10 year Model/Close Feature/75/10Model_13_23_75.ipynb

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            "Requirement already satisfied: pandas-datareader in /usr/local/lib/python3.10/dist-packages (0.10.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (4.9.4)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (2.0.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (2.31.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (2024.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (1.25.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (2024.7.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=0.23->pandas-datareader) (1.16.0)\n"
          ]
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      "source": [
        "!pip install pandas-datareader"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas_datareader as data"
      ],
      "metadata": {
        "id": "moPEPeDaGAmf"
      },
      "execution_count": 42,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "import pandas_datareader.data as web\n",
        "start='2013-01-01'\n",
        "end='2023-01-01'\n",
        "\n",
        "df=web.DataReader('JPM','stooq',start,end)\n",
        "df.head()"
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      "execution_count": 43,
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        {
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              "              Open    High     Low   Close     Volume\n",
              "Date                                                 \n",
              "2022-12-30  132.81  134.33  132.44  134.10  9292519.0\n",
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              "2022-12-27  131.57  132.22  130.55  131.74  5410991.0\n",
              "2022-12-23  130.58  131.44  129.64  131.28  5091905.0"
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          "metadata": {},
          "execution_count": 43
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        "df.tail()"
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      "execution_count": 44,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "               Open     High      Low    Close        Volume\n",
              "Date                                                        \n",
              "2013-01-08  34.6350  34.7126  34.3020  34.7126  2.572222e+07\n",
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              "      <td>3.205830e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-04</th>\n",
              "      <td>33.9100</td>\n",
              "      <td>34.6606</td>\n",
              "      <td>33.9011</td>\n",
              "      <td>34.6036</td>\n",
              "      <td>3.210123e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-03</th>\n",
              "      <td>34.1320</td>\n",
              "      <td>34.1379</td>\n",
              "      <td>33.7164</td>\n",
              "      <td>34.0023</td>\n",
              "      <td>3.175870e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-01-02</th>\n",
              "      <td>34.3177</td>\n",
              "      <td>34.3324</td>\n",
              "      <td>33.7341</td>\n",
              "      <td>34.0721</td>\n",
              "      <td>4.118124e+07</td>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2013-01-02 00:00:00\",\n        \"max\": \"2013-01-08 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2013-01-07 00:00:00\",\n          \"2013-01-02 00:00:00\",\n          \"2013-01-04 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.278905042980582,\n        \"min\": 33.91,\n        \"max\": 34.635,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.4385,\n          34.3177,\n          33.91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2604247837668299,\n        \"min\": 34.1379,\n        \"max\": 34.7126,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.7038,\n          34.3324,\n          34.6606\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30494729544627996,\n        \"min\": 33.7164,\n        \"max\": 34.3462,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.3462,\n          33.7341,\n          33.9011\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34045241223994954,\n        \"min\": 34.0023,\n        \"max\": 34.7126,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.6429,\n          34.0721,\n          34.6036\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5526870.271840395,\n        \"min\": 25722217.115122,\n        \"max\": 41181238.670225,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          32058302.718681,\n          41181238.670225,\n          32101233.49462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  Date becomes column and are removable.\n",
        "df=df.reset_index()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "U3ivPfLhGP92",
        "outputId": "da9ab653-52cf-4fee-907a-8859338a6874"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date    Open    High     Low   Close     Volume\n",
              "0 2022-12-30  132.81  134.33  132.44  134.10  9292519.0\n",
              "1 2022-12-29  132.93  133.27  132.31  133.22  6585228.0\n",
              "2 2022-12-28  131.85  133.41  131.01  132.46  8827738.0\n",
              "3 2022-12-27  131.57  132.22  130.55  131.74  5410991.0\n",
              "4 2022-12-23  130.58  131.44  129.64  131.28  5091905.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d9009e65-fab4-402f-ad1b-24d3ea1af53e\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
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              "      <th>0</th>\n",
              "      <td>2022-12-30</td>\n",
              "      <td>132.81</td>\n",
              "      <td>134.33</td>\n",
              "      <td>132.44</td>\n",
              "      <td>134.10</td>\n",
              "      <td>9292519.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-12-29</td>\n",
              "      <td>132.93</td>\n",
              "      <td>133.27</td>\n",
              "      <td>132.31</td>\n",
              "      <td>133.22</td>\n",
              "      <td>6585228.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-12-28</td>\n",
              "      <td>131.85</td>\n",
              "      <td>133.41</td>\n",
              "      <td>131.01</td>\n",
              "      <td>132.46</td>\n",
