LSTM(RealTime).ipynb

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      "source": [
        "!pip install pandas-datareader"
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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) (1.5.3)\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>=2.8.1 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: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (1.25.2)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (3.6)\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) (2024.2.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.23->pandas-datareader) (1.16.0)\n"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas_datareader as data"
      ]
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    {
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        "import pandas_datareader.data as web\n",
        "import datetime  # Import datetime for current time\n",
        "\n",
        "start = '2010-01-01'\n",
        "end = datetime.date.today()  # Set 'end' to today's date\n",
        "\n",
        "df = web.DataReader('GOOG', 'stooq', start, end)\n",
        "df.head()\n"
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              "               Open     High       Low   Close    Volume\n",
              "Date                                                    \n",
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              "               Open     High      Low    Close   Volume\n",
              "Date                                                   \n",
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              "      <td>28.3500</td>\n",
              "      <td>27.8465</td>\n",
              "      <td>27.8485</td>\n",
              "      <td>215440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-28</th>\n",
              "      <td>28.0600</td>\n",
              "      <td>28.3215</td>\n",
              "      <td>27.9335</td>\n",
              "      <td>27.9995</td>\n",
              "      <td>820060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-27</th>\n",
              "      <td>28.4000</td>\n",
              "      <td>28.4000</td>\n",
              "      <td>27.6460</td>\n",
              "      <td>27.9230</td>\n",
              "      <td>261040</td>\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\": \"2014-03-27 00:00:00\",\n        \"max\": \"2014-04-02 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2014-04-01 00:00:00\",\n          \"2014-03-27 00:00:00\",\n          \"2014-03-31 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8341865348949244,\n        \"min\": 27.9355,\n        \"max\": 29.9995,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          27.9355,\n          28.4,\n          28.3445\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8363460258768489,\n        \"min\": 28.3215,\n        \"max\": 30.2415,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          28.4225,\n          28.4,\n          28.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16843604127383185,\n        \"min\": 27.646,\n        \"max\": 28.1095,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          27.9355,\n          27.646,\n          27.8465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2416905562904769,\n        \"min\": 27.8485,\n        \"max\": 28.358,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          28.358,\n          27.923,\n          27.8485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1179761,\n        \"min\": 158640,\n        \"max\": 2933940,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          158640,\n          261040,\n          215440\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "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": "Qe-B1TqDyHzS",
        "outputId": "c840fb9e-cd4b-419e-ac8f-b465d0d0ebd1"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date     Open     High       Low   Close    Volume\n",
              "0 2024-04-09  157.350  159.890  156.6400  158.14  21538140\n",
              "1 2024-04-08  154.015  156.655  153.9900  156.19  16629665\n",
              "2 2024-04-05  151.680  154.840  151.0810  153.94  16297319\n",
              "3 2024-04-04  155.080  156.180  151.8800  151.94  24184842\n",
              "4 2024-04-03  154.920  156.550  154.1321  156.37  17266175"
            ],
            "text/html": [
              "\n",
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              "      <th>Date</th>\n",
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              "      <td>2024-04-09</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024-04-08</td>\n",
              "      <td>154.015</td>\n",
              "      <td>156.655</td>\n",
              "      <td>153.9900</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024-04-05</td>\n",
              "      <td>151.680</td>\n",
              "      <td>154.840</td>\n",
              "      <td>151.0810</td>\n",
              "      <td>153.94</td>\n",
              "      <td>16297319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024-04-04</td>\n",
              "      <td>155.080</td>\n",
              "      <td>156.180</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024-04-03</td>\n",
              "      <td>154.920</td>\n",
              "      <td>156.550</td>\n",
              "      <td>154.1321</td>\n",
              "      <td>156.37</td>\n",
              "      <td>17266175</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  }\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-a17b15aa-adcc-4d6e-ab72-1834f6d4fd7a 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\": 2526,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2014-03-27 00:00:00\",\n        \"max\": \"2024-04-09 00:00:00\",\n        \"num_unique_values\": 2526,\n        \"samples\": [\n          \"2016-07-01 00:00:00\",\n          \"2021-04-19 00:00:00\",\n          \"2023-06-30 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.66971055796878,\n        \"min\": 24.7325,\n        \"max\": 157.35,\n        \"num_unique_values\": 2444,\n        \"samples\": [\n          45.5,\n          37.352,\n          26.3635\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.132041105906595,\n        \"min\": 24.799,\n        \"max\": 159.89,\n        \"num_unique_values\": 2475,\n        \"samples\": [\n          72.1854,\n          36.737,\n          72.3276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.269280130531115,\n        \"min\": 24.378,\n        \"max\": 156.64,\n        \"num_unique_values\": 2485,\n        \"samples\": [\n          47.457,\n          51.1795,\n          25.985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.705193423594416,\n        \"min\": 24.6275,\n        \"max\": 158.14,\n        \"num_unique_values\": 2482,\n        \"samples\": [\n          106.06,\n          139.646,\n          41.216\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15693339,\n        \"min\": 158640,\n        \"max\": 223298000,\n        \"num_unique_values\": 2526,\n        \"samples\": [\n          18206520,\n          24688900,\n          23871764\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Information\n",
        "\n",
        "This code deletes \"Date\" column from the dataset as they are not useful in our plotting."
