8 year Model/Close Feature/75/8model_15_23.ipynb

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          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pandas-datareader in c:\\python312\\lib\\site-packages (0.10.0)\n",
            "Requirement already satisfied: lxml in c:\\python312\\lib\\site-packages (from pandas-datareader) (5.1.0)\n",
            "Requirement already satisfied: pandas>=0.23 in c:\\python312\\lib\\site-packages (from pandas-datareader) (2.2.1)\n",
            "Requirement already satisfied: requests>=2.19.0 in c:\\python312\\lib\\site-packages (from pandas-datareader) (2.31.0)\n",
            "Requirement already satisfied: numpy<2,>=1.26.0 in c:\\python312\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\python312\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in c:\\python312\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.7 in c:\\python312\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2024.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\python312\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in c:\\python312\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\python312\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2.2.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in c:\\python312\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2024.2.2)\n",
            "Requirement already satisfied: six>=1.5 in c:\\python312\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>=0.23->pandas-datareader) (1.16.0)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "[notice] A new release of pip is available: 24.0 -> 24.2\n",
            "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas-datareader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "moPEPeDaGAmf"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas_datareader as data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "7BkfhhO_GCEC",
        "outputId": "c8ed065f-fbdb-4485-c009-7e6bdce8dc1e"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-12-31</th>\n",
              "      <td>50.6311</td>\n",
              "      <td>50.7844</td>\n",
              "      <td>50.0555</td>\n",
              "      <td>50.0555</td>\n",
              "      <td>2.316494e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-12-30</th>\n",
              "      <td>50.1684</td>\n",
              "      <td>50.6135</td>\n",
              "      <td>50.0475</td>\n",
              "      <td>50.5113</td>\n",
              "      <td>9.399110e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-12-29</th>\n",
              "      <td>49.9267</td>\n",
              "      <td>50.6655</td>\n",
              "      <td>49.7931</td>\n",
              "      <td>50.3590</td>\n",
              "      <td>1.325363e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-12-26</th>\n",
              "      <td>50.1271</td>\n",
              "      <td>50.3039</td>\n",
              "      <td>49.9209</td>\n",
              "      <td>50.0329</td>\n",
              "      <td>7.609393e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-12-24</th>\n",
              "      <td>50.2087</td>\n",
              "      <td>50.2883</td>\n",
              "      <td>49.8059</td>\n",
              "      <td>49.9759</td>\n",
              "      <td>9.275697e+06</td>\n",
              "    </tr>\n",
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              "</table>\n",
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            ],
            "text/plain": [
              "               Open     High      Low    Close        Volume\n",
              "Date                                                        \n",
              "2014-12-31  50.6311  50.7844  50.0555  50.0555  2.316494e+07\n",
              "2014-12-30  50.1684  50.6135  50.0475  50.5113  9.399110e+06\n",
              "2014-12-29  49.9267  50.6655  49.7931  50.3590  1.325363e+07\n",
              "2014-12-26  50.1271  50.3039  49.9209  50.0329  7.609393e+06\n",
              "2014-12-24  50.2087  50.2883  49.8059  49.9759  9.275697e+06"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas_datareader.data as web\n",
        "start='2011-01-01'\n",
        "end='2015-01-01'\n",
        "\n",
        "df=web.DataReader('JPM','stooq',start,end)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "2Y0r9hQ4GOkv",
        "outputId": "b67104bb-3f17-4367-a368-ba94643a0798"
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              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2011-01-07</th>\n",
              "      <td>32.1151</td>\n",
              "      <td>32.1465</td>\n",
              "      <td>30.7505</td>\n",
              "      <td>31.4667</td>\n",
              "      <td>9.643175e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-06</th>\n",
              "      <td>32.0119</td>\n",
              "      <td>32.2977</td>\n",
              "      <td>31.9039</td>\n",
              "      <td>32.0699</td>\n",
              "      <td>4.382056e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-05</th>\n",
              "      <td>31.6621</td>\n",
              "      <td>32.4078</td>\n",
              "      <td>31.6503</td>\n",
              "      <td>32.2309</td>\n",
              "      <td>6.934255e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-04</th>\n",
              "      <td>31.5659</td>\n",
              "      <td>31.9401</td>\n",
              "      <td>31.2505</td>\n",
              "      <td>31.8380</td>\n",
              "      <td>5.549451e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-03</th>\n",
              "      <td>31.0049</td>\n",
              "      <td>31.7457</td>\n",
              "      <td>30.9725</td>\n",
              "      <td>31.4215</td>\n",
              "      <td>5.987688e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Open     High      Low    Close        Volume\n",
              "Date                                                        \n",
              "2011-01-07  32.1151  32.1465  30.7505  31.4667  9.643175e+07\n",
              "2011-01-06  32.0119  32.2977  31.9039  32.0699  4.382056e+07\n",
              "2011-01-05  31.6621  32.4078  31.6503  32.2309  6.934255e+07\n",
              "2011-01-04  31.5659  31.9401  31.2505  31.8380  5.549451e+07\n",
              "2011-01-03  31.0049  31.7457  30.9725  31.4215  5.987688e+07"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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        },
        "id": "U3ivPfLhGP92",
        "outputId": "085b2278-099b-4032-da7d-71084bae3b14"
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              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
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              "      <th>0</th>\n",
              "      <td>2014-12-31</td>\n",
              "      <td>50.6311</td>\n",
              "      <td>50.7844</td>\n",
              "      <td>50.0555</td>\n",
              "      <td>50.0555</td>\n",
              "      <td>2.316494e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-12-30</td>\n",