              "      <td>8827738.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-12-27</td>\n",
              "      <td>131.57</td>\n",
              "      <td>132.22</td>\n",
              "      <td>130.55</td>\n",
              "      <td>131.74</td>\n",
              "      <td>5410991.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-12-23</td>\n",
              "      <td>130.58</td>\n",
              "      <td>131.44</td>\n",
              "      <td>129.64</td>\n",
              "      <td>131.28</td>\n",
              "      <td>5091905.0</td>\n",
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              "        const element = document.querySelector('#df-d9009e65-fab4-402f-ad1b-24d3ea1af53e');\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",
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              "        element.appendChild(docLink);\n",
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              "  [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-66313c15-b7fb-4527-adf2-b7650fa21da0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2518,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2013-01-02 00:00:00\",\n        \"max\": \"2022-12-30 00:00:00\",\n        \"num_unique_values\": 2518,\n        \"samples\": [\n          \"2014-04-11 00:00:00\",\n          \"2021-05-17 00:00:00\",\n          \"2017-06-26 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.86506941484165,\n        \"min\": 33.91,\n        \"max\": 167.428,\n        \"num_unique_values\": 2332,\n        \"samples\": [\n          91.7178,\n          46.6061,\n          40.1327\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36.23166539952852,\n        \"min\": 34.1379,\n        \"max\": 167.664,\n        \"num_unique_values\": 2355,\n        \"samples\": [\n          48.4157,\n          84.4321,\n          108.815\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.48488001426454,\n        \"min\": 33.7164,\n        \"max\": 165.316,\n        \"num_unique_values\": 2338,\n        \"samples\": [\n          82.3257,\n          65.7686,\n          153.595\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.842818166105744,\n        \"min\": 34.0023,\n        \"max\": 166.524,\n        \"num_unique_values\": 2335,\n        \"samples\": [\n          46.4322,\n          55.507,\n          39.5403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8402918.440748073,\n        \"min\": 3220496.0,\n        \"max\": 79838537.152985,\n        \"num_unique_values\": 2518,\n        \"samples\": [\n          59879591.200496,\n          10319486.920281,\n          11785886.881119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(['Date'],axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wIHsMsmxGSJC",
        "outputId": "f8896f04-5ca2-4ded-fc9c-2248ba4ea1ce"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Open    High     Low   Close     Volume\n",
              "0  132.81  134.33  132.44  134.10  9292519.0\n",
              "1  132.93  133.27  132.31  133.22  6585228.0\n",
              "2  131.85  133.41  131.01  132.46  8827738.0\n",
              "3  131.57  132.22  130.55  131.74  5410991.0\n",
              "4  130.58  131.44  129.64  131.28  5091905.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c83db7e4-70bf-4cac-b8b1-7a040cfc3323\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>132.81</td>\n",
              "      <td>134.33</td>\n",
              "      <td>132.44</td>\n",
              "      <td>134.10</td>\n",
              "      <td>9292519.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>132.93</td>\n",
              "      <td>133.27</td>\n",
              "      <td>132.31</td>\n",
              "      <td>133.22</td>\n",
              "      <td>6585228.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>131.85</td>\n",
              "      <td>133.41</td>\n",
              "      <td>131.01</td>\n",
              "      <td>132.46</td>\n",
              "      <td>8827738.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>131.57</td>\n",
              "      <td>132.22</td>\n",
              "      <td>130.55</td>\n",
              "      <td>131.74</td>\n",
              "      <td>5410991.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130.58</td>\n",
              "      <td>131.44</td>\n",
              "      <td>129.64</td>\n",
              "      <td>131.28</td>\n",
              "      <td>5091905.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c83db7e4-70bf-4cac-b8b1-7a040cfc3323')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c83db7e4-70bf-4cac-b8b1-7a040cfc3323 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-c83db7e4-70bf-4cac-b8b1-7a040cfc3323');\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-d86ce124-734a-4531-b0da-e27ebf4813cd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d86ce124-734a-4531-b0da-e27ebf4813cd')\"\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-d86ce124-734a-4531-b0da-e27ebf4813cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2518,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.86506941484165,\n        \"min\": 33.91,\n        \"max\": 167.428,\n        \"num_unique_values\": 2332,\n        \"samples\": [\n          91.7178,\n          46.6061,\n          40.1327\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36.23166539952852,\n        \"min\": 34.1379,\n        \"max\": 167.664,\n        \"num_unique_values\": 2355,\n        \"samples\": [\n          48.4157,\n          84.4321,\n          108.815\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.48488001426454,\n        \"min\": 33.7164,\n        \"max\": 165.316,\n        \"num_unique_values\": 2338,\n        \"samples\": [\n          82.3257,\n          65.7686,\n          153.595\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.842818166105744,\n        \"min\": 34.0023,\n        \"max\": 166.524,\n        \"num_unique_values\": 2335,\n        \"samples\": [\n          46.4322,\n          55.507,\n          39.5403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8402918.440748073,\n        \"min\": 3220496.0,\n        \"max\": 79838537.152985,\n        \"num_unique_values\": 2518,\n        \"samples\": [\n          59879591.200496,\n          10319486.920281,\n          11785886.881119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "72QtumHDGToX",
