Stock_Price_Prediction (1).ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import keras\n",
        "from keras.models import Model\n",
        "from keras.layers import Dense, Input\n",
        "from keras.layers import Dropout\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from keras import Sequential\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdpmenTdgvSK",
        "outputId": "3e973f05-51d3-4ba9-829d-cc477c32a0a1"
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      "execution_count": 2,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/Google_Stock_Price_Train.csv\")"
      ],
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        "id": "T10Itz3QhpHG"
      },
      "execution_count": 3,
      "outputs": []
    },
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        "data.head()"
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              "       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"
            ],
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              "      <th>Volume</th>\n",
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              "      <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",
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              "    <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",
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              "    <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",
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          "data": {
            "text/plain": [
              "              Open         High          Low\n",
              "count  1258.000000  1258.000000  1258.000000\n",
              "mean    533.709833   537.880223   529.007409\n",
              "std     151.904442   153.008811   150.552807\n",
              "min     279.120000   281.210000   277.220000\n",
              "25%     404.115000   406.765000   401.765000\n",
              "50%     537.470000   540.750000   532.990000\n",
              "75%     654.922500   662.587500   644.800000\n",
              "max     816.680000   816.680000   805.140000"
            ],
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              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>1258.000000</td>\n",
              "      <td>1258.000000</td>\n",
              "      <td>1258.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>533.709833</td>\n",
              "      <td>537.880223</td>\n",
              "      <td>529.007409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>151.904442</td>\n",
              "      <td>153.008811</td>\n",
              "      <td>150.552807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>279.120000</td>\n",
              "      <td>281.210000</td>\n",
              "      <td>277.220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>404.115000</td>\n",
              "      <td>406.765000</td>\n",
              "      <td>401.765000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>537.470000</td>\n",
              "      <td>540.750000</td>\n",
              "      <td>532.990000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>654.922500</td>\n",
              "      <td>662.587500</td>\n",
              "      <td>644.800000</td>\n",
              "    </tr>\n",
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              "      <th>max</th>\n",
              "      <td>816.680000</td>\n",
              "      <td>816.680000</td>\n",
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      "source": [
        "data.isna().sum()"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date      0\n",
              "Open      0\n",
              "High      0\n",
              "Low       0\n",
              "Close     0\n",
              "Volume    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "DATA PREPROCESSING FOR VISUALISATION"
      ],
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      }
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      "cell_type": "code",
      "source": [
        "train_close = data.iloc[:, 4:5].values\n",
        "train_vol = data.iloc[:, 5:6].values\n",
        "print(train_close)\n",
        "print(train_vol)"
      ],
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        "colab": {
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        },
        "id": "o87DYis6EDWQ",
        "outputId": "1b25b84a-253e-49b1-99ae-c040611c734f"
      },
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['663.59']\n",
            " ['666.45']\n",
            " ['657.21']\n",
            " ...\n",
            " ['785.05']\n",
            " ['782.79']\n",
            " ['771.82']]\n",
            "[['7,380,500']\n",
            " ['5,749,400']\n",
            " ['6,590,300']\n",
            " ...\n",
            " ['1,153,800']\n",
            " ['744,300']\n",
            " ['1,770,000']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_close_=[]\n",
        "train_close = train_close.flatten()\n",
        "for i in range(len(train_close)):\n",
        "\n",
        "  b = ''.join(str(train_close[i]).split(','))\n",
        "  train_close_.append(float(b))\n",
        "train_close_ = np.array(train_close_)"
      ],
      "metadata": {
        "id": "qy5HfmtuENKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_vol_=[]\n",
        "train_vol = train_vol.flatten()\n",
        "for i in range(len(train_vol)):\n",
        "\n",
        "  b = ''.join(str(train_vol[i]).split(','))\n",
        "  train_vol_.append(float(b))\n",
        "train_vol_ = np.array(train_vol_)"
      ],
      "metadata": {
        "id": "T_UXO-bqERIc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_close_2D = np.reshape(train_close_, (len(train_close), 1))\n",
        "train_vol_2D = np.reshape(train_vol_, (len(train_vol), 1))"
      ],
      "metadata": {
        "id": "wseR4FJWEboX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = data.copy()\n",
        "df.pop(\"Close\")\n",
        "df.pop(\"Volume\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aaReEddFEh5a",
        "outputId": "d64fda03-2862-4819-a9a7-b19c7b85a50d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        7,380,500\n",
              "1        5,749,400\n",
              "2        6,590,300\n",
              "3        5,405,900\n",
              "4       11,688,800\n",
              "           ...    \n",
              "1253       623,400\n",
              "1254       789,100\n",
              "1255     1,153,800\n",
              "1256       744,300\n",
              "1257     1,770,000\n",
              "Name: Volume, Length: 1258, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Close\"] = train_close_2D\n",
        "df[\"Volume\"] = train_vol_2D\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-PFcHK_1E1tB",
        "outputId": "3627b1de-4ae1-432b-ef38-177de66c163d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Date    Open    High     Low   Close      Volume\n",
              "0  1/3/2012  325.25  332.83  324.97  663.59   7380500.0\n",
              "1  1/4/2012  331.27  333.87  329.08  666.45   5749400.0\n",
              "2  1/5/2012  329.83  330.75  326.89  657.21   6590300.0\n",
              "3  1/6/2012  328.34  328.77  323.68  648.24   5405900.0\n",
              "4  1/9/2012  322.04  322.29  309.46  620.76  11688800.0"
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              "      <td>1/3/2012</td>\n",
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              "      <td>7380500.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1/4/2012</td>\n",
              "      <td>331.27</td>\n",
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              "      <td>666.45</td>\n",
              "      <td>5749400.0</td>\n",
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              "    <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>6590300.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1/6/2012</td>\n",
              "      <td>328.34</td>\n",
              "      <td>328.77</td>\n",
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              "      <td>5405900.0</td>\n",
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              "    <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",
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "              Open         High          Low        Close        Volume\n",
              "count  1258.000000  1258.000000  1258.000000  1258.000000  1.258000e+03\n",
              "mean    533.709833   537.880223   529.007409   712.669666  3.158107e+06\n",
              "std     151.904442   153.008811   150.552807   164.752591  2.273726e+06\n",
              "min     279.120000   281.210000   277.220000   491.200000  7.900000e+03\n",
              "25%     404.115000   406.765000   401.765000   576.740000  1.621700e+06\n",
              "50%     537.470000   540.750000   532.990000   695.675000  2.566150e+06\n",
              "75%     654.922500   662.587500   644.800000   782.105000  4.122500e+06\n",
              "max     816.680000   816.680000   805.140000  1216.830000  2.497790e+07"
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              "      <th>Low</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1258.000000</td>\n",
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              "      <td>1258.000000</td>\n",
              "      <td>1258.000000</td>\n",
              "      <td>1.258000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>533.709833</td>\n",
              "      <td>537.880223</td>\n",
              "      <td>529.007409</td>\n",
              "      <td>712.669666</td>\n",
              "      <td>3.158107e+06</td>\n",
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              "      <th>std</th>\n",
              "      <td>151.904442</td>\n",
              "      <td>153.008811</td>\n",
              "      <td>150.552807</td>\n",
              "      <td>164.752591</td>\n",
              "      <td>2.273726e+06</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>279.120000</td>\n",
              "      <td>281.210000</td>\n",
              "      <td>277.220000</td>\n",
              "      <td>491.200000</td>\n",
              "      <td>7.900000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>404.115000</td>\n",
              "      <td>406.765000</td>\n",
              "      <td>401.765000</td>\n",
              "      <td>576.740000</td>\n",
              "      <td>1.621700e+06</td>\n",
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              "      <th>50%</th>\n",
              "      <td>537.470000</td>\n",
              "      <td>540.750000</td>\n",
              "      <td>532.990000</td>\n",
              "      <td>695.675000</td>\n",
              "      <td>2.566150e+06</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>654.922500</td>\n",
              "      <td>662.587500</td>\n",
              "      <td>644.800000</td>\n",
              "      <td>782.105000</td>\n",
              "      <td>4.122500e+06</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>816.680000</td>\n",
              "      <td>816.680000</td>\n",
              "      <td>805.140000</td>\n",
              "      <td>1216.830000</td>\n",
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      "cell_type": "markdown",
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        "DATA VISUALISATION\n"
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        "id": "l3T-uCUuDs1V"
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fc84a691c50>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fc84a676350>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fc84a632690>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fc84a5e99d0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fc84a5a2d10>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x864 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Itz9PMveQewl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rolling\n"
      ],
      "metadata": {
        "id": "biDyeXW6Qfye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 8))\n",
        "df_open = df[['Date', 'Open']]\n",
        "df_open['Date'] = pd.to_datetime(df_open['Date'])\n",
        "rolling_open = df_open.Open.rolling(10).mean()\n",
        "plt.plot(df_open.Date, df_open.Open)\n",
        "plt.plot(df_open.Date, rolling_open)\n",
        "plt.legend(['Open_Price', 'Rolling Price'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "-Misg8gVGhcZ",
        "outputId": "47ed9a2c-649a-478f-9276-9e6dc15fec09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "df_open['Date'] = pd.to_datetime(df_open['Date'])\n",
        "\n",
        "components = seasonal_decompose(df_open['Open'], model='multiplicative', freq = 360)\n",
        " \n",
        "components.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 741
        },
        "id": "DVveSyulSh0q",
        "outputId": "da90ac21-0738-4ce5-9208-b663c46d7abc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        "plot_acf(df_open['Open'])\n",
        "plt.show ()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "h2ZThambV7tQ",
        "outputId": "18b43948-8c09-4d28-eec6-5bce8d5c5c79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Preprocessing for Model Training"
      ],
      "metadata": {
        "id": "O9E2IhoKTyfH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_open = data.iloc[:, 1:2].values\n",
        "train_high = data.iloc[:, 2:3].values\n",
        "train_low = data.iloc[:, 3:4].values\n",
        "train_close = data.iloc[:, 4:5].values\n",
        "train_vol = data.iloc[:, 5:6].values"
      ],
      "metadata": {
        "id": "TG4Aw9uSiJWF"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_close"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AF1H3jQx2eGW",
        "outputId": "440a7729-91c2-4a94-f549-2e0fff93fcde"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['663.59'],\n",
              "       ['666.45'],\n",
              "       ['657.21'],\n",
              "       ...,\n",
              "       ['785.05'],\n",
              "       ['782.79'],\n",
              "       ['771.82']], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_close_=[]\n",
        "train_close = train_close.flatten()\n",
        "for i in range(len(train_close)):\n",
        "\n",
        "  b = ''.join(str(train_close[i]).split(','))\n",
        "  train_close_.append(float(b))\n",
        "train_close_ = np.array(train_close_)"
      ],
      "metadata": {
        "id": "dMN5XbA_MFPM"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_vol_=[]\n",
        "train_vol = train_vol.flatten()\n",
        "for i in range(len(train_vol)):\n",
        "\n",
        "  b = ''.join(str(train_vol[i]).split(','))\n",
        "  train_vol_.append(float(b))\n",
        "train_vol_ = np.array(train_vol_)"
      ],
      "metadata": {
        "id": "zUppK1tmQExb"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_close_2D = np.reshape(train_close_, (len(train_close), 1))\n",
        "train_vol_2D = np.reshape(train_vol_, (len(train_vol), 1))\n"
      ],
      "metadata": {
        "id": "qu0-ZfoLMbx3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "mms_open = MinMaxScaler(feature_range = (0, 1))\n",
        "mms_high = MinMaxScaler(feature_range = (0, 1))\n",
        "mms_low = MinMaxScaler(feature_range = (0, 1))\n",