      ],
      "metadata": {
        "id": "jyg6ZVa5xV1c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(['Date'],axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rkUI9QkayJqt",
        "outputId": "0ecbcdaf-1a4a-4c83-b21f-c2bacf052168"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Open     High       Low   Close    Volume\n",
              "0  157.350  159.890  156.6400  158.14  21538140\n",
              "1  154.015  156.655  153.9900  156.19  16629665\n",
              "2  151.680  154.840  151.0810  153.94  16297319\n",
              "3  155.080  156.180  151.8800  151.94  24184842\n",
              "4  154.920  156.550  154.1321  156.37  17266175"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2832ead0-0434-409a-8e24-01eaca9db468\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <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>157.350</td>\n",
              "      <td>159.890</td>\n",
              "      <td>156.6400</td>\n",
              "      <td>158.14</td>\n",
              "      <td>21538140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>154.015</td>\n",
              "      <td>156.655</td>\n",
              "      <td>153.9900</td>\n",
              "      <td>156.19</td>\n",
              "      <td>16629665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>151.680</td>\n",
              "      <td>154.840</td>\n",
              "      <td>151.0810</td>\n",
              "      <td>153.94</td>\n",
              "      <td>16297319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>155.080</td>\n",
              "      <td>156.180</td>\n",
              "      <td>151.8800</td>\n",
              "      <td>151.94</td>\n",
              "      <td>24184842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>154.920</td>\n",
              "      <td>156.550</td>\n",
              "      <td>154.1321</td>\n",
              "      <td>156.37</td>\n",
              "      <td>17266175</td>\n",
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              "        const element = document.querySelector('#df-2832ead0-0434-409a-8e24-01eaca9db468');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\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",
              "      --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-5b90a0d4-22f6-4467-937d-7b6ed5fefabe 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\": 2526,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.66971055796878,\n        \"min\": 24.7325,\n        \"max\": 157.35,\n        \"num_unique_values\": 2444,\n        \"samples\": [\n          45.5,\n          37.352,\n          26.3635\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.132041105906595,\n        \"min\": 24.799,\n        \"max\": 159.89,\n        \"num_unique_values\": 2475,\n        \"samples\": [\n          72.1854,\n          36.737,\n          72.3276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.269280130531115,\n        \"min\": 24.378,\n        \"max\": 156.64,\n        \"num_unique_values\": 2485,\n        \"samples\": [\n          47.457,\n          51.1795,\n          25.985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.705193423594416,\n        \"min\": 24.6275,\n        \"max\": 158.14,\n        \"num_unique_values\": 2482,\n        \"samples\": [\n          106.06,\n          139.646,\n          41.216\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15693339,\n        \"min\": 158640,\n        \"max\": 223298000,\n        \"num_unique_values\": 2526,\n        \"samples\": [\n          18206520,\n          24688900,\n          23871764\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The project deals with the \"close\" column.\n",
        "```py\n",
        "plt.plot(df.Close)\n",
        "```\n",
        "- We can also create for ther columns also.\n",
        "```py\n",
        "plt.plot(df.Open)\n",
        "```\n",
        "```py\n",
        "plt.plot(df.High)\n",
        "```\n",
        "```py\n",
        "plt.plot(df.Low)\n",
        "```\n",
        "```py\n",
        "plt.plot(df.Volume)\n",
        "```"
      ],
      "metadata": {
        "id": "-7K6Hxu9o7z1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df.Close,color='r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Vzm2S232yb5Z",
        "outputId": "ef911c15-725e-4c93-b05b-d835eb604820"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fed476999c0>]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "mQ6eYTS4ys8M",
        "outputId": "066c0d3e-a431-478d-ab8b-181c82f89060"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Open      High       Low     Close    Volume\n",
              "0     157.3500  159.8900  156.6400  158.1400  21538140\n",
              "1     154.0150  156.6550  153.9900  156.1900  16629665\n",
              "2     151.6800  154.8400  151.0810  153.9400  16297319\n",
              "3     155.0800  156.1800  151.8800  151.9400  24184842\n",
              "4     154.9200  156.5500  154.1321  156.3700  17266175\n",
              "...        ...       ...       ...       ...       ...\n",
              "2521   29.9995   30.2415   28.1095   28.3500   2933940\n",
              "2522   27.9355   28.4225   27.9355   28.3580    158640\n",
              "2523   28.3445   28.3500   27.8465   27.8485    215440\n",
              "2524   28.0600   28.3215   27.9335   27.9995    820060\n",
              "2525   28.4000   28.4000   27.6460   27.9230    261040\n",
              "\n",
              "[2526 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <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>157.3500</td>\n",
              "      <td>159.8900</td>\n",
              "      <td>156.6400</td>\n",
              "      <td>158.1400</td>\n",
              "      <td>21538140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>154.0150</td>\n",
              "      <td>156.6550</td>\n",
              "      <td>153.9900</td>\n",
              "      <td>156.1900</td>\n",