        "outputId": "e16e1ef7-ad5f-450f-b894-82d781df4bcf"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "cDZfmvIlGVks",
        "outputId": "9dcd291f-7e90-4277-f497-10cc6ebac209"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Open      High       Low     Close        Volume\n",
              "0     132.8100  134.3300  132.4400  134.1000  9.292519e+06\n",
              "1     132.9300  133.2700  132.3100  133.2200  6.585228e+06\n",
              "2     131.8500  133.4100  131.0100  132.4600  8.827738e+06\n",
              "3     131.5700  132.2200  130.5500  131.7400  5.410991e+06\n",
              "4     130.5800  131.4400  129.6400  131.2800  5.091905e+06\n",
              "...        ...       ...       ...       ...           ...\n",
              "2513   34.6350   34.7126   34.3020   34.7126  2.572222e+07\n",
              "2514   34.4385   34.7038   34.3462   34.6429  3.205830e+07\n",
              "2515   33.9100   34.6606   33.9011   34.6036  3.210123e+07\n",
              "2516   34.1320   34.1379   33.7164   34.0023  3.175870e+07\n",
              "2517   34.3177   34.3324   33.7341   34.0721  4.118124e+07\n",
              "\n",
              "[2518 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4c443c33-c11b-4f22-aaf8-adbfde7b723d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>132.8100</td>\n",
              "      <td>134.3300</td>\n",
              "      <td>132.4400</td>\n",
              "      <td>134.1000</td>\n",
              "      <td>9.292519e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>132.9300</td>\n",
              "      <td>133.2700</td>\n",
              "      <td>132.3100</td>\n",
              "      <td>133.2200</td>\n",
              "      <td>6.585228e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>131.8500</td>\n",
              "      <td>133.4100</td>\n",
              "      <td>131.0100</td>\n",
              "      <td>132.4600</td>\n",
              "      <td>8.827738e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>131.5700</td>\n",
              "      <td>132.2200</td>\n",
              "      <td>130.5500</td>\n",
              "      <td>131.7400</td>\n",
              "      <td>5.410991e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130.5800</td>\n",
              "      <td>131.4400</td>\n",
              "      <td>129.6400</td>\n",
              "      <td>131.2800</td>\n",
              "      <td>5.091905e+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>2513</th>\n",
              "      <td>34.6350</td>\n",
              "      <td>34.7126</td>\n",
              "      <td>34.3020</td>\n",
              "      <td>34.7126</td>\n",
              "      <td>2.572222e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2514</th>\n",
              "      <td>34.4385</td>\n",
              "      <td>34.7038</td>\n",
              "      <td>34.3462</td>\n",
              "      <td>34.6429</td>\n",
              "      <td>3.205830e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2515</th>\n",
              "      <td>33.9100</td>\n",
              "      <td>34.6606</td>\n",
              "      <td>33.9011</td>\n",
              "      <td>34.6036</td>\n",
              "      <td>3.210123e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2516</th>\n",
              "      <td>34.1320</td>\n",
              "      <td>34.1379</td>\n",
              "      <td>33.7164</td>\n",
              "      <td>34.0023</td>\n",
              "      <td>3.175870e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2517</th>\n",
              "      <td>34.3177</td>\n",
              "      <td>34.3324</td>\n",
              "      <td>33.7341</td>\n",
              "      <td>34.0721</td>\n",
              "      <td>4.118124e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2518 rows × 5 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "        document.querySelector('#df-4c443c33-c11b-4f22-aaf8-adbfde7b723d button.colab-df-convert');\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      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",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\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",
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              "        document.querySelector('#df-7417316f-06a9-4938-ab7f-cb11aabd35be button');\n",
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              "\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    <script>\n",
              "      (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2518,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.86506941484165,\n        \"min\": 33.91,\n        \"max\": 167.428,\n        \"num_unique_values\": 2332,\n        \"samples\": [\n          91.7178,\n          46.6061,\n          40.1327\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36.23166539952852,\n        \"min\": 34.1379,\n        \"max\": 167.664,\n        \"num_unique_values\": 2355,\n        \"samples\": [\n          48.4157,\n          84.4321,\n          108.815\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.48488001426454,\n        \"min\": 33.7164,\n        \"max\": 165.316,\n        \"num_unique_values\": 2338,\n        \"samples\": [\n          82.3257,\n          65.7686,\n          153.595\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.842818166105744,\n        \"min\": 34.0023,\n        \"max\": 166.524,\n        \"num_unique_values\": 2335,\n        \"samples\": [\n          46.4322,\n          55.507,\n          39.5403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8402918.440748073,\n        \"min\": 3220496.0,\n        \"max\": 79838537.152985,\n        \"num_unique_values\": 2518,\n        \"samples\": [\n          59879591.200496,\n          10319486.920281,\n          11785886.881119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UG_FGvSlGXC3",
        "outputId": "8cfd1098-d4f8-44e3-a0fd-b3db5eabb0b2"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0             NaN\n",
              "1             NaN\n",
              "2             NaN\n",
              "3             NaN\n",
              "4             NaN\n",
              "          ...    \n",
              "2513    37.397394\n",
              "2514    37.324762\n",
              "2515    37.243809\n",
              "2516    37.164133\n",
              "2517    37.085725\n",
