n",
    "# print(\"The R2 score on the Test set is:\\t{:0.3f}\".format(r2_score(test_label, y_pred_test_lstm)))\n",
    "# r2_test = r2_score(y_test, y_pred_test_lstm)\n",
    "score_lstm= model.evaluate(test_X,test_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd327b77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted  = model.predict(test_X)\n",
    "test_label = (test_label[:,0])\n",
    "predicted = np.array(predicted[:,0]).reshape(-1,1)\n",
    "for j in range(len_t , len_t + len(test_X)):\n",
    "    temp =stock.iloc[j,4]\n",
    "    test_label[j - len_t] = test_label[j - len_t] * temp + temp\n",
    "    predicted[j - len_t] = predicted[j - len_t] * temp + temp\n",
    "plt.plot(test_label, color = 'blue', label = ' Stock Price')\n",
    "plt.plot(predicted, color = 'orange', label = 'Predicted  Stock Price')\n",
    "plt.title(' Stock Price Prediction for Tata Motors')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel(' Stock Price')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "# predicted  = model.predict(test_X)\n",
    "# test_label[:,0] = y_scaler.inverse_transform(test_label[:,0])\n",
    "# predicted = np.array(predicted[:,0]).reshape(-1,1)\n",
    "# predicted = y_scaler.inverse_transform(predicted)\n",
    "# plt.plot(test_label[:,0], color = 'black', label = ' Stock Price')\n",
    "# plt.plot(predicted, color = 'green', 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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "102e097e",
   "metadata": {},
   "outputs": [],
   "source": [
    "col1 = pd.DataFrame(test_label, columns=['True'])\n",
    "\n",
    "col2 = pd.DataFrame(predicted, columns=['CNN_prediction'])\n",
    "\n",
    "# col3 = pd.DataFrame(history_model_lstm.history['loss'], columns=['Loss_LSTM'])\n",
    "results = pd.concat([col1, col2], axis=1)\n",
    "results.to_excel('PredictionResults_CNN.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48e61b0e",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}