        "mms_close = MinMaxScaler(feature_range = (0, 1))\n",
        "mms_vol = MinMaxScaler(feature_range = (0, 1))\n",
        "\n",
        "train_open_mms = mms_open.fit_transform(train_open)\n",
        "train_high_mms = mms_high.fit_transform(train_high)\n",
        "train_low_mms = mms_low.fit_transform(train_low)\n",
        "train_close_mms = mms_close.fit_transform(train_close_2D)\n",
        "train_vol_mms = mms_vol.fit_transform(train_vol_2D)"
      ],
      "metadata": {
        "id": "7osxhQ6OKOP-"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_open_mms.flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CSpqbbZmtYm8",
        "outputId": "00dc4fcf-b7d8-484f-b577-e4fb69821f7d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.08581368, 0.09701243, 0.09433366, ..., 0.95725128, 0.93796041,\n",
              "       0.93688146])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "GN0zP_bBSMio"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_test = pd.read_csv(\"/content/drive/MyDrive/Google_Stock_Price_Test.csv\")"
      ],
      "metadata": {
        "id": "hMI1dTjmVxlN"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "yrJ_021IV31G",
        "outputId": "e5929d13-6a55-42e5-ba44-a551fdb59dc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date    Open    High     Low   Close     Volume\n",
              "0    1/3/2017  778.81  789.63  775.80  786.14  1,657,300\n",
              "1    1/4/2017  788.36  791.34  783.16  786.90  1,073,000\n",
              "2    1/5/2017  786.08  794.48  785.02  794.02  1,335,200\n",
              "3    1/6/2017  795.26  807.90  792.20  806.15  1,640,200\n",
              "4    1/9/2017  806.40  809.97  802.83  806.65  1,272,400\n",
              "5   1/10/2017  807.86  809.13  803.51  804.79  1,176,800\n",
              "6   1/11/2017  805.00  808.15  801.37  807.91  1,065,900\n",
              "7   1/12/2017  807.14  807.39  799.17  806.36  1,353,100\n",
              "8   1/13/2017  807.48  811.22  806.69  807.88  1,099,200\n",
              "9   1/17/2017  807.08  807.14  800.37  804.61  1,362,100\n",
              "10  1/18/2017  805.81  806.21  800.99  806.07  1,294,400\n",
              "11  1/19/2017  805.12  809.48  801.80  802.17    919,300\n",
              "12  1/20/2017  806.91  806.91  801.69  805.02  1,670,000\n",
              "13  1/23/2017  807.25  820.87  803.74  819.31  1,963,600\n",
              "14  1/24/2017  822.30  825.90  817.82  823.87  1,474,000\n",
              "15  1/25/2017  829.62  835.77  825.06  835.67  1,494,500\n",
              "16  1/26/2017  837.81  838.00  827.01  832.15  2,973,900\n",
              "17  1/27/2017  834.71  841.95  820.44  823.31  2,965,800\n",
              "18  1/30/2017  814.66  815.84  799.80  802.32  3,246,600\n",
              "19  1/31/2017  796.86  801.25  790.52  796.79  2,160,600"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-363ce3df-f29a-478c-954f-91e835fff365\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>1/3/2017</td>\n",
              "      <td>778.81</td>\n",
              "      <td>789.63</td>\n",
              "      <td>775.80</td>\n",
              "      <td>786.14</td>\n",
              "      <td>1,657,300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1/4/2017</td>\n",
              "      <td>788.36</td>\n",
              "      <td>791.34</td>\n",
              "      <td>783.16</td>\n",
              "      <td>786.90</td>\n",
              "      <td>1,073,000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1/5/2017</td>\n",
              "      <td>786.08</td>\n",
              "      <td>794.48</td>\n",
              "      <td>785.02</td>\n",
              "      <td>794.02</td>\n",
              "      <td>1,335,200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1/6/2017</td>\n",
              "      <td>795.26</td>\n",
              "      <td>807.90</td>\n",
              "      <td>792.20</td>\n",
              "      <td>806.15</td>\n",
              "      <td>1,640,200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1/9/2017</td>\n",
              "      <td>806.40</td>\n",
              "      <td>809.97</td>\n",
              "      <td>802.83</td>\n",
              "      <td>806.65</td>\n",
              "      <td>1,272,400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1/10/2017</td>\n",
              "      <td>807.86</td>\n",
              "      <td>809.13</td>\n",
              "      <td>803.51</td>\n",
              "      <td>804.79</td>\n",
              "      <td>1,176,800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1/11/2017</td>\n",
              "      <td>805.00</td>\n",
              "      <td>808.15</td>\n",
              "      <td>801.37</td>\n",
              "      <td>807.91</td>\n",
              "      <td>1,065,900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1/12/2017</td>\n",
              "      <td>807.14</td>\n",
              "      <td>807.39</td>\n",
              "      <td>799.17</td>\n",
              "      <td>806.36</td>\n",
              "      <td>1,353,100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1/13/2017</td>\n",
              "      <td>807.48</td>\n",
              "      <td>811.22</td>\n",
              "      <td>806.69</td>\n",
              "      <td>807.88</td>\n",
              "      <td>1,099,200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1/17/2017</td>\n",
              "      <td>807.08</td>\n",
              "      <td>807.14</td>\n",
              "      <td>800.37</td>\n",
              "      <td>804.61</td>\n",
              "      <td>1,362,100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1/18/2017</td>\n",
              "      <td>805.81</td>\n",
              "      <td>806.21</td>\n",
              "      <td>800.99</td>\n",
              "      <td>806.07</td>\n",
              "      <td>1,294,400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1/19/2017</td>\n",
              "      <td>805.12</td>\n",
              "      <td>809.48</td>\n",
              "      <td>801.80</td>\n",
              "      <td>802.17</td>\n",
              "      <td>919,300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1/20/2017</td>\n",
              "      <td>806.91</td>\n",
              "      <td>806.91</td>\n",
              "      <td>801.69</td>\n",
              "      <td>805.02</td>\n",
              "      <td>1,670,000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1/23/2017</td>\n",
              "      <td>807.25</td>\n",
              "      <td>820.87</td>\n",
              "      <td>803.74</td>\n",
              "      <td>819.31</td>\n",
              "      <td>1,963,600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1/24/2017</td>\n",
              "      <td>822.30</td>\n",
              "      <td>825.90</td>\n",
              "      <td>817.82</td>\n",
              "      <td>823.87</td>\n",
              "      <td>1,474,000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1/25/2017</td>\n",
              "      <td>829.62</td>\n",
              "      <td>835.77</td>\n",
              "      <td>825.06</td>\n",
              "      <td>835.67</td>\n",
              "      <td>1,494,500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1/26/2017</td>\n",
              "      <td>837.81</td>\n",
              "      <td>838.00</td>\n",
              "      <td>827.01</td>\n",
              "      <td>832.15</td>\n",
              "      <td>2,973,900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1/27/2017</td>\n",
              "      <td>834.71</td>\n",
              "      <td>841.95</td>\n",
              "      <td>820.44</td>\n",
              "      <td>823.31</td>\n",
              "      <td>2,965,800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1/30/2017</td>\n",
              "      <td>814.66</td>\n",
              "      <td>815.84</td>\n",
              "      <td>799.80</td>\n",
              "      <td>802.32</td>\n",
              "      <td>3,246,600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1/31/2017</td>\n",
              "      <td>796.86</td>\n",
              "      <td>801.25</td>\n",
              "      <td>790.52</td>\n",
              "      <td>796.79</td>\n",
              "      <td>2,160,600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-363ce3df-f29a-478c-954f-91e835fff365')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-363ce3df-f29a-478c-954f-91e835fff365 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-363ce3df-f29a-478c-954f-91e835fff365');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_open = data_test.iloc[:, 1:2].values\n",
        "test_high = data_test.iloc[:, 2:3].values\n",
        "test_low = data_test.iloc[:, 3:4].values\n",
        "test_close = data_test.iloc[:, 4:5].values\n",
        "test_vol = data_test.iloc[:, 5:6].values\n"
      ],
      "metadata": {
        "id": "7hnwH2QdV-Hr"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_close_=[]\n",
        "test_close = test_close.flatten()\n",
        "for i in range(len(test_close)):\n",
        "\n",
        "  b = ''.join(str(test_close[i]).split(','))\n",
        "  test_close_.append(float(b))\n",
        "test_close_ = np.array(test_close_)"
      ],
      "metadata": {
        "id": "g8tHwL8SWRnM"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_vol_=[]\n",
        "test_vol = test_vol.flatten()\n",
        "for i in range(len(test_vol)):\n",
        "\n",
        "  b = ''.join(str(test_vol[i]).split(','))\n",
        "  test_vol_.append(float(b))\n",
        "test_vol_ = np.array(test_vol_)"
      ],
      "metadata": {
        "id": "D991WTc2Wgp8"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_close_2D = np.reshape(test_close_, (len(test_close), 1))\n",
        "test_vol_2D = np.reshape(test_vol_, (len(test_vol), 1))\n"
      ],
      "metadata": {
        "id": "LpbkeVSFWyK0"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def visualize(x, count):\n",
        "  plt.plot(x, color = 'red')\n",
        "\n",
        "  plt.title(count)\n",
        "  \n",
        "  plt.xlabel('Time')\n",
        "  plt.ylabel('Google Stock Price')\n",
        "\n",
        "  plt.show()\n",
        "  "
      ],
      "metadata": {
        "id": "FpHCBnMfXZol"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "visualize(test_open, \"Open\")\n",
        "visualize(test_high, \"High\")\n",
        "visualize(test_low, \"Low\")\n",
        "visualize(test_close_2D, \"Close\")\n",
        "visualize(test_vol_2D, \"Vol\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "l4nHcMUfSC-4",
        "outputId": "528aa76a-9b08-47b7-adc6-4e957569591f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trying various models and generating training and testing set "
      ],
      "metadata": {
        "id": "7cEJT5SZuKvv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "With last 20 timesteps as features"
      ],
      "metadata": {
        "id": "4IaRi1Emx6U-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_open_20 = []\n",
        "y_train_data_open_20 = []\n",
        "for i in range(20, len(train_open_mms)):\n",
        "    train_data_open_20.append(train_open.flatten()[i-20:i])\n",
        "    y_train_data_open_20.append(train_open.flatten()[i])\n",
        "train_data_open_20 = np.array(train_data_open_20)\n",
        "y_train_data_open_20 = np.array(y_train_data_open_20)\n",
        "\n",
        "train_data_open_20.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRLvmdG_vRfR",
        "outputId": "10bb0d6d-8e49-43e5-fb46-02a25e85e818"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1238, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_20 = train_open.flatten()\n",
        "req_train_20 = x_20[len(x_20)-20:]\n",
        "y_20 = test_open.flatten()\n",
        "z_20 = np.concatenate([req_train_20, y_20])\n",
        "z_1_20 = np.reshape(z_20, (len(z_20), 1))\n",
        "\n",
        "X_test_20 = []\n",
        "for i in range(20, 40):\n",
        "    X_test_20.append(z_1_20.flatten()[i-20:i])\n",
        "X_test_20 = np.array(X_test_20)\n",
        "X_test_20.shape"
      ],
      "metadata": {
        "id": "F7NYGxX7vv7B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f71858fb-2d8d-4122-c0a7-29948fbe3662"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg_20 = LinearRegression()\n",
        "reg_20.fit(train_data_open_20, y_train_data_open_20)\n",
        "y_pred_lr_20 = reg_20.predict(X_test_20)\n",
        "y_pred_lr_20"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NoRQF8CTxkAR",
        "outputId": "5bb89e37-26b1-4f1d-9f23-757f20270122"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([783.71492038, 779.67245821, 789.16616586, 787.84449028,\n",
              "       794.92978544, 805.90166216, 808.44524609, 805.12993882,\n",
              "       805.12627288, 808.0811952 , 806.83612928, 806.29593329,\n",
              "       803.74144603, 806.32952298, 807.43572478, 821.03467426,\n",
              "       829.25947228, 837.60018348, 834.61241803, 813.52331716])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = test_open.flatten()\n",
        "err=0\n",
        "b = y_pred_lr_20.flatten()\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "err = mse(a, b)\n",
        "print(err)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KqONeXSyiMz",
        "outputId": "fd5cc088-d003-48f4-8040-67e1c0ce3fe6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "69.3553153187003\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_lr_20 = r2_score(a, b)\n",
        "r2_lr_20"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_Eozz6L6nxz",
        "outputId": "3c0df04a-2ef3-47d2-e8f0-bf082d937c16"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6808897316147363"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_open.flatten(), color = 'red', label = 'Real Open Google Stock Price')\n",
        "plt.plot(y_pred_lr_20, color = 'blue', label = 'Predicted Open Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction using LR(20)')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Open Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "wQuM6jDrzBXG",