              "Name: Close, Length: 2518, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "-_mMtOTGGYwa",
        "outputId": "dd7d97ca-db16-4643-e6a7-6a8f5b2ebb9d"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ma200=df.Close.rolling(200).mean()\n",
        "ma200"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0RhyVJnqGaWj",
        "outputId": "b73838d7-c61d-4718-df25-bca48b6cc5a6"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0             NaN\n",
              "1             NaN\n",
              "2             NaN\n",
              "3             NaN\n",
              "4             NaN\n",
              "          ...    \n",
              "2513    39.338182\n",
              "2514    39.302568\n",
              "2515    39.264229\n",
              "2516    39.222755\n",
              "2517    39.181974\n",
              "Name: Close, Length: 2518, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "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)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "EvRw-YeJGcrZ",
        "outputId": "559f3b99-923d-47d1-cdb7-647c677478f4"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Closing Price (USD)')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUYMtUqkGg9b",
        "outputId": "99a13aa6-e61d-401f-d7d1-ee60191b398d"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2518, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "egA11-0FGkip",
        "outputId": "c26d0407-0320-435b-f398-22b8fea7da6b"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Used data for training:  (1762, 1)\n",
            "Used data for testing:  (756, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_training.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zp5Q3pJrGmiq",
        "outputId": "1ffc516b-8223-4d72-88d1-ba20ae86a920"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Close\n",
              "0  134.10\n",
              "1  133.22\n",
              "2  132.46\n",
              "3  131.74\n",
              "4  131.28"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-52c28d44-d724-4c0e-8052-4cb173a356a7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>134.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>133.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>132.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>131.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>131.28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52c28d44-d724-4c0e-8052-4cb173a356a7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-52c28d44-d724-4c0e-8052-4cb173a356a7 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-52c28d44-d724-4c0e-8052-4cb173a356a7');\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",
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              "\n",
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              "<div id=\"df-adbadc2a-5338-4428-8f57-0667cbbc9c23\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-adbadc2a-5338-4428-8f57-0667cbbc9c23')\"\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-adbadc2a-5338-4428-8f57-0667cbbc9c23 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_training",
              "summary": "{\n  \"name\": \"data_training\",\n  \"rows\": 1762,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.086325257419244,\n        \"min\": 43.8906,\n        \"max\": 166.524,\n        \"num_unique_values\": 1629,\n        \"samples\": [\n          87.5022,\n          153.32,\n          94.3763\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_testing.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QSW9UigXGn-f",
        "outputId": "ba0a24d4-ab97-4332-f535-3056a1d50d80"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Close\n",
              "1762  54.2504\n",
              "1763  54.7072\n",
              "1764  55.0983\n",
              "1765  54.5364\n",
              "1766  54.7162"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-718e2bfd-1d5a-4222-a59c-969640bd9396\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
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              "    <tr>\n",
              "      <th>1762</th>\n",
              "      <td>54.2504</td>\n",
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              "      <td>54.7072</td>\n",
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              "      <td>54.5364</td>\n",
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              "      <th>1766</th>\n",
              "      <td>54.7162</td>\n",
              "    </tr>\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-718e2bfd-1d5a-4222-a59c-969640bd9396 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-718e2bfd-1d5a-4222-a59c-969640bd9396');\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",
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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",
              "</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",
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              "  }\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-a89d7a0d-b3a4-4de8-9993-4a6a6a8532f3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_testing",
              "summary": "{\n  \"name\": \"data_testing\",\n  \"rows\": 756,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.575330900745263,\n        \"min\": 34.0023,\n        \"max\": 57.1634,\n        \"num_unique_values\": 715,\n        \"samples\": [\n          55.0728,\n          41.8265,\n          54.0962\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUOkXv2mGqN3",
        "outputId": "bf5dee36-57a4-4865-fb64-411e6c7f24c6"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.73560221],\n",
              "       [0.72842635],\n",
              "       [0.72222902],\n",
              "       ...,\n",
              "       [0.06570804],\n",
              "       [0.07190048],\n",
              "       [0.07117148]])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "xdoWv08nGrgo"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow\n",
        "!pip install tensorflow-addons==0.17.1\n",
        "\n",
        "!pip install --upgrade tensorflow\n",
        "!pip install --upgrade tensorflow-addons"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "peb5Uqz7G8wj",