              "      <td>16629665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>151.6800</td>\n",
              "      <td>154.8400</td>\n",
              "      <td>151.0810</td>\n",
              "      <td>153.9400</td>\n",
              "      <td>16297319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>155.0800</td>\n",
              "      <td>156.1800</td>\n",
              "      <td>151.8800</td>\n",
              "      <td>151.9400</td>\n",
              "      <td>24184842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>154.9200</td>\n",
              "      <td>156.5500</td>\n",
              "      <td>154.1321</td>\n",
              "      <td>156.3700</td>\n",
              "      <td>17266175</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>2521</th>\n",
              "      <td>29.9995</td>\n",
              "      <td>30.2415</td>\n",
              "      <td>28.1095</td>\n",
              "      <td>28.3500</td>\n",
              "      <td>2933940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2522</th>\n",
              "      <td>27.9355</td>\n",
              "      <td>28.4225</td>\n",
              "      <td>27.9355</td>\n",
              "      <td>28.3580</td>\n",
              "      <td>158640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2523</th>\n",
              "      <td>28.3445</td>\n",
              "      <td>28.3500</td>\n",
              "      <td>27.8465</td>\n",
              "      <td>27.8485</td>\n",
              "      <td>215440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2524</th>\n",
              "      <td>28.0600</td>\n",
              "      <td>28.3215</td>\n",
              "      <td>27.9335</td>\n",
              "      <td>27.9995</td>\n",
              "      <td>820060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2525</th>\n",
              "      <td>28.4000</td>\n",
              "      <td>28.4000</td>\n",
              "      <td>27.6460</td>\n",
              "      <td>27.9230</td>\n",
              "      <td>261040</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2526 rows × 5 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2526,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.66971055796878,\n        \"min\": 24.7325,\n        \"max\": 157.35,\n        \"num_unique_values\": 2444,\n        \"samples\": [\n          45.5,\n          37.352,\n          26.3635\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.132041105906595,\n        \"min\": 24.799,\n        \"max\": 159.89,\n        \"num_unique_values\": 2475,\n        \"samples\": [\n          72.1854,\n          36.737,\n          72.3276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.269280130531115,\n        \"min\": 24.378,\n        \"max\": 156.64,\n        \"num_unique_values\": 2485,\n        \"samples\": [\n          47.457,\n          51.1795,\n          25.985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.705193423594416,\n        \"min\": 24.6275,\n        \"max\": 158.14,\n        \"num_unique_values\": 2482,\n        \"samples\": [\n          106.06,\n          139.646,\n          41.216\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15693339,\n        \"min\": 158640,\n        \"max\": 223298000,\n        \"num_unique_values\": 2526,\n        \"samples\": [\n          18206520,\n          24688900,\n          23871764\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "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": "l-3tTxD0yz27",
        "outputId": "68efea46-9d82-4bdc-b303-90ffa2f47324"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            NaN\n",
              "1            NaN\n",
              "2            NaN\n",
              "3            NaN\n",
              "4            NaN\n",
              "          ...   \n",
              "2521    27.86985\n",
              "2522    27.86215\n",
              "2523    27.84895\n",
              "2524    27.83670\n",
              "2525    27.82250\n",
              "Name: Close, Length: 2526, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(12,6))\n",
        "\n",
        "plt.plot(df.Close,'black')\n",
        "plt.plot(ma100,'g') # this is the mean of 100 values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "GjhnDPGQy-CM",
        "outputId": "c561ad4b-736a-4de3-e1fc-24faf374cff1"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fed47539b40>]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "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": [
        "ma200=df.Close.rolling(200).mean()\n",
        "ma200"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_DGGl7bSqFzZ",
        "outputId": "bddc214f-0a4b-4138-c26d-243224dad4ff"
      },
      "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",
              "2521    27.575317\n",
              "2522    27.591660\n",
              "2523    27.605685\n",
              "2524    27.621637\n",
              "2525    27.638115\n",
              "Name: Close, Length: 2526, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(12,6))\n",
        "\n",
        "ma100=df.Close.rolling(100).mean()\n",
        "ma100\n",
        "\n",
        "plt.plot(df.Close,'b')\n",
        "plt.plot(ma100,'g') # this is the mean of 100 values\n",
        "plt.plot(ma200,'r') # this is the mean of 200 values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "AHCOCIekqQe2",
        "outputId": "3674f4dd-5796-4e1d-c047-bcbd097eab99"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fed475b2740>]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "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": "iwrQSZVgqt9l",
        "outputId": "fffb192f-4b20-4451-d520-d04cf5e5b6e2"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2526, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### This the section\n",
        "\n",
        "- We are splitting the data into 70% for training data **data_training** and for testing **data_testing** will be remaining 30% from the 70% value till last."