        "outputId": "0f95019a-b099-40fb-d039-bfcb82d1b90a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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UqVIMGTIk7ZihQ4fSoEEDGjRowEsvvZTl9scff5w6derQqlUrevfuzfPPP3/SeRcvXkybNm2Ii4ujS5cubNt2ci6Ubdu2UbXqsayiUVFRHDlyhEcffZQJEyYQGxvLhAkT2LlzJz179iQ6OprmzZunKdGkpCT69OlDVFQU0dHRfPjhh8fV/9dff9GiRQs+/fTTTJ4KJCQksHbtWmbNmkVCQgLdu3enXr16wPG9jmeeeYaoqChiYmJ44AHzp8jot+EIM8OGwdq19vnuu/DJJ7BnD7RoAXffDfv3Z1o8MdH0SJcuMG0a7N4NLVvCzTfb91MCVS2wS1xcnJ7IqlWrjq3ccYdqmzbhXe6446RznkjJkiVVVfXo0aN62WWX6eeff66qqu3bt9c1a9aoquqCBQu0Xbt2qqq6c+dOTU1NVVXVN954Q++++25VVR0zZozeeuutJ9XfsGFDXbp06XHbli5dqg0bNlRV1TZt2mj//v1VVfXbb7/V+vXrq6rq4MGD9Z133lFV1V27dmnt2rU1KSlJx4wZozVr1tTdu3frwYMH9fzzz9eNGzceV/+yZcs0NjY2w2tetGiRNmjQQJOSknTfvn1ar149XbJkSYbbv//+e42JidGDBw/q3r17tVatWvrcc8+pqup1112nEydO1CNHjmiLFi10+/btqqr6/vvva58+fU4695tvvqmlS5fWiy++WIcOHaq7du1K9/4NHDhQhwwZoqqqX3/9tcbExKiq6n333ad3BD3XnTt3qqo9xz/++EObNm2q06dPT/e6A886OTlZu3fvrq+88orOnDlTS5Qoob/99ttJx3322WfaokUL3b9/v6qq7tixQ1Uz/m3kBcf9h04ltm5VPess1e7dj9++Z4/qzTergmrNmqpffZVhFTffrFq4sOpPP9n6vn2qd9+tWqiQ6rnnqr7/vqr3V87XAIs0g3Y1ot5HpysHDx4kNjaWLVu2ULduXTp16kRSUhLz5s3j8suPOVgd9myZmzdv5sorr2Tbtm0cOXIkLD7ivXv3BqB169bs3buX3bt3M336dD766KO0N/JDhw6xceNGADp06ECZMmUAqFevHr///jvVqlXLsP4xY8YwbNgwduzYwbx585g7dy69evWiZMmSAFx66aXMmTMHVU13e2pqKj169KB48eIUL16cbt26nXSOX375hZUrV9KpUycAUlJSOO+88046rk+fPnTp0oUvvviCadOm8frrr7Ns2bKTjps7d25aL6B9+/bs2LGDvXv3MmPGDN5///20484+22I1Jicn06FDB0aOHEmbNm3SvQ+BZw3WU+jXrx/z5s2jadOm6T7HGTNm0KdPH0qUKAFAuXLlMv1tOMLIAw/YgMDQocdvL10aXnkFrroK+veHTp2gb1944QUoWzbtsBUr4PXX4dZbwesActZZdtjVV8NNN1kVY8dadQV1qseprRSCTBW5ScDOfODAAbp06cLIkSO5/vrrKVu2LEuXLj3p+Ntuu427776b7t27M2vWrONMMulRr149Fi9eTExMTNq2xYsXU79+/bT1E10LRQRV5cMPP6ROnTrH7Vu4cCHFihVLWy9cuHCaLTxArVq12LhxI/v27aNUqVL06dOHPn360KBBA1JSUrK8J35QVerXr8/8EIy2lStXpm/fvvTt25cGDRqwcuXKHJ+/SJEixMXF8eWXX2aoFALP+kQCSjAUUlNTM/xtOMLEggXw9tsweDBceGH6x7RuDcuWmX3o+efh88+tde/ZE1W46y4oUwYeS8fJvlEjO8Urr8CDD0L9+nbc3XdDQZsc7sYUIkiJEiUYPnw4L7zwAiVKlKBmzZpMnDgRsAYv8Da7Z88eqlSpAsBbb72VYX0BBg0axH//+182bNgAwIYNG3jqqae455570o6ZMGECYG/HZcqUoUyZMnTp0oURI0ag3kTxH3/8MVvX0q9fPwYOHJg2uSklJYUjR44A9pY8depUDhw4wP79+5kyZQoJCQkZbm/ZsiUff/wxhw4dIikpiU8++eSkc9apU4fExMQ0pZCcnMxP6bgRfvHFF2kD3n/88Qc7duygSpUqlCpVin379qUdl5CQwHvvvQfArFmzKF++PKVLl6ZTp07HOQPs2rULMEX65ptv8vPPP/PMM8+EfK8yo1OnTowZMybNS2nnzp2ULl06w9+GIwykpsJtt0HlytZiZ8aZZ8LTT9vAc8WK0KsXXHEFH729m6+/Nn1xzklhOo3Che00q1dD167WMWnUqOANRDulEGEaNmxIdHQ048eP57333mP06NHExMRQv379tIHeIUOGcPnllxMXF0f58uWzrDM2NpZnnnmGbt26cdFFF9GtWzeeffbZNDMGWAybhg0bMmDAAEaPHg3AI488QnJyMtHR0dSvX59HHnkkW9fy5JNPct5559GgQQMaNmxIQkIC1113HZUrV6ZRo0Zcf/31NG3alGbNmtG/f38aNmyY4fYmTZrQvXt3oqOj6dq1K1FRUWnmqwBFixZl0qRJ3H///cTExBAbG5uuF8/06dNp0KABMTExdOnSheeee45KlSrRrl07Vq1alTbQPGTIEBYvXkx0dDQPPPBAmgJ++OGH2bVrV1odM2fOTKu7cOHCjB8/nm+++YZXXnklW/crPS6++GK6d+9O48aNiY2NTTPlZfTbcISBsWNh0SLzIQ3BzRgwt6IffoAnn+Tw1M+5p+9O6lbezYCbso68U7UqfPihDUTv2QPx8TBgAHjvGvmfjAYbCsKS5UDzaUqbNm30hx9+yGsxsmTfvn2qqrp//36Ni4vTxYsX57FEDtVT7D+0e7dqxYqq8fG+R4Cfu/dPBdUv6KzaubPq+vUhlz1xIHr8+PwxEE0mA82up+DIM2688UZiY2Np1KgR//jHP2jUqFFei+Q41fjPf8yPdPhw8BHCY/t2ePz1ivy9q9JlRDf47jto0ABGjDCzVBYEBqJ/+AGqVYPevc209Ntvfi4ml8hIWxSExfUUHI7wc8r8h1avVi1SRNVzz/bDDTdYFT//7G3YsEG1SxdzX33xxWzVdfSo6vDhqqVKqRYvrvrll77FyjG4noLD4TitULWZyyVLgs9QLEuXwv/+BwMH2gRoAKpXN6+k+vXtMxsED0SXLQtjxvgSK+I4peBwOE49PvkEvvzSYhtVrJjt4gEX1HLl4NFHT9gpYu6r8+bBCa7boVClCrRpY8XzI04pOByOU4vDh61Fr1vXZpr5YMoUmDXLhiS8uYzHk5AASUk2r8EH8fGwcSNs3uyreERxSsHhcJxavPgirFtn8Y18zBw7dAgGDbLxZC/g7skkJNjnnDm+RAwEzc2PcxicUogAoYZTDoXgMNL9+/dn1apVGR47a9YsX9E4a9SowV9//XXS9j179nDttddSq1YtLrzwQq699lr27NmT7fqzy/fff0/btm2pXbs2jRo14pJLLmHFihVhPceGDRto0KBBtso8+eST1K9fn+joaGJjY1noRdZ86aWXfD/jIUOGpBsI8MRjqlSpkvab+uijj9I97rXXXuPtt9/2Jccpw5Yt8MQT0KOHhavwwUsvwfr1pluKZBTzoWpVqFHDt1KIibF5cvnRhOSUQgQINZxydvnf//6XFnUzPfwqhYzo168fF1xwAWvXrmXdunXUrFmT/v37h63+9Pjzzz+54ooreOqpp/j1119ZsmQJgwcPZt26dRE9b1bMnz+fTz75hCVLlrB8+XJmzJiRFhsqJ0ohVAKhwSdOnEjfvn1JPcEd8ujRowwYMIBrr702onLkex54wOz8J8Y3CpE//rBx6e7doWPHLA5OSDCloNlOJc8ZZ0DTpk4pnJZkFE45JSWFe++9lyZNmhAdHc3rr78OmIvwwIEDqVOnDh07dmT79u1pdbVt25ZFixYBFtqhUaNGxMTE0KFDBzZs2MBrr73Giy++SGxsLHPmzCExMZF//OMfNGnShCZNmvDdd98BsGPHDjp37kz9+vXp379/WtiLYNauXcvixYuPm/X86KOPsmjRItatW8esWbNo3bo1l1xyCXXq1GHAgAFpDdX06dNp0aIFjRo14vLLLycpKQmwHsljjz1Go0aNiIqKSjc89Msvv8x11113XFKaVq1a0bNnT8De8Nu3b090dDQdOnRIC+iX0fZ169bRvHlzoqKiePjhh9NNnJPRswhm27ZtlC9fPi1GVPny5alcuTLDhw9n69attGvXjnbt2gEwfvx4oqKiaNCgAffff39aHSc+sxN544036Nq1KwcPHjxpX4C6detSpEgR/vrrL9q2bcudd95J48aNGTZs2HG9jrVr19KxY0diYmJo1KhRmlJ97rnn0q7zsfSC+BRk5s2zcNj33AMXXOCriocesiGJLDpvRkKCzYFYs8bXueLjYckSyORx5w0Z+aoWhCWreQp5FDk7pHDKr7/+uj7++OOqqnro0CGNi4vT3377TT/88EPt2LGjHj16VLds2aJlypTRiRMnquqxmcrbt2/XqlWrptUVCL/82GOPpYWfVlXt3bu3zpkzR1VVf//9d73oootUVfW2227Tf//736qq+sknnyigiYmJx13DtGnTtGfPniddW8+ePXXatGk6c+ZMLVasmK5bt06PHj2qHTt21IkTJ2piYqImJCRoUlKSqqo+/fTTaeeqXr26Dh8+XFVVR44cqf369Tup/l69eunUqVMzvLf/93//p2PHjlVV1dGjR2uPHj0y3X7JJZfouHHjVFX11VdfTXs269evTwspntGzCGbfvn0aExOjtWvX1ptvvllnzZqVtq969epp92/Lli1arVo13b59uyYnJ2u7du10ypQpWT6zESNGaPfu3fXQoUMnXXPwc12wYIGed955mpqaqm3atNGbb7453eOaNm2qkydPVlXVgwcP6v79+/XLL7/UG264QVNTUzUlJUUvueQS/fbbb086X4Gcp5CSohoXp1qlik0j9sHixaoiqvfcE2KB1attvsIbb/g638cfW/HZs30VzxG40Nm5SyjhlKdPn87y5cvTxgv27NnDr7/+yuzZs+nduzeFCxemcuXKtG/f/qT6FyxYQOvWrdPqKleuXLpyzJgx47gxiL1795KUlMTs2bOZPHkyAJdccklaqOjs0rRpUy7w3sh69+7N3LlzKV68OKtWraJly5YAHDlyhBYtWqSVufTSSwGIi4tLkyEzmjVrxt69e+ncuTPDhg1j/vz5aeWuueaatIRAmW0PpCr95z//yaBBg046R0bPIjj09VlnncXixYuZM2cOM2fO5Morr+Tpp5/m+uuvP66uH374gbZt21KhQgUArr76ambPnk3hwoUzfGZvv/021apVY+rUqZyRwcDoiy++yLvvvkupUqWYMGFCWhTcK6+88qRj9+3bx5YtW+jVqxdAWlKk6dOnM336dBo2bAhYcqFff/2V1q1bp3vOAsWYMbB4seVeDjW+URCqcMcdUL48hBwSrE4dqFDBTEg+zKrNm9vnvHnHxq3zA6e0UsijyNkhhVNWVUaMGEGXLl2OOyac6SNTU1NZsGBBWqOQHerVq8fSpUtJTU2lUKFCafUtXbqUevXqsXnz5gzDc3fq1Inx48enW2/A/JJeeG6A+vXrs2TJEnr06AFYWO9JkyalG0U1XGT0LE6kcOHCtG3blrZt2xIVFcVbb711klLwQ1RUFEuXLmXz5s0Z5tK466670lVo2QnRraoMHjyYm266ybes+ZLduy0kdsuWFkfCB5Mmwdy5li/hhLiMGSMCrVr5HmwuX970Sn4bV3BjCnlEly5dePXVV9NCPq9Zs4b9+/fTunVrJkyYQEpKCtu2bTsuYmeA5s2bM3v2bNavXw9Y+GXgpFDRnTt3ZsSIEWnrAUXVunVrxo0bB8Dnn3+eFio6mFq1atGwYUOeeOKJtG1PPPEEjRo1olatWoB5Ca1fv57U1FQmTJhAq1ataN68Od999x1r164FLAXommzYXG+99VbGjh173IB58CBufHx8WkKc9957jwTvFSuj7c2bN09LrBOcSCeYjJ5FML/88gu//vpr2vrSpUupXr06cPx9b9q0Kd9++y1//fUXKSkpjB8/njZt2mT4zMAi6b7++ut0796drVu3hnyvMqJUqVJUrVo1rYd0+PDhtNweb775ZtoYz5YtW8/u6OwAACAASURBVI4bsyqw/Oc/8NdfFo/IR3yjgwfh3nshOhr69ctm4YQEc1XyUu5ml/h4Uwo+xqojhlMKeUT//v2pV68ejRo1okGDBtx0000cPXqUXr16Ubt2berVq8e11157nOklQIUKFRg1ahSXXnopMTExaSaEbt26MWXKlLSB5uHDh7No0SKio6OpV69emhfUY489xuzZs6lfvz6TJ0/m/PPPT1fG0aNHs2bNGi688EIuvPBC1qxZkxaGG6BJkyYMHDiQunXrUrNmTXr16kWFChUYO3YsvXv3Jjo6mhYtWmQr33ClSpWYMGECgwcPplatWsTHxzNp0iQGDhwIwIgRIxgzZgzR0dG88847DBs2LNPtL730EkOHDiU6Opq1a9eeFJ47s2cRTFJSEtdddx316tUjOjqaVatWpSVDuvHGG7n44otp164d5513Hk8//TTt2rUjJiaGuLg4evTokeEzC9CqVSuef/55LrnkknTdg7PLO++8w/Dhw4mOjiY+Pp4//viDzp07889//pMWLVoQFRXFZZdddtxLRIFk9WpTBjfcAJ5ZLLsMHQq//26WhcKFs1k4DPMV/vrL0kbnGzIabAjHAtwF/ASsBMYDxYHRwDJgOTAJOMs7thgwAVgLLARqZFW/C4iXd8ycOVMvueSSvBYjS/bv35+W/3r8+PHa/cT8vI6TKDD/odRUC2Vdpoyql8c7u2zZolqypGqvXj5lSE62vM+33OKr+E8/2WCz5yORa5AXAfFEpApwO9BYVRsAhYGrgLtUNUZVo4GNwECvSD9gl6rWAl4EwpPqynFas3jxYmJjY4mOjuaVV17hhRdeyGuRHOHio49g+nRLh+YN7GeXwYMhORmee86nDEWKQIsWvnsKF11kwfHy07hCpAeaiwBnikgyUALYqqp7AcRGKc8EAta0HsAQ7/sk4GUREU+rOfIZgQHX/E5CQoJLbXkqcuiQJUCuVw9uucVXFT/8YGmb77sv47TNIZGQYAmZd+3KIFBSxhQqZDolPymFiPUUVHUL8DzWG9gG7FHV6QAiMgb4A7gICIyEVgE2eWWPAnuAk7KhisiNIrJIRBYlJiZmdO7wXozDcZpQYP47Q4daphqf8Y0CkbXPPdcmrOWIhASr0Jscml3i4+Gnn8yJKj8QSfPR2djbf02gMlBSRP4FoKp9vG2rgZMdrTNBVUepamNVbVwhnS5j8eLF2bFjR8H5cTsc+QRVZceOHb5cmHOVDRssFkWvXiHEokif99+3t/Mnn4TSpXMoT7NmpphyMNisCgsW5FCOMBFJ81FHYL2qJgKIyGQgHngXQFVTROR94D5gDLAFqAZsFpEiQBlgR3ZPWrVqVTZv3kxGvQiHw5ExxYsXp2rVqnktRsaoWujSQoUsYp0PDhyA++83Z6UwTDOxyHZNmvhWCk2b2uXMmwcXXxwGeXJIJJXCRqC5iJQADgIdgEUiUktV13pjCt2BgL/iR8B1wHzgMuAbP+MJZ5xxRoYTgBwORwFnzBj46isYOdKyoPng2Wdh0yYLk5RtF9SMSEgwk9bBg6YkssFZZ1nU1PwyrhDJMYWF2IDxEmCFd65RwFsissLbdh7wH6/IaOAcEVkL3A08ECnZHA5HAWTrVhtcbt0aBgzwVcXvv8Mzz8CVV1o1YSMhwdyYvHDq2SU+3or6DKAcViI6eU1VH1PVi1S1gapeo6qHVbWlqkZ5264OeCOp6iFVvVxVa6lqU1X9LZKyORyOAoSqeRkdPmyJkwv5a7ruvdcmPT/7bJjla9nSKp4921fx+HhL5LZyZZjl8oGb0exwOPI/H3wA06bB449D7dq+qpg1CyZOtPGEDCbx+6dsWYiKynEmtvxgQnJKweFw5G8SE+G222ww9847fVVx9KhFQT3/fOstRISEBMuv6cMGVL06nHeeUwoOh8ORNXfcYU78b76ZSX7MzPnf/2D5ckueU6JEmOULkJAA+/fDjz9mu6jIseB4eY1TCg6HI//y8ccwfrzNMMtmTu0Au3bBww9DmzZw2WVhli+YMATHW78etm0Lo0w+cErB4XDkT3bvNi+jqCgLUuSTQASK4cN9RdYOncqVLQ2oT6Xg5aVi/vwwyuQDpxQcDkf+5N574Y8/zGxUtKivKlauhFdegZtusnwJESchwbL1+Iio0LAhFCuW9yYkpxQcDkf+Y8YMGwgYNAgaN/ZVRSC+UalSlocnV0hIsAQJ2cghEqBoURtLLxBKQUSqi0hH7/uZIlIqsmI5HI7TlqQkS5pTuzZ4iYz8MG0afP21KYTy5cMnXqaEYVxh8WILAptXZKkUROQGbGby696mqsDUSArlcDhOYx56yILejR6d7ZARAQKRtevXh5tvDq94mVK7NlSsmCOlcOQILFkSZrmyQSg9hVuBlkBg5vGvQMVICuVwOE5TvvvO0mveeuuxt24fDB1qnjzDhvn2YvWHiMntUykEsu/mpQkpFKVwWFWPBFa8CKYuLrXD4Qgvhw5Bv342w+y///VdzZYt8NRTFlm7Q4cwyhcqCQkWZGnTpmwXrVgRatXK/0rhWxF5EMug1gmYCHwcWbEcDsdpx7//Db/8AqNG2eiwTx54wCYVP/98GGXLDmEYV5g3z5cDU1gIRSk8ACRiUU1vAj4DHo6kUA6H4zRjyRJLlNynD3Tu7Lua+fMtJPY999iUgTwhJsaUWg6Uwp9/mvkrLwjF2nYm8KaqvgEgIoW9bQciKZjD4ThNSE6Gvn2hQgV44QXf1aSmwu232xyyHMx1yzmFC1vLHobgeHmh2ELpKXyNKYEAZwIzIiOOw+E47XjmGVi2DF57LduJ74MZOxYWLbKw2GedFT7xfNG6tSVe3pHt5JHUq2cpQvNqXCEUpVBcVZMCK973SIWUcjgcpxM//WThsK+8Enr08F3Nnj3WO4iPh3/+M4zy+SUwrvDdd9kuWrgwNG+ev5XCfhFpFFgRkTgsvabD4XD4JyXFvI1KlTI31BzwxBMWYTvi8Y1CpUkTm6KcAxPSihWwd2+Y5QqBUMYU7gQmishWQIBKwJURlcrhcJz6DBtmOSjfe8/GE3yyZo1V1acPxMWFUb6cULw4NG2aI6WQmmq3p1OnMMuWBVn2FFT1B+Ai4GZgAFBXVRdHWjCHw3EKs3atxbP+v/+D3r1zVNVdd9nE56eeCpNs4SIhwWJW7N+f7aLNmlmPJy9MSBkqBRFp731eCnQD/uYt3bxtDofDkX1ULbbRGWfY4HIO7D2ffWbLo4/CueeGUcZwkJBgEyYWLsx20dKlLWJ4XiiFzMxHbYBvMIVwIgpMjohEDofj1ObHHy1h8rBhUKWK72qOHLFeQp06lq0z3xEfbwpvzhxo395X8XHjbOilcOEIyJcBGSoFVX1MRAoBn6vqB7knksPhOKWZMgUKFcqxm9CIETae8NlnvtMtRJYyZWwi2+zZvorHx1tHatUq6zXkFpmOKahqKnBfLsnicDhOByZPNj/+HMSz/vNPC4n9979D165hlC3cJCTAggU2QS+bBE9iy01CcUmdISKDRKSaiJQLLBGXzOFwnHqsWWOvvpfmbFjywQfh4EF48cUwyRUpEhLgwAFfsbAvuMAC5OW2UgjFJTXgfnpr0DYF8iqyiMPhKKhMmWKfPXv6ruL332HMGBtP+NvfwiRXpAgOjtesWbaKihwLjpebhOKSWjOdJSSFICJ3ichPIrJSRMaLSHEReU9EfvG2vSkiZ3jHiogMF5G1IrI8eMKcw+E4RZg82dJrVqvmu4r33zcHpltvzfrYPKdSJYuFnYP5CmvXwvbtYZYrEzJzSW0mIstEJElE5otI3exULCJVgNuBxqraACgMXAW8h817iMLiKPX3inQFanvLjcCr2b0Yh8ORj9myBb7/Psemo3HjLAxEnkVBzS4JCTB3rs1GyyaBcYX588MsUyZk1lMYCQwCzgGGAi/5qL8IloehCBYvaauqfqYewPdYek+AHsDb3q4FQFkROc/HOR0OR35kqpfFt1cv31WsXAnLl+eT+EahkpAAO3fC6tXZLhoXZ9M5ctOElJlSKKSqX6nqYVWdCGRrHrqqbgGeBzYC24A9qjo9sN8zG10DfOFtqgIEpyra7G07DhG5UUQWiciixMTE7IjkcDjyksmT4aKLbPHJ+PHmzXrFFWGUK9LkIOlO8eKmGPKLUigrIpcGlnTWM0VEzsbe/msClYGSIvKvoENeAWararbulKqOUtXGqtq4Qg7ipTgcjlxkxw749tscmY5UzXTUsWM+nL2cGRdeaGMLORhX+OEHm6yXG2SmFL7FZjMHluD1/wuh7o7AelVNVNVkbAZ0PICIPIb1PO4OOn4LEDz6VNXb5nA4Cjoff2xTc3NgOlqwADZsKGCmIzA3ooSEHCmFw4dtInhukNmM5j45rHsj0FxESmChtjsAi0SkP9AF6OBNjgvwETBQRN4HmmHmpm05lMHhcOQHpkwxj6MchDEdPx6KFcuRXsk7EhJg4kTzp61ePVtFW7Swz3nzsu3V6otQJq/5QlUXApOAJVh+50LAKOA14FxgvogsFZFHvSKfAb8Ba4E3gFsiJZvD4chFkpJg+nRrzX0Gvzt6FCZMgG7dLFhcgaN1a/v00VuoXBlq1Mi9cYVQJq/5RlUfAx4L5ZyeN1JB8Dx2OBzZ4Ysv4NChHL3if/ON+eoXONNRgAYNLBbSnDnwr39lffwJxMdbDEHVyCcRyrKnIHKyCCJSLDLiOByOU44pU+Ccc6BVK99VjBtnbWq+jnOUGYULQ8uWORpX2LoVNm4Ms1zpEIr5aHTwioichZl6HA6HI3OOHIFPPrH8y0X8GSYOHjRv1n/8w1w0CywJCTZX4a+/sl00N4PjhaIUNovIK5DmZjodeDeiUjkcjlODb76xRMM5MB19+ins21eATUcBAvMV5s7NdtGoKChZMp8oBVV9FEgSkdcwhfCCqo6JuGQOh6PgM2UKnHWWTS7wybhx5ubftm34xMoTGjc29ykfJqQiRczz6LvvIiDXCWQW+yh4otpCoDnwI6AuHafD4ciSlBSYNs0GAnzafXbvtp7CVVflbvaxiFCsmLXsORhXWLbMnLkiSWY9heCJa/+HKYQzCH3ymsPhOJ2ZP9+y4eRgFvPkyTYsUeBNRwESEiy3go+WPT7eYup9/30E5AoikpPXHA7H6cyUKZYn8+9/913FuHEWebpx4zDKlZckJMCTT9r07Gya1IInsflI+RwyobikviUiZYPWzxaRNyMnksPhKPComlLo0MH3bLNt22yc+p//jLxvfq7RooVF9PNhQipbFurXj/xgcyjeR9Gqujuwoqq7gIaRE8nhcBR4li2D9etzZDqaMMF0S+/eYZQrryldGmJjczSuMH++r9QMIROKUijkuaIC4OVnjuhMaIfDUcCZMsXeiLt3913FuHHQqFGOIm3nTxISzHzkI+xpfLwNvv/8cwTk8ghFKbyAxSl6XESeAOYBz0ZOJIfDUeCZMsVm8Fas6Kv4r79auOhTZoA5mIQEm5G3eHG2i+bGJLZQ5im8DVwK/Ikly7lUVd+JnEgOh6NAs3YtrFiRI9PR+PE2jnDllWGUK78QmMQ2e3a2i9aubRFD8lQpeJwBiLecETlxHA5HgWfKFPvs2dNX8UAynTZtoGrVrI8vcFSsCPXq2Sh6NhGx3kKeKgURuQN4DygPVATeFZHbIieSw+Eo0EyZAg0bWrxnH/z4I/zyyylqOgrQqZP1FA4dynbR+Hi7Pz5CKIVEKD2FfkAzVX3MC3nRHLghMuI4HI4Czdat5h6TA9PRuHGWrP4f/wijXPmNTp1MIfiIWxEYV1iwIMwyeYSiFARICVpP8bY5HA7H8UybZp8+A+ClpNh4QteuUK5cGOXKb7RpYwGNvvoq20UbN7aikTIhheJaOgZYKCKeoZCegJu85nA4TmbKFBsNrVfPV/E5c6yzcUqbjsCCBLZoATNmZLtoiRLw5ptmoYsEoXgfDQX6ADu9pY+qvhgZcRwOR4Fl1y6YOdNMRz6nII8bZyGiu3ULs2z5kU6dLA7Sjh3ZLnrNNZbMLRKEMtD8jqouUdXh3vKjiDiXVIfDcTyffGLJlH2ajg4fhkmTrHiJEmGWLT/SqZO5Wn39dV5LchyhjCnUD14RkcJAXGTEcTgcBZYpUyzLfJMmvop/+aV1Nk5501GAxo0tx6iPcYVIklk+hcEisg+IFpG9IrLPW98OTMs1CR0OR/7nwAH44gt7zS8U6vSn4xk3DsqXz1E+noJFkSIW7vSrr6zHkE/I8Omp6n9VtRTwnKqWVtVS3nKOqg7ORRkdDkd+58svLXSDT9PRvn3w0UdwxRXmjnra0LEj/P47rFuX15KkkVlPobqIlAkoABFpJyLDROQuESmaeyI6HI58z5QpcPbZ0Lq1r+LTpplOOW1MRwE6dbLPfGRCyqyf9wFQEkBEYoGJwEYgFngl8qI5HI4CQXIyfPyxRUT1+Zo/bhxUr34skcxpQ61aduEFRCmcqapbve//At5U1Rcw99SmoVTu9Sp+EpGVIjJeRIqLyEARWSsiKiLlg44VERnu7VsuIo18X5XD4cg9Zs2yeM4+TUeJiTB9uuVN8DkcUXARsd7CN9/YzL18QGaPINjRuD3wNYCqhpTeQUSqALcDjVW1AVAYuAr4DugI/H5Cka5AbW+5EXg1lPM4HI48ZsoU8yHt3NlX8YkTrT087UxHATp2hD17YNGivJYEyFwpfCMiH4jIMOBs4BsAETkPCDU7RBHgTBEpApQAtqrqj6q6IZ1jewBvq7EAKOudy+Fw5FdSU2HqVItLceaZvqoYN84mYkVFhVm2gkKHDtZjyCcmpMyUwp3AZGAD0EpVk73tlYCHsqpYVbcAz2PjENuAPao6PZMiVYBNQeubvW3HISI3isgiEVmUmJiYlRgOhyOSLFxoyZR9mo42bLCYcKdtLwHMD7dhw/yvFLw39vdV9UWvgQ9s/1FVv8yqYi+FZw+gJlAZKCki/8qpwKo6SlUbq2rjChUq5LQ6h8ORE6ZMMX/7Sy7xVfz99+3zqqvCKFNBpFMniy6blJTXkoScZMcPHYH1qpro9TImA/GZHL8FqBa0XtXb5nA48iOqMHmymT/KlvVVxbhxFgq6Zs0wy1bQ6NjRvLh8ZGMLN5FUChuB5iJSQkQE6ACszuT4j4BrPS+k5pi5aVsE5XM4HDlh5UqbdOXTdLRihS2ntekoQKtWULx4vjAhRUwpqOpCYBKwBFjhnWuUiNwuIpuxnsByEfmfV+Qz4DdgLfAGcEukZHPkPXv35rUEjhwzZYoNkPbo4av4+PFQuDBcfnmY5SqIFC9uuZsLglIQkZYi8pWIrBGR30RkvYj8FkrlXra2i1S1gapeo6qHvUirVVW1iKpWVtX+3rGqqreq6oWqGqWq+cM/yxFWVq+2jFplykDfvk45FGgmTzbbT6VK2S4ayMPcqZOlLHZgN+OnnyyhRB4SSk9hNDAUaAU0ARp7n47TFVXYvj1bRTZtgn79zPXwq68sxs1bb0FMjCVWKSiowttvQ3S0TeAdNsxMIPkonlnusH49LFvm23Q0f76F/HGmoyACIS/yOJR2KEphj6p+rqrbVXVHYIm4ZI78xeHDFvTsllvg/PPh3HMtE8pvmXcad+yAe+6xZFzvvgt33GFm6AkTTBkULmyZCe+/306Rn/n5Zwtqed11NvN21Sq4805TEJUq2YzcN97I8pacGkzxEjH6VArjxpnFpGfPMMpU0ImOhgoV8tyEFEo6zpki8hzmPZT2t1XVJRGTypE/2LEDPvvMwld+8YW5y5UoAV26wNVXw8svW9rFBx6wVj1o8lJSErz0Ejz3nH2/7jp47DEL8xIgPh6WLjWl8eyzdop33rH/RrY5eBB27jSZd+48eTlx+549ZsOqUMHsFxUqHP89aNvBYmV56r/CM89YVrDXX4f+/U0x/P67RSj45ht7wQu4WNaoYQqkQwf79GFhyZ+oWpjsSZOsm3fBBSEX3b/fpjV89x289571tEqViqCsBY1ChewHM2OG3Wef2etyimgW/V4RmZnOZlXV9pERKXQaN26si/LJ1PBThrVrTQl89BHMnWvxB847z/7B3btbC1e8uB27ZQsMGmQtYc2a8NJLHOnSjVFvCI8/bhamnj3hySezTtn7ySfW0O7aBU88AXffbb2Ik0hOtrgIb71lttdAI3/oUMaVFy1qWeDPOcc+y5WD0qVNMSQmmqCJiekOcHxJZ27hFX7jQq45+2Oej3qbilWLWk+peXO7H+UthJeq9SYCCmLmTAsJBHb9HTrY0qaNbw/O8BFo3NNTmFmtB7p0//43PPpohqfYutUUwNy59rl0qf2cRGz28ttvm15xBPHmm2ZnXbEicvk2ARFZrKqN092XlVLIzzilEAZSUuz1LaAIVntewwGjeffuEBeXeaSymTNJvfU2xq+O4ZEzX2D9wUq0aQNPP23tZqgkJsJNN5llonVra/dr1PB27t4No0bB8OGmjGrVsj/NiY19YAneVqJEaG9dhw+bEImJbFu9m7uG12TCwhr8rVwirzZ7i/ZnzDmmQLZutd6JCMTGmp95x47mWujlkkxJsYbw669tmTPHihQqBI0aQZ06UK3ayUu5chF6SVSFefNg7Fh70w9orPQ488yM72e5ctaLuvzytFf91FQbIw0ogO++s9nKgaqaNbNb07Kl/SbyXCnmVzZutO70iy+abTJC5EgpiMi5wFNAZVXtKiL1gBaqOjr8omYPpxRywOefW8PwySfW0BUpAm3bmhLo1i2oNc4cVbMwPThYWb5CiC20jKcLPUTn+xsiDw7OdrLdwEDubbfZ+vBHErlu85PIm6PNDtW+vXUjunaNSEjNlBR49VV46CHTEQ89BPfdB8WKnXDg0aOweLF19WfMsFYwOdl6JS1bHlMScXFpXZ7Dh03/fvONzVFavx42b7aqgjnzTKhaNX2FEVjKlMnGRW3caHa5sWOtJ1iyJFx66TGlml6jn0UcowMH4PvvjymB+fOt4wVmKmvZ8pgSiI09zRLn5JQ6deyl59NPI3aKzJQCqprpAnwOXAEs89aLACuyKpcbS1xcnDp8MGmSKqiWKaPau7fq+PGqu3Zlu5q5c1UTEqyqCy+0alI2b1W9+mrbWL266pQpqqmp2a57/YeLtXX5nxRUe8oU3X75LapLlmS7nuywaJFqXJyJ3qmT6q+/ZqNwUpLqF1+oDhqkGhtrlYBq2bKqvXqpjhyp+ssvJ92LlBTVrVtVFy60x/Lii6p33616+eWqzZurVqmiWqjQseoCS5kyqnfeqZqYmIE8+/ervvOOaocOqiJWqG1b1bFjVfft832P9uxRffhh1RIljslSv77qjTeqvv226rp1vh63I5hbb1UtWVL18OGInQJYpBm1+RntSDsAfvA+fwzatjSrcrmxOKXgk1atVC+4wPePbu9e1R497NdTqZLqq6+qHjlywkHffqsaFWUHXXyxNYhZcfSo6sSJqi1aqIIeLVNOn2v3qRYtmqoVK6p+/LEvcbNkzx7V22+3xrdSJdX33w9Dw7Z9u1XUv78px0ALWq2aap8+qu++q7psmTXeWXDkiOrvv5sSHj9e9dlnVf/5T5O3VCnV//zHa+dTU1XnzFHt1892gGrNmqpDhqj+9luOLufwYdVhw1TLl7dqr7hC9dNPVXfuzFG1jvSYOtVu8qxZETtFTpXCLOAcYIm33hz4NqtyubE4peCDxYvtsQ8d6ruKZ5+1Kp54wl6QMyQ5WfWll1RLl1YtWlR18OD0C+zda8fVrGkVX3CB6vDhaW+0y5erRkfbrhtuyNGL7nGkpqp+8IHqeefZy/Stt/rqMIV2orVrVV97TfWyy1TPPvv41/4qVewt/oYb7OZOmaK6cqXqwYOZVrtqlXVCQLViyX06ovwQPcwZ9pbZp48p5pSUHImekmKK6IIL7Dzt26v+8EOOqnRkxe7dqoULW5csQuRUKTTCEuPs8T7XANFZlcuNxSkFH1x/vTUaPlu/5GR72W3XLhuFtm1TvfbaY2/KkyZZQ7lpk+q995otBFTj41U//NB6DCdw6JDq/fdb433BBfbWnJx88nL0aPpLSsrxy7p1ql272mkbNlT9/ntft8MfR4+q/vijtbb/+Y/qNdeYreicc45XFiKq559vJqABA1RfeEF12jTTBjt3mr2mfXudTzNtw0zTpxX26nujD+ZUF6iq6owZx8xp0dGqn3/uTEO5Rny8arNmEas+R0rBylMEqA80AM4IpUxuLE4pZJM//7Q39ltu8V3FBx/Yr2bqVB+F58w59sofFaVapIjZQC6/XHX+/JCqmD1btUaN49tOv0upUmYSSU72cS2RYudOG2B4910z+1x9tWrTpif3LgLLBReo/uc/mvrbev38c9WYGNscE6P62Wf+GvEff1Tt3NnqOf980z3hUDKObPDoo/bfiJB9LjOlEIr3UXEsOF0rQIE5wGuqmoljeO7gvI+yyZNPwsMP21TcunV9VdGqleVUWbMmg3kEWXH0qLn3jBpl3jl33BGyp1OAfftg9OiTQ89n9FNOb3