        "outputId": "9f8ac6b7-21cc-4b76-e4fd-51df888e4c29"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.17.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.11.0)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "vmNnI8vNG_cv"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0q59FGqEHAt7",
        "outputId": "031f085d-5573-45f8-8099-4f2be9457201"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "nJMjacjMHJ7o",
        "outputId": "78a34db1-5e38-444d-d6f6-9d1e1ff2ed1d"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "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_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "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_4 (\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_4 (\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_5 (\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_5 (\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_6 (\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_6 (\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_7 (\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_7 (\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_1 (\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"
            ],
            "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_4 (<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_4 (<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_5 (<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_5 (<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_6 (<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_6 (<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_7 (<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_7 (<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_1 (<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"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m178,761\u001b[0m (698.29 KB)\n"
            ],
            "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"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m178,761\u001b[0m (698.29 KB)\n"
            ],
            "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"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "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"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow-addons==0.17.1\n",
        "\n",
        "!pip install --upgrade tensorflow\n",
        "!pip install --upgrade tensorflow-addons"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGCc5GckHNlu",
        "outputId": "fb2cb26a-922f-4d6f-b087-9f8ddbca6893"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow-addons==0.17.1) (24.1)\n",
            "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.10/dist-packages (from tensorflow-addons==0.17.1) (2.13.3)\n",
            "Using cached tensorflow_addons-0.17.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
            "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",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.17.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.11.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.1)\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 /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.31.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (71.0.4)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.14.1)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.64.1)\n",
            "Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.17.1)\n",
            "Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.5.0)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.1)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.25.2)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.43.0)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (13.7.1)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.3.2)\n",
            "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",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.0.7)\n",
            "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1QksPv1HPYj",
        "outputId": "301d1b45-63d9-48e0-9748-d502c95073df"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 411ms/step - accuracy: 0.0026 - loss: 0.1293 - mean_absolute_error: 0.3639 - mean_squared_error: 0.1900\n",
            "Epoch 2/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 294ms/step - accuracy: 0.0025 - loss: 0.0160 - mean_absolute_error: 0.1707 - mean_squared_error: 0.0559\n",
            "Epoch 3/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 320ms/step - accuracy: 3.9426e-04 - loss: 0.0104 - mean_absolute_error: 0.1322 - mean_squared_error: 0.0366\n",
            "Epoch 4/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 286ms/step - accuracy: 3.2142e-04 - loss: 0.0086 - mean_absolute_error: 0.1139 - mean_squared_error: 0.0285\n",
            "Epoch 5/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 318ms/step - accuracy: 3.4423e-04 - loss: 0.0091 - mean_absolute_error: 0.1037 - mean_squared_error: 0.0241\n",
            "Epoch 6/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 290ms/step - accuracy: 0.0027 - loss: 0.0084 - mean_absolute_error: 0.0967 - mean_squared_error: 0.0211\n",
            "Epoch 7/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 290ms/step - accuracy: 0.0023 - loss: 0.0082 - mean_absolute_error: 0.0911 - mean_squared_error: 0.0190\n",
            "Epoch 8/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 298ms/step - accuracy: 0.0032 - loss: 0.0074 - mean_absolute_error: 0.0872 - mean_squared_error: 0.0174\n",
            "Epoch 9/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 292ms/step - accuracy: 0.0011 - loss: 0.0078 - mean_absolute_error: 0.0842 - mean_squared_error: 0.0162\n",
            "Epoch 10/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 309ms/step - accuracy: 0.0011 - loss: 0.0074 - mean_absolute_error: 0.0816 - mean_squared_error: 0.0152\n",
            "Epoch 11/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 290ms/step - accuracy: 0.0014 - loss: 0.0067 - mean_absolute_error: 0.0796 - mean_squared_error: 0.0145\n",
            "Epoch 12/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 307ms/step - accuracy: 0.0011 - loss: 0.0068 - mean_absolute_error: 0.0778 - mean_squared_error: 0.0138\n",
            "Epoch 13/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 287ms/step - accuracy: 0.0016 - loss: 0.0061 - mean_absolute_error: 0.0762 - mean_squared_error: 0.0132\n",