      ],
      "metadata": {
        "id": "K0UVJWB6rxB5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# splitting the data into training and testing\n",
        "import pandas as pd\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": "DeM66uKVqxSL",
        "outputId": "469b79dc-cf43-4809-e7e7-bccfc9b9912a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Used data for training:  (1768, 1)\n",
            "Used data for testing:  (758, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_training.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "P9_UnHdpsTtO",
        "outputId": "e721b806-5944-4710-8c73-610caaa4c3ec"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Close\n",
              "0  158.14\n",
              "1  156.19\n",
              "2  153.94\n",
              "3  151.94\n",
              "4  156.37"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2017a711-f482-4ecc-844c-3da85903602b\" 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>158.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>156.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>156.37</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-2017a711-f482-4ecc-844c-3da85903602b')\"\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-2017a711-f482-4ecc-844c-3da85903602b 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-2017a711-f482-4ecc-844c-3da85903602b');\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-7282ad88-f7a0-4edc-b9ac-fac90de69aa0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7282ad88-f7a0-4edc-b9ac-fac90de69aa0')\"\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-7282ad88-f7a0-4edc-b9ac-fac90de69aa0 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\": 1768,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.375265981166514,\n        \"min\": 41.1675,\n        \"max\": 158.14,\n        \"num_unique_values\": 1740,\n        \"samples\": [\n          130.286,\n          55.482,\n          70.892\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_testing.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "B7OPmld6sW-6",
        "outputId": "da00518a-0997-4e95-d74f-214e51cdbbff"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Close\n",
              "1768  41.5705\n",
              "1769  41.0460\n",
              "1770  40.9755\n",
              "1771  40.7215\n",
              "1772  40.8790"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-af717678-de11-4b1b-9760-1e307ef6cbed\" 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>1768</th>\n",
              "      <td>41.5705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1769</th>\n",
              "      <td>41.0460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1770</th>\n",
              "      <td>40.9755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1771</th>\n",
              "      <td>40.7215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1772</th>\n",
              "      <td>40.8790</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-af717678-de11-4b1b-9760-1e307ef6cbed')\"\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-af717678-de11-4b1b-9760-1e307ef6cbed 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-af717678-de11-4b1b-9760-1e307ef6cbed');\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-1c0abee0-65c9-4a30-b515-8b476bd44664\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c0abee0-65c9-4a30-b515-8b476bd44664')\"\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-1c0abee0-65c9-4a30-b515-8b476bd44664 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\": 758,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.2542251608175246,\n        \"min\": 24.6275,\n        \"max\": 42.606,\n        \"num_unique_values\": 743,\n        \"samples\": [\n          28.044,\n          25.9315,\n          28.106\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For LSTM , we need to scale the data into the range of [-1,1] and **Scaling is required.**"
      ],
      "metadata": {
        "id": "g2EfBrpHsiHr"
      }
    },
    {
      "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": "J5pRnSooshVI",
        "outputId": "7569afa0-474f-4f3a-aca9-6ee964e13b5f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        ],\n",
              "       [0.98332942],\n",
              "       [0.96409412],\n",
              "       ...,\n",
              "       [0.00649725],\n",
              "       [0.00267157],\n",
              "       [0.00348372]])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The above **data_training_array** needs to be divided into following **x_train** and **y_train** variable.\n"
      ],
      "metadata": {
        "id": "89Rg1WKxtR4k"
      }
    },
    {
      "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": "9EU0bp64tPec"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ML Model"
      ],
      "metadata": {
        "id": "rKKTrYdovbGh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eh2mJggkwe2a",
        "outputId": "87e79250-ab32-459a-e23b-b7666d3f14bc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Dense, Dropout, LSTM\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, LSTM, BatchNormalization\n",