              "      <td>50.1684</td>\n",
              "      <td>50.6135</td>\n",
              "      <td>50.0475</td>\n",
              "      <td>50.5113</td>\n",
              "      <td>9.399110e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-12-29</td>\n",
              "      <td>49.9267</td>\n",
              "      <td>50.6655</td>\n",
              "      <td>49.7931</td>\n",
              "      <td>50.3590</td>\n",
              "      <td>1.325363e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-12-26</td>\n",
              "      <td>50.1271</td>\n",
              "      <td>50.3039</td>\n",
              "      <td>49.9209</td>\n",
              "      <td>50.0329</td>\n",
              "      <td>7.609393e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-12-24</td>\n",
              "      <td>50.2087</td>\n",
              "      <td>50.2883</td>\n",
              "      <td>49.8059</td>\n",
              "      <td>49.9759</td>\n",
              "      <td>9.275697e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "        Date     Open     High      Low    Close        Volume\n",
              "0 2014-12-31  50.6311  50.7844  50.0555  50.0555  2.316494e+07\n",
              "1 2014-12-30  50.1684  50.6135  50.0475  50.5113  9.399110e+06\n",
              "2 2014-12-29  49.9267  50.6655  49.7931  50.3590  1.325363e+07\n",
              "3 2014-12-26  50.1271  50.3039  49.9209  50.0329  7.609393e+06\n",
              "4 2014-12-24  50.2087  50.2883  49.8059  49.9759  9.275697e+06"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#  Date becomes column and are removable.\n",
        "df=df.reset_index()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wIHsMsmxGSJC",
        "outputId": "38f134e8-7175-43fd-9da0-9a29a014e406"
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              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
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              "      <th>0</th>\n",
              "      <td>50.6311</td>\n",
              "      <td>50.7844</td>\n",
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              "      <td>50.0555</td>\n",
              "      <td>2.316494e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50.1684</td>\n",
              "      <td>50.6135</td>\n",
              "      <td>50.0475</td>\n",
              "      <td>50.5113</td>\n",
              "      <td>9.399110e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49.9267</td>\n",
              "      <td>50.6655</td>\n",
              "      <td>49.7931</td>\n",
              "      <td>50.3590</td>\n",
              "      <td>1.325363e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.1271</td>\n",
              "      <td>50.3039</td>\n",
              "      <td>49.9209</td>\n",
              "      <td>50.0329</td>\n",
              "      <td>7.609393e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50.2087</td>\n",
              "      <td>50.2883</td>\n",
              "      <td>49.8059</td>\n",
              "      <td>49.9759</td>\n",
              "      <td>9.275697e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Open     High      Low    Close        Volume\n",
              "0  50.6311  50.7844  50.0555  50.0555  2.316494e+07\n",
              "1  50.1684  50.6135  50.0475  50.5113  9.399110e+06\n",
              "2  49.9267  50.6655  49.7931  50.3590  1.325363e+07\n",
              "3  50.1271  50.3039  49.9209  50.0329  7.609393e+06\n",
              "4  50.2087  50.2883  49.8059  49.9759  9.275697e+06"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df=df.drop(['Date'],axis=1)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
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          "height": 415
        },
        "id": "72QtumHDGToX",
        "outputId": "ccc7990b-06fb-498e-bb26-38b4ec539401"
      },
      "outputs": [
        {
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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "fig=plt.figure(figsize=(10,6))\n",
        "fig.patch.set_facecolor('#42A5F5')\n",
        "\n",
        "ax=plt.axes()\n",
        "ax.set_facecolor('#C7E1F4')\n",
        "\n",
        "plt.plot(df.Close, '#FF8F00', label='Closing Price')\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Closing Price (USD)')\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "plt.grid(True,linestyle='--',color='#BDBDBD')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "cDZfmvIlGVks",
        "outputId": "e6817367-ba85-4b78-9bd4-9c46798f56d5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>50.6311</td>\n",
              "      <td>50.7844</td>\n",
              "      <td>50.0555</td>\n",
              "      <td>50.0555</td>\n",
              "      <td>2.316494e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50.1684</td>\n",
              "      <td>50.6135</td>\n",
              "      <td>50.0475</td>\n",
              "      <td>50.5113</td>\n",
              "      <td>9.399110e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49.9267</td>\n",
              "      <td>50.6655</td>\n",
              "      <td>49.7931</td>\n",
              "      <td>50.3590</td>\n",
              "      <td>1.325363e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.1271</td>\n",
              "      <td>50.3039</td>\n",
              "      <td>49.9209</td>\n",
              "      <td>50.0329</td>\n",
              "      <td>7.609393e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50.2087</td>\n",
              "      <td>50.2883</td>\n",
              "      <td>49.8059</td>\n",
              "      <td>49.9759</td>\n",
              "      <td>9.275697e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1001</th>\n",
              "      <td>32.1151</td>\n",
              "      <td>32.1465</td>\n",
              "      <td>30.7505</td>\n",
              "      <td>31.4667</td>\n",
              "      <td>9.643175e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1002</th>\n",
              "      <td>32.0119</td>\n",
              "      <td>32.2977</td>\n",
              "      <td>31.9039</td>\n",
              "      <td>32.0699</td>\n",
              "      <td>4.382056e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1003</th>\n",
              "      <td>31.6621</td>\n",
              "      <td>32.4078</td>\n",
              "      <td>31.6503</td>\n",
              "      <td>32.2309</td>\n",
              "      <td>6.934255e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1004</th>\n",
              "      <td>31.5659</td>\n",
              "      <td>31.9401</td>\n",
              "      <td>31.2505</td>\n",
              "      <td>31.8380</td>\n",
              "      <td>5.549451e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1005</th>\n",
              "      <td>31.0049</td>\n",
              "      <td>31.7457</td>\n",
              "      <td>30.9725</td>\n",
              "      <td>31.4215</td>\n",