            "Epoch 14/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 304ms/step - accuracy: 9.2118e-05 - loss: 0.0060 - mean_absolute_error: 0.0746 - mean_squared_error: 0.0126\n",
            "Epoch 15/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 294ms/step - accuracy: 0.0013 - loss: 0.0056 - mean_absolute_error: 0.0731 - mean_squared_error: 0.0122\n",
            "Epoch 16/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 293ms/step - accuracy: 0.0030 - loss: 0.0054 - mean_absolute_error: 0.0719 - mean_squared_error: 0.0117\n",
            "Epoch 17/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 315ms/step - accuracy: 2.6011e-04 - loss: 0.0062 - mean_absolute_error: 0.0708 - mean_squared_error: 0.0114\n",
            "Epoch 18/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 291ms/step - accuracy: 4.8717e-04 - loss: 0.0066 - mean_absolute_error: 0.0699 - mean_squared_error: 0.0110\n",
            "Epoch 19/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 291ms/step - accuracy: 0.0011 - loss: 0.0048 - mean_absolute_error: 0.0691 - mean_squared_error: 0.0108\n",
            "Epoch 20/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 291ms/step - accuracy: 2.4573e-04 - loss: 0.0053 - mean_absolute_error: 0.0682 - mean_squared_error: 0.0105\n",
            "Epoch 21/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 300ms/step - accuracy: 3.4349e-04 - loss: 0.0052 - mean_absolute_error: 0.0673 - mean_squared_error: 0.0102\n",
            "Epoch 22/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 290ms/step - accuracy: 1.2898e-04 - loss: 0.0047 - mean_absolute_error: 0.0665 - mean_squared_error: 0.0100\n",
            "Epoch 23/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 345ms/step - accuracy: 0.0014 - loss: 0.0046 - mean_absolute_error: 0.0658 - mean_squared_error: 0.0097\n",
            "Epoch 24/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 289ms/step - accuracy: 1.3155e-04 - loss: 0.0053 - mean_absolute_error: 0.0651 - mean_squared_error: 0.0095\n",
            "Epoch 25/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 305ms/step - accuracy: 6.9003e-04 - loss: 0.0044 - mean_absolute_error: 0.0644 - mean_squared_error: 0.0093\n",
            "Epoch 26/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 284ms/step - accuracy: 0.0010 - loss: 0.0050 - mean_absolute_error: 0.0638 - mean_squared_error: 0.0091\n",
            "Epoch 27/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 290ms/step - accuracy: 9.4649e-04 - loss: 0.0042 - mean_absolute_error: 0.0632 - mean_squared_error: 0.0090\n",
            "Epoch 28/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 283ms/step - accuracy: 8.5157e-04 - loss: 0.0041 - mean_absolute_error: 0.0626 - mean_squared_error: 0.0088\n",
            "Epoch 29/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 314ms/step - accuracy: 0.0017 - loss: 0.0053 - mean_absolute_error: 0.0620 - mean_squared_error: 0.0086\n",
            "Epoch 30/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 286ms/step - accuracy: 0.0014 - loss: 0.0040 - mean_absolute_error: 0.0615 - mean_squared_error: 0.0085\n",
            "Epoch 31/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 293ms/step - accuracy: 0.0027 - loss: 0.0039 - mean_absolute_error: 0.0610 - mean_squared_error: 0.0084\n",
            "Epoch 32/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 291ms/step - accuracy: 4.8341e-04 - loss: 0.0037 - mean_absolute_error: 0.0605 - mean_squared_error: 0.0082\n",
            "Epoch 33/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 309ms/step - accuracy: 3.1647e-04 - loss: 0.0037 - mean_absolute_error: 0.0599 - mean_squared_error: 0.0081\n",
            "Epoch 34/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 293ms/step - accuracy: 0.0013 - loss: 0.0031 - mean_absolute_error: 0.0594 - mean_squared_error: 0.0079\n",
            "Epoch 35/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 292ms/step - accuracy: 0.0011 - loss: 0.0034 - mean_absolute_error: 0.0589 - mean_squared_error: 0.0078\n",
            "Epoch 36/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 323ms/step - accuracy: 0.0011 - loss: 0.0033 - mean_absolute_error: 0.0585 - mean_squared_error: 0.0077\n",
            "Epoch 37/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 290ms/step - accuracy: 8.0097e-04 - loss: 0.0034 - mean_absolute_error: 0.0580 - mean_squared_error: 0.0076\n",
            "Epoch 38/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 287ms/step - accuracy: 0.0020 - loss: 0.0032 - mean_absolute_error: 0.0576 - mean_squared_error: 0.0075\n",
            "Epoch 39/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 292ms/step - accuracy: 6.0809e-04 - loss: 0.0030 - mean_absolute_error: 0.0572 - mean_squared_error: 0.0073\n",
            "Epoch 40/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 305ms/step - accuracy: 3.1682e-04 - loss: 0.0036 - mean_absolute_error: 0.0568 - mean_squared_error: 0.0072\n",
            "Epoch 41/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 291ms/step - accuracy: 2.4573e-04 - loss: 0.0031 - mean_absolute_error: 0.0564 - mean_squared_error: 0.0071\n",
            "Epoch 42/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 312ms/step - accuracy: 0.0017 - loss: 0.0034 - mean_absolute_error: 0.0560 - mean_squared_error: 0.0070\n",
            "Epoch 43/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 289ms/step - accuracy: 0.0015 - loss: 0.0039 - mean_absolute_error: 0.0557 - mean_squared_error: 0.0070\n",
            "Epoch 44/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 306ms/step - accuracy: 2.7191e-04 - loss: 0.0028 - mean_absolute_error: 0.0554 - mean_squared_error: 0.0069\n",
            "Epoch 45/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 289ms/step - accuracy: 4.6006e-04 - loss: 0.0030 - mean_absolute_error: 0.0550 - mean_squared_error: 0.0068\n",
            "Epoch 46/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 291ms/step - accuracy: 1.1719e-04 - loss: 0.0032 - mean_absolute_error: 0.0547 - mean_squared_error: 0.0067\n",
            "Epoch 47/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 291ms/step - accuracy: 0.0012 - loss: 0.0032 - mean_absolute_error: 0.0544 - mean_squared_error: 0.0066\n",
            "Epoch 48/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 289ms/step - accuracy: 9.2358e-05 - loss: 0.0031 - mean_absolute_error: 0.0541 - mean_squared_error: 0.0066\n",