        "from keras.callbacks import TensorBoard\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import tensorflow as tf\n"
      ],
      "metadata": {
        "id": "qe2wjIfLvbd9"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Dense, Dropout, LSTM, BatchNormalization\n",
        "from keras.callbacks import TensorBoard\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.initializers import Orthogonal\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": {
        "id": "C0TnEgBTw7n7"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLs2-SuTyHu0",
        "outputId": "04318069-6ac2-473a-f321-eab5b103201e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 100, 50)           10400     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 100, 50)           0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 100, 60)           26640     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 100, 60)           0         \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 100, 80)           45120     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 100, 80)           0         \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 120)               96480     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 120)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 121       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178761 (698.29 KB)\n",
            "Trainable params: 178761 (698.29 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow-addons"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7q71LUuyFOGD",
        "outputId": "32ca32e4-2020-4ae4-dfe6-2609ac964be9"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow-addons\n",
            "  Downloading tensorflow_addons-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (611 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m611.8/611.8 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow-addons) (24.0)\n",
            "Collecting typeguard<3.0.0,>=2.7 (from tensorflow-addons)\n",
            "  Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n",
            "Installing collected packages: typeguard, tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.23.0 typeguard-2.13.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.metrics import MeanSquaredError, MeanAbsoluteError\n",
        "from tensorflow_addons.metrics import RSquare\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",
        "                       RSquare()])\n",
        "model.fit(x_train,y_train,epochs=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pyLNL7ooyMei",
        "outputId": "108f4c5e-11af-4161-ee86-d81059a072cf"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
            "\n",
            "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
            "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
            "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
            "\n",
            "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
            "\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "76/76 [==============================] - 39s 298ms/step - loss: 0.0994 - accuracy: 4.1442e-04 - mean_squared_error: 0.0994 - mean_absolute_error: 0.1986 - r_square: -0.0602\n",
            "Epoch 2/50\n",
            "76/76 [==============================] - 20s 267ms/step - loss: 0.0121 - accuracy: 4.1442e-04 - mean_squared_error: 0.0121 - mean_absolute_error: 0.0686 - r_square: 0.8712\n",
            "Epoch 3/50\n",
            "76/76 [==============================] - 22s 287ms/step - loss: 0.0085 - accuracy: 4.1442e-04 - mean_squared_error: 0.0085 - mean_absolute_error: 0.0555 - r_square: 0.9095\n",
            "Epoch 4/50\n",
            "76/76 [==============================] - 23s 305ms/step - loss: 0.0074 - accuracy: 4.1442e-04 - mean_squared_error: 0.0074 - mean_absolute_error: 0.0533 - r_square: 0.9207\n",
            "Epoch 5/50\n",
            "76/76 [==============================] - 21s 281ms/step - loss: 0.0068 - accuracy: 4.1442e-04 - mean_squared_error: 0.0068 - mean_absolute_error: 0.0506 - r_square: 0.9271\n",
            "Epoch 6/50\n",
            "76/76 [==============================] - 20s 268ms/step - loss: 0.0063 - accuracy: 4.1442e-04 - mean_squared_error: 0.0063 - mean_absolute_error: 0.0479 - r_square: 0.9332\n",
            "Epoch 7/50\n",
            "76/76 [==============================] - 21s 280ms/step - loss: 0.0064 - accuracy: 4.1442e-04 - mean_squared_error: 0.0064 - mean_absolute_error: 0.0482 - r_square: 0.9321\n",
            "Epoch 8/50\n",
            "76/76 [==============================] - 22s 284ms/step - loss: 0.0049 - accuracy: 4.1442e-04 - mean_squared_error: 0.0049 - mean_absolute_error: 0.0427 - r_square: 0.9477\n",
            "Epoch 9/50\n",
            "76/76 [==============================] - 21s 272ms/step - loss: 0.0048 - accuracy: 4.1442e-04 - mean_squared_error: 0.0048 - mean_absolute_error: 0.0424 - r_square: 0.9484\n",
            "Epoch 10/50\n",