              "      <td>5.987688e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1006 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Open     High      Low    Close        Volume\n",
              "0     50.6311  50.7844  50.0555  50.0555  2.316494e+07\n",
              "1     50.1684  50.6135  50.0475  50.5113  9.399110e+06\n",
              "2     49.9267  50.6655  49.7931  50.3590  1.325363e+07\n",
              "3     50.1271  50.3039  49.9209  50.0329  7.609393e+06\n",
              "4     50.2087  50.2883  49.8059  49.9759  9.275697e+06\n",
              "...       ...      ...      ...      ...           ...\n",
              "1001  32.1151  32.1465  30.7505  31.4667  9.643175e+07\n",
              "1002  32.0119  32.2977  31.9039  32.0699  4.382056e+07\n",
              "1003  31.6621  32.4078  31.6503  32.2309  6.934255e+07\n",
              "1004  31.5659  31.9401  31.2505  31.8380  5.549451e+07\n",
              "1005  31.0049  31.7457  30.9725  31.4215  5.987688e+07\n",
              "\n",
              "[1006 rows x 5 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "UG_FGvSlGXC3",
        "outputId": "11badcf4-2e31-4bc7-91b5-68f14269a3b8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0             NaN\n",
              "1             NaN\n",
              "2             NaN\n",
              "3             NaN\n",
              "4             NaN\n",
              "          ...    \n",
              "1001    32.650014\n",
              "1002    32.668022\n",
              "1003    32.676892\n",
              "1004    32.685105\n",
              "1005    32.691599\n",
              "Name: Close, Length: 1006, dtype: float64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ma100 is created for finding the mean of the 100 values and displaying them.\n",
        "# for that rolling() function is used.\n",
        "\n",
        "ma100=df.Close.rolling(100).mean()\n",
        "ma100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "-_mMtOTGGYwa",
        "outputId": "d6d74aea-660b-4471-d869-25c66c7eadc2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "fig.patch.set_facecolor('#42A5F5')\n",
        "\n",
        "ax=plt.axes()\n",
        "ax.set_facecolor('#C7E1F4')\n",
        "\n",
        "plt.grid(True,linestyle='--',color='#BDBDBD')\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.plot(df.Close, '#FF8F00', label='Closing Price')\n",
        "plt.plot(ma100,'g--',label='Mean (100 val)') # this is the mean of 100 values\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Closing Price (USD)')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "0RhyVJnqGaWj",
        "outputId": "6d6d0537-195a-4bd9-c628-2d7f55475094"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0             NaN\n",
              "1             NaN\n",
              "2             NaN\n",
              "3             NaN\n",
              "4             NaN\n",
              "          ...    \n",
              "1001    29.685987\n",
              "1002    29.723384\n",
              "1003    29.762667\n",
              "1004    29.803217\n",
              "1005    29.839391\n",
              "Name: Close, Length: 1006, dtype: float64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ma200=df.Close.rolling(200).mean()\n",
        "ma200"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "EvRw-YeJGcrZ",
        "outputId": "d8ba7043-edc8-4c09-8faf-06d86198ad18"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Closing Price (USD)')"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "\n",
        "ma100=df.Close.rolling(100).mean()\n",
        "ma100\n",
        "\n",
        "fig=plt.figure(figsize=(12,6))\n",
        "fig.patch.set_facecolor('#A59DDF')\n",
        "\n",
        "ax=plt.axes()\n",
        "ax.set_facecolor('#211970')\n",
        "\n",
        "plt.grid(True,linestyle='--',color='#626784')\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "plt.plot(df.Close,'#A720C4',label='Closing Price')\n",
        "plt.plot(ma100,'g',label='Mean (100 val)') # this is the mean of 100 values\n",
        "plt.plot(ma200,'r',label='Mean (200 val)') # this is the mean of 200 values\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Closing Price (USD)')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUYMtUqkGg9b",
        "outputId": "d56c695d-00f1-4481-e45d-148185dd81ba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1006, 5)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "egA11-0FGkip",
        "outputId": "b2a2e41d-ed76-4d76-f80d-9f04cdf28dfc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Used data for training:  (704, 1)\n",
            "Used data for testing:  (302, 1)\n"
          ]
        }
      ],
      "source": [
        "# splitting the data into training and testing\n",
        "import pandas as pd\n",
        "\n",
        "data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])\n",
        "data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70):int(len(df))])\n",
        "\n",
        "print(\"Used data for training: \", data_training.shape)\n",
        "print(\"Used data for testing: \", data_testing.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zp5Q3pJrGmiq",
        "outputId": "85a06034-cf63-4326-8e08-763255532071"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50.0555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50.5113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50.3590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.0329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49.9759</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Close\n",
              "0  50.0555\n",
              "1  50.5113\n",
              "2  50.3590\n",
              "3  50.0329\n",
              "4  49.9759"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_training.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QSW9UigXGn-f",
        "outputId": "d27cc686-a8e2-4118-d8f5-5ce6d75f4639"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>704</th>\n",
              "      <td>32.2939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705</th>\n",
              "      <td>32.1533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>706</th>\n",
              "      <td>30.0421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707</th>\n",
              "      <td>30.4056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>708</th>\n",
              "      <td>29.9713</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Close\n",
              "704  32.2939\n",
              "705  32.1533\n",
              "706  30.0421\n",
              "707  30.4056\n",
              "708  29.9713"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "data_testing.head()"
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    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "QUOkXv2mGqN3",
        "outputId": "fafc9b41-901c-422d-e2df-26471f24a734"
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      "outputs": [
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              "       [0.3651397 ],\n",
              "       [0.39276169],\n",
              "       [0.40395288],\n",