vRonDNNVDlpDRO+ZgdO+DXX23ZtMkCqLVqdZz/amqqTRt55BGbWZ0d1+ANG+zn8d575oD00EM2gT0wLcWRi8yda8/3ww/NUyzM5NT76AMs/lE7b3kDmJhVudxYXE8hGxw5olq5srnV+OSHH+zt8aWXwiiXIyIcPqz68suqFSvaM+vZ06xO6ZGYaJ5MRYuqFi+u+sADERpbcYTOkSPWlR0wICLVk8MxhVWhbMuLxSmFbDBhgj3uHLjw/Otf9jvdsyeMcjkiyr59NmxRqpRZI/r2Vd240fbt36/61FPmB1CokDktbdqUt/I6gujWTbVWrYhUnZlSCGX2zxIv6U2g29EMcDabgsbw4XDhhfD3v/sqvm2bBbHr29ciRDgKBmeddcyUdMcdFpSwdm2LpFC7Njz4oM1ZXL4c/vc/mzTnMQcn/wAAGV9JREFUyCd07GiTDQNTw3OJUJRCHDBPRDaIyAZgPtBERFaIyPKISucID4sX27TTgQN9zwJ+9VUbDgjMNHYULMqXh6FDbSzoqqtgzBgLdjt7NkybBvXr57WEjpMIhNLO5aipoQw0V89sv6r+HlaJsoEbaA6R66+3kBZbtmQzPoJx6JA1IC1aWAPiKPgcOGCRLPIoEKcjFFQtpkl8PHzwQVirzmygOZTQ2Rdg7qgAP6lqelFTHfmV7dst7+ENN/hSCGDFExPN/OA4NchmSCpHXiBivYWPPjK3sgjE+kqPDM8iIlVEZCEwBFMMFwBDROR7ESlIjnynN6NGwZEjZjrygarlRYiKgnbtwiybw+HInE6dLFz5jz/m2ikz6ym8DLyqqmODN4rItcArgL9s3Y7cIzkZXnnFkuJcdJGvKmbNOjYI6UwNDkcu06GDfX71lUXczQUy64/UO1EhAKjq24C/FsaRu3z4obkN3X677yqGDbNBSpdL1+HIA84913Kb5OJgc2ZKId19IlII8DOX1ZHbjBhhcdkvvthX8XXrzJw5YECW4fUdDkek6NTJZjgfOJArp8tMKXwiIm+ISMnABu/7a8BnEZfMkTMWLbIsWzlwQ335ZQtlcfPNYZbN4XCETqdONi44d26unC6z1uI+LDLq7yKyWEQWAxuAvcCgXJDNkRNGjLCZS9df76v43r0WX+jKK6Fy5fCK5nA4skFCggXryiUTUoYDzaqaDAwSkUeAWt7mdaqaO30Yh3/+/NOiot14o2831LFjLfCcc0N1OPKYEiUsr2kuKYUs7QqqelBVV3hLthSCiNwlIj+JyEoRGS8ixUWkpogsFJG1IjJBRIp6xxbz1td6+2v4uyRHTt1QU1IsKkZ8PDRpEmbZHA5H9unUCZYts3lHESZisyG8uQy3A41VtQE2OH0V8AzwoqrWAnYB/bwi/YBd3vYXveMc2eXIEYtJcfHFlgDcB599ZoPMrpfgcOQTAiEvvv464qeK9BS5IsCZIlIEKAFsA9oDk7z9bwE9ve89vHW8/R1EnGd8tgmDG+pLL9ns+giEcXc4HH5o2BDOPjtXTEhZKgUx/iUij3rr54tI06zKqeoW4HlgI6YM9gCLgd2qetQ7bDMQmB1dBdjklT3qHX9OOvLcKCKLRGRRYmJiVmKcfgwfbuEvu3TxVXz5cvjmG7j1VigSShAUh8MReQoXtolsX32VcTapMBFKT+EVoAXQ21vfB4zMqpCInI29/dcEKgMlAX8O80Go6ihVbayqjStUqJDT6k4tfvgBFizIkRvq8OE2J+GGG8Ism8PhyBmdOsHmzfDLLxE9TSgtRzNVvRU4BKCqu4CiIZTrCKxX1UTPk2ky0BIo65mTAKoCW7zvW4BqAN7+MsCOUC/EQY7dUBMTLd7+dddZOkaHw5GPCIwrzJgR0dOEohSSRaQwlp8ZEakApIZQbiPQXERKeGMDHYBVwEzgMu+Y64BAMOaPvHW8/d9oVnG9Hcf44w9zQ+3Tx3cWnFGj4PDhHA1HOByOSFGzpiXKivC4QihKYTgwBagoIk8Cc4GnsiqkqguxAeMlwArvXKOA+4G7RWQtNmYw2isyGjjH23438ED2LuU0Z9QoC4Dn0w31yBEYOdKGIurWDbNsDocjPHTsCDNn2n89QmSZZAdARC7C3vQF+FpVV0dMomzgkux4HDkC1aubh8Jn/iKQjBsHV19txbt2DbN8DocjPHz4IVx2mYW8aNnSdzW+kuyISLBVeTswPnifqu70LZEjvEyaZOYjn3afQM6EOnV8Oy05HI7coH17cyKZMSNHSiEzMnM6XIyNIwTPFQisK5Z0x5EfGD4c/vY36NzZV/EFC8xxaeTIXEvu5HA4/HD22dC4sY0rPPZYRE6RWeyjmhE5oyO8fP89LFxonkc+W/SXXoKyZeHaa8Msm8PhCD8dO8Izz1jUSp9OJZkRyuS1RuksFwa5lTrykhEjoFQp8yP1waZNZqa84QbzZnU4HPmcTp0sQNmsWRGpPtTJawswz6E3vO8TgV9ExJ+9whEe/vgDJkwwN9RSpXxVMXKkjSncemuYZXM4HJGhRQuLnBoh19RQlMJWoKE3izgOiAV+AzoBz0ZEKkdovP56jtxQ9+83T9ZLLzXnJYfDUQAoVszcUv/734hUH4oJ6G+q+lNgRVVXichFqvqbi1eXhwSiof797xbryAfvvgu7drloqA5HgaNpluHnfBOKUvhJRF4F3vfWrwRWiUgxIHIzKByZM3GiJdPJgRvqsGEQFxcxzzaHw1EACUUpXA/cAtzprX+HpeNMBtpFRixHlgwfbhMLAvFQsslXX8Hq1fD22+A6fA6HI0CWSkFVD4rICGA6Nj/hFy/AHUBSJIVzZMC8eeaK+vLLOXJDrVQJrrgizLI5HI4CTSguqW2BX4GXMU+kNSLSOsJyOTLjkUegQgXfbqgrVsDnn8PNN9uYlcPhcAQIxXz0AtBZVX8B+P/27jxIqvLc4/j3YZO4ICioIHAVymCpMSi4XrcSI0Iui1ESvJbBSEAULVFxiwu7C3i1rnErxB2TeImiqCAgpC6aEnSgWI3AgKAii6KRq0YF57l/vGeaduie6Z6e093T8/tUdc3p0+d0P3Omu595z3nf5zWznxJKXnSLMzBJY/78cLv//loNLNi5M/Rgbd0arrwyhvhEpF7LJCk0rUwIAO6+xsyaxhiTpOMOt94K7dvDsGG1eop77oHFi0O5pNat6zg+Ean3MkkKZWY2BZga3b8YUGnSQnj11VCoaPJkaN48692XLYOxY2HgQLjgghjiE5F6r8bS2VHX0+HAadGqN4GH3f27mGOrUYMqnV1REUpjf/116DbUNLvG2vffwwknwLZtsHIlHLjH7Nci0lDUqnR2JXf/zsweBOayZ+8jyZdp02D5cnjuuawTAsC4cWH3GTOUEEQkvRqTQtT76GlgA6FsdgczG+TuC+INTRJ27YI77oBjjgnnfrJUVhZGxA8aBH36xBCfiJQM9T6qD555BtasgenTsx6X8O23IRkcckgYmyAiUh31Pip2330HY8aECwL9+mW9+6hR8N57YVxCy5YxxCciJUW9j4rd5Mnw4YcwZUrW9SjefhvuvTfMlXDeeTHFJyIlRb2PitnXX0PnznDkkaFUbhZJ4ZtvoGvX0OtoxYpaT7cgIiUo595HwH3RTfLpwQdDJdQXXsi6lXDrrbB2Lcybp4QgIplLe9XSzPqZ2fCk+4vMbH10G5Cf8Bqwf/4zDD/u3Tvr2tYLFoSy2MOHw9lnxxSfiJSk6rqy3AjMSLq/F3ACcBZQuxoLkrn77gsz4Iwfn9VuX30Fl14KnTqFnCIiko3qkkIzd/8o6f5b7r7d3T8E9qnpic2si5ktTbrtMLMRZvZzM3vbzFaY2Stm1iJpn1vMrNzMVptZzxx+r/rt009DwbsBA8Io5izcdBNs2ABPPgn71PhXEhH5seqSQqvkO+6ePBFwm5qe2N1Xu3tXd+9KGNPwDTAdmALc7O4/i+7fAGBmRwEDgaOB84CHzaxxFr9L6bj77nCleOzYrHabNw8efhhGjIDTT48pNhEpadUlhUVmNqTqSjO7HHgny9fpAaxz943AT4HK0dBzgcrSbP2Av7j7d+7+AVAOxDcRabHatAkeegguuST0OsrQjh1w2WVhMrYJE2KMT0RKWnW9j64FXjKz/wSWROu6Ea4t9M/ydQYSRkEDrCIkgJeAAUCHaP2hwMKkfT6O1v2ImQ0FhgJ07NgxyzDqgfHjQ/G7UaOy2u266+Djj8OkbD/5SUyxiUjJS9tScPdt7n4qMI5Q92gDMNbdT3H3rZm+gJk1A/oC06JVlwFXmtliYD/g+2wCdvfJ7t7d3bu3aVPjWaz6Zf36MEhtyBA4/PCMd5s1Cx5/HG68EU46Kcb4RKTkZTJOYT4wP4fX6AUsqUwk7v4+cC4k6ij9MtpuE7tbDQDto3UlZ8cOeOSRcA359NOT/rMfPRqaNAmDDDL0xRfw+9/D0UeH3UVEcpFJmYtcXcTuU0eY2UHuvs3MGgG3AY9GD80A/mRm9wHtgCPI/tpFvXDnnbu7izZvDmeeCeceu4Wezy7hqJFXY+3aZfxc11wTxrfNmKH5lkUkd9mV3MySme0D/AJ4MWn1RWa2Bngf+AR4EsDdVwH/A7wHvA4Md/cf4oyvED79NAxUHjAAZs6Eyy+HjRvh+kmHcAwr6finuxg8GJ5/HrZvr/65Xn4Znn02NCy6qWatiNSBGmsfFbOC1T5atQquugp+85swUiyLqTFvuikUqVu1Kqlz0eLFfNj9fOb0eZDZe/XljTfCgGazUBy1Z89wO+mkcHYJ4LPPwimjdu1g0SJo1qzOf0sRKVHV1T5SUqiNvn3DfMnuYaKC668P//LXUGRo27Zw/fj882Hq1KQHevWCd96BDz6AFi3YtQvefRfmzIHZs8OXfkUFtGgBPXqEBDF7dgihrAyOPTbeX1dESkt1SQF3r7e3bt26ed6VlbmD+7hx7vPmuffoEe63auU+apT7Z5+l3XXkSPdGjdxXr05auWBB2H/ixLT7ff65+7Rp7kOGuHfsGDYH9/Hj6+7XEpGGAyjzNN+railkq29feOutUEuiRVShY9GiMN/lyy+H2hLDhoXWQ9u2id22bg2thAsvDBOpAeG7/cwzQznTdetg771rfHl3WL06nH7q3x8aN8wx3yKSg+paCrFeaC45ixfDK6+EL/wWLXavP+kkeOmlMHFB//6hbtFhh8EVV4SxB8DEiWEStdtvT3q+OXPgzTfDygwSAoTrDEceCRdcoIQgInVPLYVspGolpLJuHUyaFKrS/fADW/oPo9NrDzDg1414+uloG/dwFXn79vCvv64Ui0ieqKVQF8rKUrcSUuncGR59NFw4HjGCe14+ku+/reD2T64IV5ABpk8PLY9Ro5QQRKRoqKWQqT594O9/r7mVUMXmzdCpkzPwyKU8ubFHGIJ8zjlhcELjxuGUU5N8jCEUEQnUUshVWVno/5lJK6GKu++GnTuN2/56XEgEkybBypXh4vLYsUoIIlJU1FLIRJ8+ofxoNI4gU5s2hTNJF18cCtYlfPstLF8erilkOfeyiEiu1FLIRY6thB9+gNtuq/JA8+Zw4olKCCJSdJQUajJ6NBxwQChrkYVNm2Dy5FAFI4sq2CIiBaWkUJ1334XXXqtVK+Guu0JpiiyqYIuIFJySQnXGjKlVK+Gjj+Cxx8L0mIcdFk9oIiJxUFJIJ8dWgrtaCSJS/zTIpLBrF7z4YvjiTquylXD11Vk994cfhhk1Bw+GUpxCWkRKW4NMCk8+GWoHXXVVSBB7eOed0EoYObLGcthV3Xln6FT0hz/UTawiIvnUIEdODR4M5eWhSF15eZjlrGXLpA1qeS1h40Z44gkYMgQ6dKh5exGRYtMgWwqNGoU5kqdMgfnz4dRTE8VMQyth5sxatRImTAithFtuqfuYRUTyoUEmhUqDB8PcubBlS6h+/dZb1LqV8MEH4bTUkCHQvn088YqIxK1BJwWAs84Kc+S0agU9zq5g6sxWtW4lNG6sVoKI1G8NPikAHHEELFwI/95iBZcwldu/uI6Kisz3X78ennoKhg6FQw+NLUwRkdgpKUQOWLuI17efwODuyxg/aS8uugj+9a/M9h0/Hpo2hZtvjjdGEZG4KSlUGjOGZge24LF5nZg0CaZNC6eWtmypfrfy8jDn8uWXQ7t2eYlURCQ2SgoQLirMmgUjR2It9mPkyDC4beXKUMx02bL0u06YEFoJN92Uv3BFROKipAChx9GBB8Lw4YlV/fuH3kgVFXDaaaF6dlXl5fDss3DFFdC2bR7jFRGJSWxJwcy6mNnSpNsOMxthZl3NbGG0rszMToy2NzN7wMzKzWy5mR0fV2w/ktRKqNrj6LjjwrCFLl2gb1+4//4fl8YYNy5Mr6xWgoiUitiSgruvdveu7t4V6AZ8A0wHJgJjovV3RPcBegFHRLehwCNxxfYjo0fv0UpI1q4dLFgA558P110XWgU7d8KaNTB1Klx5JRx8cF4iFRGJXb7KXPQA1rn7RjNzoLLs6P7AJ9FyP+AZD/ODLjSzlmbW1t03xxbVwoXw+uthirRqxiXsvXe48HzbbaECank57L9/mEDtxhtji05EJO/ylRQGAn+OlkcAs83sXkJL5dRo/aHAR0n7fByt+1FSMLOhhJYEHXMtQ5riWkI6jRqFYndduoRRyzt3wg03wEEH5RaCiEgxif1Cs5k1A/oC06JVVwDXunsH4Frg8XT7puLuk929u7t3b9OmTe0Dq2wl3HAD7LtvxrsNGgRvvAEDB6qVICKlJx8thV7AEnffGt0fBFwTLU8DpkTLm4Dk2qLto3XxyKKVUNUZZ4SbiEipyUeX1IvYfeoIwjWEM6Pls4G10fIM4LdRL6STgS9ju55Qy1aCiEipi7WlYGb7AL8ALk9aPQT4bzNrAnxLdH0AmAn0BsoJPZV+F2ds9OxZq1aCiEgpM692Tsri1r17dy8rKyt0GCIi9YqZLXb37qke04hmERFJUFIQEZEEJQUREUlQUhARkQQlBRERSVBSEBGRBCUFERFJUFIQEZGEej14zcw+BTbWcvfWwGd1GE5dK/b4oPhjVHy5UXy5Keb4/s3dU1YUrddJIRdmVpZuRF8xKPb4oPhjVHy5UXy5Kfb40tHpIxERSVBSEBGRhIacFCYXOoAaFHt8UPwxKr7cKL7cFHt8KTXYawoiIrKnhtxSEBGRKpQUREQkoeSTgpmdZ2arzazczG5O8fheZvZ89PgiMzssj7F1MLO/mdl7ZrbKzK5Jsc1ZZvalmS2NbnfkK77o9TeY2YrotfeY0SiaPvWB6PgtN7Pj8xhbl6TjstTMdpjZiCrb5P34mdkTZrbNzFYmrTvAzOaa2droZ6s0+w6KtllrZoPyGN8kM3s/+htON7OWafat9v0QY3yjzWxT0t+xd5p9q/28xxjf80mxbTCzpWn2jf345czdS/YGNAbWAZ2AZsAy4Kgq21wJPBotDwSez2N8bYHjo+X9gDUp4jsLeLWAx3AD0Lqax3sDswADTgYWFfBvvYUwKKegxw84AzgeWJm0biJwc7R8M3BPiv0OANZHP1tFy63yFN+5QJNo+Z5U8WXyfogxvtHAyAzeA9V+3uOKr8rj/wXcUajjl+ut1FsKJwLl7r7e3b8H/gL0q7JNP+DpaPmvQA8zs3wE5+6b3X1JtPx/wD+AQ/Px2nWoH/CMBwuBlmbWtgBx9ADWuXttR7jXGXdfAHxeZXXy++xpoH+KXXsCc939c3f/ApgLnJeP+Nx9jrvviu4uBNrX9etmKs3xy0Qmn/ecVRdf9N3xa+DPdf26+VLqSeFQ4KOk+x+z55duYpvoQ/ElcGBeoksSnbY6DliU4uFTzGyZmc0ys6PzGhg4MMfMFpvZ0BSPZ3KM82Eg6T+IhTx+lQ52983R8hbg4BTbFMuxvIzQ+kulpvdDnK6KTm89keb0WzEcv9OBre6+Ns3jhTx+GSn1pFAvmNm+wAvACHffUeXhJYRTIj8H/gi8lOfwTnP344FewHAzOyPPr18jM2sG9AWmpXi40MdvDx7OIxRlX3AzuxXYBTyXZpNCvR8eAToDXYHNhFM0xegiqm8lFP3nqdSTwiagQ9L99tG6lNuYWRNgf2B7XqILr9mUkBCec/cXqz7u7jvc/atoeSbQ1Mxa5ys+d98U/dwGTCc00ZNlcozj1gtY4u5bqz5Q6OOXZGvlabXo57YU2xT0WJrZpcB/ABdHiWsPGbwfYuHuW939B3evAB5L87qFPn5NgF8Bz6fbplDHLxulnhTeBY4ws8Oj/yYHAjOqbDMDqOzlcSEwP90Hoq5F5x8fB/7h7vel2eaQymscZnYi4W+Wl6RlZvuY2X6Vy4SLkSurbDYD+G3UC+lk4Muk0yT5kva/s0IevyqS32eDgJdTbDMbONfMWkWnR86N1sXOzM4DbgT6uvs3abbJ5P0QV3zJ16nOT/O6mXze43QO8L67f5zqwUIev6wU+kp33DdC75g1hF4Jt0brxhLe/ADNCacdyoF3gE55jO00wmmE5cDS6NYbGAYMi7a5ClhF6EmxEDg1j/F1il53WRRD5fFLjs+Ah6LjuwLonue/7z6EL/n9k9YV9PgREtRmYCfhvPZgwnWqecBa4A3ggGjb7sCUpH0vi96L5cDv8hhfOeF8fOX7sLJHXjtgZnXvhzzF92z0/lpO+KJvWzW+6P4en/d8xBetf6ryfZe0bd6PX643lbkQEZGEUj99JCIiWVBSEBGRBCUFERFJUFIQEZEEJQUREUlQUhDJgJkdmFQFc0tSxc6vzOzhQscnUlfUJVUkS2Y2GvjK3e8tdCwidU0tBZEcWJiv4dVoebSZPW1mb5rZRjP7lZlNjOrnvx6VNMHMupnZ/0ZF0WYXqKqsSEpKCiJ1qzNwNqFA31Tgb+7+M+BfwC+jxPBH4EJ37wY8AUwoVLAiVTUpdAAiJWaWu+80sxWESV9ej9avAA4DugDHAHOjkkyNCSUTRIqCkoJI3foOwN0rzGyn775oV0H4vBmwyt1PKVSAItXR6SOR/FoNtDGzUyCUTi/gxD8ie1BSEMkjD9NEXgjcY2bLCBVJTy1sVCK7qUuqiIgkqKUgIiIJSgoiIpKgpCAiIglKCiIikqCkICIiCUoKIiKSoKQgIiIJ/w/tbMt+w4fBogAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.datasets import make_regression\n",
        "regr_rfr_20 = RandomForestRegressor()\n",
        "regr_rfr_20.fit(train_data_open_20, y_train_data_open_20)\n",
        "y_pred_rfr_20 = regr_rfr_20.predict(X_test_20)\n",
        "y_pred_rfr_20"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaEwZjcm0K8_",
        "outputId": "f97eeac0-0211-46bc-d824-5cfcdf3a3d29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([781.0385, 780.5835, 786.9195, 784.1366, 794.6134, 802.0439,\n",
              "       804.7469, 806.4532, 805.0526, 802.5785, 800.8086, 801.6135,\n",
              "       800.8442, 800.3343, 800.6941, 800.1165, 800.1156, 799.8669,\n",
              "       797.4077, 798.2403])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = test_open.flatten()\n",
        "err=0\n",
        "b = y_pred_rfr_20.flatten()\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "err = mse(a, b)\n",
        "print(err)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ocx4zRKo2OCF",
        "outputId": "e1079769-f333-4f5b-c7db-2a788c4db3f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "239.14878688450617\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_rfr_20 = r2_score(a, b)\n",
        "r2_rfr_20"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VlWtji3G68SD",
        "outputId": "df849a0f-ded4-46d7-83a5-fbdcefa37f33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.10034585260040174"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_open.flatten(), color = 'red', label = 'Real Open Google Stock Price')\n",
        "plt.plot(y_pred_rfr_20, color = 'blue', label = 'Predicted Open Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction using RFR(20)')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Open Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "I0WT3OrF2Vg_",
        "outputId": "a2903329-c9f8-4cca-90fd-8b39dd6badd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install lightgbm\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZyJ1alFi2ekK",
        "outputId": "e801452f-bbb6-4483-c6e1-d558a9458b98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.7/dist-packages (2.2.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from lightgbm) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from lightgbm) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from lightgbm) (1.0.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->lightgbm) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->lightgbm) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "regr_lgb_20 = lgb.LGBMRegressor()\n",
        "regr_lgb_20.fit(train_data_open_20, y_train_data_open_20)\n",
        "y_pred_regr_lgb_20 = regr_lgb_20.predict(X_test_20)\n",
        "y_pred_regr_lgb_20\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2eDBvOY4e25",
        "outputId": "2771e8d1-5e91-4591-d324-5867d6838001"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([777.10881158, 770.68092926, 782.10230909, 785.32644472,\n",
              "       784.55580695, 787.18446582, 786.37502959, 786.25344134,\n",
              "       786.57342347, 787.9773825 , 785.79805298, 786.39059616,\n",
              "       786.28500613, 786.1354202 , 787.98968807, 792.55635356,\n",
              "       788.08172543, 788.42485383, 788.52891369, 783.15203706])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = test_open.flatten()\n",
        "err=0\n",
        "b = y_pred_regr_lgb_20.flatten()\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "err = mse(a, b)\n",
        "print(err)  \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOQ7HjlX5G-1",
        "outputId": "2595dec5-89b6-49a6-82f2-1fa738afadcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "628.9537104752876\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_lgb_20 = r2_score(a, b)\n",
        "r2_lgb_20"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_A6K1l67IxM",
        "outputId": "030ccae5-0ce2-4554-c819-447221edee81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.893874628489507"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_open.flatten(), color = 'red', label = 'Real Open Google Stock Price')\n",
        "plt.plot(y_pred_regr_lgb_20, color = 'blue', label = 'Predicted Open Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction using RFR(20)')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Open Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "4ZtN690A54Ry",
        "outputId": "779bfeed-a4a2-4610-906a-ec25162f3c2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Transforming input for RNN feeding"
      ],
      "metadata": {
        "id": "IlPMWFnU8icD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_open = []\n",
        "test_data_open = []\n",
        "for i in range(20, len(train_open_mms)):\n",
        "    train_data_open.append(train_open_mms.flatten()[i-20:i])\n",
        "    test_data_open.append(train_open_mms.flatten()[i])\n",
        "train_data_open = np.array(train_data_open)\n",
        "test_data_open = np.array(test_data_open)\n",
        "\n",
        "\n",
        "from numpy import newaxis\n",
        "train_data_open = train_data_open[:, :, newaxis]\n",
        "train_data_open\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WvH_7OgH8gsc",
        "outputId": "6e4edcf7-f1a6-416b-c7b9-b3487855aa41"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.08581368],\n",
              "        [0.09701243],\n",
              "        [0.09433366],\n",
              "        ...,\n",
              "        [0.00967334],\n",
              "        [0.01642607],\n",
              "        [0.02100231]],\n",
              "\n",
              "       [[0.09701243],\n",
              "        [0.09433366],\n",
              "        [0.09156187],\n",
              "        ...,\n",
              "        [0.01642607],\n",
              "        [0.02100231],\n",
              "        [0.02280676]],\n",
              "\n",
              "       [[0.09433366],\n",
              "        [0.09156187],\n",
              "        [0.07984225],\n",
              "        ...,\n",
              "        [0.02100231],\n",
              "        [0.02280676],\n",
              "        [0.02273235]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[0.91600938],\n",
              "        [0.9132934 ],\n",
              "        [0.88979835],\n",