            "76/76 [==============================] - 22s 286ms/step - loss: 0.0052 - accuracy: 4.1442e-04 - mean_squared_error: 0.0052 - mean_absolute_error: 0.0447 - r_square: 0.9441\n",
            "Epoch 11/50\n",
            "76/76 [==============================] - 22s 287ms/step - loss: 0.0051 - accuracy: 4.1442e-04 - mean_squared_error: 0.0051 - mean_absolute_error: 0.0435 - r_square: 0.9459\n",
            "Epoch 12/50\n",
            "76/76 [==============================] - 22s 284ms/step - loss: 0.0046 - accuracy: 4.1442e-04 - mean_squared_error: 0.0046 - mean_absolute_error: 0.0434 - r_square: 0.9513\n",
            "Epoch 13/50\n",
            "76/76 [==============================] - 21s 274ms/step - loss: 0.0041 - accuracy: 4.1442e-04 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0403 - r_square: 0.9563\n",
            "Epoch 14/50\n",
            "76/76 [==============================] - 22s 289ms/step - loss: 0.0045 - accuracy: 4.1442e-04 - mean_squared_error: 0.0045 - mean_absolute_error: 0.0419 - r_square: 0.9525\n",
            "Epoch 15/50\n",
            "76/76 [==============================] - 22s 284ms/step - loss: 0.0042 - accuracy: 4.1442e-04 - mean_squared_error: 0.0042 - mean_absolute_error: 0.0403 - r_square: 0.9556\n",
            "Epoch 16/50\n",
            "76/76 [==============================] - 23s 301ms/step - loss: 0.0041 - accuracy: 4.1442e-04 - mean_squared_error: 0.0041 - mean_absolute_error: 0.0411 - r_square: 0.9565\n",
            "Epoch 17/50\n",
            "76/76 [==============================] - 22s 282ms/step - loss: 0.0037 - accuracy: 4.1442e-04 - mean_squared_error: 0.0037 - mean_absolute_error: 0.0382 - r_square: 0.9603\n",
            "Epoch 18/50\n",
            "76/76 [==============================] - 22s 287ms/step - loss: 0.0040 - accuracy: 4.1442e-04 - mean_squared_error: 0.0040 - mean_absolute_error: 0.0416 - r_square: 0.9569\n",
            "Epoch 19/50\n",
            "76/76 [==============================] - 22s 285ms/step - loss: 0.0036 - accuracy: 4.1442e-04 - mean_squared_error: 0.0036 - mean_absolute_error: 0.0380 - r_square: 0.9619\n",
            "Epoch 20/50\n",
            "76/76 [==============================] - 20s 269ms/step - loss: 0.0036 - accuracy: 4.1442e-04 - mean_squared_error: 0.0036 - mean_absolute_error: 0.0387 - r_square: 0.9613\n",
            "Epoch 21/50\n",
            "76/76 [==============================] - 22s 288ms/step - loss: 0.0035 - accuracy: 4.1442e-04 - mean_squared_error: 0.0035 - mean_absolute_error: 0.0382 - r_square: 0.9631\n",
            "Epoch 22/50\n",
            "76/76 [==============================] - 22s 287ms/step - loss: 0.0033 - accuracy: 4.1442e-04 - mean_squared_error: 0.0033 - mean_absolute_error: 0.0378 - r_square: 0.9651\n",
            "Epoch 23/50\n",
            "76/76 [==============================] - 21s 279ms/step - loss: 0.0030 - accuracy: 4.1442e-04 - mean_squared_error: 0.0030 - mean_absolute_error: 0.0364 - r_square: 0.9681\n",
            "Epoch 24/50\n",
            "76/76 [==============================] - 21s 273ms/step - loss: 0.0032 - accuracy: 4.1442e-04 - mean_squared_error: 0.0032 - mean_absolute_error: 0.0373 - r_square: 0.9660\n",
            "Epoch 25/50\n",
            "76/76 [==============================] - 21s 283ms/step - loss: 0.0031 - accuracy: 4.1442e-04 - mean_squared_error: 0.0031 - mean_absolute_error: 0.0371 - r_square: 0.9672\n",
            "Epoch 26/50\n",
            "76/76 [==============================] - 21s 281ms/step - loss: 0.0029 - accuracy: 4.1442e-04 - mean_squared_error: 0.0029 - mean_absolute_error: 0.0372 - r_square: 0.9688\n",
            "Epoch 27/50\n",
            "76/76 [==============================] - 21s 271ms/step - loss: 0.0028 - accuracy: 4.1442e-04 - mean_squared_error: 0.0028 - mean_absolute_error: 0.0361 - r_square: 0.9702\n",
            "Epoch 28/50\n",
            "76/76 [==============================] - 22s 294ms/step - loss: 0.0031 - accuracy: 4.1442e-04 - mean_squared_error: 0.0031 - mean_absolute_error: 0.0377 - r_square: 0.9666\n",
            "Epoch 29/50\n",
            "76/76 [==============================] - 22s 289ms/step - loss: 0.0031 - accuracy: 4.1442e-04 - mean_squared_error: 0.0031 - mean_absolute_error: 0.0385 - r_square: 0.9674\n",
            "Epoch 30/50\n",
            "76/76 [==============================] - 21s 271ms/step - loss: 0.0026 - accuracy: 4.1442e-04 - mean_squared_error: 0.0026 - mean_absolute_error: 0.0356 - r_square: 0.9721\n",
            "Epoch 31/50\n",
            "76/76 [==============================] - 23s 299ms/step - loss: 0.0027 - accuracy: 4.1442e-04 - mean_squared_error: 0.0027 - mean_absolute_error: 0.0363 - r_square: 0.9708\n",
            "Epoch 32/50\n",
            "76/76 [==============================] - 22s 285ms/step - loss: 0.0029 - accuracy: 4.1442e-04 - mean_squared_error: 0.0029 - mean_absolute_error: 0.0370 - r_square: 0.9690\n",
            "Epoch 33/50\n",
            "76/76 [==============================] - 22s 286ms/step - loss: 0.0031 - accuracy: 4.1442e-04 - mean_squared_error: 0.0031 - mean_absolute_error: 0.0388 - r_square: 0.9669\n",