              "       [0.39989995],\n",
              "       [0.39139976],\n",
              "       [0.40772101],\n",
              "       [0.39735136],\n",
              "       [0.40495699],\n",
              "       [0.37773299],\n",
              "       [0.36388366],\n",
              "       [0.37651346],\n",
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              "       [0.37339161],\n",
              "       [0.36166002],\n",
              "       [0.3651397 ]])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "\n",
        "data_training_array = scaler.fit_transform(data_training)\n",
        "data_training_array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "xdoWv08nGrgo"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "# The data is dependant on the previous result. (ma100) and (ma200)\n",
        "# So, to calculate the next day value we train the following array.\n",
        "# 100 days are x_train and 101 day is y_train and it goes on changing.\n",
        "\n",
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "\n",
        "for i in range(100,data_training_array.shape[0]):\n",
        "  x_train.append(data_training_array[i-100: i])\n",
        "  y_train.append(data_training_array[i, 0])\n",
        "\n",
        "\n",
        "x_train\n",
        "\n",
        "# Now, converting x_train into numpy value, so that it can be provided to LSTM.\n",
        "x_train,y_train = np.array(x_train), np.array(y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "peb5Uqz7G8wj",
        "outputId": "4acdd264-a15b-4f61-b51d-dd1537133b73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow in c:\\python312\\lib\\site-packages (2.16.1)\n",
            "Requirement already satisfied: tensorflow-intel==2.16.1 in c:\\python312\\lib\\site-packages (from tensorflow) (2.16.1)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.1.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (24.3.7)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.5.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.10.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (16.0.6)\n",
            "Requirement already satisfied: ml-dtypes~=0.3.1 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.3.2)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.3.0)\n",
            "Requirement already satisfied: packaging in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (23.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.25.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.31.0)\n",
            "Requirement already satisfied: setuptools in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (69.2.0)\n",
            "Requirement already satisfied: six>=1.12.0 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (4.10.0)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.16.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.62.1)\n",
            "Requirement already satisfied: tensorboard<2.17,>=2.16 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.16.2)\n",
            "Requirement already satisfied: keras>=3.0.0 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.2.1)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.26.0 in c:\\python312\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.26.4)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\python312\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.16.1->tensorflow) (0.43.0)\n",
            "Requirement already satisfied: rich in c:\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (13.7.1)\n",
            "Requirement already satisfied: namex in c:\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.0.7)\n",
            "Requirement already satisfied: optree in c:\\python312\\lib\\site-packages (from keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\python312\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.16.1->tensorflow) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in c:\\python312\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.16.1->tensorflow) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\python312\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.16.1->tensorflow) (2.2.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in c:\\python312\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.16.1->tensorflow) (2024.2.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in c:\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.5.2)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in c:\\python312\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.0.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\python312\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\python312\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\python312\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (2.17.2)\n",
            "Requirement already satisfied: mdurl~=0.1 in c:\\python312\\lib\\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.1.2)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "[notice] A new release of pip is available: 24.0 -> 24.2\n",
            "[notice] To update, run: python.exe -m pip install --upgrade pip\n",
            "ERROR: Could not find a version that satisfies the requirement tensorflow-addons==0.17.1 (from versions: none)\n",
            "ERROR: No matching distribution found for tensorflow-addons==0.17.1\n",
            "\n",
            "[notice] A new release of pip is available: 24.0 -> 24.2\n",
            "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "^C\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "ERROR: Could not find a version that satisfies the requirement tensorflow-addons (from versions: none)\n",
            "ERROR: No matching distribution found for tensorflow-addons\n",
            "\n",
            "[notice] A new release of pip is available: 24.0 -> 24.2\n",
            "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow\n",
        "!pip install tensorflow-addons==0.17.1\n",
        "\n",
        "!pip install --upgrade tensorflow\n",
        "!pip install --upgrade tensorflow-addons"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vmNnI8vNG_cv"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# Use the recommended import path for LSTM\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.callbacks import TensorBoard\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0q59FGqEHAt7",
        "outputId": "3a07b753-8aad-4006-dc74-cb7893b77560"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Use the recommended import path for LSTM\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization\n",
        "from tensorflow.keras.callbacks import TensorBoard\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.initializers import Orthogonal\n",
        "\n",
        "# ... rest of your code ...\n",
        "import tensorflow as tf\n",
        "\n",
        "\n",
        "model = tf.keras.Sequential()\n",
        "model.add(LSTM(units=50,\n",
        "               activation ='relu',\n",
        "               return_sequences = True,\n",
        "               input_shape=(x_train.shape[1],1),\n",