              "        ...,\n",
              "        [0.95475854],\n",
              "        [0.95204256],\n",
              "        [0.95163331]],\n",
              "\n",
              "       [[0.9132934 ],\n",
              "        [0.88979835],\n",
              "        [0.86589404],\n",
              "        ...,\n",
              "        [0.95204256],\n",
              "        [0.95163331],\n",
              "        [0.95725128]],\n",
              "\n",
              "       [[0.88979835],\n",
              "        [0.86589404],\n",
              "        [0.89030062],\n",
              "        ...,\n",
              "        [0.95163331],\n",
              "        [0.95725128],\n",
              "        [0.93796041]]])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "from keras.layers.recurrent import LSTM\n",
        "model.add(LSTM(128, input_shape=(20, 1), activation='relu', return_sequences=True))\n",
        "\n",
        "\n",
        "model.add(LSTM(64, activation='relu'))\n",
        "\n",
        "\n",
        "model.add(Dense(32, activation='relu'))\n",
        "\n",
        "\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "\n",
        "model.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
        "final = model.fit(train_data_open, test_data_open, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJEfO8ke5H1C",
        "outputId": "c3e7a460-32ff-4179-f03b-b47e9d0d2601"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "39/39 [==============================] - 7s 64ms/step - loss: 0.1846\n",
            "Epoch 2/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0318\n",
            "Epoch 3/100\n",
            "39/39 [==============================] - 3s 64ms/step - loss: 0.0227\n",
            "Epoch 4/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0166\n",
            "Epoch 5/100\n",
            "39/39 [==============================] - 3s 64ms/step - loss: 0.0122\n",
            "Epoch 6/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0089\n",
            "Epoch 7/100\n",
            "39/39 [==============================] - 2s 64ms/step - loss: 0.0065\n",
            "Epoch 8/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0051\n",
            "Epoch 9/100\n",
            "39/39 [==============================] - 3s 64ms/step - loss: 0.0038\n",
            "Epoch 10/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0031\n",
            "Epoch 11/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0026\n",
            "Epoch 12/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0021\n",
            "Epoch 13/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0018\n",
            "Epoch 14/100\n",
            "39/39 [==============================] - 3s 64ms/step - loss: 0.0016\n",
            "Epoch 15/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0020\n",
            "Epoch 16/100\n",
            "39/39 [==============================] - 3s 64ms/step - loss: 0.0016\n",
            "Epoch 17/100\n",
            "39/39 [==============================] - 3s 64ms/step - loss: 0.0014\n",
            "Epoch 18/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0016\n",
            "Epoch 19/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0013\n",
            "Epoch 20/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0011\n",
            "Epoch 21/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0011\n",
            "Epoch 22/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 9.8165e-04\n",
            "Epoch 23/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0011\n",
            "Epoch 24/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 8.9511e-04\n",
            "Epoch 25/100\n",
            "39/39 [==============================] - 3s 64ms/step - loss: 9.5969e-04\n",
            "Epoch 26/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 8.4592e-04\n",
            "Epoch 27/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 8.5773e-04\n",
            "Epoch 28/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 7.5249e-04\n",
            "Epoch 29/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 9.0457e-04\n",
            "Epoch 30/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 8.5836e-04\n",
            "Epoch 31/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 8.8364e-04\n",
            "Epoch 32/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 8.1351e-04\n",
            "Epoch 33/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 8.2895e-04\n",
            "Epoch 34/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 8.7221e-04\n",
            "Epoch 35/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 6.3032e-04\n",
            "Epoch 36/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 6.2643e-04\n",
            "Epoch 37/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 6.7386e-04\n",
            "Epoch 38/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 0.0011\n",
            "Epoch 39/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 6.8107e-04\n",
            "Epoch 40/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 6.1647e-04\n",
            "Epoch 41/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 5.2141e-04\n",
            "Epoch 42/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 6.4861e-04\n",
            "Epoch 43/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 6.7905e-04\n",
            "Epoch 44/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 6.1775e-04\n",
            "Epoch 45/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 5.5475e-04\n",
            "Epoch 46/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.8536e-04\n",
            "Epoch 47/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 5.0072e-04\n",
            "Epoch 48/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.7757e-04\n",
            "Epoch 49/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 5.7532e-04\n",
            "Epoch 50/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.8771e-04\n",
            "Epoch 51/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.6952e-04\n",
            "Epoch 52/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.4246e-04\n",
            "Epoch 53/100\n",
            "39/39 [==============================] - 3s 64ms/step - loss: 5.0073e-04\n",
            "Epoch 54/100\n",
            "39/39 [==============================] - 3s 64ms/step - loss: 4.3221e-04\n",
            "Epoch 55/100\n",
            "39/39 [==============================] - 3s 64ms/step - loss: 4.0800e-04\n",
            "Epoch 56/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 4.1667e-04\n",
            "Epoch 57/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.2422e-04\n",
            "Epoch 58/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 4.4842e-04\n",
            "Epoch 59/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.4884e-04\n",
            "Epoch 60/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.9784e-04\n",
            "Epoch 61/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.9370e-04\n",
            "Epoch 62/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.7282e-04\n",
            "Epoch 63/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.5673e-04\n",
            "Epoch 64/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.1889e-04\n",
            "Epoch 65/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.7173e-04\n",
            "Epoch 66/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.0719e-04\n",
            "Epoch 67/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.2830e-04\n",
            "Epoch 68/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.8677e-04\n",
            "Epoch 69/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.1239e-04\n",
            "Epoch 70/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.9359e-04\n",
            "Epoch 71/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.3108e-04\n",
            "Epoch 72/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.5432e-04\n",
            "Epoch 73/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.8199e-04\n",
            "Epoch 74/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.3788e-04\n",
            "Epoch 75/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.7015e-04\n",
            "Epoch 76/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.6929e-04\n",
            "Epoch 77/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 4.1260e-04\n",
            "Epoch 78/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.6306e-04\n",
            "Epoch 79/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 3.2680e-04\n",
            "Epoch 80/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.6002e-04\n",
            "Epoch 81/100\n",
            "39/39 [==============================] - 3s 67ms/step - loss: 3.6279e-04\n",
            "Epoch 82/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 3.0314e-04\n",
            "Epoch 83/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.0838e-04\n",
            "Epoch 84/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.8557e-04\n",
            "Epoch 85/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 3.6021e-04\n",
            "Epoch 86/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.1285e-04\n",
            "Epoch 87/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.1810e-04\n",
            "Epoch 88/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 3.8414e-04\n",
            "Epoch 89/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 3.0206e-04\n",
            "Epoch 90/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 3.3354e-04\n",
            "Epoch 91/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.3717e-04\n",
            "Epoch 92/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 4.6079e-04\n",
            "Epoch 93/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 3.6025e-04\n",
            "Epoch 94/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 3.4567e-04\n",
            "Epoch 95/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.5866e-04\n",
            "Epoch 96/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 2.9673e-04\n",
            "Epoch 97/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 3.3416e-04\n",
            "Epoch 98/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 3.1804e-04\n",
            "Epoch 99/100\n",
            "39/39 [==============================] - 3s 65ms/step - loss: 3.0016e-04\n",
            "Epoch 100/100\n",
            "39/39 [==============================] - 3s 66ms/step - loss: 2.8165e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(final.history['loss'], label='train loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "AE5VaOh1kLic",
        "outputId": "261be770-ce2a-400e-8dc6-3fb5a3e7ef41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = train_open.flatten()\n",
        "req_train = x[len(x)-20:]\n",
        "y = test_open.flatten()\n",
        "z = np.concatenate([req_train, y])\n",
        "z_1 = np.reshape(z, (len(z), 1))\n",
        "mms = MinMaxScaler(feature_range = (0, 1))\n",
        "\n",
        "z_2 = mms.fit_transform(z_1)\n",
        "X_test = []\n",
        "for i in range(20, 40):\n",
        "    X_test.append(z_2.flatten()[i-20:i])\n",
        "X_test = np.array(X_test)\n"
      ],
      "metadata": {
        "id": "haorNWKD-5mu"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_open = model.predict(X_test)\n",
        "\n",
        "pred_open_ = mms.inverse_transform(pred_open)\n",
        "plt.plot(test_open, color = 'red', label = 'Real Open Google Stock Price')\n",
        "plt.plot(pred_open_, color = 'blue', label = 'Predicted Open Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Open Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jGUIXwqwBUas",
        "outputId": "a5a601c9-ccd6-4dac-fee0-640dde750801"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = test_open.flatten()\n",
        "err=0\n",
        "b = pred_open_.flatten()\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "err = mse(a, b)\n",
        "print(err)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fy-j45nnCiLs",
        "outputId": "873db92e-c585-4141-fe17-814d5c09a07a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79.83507330259104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_lstm_20 = r2_score(a, b)\n",
        "r2_lstm_20"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NE9tzKKp7Wd9",
        "outputId": "e45a00ea-47bc-4f80-dac4-600f8e3dea68"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6326713886155764"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With last 40 timesteps as features"
      ],
      "metadata": {
        "id": "5WZRVfC-zod9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_open_40 = []\n",
        "y_train_data_open_40 = []\n",
        "for i in range(40, len(train_open_mms)):\n",
        "    train_data_open_40.append(train_open.flatten()[i-40:i])\n",
        "    y_train_data_open_40.append(train_open.flatten()[i])\n",
        "train_data_open_40 = np.array(train_data_open_40)\n",
        "y_train_data_open_40 = np.array(y_train_data_open_40)\n",
        "\n",
        "train_data_open_40.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1O3-8tYMzrlE",
        "outputId": "57d88043-5434-4c4a-ce83-18fe8f025f9c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1218, 40)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_40 = train_open.flatten()\n",
        "req_train_40 = x_40[len(x_40)-40:]\n",
        "y_40 = test_open.flatten()\n",
        "z_40 = np.concatenate([req_train_40, y_40])\n",
        "z_1_40 = np.reshape(z_40, (len(z_40), 1))\n",
        "\n",
        "X_test_40 = []\n",
        "for i in range(40, 60):\n",
        "    X_test_40.append(z_1_40.flatten()[i-40:i])\n",
        "X_test_40 = np.array(X_test_40)"
      ],
      "metadata": {
        "id": "p1msCBRxzwbQ"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg_40 = LinearRegression()\n",
        "reg_40.fit(train_data_open_40, y_train_data_open_40)\n",
        "y_pred_lr_40 = reg_40.predict(X_test_40)\n",
        "y_pred_lr_40"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aEoitzpyzy_x",
        "outputId": "86d25e78-aa95-4d72-b0b1-395c3a3c72fe"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([782.57954199, 781.63285608, 789.56298071, 789.78010728,\n",
              "       795.67607857, 806.55401922, 809.94430951, 801.57222844,\n",
              "       804.72324558, 810.83411684, 806.363678  , 804.20072738,\n",
              "       803.3884097 , 805.72419185, 807.62786644, 818.79541015,\n",
              "       828.55181016, 839.85471044, 830.94156283, 812.29458551])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = test_open.flatten()\n",
        "err=0\n",
        "b = y_pred_lr_40.flatten()\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "err = mse(a, b)\n",
        "print(err)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SAzmpF2_z1ur",
        "outputId": "eaf9c106-e7cd-40b1-fe01-031acf420f29"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "62.96470672035705\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_lr_40 = r2_score(a, b)\n",
        "r2_lr_40"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eOj9_HRw9hdD",