            "Epoch 34/50\n",
            "76/76 [==============================] - 21s 274ms/step - loss: 0.0028 - accuracy: 4.1442e-04 - mean_squared_error: 0.0028 - mean_absolute_error: 0.0369 - r_square: 0.9705\n",
            "Epoch 35/50\n",
            "76/76 [==============================] - 22s 286ms/step - loss: 0.0028 - accuracy: 4.1442e-04 - mean_squared_error: 0.0028 - mean_absolute_error: 0.0364 - r_square: 0.9704\n",
            "Epoch 36/50\n",
            "76/76 [==============================] - 22s 289ms/step - loss: 0.0027 - accuracy: 4.1442e-04 - mean_squared_error: 0.0027 - mean_absolute_error: 0.0369 - r_square: 0.9712\n",
            "Epoch 37/50\n",
            "76/76 [==============================] - 21s 273ms/step - loss: 0.0027 - accuracy: 4.1442e-04 - mean_squared_error: 0.0027 - mean_absolute_error: 0.0370 - r_square: 0.9707\n",
            "Epoch 38/50\n",
            "76/76 [==============================] - 22s 290ms/step - loss: 0.0027 - accuracy: 4.1442e-04 - mean_squared_error: 0.0027 - mean_absolute_error: 0.0367 - r_square: 0.9713\n",
            "Epoch 39/50\n",
            "76/76 [==============================] - 22s 287ms/step - loss: 0.0025 - accuracy: 4.1442e-04 - mean_squared_error: 0.0025 - mean_absolute_error: 0.0363 - r_square: 0.9732\n",
            "Epoch 40/50\n",
            "76/76 [==============================] - 22s 288ms/step - loss: 0.0025 - accuracy: 4.1442e-04 - mean_squared_error: 0.0025 - mean_absolute_error: 0.0361 - r_square: 0.9734\n",
            "Epoch 41/50\n",
            "76/76 [==============================] - 21s 273ms/step - loss: 0.0028 - accuracy: 4.1442e-04 - mean_squared_error: 0.0028 - mean_absolute_error: 0.0379 - r_square: 0.9701\n",
            "Epoch 42/50\n",
            "76/76 [==============================] - 22s 290ms/step - loss: 0.0026 - accuracy: 4.1442e-04 - mean_squared_error: 0.0026 - mean_absolute_error: 0.0370 - r_square: 0.9720\n",
            "Epoch 43/50\n",
            "76/76 [==============================] - 22s 292ms/step - loss: 0.0026 - accuracy: 4.1442e-04 - mean_squared_error: 0.0026 - mean_absolute_error: 0.0364 - r_square: 0.9720\n",
            "Epoch 44/50\n",
            "76/76 [==============================] - 21s 272ms/step - loss: 0.0024 - accuracy: 4.1442e-04 - mean_squared_error: 0.0024 - mean_absolute_error: 0.0354 - r_square: 0.9742\n",
            "Epoch 45/50\n",
            "76/76 [==============================] - 23s 300ms/step - loss: 0.0025 - accuracy: 4.1442e-04 - mean_squared_error: 0.0025 - mean_absolute_error: 0.0364 - r_square: 0.9729\n",
            "Epoch 46/50\n",
            "76/76 [==============================] - 22s 295ms/step - loss: 0.0028 - accuracy: 4.1442e-04 - mean_squared_error: 0.0028 - mean_absolute_error: 0.0381 - r_square: 0.9697\n",
            "Epoch 47/50\n",
            "76/76 [==============================] - 22s 285ms/step - loss: 0.0026 - accuracy: 4.1442e-04 - mean_squared_error: 0.0026 - mean_absolute_error: 0.0373 - r_square: 0.9722\n",
            "Epoch 48/50\n",
            "76/76 [==============================] - 20s 268ms/step - loss: 0.0025 - accuracy: 4.1442e-04 - mean_squared_error: 0.0025 - mean_absolute_error: 0.0364 - r_square: 0.9735\n",
            "Epoch 49/50\n",
            "76/76 [==============================] - 21s 281ms/step - loss: 0.0025 - accuracy: 4.1442e-04 - mean_squared_error: 0.0025 - mean_absolute_error: 0.0367 - r_square: 0.9729\n",
            "Epoch 50/50\n",
            "76/76 [==============================] - 22s 295ms/step - loss: 0.0023 - accuracy: 4.1442e-04 - mean_squared_error: 0.0023 - mean_absolute_error: 0.0350 - r_square: 0.9750\n"
          ]
        },
        {
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            "text/plain": [
              "<keras.src.callbacks.History at 0x7fed46b330d0>"
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    },
    {
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        "model.save('keras_model_real_time_data.h5')\n"
      ],
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        "id": "89MDXI8W2Tob",
        "outputId": "ab1222f5-9f0d-4267-bbc4-f44ba2c18a7e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
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      "execution_count": 58,
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        {
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>156.37</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-04f1e304-c548-470f-bc23-5d96d6182a85')\"\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-04f1e304-c548-470f-bc23-5d96d6182a85 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-04f1e304-c548-470f-bc23-5d96d6182a85');\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-fe07cfd1-a2b0-4d2e-8775-76137fa40c06\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fe07cfd1-a2b0-4d2e-8775-76137fa40c06')\"\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-fe07cfd1-a2b0-4d2e-8775-76137fa40c06 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\": 1768,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.375265981166514,\n        \"min\": 41.1675,\n        \"max\": 158.14,\n        \"num_unique_values\": 1740,\n        \"samples\": [\n          130.286,\n          55.482,\n          70.892\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We need previous 100 days data and we need to comapre 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": "2OSNuT2Z2lp4"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wbowngcM28CD",