        "                recurrent_initializer=\"orthogonal\"\n",
        "               )\n",
        ")\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "# Second Layer\n",
        "model.add(LSTM(units=60,\n",
        "               activation ='relu',\n",
        "               return_sequences = True,\n",
        "               recurrent_initializer=\"orthogonal\"\n",
        "               )\n",
        ")\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "\n",
        "\n",
        "model.add(LSTM(units=80,\n",
        "               activation ='relu',\n",
        "               return_sequences = True,\n",
        "               recurrent_initializer=\"orthogonal\"\n",
        "               )\n",
        ")\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "\n",
        "\n",
        "model.add(LSTM(units=120,\n",
        "               activation ='relu',\n",
        "               recurrent_initializer=\"orthogonal\"\n",
        "               )\n",
        ")\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "\n",
        "\n",
        "# Dense Layer - to connect all the layers\n",
        "# model.add(Dense(units=1))\n",
        "model.add(tf.keras.layers.Dense(units=1, kernel_initializer=Orthogonal(gain=1.0, seed=None)))\n",
        "\n",
        "# Set Optimizer\n",
        "opt = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "nJMjacjMHJ7o",
        "outputId": "23b0972f-3026-4e63-d982-e165dd1a0cf6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">26,640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">45,120</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">96,480</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">121</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ],
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │          \u001b[38;5;34m10,400\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m60\u001b[0m)             │          \u001b[38;5;34m26,640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m60\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m80\u001b[0m)             │          \u001b[38;5;34m45,120\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m80\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m)                 │          \u001b[38;5;34m96,480\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m121\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">178,761</span> (698.29 KB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m178,761\u001b[0m (698.29 KB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">178,761</span> (698.29 KB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m178,761\u001b[0m (698.29 KB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGCc5GckHNlu",
        "outputId": "473a0977-d0ea-4e0a-ceb7-5332eae31c4d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-addons==0.17.1\n",
            "  Using cached tensorflow_addons-0.17.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.8 kB)\n",
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            "Installing collected packages: tensorflow-addons\n",
            "  Attempting uninstall: tensorflow-addons\n",
            "    Found existing installation: tensorflow-addons 0.23.0\n",
            "    Uninstalling tensorflow-addons-0.23.0:\n",
            "      Successfully uninstalled tensorflow-addons-0.23.0\n",
            "Successfully installed tensorflow-addons-0.17.1\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.7)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2024.7.4)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.6)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.0.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (2.16.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow) (0.1.2)\n",
            "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.10/dist-packages (0.17.1)\n",
            "Collecting tensorflow-addons\n",
            "  Using cached tensorflow_addons-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.8 kB)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow-addons) (24.1)\n",
            "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.10/dist-packages (from tensorflow-addons) (2.13.3)\n",
            "Using cached tensorflow_addons-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (611 kB)\n",
            "Installing collected packages: tensorflow-addons\n",
            "  Attempting uninstall: tensorflow-addons\n",
            "    Found existing installation: tensorflow-addons 0.17.1\n",
            "    Uninstalling tensorflow-addons-0.17.1:\n",
            "      Successfully uninstalled tensorflow-addons-0.17.1\n",
            "Successfully installed tensorflow-addons-0.23.0\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow-addons==0.17.1\n",
        "\n",
        "!pip install --upgrade tensorflow\n",
        "!pip install --upgrade tensorflow-addons"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1QksPv1HPYj",
        "outputId": "6350918b-22e8-4f33-99a5-e3d462db1cd5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 290ms/step - accuracy: 0.0056 - loss: 0.2786 - mean_absolute_error: 0.5263 - mean_squared_error: 0.3435\n",
            "Epoch 2/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 236ms/step - accuracy: 0.0012 - loss: 0.0339 - mean_absolute_error: 0.2972 - mean_squared_error: 0.1447\n",
            "Epoch 3/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 331ms/step - accuracy: 0.0021 - loss: 0.0183 - mean_absolute_error: 0.2239 - mean_squared_error: 0.0920\n",
            "Epoch 4/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 271ms/step - accuracy: 1.6556e-04 - loss: 0.0151 - mean_absolute_error: 0.1875 - mean_squared_error: 0.0691\n",
            "Epoch 5/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 239ms/step - accuracy: 0.0016 - loss: 0.0145 - mean_absolute_error: 0.1673 - mean_squared_error: 0.0568\n",
            "Epoch 6/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 329ms/step - accuracy: 0.0056 - loss: 0.0143 - mean_absolute_error: 0.1547 - mean_squared_error: 0.0492\n",
            "Epoch 7/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 248ms/step - accuracy: 1.6556e-04 - loss: 0.0133 - mean_absolute_error: 0.1447 - mean_squared_error: 0.0436\n",
            "Epoch 8/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 293ms/step - accuracy: 4.4194e-04 - loss: 0.0140 - mean_absolute_error: 0.1376 - mean_squared_error: 0.0395\n",
            "Epoch 9/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 332ms/step - accuracy: 0.0016 - loss: 0.0113 - mean_absolute_error: 0.1317 - mean_squared_error: 0.0364\n",
            "Epoch 10/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 567ms/step - accuracy: 0.0011 - loss: 0.0100 - mean_absolute_error: 0.1266 - mean_squared_error: 0.0337\n",
            "Epoch 11/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 366ms/step - accuracy: 0.0016 - loss: 0.0117 - mean_absolute_error: 0.1221 - mean_squared_error: 0.0315\n",