        "outputId": "168a15a1-d010-4ffc-c80d-7412f45f061f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7102935172595928"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_open.flatten(), color = 'red', label = 'Real Open Google Stock Price')\n",
        "plt.plot(y_pred_lr_40, color = 'blue', label = 'Predicted Open Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Open Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "d2o49ihvz5LX",
        "outputId": "abb99022-7423-4d8c-89eb-ca97a8d6291e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_open = []\n",
        "test_data_open = []\n",
        "for i in range(40, len(train_open_mms)):\n",
        "    train_data_open.append(train_open_mms.flatten()[i-40:i])\n",
        "    test_data_open.append(train_open_mms.flatten()[i])\n",
        "train_data_open = np.array(train_data_open)\n",
        "test_data_open = np.array(test_data_open)\n",
        "\n",
        "\n",
        "from numpy import newaxis\n",
        "train_data_open = train_data_open[:, :, newaxis]\n",
        "train_data_open\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vr_dJWrfByqc",
        "outputId": "3e2213dc-4915-42b3-ce97-541a459edb04"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.08581368],\n",
              "        [0.09701243],\n",
              "        [0.09433366],\n",
              "        ...,\n",
              "        [0.04286033],\n",
              "        [0.04602277],\n",
              "        [0.05398467]],\n",
              "\n",
              "       [[0.09701243],\n",
              "        [0.09433366],\n",
              "        [0.09156187],\n",
              "        ...,\n",
              "        [0.04602277],\n",
              "        [0.05398467],\n",
              "        [0.05738894]],\n",
              "\n",
              "       [[0.09433366],\n",
              "        [0.09156187],\n",
              "        [0.07984225],\n",
              "        ...,\n",
              "        [0.05398467],\n",
              "        [0.05738894],\n",
              "        [0.05714711]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[0.96054394],\n",
              "        [0.9371419 ],\n",
              "        [0.92841729],\n",
              "        ...,\n",
              "        [0.95475854],\n",
              "        [0.95204256],\n",
              "        [0.95163331]],\n",
              "\n",
              "       [[0.9371419 ],\n",
              "        [0.92841729],\n",
              "        [0.90804747],\n",
              "        ...,\n",
              "        [0.95204256],\n",
              "        [0.95163331],\n",
              "        [0.95725128]],\n",
              "\n",
              "       [[0.92841729],\n",
              "        [0.90804747],\n",
              "        [0.8771858 ],\n",
              "        ...,\n",
              "        [0.95163331],\n",
              "        [0.95725128],\n",
              "        [0.93796041]]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_40 = Sequential()\n",
        "from keras.layers.recurrent import LSTM\n",
        "model_40.add(LSTM(128, input_shape=(40, 1), activation='relu', return_sequences=True))\n",
        "model_40.add(LSTM(64, activation='relu'))\n",
        "\n",
        "\n",
        "model_40.add(Dense(32, activation='relu'))\n",
        "\n",
        "\n",
        "model_40.add(Dense(10, activation='softmax'))\n",
        "model_40.add(Dense(1))\n",
        "\n",
        "model_40.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
        "final = model_40.fit(train_data_open, test_data_open, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzUyHlr0BzM_",
        "outputId": "81d8c14c-015d-4a6c-d800-15e5049ffbec"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "39/39 [==============================] - 10s 125ms/step - loss: 0.0928\n",
            "Epoch 2/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0090\n",
            "Epoch 3/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 0.0131\n",
            "Epoch 4/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 0.0060\n",
            "Epoch 5/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 0.0036\n",
            "Epoch 6/100\n",
            "39/39 [==============================] - 5s 123ms/step - loss: 0.0029\n",
            "Epoch 7/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0024\n",
            "Epoch 8/100\n",
            "39/39 [==============================] - 5s 123ms/step - loss: 0.0027\n",
            "Epoch 9/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 0.0024\n",
            "Epoch 10/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0021\n",
            "Epoch 11/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0021\n",
            "Epoch 12/100\n",
            "39/39 [==============================] - 5s 123ms/step - loss: 0.0020\n",
            "Epoch 13/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 0.0019\n",
            "Epoch 14/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 0.0018\n",
            "Epoch 15/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0020\n",
            "Epoch 16/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 0.0018\n",
            "Epoch 17/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0017\n",
            "Epoch 18/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0018\n",
            "Epoch 19/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0017\n",
            "Epoch 20/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 0.0017\n",
            "Epoch 21/100\n",
            "39/39 [==============================] - 5s 123ms/step - loss: 0.0016\n",
            "Epoch 22/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0016\n",
            "Epoch 23/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 0.0017\n",
            "Epoch 24/100\n",
            "39/39 [==============================] - 5s 132ms/step - loss: 0.0017\n",
            "Epoch 25/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0016\n",
            "Epoch 26/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 0.0016\n",
            "Epoch 27/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 0.0022\n",
            "Epoch 28/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 0.0014\n",
            "Epoch 29/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 0.0012\n",
            "Epoch 30/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0013\n",
            "Epoch 31/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0011\n",
            "Epoch 32/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 9.7220e-04\n",
            "Epoch 33/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 9.5077e-04\n",
            "Epoch 34/100\n",
            "39/39 [==============================] - 5s 123ms/step - loss: 0.0011\n",
            "Epoch 35/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0013\n",
            "Epoch 36/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 9.3110e-04\n",
            "Epoch 37/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 8.2236e-04\n",
            "Epoch 38/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 7.9734e-04\n",
            "Epoch 39/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 7.9111e-04\n",
            "Epoch 40/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 7.5818e-04\n",
            "Epoch 41/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 7.4490e-04\n",
            "Epoch 42/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 8.7786e-04\n",
            "Epoch 43/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 7.3985e-04\n",
            "Epoch 44/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 7.4878e-04\n",
            "Epoch 45/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 6.7751e-04\n",
            "Epoch 46/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 6.4121e-04\n",
            "Epoch 47/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 6.3645e-04\n",
            "Epoch 48/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 5.8517e-04\n",
            "Epoch 49/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 5.6533e-04\n",
            "Epoch 50/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 5.2254e-04\n",
            "Epoch 51/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 5.5787e-04\n",
            "Epoch 52/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 5.0670e-04\n",
            "Epoch 53/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 5.3548e-04\n",
            "Epoch 54/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 5.6560e-04\n",
            "Epoch 55/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 5.2727e-04\n",
            "Epoch 56/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 4.6570e-04\n",
            "Epoch 57/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 4.7394e-04\n",
            "Epoch 58/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 4.5185e-04\n",
            "Epoch 59/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 4.5751e-04\n",
            "Epoch 60/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 4.4041e-04\n",
            "Epoch 61/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 5.9849e-04\n",
            "Epoch 62/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 5.0587e-04\n",
            "Epoch 63/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 4.5260e-04\n",
            "Epoch 64/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 4.2081e-04\n",
            "Epoch 65/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 5.4206e-04\n",
            "Epoch 66/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 4.3303e-04\n",
            "Epoch 67/100\n",
            "39/39 [==============================] - 5s 127ms/step - loss: 4.6945e-04\n",
            "Epoch 68/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 5.3951e-04\n",
            "Epoch 69/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 4.7945e-04\n",
            "Epoch 70/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 4.1015e-04\n",
            "Epoch 71/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 3.7463e-04\n",
            "Epoch 72/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 4.3958e-04\n",
            "Epoch 73/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 4.3091e-04\n",
            "Epoch 74/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 3.7920e-04\n",
            "Epoch 75/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 3.9338e-04\n",
            "Epoch 76/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 3.7999e-04\n",
            "Epoch 77/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 4.0204e-04\n",
            "Epoch 78/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 4.0758e-04\n",
            "Epoch 79/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 4.1962e-04\n",
            "Epoch 80/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 3.7948e-04\n",
            "Epoch 81/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 3.8552e-04\n",
            "Epoch 82/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 4.0678e-04\n",
            "Epoch 83/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 3.7097e-04\n",
            "Epoch 84/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 3.6786e-04\n",
            "Epoch 85/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 0.0012\n",
            "Epoch 86/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 4.1972e-04\n",
            "Epoch 87/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 4.2220e-04\n",
            "Epoch 88/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 3.6689e-04\n",
            "Epoch 89/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 4.2070e-04\n",
            "Epoch 90/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 4.2886e-04\n",
            "Epoch 91/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 3.7532e-04\n",
            "Epoch 92/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 3.3611e-04\n",
            "Epoch 93/100\n",
            "39/39 [==============================] - 5s 126ms/step - loss: 3.5362e-04\n",
            "Epoch 94/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 3.7364e-04\n",
            "Epoch 95/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 3.9675e-04\n",
            "Epoch 96/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 3.1020e-04\n",
            "Epoch 97/100\n",
            "39/39 [==============================] - 5s 124ms/step - loss: 3.4677e-04\n",
            "Epoch 98/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 3.6903e-04\n",
            "Epoch 99/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 3.2879e-04\n",
            "Epoch 100/100\n",
            "39/39 [==============================] - 5s 125ms/step - loss: 3.6891e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(final.history['loss'], label='train loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "cUJIAEY1dI2d",
        "outputId": "f1607b38-76e4-4ca1-a4f4-78f0c4677bc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = train_open.flatten()\n",
        "req_train = x[len(x)-40:]\n",
        "y = test_open.flatten()\n",
        "z = np.concatenate([req_train, y])\n",
        "z_1 = np.reshape(z, (len(z), 1))\n",
        "mms = MinMaxScaler(feature_range = (0, 1))\n",
        "\n",
        "z_2 = mms.fit_transform(z_1)\n",
        "X_test = []\n",
        "for i in range(40, 60):\n",
        "    X_test.append(z_2.flatten()[i-40:i])\n",
        "X_test = np.array(X_test)\n"
      ],
      "metadata": {
        "id": "M8uGxEutB53z"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_open = model_40.predict(X_test)\n",
        "\n",
        "pred_open_ = mms.inverse_transform(pred_open)\n",
        "plt.plot(test_open, color = 'red', label = 'Real Open Google Stock Price')\n",
        "plt.plot(pred_open_, color = 'blue', label = 'Predicted Open Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Open Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "amdlaJVCB9_Y",
        "outputId": "8c418ba6-62b9-4170-af46-0b8a0630c912"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = test_open.flatten()\n",
        "err=0\n",
        "b = pred_open_.flatten()\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "err = mse(a, b)\n",
        "print(err)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBFA--wUDDHo",
        "outputId": "85733e3f-8b36-41c0-9ec5-0158926df105"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "87.9580875999274\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_lstm_40 = r2_score(a, b)\n",
        "r2_lstm_40"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TVFrzIZJ9z7j",
        "outputId": "d7dbe255-5e06-4ec8-b415-e750ff077a1d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5952966429222002"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With last 60 timesteps as features"
      ],
      "metadata": {
        "id": "5J-kczQx6qf9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_open = []\n",
        "test_data_open = []\n",
        "for i in range(60, len(train_open_mms)):\n",
        "    train_data_open.append(train_open_mms.flatten()[i-60:i])\n",
        "    test_data_open.append(train_open_mms.flatten()[i])\n",
        "train_data_open = np.array(train_data_open)\n",
        "test_data_open = np.array(test_data_open)\n",