            "Epoch 49/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 347ms/step - accuracy: 8.9215e-04 - loss: 0.0030 - mean_absolute_error: 0.0538 - mean_squared_error: 0.0065\n",
            "Epoch 50/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 290ms/step - accuracy: 4.2730e-04 - loss: 0.0036 - mean_absolute_error: 0.0535 - mean_squared_error: 0.0064\n",
            "Epoch 51/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 295ms/step - accuracy: 0.0027 - loss: 0.0026 - mean_absolute_error: 0.0532 - mean_squared_error: 0.0063\n",
            "Epoch 52/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 292ms/step - accuracy: 4.5983e-04 - loss: 0.0025 - mean_absolute_error: 0.0529 - mean_squared_error: 0.0063\n",
            "Epoch 53/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 310ms/step - accuracy: 0.0010 - loss: 0.0030 - mean_absolute_error: 0.0526 - mean_squared_error: 0.0062\n",
            "Epoch 54/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 291ms/step - accuracy: 4.7553e-04 - loss: 0.0027 - mean_absolute_error: 0.0523 - mean_squared_error: 0.0061\n",
            "Epoch 55/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 333ms/step - accuracy: 8.1599e-04 - loss: 0.0027 - mean_absolute_error: 0.0521 - mean_squared_error: 0.0061\n",
            "Epoch 56/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 292ms/step - accuracy: 0.0013 - loss: 0.0027 - mean_absolute_error: 0.0518 - mean_squared_error: 0.0060\n",
            "Epoch 57/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 294ms/step - accuracy: 1.7922e-04 - loss: 0.0026 - mean_absolute_error: 0.0515 - mean_squared_error: 0.0060\n",
            "Epoch 58/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 311ms/step - accuracy: 0.0016 - loss: 0.0027 - mean_absolute_error: 0.0513 - mean_squared_error: 0.0059\n",
            "Epoch 59/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 291ms/step - accuracy: 9.7962e-04 - loss: 0.0024 - mean_absolute_error: 0.0511 - mean_squared_error: 0.0058\n",
            "Epoch 60/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 309ms/step - accuracy: 0.0018 - loss: 0.0029 - mean_absolute_error: 0.0508 - mean_squared_error: 0.0058\n",
            "Epoch 61/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 292ms/step - accuracy: 2.0458e-04 - loss: 0.0025 - mean_absolute_error: 0.0506 - mean_squared_error: 0.0057\n",
            "Epoch 62/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 322ms/step - accuracy: 0.0012 - loss: 0.0028 - mean_absolute_error: 0.0504 - mean_squared_error: 0.0057\n",
            "Epoch 63/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 290ms/step - accuracy: 0.0021 - loss: 0.0024 - mean_absolute_error: 0.0502 - mean_squared_error: 0.0056\n",
            "Epoch 64/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 301ms/step - accuracy: 2.8595e-04 - loss: 0.0025 - mean_absolute_error: 0.0500 - mean_squared_error: 0.0056\n",
            "Epoch 65/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 293ms/step - accuracy: 0.0023 - loss: 0.0025 - mean_absolute_error: 0.0498 - mean_squared_error: 0.0055\n",
            "Epoch 66/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 294ms/step - accuracy: 0.0014 - loss: 0.0026 - mean_absolute_error: 0.0496 - mean_squared_error: 0.0055\n",
            "Epoch 67/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 292ms/step - accuracy: 0.0025 - loss: 0.0024 - mean_absolute_error: 0.0494 - mean_squared_error: 0.0054\n",
            "Epoch 68/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 291ms/step - accuracy: 0.0012 - loss: 0.0023 - mean_absolute_error: 0.0491 - mean_squared_error: 0.0054\n",
            "Epoch 69/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 297ms/step - accuracy: 1.5866e-04 - loss: 0.0025 - mean_absolute_error: 0.0490 - mean_squared_error: 0.0054\n",
            "Epoch 70/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 293ms/step - accuracy: 0.0012 - loss: 0.0024 - mean_absolute_error: 0.0488 - mean_squared_error: 0.0053\n",
            "Epoch 71/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 313ms/step - accuracy: 1.9176e-04 - loss: 0.0027 - mean_absolute_error: 0.0486 - mean_squared_error: 0.0053\n",
            "Epoch 72/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 296ms/step - accuracy: 3.5823e-04 - loss: 0.0026 - mean_absolute_error: 0.0484 - mean_squared_error: 0.0052\n",
            "Epoch 73/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 323ms/step - accuracy: 6.5494e-04 - loss: 0.0024 - mean_absolute_error: 0.0482 - mean_squared_error: 0.0052\n",
            "Epoch 74/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 303ms/step - accuracy: 2.4029e-04 - loss: 0.0026 - mean_absolute_error: 0.0481 - mean_squared_error: 0.0052\n",
            "Epoch 75/75\n",
            "\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 290ms/step - accuracy: 0.0011 - loss: 0.0026 - mean_absolute_error: 0.0479 - mean_squared_error: 0.0051\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x79d7ea65bdc0>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('model_5_years.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZtdL6iHHoMA",
        "outputId": "eed0b0ed-1657-4052-b8bb-f40b1e14a26e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    },
    {
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        "data_testing.head()"
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        "colab": {
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          "height": 206
        },
        "id": "w6KaeZCQHufr",
        "outputId": "6d8d426a-ce8b-46e0-9932-0e54d843fb02"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
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              "        Close\n",
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              "    Close\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3745739-5253-481f-922d-5984751ea7b6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f3745739-5253-481f-922d-5984751ea7b6 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-f3745739-5253-481f-922d-5984751ea7b6');\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-04ca5eee-da07-4972-a981-1bee83cfedfe\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-04ca5eee-da07-4972-a981-1bee83cfedfe')\"\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-04ca5eee-da07-4972-a981-1bee83cfedfe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_training",