            "Epoch 12/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 601ms/step - accuracy: 0.0016 - loss: 0.0128 - mean_absolute_error: 0.1190 - mean_squared_error: 0.0298\n",
            "Epoch 13/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 326ms/step - accuracy: 6.5771e-04 - loss: 0.0103 - mean_absolute_error: 0.1160 - mean_squared_error: 0.0283\n",
            "Epoch 14/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 332ms/step - accuracy: 0.0021 - loss: 0.0103 - mean_absolute_error: 0.1130 - mean_squared_error: 0.0270\n",
            "Epoch 15/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 253ms/step - accuracy: 1.6556e-04 - loss: 0.0107 - mean_absolute_error: 0.1105 - mean_squared_error: 0.0258\n",
            "Epoch 16/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 331ms/step - accuracy: 0.0024 - loss: 0.0108 - mean_absolute_error: 0.1081 - mean_squared_error: 0.0247\n",
            "Epoch 17/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 237ms/step - accuracy: 2.5237e-04 - loss: 0.0081 - mean_absolute_error: 0.1059 - mean_squared_error: 0.0238\n",
            "Epoch 18/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 336ms/step - accuracy: 7.7790e-04 - loss: 0.0092 - mean_absolute_error: 0.1040 - mean_squared_error: 0.0229\n",
            "Epoch 19/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 396ms/step - accuracy: 0.0018 - loss: 0.0098 - mean_absolute_error: 0.1024 - mean_squared_error: 0.0222\n",
            "Epoch 20/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 316ms/step - accuracy: 0.0021 - loss: 0.0085 - mean_absolute_error: 0.1008 - mean_squared_error: 0.0215\n",
            "Epoch 21/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 556ms/step - accuracy: 6.5771e-04 - loss: 0.0111 - mean_absolute_error: 0.0995 - mean_squared_error: 0.0209\n",
            "Epoch 22/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 530ms/step - accuracy: 0.0021 - loss: 0.0089 - mean_absolute_error: 0.0983 - mean_squared_error: 0.0204\n",
            "Epoch 23/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 421ms/step - accuracy: 0.0014 - loss: 0.0095 - mean_absolute_error: 0.0972 - mean_squared_error: 0.0199\n",
            "Epoch 24/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 279ms/step - accuracy: 0.0018 - loss: 0.0073 - mean_absolute_error: 0.0959 - mean_squared_error: 0.0194\n",
            "Epoch 25/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 292ms/step - accuracy: 4.4194e-04 - loss: 0.0092 - mean_absolute_error: 0.0949 - mean_squared_error: 0.0190\n",
            "Epoch 26/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 238ms/step - accuracy: 0.0012 - loss: 0.0088 - mean_absolute_error: 0.0940 - mean_squared_error: 0.0186\n",
            "Epoch 27/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 361ms/step - accuracy: 2.5237e-04 - loss: 0.0079 - mean_absolute_error: 0.0930 - mean_squared_error: 0.0182\n",
            "Epoch 28/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 298ms/step - accuracy: 0.0028 - loss: 0.0088 - mean_absolute_error: 0.0921 - mean_squared_error: 0.0178\n",
            "Epoch 29/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 272ms/step - accuracy: 4.4194e-04 - loss: 0.0081 - mean_absolute_error: 0.0913 - mean_squared_error: 0.0174\n",
            "Epoch 30/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 338ms/step - accuracy: 1.6556e-04 - loss: 0.0077 - mean_absolute_error: 0.0905 - mean_squared_error: 0.0171\n",
            "Epoch 31/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 381ms/step - accuracy: 6.5771e-04 - loss: 0.0074 - mean_absolute_error: 0.0897 - mean_squared_error: 0.0168\n",
            "Epoch 32/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 404ms/step - accuracy: 2.5237e-04 - loss: 0.0085 - mean_absolute_error: 0.0890 - mean_squared_error: 0.0165\n",
            "Epoch 33/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 251ms/step - accuracy: 0.0024 - loss: 0.0070 - mean_absolute_error: 0.0884 - mean_squared_error: 0.0163\n",
            "Epoch 34/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 323ms/step - accuracy: 2.5237e-04 - loss: 0.0073 - mean_absolute_error: 0.0876 - mean_squared_error: 0.0160\n",
            "Epoch 35/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 330ms/step - accuracy: 0.0021 - loss: 0.0084 - mean_absolute_error: 0.0871 - mean_squared_error: 0.0158\n",
            "Epoch 36/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 238ms/step - accuracy: 9.0811e-04 - loss: 0.0075 - mean_absolute_error: 0.0865 - mean_squared_error: 0.0155\n",
            "Epoch 37/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 331ms/step - accuracy: 9.0811e-04 - loss: 0.0070 - mean_absolute_error: 0.0859 - mean_squared_error: 0.0153\n",
            "Epoch 38/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 251ms/step - accuracy: 0.0021 - loss: 0.0070 - mean_absolute_error: 0.0853 - mean_squared_error: 0.0151\n",
            "Epoch 39/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 306ms/step - accuracy: 4.4194e-04 - loss: 0.0078 - mean_absolute_error: 0.0849 - mean_squared_error: 0.0149\n",
            "Epoch 40/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 320ms/step - accuracy: 0.0018 - loss: 0.0081 - mean_absolute_error: 0.0845 - mean_squared_error: 0.0147\n",
            "Epoch 41/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 240ms/step - accuracy: 0.0024 - loss: 0.0088 - mean_absolute_error: 0.0841 - mean_squared_error: 0.0146\n",
            "Epoch 42/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 239ms/step - accuracy: 7.7790e-04 - loss: 0.0083 - mean_absolute_error: 0.0839 - mean_squared_error: 0.0145\n",
            "Epoch 43/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 335ms/step - accuracy: 7.7790e-04 - loss: 0.0087 - mean_absolute_error: 0.0835 - mean_squared_error: 0.0143\n",
            "Epoch 44/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 242ms/step - accuracy: 0.0056 - loss: 0.0080 - mean_absolute_error: 0.0831 - mean_squared_error: 0.0141\n",
            "Epoch 45/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 310ms/step - accuracy: 6.5771e-04 - loss: 0.0078 - mean_absolute_error: 0.0828 - mean_squared_error: 0.0140\n",
            "Epoch 46/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 265ms/step - accuracy: 5.4610e-04 - loss: 0.0063 - mean_absolute_error: 0.0824 - mean_squared_error: 0.0139\n",
            "Epoch 47/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 335ms/step - accuracy: 0.0012 - loss: 0.0070 - mean_absolute_error: 0.0819 - mean_squared_error: 0.0137\n",
            "Epoch 48/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 236ms/step - accuracy: 0.0014 - loss: 0.0079 - mean_absolute_error: 0.0815 - mean_squared_error: 0.0136\n",
            "Epoch 49/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 271ms/step - accuracy: 9.0811e-04 - loss: 0.0099 - mean_absolute_error: 0.0813 - mean_squared_error: 0.0135\n",
            "Epoch 50/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 242ms/step - accuracy: 6.5771e-04 - loss: 0.0064 - mean_absolute_error: 0.0809 - mean_squared_error: 0.0133\n",