        "\n",
        "train_data_open.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXPqDxjBV3BV",
        "outputId": "8e2bd6b8-30d2-450c-b719-8fd67b6377d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1198, 60)"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "x_60 = train_open.flatten()\n",
        "req_train_60 = x_60[len(x_60)-60:]\n",
        "y_60 = test_open.flatten()\n",
        "z_60 = np.concatenate([req_train_60, y_60])\n",
        "z_1_60 = np.reshape(z_60, (len(z_60), 1))\n",
        "\n",
        "X_test_60 = []\n",
        "for i in range(60, 80):\n",
        "    X_test_60.append(z_1_60.flatten()[i-60:i])\n",
        "X_test_60 = np.array(X_test_60)"
      ],
      "metadata": {
        "id": "QykcRrCx7Nqp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg_60 = LinearRegression()\n",
        "reg_60.fit(train_data_open, test_data_open)\n",
        "y_pred_lr_60 = reg_60.predict(X_test_60)\n",
        "y_pred_lr_60"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTZF1RxH61j1",
        "outputId": "40b17912-8fc0-477a-9d73-4e5476b04037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([782.22031572, 778.7615305 , 787.62804266, 789.60752677,\n",
              "       796.67161457, 805.95309191, 808.73970387, 802.80821573,\n",
              "       802.51349074, 813.58879383, 805.06560028, 808.18558086,\n",
              "       801.997823  , 798.91691933, 810.06171943, 817.99885072,\n",
              "       825.27719151, 843.26511973, 828.21059641, 809.02859332])"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = test_open.flatten()\n",
        "err=0\n",
        "b = y_pred_lr_60.flatten()\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "err = mse(a, b)\n",
        "print(err)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avDFZl9f62r6",
        "outputId": "9014ccd9-2904-49db-eac2-f7bee959cd32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64.32344182853454\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_lr_60 = r2_score(a, b)\n",
        "r2_lr_60"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcLqvtICLl-Z",
        "outputId": "e3bf7534-f9e9-433b-b932-f53cbc571e11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7040418504184488"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_open.flatten(), color = 'red', label = 'Real Open Google Stock Price')\n",
        "plt.plot(y_pred_lr_60, color = 'blue', label = 'Predicted Open Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Open Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Ib_V9iik649M",
        "outputId": "123c77ad-746e-437e-d090-879151339ad4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_high = []\n",
        "test_data_high = []\n",
        "for i in range(60, len(train_high_mms)):\n",
        "    train_data_high.append(train_high_mms.flatten()[i-60:i])\n",
        "    test_data_high.append(train_high_mms.flatten()[i])\n",
        "train_data_high = np.array(train_data_high)\n",
        "test_data_high = np.array(test_data_high)\n",
        "\n",
        "train_data_high.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vYJcUtIr6w4E",
        "outputId": "404db555-aec3-4524-ae65-d3a56abd9840"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1198, 60)"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_low = []\n",
        "test_data_low = []\n",
        "for i in range(60, len(train_low_mms)):\n",
        "    train_data_low.append(train_low_mms.flatten()[i-60:i])\n",
        "    test_data_low.append(train_low_mms.flatten()[i])\n",
        "train_data_low = np.array(train_data_low)\n",
        "test_data_low = np.array(test_data_low)\n",
        "\n",
        "train_data_low.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjI3lsoC7XLI",
        "outputId": "f0a6ca01-2851-4165-c0d4-39063efc425f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1198, 60)"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_close = []\n",
        "test_data_close = []\n",
        "for i in range(60, len(train_close_mms)):\n",
        "    train_data_close.append(train_close_mms.flatten()[i-60:i])\n",
        "    test_data_close.append(train_close_mms.flatten()[i])\n",
        "train_data_close = np.array(train_data_close)\n",
        "test_data_close = np.array(test_data_close)\n",
        "\n",
        "train_data_close.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IGS2E2bF7m-I",
        "outputId": "e251453e-2df9-4d62-de9b-08e34a2e4647"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1198, 60)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_vol = []\n",
        "test_data_vol = []\n",
        "for i in range(60, len(train_vol_mms)):\n",
        "    train_data_vol.append(train_vol_mms.flatten()[i-60:i])\n",
        "    test_data_vol.append(train_vol_mms.flatten()[i])\n",
        "train_data_vol = np.array(train_data_vol)\n",
        "test_data_vol = np.array(test_data_vol)\n",
        "\n",
        "train_data_vol.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YC6D4R3X8RKr",
        "outputId": "e2917e63-163e-486b-aaa0-258475d41cc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1198, 60)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converting to RNN required input"
      ],
      "metadata": {
        "id": "OX6Qi1gmxboc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import newaxis\n",
        "\n",
        "train_data_open = train_data_open[:, :, newaxis]\n",
        "train_data_open.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P7hOP2UZqkMb",
        "outputId": "145bdcc3-41a5-46a6-973a-fc6fd860a81a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1198, 60, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_high = train_data_high[:, :, newaxis]\n",
        "train_data_high.shape\n",
        "train_data_low = train_data_low[:, :, newaxis]\n",
        "\n",
        "train_data_close = train_data_close[:, :, newaxis]\n",
        "train_data_vol = train_data_vol[:, :, newaxis]\n"
      ],
      "metadata": {
        "id": "Ol8XzOtt8r6V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = train_open.flatten()\n",
        "req_train = x[len(x)-60:]"
      ],
      "metadata": {
        "id": "zQtI3mraq4Tq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "req_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1spDgZHAz4x5",
        "outputId": "3de9995c-fafc-4e07-b657-6742502e2eda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60,)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = test_open.flatten()\n",
        "z = np.concatenate([req_train, y])"
      ],
      "metadata": {
        "id": "hcmAMwRJ1QtZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z_1 = np.reshape(z, (len(z), 1))\n"
      ],
      "metadata": {
        "id": "MKBrebPA1sDY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mms = MinMaxScaler(feature_range = (0, 1))\n",
        "\n",
        "z_2 = mms.fit_transform(z_1)"
      ],
      "metadata": {
        "id": "B1oU1FB32O4v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = []\n",
        "for i in range(60, 80):\n",
        "    X_test.append(z_2.flatten()[i-60:i])\n",
        "X_test = np.array(X_test)\n"
      ],
      "metadata": {
        "id": "OPpz_mrd2-0s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = train_high.flatten()\n",
        "req_train = x[len(x)-60:]\n",
        "y = test_high.flatten()\n",
        "z = np.concatenate([req_train, y])\n",
        "z_1 = np.reshape(z, (len(z), 1))\n",
        "mms_test_high = MinMaxScaler(feature_range = (0, 1))\n",
        "\n",
        "z_2 = mms_test_high.fit_transform(z_1)\n",
        "X_test_high = []\n",
        "for i in range(60, 80):\n",
        "    X_test_high.append(z_2.flatten()[i-60:i])\n",
        "X_test_high = np.array(X_test_high)\n"
      ],
      "metadata": {
        "id": "wGvZ4oYC9jwb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = train_low.flatten()\n",
        "req_train = x[len(x)-60:]\n",
        "y = test_low.flatten()\n",
        "z = np.concatenate([req_train, y])\n",
        "z_1 = np.reshape(z, (len(z), 1))\n",
        "mms_test_low = MinMaxScaler(feature_range = (0, 1))\n",
        "\n",
        "z_2 = mms_test_low.fit_transform(z_1)\n",
        "X_test_low = []\n",
        "for i in range(60, 80):\n",
        "    X_test_low.append(z_2.flatten()[i-60:i])\n",
        "X_test_low = np.array(X_test_low)\n"
      ],
      "metadata": {
        "id": "6r24fPtY9kGt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = train_close_2D.flatten()\n",
        "req_train = x[len(x)-60:]\n",
        "y = test_close_2D.flatten()\n",
        "z = np.concatenate([req_train, y])\n",
        "z_1 = np.reshape(z, (len(z), 1))\n",
        "mms_test_close = MinMaxScaler(feature_range = (0, 1))\n",
        "\n",
        "z_2 = mms_test_close.fit_transform(z_1)\n",
        "X_test_close = []\n",
        "for i in range(60, 80):\n",
        "    X_test_close.append(z_2.flatten()[i-60:i])\n",
        "X_test_close = np.array(X_test_close)\n"
      ],
      "metadata": {
        "id": "10rO1lj3-JKN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = train_vol_2D.flatten()\n",
        "req_train = x[len(x)-60:]\n",
        "y = test_vol_2D.flatten()\n",
        "z = np.concatenate([req_train, y])\n",
        "z_1 = np.reshape(z, (len(z), 1))\n",
        "mms_test_vol = MinMaxScaler(feature_range = (0, 1))\n",
        "\n",
        "z_2 = mms_test_vol.fit_transform(z_1)\n",
        "X_test_vol = []\n",
        "for i in range(60, 80):\n",
        "    X_test_vol.append(z_2.flatten()[i-60:i])\n",
        "X_test_vol = np.array(X_test_high)\n"
      ],
      "metadata": {
        "id": "qtjDftRF-WeU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Regression Model"
      ],
      "metadata": {
        "id": "vfzRPVSF4pzP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import keras\n",
        "from keras.models import Model\n",
        "from keras.layers import Dense, Input\n",
        "from keras.layers import Dropout\n",
        "import numpy as np\n"
      ],
      "metadata": {
        "id": "gnka1Wq_33ez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from keras import Sequential\n"
      ],
      "metadata": {
        "id": "F_lZUUJz4Ark"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_open.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OH38CIbDA1Db",
        "outputId": "4d10c8b4-76c9-4717-d6a5-0d7c16dbad81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1198, 60, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_60 = Sequential()\n",
        "from keras.layers.recurrent import LSTM\n",
        "model_60.add(LSTM(128, input_shape=(60, 1), activation='relu', return_sequences=True))\n",
        "\n",
        "\n",
        "model_60.add(LSTM(96, activation='relu'))\n",
        "\n",
        "model_60.add(Dense(1))\n",
        "\n",
        "\n",
        "model_60.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
        "model_60.fit(train_data_open, test_data_open, epochs = 83)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WuzFodNg_gC1",
        "outputId": "eaf5c378-b000-4edc-8947-3a76d2f73e0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/83\n",
            "38/38 [==============================] - 38s 253ms/step - loss: 0.0427\n",
            "Epoch 2/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 0.0013\n",
            "Epoch 3/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 0.0010\n",
            "Epoch 4/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 0.0010\n",
            "Epoch 5/83\n",
            "38/38 [==============================] - 10s 254ms/step - loss: 9.8746e-04\n",
            "Epoch 6/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 9.9017e-04\n",
            "Epoch 7/83\n",
            "38/38 [==============================] - 10s 249ms/step - loss: 9.2454e-04\n",
            "Epoch 8/83\n",
            "38/38 [==============================] - 10s 252ms/step - loss: 8.5499e-04\n",
            "Epoch 9/83\n",
            "38/38 [==============================] - 10s 252ms/step - loss: 8.0066e-04\n",
            "Epoch 10/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 8.3279e-04\n",
            "Epoch 11/83\n",
            "38/38 [==============================] - 10s 252ms/step - loss: 0.0010\n",
            "Epoch 12/83\n",
            "38/38 [==============================] - 10s 253ms/step - loss: 7.7149e-04\n",
            "Epoch 13/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 7.6583e-04\n",
            "Epoch 14/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 6.3680e-04\n",
            "Epoch 15/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 6.4014e-04\n",
            "Epoch 16/83\n",
            "38/38 [==============================] - 10s 250ms/step - loss: 7.1691e-04\n",
            "Epoch 17/83\n",
            "38/38 [==============================] - 10s 250ms/step - loss: 5.7373e-04\n",
            "Epoch 18/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 5.3216e-04\n",
            "Epoch 19/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 5.9255e-04\n",
            "Epoch 20/83\n",
            "38/38 [==============================] - 9s 249ms/step - loss: 5.4366e-04\n",
            "Epoch 21/83\n",
            "38/38 [==============================] - 9s 248ms/step - loss: 5.3781e-04\n",
            "Epoch 22/83\n",
            "38/38 [==============================] - 10s 252ms/step - loss: 4.5670e-04\n",
            "Epoch 23/83\n",
            "38/38 [==============================] - 9s 248ms/step - loss: 5.4856e-04\n",
            "Epoch 24/83\n",
            "38/38 [==============================] - 9s 247ms/step - loss: 5.4258e-04\n",
            "Epoch 25/83\n",
            "38/38 [==============================] - 9s 247ms/step - loss: 4.3025e-04\n",
            "Epoch 26/83\n",
            "38/38 [==============================] - 9s 249ms/step - loss: 5.2835e-04\n",
            "Epoch 27/83\n",
            "38/38 [==============================] - 9s 248ms/step - loss: 4.3077e-04\n",
            "Epoch 28/83\n",
            "38/38 [==============================] - 13s 337ms/step - loss: 3.6779e-04\n",
            "Epoch 29/83\n",
            "38/38 [==============================] - 12s 307ms/step - loss: 3.5238e-04\n",
            "Epoch 30/83\n",
            "38/38 [==============================] - 10s 250ms/step - loss: 3.5257e-04\n",
            "Epoch 31/83\n",
            "38/38 [==============================] - 10s 256ms/step - loss: 3.6381e-04\n",
            "Epoch 32/83\n",
            "38/38 [==============================] - 10s 254ms/step - loss: 3.4898e-04\n",
            "Epoch 33/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 3.4253e-04\n",
            "Epoch 34/83\n",
            "38/38 [==============================] - 9s 249ms/step - loss: 4.1783e-04\n",
            "Epoch 35/83\n",
            "38/38 [==============================] - 9s 247ms/step - loss: 4.4105e-04\n",
            "Epoch 36/83\n",
            "38/38 [==============================] - 9s 249ms/step - loss: 3.9309e-04\n",
            "Epoch 37/83\n",
            "38/38 [==============================] - 9s 248ms/step - loss: 3.6755e-04\n",
            "Epoch 38/83\n",
            "38/38 [==============================] - 9s 245ms/step - loss: 3.5328e-04\n",
            "Epoch 39/83\n",
            "38/38 [==============================] - 9s 248ms/step - loss: 3.8982e-04\n",