        "outputId": "badcb6a1-fadb-4109-97ad-74d0056826e6"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Close\n",
              "0  45.3330\n",
              "1  45.5335\n",
              "2  45.5490\n",
              "3  46.3480\n",
              "4  46.1110"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-080f8dc9-88d7-4e25-baf4-3719a3936cce\" 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>45.3330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45.5335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45.5490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>46.3480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>46.1110</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-080f8dc9-88d7-4e25-baf4-3719a3936cce')\"\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-080f8dc9-88d7-4e25-baf4-3719a3936cce 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-080f8dc9-88d7-4e25-baf4-3719a3936cce');\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-01d26bb5-a3fe-480f-b1c7-04fcc1b94d28\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-01d26bb5-a3fe-480f-b1c7-04fcc1b94d28')\"\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",
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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",
              "      --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-01d26bb5-a3fe-480f-b1c7-04fcc1b94d28 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\": 858,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.5737535545818,\n        \"min\": 24.6275,\n        \"max\": 49.184,\n        \"num_unique_values\": 842,\n        \"samples\": [\n          31.8805,\n          39.297,\n          27.1435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Scaling of final prepared dataframe."
      ],
      "metadata": {
        "id": "nv-CHBLK3iYy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = scaler.fit_transform(final_df)\n",
        "input_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4BgJoC4u3GD0",
        "outputId": "81c833aa-279c-4724-d42e-dafe003a5c8f"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [0.15716816],\n",
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              "       [0.13731599],\n",
              "       [0.13420072]])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxjQHpo53nLz",
        "outputId": "768ee30b-cb9c-4145-dad2-8e091c0f426d"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(858, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "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": "ub8CV6VD3pgX"
      },
      "execution_count": 67,
      "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": "DTGYD9sr38O6",
        "outputId": "62bb3d35-efd8-4f64-eaf2-fae63e505c70"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(758, 100, 1)\n",
            "(758,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making Predictions\n",
        "\n",
        "y_predicted = model.predict(x_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HgV8b5n94LHB",
        "outputId": "708cc55e-b34b-4778-f3bc-0d1f0099c7f7"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24/24 [==============================] - 3s 119ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zpYu_zOf4Wvw",
        "outputId": "8f3609cc-c243-4995-dde8-ebe3aedc90e4"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(758, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5zVleJOH4bx-",
        "outputId": "9683d4d0-c609-4fe7-ae48-4089fadb6579"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       0.1311669 , 0.13731599, 0.13420072])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkz4t-gd4eSs",
        "outputId": "8dd1d57d-8332-4329-b6e8-ea7784f1889d"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [0.11206758]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.scale_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0zG4yFx4h2B",
        "outputId": "cb333637-3002-4259-e845-9bc99a5ae7ef"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.04072242])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scale_factor=1/0.04072242\n",
        "y_predicted= y_predicted* scale_factor\n",
        "y_test = y_test * scale_factor\n"
      ],
      "metadata": {
        "id": "bPVefTyM4mev"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.plot(y_test,'b',label=\"Original price\")\n",
        "plt.plot(y_predicted,'r',label=\"Predicted price\")\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ttrfU3Vz44xJ",
        "outputId": "88fab47a-1a41-4b58-89aa-d59b712763e0"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}