            "Epoch 51/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 333ms/step - accuracy: 5.4610e-04 - loss: 0.0058 - mean_absolute_error: 0.0806 - mean_squared_error: 0.0132\n",
            "Epoch 52/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 248ms/step - accuracy: 0.0028 - loss: 0.0065 - mean_absolute_error: 0.0802 - mean_squared_error: 0.0131\n",
            "Epoch 53/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 313ms/step - accuracy: 0.0024 - loss: 0.0065 - mean_absolute_error: 0.0798 - mean_squared_error: 0.0129\n",
            "Epoch 54/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 366ms/step - accuracy: 3.4428e-04 - loss: 0.0069 - mean_absolute_error: 0.0795 - mean_squared_error: 0.0128\n",
            "Epoch 55/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 239ms/step - accuracy: 0.0011 - loss: 0.0066 - mean_absolute_error: 0.0791 - mean_squared_error: 0.0127\n",
            "Epoch 56/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 239ms/step - accuracy: 0.0056 - loss: 0.0064 - mean_absolute_error: 0.0787 - mean_squared_error: 0.0126\n",
            "Epoch 57/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 328ms/step - accuracy: 6.5771e-04 - loss: 0.0067 - mean_absolute_error: 0.0784 - mean_squared_error: 0.0125\n",
            "Epoch 58/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 239ms/step - accuracy: 1.6556e-04 - loss: 0.0058 - mean_absolute_error: 0.0780 - mean_squared_error: 0.0123\n",
            "Epoch 59/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 322ms/step - accuracy: 0.0011 - loss: 0.0064 - mean_absolute_error: 0.0777 - mean_squared_error: 0.0122\n",
            "Epoch 60/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 239ms/step - accuracy: 0.0011 - loss: 0.0063 - mean_absolute_error: 0.0775 - mean_squared_error: 0.0122\n",
            "Epoch 61/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 334ms/step - accuracy: 1.6556e-04 - loss: 0.0065 - mean_absolute_error: 0.0772 - mean_squared_error: 0.0121\n",
            "Epoch 62/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 237ms/step - accuracy: 6.5771e-04 - loss: 0.0070 - mean_absolute_error: 0.0770 - mean_squared_error: 0.0120\n",
            "Epoch 63/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 336ms/step - accuracy: 4.4194e-04 - loss: 0.0058 - mean_absolute_error: 0.0767 - mean_squared_error: 0.0119\n",
            "Epoch 64/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 262ms/step - accuracy: 5.4610e-04 - loss: 0.0074 - mean_absolute_error: 0.0765 - mean_squared_error: 0.0118\n",
            "Epoch 65/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 293ms/step - accuracy: 0.0028 - loss: 0.0064 - mean_absolute_error: 0.0763 - mean_squared_error: 0.0117\n",
            "Epoch 66/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 241ms/step - accuracy: 6.5771e-04 - loss: 0.0062 - mean_absolute_error: 0.0760 - mean_squared_error: 0.0116\n",
            "Epoch 67/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 332ms/step - accuracy: 2.5237e-04 - loss: 0.0062 - mean_absolute_error: 0.0758 - mean_squared_error: 0.0116\n",
            "Epoch 68/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 238ms/step - accuracy: 5.4610e-04 - loss: 0.0063 - mean_absolute_error: 0.0755 - mean_squared_error: 0.0115\n",
            "Epoch 69/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 325ms/step - accuracy: 0.0033 - loss: 0.0058 - mean_absolute_error: 0.0752 - mean_squared_error: 0.0114\n",
            "Epoch 70/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 240ms/step - accuracy: 6.5771e-04 - loss: 0.0056 - mean_absolute_error: 0.0750 - mean_squared_error: 0.0113\n",
            "Epoch 71/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 319ms/step - accuracy: 0.0033 - loss: 0.0064 - mean_absolute_error: 0.0747 - mean_squared_error: 0.0112\n",
            "Epoch 72/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 238ms/step - accuracy: 2.5237e-04 - loss: 0.0052 - mean_absolute_error: 0.0744 - mean_squared_error: 0.0112\n",
            "Epoch 73/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 242ms/step - accuracy: 4.4194e-04 - loss: 0.0057 - mean_absolute_error: 0.0742 - mean_squared_error: 0.0111\n",
            "Epoch 74/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 293ms/step - accuracy: 5.4610e-04 - loss: 0.0064 - mean_absolute_error: 0.0739 - mean_squared_error: 0.0110\n",
            "Epoch 75/75\n",
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 332ms/step - accuracy: 0.0018 - loss: 0.0063 - mean_absolute_error: 0.0737 - mean_squared_error: 0.0109\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7ea41cf5eec0>"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Try importing AbstractRNNCell from tensorflow.python.keras.layers instead\n",
        "\n",
        "from tensorflow.python.keras.layers import AbstractRNNCell\n",
        "\n",
        "# Remove the incorrect import and keep the original correct one\n",
        "# from tensorflow.python.keras.layers import Dense, Dropout, LSTM, BatchNormalization\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization # This line was already in a previous cell, keep it\n",
        "from tensorflow.python.keras.callbacks import TensorBoard\n",
        "from tensorflow.python.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "from tensorflow.python.keras.initializers import Orthogonal\n",
        "from tensorflow.python.keras.metrics import MeanSquaredError, MeanAbsoluteError\n",
        "\n",
        "model.compile(optimizer=opt,\n",
        "              loss='mean_squared_error',  # Retain MSE for regression\n",
        "              metrics=['accuracy',  # Include accuracy for reference (optional)\n",
        "                       MeanSquaredError(),\n",
        "                       MeanAbsoluteError()])\n",
        "\n",
        "model.fit(x_train,y_train,epochs=75)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZtdL6iHHoMA",
        "outputId": "757509c0-0d00-498f-f0a5-5db08b87b66a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ],
      "source": [
        "model.save('model_5_years.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "w6KaeZCQHufr",
        "outputId": "20fb4e3d-f3a6-4fab-c5ef-9d40898e4b99"
      },
      "outputs": [
        {
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              "type": "dataframe",
              "variable_name": "data_testing"
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              "        document.querySelector('#df-6e7cb7a9-1734-41de-8bdf-dda7e00f90d6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6e7cb7a9-1734-41de-8bdf-dda7e00f90d6');\n",
              "        const dataTable =\n",
              "          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",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f2e5b0ef-f860-405d-95dd-b381acbe2333\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f2e5b0ef-f860-405d-95dd-b381acbe2333')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f2e5b0ef-f860-405d-95dd-b381acbe2333 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       Close\n",