            "Epoch 40/83\n",
            "38/38 [==============================] - 9s 247ms/step - loss: 3.5308e-04\n",
            "Epoch 41/83\n",
            "38/38 [==============================] - 9s 249ms/step - loss: 3.3859e-04\n",
            "Epoch 42/83\n",
            "38/38 [==============================] - 10s 250ms/step - loss: 3.0854e-04\n",
            "Epoch 43/83\n",
            "38/38 [==============================] - 10s 253ms/step - loss: 3.5927e-04\n",
            "Epoch 44/83\n",
            "38/38 [==============================] - 9s 250ms/step - loss: 3.6245e-04\n",
            "Epoch 45/83\n",
            "38/38 [==============================] - 9s 248ms/step - loss: 3.0984e-04\n",
            "Epoch 46/83\n",
            "38/38 [==============================] - 9s 247ms/step - loss: 3.4794e-04\n",
            "Epoch 47/83\n",
            "38/38 [==============================] - 9s 248ms/step - loss: 3.2733e-04\n",
            "Epoch 48/83\n",
            "38/38 [==============================] - 9s 245ms/step - loss: 3.7850e-04\n",
            "Epoch 49/83\n",
            "38/38 [==============================] - 9s 246ms/step - loss: 2.9604e-04\n",
            "Epoch 50/83\n",
            "38/38 [==============================] - 9s 247ms/step - loss: 3.0020e-04\n",
            "Epoch 51/83\n",
            "38/38 [==============================] - 9s 248ms/step - loss: 3.0948e-04\n",
            "Epoch 52/83\n",
            "38/38 [==============================] - 9s 248ms/step - loss: 2.9344e-04\n",
            "Epoch 53/83\n",
            "38/38 [==============================] - 10s 257ms/step - loss: 3.0463e-04\n",
            "Epoch 54/83\n",
            "38/38 [==============================] - 10s 257ms/step - loss: 3.4448e-04\n",
            "Epoch 55/83\n",
            "38/38 [==============================] - 10s 259ms/step - loss: 3.1671e-04\n",
            "Epoch 56/83\n",
            "38/38 [==============================] - 10s 259ms/step - loss: 3.1989e-04\n",
            "Epoch 57/83\n",
            "38/38 [==============================] - 10s 257ms/step - loss: 3.3755e-04\n",
            "Epoch 58/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 3.4975e-04\n",
            "Epoch 59/83\n",
            "38/38 [==============================] - 10s 255ms/step - loss: 3.3777e-04\n",
            "Epoch 60/83\n",
            "38/38 [==============================] - 10s 252ms/step - loss: 2.8910e-04\n",
            "Epoch 61/83\n",
            "38/38 [==============================] - 10s 252ms/step - loss: 2.7731e-04\n",
            "Epoch 62/83\n",
            "38/38 [==============================] - 10s 253ms/step - loss: 3.4669e-04\n",
            "Epoch 63/83\n",
            "38/38 [==============================] - 10s 257ms/step - loss: 3.2499e-04\n",
            "Epoch 64/83\n",
            "38/38 [==============================] - 10s 257ms/step - loss: 2.9645e-04\n",
            "Epoch 65/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 3.5247e-04\n",
            "Epoch 66/83\n",
            "38/38 [==============================] - 10s 253ms/step - loss: 3.0877e-04\n",
            "Epoch 67/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 3.4443e-04\n",
            "Epoch 68/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 3.0688e-04\n",
            "Epoch 69/83\n",
            "38/38 [==============================] - 9s 248ms/step - loss: 2.7790e-04\n",
            "Epoch 70/83\n",
            "38/38 [==============================] - 9s 248ms/step - loss: 3.4511e-04\n",
            "Epoch 71/83\n",
            "38/38 [==============================] - 10s 250ms/step - loss: 3.2019e-04\n",
            "Epoch 72/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 3.1883e-04\n",
            "Epoch 73/83\n",
            "38/38 [==============================] - 9s 249ms/step - loss: 3.1098e-04\n",
            "Epoch 74/83\n",
            "38/38 [==============================] - 10s 253ms/step - loss: 3.5533e-04\n",
            "Epoch 75/83\n",
            "38/38 [==============================] - 10s 255ms/step - loss: 3.4152e-04\n",
            "Epoch 76/83\n",
            "38/38 [==============================] - 10s 254ms/step - loss: 3.1971e-04\n",
            "Epoch 77/83\n",
            "38/38 [==============================] - 10s 251ms/step - loss: 3.2690e-04\n",
            "Epoch 78/83\n",
            "38/38 [==============================] - 10s 264ms/step - loss: 3.4662e-04\n",
            "Epoch 79/83\n",
            "38/38 [==============================] - 10s 256ms/step - loss: 3.0993e-04\n",
            "Epoch 80/83\n",
            "38/38 [==============================] - 10s 257ms/step - loss: 2.7768e-04\n",
            "Epoch 81/83\n",
            "38/38 [==============================] - 10s 268ms/step - loss: 2.6854e-04\n",
            "Epoch 82/83\n",
            "38/38 [==============================] - 13s 352ms/step - loss: 3.6762e-04\n",
            "Epoch 83/83\n",
            "38/38 [==============================] - 11s 293ms/step - loss: 3.2790e-04\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc82f10aa50>"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_open = model_60.predict(X_test)\n",
        "\n",
        "pred_open_ = mms.inverse_transform(pred_open)\n",
        "plt.plot(test_open, color = 'red', label = 'Real Open Google Stock Price')\n",
        "plt.plot(pred_open_, color = 'blue', label = 'Predicted Open Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Open Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "qGWKudagA7kh",
        "outputId": "5d8ea793-d8ae-4dba-8572-390b9de9a8cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = test_open.flatten()\n",
        "err=0\n",
        "b = pred_open_.flatten()\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "err = mse(a, b)\n",
        "print(err)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTzt645xDuky",
        "outputId": "34011fff-4859-40bd-f8fd-1641536edb27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "85.99185800090599\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_lstm_60 = r2_score(a, b)\n",
        "r2_lstm_60"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "10i0r9W1L1aE",
        "outputId": "9f4fd1a6-6523-416b-c26d-b9e85f1a8da1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6043434485227163"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Future Predictions for the Stock Opening Prices"
      ],
      "metadata": {
        "id": "2ZJHSAoaxipj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "AEm6r3Wlxdp7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_1 = Sequential()\n",
        "from keras.layers.recurrent import LSTM\n",
        "model_1.add(LSTM(200, input_shape=(60, 1), activation='relu', return_sequences=True))\n",
        "\n",
        "model_1.add(Dropout(0.2))\n",
        "model_1.add(LSTM(100, activation='relu'))\n",
        "model_1.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "model_1.add(Dense(100, activation='relu'))\n",
        "model_1.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model_1.add(Dropout(0.2))\n",
        "model_1.add(Dense(1))\n",
        "\n",
        "\n",
        "model_1.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
        "model_1.fit(train_data_open, test_data_open, epochs = 100)"
      ],
      "metadata": {
        "id": "ydqWTs8MHZGJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_open_2 = model_1.predict(X_test)\n",
        "\n",
        "pred_open__2 = mms.inverse_transform(pred_open_2)\n",
        "plt.plot(test_open, color = 'red', label = 'Real Open Google Stock Price')\n",
        "plt.plot(pred_open__2, color = 'blue', label = 'Predicted Open Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Open Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "KzTng8I8I0jL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = test_open.flatten()\n",
        "err=0\n",
        "b = pred_open__2.flatten()\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "err = mse(a, b)\n",
        "print(err)"
      ],
      "metadata": {
        "id": "4nZQsxHnGyMb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def vis(data_org, data_gen, x):\n",
        "  plt.plot(data_org, color = 'red', label = 'Real' + x + 'Google Stock Price')\n",
        "  plt.plot(data_gen, color = 'blue', label = 'Predicted' + x + 'Google Stock Price')\n",
        "  plt.title('Google Stock Price Prediction')\n",
        "  plt.xlabel('Time')\n",
        "  plt.ylabel(x)\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "uV-1Fo9nKaUJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Future Prediction Pipeline Using some of the above used models\n"
      ],
      "metadata": {
        "id": "B7Wu4KDX4m_R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_open_20 = []\n",
        "y_train_data_open_20 = []\n",
        "x_lr_20 = train_open.flatten()\n",
        "y_lr_20 = test_open.flatten()\n",
        "\n",
        "full_train_lr_20 = np.concatenate([x_lr_20, y_lr_20])\n",
        "full_train_lr_20_ = np.reshape(full_train_lr_20, (len(full_train_lr_20), 1))\n",
        "for i in range(20, len(full_train_lr_20)):\n",
        "    train_data_open_20.append(full_train_lr_20_.flatten()[i-20:i])\n",
        "    y_train_data_open_20.append(full_train_lr_20_.flatten()[i])\n",
        "train_data_open_20 = np.array(train_data_open_20)\n",
        "y_train_data_open_20 = np.array(y_train_data_open_20)\n",
        "\n",
        "train_data_open_20.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hpy1svMO4uRs",
        "outputId": "cdf0038b-2378-42fc-8a57-2731eaff5e2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1258, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg_20 = LinearRegression()\n",
        "reg_20.fit(train_data_open_20, y_train_data_open_20)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YHo8EdxPC5xq",
        "outputId": "617e705e-888b-4382-d948-d66e388e1218"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_lr_20 = []\n",
        "def future_pred_lr_20(point):\n",
        "    new_test_data = test_open.flatten()\n",
        "    new_test_data_20 = np.reshape(new_test_data, (len(new_test_data), 1))\n",
        "    \n",
        "    for i in range(point):\n",
        "      X_test_20 = []\n",
        "      X_test_20.append(new_test_data_20.flatten()[i:])\n",
        "      X_test_20 = np.array(X_test_20)\n",
        "      a = reg_20.predict(X_test_20)\n",
        "      y_pred_lr_20.append(a)\n",
        "      new_test_data_20 = np.concatenate([new_test_data_20.flatten(), a])\n",
        "      new_test_data_20 = np.reshape(new_test_data_20, (len(new_test_data_20), 1))\n",
        "\n",
        "  \n",
        "future_pred_lr_20(10)\n",
        "y_pred_lr_20\n",
        "      \n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JHEkgC2U_U3c",
        "outputId": "8495a34c-04f7-46ee-c617-9e5376ee7ac5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([797.17482875]),\n",
              " array([797.43289792]),\n",
              " array([799.65239912]),\n",
              " array([801.47413478]),\n",
              " array([802.3494023]),\n",
              " array([801.53129396]),\n",
              " array([801.50492645]),\n",
              " array([802.16384904]),\n",
              " array([803.02921732]),\n",
              " array([804.28401083])]"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_future_lr_20 = np.concatenate([test_open.flatten(), np.array(y_pred_lr_20).flatten()])\n"
      ],
      "metadata": {
        "id": "9YgTe--k-_Gg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y_future_lr_20, color = 'blue', label = 'Including Future Google Stock Price')\n",
        "\n",
        "plt.plot(test_open.flatten(), color = 'red', label = 'Real Open Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "T8likmemGdHv",
        "outputId": "bc694f82-900d-4e4f-bf55-4009dd2b8f8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_open_40 = []\n",
        "y_train_data_open_40 = []\n",
        "x_lr_40 = train_open.flatten()\n",
        "y_lr_40 = test_open.flatten()\n",
        "\n",
        "full_train_lr_40 = np.concatenate([x_lr_40, y_lr_40])\n",
        "full_train_lr_40_ = np.reshape(full_train_lr_40, (len(full_train_lr_40), 1))\n",
        "for i in range(40, len(full_train_lr_40)):\n",
        "    train_data_open_40.append(full_train_lr_40_.flatten()[i-40:i])\n",
        "    y_train_data_open_40.append(full_train_lr_40_.flatten()[i])\n",
        "train_data_open_40 = np.array(train_data_open_40)\n",
        "y_train_data_open_40 = np.array(y_train_data_open_40)\n",
        "\n",
        "train_data_open_40.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqsxuE4cIYgm",
        "outputId": "50df6e7a-7155-4ef8-a0c2-ad1f238c13fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1238, 40)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg_40 = LinearRegression()\n",
        "reg_40.fit(train_data_open_40, y_train_data_open_40)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-KMz0F_NIbf3",
        "outputId": "867eef36-ae73-4070-f7c8-4c9ea8cba7b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_lr_40 = []\n",
        "def future_pred_lr_40(point):\n",
        "    x_40 = train_open.flatten()\n",
        "    req_train_40 = x_40[len(x_40)-20:]\n",
        "    y_40 = test_open.flatten()\n",
        "    z_40 = np.concatenate([req_train_40, y_40])\n",
        "    new_test_data = z_40.flatten()\n",
        "    new_test_data_40 = np.reshape(new_test_data, (len(new_test_data), 1))\n",
        "    \n",
        "    for i in range(point):\n",
        "      X_test_40 = []\n",
        "      X_test_40.append(new_test_data_40.flatten()[i:])\n",
        "      X_test_40 = np.array(X_test_40)\n",
        "      a = reg_40.predict(X_test_40)\n",
        "      y_pred_lr_40.append(a)\n",
        "      new_test_data_40 = np.concatenate([new_test_data_40.flatten(), a])\n",
        "      new_test_data_40 = np.reshape(new_test_data_40, (len(new_test_data_40), 1))\n",
        "\n",
        "  \n",
        "future_pred_lr_40(10)\n",
        "y_pred_lr_40\n",
        "      \n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NVyenxUKKI2Y",
        "outputId": "42754f9b-06f3-4d0d-b945-c1cd42e7b339"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([798.34094866]),\n",
              " array([799.10951839]),\n",
              " array([800.68334755]),\n",
              " array([803.84371636]),\n",
              " array([806.22362898]),\n",
              " array([806.62447378]),\n",
              " array([806.92074628]),\n",
              " array([806.02770848]),\n",
              " array([807.88213341]),\n",
              " array([808.53878628])]"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_future_lr_40 = np.concatenate([test_open.flatten(), np.array(y_pred_lr_40).flatten()])\n"
      ],
      "metadata": {
        "id": "b9w6VGD2LLZv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y_future_lr_40, color = 'blue', label = 'Including Future Google Stock Price')\n",
        "\n",
        "plt.plot(test_open.flatten(), color = 'red', label = 'Real Open Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "qVcu7_bNLN34",
        "outputId": "dc14651c-d57e-448f-e796-c965c43b8463"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "w9gT7cdGLWdw"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}