              "summary": "{\n  \"name\": \"data_training\",\n  \"rows\": 1762,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.086325257419244,\n        \"min\": 43.8906,\n        \"max\": 166.524,\n        \"num_unique_values\": 1629,\n        \"samples\": [\n          87.5022,\n          153.32,\n          94.3763\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "SRF8WNTxHwz6"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GnC7ViwyHw2Z",
        "outputId": "894cc028-2b1c-4faa-fe53-88505b680551"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Close\n",
              "0  54.5884\n",
              "1  53.7700\n",
              "2  52.8740\n",
              "3  52.9163\n",
              "4  52.8151"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dfa93746-f985-4225-9d96-9ef31121bf48\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>54.5884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>53.7700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>52.8740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>52.9163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52.8151</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dfa93746-f985-4225-9d96-9ef31121bf48')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dfa93746-f985-4225-9d96-9ef31121bf48 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-dfa93746-f985-4225-9d96-9ef31121bf48');\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-82039ca5-01c4-4f41-b644-e022c3e79bcc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-82039ca5-01c4-4f41-b644-e022c3e79bcc')\"\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-82039ca5-01c4-4f41-b644-e022c3e79bcc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_df",
              "summary": "{\n  \"name\": \"final_df\",\n  \"rows\": 856,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.422813332734958,\n        \"min\": 34.0023,\n        \"max\": 57.1634,\n        \"num_unique_values\": 800,\n        \"samples\": [\n          41.07,\n          43.7924,\n          48.6043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = scaler.fit_transform(final_df)\n",
        "input_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJQ2pQN3Hw4r",
        "outputId": "bf477a63-f39c-4878-dfbd-fe17fe23b410"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [0.0593452 ],\n",
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              "       [0.06154716],\n",
              "       [0.07414587],\n",
              "       [0.05861984],\n",
              "       [0.04309813],\n",
              "       [0.05175056],\n",
              "       [0.05204848],\n",
              "       [0.02969203],\n",
              "       [0.0306678 ],\n",
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              "       [0.02596163],\n",
              "       [0.        ],\n",
              "       [0.00301367]])"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwmB2R2ZHw8I",
        "outputId": "2d60f40b-40ee-46f4-d23a-2565bad8074a"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(856, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "UY5DWY0HIWHN"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test,y_test= np.array(x_test),np.array(y_test)\n",
        "\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mU-vpre6IWM2",
        "outputId": "1ebdff87-3838-47f8-fd50-a1c2b9d76f08"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(756, 100, 1)\n",
            "(756,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making Predictions\n",
        "\n",
        "y_predicted = model.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_p6o-meIWPO",
        "outputId": "1d63a1d1-befa-4fa0-cc17-003f2a513a4c"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 231ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VXJiuzZIWR2",
        "outputId": "7e4a9fee-a415-4ff6-c8ae-a18ab8e9a835"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(756, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_jqGb_iIWVT",
        "outputId": "d751b283-85c8-4f24-933d-6aabb2d8af54"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       0.86388384, 0.85794284, 0.8763962 , 0.85577542, 0.83694643,\n",
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              "       0.60394368, 0.63342415, 0.65696362, 0.67818022, 0.65904469,\n",
              "       0.67818022, 0.67330135, 0.68169474, 0.65212792, 0.64339345,\n",
              "       0.62566113, 0.61590771, 0.66939826, 0.64831118, 0.68687152,\n",
              "       0.69145248, 0.68577054, 0.67876742, 0.66192884, 0.67219605,\n",
              "       0.65696362, 0.64589333, 0.59465656, 0.61043733, 0.5905419 ,\n",
              "       0.59537759, 0.62082544, 0.60580888, 0.60233322, 0.56059514,\n",
              "       0.56394558, 0.5415071 , 0.54409333, 0.50510986, 0.4916649 ,\n",
              "       0.50333102, 0.45985294, 0.42205681, 0.46689061, 0.43478505,\n",
              "       0.48525761, 0.50510986, 0.50197529, 0.53357138, 0.4744032 ,\n",
              "       0.46825064, 0.47600934, 0.44330796, 0.50650876, 0.57709263,\n",
              "       0.57675585, 0.59431547, 0.63079474, 0.58498517, 0.58192832,\n",
              "       0.63643782, 0.70392166, 0.69311043, 0.71278998, 0.7062143 ,\n",
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              "       0.00301367])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kDIEyeNIfGd",
        "outputId": "be3c84be-faa5-4df4-a023-cfdc81118996"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [0.11990035]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.scale_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gd2fc37AIhay",
        "outputId": "c9c571c4-214e-41e1-a36b-89c5a5000432"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.04317584])"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scale_factor=1/scaler.scale_\n",
        "y_predicted= y_predicted* scale_factor\n",
        "y_test = y_test * scale_factor"
      ],
      "metadata": {
        "id": "AchcQiJXIija"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "eBPuqVpAImCW",
        "outputId": "1cd4f27e-7b33-40dd-e228-4069df86d380"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}