              "704  32.2939\n",
              "705  32.1533\n",
              "706  30.0421\n",
              "707  30.4056\n",
              "708  29.9713"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_testing.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ve8KnrCaHwxh",
        "outputId": "03de8d8a-21ea-4cc1-d257-aa818896c6c7"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"data_training\",\n  \"rows\": 704,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.04242446331117,\n        \"min\": 23.1237,\n        \"max\": 50.5113,\n        \"num_unique_values\": 666,\n        \"samples\": [\n          24.7251,\n          42.0869,\n          32.2261\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "data_training"
            },
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              "\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50.0555</td>\n",
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              "      <th>1</th>\n",
              "      <td>50.5113</td>\n",
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              "      <td>50.0329</td>\n",
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              "      <td>49.9759</td>\n",
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              "        document.querySelector('#df-d9baf4fe-67a7-4dcb-8fba-7278dd15a3cb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d9baf4fe-67a7-4dcb-8fba-7278dd15a3cb');\n",
              "        const dataTable =\n",
              "          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",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2d958e95-8438-4c6a-82e4-dc485181bb39 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     Close\n",
              "0  50.0555\n",
              "1  50.5113\n",
              "2  50.3590\n",
              "3  50.0329\n",
              "4  49.9759"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_training.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SRF8WNTxHwz6"
      },
      "outputs": [],
      "source": [
        "# We need previous 100 days data and we need to compare and append the list.\n",
        "past_100_days=data_training.tail(100)\n",
        "final_df=pd.concat([past_100_days,data_testing],ignore_index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GnC7ViwyHw2Z",
        "outputId": "047d683a-31e1-425f-8bd7-3824d31d1b38"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"final_df\",\n  \"rows\": 402,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.6639986944150023,\n        \"min\": 20.8818,\n        \"max\": 34.6056,\n        \"num_unique_values\": 381,\n        \"samples\": [\n          28.6274,\n          24.1867,\n          24.9855\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "final_df"
            },
            "text/html": [
              "\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27.3010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27.1448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26.4532</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     Close\n",
              "0  27.3010\n",
              "1  27.1448\n",
              "2  26.4532\n",
              "3  27.0780\n",
              "4  27.0780"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJQ2pQN3Hw4r",
        "outputId": "3fb8cc20-2c94-478b-b008-2928b4109103"
      },
      "outputs": [
        {
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              "       [0.77470526],\n",
              "       [0.82926012],\n",
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              "       [0.81358662],\n",
              "       [0.82740203],\n",
              "       [0.76912371],\n",
              "       [0.75839053],\n",
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              "       [0.767987  ]])"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "input_data = scaler.fit_transform(final_df)\n",
        "input_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwmB2R2ZHw8I",
        "outputId": "a10d7775-4e44-4ea2-91fc-961d00dc8793"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(402, 1)"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "input_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UY5DWY0HIWHN"
      },
      "outputs": [],
      "source": [
        "x_test = []\n",
        "y_test = []\n",
        "\n",
        "for i in range(100,input_data.shape[0]):\n",
        "  x_test.append(input_data[i-100: i])\n",
        "  y_test.append(input_data[i, 0])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mU-vpre6IWM2",
        "outputId": "7c8851c5-8e6a-4223-bfff-1e7cb9e1c6bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(302, 100, 1)\n",
            "(302,)\n"
          ]
        }
      ],
      "source": [
        "x_test,y_test= np.array(x_test),np.array(y_test)\n",
        "\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_p6o-meIWPO",
        "outputId": "4c41eb36-d028-4446-f48c-eab731dcba6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 151ms/step\n"
          ]
        }
      ],
      "source": [
        "# Making Predictions\n",
        "\n",
        "y_predicted = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VXJiuzZIWR2",
        "outputId": "c20dc9b0-de95-4f20-aeea-39535ea07d3b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(302, 1)"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_predicted.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_jqGb_iIWVT",
        "outputId": "1ed10e42-2ce0-4544-c5a2-4fe6dd4b0732"
      },
      "outputs": [
        {
          "data": {
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          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "_kDIEyeNIfGd",
        "outputId": "c992e140-5ae3-4243-8476-50364a2e06eb"
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            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gd2fc37AIhay",
        "outputId": "9d8c04d2-d875-4e3d-bc72-193fb9e9f2fd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.07286612])"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scaler.scale_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AchcQiJXIija"
      },
      "outputs": [],
      "source": [
        "scale_factor=1/0.07286612\n",
        "y_predicted= y_predicted* scale_factor\n",
        "y_test = y_test * scale_factor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "eBPuqVpAImCW",
        "outputId": "2ab96d28-fbb4-4d5c-e19b-735bd229591a"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig=plt.figure(figsize=(12,6))\n",
        "fig.patch.set_facecolor('#A59DDF')\n",
        "\n",
        "ax=plt.axes()\n",
        "ax.set_facecolor('#211970')\n",
        "\n",
        "plt.grid(True,linestyle='--',color='#626784')\n",
        "plt.plot(y_test,'#A720C4',label=\"Original price\")\n",
        "plt.plot(y_predicted,'#DFB729',label=\"Predicted price\")\n",
        "\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Stock Price($)')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}